Using Multiple Sources of Data to Assess the Prevalence of Diabetes at the Subcounty Level, Duval County, Florida, 2007 (original) (raw)
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Preventing Chronic Disease, 2010
IntroductionDiabetes rates continue to grow in the United States. Effectively addressing the epidemic requires better understanding of the distribution of disease and the geographic clustering of factors that influence it. Variations in the prevalence of diabetes at the local level are largely unreported, making understanding the disparities associated with the disease more difficult. Diabetes death rates during the past 15 years in Duval County, Florida, have been disproportionately high compared with the rest of the state.MethodsWe analyzed multiple sources of secondary data related to diabetes illness and death in Duval County, including data on hospital discharge, emergency department (ED) use, and vital statistics. We accessed diabetes and diabetes-related ED use and hospitalization and death data by using codes from the International Classification of Diseases versions 9 and 10. We analyzed data from the Behavioral Risk Factor Surveillance System survey for Duval County and adapted Centers for Disease Control and Prevention weighting formulas for subcounty analysis. We used relative risk-type disease ratios and geographic information systems mapping to analyze data.ResultsThe urban, mostly minority, low-socioeconomic area of Duval County had twice the rate of diabetes-related illness and death as other areas of the county, and the inner-city, poor area of the county had almost 3 times the rate of hospitalization and ED use for diabetes and diabetes-related conditions compared with the other areas of the county.ConclusionOur analyses show that diabetes-related disparities affect not only people and their families but also the community that absorbs the costs associated with the disproportionate health care use that results from these disparities. Analyzing data at the subcounty level has implications for health care planning and public health policy development at the local level.
International Journal of Health Geographics, 2024
Background Early diagnosis, control of blood glucose levels and cardiovascular risk factors, and regular screening are essential to prevent or delay complications of diabetes. However, most adults with diabetes do not meet recommended targets, and some populations have disproportionately high rates of potentially preventable diabetes-related hospitalizations. Understanding the factors that contribute to geographic disparities can guide resource allocation and help ensure that future interventions are designed to meet the specific needs of these communities. Therefore, the objectives of this study were (1) to identify determinants of diabetes-related hospitalization rates at the ZIP code tabulation area (ZCTA) level in Florida, and (2) assess if the strengths of these relationships vary by geographic location and at different spatial scales. Methods Diabetes-related hospitalization (DRH) rates were computed at the ZCTA level using data from 2016 to 2019. A global ordinary least squares regression model was fit to identify socioeconomic, demographic, healthcarerelated, and built environment characteristics associated with log-transformed DRH rates. A multiscale geographically weighted regression (MGWR) model was then fit to investigate and describe spatial heterogeneity of regression coefficients. Results Populations of ZCTAs with high rates of diabetes-related hospitalizations tended to have higher proportions of older adults (p < 0.0001) and non-Hispanic Black residents (p = 0.003). In addition, DRH rates were associated with higher levels of unemployment (p = 0.001), uninsurance (p < 0.0001), and lack of access to a vehicle (p = 0.002). Population density and median household income had significant (p < 0.0001) negative associations with DRH rates. Non-stationary variables exhibited spatial heterogeneity at local (percent non-Hispanic Black, educational attainment), regional (age composition, unemployment, health insurance coverage), and statewide scales (population density, income, vehicle access).
PloS one, 2024
Background Hospitalizations due to diabetes complications are potentially preventable with effective management of the condition in the outpatient setting. Diabetes-related hospitalization (DRH) rates can provide valuable information about access, utilization, and efficacy of healthcare services. However, little is known about the local geographic distribution of DRH rates in Florida. Therefore, the objectives of this study were to investigate the geographic distribution of DRH rates at the ZIP code tabulation area (ZCTA) level in Florida, identify significant local clusters of high hospitalization rates, and describe characteristics of ZCTAs within the observed spatial clusters. Methods Hospital discharge data from 2016 to 2019 were obtained from the Florida Agency for Health Care Administration through a Data Use Agreement with the Florida Department of Health. Raw and spatial empirical Bayes smoothed DRH rates were computed at the ZCTA level. High-rate DRH clusters were identified using Tango's flexible spatial scan statistic. Choropleth maps were used to display smoothed DRH rates and significant high-rate spatial clusters. Demographic, socioeconomic, and healthcare-related characteristics of cluster and non-cluster ZCTAs were compared using the Wilcoxon rank sum test for continuous variables and Chi-square test for categorical variables. Results There was a total of 554,133 diabetes-related hospitalizations during the study period. The statewide DRH rate was 8.5 per 1,000 person-years, but smoothed rates at the ZCTA level ranged from 0 to 101.9. A total of 24 significant high-rate spatial clusters were identified. High-rate clusters had a higher percentage of rural ZCTAs (60.9%) than non-cluster ZCTAs (41.8%). The median percent of non-Hispanic Black residents was significantly (p < 0.0001) higher in cluster ZCTAs than in non-cluster ZCTAs. Populations of cluster ZCTAs also had
Investigation of geographic disparities of pre-diabetes and diabetes in Florida
BMC Public Health, 2020
Background Diabetes is a leading cause of death and disability in the United States, and its precursor, pre-diabetes, is estimated to occur in one-third of American adults. Understanding the geographic disparities in the distribution of these conditions and identifying high-prevalence areas is critical to guiding control and prevention programs. Therefore, the objective of this study was to investigate clusters of pre-diabetes and diabetes risk in Florida and identify significant predictors of the conditions. Methods Data from the 2013 Behavioral Risk Factor Surveillance System were obtained from the Florida Department of Health. Spatial scan statistics were used to identify and locate significant high-prevalence local clusters. The county prevalence proportions of pre-diabetes and diabetes and the identified significant clusters were displayed in maps. Logistic regression was used to identify significant predictors of the two conditions for individuals living within and outside hig...
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...
Journal of the Endocrine Society
Geographic surveillance can identify hotspots of disease and reveal associations between health and the environment. Our study used emergency department surveillance to investigate geographic disparities in type 1 and type 2 diabetes prevalence among adults and children. Using all-payer emergency claims data from 2009 to 2013, we identified unique New York City residents with diabetes and geocoded their location using home addresses. Geospatial analysis was performed to estimate diabetes prevalence by New York City Census tract. We also used multivariable regression to identify neighborhood-level factors associated with higher diabetes prevalence. We estimated type 1 and type 2 diabetes prevalence at 0.23% and 10.5%, respectively, among adults and 0.20% and 0.11%, respectively, among children in New York City. Pediatric type 1 diabetes was associated with higher income (P = 0.001), whereas adult type 2 diabetes was associated with lower income (P , 0.001). Areas with a higher proportion of nearby restaurants categorized as fast food had a higher prevalence of all types of diabetes (P , 0.001) except for pediatric type 2 diabetes. Type 2 diabetes among children was only higher in neighborhoods with higher proportions of African American residents (P , 0.001). Our findings identify geographic disparities in diabetes prevalence that may require special attention to address the specific needs of adults and children living in these areas. Our results suggest that the food environment may be associated with higher type 1 diabetes prevalence. However, our analysis did not find a robust association with the food environment and pediatric type 2 diabetes, which was predominantly focused in African American neighborhoods.
Spatial patterns of diabetes related health problems for vulnerable populations in Los Angeles
International Journal of Health Geographics, 2010
Background: Rates for Diabetes Mellitus continue to rise in most urban areas of the United States, with a disproportionate burden suffered by minorities and low income populations. This paper presents an approach that utilizes address level data to understand the geography of this disease by analyzing patients seeking diabetes care through an emergency department in a Los Angeles County hospital. The most vulnerable frequently use an emergency room as a common care access point, and such care is especially costly. A fine scale GIS analysis reveals hotspots of diabetes related health problems and provides output useful in a clinic setting. Indeed these results were used to support the work of a progressive diabetes clinic to guide management and intervention strategies. Results: Hotspots of diabetes related health problems, including neurological and kidney issues were mapped for vulnerable populations in a central section of Los Angeles County. The resulting spatial grid of rates and significance were overlaid with new patient residential addresses attending an area clinic. In this way neighbourhood diabetes health characteristics are added to each patient's individual health record. Of the 29 patients, 4 were within statistically significant hotspots for at least one of the conditions being investigated. Conclusions: Although exploratory in nature, this approach demonstrates a novel method to conduct GIS based investigations of urban diabetes while providing support to a progressive diabetes clinic looking for novel means of managing and intervention. In so doing, this analysis adds to a relatively small literature on fine scale GIS facilitated diabetes research. Similar data should be available for most hospitals, and with due consideration for preserving spatial confidentiality, analysis outputs such as those presented here should become more commonly employed in other investigations of chronic diseases.
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.