poverty and health essay

Poverty and Health

Poverty is a major cause of ill health and a barrier to accessing health care when needed. This relationship is financial: the poor cannot afford to purchase those things that are needed for good health, including sufficient quantities of quality food and health care. But, the relationship is also related to other factors related to poverty, such as lack of information on appropriate health-promoting practices or lack of voice needed to make social services work for them.

Ill health, in turn, is a major cause of poverty. This is partly due to the costs of seeking health care, which include not only out-of-pocket spending on care (such as consultations, tests and medicine), but also transportation costs and any informal payments to providers. It is also due to the considerable loss of income associated with illness in developing countries, both of the breadwinner, but also of family members who may be obliged to stop working or attending school to take care of an ill relative. In addition, poor families coping with illness might be forced to sell assets to cover medical expenses, borrow at high interest rates or become indebted to the community.

Strong  health systems  improve the health status of the whole population, but especially of the poor among whom ill health and poor access to health care tends to be concentrated, as well as protect households from the potentially catastrophic effects of out-of-pocket health care costs. In general, poor health is disproportionately concentrated among the poor.

The World Bank’s work in the area of health equity and financial protection is defined by the  2007 Health, Nutrition and Population Strategy . The strategy identifies “preventing poverty due to illness (by improving financial protection)” as one of its four strategic objectives and commits the Bank’s health team, both through its analytical work and its regional operations, to addressing vulnerability that arises from health shocks.

The strategy also stresses the importance of equity in health outcomes in a second strategic objective to "improve the level and distribution of key health, nutrition and population outcomes... particularly for the poor and the vulnerable".

The Bank supports governments to implement a variety of policies and programs to reduce inequalities in health outcomes and enhance financial protection. Generally, this involves mechanisms that help overcome geographic, social and psychological barriers to accessing care and reducing out-of-pocket cost of treatment. Examples include:

• Reducing the direct cost of care at the point of service, e.g. through reducing/abolishing user fees for the poor or expanding health insurance to the poor (including coverage, depth and breadth).
• Increasing efficiency of care to reduce total consumption of care, e.g. by limiting “irrational drug prescribing,” strengthening the referral system, or improving the quality of providers (especially at the lower level).
• Reducing inequalities in determinants of health status or health care utilization, such as reducing distance (through providing services closer to the poor), subsidizing travel costs, targeted health promotion, conditional cash transfers.
• Expanding access to care by using the private sector or public-private partnerships.

The Bank’s health team also promotes the monitoring of equity and financial protection by publishing global statistics on inequalities in health status, access to care and financial protection, as well as training government officials, policymakers and researchers in how to measure and monitor the same.

Examples of how World Bank projects have improved health coverage for the poor and reduced financial vulnerability include:

The  Rajasthan Health Systems Development Project resulted in improved access to care for vulnerable Indians. The share of below-poverty line Indians in the overall inpatient and outpatient load at secondary facilities more than doubled between 2006 and 2011, well exceeding targets. In the same period, the share of the vulnerable tribal populations in the overall patient composition tripled.

The  Georgia Health Sector Development Project  supported the government of Georgia in implementing the Medical Insurance Program for the Poor, effectively increasing the share of the government health expenditure earmarked for the poor from 4% in 2006 to 38% in 2011. It also increased the number of health care visits of both the general population and the poor, but by more for the poor (from 2 per capita per year to 2.6) than for the general population (from 2 to 2.3) over the same time period.

The  Mekong Regional Health Support Project  helped the government of Vietnam to increase access to (government) health insurance from 29% to 94% among the poor, as well as from 7% to 68% among the near-poor. Hospitalization and consultation rates, at government facilities, also increased among both the poor and near-poor.

Analyzing Health Equity Using Household Survey Data

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Why Poverty Persists in America

A Pulitzer Prize-winning sociologist offers a new explanation for an intractable problem.

A mother and son living in a Walmart parking lot in North Dakota in 2012. Credit... Eugene Richards

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By Matthew Desmond

• Published March 9, 2023 Updated April 3, 2023

In the past 50 years, scientists have mapped the entire human genome and eradicated smallpox. Here in the United States, infant-mortality rates and deaths from heart disease have fallen by roughly 70 percent, and the average American has gained almost a decade of life. Climate change was recognized as an existential threat. The internet was invented.

On the problem of poverty, though, there has been no real improvement — just a long stasis. As estimated by the federal government’s poverty line, 12.6 percent of the U.S. population was poor in 1970; two decades later, it was 13.5 percent; in 2010, it was 15.1 percent; and in 2019, it was 10.5 percent. To graph the share of Americans living in poverty over the past half-century amounts to drawing a line that resembles gently rolling hills. The line curves slightly up, then slightly down, then back up again over the years, staying steady through Democratic and Republican administrations, rising in recessions and falling in boom years.

What accounts for this lack of progress? It cannot be chalked up to how the poor are counted: Different measures spit out the same embarrassing result. When the government began reporting the Supplemental Poverty Measure in 2011, designed to overcome many of the flaws of the Official Poverty Measure, including not accounting for regional differences in costs of living and government benefits, the United States officially gained three million more poor people. Possible reductions in poverty from counting aid like food stamps and tax benefits were more than offset by recognizing how low-income people were burdened by rising housing and health care costs.

The American poor have access to cheap, mass-produced goods, as every American does. But that doesn’t mean they can access what matters most.

Any fair assessment of poverty must confront the breathtaking march of material progress. But the fact that standards of living have risen across the board doesn’t mean that poverty itself has fallen. Forty years ago, only the rich could afford cellphones. But cellphones have become more affordable over the past few decades, and now most Americans have one, including many poor people. This has led observers like Ron Haskins and Isabel Sawhill, senior fellows at the Brookings Institution, to assert that “access to certain consumer goods,” like TVs, microwave ovens and cellphones, shows that “the poor are not quite so poor after all.”

No, it doesn’t. You can’t eat a cellphone. A cellphone doesn’t grant you stable housing, affordable medical and dental care or adequate child care. In fact, as things like cellphones have become cheaper, the cost of the most necessary of life’s necessities, like health care and rent, has increased. From 2000 to 2022 in the average American city, the cost of fuel and utilities increased by 115 percent. The American poor, living as they do in the center of global capitalism, have access to cheap, mass-produced goods, as every American does. But that doesn’t mean they can access what matters most. As Michael Harrington put it 60 years ago: “It is much easier in the United States to be decently dressed than it is to be decently housed, fed or doctored.”

Why, then, when it comes to poverty reduction, have we had 50 years of nothing? When I first started looking into this depressing state of affairs, I assumed America’s efforts to reduce poverty had stalled because we stopped trying to solve the problem. I bought into the idea, popular among progressives, that the election of President Ronald Reagan (as well as that of Prime Minister Margaret Thatcher in the United Kingdom) marked the ascendancy of market fundamentalism, or “neoliberalism,” a time when governments cut aid to the poor, lowered taxes and slashed regulations. If American poverty persisted, I thought, it was because we had reduced our spending on the poor. But I was wrong.

Reagan expanded corporate power, deeply cut taxes on the rich and rolled back spending on some antipoverty initiatives, especially in housing. But he was unable to make large-scale, long-term cuts to many of the programs that make up the American welfare state. Throughout Reagan’s eight years as president, antipoverty spending grew, and it continued to grow after he left office. Spending on the nation’s 13 largest means-tested programs — aid reserved for Americans who fall below a certain income level — went from $1,015 a person the year Reagan was elected president to $3,419 a person one year into Donald Trump’s administration, a 237 percent increase.

Most of this increase was due to health care spending, and Medicaid in particular. But even if we exclude Medicaid from the calculation, we find that federal investments in means-tested programs increased by 130 percent from 1980 to 2018, from $630 to $1,448 per person.

“Neoliberalism” is now part of the left’s lexicon, but I looked in vain to find it in the plain print of federal budgets, at least as far as aid to the poor was concerned. There is no evidence that the United States has become stingier over time. The opposite is true.

This makes the country’s stalled progress on poverty even more baffling. Decade after decade, the poverty rate has remained flat even as federal relief has surged.

If we have more than doubled government spending on poverty and achieved so little, one reason is that the American welfare state is a leaky bucket. Take welfare, for example: When it was administered through the Aid to Families With Dependent Children program, almost all of its funds were used to provide single-parent families with cash assistance. But when President Bill Clinton reformed welfare in 1996, replacing the old model with Temporary Assistance for Needy Families (TANF), he transformed the program into a block grant that gives states considerable leeway in deciding how to distribute the money. As a result, states have come up with rather creative ways to spend TANF dollars. Arizona has used welfare money to pay for abstinence-only sex education. Pennsylvania diverted TANF funds to anti-abortion crisis-pregnancy centers. Maine used the money to support a Christian summer camp. Nationwide, for every dollar budgeted for TANF in 2020, poor families directly received just 22 cents.

We’ve approached the poverty question by pointing to poor people themselves, when we should have been focusing on exploitation.

A fair amount of government aid earmarked for the poor never reaches them. But this does not fully solve the puzzle of why poverty has been so stubbornly persistent, because many of the country’s largest social-welfare programs distribute funds directly to people. Roughly 85 percent of the Supplemental Nutrition Assistance Program budget is dedicated to funding food stamps themselves, and almost 93 percent of Medicaid dollars flow directly to beneficiaries.

There are, it would seem, deeper structural forces at play, ones that have to do with the way the American poor are routinely taken advantage of. The primary reason for our stalled progress on poverty reduction has to do with the fact that we have not confronted the unrelenting exploitation of the poor in the labor, housing and financial markets.

As a theory of poverty, “exploitation” elicits a muddled response, causing us to think of course and but, no in the same instant. The word carries a moral charge, but social scientists have a fairly coolheaded way to measure exploitation: When we are underpaid relative to the value of what we produce, we experience labor exploitation; when we are overcharged relative to the value of something we purchase, we experience consumer exploitation. For example, if a family paid $1,000 a month to rent an apartment with a market value of $20,000, that family would experience a higher level of renter exploitation than a family who paid the same amount for an apartment with a market valuation of $100,000. When we don’t own property or can’t access credit, we become dependent on people who do and can, which in turn invites exploitation, because a bad deal for you is a good deal for me.

Our vulnerability to exploitation grows as our liberty shrinks. Because labor laws often fail to protect undocumented workers in practice, more than a third are paid below minimum wage, and nearly 85 percent are not paid overtime. Many of us who are U.S. citizens, or who crossed borders through official checkpoints, would not work for these wages. We don’t have to. If they migrate here as adults, those undocumented workers choose the terms of their arrangement. But just because desperate people accept and even seek out exploitative conditions doesn’t make those conditions any less exploitative. Sometimes exploitation is simply the best bad option.

Consider how many employers now get one over on American workers. The United States offers some of the lowest wages in the industrialized world. A larger share of workers in the United States make “low pay” — earning less than two-thirds of median wages — than in any other country belonging to the Organization for Economic Cooperation and Development. According to the group, nearly 23 percent of American workers labor in low-paying jobs, compared with roughly 17 percent in Britain, 11 percent in Japan and 5 percent in Italy. Poverty wages have swollen the ranks of the American working poor, most of whom are 35 or older.

One popular theory for the loss of good jobs is deindustrialization, which caused the shuttering of factories and the hollowing out of communities that had sprung up around them. Such a passive word, “deindustrialization” — leaving the impression that it just happened somehow, as if the country got deindustrialization the way a forest gets infested by bark beetles. But economic forces framed as inexorable, like deindustrialization and the acceleration of global trade, are often helped along by policy decisions like the 1994 North American Free Trade Agreement, which made it easier for companies to move their factories to Mexico and contributed to the loss of hundreds of thousands of American jobs. The world has changed, but it has changed for other economies as well. Yet Belgium and Canada and many other countries haven’t experienced the kind of wage stagnation and surge in income inequality that the United States has.

Those countries managed to keep their unions. We didn’t. Throughout the 1950s and 1960s, nearly a third of all U.S. workers carried union cards. These were the days of the United Automobile Workers, led by Walter Reuther, once savagely beaten by Ford’s brass-knuckle boys, and of the mighty American Federation of Labor and Congress of Industrial Organizations that together represented around 15 million workers, more than the population of California at the time.

In their heyday, unions put up a fight. In 1970 alone, 2.4 million union members participated in work stoppages, wildcat strikes and tense standoffs with company heads. The labor movement fought for better pay and safer working conditions and supported antipoverty policies. Their efforts paid off for both unionized and nonunionized workers, as companies like Eastman Kodak were compelled to provide generous compensation and benefits to their workers to prevent them from organizing. By one estimate, the wages of nonunionized men without a college degree would be 8 percent higher today if union strength remained what it was in the late 1970s, a time when worker pay climbed, chief-executive compensation was reined in and the country experienced the most economically equitable period in modern history.

It is important to note that Old Labor was often a white man’s refuge. In the 1930s, many unions outwardly discriminated against Black workers or segregated them into Jim Crow local chapters. In the 1960s, unions like the Brotherhood of Railway and Steamship Clerks and the United Brotherhood of Carpenters and Joiners of America enforced segregation within their ranks. Unions harmed themselves through their self-defeating racism and were further weakened by a changing economy. But organized labor was also attacked by political adversaries. As unions flagged, business interests sensed an opportunity. Corporate lobbyists made deep inroads in both political parties, beginning a public-relations campaign that pressured policymakers to roll back worker protections.

A national litmus test arrived in 1981, when 13,000 unionized air traffic controllers left their posts after contract negotiations with the Federal Aviation Administration broke down. When the workers refused to return, Reagan fired all of them. The public’s response was muted, and corporate America learned that it could crush unions with minimal blowback. And so it went, in one industry after another.

Today almost all private-sector employees (94 percent) are without a union, though roughly half of nonunion workers say they would organize if given the chance. They rarely are. Employers have at their disposal an arsenal of tactics designed to prevent collective bargaining, from hiring union-busting firms to telling employees that they could lose their jobs if they vote yes. Those strategies are legal, but companies also make illegal moves to block unions, like disciplining workers for trying to organize or threatening to close facilities. In 2016 and 2017, the National Labor Relations Board charged 42 percent of employers with violating federal law during union campaigns. In nearly a third of cases, this involved illegally firing workers for organizing.

Corporate lobbyists told us that organized labor was a drag on the economy — that once the companies had cleared out all these fusty, lumbering unions, the economy would rev up, raising everyone’s fortunes. But that didn’t come to pass. The negative effects of unions have been wildly overstated, and there is now evidence that unions play a role in increasing company productivity, for example by reducing turnover. The U.S. Bureau of Labor Statistics measures productivity as how efficiently companies turn inputs (like materials and labor) into outputs (like goods and services). Historically, productivity, wages and profits rise and fall in lock step. But the American economy is less productive today than it was in the post-World War II period, when unions were at peak strength. The economies of other rich countries have slowed as well, including those with more highly unionized work forces, but it is clear that diluting labor power in America did not unleash economic growth or deliver prosperity to more people. “We were promised economic dynamism in exchange for inequality,” Eric Posner and Glen Weyl write in their book “Radical Markets.” “We got the inequality, but dynamism is actually declining.”

As workers lost power, their jobs got worse. For several decades after World War II, ordinary workers’ inflation-adjusted wages (known as “real wages”) increased by 2 percent each year. But since 1979, real wages have grown by only 0.3 percent a year. Astonishingly, workers with a high school diploma made 2.7 percent less in 2017 than they would have in 1979, adjusting for inflation. Workers without a diploma made nearly 10 percent less.

Lousy, underpaid work is not an indispensable, if regrettable, byproduct of capitalism, as some business defenders claim today. (This notion would have scandalized capitalism’s earliest defenders. John Stuart Mill, arch advocate of free people and free markets, once said that if widespread scarcity was a hallmark of capitalism, he would become a communist.) But capitalism is inherently about owners trying to give as little, and workers trying to get as much, as possible. With unions largely out of the picture, corporations have chipped away at the conventional midcentury work arrangement, which involved steady employment, opportunities for advancement and raises and decent pay with some benefits.

As the sociologist Gerald Davis has put it: Our grandparents had careers. Our parents had jobs. We complete tasks. Or at least that has been the story of the American working class and working poor.

Poor Americans aren’t just exploited in the labor market. They face consumer exploitation in the housing and financial markets as well.

There is a long history of slum exploitation in America. Money made slums because slums made money. Rent has more than doubled over the past two decades, rising much faster than renters’ incomes. Median rent rose from $483 in 2000 to $1,216 in 2021. Why have rents shot up so fast? Experts tend to offer the same rote answers to this question. There’s not enough housing supply, they say, and too much demand. Landlords must charge more just to earn a decent rate of return. Must they? How do we know?

We need more housing; no one can deny that. But rents have jumped even in cities with plenty of apartments to go around. At the end of 2021, almost 19 percent of rental units in Birmingham, Ala., sat vacant, as did 12 percent of those in Syracuse, N.Y. Yet rent in those areas increased by roughly 14 percent and 8 percent, respectively, over the previous two years. National data also show that rental revenues have far outpaced property owners’ expenses in recent years, especially for multifamily properties in poor neighborhoods. Rising rents are not simply a reflection of rising operating costs. There’s another dynamic at work, one that has to do with the fact that poor people — and particularly poor Black families — don’t have much choice when it comes to where they can live. Because of that, landlords can overcharge them, and they do.

A study I published with Nathan Wilmers found that after accounting for all costs, landlords operating in poor neighborhoods typically take in profits that are double those of landlords operating in affluent communities. If down-market landlords make more, it’s because their regular expenses (especially their mortgages and property-tax bills) are considerably lower than those in upscale neighborhoods. But in many cities with average or below-average housing costs — think Buffalo, not Boston — rents in the poorest neighborhoods are not drastically lower than rents in the middle-class sections of town. From 2015 to 2019, median monthly rent for a two-bedroom apartment in the Indianapolis metropolitan area was $991; it was $816 in neighborhoods with poverty rates above 40 percent, just around 17 percent less. Rents are lower in extremely poor neighborhoods, but not by as much as you would think.

Yet where else can poor families live? They are shut out of homeownership because banks are disinclined to issue small-dollar mortgages, and they are also shut out of public housing, which now has waiting lists that stretch on for years and even decades. Struggling families looking for a safe, affordable place to live in America usually have but one choice: to rent from private landlords and fork over at least half their income to rent and utilities. If millions of poor renters accept this state of affairs, it’s not because they can’t afford better alternatives; it’s because they often aren’t offered any.

You can read injunctions against usury in the Vedic texts of ancient India, in the sutras of Buddhism and in the Torah. Aristotle and Aquinas both rebuked it. Dante sent moneylenders to the seventh circle of hell. None of these efforts did much to stem the practice, but they do reveal that the unprincipled act of trapping the poor in a cycle of debt has existed at least as long as the written word. It might be the oldest form of exploitation after slavery. Many writers have depicted America’s poor as unseen, shadowed and forgotten people: as “other” or “invisible.” But markets have never failed to notice the poor, and this has been particularly true of the market for money itself.

The deregulation of the banking system in the 1980s heightened competition among banks. Many responded by raising fees and requiring customers to carry minimum balances. In 1977, over a third of banks offered accounts with no service charge. By the early 1990s, only 5 percent did. Big banks grew bigger as community banks shuttered, and in 2021, the largest banks in America charged customers almost $11 billion in overdraft fees. Previous research showed that just 9 percent of account holders paid 84 percent of these fees. Who were the unlucky 9 percent? Customers who carried an average balance of less than $350. The poor were made to pay for their poverty.

In 2021, the average fee for overdrawing your account was $33.58. Because banks often issue multiple charges a day, it’s not uncommon to overdraw your account by $20 and end up paying $200 for it. Banks could (and do) deny accounts to people who have a history of overextending their money, but those customers also provide a steady revenue stream for some of the most powerful financial institutions in the world.

Every year: almost $11 billion in overdraft fees, $1.6 billion in check-cashing fees and up to $8.2 billion in payday-loan fees.

According to the F.D.I.C., one in 19 U.S. households had no bank account in 2019, amounting to more than seven million families. Compared with white families, Black and Hispanic families were nearly five times as likely to lack a bank account. Where there is exclusion, there is exploitation. Unbanked Americans have created a market, and thousands of check-cashing outlets now serve that market. Check-cashing stores generally charge from 1 to 10 percent of the total, depending on the type of check. That means that a worker who is paid $10 an hour and takes a $1,000 check to a check-cashing outlet will pay $10 to $100 just to receive the money he has earned, effectively losing one to 10 hours of work. (For many, this is preferable to the less-predictable exploitation by traditional banks, with their automatic overdraft fees. It’s the devil you know.) In 2020, Americans spent $1.6 billion just to cash checks. If the poor had a costless way to access their own money, over a billion dollars would have remained in their pockets during the pandemic-induced recession.

Poverty can mean missed payments, which can ruin your credit. But just as troublesome as bad credit is having no credit score at all, which is the case for 26 million adults in the United States. Another 19 million possess a credit history too thin or outdated to be scored. Having no credit (or bad credit) can prevent you from securing an apartment, buying insurance and even landing a job, as employers are increasingly relying on credit checks during the hiring process. And when the inevitable happens — when you lose hours at work or when the car refuses to start — the payday-loan industry steps in.

For most of American history, regulators prohibited lending institutions from charging exorbitant interest on loans. Because of these limits, banks kept interest rates between 6 and 12 percent and didn’t do much business with the poor, who in a pinch took their valuables to the pawnbroker or the loan shark. But the deregulation of the banking sector in the 1980s ushered the money changers back into the temple by removing strict usury limits. Interest rates soon reached 300 percent, then 500 percent, then 700 percent. Suddenly, some people were very interested in starting businesses that lent to the poor. In recent years, 17 states have brought back strong usury limits, capping interest rates and effectively prohibiting payday lending. But the trade thrives in most places. The annual percentage rate for a two-week $300 loan can reach 460 percent in California, 516 percent in Wisconsin and 664 percent in Texas.

Roughly a third of all payday loans are now issued online, and almost half of borrowers who have taken out online loans have had lenders overdraw their bank accounts. The average borrower stays indebted for five months, paying $520 in fees to borrow $375. Keeping people indebted is, of course, the ideal outcome for the payday lender. It’s how they turn a $15 profit into a $150 one. Payday lenders do not charge high fees because lending to the poor is risky — even after multiple extensions, most borrowers pay up. Lenders extort because they can.

Every year: almost $11 billion in overdraft fees, $1.6 billion in check-cashing fees and up to $8.2 billion in payday-loan fees. That’s more than $55 million in fees collected predominantly from low-income Americans each day — not even counting the annual revenue collected by pawnshops and title loan services and rent-to-own schemes. When James Baldwin remarked in 1961 how “extremely expensive it is to be poor,” he couldn’t have imagined these receipts.

“Predatory inclusion” is what the historian Keeanga-Yamahtta Taylor calls it in her book “Race for Profit,” describing the longstanding American tradition of incorporating marginalized people into housing and financial schemes through bad deals when they are denied good ones. The exclusion of poor people from traditional banking and credit systems has forced them to find alternative ways to cash checks and secure loans, which has led to a normalization of their exploitation. This is all perfectly legal, after all, and subsidized by the nation’s richest commercial banks. The fringe banking sector would not exist without lines of credit extended by the conventional one. Wells Fargo and JPMorgan Chase bankroll payday lenders like Advance America and Cash America. Everybody gets a cut.

Poverty isn’t simply the condition of not having enough money. It’s the condition of not having enough choice and being taken advantage of because of that. When we ignore the role that exploitation plays in trapping people in poverty, we end up designing policy that is weak at best and ineffective at worst. For example, when legislation lifts incomes at the bottom without addressing the housing crisis, those gains are often realized instead by landlords, not wholly by the families the legislation was intended to help. A 2019 study conducted by the Federal Reserve Bank of Philadelphia found that when states raised minimum wages, families initially found it easier to pay rent. But landlords quickly responded to the wage bumps by increasing rents, which diluted the effect of the policy. This happened after the pandemic rescue packages, too: When wages began to rise in 2021 after worker shortages, rents rose as well, and soon people found themselves back where they started or worse.

Antipoverty programs work. Each year, millions of families are spared the indignities and hardships of severe deprivation because of these government investments. But our current antipoverty programs cannot abolish poverty by themselves. The Johnson administration started the War on Poverty and the Great Society in 1964. These initiatives constituted a bundle of domestic programs that included the Food Stamp Act, which made food aid permanent; the Economic Opportunity Act, which created Job Corps and Head Start; and the Social Security Amendments of 1965, which founded Medicare and Medicaid and expanded Social Security benefits. Nearly 200 pieces of legislation were signed into law in President Lyndon B. Johnson’s first five years in office, a breathtaking level of activity. And the result? Ten years after the first of these programs were rolled out in 1964, the share of Americans living in poverty was half what it was in 1960.

But the War on Poverty and the Great Society were started during a time when organized labor was strong, incomes were climbing, rents were modest and the fringe banking industry as we know it today didn’t exist. Today multiple forms of exploitation have turned antipoverty programs into something like dialysis, a treatment designed to make poverty less lethal, not to make it disappear.

This means we don’t just need deeper antipoverty investments. We need different ones, policies that refuse to partner with poverty, policies that threaten its very survival. We need to ensure that aid directed at poor people stays in their pockets, instead of being captured by companies whose low wages are subsidized by government benefits, or by landlords who raise the rents as their tenants’ wages rise, or by banks and payday-loan outlets who issue exorbitant fines and fees. Unless we confront the many forms of exploitation that poor families face, we risk increasing government spending only to experience another 50 years of sclerosis in the fight against poverty.

The best way to address labor exploitation is to empower workers. A renewed contract with American workers should make organizing easy. As things currently stand, unionizing a workplace is incredibly difficult. Under current labor law, workers who want to organize must do so one Amazon warehouse or one Starbucks location at a time. We have little chance of empowering the nation’s warehouse workers and baristas this way. This is why many new labor movements are trying to organize entire sectors. The Fight for $15 campaign, led by the Service Employees International Union, doesn’t focus on a single franchise (a specific McDonald’s store) or even a single company (McDonald’s) but brings together workers from several fast-food chains. It’s a new kind of labor power, and one that could be expanded: If enough workers in a specific economic sector — retail, hotel services, nursing — voted for the measure, the secretary of labor could establish a bargaining panel made up of representatives elected by the workers. The panel could negotiate with companies to secure the best terms for workers across the industry. This is a way to organize all Amazon warehouses and all Starbucks locations in a single go.

Sectoral bargaining, as it’s called, would affect tens of millions of Americans who have never benefited from a union of their own, just as it has improved the lives of workers in Europe and Latin America. The idea has been criticized by members of the business community, like the U.S. Chamber of Commerce, which has raised concerns about the inflexibility and even the constitutionality of sectoral bargaining, as well as by labor advocates, who fear that industrywide policies could nullify gains that existing unions have made or could be achieved only if workers make other sacrifices. Proponents of the idea counter that sectoral bargaining could even the playing field, not only between workers and bosses, but also between companies in the same sector that would no longer be locked into a race to the bottom, with an incentive to shortchange their work force to gain a competitive edge. Instead, the companies would be forced to compete over the quality of the goods and services they offer. Maybe we would finally reap the benefits of all that economic productivity we were promised.

We must also expand the housing options for low-income families. There isn’t a single right way to do this, but there is clearly a wrong way: the way we’re doing it now. One straightforward approach is to strengthen our commitment to the housing programs we already have. Public housing provides affordable homes to millions of Americans, but it’s drastically underfunded relative to the need. When the wealthy township of Cherry Hill, N.J., opened applications for 29 affordable apartments in 2021, 9,309 people applied. The sky-high demand should tell us something, though: that affordable housing is a life changer, and families are desperate for it.

We could also pave the way for more Americans to become homeowners, an initiative that could benefit poor, working-class and middle-class families alike — as well as scores of young people. Banks generally avoid issuing small-dollar mortgages, not because they’re riskier — these mortgages have the same delinquency rates as larger mortgages — but because they’re less profitable. Over the life of a mortgage, interest on $1 million brings in a lot more money than interest on $75,000. This is where the federal government could step in, providing extra financing to build on-ramps to first-time homeownership. In fact, it already does so in rural America through the 502 Direct Loan Program, which has moved more than two million families into their own homes. These loans, fully guaranteed and serviced by the Department of Agriculture, come with low interest rates and, for very poor families, cover the entire cost of the mortgage, nullifying the need for a down payment. Last year, the average 502 Direct Loan was for $222,300 but cost the government only $10,370 per loan, chump change for such a durable intervention. Expanding a program like this into urban communities would provide even more low- and moderate-income families with homes of their own.

We should also ensure fair access to capital. Banks should stop robbing the poor and near-poor of billions of dollars each year, immediately ending exorbitant overdraft fees. As the legal scholar Mehrsa Baradaran has pointed out, when someone overdraws an account, banks could simply freeze the transaction or could clear a check with insufficient funds, providing customers a kind of short-term loan with a low interest rate of, say, 1 percent a day.

States should rein in payday-lending institutions and insist that lenders make it clear to potential borrowers what a loan is ultimately likely to cost them. Just as fast-food restaurants must now publish calorie counts next to their burgers and shakes, payday-loan stores should publish the average overall cost of different loans. When Texas adopted disclosure rules, residents took out considerably fewer bad loans. If Texas can do this, why not California or Wisconsin? Yet to stop financial exploitation, we need to expand, not limit, low-income Americans’ access to credit. Some have suggested that the government get involved by having the U.S. Postal Service or the Federal Reserve issue small-dollar loans. Others have argued that we should revise government regulations to entice commercial banks to pitch in. Whatever our approach, solutions should offer low-income Americans more choice, a way to end their reliance on predatory lending institutions that can get away with robbery because they are the only option available.

In Tommy Orange’s novel, “There There,” a man trying to describe the problem of suicides on Native American reservations says: “Kids are jumping out the windows of burning buildings, falling to their deaths. And we think the problem is that they’re jumping.” The poverty debate has suffered from a similar kind of myopia. For the past half-century, we’ve approached the poverty question by pointing to poor people themselves — posing questions about their work ethic, say, or their welfare benefits — when we should have been focusing on the fire. The question that should serve as a looping incantation, the one we should ask every time we drive past a tent encampment, those tarped American slums smelling of asphalt and bodies, or every time we see someone asleep on the bus, slumped over in work clothes, is simply: Who benefits? Not: Why don’t you find a better job? Or: Why don’t you move? Or: Why don’t you stop taking out payday loans? But: Who is feeding off this?

Those who have amassed the most power and capital bear the most responsibility for America’s vast poverty: political elites who have utterly failed low-income Americans over the past half-century; corporate bosses who have spent and schemed to prioritize profits over families; lobbyists blocking the will of the American people with their self-serving interests; property owners who have exiled the poor from entire cities and fueled the affordable-housing crisis. Acknowledging this is both crucial and deliciously absolving; it directs our attention upward and distracts us from all the ways (many unintentional) that we — we the secure, the insured, the housed, the college-educated, the protected, the lucky — also contribute to the problem.

Corporations benefit from worker exploitation, sure, but so do consumers, who buy the cheap goods and services the working poor produce, and so do those of us directly or indirectly invested in the stock market. Landlords are not the only ones who benefit from housing exploitation; many homeowners do, too, their property values propped up by the collective effort to make housing scarce and expensive. The banking and payday-lending industries profit from the financial exploitation of the poor, but so do those of us with free checking accounts, as those accounts are subsidized by billions of dollars in overdraft fees.

Living our daily lives in ways that express solidarity with the poor could mean we pay more; anti-exploitative investing could dampen our stock portfolios. By acknowledging those costs, we acknowledge our complicity. Unwinding ourselves from our neighbors’ deprivation and refusing to live as enemies of the poor will require us to pay a price. It’s the price of our restored humanity and renewed country.

Matthew Desmond is a professor of sociology at Princeton University and a contributing writer for the magazine. His latest book, “Poverty, by America,” from which this article is adapted, is being published on March 21 by Crown.

An earlier version of this article referred incorrectly to the legal protections for undocumented workers. They are afforded rights under U.S. labor laws, though in practice those laws often fail to protect them.

An earlier version of this article implied an incorrect date for a statistic about overdraft fees. The research was conducted between 2005 and 2012, not in 2021.

How we handle corrections

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11 Poverty, Health, and Healthcare

Darrell J. Gaskin is an associate professor of health economics at Johns Hopkins University.

Eric T. Roberts is a graduate student in the department of health policy and management at Johns Hopkins University.

• Published: 28 December 2012
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This article discusses the relationship between poverty status and healthcare. The article is organized as follows. Section 2 reviews the empirical literature on the relationship between poverty and health. Section 3 discusses and gives an overview of Medicaid, the nation's public health insurance program for poor and disabled individuals. It then discusses five major issues that health economists have studied about the Medicaid program, including its impact on health outcomes for the poor and the extent to which it crowds out private insurance. Section 4 offers a conceptual framework to explain the relationships between poverty and health, and poverty and healthcare. Section 5 presents analyses of the association between poverty status and health and healthcare utilization by using the 2009 National Health Interview Survey and 2006 Medical Expenditure Survey. Section 6 concludes with a discussion of how public policy can address the problems created by the poverty-health and poverty-healthcare relationships.

1. Introduction

Health and healthcare disparities by socioeconomic status are pervasive. Poverty status influences every aspect of a person’s health, from the risk of dying at birth to the likelihood of dying from a chronic disease. Poverty status determines access to food, housing, healthcare, and community-level risks that influence a person’s general health status and risks of mortality and morbidity. Poverty status affects access to healthcare, the quality of healthcare, and healthcare outcomes. This chapter discusses the relationship between poverty status and healthcare and suggests ways in which society should address how those in poverty have difficulty with their healthcare.

This chapter has six sections. Section 2 reviews the empirical literature on the relationship between poverty and health. This section draws on the fields of economics, public health, health-services research, and sociology. We discuss the health-wealth gradient and provide an overview of research that explores the relationship between poverty and mortality, chronic conditions, self-reported health status, and other priority conditions. Section 3 discusses and gives an overview of Medicaid, the nation’s public health insurance program for poor and disabled individuals. We then discuss five major issues that health economists have studied about the Medicaid program, including its impact on health outcomes for the poor and the extent to which it crowds out private insurance. Section 4 offers a conceptual framework to explain the relationships between poverty and health, and poverty and healthcare. Section 5 presents analyses of the association between poverty status and health and healthcare utilization by using the 2009 National Health Interview Survey and 2006 Medical Expenditure Survey. Specifically, we present findings showing the association between poverty status, and access to office-based care, hospital-based care, dental and pharmacy care, and preventive and screening services. Section 6 concludes the chapter with a discussion of how public policy can address the problems created by the poverty-health and poverty-healthcare relationships. In particular, we outline policies in the Affordable Care Act as well as other strategies that policy makers should consider. Finally, we suggest directions for future research with a focus on the potential implications of health reform for low-income populations.

2. Literature Review: Poverty and Health

In 2009, 14.3 percent of the US population was poor and another 4.4 percent was near poor (between 100 and 125 percent of the federal poverty level) (DeNavas-Walt, Proctor, Smith, and US Census Bureau 2010 ). This means that over one in six persons in the nation are at risk of poor health and poor access to healthcare. Poverty can negatively affect every aspect of a person’s health because it negatively impacts the determinants of health, that is, access to quality food, clothing, shelter, transportation, healthcare, and a healthy environment. These are key risk factors for mortality and morbidity. The gradient between socioeconomic status and health is a well-established empirical observation (Adler and Ostrove 1999 ; Deaton 2002 ; Duncan et al. 2002 ; Mechanic 1989 ). The literature on the association between socioeconomic status and health indicates that the poor and disadvantaged are most at risk (Deaton 2002 ). Mortality, morbidity rates, and self-reported health status are inversely related to many correlates of socioeconomic status such as income, wealth, education, and social class (Kitagawa and Hauser 1973 ; Williams and Collins 1995 ). According to the National Longitudinal Mortality Study, people with family incomes in 1980 of less than $5,000 had a life expectancy 25 percent lower than those with family incomes above $50,000 (Roget et al., 1992 ). Data from the Panel Survey of Income Dynamics from 1984–94 shows a nearly four-fold difference in the median wealth of households when the head of household reported excellent health ($127,900) compared to when the head of household reported poor health ($34,700) (Smith 1999 ). While there is strong evidence that the gradient exists at all income/wealth levels, the relationship is nonlinear with effects diminishing across income and wealth distributions (Deaton 2002 ; Rodgers 2002 ; Smith 1999 ; Wilkinson 1986 , 1990 , 1992 , 2000 and 2002 ). The strongest effects were observed among the poor and weakest among the affluent.

The negative health consequences for the poor may be greater in areas with substantial income inequality. Several researchers have found that mortality has a strong association with income inequality (Cooper et al. 2001 ; Fang et al. 1999 ; Franzini et al. 2001 ; Wilkinson 1990 , 1992 , 2000 ). Rodgers ( 2002 ) reported that there is as much as a five-year difference in average life expectancy between members of relatively egalitarian societies and members of relatively less egalitarian societies. Theoretically, income inequality adversely affects population health because it erodes community cohesion and social capital (Kawachi et al. 1997 ; Putnam 2000 ). People who live in areas with high levels of income inequality are less likely to trust their neighbors. This lack of trust is associated with increased risk of mortality (Szreter and Woolcock 2004 ). This finding, however, has been disputed. Other researchers have not found a positive relationship between income inequality and mortality after adjusting for other factors such as race, education, and urbanization (Deaton and Lubotsky 2002 ; Deaton and Paxson 2001 ; Mellor and Milyo 2001 ; Muller 2002 ). These studies suggest that the association between income inequality and mortality found in area-level cross-sectional data analyses is an ecological fallacy. Research focusing on individuals also casts doubt on the association between income inequality and health. LeClere and Soobader ( 2000 ) reported that, after adjusting for family income and other individual covariates, they did not find a relationship between income inequality and health for African Americans of all ages, elder whites, and middle-aged whites who resided in areas with low- to moderate-income inequality. LaVeist and colleagues ( 2011 ) found that community factors account for a substantial portion of health disparities observed between African Americans and whites. The authors found that race-based disparities in health status and access to care, which typically are observed in national surveys, substantially disappeared in a survey of residents of a low-income, socioeconomically integrated neighborhood in Baltimore. Their findings highlight the importance of examining community-level determinants of health.

Higher mortality and morbidity rates among poor persons are also related to their labor market opportunities. The two Whitehall studies of British civil servants documented the strong relationship between occupation and health status (Wilkinson, 1986 ). The first Whitehall study, which began in 1967 and focused exclusively on men, found a steep inverse relationship between employment grade and poor health outcomes, including mortality for many diseases. The second Whitehall study, which was conducted 20 years later and included women, demonstrated that there was a four-fold higher relative risk of morbidity from the lowest to the highest grade (Marmot et al. 1991 ). Psychosocial factors such as work-related stress and limited social support networks are offered as explanations for the observed gradient. Singh-Manoux and colleagues ( 2003 ) present evidence that low subjective social status is a strong predictor of ill health. Subjective social status reflects individuals’ self-assessment of their position measured by money, education, and occupation in society relative to others.

2.1. Effects of Education on Health

Because education affects income, wealth, and occupational status, it may be the case that education, and not income, is protective of health (Fuchs 1989 , 1993 ; Garber 1989 ; Grossman 1975 ). A study using data from the National Health and Nutrition Examination Survey I Epidemiologic Follow-Up Study found that increasing education by one additional year increases life expectancy at age 35 by at least 1.2 years (Lleras-Muney 2001 ). Ross and Mirowsky ( 1999 ) tested three models to explain the relationship between education and health: the quantity model, the credential model, and the selectivity model. Each of these models works through the labor market. The quantity model argues that schooling builds human capital and this leads to better labor market success. In turn, labor market success promotes a sense of personal control, social support, and a healthy lifestyle. In the credential model, formal education is a screening tool used by employers to make hiring decisions (Spence 1973 ). Thus, an extra year of schooling only improves labor market opportunities if it results in a degree. In the selectivity models, it is the quality of the education, the extent of the college’s credential or network, the school culture, and selection of better students that improve job market opportunities. Ross and Mirowsky found evidence to support the quantity model. They identify full-time employment, fulfilling work, high household income, high level of personal control, and social support as possible mediators. Adjusting for these variables reduces the estimated effect of years of school on perceived health status by 60 percent. Ross and Mirowsky did not estimate the effect of schooling on these mediators to see how much of the variables’ effect can be attributed to education.

In addition to work and economic conditions, education also affects health through social psychological resources of perceived control, social support, and health lifestyle (Murrell and Meeks 2002 ; Ross and Wu 1995 ). In two national samples of US households, Ross and Wu ( 1995 ) demonstrated that education is strongly and positively associated with two measures of health—self-reported health status and physical functioning. By comparing a base regression model that includes education and demographic information to ones that successively add work and economic conditions, measures of social-psychological resources, and health lifestyle measures, they conclude that the diminishing coefficient on education is evidence that these other factors are mediators. Including social-psychological resources and health lifestyle measures reduces the estimated effect of education by 36 percent. It is hard to interpret the results of this analysis because the change in the coefficient on education could simply be due to omitted variable bias. The main limitation of this analysis is that it is basically a cross-sectional analysis. A true longitudinal analysis would examine the effects of educational attainment on these pathway variables and would attempt to parse out the portion of the impact of these pathway variables that is due to education.

2.2. Long-Term Effects of Being Born Poor

Poor adult health may be a legacy of childhood poverty. Barker ( 1997 ) investigates the lasting effects of the fetal environment on future health. He and his colleagues argue that an embryo depends on a steady supply of nutrients and oxygen and that the size of the uterus is also important. A critical period of intra-uterine life occurs when cells are dividing rapidly. A reaction to lack of nutrients or oxygen is to slow rates of cell development of some organs, thereby “programming” the body to the onset of later life diseases such as coronary heart disease, stroke, diabetes, and hypertension (Barker et al. 1989 ).

A study of the health-wealth gradient in children in the United States by Case and colleagues found that family poverty worsens the effects of childhood diseases. For instance, poverty tends to increase the number of days an asthmatic child spends in bed. The study found that family poverty slowed gains in health as children matured. The authors also examined how family income, child’s age, poor health at birth, and interactions of these factors influence a child’s health status. The study found that poverty moderates the relationship between poor health at birth and current health status (Case, Lubotsky, and Paxson 2002 ).

These results lend support to the hypothesis that childhood poverty can have sustained health consequences during a person’s adulthood. In a British 1946 national cohort study, events of early childhood have been shown to be predictors of cardiovascular, respiratory, and neurological health for middle-aged adults (Wadsworth and Kuh 1997 ). Other studies concur that childhood exposure to poverty is associated with long-term adverse health outcomes (Power et al. 1999 ; Rahkonen et al. 1997 ).

2.3. Poverty and Health Outcomes

Poverty impedes access to healthcare and is itself associated with poor health outcomes. For example, impoverished cancer patients are less likely to be diagnosed at an early disease stage, often receive less timely treatment, and have lower survival rates than nonpoor cancer patients (Albano et al. 2007 ; American Cancer Society 2008 ; Bradley et al. 2001 ). Poor persons with diabetes are likely to have poor glycemic control and to suffer higher rates of complications such as amputations and blindness (Agency for Healthcare Research and Quality 2010 ; Braunwald et al. 2004 ; Elders and Murphy 2001 ; Ostchega et al. 2008 ). Socioeconomic status is negatively associated with poor outcomes and lower quality of life for cardiac patients (Macabasco-O’Connell et al. 2010 ; Shaw et al. 2008 ; Skodova et al. 2009 ). Hypertensive patients were less likely to have their blood pressure controlled and were at greater risk for complications and mortality (Bell et al. 2004 ; Colhoun et al. 1998 ; Sharma et al. 2004 ). Birth outcomes are associated with poverty status. Poverty elevates the risk of infant mortality, low birth weight, and preterm birth (Hughes and Simpson, 1995 ; Olson et al., 2010 ).

3. Medicaid Overview

Poor and near-poor persons in the United States depend on Medicaid and the State Children’s Health Insurance Program (SCHIP) for access to healthcare services. Medicaid and SCHIP finance healthcare services for low-income families, individuals with disabilities, and elderly poor adults. Medicaid covers more than 58 million persons and SCHIP covers an additional 5 million children in near-poor families. In 2010, Medicaid provided coverage for approximately 56 percent of low-income children and 21 percent of low-income adults. Medicaid also supplements Medicare benefits for about 9 million low-income seniors and younger individuals with disabilities, known as “dual eligibles.” Historically, childless adults have been excluded from Medicaid eligibility; however, as a result of the 2010 Patient Protection and Affordable Care Act (PPACA), those with incomes below 133 percent of the federal poverty level (FPL) will be brought into the program beginning in 2014.

The federal government and states jointly finance Medicaid and SCHIP. The Centers for Medicare and Medicaid Services (CMS) oversees the programs and ensures that they meet federal standards. Each state, however, is responsible for administering its own programs. CMS mandates that Medicaid provide a minimum set of benefits to enrollees, although states can choose to supplement these benefits. Medicaid’s benefits package is more generous than low-cost private insurance plans, and it requires little to no enrollee cost sharing. Covered services for children in both Medicaid and SCHIP are comprehensive, including basic healthcare services (physician, hospital, laboratory, and x-ray services; early and periodic screening; diagnostic and treatment services for individuals under age 21; and nursing facility services for persons over 21). States have the option of covering other services such as prescription drugs, dental, vision, physical therapy, and prosthetic devices. While the benefit package is generous, recipients face barriers to care because low reimbursement rates deter provider participation (Bronstein et al. 2004 ; Mitchell 1991 ; Perloff et al. 1997 ).

Medicaid cost taxpayers about $339 billion in 2009. The federal and state shares of the program’s cost are determined by the federal matching assistance percentage (FMAP), which varies by state based on states’ personal income levels, and the FMAP ranges from 50 to 75 percent. On average, the federal government finances 57 percent of the program’s costs, while states contribute the remaining 43 percent. To manage the ongoing cost of financing the program, states have increasingly contracted with health maintenance organizations (HMOs), on a capitated basis, to manage benefits for Medicaid enrollees. Under capitation, HMOs receive a fixed monthly payment per Medicaid enrollee, independent of the services provided. This flat fee structure helps Medicaid programs control costs and may also help to achieve more predictable expenditures. As of 2010, nearly 75 percent of Medicaid beneficiaries were enrolled in managed care plans. Most enrollees in Medicaid managed care plans are women and their children; similarly, the majority of children in the SCHIP program are enrolled in HMOs. The so-called Medicaid managed care population tends to have relatively low healthcare expenditures. Dual eligibles, who represent 15 percent of Medicaid’s enrolled population, account for 40 percent of program costs. The states’ ability to finance their share of Medicaid costs becomes especially challenging during recessions, because higher unemployment increases Medicaid enrollment, while, simultaneously, tax revenues decline.

Economists have studied several aspects of the Medicaid program. We focus on six major issues: (1) Does Medicaid enrollment improve health outcomes for the poor? (2) How do eligibility criteria, family circumstances, and availability of charity care affect Medicaid take-up rates? (3) How does Medicaid reimbursement affect provider participation and the availability of services? (4) Does Medicaid crowd out the demand for private insurance? (5) How has managed care impacted Medicaid? (6) How is Medicaid involved in the structure of the heathcare safety net, and what are other determinants of access to free and reduced-cost care for those without access to Medicaid coverage?

3.1. Medicaid and Health Outcomes

The infant mortality rate in the United States has declined from 25.9 deaths per 1,000 live births in 1960 to 6.8 deaths per 1,000 live births in the 2000s (National Center for Health Statistics, 2008). This substantial decline is driven by a number of factors, including improvements in the availability and quality of healthcare for pregnant mothers and newborns, as well as reductions in maternal smoking and education gains for women. Some of the improvement in birth outcomes can be attributed to the introduction of Medicaid, and expansions of the program in the late 1980s and 1990s had helped mothers and their children. Medicaid has been shown to have a protective effect against poverty’s harmful influence on health. The benefits provided by Medicaid, however, do not accrue evenly across the population.

Using data on newborn health and mortality, Lin ( 2009 ) examined which factors contributed to reductions in infant mortality and health disparities from the 1980s to 2000. The study assessed the gap in infant mortality rates and physician-assessed scores of infant health for children born to mothers with high levels of educational attainment versus mothers with low levels of education. Infant health was measured with an Apgar score, which reflects the condition of a newborn child’s breathing, heart rate, skin condition, muscle tone, and response to stimuli. Using a regression-based decomposition model, Lin explored which parental and insurance status characteristics contributed to widening or narrowing disparities in infant mortality and Apgar scores by maternal education level. Having an African American mother widened the gap by 4.8 percent, while having a Hispanic mother reduced the gap by 5.9 percent. Notably, the greatest single contributor to reductions in the health gap was adequate prenatal care, accounting for 37 percent of the convergence in infant health outcomes.

Expansions in Medicaid eligibility and the introduction of SCHIP have increased access to care for low-income children during their developmental years. Currie, Decker, and Lin ( 2008 ) assessed how Medicaid eligibility expansions for young children in the late 1980s and early 1990s changed the effect of income as a predictor of a child’s health status and use of primary care, as well as health during adolescence. The authors estimated probability and two-stage least squares models that included as independent variables family income and interactions of family income, child age, and an indicator for the time period of the observation. The study found income to be a consistent predictor of health status and utilization. Moreover, the significance of income did not diminish in models that also controlled for Medicaid eligibility. The authors observed that eligibility expansions during early childhood, however, did exercise a protective effect against poverty on health status during adolescence. Another model revealed a significant effect of expansions in Medicaid eligibility during early childhood (ages 2–4) on health status between the ages of 9 and 17. Quite reasonably, the effect of the Medicaid expansion on use of healthcare services was more immediate (Currie et al. 2008 ).

This finding supports the hypothesis that childhood poverty has a cumulative effect on health over the life cycle. Early and sustained investment in a child’s health, enabled by expanded Medicaid eligibility, counteracted—but did not completely eliminate—the health-wealth gradient in Currie, Decker, and Lin’s analysis. Likewise, Barker ( 1997 ) found that interventions earlier in life to enhance nutrition and development produce greater health gains than interventions at later stages of development. Barker’s epidemiological findings are supported by Case, Lubotsky, and Paxson ( 2002 ), who found that low parental income prior to and immediately following birth was a strong predictor of the child’s poor health. Further, improvements in health with age are slowed by poverty. The authors found that Medicaid coverage only mitigates, but does not remove, the effect of income on health. Consequently, they speculated that the best defense against poor child health is an early and continual absence of household poverty, which can promote ongoing investment in health.

A challenge that researchers often face in estimating the effect of Medicaid enrollment on health and healthcare utilization is the absence of a true experimental study, in which a population is randomized to intervention and control groups. A recent study by Finkelstein and colleagues ( 2011 ) took advantage of a Medicaid expansion by the Oregon Health Plan, which in 2008 allowed low-income adults who were not categorically eligible for Medicaid (by federal law) to apply for a limited number of openings in the state’s Medicaid program by lottery. The random assignment of applicants to Medicaid or no insurance offers a true randomized, controlled experimental design. The study also used longitudinal data spanning the time of insurance assignment, enabling the authors to make causal inferences about the effect of gaining insurance among a population of low-income adults. The authors analyzed the effect of Medicaid enrollment on hospital admissions, outpatient visits, use of prescription drugs, self-reported health, and the accumulation of unpaid medical debt.

The authors estimated the effect of Medicaid enrollment on these outcome variables using two-stage models. The first stage used the lottery to estimate the likelihood of insurance coverage. The second stage estimated the impact of having insurance on the various outcome variables. Because the lottery was random, it served as an excellent instrument to isolate the effect of gaining Medicaid coverage on healthcare utilization, financial liabilities, and health status. The authors found that gaining insurance via Medicaid increased an individual’s probability of having a hospital admission, but this increase was concentrated in nonemergency admissions. They speculated that this reflected individuals’ price sensitivity to voluntary, as opposed to emergency, medical care. Because Medicaid imposes minimal cost-sharing requirements on enrollees, enrollment in the program reduced financial barriers to accessing nonemergency forms of care. The study also found that gaining insurance increased the use of preventive services, prescription drugs, and outpatient services, as well as improved patient-reported general health status and mental health. Finally, gaining Medicaid coverage reduced the amount of medical debt sent to collection agencies, relative to the control group. These results are especially salient to policy makers and researchers in light of the national Medicaid expansion planned for 2014. The Oregon study suggests that Medicaid expansion can increase the poor’s healthcare utilization and improve their health status relatively quickly (Finkelstein et al. 2011 ). Future research will need to examine the long-term effects of this population’s uptake of insurance.

3.2. Determinants of Medicaid Take-Up Rates

Remler, Rachlin, and Glied ( 2001 ), in a review of the literature on take-up rates for public safety-net programs, concluded that the value of Medicaid benefits for potential enrollees, the administrative hassles associated with enrolling, and the availability and clarity of information about the program were all factors in determining Medicaid take-up rates. They also concluded that culture and stigma were not important predictors of enrolling in Medicaid. Aizer ( 2003 , 2007 ) showed that community health outreach workers can improve take-up rates, especially among non-English speakers. She also demonstrated that these outreach programs can improve healthcare utilization by reducing hospital stays that may have been prevented had the patient received timely and appropriate primary care. Remler and colleagues suggest that a passive automatic enrollment process would be more effective at ensuring that eligible persons were enrolled. The administrative barriers associated with Medicaid enrollment affect many uninsured children. Sommers ( 2007 ) reported that 42 percent of uninsured children who were eligible for Medicaid or SCHIP in 2006 had been enrolled during the pervious year. Sommer notes that this high rate of disenrollment is caused by requirements that parents re-enroll their children in the program annually. Likewise, Bindman and colleagues ( 2008 ) noted that frequent re-enrollment requirements for children in California’s Medicaid program disrupted access to primary and care.

Having children in the household is a strong predictor of an adult’s enrollment in Medicaid. This is largely a function of the guidelines that states established for Medicaid eligibility, which provide the most generous entry criteria for women and their children. Rask and Rask ( 2000 ) found that the presence of children and family size increased low-income families’ probabilities of enrolling in Medicaid. A child’s enrollment in Medicaid or SCHIP may also depend on parental enrollment. Sommers ( 2006 ) found that a child’s probability of losing Medicaid coverage, despite being eligible on the basis of family income, was 37 percent lower when a parent was also enrolled in Medicaid. Sommers’s study underscores the fact that Medicaid enrollment is a family phenomenon. Parent and child enrollment in public insurance can be self-reinforcing, and efforts to increase child coverage may need to factor in a parent’s incentives to enroll.

3.3. Supply of Medicaid Services

Medicaid is expected to add some 16 million new enrollees in 2014, almost one-half of the current uninsured, legal-resident population. Yet it is not clear if healthcare providers will be able to furnish the services needed to care for this population, or how the effects of the expansion will be distributed across the US healthcare system. Baker and Royalty ( 2000 ) used a model originally developed by Sloan and colleagues ( 1978 ) to interpret the effect of changes in reimbursement rates and expansions in Medicaid eligibility on the supply of “private” physician services for Medicaid patients. Consistent with economic theory, they conclude that increasing Medicaid’s reimbursement rates would cause physicians to see more Medicaid patients. The effect of an eligibility expansion without higher provider reimbursement rates is less clear. If the expansion simply adds formerly uninsured individuals to Medicaid, the model suggests that the number of Medicaid patients seen in private practices would remain unchanged. Care for new Medicaid enrollees would be handled by doctors practicing in “public” facilities, such as clinics or hospitals. If some individuals chose to drop private coverage and enroll in Medicaid, however, the model predicts that private physicians will see more Medicaid patients, who would have switched from private to public insurance. This phenomenon of switching from private to public insurance is termed “crowd-out,” and is discussed in Section 3.4 .

Baker and Royalty tested their hypotheses about reimbursement rates and eligibility expansions empirically by using the 1987 and 1991 Survey of Young Physicians. The survey asked participating doctors to report the percentage of Medicaid and poor patients they saw in their practices. The authors estimated that a 10 percent increase in the ratio of Medicaid to private insurer reimbursement rates would increase the number of poor patients seen in private practices by 3.4 percent, consistent with the theoretical model. Accounting for some reduction in public provider visits, the higher reimbursement rate was estimated to increase total physician office visits for the poor by 2.5 percent.

This finding is consistent with results from Currie, Gruber, and Fischer ( 1995 ), who suggest that increasing Medicaid reimbursements gives doctors an incentive to provide more prenatal care to pregnant women, with the effect of lowering the infant mortality rate. Similarly, Aizer and colleagues ( 2005 ) found that, in California, the introduction of supplemental payments to hospitals that treated a disproportionate share of poor patients (known as Disproportionate Share Hospital, or DSH, payments) gave private hospitals an incentive to treat a larger share of Medicaid enrollees. Following the introduction of DSH payments, hospital segregation declined and neonatal mortality among African Americans improved. In this study, segregation was measured as dissimilarity in the insurance characteristics of delivering mothers in a particular hospital, relative to the insurance characteristics of mothers in each woman’s county of residence. Quast and colleagues ( 2008 ) found that Medicaid providers that reimbursed on a fee-for-service (FFS) basis offered more well-child visits to children; however, capitated payments improved children’s compliance with asthma medication. These findings may reflect the fact that FFS-based reimbursement gives providers incentives to provide a high volume of care, such as office-based visits, while capitated payment systems encourage the use of care to prevent potentially expensive hospitalizations related to long-term diseases.

Other researchers have observed a weaker association between reimbursement rates and Medicaid participation, as well as greater barriers to mobility between different types of providers. Bronstein, Adams, and Florence ( 2004 ) studied the impact of SCHIP’s creation on Medicaid participation among physicians in Alabama and Georgia who were already a part of the states’ Medicaid networks. In Alabama, whose SCHIP program mirrored private insurance plans, the authors found that SCHIP enrollment did not have a significant effect on Medicaid participation among the state’s traditional Medicaid providers. In Alabama, the authors conclude, SCHIP enrollees gained access to networks of physicians that were previously accessed by the privately insured, resulting in little impact on the demand for services faced by established Medicaid providers. In urban communities in Georgia, however, physician participation in Medicaid declined as SCHIP enrollment grew. This suggests that there was some displacement of Medicaid with SCHIP enrollees, as theory would predict when SCHIP reimbursement rates are higher.

3.4. Medicaid Crowd-Out

Health economists, health services researchers, and policy makers have been concerned with the extent to which Medicaid take-up crowds out enrollment in private insurance plans. Crowd-out is formally defined as the percentage of persons who are enrolled in Medicaid despite being able to purchase private insurance. Conceptually, a Medicaid expansion induces crowd-out because eligible individuals are able to obtain free healthcare benefits with little or no cost sharing, compared to private insurance coverage, which includes paying premiums, co-pays, co-insurance, and deductibles. Limited physician participation and the administrative burden associated with enrolling in Medicaid probably deter crowd-out, however. The incentive to seek Medicaid coverage is probably greater for individuals and families who would otherwise purchase insurance in the nongroup market, where premiums are often much higher and not offset by employer contributions.

If the objective of a Medicaid expansion is to increase insurance coverage, a high crowd-out rate suggests that the expansion is inefficient, because it results in replacement of private insurance coverage and a lower rate of coverage expansion among those who cannot afford private insurance. Assessed in light of other objectives, however, crowd-out may not be as serious a concern. For example, because public insurance eliminates most or all enrollee cost-sharing requirements, which are typically a feature of private insurance, a low-income family that switches enrollment from private to public insurance may face fewer financial obstacles in seeking care. Also, low-income families may use the savings from reduced cost-sharing to purchase other essential goods that promote good health such as quality food, shelter, transportation, and clothing.

The research on crowd-out has been motivated by two waves of reform to the Medicaid program. The first, which occurred in 1987, decoupled Medicaid eligibility from enrollment in the Aid to Families with Dependent Children (AFDC) program, and it allowed states to expand Medicaid eligibility to pregnant women and low-income children. This was followed by federally mandated expansions in Medicaid eligibility for young, low-income children. The second wave of coverage expansions occurred with the 1997 creation of SCHIP, which dramatically expanded coverage for children, and with the passage of the Personal Responsibility and Work Opportunity Reconciliation Act, which allowed states to further expand Medicaid eligibility to adults and children. Studies based on these different expansions have estimated crowd-out rates between 4 percent and 60 percent (Gruber and Simon 2008 ).

Cutler and Gruber ( 1996 a) published one of the first articles to address this issue. They analyzed the effect of the first wave of Medicaid expansions on crowd-out for children and women aged 15–44. Using data from the Current Population Survey (CPS; March 1996), they identified a subset of the population of women and children that became newly eligible for Medicaid coverage between 1987 and 1992. Low-income populations that were eligible for Medicaid throughout the expansions, or that remained ineligible for public insurance, served as controls. Cutler and Gruber estimated the impact of an individual’s Medicaid eligibility on his or her propensity to be covered by Medicaid, to have private insurance coverage, or to be uninsured. Complicating the analysis was the fact that Medicaid eligibility is endogenous, since factors associated with demand for insurance, such as income, are correlated with Medicaid eligibility. The authors also sought to address the imprecision of measuring eligibility, which in practice is determined monthly, using annual income data. To address these concerns, the authors developed a state-level measure of Medicaid generosity using a random national sample, which they used as an instrumental variable in lieu of person-level eligibility. With this technique, Cutler and Gruber estimated a crowd-out rate of 49 percent.

Efforts to replicate Cutler and Gruber’s work have yielded a broad range of crowd-out estimates. The variations in estimates are due to estimation techniques, the data, and the definition of crowd-out. Thorpe and Florence ( 1999 ) noted that Cutler and Gruber’s analysis relied on cross-sectional data, which provide an aggregate picture of insurance coverage but not information on insurance switching within a family over time. It is possible, for example, that families drop their private insurance coverage for certain members who gain Medicaid eligibility. Thorpe and Florence addressed this latter concern directly in their study of the first Medicaid expansion’s effect on crowd-out for children. They used panel data from the National Longitudinal Survey of Youth (NLSY), which followed a cohort of young people beginning in 1979. The insurance statuses of the original cohort members and their children are collected in each year of the panel, which spanned the first wave of Medicaid expansions. The authors used the parents’ insurance statuses as an estimate of their children’s insurance coverage in the absence of eligibility expansions. They found that the parents of nearly all poor children (families with incomes under 100 percent of the FPL) and of most near-poor children (families with incomes between 100 percent and 200 percent of the FPL) who enrolled in Medicaid had lost employer-sponsored insurance. Job loss explained most of the decline in parents’ loss of private insurance coverage. The authors estimated that only 16 percent of the parents of near-poor children enrolling in Medicaid continued to retain private insurance coverage, suggesting a much lower crowd-out rate than Cutler and Gruber’s estimate.

Blumberg, Dubay, and Norton ( 2000 ) estimated crowd-out rates separately for previously insured and uninsured groups of low-income children, in addition to a combined group, by using panel data from the Survey of Income and Program Participation (SIPP). The authors employed a differences-in-differences study design, using low-income children who were slightly older than the cohort that gained Medicaid eligibility as the control group. Among children who were previously insured, the authors estimated a crowd-out rate of 23 percent. The authors found no significant evidence of crowd-out in the group of previously uninsured children. That is, formerly uninsured children who enrolled in Medicaid were unlikely to have otherwise gained private coverage. The pooled crowd-out estimate that the authors calculated, which effectively averaged the crowd-out rates for the first two groups, was 4.4 percent.

Looking at the second wave of Medicaid expansions in the late 1990s, Gruber and Simon ( 2008 ) estimated the crowd-out rate for children who became newly eligible for Medicaid or SCHIP to be between 61 percent and 68 percent. This result followed from a modeling strategy similar to Cutler and Gruber ( 1996a ), but which also assumed that eligibility of an individual family member for Medicaid or SCHIP will increase the likelihood that other family members will enroll. This assumption that coverage of other family members improves a child’s continuity of Medicaid enrollment has been borne out in research by Sommers ( 2006 ) and by Carroll and colleagues ( 2007 ). Without family-level eligibility effects, Gruber and Simon estimated a crowd-out rate between 24 percent and 37 percent. With family-level eligibility effects, the crowd-out estimate would increase to 61–68 percent, indicating that making an entire family eligible for Medicaid provides greater incentives for enrollment. Busch and Duchovny ( 2005 ) found that, in states that raised income eligibility levels for parents in the late 1990s, 24 percent of the take-up in Medicaid coverage was due to a reduction in private insurance coverage.

Concerns about crowd-out caused policy makers to enact anti-crowd-out rules in SCHIP. Gruber and Simon ( 2008 ) analyzed these potential disincentives to crowd-out in their study of the SCHIP expansions in the late 1990s. They found that increasing cost sharing resulted in a significant reduction in the probability of take-up. They concluded, however, that longer waiting periods may simply increase the amount of time a family waits to gain eligibility of SCHIP, without significantly reducing crowd-out. Lo Sasso and Buchmueller ( 2004 ) estimated a 47 percent crowd-out rate of SCHIP expansion on private health insurance. They noted that states that implemented an anti-crowd-out policy of a six-month waiting period reduced crowd-out to almost zero.

The literature on the crowd-out effect of public insurance is similar to research on the impact of other safety-net programs on health insurance enrollment. Rask and Rask ( 2000 ) found that, among groups of individuals with incomes up to 400 percent of the FPL, medically needy programs and welfare generosity increased the probability of Medicaid enrollment, while the availability of an uncompensated care funds increased the probability of being uninsured. The authors also found that the presence of a public hospital, typically a major provider of charity care, lowered the probability of enrolling in Medicaid. These findings suggested that the availability of charity care acts as a substitute for Medicaid and private insurance enrollment. Similarly, Herring ( 2005 ) and Chernew and colleagues ( 2005 ) found that the availability of charity care was a significant predictor of an individual’s decision to forego health insurance enrollment.

3.5. Medicaid Managed Care

Since its first introduction in Medicaid plans in the 1980s, managed care has grown to become the dominant model for delivering healthcare services to Medicaid beneficiaries. About three-quarters of Medicaid recipients are enrolled in managed care plans. Two managed care models operate in Medicaid: fully capitated managed care, in which an HMO bears financial risk for the care of enrollees, and primary care case management (PCCM), in which primary care providers are paid a supplemental fee to coordinate the care of their patients. Commonly cited rationales for employing managed care are (1) to enhance access to, and use of, primary and preventive care services, (2) to reduce expensive healthcare utilization, and (3) to provide states with more predictable program expenses (Kaiser Commission on Medicaid and the Uninsured, 2010 ).

Managed care may improve access to essential healthcare services via two pathways: facilitating care coordination, and providing financial incentives to primary care providers to increase the amount of Medicaid patients they see in their practices. A regression-based comparison of HMO versus FFS Medicaid enrollees found higher rates of primary care access and use, contact with specialists, and receipt of flu shots among the disabled (i.e., SSI-eligible) HMO Medicaid enrollees in urban areas. These benefits largely disappeared among rural HMO enrollees who also had significantly higher emergency-room use than the FFS comparison group. Similar effects were observed for PCCM patients, and again, the benefits were mostly confined to urban areas (Coughlin et al. 2008 ). One study of a Medicaid HMO’s pregnancy management program, which focused on care coordination, found that it significantly lowered the odds of infants’ low birth weight (Mason et al. 2011 ). In Washington, D.C., disabled children in a Medicaid managed care plan, which provided both care coordination and enhanced reimbursement for primary care services, were significantly more likely to have the recommended number of pediatrician visits for their age group versus FFS enrollees (Mitchell et al. 2008 ). Another study that involved the same population found a significantly higher likelihood of receiving dental exams and care in the managed care population, although both the managed care and FFS groups continued to have suboptimal use of dental care (Mitchell and Gaskin 2008 ).

The techniques employed by managed care providers to increase utilization of primary and preventive care should, in theory, help to avoid more costly medical care down the line. Studies have produced mixed findings about the cost savings that can be attributed to Medicaid managed care. The Lewin Group’s ( 2009 ) synthesis of 24 evaluations of managed care programs found evidence of cost savings between 0.5 percent and 20 percent, compared to traditional FFS Medicaid plans. By contrast, a difference-in-differences regression analysis found that expenditures actually increased slightly in Florida counties that implemented a managed care demonstration program, compared to those that retained FFS Medicaid. Populations enrolled for at least three months, however, cost less in managed care than FFS plans, potentially demonstrating that continuity of enrollment determines whether a managed care program can generate savings (Harman et al. 2011 ).

Aizer, Currie, and Moretti ( 2007 ) examined the effect of California’s adoption of Medicaid managed care plans on the delivery of prenatal services and birth outcomes. In an effort to control costs, California moved many patients in its traditional FFS Medicaid program, Medi-Cal, into managed care plans in the 1990s. Most counties entered into contracts with several private insurers to administer their managed care plans. Five counties, however, ultimately adopted a single, publicly run managed care plan known as the County Organized Health System (COHS). The authors used patient-level longitudinal data for mothers who had more than one singleton pregnancy before and after the introduction of managed care to examine the effect of this transition to managed care on the women’s pregnancies. The authors found that the adoption of managed care significantly lowered a mother’s probability of receiving first trimester prenatal care. In counties that used multiple private insurers, the probability of low birth weight, a short-gestation pregnancy, and neonatal death all increased following the adoption of managed care. These negative effects diminished in an analysis that focused only on COHS plans, although the probability of low birth weight remained elevated. These results cast a skeptical light on the notion that managed care plans provide better preventive care that is supposed to reduce the risk of adverse health outcomes among their enrollees. Because of the limited financing available for Medicaid patients and insurers’ incentives to control costs, managed care plans may still not improve access to care for vulnerable populations.

Lastly, Herring and Adams ( 2011 ) examined the effect of Medicaid HMO penetration on medical utilization and expenditures in urban markets. They found no significant effect on a Medicaid enrollee’s expenditures from the degree of HMO penetration in a given market. Increases in the market penetration of HMOs with ≥75 percent Medicaid enrollment raised medical practitioner visits and reduced some measures of hospital utilization but, unexpectedly, increased emergency room visits. Greater market penetration of HMOs with 〈75 percent Medicaid enrollment increased inpatient surgeries but lowered outpatient surgical procedures.

4. Conceptual Framework

Poverty status primarily influences healthcare use in two ways: through its impact on health production, and through its impact on the demand for healthcare services. Poverty status reflects family income that determines one’s ability to purchase goods and services required to maintain health, including medical services. The demand for healthcare is a derived demand that is rooted in the demand for good health. Hence, to model demand for healthcare, one must begin with an individual’s health-production function. For simplification purposes, suppose health is a function of healthcare services and a composite good. The composite good includes food, housing, recreation, and other goods and services that influence one’s health. These goods and services can have both a positive or negative influence, for example, one can eat fresh fruits and vegetables, or one can indulge in double chocolate fudge cake. Individuals choose their consumption of the composite good and healthcare services that maximizes their utility subject to their budget constraints. Based on this simple framework, one can derive demand functions for healthcare services and the composite good that are based on the prices and income from the budget constraints and from the parameters and exogenous factors in the health-production and utility functions (Grossman 1972a , 1972b ).

Poverty status, a measure of household income relative to household size, affects an individual’s or family’s budget constraints and consequently the demand for healthcare services. As long as healthcare services are normal goods, declines in income will reduce the demand for these services. The income effect, however, is counterbalanced by poverty’s negative impact on health status. The reduction in purchasing power due to poverty lowers the poor’s demand for goods and services such as quality housing, food, transportation, and so on, which improve health. Lower consumption of these goods and services reduce health status and increase healthcare needs. This is true for the most part unless affluent persons choose to purchase goods and services that are unhealthy for them (e.g., excessive alcohol, tobacco, illegal drugs, and foods with high fat and high cholesterol content). This negative relationship between health status and income is probably a phenomenon for very high-income individuals. The poor’s health is probably subject to the impact of a low value composite good. Consequently, poverty lowers health status, which increases the need for healthcare services.

If elements of the composite good and healthcare services are substitutes in the production of health, poorer persons may substitute services provided by Medicaid or by safety-net institutions for other components of the composite good. For example, higher-income diabetic and hypertensive patients may be able to manage their conditions with better diets and health habits (i.e., high-quality food and regular exercise at a health club). Poorer diabetics and hypertension sufferers, who may live in areas without access to high-quality food or safe environments for exercise, may rely more heavily on healthcare services to manage their conditions.

5. The Relationship between Poverty Status and Healthcare Utilization

We analyze here the Medical Expenditure Panel Survey (MEPS) to illustrate the relationship between healthcare utilization and poverty status. The MEPS is an annual longitudinal survey that covers the civilian noninstitutionalized population within the United States. It is conducted by the Agency for Healthcare Research and Quality (AHRQ) and is based on a subsample of the National Health Interview Survey. The MEPS is widely used as an authoritative source of information on the nation’s use of healthcare services. AHRQ uses the MEPS to monitor the nation’s progress on disparities in healthcare by race and ethnicity, economic status, and geography (Cohen et al. 1996 ). More information about the MEPS is available on AHRQ’s website ( www.meps.ahrq.gov/mepsweb ). In this analysis, we use the 2006 MEPS adult nonelderly sample, which consisted of 23,264 noninstitutionalized adults between the ages of 18 and 64.

To measure healthcare, we use 16 variables that cover the five following domains:

office-based care—physician and nonphysician

hospital-based care—inpatient, outpatient, and emergency room

dental care

pharmacy services

preventive and screening services—cholesterol check, routine checkup, flu shot, prostate antigen test (PSA), pap smear, clinical breast exam, mammogram, blood stool, and sigmoidoscopy or colonoscopy

The dependent variables we analyze are dichotomous, as follows. For office-based, hospital, dental, and pharmacy services, we construct indicators for whether the respondent had at least one visit during the previous 12 months. We employ variable time frames to analyze the use of preventive services. The indicator variables for flu shots, PSA tests, and routine checkups cover the previous 12 months. The variables for pap smears, clinical breast exams, and mammograms indicate whether these services were received within the previous three years. We use a five-year window for the cholesterol check. The blood stool and sigmoidoscopy or colonoscopy variables indicate whether the respondent ever had these tests. For each preventive service, we only consider the recommended population (e.g., mammograms for women ages 40 years and older; PSA tests for men ages 40 years and older).

Poverty status is the primary independent variable of interest. We include it in the model as a categorical variable: poor (less than 100 percent of the FPL), near-poor (100 to 125 percent of the FPL), low-income (125 to 200 percent of the FPL), middle-income (200 to 300 percent of the FPL) or high-income (greater than 300 percent of the FPL) participants. High-income individuals are the reference group in the regression models. We also examine the association between healthcare use and two additional socioeconomic variables that are closely associated with poverty status: educational attainment and health insurance status. Educational attainment is measured by using a set of categorical variables: 8 years or less, 9 to 12 years, some college, and a college degree. (The reference group is persons with a high school diploma or GED.) We categorize health insurance status as private, public (Medicaid, Medicare, other public), and uninsured. Private insurance is the reference category.

The other domains we control for are demographic characteristics, employment status, location, functional status, and the presence of chronic health conditions. Race and ethnicity, gender, and marital status are included as categorical variables with white, female, and married persons as the respective reference groups. The race and ethnicity categories are Hispanics and non-Hispanic African Americans and Asians. Marital status is divided into three groups: married, never married, and widowed or divorced/separated. To control for differences in healthcare use related to age, we use age minus 18 years and age minus 18 years squared. Employment status is divided into three categories: full-time, part-time, and not employed, with full-time employment as the reference group. We use several variables as measures of respondents’ health status. Respondents rate their general health status and mental health status on a five-point scale: poor, fair, good, very good, and excellent. For each, we create a set of categorical variables combining poor and fair health, and good and very good health, and we use excellent health as the reference category. We use three measures of functional status. The first two indicate whether the respondent received help or supervision with activities of daily living (ADLs) or with instrument activities of daily living (IADLs). ADLs are activities such as bathing, dressing, getting around the house, walking, climbing stairs, grasping objects, reaching overhead, lifting, bending or stooping, or standing for long periods of time. IADLs are activities such as using the telephone, paying bills, taking medications, preparing light meals, doing laundry, or going shopping. The third functional status measure indicates whether respondents had problems working, doing homework, or going to school. The presence of a chronic condition is ascertained by patients’ responses to a question that asks if a “doctor or healthcare professional” had informed them that they had hypertension, heart problems, stroke, cancer, asthma, diabetes, and/or joint pain. We create two variables: whether a respondent had only one chronic condition, and whether a respondent had two or more chronic conditions.

We estimate the impact of poverty status, educational attainment, and health insurance status on healthcare utilization. Because almost all persons age 65 and older are enrolled in Medicare, we estimate models for nonelderly adults and seniors separately. The data analysis is conducted with STATA 10 statistical software. We estimate separate regression models for each outcome using logistic regression. Logistic regression is selected because the dependent variables are dichotomous, and because of the convenience of reporting the associations as odds ratios (OR). We use the sampling weights and account for the complex survey design of the MEPS by using STATA’s survey regression procedures to produce national estimates.

Consistent with prior studies, Tables 11.1 and 11.2 illustrate the wealth-health gradient. Health status declines with an increase in poverty status. In 2006, poor adults had the lowest self-reported general and mental health status and had the lowest functional status. Data from the National Health Interview Survey (NHIS) show that, in 2009, poor and near-poor adults had higher prevalence rates for asthma, heart disease, hypertension, arthritis, diabetes, chronic joint pain, lower back pain, vision problems, hearing loss, and dental problems (Pleis et al. 2010 ). Cancer is the only condition for which nonpoor adults have a highest prevalence rate. The greater cancer disease burden for the more affluent is probably due to their higher survival rates and early detection rates relative to poor cancer patients. Poor cancer patients are less likely to have their cancer detected at early stages when it is treatable and therefore have shorter survival times than more affluent cancer patients.

Despite their poor health status, poor adults are less likely to use healthcare services when compared to more affluent adults (see Table 11.3 ). With the exception of hospital stays and emergency room visits, poor and near-poor adults are less likely to use 14 of the 16 services examined in this study. In Tables 11.4 through 11.9 , we report the ORs for poverty status, education level, and health insurance status adjusted for demographic, socioeconomic, and health factors. The regression analyses reveal the same associations between poverty status and healthcare utilization. We present here the results for office-based services followed by hospital-based services, dental and pharmacy services, and then preventive services.

Notes: Cell entries are percentages. Standard errors are in parentheses.

Source: Authors’ calculations from Cohen et al. ( 2006 )

Source: Summary Health Statistics for US Adults, National Health Interview Survey, 2009.

5.1. Office-Based Services

Table 11.4 reports that, in comparison to high-income nonelderly adults, the odds of poor nonelderly adults having at least one office-based physician visit and nonphysician visit during the year are 26.4 percent and 28.7 percent lower, respectively. Even for poor seniors who are covered by Medicare, there is a difference in the use of office-based services. Poor seniors are less likely to have at least one office-based physician visit (OR = 0.562) and office-based nonphysician visit (OR = 0.609), versus the high-income reference group. There is also a positive gradient between educational attainment and the odds of having an office-based visit. Although the estimates are not always significant, the relationship appears to be monotonically increasing, where the odds of using office-based services increase as educational attainment increases from no high school to an advanced degree. Persons with no high school training, who are the least educated group in our models, are the most disadvantaged. Nonelderly and elderly adults with less than a ninth-grade education have 28.2 percent and 39.7 percent lower odds, respectively, of a physician visit than adults with a high school diploma. Compared to privately insured adults, the uninsured are significantly less likely to use office-based services. The uninsured are almost 70 percent less likely to use office-based physician services and more than 53 percent less likely to use office-based nonphysician services.

Notes: These are results from logistic regression models that control for age, gender, race and Hispanic origin, urban/rural location, employment status, region, health status, and functional status.

The results reported in the table are the exponentiated results of logistic regressions. Statistical significance is determined on the logarithmic scale.

Standard errors are in parentheses.

Significance levels denoted as follows:

Source: Cohen et al. ( 2006 )

5.2. Hospital-Based Services

Table 11.5 indicates that the association between poverty status and hospital use is different. The poverty status association with outpatient services is similar to that of office-based services; however, for hospital stays and emergency room visits the association is different. For outpatient services, the poor elderly are nearly 30 percent less likely to use services than their high-income counterparts. The ORs for poor and near-poor nonelderly adults are less than one but statistically insignificant; however, the odds for low-income nonelderly adults is approximately 28 percent lower compared to high-income nonelderly adults. For hospital stays, nonelderly poor adults are more likely to have at least one stay or visit during the year than high-income adults. The pattern of the ORs suggests that the likelihood of a hospital stay declines with a higher poverty status. We observe a similar pattern for emergency room visits, although the estimates are not statistically significant. For seniors, the odds of a hospital stay and emergency room visits for the poor and near-poor are not statistically different from one, and the patterns of the estimates do not suggest an underlying monotonic association.

The impact of educational attainment and health insurance status on hospital-based services is similar to the results for office-based services, particularly for nonelderly adults. The pattern of estimates for hospital stays and outpatient services suggests that as education increases the use of hospital services increases. Nonelderly adults with no high school diploma are less likely to use outpatient (OR = 0.717) and emergency room services (OR = 0.716). We do not find the same association for seniors. Similar to office-based services, the uninsured are less likely to have a hospital stay and outpatient visit. We do not find the same result with emergency room visits, but this is consistent with hospitals’ legal obligation under the Emergency Medical Treatment and Active Labor Act, which requires hospitals to treat persons with an emergency medical condition regardless of the patients’ ability to pay for treatment.

These are results from logistic regression models that control for age, gender, race and Hispanic origin, urban/rural location, employment status, region, health status, and functional status.

5.3. Dental and Pharmacy Services

Use of dental services has a strong negative association with poverty status, as shown in Table 11.6 . The odds of using dental services decline as poverty status increases. The poor are most disadvantaged, followed by the near-poor, low-income, and middle-income participants, who are all less likely to have a dental visit than high-income adults. This association is the same for nonelderly adults and for seniors. This is an expected finding, given that private health insurance and Medicare do not provide dental coverage and because Medicaid dental coverage is inadequate as relatively few dentists participate in the program. Consequently, nonelderly adults and seniors must have out-of-pocket costs for dental care, which produces strong poverty status effects. We do not find a strong relationship between poverty status and the use of pharmacy services. The low-income and middle-income groups appear to be the most vulnerable. This finding may also be the product of how private health insurance, Medicare, and Medicaid provide coverage for pharmacy services. Poor and near-poor nonelderly adults may have first dollar coverage through Medicaid or access to indigent pharmacy assistance programs. High-income persons have more resources to afford the co-pays and deductibles associated with Medicare and private health insurance, while the nonelderly in the middle of the income spectrum may lack support from public programs or may not have the independent means to purchase prescription drugs.

* p 〈 0.05.

The impacts of educational attainment and health insurance status parallel the associations observed for office-based and hospital-based services. Adults with less education are most likely to use fewer dental and pharmacy services. The patterns of the ORs indicate monotonic relationships (i.e., as educational attainment declines, the odds of using dental or pharmacy services declines). The results are particularly strong for dental services. Adults with no high school are 44 percent (for the nonelderly) and 47 percent (for the elderly) less likely to have a dental visit. The ORs for prescription drug use follow a similar pattern, although the results are not significant for elderly adults. Among nonelderly adults, however, those with a college education (adults with some college and those with a college degree) are more likely have at least one prescription filled during the year. Similar to the other healthcare services, the uninsured are vulnerable. They have a 68 percent lower odds of using dental care and a 61 percent lower odds of using pharmacy services, compared to the privately insured.

5.4. Prevention and Screening Services

Table 11.7 indicates that nonelderly poor adults are less likely to receive preventive services. For services that pertain to the entire population and that can be performed during a routine physician visit, poor nonelderly adults have 54 percent lower odds of receiving a cholesterol check and 31 percent lower odds of receiving a routine checkup. This association does not hold for flu shots, for which only low-income adults are at risk. This may be due to public health departments’ efforts to ensure that poor people have access to flu vaccines. These free programs are typically targeted to poor and near-poor populations. We do not find statistically significant differences by poverty status among elderly adults. The education and health insurance effects are similar to other healthcare services. The patterns of ORs for the education variables indicate a monotonic association, where persons with lower educational attainment are most at risk. This association is strongest for nonelderly adults. Uninsured nonelderly adults have lower odds for receiving all three services.

For gender- and age-specific screening services, such as a clinical breast exam, PSA, PAP smear, or mammogram, the impact of poverty status varies by age as reported in Table 11.8 . Among the nonelderly, poor, low-income, and middle-income adults are less likely to receive these services than high-income adults, with the exception of clinical breast exams. In general, we do not observe significantly lower odds of the use of these services among the elderly. We observe the education-to-health-service-use gradient among these services, too. The patterns of ORs show that college-educated adults are more likely to receive these services. This association holds for both nonelderly and elderly adults. Similar to other services, the uninsured are significantly less likely to use these services.

1 Adults ages 20–64.

Source: Cohen et al. ( 2006 ).

Adults ages 41 to 64.

Adults ages 40 to 64.

As reported in Table 11.9 , poverty status is associated with the receipt of screenings for digestive disorders and colon cancer. Specifically, compared to high-income adults, the odds that a poor adult received a sigmoidoscopy or colonoscopy are 32 percent lower in the nonelderly sample and 35 percent lower among elderly adults. The education-to-health-services gradient is observed for these services as well. Persons without a high school diploma are the most at risk and persons with more than a bachelor’s degree are least at risk. Similar to all the other services, uninsured nonelderly adults are most vulnerable.

In summary, we find that poor and near-poor nonelderly adults are at significant risk of not receiving healthcare and preventive services. For the elderly, poverty status sometimes is not a disadvantage, especially for services that are covered by Medicare or Medicaid. Dental services are particularly subject to the poverty – health services gradient. We also find a consistent relationship between educational attainment and health services use. Adults without a high school diploma are disadvantaged while college-trained adults are more likely to use healthcare services. Finally, uninsured nonelderly adults are uniformly less likely to use services compared to privately insured nonelderly adults. The disadvantage is large and statistically significant, ranging from 35 percent to 60 percent lower odds, depending on the service.

6. Conclusion

The problems that poverty creates for health and healthcare use can be addressed with demand-side and supply-side policy levers. On the demand side, government can implement policies to expand health insurance coverage for the poor and to facilitate their use of healthcare services. On the supply side, government can implement policies to increase the number of healthcare providers for the poor and underserved communities. The Patient Protection and Affordable Care Act of 2010 (ACA) attempts to use both demand and supply remedies to improve the poor’s access to healthcare.

On the demand side, the ACA expands Medicaid coverage to poor individuals who are not currently eligible for the program. Virtually all US citizens under age 65 with incomes below 133 percent of the FPL will become eligible for Medicaid beginning in 2014. The ACA also authorizes states to create health insurance exchanges to organize and facilitate individual health insurance market purchases. The federal government will give individuals up to 400 percent of FPL tax credits and out-of-pocket subsidies to help them purchase plans in the exchanges. Plans offered on the exchanges must provide preventive services, such as immunizations and screenings, without cost-sharing requirements. States also have the option of insuring adults between 133 and 200 percent of the FPL in a Basic Health Plan. The Basic Health Plan would be a public plan similar to Medicaid or SCHIP. States could use 95 percent of the federal government subsidy these adults would have received in the health insurance exchange to support the Basic Health Plan. This essentially gives states the option of expanding their Medicaid coverage to adults up to 200 percent of the FPL.

The exchanges will make insurance more affordable by pooling risk across participants and lowering administrative costs for insurance carriers. Adults with incomes that are more than 133 percent of the FPL will be required to obtain health insurance through an employer or via an exchange. Those with incomes below 400 percent of the FPL will be eligible to receive tax-credit subsidies for insurance purchased in an exchange. The subsidies are structured to reflect individual income and the cost of insurance in the exchange. The ACA is expected to lower the proportion of the US population that is uninsured to 7 percent from its current level of 15 percent. One half of newly insured adults will receive coverage through Medicaid, growing the number of Medicaid enrollees by 30 percent from 2010 levels.

On the supply side, the ACA provides $11 billion over five years to expand the healthcare safety net through community health centers and the National Health Service Corp. Federally qualified health centers (FQHCs) are a primary source of care for the uninsured and for Medicaid patients. The funding for FQHCs will be used to both expand the number of health centers in operation and to recruit and train medical personnel. To encourage more private providers to participate in Medicaid, reimbursement rates for primary care physicians will be increased temporarily in 2013 and 2014. States are also encouraged to implement patient-centered medical homes for their dual eligible populations with multiple chronic conditions in order to improve their quality of treatment and possibly lower their cost of care.

The implementation of the ACA should spur numerous new studies. Economists and health services researchers engaged in evaluating the ACA must address a number of questions, several of which are outlined below. Has the ACA effectively reduced the number of uninsured people and improved their access to healthcare services? How did the expansion of the insured population impact persons who had insurance prior to implementation of the ACA? Did the Medicaid expansion crowd out private insurance participation? The variability of health benefit exchanges across states will invite studies comparing the efficiency of such exchanges. The ACA created a Center for Medicare Innovation that is charged with studying new healthcare delivery and payment methods that provide care as efficiently as possible to high-need populations. Potential programs to be evaluated include medical homes for individuals with multiple chronic conditions, as well as bundled payment systems that enhance provider coordination across multiple delivery settings. Evaluating these programs will be of particular interest to health services researchers, economists, and policy makers.

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Poverty and Health Disparities: What Can Public Health Professionals Do?

Affiliations.

• 1 1 University of Toledo, Toledo, OH, USA.
• 2 2 Ball State University, Muncie, IN, USA.
• 3 3 University of Florida College of Medicine, Jacksonville, FL, USA.
• PMID: 29363333
• DOI: 10.1177/1524839918755143

More than a tenth of the U.S. population (13% = 41 million people) is currently living in poverty. In this population, the socioeconomic, cultural, and environmental conditions have detrimental health effects such as higher rates of chronic diseases, communicable illnesses, health risk behaviors, and premature mortality. People living in poverty are also deprived of social, psychological, and political power, leading to continuation of worsening health and chronic deprivation over generations. The health of individuals living in poverty poses greater challenges from policy, practice, and research standpoints. Public health professionals are poised uniquely to be advocates for the marginalized, be the resource persons for health education, implement health promotion programs, and conduct research to understand health effects of poverty and design tailored and targeted public health interventions. In this article, we summarize the opportunities for public health practice with individuals living in poverty.

Keywords: health disparities; poverty; public health policies; social determinants of health; social policy.

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Perspectives in poverty and mental health

Derin marbin.

1 Department of Psychiatry and Neurosciences, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany

2 Department of Psychiatry of University Hospital Charité in St. Hedwig Hospital Berlin, Germany

Stefan Gutwinski

Stefanie schreiter, andreas heinz, associated data.

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

In recent years, different forms of poverty and their interaction with mental illness have been in the focus of research, although the implementation of action in mental health care and policy making so far is scarce. This perspective article offers different perspectives of poverty and its reciprocal association with mental illness and outlines possible future research and policy implications. We will approach the topic of poverty from various levels: On a micro-level, focusing on absolute poverty with precarious housing and malnutrition. On a meso-level, on neighborhood-related poverty as a factor in individuals' mental illness. On a macro-level, on effects of income inequality on mental health. In several studies, it has been shown that on each level, poverty has a profound impact on mental health, though it must be noted that in some fields, research is still scarce. In the future, an inter- and transdisciplinary approach is of considerable importance, since poverty and its impact on mental health should be addressed from different perspectives, reaching from targeted programs for individual groups (e.g., homeless people) up to national policy measures.

Introduction

The Global Burden of Disease study estimated that, in 2017, 792 million people worldwide reported impaired mental health, which represents almost 11% of the global population ( 1 ). According to The World Health Organization, mental health conditions produce economical losses of one trillion USD, with depression being the leading cause of ill health and disability ( 2 , 3 ).

Especially people living in poverty are unequally affected by mental illness ( 4 ). The United Nations (UN) defines poverty as “…a condition characterized by severe deprivation of basic human needs, including food, safe drinking water, sanitation facilities, health, shelter, education and information. It depends not only on income but also on access to social services” ( 5 ). According to the World Bank, the extreme poverty line concerning daily expenses is under 2.15$ based on 15 national poverty lines from some of the poorest countries in the World ( 6 ). National poverty lines vary depending on the respective costs to cover one's basic needs, e.g., in the European Union, someone earning <60% of the median income is “at-risk-of-poverty” ( 7 ). Relative poverty on the other hand, is defined as not having enough material, cultural, and social resources and thus be excluded from a lifestyle, which other individuals from the respective country can maintain ( 8 ).

Poverty has increased globally since the COVID-19 pandemic. The United Nations University reported that after 30 years of decline of poverty, the global pandemic could lead to an increase of global poverty by 8% since 2020, with rates being three times higher in rural compared to urban areas ( 9 ).

In this perspective article, we focus on the impact of poverty on mental health in high-income countries. We discuss effects of poverty on a micro-, meso- and macro-level and outline implications for future research and mental health policies. On the micro-level, we discuss individual characteristics including material, psychosocial, and behavioral risk factors, using the example of precarious housing and malnutrition. On a meso-level, we refer to community- and neighborhood-related circumstances with a focus on local poverty, social exclusion and discrimination, and their respective effects on individual mental health. On a macro-level, we focus on the association between income inequality within a nation and the national mental health burden.

Absolute poverty in high-income countries

Rates of poverty in the Organization for Economic Co-operation and Development (OECD) countries vary between 4.9% in Iceland as the lowest rate up to 19.9% in Costa Rica, whereby the highest poverty rate among high-income countries can be found in the United States with 17.8% ( 10 ). One subgroup affected by poverty in high-income countries are people exposed to homelessness. With an estimation of 1.9 million persons without a home, and increasing numbers in countries like the United States, United Kingdom and Germany ( 11 ), several research and policy initiatives focus on the interrelation between mental health and living conditions in poverty.

In a systematic review and meta-analysis of 39 studies with altogether 8,049 homeless persons in Germany, a pooled prevalence of a current mental illness of 76.2% (95% CI 64.0–86.6) was reported, with alcohol dependence being the most common disorder (pooled prevalence 36.7% [95%CI 27.7–46.2]) ( 12 ). In a systematic review and meta-regression among western countries, 29 studies with 5,684 individuals reported a pooled prevalence of 37.9% (95%CI 27.8–48.0) for alcohol dependence and 24.4% (95%CI 13.2–35.6) for drug dependence ( 13 ). Substance use among homeless people is often heavily stigmatized, and even healthcare professionals have displayed a rather negative attitude toward patients with substance use, which might have an influence of the self-esteem and empowerment of patients and thus affect treatment outcomes such as treatment completion ( 14 ). Beyond drug use, adverse life events, suicidality and mental illness are important predictors of becoming homeless ( 15 ). There is a complex interplay between homelessness and mental illness, with mental health challenges increasing the risk of homelessness, and homelessness promoting poor mental health including depression and suicidality ( 16 ).

Persons with debts and substantial loans represent another group affected by poverty in high-income countries. In Western countries like Germany, 8.6% of the general population have debts that cannot be cleared because of insufficient income and assets ( 17 ). Again, persons with mental health issues are disproportionately affected by debt. In a study with 486 psychiatric patients, 55.1% had outstanding debts, loans, or unpaid bills, of which more than a third (36.3%) reported debts between 10.000 and 99.999 e ( 18 ). Here, binary regression analysis identified younger age and substance use disorders as being significantly associated with outstanding debts [OR 0.98 (95%CI 0.96–1.00) and OR 2.41 (95%CI 1.48–3.92)] ( 18 ).

Another important aspect of absolute poverty is insufficient nutrition. There is an increasing focus on the interaction between food security and mental health as main sources of global mortality and disease ( 19 ). For example, Fang et al. conducted a study during the COVID-19 pandemic among 2,714 low-income participants in the United States and observed that food insecurity was associated with a 257% higher risk of anxiety [as measured by the GAD-7; OR 3.57 (95%CI 3.01–4.23)] and a 253% higher risk of depression [measured by PHQ-9; OR 3.53 (95%CI 2.99–4.17)] ( 20 ). Insufficient nutrition is a risk factor, while income stability was detected as a protective factor for depression [OR 0.77 after adjusting for income stability (95%CI 0.66–0.91)]. Especially respondents with children were identified as the most vulnerable subgroup. This evidence is supported by findings of the Global Burden of Disease Study 2019 ( 21 ), which reported that child and maternal malnutrition was one of the leading risk factors for disability-adjusted life-years. This study emphasizes the pivotal importance of targeted nutritional programs as a part of women's health in the context of mental health care. It also implicates that reaching out to vulnerable groups should not be restricted to mental health care settings, but that including interventions in the general community is essential, where e.g. mothers can be provided with adequate resources.

Poverty in the neighborhood

People living in socially underprivileged and poor city areas suffer more often from mental health conditions like depression, anxiety and psychosis than persons living in high-income neighborhoods ( 22 – 24 ). For example, Fone et al. reported that regional income inequality was significantly associated with more common mental disorders [measured by the Gini coefficient 1 and the Mental Health Inventory MHI-5; Odds Ratio = 1.13 (95%CI 1.04–1.22)] ( 22 ). In addition, more than half of the world's population live in cities, and the continuous urbanization of city areas lead to an aggravation of community-level risk factors for mental health conditions, including physical environmental challenges or chronic stress exposition, even though city residents have more access to education and healthcare ( 25 , 26 ). Accordingly, in a meta-analysis, Vassos et al. reported a 2.37 higher risk for schizophrenia for people living in urban environments compared to rural environments ( 24 ).

During the last decade, the effect of neighborhood-related factors (including social cohesion, income deprivation as well as traffic or air pollution) on the mental health status of residents has gained attention ( 27 ). It has been suggested to interpret data on individual risk factors including income and education with respect to their interaction with the social environment ( 28 , 29 ). The relationship between factors on a neighborhood level and individual outcomes can be complex ( 30 ), with multiple, potentially bidirectional pathways to explain the association between community-level and individual factors. In spite of these potential complexities, there is evidence that poverty in the neighborhood is associated with poor mental health (measured by the General Health Questionnaire GHQ-28) above and beyond the effects of individual education or income ( 31 ). This effect was even more pronounced among persons with a minority status (persons with a Turkish migration background in Berlin, Germany), and again independent of individual factors like age or income (beta = 1.12, Standard Error = 0.26, p < 0.001) ( 31 ). This observation is supported by a longitudinal cohort study with 1,120 participants in New York, which observed that the socioeconomic status (SES) of the neighborhood was associated with the incidence of depression, independently of the individual SES ( 28 ).

Next to economic deprivation, social-interactive aspects of the neighborhood should be taken into account. For example, a longitudinal multilevel analysis with 4,426 participants over the course of 7 years examined quality of life and mental health (36-Item Short Form Survey and Mental Health Inventory-5), neighborhood deprivation (gross household income) and social cohesion, which refers to a sense of belonging and solidarity within a community (Buckner's Neighborhood Cohesion Scale) ( 32 ). This study reported a negative association between neighborhood challenges due to poverty on the one hand and low levels of mental health and quality of life on the other, again after adjusting for individual socio-economic risk factors and transitions in life events. Interestingly, a protective effect of solidarity and social cohesion on this association was found. These findings suggest that individual mental health is substantially influenced by local poverty, and that solidarity and social support is of key importance for mental wellbeing ( 33 – 35 ).

Data on mediating processes between neighborhood poverty and mental wellbeing are rare and may include aspects of family and peer support structures ( 4 , 30 ). Lack of family and neighborhood resources can lead to more stress, social isolation, discrimination and susceptibility for mental disorders ( 31 ). Mental illness, on the other hand, can increase stigmatization, social exclusion and marginalisation ( 4 , 36 ). Also, income in people with mental health conditions is usually decreased, reducing resources and leading to a higher probability to live in a poor neighbourhood ( 4 ). Physical aspects of neighborhoods may also play an important role for mental wellbeing. In Greenwich, UK, it has been shown that physical environment and mental wellbeing are associated with each other on many domains ( 37 ). In this study, living conditions for persons registering in the lowest quartile for mental health were characterized by neighborhood noise [OR 2.71 (95%CI 1.48–4.98)], feeling overcrowded in the home [OR 1.42 (95%CI 1.42–3.48)], being dissatisfied with access to green open spaces [OR 1.69 (95%CI 1.05–2.74)], and feeling unsafe to go out in the day [OR 1.64 (1.02–2.64)].

Prospective studies are required to disentangle the interaction of individual, environmental and community level effects on mental health.

Income inequality and its effects on mental health

Looking at relative poverty on a macro-level, general effects of income inequalities on mental health have to be considered, as numerous studies have shown the effect of income inequality on overall health and mortality in high income countries ( 38 – 41 ). In their list of 17 sustainable development goals, the UN declared reducing income inequalities within and among countries as one aim ( 5 ). In the last decades, income inequality has dramatically increased in Western industrialized countries ( 42 ). According to the epidemiologists Pickett and Wilkinson, income inequality is linearly associated with higher rates of mental illness in high income countries ( 38 , 43 ). Industrialized countries with high income inequality, like the United States (measured by the ratio of income among the wealthiest compared with the poorest 20% in each country) showed a high index of health and social problems (e.g., life expectancy, mental illness, homicides, distrust, social mobility) ( 44 ). In 2022, Tibber et al. included 42 studies with data from 7,744,469 participants and found higher income inequality to be associated with poor general mental health, depression and psychosis in adults ( 45 ). Likewise, a review of 26 studies from mostly high-income countries reported a positive relationship between income inequality and depression, with greater impact for women and low-income subpopulations ( 41 ). Regarding schizophrenia, Burns et al. investigated incidence rates across 26 mostly high-income countries and found a positive relationship between income inequality, measured by the Gini coefficient, and the incidence rate of schizophrenia [beta = 1.02; Z = 2.28; p = 0.02; (95%CI 1.00–1.03)]: for every point in income inequality increase, a two-point increase in incidence rate of schizophrenia followed ( 46 ). After correction for potential confounders like rates of urbanization, Gross Domestic Product per capita, migrant population and unemployment rate, the effect still remained significant ( 46 ).

Regarding mortality caused by mental health conditions, suicide rates among young men in England and Wales increased over the period of 1950–1998, which was associated with an increase in income inequality and divorce and a decline in marriage ( 47 ). A longitudinal study from Canada investigated mental health in 2,461 mothers during pregnancy and after birth and found a significant interaction between income inequality measured by the Gini coefficient and anxiety symptoms, but not depressive symptoms ( 48 ). In a register-based cohort study with 1,354,393 children, mental disorders were three–four times more prevalent in children who had parents in the lowest income percentiles ( 49 ). Differences were detected concerning attention-deficit hyperactivity disorder in boys and depression and anxiety in girls. Wilkinson and Pickett elaborated on the importance of early childhood interventions to reduce developmental risk factors for health ( 50 ).

To explain these findings, several causal hypotheses are discussed. For example, the Social Capital Hypothesis suggests high income inequality affects (mental) health because of a breakdown of social capital, which includes social trust and safety, a sense of belonging, and participation ( 38 , 39 , 51 ). The Status Anxiety Hypothesis states that high income inequality fuels a feeling of inferiority because of high status competition and comparison, which causes chronic stress ( 52 ). In his book, “Status Syndrome,” Marmot states that position in hierarchy, which is linked to control over life and social engagement, is the most important factor for health inequalities, rather than factors like genetics or behaviour ( 53 , 54 ). This hypothesis is supported by a study using data of 34,000 study participants across 31 European countries, conducting multi-level models with the Gini coefficient, several sociodemographic factors, and status anxiety, as assessed by the multi-scale question “some people look down on me because of my job situation or income” ( 52 ). In this latter study, status anxiety was inversely associated with income rank; moreover, status anxiety was also lower in countries with lower income inequality. Beyond status anxiety, perception of unfair income distribution may be particularly pronounced in cities with relatively segregated high and low income neighborhoods and exposure to dramatic differences in resources and privileges ( 55 ). Again, longitudinal studies can help to disentangle complex interactions between individual risk and resilience factors, community resources and environmental challenges.

Discussion: Future policy implications

The reviewed studies suggest that a person's mental health is not only and even not primarily explained by individual risk factors, but includes closely related community and environmental processes that reflect social differences and justice. In this context, Amartya Sen's capability framework emphasizes the responsibility of the society to contribute to each of its members' self-fulfilment ( 56 ). Together with the conceptual work of Michael Marmot ( 54 ), these considerations shift the focus away from stigmatizing socially and economically excluded individuals toward a reflection of multidimensional processes within a society, which steer vulnerable people in the direction of poverty and compromised somatic as well as mental health.

Among many conceptual frameworks, the World Health Organization formulated three levels of policy approaches in their call for action on the social determinants of health, to tackle general health inequities ( 57 ). Based on this, we elaborate on specific mental health and poverty strategies and recommend:

Targeted programs for groups with a low socioeconomic status, including homeless persons with mental illness. Financial inclusion of people with mental illness, especially those in unstable housing, should be in the focus of targeted programs. Indeed, our own research showed that in Berlin Germany, about 10.1% of all patients have no bank account, thus severely restricting access to social aid and limiting participation ( 58 ). Strategies of permanent supportive housing like Housing First have proven to be effective on housing stability as well as health outcomes ( 15 ). Other examples for targeted programs might be nutritional programs for women or young mothers, since child and maternal malnutrition are one of the leading risk factors for disability-adjusted life-years ( 21 ).

Policies for closing the gap of social inequalities. Here, scientists from different disciplines should cooperate to assess the impact of poverty on the general mental health of a community, and to disentangle complex interactions on the level of communities, environments and individuals. Our own research emphasizes the impact of local poverty above and beyond individual income ( 31 ). This observation supports the implementation and examination of effects of anti-poverty programs including Universal Basic Income (UBI) ( 59 ). A study on the effects of basic income provided by cash payments in Finland reported significant improvements in mental wellbeing; mediating factors were associated with a reduction in perceived stigma, more time with family and friends, and a new sense of hope for the future ( 60 ). Interestingly, improvements for children were amplified when the payments were given early during their individual development ( 60 ). Nevertheless, it should be considered that UBI might also be discriminating toward people with different needs and thus higher living expenses, for example for people with chronic diseases and higher health expenditures. Also, UBI might only be offered to people with a certain citizenship, excluding already marginalized people without any citizenship for example. In light of the effects of income inequality on mental health, economic growth per se will not lead to an increased mental wellbeing, and a balanced distribution of wealth should be considered in policy including metal health strategies ( 61 ).

Promoting interdisciplinary and participatory research on social interactions in societies. To date, participatory research and peer involvement is widely underrepresented and should play a much more influential role in scientific studies and policies for mental health ( 62 ). On a neighborhood level, solidarity and mutual support appears to represent important mediators between neighborhood poverty and individual mental health. It should be an inter- as well as transdisciplinary effort including social sciences to disentangle these processes and their complex interactions. We and others have suggested to promote topics relevant for mental health in all aspects of city planning, including exposure to air pollution, traffic noise, also at nights, provision of green spaces, accessible community centers and spaces for social interaction ( 25 , 26 ).

To disentangle the complex impact of absolute and relative poverty on mental health, studies should be designed longitudinally and measurements should be included that address mental as well as somatic health, risk factors such as racism and discrimination and potential resilience factors such as solidarity and mutual support. Facing the increasing digitization of health care, the use of digital tools and digital interventions to collect data can be of help with this endeavour ( 63 , 64 ).

Altogether, an inter- and transdisciplinary approach can promote understanding of the complex and multileveled interactions between individual- and community-based risk factors. The aim is to address mental health in populations with evidence-based public health policies that target social and physical environments and foster solidarity and mutual support. Medical prevention and intervention strategies targeted at the provision of adequate mental health care for persons with mental illness should be complemented by policies that promote social participation and empowerment within societies.

Data availability statement

Author contributions.

DM and AH were responsible for drafting and revising the manuscript. DM wrote the first manuscript draft. AH, SS, and SG revised the manuscript. All authors contributed to and approved the final manuscript.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The handling editor [WF] declared a shared affiliation with the author(s) at the time of review.

Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

1 To objectify income inequality, several measures have been established, of which the Gini index is one of the most commonly used ( 65 ). It ranges from 0 to 1, with higher index indicating greater income inequality. The calculation relates to the Lorenz curve, which is a graphical representation of distribution of income.

The Countless Ways Poverty Affects People's Health

Many gaps persist, from lower birth weights to shorter life spans.

The Countless Ways Poverty Affects People's Health

Getty Images

Families in poor neighborhoods are at a higher risk for a host of health problems that are perpetuated by their environment.

Poverty's harsh effects on health start before babies are born and pile up throughout their adult lives. With stressed-filled homes, shaky nutrition, toxic environments and health-care gaps of every kind, kids in very low-income families may never catch up when it comes to their health. Below, experts spell out the strong link between poverty and illness and discuss efforts to lift people to better health.

[See: 10 Concerns Parents Have About Their Kids' Health .]

Between 10 and nearly 15 years – that's the difference in life expectancy between the poorest and richest people in the United States, according to findings just published online in the medical journal JAMA. In the huge, long-term study encompassing 1.4 billion Americans, researchers matched income levels and mortality data between 1999 and 2014 to reach their conclusion. And low income has long been linked to lower birth weight, which increases the risk of a host of health and educational problems.

Gaps like this come as no surprise to Dr. James Duffee, who's been a community pediatrician in Springfield, Ohio, for more than 20 years, serving mostly low-income children and families. "Poverty is a negative, independent factor that influences lifelong health," he says. "Through the science of toxic stress, we understand that early childhood adversity and poverty is a factor that affects not only brain architecture and [neurologic and endocrine] function, but affects the probability of lifelong illness, including cardiac disease and diabetes."

The insidious role of stress starts early. "If a child is exposed to constant stress in childhood, essentially their stress mechanism is never turned off," Duffee says. "So it resets at a higher level, a higher heart rate, higher blood pressure." Adults living in poverty, among other stresses, are much more likely to have inflammatory diseases, he says, with an increased risk for heart attack and stroke.

"Many, many adult illnesses have thdepressioneir foundation in early childhood," Duffee says. People living in poverty are more likely to smoke , he points out, putting them at higher risk for lung cancer and respiratory conditions.

Injuries and Low Income

"People are often surprised at the increase in accidents and actual deaths that happen to poor children," says Dr. Benard Dreyer, president of the American Academy of Pediatrics. But it's not really that hard to understand.

"They live in more dangerous environments," he says. "Their parents are stressed out, often working multiple jobs. They live in substandard housing. They live in dangerous neighborhoods where they can be shot or injured."

[See: How Social Workers Help Your Health .]

Food on the Table?

It seems like a paradox: increased hunger yet more obesity among poor people. "The answer is pretty simple," says Dreyer, who is also the director of developmental-behavioral pediatrics at the New York University School of Medicine. "The cheapest food you can buy is usually empty calories – high-calorie, high-fat food."

People may live in food deserts, Dreyer says – neighborhoods with nowhere to get fresh vegetables but plenty of access to fast food . With this combination, he says, "people are hungry, and when they get food, they're going to try to eat a lot of it."

Sometimes there just isn't enough to eat. When that happens, Duffee says, "The truth of the matter is, most of the time parents are forgoing their meals to feed the kids."

Emotional Health Suffers

Almost half of children who live in poverty have mothers with at least some symptoms of depression , because of the stresses of raising a family in these circumstances, Duffee says.

"Mothers who are depressed interact with their children very differently," he says. "Those interactions – the lack of stimulation and socio-emotional connections, what we call attachment – also have long-term effects, if not lifelong effects, on children."

Duffee recalls a recent situation. "I saw a family with two children and the parents and an older adult child in the room," he says. "And the identified patient was a 9-year-old boy who was very upset. The father said to me, 'Why doesn't he follow any instructions I give him? I don't understand why he's so disrespectful, and he's always hateful to me.'"

There was more to the story, Duffee learned. "Halfway through the interview, the mother turned to me and said, 'There's something you probably need to know: We're homeless and living in one room in a hotel.' So I said to [the father], 'Can we understand that this boy is extremely upset because you live in a hotel room and don't have a home, and all five of you live in the same room? And how would you expect him to follow directions or not respond to that?'"

Kids respond to that kind of adversity in two ways, Duffee says: either with outward behavior or becoming depressed and internalizing their reaction. "This boy was expressing his distress by becoming oppositional," Duffee says.

Toxic Infrastructures

Lead-contaminated water used to be a widespread health hazard, Duffee says, although it has been less so in the past few decades. But the threat remains. "Because of the lack of infrastructure in cities, lead is becoming more of a concern," he says. "As the infrastructure deteriorates, lead is released in some of the older pipes."

Duffee points to the Flint, Michigan, water crisis as a recent example. "There's no safe level of lead exposure," he says. "Exposure to lead in the first couple of years can cause lifelong, irreparable damage" to the brain, he says. "It doesn't always, but it can."

Dental and Vision Disparities

Pediatricians are starting to take more interest in early oral-health promotion, Duffee says. Applying fluoride varnish during well-child visits significantly reduces childhood dental cavities, he says. Also, he adds, educating mothers to not prop bottles of milk during feeding helps prevent rotting front teeth.

Vision health, although covered by Medicaid, can still be a problem. Kids often break their glasses, Duffee notes. "And so they walk around without any glasses because they've broken them, and there's not enough money to replace them." When kids can't see properly, that "absolutely" has an educational impact, he says.

[See: 10 Reasons to See a Physician Assistant .]

Two-Way Street

Inadequate dental coverage helps perpetuate the poverty cycle, says Barbara Wolfe, a professor who specializes in health economics at the University of Wisconsin–Madison. "If an individual hasn't gotten appropriate oral health care, as a child or an adolescent, and then loses teeth, it makes it really hard to get a job," she says.

The tie between poverty and health most likely goes in both directions, Wolfe says, with an adult in poor health more likely to have lower earnings.

On the other hand, a boost in income can boost health. In research Wolfe conducted involving Native American tribes included in reservation gaming compacts, her group found mental-health improvements for depression and anxiety. Other studies of groups with an influx of income have found small improvements in blood pressure among people with hypertension, she says, and needed eyesight corrections being made.

Maternal Health Focus

Narrowing the health-care gap starts at the beginning of life. "We have gone a long way to provide health care, mainly through Medicaid, to low-income women," Wolfe says. "We do have policies in this country that are targeted on trying to reduce outcomes that could be prevented just by prenatal care."

The Affordable Care Act appears to be helping, she says, not only through earlier access to prenatal care, but also in terms of providing greater services to young women from low-income families in the first few months of a child's life. "That focus on intensive services in those early months is an effort to educate parents," she says. That includes raising their awareness of children's health and teaching them about kids' nutritional needs.

Programs That Help

"Without government programs, instead of one out of five children living in poverty, it would be one in three children living in poverty," Dreyer says. "So they really do help keep children out of poverty. The problem is, we need to expand and enhance them rather than fight just to keep them alive." The good news, he says, is programs such as the Supplemental Nutrition Assistance Program (or food stamps), WIC and school-lunch programs and certain tax credits that are permanently in place.

Community health centers are improving access , Wolfe says. "We have spread these health care centers to many more areas that did not have adequate providers," she says.

The National Health Service Corps and NURSE Corps programs provide funding and scholarships to primary care clinicians and students in exchange for their service in underserved communities. According to the NHSC website, nearly 10,000 of these medical, dental, nursing, physician assistant and behavioral and mental health practitioners are providing primary care to millions of medically underserved people.

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Poverty And Health Essay

Type of paper: Essay

Topic: Health Care , Social Issues , Health , Politics , Children , Family , Poverty , Leadership

Published: 01/25/2020

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The below mentioned paper is a reaction paper on Poverty and Health. Both of them are correlated but through this paper we shall go through and discuss whether poverty leads to ill health or poor health leads to poverty. Several research results and findings have been mentioned to support the fact.

Introduction

Poor health been considered as a result of poverty for long. It has been widely acknowledged that areas with higher figures of ill-health and malnutrition are prone to widespread poverty. Improper and insufficient diet intake among children leads to infection and malnutrition respectively and thus leading to poor health of the nation. Children are considered to be the future of every country and ensuring their good health is the collective responsibility of parents, general public and the government.

Cause Analysis:

According to me, the factors that give rise to poverty and ill health include the following: - Lack of basic amenities - Gender inequality - Lack of cleanliness - Lack of education and basic knowledge about sanitation - Lack of sources of earnings - Poor environment - Inadequate care - Deprived health services - Marginal diet intake - Limited employability - High health care expense - Lack of skills and abilities - Lack of energy and clarity of thought.

Poverty and Health are correlated by two major indicators-

Access to health care and living conditions. Poor people are more prone to experience stern living conditions that contribute more towards poor health. Conditions such as illness, risk of accidents and injury. Moreover, poor people do not have health insurance cover and have less access to health care facilities. They can hardly spend on health expenditures. They do not prefer visiting doctors to avoid expenses as they cannot afford high fees of well-qualified ones. Keeping good health is a significant prerequisite for absconding poverty and undoubtedly, improved health contributes a lot towards growth and prosperity of a nation. The government of a country should aim at investing in common people’s health in order to improve a country’s common welfare and to promote economic growth. Most of the time they are unaware of the fact that they are eligible for med claims or not.

Analysis and Findings

Latest studies and findings have shown that women who are more unfit and malnourished tend to deliver low birth weight kids and therefore become the foremost cause of child nutrition. The foremost significance of child malnutrition as a cause of poverty is that it is independent of inflation figures and any sort of price information analysis. Studies reveal that the association between poverty and child nutrition is tough at the end of lower income range. There is a reduction in the occurrence of malnourished children from 34% to 17% with increasing GNP per capita from $300 to $400. Thus countries which rank lower on the GNP per capita are further prone to occurrence of underweight children. A research finding reveals that 23.3% of poor and uninsured children had not seen a doctor in one year.

Considering all the data and findings above, poor health appears to be the foremost cause and factor of poverty indicator. The data analysis of this paper very strongly supported by the fact that poor health/malnutrition is a poverty pointer and analytical of other advantageous results of development such as intra-household distribution, enhancement in gender empowerment etc.

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Economic Stability

About This Literature Summary

This summary of the literature on Poverty as a social determinant of health is a narrowly defined examination that is not intended to be exhaustive and may not address all dimensions of the issue. Please note: The terminology used in each summary is consistent with the respective references. For additional information on cross-cutting topics, please see the Discrimination , Employment , Housing Instability , and Incarceration literature summaries. 

Related Objectives (4)

Here's a snapshot of the objectives related to topics covered in this literature summary. Browse all objectives .

• Reduce the proportion of people living in poverty — SDOH‑01
• Increase employment in working-age people — SDOH‑02
• Increase the proportion of children living with at least 1 parent who works full time — SDOH‑03
• Reduce the proportion of families that spend more than 30 percent of income on housing — SDOH‑04

Related Evidence-Based Resources (3)

Here's a snapshot of the evidence-based resources related to topics covered in this literature summary. Browse all evidence-based resources .

• Social Determinants of Health: Tenant-Based Housing Voucher Programs
• New Perspectives on Creating Jobs: Final Impacts of the Next Generation of Subsidized Employment Programs
• Strengthening TANF Outcomes By Developing Two-Generation Approaches To Build Economic Security

Literature Summary

The United States measures poverty based on how an individual’s or family’s income compares to a set federal threshold. 1 For example, in the 2021 definition, people are considered impoverished if their individual income is below $12,880 or their household income is below $26,500 for a family of 4. 2 After 5 consecutive years in decline, the U.S. poverty rate increased to 11.4 percent in 2020, or a total of 37.2 million people. 3  

Poverty often occurs in concentrated areas and endures for long periods of time. 1 Some communities, such as certain racial and ethnic groups, people living in rural areas, and people with disabilities, have a higher risk of poverty for a myriad of factors that extend beyond individual control. 1 , 4 – 8 For example, institutional racism and discrimination contribute to unequal social and economic opportunities. 4 Residents of impoverished communities often have reduced access to resources that are needed to support a healthy quality of life, such as stable housing , healthy foods , and safe neighborhoods. 1 , 4 , 9 Poverty can also limit access to educational and employment opportunities, which further contributes to income inequality and perpetuates cyclical effects of poverty. 1  

Unmet social needs, environmental factors, and barriers to accessing health care contribute to worse health outcomes for people with lower incomes. 10 , 11 For example, people with limited finances may have more difficulty obtaining health insurance or paying for expensive procedures and medications. 12 In addition, neighborhood factors, such as limited access to healthy foods and higher instances of violence , can affect health by influencing health behaviors and stress. 12  

Across the lifespan, residents of impoverished communities are at increased risk for mental illness, chronic disease, higher mortality, and lower life expectancy. 9 , 13 – 17 Children make up the largest age group of those experiencing poverty. 18 , 19 Childhood poverty is associated with developmental delays, toxic stress, chronic illness, and nutritional deficits. 20 – 24 Individuals who experience childhood poverty are more likely to experience poverty into adulthood, which contributes to generational cycles of poverty. 25 In addition to lasting effects of childhood poverty, adults living in poverty are at a higher risk of adverse health effects from obesity, smoking, substance use, and chronic stress. 12 Finally, older adults with lower incomes experience higher rates of disability and mortality. 6 One study found that men and women in the top 1 percent of income were expected to live 14.6 and 10.1 years longer respectively than men and women in the bottom 1 percent. 26

Poverty is a multifaceted issue that will require multipronged approaches to address. Strategies that improve the economic mobility of families may help to alleviate the negative effects of poverty. 27 – 29 For example, tax credits such as the Earned Income Tax Credit and Child Tax Credit alleviate financial burdens for families with lower and middle incomes by reducing the amount of taxes owed. 30 In addition, federal social assistance programs are designed to provide safety net services and specifically benefit individuals and families with lower incomes. 31 Two of the nation’s largest social assistance programs are Medicaid, which provides health coverage, and the Supplemental Nutrition Assistance Program (SNAP), which provides food assistance. Medicaid and SNAP serve millions of people each year and have been associated with reductions in poverty along with overall health benefits. 32 , 33 In order to reduce socioeconomic inequality, it may also be important to address factors that are associated with the health status of poor communities. 27 Additional research and interventions are needed to address the effects of poverty on health outcomes and disparities. 

U.S. Department of Agriculture, Economic Research Service. (n.d.) Rural poverty & well-being . Retrieved December 13, 2021, from https://www.ers.usda.gov/topics/rural-economy-population/rural-poverty-well-being/

U.S. Department of Agriculture, Office of the Assistant Secretary for Planning and Evaluation. (2021, February 1). 2021 Poverty guidelines . https://aspe.hhs.gov/topics/poverty-economic-mobility/poverty-guidelines/prior-hhs-poverty-guidelines-federal-register-references/2021-poverty-guidelines

Shrider, E. A., Kollar, M., Chen, F., & Semega, J. (2021, September 14). Income and poverty in the United States: 2020 . U.S. Census Bureau. https://www.census.gov/library/publications/2021/demo/p60-273.html

Williams, D. R., Mohammed, S. A., Leavell, J., & Collins, C. (2010). Race, socioeconomic status, and health: Complexities, ongoing challenges, and research opportunities. Annals of the New York Academy of Sciences, 1186 (1), 69–101. https://doi.org/10.1111/j.1749-6632.2009.05339.x

Kaiser Family Foundation. (n.d.). Poverty rate by race/ethnicity . https://www.kff.org/other/state-indicator/poverty-rate-by-raceethnicity/

Minkler, M., Fuller-Thomson, E., & Guralnik, J. M. (2006). Gradient of disability across the socioeconomic spectrum in the United States. New England Journal of Medicine, 355 (7), 695–703. https://doi.org/10.1056/NEJMsa044316

Brucker, D. L., Mitra, S., Chaitoo, N., & Mauro, J. (2015). More likely to be poor whatever the measure: Working-age persons with disabilities in the United States. Social Science Quarterly, 96 (1), 273–296. https://doi.org/10.1111/ssqu.12098

Rank, M. R., & Hirschl, T. A. (2015). The likelihood of experiencing relative poverty over the life course. PLoS ONE, 10 (7), e0133513. https://doi.org/10.1371/journal.pone.0133513

Singh, G. K., & Siahpush, M. (2006). Widening socioeconomic inequalities in US life expectancy, 1980–2000. International Journal of Epidemiology, 35 (4), 969–979. https://doi.org/10.1093/ije/dyl083

Phelan, J. C., Link, B. G., & Tehranifar, P. (2010). Social conditions as fundamental causes of health inequalities: Theory, evidence, and policy implications. Journal of Health and Social Behavior, 51(Suppl 1) , S28–S40. https://doi.org/10.1177/0022146510383498

Thompson, T., McQueen, A., Croston, M., Luke, A., Caito, N., Quinn, K., Funaro, J., & Kreuter, M. W. (2019). Social needs and health-related outcomes among Medicaid beneficiaries. Health Education & Behavior: The Official Publication of the Society for Public Health Education, 46 (3), 436–444. https://doi.org/10.1177/1090198118822724

Khullar, D., & Chokshi, D. A. (2018). Health, income, & poverty: Where we are & what could help . Health Affairs Health Policy Brief. https://doi.org/10.1377/hpb20180817.901935

Braveman, P. A., Cubbin, C., Egerter, S., Williams, D. R., & Pamuk, E. (2010). Socioeconomic disparities in health in the United States: What the patterns tell us. American Journal of Public Health, 100 (Suppl 1), S186–S196. https://doi.org/10.2105/AJPH.2009.166082

Belle, D., & Doucet, J. (2003). Poverty, inequality, and discrimination as sources of depression among U.S. women. Psychology of Women Quarterly, 27 (2), 101–113. https://doi.org/10.1111/1471-6402.00090

Caughy, M. O., O’Campo, P. J., & Muntaner, C. (2003). When being alone might be better: Neighborhood poverty, social capital, and child mental health. Social Science & Medicine, 57 (2), 227–237. https://doi.org/10.1016/S0277-9536(02)00342-8

Ward-Smith, P. (2007). The effects of poverty on urologic health. Urologic Nursing, 27 (5), 445–446.

Mode, N. A., Evans, M. K., & Zonderman, A. B. (2016). Race, neighborhood economic status, income inequality and mortality. PLoS ONE, 11 (5), e0154535. https://doi.org/10.1371/journal.pone.0154535

Kaiser Family Foundation. (n.d.). Poverty rate by age . https://www.kff.org/other/state-indicator/poverty-rate-by-age/

Cellini, S. R., McKernan, S. M., & Ratcliffe, C. (2008). The dynamics of poverty in the United States: A review of data, methods, and findings. Journal of Policy Analysis and Management, 27 (3), 577–605.   https://onlinelibrary.wiley.com/doi/abs/10.1002/pam.20337

Eamon, M. K. (2001). The effects of poverty on children’s socioemotional development: An ecological systems analysis. Social Work, 46 (3), 256–266.

Evans, G. W., & Kim, P. (2013). Childhood poverty, chronic stress, self-regulation, and coping. Child Development Perspectives, 7 (1), 43–48. https://doi.org/10.1111/cdep.12013

Shaw, D. S., & Shelleby, E. C. (2014). Early-starting conduct problems: Intersection of conduct problems and poverty. Annual Review of Clinical Psychology, 10 (1), 503–528. https://doi.org/10.1146/annurev-clinpsy-032813-153650

Justice, L. M., Jiang, H., Purtell, K. M., Schmeer, K., Boone, K., Bates, R., & Salsberry, P. J. (2019). Conditions of poverty, parent-child interactions, and toddlers’ early language skills in low-income families. Maternal and Child Health Journal, 23 (7), 971–978. https://doi.org/10.1007/s10995-018-02726-9

Council on Community Pediatrics, Gitterman, B. A., Flanagan, P. J., Cotton, W. H., Dilley, K. J., Duffee, J. H., Green, A. E., Keane, V. A., Krugman, S. D., Linton, J. M., McKelvey, C. D., & Nelson, J. L. (2016). Poverty and child health in the United States. Pediatrics, 137 (4), e20160339. https://doi.org/10.1542/peds.2016-0339

Wagmiller Jr, R. L., & Adelman, R. M. (2009). Childhood and intergenerational poverty: The long-term consequences of growing up poor . National Center for Children in Poverty. https://www.nccp.org/publication/childhood-and-intergenerational-poverty/

Chetty, R., Stepner, M., Abraham, S., Lin, S., Scuderi, B., Turner, N., Bergeron, A., & Cutler, D. (2016). The association between income and life expectancy in the United States, 2001–2014. JAMA, 315 (16), 1750–1766. https://doi.org/10.1001/jama.2016.4226

Yoshikawa, H., Aber, J. L., & Beardslee, W. R. (2012). The effects of poverty on the mental, emotional, and behavioral health of children and youth: Implications for prevention. The American Psychologist, 67 (4), 272–284. https://doi.org/10.1037/a0028015

Riccio, J. A., Dechausay, N., Greenberg, D. M., Miller, C., Rucks, Z., & Verma, N. (2010). Toward reduced poverty across generations: Early findings from New York City’s conditional cash transfer program . MDRC.

Love, J. M., Kisker, E. E., Ross, C. M., Schochet, P. Z., Brooks-Gunn, J., Paulsell, D., Boller, K., Constantine, J., Vogel, C., Fuligni, A. S., & Brady-Smith, C. (2002). Making a difference in the lives of infants and toddlers and their families: The impacts of early Head Start. Volumes I–III: Final technical report and appendixes and local contributions to understanding the programs and their impacts . U.S. Department of Health and Human Services, Administration for Children and Families.

Maag, E., & Airi, N. (2020). Moving forward with the earned income tax credit and child tax credit: Analysis of proposals to expand refundable tax credits. National Tax Journal, 73 (4), 1163–1186. https://doi.org/10.17310/ntj.2020.4.11

Blank, R. M. (2002). Evaluating welfare reform in the United States. Journal of Economic Literature, 40 (4), 1105–1166.

Currie, J., & Chorniy, A. (2021). Medicaid and Child Health Insurance Program improve child health and reduce poverty but face threats. Academic Pediatrics, 21 (8), S146–S153. https://doi.org/10.1016/j.acap.2021.01.009

Keith-Jennings, B., Llobrera, J., & Dean, S. (2019). Links of the Supplemental Nutrition Assistance Program with food insecurity, poverty, and health: Evidence and potential. American Journal of Public Health, 109 (12), 1636–1640. https://doi.org/10.2105/AJPH.2019.305325

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The Connection Between Poverty and Mental Health Problems Essay

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Introduction

References list.

Poverty is linked to various health problems affecting an individual. According to Canadian Mental Health Association (2009) people living in severe poverty conditions are susceptible to the increased rate of mental health problems. David et al (1996) agrees with the latter assertion by claiming the connection between the health morbidity, and socio-economic weakness is not unique, therefore, a possibility of a person developing mental health problems is normal.

For instance, Canadian Mental Health Association (2009) contends that a person from an economically deprived environment is more likely to experience, injuries, exposure to toxins, prenatal complications and respiratory disease. Among the children, the environment leads to retarded growth and poor dental health problems.

The connection between poverty and mental health problems of an individual has been explained by various authors. Hence, the trend encompasses a developmental linkage with varying definitions of poverty and methods of examining mental health outcomes.

The trend has consistently shown that poverty upholds the stress level in an individual’s life. The daily struggle to earn a daily bread takes a toll on an individual mental health and contributes to mental health problem. Thus, it is easy for a person to have a strong mental health when he/she has material comfort rather than living in poverty.

However, the causes of mental health vary and are complex. Saraceno and Barbui admits that it is difficult to point out poverty alone contributes to mental health problem (1997). Mental illness is a condition which affects all classes of people in the society, and many people who experiences poverty do not have mental illness.

Similarly, a mental problem complicates a person life in gaining a stable employment. Hence, this makes the door of becoming poor to be wide open. For example, in Canada, Canadian Mental Health Association (2009) explains that many homeless people with mental illness are increasing. Besides, many people with mental challenges hold to low paying jobs. Therefore, we can infer that poverty contributes to mental health problems and that mental illness can make it difficult to stay above the poverty line.

The bond between poverty and mental health is not a new idea. Various researchers have expressed arguments to support this assertion. Weich and Lewis (1998) is one of the authors supporting this argument. He notes the connection between the two variables is bi-directional.

First, a person with a low income is more prone to experience poor mental health and poverty contributing or causing mental health. Observationally, a person and neighbourhood shortfall surges the risk of poor mental health. In their studies, Wicks et al (2005) argues the rate of prevalence of chronic psychiatric condition is higher in deprived areas than in areas with sufficient economic background.

Equally, in a research conducted on the older population in Hertfordshire in the United Kingdom, the result showed the older people who had a strong cohesion within their neighbourhood reported fewer neighbourhood problems. They had a higher level of mental wellness (Jenkins, 2008).

Whereas, those in deprived neighbourhoods showed more serious mental illness, this suggests that poverty does subscribe to mental illness. Second, people with mental illness are more likely to succumb to poverty once a person is incapacitated his/her personal socio-economic standing falls, or a selective glide occurs.

For example, Canadian Mental Health Association (2009) noted a decline in the social position and financial condition overtime in individuals who had depression problem. In a similar study, the GHQ-28, a multipurpose health questionnaire used to determine psychiatric conditions were used to evaluate a group of individuals born in 1947. The men who were poor recorded mental health problem connected with the descending socio-economic arc over their entire life than in women.

Being mentally challenged poses a challenge on individual ability to earn. A person with mental health problem is more likely to be discriminated at work than a medically fit person. According to Wilkinson and Pickett (2006) a person with a general mental problem is four to five times likely to be rejected for employment.

He/she is also twice likely to be on income support system and four to five times likely to receive invalidity reimbursement in contrast to the larger population. Besides, people with a psychotic problem have four chances of securing employment (Wilkinson and Pickett, 2006). People with mental health problems may also have inferior or weaker social networks and educational achievements.

It is more exigent to get a job when a person is mentally challenged. This is because of the social stigma attached to the illness. Wicks et al (2005) notes that studies have shown that most employers are reluctant to employ people with mental problems at any level in their organizations.

Hence, individuals without medical complications are favoured to those with mental or physical disabilities. An individual with mental disability encounters a greater difficulty in regard to retaining a job. This is because; he/she may need unpredictable and intermittent time off the work when the disease needs acute treatment (Wilkinson and Pickett, 2006). Employers may consider this as unreliability and cast doubts on the individual potential while working.

People who use the mental health services cite that personal finances are is a serious issue affecting their daily lives. Hence, according to Weich and Lewis (1998) one out of three people suffering from mental illness is likely to be in debt. Concerns and anxieties about personal finance mount to a significant stressor to an individual in many ways. First, the financial strain is regarded by many researchers as a good predictor of future psychiatric morbidity.

This is more than unemployment or poverty. However, the nature of this threat and its association with unemployment and poverty has not been fixed coherently (Weich and Lewis, 1998). Second, Jenkins et al (2008) in his research in the UK showed the link between poverty and mental illness was coined around debt.

A cross-sectional premeditated illustrated that people with low-income were susceptible to mental illnesses. However, this connection was significantly attenuated after the debt was adjusted. According to Jenkins, an individual with six or more distinct debt had about the six-fold increase in mental illness after their income was adjusted (2008).

As on physical ill health, major general mental problems are linked with poor material standards of living and autonomous of working social class (Weich and Lewis, 1998). He contends the gap between those living comfortable lifestyles and those living in poverty has increased during the last few decades in the UK. This has created a cardinal health risk besides raising the issue of social justice in the society (Ford et al, 2004).

A study in the UK extracting evidence on a population supported the theory that higher income inequality was connected with lower standards of population health (Wilkinson and Pickett, 2006). In the United States, Olson suggests the income and income inequality is the crowning issue affecting the infant health outcome. Hence, the health of the poor infants is affected by absolute wealth rather than comparative wealth (2010).

The social decline support connected with mental illness has declined over the years in most countries. Hence, this has compelled many social researchers to explore the mental health of young people who still depend on their parents for their support and maintenance. According to surveys conducted on families earning low incomes, Ford et al noted there is increasing proportion of mental health problems in these families compared to those families which have better household income (2004).

The difference is worse in boys with a double risk. This risks involve varying social patterns of mental related illnesses such as; attention deficit, bed-wetting, hyperactivity disorder and self-harming behaviours. However, Ford et al found out that none of the studied variables was connected with all major types of disorders.

These disorders included; the conduct disorders were tied to family variables and the life events, and poor general health were because of emotional disorders. More interestingly, Wicks et al (2005) argues that children who have been brought up in difficult financial environment, when the graduate into adulthood, the experience mental health disorders, termed as adult poverty.

He connects this declaration to a Swedish study where a study showed the risk of developing a psychoses increase with an increased exposure to measures of social deprivation in childhood. These adversities are based on the low-income status, rented accommodation, unemployment, social welfares and single parenthood.

Mental disorder in itself is a key reason to child poverty. About 1.25 million in Canada lives with caregivers or parents with a mental health disorder (Saraceno and Barbui, 1997). Owing to the over- representation of unemployment and benefits reliance among those with mental disorder, it is estimated that about 370,000 are likely to live in financial hardships.

An individual abusing drugs or alcohol is likely to experience a social decline. Abusing substances drains an individual’s finances and renders a person unemployable besides linking a person to criminal activities or behaviour.

Although there is evidence to link substance abuse directly to mental illness, it is remarkable to find out there exist a dual diagnosis of mental illness and substance abuse (Saraceno and Barbui, 1997). Hence, substance abuse causes the mental illness because it makes a person to engage in “self-medication” with alcohol and illicit drugs.

The aetiology of mental illness is undeniably multimodal. Many social scientists researchers have put forward arguments on its connection to poverty. Thus, poverty in itself is neither necessary nor sufficient to translate to mental illness, and the impact of social problem may also be distinct for different kinds of mental illness problems.

An individual living in poverty experiences and chronic stress, perhaps, this might serve as an essential biological impact on brain function. This is crucial especially if a person experienced a certain amount of key points in life during his/her growth and development. According to Olson et al (2010) schizophrenia result to chronic experience of social failure that disturbs the dopaminergic working of the brain. Hence, we can deduce that poverty plays a significant role in creating mental illness in people.

Canadian Mental Health Association (2009) CMHA, National supports Senate report on poverty, housing and homelessness: Report addresses mental health issues . Web.

David, R., and Offord et al. (1996). “One-Year Prevalence of Psychiatric Disorder in Ontarians 15 to 64 Years of Age. Canadian Journal of Psychiatry , (41), 9, pp. 559- 63.

Ford, T., Goodman, R., and Meltzer, H., (2004) The relative importance of child, family, school and neighbourhood correlates of childhood psychiatric disorder. Soc Psychiatry Psychiatr Epidemiol, (39),6, pp.487-96.

Jenkins, R., Bhugra, D., and Bebbington, P., (2008) Debt, income and mental disorder in the general population. Psychol Med, ( 38),10, pp.1485-93

Olson, M.E., Diekema, D., and Elliott, B.A., (2010) Impact of income and income inequality on infant health outcomes in the United Pediatrics. Epub . (126), 6,pp.1165-73.

Saraceno, B., and Barbui, C., (1997) Poverty and Mental Illness. Canadian Journal of Pyschatry, (42), 3,285-290

Weich, S., and Lewis, G., (1998) Poverty, unemployment, and common mental disorders: Population Based Cohort Study. BMJ , (11), 317, pp.115-9.

Wicks, S., Hjern, A., and Gunnell, D., (2005) Social adversity in childhood and the risk of developing psychosis: a national cohort study. Am J Psychiatry . (162), 9, pp.1652-7

Wilkinson, R.G., and Pickett, K.E., (2006) Income inequality and population health: A Review and Explanation of the Soc Sci Med, (62), 7, pp. 1768-84.

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Home — Essay Samples — Nursing & Health — Healthcare Crisis — Problematic Issues in America: Poverty and Health

Problematic Issues in America: Poverty and Health

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Words: 2465 |

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Published: Apr 11, 2019

Words: 2465 | Pages: 5 | 13 min read

Works Cited

• Baicker, K., & Taubman, S. L. (2013). The effect of Medicaid on labor market activity and program participation: Evidence from the Oregon Health Insurance Experiment. Journal of Public Economics, 99, 1-19.
• Braveman, P. A., Egerter, S. A., & Mockenhaupt, R. E. (2011). Broadening the focus: The need to address the social determinants of health. American Journal of Preventive Medicine, 40(1 Suppl 1), S4-S18.
• Center on Budget and Policy Priorities. (2021). Policy basics: Introduction to Medicaid. Retrieved from https://www.cbpp.org/research/health/policy-basics-introduction-to-medicaid
• DeNavas-Walt, C., Proctor, B. D., & Smith, J. C. (2021). Income, poverty, and health insurance coverage in the United States: 2020. U.S. Census Bureau, Current Population Reports, P60-275.
• Fiscella, K., & Williams, D. R. (2004). Health disparities based on socioeconomic inequities: Implications for urban health care. Academic Medicine, 79(12), 1139-1147.
• HealthCare.gov. (n.d.). What is the Affordable Care Act (ACA)? Retrieved from https://www.healthcare.gov/glossary/affordable-care-act/
• Lantz, P. M., Golberstein, E., House, J. S., & Morenoff, J. (2010). Socioeconomic and behavioral risk factors for mortality in a national 19-year prospective study of U.S. adults. Social Science & Medicine, 70(10), 1558-1566.
• National Academies of Sciences, Engineering, and Medicine. (2017). Communities in action: Pathways to health equity. National Academies Press.
• National Center for Health Statistics. (2020). Health, United States, 2019: With special feature on mortality. Retrieved from https://www.cdc.gov/nchs/data/hus/hus19.pdf
• Shonkoff, J. P., Boyce, W. T., & McEwen, B. S. (2009). Neuroscience, molecular biology, and the childhood roots of health disparities: Building a new framework for health promotion and disease prevention. JAMA, 301(21), 2252-2259.

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Poverty and Health - The Family Medicine Perspective (Position Paper)

Introduction

Poverty is a complex and insidious determinant of health caused by systemic factors that can persist for generations in a family. Beginning before birth and continuing throughout an individual’s life, poverty can significantly impact health and health outcomes. The vision of the American Academy of Family Physicians (AAFP) is to transform health care to achieve optimal health for everyone. Primary care physicians and public health professionals continue to collaborate on a shared vision of improving population health. As the integration of primary care and public health continues, this shared vision becomes even more relevant, focused, and clear. Success in this new era means achieving better outcomes by transforming health care to overcome obstacles related to the  social, environmental, and community determinants of health – including poverty. 1,2,3,4

Family physicians have a unique perspective on local population’s health challenges because we serve generations of families and follow individual patients through different life stages. We are privileged to share the complex stories of individuals and families in sickness and health over long periods and across different care settings. Rather than viewing a single snapshot of a patient during an episode of illness, we know the patient’s whole story. We know the environmental, patient, and family factors that lead to illness and disease – and the patient’s need to manage their condition effectively. As lifelong collaborators in care, family physicians are well-positioned to understand each patient’s unique obstacles to better health and help overcome them.

Call to Action

The AAFP urges its members to become informed about the impact of poverty on health. Achieving the vision of optimal health for everyone requires a culturally proficient care team and a well-resourced medical neighborhood that supplies readily accessible solutions. Family physicians play a critical role in community health and can contribute through bold efforts in many areas. When these solutions are incorporated seamlessly into everyday practice workflows, family physicians and care teams can be true to the AAFP’s vision by achieving positive change for individuals, families, and communities, and improve population health.

The AAFP calls for action in the following areas:

Physician Level

• Become more informed about the impact of the social determinants of health (SDoH) and identify tangible next steps you can take to address and reduce health inequities
• Be aware of, and sensitive to, your patient’s specific circumstances to help them achieve their health goals

Practice Level

• Identify critical factors that impact patient health, leveraging  The EveryONE Project and data collection on SDoH in electronic health records (EHRs)
• Understand each patient’s unique challenges and coping strategies and know what community resources are available

Community-Leadership Level

• Promote alignment with other private and public community resources to help advance the integration of primary care and public health
• Partner with other health care and social service organizations to connect directly to resources that mitigate poverty’s effect on health

Educational Level

• Drive change in undergraduate and graduate medical education to ensure future physicians are adequately prepared to prevent and address disparities caused by SDoH

Advocacy Level

• Work with local, state, and national governments to adopt a Health in All Policies approach that prioritizes health within goals and agenda-setting
• Advocate for regulatory frameworks and economic incentives to ensure public health and population health are critical to individual health care efforts

Understanding Poverty and Low-income Status

Poverty occurs when an individual or family lacks the resources to provide life necessities, such as food, clean water, shelter, and clothing. It also includes a lack of access to such resources as health care, education, and transportation. 5 In the United States, federal poverty is expressed as an annual pre-tax income level indexed by the size of household and age of household members. For example, in 2020, the federal poverty income level was $12,760 for an individual younger than 65 years and $26,200 for a family of four. 6  In 2019, approximately 10.5% of Americans were living below the poverty line. While overall poverty rates had been declining in the past several years, inequalities remain by SDoH, including race and racism, ethnicity, educational attainment, and disability status. 7

The term “low income” generally describes individuals and families whose annual income is less than 130-150% of the federal poverty income level. For example, the Supplemental Nutrition Assistance Program (SNAP) is available to individuals with a gross monthly income of 130% of the federal poverty income level. 8 Medicaid is open to families with an income of 138% of the poverty income level. 9

Poverty and low-income status are associated with various adverse health outcomes, including shorter life expectancy, higher infant mortality rates, and higher death rates for the 14 leading causes of death. 10,11  Individual- and community-level mechanisms mediate these effects. 12  For individuals, poverty restricts the resources used to avoid risks and adopt healthy behaviors. 13  Poverty also affects the built environment (i.e., the human-made physical parts of the places where people live, work, and play, including buildings, open spaces, and infrastructure), services, culture, and communities’ reputation, all of which have independent effects on health outcomes. 14

Location matters, and there are often dramatic differences in health care delivery and health outcomes between communities that are only a few miles apart. For example, the Robert Wood Johnson Foundation (RWJF) found a 25-year difference in average life expectancy in New Orleans, LA, between inner city and suburban neighborhoods. Similarly, there is a 14-year difference in average life expectancy between two Kansas City, MO, neighborhoods that are roughly three miles apart. 15

A study by The Commonwealth Fund assessed 30 indicators of access, prevention, quality, potentially avoidable hospital use, and health outcomes. The study found that populations with low-income status suffer disparities in every state. However, it also identified significant differences among states’ performances. For top-performing states, many health care measures of populations with low income were better than average and better than those for individuals with higher income or more education in lagging states. These findings indicate that low-income status does not have to determine poor health or poor care experience. Interventions seen in top-performing states, such as expanded insurance coverage, access, and coordination of social and medical services, can help mitigate poverty’s effects on health. 16

Poverty and Health

SDoH are the conditions under which people are born, grow, live, work, and age, and include factors such as socioeconomic status, education, employment, social support networks, and neighborhood characteristics. 4 These social factors have a more significant collective impact on health and health outcomes than health behavior, health care, and the physical environment. 17,18 SDoH, especially poverty, structural racism, and discrimination, are the primary drivers of health inequities. 19,20

Economic prosperity can provide individuals access to resources to avoid or buffer exposure to health risks. 21  Research shows that individuals with higher incomes consistently experience better health outcomes than individuals with low incomes and those living in poverty. 22 Poverty affects health by limiting access to proper nutrition and healthy foods; shelter; safe neighborhoods to learn, live, and work; clean air and water; utilities; and other elements that define an individual’s standard of living. Individuals who live in low-income or high-poverty neighborhoods are likely to experience poor health due to a combination of these factors. 23,24

Violence is also more prevalent in areas with greater poverty. From 2008 to 2012, individuals in households at or below the poverty level experienced more than double the rate of violent victimization than individuals in high-income households. 25  This pattern of victimization by violent behavior was consistent for both Black and white individuals. It significantly impacts the victim’s family and perpetrator’s family (through incarceration).

Because they intersect with so many SDoH, poverty and low-income status dramatically affects life expectancy. 26 Education and its socioeconomic status correlate to income and wealth. These have powerful associations with life expectancy for both sexes and all races at all ages. Students from families with low income are five times more likely to drop out of high school than students from families with high income. 27  In 2008, the life expectancy among U.S. adult men and women with fewer than 12 years of education was not much better than the life expectancy among all adults in the 1950s and 1960s. 28

Poverty affects individuals insidiously in other ways that we are just beginning to understand. Mental illness, chronic health conditions, and substance use disorders are all more prevalent in populations with low income. 29  Poor nutrition, toxic exposures (e.g., lead), and elevated levels of the stress hormone cortisol are factors associated with poverty that may have lasting effects on children beginning before birth and continuing after birth. These effects, which can influence cognitive development and chronic disease development, are dose-dependent (i.e., the duration of exposure matters). 30,31,32  For example, the greater the number of years a child spends living in poverty, the more elevated the child’s overnight cortisol level and the more dysregulated the child’s cardiovascular response to acute stressors. 31  Impaired development of the nervous system affects cognitive and socioemotional development and increases the risk of behavioral challenges, adverse health behaviors, and poor school performance. 31,32  Recent studies have even identified a strong association between pediatric suicide and county-level poverty rates. 33

However, the effects of poverty are not predictably uniform. Longitudinal studies of health behavior describe positive (e.g., tobacco use cessation) and negative (e.g., decrease in physical activity) health behavior trends in populations with lower and higher socioeconomic status. However, there is a socioeconomic gradient in health improvement. In other words, populations with lower socioeconomic status lag behind populations with higher socioeconomic status in positive gains from health behavior trends. Health behaviors are important in that they account for differences in mortality. 34  The fact that positive changes in health behaviors are possible despite the challenges of poverty points to the importance of developing and implementing interventions that promote healthy behaviors in populations with low income.

Risk Regulators and Intervention

Poverty affects health in many different ways through complex mechanisms that we are just beginning to understand and describe. Living in poverty does not necessarily predetermine poor health. 35  Poverty will not “cause” a disease. Instead, poverty affects both the likelihood that an individual will have risk factors for disease and its ability and opportunity to prevent and manage disease. An individual’s health outcomes (a physiologic expression) ultimately will be influenced by genetic and environmental factors, as well as health behaviors – all of which may be affected by poverty. Material conditions, discriminatory practices, neighborhood conditions, behavioral norms, work conditions, as well as laws, policies, and regulations associated with poverty make it a “risk regulator.” 35  This means that poverty functions as a control parameter at a system level to influence the probability of exposure to key risk factors (e.g., behaviors, environmental risks) that lead to disease (Figure 1).

Figure 1: An Illustration of Risk Regulators in Social and Biological Context

Reprinted with permission from Glass TA, McAtee MJ. Behavioral science at the crossroads in public health: extending horizons, envisioning the future. Soc Sci Med. 2006;62(7):1650-1671.

Thinking of poverty as a risk regulator rather than a rigid determinant of health allows family physicians to relinquish the feeling of helplessness when providing medical care to families and individuals with low income.

Family physicians are uniquely positioned to devise solutions to mitigate the development of risk factors that lead to disease and the conditions unique to populations with low income that interfere with effective disease prevention and management. They can boost an individual or family’s “host resistance” to the health effects of poverty and tap into a growing array of aligned resources that provide patients and families with tangible solutions so that health maintenance can be a realistic goal.

Role of the Family Physician

Community-Oriented Primary Care (COPC)

Strong primary care teams are critical in the care of patients with low income. These populations often have higher rates of chronic disease and difficulty navigating health care systems. They benefit from care coordination and team-based care that addresses medical and socioeconomic needs.

In the United States, there is a move toward increased payment from government and commercial payers to offset the cost of providing coordinated and team-based care. Some payment models provide shared savings or care coordination payments in addition to traditional fee-for-service reimbursement. The practice transformations from COPC and payment models based on targets and meaningful use alter how we approach patient panels and communities. 36 The rationale behind alternative payment models – particularly regarding the care of lower socioeconomic populations – is that significant cost savings can be realized when care moves toward prevention and self-management and away from crisis-driven, fragmented care provided in the emergency department or a hospital setting. By recognizing and treating disease earlier – and actively partnering with local public health services like health educators, community health workers, and outreach services – family physicians can help prevent costly, avoidable complications and reduce the total cost of care.

Community Responsive Care

Care team members can positively affect the health of patients with low income by creating a welcoming, nonjudgmental environment that supports a long-standing therapeutic relationship built on trust. Familiarity with the National Standards for Culturally and Linguistically Appropriate Services (CLAS) in Health and Health Care can prepare practices and institutions to provide care in a manner that promotes health equity. 37

Patients with low income may be unintentionally shamed by the care team when their behaviors are seen as evidence of being “noncompliant” (e.g., missing appointments, not adhering to a medical regimen, not getting tests done). These patients may not be comfortable sharing information about the challenges that lead to their “noncompliant” behaviors. For example, a patient with low income may arrive 15 minutes late to an appointment because they have to rely on someone else for transportation. A patient may not take prescribed medication because it is too expensive. A patient may not get tests done because their employer will not allow time off from work. A patient may not understand printed care instructions because of low-literacy skills. Such patients may be turned away by staff because their tardiness disrupts the schedule, or they may even be dismissed from the practice altogether because of repeated noncompliance. Physicians and care team members should learn why the patient was noncompliant and promote an atmosphere of tolerance and adaptation.

Patients with low socioeconomic status and other marginalized populations rarely respond well to dictation from health care professionals. Instead, interventions that rely on peer-to-peer storytelling or coaching are more effective in overcoming cognitive resistance to positive health behavior changes. 38  Physicians and care team members can identify local groups that provide peer-to-peer support. Such activities are typically hosted by local hospitals, faith-based organizations, health departments, or senior centers.

Screen for Socioeconomic Challenges

Family physicians regularly screen for risk factors for disease. Screening to identify patients’ socioeconomic challenges and other SDoH can be incorporated into practices using EveryONE Project tools. Once socioeconomic challenges are identified, physicians and their care teams can work with patients to design achievable, sustainable treatment plans. The simple question, “Do you (ever) have difficulty making ends meet at the end of the month?” has a sensitivity of 98% and specificity of 60% in predicting poverty. 39  A casual inquiry about the cost of a patient’s medications is another way to start a conversation about socioeconomic obstacles to care.

A patient’s home and neighborhood affect health. 40  The care team should ask the patient whether their home is adequate to support healthy behaviors. For example, crowding, infestations, and lack of utilities are all risk factors for disease. Knowing that a patient is homeless or has poor, inadequate housing will help guide care.

Set Priorities and Make a Realistic Plan of Action

Family physicians direct the therapeutic process by working with the patient and care team to identify priorities so treatment goals are clear and achievable. In many cases, suspending a “fix everything right now” agenda in favor of a treatment plan of small steps that incorporate shared decision making can help this process. It is likely that a patient with low income will not have the resources (e.g., on-demand transportation, forgiving work schedule, available child care) to comply with an ideal treatment plan. Formulating a treatment plan that makes sense for the patient’s life circumstances is vital to success.

For example, for a patient with limited means and multiple chronic conditions – including hypertension and diabetes – start by addressing these conditions. Colon cancer screening or a discussion about beginning statin therapy can come later. It may be easier for this patient to adhere to an insulin regimen involving vials and syringes instead of insulin pens, which are much more expensive. The “best” medication for a patient with low income is the one that the patient can afford and self-administer reliably. Celebrate success with each small step that takes a patient closer to disease control and improved self-management.

Help Newly Insured Patients Navigate the Health Care System

In many states, the expansion of Medicaid has allowed individuals and families with low income to become insured – perhaps for the first time. A newly insured individual with low income will not necessarily know how or when to make, keep, or reschedule an appointment; develop a relationship with a family physician; manage medication refills; or obtain referrals. They may be embarrassed to reveal this lack of knowledge to the care team. Physicians and care team members can help by providing orientation to newly insured patients within the practice. For example, ensure that all patients know where to pick up medication, how to take it and why, when to return for a follow-up visit and why, and how to follow their treatment plan from one appointment to the next. Without this type of compassionate intervention, patients may revert to an old pattern of seeking crisis-driven care often provided by the emergency department or a local hospital.

Provide Material Support to Families with Low Income

Resources that make it easier for busy physicians to provide support to families with low income include the following:

●       Reach Out and Read is a program that helps clinicians provide books for parents to take home to read to their children. Studies have shown that Reach Out and Read improve children’s language skills. 41

●       2-1-1 is a free, confidential service that patients or staff can access 24 hours a day by phone. 2-1-1 is staffed by community resource specialists who can connect patients to resources such as food, clothing, shelter, utility bill relief, social services, and even employment opportunities. Follow-up calls are made to ensure clients connect successfully with the resource referrals.

●       The National Domestic Violence Hotline is staffed 24 hours a day by trained advocates who provide confidential help and information to patients who are experiencing domestic violence.

Local hospitals, health departments, and faith-based organizations often are connected to community health resources that offer services such as installing safety equipment in homes; providing food resources; facilitating behavioral health evaluation and treatment; and providing transportation, vaccinations, and other benefits to individuals and families with low income.

Practices can make a resource folder of information about local community services that can be easily accessed when taking care of patients in need. This simple measure incorporates community resources into the everyday workflow of patient care, thus empowering the care team.

Participate in Research that Produces Relevant Evidence

Much of the research about the effects of poverty on health is limited to identifying health disparities. This is insufficient. Research that evaluates specific interventions is needed to gain insight into what effectively alleviates poverty’s effects on health care delivery and outcomes. Family physicians can serve a critical role in this research because we have close relationships with patients with low income. 42

Advocate on Behalf of Neighborhoods and Communities with Low Income

Family physicians are community leaders, so we can advocate effectively for initiatives that improve the quality of life in neighborhoods with low income. Some forms of advocacy are apparent, such as promoting a state’s expansion of Medicaid. Other efforts may be specific to the community served. For example, a vacant lot can be converted to a basketball court or soccer field. A community center can expand programs that involve peer-to-peer health coaching. A walking program can be started among residents in a public housing unit. Collaboration with local law enforcement agencies can foster the community’s trust and avoid the potential for oppression. 43

Family physicians have local partners in advocacy, so we do not have to act in isolation. As a result of the Patient Protection and Affordable Care Act (ACA), nonprofit hospitals regularly report community needs assessments and work with local health departments to establish action plans that address identified needs. A Community Health Needs Assessment (CHNA) reflects a specific community’s perception of need, and each action plan outlines multi-sectoral solutions to meet local health needs. Local CHNAs are typically available online, as are the associated action plans. Family physicians can use information in the CHNA to access local health care leadership and join aligned forces in the communities we serve, thereby supporting the AAFP’s vision of achieving optimal health for everyone.

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2.     Commission on Social Determinants of Health. Closing the gap in a generation. Health equity through action on the social determinants of health. World Health Organization (WHO). Accessed March 22, 2021. www.who.int/social_determinants/final_report/csdh_finalreport_2008.pdf   

3.     Kovach KA, Reid K, Grandmont J, et al. How engaged are family physicians in addressing the social determinants of health? A survey supporting the American Academy of Family Physician’s health equity environmental scan.  Health Equity . 2019;3(1):449-457.

4.     American Academy of Family Physicians (AAFP). Advancing health equity by addressing the social determinants of health in family medicine (position paper). Accessed March 22, 2021. 

5.     World Vision. What is poverty? It’s not as simple as you think. Accessed March 22, 2021. www.worldvision.ca/stories/child-sponsorship/what-is-poverty#:~:text=1.-,What%20is%20the%20definition%20of%20poverty%3F,care%2C%20education%20and%20even%20transportation

6.     Office of the Assistant Secretary for Planning and Evaluation. 2020 poverty guidelines. Accessed March 22, 2021. https://aspe.hhs.gov/2020-poverty-guidelines

7.     United States Census Bureau. Income, poverty and health insurance coverage in the United States: 2019. Accessed March 22, 2021. www.census.gov/newsroom/press-releases/2020/income-poverty.html

8.     United States Department of Agriculture. SNAP special rules for the elderly or disabled. Accessed March 22, 2021. www.fns.usda.gov/snap/eligibility/elderly-disabled-special-rules

9.     U.S. Centers for Medicare & Medicaid Services. Federal poverty level (FPL). Accessed March 22, 2021. www.healthcare.gov/glossary/federal-poverty-level-fpl/  

10.  Link BG, Phelan J. Social conditions as fundamental causes of disease. J Health Soc Behav . 1995;Spec No:80-94.

11.  Brooks-Gunn J, Duncan GJ. The effects of poverty on children. Future Child . 1997;7(2):55-71.

12.  Berkman LF, Kawachi I. A historical framework for social epidemiology. In: Berkman LF, Kawachi I, eds. Social Epidemiology . New York, NY: Oxford University Press; 2014.

13.  Phelan JC, Link BG, Tehranifar P. Social conditions as fundamental causes of health inequalities: theory, evidence, and policy implications. J Health Soc Behav . 2010;51 Suppl:S28-S40.

14.  Macintyre S, Ellaway A, Cummins S. Place effects on health: how can we conceptualise, operationalise and measure them? Soc Sci Med . 2002;55(1):125-139.

15.  Robert Wood Johnson Foundation. Mapping life expectancy. Short distances to large gaps in health. Accessed March 22, 2021. www.rwjf.org/en/library/articles-and-news/2015/09/city-maps.html

16.  Schoen C, Radley D, Riley P, et al. Health care in the two Americas. Findings from the Scorecard on State Health System Performance for Low-Income Populations, 2013. Accessed March 22, 2021. www.commonwealthfund.org/sites/default/files/documents/___media_files_publications_fund_report_2013_sep_1700_schoen_low_income_scorecard_full_report_final_v4.pdf    

17.  Booske BC, Athens JK, Kindig DA, Park H, Remington PL. County health rankings working paper. Different perspectives for assigning weights to determinants of health. University of Wisconsin Population Health Institute. Accessed March 22, 2021. www.countyhealthrankings.org/sites/default/files/differentPerspectivesForAssigningWeightsToDeterminantsOfHealth.pdf

18.  County Health Rankings & Roadmaps. County Health Rankings model. Accessed March 22, 2021. www.countyhealthrankings.org/explore-health-rankings/measures-data-sources/county-health-rankings-model  

19.  Phelan JC, Link BG, Tehranifar P. Social conditions as fundamental causes of health inequalities: theory, evidence, and policy implications.  J Health Soc Behav . 2010;51 Suppl:S28-S40.

20.  National Academies of Sciences, Engineering, and Medicine. Communities in action. Pathways to health equity. Accessed March 22, 2021. www.nap.edu/catalog/24624/communities-in-action-pathways-to-health-equity

21.  Anderson LM, Scrimshaw SC, Fullilove MT, Fielding JE, Task Force on Community Preventive Services. The Community Guide’s model for linking the social environment to health. Am J Prev Med . 2003;24(3 Suppl):12-20.

22.  Berkman LF, Kawachi I, eds.  Social Epidemiology . New York, NY: Oxford University Press; 2000.

23.  Riste L, Khan F, Cruickshank K. High prevalence of type 2 diabetes in all ethnic groups, including Europeans, in a British inner city: relative poverty, history, inactivity, or 21st century Europe? Diabetes Care . 2001;24(8):1377-1383.

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25.  Bureau of Justice Statistics. Household poverty and nonfatal violent victimization, 2008-2012. Accessed March 24, 2021. www.bjs.gov/index.cfm?ty=pbdetail&iid=5137

26.  Chetty R, Stepner M, Abraham S, et al. The association between income and life expectancy in the United States, 2001–2014. JAMA . 2016;315(16):1750-1766.

27.  National Center for Education Statistics. Trends in high school dropout and completion rates in the United States: 1972–2009. Accessed March 24, 2021.  https://nces.ed.gov/pubs2012/2012006.pdf  

28.  Olshansky SJ, Antonucci T, Berkman L, et al. Differences in life expectancy due to race and educational differences are widening, and many may not catch up. Health Aff (Millwood). 2012;31(8):1803-1813.

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33.  Hoffmann JA, Farrell CA, Monuteaux MC, et al. Association of pediatric suicide with county-level poverty in the United States, 2007-2016. JAMA Pediatr . 2020;174(3):287-294.

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37.  U.S. Department of Health and Human Services. National CLAS Standards. Accessed March 24, 2021. https://thinkculturalhealth.hhs.gov/clas

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39.  Brcic V, Eberdt C, Kaczorowski J. Development of a tool to identify poverty in a family practice setting: a pilot study. Int J Family Med . 2011;2011:812182.

40.  Braveman P, Dekker M, Egerter S, Sadegh-Nobari T, Pollack C. Housing and health. Robert Wood Johnson Foundation. Accessed March 24, 2021. www.dhss.delaware.gov/dhss/dph/mh/files/housingandhealth.pdf

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(2015 COD) (January 2022 COD)

Copyright © 2024 American Academy of Family Physicians. All Rights Reserved.

Essay on poverty and economic development

28 May 2024 09:34

• Development Economics

On Monday 10 June 2024 Arne Nasgowitz will hold a trial lecture on a prescribed topic and defend his thesis for the PhD degree at NHH.

This thesis explores the importance of time and capital constraints for women’s labor supply, business development and households’ transition out of poverty in a developing-country context.

Janet Currie to speak on child mental health

The first chapter focuses on mothers of pre-school children in Uganda who are provided with either a cash transfer, free childcare, or both. We find that the cash transfer allowed the women to start their own business, while free access to childcare increased the labor supply of single women and, in households with couples, the father. In addition, access to childcare improved child development. Our study thus shows that. Our study suggests that subsidizing childcare can be a cost-effective way to improve household economics status and child development.

The second chapter investigates the effects of cash transfers before and during the COVID19-pandemic in Uganda. The study starts by documenting a large drop in incomes at the onset of the pandemic, and a relatively quick recovery. It then shows that cash transfers shielded incomes and businesses, as well as increased food security during the pandemic. The effects on business incomes persist after the pandemic and are accompanied by a reduction of violence against children in the long run. This paper shows that cash transfers were and can be a successful tool to assist households in times of crises.

Essays on Staffing and Transportation

The last chapter discusses a refined measurement of poverty that considers not only the standard monetary dimension but also time. This measure would include individuals who are not monetarily poor simply because they are working very long hours. Following households over time suggests that such time-constrained households are particularly vulnerable to fall into income poverty. Considering the time people spend working is therefore important to get a more comprehensive understanding of poverty and to provide appropriate advice on the design of anti-poverty policies.

Prescribed topic for the trial lecture:

T he impact of Covid-19 on children’s education in low-income countries

Trial lecture:

Karl Borch, NHH, 09:15

Title of the thesis:

«Essays on poverty and economic development»

Karl Borch, NHH, 12:15

Watch out for NHH's new rising star

Members of the evaluation committee:.

Associate Professor Justin Valasek (leader of the committee), Department of Economics, NHH

Associate Professor Isabel Günther, Federal Institute of Technology Zurich

Associate Professor Jonathan de Quidt, Queen Mary University of London

Supervisors:

Professor Kjetil Bjorvatn (main supervisor), Department of Economics, NHH

Associate Professor Selim Gulesci, Trinity College Dublin

The trial lecture and thesis defense will be open to the public.                                                  

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