Implementation of Prevention Science to Eliminate Health Care Inequities in Achieving Cardiovascular Health: A Scientific Statement From the American Heart Association.

Prevention of cardiovascular and related diseases is foundational to attaining ideal cardiovascular health to improve the overall health and well-being of individuals and communities. Social determinants of health and health care inequities adversely affect ideal cardiovascular health and prevention of disease. Achieving optimal cardiovascular health in an effective and equitable manner requires a coordinated multidisciplinary and multilayered approach. In this scientific statement, we examine barriers to ideal cardiovascular health and its related conditions in the context of leveraging existing resources to reduce health care inequities and to optimize the delivery of preventive cardiovascular care. We systematically discuss (1) interventions across health care environments involving direct patient care, (2) leveraging health care technology, (3) optimizing multispecialty/multiprofession collaborations and interventions, (4) engaging local communities, and (5) improving the community environment through health-related government policies, all with a focus on making ideal cardiovascular health equitable for all individuals.

A retrospective investigation of spatial clusters and determinants of diabetes prevalence: scan statistics and geographically weighted regression modeling approaches.

Background: Diabetes and its complications represent a significant public health burden in the United States. Some communities have disproportionately high risks of the disease. Identification of these disparities is critical for guiding policy and control efforts to reduce/eliminate the inequities and improve population health. Thus, the objectives of this study were to investigate geographic high-prevalence clusters, temporal changes, and predictors of diabetes prevalence in Florida. Methods: Behavioral Risk Factor Surveillance System data for 2013 and 2016 were provided by the Florida Department of Health. Tests for equality of proportions were used to identify counties with significant changes in the prevalence of diabetes between 2013 and 2016. The Simes method was used to adjust for multiple comparisons. Significant spatial clusters of counties with high diabetes prevalence were identified using Tango's flexible spatial scan statistic. A global multivariable regression model was fit to identify predictors of diabetes prevalence. A geographically weighted regression model was fit to assess for spatial non-stationarity of the regression coefficients and fit a local model. Results: There was a small but significant increase in the prevalence of diabetes in Florida (10.1% in 2013 to 10.4% in 2016), and statistically significant increases in prevalence occurred in 61% (41/67) of counties in the state. Significant, high-prevalence clusters of diabetes were identified. Counties with a high burden of the condition tended to have high proportions of the population that were non-Hispanic Black, had limited access to healthy foods, were unemployed, physically inactive, and had arthritis. Significant non-stationarity of regression coefficients was observed for the following variables: proportion of the population physically inactive, proportion with limited access to healthy foods, proportion unemployed, and proportion with arthritis. However, density of fitness and recreational facilities had a confounding effect on the association between diabetes prevalence and levels of unemployment, physical inactivity, and arthritis. Inclusion of this variable decreased the strength of these relationships in the global model, and reduced the number of counties with statistically significant associations in the local model. Conclusions: The persistent geographic disparities of diabetes prevalence and temporal increases identified in this study are concerning. There is evidence that the impacts of the determinants on diabetes risk vary by geographical location. This implies that a one-size-fits-all approach to disease control/prevention would be inadequate to curb the problem. Therefore, health programs will need to use evidence-based approaches to guide health programs and resource allocation to reduce disparities and improve population health.

Prevalence and predictors of stroke among individuals with prediabetes and diabetes in Florida.

BACKGROUND: The prevalence of both prediabetes and diabetes have been increasing in Florida. These increasing trends will likely result in increases of stroke burden since both conditions are major risk factors of stroke. However, not much is known about the prevalence and predictors of stroke among adults with prediabetes and diabetes and yet this information is critical for guiding health programs aimed at reducing stroke burden. Therefore, the objectives of this study were to estimate the prevalence and identify predictors of stroke among persons with either prediabetes or diabetes in Florida. METHODS: The 2019 Behavioral Risk Factor Surveillance System (BRFSS) survey data were obtained from the Florida Department of Health and used for the study. Weighted prevalence estimates of stroke and potential predictor variables as well as their 95% confidence intervals were computed for adults with prediabetes and diabetes. A conceptual model of predictors of stroke among adults with prediabetes and diabetes was constructed to guide statistical model building. Two multivariable logistic models were built to investigate predictors of stroke among adults with prediabetes and diabetes. RESULTS: The prevalence of stroke among respondents with prediabetes and diabetes were 7.8% and 11.2%, respectively. The odds of stroke were significantly (p ≤ 0.05) higher among respondents with prediabetes that were ≥ 45 years old (Odds ratio [OR] = 2.82; 95% Confidence Interval [CI] = 0.74, 10.69), had hypertension (OR = 5.86; CI = 2.90, 11.84) and hypercholesterolemia (OR = 3.93; CI = 1.84, 8.40). On the other hand, the odds of stroke among respondents with diabetes were significantly (p ≤ 0.05) higher if respondents were non-Hispanic Black (OR = 1.79; CI = 1.01, 3.19), hypertensive (OR = 3.56; CI = 1.87, 6.78) and had depression (OR = 2.02; CI = 1.14, 3.59). CONCLUSIONS: Stroke prevalence in Florida is higher among adults with prediabetes and diabetes than the general population of the state. There is evidence of differences in the importance of predictors of stroke among populations with prediabetes and those with diabetes. These findings are useful for guiding health programs geared towards reducing stroke burden among populations with prediabetes and diabetes.

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.

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

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.

The Florida Department of Health STEPS Public Health Approach: The COVID-19 Response Plan and Outcomes Through May 31, 2020.

In January 2020, the Florida Department of Health began planning for a potential coronavirus disease 2019 (COVID-19) outbreak. The first 2 cases of COVID-19 in Florida were confirmed on March 1, 2020. The state's multiagency response to the COVID-19 pandemic was based on the Florida STEPS plan: (1) social distancing, (2) testing and contact tracing, (3) elderly and medically vulnerable population protection, (4) preparing hospitals for a patient surge and health care worker protection, and (5) stopping the introduction of COVID-19 into the state. This brief report describes COVID-19 response strategies and outcomes in Florida through May 31, 2020.

Investigation of geographic disparities of pre-diabetes and diabetes in Florida.

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 high-prevalence clusters. RESULTS: The study included a total of 34,186 respondents. The overall prevalence of pre-diabetes and diabetes were 8.2 and 11.5%, respectively. Three significant (p < 0.05) local, high-prevalence spatial clusters were detected for pre-diabetes, while five were detected for diabetes. The counties within the high-prevalence clusters had prevalence ratios ranging from 1.29 to 1.85. There were differences in the predictors of the conditions based on whether respondents lived within or outside high-prevalence clusters. Predictors of both pre-diabetes and diabetes regardless of region or place of residence were obesity/overweight, hypertension, and hypercholesterolemia. Income and physical activity level were significant predictors of diabetes but not pre-diabetes. Arthritis, sex, and marital status were significant predictors of diabetes only among residents of high-prevalence clusters, while educational attainment and smoking were significant predictors of diabetes only among residents of non-cluster counties. CONCLUSIONS: Geographic disparities of pre-diabetes and diabetes exist in Florida. Information from this study is useful for guiding resource allocation and targeting of intervention programs focusing on identified modifiable predictors of pre-diabetes and diabetes so as to reduce health disparities and improve the health of all Floridians.

An exploratory investigation of geographic disparities of stroke prevalence in Florida using circular and flexible spatial scan statistics.

BACKGROUND: Stroke is a major public health concern due to the morbidity and mortality associated with it. Identifying geographic areas with high stroke prevalence is important for informing public health interventions. Therefore, the objective of this study was to investigate geographic disparities and identify geographic hotspots of stroke prevalence in Florida. MATERIALS AND METHODS: County-level stroke prevalence data for 2013 were obtained from the Florida Department of Health's Behavioral Risk Factor Surveillance System (BRFSS). Geographic clusters of stroke prevalence were investigated using the Kulldorff's circular spatial scan statistics (CSSS) and Tango's flexible spatial scan statistics (FSSS) under Poisson model assumption. Exact McNemar's test was used to compare the proportion of cluster counties identified by each of the two methods. Both Cohen's Kappa and bias adjusted Kappa were computed to assess the level of agreement between CSSS and FSSS methods of cluster detection. Goodness-of-fit of the models were compared using Cluster Information Criterion. Identified clusters and selected stroke risk factors were mapped. RESULTS: Overall, 3.7% of adults in Florida reported that they had been told by a healthcare professional that they had suffered a stroke. Both CSSS and FSSS methods identified significant high prevalence stroke spatial clusters. However, clusters identified using CSSS tended to be larger than those identified using FSSS. The FSSS had a better fit than the CSSS. Most of the identified clusters are explainable by the prevalence distributions of the known risk factors assessed. CONCLUSIONS: Geographic disparities of stroke risk exists in Florida with some counties having significant hotspots of high stroke prevalence. This information is important in guiding future research and control efforts to address the problem. Kulldorff's CSSS and Tango's FSSS are complementary to each other and should be used together to provide a more complete picture of the distributions of spatial clusters of health outcomes.

Geographic disparities and temporal changes in risk of death from myocardial infarction in Florida, 2000-2014.

BACKGROUND: Identifying disparities in myocardial infarction (MI) burden and assessing its temporal changes are critical for guiding resource allocation and policies geared towards reducing/eliminating health disparities. Our objectives were to: (a) investigate the spatial distribution and clusters of MI mortality risk in Florida; and (b) assess temporal changes in geographic disparities in MI mortality risks in Florida from 2000 to 2014. METHODS: This is a retrospective ecologic study with county as the spatial unit of analysis. We obtained data for MI deaths occurring among Florida residents between 2000 and 2014 from the Florida Department of Health, and calculated county-level age-adjusted MI mortality risks and Spatial Empirical Bayesian smoothed MI mortality risks. We used Kulldorff's circular spatial scan statistics and Tango's flexible spatial scan statistics to identify spatial clusters. RESULTS: There was an overall decline of 48% in MI mortality risks between 2000 and 2014. However, we found substantial, persistent disparities in MI mortality risks, with high-risk clusters occurring primarily in rural northern counties and low-risk clusters occurring exclusively in urban southern counties. MI mortality risks declined in both low- and high-risk clusters, but the latter showed more dramatic decreases during the first nine years of the study period. Consequently, the risk difference between the high- and low-risk clusters was smaller at the end than at the beginning of the study period. However, the rates of decline levelled off during the last six years of the study, and there are signs that the risks may be on an upward trend in parts of North Florida. Moreover, MI mortality risks for high-risk clusters at the end of the study period were on par with or above those for low-risk clusters at the beginning of the study period. Thus, high-risk clusters lagged behind low-risk clusters by at least 1.5 decades. CONCLUSION: Myocardial infarction mortality risks have decreased substantially during the last 15 years, but persistent disparities in MI mortality burden still exist across Florida. Efforts to reduce these disparities will need to target prevention programs to counties in the high-risk clusters.

Does Place of Residence or Time of Year Affect the Risk of Stroke Hospitalization and Death? A Descriptive Spatial and Temporal Epidemiologic Study.

BACKGROUND: Identifying geographic areas with significantly high risks of stroke is important for informing public health prevention and control efforts. The objective of this study was to investigate geographic and temporal patterns of stroke hospitalization and mortality risks so as to identify areas and seasons with significantly high burden of the disease in Florida. The information obtained will be useful for resource allocation for disease prevention and control. METHODS: Stroke hospitalization and mortality data from 1992 to 2012 were obtained from the Florida Agency for Health Care Administration. Age-adjusted stroke hospitalization and mortality risks for time periods 1992-94, 1995-97, 1998-2000, 2001-03, 2004-06, 2007-09 and 2010-12 were computed at the county spatial scale. Global Moran's I statistics were computed for each of the time periods to test for evidence of global spatial clustering. Local Moran indicators of spatial association (LISA) were also computed to identify local areas with significantly high risks. RESULTS: There were approximately 1.5 million stroke hospitalizations and over 196,000 stroke deaths during the study period. Based on global Moran's I tests, there was evidence of significant (p<0.05) global spatial clustering of stroke mortality risks but no evidence (p>0.05) of significant global clustering of stroke hospitalization risks. However, LISA showed evidence of local spatial clusters of both hospitalization and mortality risks with significantly high risks being observed in the north while the south had significantly low risks of stroke deaths. There were decreasing temporal trends and seasonal patterns of both hospitalization and mortality risks with peaks in the winter. CONCLUSIONS: Although stroke hospitalization and mortality risks have declined in the past two decades, disparities continue to exist across Florida and it is evident from the results of this study that north Florida may, in fact, be part of the stroke belt despite not being in any of the traditional stroke belt states. These findings are useful for guiding public health efforts to reduce/eliminate inequities in stroke outcomes and inform policy decisions. There is need to continually identify populations with significantly high risks of stroke to better guide the targeting of limited resources to the highest risk populations.

Prevalence and Predictors of Pre-Diabetes and Diabetes among Adults 18 Years or Older in Florida: A Multinomial Logistic Modeling Approach.

BACKGROUND: Individuals with pre-diabetes and diabetes have increased risks of developing macro-vascular complications including heart disease and stroke; which are the leading causes of death globally. The objective of this study was to estimate the prevalence of pre-diabetes and diabetes, and to investigate their predictors among adults ≥18 years in Florida. METHODS: Data covering the time period January-December 2013, were obtained from Florida's Behavioral Risk Factor Surveillance System (BRFSS). Survey design of the study was declared using SVYSET statement of STATA 13.1. Descriptive analyses were performed to estimate the prevalence of pre-diabetes and diabetes. Predictors of pre-diabetes and diabetes were investigated using multinomial logistic regression model. Model goodness-of-fit was evaluated using both the multinomial goodness-of-fit test proposed by Fagerland, Hosmer, and Bofin, as well as, the Hosmer-Lemeshow's goodness of fit test. RESULTS: There were approximately 2,983 (7.3%) and 5,189 (12.1%) adults in Florida diagnosed with pre-diabetes and diabetes, respectively. Over half of the study respondents were white, married and over the age of 45 years while 36.4% reported being physically inactive, overweight (36.4%) or obese (26.4%), hypertensive (34.6%), hypercholesteremic (40.3%), and 26% were arthritic. Based on the final multivariable multinomial model, only being overweight (Relative Risk Ratio [RRR] = 1.85, 95% Confidence Interval [95% CI] = 1.41, 2.42), obese (RRR = 3.41, 95% CI = 2.61, 4.45), hypertensive (RRR = 1.69, 95% CI = 1.33, 2.15), hypercholesterolemic (RRR = 1.94, 95% CI = 1.55, 2.43), and arthritic (RRR = 1.24, 95% CI = 1.00, 1.55) had significant associations with pre-diabetes. However, more predictors had significant associations with diabetes and the strengths of associations tended to be higher than for the association with pre-diabetes. For instance, the relative risk ratios for the association between diabetes and being overweight (RRR = 2.00, 95% CI = 1.55, 2.57), or obese (RRR = 4.04, 95% CI = 3.22, 5.07), hypertensive (RRR = 2.66, 95% CI = 2.08, 3.41), hypercholesterolemic (RRR = 1.98, 95% CI = 1.61, 2.45) and arthritic (RRR = 1.28, 95% CI = 1.04, 1.58) were all further away from the null than their associations with pre-diabetes. Moreover, a number of variables such as age, income level, sex, and level of physical activity had significant association with diabetes but not pre-diabetes. The risk of diabetes increased with increasing age, lower income, in males, and with physical inactivity. Insufficient physical activity had no significant association with the risk of diabetes or pre-diabetes. CONCLUSIONS: There is evidence of differences in the strength of association of the predictors across levels of diabetes status (pre-diabetes and diabetes) among adults ≥18 years in Florida. It is important to monitor populations at high risk for pre-diabetes and diabetes, so as to help guide health programming decisions and resource allocations to control the condition.