lack of health insurance essay

Health Insurance Reform Has Surprisingly Little Impact on Actual Health

T he typical American’s health compares poorly to that of their counterparts in other high-income countries, even though the U.S. spends twice as much as these countries do on medical care. Behind that middling average lies substantial health inequality. A 40-year-old American male can expect to live 15 years less if he’s one of the poorest 1% of Americans rather than one of the richest 1%. Black children who live in the richest parts of the United States have higher mortality rates than White children in the poorest parts of the country.

Many have put these observations together with another aspect of U.S. “exceptionalism”: We are the only high-income country without universal health insurance coverage. And they have concluded that the key to improving health and reducing health inequality in the U.S. is to finally enact universal coverage.

They’re wrong. While these two facts are correct, they have very little to do with each other. There are good reasons to support universal health coverage, but noticeably improving population health is not one of them.

Indeed, the evidence suggests that the health disparities among Americans are not driven by differences in access to health insurance or to medical care. Rather, the key to improving health is far more complex: It lies in changing health behaviors and reducing exposure to external sources of poor health.

Perhaps the clearest evidence for how little impact health insurance reform has on health comes from the experience of other countries which have universal health insurance but also experience substantial health inequality. Consider Sweden and Norway , two Nordic countries with universal health insurance as well as a cradle-to-grave generous social safety net. Yet differences in life expectancy between adults in the top 10% and bottom 10% of the national income distribution in those countries are similar to the disparities in the United States.

Read More: Long Waits, Short Appointments, Huge Bills: U.S. Health Care Is Causing Patient Burnout

Or consider the enormous differences across the country in remaining life expectancy for elderly Americans, all of whom are covered by the same Medicare health insurance program. Researchers have identified which cities in the U.S. are better or worse for elderly longevity , and also which tend to provide more medical care than others . But, the evidence indicates, the places you’d want to move to in order to increase your life expectancy in retirement aren’t the same as the places to move to if you want to receive more medical care.

Indeed, there is widespread agreement among researchers that medical care, let alone health insurance, is not the only—or even the most important—determinant of health. Rather, the key to better health and smaller health disparities lies in the air we breathe, the food we eat, and the cigarettes we do or do not smoke. Which means that the key public policies for improving health must be those that tackle these sources of poor health through pollution regulation, or soda and cigarette taxes. The path to major health improvements doesn’t run through health insurance and health care policy.

How can this possibly be?

It is not because health insurance is not important for health. Of course it is .  But its effects are too small for health insurance reform to make much of a dent in the large U.S. income-health gradient, or to substantially improve the poor health of average Americans.

Behind this relative unimportance of health insurance coverage for health is a startling, but little-understood reality: No one in America is actually uninsured when it comes to their health care. Rather, the nominally “uninsured”—those who lack formal health insurance coverage—nonetheless receive a substantial amount of medical care which they don’t pay for.  

There is a vast web of public policy requirements and dedicated public funding to provide the nominally uninsured with free or heavily discounted medical care. And no, we’re not just talking about the emergency room. Through a piecemeal slew of policies at the federal, state, and local level, the government has created a large, complex web of publicly-regulated, publicly-funded programs that provide free or low-fee preventive care, care management for chronic health problems, and non-emergency hospital care for the uninsured and under-insured.

This point was made clear by data from Oregon, where the state ran a lottery for health insurance coverage in 2008. The process was similar to a clinical trial for a new drug, in which some patients are randomly assigned the new drug and others are assigned an older drug or a sugar pill. Except in this case, Oregon randomly assigned public health insurance coverage to about 10,000 low-income, uninsured adults but not to the thousands of others who had signed up to “win” free public health insurance. The results of this lottery made clear that providing formal health insurance coverage to the uninsured provides them with real benefits: better protection against expensive medical bills, greater likelihood of having a medical home, more access to medical care, and ultimately, improved health.

But the experiment’s results also revealed something striking about the experience of the uninsured: The uninsured receive about four-fifths of the medical care that they would get had they been insured. This medical care includes primary care, preventive care, prescription drugs, emergency room visits, and hospital admissions. And they pay for only about 20 cents out of every dollar of medical care that they receive. In other words, they are not actually uninsured. Rather, there’s a lot more commonality in the medical care received and (not) paid for by the insured and the uninsured than those labels might suggest.

And once we realize that everyone in America can access medical care, it becomes much clearer why formalizing this access – while important for other reasons – is unlikely to make an important difference for people’s health, or substantially reduce the large disparities in population health.

The surprisingly limited role for health care policy or health insurance in driving population health is not a new observation. A half century ago, the economist Victor Fuchs – who at age 99 is now widely considered to be the founding father of the economic study of health – made this point in his now-famous “ Tale of Two States. ” He described two neighboring states in the Western U.S. that were similar along many of the dimensions believed to be important for health – including medical care, income, schooling, climate, and urbanicity. Yet in one state, the people were among the U.S. healthiest. Their neighbors in the other state were among the least healthy, with annual death rates that were 40% to 50% higher.

You may get an inkling of where Fuchs was going with this comparison when we tell you that the two states were Utah and Nevada. And that the residents of Utah were the ones enjoying much better health.

Fuchs famously attributed the lower-mortality rates of the clean-living, predominantly Mormon residents of Utah to their better health behaviors. Their Nevada neighbors enjoyed what he referred to as “more permissive” norms. Rates of smoking and drinking were much lower in Utah than in Nevada. And differences in mortality between the two states were particularly pronounced for diseases for which there was a direct link to such behaviors, such as lung cancer and cirrhosis of the liver.

Fuchs’s simple tabulations of publicly reported death rates by age and gender for Utah and Nevada appear antiquated by modern data science standards. But his central argument has stood the test of time. A subsequent half-century of confirmatory work has hammered home an important but often overlooked point: when it comes to improving health outcomes and reducing health disparities, health insurance policy is not the lever to lean on.

Adapted from We’ve Got You Covered: Rebooting American Health Care by Liran Einav and Amy Finkelstein, in agreement with Portfolio, an imprint of Penguin Publishing Group, a division of Penguin Random House LLC. Copyright © Liran Einav and Amy Finkelstein, 2023.

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The Challenges of Health Insurance: Barriers to Affordable and Accessible Healthcare

Rising out-of-pocket costs and barriers preventing or delaying the utilization of healthcare coverage highlight the need for a comprehensive approach that goes beyond insurance coverage alone and addresses the underlying issues affecting healthcare accessibility and affordability.

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Health insurance is often seen as a safety net, providing individuals with access to affordable and accessible healthcare. However, results from a recent survey conducted by PhRMA shed light on the challenges faced by insured Americans by revealing that having insurance doesn’t always guarantee optimal healthcare outcomes. From concerns about out-of-pocket costs to difficulties in accessing necessary medications, many people are encountering significant barriers to accessing and receiving the care they need.

Barriers to care

Health Benefit Consultants, Share Your Expert Insights in Our Survey

Paul Ketchel Paul Ketchel is President and Chief Executive Officer of MDsave Inc. Mr. Ketchel has over 10 years of combined experience in the health care industry. Mr. Ketchel is the founder of MDsave, Inc., which is the world's first healthcare marketplace.

One of the major concerns for insured individuals is the burden of out-of-pocket healthcare costs. Forty percent of insured adults report that they had put off or postponed medical treatment in the last 12 months due to cost. Americans are increasingly worried about their ability to pay for healthcare. And many people are more anxious about affording medical bills than they are about paying for food ‌or housing.

Another critical issue faced by many insured Americans is the difficulty in accessing necessary medications. Approximately 38% of insured individuals taking prescription drugs reported encountering barriers due to insurance coverage restrictions in the past year. These obstacles include prior authorization requirements, high copayments, and formulary restrictions. Such hurdles not only hinder timely access to medication but also contribute to increased healthcare costs and potential health complications.

These concerns serve as a reminder that insurance alone is not enough to ensure access to adequate healthcare. While the Affordable Care Act has helped many Americans gain access to healthcare coverage, it hasn’t eliminated all the challenges insured individuals face. Rising out-of-pocket costs and barriers preventing or delaying the utilization of healthcare coverage highlight the need for a comprehensive approach that goes beyond insurance coverage alone and addresses the underlying issues affecting healthcare accessibility and affordability.

Potential solutions

A Deep-dive Into Specialty Pharma

A specialty drug is a class of prescription medications used to treat complex, chronic or rare medical conditions. Although this classification was originally intended to define the treatment of rare, also termed “orphan” diseases, affecting fewer than 200,000 people in the US, more recently, specialty drugs have emerged as the cornerstone of treatment for chronic and complex diseases such as cancer, autoimmune conditions, diabetes, hepatitis C, and HIV/AIDS.

To combat these challenges, several possible solutions can be explored. Government policies can play a crucial role in incentivizing healthcare providers to prioritize patient-centered care and improve overall quality. Promoting a value-based care model that incentivizes providers based on patient outcomes could help lower patient costs in the long term. In addition, public-private partnerships can be formed to increase the availability of healthcare services in underserved communities. These collaborations can focus on creating accessible care options, such as telemedicine programs, to expand access to care. Furthermore, insurance companies can look to reduce out-of-pocket costs by negotiating lower prices for common treatments and medications.

Additionally, platforms that promote price transparency and prevent surprise out-of-pocket costs are essential. By empowering people with information about healthcare costs, patients can make more informed decisions and better prepare for potential expenses. Price transparency can also foster competition among healthcare providers, leading to more affordable options for patients.

A greater emphasis on preventive care can also help address the challenges faced by insured individuals. By promoting regular check-ups, screenings, and healthy lifestyle choices, the need for emergency care and costly interventions can be reduced. When providers, patients, and insurance companies work together to keep patients healthy, cost of care can be improved for everyone involved. This not only improves overall patient health outcomes but also helps lower overall healthcare costs, making quality care more accessible to all.

While health insurance is important, it can only solve some of the healthcare problems insured people face. From being denied coverage to facing exorbitant out-of-pocket costs, insured individuals still face significant hurdles to accessing and receiving quality care. These issues highlight the need for comprehensive solutions that prioritize affordability and accessibility. With more affordable healthcare readily available, insured individuals can receive the care they need without excessive financial burden. By implementing policies that incentivize quality care, promote price transparency, and emphasize preventive measures, we can work towards a healthcare system that ensures affordable and accessible care for all.

Photo: eakrin rasadonyindee, Getty Images

Paul Ketchel

Paul Ketchel is President and Chief Executive Officer of MDsave Inc. Mr. Ketchel has over 10 years of combined experience in the health care industry. Mr. Ketchel is the founder of MDsave, Inc., which is the world's first healthcare marketplace.

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Open Access

Peer-reviewed

Research Article

The impact of public health insurance on health care utilisation, financial protection and health status in low- and middle-income countries: A systematic review

Roles Conceptualization, Data curation, Formal analysis, Investigation, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliation Department of Health Sciences, University of York, York, England, United Kingdom

Roles Investigation, Methodology, Supervision, Writing – review & editing

Affiliations Centre of Health Economics, University of York, York, England, United Kingdom, Luxembourg Institute of Socio-economic Research (LISER), Luxembourg

Roles Conceptualization, Methodology, Supervision, Writing – review & editing

Affiliations Department of Health Sciences, University of York, York, England, United Kingdom, Department of Epidemiology and Biostatistics, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada

Roles Conceptualization, Investigation, Supervision, Writing – review & editing

• Darius Erlangga, 
• Marc Suhrcke, 
• Shehzad Ali, 
• Karen Bloor

• Published: August 28, 2019
• https://doi.org/10.1371/journal.pone.0219731
• Reader Comments

7 Nov 2019: Erlangga D, Suhrcke M, Ali S, Bloor K (2019) Correction: The impact of public health insurance on health care utilisation, financial protection and health status in low- and middle-income countries: A systematic review. PLOS ONE 14(11): e0225237. https://doi.org/10.1371/journal.pone.0225237 View correction

Expanding public health insurance seeks to attain several desirable objectives, including increasing access to healthcare services, reducing the risk of catastrophic healthcare expenditures, and improving health outcomes. The extent to which these objectives are met in a real-world policy context remains an empirical question of increasing research and policy interest in recent years.

We reviewed systematically empirical studies published from July 2010 to September 2016 using Medline, Embase, Econlit, CINAHL Plus via EBSCO, and Web of Science and grey literature databases. No language restrictions were applied. Our focus was on both randomised and observational studies, particularly those including explicitly attempts to tackle selection bias in estimating the treatment effect of health insurance. The main outcomes are: (1) utilisation of health services, (2) financial protection for the target population, and (3) changes in health status.

8755 abstracts and 118 full-text articles were assessed. Sixty-eight studies met the inclusion criteria including six randomised studies, reflecting a substantial increase in the quantity and quality of research output compared to the time period before 2010. Overall, health insurance schemes in low- and middle-income countries (LMICs) have been found to improve access to health care as measured by increased utilisation of health care facilities (32 out of 40 studies). There also appeared to be a favourable effect on financial protection (26 out of 46 studies), although several studies indicated otherwise. There is moderate evidence that health insurance schemes improve the health of the insured (9 out of 12 studies).

Interpretation

Increased health insurance coverage generally appears to increase access to health care facilities, improve financial protection and improve health status, although findings are not totally consistent. Understanding the drivers of differences in the outcomes of insurance reforms is critical to inform future implementations of publicly funded health insurance to achieve the broader goal of universal health coverage.

Citation: Erlangga D, Suhrcke M, Ali S, Bloor K (2019) The impact of public health insurance on health care utilisation, financial protection and health status in low- and middle-income countries: A systematic review. PLoS ONE 14(8): e0219731. https://doi.org/10.1371/journal.pone.0219731

Editor: Sandra C. Buttigieg, University of Malta Faculty of Health Sciences, MALTA

Received: March 19, 2018; Accepted: July 2, 2019; Published: August 28, 2019

Copyright: © 2019 Erlangga et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The search strategy for this review is available in Supporting Information files.

Funding: The authors received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

Introduction

In recent decades, achieving universal health coverage (UHC) has been a major health policy focus globally.[ 1 – 3 ] UHC entitles all people to access healthcare services through publicly organised risk pooling,[ 4 ] safeguarding against the risk of catastrophic healthcare expenditures.[ 5 ] Low- and middle-income countries (LMICs) face particular challenges in achieving UHC due to particularly limited public resources for health care, inefficient allocation, over-reliance on out-of-pocket payments, and often large population size.[ 5 ] As a result, access to health care and the burden of financial cost in LMICs tends to be worse for the poor, often resulting in forgone care.[ 6 – 8 ]

Introducing and increasing the coverage of publicly organised and financed health insurance is widely seen as the most promising way of achieving UHC,[ 9 , 10 ] since private insurance is mostly unaffordable for the poor.[ 11 ] Historically, social health insurance, tax-based insurance, or a mix of the two have been the dominant health insurance models amongst high income countries and some LMICs, including Brazil, Colombia, Costa Rica, Mexico, and Thailand.[ 12 ] This is partly influenced by the size of the formal sector economy from which taxes and payroll contributions can be collected. In recent decades, community-based health insurance (CBHI) or “mutual health organizations” have become increasingly popular among LMICs, particularly in Sub-Saharan Africa (e.g. Burkina Faso,[ 13 ] Senegal[ 14 ] and Rwanda[ 15 ]) as well as Asia (e.g. China[ 16 ] and India[ 17 ]). CBHI has emerged as an alternative health financing strategy, particularly in cases where the public sector has failed to provide adequate access to health care.[ 18 ]

We searched for existing systematic reviews on health insurance in the Cochrane Database for Systematic Reviews, Medline, Embase, and Econlit. Search terms “health insurance”, “low-middle income countries”, and “utilisation” were used alongside methodological search strategy to locate reviews. Seven systematic reviews were identified of varying levels of quality, [ 19 – 26 ] with Acharya et al.[ 27 ] being the most comprehensive. The majority of existing reviews has suggested that publicly-funded health insurance has typically shown a positive impact on access to care, while the picture for financial protection was mixed, and evidence of the impact on health status was very sparse.

This study reviews systematically the recent fast-growing evidence on the impact of health insurance on health care utilisation, financial protection and health status in LMICs. Since the publication of Acharya et al. (which conducted literature searches in July 2010), the empirical evidence on the impact of health insurance has expanded significantly in terms of quantity and quality, with growing use of sophisticated techniques to account for statistical challenges[ 28 ] (particularly insurance selection bias). This study makes an important contribution towards our understanding of the impact of health insurance in LMICs, taking particular care in appraising the quality of studies. We recognise the heterogeneity of insurance schemes implemented in LMICs and therefore do not attempt to generalise findings, but we aim to explore the pattern emerging from various studies and to extract common factors that may affect the effectiveness of health insurance, that should be the focus of future policy and research. Furthermore, we explore evidence of moral hazard in insurance membership, an aspect that was not addressed in the Acharya et al review.[ 27 ]

This review was planned, conducted, and reported in adherence with PRISMA standards of quality for reporting systematic reviews.[ 29 ]

Participants

Studies focusing on LMICs are included, as measured by per capita gross national income (GNI) estimated using the World Bank Atlas method per July 2016.[ 30 ]

Intervention

Classification of health insurance can be complicated due to the many characteristics defining its structure, including the mode of participation (compulsory or voluntary), benefit entitlement, level of membership (individual or household), methods for raising funds (taxes, flat premium, or income-based premium) and the mechanism and extent of risk pooling [ 31 ]. For the purpose of this review, we included all health insurance schemes organised by government, comprising social health insurance and tax-based health insurance. Private health insurance was excluded from our review, but we recognise the presence of community-based health insurance (CBHI) in many LMICs, especially in Africa and Asia [ 18 ]. We also therefore included CBHI if it was scaled up nationally or was actively promoted by national government. Primary studies that included both public and private health insurance were also considered for inclusion if a clear distinction between the two was made in the primary paper. Studies examining other types of financial incentives to increase the demand for healthcare services, such as voucher schemes or cash transfers, were excluded.

Control group

In order to provide robust evidence on the effect on insurance, it is necessary to compare an insured group with an appropriate control group. In this review, we selected studies that used an uninsured population as the control group. Multiple comparison groups were allowed, but an uninsured group had to be one of them.

Outcome measures

We focus on three main outcomes:

• Utilisation of health care facilities or services (e.g. immunisation coverage, number of visits, rates of hospitalisation).
• Financial protection, as measured by changes in out-of-pocket (OOP) health expenditure at household or individual level, and also catastrophic health expenditure or impoverishment from medical expenses.
• Health status, as measured by morbidity and mortality rates, indicators of risk factors (e.g. nutritional status), and self-reported health status.

The scope of this review is not restricted to any level of healthcare delivery (i.e. primary or secondary care). All types of health services were considered in this review.

Types of studies

The review includes randomized controlled trials, quasi-experimental studies (or “natural experiments”[ 32 ]), and observational studies that account for selection bias due to insurance endogeneity (i.e. bias caused by insurance decisions that are correlated with the expected level of utilisation and/or OOP expenditure). Observational studies that did not take account of selection bias were excluded.

Databases and search terms

A search for relevant articles was conducted on 6 September 2016 using peer-reviewed databases (Medline, Embase, Econlit, CINAHL Plus via EBSCO and Web of Science) and grey literatures (WHO, World Bank, and PAHO). Our search was restricted to studies published since July 2010, immediately after the period covered by the earlier Acharya et al. (2012) review. No language restrictions were applied. Full details of our search strategy are available in the supporting information ( S1 Table ).

Screening and data extraction

Two independent reviewers (DE and MS) screened all titles and abstracts of the initially identified studies to determine whether they satisfied the inclusion criteria. Any disagreement was resolved through mutual consensus. Full texts were retrieved for the studies that met the inclusion criteria. A data collection form was used to extract the relevant information from the included studies.

Assessment of study quality

We used the Grades of Assessment, Development and Evaluation (GRADE) system checklist[ 33 , 34 ] which is commonly used for quality assessment in systematic reviews. However, GRADE does not rate observational studies based on whether they controlled for selection bias. Therefore, we supplemented the GRADE score with the ‘Quality of Effectiveness Estimates from Non-randomised Studies’ (QuEENS) checklist.[ 35 ]

cRandomised studies were considered to have low risk of bias. Non-randomised studies that account for selection on observable variables, such as propensity score matching (PSM), were categorised as high risk of bias unless they provided adequate assumption checks or compared the results to those from other methods, in which case they may be classed as medium risk. Non-randomised studies that account for selection on both observables and unobservables, such as regression with difference-in-differences (DiD) or Heckman sample selection models, were considered to have medium risk of bias–some of these studies were graded as high or low risk depending on sufficiency of assumption checks and comparison with results from other methods.

Heterogeneity of health insurance programmes across countries and variability in empirical methods used across studies precluded a formal meta-analysis. We therefore conducted a narrative synthesis of the literature and did not report the effect size. Throughout this review, we only considered three possible effects: positive outcome, negative outcome, or no statistically significant effect (here defined as p-value > 0.1).

Results of the search

Our database search identified 8,755 studies. Five additional studies were retrieved from grey literature. After screening of titles and abstracts, 118 studies were identified as potentially relevant. After reviewing the full-texts, 68 studies were included in the systematic review (see Fig 1 for the PRISMA diagram). A full description of the included studies is presented in the supporting information ( S2 Table ). Of the 68 included studies, 40 studies examined the effect on utilisation, 46 studies on financial protection, and only 12 studies on health status (see Table 1 ).

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https://doi.org/10.1371/journal.pone.0219731.g001

https://doi.org/10.1371/journal.pone.0219731.t001

Utilisation of health care

Table 2 collates evidence on the effects of health insurance on utilisation of healthcare services. Three main findings were observed:

• Evidence on utilisation of curative care generally suggested a positive effect, with 30 out of 38 studies reporting a statistically significant positive effect.
• Evidence on preventive care is less clear with 4 out of 7 studies reporting a positive effect, two studies finding a negative effect and one study reporting no effect.
• Among the higher quality studies, i.e. those that suitably controlled for selection bias reflected by moderate or low GRADE score and low risk of bias (score = 3) on QuEENS, seven studies reported a positive relationship between insurance and utilisation. One study[ 36 ] reported no statistically significant effect, and another study found a statistically significant negative effect.[ 37 ]

https://doi.org/10.1371/journal.pone.0219731.t002

Financial protection

Overall, evidence on the impact of health insurance on financial protection is less clear than that for utilisation (see Table 3 ). 34 of the 46 studies reported the impact of health insurance on the level of out-of-pocket health expenditure. Among those 34 studies, 17 found a positive effect (i.e. a reduction in out-of-pocket expenditure), 15 studies found no statistically significant effect, and two studies–from Indonesia[ 59 ] and Peru[ 62 ]–reported a negative effect (i.e. an increase in out-of-pocket expenditure).

https://doi.org/10.1371/journal.pone.0219731.t003

Another financial protection measure is the probability of incurring catastrophic health expenditure defined as OOP exceeding a certain threshold percentage of total expenditure or income. Of the 14 studies reporting this measure, nine reported reduction in the risk of catastrophic expenditure, three found no statistically significant difference, and two found a negative effect of health insurance. Only four studies reported sensitivity analysis varying changes in the threshold level,[ 59 , 62 , 75 , 76 ] though this did not materially affect the findings.

• Two studies used a different measure of financial protection, the probability of impoverishment due to catastrophic health expenditure, reporting conflicting findings.[ 77 , 78 ] Finally, four studies evaluated the effect on financial protection by assessing the impact of insurance on non-healthcare consumption or saving behaviour, such as non-medical related consumption[ 79 ], probability of financing medical bills via asset sales or borrowing[ 40 ], and household saving[ 80 ]. No clear pattern can be observed from those four studies.

Health status

Improving health is one of the main objectives of health insurance, yet very few studies thus far have attempted to evaluate health outcomes. We identified 12 studies, with considerable variation in the precise health measure considered (see Table 4 ). There was some evidence of positive impact on health status: nine studies found a positive effect, one study reported a negative effect, and two studies reported no effect.

https://doi.org/10.1371/journal.pone.0219731.t004

Type of insurance and countries

Considering the heterogeneity of insurance schemes among different countries, we attempted to explore the aggregate results by the type of insurance scheme and by country. Table 5 provides a summary of results classified by three type of insurance scheme: community-based health insurance, voluntary health insurance (non-CBHI), and compulsory health insurance. This division is based on the mode of participation (compulsory vs voluntary), which may affect the presence of adverse selection and moral hazard. Premiums are typically community-rated in CBHI, risk-rated in voluntary schemes and income-rated in compulsory schemes.

https://doi.org/10.1371/journal.pone.0219731.t005

In principle, CBHI is also considered a voluntary scheme, but we separated it to explore whether the larger size of pooling from non-CBHI schemes may affect the outcomes. Social health insurance is theoretically a mandatory scheme that requires contribution from the enrolees. However, in the context of LMICs, the mandatory element is hard to enforce, and in practice the scheme adopts a voluntary enrolment. Additionally, the government may also want to subsidise the premium for poor people. Therefore, in this review SHI schemes can fall into either the voluntary health insurance (non-CBHI) or compulsory health insurance (non-CBHI), depending on the target population defined in the evaluation study. Lastly, we chose studies with high quality/low risk only to provide more robust results.

Based on the summary in Table 5 , the effect on utilisation overall does not differ based on type of insurance, with most evidence suggesting an overall increase in utilisation by the insured. The two studies showing no effect or reduced consumption of care were conducted in two different areas of India, which may–somewhat tentatively–suggest a common factor unique to India’s health system that may compromise the effectiveness of health insurance in increasing utilisation.

Regarding financial protection, the evidence for both CBHI and non-CBHI voluntary health insurance is inconclusive. Furthermore, there is an indication of heterogeneity by supply side factors captured by proximity to health facilities. Evidence from studies exploring subsidised schemes suggests no effect on financial protection, even a negative effect among the insured in Peru.

Lastly, evidence for health status may be influenced by how health outcomes are measured. Studies exploring specific health status, (examples included health indexes, wasting, C-reactive protein, and low birth weight), show a positive effect, whereas studies using mortality rates tends to show no effect or even negative effects. Studies exploring CBHI scheme did not find any evidence of positive effect on health status, as measured either by mortality rate or specific health status.

This review synthesises the recent, burgeoning empirical literature on the impact of health insurance in LMICs. We identified a total of 68 eligible studies over a period of six years–double the amount identified by the previous review by Acharya et al. over an approximately 60-year time horizon (1950—July 2010). We used two quality assessment checklists to scrutinise the study methodology, taking more explicit account of the methodological robustness of non-experimental designs.

Programme evaluation has been of interest to many researchers for reporting on the effectiveness of a public policy to policymakers. In theory, the gold standard for a programme evaluation is the randomised control trial, in which the treatment is randomly assigned to the participants. The treatment assignment process has to be exogenous to ensure that any observed effect between the treated and control groups can only be caused by the difference in the treatment assignment. Unfortunately, this ideal scenario is often not feasible in a public policy setting. Our findings showed that only three papers between 2010 and 2016 were able to conduct a randomised study to evaluate the impact of health insurance programmes in developing countries, particularly CBHI [ 38 , 75 , 103 ]. Policymakers may believe in the value of an intervention regardless of its actual evidence base, or they may believe that the intervention is beneficial and that no one in need should be denied it. In addition, policymakers are inclined to demonstrate the effectiveness of an intervention that they want implemented in the most promising contexts, as opposed to random allocation [ 104 ].

Consequently, programme evaluators often have to deal with a non-randomised treatment assignment which may result in selection bias problems. Selection bias is defined as a spurious relationship between the treatment and the outcome of interest due to the systematic differences between the treated and the control groups [ 105 ]. In the case of health insurance, an individual who chooses to enrol in the scheme may have different characteristics to an individual who chooses not to enrol. When those important characteristics are unobservable, the analyst needs to apply more advanced techniques and, sometimes, stronger assumptions. Based on our findings, we noted several popular methods, including propensity score matching (N = 8), difference-in-difference (N = 10), fixed or random effects of panel data (N = 6), instrumental variables (N = 12) and regression discontinuity (N = 6). Those methods have varying degree of success in controlling the unobserved selection bias and analysts should explore the robustness of their findings by comparing initial findings with other methods by testing important assumptions. We noted some papers combining two common methods, such as difference-in-difference with propensity score matching (N = 10) and fixed effects with instrumental variables (N = 8), in order to obtain more robust results.

Overall effect

Compared with the earlier review, our study has found stronger and more consistent evidence of positive effects of health insurance on health care utilisation, but less clear evidence on financial protection. Restricting the evidence base to the small subset of randomised studies, the effects on financial protection appear more consistently positive, i.e. three cluster randomised studies[ 39 , 75 , 76 ] showed a decline in OOP expenditure and one randomised study[ 36 ] found no significant effect.

Besides the impact on utilisation and financial protection, this review identified a number of good quality studies measuring the impact of health insurance on health outcomes. Twelve studies were identified (i.e. twice as many as those published before 2010), nine of which showed a beneficial health effect. This holds for the subset of papers with stronger methodology for tackling selection bias.[ 39 , 49 , 89 , 103 ] In cases where a health insurance programme does not have a positive effect on either utilisation, financial protection, and health status, it is particularly important to understand the underlying reasons.

Possible explanation of heterogeneity

Payment system..

Heterogeneity of the impact of health insurance may be explained by differences in health systems and/or health insurance programmes. Robyn et al. (2012) and Fink et al (2013) argued that the lack of significant effect of insurance in Burkina Faso may have been partially influenced by the capitation payment system. As the health workers relied heavily on user fees for their income, the change of payment system from fee-for-services to capitation may have discouraged provision of high quality services. If enrolees perceive the quality of contracted providers as bad, they might delay seeking treatment, which in turn could impact negatively on health.

Several studies from China found the utilisation of expensive treatment and higher-level health care facilities to have increased following the introduction of the insurance scheme.[ 41 , 44 , 45 , 88 ] A fee-for-service payment system may have incentivised providers to include more expensive treatments.[ 43 , 83 , 88 ] Recent systematic reviews suggested that payment systems might play a key role in determining the success of insurance schemes,[ 23 , 106 ] but this evidence is still weak, as most of the included studies were observational studies that did not control sufficiently for selection bias.

Uncovered essential items.

Sood et al. (2014) found no statistically significant effect of community-based health insurance on utilisation in India. They argued that this could be caused by their inability to specify the medical conditions covered by the insurance, causing dilution of a potential true effect. In other countries, transportation costs[ 69 ] and treatments that were not covered by the insurance[ 59 , 60 ] may explain the absence of a reduction in out-of-pocket health expenditures.

Methodological differences.

Two studies in Georgia evaluated the same programme but with different conclusions.[ 50 , 51 ] This discrepancy may be explained by the difference in the estimated treatment effect: one used average treatment effect (ATE), finding no effect, and another used average treatment effect on the treated (ATT), reporting a positive effect. ATE is of prime interest when policymakers are interested in scaling up the programme, whereas ATT is useful to measure the effect on people who were actually exposed to insurance.[ 107 ]

Duration of health insurance.

We also found that the longer an insurance programme has been in place prior to the timing of the evaluation, the higher the odds of improved health outcomes. It is plausible that health insurance would not change the health status of population instantly upon implementation.[ 21 ] While there may be an appetite among policymakers to obtain favourable short term assessments, it is important to compare the impact over time, where feasible.

Moral hazard.

Acharya et al (2012) raised an important question about the possibility of a moral hazard effect as an unintended consequence of introducing (or expanding) health insurance in LMICs. We found seven studies exploring ex-ante moral hazard by estimating the effect on preventive care. If uninsured individuals expect to be covered in the future, they may reduce the consumption of preventive care or invest less in healthy behaviours.[ 108 , 109 ] Current overall evidence cannot suggest a definite conclusion considering the heterogeneity in chosen outcomes. One study found that the use of a self-treated bed nets to prevent malaria declined among the insured group in Ghana[ 54 ] while two studies reported an increase in vaccination rates[ 62 ] and the number of prenatal care visits[ 55 , 62 ]among the insured group. Another study reported no evidence that health insurance encouraged unhealthy behaviour or reduction of preventive efforts in Thailand.[ 66 ]

Two studies from Colombia found that the insured group is more likely to increase their demand for preventive treatment.[ 47 , 49 ] As preventive treatment is free for all, both authors attributed this increased demand to the scheme’s capitation system, incentivising providers to promote preventive care to avoid future costly treatments.[ 110 ] Another study of a different health insurance programme in Colombia found an opposite effect.[ 48 ]

Study limitations.

This review includes a large variety of study designs and indicators for assessing the multiple potential impacts of health insurance, making it hard to directly compare and aggregate findings. For those studies that used a control group, the use of self-selected controls in many cases creates potential bias. Studies of the effect of CBHI are often better at establishing the counterfactual by allowing the use of randomisation in a small area, whereas government schemes or social health insurance covering larger populations have limited opportunity to use randomisation. Non-randomised studies are more susceptible to confounding factors unobserved by the analysts. For a better understanding of the links between health insurance and relevant outcomes, there is also a need to go beyond quantitative evidence alone and combine the quantitative findings with qualitative insights. This is particularly important when trying to interpret some of the counterintuitive results encountered in some studies.

The impact of different health insurance schemes in many countries on utilisation generally shows a positive effect. This is aligned with the supply-demand theory in whichhealth insurance decreases the price of health care services resulting in increased demand. It is difficult to draw an overall conclusion about the impact of health insurance on financial protection, most likely because of differences in health insurance programmes. The impact of health insurance on health status suggests a promising positive effect, but more studies from different countries is required.

The interest in achieving UHC via publicly funded health insurance is likely to increase even further in the coming years, and it is one of the United Nation’s Sustainable Development Goals (SDGs) for 2030[ 111 ]. As public health insurance is still being widely implemented in many LMICs, the findings from this review should be of interest to health experts and policy-makers at the national and the international level.

Supporting information

S1 table. search strategies..

https://doi.org/10.1371/journal.pone.0219731.s001

S2 Table. Study characteristic and reported effect from the included studies (N = 68).

https://doi.org/10.1371/journal.pone.0219731.s002

S3 Table. PRISMA 2009 checklist.

https://doi.org/10.1371/journal.pone.0219731.s003

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Health Insurance is a Family Matter (2002)

Chapter: 7 conclusions, 7 conclusions.

The Committee’s overarching conclusion is that insurance coverage within a family concerns and may affect the entire family unit. The lack of insurance of any family member has the potential to affect the financial and emotional well-being of all members of the family. This suggests that we focus not only on the more than 38 million uninsured adults and children in the United States, but also on the 17 million families in which some or all members are uninsured. 1

A FAMILY PERSPECTIVE

Of the 85 million families in the United States, 17 million have one or more members who lack health insurance. Narrowing the focus to the roughly 38 million families with children, in 3.2 million of these families all members lack insurance and in an additional 4.3 million families some but not all members are uninsured (see Chapter 2, Table 2.1 ). Together these uninsured families with children account for about one-fifth of all families with children. Among married, childless couples, an additional 3.7 million family units have one or both members uninsured. More than 38 million uninsured people live in the 11.1 million family units mentioned above, with relatives other than their own children under age 18, with people other than conventionally recognized kin, or alone. Because of family relationships—financial responsibilities, psychosocial ties, and traditional child rearing obligations—an uninsured individual may affect the lives of other immediate family members, even if they have coverage. Thus, the consequences of not

having health insurance may intimately touch the lives of more than 58 million of the 276.5 million people in this country.

FINANCIAL AND HEALTH CONSEQUENCES FOR FAMILIES

Many of these 58 million feel the impact of living with uninsured family members as merely an insecurity or worry about the possibility of a very large health-related expense. Fortunately, very serious and expensive illnesses and accidents occur relatively rarely, although chronic and expensive conditions are more common. Uninsured families do have reason to worry. More than 15 percent of families with all members uninsured for the full year experience health expenditures that exceed 5 percent of their family income in a year compared with 9 percent of families in which all members are either privately insured or covered by Medicaid. Expenditures are also higher for families whose members are uninsured for the full year than for those who may have lacked coverage for a shorter period. Because families with at least one uninsured member tend to have lower incomes than do fully insured families, along with very few assets, they generally have fewer financial resources to help cope with these higher expenses. This may financially destabilize the entire family. The Committee recognizes, however, that high out-of-pocket medical bills can be damaging to families at almost any income level, whether or not they are insured.

For uninsured families, what is more common than ruinous health costs is the likelihood that they will go without needed care. Although uninsured people tend to have poorer health status than otherwise comparable insured people, they are less likely to visit a physician, fill prescriptions, and obtain preventive care and other services. Chapter 6 of this report presents strong evidence that insured children have better access to and use more health care services than do uninsured children. Uninsured children are less likely to receive the routine medical attention that is considered necessary for quality preventive care than are insured children. Low-income, minority, non-citizen, or uninsured children consistently have worse access and use than do children without those characteristics. Uninsured adolescents are more likely than those with insurance to have no regular source of care, fewer visits, and unmet health needs. Similarly, uninsured children with special health care needs, whose medical conditions require significantly more than routine well-child care, also have less access to a usual source of care, are less likely to have seen a doctor in the past year, and are less able to get needed medical, dental, prescription, and other care compared to children with special health care needs who do have insurance.

Many of the health and developmental implications of the reduced access to and use of services by uninsured children may not become apparent on a population-wide basis, at least not for many years, because most children tend to be healthy and have many fewer chronic conditions than their elders. Nonetheless, studies demonstrate that parents delay seeking care for their uninsured children

until the symptoms are more severe. These delays may result in unnecessary hospitalizations for conditions that could have been treated on an ambulatory basis and, in some cases, place uninsured children at a higher risk of premature death. If left untreated, some of the common childhood illnesses that can be detected and treated with routine care can also have long-term negative impacts on children’s development, including middle-ear infections, asthma, and iron deficiency. To the extent that timely and appropriate medical care might ameliorate or even prevent these conditions, insurance contributes to better future functioning and life chances for children. Further, provision of preventive care to children can have beneficial long-term effects that extend beyond health, so that society can reap the rewards in the future. The Committee recognizes, however, that there are many factors in addition to medical care that influence children’s health and development.

IMPLICATIONS OF PARENTAL COVERAGE

The Committee’s second report, Care Without Coverage: Too Little, Too Late, shows that the 30 million adults without coverage, many of whom are parents, are less likely to receive appropriate, timely care, particularly for chronic illnesses and certain life-threatening conditions, such as cancer, than are insured adults. Health policy researchers and health care professionals understand the financial and health risks of having family members without insurance. The public also appreciates these risks by showing a strong preference for insuring their families, when given a realistic and affordable option for family coverage. The Committee’s analyses in this report reveal another, more insidious and subtle consequence of uninsurance, namely that if a parent is uninsured, the children in the family may be less likely to get the medical care they need, even if the children have coverage.

Because children depend upon their parents and guardians as decision makers as well as caregivers, parents’ experiences with the health care system and their beliefs about health care are important. Parents’ ability to negotiate that system on behalf of their children affects how children benefit from their insurance eligibility and coverage. In Chapter 5 , the Committee shows that parents’ own use of health care, including whether they have a usual source of care and are connected to the health care system, are powerful predictors of their children’s use of services. Compared to insured adults, uninsured adults are more likely to have no doctor visit in the previous year, to use fewer medical services, and to have negative experiences when they finally obtain health care. The evidence suggests that children of uninsured parents may be less likely to get the full benefit of their own coverage than are children whose parents are also insured.

Not only may parental coverage be an important determinant of children’s access to care, it also can affect the parents’ health. The mental and physical health of parents plays an important role in child well-being. Being in poor physical or mental health, which is more likely for those of low income and those without insurance, has a bearing on a parent’s child rearing practices and ability to cope

with the stresses of raising a family. The physical and emotional health and development of their children may suffer as a result of parents’ poor health.

A key example of a parent’s health affecting that of the child can be seen during pregnancy. Providing public health insurance to previously uninsured pregnant women increases the use of prenatal care but not to the level seen with privately insured women. Uninsured women and their newborns receive less prenatal care and fewer expensive perinatal services than do insured women. Uninsured newborns are more likely to have adverse outcomes than are their insured counterparts. The evidence to date on whether expanding coverage improves an outcome such as low birthweight is not definitive, however.

POPULATIONS AT RISK

Families having some or all members with no insurance for extended periods are at greater risk of adverse consequences than are those with brief gaps in coverage. The Committee has shown that families with members uninsured for long periods are more likely to incur substantial health care costs for services and to suffer adverse consequences to health. These risks have added significance because of the types of families most likely to have some or all members uninsured.

The families in which some or all members lack insurance disproportionately are low income, single parent, immigrant, and racial and ethnic minorities. They face multiple barriers to care—of culture, education, and language—in addition to lack of financial means. The percentage of families with children in which no members are insured increases as family income declines. Also, minority population families are more likely to be wholly uninsured or have some members without coverage than are other families. The uninsured rate for immigrants and naturalized citizens has been significantly higher than that of U.S.-born residents.

In addition, there are families more likely to suffer negative consequences of having uninsured members, even though they are relatively more likely to have insurance than are the populations above. These families have members in late middle age, approaching retirement. Their increased risk comes from the fact that their health care needs and costs are likely to be higher than those of younger families. The limitations of employment-based insurance and the frequency of retirement before the age of Medicare eligibility put both the early retiree and the dependent spouse in danger of losing coverage. In fact, some health conditions and certain chronic illnesses can precipitate early retirements, either for the worker to care for an ill spouse or because work is no longer possible for the ill member of the family.

A PUBLIC POLICY PERSPECTIVE

Public policies that affect opportunities for and the structure of health insurance coverage have great societal significance, given the harmful impacts on families as well as on individuals that are associated with the lack of insurance.

What can the Committee’s analysis in this report on families contribute to policy makers dealing with issues related to health insurance coverage?

In its previous report, the Committee highlighted the importance of ease of access to a regular and continuing relationship with a health care professional, which is associated with better health outcomes and is usually facilitated through insurance. In this study the evidence demonstrates that uninsured children are less likely than insured children to have a usual source of health care or a regular physician. For children, gaps in coverage are associated with health access and use that resemble those of chronically uninsured children. There are several limitations of current insurance arrangements that hinder ease of access to a usual source of care for families. There is also evidence that expanding public programs to previously uninsured children brings a significant increase in access to and use of health services.

The nature of private and public health insurance means that transitions over the course of family life—job changes, divorce, retirement, death of an insured member—often disrupt health coverage for those who had it. Eligibility for private insurance may exclude some family members because they do not meet specific legal definitions or because a child ages beyond a specified limit. Definitions of eligibility and requirements for re-enrollment in public programs may also contribute to gaps in coverage. While some rules for insurance programs are unavoidable, from the family perspective, some of these definitions and limits may cause disruption and discontinuities that are counterproductive to promoting healthy families. Policy efforts targeted at expanding the limits and definitions of insurance eligibility and smoothing the discontinuities will be examined further in the Committee’s sixth report.

Approximately 20 million children are currently covered by Medicaid and the State Children’s Health Insurance Program (SCHIP) program expansions. Nonetheless, almost 5 million children who are potentially eligible for these programs remain uninsured (Urban Institute, 2002a). Recent efforts to simplify the application and re-enrollment processes in many states have contributed to increased coverage. The Committee’s evidence-based review shows clearly that lack of insurance for children reduces access, appropriate utilization, and some health outcomes. In addition, lack of coverage for parents means they are less likely to obtain care or to have positive experiences with the health care system and that this is likely to have a negative impact on their seeking care for their children.

The perspective of this report on coverage of families also highlights the importance of the interdependence of individuals within families, the shared health and economic consequences of uninsurance, and the importance of stronger efforts to view the family in its entirety and to consider health insurance for the whole family. Among private, employment-based insurance plans there has been a small but promising trend to expand the definition of family to include both partners in a relationship, for example, unmarried couples, both mixed sex and same sex. This development increases the opportunity for some adults to receive coverage as dependents.

While enrollment in the employment-based insurance market grew during the strong economy of the past decade, continuing growth in enrollment seems less promising now. Recent economic trends relating to recession, a soft labor market, an increasing rate of health cost inflation, and resulting premium increases all support the expectation that employers will be shifting more costs onto their employees. Higher premiums, copayments, and deductibles are likely to result in fewer employees deciding they can afford to take up the offer of coverage for themselves and their families. There are also indications that the trend for employers to reduce the amount of health insurance they offer to their retirees will continue.

The Committee notes the recent policy discussions regarding subsidizing Consolidated Omnibus Budget Reconciliation Act (COBRA) coverage for workers who lose their jobs under particular circumstances. 1 The discussions recognize the value of health insurance and the need to make it more affordable for workers and their families to keep. Although many workers cannot benefit from COBRA protections (e.g., those whose jobs do not offer health benefits), it could help some workers and their families through some employment-related transitions if it were affordable. The limited real opportunities for coverage available to uninsured workers has recently become more widely understood by the public, but political solutions are yet to be found.

The outlook for continuing expansions of Medicaid and SCHIP may also be affected by the recession. Eligibility for Medicaid coverage is likely to grow as unemployment rises. Most state budgets are feeling the constraints of lower-than-forecasted revenues and some may be tempted to cut back on public coverage rather than to expand it (Kaiser, 2001a). Even without formal changes in eligibility, there has been discussion in some states to stop aggressive campaigns to enroll currently eligible children in their SCHIP program because the campaigns are perceived as sufficiently successful that they are increasing program costs. Such cutbacks might mean that fewer of the millions of eligible children will enroll than might have done.

The Committee’s final report will examine in further depth both the implications for public policy of the consequences of uninsurance on families and the impact of various programs and policies designed to counteract the negative effects.

Health Insurance is a Family Matter is the third of a series of six reports on the problems of uninsurance in the United Sates and addresses the impact on the family of not having health insurance. The book demonstrates that having one or more uninsured members in a family can have adverse consequences for everyone in the household and that the financial, physical, and emotional well—being of all members of a family may be adversely affected if any family member lacks coverage. It concludes with the finding that uninsured children have worse access to and use fewer health care services than children with insurance, including important preventive services that can have beneficial long-term effects.

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The costs of inequality: money = quality health care = longer life.

Federal insurance has helped many, but system’s holes limit gains, Harvard analysts say

Alvin Powell

Harvard Staff Writer

Fourth in a series on what Harvard scholars are doing to identify and understand inequality, in seeking solutions to one of America’s most vexing problems.

If you want to get an idea of the gap between the world’s sickest and healthiest people, don’t fly to a faraway land. Just look around the United States.

Health inequality is part of American life, so deeply entangled with other social problems — disparities in income , education , housing, race, gender, and even geography — that analysts have trouble saying which factors are cause and which are effect. The confusing result, they say, is a massive chicken-and-egg puzzle, its solution reaching beyond just health care. Because of that, everyday realities often determine whether people live in health or infirmity, to a ripe old age or early death.

“There are huge inequalities in this country that often get overlooked … If you want to observe the problems of poverty and inequality, you don’t need to travel all the way to Malawi. You can go to a rural house in America,” said Ichiro Kawachi, John L. Loeb and Frances Lehman Loeb Professor of Social Epidemiology and chair of the Harvard T.H. Chan School of Public Health’s Department of Social and Behavioral Sciences. “If you’re born a black man in, let’s say, New Orleans Parish, your average life expectancy is worse than the male average of countries that are much poorer than America.”

Scholars say that inequality in health is actually three related problems. The first, and most critical, involves disparities in health itself: rates of asthma, diabetes, heart disease, cancer, drug abuse, violence, and other afflictions. The second problem involves disparities in care, including access to hospitals, clinics, doctors’ offices, skilled professionals, medical technology, essential medicine, and proper procedures to deal with illness and disease. The third problem, one that has grabbed national headlines in recent years, is inequality in health insurance, the financial means to pay for the care people get to stay well.

The three problems, scholars say, require interlaced solutions. President Obama’s signature health care law, the Patient Protection and Affordable Care Act (ACA), has taken important strides in narrowing the gap in health insurance coverage, but those gains so far have translated to limited advances in ensuring access to quality medical care and even less progress in making diverse groups equally healthy.

“That’s an area where there’s less progress and more disappointment,” said John McDonough, professor of the practice of public health at the Harvard Chan School. McDonough has worked on health care reform both in Massachusetts, which created the model for national care, and at the federal level.

Disparities are built into the health care landscape, but there has been great progress in recent decades, according to S.V. Subramanian, professor of population health and geography at the Harvard Chan School and the Harvard Center for Population and Development Studies . Life expectancy is increasing for African-Americans and the poor, albeit at a lower rate than for wealthy whites. Although stark disparities remain, the overall health picture in this country is one of improvement, analysts say.

“I sometimes feel that the public health narrative is all doom and gloom, but that’s inaccurate,” Subramanian said.

Still, the remaining disparities are bell-clear in one of the bluntest measures of health: life expectancy, which varies, depending on a resident’s race and ethnicity, as much as 30 years between the richest and poorest U.S. counties. That gap, Kawachi said, covers about two-thirds of the range seen between the world’s healthiest and unhealthiest populations.

In addition, a new study by the Brookings Institution found that the gap has widened considerably. An upper-income man born in 1920, for example, could expect to live five years longer than someone at the lower end of the income spectrum. But for men born in 1940, that life-expectancy divide based on income has more than doubled, to 12 years.

Health disparities are a major reason why U.S. life expectancy trails many peer nations, Kawachi said. According to a 2014 report by the Organization for Economic Cooperation and Development (OECD), U.S. life expectancy in 2012 was 78.7 years, 27th out of the group’s 34 industrialized democracies.

The fault line of income, and care

Health disparities form along several societal fault lines, but analysts say the deepest and most persistent divide surrounds income. America’s poor — of any race or ethnicity — are sicker than well-off Americans, Kawachi said. They get substandard care, live in poor housing and degraded environments, and have higher rates of suicide, violence, drug overdose, accidents, and smoking.

“It’s not only a question of racial disparities,” Kawachi said. “At the root of it are unequal economic opportunities, unequal education, and despair.”

Disparities due to poverty hurt racial and ethnic minorities more than other groups because they make up a large proportion of the poor. Not only do they have more ailments, but they often get worse care.

“If you’re having a heart attack, there are very standardized protocols. If you’re African-American, you’re less likely to get those, even with the same health insurance, even with the same presentation,” said Ashish Jha, the K.T. Li Professor of International Health, professor of medicine, and director of the Harvard Global Health Institute. “It’s things like that that trouble us.”

Disparities in health, Jha said, begin at birth for many African-Americans and persist through life.

“One thing we hoped is that the health care system would be part of the solution. What we find, over and over, is that not only does it not do that consistently, sometimes it makes things worse,” Jha said. “It’s obviously deeply disappointing.”

Though health professionals generally care deeply about their poor and minority patients, the problem nonetheless may be rooted in racism, according to David Williams, Florence Sprague Norman and Laura Smart Norman Professor of Public Health at the Harvard Chan School and professor of African and African-American studies in Harvard’s Faculty of Arts and Sciences.

In a recent article in the Journal of the American Medical Association, Williams, along with colleague Ronald Wyatt, cited a 2003 Institute of Medicine report that labeled widespread “implicit bias” for differences in the care that African-Americans and other minorities receive. They said that substandard care leads to an estimated 260 premature African-American deaths each day.

“Insurance is not just supposed to get you access to care, it’s supposed to keep you from getting evicted from your apartment because you paid your hospital bill instead of your rent.” Katherine Baicker

“African-American individuals and those in other minority groups receive fewer procedures and poorer-quality medical care than white individuals,” Williams and Wyatt wrote. “These differences existed even after statistical adjustment for variations in health insurance, stage and severity of disease, income or education, comorbid disease, and the type of health care facility.”

The result of that disparity and others fuels another one: shorter life spans for African-Americans, according to Thomas McGuire, professor of health economics in Harvard Medical School’s (HMS) Department of Health Care Policy.

“In terms of health, there’s approximately a five-year penalty for being African-American compared to being a white male,” McGuire said.

While poverty, race, and ethnicity are key divides between wellness and ill health, another factor — often ignored — is geography, according to Katherine Baicker, C. Boyden Gray Professor of Health Economics at the Harvard Chan School and acting chair of the Department of Health Policy and Management.

Health disparities exist regionally across America — Southern states, for example, have poorer care, according to a 2014 government report. There also are smaller pockets of poverty, such as depressed urban areas.

“I think an important factor that is sometimes overlooked is there are a lot of observed disparities in care … based on income, race, or ethnicity, that are attributable to the quality of care in some parts of the country lagging behind other parts of the country,” Baicker said. “So it’s as much about where you live as what your characteristics are.”

Necessary, but not sufficient

Easily the most significant recent step to lessen health disparities came when Congress passed the ACA in 2010. The law requires people above a certain income to have health insurance, and it expands the Medicaid program to cover those who can’t afford it. A 2012 Supreme Court ruling created a significant pothole on the road to universal coverage, however, allowing states to opt out of the Medicaid expansion. That left 4 million poor Americans in 20 states ineligible and on their own.

“Under the ACA, we created a new structure where just about every American citizen and legal resident has access to some kind of affordable health insurance coverage, except for poor adults in states that have not accepted Medicaid expansion under the ACA,” McDonough said.

Despite that large pool of uncovered people, McDonough said, the ACA has clearly reduced health inequalities in much of the nation, particularly for minority and ethnic groups.

“The ACA has succeeded in taking a major step forward in reducing inequalities as pertains to access in health insurance coverage,” McDonough said. “It has not solved it, but it’s a major step forward.”

The act also has reduced disparities in medical care and in health status, according to McDonough and McGuire. By requiring insurance companies to cover people regardless of pre-existing conditions, it has worked to level the financial gap between the sick and the well.

Though researchers will require time to prove whether illness and chronic disease have dropped as a result, early studies — including one in 2012 by Benjamin Sommers and another last September led by Joshua Salomon, both at the Harvard Chan School — indicate that health insurance coverage can prevent tens of thousands of premature deaths and prompt more than 650,000 people to control chronic conditions such as diabetes, high blood pressure, and high cholesterol.

Substandard care leads to 260 premature African-American deaths daily.

“What’s fairly indisputable is that by expanding coverage to so many millions of otherwise uninsured Americans, we’re saving lives,” McDonough said.

McDonough said that Oregon presented researchers, led by the Chan School’s Baicker and Amy Finkelstein at the Massachusetts Institute of Technology, with a natural experiment when it used a lottery to expand its Medicaid program in 2008. The lottery was a randomized control designed to show that people newly covered by Medicaid took advantage of more preventive care, prescription drugs, and doctors’ office visits to stay healthy, as well as more hospital stays and emergency department visits to treat worsening conditions. Their self-reported access to care and quality of care both rose. In addition, they reported better health and lower rates of depression.

The study did not show improvement in several measures of health involving chronic conditions such as high blood pressure, high cholesterol, or diabetes, Baicker said. But there was progress in improving patients’ financial stability. People covered by Medicaid had far fewer bills sent to collectors, and catastrophic out-of-pocket medical expenses were virtually eliminated.

“Having insurance improved access to care. It also provided financial protection, which is a component of insurance that people don’t talk about but which is really important,” Baicker said. “Insurance is not just supposed to get you access to care; it’s supposed to keep you from getting evicted from your apartment because you paid your hospital bill instead of your rent.”

While the ACA was designed to provide that stability nationally, it isn’t perfect, Jha said, and the endless partisan debate surrounding it in Washington, D.C., and in some states has blocked needed fixes, according to analysts. One provision of the measure that Jha finds troubling was supposed to improve patient care by penalizing hospitals with poor patient outcomes. But that provision, he said, backfires where large, urban, safety-net hospitals are involved. Since they serve a disproportionate number of poor people with chronic ailments, they often have worse outcomes that are unrelated to care.

“I do worry that it will worsen disparities,” Jha said. “There’s good empirical data; the penalties are disproportionately targeting safety-net” hospitals.

A multipronged approach

While the ACA was a giant step in bridging America’s health divide, analysts say that merely providing health insurance isn’t enough. Insurance helps equalize access to care, but disparities remain in the quality of that care. However, there are levers to tackle that problem too, according to Amitabh Chandra, Malcolm Wiener Professor of Social Policy at the Harvard Kennedy School (HKS).

Identifying poor-quality facilities provides an opportunity for rapid improvement by bringing best practices to bear, Chandra said. Baicker agreed, saying such targeted interventions can prove both effective and inexpensive.

“Part of it is about resources,” Baicker said. “There’s also fairly strong evidence that quality could be improved without costing a lot of money in some places, where best practices are not being implemented. Sometimes, it’s not clear what the right thing to do is. But sometimes it is, and our system does not generate high-quality care and best practices nearly as consistently as we would like it to.”

Government-led, policy-driven shifts on health care have led to dramatic improvements in the past and could again, Chandra said. When U.S. hospitals were desegregated in the 1960s, there was “overnight” improvement in African-American infant mortality. Similarly, Chandra said, new technology could help disadvantaged populations by improving their care condition by condition. The discovery of surfactants, for example, helped all premature babies breathe better, but benefitted African-American babies in particular because they were more often premature.

Similarly, McGuire said, attitudinal health reforms, such as against stereotypes and race-related disparities, could create a wide ripple effect. “Activation” training, during which patients from disadvantaged populations are encouraged to question physicians and immerse themselves in their own care, could improve their interactions in everyday life as well.

“The next time they interact with someone outside the health care system, this is going to help them there, too,” McGuire said.

Targeting the factors influencing health

While policy shifts can be powerful tools against health inequities, Subramanian said that such initiatives should be aimed not at outcomes, but earlier in the chain at the factors influencing health. “From a health perspective, the most important thing to think about would be inequalities in opportunities that are health-promoting, rather than inequalities in health outcomes per se,” Subramanian said.

Another helpful policy change would relax America’s relatively stingy family-leave and sick-time policies, Kawachi said.

“If you’re having a heart attack, there are very standardized protocols. If you’re African-American, you’re less likely to get those, even with the same health insurance, even with the same presentation.” Ashish Jha

Among its less-publicized features, the ACA promotes “accountable care organizations,” whose financial rewards are linked to better patient outcomes rather than to just providing treatment, thereby linking financial success to patient health.

“Many health care providers are acutely aware of the inadequacy of the office visit alone to improve population health,” Baicker said. “They sometimes lack flexibility [to try new things and] they sometimes lack resources. To the extent that they can be given flexibility, coupled with responsibility [for population health], that’s an avenue toward better disease management and health promotion.”

But eliminating health disparities outright remains problematic because some causes lie outside the care system. Poor education often leads to low-wage jobs, leading to substandard housing and poor diets and smoking, further leading to diabetes and asthma. So any overarching solution would require massive breadth.

“Health insurance affects health care. Health care affects health. But there are other things that affect health,” Baicker said, including social determinants, quality food, proper exercise, and wise behavior. “Being poor is really hard on your health in and of itself. Then having limited access to health care is another big hit. But there are lots of things about poverty that are harmful to health that really have nothing to do with health care per se.”

Addressing those social and behavioral factors would require government officials and community leaders to think innovatively and cooperatively about the everyday realities that affect health, even down to the designs of neighborhoods and transportation systems. There would have to be more flexibility for health care spending to help, for example, an asthmatic child whose medication might be covered but whose need for an air conditioner is not. Physicians would need to be aware of nonmedical pressures that patients face after leaving their offices that might, for instance, leave them without transportation to follow-up appointments or to pharmacies for medicine. Partner organizations would need to help meet routine needs by such things as grocery shopping and cooking for postoperative patients who are well enough to go home, but can’t yet push carts down store aisles.

“It’s not just about what we do in the hospital, what we do in the doctor’s office. It’s about all the things that happen outside of it,” Jha said. “We’ve come to realize that whole processes are important … not just prescribing right.”

Nancy Oriol, HMS dean of students, has been part of a 24-year experiment to hurdle some of those barriers, seeing patients as whole people and meeting them literally where they live.

As director of obstetric anesthesia for Harvard-affiliated Beth Israel Deaconess Medical Center in the 1980s, Oriol became concerned about infant mortality rates in the poor neighborhoods near Boston’s Longwood section, where HMS and several of its most prominent affiliate hospitals are located.

She and Cheryl Dorsey, then a third-year medical student, began to develop a mobile clinic, the Family Van, that provides basic health education, screenings, and referrals to residents of poor neighborhoods. Though Oriol no longer works on van issues day-to-day, she is probing how the nation’s estimated 2,000 mobile clinics can fill a gap in America’s health care network.

Early on, she hoped that the Family Van would become obsolete as the health care system improved. But now Oriol sees the mobile clinics as an emerging sector, a permanent player with their own mission.

“I actually have come to believe that mobile clinics are not a bridge over a gap, but a different form of health care,” Oriol said. “It is the health care that brings you health in the same place that you live your life.”

60 million with little primary care

In another example of fresh thinking, at HMS’s five-year-old Center for Primary Care, co-director Andrew Ellner, an assistant professor of medicine, is tackling a root cause of health disparities: poor primary care.

According to a 2009 report by the National Association of Community Health Centers, a whopping 60 million Americans lack adequate access to primary care. Ellner said that even people who do have access are often poorly served by what they get.

“I think the lack of primary care certainly is part of the problem, but it is also a reflection of the problem,” Ellner said. “There’s less primary care because there’s less financing available to offer care to disadvantaged patient populations.”

Good primary care requires correct responses to illnesses, injuries, and other conditions, coupled with strong preventive medicine, including administering vaccines and routine screenings, while encouraging healthy diet and exercise. Another critical factor, Ellner said, is properly coordinating care with other providers, including specialists, therapists, and technicians.

“That’s especially important in the U.S. because the health care system is fragmented, and people can get lost. We have to make sure they don’t fall through the cracks,” he said. “It’s definitely part of the solution. Primary care is really on the front lines of health care. It’s a critical factor in making the health system work — not just in providing care, but also in making it accessible to people.”

Internationally, the United States has a reputation as a country with poor primary care. That reality is a significant factor in high health care costs, even though, Ellner said, there are pockets of excellence that provide primary care as good as anywhere in the world. The challenge, he said, is to scale up those examples nationally and make them the rule rather than the exception.

A team approach to care

In another shift, the HMS center has partnered with seven Harvard-affiliated health systems to install a team-based approach to care. The teams, which are being tested in 28 Boston-area practices serving 300,000 patients, include physicians, nurses, social workers, community health workers, and pharmacy technicians.

“Physicians are important, but a lot of primary-care functions should be filled by non-physicians,” Ellner said. “The real fix [to U.S. primary care] will be, over time, to change the ratio of primary care and specialist physicians … [and] to fundamentally change the organization of primary care into a team approach that allows doctors to focus on the parts of health care for which they are uniquely trained: complex diagnosis and management.”

Possible solutions to health care inequality

• Earlier diagnoses of ailments
• Universal health insurance
• Better patient education
• More preventive care
• Fewer penalties on safety-net hospitals
• Monitoring, upgrading struggling facilities
• Wider access to technology
• Stronger primary care
• Broader family-, sick-leave policies
• Social systems supporting health
• Team approaches to care

Larger social factors play a role in health. Kawachi said studies have shown that preschool programs deliver $1.17 of benefits for each $1 invested. Since poverty is a root cause of health disparities, an increase in the federal minimum wage — which at $7.25 an hour leaves a family of four well below the federal poverty line ― would prove a health boon as well, he said.

The ACA recognizes the need to address the exterior causes of health disparities, McDonough said, but those aspects of the law haven’t been taken seriously. For instance, political opponents have attacked the $15 million Prevention and Public Health Trust Fund that provides grants to address poor housing, air quality, and lack of exercise, calling the spending wasteful. Legislators have cut a third of its funds.

“It has been under constant, ruthless assault by opponents of the law as fluff, as paying for jungle gym sets,” McDonough said. “Public health and public health programs have always taken a beating in difficult economic times. There was hope that the ACA represented the dawn of a new day and we would break out of that cycle. That hasn’t happened.”

Despite the endless political tug-of-war over the ACA , Baicker believes that there will be more opportunities soon to experiment with health care reforms, if only to slow cost increases, which have picked up speed.

“There are a lot of positive developments, but I don’t think anybody would argue that we solved this problem,” Baicker said. “There are serious financial problems with the health care system that necessitate bigger change than we’ve seen so far.”

Illustration by Kathleen M.G. Howlett.

Next Tuesday: Inequality in law

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Health Insurance in the USA: A Basic Necessity for the Population Essay

Introduction, present solutions, plan to implement solutions, works cited.

Health insurance has become an important form of security in the modern world. In a country like the United States, health insurance is sought part of being a US citizen and has become a basic necessity for the population. With this thought, there are still millions of people that are uninsured and form one of the biggest social and public sector issues today. Solutions to this problem come in all forms but only a few can be logically implemented, accepted, and benefit all parties.

There are several planned, proposed, and potential solutions to the issue of uninsured people. These are mainly different ways of covering the uninsured and vary in cost, philosophy, and methodology. Some of these are discussed briefly as follows.

The basic idea is to provide health benefits and insurance for all Americans. This can be done only when there is willingness and participation from all influential actors including the healthcare provider and the patient. Controlling costs will result in efficiency and a greater share that can be used on the solutions to this problem through proper managerial practices and infrastructure. This can also be done by the simplification of the administrative side of the healthcare sector. Finally, affordable solutions for both the public and the private and public financers are the key to proper implementation and running (Battista).

One of the basic solutions is to use the federal funds to cover uninsured people several hundred percent below the poverty line. This will redirect a chunk of the federal funds towards healthcare expenditure, which is the only problematic aspect although it would somewhat lower the uninsured population.

High deductible health insurance is another solution that will help the uninsured through healthcare savings accounts and will use taxes to fund coverage solutions. Another basic but large-scale solution is a single large pool of healthcare that all US citizens will be a part of, which will be publically funded. Another small-scale solution with limited effects would be to mandate employers to provide health insurance to employees that work for a certain amount of time during a week. Like these, several other solutions have been proposed by experts but only a few can be applicable. (Healthcare coverage in America: Understanding the issues and proposed solutions).

Currently, few of these solutions are being implemented. That does not mean that nothing is being done about the uninsured issue, just that the current solutions are a little different than the ones mentioned above, which seems ideal (Kennedy).

The current measures being taken are similar yet different. The new US administration is focusing on higher discount rates from drug companies to medical insurance providers. Among the solutions mentioned above, redirection of federal funds is being proposed with the idea of funding medical research and financing the insurance-providing pool for the public. This also includes expenditure on outreach programs that help the issue of immigrants and minority uninsured people (Robert).

In my view, a good plan to start the solution chain for this problem would be the proper reallocation of federal funds in favor of healthcare research and insurance coverage. At the same time, in parallel, the US administration needs to plan out and implement a single publically funded pool and provide health insurance for all US citizens at nominal rates, a system similar to the Canadian counterpart (Varnon).

• Battista, John R. “Solving The Problem Of The Uninsured.” 2004. Connecticut Coalition for Universal healthcare.
• “Healthcare coverage in America: Understanding the issues and proposed solutions.” 2008. cover the uninsured.org.
• Rob Varnon. “Many arguments, few solutions to growing problem of uninsured Americans.” Connecticut Post (Bridgeport, CT) (n.d.). Newspaper Source. EBSCO. [Library name], [City], [State abbreviation].
• Robert, Pear. Obama Offers Broad Plan to Revamp Health Care . 2009. Web.
• Sheryl Kennedy. “Detroit Health Care Providers, Politicians, Seek Solutions for Uninsured.” Detroit Free Press (MI) (n.d.). Newspaper Source. EBSCO. [Library name], [City], [State abbreviation].
• Chicago (A-D)
• Chicago (N-B)

IvyPanda. (2022, March 13). Health Insurance in the USA: A Basic Necessity for the Population. https://ivypanda.com/essays/health-insurance-in-the-usa/

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5 facts about Hispanic Americans and health care

Hispanic Americans have long faced health care challenges in the United States, including lower health insurance coverage rates and less access to preventative care.

Language and cultural barriers, as well as higher levels of poverty, are among the social and economic factors contributing to disparate health outcomes for Hispanic Americans. These disparities were apparent during the early stages of the COVID-19 pandemic , when Hispanics were far more likely than White Americans to have died from the virus .

Pew Research Center conducted this analysis to highlight Hispanic Americans’ attitudes about and experiences with health care. We surveyed U.S. adults from Nov. 30 to Dec. 12, 2021, including 3,716 Hispanic adults (inclusive of those who identify as any race). A total of 14,497 U.S. adults completed the survey.

The survey was conducted on the Center’s American Trends Panel (ATP) and included an oversample of Black and Hispanic adults from the Ipsos KnowledgePanel. Respondents on both panels are recruited through national, random sampling of residential addresses. This way nearly all U.S. adults have a chance of selection. The survey is weighted to be representative of the U.S. adult population by gender, race, ethnicity, partisan affiliation, education and other categories. Read more about the  ATP’s methodology .

Here are the survey  questions used for this analysis , along with responses, and its methodology .

This study was informed by a group of advisers with expertise related to Black and Hispanic Americans’ attitudes and experiences in science, health, STEM education and other areas. Pew Research Center remains solely responsible for all aspects of the research, including any errors associated with its products and findings.

This analysis includes additional information from sources including KFF and the U.S. Census Bureau. Further information about these sources can be found through the links in the text.

Here are five key facts about Hispanic Americans and health care, based on a 2021 Pew Research Center survey of Hispanic adults and other sources:

Hispanic adults are less likely than other Americans to have seen a health care provider recently and to have a primary care provider. Seven-in-ten say they’ve seen a doctor or other health care provider in the past year, compared with 82% among Americans overall. Hispanics are also slightly less likely than Americans overall to say they have a primary care provider (68% vs. 76%).

Health care access among Hispanic immigrants differs markedly based on how long they have lived in the U.S. More recent arrivals are less likely than those who have been in the country longer to have seen a doctor recently and to have a primary care provider. For example, 48% of Hispanic immigrants who have been in the U.S. for a decade or less report having a primary care provider, compared with 79% among those who have been in the U.S. for more than two decades.

Recent arrivals make up a declining share of Hispanic immigrants in the U.S. And more broadly, immigrants account for a declining share of the overall U.S. Hispanic population . In 2021, they made up 32% of all Hispanic Americans, down from 37% in 2010.

Hispanic Americans are less likely than people of other racial and ethnic backgrounds to have health insurance. As of 2021, the uninsured rate among Hispanics under age 65 was 19%, according to KFF, formerly known as the Kaiser Family Foundation . That was higher than the share among Black (11%), White (7%) and Asian Americans (6%). (These figures include rates among children as well as adults.)

While comparatively high, the uninsured rate among Hispanic Americans under age 65 in 2021 was down from 33% in 2010, before the implementation of the Affordable Care Act, according to KFF.

Lower rates of health insurance coverage play a major role in Hispanic Americans’ less frequent interactions with health care providers.

The relative youth of the U.S. Hispanic population may be another factor at play. The median age of Hispanic Americans was 30 as of 2020, compared with 41 for non-Hispanic Americans, according to the U.S. Census Bureau . Among both Hispanic and non-Hispanic Americans, younger people are less likely than their elders to have seen a health care provider recently and to have a primary care provider.

Many Hispanic Americans say worse health outcomes for Hispanics are tied to occupational and structural factors. Some 53% of Hispanic adults say a major reason why Hispanic people generally have worse health outcomes is that they’re more likely to work in jobs that put them at risk for health problems. About half (48%) say a major reason is that Hispanic people have less access to quality medical care where they live.

At least four-in-ten Hispanic adults also point to communication problems arising from language or cultural differences (44%) and preexisting health conditions (40%) as major reasons. (Majorities view all of these factors as at least minor reasons for disparate health outcomes among Hispanic adults.)

The coronavirus outbreak took an especially heavy toll on Hispanic Americans when compared with White Americans. Hispanics also face higher rates of certain diseases like diabetes than some other Americans.

When it comes to progress in health outcomes for Hispanic people, 51% of Hispanic adults say health outcomes have gotten a lot or a little better over the past two decades, compared with 13% who say they’ve gotten a lot or a little worse; 34% say they’ve stayed about the same.

About a third of Hispanic Americans – including 58% of Hispanic immigrants – say they prefer to see a Spanish-speaking health care provider. Overall, 35% of Hispanic adults strongly or somewhat prefer seeing a Spanish-speaking doctor or other health care provider for routine care. A larger share (51%) say it makes no difference whether the doctor they see speaks Spanish or not. And 13% say they would rather not see a Spanish-speaking doctor.

Attitudes are broadly similar when it comes to seeing a Hispanic doctor or health care provider. A third of Hispanic adults say they would prefer to see a Hispanic doctor for routine care, while 59% say it makes no difference and 7% would rather not.

Among Hispanic adults, immigrants are much more likely than those born in the U.S. to prefer seeing a Spanish-speaking doctor (58% vs. 12%) and to prefer seeing a Hispanic doctor (47% vs. 20%). About half of Hispanic immigrants in the U.S. mostly speak and read in Spanish.

Hispanic Americans account for 19% of the U.S. population . But only 9% of the nation’s health care practitioners and technicians are Hispanic, according to a 2021 Pew Research Center analysis of federal government data . And just 7% of all U.S. physicians and surgeons and 7% of registered nurses are Hispanic.

Black Hispanic adults are more likely to report negative health care experiences than other Hispanic adults. Overall, about half of Hispanic adults (52%) say they’ve had at least one of six negative health care experiences asked about in the Center’s 2021 survey, including feeling rushed or having to speak up to get the proper care. This is similar to the share of all U.S. adults who report having at least one of these types of negative experiences.

However, there are notable differences among Hispanics by race. Hispanic Americans who identify as Black are much more likely than White Hispanic adults to have faced negative health care experiences. For instance, 52% of Black Hispanic adults say they’ve had to speak up to get proper care, compared with 31% of White Hispanic adults. And Black Hispanic adults are 15 percentage points more likely than White Hispanic adults to say they’ve received lower-quality care (37% vs. 22%).

While negative health care experiences are fairly common, most Hispanic adults have generally positive opinions about their latest health care interaction. A 56% majority say the quality of care they most recently received from doctors or other health care providers was excellent or very good, while another 28% say it was good. Fewer (14%) say the care they received was only fair or poor. Black and White Hispanic adults are about equally likely to give positive ratings of their most recent health care experience.

Note: Here are the survey  questions used for this analysis , along with responses, and its methodology .

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5 facts about black americans and health care , 8 facts about americans with disabilities, inflation, health costs, partisan cooperation among the nation’s top problems, by more than two-to-one, americans say medication abortion should be legal in their state, most popular.

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Taking Account of Rising Health Care Costs

Have your out-of-network insurance bills skyrocketed? Chris Hamby, an investigative reporter for The Times, may have an explanation.

By Josh Ocampo

Navigating the health care system in the United States can often feel like being lost in a maze. What kind of doctor should I see? Who takes my insurance? What even is a co-pay, anyway?

For that reason, Chris Hamby, an investigative reporter, has devoted much of his five-year career at The New York Times to guiding readers through such dizzying questions. His latest article, which was published online this month , explored the complex subject of insurance bills.

Last year, Mr. Hamby began investigating MultiPlan, a data firm that works with several major health insurance companies, including UnitedHealthcare, Cigna and Aetna. After a patient sees an out-of-network medical provider, the insurer often uses MultiPlan to recommend how much to reimburse the provider.

Mr. Hamby’s investigation revealed that MultiPlan and the insurers are incentivized to reduce payments to providers; in doing so, they score larger fees, which are paid by the patient’s employer. Many patients are forced to foot the rest of the bill. (MultiPlan said in a statement to The Times that it uses “well-recognized and widely accepted solutions” to promote “affordability, efficiency and fairness” by recommending a “reimbursement that is fair and that providers are willing to accept in lieu of billing plan members for the balance.”)

In an interview, Mr. Hamby shared his experience poring over more than 50,000 pages of documents and interviewing more than 100 people. This conversation has been edited.

Where did your investigation begin?

We were broadly looking at issues in health insurance last year. MultiPlan kept coming up in my conversations with physician groups, doctors and patients. At first, it was unclear what exactly MultiPlan did. There were some lawsuits regarding its work with UnitedHealthcare, but it was difficult to understand the company’s role in the industry. We eventually accumulated more information about MultiPlan’s relationship with big insurance companies.

What were doctors and other providers saying?

Mostly that they’d seen their reimbursements dramatically cut in recent years and that it was becoming difficult for them to sustain their practices. They said they previously had more success negotiating and obtaining higher payments.

Of your findings, perhaps the most surprising is that MultiPlan receives a cut of the money it saves employers.

Yes, but I wouldn’t call it a cut. It’s very complicated. MultiPlan charges a fee based on the savings that they obtain for employers. But in some cases, that savings is passed onto a patient as a bill. Both insurers and MultiPlan have financial incentives to keep payments low because they receive more money, in many cases.

But it wasn’t always that way, correct?

Right. MultiPlan was founded in 1980, and it was a fairly traditional out-of-network cost containment company. Doctors and hospitals agreed to modest discounts with MultiPlan, and agreed not to try and collect more money from patients. It was a balancing act.

But that balancing act changed over time. MultiPlan’s founder sold the company to the Carlyle Group, a big private equity firm, in 2006. It moved away from negotiations and toward automated pricing. They bought one company in 2010, and another, key company in 2011, and in doing so, acquired these algorithm-driven tools that became the backbone of MultiPlan’s business.

You read more than 50,000 pages of documents for your investigation. How does one begin to sift through that much information?

I love a good trove of documents. There wasn’t some big leak. It was more about piecing together information from many different sources — legal filings, documents that providers and patients shared with me, their communications with MultiPlan and insurers. We asked federal judges to unseal a few documents that had previously been confidential, including emails between Cigna executives, paperwork describing how some of MultiPlan’s tools worked and data on thousands of medical claims.

What was the greatest challenge in your reporting?

Finding patients and providers who were willing to speak on the record about their experiences, because this is a really sensitive subject. A number of providers were concerned that if they spoke on the record, insurance companies would retaliate. For many of the patients I spoke with, it also meant putting their personal medical history out there for the public to read.

What about health care and the pharmaceutical industry drew your interest as a reporter?

For many Americans, health care is an almost universally frustrating or confusing experience. It’s one that has direct effects on people’s health, their pocketbooks or both. I really like learning about the stuff that impacts people’s health. I try to make that information accessible to millions of people who are affected by it but who might not have a lot of time to understand it.

How You Pay Drives What You Choose: Health Savings Accounts versus Cash in Health Insurance Plan Choice

A marked feature of health insurance plan choice is inconsistent choices through the overweighting of premiums relative to out-of-pocket spending. We show that this source of inconsistency disappears when both types of spending come from the same source of designated funds. We focus on the MediSave program in Singapore, whereby residents can pay their health insurance premiums with cash or MediSave funds, but are subject to limits that vary by age and over time. By exploiting variations in those limits, we consistently find that when individuals are able to pay their health insurance premiums with MediSave funds, they are less price sensitive and more willing to enroll in more generous plans—which results in lower spending levels and variance, and lower adverse selection in the market. The results suggest a strong role for mental accounting in insurance decisions.

Lin, Liu, and Yi gratefully acknowledge support from Singapore’s Ministry of Education Academic Research Fund Tier 1 (WBS R-122-000-303- 115). The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.

MARC RIS BibTeΧ

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Report: employers unaware of ethical, safety implications of health insurance 'alternative funding programs'.

Survey of human resource professionals finds most respondents lack understanding of the misuse of charitable programs and risks of requiring employees to obtain foreign drugs

WASHINGTON, DC / ACCESSWIRE / April 22, 2024 / A new survey of human resource professionals found that the cost of offering affordable health insurance to employees is a large concern for employers. Yet these professionals may be unaware of the ethical and safety implications of requiring health insurance participants to participate in "alternative funding programs" (AFPs) to access their medications.

HR.com's HR Research Institute partnered with Aimed Alliance and the Alliance for Patient Access (AfPA) to examine how employers perceive and utilize AFPs with the overarching goal of addressing misconceptions and inaccuracies surrounding these programs. The report of the survey's findings, Managing Prescription Drug Costs 2023-24 , is available for download.

Almost nine out of 10 respondents (88%) said that the cost of offering affordable health insurance to employees is a large concern for their employer. In an effort to lower health insurance costs, some employer health plans have turned to third-party AFPs. When working with an AFP, the health plan "carves out" coverage of certain medications, requiring plan participants to obtain those medications through the AFP. The AFP procures participants' medications through "alternative sources," such as charitable assistance programs and foreign drug suppliers.

Although contracting with an AFP might appear to be a cost-effective way of managing drug costs for the employer, there are ethical and safety concerns associated with sourcing medications through alternative sources. However, employer respondents may need greater education on these challenges.

Over half (53%) of respondents agreed that enrolling employees in charitable medication assistance programs is an effective way to reduce employers' prescription drug costs. Charitable medication assistance programs are intended to benefit people who have no health insurance or only minimal coverage. When charitable programs' resources are depleted, uninsured or underinsured individuals who rely on these programs have no other means of accessing their medications.

Similarly, four out of five survey respondents (82%) agreed that getting prescription drugs from foreign countries is a safe and ethical means for reducing drug spending for employers. According to the U.S. Food and Drug Administration (FDA), "medicines from outside the legitimate U.S. drug supply chain do not have the same assurance of safety, effectiveness and quality as drugs subject to FDA oversight." For these reasons, the FDA recommends only obtaining medicines from legal sources in the U.S.

Respondents' lack of understanding of the ethical and safety implications of requiring health plan participants to obtain medications through AFPs may be due to a lack of knowledge. Almost nine out of 10 respondents (88%) say that their internal compliance team had not raised concerns about using AFPs. Less than half of respondents (46%) said they had heard of AFPs. Over two-thirds (67%) of respondents said that they had not used an AFP and were not considering doing so.

"The rise in use of alternative funding programs is worrisome for insured employees, who rely on their insurance in order to access the medications they need," said Josie Cooper, Executive Director of the Alliance for Patient Access. "We urge employers to consider the implications of these types of programs on employee health and safety prior to implementing an AFP."

More takeaways from the survey and recommendations for managing employer health insurance costs without resorting to the use of AFPs are available in the full report .

About Aimed Alliance

Established in 2013 and based in Washington, DC, Aimed Alliance is an independent, not-for-profit health policy organization that works to protect and enhance the rights of health care consumers and providers. For more information on Aimed Alliance and its initiatives, go to aimedalliance.org and follow @aimedalliance on X, Instagram, and Facebook.

About Alliance for Patient Access (AfPA)

The Alliance for Patient Access is a national network of policy-minded health care providers who advocate for patient-centered care and participate in clinician working groups, advocacy initiatives, stakeholder coalitions and the creation of educational materials.

About HR.com and the HR Research Institute

HR.com's HR Research Institute helps HR departments keep their finger on the pulse of HR. HR.com is committed to creating inspired and informed workforces by maximizing the potential of HR professionals around the world. Over 1.88 million HR professionals rely on HR.com as the foremost, trusted industry resource for education, career development, and compliance.

Media Contact:

Ashira Vantrees, Esq. [email protected]

SOURCE: Aimed Alliance

View the original press release on accesswire.com

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• v.43(3); 2008 Jun

Effects of Poverty and Lack of Insurance on Perceptions of Racial and Ethnic Bias in Health Care

To investigate whether poverty and lack of insurance are associated with perceived racial and ethnic bias in health care.

Data Source

2001 Survey on Disparities in Quality of Health Care, a nationally representative telephone survey. We use data on black, Hispanic, and white adults who have a regular physician ( N =4,556).

Study Design

We estimate multivariate logistic regression models to examine the effects of poverty and lack of health insurance on perceived racial and ethnic bias in health care for all respondents and by racial, ethnic, and language groups.

Principal Findings

Controlling for sociodemographic and other factors, uninsured blacks and Hispanics interviewed in English are more likely to report racial and ethnic bias in health care compared with their privately insured counterparts. Poor whites are more likely to report racial and ethnic bias in health care compared with other whites. Good physician–patient communication is negatively associated with perceived racial and ethnic bias.

Conclusions

Compared with their more socioeconomically advantaged counterparts, poor whites, uninsured blacks, and some uninsured Hispanics are more likely to perceive that racial and ethnic bias operates in the health care they receive. Providing health insurance for the uninsured may help reduce this perceived bias among some minority groups.

Patients' perceptions of racial and ethnic bias and discrimination in health care are not uncommon among minority health care users. Based on national studies, as many as 15 percent of Latinos/Latinas and 12 percent of blacks report that they had been judged unfairly or treated with disrespect by a health care provider because of their race or ethnicity ( Lillie-Blanton et al. 2000 ). Twenty-three percent of blacks and 15 percent of Hispanics believe they would have received better medical care if they were of a different race or ethnicity ( LaVeist, Rolley, and Diala 2003 ). In some special patient populations, perceived discrimination is even more prevalent. A study by Bird, Bogart, and Delahanty (2004) , for instance, reveals that more than 70 percent of HIV-positive patients report having experienced discrimination based on their race or color while receiving treatment. Because this study did not control for race, the overrepresentation of blacks among the HIV-positive patients may have contributed to the finding of a high prevalence of perceived racial discrimination.

As one might expect, perceptions of racial and ethnic discrimination are much less common among white Americans. In one national study, only 1 percent of the whites reported having been judged unfairly or treated with disrespect because of their race or ethnicity ( Lillie-Blanton et al. 2000 ). Yet, 16 percent of whites, 30 percent of Hispanics, and 35 percent of blacks believed racism was a “major problem” in health care, suggesting that many Americans are concerned about equity in the health care system.

Perceptions of racial and ethnic bias and discrimination in health care are linked to a number of undesirable outcomes, including lower overall satisfaction with care ( Bird et al. 2004 ), a higher likelihood of putting off medical tests and treatment ( Van Houtven et al. 2005 ), lower likelihood of receiving preventive health care services (such as cholesterol testing, diabetic foot exams, flu vaccinations, and hemoglobin a1c testing [ Trivedi et al. 2005 ]), poorer self-rated health, increased depression, and increased AIDS-related symptoms among HIV patients ( Bird et al. 2004 ), as well as poorer glycemic control, poorer physical functioning, and higher symptom burden among diabetic patients ( Piette, Bibbins-Domingo, and Schillinger 2006 ). This evidence is consistent with the broader literature indicating that the physical and mental health of Americans who experience discrimination in their daily lives tends to suffer ( Kessler, Mickelson, and Williams 1999 ; Krieger 2000 ; Williams and Neighbors 2001 ; Williams, Neighbors, and Jackson 2003 ).

Only a handful of studies have addressed factors beyond race and ethnicity that may contribute to perceptions of racial and ethnic bias in health care. One study found that the odds of reporting racial discrimination in health care decreased with increasing income and self-rated health, even after controlling for race and ethnicity ( LaVeist, Rolley, and Diala 2003 ). Another study revealed that black patients who reported perceptions of race-based discrimination in interactions with their health care providers were more educated and more aware of the problem of racial stigmatization compared with other black patients ( Bird and Bogart 2001 ). Little is known about how other factors relate to perceived discrimination in health care.

Two factors that are especially important in the context of health care delivery are poverty and the lack of health insurance. We know that they represent formidable barriers to obtaining high-quality health care ( IOM 2002 ; AHRQ 2003 ). These structural obstacles affect minorities more commonly than whites ( Hargraves 2004 ). As the numbers of the poor and the uninsured, who are disproportionately nonwhite, continue to rise in America ( Strunk and Reschovsky 2004 ; The Kaiser Commission of Medicaid and the Uninsured 2005 ; DeNavas-Walt, Proctor, and Lee 2006 ), it is important to better understand the implications of poverty and lack of insurance for patients' experiences, especially among nonwhites and people with limited English skills, who also face language barriers to high-quality care ( David and Rhee 1998 ; Carrasquillo et al. 1999 ; Weech-Maldonado et al. 2001 ).

The purpose of this paper is to investigate whether poverty and lack of insurance are related to perceived racial and ethnic bias in health care. While this problem is not well understood in the context of health care, the sociological literature suggests that socioeconomic disadvantage is linked to perceived racial and ethnic discrimination in other areas of daily living ( Kessler, Mickelson, and Williams 1999 ; Watson, Scarinci, Klesges, Slawson and Beech 2002 ). This literature leads us to expect that the poor and the uninsured will be more likely than others to report that they have experienced racial and ethnic bias in health care . We test the relationships between poverty and lack of insurance using a recent national sample of health care users who have a regular physician. We also examine whether the effects of poverty and lack of insurance vary among people of different racial, ethnic, and language backgrounds.

The data come from the 2001 Survey on Disparities in Quality of Health Care, a random-digit-dial survey with 6,722 adults (age 18 and older) residing in the continental United States. The survey was sponsored by the Commonwealth Fund and administered over the telephone in English, Spanish, Mandarin, Cantonese, Vietnamese, and Korean. Telephone numbers from areas with higher than average densities of minority households were oversampled. Respondents answered questions about their sources of, access to, utilization of, and experiences with health care, their sociodemographic characteristics, and their health. The response rate was 54.3 percent.

Our analyses include whites, blacks, and Hispanics. We excluded Asians, Native Hawaiians/Pacific Islanders, American Indians/Alaska Natives, and members of the “Other” racial category because their numbers were too small for meaningful statistical analysis. Furthermore, because our multivariate analyses include an indicator of physician–patient racial and ethnic concordance, which is available only for patients with a regular physician, the analyses necessarily exclude respondents without a regular physician. The final nonmissing N after these exclusions is 4,556.

Dependent Variables

An indicator of perceived racial and ethnic bias in health care was constructed from the following two questions: “Thinking of all of the experiences you have had with health care visits in the last 2 years, have you ever felt that the doctor or medical staff you saw judged you unfairly or treated you with disrespect because of your race or ethnic background?” and “Do you think there was ever a time when you would have gotten better medical care if you had belonged to a different race or ethnic group?” Respondents who answered “Yes” to one of these two questions received a code of 1 for perceived racial and ethnic bias in health care. All others were coded as 0 for this indicator.

Main Explanatory Variables

The lack of health insurance was coded as 1 for respondents who reported being uninsured and 0 for all others. The insurance type was categorized as private insurance or public insurance (Medicaid, Medicare, and other public insurances, including CHAMPUS, TRICAP, or VA). A dichotomous indicator of poverty was calculated according to the U.S. Census Bureau's definition of poverty for 2001 for each region and household size.

Control Variables

To ascertain race and ethnicity , respondents were first asked whether they were Latino/a or Hispanic. Those who did not self-identify as Latino/a or Hispanic were asked whether they were white or black/African American. 1 To approximate English proficiency, we further distinguished between Hispanics who chose a Spanish interview and Hispanics who chose an English interview.

Physician–patient racial and ethnic concordance was based on the race and ethnicity of the respondent and of the respondent's regular physician. Respondents were asked whether their regular physician was white, black/African American, Hispanic, Asian, Native Hawaiian or other Pacific Islander, American Indian or Alaskan Native, or some other race. Racial and ethnic concordance was coded as 1 if the patient's and physician's race and ethnicity matched and 0 otherwise.

A composite indicator for good physician-patient communication was based on three questions: (i) “The last time you visited a doctor, did the doctor listen to everything you had to say, to most, to some, or only a little of what you had to say?” (ii) “During the visit, did you understand everything the doctor said, most of what the doctor said, some, or only a little of what the doctor said?” (iii) “Did you have questions about your care or treatment that you wanted to discuss but did not?” Respondents who selected “everything” or “most” on the first two questions and “no” on the third question were coded as 1 on this variable; all others were coded as 0.

Categories for usual place of care included (i) doctor's office, private clinic or hospital outpatient department; (ii) community health center or public clinic; and (iii) emergency room or other place. Subjective health status was reported on a five-point scale ranging from “ poor” to “excellent.” 2 We also controlled for years of education, age, gender, being born in the United States, and region (midwest, northeast, south, and west).

Analytic Strategy

After obtaining univariate and bivariate statistics, we estimated a logistic regression model of perceived racial and ethnic bias on poverty and lack of insurance for the entire sample, controlling for sociodemographic and other variables. To clarify whether the role of insurance status and poverty in perceptions of racial and ethnic bias varied among patients of different racial, ethnic, and language backgrounds, we further estimated logistic regression models of the perceptions of racial and ethnic bias in health care for each racial, ethnic, and language group. We used the same independent variables as in the overall model, with the exception of models for Hispanics interviewed in Spanish. Because very few Hispanics interviewed in Spanish reported being born in the United States, this variable was not used in the model for Hispanics interviewed in Spanish. To ensure that the estimates were unbiased and representative of the national population, we used complex survey design commands in Stata 8.2 statistical software ( StataCorp 2005 ). These commands adjusted for stratification by region, clustering within census tracts, and probability weights. Weights were constructed to account for oversampling in high-density minority areas, the household characteristics of each region, the number of eligible household members, and demographic distortions due to nonresponse.

Table 1 displays the descriptive statistics for variables used in this study along with bivariate statistics comparing respondents who reported racial and ethnic bias in health care to those who did not. In the sample including all racial and ethnic groups, 5 percent of the respondents reported having experienced racial or ethnic bias in health care. Three percent reported having felt that that the doctor or medical staff judged them unfairly or treated them with disrespect because of their racial and ethnic background; and 5 percent reported that they thought they would have received better medical care if they had belonged to a different racial and ethnic group (results not shown). The relatively low rates of reported bias were driven by the preponderance of whites, who accounted for 78 percent of our sample and who were considerably less likely to report having experienced racial or ethnic bias than minorities. Living in poverty and being uninsured were also positively associated with perceived racial and ethnic bias in health care. People usually receiving care in community health centers or public clinics and those residing in the south were more likely than others to report perceived racial and ethnic bias in health care. In contrast, individuals who usually received their care in doctors' offices, private clinics, or outpatient departments, saw a physician of their own race and ethnicity, reported good communication with their physicians, had private insurance, were born in the United States, and resided in the northeast were less likely than their counterparts to report racial and ethnic bias in health care. Perceived racial and ethnic bias was also negatively associated with age, education, and subjective health.

Characteristics of the Final Sample

Source : Health Care Quality Survey (The Commonwealth Fund, 2001). Analyses are limited to blacks, Hispanics, and whites. All estimates are corrected for survey design. Standard errors appear in square brackets.

Wald statistic for Pearson χ 2 test for independence comparing respondents who reported perceived racial and ethnic bias in health care to those who did not:

t -test for differences in means comparing respondents who reported perceived racial and ethnic bias in health care to those who did not:

Table 2 contains the results of multivariate logistic regression models of perceived racial and ethnic bias in health care. The model for all respondents, displayed in the first column, shows that net of the effects of the control variables, the uninsured individuals had a 2.39 times higher odds of reporting racial and ethnic bias in their health care when compared with the privately insured individuals . Respondents living in poverty were more likely to report racial or ethnic bias than other respondents, but this difference was not statistically significant. Several control variables in the model for all respondents performed as previous research ( Lillie-Blanton et al. 2000 ; LaVeist, Rolley, and Diala 2003 ; Johnson, Roter, Powe, and Cooper 2004 ) would predict. The odds of reporting racial or ethnic bias were higher for minority individuals compared with whites (almost eight times higher for blacks and Hispanics interviewed in Spanish, and over four times higher for Hispanics interviewed in English). Among the other variables, the quality of physician–patient communication displayed the strongest association with perceived racial and ethnic bias in health care. Good physician–patient communication was associated with a 71 percent decrease in the odds of reporting racial and ethnic bias in health care . 3 Other characteristics were not related to perceived racial and ethnic bias.

Estimates of Odds Ratios from Logistic Regression Models of Perceived Racial and Ethnic Bias in Health Care for All Respondents and by Respondents' Race, Ethnicity, and Language

Source : Health Care Quality Survey (The Commonwealth Fund 2001). Analyses are limited to blacks, Hispanics, and whites. All estimates are corrected for survey design. Standard errors appear in square brackets.

The remainder of Table 2 displays separate models for blacks, Hispanics interviewed in English, Hispanics interviewed in Spanish, and whites. Compared with the privately insured, the odds of reporting racial and ethnic bias in health care for the uninsured increased by a factor of 3.19 among blacks and 2.93 among Hispanics interviewed in English. Moreover, whites living in poverty had 3.97 higher odds of reporting perceived racial and ethnic bias in health care compared with other whites . Poverty, however, was not associated with perceived bias among minority respondents.

The effects of several control variables varied by respondents' race, ethnicity, and language. Good physician–patient communication was negatively related to perceived racial and ethnic bias in health care among all racial and ethnic groups. Its effects were largest among Hispanics interviewed in Spanish, followed by blacks, whites, and Hispanics interviewed in English. Physician–patient racial and ethnic concordance was associated with a decreased odds of perceived racial and ethnic bias in health care among white respondents, but not among minority respondents. Among Hispanics interviewed in English, those who usually obtained their care in a community health care center or public clinic had a higher odds of perceived racial and ethnic bias in health care compared with those who usually obtained their care in a doctor's office. Education and being born in the United States were positively associated with perceptions of racial and ethnic bias among blacks but not among other groups. White females were less likely to report racial and ethnic bias in health care compared with white males. Hispanics interviewed in English who lived in the northeast, west, or south were less likely to report racial and ethnic bias in health care than their counterparts living in the midwest.

DISCUSSION AND CONCLUSIONS

This study examined how poverty and the lack of health insurance coverage were related to perceptions of racial and ethnic bias in health care in a national sample of blacks, Hispanics, and whites who had a regular physician. Our results indicated that uninsured blacks and Hispanics were more likely to report that they had experienced racial and ethnic bias in the health care they received than did their privately insured counterparts. In addition, poverty was associated with an increased likelihood of perceived racial and ethnic bias among white respondents but not among members of the other racial and ethnic groups.

We cannot determine to what degree the respondents' reports of racial and ethnic bias reflected actual instances of biased behavior by their health care providers or to what degree they resulted from other factors not examined in this study. The actual nature of the association of provider behavior with perceptions of racial and ethnic bias among patients is not well understood; yet, we know that in contemporary America, subtle, often unconscious, forms of bias against racial and ethnic minorities are prevalent (e.g., Bargh, Chen, and Burrows 1996 ; Bobo 2001 ; Dovidio 2001 ; Dovidio and Gaertner 2002 ). Even individuals who explicitly disavow racial and ethnic stereotypes can unwittingly exhibit biased perceptions and behaviors under certain conditions ( Stepanikova 2006 ). Arguably, stereotypes and biased perceptions may affect how some health care providers interpret information about minority patients, how they behave during patient visits, and how they make decisions about what types of treatment are appropriate ( Schulman et al. 1999 ; Bogart, Kelly, Catz, and Sosman 2000 ; Rathore et al. 2000 ; van Ryn and Burke 2000 ; van Ryn and Fu 2003 ).

For a variety of reasons, some minority patients may have concluded that racial and ethnic biases negatively influenced the quality of their health care, even if they received care that was appropriate. This is not surprising, given that minority individuals commonly experience discrimination in their daily lives ( Kessler, Mickelson, and Williams 1999 ). As a result, they may develop a kind of stigma consciousness that makes them more likely to interpret daily events through the lens of race and ethnicity ( Bird and Bogart 2001 ). Importantly, some patients experience barriers to high-quality health care that are unrelated to their race and ethnicity; yet, because of their high-stigma consciousness, they may attribute these difficulties to racial and ethnic discrimination. Lack of insurance could be one such barrier that potentially contributes to an increased likelihood of reporting racial and ethnic bias among the uninsured blacks and Hispanics in our study.

Evidence from social psychology (e.g., Bargh, Chen, and Burrows 1996 ; Blair and Banaji 1996 ) suggests that if providers have racial and ethnic biases, they may play a stronger role in the delivery of care when providers face increased levels of stress ( Stepanikova 2006 ). Consequently, we might expect to find a larger racial and ethnic disparity in the quality of care in health care settings that serve large numbers of socioeconomically disadvantaged patients, because the providers working in these settings may experience increased levels of stress and fatigue. They often see large numbers of patients, have inadequate administrative support, and face other stressors. The poor and the uninsured receiving their care in such resource-poor settings may therefore be more likely to experience, and to report, racial and ethnic bias in health care, potentially contributing to the association of poverty and lack of insurance with perceived racial and ethnic bias revealed by our study. Also consistent with this argument is our finding that Hispanics interviewed in English who receive care in community clinics, which are typically resource-poor, are more likely to report bias compared with those who receive their care in private practices or outpatient hospital departments, which are typically more resource-rich.

Our data do not enable us to determine precisely which of these explanations, if any, reflect an accurate understanding of the processes leading to the associations between socioeconomic disadvantage and perceived racial and ethnic bias found in our study. Our data also do not specify why lack of health insurance was associated with perceived racial and ethnic bias among some minority respondents but not among white respondents, and why poverty was associated with perceived racial and ethnic bias among whites, but not among racial and ethnic minorities, although these findings are not particularly surprising to those who study poverty and inequality more broadly. The broader literature which is not limited to health care suggests that some whites believe they have been victims of reverse discrimination ( Fraser and Kick 2000 ). In addition, we know that poverty is often viewed as a stigma, perhaps even more so by whites ( Amato and Zuo 1992 ).

An important limitation in this study concerns the lack of information in the survey about the identities of the regular physician, the physician seen in the last visit, and the physician delivering care on the occasion that lead to the report of bias. Without such information, we cannot determine whether in the questions about racial concordance, communication, and bias a respondent referred to a single physician or to multiple physicians. It seems reasonable to expect that many respondents referred to a single physician but those who referred to multiple physicians potentially increased the measurement error in our data.

The subjective nature of the patients' reports of racial and ethnic bias in health care can be considered another limitation, because these reports do not necessarily measure whether the patient's racial and ethnic background actually had an independent impact on the quality of health care delivered to the patient. At the same time, the fact that these reports reflect patients' subjective experiences can be considered a strength of the study. In recent years, scholars and policy makers have called for increased attention to patients' experiences with health care as one part of their efforts to improve the quality of care. They have also stressed the importance of culturally sensitive care, arguing that such care could improve the overall quality of health care for minority patients. Yet, as LaVeist, Rolley, and Diala (2003) point out, there are few studies of cultural competence from the perspective of patients. Our study sought to contribute to the understanding of one aspect of patients' experiences with cultural sensitivity (or the lack of it) as reflected in their subjective perceptions of racial and ethnic bias in the health care they receive.

Another strength of our study is the use of a national sample, which makes the results more generalizable to the U.S. population (and subsets of it) compared with some of the earlier studies that used samples consisting of special patient populations. At the same time, the scope of our study is limited to blacks, Hispanics, and whites. Our results are not generalizable to individuals of other racial and ethnic backgrounds. In addition, because we only studied people with a regular physician, our results do not generalize to people without a regular physician. More research is needed to determine whether an association between perceived racial and ethnic bias and socioeconomic disadvantage extends to those without a regular physician, especially because these individuals tend to be affected by poverty and lack of insurance more often than others.

What are the implications of the findings described in this study? In addition to the benefits of health insurance for access to health care services and for the overall health of the population, which have been amply documented, universal health insurance coverage may help reduce perceptions of racial and ethnic bias among some minority patients. These implications are preliminary and must be supported by future research on the direction of causality and on how various aspects of socioeconomic disadvantage are linked to racial and ethnic disparities in the quality of health care.

Our results reveal that several factors beyond the lack of insurance and poverty contribute independently to patients' perceptions of racial and ethnic bias in health care. Especially important is the quality of physician–patient communication. Previous research suggests that physician–patient communication characterized by clarity, attentiveness, and empathy is associated with patients' more positive experiences, including higher perceived respectfulness of the health care providers (  Johnson, Roter, Powe, and Cooper 2004 ), higher patient satisfaction, and reduced emotional distress following consultation ( Zachariae et al. 2003 ). Communication breakdowns may lead to patients' perceptions of racial and ethnic discrimination, regardless of whether providers' racial and ethnic biases actually influenced the quality of care delivered to the patient. If further research finds causal effects between physician–patient communication and patients' perceptions of racial and ethnic bias, we will have evidence suggesting the importance of training physicians in culturally sensitive communication skills to improve those aspects of the quality of health care that are reflected in patients' experiences, and, ultimately, to assist in designing a health care system that provides high-quality care to all patients regardless of their race, ethnicity, and socioeconomic status. Programs currently being developed to increase the extent to which physicians in training are culturally sensitive to their patients' needs and behaviors should be investigated as part of the broader effort to improve health care.

Acknowledgments

We are grateful to The Commonwealth Fund for providing the data for this research.

1 Response categories also included “Asian,”“Native Hawaiian or other Pacific Islander,”“American Indian or Alaskan Native,” and “some other background,” but respondents in these categories were excluded from the analysis.

2 This variable was treated as continuous.

3 We also estimated a constrained model without variables that might suffer from the endogeneity problem, including racial/ethnic concordance, communication, and usual source of care. With a single exception of a coefficient for health, which was significant in the constrained model but not in the full model, no other coefficient differed in its direction and statistical significance. This result suggests that the statistical effects of the explanatory variables, i.e., poverty and the lack of insurance, are robust to the inclusion and exclusion of racial/ethnic concordance, communication, and usual source of care.

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