Dataset Information

Assessing United States County-Level Exposure for Research on Tropical Cyclones and Human Health.

ABSTRACT: BACKGROUND:Tropical cyclone epidemiology can be advanced through exposure assessment methods that are comprehensive and consistent across space and time, as these facilitate multiyear, multistorm studies. Further, an understanding of patterns in and between exposure metrics that are based on specific hazards of the storm can help in designing tropical cyclone epidemiological research. OBJECTIVES:a) Provide an open-source data set for tropical cyclone exposure assessment for epidemiological research; and b) investigate patterns and agreement between county-level assessments of tropical cyclone exposure based on different storm hazards. METHODS:We created an open-source data set with data at the county level on exposure to four tropical cyclone hazards: peak sustained wind, rainfall, flooding, and tornadoes. The data cover all eastern U.S. counties for all land-falling or near-land Atlantic basin storms, covering 1996-2011 for all metrics and up to 1988-2018 for specific metrics. We validated measurements against other data sources and investigated patterns and agreement among binary exposure classifications based on these metrics, as well as compared them to use of distance from the storm's track, which has been used as a proxy for exposure in some epidemiological studies. RESULTS:Our open-source data set was typically consistent with data from other sources, and we present and discuss areas of disagreement and other caveats. Over the study period and area, tropical cyclones typically brought different hazards to different counties. Therefore, when comparing exposure assessment between different hazard-specific metrics, agreement was usually low, as it also was when comparing exposure assessment based on a distance-based proxy measurement and any of the hazard-specific metrics. DISCUSSION:Our results provide a multihazard data set that can be leveraged for epidemiological research on tropical cyclones, as well as insights that can inform the design and analysis for tropical cyclone epidemiological research. https://doi.org/10.1289/EHP6976.

SUBMITTER: Anderson GB

PROVIDER: S-EPMC7592507 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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Assessing United States County-Level Exposure for Research on Tropical Cyclones and Human Health.

Anderson G Brooke GB Ferreri Joshua J Al-Hamdan Mohammad M Crosson William W Schumacher Andrea A Guikema Seth S Quiring Steven S Eddelbuettel Dirk D Yan Meilin M Peng Roger D RD

Environmental health perspectives 20201028 10

<h4>Background</h4>Tropical cyclone epidemiology can be advanced through exposure assessment methods that are comprehensive and consistent across space and time, as these facilitate multiyear, multistorm studies. Further, an understanding of patterns in and between exposure metrics that are based on specific hazards of the storm can help in designing tropical cyclone epidemiological research.<h4>Objectives</h4><i>a</i>) Provide an open-source data set for tropical cyclone exposure assessment for ...[more]

PMID: 33112191

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Project description:ImportanceTropical cyclones have a devastating effect on society, but a comprehensive assessment of their association with cause-specific mortality over multiple years of study is lacking.ObjectiveTo comprehensively evaluate the association of county-level tropical cyclone exposure and death rates from various causes in the US.Design, setting, and participantsA retrospective observational study using a Bayesian conditional quasi-Poisson model to examine how tropical cyclones were associated with monthly death rates. Data from 33.6 million deaths in the US were collected from the National Center for Health Statistics over 31 years (1988-2018), including residents of the 1206 counties in the US that experienced at least 1 tropical cyclone during the study period.ExposuresTropical cyclone days per county-month, defined as number of days in a month with a sustained maximal wind speed 34 knots or greater.Main outcomes and measuresMonthly cause-specific county-level death rates by 6 underlying causes of death: cancers, cardiovascular diseases, infectious and parasitic diseases, injuries, neuropsychiatric conditions, and respiratory diseases. The model yielded information about the association between each additional cyclone day per month and monthly county-level mortality compared with the same county-month in different years, up to 6 months after tropical cyclones, and how these estimated associations varied by age, sex, and social vulnerability. The unit of analysis was county-month.ResultsThere were 33 619 393 deaths in total (16 691 681 females and 16 927 712 males; 8 587 033 aged 0-64 years and 25 032 360 aged 65 years or older) from the 6 causes recorded in 1206 US counties. There was a median of 2 tropical cyclone days experienced in total in included US counties. Each additional cyclone day was associated with increased death rates in the month following the cyclone for injuries (3.7% [95% credible interval {CrI}, 2.5%-4.9%]; 2.0 [95% CrI, 1.3-2.7] additional deaths per 1 000 000 for 2018 monthly age-standardized median rate [DPM]; 54.3 to 56.3 DPM), infectious and parasitic diseases (1.8% [95% CrI, 0.1%-3.6%]; 0.2 [95% CrI, 0.0-0.4] additional DPM; 11.7 to 11.9 DPM), respiratory diseases (1.3% [95% CrI, 0.2%-2.4%]; 0.6 [95% CrI, 0.1-1.1] additional DPM; 44.9 to 45.5 DPM), cardiovascular diseases (1.2% [95% CrI, 0.6%-1.7%]; 1.5 [95% CrI, 0.8-2.2] additional DPM; 129.6 to 131.1 DPM), neuropsychiatric conditions (1.2% [95% CrI, 0.1%-2.4%]; 0.6 [95% CrI, 0.1-1.2] additional DPM; 52.1 to 52.7 DPM), with no change for cancers (-0.3% [95% CrI, -0.9% to 0.3%]; -0.3 [95% CrI, -0.9 to 0.3] additional DPM; 100.4 to 100.1 DPM).Conclusions and relevanceAmong US counties that experienced at least 1 tropical cyclone from 1988-2018, each additional cyclone day per month was associated with modestly higher death rates in the months following the cyclone for several causes of death, including injuries, infectious and parasitic diseases, cardiovascular diseases, neuropsychiatric conditions, and respiratory diseases.

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:ObjectiveTo 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 findingsWe 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.ConclusionsLarge 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.

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Dataset Information

Assessing United States County-Level Exposure for Research on Tropical Cyclones and Human Health.

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Assessing United States County-Level Exposure for Research on Tropical Cyclones and Human Health.

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