Life Expectancy Changes During the COVID-19 Pandemic, 2019-2021: Highly Racialized Deaths in Young and Middle Adulthood in the United States as Compared With Other High-Income Countries.

We estimated changes in life expectancy between 2019 and 2021 in the United States (in the total population and separately for 5 racial/ethnic groups) and 20 high-income peer countries. For each country's total population, we decomposed the 2019-2020 and 2020-2021 changes in life expectancy by age. For US populations, we also decomposed the life expectancy changes by age and number of coronavirus disease 2019 (COVID-19) deaths. Decreases in US life expectancy in 2020 (1.86 years) and 2021 (0.55 years) exceeded mean changes in peer countries (a 0.39-year decrease and a 0.23-year increase, respectively) and disproportionately involved COVID-19 deaths in midlife. In 2020, Native American, Hispanic, Black, and Asian-American populations experienced larger decreases in life expectancy and greater losses in midlife than did the White population. In 2021, the White population experienced the largest decrease in US life expectancy, although life expectancy in the Native American and Black populations remained much lower. US losses during the pandemic were more severe than in peer countries and disproportionately involved young and middle-aged adults, especially adults of this age in racialized populations. The mortality consequences of the COVID-19 pandemic deepened a US disadvantage in longevity that has been growing for decades and exacerbated long-standing racial inequities in US mortality.

Neighborhood Predictors of Poor Prenatal Care and Well-Child Visit Attendance.

PURPOSE: Women and children continue to miss preventive visits. Which neighborhood factors predict inadequate prenatal care (PNC) and well-child visit (WCV) attendance remain unclear. DESCRIPTION: In a retrospective case-control study at Virginia Commonwealth University Health System, mothers with less than 50% adherence or initiation after 5 months gestation were eligible as cases and those with ≥ 80% adherence and initiation before 5 months were eligible as controls. Children in the lowest quintile of adherence were eligible as cases and those with ≥ 80% of adherence were eligible as controls. Cases and controls were randomly selected at a 1:2 ratio and matched on birth month. Covariates were derived from the 2018 American Community Survey. A hotspot was defined as a zip code tabulation area (ZCTA) with a proportion of controls less than 0.66. ZCTAs with fewer than 5 individuals were excluded. Weighted quantile regression was used to determine which covariates were most associated with inadequate attendance. ASSESSMENT: We identified 38 and 35 ZCTAs for the PNC and WCV analyses, respectively. Five of 11 hotspots for WCV were also hotspots for PNC. Education and income predicted 51% and 34% of the variation in missed PNCs, respectively; language, education and transportation difficulties explained 33%, 29%, and 17% of the variation in missed WCVs, respectively. Higher proportions of Black residents lived in hotspots of inadequate PCV and WCV attendance. CONCLUSION: Neighborhood-level factors performed well in predicting inadequate PCV and WCV attendance. The disproportionate impact impact of inadequate PCV and WCV in neighborhoods where higher proportions of Black people lived highlights the potential influence of systemic racism and segregation on healthcare utilization.

Racial and Ethnic Disparities in All-Cause and Cause-Specific Mortality Among US Youth.

Importance: Mortality rates in US youth have increased in recent years. An understanding of the role of racial and ethnic disparities in these increases is lacking. Objective: To compare all-cause and cause-specific mortality trends and rates among youth with Hispanic ethnicity and non-Hispanic American Indian or Alaska Native, Asian or Pacific Islander, Black, and White race. Design, Setting, and Participants: This cross-sectional study conducted temporal analysis (1999-2020) and comparison of aggregate mortality rates (2016-2020) for youth aged 1 to 19 years using US Centers for Disease Control and Prevention Wide-Ranging Online Data for Epidemiologic Research database. Data were analyzed from June 30, 2023, to January 17, 2024. Main Outcomes and Measures: Pooled, all-cause, and cause-specific mortality rates per 100 000 youth (hereinafter, per 100 000) for leading underlying causes of death were compared. Injuries were classified by mechanism and intent. Results: Between 1999 and 2020, there were 491 680 deaths among US youth, including 8894 (1.8%) American Indian or Alaska Native, 14 507 (3.0%) Asian or Pacific Islander, 110 154 (22.4%) Black, 89 251 (18.2%) Hispanic, and 267 452 (54.4%) White youth. Between 2016 and 2020, pooled all-cause mortality rates were 48.79 per 100 000 (95% CI, 46.58-51.00) in American Indian or Alaska Native youth, 15.25 per 100 000 (95% CI, 14.75-15.76) in Asian or Pacific Islander youth, 42.33 per 100 000 (95% CI, 41.81-42.86) in Black youth, 21.48 per 100 000 (95% CI, 21.19-21.77) in Hispanic youth, and 24.07 per 100 000 (95% CI, 23.86-24.28) in White youth. All-cause mortality ratios compared with White youth were 2.03 (95% CI, 1.93-2.12) among American Indian or Alaska Native youth, 0.63 (95% CI, 0.61-0.66) among Asian or Pacific Islander youth, 1.76 (95% CI, 1.73-1.79) among Black youth, and 0.89 (95% CI, 0.88-0.91) among Hispanic youth. From 2016 to 2020, the homicide rate in Black youth was 12.81 (95% CI, 12.52-13.10) per 100 000, which was 10.20 (95% CI, 9.75-10.66) times that of White youth. The suicide rate for American Indian or Alaska Native youth was 11.37 (95% CI, 10.30-12.43) per 100 000, which was 2.60 (95% CI, 2.35-2.86) times that of White youth. The firearm mortality rate for Black youth was 12.88 (95% CI, 12.59-13.17) per 100 000, which was 4.14 (95% CI, 4.00-4.28) times that of White youth. American Indian or Alaska Native youth had a firearm mortality rate of 6.67 (95% CI, 5.85-7.49) per 100 000, which was 2.14 (95% CI, 1.88- 2.43) times that of White youth. Black youth had an asthma mortality rate of 1.10 (95% CI, 1.01-1.18) per 100 000, which was 7.80 (95% CI, 6.78-8.99) times that of White youth. Conclusions and Relevance: In this study, racial and ethnic disparities were observed for almost all leading causes of injury and disease that were associated with recent increases in youth mortality rates. Addressing the increasing disparities affecting American Indian or Alaska Native and Black youth will require efforts to prevent homicide and suicide, especially those events involving firearms.

Association Between Partisan Affiliation of State Governments and State Mortality Rates Before and During the COVID-19 Pandemic.

Policy Points The increasing political polarization of states reached new heights during the COVID-19 pandemic, when response plans differed sharply across party lines. This study found that states with Republican governors and larger Republican majorities in legislatures experienced higher death rates during the COVID-19 pandemic-and in preceding years-but these associations often lost statistical significance after adjusting for the average income and health status of state populations and for the policy orientations of the states. Future research may help clarify whether the higher death rates in these states result from policy choices or have other explanations, such as the tendency of voters with lower incomes or poorer health to elect Republican candidates. CONTEXT: Increasing polarization of states reached a high point during the COVID-19 pandemic, when the party affiliation of elected officials often predicted their policy response. The health consequences of these divisions are unclear. Prior studies compared mortality rates based on presidential voting patterns, but few considered the partisan orientation of state officials. This study examined whether the partisan orientation of governors or legislatures was associated with mortality outcomes during the COVID-19 pandemic. METHODS: Data on deaths and the partisan orientation of governors and legislators were obtained from the Centers for Disease Control and Prevention and the National Conference of State Legislatures, respectively. Linear regression was used to measure the association between Republican representation (percentage of seats held) in legislatures and (1) age-adjusted, all-cause mortality rates (AAMRs) in 2015-2021 and (2) excess death rates during three phases of the COVID-19 pandemic, controlling for median household income, the prevalence of four risk factors (obesity, chronic obstructive pulmonary disease, heart attack, stroke), and state policy orientation. Associations between excess death rates and the governor's party were also examined. FINDINGS: States with Republican governors or greater Republican representation in legislatures experienced higher AAMRs during 2015-2021, lower excess death rates during Phase 1 of the COVID-19 pandemic (weeks ending March 28, 2020, through June 13, 2020), and higher excess death rates in Phases 2 and 3 (weeks ending June 20, 2020, through April 30, 2022; p < 0.05). Most associations lost statistical significance after adjustment for control variables. CONCLUSIONS: Mortality was higher in states with Republican governors and greater Republican legislative representation before and during much of the pandemic. Observed associations could be explained by the adverse effects of policy choices, reverse causality (e.g., popularity of Republican candidates in states with lower socioeconomic and health status), or unmeasured factors that predominate in states with Republican leaders.

Falling Behind: The Growing Gap in Life Expectancy Between the United States and Other Countries, 1933-2021.

Objectives. To document the evolution of the US life expectancy disadvantage and regional variation across the US states. Methods. I obtained life expectancy estimates in 2022 from the United Nations, the Human Mortality Database, and the US Mortality Database, and calculated changes in growth rates, US global position (rank), and state-level trends. Results. Increases in US life expectancy slowed from 1950 to 1954 (0.21 years/annum) and 1955 to 1973 (0.10 years/annum), accelerated from 1974 to 1982 (0.34 years/annum), and progressively deteriorated from 1983 to 2009 (0.15 years/annum), 2010 to 2019 (0.06 years/annum), and 2020 to 2021 (-0.97 years/annum). Other countries experienced faster growth in each phase except 1974 to 1982. During 1933 to 2021, 56 countries on 6 continents surpassed US life expectancy. Growth in US life expectancy was slowest in Midwest and South Central states. Conclusions. The US life expectancy disadvantage began in the 1950s and has steadily worsened over the past 4 decades. Dozens of globally diverse countries have outperformed the United States. Causal factors appear to have been concentrated in the Midwest and South. Public Health Implications. Policies that differentiate the United States from other countries and circumstances associated with the Midwest and South may have contributed. (Am J Public Health. 2023;113(9):970-980. https://doi.org/10.2105/AJPH.2023.307310).

Changes in the Relationship Between Income and Life Expectancy Before and During the COVID-19 Pandemic, California, 2015-2021.

Importance: The COVID-19 pandemic caused a large decrease in US life expectancy in 2020, but whether a similar decrease occurred in 2021 and whether the relationship between income and life expectancy intensified during the pandemic are unclear. Objective: To measure changes in life expectancy in 2020 and 2021 and the relationship between income and life expectancy by race and ethnicity. Design, Setting, and Participants: Retrospective ecological analysis of deaths in California in 2015 to 2021 to calculate state- and census tract-level life expectancy. Tracts were grouped by median household income (MHI), obtained from the American Community Survey, and the slope of the life expectancy-income gradient was compared by year and by racial and ethnic composition. Exposures: California in 2015 to 2019 (before the COVID-19 pandemic) and 2020 to 2021 (during the COVID-19 pandemic). Main Outcomes and Measures: Life expectancy at birth. Results: California experienced 1 988 606 deaths during 2015 to 2021, including 654 887 in 2020 to 2021. State life expectancy declined from 81.40 years in 2019 to 79.20 years in 2020 and 78.37 years in 2021. MHI data were available for 7962 of 8057 census tracts (98.8%; n = 1 899 065 deaths). Mean MHI ranged from $21 279 to $232 261 between the lowest and highest percentiles. The slope of the relationship between life expectancy and MHI increased significantly, from 0.075 (95% CI, 0.07-0.08) years per percentile in 2019 to 0.103 (95% CI, 0.098-0.108; P < .001) years per percentile in 2020 and 0.107 (95% CI, 0.102-0.112; P < .001) years per percentile in 2021. The gap in life expectancy between the richest and poorest percentiles increased from 11.52 years in 2019 to 14.67 years in 2020 and 15.51 years in 2021. Among Hispanic and non-Hispanic Asian, Black, and White populations, life expectancy declined 5.74 years among the Hispanic population, 3.04 years among the non-Hispanic Asian population, 3.84 years among the non-Hispanic Black population, and 1.90 years among the non-Hispanic White population between 2019 and 2021. The income-life expectancy gradient in these groups increased significantly between 2019 and 2020 (0.038 [95% CI, 0.030-0.045; P < .001] years per percentile among Hispanic individuals; 0.024 [95% CI: 0.005-0.044; P = .02] years per percentile among Asian individuals; 0.015 [95% CI, 0.010-0.020; P < .001] years per percentile among Black individuals; and 0.011 [95% CI, 0.007-0.015; P < .001] years per percentile among White individuals) and between 2019 and 2021 (0.033 [95% CI, 0.026-0.040; P < .001] years per percentile among Hispanic individuals; 0.024 [95% CI, 0.010-0.038; P = .002] years among Asian individuals; 0.024 [95% CI, 0.011-0.037; P = .003] years per percentile among Black individuals; and 0.013 [95% CI, 0.008-0.018; P < .001] years per percentile among White individuals). The increase in the gradient was significantly greater among Hispanic vs White populations in 2020 and 2021 (P < .001 in both years) and among Black vs White populations in 2021 (P = .04). Conclusions and Relevance: This retrospective analysis of census tract-level income and mortality data in California from 2015 to 2021 demonstrated a decrease in life expectancy in both 2020 and 2021 and an increase in the life expectancy gap by income level relative to the prepandemic period that disproportionately affected some racial and ethnic minority populations. Inferences at the individual level are limited by the ecological nature of the study, and the generalizability of the findings outside of California are unknown.

US State Policies, Politics, and Life Expectancy.

Policy Points Changes in US state policies since the 1970s, particularly after 2010, have played an important role in the stagnation and recent decline in US life expectancy. Some US state policies appear to be key levers for improving life expectancy, such as policies on tobacco, labor, immigration, civil rights, and the environment. US life expectancy is estimated to be 2.8 years longer among women and 2.1 years longer among men if all US states enjoyed the health advantages of states with more liberal policies, which would put US life expectancy on par with other high-income countries. CONTEXT: Life expectancy in the United States has increased little in previous decades, declined in recent years, and become more unequal across US states. Those trends were accompanied by substantial changes in the US policy environment, particularly at the state level. State policies affect nearly every aspect of people's lives, including economic well-being, social relationships, education, housing, lifestyles, and access to medical care. This study examines the extent to which the state policy environment may have contributed to the troubling trends in US life expectancy. METHODS: We merged annual data on life expectancy for US states from 1970 to 2014 with annual data on 18 state-level policy domains such as tobacco, environment, tax, and labor. Using the 45 years of data and controlling for differences in the characteristics of states and their populations, we modeled the association between state policies and life expectancy, and assessed how changes in those policies may have contributed to trends in US life expectancy from 1970 through 2014. FINDINGS: Results show that changes in life expectancy during 1970-2014 were associated with changes in state policies on a conservative-liberal continuum, where more liberal policies expand economic regulations and protect marginalized groups. States that implemented more conservative policies were more likely to experience a reduction in life expectancy. We estimated that the shallow upward trend in US life expectancy from 2010 to 2014 would have been 25% steeper for women and 13% steeper for men had state policies not changed as they did. We also estimated that US life expectancy would be 2.8 years longer among women and 2.1 years longer among men if all states enjoyed the health advantages of states with more liberal policies. CONCLUSIONS: Understanding and reversing the troubling trends and growing inequalities in US life expectancy requires attention to US state policy contexts, their dynamic changes in recent decades, and the forces behind those changes. Changes in US political and policy contexts since the 1970s may undergird the deterioration of Americans' health and longevity.

Educational Disparities in Adult Mortality Across U.S. States: How Do They Differ, and Have They Changed Since the Mid-1980s?

Adult mortality varies greatly by educational attainment. Explanations have focused on actions and choices made by individuals, neglecting contextual factors such as economic and policy environments. This study takes an important step toward explaining educational disparities in U.S. adult mortality and their growth since the mid-1980s by examining them across U.S. states. We analyzed data on adults aged 45-89 in the 1985-2011 National Health Interview Survey Linked Mortality File (721,448 adults; 225,592 deaths). We compared educational disparities in mortality in the early twenty-first century (1999-2011) with those of the late twentieth century (1985-1998) for 36 large-sample states, accounting for demographic covariates and birth state. We found that disparities vary considerably by state: in the early twenty-first century, the greater risk of death associated with lacking a high school credential, compared with having completed at least one year of college, ranged from 40 % in Arizona to 104 % in Maryland. The size of the disparities varies across states primarily because mortality associated with low education varies. Between the two periods, higher-educated adult mortality declined to similar levels across most states, but lower-educated adult mortality decreased, increased, or changed little, depending on the state. Consequently, educational disparities in mortality grew over time in many, but not all, states, with growth most common in the South and Midwest. The findings provide new insights into the troubling trends and disparities in U.S. adult mortality.

A state-level study of opioid use disorder treatment access and neonatal abstinence syndrome.

BACKGROUND: Adult opioid use and neonatal abstinence syndrome (NAS) are growing public health problems in the United States (U.S.). Our objective was to determine how opioid use disorder treatment access impacts the relationship between adult opioid use and NAS. METHODS: We conducted a cross-sectional state-level ecologic study using 36 states with available Healthcare Cost and Utilization Project State Inpatient Databases in 2014. Opioid use disorder treatment access was determined by the: 1) proportion of people needing but not receiving substance use treatment, 2) density of buprenorphine-waivered physicians, and 3) proportion of individuals in outpatient treatment programs (OTPs). The incidence of NAS was defined as ICD-9 code 779.5 (drug withdrawal syndrome in newborn) from any discharge diagnosis field per 1000 live births in that state. RESULTS: Unmet need for substance use disorder treatment correlated with NAS (r = 0.54, 95% CI: 0.26-0.73). The correlation between adult illicit drug use/dependence and NAS was higher in states with a lower density of buprenorphine-waivered physicians and individuals in OTPs. CONCLUSIONS: Measures of opioid use disorder treatment access dampened the correlation between illicit drug use/dependence and NAS. Future studies using community- or individual-level data may be better poised to answer the question of whether or not opioid use disorder treatment access improves NAS relative to adult opioid use.

Life Expectancy and Mortality Rates in the United States, 1959-2017.

Importance: US life expectancy has not kept pace with that of other wealthy countries and is now decreasing. Objective: To examine vital statistics and review the history of changes in US life expectancy and increasing mortality rates; and to identify potential contributing factors, drawing insights from current literature and an analysis of state-level trends. Evidence: Life expectancy data for 1959-2016 and cause-specific mortality rates for 1999-2017 were obtained from the US Mortality Database and CDC WONDER, respectively. The analysis focused on midlife deaths (ages 25-64 years), stratified by sex, race/ethnicity, socioeconomic status, and geography (including the 50 states). Published research from January 1990 through August 2019 that examined relevant mortality trends and potential contributory factors was examined. Findings: Between 1959 and 2016, US life expectancy increased from 69.9 years to 78.9 years but declined for 3 consecutive years after 2014. The recent decrease in US life expectancy culminated a period of increasing cause-specific mortality among adults aged 25 to 64 years that began in the 1990s, ultimately producing an increase in all-cause mortality that began in 2010. During 2010-2017, midlife all-cause mortality rates increased from 328.5 deaths/100 000 to 348.2 deaths/100 000. By 2014, midlife mortality was increasing across all racial groups, caused by drug overdoses, alcohol abuse, suicides, and a diverse list of organ system diseases. The largest relative increases in midlife mortality rates occurred in New England (New Hampshire, 23.3%; Maine, 20.7%; Vermont, 19.9%) and the Ohio Valley (West Virginia, 23.0%; Ohio, 21.6%; Indiana, 14.8%; Kentucky, 14.7%). The increase in midlife mortality during 2010-2017 was associated with an estimated 33 307 excess US deaths, 32.8% of which occurred in 4 Ohio Valley states. Conclusions and Relevance: US life expectancy increased for most of the past 60 years, but the rate of increase slowed over time and life expectancy decreased after 2014. A major contributor has been an increase in mortality from specific causes (eg, drug overdoses, suicides, organ system diseases) among young and middle-aged adults of all racial groups, with an onset as early as the 1990s and with the largest relative increases occurring in the Ohio Valley and New England. The implications for public health and the economy are substantial, making it vital to understand the underlying causes.

The New Crisis of Increasing All-Cause Mortality in US Children and Adolescents.

This Viewpoint discusses increased rates in pediatric mortality by age and cause between 1999 and 2021.

Harnessing Information Technology to Inform Patients Facing Routine Decisions: Cancer Screening as a Test Case.

PURPOSE: Technology could transform routine decision making by anticipating patients' information needs, assessing where patients are with decisions and preferences, personalizing educational experiences, facilitating patient-clinician information exchange, and supporting follow-up. This study evaluated whether patients and clinicians will use such a decision module and its impact on care, using 3 cancer screening decisions as test cases. METHODS: Twelve practices with 55,453 patients using a patient portal participated in this prospective observational cohort study. Participation was open to patients who might face a cancer screening decision: women aged 40 to 49 who had not had a mammogram in 2 years, men aged 55 to 69 who had not had a prostate-specific antigen test in 2 years, and adults aged 50 to 74 overdue for colorectal cancer screening. Data sources included module responses, electronic health record data, and a postencounter survey. RESULTS: In 1 year, one-fifth of the portal users (11,458 patients) faced a potential cancer screening decision. Among these patients, 20.6% started and 7.9% completed the decision module. Fully 47.2% of module completers shared responses with their clinician. After their next office visit, 57.8% of those surveyed thought their clinician had seen their responses, and many reported the module made their appointment more productive (40.7%), helped engage them in the decision (47.7%), broadened their knowledge (48.1%), and improved communication (37.5%). CONCLUSIONS: Many patients face decisions that can be anticipated and proactively facilitated through technology. Although use of technology has the potential to make visits more efficient and effective, cultural, workflow, and technical changes are needed before it could be widely disseminated.

Methods for Evaluating Respondent Attrition in Web-Based Surveys.

BACKGROUND: Electronic surveys are convenient, cost effective, and increasingly popular tools for collecting information. While the online platform allows researchers to recruit and enroll more participants, there is an increased risk of participant dropout in Web-based research. Often, these dropout trends are simply reported, adjusted for, or ignored altogether. OBJECTIVE: To propose a conceptual framework that analyzes respondent attrition and demonstrates the utility of these methods with existing survey data. METHODS: First, we suggest visualization of attrition trends using bar charts and survival curves. Next, we propose a generalized linear mixed model (GLMM) to detect or confirm significant attrition points. Finally, we suggest applications of existing statistical methods to investigate the effect of internal survey characteristics and patient characteristics on dropout. In order to apply this framework, we conducted a case study; a seventeen-item Informed Decision-Making (IDM) module addressing how and why patients make decisions about cancer screening. RESULTS: Using the framework, we were able to find significant attrition points at Questions 4, 6, 7, and 9, and were also able to identify participant responses and characteristics associated with dropout at these points and overall. CONCLUSIONS: When these methods were applied to survey data, significant attrition trends were revealed, both visually and empirically, that can inspire researchers to investigate the factors associated with survey dropout, address whether survey completion is associated with health outcomes, and compare attrition patterns between groups. The framework can be used to extract information beyond simple responses, can be useful during survey development, and can help determine the external validity of survey results.