A Risk Prediction Model to Identify People Living with HIV Who are High-risk for Disengagement from Care after HIV Diagnosis in South Africa.

The provision of ART in South Africa has transformed the HIV epidemic, resulting in an increase in life expectancy by over 10 years. Despite this, nearly 2 million people living with HIV are not on treatment. The objective of this study was to develop and externally validate a practical risk assessment tool to identify people with HIV (PWH) at highest risk for attrition from care after testing. A machine learning model incorporating clinical and psychosocial factors was developed in a primary cohort of 498 PWH. LASSO regression analysis was used to optimize variable selection. Multivariable logistic regression analysis was applied to build a model using 80% of the primary cohort as a training dataset and validated using the remaining 20% of the primary cohort and data from an independent cohort of 96 participants. The risk score was developed using the Sullivan and D'Agostino point based method. Of 498 participants with mean age 35.7 years, 192 (38%) did not initiate ART after diagnosis. Controlling for site, factors associated with non-engagement in care included being < 35 years, feeling abandoned by God, maladaptive coping strategies using alcohol or other drugs, no difficulty concentrating, and having high levels of confidence in one's ability to handle personal challenges. An effective risk score can enable clinicians and implementers to focus on tailoring care for those most in need of ongoing support. Further research should focus on potential strategies to enhance the generalizability and evaluate the implementation of the proposed risk prediction model in HIV treatment programs.

RESUMEN: La provisión de TAR (Terapia Antirretroviral) en Sudáfrica ha transformado la epidemia del VIH, resultando en un aumento de la esperanza de vida de más de 10 años. Los últimos objetivos de tratamiento del VIH se sitúan en 94-75-92, con brechas notables después de las pruebas. El objetivo de este estudio fue desarrollar y validar externamente una herramienta práctica de evaluación de riesgos para identificar a las personas con VIH (PVH) con mayor riesgo de deserción del cuidado después de las pruebas. Se desarrolló un modelo sencillo de aprendizaje automático que incorpora factores clínicos y psicosociales en una cohorte primaria de 498 PVH. Se utilizó el análisis de regresión LASSO para optimizar la selección de variables. Se aplicó un análisis de regresión logística multivariable para construir un modelo usando el 80% de la cohorte primaria como conjunto de datos de entrenamiento y validado usando el 20% restante de la cohorte primaria y datos de una cohorte independiente de 96 participantes. El puntaje de riesgo se desarrolló utilizando el método basado en puntos de Sullivan y D'Agostino. De los 498 participantes con una edad media de 35,7 años, 192 (38%) no iniciaron TAR después del diagnóstico. Controlando por sitio, los factores asociados con la no participación en el cuidado incluyeron tener menos de 35 años, sentirse abandonado por Dios, estrategias de afrontamiento desadaptativas usando alcohol u otras drogas, no tener dificultades para concentrarse y tener altos niveles de confianza en la capacidad de manejar desafíos personales. Un puntaje de riesgo efectivo puede permitir a los clínicos y ejecutores enfocarse en personalizar el cuidado para aquellos que más necesitan apoyo continuo. Las investigaciones futuras deben centrarse en estrategias potenciales para mejorar la generalización y evaluar la implementación del modelo de predicción de riesgo propuesto en los programas de tratamiento del VIH.

Missing Americans: Early Death in the United States, 1933-2021

We assessed how many U.S. deaths would have been averted each year, 1933-2021, if U.S. age-specific mortality rates had equaled those of other wealthy nations. The annual number of excess deaths in the U.S. increased steadily beginning in the late 1970s, reaching 626,353 in 2019. Excess deaths surged during the COVID-19 pandemic. In 2021, there were 1,092,293 "Missing Americans" and 25 million years of life lost due to excess mortality relative to peer nations. In 2021, half of all deaths under 65 years and 91% of the increase in under-65 mortality since 2019 would have been avoided if the U.S. had the mortality rates of its peers. Black and Native Americans made up a disproportionate share of Missing Americans, although the majority were White. One sentence summaryIn 2021, 1.1 million U.S. deaths - including 1 in 2 deaths under age 65 years - would have been averted if the U.S. had the mortality rates of other wealthy nations.

Excess all-cause mortality across counties in the United States, March 2020 to December 2021

Excess mortality is the difference between expected and observed mortality in a given period and has emerged as a leading measure of the overall impact of the Covid-19 pandemic that is not biased by differences in testing or cause-of-death assignment. Spatially and temporally granular estimates of excess mortality are needed to understand which areas have been most impacted by the pandemic, evaluate exacerbating and mitigating factors, and inform response efforts, including allocating resources to affected communities. We estimated all-cause excess mortality for the United States from March 2020 through February 2022 by county and month using a Bayesian hierarchical model trained on data from 2015 to 2019. An estimated 1,159,580 excess deaths occurred during the first two years of the pandemic (first: 620,872; second: 538,708). Overall, excess mortality decreased in large metropolitan counties, but increased in nonmetro counties, between the first and second years of the pandemic. Despite the initial concentration of mortality in large metropolitan Northeast counties, beginning in February 2021, nonmetro South counties had the highest cumulative relative excess mortality. These results highlight the need for investments in rural health as the pandemics disproportionate impact on rural areas continues to grow.

Inequities in COVID-19 vaccine and booster coverage across Massachusetts ZIP codes: large gaps persist after the 2021/22 Omicron wave

BackgroundInequities in COVID-19 vaccine coverage may contribute to future disparities in morbidity and mortality between Massachusetts (MA) communities. MethodsWe obtained public-use data on residents vaccinated and boosted by ZIP code (and by age group: 5-19, 20-39, 40-64, 65+) from MA Department of Public Health. We constructed population denominators for postal ZIP codes by aggregating Census-tract population estimates from the 2015-2019 American Community Survey. We excluded non-residential ZIP codes and the smallest ZIP codes containing 1% of the states population. We mapped variation in ZIP-code level primary series vaccine and booster coverage and used regression models to evaluate the association of these measures with ZIP-code-level socioeconomic and demographic characteristics. Because age is strongly associated with COVID-19 severity and vaccine access/uptake, we assessed whether observed socioeconomic and racial inequities persisted after adjusting for age composition and plotted age-specific vaccine and booster coverage by deciles of ZIP-code characteristics. ResultsWe analyzed data on 418 ZIP codes. We observed wide geographic variation in primary series vaccination and booster rates, with marked inequities by ZIP-code-level education, median household income, essential worker share, and racial-ethnic composition. In age-stratified analyses, primary series vaccine coverage was very high among the elderly. However, we found large inequities in vaccination rates among younger adults and children, and very large inequities in booster rates for all age groups. In multivariable regression models, each 10 percentage point increase in "percent college educated" was associated with a 5.0 percentage point increase in primary series vaccine coverage and a 4.9 percentage point increase in booster coverage. Although ZIP codes with higher "percent Black/Latino/Indigenous" and higher "percent essential workers" had lower vaccine coverage, these associations became strongly positive after adjusting for age and education, consistent with high demand for vaccines among Black/Latino/Indigenous and essential worker populations. ConclusionOne year into MAs vaccine rollout, large disparities in COVID-19 primary series vaccine and booster coverage persist across MA ZIP codes. O_TEXTBOXKey Messages O_LIAs of March 2022, in the wake of MAs Omicron wave, there were large inequities in ZIP-code-level vaccine and booster coverage by income, education, percent Black/Latino/Indigenous, and percent essential workers. C_LIO_LIEducation was the strongest predictor of ZIP-code vaccine coverage in MA. C_LIO_LICoverage gaps in ZIP codes with many essential workers and large Black/Latino/Indigenous populations are troubling, as these groups face disproportionate risk for COVID-19 infection and severe illness. However, we found no evidence that "hesitancy" drives vaccination gaps. After adjusting for age and education levels, vaccine uptake was higher in ZIP codes with many Black/Latino/Indigenous residents or essential workers. C_LIO_LIGaps in vaccine and booster coverage among vulnerable groups may lead to excess morbidity, mortality, and economic losses during the next COVID-19 wave. These burdens will not be equitably shared and are preventable. C_LI C_TEXTBOX

Does COVID-19 vaccination improve mental health? A difference-in-difference analysis of the UnderstandingCoronavirus in America study

BackgroundMental health problems increased during the COVID-19 pandemic. Knowledge that one is less at risk after being vaccinated may alleviate distress, but this hypothesis remains unexplored. Here we test whether psychological distress declined in those vaccinated against COVID-19 in the US and whether changes in perceived risk mediated any association. MethodsA nationally-representative cohort of U.S. adults (N=5,792) in the Understanding America Study were interviewed every two weeks from March 2020 to June 2021 (28 waves). Difference-in-difference regression tested whether getting vaccinated reduced distress (PHQ-4 scores), with mediation analysis used to identify potential mechanisms, including perceived risks of infection, hospitalization, and death. ResultsVaccination was associated with a 0.09 decline in distress scores (95% CI:-0.15 to -0.04) (0-12 scale), a 5.7% relative decrease compared to mean scores in the wave prior to vaccination. Vaccination was also associated with an 8.44 percentage point reduction in perceived risk of infection (95% CI:-9.15% to -7.73%), a 7.44-point reduction in perceived risk of hospitalization (95% CI:-8.07% to -6.82%), and a 5.03-point reduction in perceived risk of death (95% CI:-5.57% to -4.49%). Adjusting for risk perceptions decreased the vaccination-distress association by two-thirds. Event study models suggest vaccinated and never vaccinated respondents followed similar PHQ-4 trends pre-vaccination, diverging significantly post-vaccination. Analyses were robust to individual and wave fixed effects, time-varying controls, and several alternative modelling strategies. Results were similar across sociodemographic groups. ConclusionReceiving a COVID-19 vaccination was associated with declines in distress and perceived risks of infection, hospitalization, and death. Vaccination campaigns could promote these additional benefits of being vaccinated.

Assessing the Impact of the Covid-19 Pandemic on US Mortality: A County-Level Analysis

BackgroundCovid-19 excess deaths refer to increases in mortality over what would normally have been expected in the absence of the Covid-19 pandemic. Several prior studies have calculated excess deaths in the United States but were limited to the national or state level, precluding an examination of area-level variation in excess mortality and excess deaths not assigned to Covid-19. In this study, we take advantage of county-level variation in Covid-19 mortality to estimate excess deaths associated with the pandemic and examine how the extent of excess mortality not assigned to Covid-19 varies across subsets of counties defined by sociodemographic and health characteristics. Methods and FindingsIn this ecological, cross-sectional study, we made use of provisional National Center for Health Statistics (NCHS) data on direct Covid-19 and all-cause mortality occurring in U.S. counties from January 1 to December 31, 2020 and reported before March 12, 2021. We used data with a ten week time lag between the final day that deaths occurred and the last day that deaths could be reported to improve the completeness of data. Our sample included 2,096 counties with 20 or more Covid-19 deaths. The total number of residents living in these counties was 319.1 million. On average, the counties were 18.7% Hispanic, 12.7% non-Hispanic Black and 59.6% non-Hispanic White. 15.9% of the population was older than 65 years. We first modeled the relationship between 2020 all-cause mortality and Covid-19 mortality across all counties and then produced fully stratified models to explore differences in this relationship among strata of sociodemographic and health factors. Overall, we found that for every 100 deaths assigned to Covid-19, 120 all-cause deaths occurred (95% CI, 116 to 124), implying that 17% (95% CI, 14% to 19%) of excess deaths were ascribed to causes of death other than Covid-19 itself. Our stratified models revealed that the percentage of excess deaths not assigned to Covid-19 was substantially higher among counties with lower median household incomes and less formal education, counties with poorer health and more diabetes, and counties in the South and West. Counties with more non-Hispanic Black residents, who were already at high risk of Covid-19 death based on direct counts, also reported higher percentages of excess deaths not assigned to Covid-19. Study limitations include the use of provisional data that may be incomplete and the lack of disaggregated data on county-level mortality by age, sex, race/ethnicity, and sociodemographic and health characteristics. ConclusionsIn this study, we found that direct Covid-19 death counts in the United States in 2020 substantially underestimated total excess mortality attributable to Covid-19. Racial and socioeconomic inequities in Covid-19 mortality also increased when excess deaths not assigned to Covid-19 were considered. Our results highlight the importance of considering health equity in the policy response to the pandemic. Authors SummaryO_ST_ABSWhy Was This Study Done?C_ST_ABSO_LIThe Covid-19 pandemic has resulted in excess mortality that would not have occurred in the absence of the pandemic. C_LIO_LIExcess deaths include deaths assigned to Covid-19 in official statistics as well as deaths that are not assigned to Covid-19 but are attributable directly or indirectly to Covid-19. C_LIO_LIWhile prior studies have identified significant racial and socioeconomic inequities in directly assigned Covid-19 deaths, few studies have documented how excess mortality in 2020 has differed across sociodemographic or health factors in the United States. C_LI What Did the Researchers Do and Find?O_LILeveraging data from 2,096 counties on Covid-19 and all-cause mortality, we assessed what percentage of excess deaths were not assigned to Covid-19 and examined variation in excess deaths by county characteristics. C_LIO_LIIn these counties, we found that for every 100 deaths directly assigned to Covid-19 in official statistics, an additional 20 deaths occurred that were not counted as direct Covid-19 deaths. C_LIO_LIThe proportion of excess deaths not counted as direct Covid-19 deaths was even higher in counties with lower average socioeconomic status, counties with more comorbidities, and counties in the South and West. Counties with more non-Hispanic Black residents who were already at high risk of Covid-19 death based on direct counts, also reported a higher proportion of excess deaths not assigned to Covid-19. C_LI What Do These Findings Mean?O_LIDirect Covid-19 death counts significantly underestimate excess mortality in 2020. C_LIO_LIMonitoring excess mortality will be critical to gain a full picture of socioeconomic and racial inequities in mortality attributable to the Covid-19 pandemic. C_LIO_LITo prevent inequities in mortality from growing even larger, health equity must be prioritized in the policy response to the Covid-19 pandemic. C_LI

Unemployment insurance and food insecurity among people who lost employment in the wake of COVID-19

Forty million U.S. residents lost their jobs in the first two months of the coronavirus disease 2019 (COVID-19) pandemic. In response, the Federal Government expanded unemployment insurance benefits in both size ($600/week supplement) and scope (to include caregivers and self-employed workers). We assessed the relationship between unemployment insurance and food insecurity among people who lost their jobs during the COVID-19 pandemic in the period when the federal unemployment insurance supplement was in place. We analyzed data from the Understanding Coronavirus in America (UAC) cohort, a longitudinal survey collected by the University of Southern California Center for Economic and Social Research (CESR) every two weeks between April 1 and July 8, 2020. We limited the sample to individuals living in households earning less than $75,000 in February 2020 who lost their jobs during COVID-19. Using difference-in-differences and event study regression models, we evaluated the association between receipt of unemployment insurance and self-reported food insecurity and eating less due to financial constraints. We found that 40.5% of those living in households earning less than $75,000 and employed in February 2020 experienced unemployment during the COVID-19 pandemic. Of those who lost their jobs, 31% reported food insecurity and 33% reported eating less due to financial constraints. Food insecurity peaked in April 2020 and declined over time, but began to increase again among people receiving unemployment insurance during the final wave of the survey ahead of the federal supplement to unemployment insurance ending. Food insecurity and eating less were more common among people who were non-White, lived in lower-income households, younger, and who were sexual or gender minorities. Receipt of unemployment insurance was associated with a 4.4 percentage point (95% CI: -7.8 to -0.9 percentage points) decline in food insecurity (a 30.3% relative decline compared to the average level of food insecurity during the study period). Receipt of unemployment insurance was also associated with a 6.1 percentage point (95% CI: -9.6 to -2.7 percentage point) decline in eating less due to financial constraints (a 42% relative decline). Estimates from event study specifications revealed that reductions in food insecurity and eating less were greatest in the four-week period immediately following receipt of unemployment insurance, with no evidence of differential pre-existing trends in either outcome. We conclude that receiving unemployment insurance benefits during the period when the $600/week federal supplement was in place was associated with large reductions in food insecurity.

Neighborhood income and physical distancing during the COVID-19 pandemic in the U.S.

IntroductionAlthough physical distancing has been the primary strategy to reduce the spread of COVID-19 in the U.S., peoples ability to distance may vary by socioeconomic characteristics, leading to higher transmission risk in low-income neighborhoods. MethodsWe used mobility data from a large, anonymized sample of smartphone users to assess the relationship between neighborhood median household income and physical distancing during the COVID-19 epidemic. We assessed changes in several behaviors including: spending the day entirely at home; working outside the home; and visits to supermarkets, parks, hospitals, and other locations. We also assessed differences in effects of state policies on physical distancing across neighborhood income levels. ResultsWe found a strong gradient between neighborhood income and physical distancing. Compared to January and February 2020, the proportion of individuals spending the day entirely at home in April 2020 increased by 10.9 percentage points in low-income neighborhoods and by 27.1 percentage points in high-income neighborhoods. During April 2020, people in low-income neighborhoods were more likely to work outside the home, compared to people in higher-income neighborhoods, but not more likely to visit non-work locations. State physical distancing orders were associated with a 1.5 percentage-point increase (95% CI [0.9, 2.1], p < 0.001) in staying home in low-income neighborhoods and a 2.4 percentage point increase (95% CI [1.4, 3.4], p < 0.001) in high-income neighborhoods. DiscussionPeople in lower-income neighborhoods have faced barriers to physical distancing, particularly the need to work outside the home. State physical distancing policies have not mitigated these disparities.

Effect of education on the risk of gender-based violence in the Philippines

Background@#Gender-based violence originates when societal gender-based expectations and the reality are not consistent. One such example is: there has been a recent rise in women's education in the Philippines, yet the prevalence of traditional female role expectations in the context of the heavily Catholic Filipino society remains unchanged. @*Objectives@#In this paper, the relationship between women's education and their risk of gender-based violence (GBV) is examined and compared with the relationship between the education of their partners and the women's risk of experiencing GBV. @*Methodology@#Our sample included women living in the Philippines surveyed by the Demographic Health Survey in 2017. We used multivariate logistic regression on the respondents' and the partner's education level, with respondent's risk of experiencing GBV. @*Results@#We found that there was a slight but statistically significant decreased risk of GBV experience with increased years of education of both the female respondents and their male partners. For all female respondents, there was a 3.7% decrease in the risk of GBV per additional year of their own education. For those with partners, there was a 2.3% decrease in the risk of GBV per additional year of their partner's education. @*Conclusion@#We found that the education of male partners is as much of a factor as the women's own education in her likelihood of experiencing violence. From this, we established that partner selection based on their education levels may act as a protective factor for an individual's likelihood of experiencing GBV. Policy initiatives should address increasing male awareness of safe behavior and violence against women, especially while traditional gender roles are still predominant in the Filipino society.