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

Circulation ◽  
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.

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

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0254579
Author(s):  
Md Marufuzzaman Khan ◽  
Shamarial Roberson ◽  
Keshia Reid ◽  
Melissa Jordan ◽  
Agricola Odoi
Keyword(s):  
Geographic Distribution ◽  
Management Education ◽  
Scan Statistics ◽  
Diabetes Prevalence ◽  
Self Management ◽  
County Level ◽  
Participation Rates ◽  
Geographic Disparities ◽  
Central Florida ◽  
Diabetes Outcomes

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.

scholarly journals Evaluation of County-Level Heterogeneity in Excess Mortality in Colorado from March to September 2020

2021 ◽  
Author(s):  
Jay Chandra ◽  
Marie Charpignon ◽  
Mathew C Samuel ◽  
Anushka Bhaskar ◽  
Saketh Sundar ◽  
...  
Keyword(s):  
Public Health ◽  
Mortality Rate ◽  
Excess Mortality ◽  
Ethnic Disparities ◽  
The United States ◽  
The State ◽  
Mortality Data ◽  
County Level ◽  
All Cause Mortality ◽  
Excess Deaths

Importance: Tracking the direct and indirect impact of the coronavirus disease 2019 (COVID-19) pandemic on all-cause mortality in the United States has been hindered by the lack of testing and by reporting delays. Evaluating excess mortality, or the number of deaths above what is expected in a given time period, provides critical insights into the true burden of the COVID-19 pandemic caused by the novel Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). Stratifying mortality data by demographics such as age, sex, race, ethnicity, and geography helps quantify how subgroups of the population have been differentially affected. Similarly, stratifying mortality data by cause of death reveals the public health effects of the pandemic in terms of other acute and chronic diseases. Objective: To provide stratified estimates of excess mortality in Colorado from March to September 2020. Design, Setting, and Population: This study evaluated the number of excess deaths both directly due to SARS-CoV-2 infection and from all other causes between March and September 2020 at the county level in Colorado. Data were obtained from the Vital Statistics Program at the Colorado Department of Public Health and Environment. These estimates of excess mortality were derived by comparing population- adjusted mortality rates in 2020 with rates in the same months from 2015 to 2019. Results: We found evidence of excess mortality in Colorado between March and September 2020. Two peaks in excess deaths from all causes were recorded in the state, one mid-April and the other at the end of June. Since the first documented SARS-CoV-2 infection on March 5th, we estimated that the excess mortality rate in Colorado was two times higher than the officially reported COVID-19 mortality rate. State-level cumulative excess mortality from all causes reached 71 excess deaths per 100k residents (~4000 excess deaths in the state); in contrast, 35 deaths per 100k directly due to SARS-CoV-2 were recorded in the same period (~1980 deaths. Excess mortality occurred in 52 of 64 counties, accounting for 99% of the state's population. Most excess deaths recorded from March to September 2020 were associated with acute events (estimated at 44 excess deaths per 100k residents and at 9 after excluding deaths directly due to SARS-CoV-2) rather than with chronic conditions (~21 excess deaths per 100k). Among Coloradans aged 14-44, 1.4 times more deaths occurred in those months than during the same period in the five previous years. Hispanic White males died of COVID-19 at the highest rate during this time (~90 deaths from COVID-19 per 100k residents); however, Non-Hispanic Black/African American males were the most affected in terms of overall excess mortality (~204 excess deaths per 100k). Beyond inequalities in COVID-19 mortality per se, these findings signal considerable regional and racial-ethnic disparities in excess all-cause mortality that need to be addressed for a just recovery and in future public health crises.

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

PeerJ ◽  
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.

scholarly journals Feeding the disparities: the geography and trends of breastfeeding in the United States

2018 ◽  
Author(s):  
Romain Garnier ◽  
Ana I. Bento ◽  
Pejman Rohani ◽  
Saad B. Omer ◽  
Shweta Bansal
Keyword(s):  
Public Health ◽  
United States ◽  
Breast Milk ◽  
Low Income ◽  
Temporal Trends ◽  
State Level ◽  
The United States ◽  
Poverty Level ◽  
Future Health ◽  
Breastfeeding Rates

AbstractThere is scientific consensus on the importance of breastfeeding for the present and future health of newborns, in high- and low-income settings alike. In the United States, improving breast milk access is a public health priority but analysis of secular trends are largely lacking. Here, we used data from the National Immunization Survey of the CDC, collected between 2003 and 2016, to illustrate the temporal trends and the spatial heterogeneity in breastfeeding. We also considered the effect sizes of two key determinants of breastfeeding rates. We show that, while access to breast milk both at birth and at 6 months old has steadily increased over the past decade, large spatial disparities still remain at the state level. We also find that, since 2009, the proportion of households below the poverty level has become the strongest predictor of breastfeeding rates. We argue that, because variations in breastfeeding rates are associated with socio-economic factors, public health policies advocating for breastfeeding are still needed in particular in underserved communities. This is key to reducing longer term health disparities in the U.S., and more generally in high-income countries.

scholarly journals Geographical surveillance of COVID-19: Diagnosed cases and death in the United States

2020 ◽  
Author(s):  
Raid Amin ◽  
Terri Hall ◽  
Jacob Church ◽  
Daniela Schlierf ◽  
Martin Kulldorff
Keyword(s):  
Public Health ◽  
New York ◽  
New England ◽  
Disease Surveillance ◽  
Multiple Testing ◽  
The United States ◽  
Space Time ◽  
Scan Statistics ◽  
Public Health Agencies ◽  
Incidence And Mortality

AbstractBackgroundCOVID-19 is a new coronavirus that has spread from person to person throughout the world. Geographical disease surveillance is a powerful tool to monitor the spread of epidemics and pandemic, providing important information on the location of new hot-spots, assisting public health agencies to implement targeted approaches to minimize mortality.MethodsCounty level data from January 22-April 28 was downloaded from USAfacts.org to create heat maps with ArcMap™ for diagnosed COVID-19 cases and mortality. The data was analyzed using spatial and space-time scan statistics and the SaTScan™ software, to detect geographical cluster with high incidence and mortality, adjusting for multiple testing. Analyses were adjusted for age. While the spatial clusters represent counties with unusually high counts of COVID-19 when averaged over the time period January 22-April 20, the space-time clusters allow us to identify groups of counties in which there exists a significant change over time.ResultsThere were several statistically significant COVID-19 clusters for both incidence and mortality. Top clusters with high rates included the areas in and around New York City, New Orleans and Chicago, but there were also several small rural clusters. Top clusters for a recent surge in incidence and mortality included large parts of the Midwest, the Mid-Atlantic Region, and several smaller areas in and around New York and New England.ConclusionsSpatial and space-time surveillance of COVID-19 can be useful for public health departments in their efforts to minimize mortality from the disease. It can also be applied to smaller regions with more granular data.

scholarly journals Characterization of influenza vaccination recommendation across spatial scales in the United States

2021 ◽  
Author(s):  
Madeline C. Kuney ◽  
Casey M. Zipfel ◽  
Shweta Bansal
Keyword(s):  
Public Health ◽  
Influenza Vaccine ◽  
Influenza Vaccination ◽  
Spatial Scales ◽  
The United States ◽  
Vaccine Uptake ◽  
County Level ◽  
Health Administration ◽  
Vaccination Recommendation ◽  
Vaccination Recommendations

AbstractThe US public health system is organized in 3 levels: national, state-level, and county-level. Public health messaging both within and across these scales may not always be consistent, and for transmissible public health threats where cases in one spatial location may impact other areas, this lack of consistency could create problems. Here, we collected and analyzed data on influenza vaccination recommendations across public health administration levels. We assess spatial heterogeneity at the county level, and analyze consistency in recommendations across spatial scales. We also compare information accessibility with influenza vaccine affordability and availability to identify factors that may be most related to vaccine uptake. We find that influenza vaccine recommendations are highly variable in both their priority group specificity and in their ease of access, and there is poor agreement across spatial scales. This lack of consistency results in a lack of clear relationship between vaccination information and vaccine uptake. This work highlights the need for greater consistency in specific, easily accessed public health information from trusted sources.

Iterative near-term forecasting of the transmission and management of SARS-CoV-2/COVID-19 using social interventions at the county-level in the United States

2020 ◽  
Author(s):  
Kenneth Newcomb ◽  
Morgan E. Smith ◽  
Rose E. Donohue ◽  
Sebastian Wyngaard ◽  
Caleb Reinking ◽  
...  
Keyword(s):  
United States ◽  
The United States ◽  
The State ◽  
Data Driven ◽  
Effective Control ◽  
County Level ◽  
Movement Restriction ◽  
Social Distancing ◽  
Social Interventions ◽  
Near Term

Abstract The control of the initial outbreak and spread of SARS-CoV-2/COVID-19 by the implementation of unprecedented population-wide non-pharmaceutical mitigation measures has led to remarkable success in dampening the pandemic globally. With many countries easing or beginning to lift these measures to restart activities presently, concern is growing regarding the impacts that such reopening of societies could have on the subsequent transmission of the virus. While mathematical models of COVID-19 transmission have played important roles in evaluating the general population-level impacts of these measures for curbing virus transmission, a key need is for models that are able to effectively capture the effects of the spatial and social heterogeneities that drive the epidemic dynamics observed at the local community level. Iterative near-term forecasting that uses new incoming epidemiological and social behavioural data to sequentially update locally-applicable transmission models can overcome this gap, potentially leading to better predictions and intervention actions. Here, we present the development of one such data-driven iterative modelling tool based on publically-available data and an extended SEIR model for forecasting SARS-CoV-2 at the county level in the United States, and demonstrate, using data from the state of Florida, how this tool can be used to explore the outcomes of the social measures proposed for containing the course of the pandemic as a result of easing the initially imposed lockdown in the state. We provide comprehensive results showing the use of the locally identified models for accessing the impacts and societal tradeoffs of using specific strategies involving movement restriction, social distancing and mass testing, and conclude that while it is absolutely vital to continue with these measures over the near-term and likely to the end of March 2021 in all counties for containing the ongoing pandemic before less socially-disruptive vaccination strategies come into play, it could be possible to lift the more disruptive movement restriction/social distancing measures by end of December 2020 if these are accompanied by widespread testing and contact tracing. Our findings further show that such intensified social interventions could potentially also bring about the control of the epidemic in low and some medium incidence counties first, supporting the development and deployment of a geographically-phased approach to reopening the economy of Florida. We have made our data-driven forecasting system publicly available for policymakers and health officials to use in their own locales, with the hope that a more efficient coordinated strategy for controlling SARS-CoV-2 state-wide, based on effective control of viral transmission at the county level, can be developed and successfully implemented.

scholarly journals GEOGRAPHIC DISPARITIES IN COUNTY-LEVEL PREVALENCE OF ALZHEIMER’S DISEASE ACROSS THE UNITED STATES

2019 ◽  
Vol 3 (Supplement_1) ◽  
pp. S782-S782 ◽  
Author(s):  
Mackenzie E Fowler ◽  
Christina DiBlasio ◽  
Michael Crowe ◽  
Richard E Kennedy
Keyword(s):  
United States ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Small Area ◽  
Empirical Bayes ◽  
Local Population ◽  
The United States ◽  
County Level ◽  
Prevalence Rates ◽  
Geographic Disparities

Abstract Small-area geographic disparities in health care delivery have been observed across multiple disorders, but remain poorly studied for Alzheimer’s disease (AD) and other dementias. While national and state estimates of the prevalence and incidence of AD are available, estimates across finer geographic regions offer an opportunity to tailor programs to the needs of the local population. We estimated prevalence of AD at the county level across the continental United States. We used prevalence rates of AD by age category and race among Medicare fee-for-service beneficiaries published by the Centers for Disease Control (CDC). These prevalence rates were projected onto bridged-race county-level population data for 2017 from the National Center for Health Statistics, with empirical Bayes spatial smoothing to reduce variability in rates due to small population sizes. Estimated prevalence of AD varied more than threefold across counties, from a low of 51.8 per 1,000 persons in Loving County, Texas to a high of 169.6 per 1,000 persons in Kalawao County, Hawaii. Higher prevalence of AD was seen in the Southeastern and Midwestern United States. However, we observed specific counties with low prevalence of AD within regions with high prevalence of AD, and vice versa. These small-area geographic variations may provide vital information about social and environmental influences on dementia care, yet little data have been available to date. Understanding geographic disparities in prevalence will be critical for addressing practice variation in the prevention and diagnosis of dementia.

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