Geographic disparities and temporal changes of diabetes prevalence and diabetes self-management education program participation in Florida

Background Although Diabetes Self-Management Education (DSME) programs are recommended to help reduce the burden of diabetes and diabetes-related complications, Florida is one of the states with the lowest DSME participation rates. Moreover, there is evidence of geographic disparities of not only DSME participation rates but the burden of diabetes as well. Understanding these disparities is critical for guiding control programs geared at improving participation rates and diabetes outcomes. Therefore, the objectives of this study were to: (a) investigate geographic disparities of diabetes prevalence and DSME participation rates; and (b) identify predictors of the observed disparities in DSME participation rates. Methods Behavioral Risk Factor Surveillance System (BRFSS) data for 2007 and 2010 were obtained from the Florida Department of Health. Age-adjusted diabetes prevalence and DSME participation rates were computed at the county level and their geographic distributions visualized using choropleth maps. Significant changes in diabetes prevalence and DSME participation rates between 2007 and 2010 were assessed and counties showing significant changes were mapped. Clusters of high diabetes prevalence before and after adjusting for common risk factors and DSME participation rates were identified, using Tango’s flexible spatial scan statistics, and their geographic distribution displayed in maps. Determinants of the geographic distribution of DSME participation rates and predictors of the identified high rate clusters were identified using ordinary least squares and logistic regression models, respectively. Results County level age-adjusted diabetes prevalence varied from 4.7% to 17.8% while DSME participation rates varied from 26.6% to 81.2%. There were significant (p≤0.05) increases in both overall age-adjusted diabetes prevalence and DSME participation rates from 2007 to 2010 with diabetes prevalence increasing from 7.7% in 2007 to 8.6% in 2010 while DSME participation rates increased from 51.4% in 2007 to 55.1% in 2010. Generally, DSME participation rates decreased in rural areas while they increased in urban areas. High prevalence clusters of diabetes (both adjusted and unadjusted) were identified in northern and central Florida, while clusters of high DSME participation rates were identified in central Florida. Rural counties and those with high proportion of Hispanics tended to have low DSME participation rates. Conclusions The findings confirm that geographic disparities in both diabetes prevalence and DSME participation rates exist. Specific attention is required to address these disparities especially in areas that have high diabetes prevalence but low DSME participation rates. Study findings are useful for guiding resource allocation geared at reducing disparities and improving diabetes outcomes.

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Abstract P296: Investigation Of Geographic Disparities Of Pre-diabetes And Diabetes And Associated County-level Risk Factors In Florida, 2016

Circulation ◽

10.1161/circ.141.suppl_1.p296 ◽

2020 ◽

Vol 141 (Suppl_1) ◽

Author(s):

Jennifer E Lord ◽

Shamarial L Roberson ◽

Agricola Odoi

Keyword(s):

Public Health ◽

Spatial Patterns ◽

The United States ◽

The State ◽

Scan Statistics ◽

Diabetes Prevalence ◽

County Level ◽

Poverty Level ◽

Geographic Disparities ◽

Strength Of Association

Background: Diabetes and its complications represent a significant public health burden in the United States, with evidence of geographic disparities. Identifying these disparities and their determinants is useful for guiding control programs. Therefore, this study investigated geographic disparities of pre-diabetes and diabetes prevalence in Florida in 2016, and identified predictors of the observed spatial patterns. Additionally, we investigated changes in geographic distribution of the two conditions between 2013 and 2016. Methods: The 2013 and 2016 Behavioral Risk Factor Surveillance System data were obtained from the Florida Department of Health. Flexible scan statistics were used to identify significant high prevalence geographic clusters. Global ordinary least squares regression and local Poisson geographically weighted generalized linear models were used to investigate predictors of the identified spatial patterns. Counties with significant changes in prevalence of the two conditions between 2013 and 2016 were identified using tests for equality of proportions adjusted for multiple comparisons using Simes method. Results: The state-wide diabetes prevalence was 11.2% in 2013, and 11.8% in 2016. Statistically significant ( p ≤0.05) increases in prevalence were identified in 73% (49/67) of the counties. Similarly, the state-wide prevalence of pre-diabetes was 7.1% in 2013 and 9.2% in 2016 with 76% (51/67) of the counties reporting statistically significant increases. Significant local hotspots were identified for both conditions. Predictors of county-level diabetes prevalence were: proportion of the obese population, number of physicians per 1000 persons, proportion of the population living below the poverty level, and proportion of the population with arthritis. Predictors of pre-diabetes prevalence included proportion of the population with arthritis and proportion of the population that identified as non-Hispanic black. There was evidence of geographical variability of all regression coefficients for both the pre-diabetes and diabetes models indicating that the strength of association of the relationships between the predictors and outcomes varied by geographic area. Conclusions: Geographic disparities of both conditions continue to exist in Florida. Moreover, there was a state-wide increase in the burden of both conditions between 2013 and 2016. The fact that the strength of association of the relationships between the predictors and outcomes varied across the counties implies that some predictors may be more important in some counties than others. These findings imply that local models provide useful information to guide public health decision-making and resource allocation. Identifying high-risk geographic areas and location-specific determinants of chronic disease prevalence should be used to inform targeted intervention programs.

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Geographic disparities and socio-demographic predictors of pertussis risk in Florida

PeerJ ◽

10.7717/peerj.11902 ◽

2021 ◽

Vol 9 ◽

pp. e11902

Author(s):

Corinne B. Tandy ◽

Agricola Odoi

Keyword(s):

Resource Allocation ◽

High Risk ◽

Geographic Distribution ◽

Temporal Changes ◽

County Level ◽

Spatial Clusters ◽

Geographic Disparities ◽

Demographic Predictors ◽

Time Period ◽

Over Time

Background Pertussis is a toxin-mediated respiratory illness caused by Bordetella pertussis that can result in severe complications and death, particularly in infants. Between 2008 and 2011, children less than 3 months old accounted for 83% of the pertussis deaths in the United States. Understanding the geographic disparities in the distribution of pertussis risk and identifying high risk geographic areas is necessary for guiding resource allocation and public health control strategies. Therefore, this study investigated geographic disparities and temporal changes in pertussis risk in Florida from 2010 to 2018. It also investigated socioeconomic and demographic predictors of the identified disparities. Methods Pertussis data covering the time period 2010–2018 were obtained from Florida HealthCHARTS web interface. Spatial patterns and temporal changes in geographic distribution of pertussis risk were assessed using county-level choropleth maps for the time periods 2010–2012, 2013–2015, 2016–2018 and 2010–2018. Tango’s flexible spatial scan statistics were used to identify high-risk spatial clusters which were displayed in maps. Ordinary least squares (OLS) regression was used to identify significant predictors of county-level risk. Residuals of the OLS model were assessed for model assumptions including spatial autocorrelation. Results County-level pertussis risk varied from 0 to 116.31 cases per 100,000 people during the study period. A total of 11 significant (p < 0.05) spatial clusters were identified with risk ratios ranging from 1.5 to 5.8. Geographic distribution remained relatively consistent over time with areas of high risk persisting in the western panhandle, northeastern coast, and along the western coast. Although county level pertussis risks generally increased from 2010–2012 to 2013–2015, risk tended to be lower during the 2016–2018 time period. Significant predictors of county-level pertussis risk were rurality, percentage of females, and median income. Counties with high pertussis risk tended to be rural (p = 0.021), those with high median incomes (p = 0.039), and those with high percentages of females (p < 0.001). Conclusion There is evidence that geographic disparities exist and have persisted over time in Florida. This study highlights the application and importance of Geographic Information Systems (GIS) technology and spatial statistical/epidemiological tools in identifying areas of highest disease risk so as to guide resource allocation to reduce health disparities and improve health for all.

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Is it possible to predict improved diabetes outcomes following diabetes self-management education: a mixed-methods longitudinal design

BMJ Open ◽

10.1136/bmjopen-2015-008781 ◽

2015 ◽

Vol 5 (11) ◽

pp. e008781 ◽

Cited By ~ 5

Author(s):

Caroline Huxley ◽

Jackie Sturt ◽

Jeremy Dale ◽

Rosie Walker ◽

Isabela Caramlau ◽

...

Keyword(s):

Mixed Methods ◽

Management Education ◽

Longitudinal Design ◽

Self Management ◽

Diabetes Outcomes

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Assessing the Effectiveness of Diabetes Self-Management Education on Improving Diabetes Outcomes

Journal of the Academy of Nutrition and Dietetics ◽

10.1016/j.jand.2013.06.264 ◽

2013 ◽

Vol 113 (9) ◽

pp. A76

Author(s):

B. Gore ◽

E.C. Okeiyi

Keyword(s):

Management Education ◽

Self Management ◽

Diabetes Outcomes

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Diabetes self-management education: Essential to improved diabetes outcomes

PsycEXTRA Dataset ◽

10.1037/e504512010-002 ◽

2007 ◽

Author(s):

Nancy L. Rhodes

Keyword(s):

Management Education ◽

Self Management ◽

Diabetes Outcomes

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Abstract P154: Disparities in Geographic and Temporal Trends of Myocardial Infarction Hospitalizations in Florida, 2005-2014

Circulation ◽

10.1161/circ.141.suppl_1.p154 ◽

2020 ◽

Vol 141 (Suppl_1) ◽

Author(s):

Evah W Odoi ◽

Nicholas Nagle ◽

Melissa Jordan ◽

Chris DuClos ◽

Kristina Kintziger

Keyword(s):

Myocardial Infarction ◽

Health Disparities ◽

High Risk ◽

Low Risk ◽

Scan Statistics ◽

Time Interval ◽

Geographic Disparities ◽

Central Florida ◽

South Central ◽

West Central

Background: Knowledge of the extent of geographic disparities in the burden of myocardial infarction (MI) is useful for allocation of scarce public health resources to reduce health disparities and improve population health, regardless of whether MI is the primary or secondary cause for hospitalization. The objectives of this study were to identify: (a) geographic disparities in hospitalization risks for MI in Florida; and (b) the temporal changes in these disparities from 2005 to 2014. Methodology: We aggregated county-level data for principal and secondary inpatient MI-related hospital discharges in Florida between 2005 and 2014 by 2-year intervals and calculated age- and sex- adjusted MI hospitalization risks for each time interval. We identified spatial clusters of low- and high-risk MI hospitalization risks using circular spatial scan statistics and tracked MI risks in clusters that persisted throughout the 10-year study period. We also assessed health disparities between persistent high- and low-risk clusters at the end of the study (2013-2014) compared to the beginning of the study (2005-2006) periods. Results: MI hospitalization risks decreased by 15% in Florida overall during the 10-year study period. However, we found persistent disparities in MI risks by geographic location, with high-risk clusters occurring in north-, west-, south-central and southeast Florida, and low-risk clusters occurring in southeast and southwest Florida. A low-risk cluster that transitioned to high-risk status in the last four years of study was identified in northwest Florida. We also found substantial differences in the magnitude of decline in MI risks amongst clusters, with risks decreasing by 5%, 16% and 31% in high-risk clusters in west central, south central and north central Florida, respectively, and by 6.5% and 26% in low-risk clusters in southwest and southeast Florida, respectively. Furthermore, the risks only decreased during the first 6-8 years of study, after which they leveled off or ticked upwards. Consequently, health disparities between high- and low-risk clusters at the end of the study compared to the beginning of the study period decreased by 57% and 31.5% in north- and south-central Florida, respectively, but they remained relatively unchanged in west central Florida. Moreover, MI hospitalization risks in high-risk clusters lag behind those in low-risk clusters by at least a decade. Conclusion: Myocardial infarction hospitalization risks declined modestly during 10-year study period. However, persistent disparities continue to exist across space and time. Addressing these disparities will require targeting intervention efforts to counties with persistently high risks.

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Diabetes Education Desert: Regional Disparity Between Diabetes Prevalence and Diabetes Self-Management Education Programs in Texas

Population Health Management ◽

10.1089/pop.2020.0006 ◽

2020 ◽

Author(s):

Juha Baek ◽

Ohbet Cheon ◽

Sayah Lee ◽

Nwabunie Nwana

Keyword(s):

Management Education ◽

Diabetes Education ◽

Regional Disparity ◽

Diabetes Prevalence ◽

Self Management ◽

Education Programs

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Geographic disparities, determinants, and temporal changes in the prevalence of pre-diabetes in Florida

PeerJ ◽

10.7717/peerj.10443 ◽

2021 ◽

Vol 9 ◽

pp. e10443

Author(s):

Jennifer Lord ◽

Shamarial Roberson ◽

Agricola Odoi

Keyword(s):

Health Planning ◽

Insurance Coverage ◽

Black Population ◽

Health Insurance Coverage ◽

The United States ◽

Scan Statistics ◽

Diabetes Prevalence ◽

Behavioral Risk ◽

Geographic Disparities ◽

High Prevalence

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 comparisons using the Simes method. Flexible scan statistics were used to identify significant high prevalence geographic clusters. Multivariable regression models were used to identify determinants of county-level pre-diabetes prevalence. Results The state-wide age-adjusted prevalence of pre-diabetes increased significantly (p ≤ 0.05) from 8.0% in 2013 to 10.5% in 2016 with 72% (48/67) of the counties reporting statistically significant increases. Significant local geographic hotspots were identified. High prevalence of pre-diabetes tended to occur in counties with high proportions of non-Hispanic black population, low median household income, and low proportion of the population without health insurance coverage. Conclusions Geographic disparities of pre-diabetes continues to exist in Florida with most counties reporting significant increases in prevalence between 2013 and 2016. These findings are critical for guiding health planning, resource allocation and intervention programs.

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