This study described spatiotemporal changes in health insurance coverage, healthcare access, and reasons for non-insurance among racial/ethnic minority populations in the United States during the COVID-19 pandemic using four national survey datasets. Getis-Ord Gi* statistic and scan statistics were used to analyze geospatial clusters of health insurance coverage by race/ethnicity. Logistic regression was used to estimate odds of reporting inability to access healthcare across two pandemic time periods by race/ethnicity. Racial/ethnic differences in insurance were observed from 2010 through 2019, with the lowest rates being among Hispanic/Latino, African American, American Indian/Alaska Native, and Native Hawaiian/Pacific Islander populations. Pre-pandemic insurance coverage rates were geographically clustered. The percentage of adults citing change in employment status as the reason for non-insurance increased by about 7% after the start of the pandemic, with a small decrease observed among African American adults. Almost half of adults reported reduced healthcare access in June 2020, with 38.7% attributing reduced access to the pandemic; however, by May 2021, the percent of respondents reporting reduced access for any reason and due to the pandemic fell to 26.9% and 12.7%, respectively. In general, racial/ethnic disparities in health insurance coverage and healthcare access worsened during the pandemic. Although coverage and access improved over time, pre-COVID disparities persisted with African American and Hispanic/Latino populations being the most affected by insurance loss and reduced healthcare access. Cost, unemployment, and eligibility drove non-insurance before and during the pandemic.

BACKGROUND: Syphilis, a sexually transmitted infection that can cause severe congenital disease when not treated during pregnancy, is on the rise in the United States. Our objective was to identify U.S. counties with elevated risk for emergence of primary and secondary (P&S) syphilis among reproductive-aged women. METHODS: Using syphilis case reports, we identified counties with no cases of P&S syphilis among reproductive-aged women in 2017 and ≥ 1 case in 2018. Using county-level syphilis and sociodemographic data, we developed a model to predict counties with emergence of P&S syphilis among women and a risk score to identify counties at elevated risk. RESULTS: Of 2,451 counties with no cases of P&S syphilis among reproductive-aged women in 2017, 345 counties (14.1%) had documented emergence of syphilis in 2018. Emergence was predicted by the county's P&S syphilis rate among men; violent crime rate; proportions of Black, White, Asian, and Hawaiian/Pacific Islander persons; urbanicity; presence of a metropolitan area; population size; and having a neighboring county with P&S syphilis among women. A risk score of ≥20 identified 75% of counties with emergence. CONCLUSIONS: Jurisdictions can identify counties at elevated risk for emergence of syphilis in women and tailor prevention efforts. Prevention of syphilis requires multidisciplinary collaboration to address underlying social factors.

OBJECTIVES: When Hurricane Harvey struck the coastline of Texas in 2017, it caused 88 fatalities and over US $125 billion in damage, along with increased emergency department visits in Houston and in cities receiving hurricane evacuees, such as the Dallas-Fort Worth metroplex (DFW).This study explored demographic indicators of vulnerability for patients from the Hurricane Harvey impact area who sought medical care in Houston and in DFW. The objectives were to characterize the vulnerability of affected populations presenting locally, as well as those presenting away from home, and to determine whether more vulnerable communities were more likely to seek medical care locally or elsewhere. METHODS: We used syndromic surveillance data alongside the Centers for Disease Control and Prevention Social Vulnerability Index to calculate the percentage of patients seeking care locally by zip code tabulation area. We used this variable to fit a spatial lag regression model, controlling for population density and flood extent. RESULTS: Communities with more patients presenting for medical care locally were significantly clustered and tended to have greater socioeconomic vulnerability, lower household composition vulnerability, and more extensive flooding. CONCLUSIONS: These findings suggest that populations remaining in place during a natural disaster event may have needs related to income, education, and employment, while evacuees may have more needs related to age, disability, and single-parent household status.