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.

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.

Project description:ImportanceSuicide is the second leading cause of death among youths aged 10 to 19 years in the United States, with rates nearly doubling during the past decade. Youths in impoverished communities are at increased risk for negative health outcomes; however, the association between pediatric suicide and poverty is not well understood.ObjectiveTo assess the association between pediatric suicide rates and county-level poverty concentration.Design, setting, and participantsThis retrospective, cross-sectional study examined suicides among US youths aged 5 to 19 years from January 1, 2007, to December 31, 2016. Suicides were identified using International Statistical Classification of Diseases, Tenth Revision, Clinical Modification codes from the Centers for Disease Control and Prevention's Compressed Mortality File. Data analysis was performed from February 1, 2019, to September 10, 2019.ExposuresCounty poverty concentration and the percentage of the population living below the federal poverty level. Counties were divided into 5 poverty concentration categories: 0% to 4.9%, 5.0% to 9.9%, 10.0% to 14.9%, 15.0% to 19.9%, and 20.0% or more of the population living below the federal poverty level.Main outcomes and measuresThe study used a multivariable negative binomial regression model to analyze the association between pediatric suicide rates and county poverty concentration, reporting adjusted incidence rate ratios (aIRRs) with 95% CIs. The study controlled for year, demographic characteristics of the children who died (age, sex, and race/ethnicity), county urbanicity, and county demographic features (age, sex, and racial composition). Subgroup analyses were stratified by method.ResultsFrom 2007 to 2016, a total of 20 982 youths aged 5 to 19 years died by suicide (17 760 [84.6%] were aged 15-19 years, 15 982 [76.2%] male, and 14 387 [68.6%] white non-Hispanic). The annual suicide rate was 3.35 per 100 000 youths aged 5 to 19 years. In the multivariable model, compared with counties with the lowest poverty concentration (0%-4.9%), counties with poverty concentrations of 10% or greater had higher suicide rates in a stepwise manner (10.0%-14.9%: aIRR, 1.25 [95% CI, 1.06-1.47]; 15.0%-19.9%: aIRR, 1.30 [95% CI, 1.10-1.54]; and 20.0% or more: aIRR, 1.37 [95% CI, 1.15-1.64]). When stratified by method, firearm suicides had the strongest association with county poverty concentration (aIRR, 1.87; 95% CI, 1.41-2.49) in counties with 20% or higher poverty concentration compared with counties with 0% to 4.9% poverty concentration.Conclusions and relevanceThe findings suggest that higher county-level poverty concentration is associated with increased suicide rates among youths aged 5 to 19 years. These findings may guide research into upstream risk factors associated with pediatric suicide to inform suicide prevention efforts.

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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