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The past half-century has been marked by major changes in the treatment of mental illness: important advances in understanding mental illnesses, increases in spending on mental health care and support of people with mental illnesses, and the availability of new medications that are easier for the patient to tolerate. Although these changes have made things better for those who have mental illness, they are not quite enough. In Better But Not Well, Richard G. Frank and Sherry A. Glied examine the well-being of people with mental illness in the United States over the past fifty years, addressing issues such as economics, treatment, standards of living, rights, and stigma. Marshaling a range of new empirical evidence, they first argue that people with mental illness—severe and persistent disorders as well as less serious mental health conditions—are faring better today than in the past. Improvements have come about for unheralded and unexpected reasons. Rather than being a result of more effective mental health treatments, progress has come from the growth of private health insurance and of mainstream social programs—such as Medicaid, Supplemental Security Income, housing vouchers, and food stamps—and the development of new treatments that are easier for patients to tolerate and for physicians to manage. The authors remind us that, despite the progress that has been made, this disadvantaged group remains worse off than most others in society. The \"mainstreaming\" of persons with mental illness has left a policy void, where governmental institutions responsible for meeting the needs of mental health patients lack resources and programmatic authority. To fill this void, Frank and Glied suggest that institutional resources be applied systematically and routinely to examine and address how federal and state programs affect the well-being of people with mental illness.

Rigorous analysis of levels and trends in exposure to leading risk factors and quantification of their effect on human health are important to identify where public health is making progress and in which cases current efforts are inadequate. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 provides a standardised and comprehensive assessment of the magnitude of risk factor exposure, relative risk, and attributable burden of disease. GBD 2019 estimated attributable mortality, years of life lost (YLLs), years of life lived with disability (YLDs), and disability-adjusted life-years (DALYs) for 87 risk factors and combinations of risk factors, at the global level, regionally, and for 204 countries and territories. GBD uses a hierarchical list of risk factors so that specific risk factors (eg, sodium intake), and related aggregates (eg, diet quality), are both evaluated. This method has six analytical steps. (1) We included 560 risk–outcome pairs that met criteria for convincing or probable evidence on the basis of research studies. 12 risk–outcome pairs included in GBD 2017 no longer met inclusion criteria and 47 risk–outcome pairs for risks already included in GBD 2017 were added based on new evidence. (2) Relative risks were estimated as a function of exposure based on published systematic reviews, 81 systematic reviews done for GBD 2019, and meta-regression. (3) Levels of exposure in each age-sex-location-year included in the study were estimated based on all available data sources using spatiotemporal Gaussian process regression, DisMod-MR 2.1, a Bayesian meta-regression method, or alternative methods. (4) We determined, from published trials or cohort studies, the level of exposure associated with minimum risk, called the theoretical minimum risk exposure level. (5) Attributable deaths, YLLs, YLDs, and DALYs were computed by multiplying population attributable fractions (PAFs) by the relevant outcome quantity for each age-sex-location-year. (6) PAFs and attributable burden for combinations of risk factors were estimated taking into account mediation of different risk factors through other risk factors. Across all six analytical steps, 30 652 distinct data sources were used in the analysis. Uncertainty in each step of the analysis was propagated into the final estimates of attributable burden. Exposure levels for dichotomous, polytomous, and continuous risk factors were summarised with use of the summary exposure value to facilitate comparisons over time, across location, and across risks. Because the entire time series from 1990 to 2019 has been re-estimated with use of consistent data and methods, these results supersede previously published GBD estimates of attributable burden. The largest declines in risk exposure from 2010 to 2019 were among a set of risks that are strongly linked to social and economic development, including household air pollution; unsafe water, sanitation, and handwashing; and child growth failure. Global declines also occurred for tobacco smoking and lead exposure. The largest increases in risk exposure were for ambient particulate matter pollution, drug use, high fasting plasma glucose, and high body-mass index. In 2019, the leading Level 2 risk factor globally for attributable deaths was high systolic blood pressure, which accounted for 10·8 million (95% uncertainty interval [UI] 9·51–12·1) deaths (19·2% [16·9–21·3] of all deaths in 2019), followed by tobacco (smoked, second-hand, and chewing), which accounted for 8·71 million (8·12–9·31) deaths (15·4% [14·6–16·2] of all deaths in 2019). The leading Level 2 risk factor for attributable DALYs globally in 2019 was child and maternal malnutrition, which largely affects health in the youngest age groups and accounted for 295 million (253–350) DALYs (11·6% [10·3–13·1] of all global DALYs that year). The risk factor burden varied considerably in 2019 between age groups and locations. Among children aged 0–9 years, the three leading detailed risk factors for attributable DALYs were all related to malnutrition. Iron deficiency was the leading risk factor for those aged 10–24 years, alcohol use for those aged 25–49 years, and high systolic blood pressure for those aged 50–74 years and 75 years and older. Overall, the record for reducing exposure to harmful risks over the past three decades is poor. Success with reducing smoking and lead exposure through regulatory policy might point the way for a stronger role for public policy on other risks in addition to continued efforts to provide information on risk factor harm to the general public. Bill & Melinda Gates Foundation.

Widening economic inequality in the USA has been accompanied by increasing disparities in health outcomes. The life expectancy of the wealthiest Americans now exceeds that of the poorest by 10–15 years. This report, part of a Series on health and inequality in the USA, focuses on how the health-care system, which could reduce income-based disparities in health, instead often exacerbates them. Other articles in this Series address population health inequalities, and the health effects of racism, mass incarceration, and the Affordable Care Act (ACA). Poor Americans have worse access to care than do wealthy Americans, partly because many remain uninsured despite coverage expansions since 2010 due to the ACA. For individuals with private insurance, rising premiums and cost sharing have undermined wage gains and driven many households into debt and even bankruptcy. Meanwhile, the share of health-care resources devoted to care of the wealthy has risen. Additional reforms that move forward, rather than backward, from the ACA are sorely needed to mitigate health and health-care inequalities and reduce the financial burdens of medical care borne by non-wealthy Americans.

Objectives. To examine whether living in a rural versus urban area differentially exposes populations to social conditions associated with disparities in access to health care. Methods. We linked Medical Expenditure Panel Survey (2005–2010) data to geographic data from the American Community Survey (2005–2009) and Area Health Resource File (2010). We categorized census tracts as rural and urban by using the Rural–Urban Commuting Area Codes. Respondent sample sizes ranged from 49 839 to 105 306. Outcomes were access to a usual source of health care, cholesterol screening, cervical screening, dental visit within recommended intervals, and health care needs met. Results. African Americans in rural areas had lower odds of cholesterol screening (odds ratio[OR] = 0.37; 95% confidence interval[CI] = 0.25, 0.57) and cervical screening (OR = 0.48; 95% CI = 0.29, 0.80) than African Americans in urban areas. Whites had fewer screenings and dental visits in rural versus urban areas. There were mixed results for which racial/ethnic group had better access. Conclusions. Rural status confers additional disadvantage for most of the health care use measures, independently of poverty and health care supply.

Although spending rates on health care and social services vary substantially across the states, little is known about the possible association between variation in state-level health outcomes and the allocation of state spending between health care and social services. To estimate that association, we used state-level repeated measures multivariable modeling for the period 2000-09, with region and time fixed effects adjusted for total spending and state demographic and economic characteristics and with one- and two-year lags. We found that states with a higher ratio of social to health spending (calculated as the sum of social service spending and public health spending divided by the sum of Medicare spending and Medicaid spending) had significantly better subsequent health outcomes for the following seven measures: adult obesity; asthma; mentally unhealthy days; days with activity limitations; and mortality rates for lung cancer, acute myocardial infarction, and type 2 diabetes. Our study suggests that broadening the debate beyond what should be spent on health care to include what should be invested in health-not only in health care but also in social services and public health-is warranted.

The Health Reform Monitoring Survey (HRMS) was launched in 2013 as a mechanism to obtain timely information on the Affordable Care Act (ACA) during the period before federal government survey data for 2013 and 2014 will be available. Based on a nationally representative, probability-based Internet panel, the HRMS provides quarterly data for approximately 7,400 nonelderly adults and 2,400 children on insurance coverage, access to health care, and health care affordability, along with special topics of relevance to current policy and program issues in each quarter. For example, HRMS data from summer 2013 show that more than 60 percent of those targeted by the health insurance exchanges struggle with understanding key health insurance concepts. This raises concerns about some people's ability to evaluate trade-offs when choosing health insurance plans. Assisting people as they attempt to enroll in health coverage will require targeted education efforts and staff to support those with low health insurance literacy. [PUBLICATION ABSTRACT]

Hospital admissions in the US fell dramatically with the onset of the coronavirus disease 2019 (COVID-19) pandemic. However, little is known about differences in admissions patterns among patient groups or the extent of the rebound. In this study of approximately one million medical admissions from a large, nationally representative hospitalist group, we found that declines in non-COVID-19 admissions from February to April 2020 were generally similar across patient demographic subgroups and exceeded 20 percent for all primary admission diagnoses. By late June/early July 2020, overall non-COVID-19 admissions had rebounded to 16 percent below prepandemic baseline volume (8 percent including COVID-19 admissions). Non-COVID-19 admissions were substantially lower for patients residing in majority-Hispanic neighborhoods (32 percent below baseline) and remained well below baseline for patients with pneumonia (-44 percent), chronic obstructive pulmonary disease/asthma (-40 percent), sepsis (-25 percent), urinary tract infection (-24 percent), and acute ST-elevation myocardial infarction (-22 percent). Health system leaders and public health authorities should focus on efforts to ensure that patients with acute medical illnesses can obtain hospital care as needed during the pandemic to avoid adverse outcomes.

Estimates of health care costs associated with excess weight are needed to inform the development of cost-effective obesity prevention efforts. However, commonly used cost estimates are not sensitive to changes in weight across the entire body mass index (BMI) distribution as they are often based on discrete BMI categories. We estimated continuous BMI-related health care expenditures using data from the Medical Expenditure Panel Survey (MEPS) 2011-2016 for 175,726 respondents. We adjusted BMI for self-report bias using data from the National Health and Nutrition Examination Survey (NHANES) 2011-2016, and controlled for potential confounding between BMI and medical expenditures using a two-part model. Costs are reported in $US 2019. We found a J-shaped curve of medical expenditures by BMI, with higher costs for females and the lowest expenditures occurring at a BMI of 20.5 for adult females and 23.5 for adult males. Over 30 units of BMI, each one-unit BMI increase was associated with an additional cost of $253 (95% CI $167-$347) per person. Among adults, obesity was associated with $1,861 (95% CI $1,656-$2,053) excess annual medical costs per person, accounting for $172.74 billion (95% CI $153.70-$190.61) of annual expenditures. Severe obesity was associated with excess costs of $3,097 (95% CI $2,777-$3,413) per adult. Among children, obesity was associated with $116 (95% CI $14-$201) excess costs per person and $1.32 billion (95% CI $0.16-$2.29) of medical spending, with severe obesity associated with $310 (95% CI $124-$474) excess costs per child. Higher health care costs are associated with excess body weight across a broad range of ages and BMI levels, and are especially high for people with severe obesity. These findings highlight the importance of promoting a healthy weight for the entire population while also targeting efforts to prevent extreme weight gain over the life course.

Under the Affordable Care Act, up to thirteen million adults have the opportunity to obtain health insurance through an expansion of the Medicaid program. A great deal of effort is currently being devoted to eligibility verification, outreach, and enrollment. We look beyond these important first-phase challenges to consider what people who are transitioning back to the community after incarceration need to receive effective care. It will be possible to deliver cost-effective, high-quality care to this population only if assistance is coordinated between the correctional facility and the community, and across diverse treatment and support organizations in the community. This article discusses several examples of successful coordination of care for formerly incarcerated people, such as Project Bridge and the Community Partnerships and Supportive Services for HIV-Infected People Leaving Jail (COMPASS) program in Rhode Island and the Transitions Clinic program that operates in ten US cities. To promote broader adoption of successful models, we offer four policy recommendations for overcoming barriers to integrating individuals into sustained, community-based care following their release from incarceration. [PUBLICATION ABSTRACT]

In this Series paper, we examine how mass incarceration shapes inequality in health. The USA is the world leader in incarceration, which disproportionately affects black populations. Nearly one in three black men will ever be imprisoned, and nearly half of black women currently have a family member or extended family member who is in prison. However, until recently the public health implications of mass incarceration were unclear. Most research in this area has focused on the health of current and former inmates, with findings suggesting that incarceration could produce some short-term improvements in physical health during imprisonment but has profoundly harmful effects on physical and mental health after release. The emerging literature on the family and community effects of mass incarceration points to negative health impacts on the female partners and children of incarcerated men, and raises concerns that excessive incarceration could harm entire communities and thus might partly underlie health disparities both in the USA and between the USA and other developed countries. Research into interventions, policies, and practices that could mitigate the harms of incarceration and the post-incarceration period is urgently needed, particularly studies using rigorous experimental or quasi-experimental designs.