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Population with household expenditures on health > 10% of total household expenditure or income (%), 2015-2021: 4.61%
Population with household expenditures on health > 25% of total household expenditure or income (%), 2015-2021: 0.89%

Source: World health statistics 2025: monitoring health for the SDGs, Sustainable Development Goals. Tables of health statistics by country and area, WHO region and globally. Geneva: World Health Organization; 2025. Licence: CC BY-NC-SA 3.0 IGO.

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Population aged 15 years and over rating their own health as good or very good, by income quintile, 2021
– Highest quintile: 92.5%
– Lowest quintile: 74.8%
– Total: 86.4%

Source: OECD (2023), Health at a Glance 2023: OECD Indicators, OECD Publishing, Paris, doi.org/10.1787/7a7afb35-en.

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Share of Household Income, 2010-2019:
    Bottom 40%: 16%; Top 20%: 47%; Bottom 20%: 5%
Gini Coefficient, 2010-2019: 42
Palma Index of Income Inequality, 2010-2019: 2.0

Note: Gini coefficient – Gini index measures the extent to which the distribution of income (or, in some cases, consumption expenditure) among individuals or households within an economy deviates from a perfectly equal distribution. A Gini index of 0 represents perfect equality, while an index of 100 implies perfect inequality.
Palma index of income inequality – Palma index is defined as the ratio of the richest 10% of the population’s share of gross national income divided by the poorest 40%’s share.

Source: United Nations Children’s Fund, The State of the World’s Children 2023: For every child, vaccination, UNICEF Innocenti – Global Office of Research and Foresight, Florence, April 2023.

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“During 2018 to 2022, we observed persistent disparities in pregnancy-related death among American Indian and Alaska Native women and non-Hispanic Black women. Late maternal death, disorders related to pregnancy, and hypertensive disorders accounted for more than 60% of the overall deaths in these groups. Previous US data suggested persistent disparities among American Indian and Alaska Native and Black persons vs Asian, Native Hawaiian, Other Pacific Islander, Hispanic, and White persons.^18,19 These disparities do not appear to have improved over time.¹⁸ In the current analysis, we found particularly higher mortality rates due to late maternal death among American Indian and Alaska Native women and non-Hispanic Black women, indicating that these groups may face disparities in access to postnatal care, as well as other socioeconomic and systemic challenges impacting maternal health outcomes.

“Moreover, our cause-specific analyses indicated that mental and behavior disorders and drug and alcohol induced death contributed to 21.2% of late maternal deaths. A similar analysis from the United Kingdom and Ireland suggested that psychiatric causes led to almost a quarter of late maternal deaths during 2009 to 2014.²⁰ Maternal anxiety and depression are the most common complications of childbirth.²¹ According to the American Psychological Association, the prevalence of depression ranges from 8.5% to 11.0% during pregnancy and from 6.5% to 12.9% during the first year post partum.²² It is critical to address mental health needs as part of efforts to reduce pregnancy-related death.”

Source: Chen Y, Shiels MS, Uribe-Leitz T, et al. Pregnancy-Related Deaths in the US, 2018-2022. JAMA Netw Open. 2025;8(4):e254325. doi:10.1001/jamanetworkopen.2025.4325

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“Hispanic and non-Hispanic Black adults experienced higher unmet need for medical care due to cost than non-Hispanic White adults in 2019.

“● Hispanic (15.6%) and non-Hispanic Black (14.4%) adults aged 18–64 delayed or did not receive needed medical care due to cost more often than non-Hispanic White adults (11.2%) in 2019 (Table UnmtNd [https://www.cdc.gov/nchs/hus/contents2020-2021.htm#Table-UnmtNd]).

“● Nonreceipt of needed prescription drugs due to cost was higher in non-Hispanic Black adults aged 18–64 (10.3%) than in Hispanic (8.2%) and non-Hispanic White (7.4%) adults in 2019 (Table UnmtNd [https://www.cdc.gov/nchs/hus/contents2020-2021.htm#Table-UnmtNd]).”

Source: National Center for Health Statistics. Health, United States, 2020–2021: Annual Perspective. Hyattsville, Maryland. 2023. dx.doi.org/10.15620/cdc:122044

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“Hispanic people were at least twice as likely as other racial and ethnic groups to lack health insurance in 2019.

“● The percentage of people under age 65 who were uninsured decreased from 17.5% in 2009 to 11.0% in 2018 (Figure 4). In 2019, 12.0% of people under age 65 were uninsured.

“● The percentage of people under age 65 who were uninsured decreased for all racial and ethnic groups from 2009 to 2018. However, in 2019, Hispanic people continued to be more likely to be uninsured (22.5%) than non-Hispanic Black (11.2%), non-Hispanic White (8.8%), and non-Hispanic Asian (6.2%) people (Figure 4).”

Source: National Center for Health Statistics. Health, United States, 2020–2021: Annual Perspective. Hyattsville, Maryland. 2023. dx.doi.org/10.15620/cdc:122044

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“Research suggests that social determinants of health are related to transportation, the environment, wealth, agriculture, education, employment and housing. Overall, the United States does poorly on social determinants of health indicators and on aligning policy across sectors (Raphael, 2007; Marmot & Bell, 2009). For example, the generosity of family policy – as measured by the total expenditure level – is correlated with child poverty levels, and the United States has the poorest performance among the high-income countries on this measure (Baker, Metzler & Galea, 2005; Commission on Social Determinants of Health, 2008, p. 11).

“While it is generally agreed that health disparities across racial and ethnic groups are mainly caused by factors outside the healthcare system, access to medical care is nonetheless one critical factor in reducing these disparities. However, there is no government department in the United States that focuses on the intersectoral policy topic of the social determinants of health and how they influence the health of the population. There is little doubt that policies related to these variables influence health. These include racism (both individual and institutional), income inequality, socioeconomic status, the distribution of power, social support networks, stress levels, early life experience, social inclusion / exclusion, unemployment, physical activity / inactivity and the redistribution of other resources (Lynch et al., 1998; Wilkinson & Marmot, 2003; Feagin and Bennefield, 2014).”

Source: Rice T, Rosenau P, Unruh LY, Barnes AJ, van Ginneken E. United States of America: Health system review. Health Systems in Transition, 2020; 22(4): pp. i–441.

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“In this study, we present estimates of mortality for 19 causes of death by county and racial–ethnic group from 2000 to 2019 in the USA. These estimates provide a far more complete and detailed view than previously available of racial–ethnic and geographical inequalities in mortality for a nearly exhaustive set of causes of death. We found that racial–ethnic disparities in mortality were ubiquitous, occurring across a range of causes of death and across locations in the USA. At the same time, our results showed remarkable heterogeneity in mortality by cause, by racial–ethnic group, by location, and over time. Collectively, these results underscore the pressing need to address widespread disparities in mortality and longevity in the USA, as well the importance of detailed local data for informing efforts to reduce or eliminate these disparities.

“Across 19 causes of death, 3110 counties, and five racial–ethnic groups, our study reveals certain repeated patterns, especially with respect to racial–ethnic disparities in mortality. For nearly all of the causes considered in this analysis, the AIAN and Black populations had substantially higher mortality rates than the White population nationally. The same was true in most counties, although the magnitude of disparity typically varied substantially. This repeated pattern of racial–ethnic disparities across causes of death and across locations strongly suggests shared root causes.²⁷ An extensive body of evidence links systemic racism to poor health and increased risk of early death,^28, 29 in large part through the impact of systemic racism on the socioeconomic status of minoritised individuals and populations,^27, 29 but also through other pathways, including residential segregation,³⁰ mass incarceration,³¹ chronic stress,^32, 33 and discrimination in health care,^34, 35 among others. Given these multiple pathways, the magnitude of the impact of systemic racism on health and longevity is likely to vary by cause of death, by local context, and by affected population group, which might explain some of the variation we observed in this study in the magnitude of racial–ethnic disparities across causes and locations. The estimates that we present here could be useful for future research aimed at better understanding the impact of systemic racism in a local context over time, and to support developing strategies to mitigate the resulting and persistent harms. However, mitigating the effects of systemic racism can only go so far, and dismantling systemic racism will ultimately be required to eliminate racial–ethnic disparities in mortality.^27, 36 To this end, there is a pressing need for better measurement of various domains of racism. Residential segregation is relatively easily quantified with use of readily available data from the census,³⁷ and thus has been studied extensively. Previous research has identified other domains of racism that have also been studied, but less extensively, including socioeconomic status, criminal justice, immigration and border enforcement, and political participation, among others.^38, 39 Nonetheless, the lack of widely available and appropriate data remains a substantial challenge for quantifying and studying most domains of racism apart from residential segregation.^38, 39 Thus, it is crucial that future research focus on data collection and operationalisation of measures of various domains of racism that identify mechanisms that increase risk of poor health outcomes and premature mortality, as well as salient intervention points.

“Mortality for Asian and Latino populations, both nationally and in many counties, is lower than for the White population for most causes of death. The reasons for this finding are complex, and not fully understood, but previous research has highlighted the central role of migration in explaining these differences.^40, 41 Approximately two-thirds of the Asian population (ie, people who identify as Asian alone, not in combination with other racial groups) and one-third of the Latino population residing in the USA were born outside of the USA, compared with 13·7% of all Americans (all racial–ethnic groups combined).⁴² Explanations for lower mortality rates among foreign-born individuals include positive selection for emigration (ie, individuals in good health are more likely to emigrate than those in poor health)^40, 41 and differences in certain health risk factors between foreign-born and US-born individuals (eg, lower cigarette smoking prevalence among foreign-born individuals).⁴³ It is also possible that some foreign-born individuals who have a decline in health might return to their country of origin before death, and thus not be included in USA mortality statistics. However, research focused on the Latino population has found that this phenomenon only accounts for a small part of the mortality difference between Latino and White populations.⁴⁴ The generally lower mortality rates observed for Asian and Latino populations should not be construed as indicating that these two populations do not experience or are not harmed by racism, as there is plentiful evidence to the contrary,^45, 46 although the effects of racism, and particularly structural or institutional forms of racism, are understudied for these populations.^38, 47 Other factors, including the mortality advantage observed among foreign-born individuals, might somewhat offset the negative impact of systemic racism, which could explain why these populations have generally lower mortality rates despite being affected by systemic racism. Moreover, previous research has highlighted important differences in mortality within the Asian and Latino groups in the USA—eg, differences within the Latino population by racial identity³ and within both groups by national origin.^{48, 49, 50, 51} Our study similarly shows considerable variation across counties in mortality within the Asian and Latino groups, and that the mortality rate for each of these populations compares less favourably with that among the White population in some locations for some causes. This spatial variation is likely to be related to these other differences that have been noted among populations within the Asian and Latino groups. For instance, NHOPI populations are known to have higher mortality and worse health outcomes than Asian populations across a range of health conditions.^{48, 50, 52, 53} Thus, unsurprisingly, we found that counties in Hawaii—where the size of the NHOPI population relative to the Asian population is much higher than in the USA overall—often had relatively high mortality for the combined Asian and NHOPI population, such that, for many causes, mortality was higher for this population than for the White population, in contrast to the national pattern.”

Source: Laura Dwyer-Lindgren, Parkes Kendrick, Yekaterina O Kelly, Mathew M Baumann, Kelly Compton, Brigette F Blacker, Farah Daoud, Zhuochen Li, Farah Mouhanna, Hasan Nassereldine, Chris Schmidt, Dillon O Sylte, Simon I Hay, George A Mensah, Anna M Nápoles, Eliseo J Pérez-Stable, Christopher J L Murray, Ali H Mokdad, Cause-specific mortality by county, race, and ethnicity in the USA, 2000–19: a systematic analysis of health disparities, The Lancet, 2023, ISSN 0140-6736, doi.org/10.1016/S0140-6736(23)01088-7.

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“Black and Indigenous individuals and other people of color face significant barriers to obtaining quality health care services in the US.¹ Inequalities by race and ethnicity in access to care have been attributed to variation in insurance coverage²; socioeconomic and geographic inequities that affect health and access to health care^3,4; and structural, institutional, and interpersonal racism within the health care system.^5,6 These barriers to health care utilization and treatment reflect and perpetuate structural racism in US society more broadly.⁷“

Source: Dieleman JL, Chen C, Crosby SW, et al. US Health Care Spending by Race and Ethnicity, 2002-2016. JAMA. 2021;326(7):649–659. doi:10.1001/jama.2021.9937

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“Evidence Review  Analysis of 2016-2019 data from the Medical Expenditure Panel Survey (MEPS) and state-level Behavioral Risk Factor Surveillance System (BRFSS) and 2016-2018 mortality data from the National Vital Statistics System and 2018 IPUMS American Community Survey. There were 87 855 survey respondents to MEPS, 1 792 023 survey respondents to the BRFSS, and 8 416 203 death records from the National Vital Statistics System.

“Findings  In 2018, the estimated economic burden of racial and ethnic health inequities was $421 billion (using MEPS) or $451 billion (using BRFSS data) and the estimated burden of education-related health inequities was $940 billion (using MEPS) or $978 billion (using BRFSS). Most of the economic burden was attributable to the poor health of the Black population; however, the burden attributable to American Indian or Alaska Native and Native Hawaiian or Other Pacific Islander populations was disproportionately greater than their share of the population. Most of the education-related economic burden was incurred by adults with a high school diploma or General Educational Development equivalency credential. However, adults with less than a high school diploma accounted for a disproportionate share of the burden. Although they make up only 9% of the population, they bore 26% of the costs.”

Source: LaVeist TA, Pérez-Stable EJ, Richard P, et al. The Economic Burden of Racial, Ethnic, and Educational Health Inequities in the US. JAMA. 2023;329(19):1682–1692. doi:10.1001/jama.2023.5965

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“Generally, after adjustment for differences in levels of education between the two groups, AIAN adults living on tribal lands had a higher prevalence of diagnosed diabetes compared with AIAN adults living off tribal lands. In contrast, AIAN adults living on tribal lands had a lower prevalence of current asthma or arthritis compared with AIAN adults living off tribal lands, although this difference was not statistically significant. The prevalence of heart attack was similar between groups.

“Compared with AIAN adults living off tribal lands, AIAN adults living on tribal lands had a higher percentage of engaging in both preventive (usual place of care and doctor’s visit in past 12 months) and emergent (urgent care visit in past 12 months and emergency room visit in past 12 months) care, although some estimates were underpowered (did not have a sufficiently large sample size) to detect a significant difference. The percentage of AIAN adults with unmet medical or mental healthcare needs in the past 12 months was lower among those who lived on tribal lands compared with those who did not. Regarding mental health, AIAN adults living on tribal lands were less likely to have regular feelings of anxiety or depression compared with those living off tribal lands, although this difference was not statistically significant. The percentage of AIAN adults who received any mental health treatment in the past 12 months was similar between these groups. The findings from this report align with previous research using the combined 2005–2014 National Survey on Drug Use and Health regarding tribal land residence and some mental health indicators (13).

“Zhao and colleagues (12) used data from the 2017 Behavioral Risk Factor Surveillance System to look at differences in current cigarette smoking, heavy drinking, binge drinking, physical activity, and obesity between non-Hispanic AIAN and non-Hispanic White adults and by region. The authors observed variation in the prevalence of these health-related behavioral risk factors by region. For instance, non-Hispanic AIAN adults in Alaska and the northern plains regions had the highest prevalence of current smoking and binge drinking, while those in the southwest and Pacific Coast regions had the lowest prevalence. Although the current report does not include health behavior estimates because they do not meet National Center for Health Statistics data presentation standards, diseases associated with these behaviors, namely diabetes, also varied by tribal land residence.”

Source: Ng AE, Adjaye-Gbewonyo D, Vahratian A. Health conditions and health care use among American Indian and Alaska Native adults by tribal land residential status: United States, 2019–2021. National Health Statistics Reports; no 185. Hyattsville, MD: National Center for Health Statistics. 2023. DOI: dx.doi.org/10.15620/cdc:125982.

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“Consistent with recent reports, our epidemiologic assessment at the county level indicates that the burden of COVID-19 mortality is higher in counties with high proportions of Black residents [4,5,6,7,8, 13]. We found that this association is independent of clinical risk factors [39] – many of which disproportionately affect Black residents [7]. Importantly, the full SDH [Social Determinants of Health] model results showed that when all SDH measures are included in a regression, there is no longer a relationship between Black race and COVID-19 mortality. Furthermore, in our subgroup analysis stratified by SDH, we found that percent Black residents in a county is a predictor of COVID-19 mortality only in counties with higher degrees of adverse SDH, thus suggesting that social constructs and policies mediate the disparate COVID-19 outcomes in Black Americans. This precludes genetic differences as a possible explanation for COVID-19 racial disparities and challenges the harmful belief that racial disparities in illness primarily have a biological basis. Overall, this study provides both qualitative and quantitative evidence that SDH play a significant role in influencing increased COVID-19 mortality for Black Americans.

“In the full SDH regression model, the two particularly relevant SDH that emerged as significant positive predictors of COVID-19 mortality included percent adults without HS diploma and percent households without internet. Education frequently emerges as a strong predictor of health outcomes, including mortality, in studies examining SDH [26, 36]. The relationship between Black race and education is largely attributable to long-standing educational discrimination, residential segregation, and marginalization [36]. The finding that internet connectivity is also associated with COVID-19 mortality is particularly relevant in the climate of a pandemic. The internet is essential for social distancing, remote work, and online learning, as well as access to timely and accurate information from public health entities. We were only able to analyze data for this study at the county level; however, a more detailed analysis that includes rural vs suburban vs urban locales may also provide more information about how regional variations in internet connectivity may impact COVID-19 mortality.

“Ultimately, these findings support the hypothesis that SDH are important drivers of COVID-19 racial disparities for Black Americans in the U.S. Our results are consistent over a diverse set of SDH variables representing areas of economic stability, healthcare access, educational attainment, and social contexts. This suggests that racial disparities in COVID-19 outcomes for Black Americans stem from multiple sources which compound to create the overall effect. This study provides a method for public health policymakers to identify areas with high adverse SDH, which is crucial because these are high-risk areas for racial disparities in COVID-19 mortality and other harmful health outcomes. Furthermore, this study raises the possibility of targeting changes to SDH as a mechanism to reduce racial disparities in COVID-19 outcomes. These findings also may allow policymakers to monitor SDH indicators as a metric for improvement in health equity in the future. Multiple prior studies have linked SDH to structural racism, which is deeply ingrained in the U.S. legal and economic systems, shaped by historical injustices, and perpetuated by bias. As a next step, further research is needed to evaluate the effect of validated markers of structural racism on COVID-19 mortality, and to explore these associations over time as the pandemic evolves [41, 42]. Additional studies related to bias experienced within the healthcare system related to testing, triage, and treatment may also shed additional insights on COVID-19 racial disparities.”

Source: Dalsania, A.K., Fastiggi, M.J., Kahlam, A. et al. The Relationship Between Social Determinants of Health and Racial Disparities in COVID-19 Mortality. J. Racial and Ethnic Health Disparities (2021). https://doi.org/10.1007/s40615-020-00952-y.

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“Most evaluations of health equity policy have focused on the effects of individual laws. However, multiple laws’ combined effects better reflect the crosscutting nature of structurally racist legal regimes. To measure the combined effects of multiple laws, we used latent class analysis, a method for detecting unobserved “subgroups” in a population, to identify clusters of US states based on thirteen structural racism–related legal domains in 2013. We identified three classes of states: one with predominantly harmful laws (n=29), another with predominantly protective laws (n=15), and a third with a mix of both (n=7). Premature mortality rates overall—defined as deaths before age seventy-five per 100,000 population—were highest in states with predominantly harmful laws, which included eighteen states with past Jim Crow laws.”

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“Inequities in care for women can stem from gender biases resulting in discrimination and stigma in clinical care settings. Structural sexism also can significantly influence health policies, including access to care and research funding. These factors affect health care access and quality and result in dismissing symptoms, which leads to underdiagnosis, misdiagnosis, and differential outcomes in treating and managing chronic conditions. For example, women’s experiences of symptoms such as pain are often underestimated compared to men, so women are less likely to receive proper treatment. Stigma and discrimination toward conditions such as HIV and substance use disorder lead to women being reluctant to seek care and preventive services. Incorporating patient-centered outcomes and the patient’s voice and lived experience can center research on women and can enhance the quality of care for women.”

Source: National Academies of Sciences, Engineering, and Medicine. 2024. Advancing Research on Chronic Conditions in Women. Washington, DC: The National Academies Press. doi.org/10.17226/27757.

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“Multiple social identities (race and ethnicity, cultural norms, gender identity, sexual orientation) interact with structural and social determinants of health to influence chronic conditions in women across the life course. Women who experience adverse childhood experiences, sexual and physical trauma, and interpersonal violence are at higher risk; these are impartially influenced by societal gender roles and expectations. Exposures to early life experiences and traumatic events across the life course shape the outcomes associated with chronic conditions, but these factors are not well incorporated in studies. In addition, adverse childhood experiences, sexual and physical trauma, and interpersonal violence have been associated with female-specific and gynecologic conditions, such as vulvodynia and menopausal symptoms, and other chronic conditions, including depression, substance use disorder, chronic pain, fibromyalgia, migraine, and Alzheimer’s disease.”

Source: National Academies of Sciences, Engineering, and Medicine. 2024. Advancing Research on Chronic Conditions in Women. Washington, DC: The National Academies Press. doi.org/10.17226/27757.

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“Wealthy and educated people are more likely to use HDHPs [High-Deductible Health Plans] with HSAs [Health Savings Accounts] and to contribute more to their accounts than people with less income and education. The inherent regressivity of this policy was originally justified by the belief that HDHPs with HSAs would generate an increase in cost-consciousness, and therefore in efficiency. In fact, however, people who have HDHPs with HSAs are becoming less likely over time to report financial barriers to access to care— the source of HDHP cost-consciousness—than are people with private insurance plans not linked to HSAs.

“In short, HSAs are a tax advantage for better-off people, masquerading as a health care efficiency increase that was never very likely and is not occurring now. There is no remaining justification for a regressive tax break that failed to achieve its policy goal and is used disproportionately by higher-income people.”

Source: Sherry A. Glied, Dahlia K. Remler, and Mikaela Springsteen, Health Savings Accounts No Longer Promote Consumer Cost-Consciousness, Health Affairs 2022 41:6, 814-820

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“We found that in the past two years, health systems in the US have publicly committed approximately $2.5 billion toward directly addressing social determinants of health such as housing, food security, and job training. This figure is dwarfed by health systems’ overall community benefit spending, which is estimated to be over $60 billion per year.¹³ Nonetheless, it represents a substantial investment.

“Historically, hospitals have tended to provide community benefit through uncompensated or subsidized care rather than through investment in activities not directly related to health. In one analysis of the $2.6 billion spent by all fifty-three North Carolina tax-exempt hospitals on community benefit, only 0.7 percent ($18.2 million) was spent on community investments such as affordable housing, economic development, and environmental improvements.²⁰ Nationally, spending on all kinds of community health improvement activities (most of which are directly related to health) is 5 percent or less of total community benefit spending.^13,14 Yet spending on community activities may be effective. For instance, although a recent study found no association between overall community benefit spending and readmission rates, hospitals in the top quintile of spending that was directed toward the community had significantly lower readmission rates than those in the bottom quintile.²¹

“We found significant differences in characteristics between health systems that publicly announced making investments focused on social determinants and those that did not. The clear predominance of sectarian and other nonprofit institutions in making these investments and the absence of for-profit institutions suggest that health systems may be driven to invest in social determinants more by mission and values than by the potential for direct financial returns. However, the fact that investments are disproportionately being made by systems that are in Medicaid expansion states, in the BPCI Initiative, or in an ACO suggests that business-case considerations may also be playing a role. The complexity of making tangible commitments to improving social determinants of health is reflected in the fact that investing systems tend to be substantially larger and therefore potentially have more capacity than noninvesting systems.

“Our results are consistent with national survey data, such as the data from a 2017 survey by the Deloitte Center for Health Solutions. In this survey of 300 hospitals and health systems, 88 percent reported screening patients for social needs (62 percent screened them systematically), but only 30 percent reported having a formal relationship with community-based providers for their entire target population.²² The survey did not explore the extent to which health systems directly funded community programs. Compared to smaller hospitals and those that were for profit or independent, respectively, larger hospitals and those that were public or not for profit were more likely to screen patients for social needs—which is consistent with our finding that those are the hospitals that are also most likely to engage in direct community investment.

“A key feature of this study was our ability to identify the specific social determinants that each program focused on. Prior studies have been able to quantify only overall community investment. By far the most popular focus area of the programs we identified was housing, which accounted for two-thirds of total investment. Housing is one social determinant in which investing has the most immediately apparent potential return, even though it is one of the determinants in which interventions are especially complex and costly. Housing investment also has face validity, and housing is a common pain point for health care professionals, who struggle with housing-insecure patients. These findings are consistent with those in the general literature.¹² In one systematic review of thirty-nine studies up to 2014 that addressed social determinants and measured health outcomes, the largest number of the studies (twelve) focused on housing, and ten of them reported benefits to health outcomes, costs, or both.¹² Several subsequent publications have also shown benefits from housing-focused interventions.^23–25

Source: Leora I. Horwitz, Carol Chang, Harmony N. Arcilla, and James R. Knickman, Quantifying Health Systems’ Investment In Social Determinants Of Health, By Sector, 2017–19, Health Affairs 2020 39:2, 192-198.

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“In general, however, the evidence for health outcome improvements from interventions focused on social determinants is thin. A different systematic review of interventions related to social determinants that included sixty-seven articles published up to 2017 found that only 30 percent (twenty articles) reported health outcomes and 27 percent (eighteen) reported health care costs.²⁶ Furthermore, only 22 percent (fifteen) showed any benefit to health outcomes, 10 percent (seven) showed a reduction in emergency department visits or hospitalizations, and 7 percent (five) showed any benefit to health care costs. In fact, programs focused on multiple social determinants, food security, and legal interventions all had more articles showing positive impacts on outcomes, compared to those focused on housing. However, the quality of studies in most of the articles reviewed was poor. This is very little evidence on which to base billions in investment and may partially explain why investments to date have lagged. In the Deloitte survey, 48 percent of respondents reported that evidence for improved outcomes would increase their investments in social needs activities.²²“

Source: Leora I. Horwitz, Carol Chang, Harmony N. Arcilla, and James R. Knickman, Quantifying Health Systems’ Investment In Social Determinants Of Health, By Sector, 2017–19, Health Affairs 2020 39:2, 192-198.

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World Health Systems Facts is a project of the Real Reporting Foundation. We provide reliable statistics and other data from authoritative sources regarding health systems and policies in the US and sixteen other nations.

Page last updated August 6, 2025 by Doug McVay, Editor.