Dataset Information

County-level factors affecting Latino HIV disparities in the United States.

ABSTRACT: OBJECTIVE:To determine which county-level social, economic, demographic, epidemiologic and access to care factors are associated with Latino/non-Latino White disparities in prevalence of diagnosed HIV infection. METHODS AND FINDINGS:We used 2016 county-level prevalence rates of diagnosed HIV infection rates for Latinos and non-Latino Whites obtained from the National HIV Surveillance System and factors obtained from multiple publicly available datasets. We used mixed effects Poisson modeling of observed HIV prevalence at the county-level to identify county-level factors that explained homogeneous effects across race/ethnicity and differential effects for Latinos and NL-Whites. Overall, the median Latinos disparity in HIV prevalence is 2.4; 94% of the counties have higher rates for Latinos than non-Latinos, and one-quarter of the counties' disparities exceeded 10. Of the 41 county-level factors examined, 24 showed significant effect modification when examined individually. In multi-variable modeling, 11 county-level factors were found that significantly affected disparities. Factors that increased disparity with higher, compared to lower values included proportion of HIV diagnoses due to injection drug use, percent Latino living in poverty, percent not English proficient, and percent Puerto Rican. Latino disparities increased with decreasing percent severe housing, drug overdose mortality rate, percent rural, female prevalence rate, social association rate, percent change in Latino population, and Latino to NL-White proportion of the population. These factors while significant had minimal effects on diminishing disparity, but did substantially reduce the variance in disparity rates. CONCLUSIONS:Large differences in HIV prevalence rates persist across almost all counties even after controlling for county-level factors. Counties that are more rural, have fewer Latinos, or have lower NL-White prevalence rates tend to have higher disparities. There is also higher disparity when community risk is low.

SUBMITTER: Benbow ND

PROVIDER: S-EPMC7423131 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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County-level factors affecting Latino HIV disparities in the United States.

Benbow Nanette D ND Aaby David A DA Rosenberg Eli S ES Brown C Hendricks CH

PloS one 20200812 8

<h4>Objective</h4>To determine which county-level social, economic, demographic, epidemiologic and access to care factors are associated with Latino/non-Latino White disparities in prevalence of diagnosed HIV infection.<h4>Methods and findings</h4>We used 2016 county-level prevalence rates of diagnosed HIV infection rates for Latinos and non-Latino Whites obtained from the National HIV Surveillance System and factors obtained from multiple publicly available datasets. We used mixed effects Poiss ...[more]

PMID: 32785252

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Project description:PurposeHIV pre-exposure prophylaxis (PrEP) is highly efficacious, and yet most individuals indicated for it are not currently using it. To provide guidance for health policymakers, researchers, and community advocates, we developed county-level PrEP use estimates and assessed locality and policy associations.MethodsUsing data from a national aggregator, we applied a validated crosswalk procedure to generate county-level estimates of PrEP users in 2018. A multilevel Poisson regression explored associations between PrEP use and (1) state policy variables of Medicaid expansion and state Drug Assistance Programs (PrEP-DAPs) and (2) county-level characteristics from the U.S. Census Bureau. Outcomes were PrEP per population (prevalence) and PrEP-to-need ratio (PnR), defined as the ratio of PrEP users per new HIV diagnosis. Higher levels of PrEP prevalence or PnR indicate more PrEP users relative to the total population or estimated need, respectively.ResultsOur 2018 county-level data set included a total of 188,546 PrEP users in the United States. Nationally, PrEP prevalence was 70.3/100,000 population and PnR was 4.9. In an adjusted model, counties with a 5% higher proportion of black residents had 5% lower PnR (rate ratio (RR): 0.95, 95% confidence interval (CI): 0.93, 0.96). Similarly, counties with higher concentration of residents uninsured or living in poverty had lower PnR. Relative to states without Medicaid expansion or PrEP-DAPs, states with only one of those programs had 25% higher PrEP prevalence (RR: 1.25, 95% CI: 1.09, 1.45), and states with both programs had 99% higher PrEP prevalence (RR: 1.99, 95% CI: 1.60, 2.48). There was a significant linear trend across the three policy groups, and similar findings for the relation between PnR and the policy groups.ConclusionsIn a data set comprising approximately 80% of PrEP users in the United States, we found that Medicaid expansion and PrEP-DAPs were associated with higher PrEP use in states that adopted those policies, after controlling for potential confounders. Future research should identify which components of PrEP support programs have the most success and how to best promote PrEP among groups most impacted by the epidemic. States should support the admirable health decisions of their residents to get on PrEP by implementing policies that facilitate access.

Project description:ImportanceUnderstanding geographic and community-level factors associated with suicide can inform targeted suicide prevention efforts.ObjectivesTo estimate suicide rates and trajectories, assess associated county-level contextual factors, and explore variation across the rural-urban continuum.Design, setting, and participantsThis cross-sectional study included all individuals aged 25 to 64 years who died by suicide from January 1, 1999, to December 31, 2016, in the United States. Spatial analysis was used to map excess risk of suicide, and longitudinal random-effects models using negative binomial regression tested associations of contextual variables with suicide rates as well as interactions among county-level contextual variables. Data analyses were conducted between January 2019 and July 2019.ExposureCounty of residence.Main outcomes and measuresThree-year county suicide rates during an 18-year period stratified by rural-urban location.ResultsBetween 1999 and 2016, 453 577 individuals aged 25 to 64 years died by suicide in the United States. Decedents were primarily male (349 082 [77.0%]) with 101 312 (22.3%) aged 25 to 34 years, 120 157 (26.5%) aged 35 to 44 years, 136 377 (30.1%) aged 45 to 54 years, and 95 771 (21.1%) aged 55 to 64 years. Suicide rates were higher and increased more rapidly in rural than in large metropolitan counties. The highest deprivation quartile was associated with higher suicide rates compared with the lowest deprivation quartile, especially in rural areas, although this association declined during the period studied (rural, 1999-2001: incidence rate ratio [IRR], 1.438; 95% CI, 1.319-1.568; P < .001; large metropolitan, 1999-2001: 1.208; 95% CI, 1.149-1.270; P < .001; rural, 2014-2016: IRR, 1.121; 95% CI, 1.032-1.219; P = .01; large metropolitan, 2014-2016: IRR, 0.942; 95% CI, 0.887-1.001; P = .06). The presence of more gun shops was associated with an increase in county-level suicide rates in all county types except the most rural (rural: IRR, 1.001; 95% CI, 0.999-1.004; P = .40; micropolitan: IRR, 1.005; 95% CI, 1.002-1.007; P < .001; small metropolitan: IRR, 1.010; 95% CI, 1.006-1.014; P < .001; large metropolitan: IRR, 1.012; 95% CI, 1.006-1.018; P < .001). High social capital was associated with lower suicide rates than low social capital (IRR, 0.917; 95% CI, 0.891-0.943; P < .001). High social fragmentation, an increasing percentage of the population without health insurance, and an increasing percentage of veterans in a county were associated with higher suicide rates (high social fragmentation: IRR, 1.077; 95% CI, 1.050-1.103; P < .001; percentage of population without health insurance: IRR, 1.005; 95% CI, 1.004-1.006; P < .001; percentage of veterans: IRR, 1.025; 95% CI, 1.021-1.028; P < .001).Conclusions and relevanceThis study found that suicide rates have increased across the nation and most rapidly in rural counties, which may be more sensitive to the impact of social deprivation than more metropolitan counties. Improving social connectedness, civic opportunities, and health insurance coverage as well as limiting access to lethal means have the potential to reduce suicide rates across the rural-urban continuum.

Project description:BackgroundCounties are the smallest unit for which mortality data are routinely available, allowing consistent and comparable long-term analysis of trends in health disparities. Average life expectancy has steadily increased in the United States but there is limited information on long-term mortality trends in the US counties This study aimed to investigate trends in county mortality and cross-county mortality disparities, including the contributions of specific diseases to county level mortality trends.Methods and findingsWe used mortality statistics (from the National Center for Health Statistics [NCHS]) and population (from the US Census) to estimate sex-specific life expectancy for US counties for every year between 1961 and 1999. Data for analyses in subsequent years were not provided to us by the NCHS. We calculated different metrics of cross-county mortality disparity, and also grouped counties on the basis of whether their mortality changed favorably or unfavorably relative to the national average. We estimated the probability of death from specific diseases for counties with above- or below-average mortality performance. We simulated the effect of cross-county migration on each county's life expectancy using a time-based simulation model. Between 1961 and 1999, the standard deviation (SD) of life expectancy across US counties was at its lowest in 1983, at 1.9 and 1.4 y for men and women, respectively. Cross-county life expectancy SD increased to 2.3 and 1.7 y in 1999. Between 1961 and 1983 no counties had a statistically significant increase in mortality; the major cause of mortality decline for both sexes was reduction in cardiovascular mortality. From 1983 to 1999, life expectancy declined significantly in 11 counties for men (by 1.3 y) and in 180 counties for women (by 1.3 y); another 48 (men) and 783 (women) counties had nonsignificant life expectancy decline. Life expectancy decline in both sexes was caused by increased mortality from lung cancer, chronic obstructive pulmonary disease (COPD), diabetes, and a range of other noncommunicable diseases, which were no longer compensated for by the decline in cardiovascular mortality. Higher HIV/AIDS and homicide deaths also contributed substantially to life expectancy decline for men, but not for women. Alternative specifications of the effects of migration showed that the rise in cross-county life expectancy SD was unlikely to be caused by migration.ConclusionsThere was a steady increase in mortality inequality across the US counties between 1983 and 1999, resulting from stagnation or increase in mortality among the worst-off segment of the population. Female mortality increased in a large number of counties, primarily because of chronic diseases related to smoking, overweight and obesity, and high blood pressure.

Project description:BackgroundCoronavirus Disease 2019 (COVID-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 US counties from January 1 to December 31, 2020 and reported before March 12, 2021. We used data with a 10-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. A total of 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 US 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.

Dataset Information

County-level factors affecting Latino HIV disparities in the United States.

Publications

County-level factors affecting Latino HIV disparities in the United States.

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