Identifying counties vulnerable to diabetes from obesity prevalence in the United States: a spatiotemporal analysis (original) (raw)
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Bayesian Small Area Estimates of Diabetes Incidence by United States County, 2009
Journal of Data Science, 2021
In the United States, diabetes is common and costly. Programs to prevent new cases of diabetes are often carried out at the level of the county, a unit of local government. Thus, efficient targeting of such programs requires county-level estimates of diabetes incidence−the fraction of the nondiabetic population who received their diagnosis of diabetes during the past 12 months. Previously, only estimates of prevalence−the overall fraction of population who have the disease−have been available at the county level. Counties with high prevalence might or might not be the same as counties with high incidence, due to spatial variation in mortality and relocation of persons with incident diabetes to another county. Existing methods cannot be used to estimate county-level diabetes incidence, because the fraction of the population who receive a diabetes diagnosis in any year is too small. Here, we extend previously developed methods of Bayesian small-area estimation of prevalence, using diffuse priors, to estimate diabetes incidence for all U.S. counties based on data from a survey designed to yield state-level estimates. We found high incidence in the southeastern United States, the Appalachian region, and in scattered counties throughout the western U.S. Our methods might be applicable in other circumstances in which all cases of a rare condition also must be cases of a more common condition (in this analysis, "newly diagnosed cases of diabetes" and "cases of diabetes"). If appropriate data are available, our methods can be used to estimate proportion of the population with the rare condition at greater geographic specificity than the data source was designed to provide.
Journal of Data Science, 2010
Information regarding small area prevalence of chronic disease is important for public health strategy and resourcing equity. This paper develops a prevalence model taking account of survey and census data to derive small area prevalence estimates for diabetes. The application involves 32000 small area subdivisions (zip code census tracts) of the US, with the prevalence estimates taking account of information from the US-wide Behavioral Risk Factor Surveillance System (BRFSS) survey on population prevalence differentials by age, gender, ethnic group and education. The effects of such aspects of population composition on prevalence are widely recognized. However, the model also incorporates spatial or contextual influences via spatially structured effects for each US state; such contextual effects are allowed to differ between ethnic groups and other demographic categories using a multivariate spatial prior. A Bayesian estimation approach is used and analysis demonstrates the considerably improved fit of a fully specified compositional-contextual model as compared to simpler 'standard' approaches which are typically limited to age and area effects.
Computational Urban Science
Heart disease is the leading cause of death in the United States. A person who has type-2 diabetes is twice as likely to have heart disease than someone who doesn’t have diabetes. Therefore, analyzing factors associated with both diseases and their interrelationships is essential for cardiovascular disease control and public health. In this article, we propose a Multi-scale Geographically Weighted Regression (MGWR) approach to observe spatial variations of environmental and demographic risk factors such as alcohol consumption behavior, lack of physical activity, obesity rate, urbanization rate, and income from 2005 to 2015 in the United States. The MGWR model has applied to eight census divisions of the United States at the county level: New England, Middle Atlantic, East North Central, West North Central, South Atlantic, East South Central, West South Central, and Mountain. Results illustrate that there are notable differences in the spatial variation of the risk factors behind the...
Spatial Clusters of County-Level Diagnosed Diabetes and Associated Risk Factors in the United States
The Open Diabetes Journal, 2012
Introduction: We examined whether spatial clusters of county-level diagnosed diabetes prevalence exist in the United States and whether socioeconomic and diabetes risk factors were associated with these clusters. Materials and Methods: We used estimated county-level age-adjusted data on diagnosed diabetes prevalence for adults in 3109 counties in the United States (2007 data). We identified four types of diabetes clusters based on spatial autocorrelations: high-prevalence counties with high-prevalence neighbors (High-High), low-prevalence counties with low-prevalence neighbors (Low-Low), low-prevalence counties with high-prevalence neighbors (Low-High), and highprevalence counties with low-prevalence neighbors (High-Low). We then estimated relative risks for clusters being associated with several socioeconomic and diabetesrisk factors. Results: Diabetes prevalence in 1551 counties was spatially associated (p<0.05) with prevalence in neighboring counties. The rate of obesity, physical inactivity, poverty, and the proportion of non-Hispanic blacks were associated with a county being in a High-High cluster versus being a non-cluster county (7% to 36% greater risk) or in a Low-Low cluster (13% to 67% greater risk). The percentage of non-Hispanic blacks was associated with a 7% greater risk for being in a Low-High cluster. The rate of physical inactivity and the percentage of Hispanics or non-Hispanic American Indians were associated with being in a High-Low cluster (5% to 21% greater risk). Discussion: Distinct spatial clusters of diabetes prevalence exist in the United States. Strong association between diabetes clusters and socioeconomic and other diabetes risk factors suggests that interventions might be tailored according to the prevalence of modifiable factors in specific counties.
Obesity (Silver Spring, Md.), 2017
To investigate differences in community characteristics associated with diabetes prevalence between the Diabetes Belt and the rest of the contiguous United States (U.S.) METHODS: County-level adult diabetes prevalence estimates (i.e., percent of people [≥20 years] with diagnosed diabetes 2009) were used from the Centers for Disease Control and Prevention, in addition to data from the U.S. Census Bureau, U.S. Department of Agriculture, and U.S. Department of Health and Human Services, to carry out a spatial regime analysis to identify county-level factors correlated with diabetes prevalence in the Diabetes Belt versus the remainder of the U.S. Counties outside of the Diabetes Belt demonstrated stronger positive associations between diabetes prevalence and persistent poverty and greater percentages of unemployed labor forces. For counties in the Diabetes Belt, diabetes prevalence showed a stronger positive association with natural amenities (e.g., temperate climate and topographic fea...
Data Science Journal
This paper assesses concordance and inconsistency among three small area estimation methods that are currently providing county-level health indicators in the United States. The three methods are multi-level logistic regression, spatial logistic regression, and spatial Poison regression, all proposed since 2010. Diabetes prevalence is estimated for each county in the continental United States from the 2012 sample of Behavioral Risk Factor Surveillance System. The mapping results show that all three methods displayed elevated diabetes prevalence in the South. While the Pearson correlation coefficients among three model-based estimates were all above 0.60, the highest one was 0.80 between the multilevel and spatial logistic methods. While point estimates are apparently different among the three small area estimate methods, their top and bottom of quintile distributions are fairly consistent based on Bangdiwala's B-statistic, suggesting that outputs from each method would support consistent policy making in terms of identifying top and bottom percent counties.
International Journal of Health Geographics, 2014
The role that environmental factors, such as neighborhood socioeconomics, food, and physical environment, play in the risk of obesity and chronic diseases is not well quantified. Understanding how spatial distribution of disease risk factors overlap with that of environmental (contextual) characteristics may inform health interventions and policies aimed at reducing the environment risk factors. We evaluated the extent to which spatial clustering of extreme body mass index (BMI) values among a large sample of adults with diabetes was explained by individual characteristics and contextual factors.
Scientific Reports
Type 2 diabetes mellitus (T2D) prevalence in the United States varies substantially across spatial and temporal scales, attributable to variations of socioeconomic and lifestyle risk factors. Understanding these variations in risk factors contributions to T2D would be of great benefit to intervention and treatment approaches to reduce or prevent T2D. Geographically-weighted random forest (GW-RF), a tree-based non-parametric machine learning model, may help explore and visualize the relationships between T2D and risk factors at the county-level. GW-RF outputs are compared to global (RF and OLS) and local (GW-OLS) models between the years of 2013–2017 using low education, poverty, obesity, physical inactivity, access to exercise, and food environment as inputs. Our results indicate that a non-parametric GW-RF model shows a high potential for explaining spatial heterogeneity of, and predicting, T2D prevalence over traditional local and global models when inputting six major risk factor...
PeerJ, 2021
Background Left unchecked, pre-diabetes progresses to diabetes and its complications that are important health burdens in the United States. There is evidence of geographic disparities in the condition with some areas having a significantly high risks of the condition and its risk factors. Identifying these disparities, their determinants, and changes in burden are useful for guiding control programs and stopping the progression of pre-diabetes to diabetes. Therefore, the objectives of this study were to investigate geographic disparities of pre-diabetes prevalence in Florida, identify predictors of the observed spatial patterns, as well as changes in disease burden between 2013 and 2016. Methods The 2013 and 2016 Behavioral Risk Factor Surveillance System data were obtained from the Florida Department of Health. Counties with significant changes in the prevalence of the condition between 2013 and 2016 were identified using tests for equality of proportions adjusted for multiple com...