Dataset Information

Use of Small-Area Estimates to Describe County-Level Geographic Variation in Prevalence of Extreme Obesity Among US Adults.

ABSTRACT: Importance:The prevalence of extreme obesity continues to increase among adults in the US, yet there is an absence of subnational estimates and geographic description of extreme obesity. This shortcoming prevents a thorough understanding of the geographic distribution of extreme obesity, which in turn limits the ability of public health agencies and policy makers to target areas with a known higher prevalence. Objectives:To use small-area estimation to create county-level estimates of extreme obesity in the US and apply spatial methods to identify clusters of high and low prevalence. Design, Setting, and Participants:A cross-sectional analysis was conducted using multilevel regression and poststratification with data from the 2012 Behavioral Risk Factor Surveillance System and the US Census Bureau to create prevalence estimates of county-level extreme obesity (body mass index ?40 [calculated as weight in kilograms divided by height in meters squared]). Data were included on adults (aged ?18 years) living in the contiguous US. Analysis was performed from June 4 to December 28, 2018. Main Outcomes and Measures:Multilevel logistic regression models estimated the probability of extreme obesity based on individual-level and area-level characteristics. Census counts were multiplied by these probabilities and summed by county to create county-level prevalence estimates. Moran index values were calculated to assess spatial autocorrelation and identify spatial clusters of hot and cold spots. Estimates of moderate obesity were obtained for comparison. Results:Overall, the weighted prevalence of extreme obesity was 4.0% (95% CI, 3.9%-4.1%) and the prevalence of moderate obesity was 23.7% (95% CI, 23.4%-23.9%). County-level prevalence of extreme obesity ranged from 1.3% (95% CI, 1.3%-1.3%) to 15.7% (95% CI, 15.3%-16.0%). The Pearson correlation coefficient comparing model-predicted estimates with direct estimates was 0.81 (P?

SUBMITTER: Mills CW

PROVIDER: S-EPMC7210484 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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Use of Small-Area Estimates to Describe County-Level Geographic Variation in Prevalence of Extreme Obesity Among US Adults.

Mills Carrie W CW Johnson Glen G Huang Terry T K TTK Balk Deborah D Wyka Katarzyna K

JAMA network open 20200501 5

<h4>Importance</h4>The prevalence of extreme obesity continues to increase among adults in the US, yet there is an absence of subnational estimates and geographic description of extreme obesity. This shortcoming prevents a thorough understanding of the geographic distribution of extreme obesity, which in turn limits the ability of public health agencies and policy makers to target areas with a known higher prevalence.<h4>Objectives</h4>To use small-area estimation to create county-level estimate ...[more]

PMID: 32383746

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Project description:ImportanceObesity is a leading cause of high health care expenditures, disability, and premature mortality. Previous studies have documented geographic disparities in obesity prevalence.ObjectiveTo identify county-level factors associated with obesity using traditional epidemiologic and machine learning methods.Design, setting, and participantsCross-sectional study using linear regression models and machine learning models to evaluate the associations between county-level obesity and county-level demographic, socioeconomic, health care, and environmental factors from summarized statistical data extracted from the 2018 Robert Wood Johnson Foundation County Health Rankings and merged with US Census data from each of 3138 US counties. The explanatory power of the linear multivariate regression and the top performing machine learning model were compared using mean R2 measured in 30-fold cross validation.ExposuresCounty-level demographic factors (population; rural status; census region; and race/ethnicity, sex, and age composition), socioeconomic factors (median income, unemployment rate, and percentage of population with some college education), health care factors (rate of uninsured adults and primary care physicians), and environmental factors (access to healthy foods and access to exercise opportunities).Main outcomes and measuresCounty-level obesity prevalence in 2018, its association with each county-level factor, and the percentage of variation in county-level obesity prevalence explained by linear multivariate and gradient boosting machine regression measured with R2.ResultsAmong the 3138 counties studied, the mean (range) obesity prevalence was 31.5% (12.8%-47.8%). In multivariate regressions, demographic factors explained 44.9% of variation in obesity prevalence; socioeconomic factors, 33.0%; environmental factors, 15.5%; and health care factors, 9.1%. The county-level factors with the strongest association with obesity were census region, median household income, and percentage of population with some college education. R2 values of univariate regressions of obesity prevalence were 0.238 for census region, 0.218 for median household income, and 0.160 for percentage of population with some college education. Multivariate linear regression and gradient boosting machine regression (the best-performing machine learning model) of obesity prevalence using all county-level demographic, socioeconomic, health care, and environmental factors had R2 values of 0.58 and 0.66, respectively (P < .001).Conclusions and relevanceObesity prevalence varies significantly between counties. County-level demographic, socioeconomic, health care, and environmental factors explain the majority of variation in county-level obesity prevalence. Using machine learning models may explain significantly more of the variation in obesity prevalence..

Project description:BackgroundModel-based small area estimation methods can help generate parameter estimates at the district level, where planned population survey sample sizes are not large enough to support direct estimates of HIV prevalence with adequate precision. We computed district-level HIV prevalence estimates and their 95% confidence intervals for districts in Uganda.MethodsOur analysis used direct survey and model-based estimation methods, including Fay-Herriot (area-level) and Battese-Harter-Fuller (unit-level) small area models. We used regression analysis to assess for consistency in estimating HIV prevalence. We use a ratio analysis of the mean square error and the coefficient of variation of the estimates to evaluate precision. The models were applied to Uganda Population-Based HIV Impact Assessment 2016/2017 data with auxiliary information from the 2016 Lot Quality Assurance Sampling survey and antenatal care data from district health information system datasets for unit-level and area-level models, respectively.ResultsEstimates from the model-based and the direct survey methods were similar. However, direct survey estimates were unstable compared with the model-based estimates. Area-level model estimates were more stable than unit-level model estimates. The correlation between unit-level and direct survey estimates was (β1 = 0.66, r2 = 0.862), and correlation between area-level model and direct survey estimates was (β1 = 0.44, r2 = 0.698). The error associated with the estimates decreased by 37.5% and 33.1% for the unit-level and area-level models, respectively, compared to the direct survey estimates.ConclusionsAlthough the unit-level model estimates were less precise than the area-level model estimates, they were highly correlated with the direct survey estimates and had less standard error associated with estimates than the area-level model. Unit-level models provide more accurate and reliable data to support local decision-making when unit-level auxiliary information is available.

Project description:BackgroundThe goal of this study was to re-estimate rates of bilateral hearing loss Nationally, and create new estimates of hearing loss prevalence at the U.S. State and County levels.MethodsWe developed small area estimation models of mild, and moderate or worse bilateral hearing loss in the U.S. using data from the National Health and Nutrition Examination Survey (2001-2012, 2015-2018), the American Community Survey (2019), Census County Business Patterns (2019); Social Security Administration Data (2019); Medicare Fee-for-Service and Advantage claims data (2019); the Area Health Resources File (2019), and other sources. We defined hearing loss as mild (>25 dB through 40 dB), moderate or worse (>40 dB), or any (>25 dB) in the better hearing ear based on a 4-frequency pure-tone-average threshold, and created estimates by age group (0-4, 5-17, 18-34, 35-64, 65-74, 75+), gender, race and ethnicity, state, and county.FindingsWe estimated that 37.9 million (95% Uncertainty Interval [U.I.] 36.6-39.1) Americans experienced any bilateral hearing loss; 24.9 million (95% U.I. 23.6-26.0) with mild and 13.0 million (95% U.I. 12.1-13.9) with moderate or worse. The prevalence rate of any hearing loss was 11.6% (95% U.I. 11.2%-12.0%). Hearing loss increased with age. Men were more likely to have hearing loss than women after age 35, and non-Hispanic Whites had higher rates of hearing loss than other races and ethnicities. Higher hearing loss prevalence was associated with smaller population size. West Virginia, Alaska, Wyoming, Oklahoma, and Arkansas had the highest standardised rate of bilateral hearing loss, and Washington D.C., New Jersey, New York, Maryland, and Connecticut had the lowest.InterpretationBilateral Hearing loss varies by State and County, with variation associated with population age, race and ethnicity, and population size. Geographic estimates can be used to raise local awareness of hearing loss as a problem, to prioritize areas for hearing loss prevention, identification, and treatment, and to guide future research on the hearing loss risk factors that contribute to these differences.FundingCDC's National Center for Chronic Disease Prevention and Health Promotion, Division of Population Health.

Project description:Bronchiolitis and pneumonia are leading causes of pediatric hospitalizations. Identifying geographic patterns in hospitalization rates across small geographic areas could be particularly relevant to targeted patient-level and population-level health care.To determine whether lower respiratory tract infection hospitalization rates varied geographically across a single county and whether such variability was associated with socioeconomic conditions.Cross-sectional, population-based study of children hospitalized at one institution for lower respiratory tract infections between January 1, 2010, and December 31, 2013. The setting was Cincinnati Children's Hospital Medical Center, a large, academic, stand-alone pediatric facility located in Hamilton County, Ohio. During the study period, 99.6% of in-county children hospitalized for lower respiratory tract infections were admitted to Cincinnati Children's Hospital Medical Center. Participants were children younger than 2 years who were hospitalized with bronchiolitis and children younger than 18 years who were hospitalized with pneumonia. Patients were identified using discharge diagnosis codes and then geocoded to their home census tract.Primary exposures, linked to each geocoded patient, included census tract-level socioeconomic measures obtained from the 2008 to 2012 American Community Survey (eg, adult educational attainment, unemployment, and poverty). Patient-level variables examined included demographics, presence of a complex chronic condition, length of stay, and cost.We calculated bronchiolitis and pneumonia hospitalization rates for Hamilton County and for each of 222 in-county census tracts. Associations between hospitalization rate quintiles and underlying socioeconomic conditions were assessed using the Kruskal-Wallis test. Geographic clustering was assessed using the Getis-Ord Gi* statistic.There were 1495 bronchiolitis hospitalizations and 1231 pneumonia hospitalizations during the study period. The county rates were 17.5 (range across census tracts, 0-71.4) hospitalizations per 1000 children per year for bronchiolitis and 1.6 (range across census tracts, 0-4.3) hospitalizations per 1000 children per year for pneumonia. There was significant variation in the median hospitalization rates by census tract quintile for bronchiolitis (32.8, 20.8, 14.0, 10.4, and 5.1 per 1000) and for pneumonia (3.3, 2.1, 1.4, 0.9, and 0.3 per 1000). There were also significant, graded differences in socioeconomic measures by hospitalization rate quintile. Hot spots were localized to inner-city, impoverished neighborhoods.Bronchiolitis and pneumonia hospitalization rates varied considerably in ways that were related to underlying socioeconomic conditions. Clinical and public health interventions, targeted accordingly, could improve patient-level and population-level management of acute conditions at a reduced cost.

Project description:IntroductionGeographic variation in the prevalence of chronic kidney disease and incidence of end-stage renal disease has been previously reported. However, the geographic epidemiology of rapid estimated glomerular filtration rate (eGFR) decline has not been examined.MethodsWe built a longitudinal cohort of 2,107,570 US veterans to characterize the spatial epidemiology of and examine the associations between US county characteristics and rapid eGFR decline.ResultsThere were 169,029 (8.02%) with rapid eGFR decline (defined as eGFR slope < -5 ml/min per 1.73 m2/year). The prevalence of rapid eGFR decline adjusted for age, race, gender, diabetes, and hypertension varied by county from 4.10%-6.72% in the lowest prevalence quintile to 8.41%-22.04% in the highest prevalence quintile (P for heterogeneity < 0.001). Examination of adjusted prevalence showed substantial geographic variation in those with and without diabetes and those with and without hypertension (P for heterogeneity < 0.001). Cohort participants had higher odds of rapid eGFR decline when living in counties with unfavorable characteristics in domains including health outcomes (odds ratio [OR] = 1.15; confidence interval [CI] = 1.09-1.22), health behaviors (OR = 1.08; CI = 1.03-1.13), clinical care (OR = 1.11; CI = 1.06-1.16), socioeconomic conditions (OR = 1.15; CI = 1.09-1.22), and physical environment (OR = 1.15; CI = 1.01-1.20); living in counties with high percentage of minorities and immigrants was associated with rapid eGFR decline (OR = 1.25; CI = 1.20-1.31). Spatial analyses suggest the presence of cluster of counties with high prevalence of rapid eGFR decline.DiscussionOur findings show substantial geographic variation in rapid eGFR decline among US veterans; the variation persists in analyses stratified by diabetes and hypertension status; results show associations between US county characteristics in domains capturing health, socioeconomic, environmental, and diversity conditions, and rapid eGFR decline.

Project description:BackgroundSuicide is one of the leading causes of death in the USA and population risk prediction models can inform decisions on the type, location, and timing of public health interventions. We aimed to develop a prediction model to estimate county-level suicide risk in the USA using population characteristics.MethodsWe obtained data on all deaths by suicide reported to the National Vital Statistics System between Jan 1, 2005, and Dec 31, 2019, and age, sex, race, and county of residence of the decedents were extracted to calculate baseline risk. We also obtained county-level annual measures of socioeconomic predictors of suicide risk (unemployment, weekly wage, poverty prevalence, median household income, and population density) and state-level prevalence of major depressive disorder and firearm ownership from US public sources. We applied conditional autoregressive models, which account for spatiotemporal autocorrelation in response and predictors, to estimate county-level suicide risk.FindingsEstimates derived from conditional autoregressive models were more accurate than from models not adjusted for spatiotemporal autocorrelation. Inclusion of suicide risk and protective covariates further reduced errors. Suicide risk was estimated to increase with each SD increase in firearm ownership (2·8% [95% credible interval (CrI) 1·8 to 3·9]), prevalence of major depressive episode (1·0% [0·4 to 1·5]), and unemployment rate (2·8% [1·9 to 3·8]). Conversely, risk was estimated to decrease by 4·3% (-5·1 to -3·2) for each SD increase in median household income and by 4·3% (-5·8 to -2·5) for each SD increase in population density. An increase in the heterogeneity in county-specific suicide risk was also observed during the study period.InterpretationArea-level characteristics and the conditional autoregressive models can estimate population-level suicide risk. Availability of near real-time situational data are necessary for the translation of these models into a surveillance setting. Monitoring changes in population-level risk of suicide could help public health agencies select and deploy targeted interventions quickly.FundingUS National Institute of Mental Health.

Dataset Information

Use of Small-Area Estimates to Describe County-Level Geographic Variation in Prevalence of Extreme Obesity Among US Adults.

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Use of Small-Area Estimates to Describe County-Level Geographic Variation in Prevalence of Extreme Obesity Among US Adults.

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