research about health insurance

Insights into the 2022 individual health insurance market

It has been eight years since the Affordable Care Act’s health insurance exchanges launched in 2014.

During that time, the individual market has been highly fluid, with insurer participation, pricing, and plan types evolving dynamically from year to year. The 2022 open-enrollment period (OEP) allows for an assessment of the latest movements in the individual health insurance market.

Several salient features have emerged, particularly in light of the substantive changes created by the American Rescue Plan Act of 2021. Using data scraped from nearly every health insurance exchange in the country, McKinsey’s Center for US Health System Reform has uncovered critical insights relevant to consumers, payers, providers, private equity sponsors, and policy analysts.

The Center for US Health System Reform’s analysis of the 2022 OEP led to the following conclusions:

• Insurer participation and new product offerings have accelerated in the past four years (the halfway point since the marketplace launched), with levels near or surpassing their all-time peaks.
• Managed-care plans—particularly health maintenance organization (HMO) plans and exclusive provider organization (EPO) plans—have grown steadily since 2014 and now account for 82 percent of plan type offerings.
• Consumers increasingly have access to more insurer choices and plans in their home counties; only about 2 percent of consumers have access to just one insurer.
• Across all plan tiers, prices (premiums) remained largely stable, with only slightly higher price increases in 2022 than in 2021; consumer cost burden was also reduced by additional subsidies created by the American Rescue Plan Act.
• Annual premium growth of plans in the 15 states with section 1332 innovation waivers was observably lower than similar plans in the 35 states plus Washington, DC, without waivers.

Overall, the individual market has continued its recent trajectory of increased participation by insurers and consumers. Pricing has largely stabilized in the past several years, and consumer access has grown as newer, tech-enabled insurers bring greater choice to the market.

But uncertainty remains, in part because the enhanced premium subsidies created by the American Rescue Plan Act are due to expire at the end of 2022. If Congress does not renew these subsidies, pricing and consumer participation may face headwinds in the 2023 OEP.

2022 brought healthy participation growth

In 2022, 55 new insurers entered the market (a 21 percent increase over 2021), marking the highest growth in participation since 2015, when participation grew 26 percent.

Participation in the past two years has exceeded the 252 insurers that initially entered in 2014, with 305 total insurers in 2022.

Insurer participation has nearly reached its 2015 high of 310 insurers.

The individual market has continued its trajectory of increased participation by insurers, with 55 insurers entering the market in 2022.

Insurer offerings continue to increase

Over the past four years, product offerings have nearly tripled, with approximately half of that growth happening between 2021 and 2022 alone.

In-market growth represented about 50 percent of total product growth between 2021 and 2022, accounting for a larger share of total growth than during any of the previous four years, highlighting insurers’ strategy of competing on the number and variety of offerings in the market.

50% of 2021–22 growth driven by in-market growth

Insurer types generally increased their participation

Participation by number of insurers is approaching its 2015 peak and has increased for four years straight.

Tech-enabled and Medicaid plans, such as Oscar and Bright Health, have seen substantial growth since 2014, contributing the most to overall participation growth, while other types have declined or stayed relatively stagnant.

Conversely, participation among Consumer Operated and Oriented Plan (CO-OP) insurers has largely declined since 2016.

Tech-enabled and Medicaid plans have contributed the most to overall participation growth since 2014.

Growth in some plan types comes at the expense of others

Since 2014, participation of exclusive provider organization (EPO) plans has quadrupled to 36 percent as a share of all plan type offerings.

Preferred provider organization (PPO) plans have sharply declined by 36 percentage points in the same time frame.

Health maintenance organization (HMO) plans have grown steadily since 2014 as a share of plan types, displacing PPO offerings.

82% of all plan type offerings are managed-care plans.

Plan tier options are evolving

Silver and bronze plans account for the majority of market offerings in 2022, representing 75 percent of available options nationally.

Bronze plans have seen the most growth of any tier type since the creation of the market.

Catastrophic and platinum plans, conversely, have gradually become less available since 2014.

37% of 2022 plan type offerings are bronze, which have seen the most growth of any tier type.

Accessibility by insurer type

Blues have been the most accessible insurer type each year since 2014, with at least 80 percent of consumers consistently having such a plan available to them.

Medicaid and tech-enabled insurers have been narrowing the accessibility gap since around 2018 , with about 75 percent and 50 percent of consumers, respectively, having access to such a plan in 2022.

CO-OP plans have precipitously become less available to consumers since 2017 as these insurers have reduced their participation in the market overall.

Consumer access has grown as newer, tech-enabled insurers bring greater choice to the market.

In 2022, fewer counties offered only one plan

Residents in many areas of the country that had access to just a single insurer in the individual market in 2018 (52 percent of counties) can now choose between multiple insurers.

Counties with high insurer participation (defined as more than five insurers) remain in the minority.

94% of counties have more than one insurer participating in the market.

Well more than half of consumers can choose from five or more plans

Counties with one participating insurer account for just 2 percent of the overall consumer population in 2022, down from one-quarter of consumers in 2018.

The proportion of consumers with access to five or more insurers jumped 17 percentage points from 44 percent in 2021 to 61 percent in 2022, indicating that the relatively few counties with such high participation levels still represent a majority of the market population.

61% of consumers in 2022 have access to five or more insurers.

Plans are becoming increasingly affordable

Largely stable or reduced gross premiums in 2022, combined with enhanced subsidies from the American Rescue Plan Act, suggest that many enrollees faced a meaningfully more predictable out-of-pocket cost burden than in prior years.

Gold plans in particular saw average price reductions in 2022—the largest reductions of any plan tier since the marketplace launched in 2014.

Also in 2022, platinum plans became less expensive for the first time, capping off years of decelerating price increases.

Premiums remained largely stable or saw reductions in 2022, and consumer cost burden was reduced by additional subsidies created by the American Rescue Plan Act.

Price changes varied by insurer type

Consumers in CO-OPs and Medicaid plans were most likely to see decreases in premiums (in the lowest-priced silver plan), while Blues consumers were most likely to see premium increases.

Enrollees in national plans were more than two times as likely to be exposed to premium increases of more than 7.5 percent (in the lowest-priced silver plan).

15% of enrollees in national plans were exposed to a significant increase (7.5 percent or more) in premiums.

Price changes by insurer type in 2022

CO-OPs experienced the most significant price decreases but have the lowest insurer presence in the market by far.

Tech-enabled plans offered substantial price decreases while also maintaining a meaningful presence in the market.

Blue and regional plans were the only plans with median price increases across the lowest-price silver plans.

3% price increase in Blues plans.

Consumers have access to increasingly affordable plans

In 2018, tech-enabled plans such as those carried by Bright Health, Friday Health Plans, and Oscar were the most affordable silver options for 1 percent of customers. That share rose to 20 percent in 2020 and 18 percent in 2022.

Blues and Medicaid account for 36 percent of participating insurers in 2022 and are most likely to offer the lowest-priced plan option.

The percentage of consumers whose most affordable silver plan option was offered by a national or CO-OP insurer dropped from 40 percent in 2015 to 6 percent in 2021 and 4 percent in 2022.

These shifts are driven partly by participation; 33 percent of insurers at the state level in 2015 were national or CO-OP, compared with only 15 percent in 2022.

Reinsurance improves on affordability

Annual premium growth for the median county’s lowest-priced silver plan was 5.2 percent in the 15 states with a section 1332 innovation waiver, compared with 8.3 percent in the 35 states plus Washington, DC, without one.

Annual premium growth of silver plans in the 15 states with reinsurance programs was observably lower than similar plans in the 35 states (plus Washington, DC) without programs, in most cases.

Methodology

The findings in this document are based on publicly available information. The 2014–22 rates come from McKinsey’s Healthcare Insights exchange tracking tool, which includes county- and plan-level information from publicly available rate filings and from HealthCare.gov. The consumer population is defined as the population that has enrolled in any type of individual coverage, including both on- and off-exchange plans. Enrollment for 2022 is projected.

Because of data availability limitations, the analysis excludes some counties:

• 2020: Herkimer, Montgomery, Orleans, Saratoga, Schuyler, Tompkins, Washington, Wayne, and Wyoming counties in New York (combined estimated enrollment is 12,969)
• 2021: Chemung, Erie, Montgomery, Orleans, Saratoga, Washington, Wayne, and Wyoming counties in New York (combined estimated enrollment is 20,427)
• 2022: All New Mexico counties because their state-based exchange limits data scraping (combined estimated enrollment is 43,018); population not available for Kalawao County, Hawaii, in 2021 and 2022 (three estimated individual market enrollees in 2020)

Pricing: All analyses in this document are for exchange plans only; this report does not include off-exchange pricing data. For consistency, prices were obtained for a 27-year-old nonsmoking individual without family or partner coverage. To understand the premium changes that individuals face, we calculated the weighted average rate change in premiums in each relevant year for the lowest-priced silver plan in each rating area or county combination and combined those data with the distribution of individuals using individual market plans in each county, as designated by Federal Information Process Standards (FIPS) county codes.

Insurer participation: To calculate insurer participation counts, we analyzed the number of unique insurer parents that are offering plans on exchange, either by state or by county, depending on the analysis. To analyze access, we combined those data with the distribution of individuals using individual market plans in each county (designated by FIPS codes).

Plan types: Plan types reported here were taken directly from insurer rate filings and summary of benefits and coverage documents. Independent assessment of plan types was not part of the analysis presented in this document. Plan types are defined as follows:

• HMO: A health maintenance organization typically centers around a primary-care physician who acts as gatekeeper to other services and referrals; it usually provides no coverage for out-of-network services, except in emergency or urgent-care situations.
• EPO: An exclusive provider organization is similar to an HMO. It usually provides no coverage for any services delivered by out-of-network providers or facilities except in emergency or urgent-care situations; however, it generally does not require members to use a primary-care physician for in-network referrals.
• PPO: A preferred provider organization typically allows members to see physicians and get services that are not part of a network, but out-of-network services often require a higher copayment.
• POS: A point-of-service plan is a hybrid of an HMO and a PPO; it is an open-access model that may assign members to a primary-care physician and usually provides partial coverage for out-of-network services.

Insurer types are defined as follows:

• Blue: A Blue Cross Blue Shield payer
• Consumer Operated and Oriented Plan (CO-OP): A recipient of federal CO-OP grant funding that was not a commercial payer before 2014
• Medicaid: An insurer that offered only Medicaid insurance prior to 2014
• National: A commercial payer with a presence on exchanges
• Provider: An insurer that also operates as a provider or health system
• Regional or local: A commercial payer with a presence typically in a single state, but may be in multiple states
• Tech-enabled: Any payer from the parent companies Bright Health, Friday Health Plans, or Oscar

Stephanie Carlton is a partner in McKinsey’s Dallas office; Mike Lee is a consultant in the Washington, DC, office; and Arjun Prakash is a consultant in the New York office.

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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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• 9. Maeda A, Araujo E, Cashin C, Harris J, Ikegami N, Reich MR. Universal Health Coverage for Inclusive and Sustainable Development: A Synthesis of 11 Country Case Studies. The World Bank; 2014. https://doi.org/10.1596/978-1-4648-0297-3
• 10. Jowett M, Kutzin J. Raising revenues for health in support of UHC: strategic issues for policy makers. world Health Organization. Geneva; 2015. Report No.: 1. https://doi.org/10.1080/13545701.2015.1088658
• 12. Wang H, Switlick K, Ortiz C, Zurita B, Connor C. Health Insurance Handbook. The World Bank; 2011. https://doi.org/10.1596/978-0-8213-8982-9
• 21. Giedion U, Alfonso EA, Díaz Y, Andrés Alfonso E, Díaz Y. The Impact of Universal Coverage Schemes in the Developing World: A Review of the Existing Evidence. Univers Heal Cover Stud Ser (UNICO), No 25. Washington DC: World; 2013;
• 31. A System of Health Accounts 2011. OECD; 2017. https://doi.org/10.1787/9789264270985-en
• 34. Cochrane . Cochrane handbook for systematic reviews of interventions. Higgins JPT, Green SE, editors. Wiley-Blackwell; 2008.
• 37. Sheth K. Evaluating Health-Seeking Behavior, Utilization of Care, and Health Risk: Evidence from a Community Based Insurance Model in India. 2014. Report No.: 36.
• 105. Wooldridge JM. Introductory Econometrics: A Modern Approach. Fifth Inte. Mason, Ohio: South-Western Cengage Learning; 2013.
• 111. World Health Organization. World Health Statistics 2016: Monitoring health for the SDGs. WHO. Geneva: World Health Organization; 2017.

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6 tips to help you pick the right health insurance plan

Selena Simmons-Duffin

If you're buying health insurance outside a job-based plan, you're in luck this fall. After years of cutbacks and — some say sabotage — of the Affordable Care Act during the Trump administration, the Biden administration is pulling out the stops to help people find good health plans on HealthCare.gov right now -- the open enrollment period starts this week. You will have more time to sign up, more free help choosing a plan, and a greater likelihood you'll be eligible for subsidies to help keep down the costs of a health plan you buy via the ACA marketplace.

Still, picking health insurance can be hard work, even if you're choosing a plan through your employer. There are a lot of confusing terms, and the process forces you to think hard about your health and your finances. Plus you have to navigate all of it on a deadline, often with only a few-week period to explore your options and make decisions.

Applying for health insurance doesn't have to be confusing. Here's a handy glossary

Whether you're aging out of your parent's plan and picking one for the first time, or you're in a plan that no longer works for you and you're ready to switch things up, or you're uninsured and want to see if you have any workable options, there's good news. Asking yourself a few simple questions can help you zero in on the right plan from all those on the market.

Here are some tips on where to look and how to get trustworthy advice and help if you need it.

Tip #1: Know where to go

It's not always obvious where to look for health insurance. "In this country it is a truly wacky patchwork quilt of options," says Sabrina Corlette , who co-directs the Center on Health Insurance Reform at Georgetown University.

If you're 65 or older, you're eligible for Medicare . It's a federally run program — the government pays for much of your health care. You might also be eligible if you have certain disabilities. For those already enrolled in Medicare or in a Medicare Advantage plan , the open enrollment period to switch up your supplemental health and prescription drug plans for 2022 runs through Dec. 7 this year .

For those under age 65, Corlette says, "the vast majority of us get our coverage through our employer. The employer typically will cover between 70% and 90% of your premium costs, which is pretty nice." Check with your supervisor or your company's human resources department to find out what, if any, plans are available to you through your job.

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12 holdout states haven't expanded medicaid, leaving 2 million people in limbo.

Then there's Medicaid , the health insurance program for people with low incomes, that covers around 80 million people — nearly one in four Americans. It's funded by both the federal and state governments, but run by each state, so whether you're eligible depends on where you live.

For practically everyone else, the place to go is Healthcare.gov , where you can shop for insurance in the marketplaces created by the Affordable Care Act, also known as Obamacare.

This is where you look for health insurance if you don't fit any of the categories we mentioned previously, Corlette says — if, for example, "your employer doesn't offer you any coverage; you're not eligible for Medicare because you're not old enough; and you're not poor enough for Medicaid. You can go to the marketplaces, apply for financial help depending on your income, and choose a plan there."

Tip #2: Overwhelmed by the options? To help you choose, think about what's predictable about your health

If you're basically healthy and picking from one or two plan options through your job, the choice may be pretty simple. You might just ask your coworkers what they like, sign up through an online benefits portal, and call it a day.

If you're shopping in the Affordable Care Act marketplaces, however, the number of choices can feel overwhelming at first. In Austin, Texas, "we had 76 plans to review with clients," says Aaron DeLaO, director of health initiatives with Foundation Communities .

Even with dozens of options, you can narrow things down with some basic questions, DeLaO says. First, ask yourself: "Do you [just] want insurance for that catastrophic event that might happen, or do you know you have a health issue now that you're going to need ongoing care for?"

Despite ACA Coverage Gains, Millions Still Suffer 'Catastrophic' Health Care Costs

If you're pretty healthy, any of a variety of plans might work. But if you or your spouse or dependent family member has particular ongoing health needs (such as an underlying medical condition, for example, or plans to undergo fertility treatments in 2022 or the need to see a particular medical specialist), that information can be really useful in helping you narrow the field to your best health insurance choice. "If there's a plan that doesn't have your provider or your medications in-network, those can be eliminated," he says.

Sometimes you can enter in your medications or doctors' names while you search for plans online to filter out plans that won't cover them. You can also just call the insurance company and ask: Is my provider in-network for this plan I'm considering? Is my medicine on the plan's formulary (the list of medications an insurance plan will cover)?

There are also two major different types of plans to consider . "You may have a choice between what's called an HMO or a PPO," says Corlette. A Health Maintenance Organization tends to have a strict network of providers — if you see a provider outside of the network, the costs are all on you. A Preferred Provider Organization "will give you a lot broader choice of providers — it might be a little bit more expensive to see than an out-of-network provider, but they'll still cover some of that cost," she explains.

Tip #3: Learn what a few of those wonky health insurance terms mean

How much can you afford to pay for health insurance every month? In order to compare the true overall cost of health plans and figure out which one might work best within your budget, you need to get familiar with several important insurance terms — words like premium, cost-sharing, deductible and copay.

Luckily, we made a handy health insurance glossary just for you.

Insurance companies use these different types of charges — the premium vs. the deductible , for example — sort of like dials to keep their own costs manageable. A basic plan they sell might dial down the monthly premium on a particular plan, so it looks inexpensive. But that same plan might have a high, "dialed up" deductible of, say, $6,000 — meaning you'll have to spend $6,000 out of your own pocket on health services each year before your insurance begins to pay its portion of the cost. If you picked that plan, you'd be betting you won't have to use a lot of health services, and so would only have to worry about your — hopefully affordable — premiums, and the costs of a few appointments.

Health Insurance For $10 Or Less A Month? You May Qualify For New Discounts

If you have a chronic medical condition or are simply more risk averse, you might instead choose a plan that has dialed up the amount of the premium. You'll be forking over quite a bit more than for the other plan every month, but your costs will be more predictable — you'll likely have a lower deductible and lower coinsurance rate. That way, you can go to a lot of appointments and pick up a lot of prescriptions and still have manageable monthly costs.

Which plans are available and affordable to you will vary a lot depending on where you live, your income and who's in your household and on your insurance policy. With the pandemic, Congress passed new temporary funding to cover more out-of-pocket costs for people — depending on your income, you may qualify for plans with premiums of $10 or less per month on HealthCare.gov or onyour state's ACA insurance exchange.

Tip #4: Get trustworthy professional help — for free

Still feeling overwhelmed with all the ACA choices? You're in luck. There is free, impartial professional help available to help you choose and enroll in a plan. Just put in your zip code at Healthcare.gov/localhelp and look for an "assister" — a person also referred to as a health care navigator on some state websites."

Aaron DeLaO is one such navigator, and notes that he and his fellow guides don't work on commission — they're paid by the government. "We're not contracted with insurance agencies," he says. "We do it completely autonomously, impartially. It's about what's best for the consumer."

In 2021, the Biden administration quadrupled the number of navigators ahead of open enrollment. (Funding for the program had been severely cut by the Trump administration .)

Insurance brokers can be helpful, too, says Corlette. "Brokers do get commissions, but in my experience, the good brokers want repeat customers and that means happy customers," she says. To find a good broker, she advises, "go through either Healthcare.gov or your local state department of insurance to find somebody that's licensed and in good standing."

Tip #5: Beware too-good-to-be-true plans sold online

The internet can be a scary place. Corlette says she warns people: Don't put your contact information in health insurance interest forms on random websites or click on online ads for insurance!

The plans that tend to crop up when you Google "I need health insurance" can seem appealing because they're often very cheap — but they might also be "short term" plans that don't cover basic things like prescription drugs or annual check ups . Many experts warn that this type of plan is not a very good deal.

Buyer Beware: New Cheaper Insurance Policies May Have Big Coverage Gaps

"Unfortunately, there are a lot of con artists out there who take advantage of the fact that people recognize health insurance is something that they should get," says Corlette. She tells people: "Just go straight to Healthcare.gov. No matter what state you live in, you can go through that portal." Any plan you find there will cover the ACA's 10 essential benefits — such as free preventive care and hospital coverage.

Tip #6: Know your deadlines

Usually you only get a few weeks in the fall to sign up. This year, the sign up period for the HealthCare.gov marketplace plans that go into effect in January 2022 starts Nov. 1, 2021 and runs until Jan.15, 2022. If you're signing up for an employer-sponsored plan or Medicare, the deadlines will be different, but probably also in the fall. For Medicaid, you can enroll at any time of the year.

DeLaO, the health navigator, says even if you're already enrolled in a plan that seems fine and it's tempting to just let it automatically renew, it's always a good idea to annually check what else is available.

"Are you eligible for additional subsidies to lower the cost of your monthly premium?" he says. "Is there a plan that — with those increased subsidies — you can now get a silver plan as opposed to a bronze plan, which lowers your deductible [and] your copayments?"

Figuring out the right plan for you doesn't have to require a huge time commitment, he says. His team aims to get people in and out — enrolled in a plan — in an hour and a half. And those appointments don't have to be in person — customers can get help by phone and can often do everything they need to do to get signed up virtually.

Though signing up for health insurance can be confusing at first, it's also very important — for your wallet and your health. Hang in there — and know there are people out there eager to help you make sure you get covered.

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Financial Burden of Health Care in the Privately Insured US Population

• 1 Section of Health Policy and Equity, Richard A. and Susan F. Smith Center for Outcomes Research, Beth Israel Deaconess Medical Center, Boston, Massachusetts
• 2 Department of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts
• 3 Harvard Medical School, Boston, Massachusetts
• 4 Cardiovascular Division, Department of Medicine, Washington University School of Medicine, St Louis, Missouri
• 5 Center for Advancing Health Services, Policy & Economics Research, Washington University, St Louis, Missouri
• 6 Department of Health Policy and Management, Harvard School of Public Health, Boston, Massachusetts
• Editor's Note Health Care Expenses and Household Resources for Families With Low Income Mitchell H. Katz, MD; Raegan W. Durant, MD, MPH; Deborah Grady, MD, MPH JAMA Internal Medicine

Improving health care affordability is a national priority, including for the nearly 180 million individuals with private insurance coverage who have experienced increased premiums and decreased benefits (eg, increasing copayments and deductibles). However, little is known about how changes in privately insured families’ contributions to insurance premiums and out-of-pocket spending have affected the financial burden of health care over the past 2 decades. 1 This issue is particularly salient for those with low incomes, who are more susceptible to debt, bankruptcy, and worse health outcomes due to poverty. 1 , 2 Understanding changes in the financial burden of health care has important implications for patients and policymakers, who have made addressing care affordability a priority.

• Editor's Note Health Care Expenses and Household Resources for Families With Low Income JAMA Internal Medicine

Read More About

Shashikumar SA , Zheng Z , Joynt Maddox KE , Wadhera RK. Financial Burden of Health Care in the Privately Insured US Population. JAMA Intern Med. Published online May 28, 2024. doi:10.1001/jamainternmed.2024.1464

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How to Choose Health Insurance: Your Step-by-Step Guide

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You typically have a limited amount of time to choose the best health insurance plan for your family, but rushing and picking the wrong coverage can be costly. Here’s a start-to-finish guide to help you find affordable health insurance, whether it’s through a state or federal marketplace or through an employer.

Step 1: Choose your health insurance marketplace

How you shop for health insurance will depend on what’s available to you.

If your employer offers health insurance

Most people with health insurance get it through an employer. If your employer offers health insurance, you won’t need to use the government insurance exchanges or marketplaces, unless you want to look for an alternative plan. But plans in the marketplace are likely to cost more than plans offered by employers. This is because most employers pay a portion of workers’ insurance premiums.

If your employer doesn’t offer health insurance

Shop your state’s online marketplace, if available, or the federal marketplace to find the plan that's best for you. Start by going to HealthCare.gov and entering your ZIP code. You’ll be sent to your state’s exchange, if there is one. Otherwise, you’ll use the federal marketplace.

You can also purchase health insurance through a private exchange or directly from an insurer. If you choose these options, you won’t be eligible for premium tax credits, which are income-based discounts on your monthly premiums.

Step 2: Compare types of health insurance plans

You’ll encounter some alphabet soup while shopping for the best health insurance plan. The most common types of health insurance policies are HMOs, PPOs, EPOs and POS plans. What you choose will help determine your out-of-pocket costs and which doctors you can see.

Comparing health insurance plans: HMO vs. PPO vs. EPO vs. POS

Look for a summary of benefits.

Online marketplaces usually provide a link to the summary of benefits, which explains all the plan's costs and coverages. A provider directory, which lists the doctors and clinics that participate in the plan’s network, should also be available. If you’re going through an employer, ask your workplace benefits administrator for the summary of benefits.

Weigh your family’s medical needs

Look at the amount and type of treatment you’ve received in the past. Though it’s impossible to predict every medical expense, being aware of trends can help you make an informed decision.

Consider whether you want a referral system of care

Plans that require referrals.

If you choose an HMO or POS plan, which require referrals, you typically must see a primary care physician before scheduling a procedure or visiting a specialist. Because of this requirement, many people prefer other plans. However, by limiting your choices to providers they've contracted with, HMOs do tend to be the cheapest type of health plan.

A benefit of HMO and POS plans is that there’s one primary doctor managing your overall medical care, which can result in greater familiarity with your needs and continuity of medical records. If you do choose a POS plan and go out-of-network, make sure to get the referral from your doctor ahead of time to reduce out-of-pocket costs. (You cannot go out-of-network with an HMO unless it's an emergency.)

Plans that don't require referrals

If you would rather see specialists without a referral, you might be happier with an EPO or a PPO. (EPOs typically don't require a referral, but some do, so read the fine print.) An EPO may help keep costs low as long as you find providers in-network; this is more likely to be the case in a larger metro area. A PPO might be better if you live in a remote or rural area with limited access to doctors and care, as you may be forced to go out-of-network.

What about an HDHP with a health savings account?

A high-deductible health plan, or HDHP, can be any one of the types of health insurance above — HMO, PPO, EPO or POS — but follows certain rules in order to be “HSA-eligible.” These HDHPs typically have lower premiums, but you pay higher out-of-pocket costs, especially at first. They're the only plans that qualify you to open a health savings account, or HSA, which is a tax-advantaged account you can use to pay health care costs. If you’re interested in this arrangement, be sure to learn the ins and outs of HSAs and HDHPs first .

» MORE: HSA vs. FSA: Differences and how to choose

Step 3: Compare health plan networks

Your health insurance “network” refers to the medical providers and facilities your health plan has contracted with to provide your care.

Why does the network matter?

Costs are lower when you go to an in-network doctor because insurance companies negotiate lower rates with in-network providers. When you go out-of-network, those doctors don’t have agreed-upon rates, and you’re typically on the hook for a higher portion of the cost.

Do you have preferred doctors?

If you want to keep seeing your current medical providers, make sure they’re in the provider directories for the plan you’re considering. You can also ask your doctors directly if they take a particular health plan.

Is a large network important?

If you don’t have a preferred doctor, it's probably a good idea to look for a plan with a large network so you have more choices. A larger network is especially important if you live in a rural community, since it'll give you better odds of finding a local doctor who takes your plan.

Eliminate any plans that don’t have local in-network doctors, if possible; you may also want to eliminate those that have very few provider options compared with other plans.

Step 4: Compare out-of-pocket costs

Out-of-pocket costs (that is, costs other than your monthly premium) are another key consideration. A plan’s summary of benefits should clearly lay out how much you’ll have to pay out of pocket for services. The federal online marketplace offers snapshots of these costs for comparison, as do many state marketplaces.

Know your health insurance terms

It’s useful to know the definitions of some key health insurance terms:

Copay: This is a flat fee (such as $20) that you pay each time you receive a health care service or procedure.

Coinsurance: This is the percentage (such as 20%) of a medical charge that you pay; the rest is covered by your health insurance plan.

Deductible: This is the amount you pay for covered medical care before your insurance starts paying.

Out-of-pocket maximum: This is the most you’ll pay in one year, out of your own pocket, for covered health care. Once you reach this maximum, your insurance pays the rest.

Out-of-pocket costs: These are all costs above a plan's premium that you must pay, including copays, coinsurance and deductibles.

Premium: This is the monthly amount you pay for your health insurance plan.

» MORE: Understanding copays, coinsurance and deductibles

Higher premiums, more coverage

In general, the higher your premium, the lower your out-of-pocket costs such as copays and coinsurance (and vice versa). A plan that pays a higher portion of your medical costs, but has higher monthly premiums, may be better if:

You see a primary physician or a specialist frequently.

You frequently need emergency care.

You take expensive or brand-name medications on a regular basis.

You're expecting a baby, plan to have a baby or have small children.

You have a planned surgery coming up.

You’ve been diagnosed with a chronic condition such as diabetes or cancer.

Lower premiums, higher out-of-pocket

A plan with higher out-of-pocket costs and lower monthly premiums might be the better choice if:

You can’t afford the higher monthly premiums for a plan with lower out-of-pocket costs.

You're in good health and rarely see a doctor.

» MORE: What is a copay?

Step 5: Compare benefits

By this step, you'll likely have your options narrowed down to just a few plans. Here are some things to consider next:

Check the scope of services

Go back to that summary of benefits to see if any of the plans cover a wider scope of services. Some may have better coverage for things like physical therapy, fertility treatments or mental health care, while others might have better emergency coverage.

If you skip this quick but important step, you could miss out on a plan that’s much better suited to you and your family.

Address any lingering questions

In some cases, calling the plans’ customer service line may be the best way to get your questions answered. Write your questions down ahead of time, and have a pen or electronic device handy to record the answers.

Here are some examples of what you could ask:

I take a specific medication. How is that medication covered under this plan?

Which drugs for my condition are covered under this plan?

What maternity services are covered?

What happens if I get sick while traveling abroad?

How do I get started signing up, and what documents will I need?

Don’t forget to discontinue your old plan, if you have one, before the new one starts.

Summary: How to choose health insurance

Here’s a quick recap:

Go to your online health insurance marketplace and view all of your plan options.

Decide which type of health insurance plan — HMO, PPO, EPO or POS — is best for you and your family, and whether you want an HSA-eligible plan.

Eliminate plans that exclude your preferred doctor or that don't have local doctors in the provider network.

Determine whether you want more health coverage and higher premiums, or lower premiums and higher-out-of-pocket costs.

Make sure any plan you choose will pay for your regular and necessary care, like prescriptions and specialists.

On a similar note...

The independent source for health policy research, polling, and news.

KFF’s Health Policy 101

Published: May 28, 2024 Edited by Dr. Drew Altman

Introduction

• The Affordable Care Act
• Employer-Sponsored Health Insurance
• The Uninsured Population and Health Coverage
• Health Care Costs and Affordability
• The Regulation of Private Health Insurance
• Health Policy Issues in Women’s Health
• Race, Inequality, and Health
• International Comparison of Health Systems
• The U.S. Government and Global Health
• Congress, the Executive Branch, and Health Policy
• The Politics of Health Care and the 2024 Election

Dr. Drew Altman, president and chief executive officer of KFF

I have long planned to create an online resource or mini “textbook” for faculty and students interested in health policy. One of the stumbling blocks is that there is no agreed upon definition of “health policy.”

We took a stab at it of sorts at KFF in our headquarters when we created a physical timeline—as shown in the photo above—of the central events in the history of our field on a wall in our headquarters in San Francisco. But, of course, you can’t all visit our offices to see our health policy history wall—and many of you may have quibbles if you did.

For us at KFF, our definition reflects our views and what we do: Health policy centers around, well policy–what the government does, and public programs like Medicare, Medicaid, and the ACA, and heavily emphasizes financing and coverage.

We also focus relentlessly on people, not health professionals and health care institutions (I have never been fond of the word “provider”). Others have a more expansive definition and that’s fine. What I ultimately settled on doing is far simpler: Organizing the basic materials we have on the issues we work on, recognizing that they do not represent every topic of interest to the faculty and students we hope to assist.

The result is the following chapters. We will add chapters over time as we develop them. Our organization changes to play our role as an independent source of analysis, polling, and journalism on national health issues, and as that happens, we will add more content on subjects not covered in this first installment. We will also add chapters as we get feedback from you. And we will update the “101” at least annually as data and circumstances change.

Let me know if this is helpful and how it can be improved. You can reach me at [email protected] .

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NCBI Bookshelf. A service of the National Library of Medicine, National Institutes of Health.

Institute of Medicine (US) Committee on the Consequences of Uninsurance. Care Without Coverage: Too Little, Too Late. Washington (DC): National Academies Press (US); 2002.

Care Without Coverage: Too Little, Too Late.

• Hardcopy Version at National Academies Press

3 Effects of Health Insurance on Health

This chapter presents the Committee's review of studies that address the impact of health insurance on various health-related outcomes. It examines research on the relationship between health insurance (or lack of insurance), use of medical care and health outcomes for specific conditions and types of services, and with overall health status and mortality. There is a consistent, positive relationship between health insurance coverage and health-related outcomes across a body of studies that use a variety of data sources and different analytic approaches. The best evidence suggests that health insurance is associated with more appropriate use of health care services and better health outcomes for adults.

The discussion of the research in this chapter is organized within sections that encompass virtually all of the research literature on health outcomes and insurance status that the Committee identified. The chapter sections include the following:

• Primary prevention and screening services
• Cancer care and outcomes
• Chronic disease management, with specific discussions of diabetes, hypertension, end-stage renal disease (ESRD), HIV disease, and mental illness
• Hospital-based care (emergency services, traumatic injury, cardiovascular disease)
• Overall mortality and general measures of health status

The Committee consolidated study results within categories that reflect both diseases and services because these frameworks helped in summarizing the individual studies and subsumed similar research structures and outcome measures. Older studies and those of lesser relevance or quality are not discussed within this chapter devoted to presenting study results and reaching Committee findings. However, all of the studies reviewed are described briefly in Appendix B .

The studies presented in some detail in this chapter are those that the Committee judged to be both methodologically sound and the most informative regarding health insurance effects on health-related outcomes. 1 Most studies report a positive relationship between health insurance coverage and measured outcomes. However, all studies with negative results that are contrary to the Committee's findings are presented and discussed in this chapter. Appendix B includes summaries of the complete set of studies that the Committee reviewed.

In the pages that follow, the Committee's findings introduce each of the five major sections listed above and also some of the subsections under chronic disease and hospital-based care. All of the Committee's specific findings are also presented together in Box 3.12 in the concluding section of this chapter. These findings are the basis for the Committee's overall conclusions in Chapter 4 .

Specific Committee Findings. Uninsured adults are less likely than adults with any kind of health coverage to receive preventive and screening services and less likely to receive these services on a timely basis. Health insurance that provides more extensive (more...)

• PRIMARY PREVENTION AND SCREENING SERVICES

Finding: Uninsured adults are less likely than adults with any kind of health coverage to receive preventive and screening services and less likely to receive these services on a timely basis. Health insurance that provides more extensive coverage of preventive and screening services is likely to result in greater and more appropriate use of these services.

Finding: Health insurance may reduce racial and ethnic disparities in the receipt of preventive and screening services.

These findings have important implications for health outcomes, as can be seen in the later sections on cancer and chronic diseases. For prevention and screening services, health insurance facilitates both the receipt of services and a continuing care relationship or regular source of care, which also increases the likelihood of receiving appropriate care.

Insurance benefits are less likely to include preventive and screening services ( Box 3.2 ) than they are physician visits for acute care or diagnostic tests for symptomatic conditions. However, over time, coverage of preventive and screening services has been increasing. In 1998, about three-quarters of adults with employment-based health insurance had a benefit package that included adult physical examinations; two years later in 2000, the proportion had risen to 90 percent (KPMG, 1998; Kaiser Family Foundation/HRET, 2000). Yet even if health insurance benefit packages do not cover preventive or screening services, those with health insurance are more likely to receive these recommended services because they are more likely to have a regular source of care, and having a regular source of care is independently associated with receiving recommended services (Bush and Langer, 1998; Gordon et al., 1998; Mandelblatt et al., 1999; Zambrana et al., 1999; Cummings et al., 2000; Hsia et al., 2000; Breen et al., 2001). The effect of having health insurance is more evident for relatively costly services, such as mammograms, than for less costly services, such as a clinical breast exam (CBE) or Pap test (Zambrana et al., 1999; Cummings et al., 2000; O'Malley et al., 2001).

Screening Services. The U.S. Preventive Services Task Force (USPSTF) recommends screening for the following conditions in the general adult population under age 65: cervical cancer (above age 18), breast and colorectal cancer (above age 50), hypertension (more...)

According to several large population surveys conducted within the past decade, adults without health insurance are less likely to receive recommended preventive and screening services and are less likely to receive them at the frequencies recommended by the United States Preventive Services Task Force than are insured adults. 2 The 1992 National Health Interview Survey (NHIS) documented receipt of mammography, CBE, Pap test, fecal occult blood test (FOBT), sigmoidoscopy, and digital rectal exam by adults under 65 (Potosky et al., 1998). Those with no health insurance had significantly lower screening rates compared to those with private coverage and compared to those with Medicaid for every service except sigmoidoscopy. The odds ratios (ORs) for receiving a screening service if uninsured compared with having private health insurance ranged from 0.27 for mammography to 0.43 for Pap test. 3

The 1998 NHIS found that, although rates of screening at appropriate intervals had increased generally over the preceding decade, they remained substantially lower for uninsured adults than for those with any kind of health insurance (Breen et al., 2001). 4 In a multivariable analysis that adjusted for age, race, education, and a regular source of care, uninsured adults were significantly less likely than those with any kind of coverage to receive a Pap test, mammography, and colorectal screening (FOBT or sigmoidoscopy) (ORs ranged from 0.37 to 0.5) (Breen et al., 2001). The study reported a strong relationship between having a regular source of care and timely receipt of these screening services in addition to the relationship between health insurance and screening.

Studies using other national samples report results consistent with those of the NHIS. A study of more than 31,000 women between ages 50 and 64 who responded to telephone surveys conducted between 1994 and 1997 about their receipt of mammograms, Pap smears, and colorectal cancer screening (either FOBT or sigmoidoscopy) found that uninsured women were significantly less likely to have received these tests than were women with private prepaid plan insurance (ORs ranging from 0.30 to 0.50) (Hsia et al., 2000). This study also found a strong relationship between having a regular source of care and receipt of screening services. Health insurance was an independently significant predictor. Another study based on several years of the Behavioral Risk Factor Surveillance System (BRFSS) for older adults (55 through 64) found that uninsured men and women were much less likely than their insured counterparts to receive cancer or heart disease screening tests and also much less likely to have a regular source of care (Powell-Griner et al., 1999; see Table 4.1 ).

Disparities Among Population Groups

A review of the literature on the interaction of race, ethnicity, and socioeconomic status (SES) with health insurance, concluded that health insurance makes a positive contribution to the likelihood of receiving appropriate screening services, although racial and ethnic disparities persist independent of health insurance (Haas and Adler, 2001). Studies of the use of preventive services by particular ethnic groups, such as Hispanics and African Americans, find that health insurance is associated with increased receipt of preventive services and increased likelihood of having a regular source of care, which improves one's chances of receiving appropriate preventive services (Solis et al., 1990; Mandelblatt et al., 1999; Zambrana et al., 1999; Wagner and Guendelman, 2000; Breen et al., 2001; O'Malley et al., 2001).

Breen and colleagues (2001) modeled the expected increase in screening rates for different ethnic groups if they were to gain health insurance coverage and a regular source of care. This “what-if” model suggests that those groups for whom screening rates are particularly low (e.g., receipt of mammography by Hispanic women, colorectal screening of African-American men) would make the largest gains (an 11 percentage-point increase in mammography rates for Hispanic women [to 77 percent] and a 5 percentage-point increase in colorectal screening for African-American men [to 31 percent] (Breen et al., 2001).

Extensiveness of Insurance Benefits

The type of health insurance and the continuity of coverage have also been found to affect receipt of appropriate preventive and screening services. Faulkner and Schauffler (1997) examined receipt of physical examinations, blood pressure screening, lipid screening for detection of cardiovascular disease, Pap test, CBE, and mammography and identified a positive and statistically significant “dose– response” relationship between the extent of coverage for preventive services (e.g., whether all such services, most, some, or none were covered by health insurance). Insurance coverage for preventive care increased men's receipt of preventive services more than it did that of women. Men with no coverage for preventive services were much less likely than men with complete coverage for such services to receive them (ORs for receipt of specific services ranged from 0.36 to 0.56). Women with no preventive services coverage also received fewer of these services than did women with full coverage for them (ORs for specific services ranged from 0.5 to 0.83) (Faulkner and Schauffler, 1997).

Ayanian and colleagues (2000) used the 1998 BRFSS data set to analyze the effect of length of time without coverage on receipt of preventive and screening services for adults between ages 18 and 65. Those without coverage for a year or longer were more likely than those uninsured for less than one year to go without appropriate preventive and screening services. For every generally recommended service (mammography, CBE, Pap smear, FOBT, sigmoidoscopy, hypertension screening, and cholesterol screening), the longer-term uninsured were significantly less likely than persons with any form of health insurance to receive these services (Ayanian et al., 2000).

Negative Findings

In the Committee's review, the one study that did not find a positive effect of insurance coverage compared mammography use among clients of various sites of care in Detroit, Michigan: two health department clinics, a health maintenance organization (HMO), and a private hospital (Burack et al., 1993). This study found no significant differences among women according to their health insurance status but did find that patients with more visits annually for any service (seven or more) were more likely to receive mammography. All women in this study had access to a primary care provider and, in the case of uninsured women, to clinics with the mission of serving the uninsured. These factors may explain why uninsured women had mammography rates as high as those of women with insurance.

• CANCER CARE AND OUTCOMES

Finding: Uninsured cancer patients generally have poorer outcomes and are more likely to die prematurely than persons with insurance, largely because of delayed diagnosis. This finding is supported by population-based studies of breast, cervical, colorectal, and prostate cancer and melanoma.

The studies analyzing health-related outcomes for cancer patients provide some of the most compelling evidence for the effect of health insurance status on health outcomes ( Box 3.3 ). This evidence comes from research based on area or statewide cancer registries, which provide large numbers of observations and reflect almost all cases occurring in a geographic region. Multivariable data analysis is used to determine the independent effects of health insurance, by controlling for demographic, SES, and clinical differences among study subjects.

Cancer. Cancers of all kinds have an overall incidence nationally of 400 cases per 100,000 people each year. More than 8.9 million Americans alive today have a history of cancer. Cancers account for approximately 550,000 deaths each year in the United (more...)

In addition to receiving fewer cancer screening services, uninsured adults are at greater risk of late-stage, often fatal cancer. Early diagnosis frequently improves the chances of surviving cancer. Generally, in studies examining the stage at which cancer is diagnosed, those with private health insurance have the best outcomes and those with no insurance have the worst (i.e., the highest proportion of late-stage diagnoses), with intermediate outcomes for Medicaid enrollees. In some studies however, the outcomes for Medicaid enrollees are comparable to those for uninsured cancer patients (Roetzheim et al., 1999). Both because of an assumption of similarity in SES between uninsured and Medicaid patients and because of small numbers of observations in the separate categories, some studies report combined results for Medicaid and uninsured patients and compare these findings with those for privately insured patients (e.g., Lee-Feldstein et al., 2000).

In studies assessing the outcomes for adults with cancer—stage of disease at diagnosis and mortality—Medicaid enrollees often do no better, and sometimes do worse, than uninsured patients. This similarity in experience between patients enrolled in Medicaid and those without any coverage may reflect the fact that uninsured persons in poor health, once they seek care, may become enrolled in Medicaid as a result of their frequent interactions with the health care system (Davidoff et al., 2001; see Box 2.1 ). Also, Medicaid enrollees tend to have discontinuous coverage and thus may have had less regular access to screening services. Consequently, persons with Medicaid at the time of a cancer diagnosis may have been without coverage for some prior period (Carrasquillo et al., 1998; IOM, 2001a; Perkins et al., 2001). For example, one study of women under 65 with Medi-Cal coverage (California's Medicaid and indigent care program) who were diagnosed with breast cancer found that, among those who had been uninsured during the year prior to their diagnosis (18 percent of all Medi-Cal enrollees), late-stage diagnosis was much more likely than among those who had been continuously enrolled for the previous 12 months (ORs of 3.9 for those who had been uninsured and 1.4 for those continuously covered by Medi-Cal, compared with all other women ages 30–64 diagnosed with breast cancer) (Perkins et al., 2001).

With this general background on the nature of the research examining health insurance status effects, the remainder of this section discusses study results for five specific cancers.

Breast Cancer

Uninsured women and women with Medicaid are more likely to receive a breast cancer diagnosis at a late stage of disease (regional or distant) and have a 30– 50 percent greater risk of dying than women with private coverage, as shown in studies based on three different state or regional cancer registries (Ayanian et al., 1993; Roetzheim et al., 1999, 2000; Lee-Feldstein et al., 2000).

In a study using the New Jersey Cancer Registry, Ayanian and colleagues (1993) identified 4,675 women 35 to 65 years of age diagnosed with breast cancer and assessed their stage of disease at diagnosis and their survival rates 4.5 to 7 years after diagnosis. The authors found that uninsured women were significantly more likely than privately insured women to be diagnosed with regional or late-stage cancer, as were patients with Medicaid. After controlling for stage of disease at diagnosis and other factors, uninsured women had an adjusted risk of death 49 percent higher than that of privately insured women, and women with Medicaid had a 40 percent higher risk of death than those who were privately insured.

Using a regional cancer registry and Census data for 1987 through 1993, Lee-Feldstein and colleagues (2000) examined the stage of disease at diagnosis, treatment, and survival experience of about 1,800 northern California women under the age of 65 diagnosed with breast cancer. They found that women who were uninsured and publicly insured (primarily Medicaid), taken together, were twice as likely as privately insured women with indemnity coverage to be diagnosed at a late stage of disease. Over a four- to ten-year follow-up, uninsured and publicly insured women had higher risks of death from both breast cancer (42 percent higher) and all causes (46 percent higher) than did privately insured women with indemnity coverage. The likelihood of receiving breast-conserving surgery did not differ between these two groups.

In a review of approximately 9,800 Florida residents diagnosed with breast cancer in 1994, Roetzheim and colleagues calculated that, after controlling for age, education, income, marital status, race, and comorbidity, women without insurance were more likely to be diagnosed with late-stage disease than women with private indemnity coverage (OR = 1.43) (Roetzheim et al., 1999). Women with Medicaid had an even greater likelihood of late-stage diagnosis compared with privately insured women (OR = 1.87). In a subsequent analysis of mortality using the same registry data, the authors estimated that the relative risk (RR) of dying was 31 percent higher for uninsured women and 58 percent higher for women with Medicaid over a three to four-year follow-up period (Roetzheim et al, 2000a). Further analysis suggested that stage of disease at diagnosis and, to a lesser extent, treatment modality appeared to account for the differences in survival by insurance status. Finally, uninsured women were less likely than women with private coverage to receive breast-conserving surgery when stage at diagnosis, comorbidities, and other personal characteristics were taken into account (OR = 0.70) (Roetzheim et al., 2000a).

Cervical Cancer

Uninsured women are more likely to receive a late-stage diagnosis for invasive cervical cancer than are privately insured women. Ferrante and colleagues (2000) analyzed 852 cases of invasive cervical cancer reported in the Florida tumor registry for 1994 to determine factors associated with late-stage diagnosis. In bivariate analysis, being uninsured was associated with an increased likelihood of late-stage diagnosis (OR = 1.6). In a multivariable analysis that adjusted for age, education, income, marital status, race, comorbidities, and smoking, uninsured women were more likely to present with a late-stage cancer compared to women with private indemnity coverage, although this finding was not statistically significant (OR = 1.49, confidence interval [CI]: 0.88–2.50). The outcome for Medicaid enrollees was similar to that of privately insured women in both bivariate and multivariable analysis (Ferrante et al., 2000).

Colorectal Cancer

Uninsured patients with colorectal cancer have a greater risk of dying than do patients with private indemnity insurance, even after adjusting for differences in the stage at which the cancer is diagnosed and the treatment modality. Using the Florida cancer registry for 1994, Roetzheim and colleagues (1999) analyzed the relative likelihood of late-stage diagnosis by insurance status for more than 8,000 cases of colorectal cancer. In a multivariable analysis adjusting for sociodemographic characteristics, smoking status, and comorbidities, uninsured patients were more likely to be diagnosed with late-stage colorectal cancer than were patients with private indemnity coverage (OR = 1.67). Medicaid enrollees had a statistically insignificant greater likelihood of late-stage disease compared to patients with indemnity coverage (OR = 1.44, CI: 0.92–2.25).

A subsequent analysis of largely the same data set (9,500 cases) that adjusted for sociodemographic factors and comorbidities but not for smoking estimated the adjusted mortality risk for uninsured patients with colorectal cancer to be 64 percent greater over a three- to four-year follow-up period than that for patients covered by private indemnity plans (Roetzheim et al., 2000b). 5 Even after adjusting for stage of disease at diagnosis, the risk of death for uninsured patients was 50 percent higher than that for the privately insured, and after further adjustment for treatment modality, the risk for uninsured patients was 40 percent higher (Roetzheim et al., 2000b).

Prostate Cancer

In addition to delayed diagnosis and greater risk of death, uninsured prostate cancer patients have been found to experience a decrease in health-related quality of life after their diagnosis and during treatment, unlike publicly and privately insured patients. A study of about 8,700 cases of newly diagnosed prostate cancer reported to the Florida cancer registry in 1994 found that uninsured men were more likely to be diagnosed at a late stage of the disease than were men with private indemnity insurance (OR = 1.47) (Roetzheim et al., 1999). A study of 860 men in 26 medical practices with newly diagnosed prostate cancer evaluated their health-related quality of life (HRQOL) at three- to six-month intervals over a two-year period (Penson et al., 2001). Although uninsured men diagnosed with prostate cancer did not have a lower HRQOL at diagnosis, their HRQOL decreased over the course of their disease and treatment, in contrast to that of HMO and Medicare patients. The authors suggest that “patients undergoing aggressive treatment, which can itself have deleterious effects on quality of life, are exposed to further hardships when they do not have comprehensive health insurance upon which to support their care” (Penson et al., 2001, p. 357).

Uninsured patients, as well as Medicaid patients have been found to be more likely to be diagnosed with late-stage melanoma than are privately insured patients. Among 1,500 patients diagnosed with melanoma, uninsured patients were more likely to have late-stage (regional or distant) disease than those with private indemnity coverage (OR = 2.6) (Roetzheim et al., 1999). The small number of Medicaid patients with melanoma (13) included in this study also had a much greater chance of being diagnosed with late-stage cancer.

• CHRONIC DISEASE CARE AND OUTCOMES

Finding: Uninsured people with chronic diseases are less likely to receive appropriate care to manage their health conditions than are those who have health insurance. For the five disease conditions that the Committee examined (diabetes, cardiovascular disease, end-stage renal disease, HIV infection, and mental illness), uninsured patients have worse clinical outcomes than insured patients.

Effective management of chronic conditions such as diabetes, hypertension, HIV, and depression ( Box 3.4 ) includes not only periodic services and care from health care professionals but also the active involvement of patients in modifying their behavior, monitoring their condition, and participating in treatment regimens (Wagner et al., 1996; Davis et al., 2000). Identifying chronic conditions early and providing appropriate health care on an ongoing and coordinated basis are health care system goals that have been developed over several decades and have been continuously refined as evidence for cost-effective interventions and practices has accumulated. Maintaining an ongoing relationship with a specific provider who keeps records, manages care, and is available for consultation between visits is a key to high-quality health care, particularly for those with chronic illnesses (O'Connor et al., 1998; IOM, 2001b).

Chronic Conditions. Chronic conditions are the leading causes of death, disability, and illness in the United States, accounting for one-third of the potential life years lost before age 65 (CDC, 2000a). Almost 100 million Americans have chronic conditions. (more...)

For persons with a chronic illness, health insurance may be most important in that it enhances the opportunities to acquire a regular source of care. If someone has coverage through a private or public managed care plan, a relationship with a primary care provider may be built into the insurance. Indemnity or fee-for-service (FFS) insurance coverage also improves the chances of having a regular source of care because having the resources to pay for services is often a prerequi-site to being seen in a medical practice. Uninsured adults are much less likely to have a regular source of care and are more likely to identify an emergency department as their regular source of care than are adults with any form of coverage (Weinick et al., 1997; Cunningham and Whitmore, 1998; Zuvekas and Weinick, 1999; Haley and Zuckerman, 2000). Loss of coverage also interrupts patterns of use of health care and results in delays in seeking needed care (Burstin et al., 1998; Kasper et al., 2000; Hoffman et al., 2001). For uninsured adults under age 65, 19 percent with heart disease and 14 percent with hypertension lack a usual source of care, compared to 8 and 4 percent, respectively, of their insured counterparts (Fish-Parcham, 2001). For uninsured patients without a regular source of care or those who identify an emergency department as their usual source, obtaining care that is consistent with recognized standards for effective disease management is a daunting challenge.

Providers with a commitment to serving uninsured clients, such as local public health and hospital clinics and federally funded community health centers, have sometimes instituted special interventions and programs for the chronically ill to promote continuity of care and disease management. These innovations are critically important to the identified, chronically ill patients who routinely receive care at such clinics and centers. The efforts of these providers, however, are limited in scale by funding and service capacity relative to the high need for care within their service areas (Baker et al., 1998; Chin et al., 2000; Piette, 2000; Philis-Tsimikas and Walker, 2001). As demonstrated in the following review of studies examining the care and outcomes for patients with specific chronic conditions, those who do not have health insurance coverage of any kind fare measurably worse than their insured counterparts.

Cardiovascular Disease

Finding: Uninsured adults with hypertension or high cholesterol have diminished access to care, are less likely to be screened, are less likely to take prescription medication if diagnosed, and experience worse health outcomes.

Across the spectrum of services and the course of development of cardiovascular disease ( Box 3.5 ), uninsured adults receive fewer services and experience worse health. They are less likely to receive screening for hypertension and high cholesterol and to have frequent monitoring of blood pressure once they develop hypertension. Uninsured adults are less likely to stay on drug therapy for hypertension both because they lack a regular provider and because they do not have insurance coverage. Loss of insurance coverage has been demonstrated to disrupt therapeutic relationships and worsen control of blood pressure.

Cardiovascular Disease. “Cardiovascular disease” encompasses a variety of diseases and conditions that affect the heart and blood vessels, including hypertension (high blood pressure), heart disease, and stroke. One-quarter of all Americans (more...)

Uninsured adults are less likely to receive routine screening services for cardiovascular disease. A nationwide household survey in 1997 found that adults who had been without health insurance for one year or longer were less likely than insured adults to have received recommended hypertension screening within the previous two years (80 percent compared with 94 percent) or cholesterol screening (60 percent compared with 82 percent) (Ayanian et al., 2000). Adults who were uninsured for less than one year received these screening services at rates intermediate between those for long-term uninsured and insured adults.

Health insurance coverage is associated with better blood pressure control for lower-income persons with hypertension, according to two studies, one prospective and experimental and the other a longitudinal analysis of a cohort of patients that either lost or maintained Medicaid coverage. The prospective study, the RAND Health Insurance Experiment, found that for patients with diagnosed hypertension, patients in the plan without any cost sharing had significantly lower blood pressure than those in health plans with any form of cost sharing (an overall difference of 1.9 mm Hg) (Keeler et al., 1985). A much greater effect of cost sharing on average blood pressure was found for low-income patients than for high-income patients (3.5 mm Hg. versus 1.1 mm Hg.). Patients in the plan without cost sharing also had greater compliance with drug and behavioral therapies. These differences were attributed to more frequent contact with health providers in the free care plan (Keeler et al., 1985). 6

In the longitudinal analysis, Lurie and colleagues (1984, 1986) followed a cohort of patients at a university ambulatory care clinic for one year after some lost their Medi-Cal coverage consequent to a state policy change. At six months after loss of coverage and again at one year, hypertensive patients who lost coverage had significantly worse blood pressure than did those who remained covered by MediCal, with an average increase in diastolic blood pressure of 6 mm Hg compared with a decrease in the insured control group of 3 mm Hg after a full year (Lurie et al., 1984, 1986). The percentage of patients with diastolic blood pressure greater than 100 mm Hg increased in the group that lost coverage from 3 percent at baseline to 31 percent at six months, and then declined to 19 percent at one year, while the proportion with diastolic blood pressure > 100 mm Hg in the continuously covered control group did not change significantly over the year (Lurie et al., 1986).

Deficits in the care of uninsured persons with hypertension place them at risk of complications and deterioration in their condition. The 1987 National Medical Expenditures Survey afforded an in-depth examination of the use of antihypertensive medications by health insurance status. Uninsured persons younger than 65 who had hypertension were less likely than either those with private insurance or Medicaid to have any antihypertensive medication therapy (ORs = 0.62 and 0.44, respectively) (Huttin et al., 2000). 7 An analysis of the third round of the National Health and Nutrition Examination Survey (NHANES), with data on 40,000 respondents for the period 1988–1994, found that 22 percent of uninsured adults under age 65 with diagnosed hypertension had gone for more than one year without a blood pressure check, compared to 10 percent of insured adults with hypertension (Fish-Parcham, 2001). While 75 percent of insured adults under 65 who had ever been diagnosed with high blood pressure and been told to take medication for it were in fact taking blood pressure medication, only 58 percent of their uninsured counterparts who had been advised to take medication were doing so. Among those adults under 65 who had been advised to take cholesterol-lowering medication, 43 percent of those without insurance failed to take such medication, compared to 29 percent among those with health insurance who did not comply with this advice (Fish-Parcham, 2001).

A study by Shea and colleagues (1992a, 1992b) of patients presenting to two New York hospital emergency departments between 1989 and 1991 found that uninsured patients were more likely to have severe, uncontrolled hypertension than were sociodemographically similar patients with any health insurance (OR = 2.2), while patients without a regular source of care had an even greater risk of severe and uncontrolled disease (OR = 4.4). When insurance status, having a regular source of care, and complying with a therapeutic regimen were all included in the analysis, the odds ratio for being uninsured was no longer statistically significant (OR = 1.9, CI: 0.8–4.6). This result is not surprising, given the strong association between having health insurance and having a regular source of care.

Finding: Uninsured persons with diabetes are less likely to receive recommended services. Lacking health insurance for longer periods increases the risk of inadequate care for this condition and can lead to uncontrolled blood sugar levels, which, over time, put diabetics at risk for additional chronic disease and disability.

Despite the demanding and costly care regimen that persons with diabetes face, adults with diabetes are almost as likely to lack health insurance as those without this disease. Of diabetic adults under age 65, 12 percent are uninsured compared with 15 percent of the comparable general population (Harris, 1999). Persons with diabetes who are uninsured are less likely to receive the professionally recommended standard of care than are those who have health insurance ( Box 3.6 ). One result of not receiving appropriate care may be uncontrolled blood sugar levels, which puts diabetics at increased risk of hospitalization for either hyper- or hypoglycemia, in addition to increasing the likelihood of comorbidities and disabilities (Palta et al., 1997).

Diabetes. Diabetes mellitus is a prevalent chronic disease that has been increasing in the U.S. population by 5–6 percent each year during the past decade. Approximately 800,000 new cases are diagnosed each year. More than 16 million Americans (more...)

Based on a 1994 survey, among adults diagnosed with diabetes who did not use insulin, those without health insurance were less likely than those with any kind of coverage to self-monitor blood glucose (OR = 0.5) or, within the past year, to have had their feet examined (OR = 0.4), or a dilated eye exam (OR = 0.5) (Beckles et al., 1998). 8 Persons with diabetes who used insulin and were uninsured were also less likely than those with health insurance to have had a foot examination (OR = 0.25) or a dilated eye examination (OR = 0.34) (Beckles et al., 1998).

A later analysis, using 1998 data from the same annual survey, found that 25 percent of adults younger than 65 who had diabetes and were uninsured for a year or more had not had a routine checkup within the past two years, compared with 7 percent of diabetics who were uninsured for less than a year and 5 percent of diabetics with health insurance (Ayanian et al., 2000). Adjusting results for the demographic characteristics of the national population, persons with diabetes who were uninsured for a year or longer were significantly less likely to have had a foot examination, a dilated eye examination, a cholesterol measurement, or a flu shot than were insured diabetics ( Figure 3.1 ) (Ayanian et al., 2000).

Diabetes management among insured and uninsured adults, ages 18–64. NOTE: Proportions adjusted to demographic characteristics of study cohort.

End-Stage Renal Disease

Finding: Uninsured patients with end-stage renal disease begin dialysis at a later stage of disease than do insured patients and have poorer clinical measures of their condition at the time they begin dialysis.

Insurance status affects the timing and quality of care ( Box 3.7 ) and may contribute to the longevity of dialysis patients, which is substantially lower than that of others of the same age (Obrador et al., 1999). The clinical goals for patients with kidney disease are to slow the progression of renal failure, manage complications, and prevent or manage comorbidities effectively. Although professional consensus about when dialysis should begin is not complete, there is agreement that the point in the progression of the disease at which dialysis begins affects patient outcomes (Kausz et al., 2000).

End-Stage Renal Disease. In 2000, 90,000 people in the United States developed end-stage renal disease (kidney failure). Dialysis and transplantation are the two standard treatments. Approximately 300,000 patients are on dialysis and 80,000 have received (more...)

The Medicare ESRD program maintains extensive clinical and sociodemographic information on all dialysis patients, including information on patient health insurance status before beginning dialysis. This database provides opportunities to analyze the health care experience of all Americans who eventually develop ESRD, rather than just a sample of the population. One study that used this database analyzed the characteristics of 155,000 chronic dialysis patients who entered dialysis over a 27-month period between 1995 and 1997 (Obrador et al., 1999). This study found that uninsured patients were sicker at initiation of dialysis and less likely to have received erythropoietin (EPO) therapy than patients with any kind of insurance pre-ESRD. Uninsured patients also had an increased likelihood of hypoalbuminemia than those who had previously been privately insured (OR = 1.37) and a greater likelihood of low hematocrit (<28 percent) 9 than the privately insured (OR = 1.34), after controlling for patients' sociodemographic and clinical characteristics, including comorbidities. Uninsured patients were also less likely than privately insured patients to have received EPO prior to dialysis (OR = 0.49) (Obrador et al., 1999). A second study based on the same data set found that patients without insurance were more likely to begin dialysis late 10 than were patients with any form of insurance (OR = 1.55) (Kausz et al., 2000).

Human Immunodeficiency Virus (HIV) Infection

Finding: Uninsured adults with HIV infection are less likely to receive highly effective medications that have been shown to improve survival.

A strong body of research about HIV infection confirms the findings of the general literature on insurance status and access to and use of services: uninsured adults diagnosed with HIV face greater delays in care than those with health insurance. They are less likely to receive regular care and drug therapy and are more likely to go without needed care than patients with any kind of coverage (Cunningham et al., 1995, 1999; Katz et al., 1995; Shapiro et al., 1999).

BOX 3.8 HIV Infection

• As of the beginning of 2000, the Centers for Disease Control and Prevention estimated that about 800,000 to 900,000 people were living with HIV infection or AIDS in the United States (CDC, 2001a).
• In each of the years 1997, 1998, and 1999, between 40,000 and 50,000 new cases of AIDS were reported.
• By 1996, combination antiretroviral therapy including protease inhibitors and nonnucleoside reverse transcriptase inhibitors, referred to as highly active antiretroviral therapies were becoming established as the treatment of choice for HIV infection (Carpenter et al., 1996). Largely as a result of these therapies, deaths among persons with AIDS dropped for the first time between 1996 and 1997 (by 42 percent) and declined 8 percent between 1998 and 1999 (CDC, 2001a).
• About half of all adults with HIV infection see a provider at least once every six months (Bozzette et al., 1998).
• Studies of HIV infection and health insurance examine a variety of health-related outcomes: general measures of access and utilization such as routine care visits and emergency department visits without hospitalization, delays between diagnosis and initiation of therapy, use of recommended drug therapies, and clinical outcomes such as CD4 lymphocyte counts.

A number of analyses have been based on national, longitudinal surveys evaluating access to care for persons with HIV infection (Niemcryk et al., 1998; Joyce et al., 1999; Shapiro et al., 1999; Andersen et al., 2000; Cunningham et al., 1999, 2000; Turner et al., 2000, Goldman et al., 2001). 11 These surveys allow assessment of the relationship between health insurance and access to care, use of services, receipt and timeliness of recommended therapies, and mortality as related to health insurance status. The research based on one of these surveys, the HIV Cost and Services Utilization Study (HCSUS), represents some of the most carefully designed studies of access to care and receipt of recommended therapies for specific conditions. In addition, there are several smaller, local studies based on hospital records or patient surveys (Katz et al., 1992, 1995; Bennett et al., 1995; Cunningham et al., 1995, 1996; Palacio et al, 1999; Sorvillo et al., 1999).

Access to a Regular Source of Care

Several studies suggest that the positive effects of health insurance for HIV-infected adults are achieved through the mechanism of having a regular source of care. Sorvillo and colleagues (1999) surveyed 339 HIV-positive adults in Los Angeles county in 1996–1997, and found that two-thirds of insured patients used protease inhibitors (PIs), while just half of uninsured patients were using them. When the site of care (private clinic, HMO, or public clinic) was included in a multivariable analysis, insurance status was no longer significantly related to receipt of PIs because of the concentration of uninsured patients in public clinics, which were less likely to prescribe PIs, especially at the beginning of the study period (Sorvillo et al., 1999).

Uninsured patients appear to face greater delays in beginning care following a diagnosis of HIV infection. In bivariate analysis of HCSUS data, uninsured patients were significantly more likely to have their first office visit more than three months after diagnosis with HIV than were privately insured patients (37 percent of uninsured patients had delays compared to 25 percent of privately insured patients in 1993; by 1995, those patients with delays decreased to 22 percent of uninsured patients and 14 percent of privately insured) (Turner et al., 2000). However, in a multivariable analysis, being uninsured was no longer a significant predictor of late initiation, while not having a regular source of care remained an important predictor (Turner et al., 2000).

Findings regarding emergency department (ED) use and hospitalization have changed over time. The most recent analysis, based on HCSUS, finds greater use of EDs, without hospitalization, and hospitalization more frequently than every six months for uninsured HIV patients (Shapiro et al., 1999), suggesting poorer access to other kinds of outpatient care. Studies based on earlier data report that uninsured patients had lower use of emergency rooms and hospitalization than either publicly or privately insured patients (Mor et al., 1992; Fleishman and Mor, 1993; Niemcryk et al., 1998; Joyce et al., 1999), suggesting poorer access even at high levels of acuity.

Receipt of Drug Therapies

Adults with HIV infection are more likely to receive effective drug therapies and to receive them earlier in the course of disease if they have health insurance. In an HCSUS analysis with extensive adjustments for sociodemographic and clinical factors, those without health insurance were much less likely to have ever received antiretroviral therapy (OR = 0.35) (Shapiro et al., 1999). Waiting times from diagnosis to the start of therapy with either PIs or nonnucleoside reverse transcriptase inhibitors, were 9.4 months for the privately insured, 12.4 months for Medicaid enrollees, and 13.9 months for uninsured patients (Shapiro et al., 2000).

Overall, many HIV-infected patients abandon recommended drug therapy over time. However, uninsured patients are more likely to stop drug therapy than are those with coverage. At the second follow-up interview of HCSUS respondents in 1997–1998, only half (53 percent) of all HIV-positive patients in care were receiving the recommended combination drug therapy, highly active antiretroviral therapy (HAART), although 71 percent had received HAART at some time in their treatment history (Cunningham et al., 2000). Uninsured patients were significantly less likely than privately insured patients with indemnity coverage (OR = 0.71) to be receiving HAART at the time of follow-up, indicating less appropriate care for uninsured patients with this disease (Cunningham et al., 2000).

Clinical Outcomes and Mortality

Studies of clinical outcomes for HIV patients present an evolving picture of both the efficacy of treatments and the impact of health insurance. A relatively early study of patients hospitalized with Pneumocystis carinii pneumonia (1987– 1990) found that uninsured patients had a higher in-hospital mortality rate than did those with private insurance (OR = 1.49), and Medicaid patients had an even higher in-hospital mortality, relative to private patients (OR = 2.1) (Bennett et al., 1995). Another early and small study (96 patients in one university clinic) found that patients with private insurance had significantly lower CD4 lymphocyte counts (a worse outcome) than either uninsured or Medicaid patients (who had the highest counts), when first treated at the clinic (Katz et al., 1992). The authors hypothesize that some relatively healthy patients with private health insurance coverage may have been reluctant to use it and thus reported their status as uninsured.

More recently, an analysis based on HCSUS examined the mortality experience of insured and uninsured HIV-infected adults and found that having health insurance of any kind reduced the risk of dying within six months of being surveyed between 71 and 85 percent, when severity of illness (measured by CD4 lymphocyte count) and sociodemographic characteristics were controlled (Goldman et al., 2001). The greater reduction in mortality risk (85 percent) was estimated for a surviving subset (2,466 participants) of the original 1996 sample of 2,864 participants a year later, when HAART was in wider use and was reducing mortality among HIV patients who used it. This impact of health insurance on mortality for HIV-infected adults within a short follow-up period, six months, demonstrates how sensitive health outcomes can be to coverage when it facilitates receipt of effective therapy.

Mental Illness

Finding: Health insurance that covers any mental health treatment is associated with the receipt of mental health care and with care consistent with clinical practice guidelines from both general medical and specialty mental health providers.

Mental disorders or illnesses are health conditions that are characterized by changes in thinking, mood, or behavior. They are often chronic conditions but may also occur as single or infrequent episodes over a lifetime. Mental illnesses represent a major source of disability in the United States that is often underestimated by the public and health care professionals alike (USDHHS, 2000). In industrialized economies, mental illness is equivalent to heart disease and cancer in terms of its impact on disability (Murray and Lopez, 1996).

Despite the differential treatment of mental health services in both public and private insurance plans, the studies reviewed by the Committee document a positive association between health insurance coverage and more appropriate care for mental illnesses ( Box 3.9 ). Health insurance plans and programs historically have excluded services related to treatment for mental illness, strictly limited coverage of mental health services, and administered mental health benefits separately from other kinds of medical care. Thus, studies that attempt to measure the effects of health insurance status on health care and outcomes for mental illnesses may be affected by the diversity of health insurance benefits and of cost sharing and administrative requirements for these services and conditions. Variability in benefits among health insurance plans and types of insurance complicates the interpretation of all observational studies of health insurance effects but poses a particular problem vis–a–vis mental health. (See the discussion of measurement bias in Chapter 2 .)

Mental Illness. About 38 million people ages 18 and older are estimated to have a single mental disorder of any severity or both a mental and an addictive disorder in a given year (Narrow et al., 2002). The most common conditions fall into the broad categories (more...)

The use of mental health services in both the general and specialty mental health sectors by adults is positively associated with health insurance coverage (Cooper-Patrick et al., 1999; Wang et al., 2000; Young et al., 2001). Between 1987 and 1997, the overall rate of treatment for depression among American adults under age 65 tripled from 1 person per 100 to 3.2 persons per 100, yet the treatment rate among those without health insurance was half that of the overall population rate in 1997, 1.5 persons treated per 100 population (Olfson et al., 2002). A longitudinal, community-based study in Baltimore, Maryland, between 1981 and 1996 documented increased use of mental health services over this period (Cooper-Patrick et al., 1999). Analyzing the experience of African Americans and whites separately, the authors found that for African Americans specifically, this increase was achieved predominantly with services provided in the general medical sector. For both African Americans and whites, being uninsured reduced the likelihood of receiving any mental health services.

At the same time, insurance coverage for adults with mental illness is less stable than average for those without this condition (Sturm and Wells, 2000; Rabinowitz et al., 2001). In a recent (1998) follow-up survey of participants in the Community Tracking Study, those who reported having symptoms of mental disorders were found to be more likely to lose coverage within a year following their diagnosis than those without a mental disorder (Sturm and Wells, 2000). As discussed below, those with severe mental illness also experience transitions in insurance coverage, frequently ending up with public program coverage (Rabinowitz et al., 2001).

The findings reported below are grouped into those for depression and anxiety disorders and those for severe mental illnesses. Depression and anxiety disorders are often treatable in the general medical sector and primarily require outpatient services. Severe mental illnesses (schizophrenia, other psychoses, and bipolar depression) require the attention of specialty mental health professionals and may require inpatient and other forms of more extensive services (e.g., partial or day hospitalization). Public health insurance, both Medicare and Medicaid, is an important source of coverage for specialty mental health services for those disabled by severe mental illness (SMI).

Depression and Anxiety Disorders

Receipt of appropriate (guideline-concordant) care for depression is associated with improved functional outcomes at two years (Sturm and Wells, 1995). Health insurance coverage specifically for mental health services is associated with an increased likelihood of receiving such care. Two studies support this claim.

The first, a nationally representative study of three prevalent disorders— depression, panic disorder, and generalized anxiety disorder—investigated the contribution of insurance coverage and health care utilization to guideline-con-cordant treatment (Wang et al., 2000). Mental health diagnoses were determined in a structured interview using a well-defined operational definition of mental health care over the previous 12 months. Treatment criteria included the combination of a prescription medication for depression or anxiety from a general medical doctor or a psychiatrist in addition to at least four visits to the same type of provider or, where medication was not prescribed, a minimum of eight visits to either a psychiatrist or a mental health specialist (Wang et al., 2000). A multivariable analysis estimated the effects of sociodemographic characteristics, various measures of clinical status including a measure of mental illness severity, insurance coverage for mental health visits, number and reasons for use of general medical services, other medications, and alternative therapies. Patients diagnosed with depression, panic disorder, or a generalized anxiety disorder who had no health insurance coverage for mental health visits were less likely to receive any mental health services (OR = 0.43). They were also less likely to receive guideline-concordant care in the general medical sector (OR = 0.24) or in the mental health treatment sector (OR = 0.36) (Wang et al., 2000).

A second study of adults with a probable 12-month diagnosis of depression or anxiety examined factors associated with receipt of appropriate care (psychiatric medication and counseling) (Young et al, 2001): 1,636 respondents were identified as having one or more depressive or anxiety disorders based on a structured diagnostic interview. Respondents with a depressive or anxiety disorder who had more education and a greater number of medical disorders were more likely to have had contact with providers than those with less education and fewer medical conditions. Those with no health insurance were less likely to have had any provider contact than were those with any form of health insurance (OR = 0.46). However, for those receiving any care, insurance status was not related to receipt of appropriate care (Young et al., 2001). These findings suggest that health insurance alone may not ensure appropriate mental health care.

Severe Mental Illness

Uninsured adults with severe mental illnesses are less likely to receive appropriate care than are those with coverage and may experience delays in receiving services until they gain public insurance.

In a study using the same sample and survey as that used by Young and colleagues, McAlpine and Mechanic (2001) investigated the association of current insurance coverage and specialty mental health utilization within the past 12 months (i.e., visits to a psychiatrist or psychologist, hospital admission, or emergency room visit for an emotional or substance use problem) for SMI. Two diagnostic indices, including a global measure of mental health, measured the need for care. Potential confounding factors such as physical symptoms and degree of dangerousness and disruptiveness were also measured. One in five respondents identified with an SMI was uninsured. Among persons with SMI, those without health insurance were far less likely to use specialty mental health services than those with Medicare or Medicaid (OR = 0.17) (McAlpine and Mechanic, 2000).

Individuals with SMIs typically lack insurance at the time of hospitalization (Rabinowitz et al., 2001). An important question regarding insurance coverage in this patient population is whether a first hospitalization for SMI results in a change in insurance status and whether such a change influences subsequent mental health care. Rabinowitz and colleagues followed the progress of 443 individuals enrolled in a county mental health project to determine whether changes in coverage followed first admission for psychosis and the association between type of insurance coverage and future care. Overall, the proportion of patients with no insurance 24 months after hospitalization decreased from 42 percent at baseline to 21 percent as a result of enrollment in public insurance programs. Men were more likely to remain uninsured than were women. The total number of days of care received (inpatient, outpatient, day hospital) was significantly higher for the publicly insured group compared to both those with private insurance and those with no insurance during the first 6 months after initial hospitalization and over the entire 24-month period. Uninsured patients with SMI were much less likely to receive outpatient care after hospitalization than patients with Medicaid or Medicare (OR = 0.24) and also less likely than those with private health insurance to receive outpatient care subsequent to hospitalization (OR = 0.56) (Rabinowitz et al., 2001).

An earlier study using the same data also reported an association between health insurance and receipt of mental health services prior to a first admission for psychotic disorder (Rabinowitz et al., 1998). Forty-four percent of patients (n = 525) were uninsured at first admission. Uninsured patients were less likely than those with private insurance to have had

• any mental health treatment prior to admission (OR = 0.53),
• specific psychotherapeutic contact (OR = 0.43),
• voluntary admission (OR = 0.56),
• less than three months between onset of psychosis and admission (OR = 0.56)

and were less likely to have been admitted to a community (versus public) hospital (OR = 0.14) (Rabinowitz et al., 1998). Uninsured patients were also less likely than those with either Medicaid or Medicare to have received antipsychotic medication (OR = 0.4), had voluntary admission (OR = 0.53), and be admitted to a community hospital (OR = 0.33).

• HOSPITAL-BASED CARE

Finding: Uninsured patients who are hospitalized for a range of conditions experience higher rates of death in the hospital, receive fewer services, and are more likely to experience an adverse medical event due to negligence than are insured patients.

Americans assume and expect that hospital-based care for serious and emergency conditions is available to everyone, regardless of health insurance coverage, while recognizing that uninsured patients may be limited to treatment at public or otherwise designated “safety-net” hospitals (IOM, 2001a). Professional and institutional standards of practice grounded in ethics, law, and licensure dictate that the care received by all patients, regardless of financial or insurance status, be of equal and high quality. Yet studies of hospital-based care conducted over the past two decades have documented differences in the services received by insured and uninsured patients, differences in the quality of their care (sometimes but not always related to the site of care), and differences in patient outcomes such as in-hospital mortality rates. 12

One of the most comprehensive of these studies of hospitalization analyzed more than 592,000 hospital discharge abstracts in 1987 (Hadley et al., 1991). The authors report that for adults ages 18–65, uninsured hospital inpatients had a significantly higher risk of dying in the hospital than their privately insured counterparts in 8 of 12 age–sex–race-specific population cohorts (relative risks ranged from 1.1 for black women ages 50–64 to 3.2 for black men ages 35–49). This analysis adjusted for patient condition on admission to the hospital. Uninsured patients were also less likely to receive endoscopic procedures in the hospital than privately insured patients, and when they did receive these diagnostic services, the resultant pathology reports were more likely to be abnormal (OR = 1.56) (Hadley et al., 1991).

This study by Hadley and colleagues also examined the relative resource use (length of stay) of uninsured hospital patients compared to privately insured patients and found that for conditions that afford high discretion in treatment decisions (e.g., tonsillitis, bronchitis, hernia), uninsured patients had significantly shorter lengths of stay (Hadley et al., 1991). However, for diagnoses that afford little discretion in treatment (e.g., gastrointestinal hemorrhage, congestive heart failure), lengths of stay were not significantly different for uninsured and privately insured patients, although uninsured patients tended to have shorter stays. This underscores the possibility that when uninsured patients are found to receive fewer services than insured patients, it may be the result of overtreatment of patients with insurance, rather than undertreatment of those without coverage.

In addition to differences in the resources devoted to the care of insured and uninsured patients, the quality of the care provided may differ. One study of more than 30,000 hospital medical records in 51 hospitals in New York State for 1984 found that the proportion of adverse medical events due to negligence was substantially greater among patients without health insurance than among privately insured patients (OR = 2.35), while the experience of Medicaid patients did not differ significantly from that of the privately insured population (Burstin et al., 1992). This increased risk for uninsured patients was attributable only in part to receiving care more frequently in emergency departments, which generally were found to have higher rates of adverse events.

Because most studies of hospital-based care and outcomes are observational, including only those who literally “show up” for care, and because appropriateness criteria are not available for many conditions, some of the strongest research on health insurance effects involves studies of specific conditions. Studies of certain conditions are less likely to be compromised by nonrandom or unrepresentative samples (selection bias) simply because a larger proportion of the population of interest—namely, acutely ill adults—is likely to be captured in the hospital-based study population. Furthermore, condition-specific studies are more likely to include evidence-based criteria for judging the appropriateness of care.

The following two sections consider research that has examined the effect of health insurance on care and outcomes for patients with (1) emergency conditions and traumatic injuries and (2) cardiovascular disease. For both categories, selection bias among those reaching treatment is minimized, and appropriateness guidelines and outcomes criteria (e.g., mortality) are definitive. Traumatic injuries (specifically automobile accidents), for example, reduce some of the unmeasured differences in propensity to seek care between insured and uninsured patients (Doyle, 2001). Another area of hospital-based services for which there is sufficient professional consensus about appropriate treatment is the use of angiography and revascularization procedures following acute myocardial infarction (AMI) or heart attack, at least for a subset of patients with severe coronary artery disease. 13

Emergency and Trauma Care

Finding: Uninsured persons with traumatic injuries are less likely to be admitted to the hospital, receive fewer services when admitted, and are more likely to die than insured trauma victims.

Two studies based on large, statewide data sets have found substantial and significant differences in the risk of dying for insured and uninsured trauma patients ( Box 3.10 ) who were admitted to hospitals as emergencies. Doyle (2001) analyzed more than 10,000 police reports of auto accidents linked to hospital records maintained by Wisconsin over 1992–1997 to ascertain the care received and the mortality of insured and uninsured crash victims. After controlling for personal, crash, and hospital characteristics, it was found that uninsured accident victims received 20 percent less care, as measured by hospital charges and length of stay, and had a 37 percent higher mortality rate than did privately insured accident victims (5.2 percent versus 3.8 percent, respectively) (Doyle, 2001). The authors conclude that these differences are attributable to provider response to insurance status because extensive patient characteristics were accounted for in the analysis and because unmeasured patient characteristics that might influence these outcomes were unlikely to be related to patients' health insurance status.

Trauma. Throughout the United States in 1997, approximately 34.4 million episodes of injury and poisoning received medical attention and 40.9 million injuries and poisonings were reported as a result (Warner et al., 2000). For injury-related deaths, 43 (more...)

Haas and Goldman (1994) evaluated the treatment experience and mortality of more than 15,000 insured and uninsured trauma patients admitted to hospitals on an emergency basis in Massachusetts in 1990. Adjusting the data for injury severity and comorbidities as well as for age, sex, and race, the authors found that uninsured trauma patients received less care and had higher in-hospital mortality than did patients with private insurance or Medicaid. Uninsured patients were just as likely to receive care in an intensive care unit (ICU) as privately insured trauma patients but were less likely to undergo an operative procedure (OR = 0.68) or to receive physical therapy (OR = 0.61). Uninsured patients were much more likely than privately insured patients to die in the hospital (OR = 2.15) (Haas and Goldman, 1994). The differences in services and mortality experience between Medicaid and privately insured patients were small and were not statistically significant.

Other studies of emergency department use and admissions and care for traumatic injuries shed some light on patient behavior and institutional responses related to health insurance status. Both lacking health insurance and not having a regular source of care have been found in surveys of patients who eventually do arrive at an ED to be related to delays in seeking care (Ell et al., 1994; Rucker et al., 2001). Braveman and colleagues (1994) examined hospital discharge records of more than 91,000 adults diagnosed with acute appendicitis in California hospitals between 1984 and 1989. They found that the risk of a ruptured appendix was 50 percent higher for both uninsured and Medicaid patients, than for privately insured patients in prepaid plans, in an analysis that controlled for age, sex, race, psychiatric diagnoses, diabetes, and hospital characteristics. Admission to a public hospital also was associated with rupture, as were diagnoses of psychiatric illness or diabetes (Braveman et al., 1994). The authors hypothesized that both Medicaid and uninsured patients incurred avoidable delays before seeking care for appendicitis.

Three separate studies that analyzed Medicaid and uninsured trauma patients together report mixed findings regarding patient outcomes and hospital care. Rhee and colleagues (1997) examined patient information for more than 2,800 persons hospitalized at a Level 1 trauma center after a motor vehicle crash in Seattle, Washington, between 1990 and 1993. 14 This study found no significant differences in mortality, hospital charges, or length of stay (LOS) between privately insured patients and those who either had Medicaid coverage or were uninsured, except for patients who ultimately were transferred to a long-term care or rehabilitation facility. In the case of patients awaiting transfer, those with Medicaid or no insurance had an adjusted LOS that was 11 percent longer than privately insured patients (Rhee et al., 1997). The authors speculate that the similarity in treatment and outcomes for patients of different insurance status could be due to the mission of the public, Level 1 trauma center to which they were admitted, which was to serve the entire state population needing that level of care and act as a provider of last resort for uninsured patients. Because this study did not differentiate results for Medicaid and uninsured patients, it provides less information about outcomes for uninsured patients than studies that analyze these groups separately.

Uninsured trauma patients may also be treated differently from insured patients in interhospital transfer decisions. Using Washington State trauma registry information, Nathens and colleagues (2001) identified 2,008 trauma patients between 16 and 64 years of age injured in King County (Seattle) and originally transported to one of seven Level 3 or 4 trauma centers in the county between 1995 and 1999. Adjusting for age, sex, type of injury, and injury severity, they looked at independent predictors of transfer to the Level 1 trauma center in the county—a public, safety-net hospital, and estimated that patients who either had Medicaid or were uninsured were more than twice as likely to be transferred to the higher level facility than were privately insured patients (OR = 2.4) and that many of these transferred patients had low injury severity scores (ISS). 15 The authors conclude that this “payer-based triage” may undermine the effectiveness of Level 1 trauma centers in serving the more critically injured patients by diverting resources to patients who could have been treated appropriately in their original hospital (Nathens et al., 2001).

Finally, the differences found between uninsured and insured patients in highly discretionary cases may reflect overtreatment of those with health insurance rather than undertreatment of uninsured patients. Svenson and Spurlock (2001) evaluated the experience of more than 8,500 patients with head injuries treated in four Kentucky hospitals between 1995 and 1997. For those with less severe head injuries (lacerations, contusion, or concussion), uninsured patients were substantially less likely than privately insured patients to be admitted to the hospital (OR = 0.14 for laceration, 0.38 for contusion or concussion). The likelihood of admission for Medicaid was also substantially lower than for privately insured patients, but not as low as for uninsured patients (ORs = 0.33 and 0.45, respectively). Little difference was found in hospital admissions for more severe head injuries among patients with different insurance status. The authors were unable to determine whether the differences in admissions for less severe head trauma are due to undertreatment of uninsured and Medicaid patients or overtreatment of privately insured patients (Svenson and Spurlock, 2001).

Finding: Uninsured patients with acute cardiovascular disease are less likely to be admitted to a hospital that performs angiography or revascularization procedures, are less likely to receive these diagnostic and treatment procedures, and are more likely to die in the short term.

Finding: Health insurance reduces the disparity in receipt of these services by members of racial and ethnic minority groups.

Health insurance is positively associated with receipt of hospital-based treatments for cardiovascular disease (specifically, coronary artery disease) and with lower patient mortality ( Box 3.11 ). One meta-analysis has credited medical advances in the treatment of cardiovascular disease, including hospital-based care following AMI, with roughly half of the reduction in post-AMI mortality between 1975 and 1995 (with a range of 20 to 85 percent) (Cutler et al., 1998). Some of the most recent studies have used appropriateness criteria to identify when a given procedure is considered necessary according to professional consensus, reducing the chances that differences in rates between uninsured and insured patients are a result of overtreatment of the insured population (i.e., Sada et al.,1998; Leape et al., 1999).

In 2001, an estimated 1.1 million Americans suffered a diagnosed heart attack. An estimated 7.3 million Americans have a history of AMI (American Heart Association, 2001). During 1998, coronary heart disease accounted for about 460,000 deaths; AMI was (more...)

Five studies that examined the mortality experience of patients hospitalized for cardiovascular disease (including AMI, angina, and chest pain) reported higher in-hospital or 30-day posthospitalization mortality for uninsured patients (Young and Cohen, 1991; Blustein et al., 1995; Kreindel et al., 1997; Sada et al., 1998; Canto et al., 2000).

The first study, of about 5,000 patients admitted on an emergency basis for AMI in 1987, found that uninsured patients were more likely to die within 30 days of admission than privately insured patients (OR = 1.5) (Young and Cohen, 1991). In a second study, Blustein and colleagues (1995) examined records for 5,800 patients under 65 who were admitted to California hospitals for AMI in 1991 and found that uninsured patients were more likely to die in the hospital than privately insured patients (OR = 1.9) and still had an increased risk of dying after adjusting for receipt of a revascularization procedure (OR = 1.7). Finally, a study in a single Massachusetts community of 3,700 patients hospitalized for AMI between 1986 and 1993 reported that uninsured patients had a slight, but statistically insignificant greater in-hospital mortality than privately insured patients (OR = 1.2, CI: 0.6–2.4) (Kreindel et al., 1997).

Two larger studies that used more recent data (1994–1996) from the National Registry of Myocardial Infarction reported higher in-hospital mortality for uninsured than for privately insured patients. In the first, Sada and colleagues (1998) reviewed records for 17,600 patients under age 65 who were admitted to hospital for AMI and found that uninsured patients had an in-hospital mortality rate of 5.4 percent, compared with 3.8 percent for private FFS patients and 3.9 percent for private HMO patients. Medicaid patients had the highest in-hospital mortality rate, 8.9 percent. In a model that adjusted for demographic and clinical factors, the likelihood of uninsured patients dying in the hospital was still higher but was not statistically significantly different from that of privately insured patients (OR = 1.2, CI: 0.8–1.6) (Sada et al, 1998). The second national study examined records for more than 332,000 patients admitted with AMI and found that after adjusting for demographics, prior disease history, and clinical characteristics, uninsured patients were more likely to die in the hospital than privately insured FFS patients (OR = 1.29) (Canto et al., 2000). The mortality experience of Medicaid patients was the same as that of uninsured patients.

Only one study, a review of hospital records of 1,556 patients undergoing coronary artery bypass graft surgery in a single Louisiana teaching hospital, found that uninsured patients had better long-term survival than did insured patients (Mancini et al., 2001). However, this study did not control for age or characteristics of the patients. The average age of uninsured patients at the time of surgery was 55, and of insured patients, 65 years. Furthermore, only 7 percent of the insured study population had private insurance, so the population was not representative of the insured population at large.

Coronary Procedures

The body of research on the use of specific procedures to diagnose and treat cardiovascular disease as a function of the insurance status of the patient consistently reports differences in utilization, with uninsured patients generally less likely to receive coronary angiography, CABG, or percutaneous transluminal coronary angioplasty (PTCA) than privately insured patients (Young and Cohen, 1991; Blustein et al., 1995; Kuykendall et al., 1995; Sada et al., 1998; Leape et al., 1999; Canto et al., 2000; Daumit et al., 2000). However, only some of these studies applied appropriateness criteria to identify cases in which the use of these procedures was considered nondiscretionary or necessary. In the studies that examined overall utilization rates, the differences found by insurance status could be attributed to overutilization as well as underutilization.

Angiography (cardiac catheterization) is an invasive diagnostic procedure that provides information to guide decisions about subsequent treatment options, including revascularization procedures. Sada and colleagues (1998) applied the criteria of the American College of Cardiology and American Heart Association Joint Task Force to a national data set of 17,600 myocardial infarction patients under 65 to identify nondiscretionary angiography for revascularization candidates considered to be at high risk. They estimated that in hospitals providing these cardiac procedures, patients with private FFS coverage who were deemed high-risk and for whom angiography was nondiscretionary were more likely than similarly high-risk uninsured patients or Medicaid patients to receive angiography. Among high-risk FFS patients, 84 percent received this service compared to 73 percent of high-risk uninsured patients and 60 percent of similar Medicaid patients (Sada et al., 1998).

Revascularization procedures (either CABG or PTCA) following a heart attack are also more likely to be performed on insured than uninsured patients. In two studies, uninsured patients were less likely to receive revascularization (either CABG or PTCA) than privately insured FFS patients (OR = 0.6 in the 1991 study and 0.8 in the 2000 study) (Young and Cohen, 1991; Canto et al., 2000). Blustein and colleagues (1995) and Kuykendall and colleagues (1995) reported similar comparative findings regarding the revascularization of uninsured and privately insured patients (ORs in these studies ranged from 0.4 to 0.6).

InterHospital Transfers to Receive Services. For patients with AMI, health insurance facilitates access to hospitals that perform angiography and revascularization, whether admission is initial or by means of an interhospital transfer (Blustein et al., 1995; Canto et al., 1999; Leape et al., 1999).

In a study of California hospital admissions for AMI, Blustein and colleagues (1995) found that uninsured patients were less likely than privately insured patients to be admitted initially to a hospital that offered revascularization and much less likely to be transferred if admitted initially to one that did not (ORs = 0.71 and 0.42, respectively).

Leape and colleagues (1999) reviewed 631 records for patients who had received angiography and subsequently met expert panel criteria for necessary revascularization. Overall, 74 percent of patients meeting these criteria received revascularization. Leape et al. found that in hospitals that also performed CABG and PTCA, there were no differences in rates of revascularization for patients with different insurance status. However, for patients initially hospitalized in facilities that did not perform CABG and PTCA, who required a transfer to another hospital to receive revascularization, the rates differed significantly by insurance status: 91 percent of Medicare patients, 82 percent of privately insured patients, 75 percent of Medicaid patients, and just 52 percent of uninsured patients received this indicated surgery (Leape et al., 1999).

Insurance Status and Racial and Gender Disparities. Health insurance has been shown to lessen disparities in the care for cardiovascular disease received by men compared to women and among members of racial and ethnic groups (Carlisle et al., 1997; Daumit et al., 1999, 2000).

An analysis of more than 100,000 hospital discharges with a principal diagnosis of cardiovascular disease in Los Angeles County between 1986 and 1988 revealed significant differences in rates of angiography, CABG, and PTCA between uninsured African-American and white patients but not between members of these ethnic groups who were privately insured (Carlisle et al., 1997). In a multivariate analysis that controlled for demographic and clinical characteristics and hospital procedure volume, the odds ratios for uninsured African Americans to receive one of these services compared with uninsured whites ranged from 0.33 to 0.5 (Carlisle et al., 1997).

A longitudinal study with a seven-year follow-up of a national random sample of patients who initially became eligible for the Medicare ESRD program in 1986 or 1987 found that once uninsured patients qualified for ESRD benefits, pronounced disparities by gender or race in their likelihood of receiving either angiography, CABG, or PTCA were eliminated (Daumit et al., 1999, 2000). In the period prior to qualifying for Medicare, uninsured African Americans were far less likely than uninsured whites to undergo a cardiac procedure (OR = 0.07) (Daumit et al., 1999). Uninsured women were also less likely than uninsured men to receive a cardiac procedure before qualifying for Medicare (OR = 0.4), and uninsured men were much less likely than men with private insurance to receive one (OR = 0.47) (Daumit et al., 2000). In the case of both race and gender, differences in the receipt of these cardiac procedures were eliminated after gaining Medicare ESRD coverage.

• GENERAL HEALTH OUTCOMES

Finding: Longitudinal population-based studies of the mortality of uninsured and privately insured adults reveal a higher risk of dying for those who were uninsured at baseline than for those who initially had private coverage.

Finding: Relatively short (one- to four-year) longitudinal studies document relatively greater decreases in general health status measures for uninsured adults and for those who lost insurance coverage during the period studied than for those with continuous coverage.

This chapter concludes with a review of the studies evaluating the overall health status and mortality experience of insured and uninsured populations. Assessments of general health outcomes such as self-reported health status and mortality or survival rates for uninsured adults under 65 compared to those with some form of health insurance (i.e., employment-sponsored, Medicaid, Medicare, individually purchased policies), present researchers with even greater challenges of analytic adjustment than those encountered in studies of specific health conditions. Not only might health insurance affect health status, but health status can affect health insurance status. Thus, it is difficult to interpret cross-sectional studies of health insurance and health status. However, several well-designed longitudinal studies with extensive analytic adjustments for covariates have found higher mortality and worse overall functional and health status among uninsured adults than among otherwise similar insured adults.

Two studies provide evidence that uninsured adults are more likely to die prematurely than are their privately insured counterparts.

Franks and colleagues (1993a) followed a national cohort of 4,700 adults age 25 or older for 13 to 17 years who, at the baseline interview, were either privately insured or uninsured. At the end of the follow-up period (1987), about twice as many participants who were uninsured at the time of the first interview had died as had those with private health insurance (18.4 percent compared with 9.6 percent). Controlling for sociodemographic characteristics, health examination findings, self-reported health status, and health behaviors, the risk of death for adults who initially were uninsured was 25 percent greater than for those who had private health insurance at the time of the initial interview (mortality hazard ratio = 1.25, CI: 1.00–1.55). The magnitude of this independent health insurance effect on mortality risk was comparable to that of being unemployed, to lacking a high school diploma, or to being in the lowest income category (Franks et al., 1993a). 16 Because insurance status was measured only at the initial interview and thus did not reflect the subjects' cumulative insurance experience over the 13–17 year follow-up period, the difference found in mortality between uninsured and privately insured persons most likely is an underestimate of differences in the mortality experience of those who are continuously uninsured and those who are continuously insured.

A study by Sorlie and colleagues (1994) tracked the mortality experience of 148,000 adults between 25 and 65 years of age until 1987, a two- to five-year follow-up period. After adjusting for age and income, this study found that uninsured white men had a 20 percent higher risk of dying than white men with employment-based health insurance. Uninsured black men and white women each had a 50 percent higher mortality risk than their counterparts with employment-based coverage (Sorlie et al., 1994). Among black women, insurance was not statistically associated with mortality. The authors also examined the mortality experience of insured and uninsured employed white men and women, adjusted for age and income. (Because of small sample size, they did not perform this analysis for black men and women.) Uninsured employed white men had a 30 percent greater risk of dying than their working counterparts with health insurance, and uninsured employed white women had a 20 percent greater risk over two to five years than their counterparts with health insurance (Sorlie et al., 1994).

Loss of Coverage and Changes in Health Status Over Time

Persons who lose health insurance have been found to experience declines in their health status. Longitudinal studies that follow a cohort of individuals over time can provide a “before-and-after” picture of health status, comparing a group that maintained coverage with one that lost it. Such a design helps to minimize the possibility that unmeasured factors that vary along with health insurance status account for differences in health, a competing hypothesis that cannot be eliminated in cross-sectional studies.

Lurie and colleagues (1984, 1986) took advantage of a natural experiment in the mid-1980s when California eliminated Medi-Cal coverage for a group of medically indigent adults. Following matched cohorts of adults seen at an internal medicine practice at a university clinic who either maintained or lost Medi-Cal coverage, the authors found that the patients who lost coverage reported significant decreases in perceived overall health at both six months and a year later, unlike those who maintained coverage. As discussed earlier in this chapter, participants in this study with hypertension who lost coverage also experienced worsening blood pressure control, while those who maintained coverage did not.

Like those with chronic health conditions, adults in late middle age are particularly susceptible to deteriorations of function and health status if they lack or lose health insurance coverage. Baker and colleagues (2001) followed a group of more than 7,500 participants in the longitudinal Health and Retirement Survey (adults ages 51 to 61 at the outset) between 1992 and 1996. The authors compared three groups:

those who were continuously insured over the first two years (measured in 1992 and 1994);

those who were continuously without insurance over that period; and

those who were intermittently uninsured , defined as those who lacked health insurance either in 1992 or in 1994, but not at both times (Baker et al., 2001).

Of those who were continuously uninsured, 22 percent had a major decline 17 in self-reported health, 16 percent of the intermittently uninsured experienced a major decline, and 8 percent of the continuously insured reported a major decline in health. In an analysis that controlled for sociodemographic characteristics, preexisting medical conditions, and health behaviors, the authors estimated a 60 percent greater risk of a major decline in health for continuously uninsured persons and a 40 percent greater risk for intermittently insured persons, as compared with continuously insured persons. Continuously or intermittently uninsured persons also had a 20 to 25 percent greater risk of developing a new difficulty in walking or climbing stairs than did those who were continuously insured (Baker et al., 2001).

Cross-Sectional Studies of Health Status

Cross-sectional studies based on large national population surveys (Medical Expenditure Panel Survey [MEPS], National Medical Expenditure Survey [NMES], and Behavioral Risk Factor Surveillance System, provide snapshots of the subjective or self-reported health status of populations according to insurance status. These surveys report worse health status among those without insurance than among those with coverage. Two large studies with careful and extensive analytic adjustments for covarying personal characteristics are presented here.

Franks and colleagues (1993b) examined the relationship between health insurance status and subjective health across several dimensions, including a general health perceptions scale, physical and role functions, and mental health, for 12,000 adults ages 25 through 64. The authors compared participants who had private health insurance for an entire year with those who had been without health insurance the entire year. In an analysis that controlled for age, sex, race, education, presence of a medical condition, and attitude toward medical care and insurance, uninsured adults had significantly lower subjective health scores across all dimensions. The effect on these measures of health of being uninsured was greater for lower-income persons than for those in families with incomes above 200 percent of the federal poverty level, although the effect persisted in both income groups. For both lower- and higher-income adults, the negative effect on perceived health of being uninsured was greater than that of having minority racial or ethnic status. Overall, the extent to which being uninsured negatively affected subjective health (a decrement of 4 points on a 100-point scale) was greater than that of having either of two diseases, cancer or gall bladder disease, and slightly lower than that for arteriosclerosis (Franks et al., 1993b).

Ayanian and colleagues' (2000) analysis of the 1998 BRFSS compared self-reported health status among adults 18-64 who were uninsured for a year or longer, those uninsured for less than a year, and those with any kind of insurance, public or private. Table 3.1 presents the unadjusted results for the approximately 163,000 adults surveyed. One in five adults uninsured for a year or longer reported being in fair or poor health, compared with one in seven among those uninsured for less than a year, and one in nine for those with health insurance.

Unadjusted Self-Reported Health Status for 18–64 Year-Old Adults, BRFSS, 1998 (percent).

The RAND Health Insurance Experiment

In an experimental study conducted between 1975 and 1982, about 4,000 participants between 14 and 61 years were randomly assigned (in family units) to health insurance plans that differed in the amount of patient cost sharing required, ranging from free care to major deductible plans (95 percent cost sharing, with a maximum of $1,000 per family per year) (Brook et al., 1983; Newhouse et al., 1993). Participants received a lump-sum payment at the beginning of the study to compensate them for their expected out-of-pocket costs if they were in cost-sharing plans. Participants were studied for a three- to five-year period. While persons in plans with any cost sharing had significantly fewer physician visits and hospitalizations than persons in a free-care plan, no difference was found overall between plans with any amount of cost sharing and those with no cost sharing. Free care did result in better outcomes for adults with hypertension, as discussed earlier in this chapter, and in improved visual acuity. This experiment demonstrates both the sensitivity of health care utilization in the general population to cost sharing and the relative insensitivity of short-term (three- to five-year) health outcomes for the general population to cost sharing.

Negative Results

Some studies have reported worse health status for those with health insurance compared to uninsured adults. This result may be attributable to the fact that worse health status may lead to coverage by Medicare or Medicaid, as discussed in Chapter 2 (see Box 2.1 ) and Chapter 4 . However, the competing hypothesis, that health insurance is not associated with overall health status, must also be considered.

Hahn and Flood (1995) used NMES to examine health status by both income level and type and duration of insurance coverage. When SES and demographic characteristics, health behaviors, health care utilization, and Social Security disability status were controlled for in the analysis, self-reported health status was seen to be arrayed from highest to lowest as follows:

• privately insured for the full year,
• privately insured for part of the year and uninsured for part of the year,
• uninsured for the full year,
• publicly insured for part of the year, and
• publicly insured for the full year.

The authors concluded that the likeliest explanation for their results was that the poorer health status of those who qualify for public coverage was not fully accounted for in their analytic model, even though qualification on the basis of disability was considered explicitly (Hahn and Flood, 1995). An alternative (and possibly supplementary) hypothesis was that public insurance—Medicaid specifically—provided enrollees with access and services that were less effective than those provided by private insurance. Neither of these possible explanations can be eliminated based on the research that the Committee has reviewed.

A second study by Ross and Mirowsky (2000) based on the Survey of Aging, Status and the Sense of Control (ASOC) examined the claim that being uninsured contributes to the worse health of persons of lower SES. The ASOC survey included 2,600 adults between ages 18 and 95 at baseline in 1995, 38 percent of whom were 60 years or older. Participants were reinterviewed in 1998 (44 percent were lost to follow-up) (Ross and Mirowsky, 2000). Health status, functional status, and chronic conditions reported by participants at baseline were used to predict health status, functional status, and chronic conditions three years later. Changes in these measures between baseline and follow-up were also included as predictors of health status, functional status, and number of chronic conditions at follow-up in 1998. The authors concluded that privately insured and uninsured persons had similar health status at a three-year follow-up, adjusted for baseline health status, chronic conditions, and sociodemo-graphic characteristics, and that publicly insured persons had worse health status than privately insured and uninsured adults (Ross and Mirowsky, 2000).

The Committee does not find this study convincing in its conclusions because of both the study sample and its analytic design. The sample included a large proportion of persons over 65, all of whom have Medicare, and the substantial fraction of participants lost to follow-up differed systematically from those who were reinterviewed. By including changes in health condition over the study period as independent variables along with health measures at baseline, the authors may have built their findings into the predictive model itself. In addition, Medicare beneficiaries with supplemental health insurance were classified as privately insured; thus, those who counted as publicly insured included only those Medicare beneficiaries without supplemental policies (a lower-income subset of all Medicare beneficiaries) and Medicaid beneficiaries. This atypical classification scheme distorts the comparison between those with public and private health insurance.

This chapter has presented studies examining the impact of health insurance status on general measures of population health, on health care and clinical outcomes for specific conditions, and on the appropriate use of preventive services for the nonelderly adult population in the United States. This body of research yields largely consistent and significant findings about the relationship between health insurance and health-related outcomes. In summary, uninsured adults receive health care services that are less adequate and appropriate than those received by patients who have either public or private health insurance, and they have poorer clinical outcomes and poorer overall health than do adults with private health insurance. The specific findings discussed throughout this chapter are presented in Box 3.12 .

The Committee has assessed the research regarding the effects of health insurance status across a range of health conditions and services affecting adults. In each domain examined—

• preventive care and screening services,
• cancer care and outcomes,
• chronic disease management and patient outcomes,
• acute care services and outcomes for hospitalized adults, and
• overall health status and mortality,

health insurance improved the likelihood of appropriate care and was associated with better health outcomes. Health insurance appears to achieve these positive effects in part through facilitating ongoing care with a regular health care provider and reducing financial barriers to obtaining those services that constitute or contribute to appropriate care, including screening services, prescription drugs, and specialty mental health services.

Chapter 4 specifically addresses the question of the difference that providing health insurance to uninsured individuals and populations would make to their health and health care. The Committee assesses the potential impact of health insurance coverage on those uninsured adults who are most at risk for poor or adverse health-related outcomes, including the chronically ill, adults in late middle age, members of ethnic minorities, and adults in lower-income households. The chapter also reviews the features and characteristics of health insurance that account for its effectiveness in achieving better health outcomes, including both continuity of coverage and scope of benefits.

BOX 4.1 Conclusions

The Committee's conclusions are supported by the evidence and findings presented in Chapter 3 , which are largely based on observational studies.

• Health insurance is associated with better health outcomes for adults and with their receipt of appropriate care across a range of preventive, chronic, and acute care services. Adults without health insurance coverage die sooner and experience greater declines in health status over time than do adults with continuous coverage.
• Adults with chronic conditions, and those in late middle age, are the most likely to realize improved health outcomes as a result of gaining health insurance coverage because of their high probability of needing health care services.
• Population groups that are most at risk of lacking stable health insurance coverage and that have worse health status, including racial and ethnic minorities and lower-income adults, particularly would benefit from increased health insurance coverage. Increased coverage would likely reduce some of the racial and ethnic disparities in the utilization of appropriate health care services and might also reduce disparities in morbidity and mortality among ethnic groups.
• When health insurance affords access to providers and includes preventive and screening services, outpatient prescription drugs, and specialty mental health care, it is more likely to facilitate the receipt of appropriate care than when insurance does not have these features.
• Broad-based health insurance strategies across the entire uninsured population would be more likely to produce the benefits of enhanced health and life expectancy than would “rescue” programs aimed only at the seriously ill.

Chapter 2 discusses the features of observational (nonexperimental) studies that are necessary for methodological soundness. All quantified study results that are presented in this chapter and in Chapter 4 are significant at least at the 95 percent confidence interval. If results do not meet this level of statistical significance, the confidence interval is reported. See “confidence interval” in Appendix C for further discussion.

Earlier studies based on the 1986 Access to Care Survey and the 1982 NHIS had findings consistent with those of the more recent nationally representative sample surveys regarding receipt of preventive and screening services by those without health insurance (Hayward et al., 1988; Woolhandler and Himmelstein, 1988).

Enrollees in private managed care plans is the reference group; however, fee-for-service enrollees did not have significantly different screening rates from those of managed care enrollees. The odds ratio is the relative odds of having an outcome in the uninsured and insured groups. For example, if the odds of receiving a Pap test are 2:1 in a group of uninsured women (i.e., two of every three women or 67 percent receive the test) and the odds are 4:1 in a group of women with insurance (i.e., four of every five women, or 80 percent, receive the test), the odds ratio of uninsured compared to insured women is 0.5 (2:1/4:1). The OR is not a good estimate of the relative risk (the probability of been screened in the uninsured group divided by the probability of being screened in the insured group) because screening is not a rare event. Throughout this report the results of particular studies, if reported as odds ratios or as relative risks, will be presented as the ratio of the uninsured to the insured rates (in this example, as an OR of 0.5).

Comparing results presented in Potosky et al., 1998, and Breen et al., 2001, the gap in screening rates between insured and uninsured adults decreased between 1992 and 1998.

Smoking has been associated with an increased risk of colorectal cancer (Chao et al., 2000).

This hypertension result was an exception to the overall results for the RAND study, which did not find significant differences in outcomes for most conditions and dimensions of health. These results are discussed further in the General Health Outcomes section later in this chapter.

Notably, this same study found that persons with hypertension who had Medicare coverage only (which does not pay for outpatient prescription drugs) did not have a statistically significant difference in their likelihood of receiving antihypertensive medication than uninsured persons, while those who had Medicare plus Medicaid coverage or Medicare with private supplemental insurance were significantly more likely to have received drug therapy than uninsured persons with hypertension.

BRFSS has documented the use of recommended services among insured and uninsured persons with diabetes for two recent years. BRFSS collected information on diabetes management in 1994 in 22 jurisdictions (21 states and the District of Columbia) and in 1998 in 37 jurisdictions, representing 70 percent of the U.S. population (Beckles et al., 1998; Ayanian et al., 2000).

This standard of low hematocrit is below the hematocrit target range of 33–36 percent recommended by the National Kidney Foundation's Dialysis Outcomes Quality Initiative (NKF, 2001).

In this study, “late initiation” is defined as glomerular filtration rate of serum creatinine of <5 ml/min per 1.73 m 2 —a level substantially below both that recommended by the National Kidney Foundation (<10.5 ml/min) and the U.S. mean value at initiation (<7.1 ml/min) (Kausz et al., 2000).

The HIV Cost and Services Utilization Study (HSCUS), conducted by RAND and the Agency for Healthcare Research and Quality, was a probability sample of persons 18 years and older in the contiguous United States known to have HIV infection who had one visit for regular care (except in a military, prison, or emergency treatment facility) within a two-month period in 1996. Three rounds of interviews were conducted over a two-year period, 1996–1998, with between 2,267 and 2,864 subjects (Shapiro et al., 1999). The AIDS Costs and Utilization Survey, a predecessor study to HCSUS, with six waves over 18 months in 1991 and 1992, was not a probability sample (see Box 2.4 for further detail on these surveys).

Older studies that examine hospital-based care and outcomes according to insurance status across a range of diagnoses are summarized in Appendix B . The results of these studies are consistent with the findings discussed in text; however, many are based on hospital records that may be less relevant to the current hospital practice environment.

See Leape et al. (1999) for a description of the RAND methodology for determining appropriateness and its application to developing criteria for revascularization procedures.

The American College of Surgeons designates hospital EDs as trauma centers based on qualifying criteria related to staffing, resources, and services. There are four designations: Level 1, the most stringent requirements, for providing tertiary care on a regional basis; Level 2, similar services to a Level 1 center but without clinical research and prevention activities; Level 3, presence of emergency services, often in a rural area, with fewer specialized services and resources than Level 1 or 2 centers; and Level 4, usually in a rural area, describing hospitals and clinics that serve a triage function (Bonnie et al., 1999).

The authors designated an ISS of <16 as “minimal to moderate injury” and >16 as more severe. Overall, 59 percent of transferred patients had an ISS of <9.

The lowest income category included those with a family income of less than $7,000 at the initial interview (1971–1975).

A “major decline” in health was defined as a change from excellent, very good, or good health in 1992 to fair or poor health in 1996, or from fair health in 1992 to poor health in 1996 (Baker et al., 2001).

• Cite this Page Institute of Medicine (US) Committee on the Consequences of Uninsurance. Care Without Coverage: Too Little, Too Late. Washington (DC): National Academies Press (US); 2002. 3, Effects of Health Insurance on Health.
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Research Finds Significant Racial Disparities in Medicaid Re-enrollment

Among those who could not complete the process of renewing their Medicaid coverage, Black and Hispanic Americans were twice as likely as white people to lose their health insurance, a new study found.

By Noah Weiland

Reporting from Washington

Black and Hispanic Americans were twice as likely as white Americans to lose Medicaid last year because of an inability to complete renewal forms during a vast trimming of the program’s rolls, according to a study published on Monday in the journal JAMA Internal Medicine.

The findings from researchers at the Oregon Health & Science University, Harvard Medical School and Northwestern University are some of the first comprehensive data on race gathered after a pandemic-era policy that allowed Medicaid recipients to keep their coverage without regular eligibility checks ended last year.

More than 22 million low-income people have lost health care coverage at some point since April 2023, when the policy allowing continuous enrollment lapsed. The process of ending that policy — what federal and state officials have called “unwinding” — was one of the most drastic ruptures in the health safety net in a generation.

“Medicaid eligibility is complex, and then applying and keeping Medicaid coverage is a huge logistical barrier,” said Dr. Jane M. Zhu, an associate professor of medicine at the Oregon Health & Science University and one of the study’s authors. “What this analysis is showing is that these barriers have downstream spillover effects on particular communities.”

Researchers have found that increases in health insurance coverage across racial and ethnic groups from 2019 to 2022 were largely driven by Medicaid .

A provision in a coronavirus relief package passed by Congress in 2020 required states to keep recipients of the joint federal-state health insurance program for the poor continuously enrolled in exchange for additional federal funding.

By early 2023, more than 90 million people were enrolled in Medicaid and the Children’s Health Insurance Program, or more than one in four Americans. That was up from about 70 million people at the start of the coronavirus pandemic. About half of Medicaid enrollees are Black or Hispanic, and around 40 percent are white.

As of May, Medicaid enrollment had declined by more than 13 million, including more than five million children, according to the Georgetown University Center for Children and Families.

Many of those who lost coverage had incomes that were too high to qualify for Medicaid or had aged out of the program. But about 70 percent of those who lost coverage might have still been eligible and fell out of Medicaid because of bureaucratic reasons, such as failing to return paperwork on time, according to KFF, a nonprofit health policy research group.

The study published on Monday, which used Census Bureau survey data on health insurance enrollment from late March 2023 to October 2023, focused on the group of Americans who lost coverage for technical reasons.

There is otherwise little data from the unwinding to help researchers and federal officials understand who has been affected most from the shrinking of the Medicaid rolls.

The Centers for Medicare and Medicaid Services did not require states to report enrollment decisions during the unwinding by race or ethnicity. Only about 10 states have shared that data with the Biden administration.

“This data is vitally important and is information that we simply don’t have,” said Jennifer Tolbert, a Medicaid and state health policy expert at KFF.

The study did not determine that Black and Hispanic people were more likely than white people to lose Medicaid overall, only that they were a disproportionate number of the large subset of people who could not complete the renewal process.

Dr. Zhu acknowledged other limitations to the study. Because the data was self-reported, she said, some Medicaid recipients might have actually lost coverage because they were no longer eligible and believed they had been dropped from the program for bureaucratic reasons.

Health policy experts said the study exposed how diffuse and varied Medicaid administration could be, with sometimes significant demands on enrollees who might not have internet access or the ability to renew their coverage in person with state officials.

Ms. Tolbert pointed to findings from a recent KFF survey that showed Black and Hispanic adults were more likely than white adults to be asked to prove their residency as part of renewing their Medicaid coverage.

States have also used different technology and procedures to vet Medicaid eligibility, some of which contained glitches that led to program recipients being unfairly disenrolled.

The differences in state unwinding strategies have had “big implications on the ability of people to enroll or renew their coverage,” Ms. Tolbert said.

Dr. Zhu said fixes for the study’s findings should be “low-hanging fruit.”

“Do we have the right contact information? Are we sending enrollment and eligibility paperwork to the right people at the right time? Are we considering all different forms of automatically re-enrolling individuals?” she said. “These are all things that are systems issues, systems barriers that should be easy to address, and by addressing them can limit disruptions.”

Noah Weiland writes about health care for The Times. More about Noah Weiland

Health Equity

How a Rising Wave of Anti-DEI Politics Harms Patients and Health Workers

Third annual sandy schwartz memorial grand rounds focuses on spread of dei restrictions.

• Hoag Levins
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Despite evidence that diversity, equity, and inclusion (DEI) policies and programs improve the quality of health care delivery, a 2023 U.S. Supreme Court (SCOTUS) ruling that struck down race-conscious college admissions has spawned a rapidly widening anti-DEI political movement that is particularly threatening to the health care workforce. That’s according to Florence Momplaisir, MD, MSHP , Assistant Professor of Infectious Diseases (ID) at the University of Pennsylvania Perelman School of Medicine and Director of Implementation Science Core at the Penn Center for AIDS Research (CFAR).

Momplaisir, an LDI Senior Fellow, made the remarks as featured speaker at the May 23 third Annual Memorial Grand Rounds for one of the Perelman School’s most esteemed faculty members, J. Sanford “Sandy” Schwartz, MD . Organized by the Penn Medicine Division of General Internal Medicine (DGIM) the event honors the Professor of Medicine and Health Care Management, and former LDI Executive Director (1989-1998) who died in 2021. He was famed for his mentoring of generations of Penn health services research students, one of whom was Florence Momplaisir.

Momplaisir’s talk was made even more poignant by the fact that she herself came through an academic health-care oriented DEI program and then went on to become the Vice Chair for DEI in the Department of Infectious Diseases at the Perelman School of Medicine.

Felt Like an Imposter

“I am not a product of Ivy League education, so I felt very much an imposter when I came to Penn,” said the Haiti-born Momplaisir. “Sandy Schwartz created a space for me in places where I didn’t really feel that I belonged.”

Along with his mentorship, Schwartz also introduced her to the Harold Amos Medical Faculty Development Program (AMFDP), a DEI initiative that mentors and supports young medical professionals from underrepresented minorities in career development.

“If Sandy had not introduced me to the AMFDP program I wouldn’t be here today,” said Momplaisir.

Against this context, Momplaisir’s presentation looked back at the 2023 Students for Fair Admissions (SFFA) v. Harvard and SFFA v. University of North Carolina (UNC) SCOTUS ruling and its accelerating impact across the country as a growing number of states pass laws to defund public schools’ DEI offices and officers and remove diversity statements from hiring practices.

Shifting Socio-Political Climate

“The results of past DEI programming are clearly visible here at Penn as well as across the country. However, we need to address the elephant in the room which is the shifting socio-political climate when it comes to DEI,” said Momplaisir.

“It’s important to acknowledge that sometimes policies are driven by sentiment rather than data,” said Momplaisir. “What this Supreme Court ruling has done is swing the pendulum away from four years ago when the murder of George Floyd set off a national wave of racial awakening as institutions and individuals responded publicly with renewed commitments to DEI. Although the SCOTUS ruling was tailored to undergraduate admissions, it has wide implications beyond undergraduate admissions, including medical training and other aspects of the medical workforce.”

Momplaisir displayed a map showing that nine states have enacted DEI legal bans, 16 other state legislatures have introduced similar anti-DEI bills, and two more states are expected to introduce anti-DEI bills soon.

“The reach of these laws and bills is beyond just undergraduate admission and this is problematic because we know there is currently attrition for underrepresented minorities across all levels of the academic health care spectrum,” said Momplaisir. “So, the concern here is that by limiting opportunities so early, we’re further limiting the reach of underrepresented minorities for achievement in higher education which is the path to so many health care professions. This is particularly worrisome in workforce areas like infectious disease, because workforce diversity positions us in a place of strength to appropriately respond to emerging pandemics and epidemics and in areas like HIV/AIDS prevention and treatment.”

Pivotal Role in Academic Medicine

Momplaisir pointed to four areas where DEI plays a pivotal role in academic medicine:

• Impact on healthcare delivery where a diverse workforce environment fosters culturally responsive care and team performance. Many studies show that DEI improves the quality of health care delivery by elevating patient satisfaction and trust levels. And this is important because when patients form trusted relationships with their clinicians, they’re more likely to adhere to treatment recommendations and more likely to achieve outcome improvement.
• Integrating DEI principles into the organization mission . This makes it likely that the organization will engage in addressing health disparities by expanding patient access and utilization of health care services.
• Higher impact by research teams . There is a lot of data showing that teams with diverse perspectives achieve higher scientific impact, and when those findings are implemented into practice, they improve patient care.
• Diversity benefits all physicians and patients , not just racial and ethnic minorities. This is too often lost in translation. Data shows that white physicians in racially diverse medical schools are more culturally responsive and report feeling more comfortable treating diverse patient populations.

DEI and Employee Retention

Momplaisir pointed to the 2021 Press Ganey survey on the impact of workforce diversity on employee retention. It queried more than 400,000 health care workers in 118 health systems to compare DEI perceptions against worker intention to leave the organization. It found that twice as many employees intended to leave when DEI was not prioritized in their organization. The risk of an employee leaving within three years was more than four times higher if employees felt that their organization undervalued people from diverse backgrounds.

“In terms of team performance, it’s very important to understand that psychological safety is a key ingredient in effective teamwork,” said Momplaisir. “That’s when there’s an environment of comfort in which team members can share ideas, take risks, and be vulnerable without fear of negative consequences.”

“A lot of this team psychological safety work data was pioneered by Harvard Business School Professor Amy C. Edmondson, PhD , who studies teaming, psychological safety, and organizational learning,” said Momplaisir. “She looked at the relationship of team diversity to team performance. What she found was that even with very diverse teams with low psychological safety levels, team performance was poor. However, when diversity and psychological safety were high, team performance went through the roof, demonstrating that both are needed to create innovative and highly performing teams.”

“This tells us why diversity is important and why we need to encourage diverse environments across academic medicine and open pathways that invite students from underrepresented backgrounds to pursue academic studies in the health care professions,” said Momplaisir.

Editor, Digital Publications

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Research shows that people in the Netherlands are overpaying for health insurance

More than half of the residents in the Netherlands paid over a hundred euros more than they should have for their health insurance last year, according to the Netherlands Authority for Consumers and Markets (ACM) and the Dutch Healthcare Authority (NZa). The reason is because a wide range of health insurance packages are offered which either does not differ from competing offers, or barely differs from other products.

As a result, people struggle to understand the differences leading to the higher payments. That is why they usually miss out on cheaper alternatives when selecting their basic health insurance, the organizations said after conducting a joint study.

An average of 103 euros per insured person could have been saved last year, according to ACM and NZa’s calculations. The research from the two regulators also showed that some people have a more expensive basic health insurance than necessary because of a condition set by the insurance provider for more extensive additional coverage.

Offering the same policies for different prices, also known as premium differentiation, is banned in the Netherlands. However, sometimes insurance companies offer relatively minor variations, such as a difference of five percentage points in the reimbursement rate of non-contracted care.

In this case, a price differential still technically exists. Prices may also vary when insurance companies offer essentially the same policy under various brand names and subsidiaries owned by that company.

The ACM and NZa want the law on health insurance premium price differentials expanded, to prevent the oversupply of similar health insurance packages.

Reporting by ANP