Sociopolitical Diagnostic Tools to Understand National and Local Response Capabilities and Vulnerabilities to Epidemics and Guide Research into How to Improve the Global Response to Pathogens.

The AIDS and COVID-19 pandemics demonstrated that nations at similar economic development levels varied widely in their capacity to protect the health of their residents. For AIDS, Britain and Australia brought gay representatives into official counsels and adopted harm reduction far more rapidly than the United States or Spain, and East African countries responded more effectively than South Africa or the Democratic Republic of the Congo. National responses to COVID-19 varied widely, with New Zealand, China, and Vietnam more effective than Italy, Brazil, or the United States. Further, as phylogenetic research has demonstrated, these pandemics spread from one country to another, with those that responded poorly acting as sources for mutations and potentially sources of transmission to countries with more effective responses. Many observers expressed surprise at the poor responses of the United States to COVID-19, but in retrospect the cutbacks in public health funding at state and national levels made it clear that this was a predictable weakness even in addition to the political vacillations that crippled the US and Brazilian responses. In a time of global sociopolitical and climate instability, it is important to measure and conduct research into spatial and time variations in 1. public health and medical funding, 2. social influence networks, social cohesion and trust, and stigmatization, 3. income inequality, 4. social conflict, and 5. other factors that affect responsiveness to pandemics.

Fairness and efficiency considerations in COVID-19 vaccine allocation strategies: A case study comparing front-line workers and 65-74 year olds in the United States.

The COVID-19 epidemic in the United States has been characterized by two stark disparities. COVID-19 burden has been unequally distributed among racial and ethnic groups and at the same time the mortality rates have been sharply higher among older age groups. These disparities have led some to suggest that inequalities could be reduced by vaccinating front-line workers before vaccinating older individuals, as older individuals in the US are disproportionately Non-Hispanic White. We compare the performance of two distribution policies, one allocating vaccines to front-line workers and another to older individuals aged 65-74-year-old. We estimate both the number of lives saved and the number of years of life saved under each of the policies, overall and in every race/ethnicity groups, in the United States and every state. We show that prioritizing COVID-19 vaccines for 65-74-year-olds saves both more lives and more years of life than allocating vaccines front-line workers in each racial/ethnic group, in the United States as a whole and in nearly every state. When evaluating fairness of vaccine allocation policies, the overall benefit to impact of each population subgroup should be considered, not only the proportion of doses that is distributed to each subgroup. Further work can identify prioritization schemes that perform better on multiple equity metrics.

Tobacco and Alcohol Retailer Availability and Neighborhood Racialized, Economic, and Racialized Economic Segregation in North Carolina.

BACKGROUND: Despite evidence of racialized and socioeconomic inequities in tobacco and alcohol outlet availability, few studies have investigated spatial inequities in areas experiencing both concentrated residential racialized segregation and socioeconomic disadvantage. This study examined whether segregation-racialized, economic or both-was associated with alcohol and tobacco retailer counts in North Carolina (NC). METHODS: The NC Alcoholic Beverage Control Commission provided lists of 2021 off-premise alcohol retailers. We created a list of 2018 probable tobacco retailers using ReferenceUSA. We calculated three census tract-level measures of the Index of Concentrations at the Extremes (ICE), indicating racialized segregation between non-Hispanic White and Black residents and economic segregation based on household income. We used negative binomial regression to test associations between quintiles of each ICE measure and tobacco and, separately, alcohol retailer counts. RESULTS: Tracts with the greatest racialized disadvantage had 38% (IRR, 1.38; 95% CI, 1.15-1.66) and 65% (IRR, 1.65; 95% CI, 1.34-2.04) more tobacco and alcohol outlets, respectively, as tracts with the lowest. Tracts with the highest racialized economic disadvantage had a predicted count of 1.51 tobacco outlets per 1000 people while those in the lowest had nearly one fewer predicted outlet. Similar inequities existed in the predicted count of alcohol outlets. DISCUSSION: Tobacco and alcohol outlet availability are higher in NC places experiencing concentrated racialized and economic segregation. A centralized agency overseeing tobacco and alcohol outlet permits and strategies to reduce the retail availability of these harmful products (e.g., capping the number of permits) are needed to intervene upon these inequities.

Examining COVID-19 mortality rates by race and ethnicity among incarcerated people in 11 U.S. state prisons (March-October 2021).

Background: Populations who are incarcerated have experienced disproportionately high coronavirus disease 2019 (COVID-2019) mortality rates compared to the general population. However, mortality rates by race/ethnicity from federal, state, and local carceral settings are largely unavailable due to unregulated reporting; therefore, racial/ethnic inequities have yet to be examined. We aimed to estimate coronavirus disease 2019 (COVID-19) mortality rates among individuals incarcerated in U.S. state prisons by race and ethnicity (RE). Methods: Public records requests to state Departments of Corrections were used to identify deaths from COVID-19 among incarcerated adults occurring from March 1-October 1, 2020. We requested race, ethnicity, and age specific data on deaths and custody populations; sufficient data to calculate age-adjusted rates were obtained for 11 state systems. Race and ethnic specific unadjusted deaths rates per 100,000 persons were calculated overall and by state, based on March 1, 2020 custody populations. Rate ratios (RR) and 95% confidence intervals (95%CI) compared aggregated age-adjusted death rates by race and ethnicity, with White individuals as the reference group. Results: Of all COVID-related deaths in U.S. prisons through October 2020, 23.35% (272 of 1165) were captured in our analyses. The average age at COVID-19 death was 63 years (SD = 10 years) and was significantly lower among Black (60 years, SD = 11 years) compared to White adults (66 years, SD = 10 years; p < 0.001). In age-standardized analysis, COVID-19 death rates were significantly higher among Black (RR = 1.93, 95% CI: 1.25-2.99), Hispanic (RR = 1.81, 95% CI: 1.10-2.96) and those of Other racial and ethnic groups (RR = 2.60, 95% CI: 1.01-6.67) when compared to White individuals. Conclusions: Age-standardized death rates were higher among incarcerated Black, Hispanic and those of Other racial and ethnic groups compared to their White counterparts. Greater data transparency from all carceral systems is needed to better understand populations at disproportionate risk of COVID-19 morbidity and mortality.

Eviction, Healthcare Utilization, and Disenrollment Among New York City Medicaid Patients.

INTRODUCTION: Although growing evidence links residential evictions to health, little work has examined connections between eviction and healthcare utilization or access. In this study, eviction records are linked to Medicaid claims to estimate short-term associations between eviction and healthcare utilization, as well as Medicaid disenrollment. METHODS: New York City eviction records from 2017 were linked to New York State Medicaid claims, with 1,300 evicted patients matched to 261,855 non-evicted patients with similar past healthcare utilization, demographics, and neighborhoods. Outcomes included patients' number of acute and ambulatory care visits, healthcare spending, Medicaid disenrollment, and pharmaceutical prescription fills during 6 months of follow-up. Coarsened exact matching was used to strengthen causal inference in observational data. Weighted generalized linear models were then fit, including censoring weights. Analyses were conducted in 2019-2021. RESULTS: Eviction was associated with 63% higher odds of losing Medicaid coverage (95% CI=1.38, 1.92, p<0.001), fewer pharmaceutical prescription fills (incidence rate ratio=0.68, 95% CI=0.52, 0.88, p=0.004), and lower odds of generating any healthcare spending (OR=0.72, 95% CI=0.61, 0.85, p<0.001). However, among patients who generated any spending, average spending was 20% higher for those evicted (95% CI=1.03, 1.40, p=0.017), such that evicted patients generated more spending on balance. Marginally significant estimates suggested associations with increased acute, and decreased ambulatory, care visits. CONCLUSIONS: Results suggest that eviction drives increased healthcare spending while disrupting healthcare access. Given previous research that Medicaid expansion lowered eviction rates, eviction and Medicaid disenrollment may operate cyclically, accumulating disadvantage. Preventing evictions may improve access to care and lower Medicaid costs.

Variation in COVID-19 Mortality in the US by Race and Ethnicity and Educational Attainment.

Importance: Racial and ethnic inequities in COVID-19 mortality have been well documented, but little prior research has assessed the combined roles of race and ethnicity and educational attainment. Objective: To measure inequality in COVID-19 mortality jointly by race and ethnicity and educational attainment. Design, Setting, and Participants: This cross-sectional study analyzed data on COVID-19 mortality from the 50 US states and the District of Columbia for the full calendar year 2020. It included all persons in the United States aged 25 years or older and analyzed them in subgroups jointly stratified by age, sex, race and ethnicity, and educational attainment. Main Outcomes and Measures: Population-based cumulative mortality rates attributed to COVID-19.F. Results: Among 219.1 million adults aged 25 years or older (113.3 million women [51.7%]; mean [SD] age, 51.3 [16.8] years), 376 125 COVID-19 deaths were reported. Age-adjusted cumulative mortality rates per 100 000 ranged from 54.4 (95% CI, 49.8-59.0 per 100 000 population) among Asian women with some college to 699.0 (95% CI, 612.9-785.0 per 100 000 population) among Native Hawaiian and Other Pacific Islander men with a high school degree or less. Racial and ethnic inequalities in COVID-19 mortality rates remained when comparing within educational attainment categories (median rate ratio reduction, 17% [IQR, 0%-25%] for education-stratified estimates vs unstratified, with non-Hispanic White individuals as the reference). If all groups had experienced the same mortality rates as college-educated non-Hispanic White individuals, there would have been 48% fewer COVID-19 deaths among adults aged 25 years or older overall, including 71% fewer deaths among racial and ethnic minority populations and 89% fewer deaths among racial and ethnic minority populations aged 25 to 64 years. Conclusions and Relevance: Public health research and practice should attend to the ways in which populations that share socioeconomic characteristics may still experience racial and ethnic inequity in the distribution of risk factors for SARS-CoV-2 exposure and infection fatality rates (eg, housing, occupation, and prior health status). This study suggests that a majority of deaths among racial and ethnic minority populations could have been averted had all groups experienced the same mortality rate as college-educated non-Hispanic White individuals, thus highlighting the importance of eliminating joint racial-socioeconomic health inequities.

Social and Economic Differences in Neighborhood Walkability Across 500 U.S. Cities.

INTRODUCTION: Neighborhood walkability has been established as a potentially important determinant of various health outcomes that are distributed inequitably by race/ethnicity and sociodemographic status. The objective of this study is to assess the differences in walkability across major urban centers in the U.S. METHODS: City- and census tract-level differences in walkability were assessed in 2020 using the 2019 Walk Score across 500 large cities in the U.S. RESULTS: At both geographic levels, high-income and majority White geographic units had the lowest walkability overall. Walkability was lower with increasing tertile of median income among majority White, Latinx, and Asian American and Native Hawaiian and Pacific Islander neighborhoods. However, this association was reversed within majority Black neighborhoods, where tracts in lower-income tertiles had the lowest walkability. Associations varied substantially by region, with the strongest differences observed for cities located in the South. CONCLUSIONS: Differences in neighborhood walkability across 500 U.S. cities provide evidence that both geographic unit and region meaningfully influence associations between sociodemographic factors and walkability. Structural interventions to the built environment may improve equity in urban environments, particularly in lower-income majority Black neighborhoods.

High School Seniors Who Used E-Cigarettes May Have Otherwise Been Cigarette Smokers: Evidence From Monitoring the Future (United States, 2009-2018).

INTRODUCTION: Studies have indicated that youth who use e-cigarettes are more likely to progress to cigarette smoking; however, the likelihood that these youth would have used tobacco products in the pre-vaping era is unclear. AIMS AND METHODS: This study sought to determine whether youth who used e-cigarettes in 2014-2018 would have likely been smokers in the period preceding e-cigarette availability. Analyzing Monitoring the Future 12th grade data (United States, 2009-2018), we forecasted the prevalence of current smoking with logistic regression-derived propensity scores. Models predicted smoking for all subsequent years, incorporating sociodemographic, family, alcohol, and school-related variables, and a linear time trend. We compared forecasted to observed smoking prevalence annually, and prevalence of current e-cigarette use among nonsmokers across smoking propensity tertiles. RESULTS: Until 2014, observed smoking prevalence mirrored forecasted prevalence. Afterward, forecasted rates consistently overestimated prevalence. Among nonsmoking youth, e-cigarette use was lowest among those with lowest predicted probability of cigarette smoking (3.8%; 95% confidence interval [CI]: 3.3, 4.4) and highest among those with highest probability (23.5%; 95% CI: 22.2, 24.9). CONCLUSIONS: Youth e-cigarette use has increased rapidly, with high prevalence among nonsmoking youth. However, the decline in current smoking among 12th graders has accelerated since e-cigarettes have become available. E-cigarette use is largely concentrated among youth who share characteristics with smokers of the pre-vaping era, suggesting e-cigarettes may have replaced cigarette smoking. IMPLICATIONS: Among nonsmoking youth, vaping is largely concentrated among those who would have likely smoked prior to the introduction of e-cigarettes, and the introduction of e-cigarettes has coincided with an acceleration in the decline in youth smoking rates. E-cigarettes may be an important tool for population-level harm reduction, even considering their impact on youth.

Subgroup Variation and Neighborhood Social Gradients-an Analysis of Hypertension and Diabetes Among Asian Patients (New York City, 2014-2017).

Diabetes and hypertension are socially patterned by individual race/ethnicity and by neighborhood economic context, but distributions among Asian subgroups are undercharacterized. We examined variation in prevalence for both conditions, comparing between US Asian subgroups, including within South Asian nationalities, and comparing within subgroups by neighborhood economic context. We obtained data on a non-probability sample of 633,664 patients ages 18-64 in New York City, NY, USA (2014-2017); 30,138 belonged to one of seven Asian subgroups (Asian Indian, Bangladeshi, Pakistani, Chinese, Korean, Japanese, and Filipino). We used electronic health records to classify disease status. We characterized census tract economic context using the Index of Concentration at the Extremes and estimated prevalence differences using multilevel models. Among Asian men, hypertension prevalence was highest for Filipinos. Among Asian women, hypertension prevalence was highest for Filipinas and Bangladeshis. Diabetes prevalence was highest among Pakistanis and Bangladeshis of both genders, exceeding all other Asian and non-Asian groups. There was consistent evidence of an economic gradient for both conditions, whereby persons residing in the most privileged neighborhood tertile had the lowest disease prevalence. The economic gradient was particularly strong for diabetes among Pakistanis, whose prevalence in the most deprived tertile exceeded that of the most privileged by 9 percentage points (95% CI 3, 14). Only Koreans departed from the trend, experiencing the highest diabetes prevalence in the most privileged tertile. US Asian subgroups largely demonstrate similar neighborhood economic gradients as other groups. Disaggregating Asian subgroups, including within South Asian nationalities, reveals important heterogeneity in prevalence.

Health and Health Determinant Metrics for Cities: A Comparison of County and City-Level Data.

We evaluated whether using county-level data to characterize public health measures in cities biases the characterization of city populations. We compared 4 public health and sociodemographic measures in 447 US cities (percent of children living in poverty, percent of non-Hispanic Black population, age-adjusted cardiovascular disease mortality, life expectancy at birth) to the same measures calculated for counties that contain those cities. We found substantial and highly variable city-county differences within and across metrics, which suggests that use of county data to proxy city measures could hamper accurate allocation of public health resources and appreciation of the urgency of public health needs in specific locales.

Longitudinal associations between the disruption of incarceration and community re-entry on substance use risk escalation among Black men who have sex with men; A causal analysis.

BACKGROUND AND AIMS: While substance use can lead to incarceration, the disruptive effects of incarceration may lead to, or increase psychosocial vulnerability and substance use. Using causal inference methods, we measured longitudinal associations between incarceration and post-release substance use among Black men who have sex with men (BMSM), populations facing disproportionate risk of incarceration and substance use. METHODS: Using data from the HIV Prevention Trials Network (HPTN 061) study (N = 1553) we estimated associations between past 6-month incarceration and binge drinking, marijuana use, and stimulant use post release (at 12-month follow-up visit). Adjusted models used inverse probability weighting (IPW) to control for baseline (pre-incarceration) substance use and additional risk factors. RESULTS: There were 1133 participants present at the twelve-month follow-up visit. Participants were predominately non-Hispanic Blacks and unemployed. At baseline, 60.1 % reported a lifetime history of incarceration, 22.9 % were HIV positive and 13.7 % had a history of an STI infection. A total of 43 % reported a history of depression. In adjusted analyses with IPW, recent incarceration was associated with crack-cocaine (adjusted odds ratio (AOR): 1.53, 95 % confidence interval (CI): 1.03, 2.23) and methamphetamine use (AOR: 1.52, 95 % CI: 0.94-2.45). Controlling for pre-incarceration binge drinking, incarceration was associated with post-release binge drinking (AOR: 1.47, 95 % CI: 1.05, 2.04); in fully adjusted models the AOR was 1.14 (95 % CI: 0.81, 1.62). Incarceration was not associated with marijuana use. CONCLUSION: Findings underscore the need to provide substance use treatment in custody and post-release, and to consider alternatives to incarceration for substance using populations.

Assessing county-level determinants of diabetes in the United States (2003-2012).

Using data from the United States Behavioral Risk Factor Surveillance System (2003-2012; N = 3,397,124 adults), we estimated associations between prevalent diabetes and four county-level exposures (fast food restaurant density, convenience store density, unemployment, active commuting). All associations confirmed our a priori hypotheses in conventional multilevel analyses that pooled across years. In contrast, using a random-effects within-between model, we found weak, ambiguous evidence that within-county changes in exposures were associated with within-county change in odds of diabetes. Decomposition revealed that the pooled associations were largely driven by time-invariant, between-county factors that may be more susceptible to confounding versus within-county associations.

Impact of Changes in the Food, Built, and Socioeconomic Environment on BMI in US Counties, BRFSS 2003-2012.

OBJECTIVE: Researchers have linked geographic disparities in obesity to community-level characteristics, yet many prior observational studies have ignored temporality and potential for bias. METHODS: Repeated cross-sectional data were used from the Behavioral Risk Factor Surveillance System (BRFSS) (2003-2012) to examine the influence of county-level characteristics (active commuting, unemployment, percentage of limited-service restaurants and convenience stores) on BMI. Each exposure was calculated using mean values over the 5-year period prior to BMI measurement; values were standardized; and then variables were decomposed into (1) county means from 2003 to 2012 and (2) county-mean-centered values for each year. Cross-sectional (between-county) and longitudinal (within-county) associations were estimated using a random-effects within-between model, adjusting for individual characteristics, survey method, and year, with nested random intercepts for county-years within counties within states. RESULTS: A negative between-county association for active commuting (ß = -0.19; 95% CI: -0.23 to -0.16) and positive associations for unemployment (ß = 0.17; 95% CI: 0.14 to 0.19) and limited-service restaurants (ß = 0.13; 95% CI: 0.11 to 0.14) were observed. An SD increase in active commuting within counties was associated with a 0.51-kg/m2 (95% CI: -0.72 to -0.31) decrease in BMI over time. CONCLUSIONS: These results suggest that community-level characteristics play an important role in shaping geographic disparities in BMI between and within communities over time.

Improving the geographical precision of rural chronic disease surveillance by using emergency claims data: a cross-sectional comparison of survey versus claims data in Sullivan County, New York.

OBJECTIVES: Some of the most pressing health problems are found in rural America. However, the surveillance needed to track and prevent disease in these regions is lacking. Our objective was to perform a comprehensive health survey of a single rural county to assess the validity of using emergency claims data to estimate rural disease prevalence at a sub-county level. DESIGN: We performed a cross-sectional study of chronic disease prevalence estimates using emergency department (ED) claims data versus mailed health surveys designed to capture a substantial proportion of residents in New York's rural Sullivan County. SETTING: Sullivan County, a rural county ranked second-to-last for health outcomes in New York State. PARTICIPANTS: Adult residents of Sullivan County aged 25 years and older who responded to the health survey in 2017-2018 or had at least one ED visit in 2011-2015. OUTCOME MEASURES: We compared age and gender-adjusted prevalence of hypertension, hyperlipidaemia, diabetes, cancer, asthma and chronic obstructive pulmonary disease/emphysema among nine sub-county areas. RESULTS: Our county-wide mailed survey obtained 6675 completed responses for a response rate of 30.4%. This sample represented more than 12% of the estimated 53 020 adults in Sullivan County. Using emergency claims data, we identified 34 576 adults from Sullivan County who visited an ED at least once during 2011-2015. At a sub-county level, prevalence estimates from mailed surveys and emergency claims data correlated especially well for diabetes (r=0.90) and asthma (r=0.85). Other conditions were not well correlated (range: 0.23-0.46). Using emergency claims data, we created more geographically detailed maps of disease prevalence using geocoded addresses. CONCLUSIONS: For select conditions, emergency claims data may be useful for tracking disease prevalence in rural areas and providing more geographically detailed estimates. For rural regions lacking robust health surveillance, emergency claims data can inform how to geographically target efforts to prevent chronic disease.