Comparing Individual and Community-level Characteristics of People with Ground Beef-associated Salmonellosis and Other Ground Beef Eaters: A Case-control Analysis.

Salmonella is estimated to be the leading bacterial cause of U.S. domestically acquired foodborne illness. Large outbreaks of Salmonella attributed to ground beef have been reported in recent years. The demographic and sociodemographic characteristics of infected individuals linked to these outbreaks are poorly understood. We employed a retrospective case-control design; case-patients were people with laboratory-confirmed Salmonella infections linked to ground beef-associated outbreaks between 2012 and 2019, and controls were respondents to the 2018-2019 FoodNet Population Survey who reported eating ground beef and denied recent gastrointestinal illness. We used county-level CDC/ATSDR Social Vulnerability Index (SVI) to compare case-patient and controls. Case-patient status was regressed on county-level social vulnerability and individual-level demographic characteristics. We identified 376 case-patients and 1,321 controls in the FoodNet sites. Being a case-patient was associated with increased overall county-level social vulnerability (OR: 1.21 [95% CI: 1.07-1.36]) and socioeconomic vulnerability (OR: 1.24 [1.05-1.47]) when adjusted for individual-level demographics. Case-patient status was not strongly associated with the other SVI themes of household composition and disability, minority status and language, and housing type and transportation. Data on individual-level factors such as income, poverty, unemployment, and education could facilitate further analyses to understand this relationship.

The impact of COVID-19 on healthcare coverage and access in racial and ethnic minority populations in the United States.

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.

Spatial, Sociodemographic, and Weather Analysis of the Zika Virus Outbreak: U.S. Virgin Islands, January 2016-January 2018.

Background: The first Zika virus outbreak in U.S. Virgin Islands identified 1031 confirmed noncongenital Zika disease (n = 967) and infection (n = 64) cases during January 2016-January 2018; most cases (89%) occurred during July-December 2016. Methods and Results: The epidemic followed a continued point-source outbreak pattern. Evaluation of sociodemographic risk factors revealed that estates with higher unemployment, more houses connected to the public water system, and more newly built houses were significantly less likely to have Zika virus disease and infection cases. Increased temperature was associated with higher case counts, which suggests a seasonal association of this outbreak. Conclusion: Vector surveillance and control measures are needed to prevent future outbreaks.

Predicting Emergence of Primary and Secondary Syphilis Among Women of Reproductive Age in US Counties.

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 US counties with elevated risk for emergence of primary and secondary (P&S) syphilis among women of reproductive age. METHODS: Using syphilis case reports, we identified counties with no cases of P&S syphilis among women of reproductive age in 2017 and 1 case or more 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 2451 counties with no cases of P&S syphilis among women of reproductive age 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 or more 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.

Social Vulnerability and Access of Local Medical Care During Hurricane Harvey: A Spatial Analysis.

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.