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Lancet ; 397(10279): 1127-1138, 2021 03 20.
Article in English | MEDLINE | ID: covidwho-1525996


In 2010, the US health insurance system underwent one of its most substantial transformations with the passage of the Affordable Care Act, which increased coverage for millions of people in the USA, including those with and at risk of HIV. Even so, the system of HIV care and prevention services in the USA is a complex patchwork of payers, providers, and financing mechanisms. People with HIV are primarily covered by Medicaid, Medicare, private insurance, or a combination of these; many get care through other programmes, particularly the Ryan White HIV/AIDS Program, which serves as the nation's safety net for people with HIV who remain uninsured or underinsured but offers modest to no support for prevention services. While uninsurance has drastically declined over the past decade, the USA trails other high-income countries in key HIV-specific metrics, including rates of viral suppression. In this paper in the Series, we provide an overview of the coverage and financing landscape for HIV treatment and prevention in the USA, discuss how the Affordable Care Act has changed the domestic health-care system, examine the major programmes that provide coverage and services, and identify remaining challenges.

Acquired Immunodeficiency Syndrome/epidemiology , COVID-19/economics , HIV Infections/drug therapy , HIV Infections/prevention & control , Insurance Coverage/legislation & jurisprudence , Insurance, Health/legislation & jurisprudence , Acquired Immunodeficiency Syndrome/drug therapy , Adult , Aged , Anti-Retroviral Agents/therapeutic use , COVID-19/diagnosis , COVID-19/epidemiology , COVID-19/virology , Female , Gender Identity , HIV Infections/economics , HIV Infections/epidemiology , Humans , Incidence , Male , Medicaid/statistics & numerical data , Medically Uninsured/statistics & numerical data , Medicare/statistics & numerical data , Middle Aged , Patient Protection and Affordable Care Act , Risk Assessment , SARS-CoV-2/genetics , United States/epidemiology
Ann Epidemiol ; 52: 46-53.e2, 2020 12.
Article in English | MEDLINE | ID: covidwho-1023435


PURPOSE: The purpose of this study was to ascertain COVID-19 transmission dynamics among Latino communities nationally. METHODS: We compared predictors of COVID-19 cases and deaths between disproportionally Latino counties (≥17.8% Latino population) and all other counties through May 11, 2020. Adjusted rate ratios (aRRs) were estimated using COVID-19 cases and deaths via zero-inflated binomial regression models. RESULTS: COVID-19 diagnoses rates were greater in Latino counties nationally (90.9 vs. 82.0 per 100,000). In multivariable analysis, COVID-19 cases were greater in Northeastern and Midwestern Latino counties (aRR: 1.42, 95% CI: 1.11-1.84, and aRR: 1.70, 95% CI: 1.57-1.85, respectively). COVID-19 deaths were greater in Midwestern Latino counties (aRR: 1.17, 95% CI: 1.04-1.34). COVID-19 diagnoses were associated with counties with greater monolingual Spanish speakers, employment rates, heart disease deaths, less social distancing, and days since the first reported case. COVID-19 deaths were associated with household occupancy density, air pollution, employment, days since the first reported case, and age (fewer <35 yo). CONCLUSIONS: COVID-19 risks and deaths among Latino populations differ by region. Structural factors place Latino populations and particularly monolingual Spanish speakers at elevated risk for COVID-19 acquisition.

Coronavirus Infections/mortality , Health Status Disparities , Pneumonia, Viral/mortality , Adult , Age Distribution , Aged , Aged, 80 and over , COVID-19 , Coronavirus Infections/ethnology , Coronavirus Infections/transmission , Humans , Local Government , Middle Aged , Pandemics , Pneumonia, Viral/ethnology , Pneumonia, Viral/transmission , Population Surveillance , Residence Characteristics , SARS-CoV-2 , United States/epidemiology
Ann Epidemiol ; 47: 37-44, 2020 07.
Article in English | MEDLINE | ID: covidwho-260620


Purpose: Given incomplete data reporting by race, we used data on COVID-19 cases and deaths in U.S. counties to describe racial disparities in COVID-19 disease and death and associated determinants. Methods: Using publicly available data (accessed April 13, 2020), predictors of COVID-19 cases and deaths were compared between disproportionately (≥13%) black and all other (<13% black) counties. Rate ratios were calculated, and population attributable fractions were estimated using COVID-19 cases and deaths via zero-inflated negative binomial regression model. National maps with county-level data and an interactive scatterplot of COVID-19 cases were generated. Results: Nearly 90% of disproportionately black counties (656/677) reported a case and 49% (330/677) reported a death versus 81% (1987/2465) and 28% (684/2465), respectively, for all other counties. Counties with higher proportions of black people have higher prevalence of comorbidities and greater air pollution. Counties with higher proportions of black residents had more COVID-19 diagnoses (Rate Ratio (RR): 1.24, 95% confidence interval: 1.17-1.33) and deaths (RR: 1.18, 95% confidence interval: 1.00-1.40), after adjusting for county-level characteristics such as age, poverty, comorbidities, and epidemic duration. COVID-19 deaths were higher in disproportionally black rural and small metro counties. The population attributable fraction of COVID-19 diagnosis due to lack of health insurance was 3.3% for counties with less than 13% black residents and 4.2% for counties with greater than or equal to 13% black residents. Conclusions: Nearly 20% of U.S. counties are disproportionately black, and they accounted for 52% of COVID-19 diagnoses and 58% of COVID-19 deaths nationally. County-level comparisons can both inform COVID-19 responses and identify epidemic hot spots. Social conditions, structural racism, and other factors elevate risk for COVID-19 diagnoses and deaths in black communities.

African Americans/statistics & numerical data , Coronavirus Infections/mortality , Coronavirus , Health Status Disparities , Pneumonia, Viral/mortality , Betacoronavirus , COVID-19 , Coronavirus Infections/ethnology , Humans , Pandemics , Pneumonia, Viral/ethnology , Rural Population , SARS-CoV-2