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Scholars rely on accurate population and mortality data to inform efforts regarding the coronavirus disease 2019 (COVID-19) pandemic, with age-specific mortality rates of high importance because of the concentration of COVID-19 deaths at older ages. Population counts, the principal denominators for calculating age-specific mortality rates, will be subject to noise infusion in the United States with the 2020 census through a disclosure avoidance system based on differential privacy. Using empirical COVID-19 mortality curves, the authors show that differential privacy will introduce substantial distortion in COVID-19 mortality rates, sometimes causing mortality rates to exceed 100 percent, hindering our ability to understand the pandemic. This distortion is particularly large for population groupings with fewer than 1,000 persons: 40 percent of all county-level age-sex groupings and 60 percent of race groupings. The U.S. Census Bureau should consider a larger privacy budget, and data users should consider pooling data to minimize differential privacy’s distortion.

The application of a currently proposed differential privacy algorithm to the 2020 United States Census data and additional data products may affect the usefulness of these data, the accuracy of estimates and rates derived from them, and critical knowledge about social phenomena such as health disparities. We test the ramifications of applying differential privacy to released data by studying estimates of US mortality rates for the overall population and three major racial/ethnic groups. We ask how changes in the denominators of these vital rates due to the implementation of differential privacy can lead to biased estimates. We situate where these changes are most likely to matter by disaggregating biases by population size, degree of urbanization, and adjacency to a metropolitan area. Our results suggest that differential privacy will more strongly affect mortality rate estimates for non-Hispanic blacks and Hispanics than estimates for non-Hispanic whites. We also find significant changes in estimated mortality rates for less populous areas, with more pronounced changes when stratified by race/ethnicity. We find larger changes in estimated mortality rates for areas with lower levels of urbanization or adjacency to metropolitan areas, with these changes being greater for non-Hispanic blacks and Hispanics. These findings highlight the consequences of implementing differential privacy, as proposed, for research examining population composition, particularly mortality disparities across racial/ethnic groups and along the urban/rural continuum. Overall, they demonstrate the challenges in using the data products derived from the proposed disclosure avoidance methods, while highlighting critical instances where scientific understandings may be negatively impacted.

Descriptions of the effect of the implementation of a new disclosure avoidance system (DAS), which relies on differential privacy, emphasize the impact of our understanding of contemporary social and health dynamics. However, focusing on overall population may obscure important changes in subpopulation indicators such as age-specific rates resulting from this implementation. The author provides a visualization that compares infant mortality rates calculated using 2009–2011 county-level average death counts and denominators derived from the traditional and proposed DASs. Death counts come from the National Center for Health Statistics and denominators come from the first U.S. Census Bureau demonstration products. These visualizations indicate that infant mortality rates produced using the proposed DAS are different from those produced using the traditional methods, with higher variation observed for nonmetropolitan counties and areas with smaller populations. These findings suggest that the proposed DAS will hinder our ability to understand contemporary health dynamics in the United States.

Few nationally representative studies examine suicidality and substance use during 2020; as such, we explored the role of substance use disorders (SUDs) on suicidality among adults and adolescents in 2020. Data were derived from N = 26,084 adult participants, representing 240 million U.S. adults weighted, and N = 5,723 adolescent participants, representing 25 million U.S. adolescents (12-17 years.). Separate logistic regressions for adults and adolescents were used to assess the association of DSM-5 SUDs, related factors, and suicidal thoughts and behaviors (ideation, planning, and attempts). In 2020, adults with SUDs were nearly 4 times more likely to seriously consider suicide (aOR = 3.94, 95% CI: 3.19, 4.86), 3 times more likely to make a suicide plan (aOR = 3.09, 95% CI: 2.25, 4.25), and nearly 4 times more likely to attempt suicide (aOR = 3.77, 95% CI: 2.29, 6.19) than adults without SUDs. Adolescents with SUDs were 4 times more likely to consider suicide (aOR = 3.69, 95% CI: 2.47, 5.51), 5 times as likely to make a suicide plan (aOR = 5.14, 95% CI: 3.25, 8.13) and to attempt suicide (aOR = 5.27, 95% CI: 2.91, 9.53) than adolescents without SUDs. Adult females and individuals experiencing poverty were twice as likely to attempt suicide than adult males and individuals not living in poverty. Adolescent females were 3-5 times more likely to seriously consider, plan, and attempt suicide than adolescent males. Interventions to curb suicidality among individuals with SUDs are crucial.

Measuring immigrant health selection is crucial for understanding population health in immigrant-receiving countries. Recently, studies have measured health selection using respondents’ perceptions of their health in comparison with those in their home countries. Yet we do not know how well this measure captures health selection. Using the New Immigrant Survey, the authors visualize respondents’ self-reported levels of health selection stratified by self-rated health and by sending country. The visualization indicates that immigrants from the same country who rate their health the same still give very different answers when asked to compare their health with those in their home countries. These variations were observed for immigrants from all top five sending countries and at every level of self-reported health but are much larger among those who rate their health less favorably. Overall, the present findings signal that U.S. immigrants have highly heterogeneous perceptions of how selected they are.

This paper evaluates racial/ethnic differences in self-rated mental health for adults in the United States, while controlling for demographic and socioeconomic characteristics as well as length of stay in the country. Using data from the 2010 National Health Interview Survey Cancer Control Supplement (NHIS-CCS), binomial logistic regression models are fit to estimate the association between race/ethnicity and poor/fair self-reported mental health among US Adults. The size of the analytical sample was 22,844 persons. Overall prevalence of poor/fair self-rated mental health was 7.72%, with lower prevalence among Hispanics (6.93%). Non-Hispanic blacks had the highest prevalence (10.38%). After controls for socioeconomic characteristics are incorporated in the models, Hispanics were found to have a lower probability of reporting poor/fair self-rated mental health in comparison to non-Hispanic whites ( OR = 0.70; 95% CI [0.55–0.90]). No difference was found for other minority groups when compared to the reference group in the final model. Contrary to global self-rated health, Hispanics were found to have a lower probability of reporting poor/fair self-rated mental health in comparison to non-Hispanic whites. No difference was found for non-Hispanic blacks when they were compared to non-Hispanic whites. Self-rated mental health is therefore one case of a self-rating of health in which evidence supporting the epidemiological paradox is found among adults in the United States.

The intersection of racism, classism, gender discrimination, and criminal justice involvement in the United States continues to manifest syndemic inequalities. In their work, Alang et al. (p. S29) describe police brutality and the adverse outcomes produced in women's lives over time. Drawing on seminal work on intersectionality and public health,1,2 Alang et al. argue for in-depth consideration of how gender and racism influence police brutality and the impact of interactions with the police on the health and well-being of racialized women. Personal and vicarious witnessing of police brutality and other adverse criminal justice contacts has been shown to affectwomen and Black individuals.3,4 Moreover, Black and Latina women are significantly more likely to fear police brutality than White women, and this anticipatory fear is linked with depressed moods.5 Furthermore, evidence suggests that even having a family member incarcerated during a woman's childhood is associated with a higher likelihood of depressedmood in adulthood.5The interaction between the criminal justice system and racial minority status is complex, as evidenced by results on the impact of a partner's incarceration on racially minoritized women and consequences for their own life. In the case of Black women, evidence suggests that partner incarceration is linked with substance use.6 Although the mechanisms through which partner incarceration leads to drug use need further exploration, the knitted relationship between gender and race can lead to heightened vulnerability and inequality. 6 Moreover, fear of harassment from police reduces access to syringe service programs and other harm reduction programs among racialized people who use drugs and may contribute to rising overdoses and fear of overdoses among minoritized groups, contributing to health disparities.

Historically, Hispanic adults that answer health surveys in Spanish report worse health than those who answer in English. This paper documents a growing English-Spanish gap in self-reported health (SRH) among Hispanic adults in the United States between 1997 and 2018. Data are from the 1997–2018 National Health Interview Survey (NHIS). The analytic sample consisted of 189,024 Hispanic adults older than 18 with valid information for the variables considered in the study. Descriptive analyses indicate that Hispanic adults who answer the NHIS in Spanish report worse health than English respondents do across the period of analysis. Multivariable logistic regression analysis was used to study the English-Spanish gap in SRH and to track its evolution over the last 22 years. At baseline, Spanish respondents exhibited significantly worse levels of SRH than those who answered in English and this gap persisted across time and older cohorts. The gap was still present when demographic/socioeconomic characteristics and assimilation are considered. In the majority of the cases, there is a significant interaction between language of interview, and period and cohort indicators. The English-Spanish gap in self-reported health is not explained by demographic/socioeconomic characteristics or assimilation. It may be possible that there are differences in how Hispanic adults understand health categories items across different languages with differences observed depending on how self-reported health is operationalized.

Climate change-related excess mortality estimates clearly demonstrate a dramatic impact on public health and human mortality. However, life expectancy at birth is more easily communicated and understood by the public. By properly situating climate change mortality within the contexts of life expectancy, we better represent the cost of climate change on longevity. In this paper, we convert excess mortality estimates due to increases in extreme weather from climate change (heat waves, cold waves, droughts, wildfires, river and coastal floods, and windstorms) into potential reductions in life expectancy at birth in thirty-one European countries. We project climate change extremes to reduce life expectancy at birth by 0.24 years for the average European country with differences in excess of 1.0 years in some countries by 2100. We only estimate the impact of mortality directly related to climate extremes, making our estimates conservative. Thus, the cost of inaction on climate change could approach, and likely to exceed, one year of life in some European countries.

The recently finalized changes to the disclosure avoidance policies of the US Census Bureau for the 2020 census, grounded in differential privacy, have faced increasing criticism from demographers and other social scientists. Scholars have found that estimates generated via census-released test data are accurate for aggregate total population statistics of larger spatial units (e.g., counties), but introduce considerable discrepancies for estimates of subgroups. At present, the ramifications of this new approach remain unclear for rural populations. In this brief, we focus on rural populations and evaluate the ability of the finalized differential privacy algorithm to provide accurate population counts and growth rates from 2000 to 2010 across the rural–urban continuum for the total, non-Hispanic white, non-Hispanic Black, Hispanic or Latino/a, and non-Hispanic American Indian population. We find the method introduces significant discrepancies relative to the prior approach into counts and growth rate estimates at the county level for all groups except the total and non-Hispanic white population. Further, discrepancies increase dramatically as we move from urban to rural. Thus, the differential privacy method likely introduced significant discrepancies for rural and non-white populations into 2020 census tabulations.