Geographic distribution of diagnosed diabetes in the U.S.: a diabetes belt

Geographic disparities and temporal changes of diabetes-related mortality risks in Florida: a retrospective study

Background

Over the last few decades, diabetes-related mortality risks (DRMR) have increased in Florida. Although there is evidence of geographic disparities in pre-diabetes and diabetes prevalence, little is known about disparities of DRMR in Florida. Understanding these disparities is important for guiding control programs and allocating health resources to communities most at need. Therefore, the objective of this study was to investigate geographic disparities and temporal changes of DRMR in Florida.

Methods

Retrospective mortality data for deaths that occurred from 2010 to 2019 were obtained from the Florida Department of Health. Tenth International Classification of Disease codes E10-E14 were used to identify diabetes-related deaths. County-level mortality risks were computed and presented as number of deaths per 100,000 persons. Spatial Empirical Bayesian (SEB) smoothing was performed to adjust for spatial autocorrelation and the small number problem. High-risk spatial clusters of DRMR were identified using Tango's flexible spatial scan statistics. Geographic distribution and high-risk mortality clusters were displayed using ArcGIS, whereas seasonal patterns were visually represented in Excel.

Results

A total of 54,684 deaths were reported during the study period. There was an increasing temporal trend as well as seasonal patterns in diabetes mortality risks with high risks occurring during the winter. The highest mortality risk (8.1 per 100,000 persons) was recorded during the winter of 2018, while the lowest (6.1 per 100,000 persons) was in the fall of 2010. County-level SEB smoothed mortality risks varied by geographic location, ranging from 12.6 to 81.1 deaths per 100,000 persons. Counties in the northern and central parts of the state tended to have high mortality risks, whereas southern counties consistently showed low mortality risks. Similar to the geographic distribution of DRMR, significant high-risk spatial clusters were also identified in the central and northern parts of Florida.

Conclusion

Geographic disparities of DRMR exist in Florida, with high-risk spatial clusters being observed in rural central and northern areas of the state. There is also evidence of both increasing temporal trends and Winter peaks of DRMR. These findings are helpful for guiding allocation of resources to control the disease, reduce disparities, and improve population health.

Regional and rural-urban patterns in the prevalence of diagnosed hypertension among older U.S. adults with diabetes, 2005-2017

BACKGROUND

Hypertension prevalence among the overall US adult population has been relatively stable during the last two decades. However, whether this stabilization has occurred across rural-urban communities and across different geographic regions is unknown, particularly among older adults with diabetes who are likely to have concomitant cardiovascular risk factors.

METHODS

This serial cross-sectional analysis used the 5% national sample of Medicare administrative claims data (n = 3,516,541) to examine temporal trends (2005-2017) in diagnosed hypertension among older adults with diabetes, across urban-rural communities and US census regions (Northeast, Midwest, South, and West). Joinpoint regression was used to obtain annual percent change (APC) in hypertension prevalence across rural-urban communities and geographic regions, and multivariable adjusted regression was used to assess associations between rural-urban communities and hypertension prevalence.

RESULTS

The APC in the prevalence of hypertension was higher during 2005-2010, and there was a slowdown in the increase during 2011-2017 across all regions, with significant variations across rural-urban communities within each of the regions. In the regression analysis, in the adjusted model, older adults living in non-core (most rural) areas in the Midwest (PR = 0.988, 95% CI: 0.981-0.995) and West (PR = 0.935, 95% CI: 0.923-0.946) had lower hypertension prevalence than their regional counterparts living in large central metro areas.

CONCLUSIONS

Although the magnitudes of these associations are small, differences in hypertension prevalence across rural-urban areas and geographic regions may have implications for targeted interventions to improve chronic disease prevention and management.

Interprofessional collaboration to improve care for patients with diabetes

BACKGROUND

The COVID-19 pandemic resulted in decreased access to routine diabetes care in rural areas and adversely affected self-management of diabetes.

METHODS

This article describes a descriptive pretest-posttest study conducted to assess efficacy in managing hemoglobin A1C (A1C) among patients with type 2 diabetes mellitus (T2DM) using a continuous glucose monitoring (CGM) system for 1 year.

RESULTS

A total of 14 participants completed the Diabetes Mellitus Self-Efficacy Scale survey. Of those 14, 11 used CGM for 1 year; of the 11 who maintained CGM use, A1C levels improved in 9.

CONCLUSIONS

Results indicate that CGM combined with medication management positively impacts self-efficacy in managing A1C levels among patients with T2DM. Interdisciplinary collaboration optimizes patient outcomes.

Food deserts exposure, density of fast-food restaurants, and park access: Exploring the association of food and recreation environments with obesity and diabetes using global and local regression models

To prevent obesity and diabetes environmental interventions such as eliminating food deserts, restricting proliferation of food swamps, and improving park access are essential. In the United States, however, studies that examine the food and park access relationship with obesity and diabetes using both global and local regression are lacking. To guide county, state, and federal policy in combating obesity and diabetes, there is a need for cross-scale analyses to identify that relationship at national and local levels. This study applied spatial regression and geographically weighted regression to the 3,108 counties in the contiguous United States. Global regression show food deserts exposure and density of fast-food restaurants have non-significant association with obesity and diabetes while park access has a significant inverse association with both diseases. Geographically weighted regression that takes into account spatial heterogeneity shows that, among southern states that show high prevalence of obesity and diabetes, Alabama and Mississippi stand out as having opportunity to improve park access. Results suggest food deserts exposure are positively associated with obesity and diabetes in counties close to Alabama, Georgia, and Tennessee while density of fast-food restaurants show positive association with two diseases in counties of western New York and northwestern Pennsylvania. These findings will help policymakers and public health agencies in determining which geographic areas need to be prioritized when implementing public interventions such as promoting healthy food access, limiting unhealthy food options, and increasing park access.

Heterogeneity in elevated glucose and A1C as predictors of the prediabetes to diabetes transition: Framingham Heart Study, Multi-Ethnic Study on Atherosclerosis, Jackson Heart Study, and Atherosclerosis Risk In Communities

Introduction

There are a number of glycemic definitions for prediabetes; however, the heterogeneity in diabetes transition rates from prediabetes across different glycemic definitions in major US cohorts has been unexplored. We estimate the variability in risk and relative risk of adiposity based on diagnostic criteria like fasting glucose and hemoglobin A1C% (HA1C%).

Research Design and Methods

We estimated transition rate from prediabetes, as defined by fasting glucose between 100-125 and/or 110-125 mg/dL, and HA1C% between 5.7-6.5% in participant data from the Framingham Heart Study, Multi-Ethnic Study on Atherosclerosis, Atherosclerosis Risk in Communities, and the Jackson Heart Study. We estimated the heterogeneity and prediction interval across cohorts, stratifying by age, sex, and body mass index. For individuals who were prediabetic, we estimated the relative risk for obesity, blood pressure, education, age, and sex for diabetes.

Results

There is substantial heterogeneity in diabetes transition rates across cohorts and prediabetes definitions with large prediction intervals. We observed the highest range of rates in individuals with fasting glucose of 110-125 mg/dL ranging from 2-18 per 100 person-years. Across different cohorts, the association obesity or hypertension in the progression to diabetes was consistent, yet it varied in magnitude. We provide a database of transition rates across subgroups and cohorts for comparison in future studies.

Conclusion

The absolute transition rate from prediabetes to diabetes significantly depends on cohort and prediabetes definitions.

A sampling strategy for longitudinal and cross-sectional analyses using a large national claims database

Importance

The United States (US) Medicare claims files are valuable sources of national healthcare utilization data with over 45 million beneficiaries each year. Due to their massive sizes and costs involved in obtaining the data, a method of randomly drawing a representative sample for retrospective cohort studies with multi-year follow-up is not well-documented.

Objective

To present a method to construct longitudinal patient samples from Medicare claims files that are representative of Medicare populations each year.

Design

Retrospective cohort and cross-sectional designs.

Participants

US Medicare beneficiaries with diabetes over a 10-year period.

Methods

Medicare Master Beneficiary Summary Files were used to identify eligible patients for each year in over a 10-year period. We targeted a sample of ~900,000 patients per year. The first year's sample is stratified by county and race/ethnicity (white vs. minority), and targeted at least 250 patients in each stratum with the remaining sample allocated proportional to county population size with oversampling of minorities. Patients who were alive, did not move between counties, and stayed enrolled in Medicare fee-for-service (FFS) were retained in the sample for subsequent years. Non-retained patients (those who died or were dropped from Medicare) were replaced with a sample of patients in their first year of Medicare FFS eligibility or patients who moved into a sampled county during the previous year.

Results

The resulting sample contains an average of 899,266 ± 408 patients each year over the 10-year study period and closely matches population demographics and chronic conditions. For all years in the sample, the weighted average sample age and the population average age differ by <0.01 years; the proportion white is within 0.01%; and the proportion female is within 0.08%. Rates of 21 comorbidities estimated from the samples for all 10 years were within 0.12% of the population rates. Longitudinal cohorts based on samples also closely resembled the cohorts based on populations remaining after 5- and 10-year follow-up.

Conclusions and relevance

This sampling strategy can be easily adapted to other projects that require random samples of Medicare beneficiaries or other national claims files for longitudinal follow-up with possible oversampling of sub-populations.

Geographic variation and racial disparities in adoption of newer glucose-lowering drugs with cardiovascular benefits among US Medicare beneficiaries with type 2 diabetes

BACKGROUND

Prior studies have shown disparities in the uptake of cardioprotective newer glucose-lowering drugs (GLDs), including sodium-glucose cotranwsporter-2 inhibitors (SGLT2i) and glucagon-like peptide-1 receptor agonists (GLP1a). This study aimed to characterize geographic variation in the initiation of newer GLDs and the geographic variation in the disparities in initiating these medications.

METHODS

Using 2017-2018 claims data from a 15% random nationwide sample of Medicare Part D beneficiaries, we identified individuals diagnosed with type 2 diabetes (T2D), who had ≥1 GLD prescriptions, and did not use SGLT2i or GLP1a in the year prior to the index date,1/1/2018. Patients were followed up for a year. The cohort was spatiotemporally linked to Dartmouth hospital-referral regions (HRRs), with each patient assigned to 1 of 306 HRRs. We performed multivariable Poisson regression to estimate adjusted initiation rates, and multivariable logistic regression to assess racial disparities in each HRR.

RESULTS

Among 795,469 individuals with T2D included in the analyses, the mean (SD) age was 73 (10) y, 53.3% were women, 12.2% were non-Hispanic Black, and 7.2% initiated a newer GLD in the follow-up year. In the adjusted model including clinical factors, compared to non-Hispanic White patients, non-Hispanic Black (initiation rate ratio, IRR [95% CI]: 0.66 [0.64-0.68]), American Indian/Alaska Native (0.74 [0.66-0.82]), Hispanic (0.85 [0.82-0.87]), and Asian/Pacific islander (0.94 [0.89-0.98]) patients were less likely to initiate newer GLDs. Significant geographic variation was observed across HRRs, with an initiation rate spanning 2.7%-13.6%.

CONCLUSIONS

This study uncovered substantial geographic variation and the racial disparities in initiating newer GLDs.

Changes in Diabetes Care and Management Practices during the COVID-19 Pandemic

Background

Evidence suggests diabetes management was negatively impacted early in the pandemic. However, the impact of the pandemic on key healthcare services for diabetes control and diabetes self-management practices is less known. We examined changes in diabetes care and management practices before and during the COVID-19 pandemic.

Methods

Population-based data regarding 4 diabetes-related healthcare engagement and 4 self-management indicators were obtained from adults with diabetes surveyed in 19 US States and Washington DC through the Behavioral Risk Factor Surveillance System. Using logistic regression, we estimated changes in the prevalence of each indicator, overall and by sociodemographic subgroups, before (2019; n = 15,307) and during (2021; n = 13,994) the COVID-19 pandemic.

Results

Between 2019 and 2021, the prevalence of biannual HbA1c tests reduced by 2.6 percentage points (pp, 95% CI :-4.8, -0.4), from 75.4-73.1%, and prevalence of annual eye exams fell by 4.0 pp (-6.2, -2.8), from 72.2-68.7%. The composite indicator of engagement with healthcare for diabetes control fell by 3.5 pp (-5.9, -1.1), from 44.9-41.9%. Reductions in engagement with healthcare were largely seen across sex, age, education, employment status, marital status, insurance status, and urbanicity; and were more pronounced among those aged 18-34 and the uninsured. Reductions in engagement with healthcare were seen in several states, with Delaware and Washington DC reporting the largest decrease. Of self-management behaviors, we only observed change in avoidance of smoking, an increase of 2.0 pp (0.4, 3.6) from 84.7-87.1%.

Conclusions

The pandemic had mixed impacts on diabetes care and self-management. The findings suggest a deterioration of the uptake of evidence-based, preventive health services requiring laboratory services and clinical examination for diabetes control during the pandemic. On the other hand, smoking rates decreased, suggesting potential positive impacts of the pandemic on health behaviors in people with diabetes.

Determinants of disparities of diabetes-related hospitalization rates in Florida: a retrospective ecological study using a multiscale geographically weighted regression approach

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, healthcare-related, 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).

CONCLUSIONS

The findings of this study underscore the importance of socioeconomic resources and rurality in shaping population health. Understanding the spatial context of the observed relationships provides valuable insights to guide needs-based, locally-focused health planning to reduce disparities in the burden of potentially avoidable hospitalizations.

Exploring facilitators and barriers to patient-provider communication regarding diabetes self-management

Objective

Long-term type 2 diabetes management requires open communication between a patient and their provider for self-care adherence. This study explored facilitators and barriers to diabetes-specific communication in West Virginia, a medically underserved state with the highest prevalence of diabetes (16.2%) in the U.S.

Methods

A secondary qualitative analysis was conducted using data from semi-structured interviews (n = 34) and 4 focus groups (n = 23) with participants of a diabetes education program. Transcripts were analyzed using thematic analysis.

Results

Three facilitators to patient-provider communication emerged: "Patient-Provider Partnership", "Provider Accessibility", and "Empowerment Through Education". Partnership with providers, especially those who were accessible outside of scheduled appointments, and empowerment obtained through diabetes education facilitated diabetes-specific patient-provider communication. However, barriers included "Providers' Focus on 'Numbers' Rather than Patient Concerns", "Patient Lack of Preparation for Appointments", and "Providers 'Talking Down to' Patients".

Conclusion

The findings highlight patient- and provider-related factors that impact diabetes-specific communication.

Innovation

Previous interventions have trained providers in communication skills. Despite some success, challenges in implementation remain considering providers' limited time and overwhelming demands. This study suggests that diabetes education programs incorporating communication and self-advocacy skills could be a promising alternative as they can empower patients to communicate their diabetes-specific needs/preferences with providers.

Regional and racial disparities in major amputation rates among medicare beneficiaries with diabetes: a retrospective study in the southeastern USA

Objective

While rates for non-traumatic lower extremity amputations (LEA) have been declining, concerns exist over disparities. Our objectives are to track major LEA (MLEA) rates over time among Medicare beneficiaries residing in a high diabetes prevalence region in the southeastern USA (the diabetes belt) and surrounding areas.

Methods

We used Medicare claims files for ~900 000 fee-for-service beneficiaries aged ≥65 years in 2006-2015 to track MLEA rates per 1000 patients with diabetes. We additionally conducted a cross-sectional analysis of data for 2015 to compare regional and racial disparities in major amputation risks after adjusting for demographic, socioeconomic, access-to-care and foot complications and other health factors. The Centers for Disease Control and Prevention defined the diabetes belt as 644 counties across Appalachian and southeastern US counties with high prevalence.

Results

MLEA rates were 3.9 per 1000 in the Belt compared with 2.8 in the surrounding counties in 2006 and decreased to 2.3 and 1.6 in 2015. Non-Hispanic black patients had 8.5 and 6.9 MLEAs per 1000 in 2006 and 4.8 and 3.5 in 2015 in the Belt and surrounding counties, respectively, while the rates were similar for non-Hispanic white patients in the two areas. Although amputation rates declined rapidly in both areas, non-Hispanic black patients in the Belt consistently had >3 times higher rates than non-Hispanic whites in the Belt. After adjusting for patient demographics, foot complications and healthcare access, non-Hispanic blacks in the Belt had about twice higher odds of MLEAs compared with non-Hispanic whites in the surrounding areas.

Discussion

Our data show persistent disparities in major amputation rates between the diabetes belt and surrounding counties. Racial disparities were much larger in the Belt. Targeted policies to prevent MLEAs among non-Hispanic black patients are needed to reduce persistent disparities in the Belt.

Investigation of predictors of severity of diabetes complications among hospitalized patients with diabetes in Florida, 2016-2019

BACKGROUND

Severe diabetes complications impact the quality of life of patients and may lead to premature deaths. However, these complications are preventable through proper glycemic control and management of risk factors. Understanding the risk factors of complications is important in guiding efforts to manage diabetes and reduce risks of its complications. Therefore, the objective of this study was to identify risk factors of severe diabetes complications among adult hospitalized patients with diabetes in Florida.

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. Adapted Diabetes Complications Severity Index (aDCSI) scores were computed for 1,061,140 unique adult patients with a diagnosis of diabetes. Severe complications were defined as those with an aDCSI ≥ 4. Population average models, estimated using generalized estimating equations, were used to identify individual- and area-level predictors of severe diabetes complications.

RESULTS

Non-Hispanic Black patients had the highest odds of severe diabetes complications compared to non-Hispanic White patients among both males (Odds Ratio [OR] = 1.20, 95% Confidence Interval [CI]: 1.17, 1.23) and females (OR = 1.27, 95% CI: 1.23, 1.31). Comorbidities associated with higher odds of severe complications included hypertension (OR = 2.30, 95% CI: 2.23, 2.37), hyperlipidemia (OR = 1.29, 95% CI: 1.27, 1.31), obesity (OR = 1.24, 95% CI: 1.21, 1.26) and depression (OR = 1.09, 95% CI: 1.07, 1.11), while the odds were lower for patients with a diagnosis of arthritis (OR = 0.81, 95% CI: 0.79, 0.82). Type of health insurance coverage was associated with the severity of diabetes complications, with significantly higher odds of severe complications among Medicare (OR = 1.85, 95% CI: 1.80, 1.90) and Medicaid (OR = 1.83, 95% CI: 1.77, 1.90) patients compared to those with private insurance. Residing within the least socioeconomically deprived ZIP code tabulation areas (ZCTAs) in the state had a protective effect compared to residing outside of these areas.

CONCLUSIONS

Racial, ethnic, and socioeconomic disparities in the severity of diabetes complications exist among hospitalized patients in Florida. The observed disparities likely reflect challenges to maintaining glycemic control and managing cardiovascular risk factors, particularly for patients with multiple chronic conditions. Interventions to improve diabetes management should focus on populations with disproportionately high burdens of severe complications to improve quality of life and decrease premature mortality among adult patients with diabetes in Florida.

Age and sex differences in the association between neighborhood socioeconomic environment and incident diabetes: Results from the diabetes location, environmental attributes and disparities (LEAD) network

Objective

Worse neighborhood socioeconomic environment (NSEE) may contribute to an increased risk of type 2 diabetes (T2D). We examined whether the relationship between NSEE and T2D differs by sex and age in three study populations.

Research design and methods

We conducted a harmonized analysis using data from three independent longitudinal study samples in the US: 1) the Veteran Administration Diabetes Risk (VADR) cohort, 2) the REasons for Geographic and Racial Differences in Stroke (REGARDS) cohort, and 3) a case-control study of Geisinger electronic health records in Pennsylvania. We measured NSEE with a z-score sum of six census tract indicators within strata of community type (higher density urban, lower density urban, suburban/small town, and rural). Community type-stratified models evaluated the likelihood of new diagnoses of T2D in each study sample using restricted cubic splines and quartiles of NSEE.

Results

Across study samples, worse NSEE was associated with higher risk of T2D. We observed significant effect modification by sex and age, though evidence of effect modification varied by site and community type. Largely, stronger associations between worse NSEE and diabetes risk were found among women relative to men and among those less than age 45 in the VADR cohort. Similar modification by age group results were observed in the Geisinger sample in small town/suburban communities only and similar modification by sex was observed in REGARDS in lower density urban communities.

Conclusions

The impact of NSEE on T2D risk may differ for males and females and by age group within different community types.

Telehealth Diabetes Prevention Program for Adults With Prediabetes in an Academic Medical Center Setting: Protocol for a Hybrid Type III Trial

BACKGROUND

Diabetes is a costly epidemic in the United States associated with both health and economic consequences. These consequences can be mitigated by participation in structured lifestyle change programs such as the National Diabetes Prevention Program (DPP) led by the Centers for Disease Control and Prevention. Mississippi consistently has among the highest rates of diabetes and prediabetes nationally. Implementing the National DPP through large health care systems can increase reach and accessibility for populations at the highest risk for diabetes. Translational research on the National DPP in Mississippi has not been studied.

OBJECTIVE

This study aims to evaluate the implementation and impact of the National DPP delivered using telehealth modalities at the University of Mississippi Medical Center in Jackson, Mississippi.

METHODS

An effectiveness-implementation hybrid type III research design is proposed. The study design is guided by the Reach, Effectiveness, Adoption, Implementation, and Maintenance framework and the Practical, Robust Implementation and Sustainability Model. Participants are being recruited via provider referral, and the DPP is being delivered by trained lifestyle coaches. Study participants include adult (≥18 years) patients eligible for the DPP with at least 1 encounter at 1 of 3 ambulatory clinic specialties (lifestyle medicine, family medicine, and internal medicine) between January 2019 and December 2023. The National DPP eligibility criteria include a BMI ≥25 kg/m2 and hemoglobin A1c between 5.7% and 6.4%. The University of Mississippi Medical Center criteria include Medicare or Medicaid beneficiaries. The University of Mississippi Medical Center's a priori implementation plan was developed using the Consolidated Framework for Implementation Research and includes 23 discrete strategies. The primary aim will use an embedded mixed method process analysis to identify and mitigate challenges to implementation. The secondary aim will use a nonrandomized quasi-experimental design to assess the comparative effectiveness of the DPP on health care expenditures. A propensity score matching method will be implemented to compare case subjects to control subjects. The primary outcomes include patient referrals, participant enrollment, retention, engagement, the incidence of diabetes, and health care resource use and costs.

RESULTS

At baseline, of the 26,151 patients across 3 ambulatory clinic specialties, 1010 (3.9%) had prediabetes and were eligible for the National DPP. Of the 1010 patients, more than half (n=562, 55.6%) were aged 65 years or older, 79.5% (n=803) were Medicare beneficiaries, 65.9% (n=666) were female, and 70.8% (n=715) were obese.

CONCLUSIONS

This is the first translational study of the National DPP in Mississippi. The findings will inform implementation strategies impacting the uptake and sustainability of the National DPP delivered in an academic medical setting using distance learning and telehealth modalities.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04822480; https://clinicaltrials.gov/study/NCT03622580.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/50183.

Rural African American Women With Severe Obesity: A Cross-Sectional Analysis of Lifestyle Behaviors and Psychosocial Characteristics

PURPOSE

To examine differences in lifestyle behavioral and psychosocial factors between rural African American women with Class 3 obesity and those with overweight, and Class 1-2 obesity.

DESIGN

Cross-sectional study.

SETTING

Rural Southeastern United States.

SUBJECTS

Participants included 289 African American women with a mean age of 56 years, 66% with a high school education or less, and a mean body mass index (BMI) of 38.6 kg/m2; 35% (n = 102) were classified with Class 3 obesity.

MEASURES

We objectively measured height, weight, and physical activity steps/day. Self-reported dietary and physical activity behaviors, general health-related quality of life, mental health, and social support were measured with validated surveys.

ANALYSIS

Chi-Square analysis for categorical variables and analysis of variance (ANOVA) - via multiple linear regression - for continuous variables.

RESULTS

There were no significant demographic differences between BMI groups, except for age, where women with Class 3 obesity were on average younger (51 vs 58 y, P < .001). Although dietary behaviors did not differ significantly between groups, we observed significant group differences in self-reported and objective measures of physical activity. The age-adjusted difference in means for self-reported total physical activity minutes/wk. was 91 minutes, with women categorized with Class 3 obesity reporting significantly fewer weekly minutes than those with overweight/Class 1-2 obesity (64.3 vs 156.4 min/wk. respectively, P < .01). Among psychosocial variables, only in the physical component scores of health-related quality of life did we find significant group differences - lower physical well-being among women with Class 3 obesity compared to those with overweight/Class 1-2 obesity (P = .02).

CONCLUSION

For African American women with Class 3 obesity living in rural setting, these findings suggest behavioral weight loss interventions may need to target physical activity strategies that address physical, psychosocial, and environmental barriers.

Education, trust, and likelihood to vaccinate against COVID-19 among patients with diabetes in the American South

OBJECTIVE

The purpose of this study was to explain the relationship among education, likelihood to vaccinate for COVID-19, and trust in healthcare providers among patients living with diabetes in the American South.

METHODS

Explanatory iterative sequential mixed methods design combined retrospective chart review, self-report surveys, and qualitative interviews.

RESULTS

Analysis of covariance revealed that severity of diabetes was not linked to vaccine acceptance. Overall, patients reported higher likelihood to vaccinate if their healthcare providers strongly recommend the vaccine. People with "some college" education reported lowest likelihood to vaccinate, before and after their healthcare providers' strong recommendation. Integrated analysis revealed the complexity of patient-provider trust and vaccination decisions.

CONCLUSIONS

In the context of COVID vaccination, particularly as conspiracy theories entered the mainstream, measures of trust in the system may be a clearer indicator of vaccine decision making than trust in personal physician.

PRACTICE IMPLICATIONS

The nonlinear relationship between education and likelihood to vaccinate challenges providers to talk to patients about knowledge and understanding beyond a superficial, quantitative screening question about education. Health systems and public health officials need to find strategies to build trusting relationships for patients across systems, such as community health workers.

Diabetes Self-management Education and Support Completion Before and During the COVID-19 Pandemic: Results From Local Health Departments in North Carolina

OBJECTIVE

To assess diabetes self-management education and support (DSMES) completion rate and explore the differences in DSMES completion by different delivery models.

METHODS

We conducted a retrospective analysis of 2017-2021 DSMES data at 2 local health departments (LHDs) in Eastern North Carolina. We evaluated DSMES completion by 2 delivery models.

RESULTS

From 2017 to 2021, the overall DSMES completion rate was 15.3%. The delivery model of two 4-hour sessions was associated with a higher completion rate than the delivery model of four 2-hour sessions ( P < .05). Patients with less than a high school education and without health insurance were less likely to have completed their DSMES training ( P < .05).

CONCLUSION

The DSMES completion rate at LHDs in North Carolina is very low. A delivery model consisting of 10 hours of education delivered in fewer sessions may contribute to a higher DSMES completion rate, but more research is needed. Targeted programs are needed to engage patients and improve DSMES completion.

Core issues, case studies, and the need for expanded Legacy African American genomics

Introduction: Genomic studies of Legacy African Americans have a tangled and convoluted history in western science. In this review paper, core issues affecting African American genomic studies are addressed and two case studies, the New York African Burial Ground and the Gullah Geechee peoples, are presented to highlight the current status of genomic research among Africa Americans. Methods: To investigate our target population's core issues, a metadatabase derived from 22 publicly accessible databases were reviewed, evaluated, and synthesized to identify the core bioethical issues prevalent during the centuries of the African American presence in North America. The sequence of metadatabase development included 5 steps: identification of information, record screening and retention of topic relevant information, identification of eligibility via synthesis for concept identifications, and inclusion of studies used for conceptual summaries and studies used for genetic and genomic summaries. To these data we added our emic perspectives and specific insights from our case studies. Results: Overall, there is a paucity of existing research on underrepresent African American genomic diversity. In every category of genomic testing (i.e., diagnostic, clinical predictive, pharmacogenomic, direct-to-consumer, and tumor testing), African Americans are disproportionately underrepresented compared to European Americans. The first of our case studies is from the New York African Burial Ground Project where genomic studies of grave soil derived aDNA yields insights into the causes of death of 17^th and 18^th Century African Americans. In the second of our case studies, research among the Gullah Geechee people of the Carolina Lowcountry reveals a connection between genomic studies and health disparities. Discussion: African Americans have historically borne the brunt of the earliest biomedical studies used to generate and refine primitive concepts in genetics. As exploited victims these investigations, African American men, women, and children were subjected to an ethics-free western science. Now that bioethical safeguards have been added, underrepresented and marginalized people who were once the convenient targets of western science, are now excluded from its health-related benefits. Recommendations to enhance the inclusion of African Americans in global genomic databases and clinical trials should include the following: emphasis on the connection of inclusion to advances in precision medicine, emphasis on the relevance of inclusion to fundamental questions in human evolutionary biology, emphasis on the historical relevance of inclusion for Legacy African Americans, emphasis on the ability of inclusion to foster expanded scientific expertise in the target population, ethical engagement with their descendants, and increase the number of science researchers from these communities.

Geographic Variation and Risk Factor Association of Early Versus Late Onset Colorectal Cancer

The proportion of patients diagnosed with colorectal cancer (CRC) at age < 50 (early-onset CRC, or EOCRC) has steadily increased over the past three decades relative to the proportion of patients diagnosed at age ≥ 50 (late-onset CRC, or LOCRC), despite the reduction in CRC incidence overall. An important gap in the literature is whether EOCRC shares the same community-level risk factors as LOCRC. Thus, we sought to (1) identify disparities in the incidence rates of EOCRC and LOCRC using geospatial analysis and (2) compare the importance of community-level risk factors (racial/ethnic, health status, behavioral, clinical care, physical environmental, and socioeconomic status risk factors) in the prediction of EOCRC and LOCRC incidence rates using a random forest machine learning approach. The incidence data came from the Surveillance, Epidemiology, and End Results program (years 2000-2019). The geospatial analysis revealed large geographic variations in EOCRC and LOCRC incidence rates. For example, some regions had relatively low LOCRC and high EOCRC rates (e.g., Georgia and eastern Texas) while others had relatively high LOCRC and low EOCRC rates (e.g., Iowa and New Jersey). The random forest analysis revealed that the importance of community-level risk factors most predictive of EOCRC versus LOCRC incidence rates differed meaningfully. For example, diabetes prevalence was the most important risk factor in predicting EOCRC incidence rate, but it was a less important risk factor of LOCRC incidence rate; physical inactivity was the most important risk factor in predicting LOCRC incidence rate, but it was the fourth most important predictor for EOCRC incidence rate. Thus, our community-level analysis demonstrates the geographic variation in EOCRC burden and the distinctive set of risk factors most predictive of EOCRC.

Correlates of Taxane-Induced Neuropathy, an Electronic Health Record Based Observational Study

BACKGROUND

Chemotherapy-induced peripheral neuropathy (CIPN) is a common therapeutic complication affecting cancer patients' quality-of-life. We evaluated clinical characteristics, demographics, and lifestyle factors in association with CIPN following taxane treatment.

METHODS

Data were extracted from the electronic health record of 3387 patients diagnosed with a primary cancer and receiving taxane (i.e., paclitaxel or docetaxel) at Vanderbilt University Medical Center. Neuropathy was assessed via a validated computer algorithm. Univariate and multivariate regression models were applied to evaluate odds ratios (ORs) and 95% confidence intervals (CIs) of CIPN-associated factors.

RESULTS

Female sex (OR = 1.28, 95% CI = 1.01-1.62), high body-mass index (BMI) (OR = 1.31, 95% CI = 1.06-1.61 for overweight, and OR = 1.49, 95% CI = 1.21-1.83 for obesity), diabetes (OR = 1.66, 95% CI = 1.34-2.06), high mean taxane dose (OR = 1.05, 95% CI = 1.03-1.08 per 10 mg/m2), and more treatment cycles (1.12, 95% CI = 1.10-1.14) were positively associated with CIPN. Concurrent chemotherapy (OR = 0.74, 95% CI = 0.58-0.94) and concurrent radiotherapy (OR = 0.77, 95% CI = 0.59-1.00) were inversely associated with CIPN. Obesity and diabetes both had a stronger association with docetaxel CIPN compared to paclitaxel, although interaction was only significant for diabetes and taxane (p = 0.019). Increased BMI was associated with CIPN only among non-diabetic patients (OR:1.34 for overweight and 1.68 for obesity), while diabetes increased CIPN risk across all BMI strata (ORs were 2.65, 2.41, and 2.15 for normal weight, overweight, and obese, respectively) compared to normal-weight non-diabetic patients (p for interaction = 0.039).

CONCLUSIONS

Female sex, obesity, and diabetes are significantly associated with taxine-induced CIPN. Further research is needed to identify clinical and pharmacologic strategies to prevent and mitigate CIPN in at-risk patient populations.

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.

Cumulative Consumption of Sulfur Amino Acids and Risk of Diabetes: A Prospective Cohort Study

BACKGROUND

Cross-sectional studies have suggested that consumption of sulfur amino acids (SAAs), including methionine and cysteine, is associated with a higher risk of type 2 diabetes (T2D) in humans and with T2D-related biomarkers in animals. But whether higher long-term SAA intake increases the risk of T2D in humans remains unknown.

OBJECTIVES

We aimed to investigate the association between long-term dietary SAA intake and risk of T2D.

METHODS

We analyzed data collected from 2 different cohorts of the Framingham Heart Study, a long-term, prospective, and ongoing study. The Offspring cohort (1991-2014) included participants from fifth through ninth examinations, and the Third-Generation cohort (2002-2011) included participants from first and second examinations. After excluding participants with a clinical history of diabetes, missing dietary data, or implausible total energy intake, 3222 participants in the Offspring cohort and 3205 participants in the Third-Generation cohort were included. Dietary intake was assessed using a validated FFQ. The relations between energy-adjusted total SAA (methionine and cysteine) intake or individual SAA intake (in quintiles) and risk of incident T2D were estimated via Cox proportional hazards models after adjusting for dietary and nondietary risk factors. Associations across the 2 cohorts were determined by direct combination and meta-analysis.

RESULTS

During the 23 y of follow-up, 472 participants reported a new diagnosis of T2D in the 2 cohorts. In the meta-analysis, the HRs of T2D comparing the highest with the lowest intake of total SAAs, methionine, and cysteine were 1.8 (95% CI: 1.3, 2.5), 1.7 (95% CI: 1.2, 2.3), and 1.4 (95% CI: 1.0, 2.1), respectively. The association of SAA intake with T2D was attenuated after adjusting animal protein intake in sensitivity analyses.

CONCLUSIONS

Our findings show that excess intake of SAAs is associated with higher risk of T2D. Dietary patterns that are low in SAAs could help in preventing T2D.

Diabetes and the treatment of ischemic stroke

This white paper examines the current challenges for treating ischemic stroke in diabetic patients. The need for a greater understanding of the mechanisms that underlie the relationship between diabetes and the cerebral vascular responses to ischemia is discussed. The critical need to improve the efficacy and safety of thrombolysis is addressed, as is the need for a better characterization the off-target actions of tPA, the only currently approved thrombolytic for the treatment of ischemic stroke.

Relationship Among Diabetes Distress, Health Literacy, Diabetes Education, Patient-Provider Communication and Diabetes Self-Care

OBJECTIVES

Mechanisms underlying relationships among patients' health literacy, diabetes distress, diabetes education, and provider counseling for self-care of chronic conditions are unclear. This study tested these relationships using SEM with adult patients with comorbid diabetes and hypertension in rural WV.

METHODS

Ninety-one participants of a 12-week self-management program reported on diabetes self-care (diet, exercise, blood glucose (BG) monitoring) and related provider counseling.

RESULTS

Based on patient report, providers' recommendations included following a low-fat diet, eating fruits/ vegetables, limiting sweets, a daily low-level of exercise and/or exercise ≥20 minutes three times/week, and BG monitoring. Provider recommendations were shown to be associated with patients' self-care behaviors (r=0.22, p<0.05). Multiple factors directly influenced provider recommendations: diabetes distress, health literacy, and family history of diabetes. A positive association was also noted between prior diabetes education and provider recommendations and diabetes self-care (r=0.44, p<0.001). A negative association was noted between diabetes distress and self-care, but a positive effect on provider recommendations was found. The model demonstrated good fit [CFI=0.94, and Root Mean Square Error of Approximation (RMSEA) =0.05].

CONCLUSIONS

To enhance diabetes self-care, providers should consistently provide education on self-care behaviors as well as partner with them to address diabetes distress.

Assessment of Disparities in Diabetes Mortality in Adults in US Rural vs Nonrural Counties, 1999-2018

IMPORTANCE

US rural vs nonrural populations have striking disparities in diabetes care. Whether rurality contributes to disparities in diabetes mortality is unknown.

OBJECTIVE

To examine rates and trends in diabetes mortality based on county urbanization.

DESIGN, SETTING, AND PARTICIPANTS

In this observational, cross-sectional study, the US Centers for Disease Control and Prevention Wide-Ranging Online Data for Epidemiologic Research database was searched from January 1, 1999, to December 31, 2018, for diabetes as a multiple cause and the underlying cause of death among residents aged 25 years or older in US counties. County urbanization was categorized as metro, medium-small, and rural. Weighted multiple linear regression models and jackknife resampling, with a 3-segment time component, were used. The models included exposures with up to 3-way interactions and were age standardized to the 2009-2010 population. The analyses were conducted from July 1, 2020, to February 1, 2022.

EXPOSURES

County urbanization (metro, medium-small, or rural), gender (men or women), age group (25-54, 55-74, or ≥75 years), and region (Midwest, Northeast, South, or West).

MAIN OUTCOMES AND MEASURES

Annual diabetes mortality rate per 100 000 people.

RESULTS

From 1999-2018, based on 4 022 238 309 person-years, diabetes was a multiple cause of death for 4 735 849 adults aged 25 years or older. As a multiple cause, diabetes mortality rates in 2017-2018 vs 1999-2000 were highest and unchanged in rural counties (157.2 [95% CI, 150.7-163.7] vs 154.1 [95% CI, 148.2-160.1]; P = .49) but lower in medium-small counties (123.6 [95% CI, 119.6-127.6] vs 133.6 [95% CI, 128.4-138.8]; P = .003) and urban counties (92.9 [95% CI, 90.5-95.3] vs 109.7 [95% CI, 105.2-114.1]; P < .001). In 2017-2018 vs 1999-2000, mortality rates were higher in rural men (+18.2; 95% CI, 14.3-22.1) but lower in rural women (-14.0; 95% CI, -17.7 to -10.3) (P < .001 for both). In the 25- to 54-year age group, mortality rates in 2017-2018 vs 1999-2000 showed a greater increase in rural counties (+9.4; 95% CI, 8.6-10.2) compared with medium-small counties (+4.5; 95% CI, 4.0-5.0) and metro counties (+0.9; 95% CI, 0.4-1.4) (P < .001 for all). Of all regions and urbanization levels, the mortality rate in 2017-2018 vs 1999-2000 was higher only in the rural South (+13.8; 95% CI, 7.6-20.0; P < .001).

CONCLUSIONS AND RELEVANCE

In this cross-sectional study, US rural counties had the highest overall diabetes mortality rate. The determinants of persistent rural disparities, in particular for rural men and for adults in the rural South, require investigation.

Impact of land use and food environment on risk of type 2 diabetes: A national study of veterans, 2008-2018

BACKGROUND

Large-scale longitudinal studies evaluating influences of the built environment on risk for type 2 diabetes (T2D) are scarce, and findings have been inconsistent.

OBJECTIVE

To evaluate whether land use environment (LUE), a proxy of neighborhood walkability, is associated with T2D risk across different US community types, and to assess whether the association is modified by food environment.

METHODS

The Veteran's Administration Diabetes Risk (VADR) study is a retrospective cohort of diabetes-free US veteran patients enrolled in VA primary care facilities nationwide from January 1, 2008, to December 31, 2016, and followed longitudinally through December 31, 2018. A total of 4,096,629 patients had baseline addresses available in electronic health records that were geocoded and assigned a census tract-level LUE score. LUE scores were divided into quartiles, where a higher score indicated higher neighborhood walkability levels. New diagnoses for T2D were identified using a published computable phenotype. Adjusted time-to-event analyses using piecewise exponential models were fit within four strata of community types (higher-density urban, lower-density urban, suburban/small town, and rural). We also evaluated effect modification by tract-level food environment measures within each stratum.

RESULTS

In adjusted analyses, higher LUE had a protective effect on T2D risk in rural and suburban/small town communities (linear quartile trend test p-value <0.001). However, in lower density urban communities, higher LUE increased T2D risk (linear quartile trend test p-value <0.001) and no association was found in higher density urban communities (linear quartile trend test p-value = 0.317). Particularly strong protective effects were observed for veterans living in suburban/small towns with more supermarkets and more walkable spaces (p-interaction = 0.001).

CONCLUSION

Among veterans, LUE may influence T2D risk, particularly in rural and suburban communities. Food environment may modify the association between LUE and T2D.

Urban and rural differences in new onset type 2 diabetes: Comparisons across national and regional samples in the diabetes LEAD network

Introduction

Geographic disparities in diabetes burden exist throughout the United States (US), with many risk factors for diabetes clustering at a community or neighborhood level. We hypothesized that the likelihood of new onset type 2 diabetes (T2D) would differ by community type in three large study samples covering the US.

Research design and methods

We evaluated the likelihood of new onset T2D by a census tract-level measure of community type, a modification of RUCA designations (higher density urban, lower density urban, suburban/small town, and rural) in three longitudinal US study samples (REGARDS [REasons for Geographic and Racial Differences in Stroke] cohort, VADR [Veterans Affairs Diabetes Risk] cohort, Geisinger electronic health records) representing the CDC Diabetes LEAD (Location, Environmental Attributes, and Disparities) Network.

Results

In the REGARDS sample, residing in higher density urban community types was associated with the lowest odds of new onset T2D (OR [95% CI]: 0.80 [0.66, 0.97]) compared to rural community types; in the Geisinger sample, residing in higher density urban community types was associated with the highest odds of new onset T2D (OR [95% CI]: 1.20 [1.06, 1.35]) compared to rural community types. In the VADR sample, suburban/small town community types had the lowest hazard ratios of new onset T2D (HR [95% CI]: 0.99 [0.98, 1.00]). However, in a regional stratified analysis of the VADR sample, the likelihood of new onset T2D was consistent with findings in the REGARDS and Geisinger samples, with highest likelihood of T2D in the rural South and in the higher density urban communities of the Northeast and West regions; likelihood of T2D did not differ by community type in the Midwest.

Conclusions

The likelihood of new onset T2D by community type varied by region of the US. In the South, the likelihood of new onset T2D was higher among those residing in rural communities.

Bayesian Multistate Life Table Methods for Large and Complex State Spaces: Development and Illustration of a New Method

Multistate life table methods are an important tool for producing easily understood measures of population health. Most contemporary uses of these methods involve sample data, thus requiring techniques for capturing uncertainty in estimates. In recent decades, several methods have been developed to do so. Among these methods, the Bayesian approach proposed by Lynch and Brown has several unique advantages. However, the approach is limited to estimating years to be spent in only two living states, such as "healthy" and "unhealthy." In this article, the authors extend this method to allow for large state spaces with "quasi-absorbing" states. The authors illustrate the new method and show its advantages using data from the Health and Retirement Study to investigate U.S. regional differences in years of remaining life to be spent with diabetes, chronic conditions, and disabilities. The method works well and yields rich output for reporting and subsequent analyses. The expanded method also should facilitate the use of multi-state life tables to address a wider array of social science research questions.

Epidemiology for public health practice: The application of spatial epidemiology

Spatial epidemiology is the description and analysis of geographic patterns and variations in disease risk factors, morbidity and mortality with respect to their distributions associated with demographic, socioeconomic, environmental, health behavior, and genetic risk factors, and time-varying changes. In the Letter to Editor, we had a brief description of the practice for the mortality and the space-time patterns of John Snow's map of cholera epidemic in London, United Kingdom in 1854. This map is one of the earliest public heath practices of developing and applying spatial epidemiology. In the early history, spatial epidemiology was predominantly applied in infectious disease and risk factor studies. However, since the recent decades, noncommunicable diseases have become the leading cause of death in both developing and developed countries, spatial epidemiology has been used in the study of noncommunicable disease. In the Letter, we addressed two examples that applied spatial epidemiology to cluster and identify stroke belt and diabetes belt across the states and counties in the United States. Similar to any other epidemiological study design and analysis approaches, spatial epidemiology has its limitations. We should keep in mind when applying spatial epidemiology in research and in public health practice.

Annual wellness visits are associated with increased use of preventive services in patients with diabetes living in the Diabetes Belt

Objective:

To examine whether Annual Wellness Visits (AWVs) were associated with increased use of preventive services in Medicare patients with diabetes living in the Diabetes Belt.

Methods:

We used a case-control design where outcomes were utilization of preventive services recommended for patients with diabetes (foot exam, eye exam, A1c test, and microalbuminuria test) and the exposure was AWVs using data for Medicare patients with diabetes in 2014 − 2015 residing in the Diabetes Belt (N = 412,009).

Results:

Only 13.4% of patients in 2014 and 17.4% in 2015 used AWVs. Eye exams (61% vs 53%), foot exams (93% vs 79%), A1c tests (81% vs 71%), and microalbuminuria tests (45% vs 28%) were more common among patients who had an AWV in the preceding year compared with those who did not. These differences remained significant after adjusting for patient demographics, comorbidities, county level medical resources, and geographic factors.

Conclusions:

AWVs were significantly associated with increased preventive care use among patients with diabetes living in the Diabetes Belt. Low AWV utilization by patients with diabetes in and around the Diabetes Belt is concerning.

Diet Quality and the Gut Microbiota in Women Living in Alabama

INTRODUCTION

The gut microbiota is associated with obesity and modulated by individual dietary components. However, the relationships between diet quality and the gut microbiota and their potential interactions with weight status in diverse populations are not well understood. This study examined the associations between overall diet quality, weight status, and the gut microbiota in a racially balanced sample of adult females.

METHODS

Female participants (N=71) residing in Birmingham, Alabama provided demographics, anthropometrics, biospecimens, and dietary data in this observational study from March 2014 to August 2014, and data analysis was conducted from August 2017 to March 2019. Weight status was defined as a BMI (weight [kg]/height [m²]) <30 kg/m² for non-obese participants and ≥30 kg/m² for participants who were obese. Dietary data collected included an Automated Self-Administered 24-Hour recall and Healthy Eating Index-2010 (HEI-2010) score. Diet quality was defined as having a high HEI score (≥median) or a low HEI score (<median). The fecal microbiota was collected, and the 16S ribosomal RNA gene was amplified to profile the microbiota composition. Differences in diet quality based on weight status were assessed using 2-sample t-tests. The associations between diet quality, gut microbiota, and weight status were analyzed using negative binomial models.

RESULTS

Participants (43 Black, 28 White) aged 40.39±13.86 years who were non-obese (56%) and obese (44%) were studied. Greater alpha diversity was observed among those with higher Healthy Eating Index scores (p=0.037) but did not differ by weight status. Higher abundances of Bacteroidetes (p=0.006) and Firmicutes (p=0.042) were associated with a higher HEI score. Higher Bacteriodetes levels were observed among non-obese (p=0.006).

CONCLUSIONS

Diet quality measured by the HEI was associated with alpha diversity of the gut microbiota among adult females. Abundances of phyla that have been linked with weight status (Bacteroidetes and Firmicutes) were positively associated with diet quality.

Regional Comparison of Diabetes Psychosocial Comorbidities Among Americans With Type 1 Diabetes During the COVID-19 Pandemic

PURPOSE

The purpose of this study was to compare diabetes psychosocial comorbidities among adults with type 1 diabetes (T1D) across the United States during the onset of COVID-19 pandemic.

METHODS

Adults with T1D from 4 main US geographic regions including South (n = 367), West (n = 234), Northeast (n = 250), and Midwest (n = 247) completed a cross-sectional online survey between April and June 2020. Data collection was done on psychosocial measures, glycemic variability, sociodemographic characteristics, and various challenges related to the COVID-19 pandemic. Chi-square, Fisher's exact, Kruskal-Wallis tests, ordinary least squares, proportional odds, and ordinal logistic regression methods were used for data analysis.

RESULTS

In the South, 51.2% of participants had moderate to high levels of diabetes distress, and this was significantly (P = .03) higher than other regions. Northeast region had the lowest prevalence of moderate to severe diabetes burnout (19.8%), but this was not significantly different compared to the other regions. Participants in the South had also the highest mean score on the 8-item Patient Health Questionnaire, with 30.3% of them reporting moderate to severe depressive symptoms. However, there were no significant differences in depressive symptoms among the regions. Glycemic control, demographic characteristics, and COVID-19-related challenges were associated with different psychosocial comorbidities in different regions.

CONCLUSIONS

When providing information and support to individuals with diabetes in time of crisis like the COVID pandemic, providers should consider psychosocial aspects of diabetes care. Diabetes disparities and contextual factors vary geographically in the United States; these factors may impact the psychosocial comorbidities of diabetes in each region.

Diabetes distress and self-reported health in a sample of Alabama Medicaid-covered adults before and during the COVID-19 pandemic

Temporary closures of outpatient health facilities and transitions to virtual care during the COVID-19 pandemic interrupted the care of millions of patients with diabetes contributing to worsening psychosocial factors and enhanced difficulty in managing type 2 diabetes mellitus. We explored associations between COVID time period and self-reported diabetes distress on self-reported health among a sample of Alabama Medicaid-covered adults with diabetes pre-COVID (2017-2019) and during-COVID (2020-2021).

Method

In this cross-sectional study, we surveyed a population-based sample of adults with type 2 diabetes covered by the Alabama Medicaid Agency. Participants were dichotomized into pre-COVID (March 2017 to October 2019) vs during-COVID (October 2020 to May 2021) groups. Participants with missing data were removed from analyses. We assessed diabetes related stress by the Diabetes Distress Scale. We measured self-reported health using a single item with a 5-point Likert scale. We ran logistic regressions modeling COVID time period on self-reported poor health controlling for demographics, severity of diabetes, and diabetes distress.

Results

In this sample of 1822 individuals, median age was 54, 74.5% were female and 59.4% were Black. Compared to pre-COVID participants, participants surveyed during COVID were younger, more likely to be Black (64.1% VS 58.2%, p=0.01) and female (81.8% VS 72.5%, p<0.001). This group also had fewer individuals from rural areas (29.2% VS 38.4%, p<0.001), and shorter diabetes duration (7 years VS 9 years, p<0.001). During COVID individuals reported modestly lower levels of diabetes distress (1.2 VS 1.4, p<0.001) when compared to the pre-COVID group. After adjusting for demographic differences, diabetes severity, and diabetes distress, participants responding during COVID had increased odds of reporting poor health (Odds ratio [OR] 1.41, 95% Confidence Interval [CI] 1.11-1.80).

Discussion

We found respondents were more likely to report poorer health during COVID compared to pre-COVID. These results suggest that increased outreach may be needed to address diabetes management for vulnerable groups, many of whom were already at high risk for poor outcomes prior to the pandemic.

Neighborhood Socioeconomic Environment and Risk of Type 2 Diabetes: Associations and Mediation Through Food Environment Pathways in Three Independent Study Samples

OBJECTIVE

We examined whether relative availability of fast-food restaurants and supermarkets mediates the association between worse neighborhood socioeconomic conditions and risk of developing type 2 diabetes (T2D).

RESEARCH DESIGN AND METHODS

As part of the Diabetes Location, Environmental Attributes, and Disparities Network, three academic institutions used harmonized environmental data sources and analytic methods in three distinct study samples: 1) the Veterans Administration Diabetes Risk (VADR) cohort, a national administrative cohort of 4.1 million diabetes-free veterans developed using electronic health records (EHRs); 2) Reasons for Geographic and Racial Differences in Stroke (REGARDS), a longitudinal, epidemiologic cohort with Stroke Belt region oversampling (N = 11,208); and 3) Geisinger/Johns Hopkins University (G/JHU), an EHR-based, nested case-control study of 15,888 patients with new-onset T2D and of matched control participants in Pennsylvania. A census tract-level measure of neighborhood socioeconomic environment (NSEE) was developed as a community type-specific z-score sum. Baseline food-environment mediators included percentages of 1) fast-food restaurants and 2) food retail establishments that are supermarkets. Natural direct and indirect mediating effects were modeled; results were stratified across four community types: higher-density urban, lower-density urban, suburban/small town, and rural.

RESULTS

Across studies, worse NSEE was associated with higher T2D risk. In VADR, relative availability of fast-food restaurants and supermarkets was positively and negatively associated with T2D, respectively, whereas associations in REGARDS and G/JHU geographies were mixed. Mediation results suggested that little to none of the NSEE-diabetes associations were mediated through food-environment pathways.

CONCLUSIONS

Worse neighborhood socioeconomic conditions were associated with higher T2D risk, yet associations are likely not mediated through food-environment pathways.

County-Level Characteristics Associated with Variation in ESKD Mortality in the United States, 2010-2018

Background

Geographic and neighborhood-level factors, such as poverty and education, have been associated with an increased risk for incident ESKD, likelihood of receiving pre-ESKD care, and likelihood of receiving a transplant. However, few studies have examined whether these same factors are associated with ESKD mortality. In this study, we examined county-level variation in ESKD mortality and identified county-level characteristics associated with this variation.

Methods

We identified 1,515,986 individuals (aged 18-84 years) initiating RRT (dialysis or transplant) between 2010 and 2018 using the United States Renal Data System. Among 2781 counties, we estimated county-level, all-cause, age-standardized mortality rates (ASMR) among patients with ESKD. We then identified county-level demographic (e.g., percent female), socioeconomic (e.g., percent unemployed), healthcare (e.g., percent without health insurance), and health behavior (e.g., percent current smokers) characteristics associated with ASMR using multivariable hierarchic linear mixed models and quantified the percentage of ASMR variation explained by county-level characteristics.

Results

County-level ESKD ASMR ranged from 45 to 1022 per 1000 person-years (PY) (mean, 119 per 1000 PY). ASMRs were highest in counties located in the Tennessee Valley and Appalachia regions, and lowest in counties located in New England, the Pacific Northwest, and Southern California. In fully adjusted models, county-level characteristics significantly associated with higher ESKD mortality included a lower percentage of Black residents (-4.94 per 1000 PY), lower transplant rate (-4.08 per 1000 PY), and higher healthcare expenditures (5.21 per 1000 PY). Overall, county-level characteristics explained 19% of variation in ESKD mortality.

Conclusions

Counties with high ESKD-related mortality may benefit from targeted and multilevel interventions that combine knowledge from a growing evidence base on the interplay between individual and community-level factors associated with ESKD mortality.

Crafting Community-Based Participatory Research Stroke Interventions

Stroke exerts a tremendous burden on individuals, families, communities, and health systems globally. Even more troublesome are the striking disparities faced across diverse populations. These disparities are further exacerbated by the COVID-19 pandemic. Despite efforts to advance stroke research, substantial gaps remain in understanding factors that contribute to stroke disparities, including the Social Determinants of Health. Strategically designed studies and tailored interventions are needed to bridge the inequities high-risk populations face and to meet their specific needs. Community-based participatory research offers an approach to equitably partner with community members to understand and work collaboratively to address community-specific health priorities. In this focused update, we highlight the main processes of community-based participatory research studies and share exemplars from our team's work in stroke research and from the literature. As we continue to face an increasing prevalence of stroke, compounded by the COVID-19 pandemic and ongoing implications of the Social Determinants of Health, partnering with communities to address community-driven health priorities can inform interventions targeted to overcome the disparities faced by certain populations.

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.

Trends and Rural-Urban Differences in Participation in Diabetes Self-management Education Among Adults in North Carolina: 2012-2017

PURPOSE

This study aimed to report recent trends in self-reported diabetes self-management education (DSME) participation rates among adults in North Carolina and to compare these rates between rural and urban residents.

METHODS

Data for this analysis were obtained from the NC Behavioral Risk Factor Surveillance System (BRFSS) for the years 2012, 2013, 2015, and 2017, when the survey included the diabetes module. Respondents were classified as having participated in DSME if they answered "Yes" to the question, "Have you ever taken a course or class in how to manage your diabetes yourself?" We used the Rural Urban Continuum Code to classify urban and rural residence. The study sample included 4368 adults 18 years or older with self-reported diabetes. We assessed the changes in DSME participation from 2012 to 2017. We used multiple logistic regression modeling to assess the association between rural residence and DSME participation. All analyses were conducted in Stata 14 and accounted for the survey design of the BRFSS. Statistical significance was set at P < .01.

RESULTS

Overall, the DSME participation rates decreased slightly in the study period, from 55.8% in 2012 to 55.6% in 2013 to 56.5% in 2015 to 52.1% in 2017. By rural-urban residence, the rates were 52.3% versus 57.8% in 2012, 54.0% versus 56.5% in 2013, 48.8% versus 62.0% in 2015, and 46.7% versus 56.1% in 2017. The multiple logistic regression model results showed that rural residents were less likely to have participated in DSME (adjusted odds ratio = 0.78; 95% confidence interval, 0.64-0.94) than urban residents. Adults with higher income and education levels were also more likely to have participated in DSME (P < .01).

CONCLUSIONS

The recent BRFSS data showed that the DSME participation rate declined slightly in North Carolina. There were persistent rural-urban disparities in DSME participation, with rural residents showing lower rates, and the gaps seemed to be widening.

IMPLICATIONS FOR POLICY OR PRACTICE

Continuous efforts are needed to bring more American Diabetes Association/American Association of Diabetes Educators programs to rural communities and assist persons with diabetes to participate in DSME training to reduce the burden of diabetes. Furthermore, those in rural areas may need additional support.

CKD Prevalence Among Patients With and Without Type 2 Diabetes: Regional Differences in the United States

Rationale & Objective

Regional variation in chronic kidney disease (CKD) prevalence in patients with or without type 2 diabetes mellitus (T2DM) has not been well characterized.

Study Design

Spatial and temporal comparative analysis.

Setting & Participants

MarketScan databases were used to identify patients with CKD overall and subgroups of patients with CKD with and without T2DM in the United States.

Outcomes

Spatial patterns in CKD prevalence based on year, regional clusters of CKD between years, and characteristics of patients in high-prevalence states.

Analytical Approach

Geomapping was used to visualize the state-level data of CKD prevalence generated from 2013 to 2018. We used univariate local indicators of spatial association (LISA) to evaluate geographic differences in prevalence, differential LISA for changes in CKD prevalence over time, and the χ² test to identify patient characteristics in the top-20th percentile states for the prevalence of CKD.

Results

In univariate LISA, low-low clusters, in which a state has a low CKD prevalence and the surrounding states have a below-average CKD prevalence, were observed in the northwest region throughout the study period, regardless of the T2DM status, indicating a consistently low prevalence of CKD clustered in these areas. High-high clusters were observed, regardless of the T2DM status, in the southeast region in more recent years, suggesting an increased CKD prevalence in this region.

Limitations

Health care insurance enrollment might not have been representative of the United States; the estimates were based on claims data that likely underestimated the true prevalence.

Conclusions

Geographic disparities in CKD prevalence appear increasingly magnified, with an increase in the southeastern region of the United States. This increase is especially problematic because patients with CKD in high-prevalence states experience a greater likelihood of chronic conditions than those in the rest of the United States.

Patient Decision-Making About Self-Disclosure of a Type 2 Diabetes Diagnosis: A Qualitative Study

BACKGROUND

Effective self-management of type 2 diabetes requires receiving support, which can result from disclosing the diagnosis to a support network, including coworkers, family, and friends. As a primarily invisible disease, diabetes allows people to choose whether to disclose. This study qualitatively explores the factors that influence a person's decision to disclose diabetes to others.

METHODS

Research coordinators recruited 22 interview participants, ranging in age from 32 to 64 years, whose medical records included a diagnosis code for type 2 diabetes. Participants received care from one of two U.S. medical centers. Semi-structured interviews lasted approximately 1 hour and were audio-recorded and professionally transcribed. Verification strategies such as memo-keeping and maintaining methodological coherence/congruence were used throughout analysis to promote rigor.

RESULTS

In patients' descriptions of their decision-making processes regarding whether to disclose their diagnosis, six themes emerged. Three motivations prompted open disclosure: 1) to seek information, 2) to seek social support, and 3) to end the succession of diabetes, and the other three motivations prompted guarded disclosure: 4) to prepare for an emergency, 5) to maintain an image of health, and 6) to protect employment.

CONCLUSION

Based on our findings, we recommend three communicative actions for clinicians as they talk to patients about a diabetes diagnosis. First, clinicians should talk about the benefits of disclosure. Second, they should directly address stereotypes in an effort to de-stigmatize diabetes. Finally, clinicians can teach the skills of disclosure. As disclosure efficacy increases, a person's likelihood to disclose also increases. Individuals can use communication as a tool to gain the knowledge and support they need for diabetes self-management and to interrupt the continuing multigenerational development of diabetes within their family.

Clinical risk factors and social needs of 30-day readmission among patients with diabetes: A retrospective study of the Deep South

Introduction

Evidence is needed for 30-day readmission risk factors (clinical factors and social needs) among patients with diabetes in the Deep South. To address this need, our objectives were to identify risk factors associated with 30-day readmissions among this population and determine the added predictive value of considering social needs.

Methods

This retrospective cohort study utilized electronic health records from an urban health system in the Southeastern U.S. The unit of analysis was index hospitalization with a 30-day washout period. The index hospitalizations were preceded by a 6-month pre-index period to capture risk factors (including social needs), and hospitalizations were followed 30 days post-discharge to evaluate all-cause readmissions (1=readmission; 0=no readmission). We performed unadjusted (chi-square and student's t-test, where applicable) and adjusted analyses (multiple logistic regression) to predict 30-day readmissions.

Results

A total of 26,332 adults were retained in the study population. Eligible patients contributed a total of 42,126 index hospitalizations, and the readmission rate was 15.21%. Risk factors associated with 30-day readmissions included demographics (e.g., age, race/ethnicity, insurance), characteristics of hospitalizations (e.g., admission type, discharge status, length of stay), labs and vitals (e.g., highest and lowest blood glucose measurements, systolic and diastolic blood pressure), co-existing chronic conditions, and preadmission antihyperglycemic medication use. In univariate analyses of social needs, activities of daily living (p<0.001), alcohol use (p<0.001), substance use (p=0.002), smoking/tobacco use (p<0.001), employment status (p<0.001), housing stability (p<0.001), and social support (p=0.043) were significantly associated with readmission status. In the sensitivity analysis, former alcohol use was significantly associated with higher odds of readmission compared to no alcohol use [aOR (95% CI): 1.121 (1.008-1.247)].

Conclusions

Clinical assessment of readmission risk in the Deep South should consider patients' demographics, characteristics of hospitalizations, labs, vitals, co-existing chronic conditions, preadmission antihyperglycemic medication use, and social need (i.e., former alcohol use). Factors associated with readmission risk can help pharmacists and other healthcare providers identify high-risk patient groups for all-cause 30-day readmissions during transitions of care. Further research is needed about the influence of social needs on readmissions among populations with diabetes to understand the potential clinical utility of incorporating social needs into clinical services.

Explainable artificial intelligence (XAI) for exploring spatial variability of lung and bronchus cancer (LBC) mortality rates in the contiguous USA

Machine learning (ML) has demonstrated promise in predicting mortality; however, understanding spatial variation in risk factor contributions to mortality rate requires explainability. We applied explainable artificial intelligence (XAI) on a stack-ensemble machine learning model framework to explore and visualize the spatial distribution of the contributions of known risk factors to lung and bronchus cancer (LBC) mortality rates in the conterminous United States. We used five base-learners-generalized linear model (GLM), random forest (RF), Gradient boosting machine (GBM), extreme Gradient boosting machine (XGBoost), and Deep Neural Network (DNN) for developing stack-ensemble models. Then we applied several model-agnostic approaches to interpret and visualize the stack ensemble model's output in global and local scales (at the county level). The stack ensemble generally performs better than all the base learners and three spatial regression models. A permutation-based feature importance technique ranked smoking prevalence as the most important predictor, followed by poverty and elevation. However, the impact of these risk factors on LBC mortality rates varies spatially. This is the first study to use ensemble machine learning with explainable algorithms to explore and visualize the spatial heterogeneity of the relationships between LBC mortality and risk factors in the contiguous USA.

Disparities in Cervical Cancer Screening with HPV Test among Females with Diabetes in the Deep South

Simple Summary

Diabetes is linked with poorer cervical cancer prognosis, and people residing in the Southern region of the U.S. are disproportionately diagnosed with diabetes and cancer. The HPV test was recently recognized as the preferred method of cervical cancer screening by the American Cancer Society. Through our observational study, we sought to investigate the HPV testing behaviors among females with and without diabetes across the U.S. Our nationally representative estimates reveal that less than half of females reported HPV testing, and females with diabetes in the Deep South have the lowest rates of HPV testing. Various risk factors were identified to significantly lower the odds of HPV testing, including a diabetes diagnosis, older age, living in the Southern region of the U.S., and absence of certain comorbidities. The lower rates of HPV testing among females with diabetes, especially those living in the Deep South, leave these populations vulnerable to cervical cancer.

Abstract

Background: Due to diabetes being linked with poorer cervical cancer prognosis, this study aimed to evaluate HPV testing behaviors among females with and without diabetes across the U.S. by geographic area in 2016, 2018, and 2020. Methods: This cross-sectional study used the Behavioral Risk Factor Surveillance System (BRFSS) from 2016, 2018, and 2020. The study population included females aged 25–69 years old, stratified by self-reported diabetes status. The primary outcome measure was cervical cancer screening behavior, which was evaluated by self-reported HPV test uptake/receipt (yes/no). Results: A total of 361,546 females from across the U.S. were sampled. Within the study population combined from all study years, the overall likelihood of receiving an HPV test was significantly lower among females with diabetes [37.95% (95% CI: 36.87–39.04)] compared to those without diabetes [46.21% (95% CI: 45.84–46.58)] (p < 0.001). Screening rates with HPV tests were lowest among females with diabetes in the South in 2016 (29.32% (95% CI: 26.82–31.83)), 2018 (39.63% (95% CI: 36.30–42.96)), and 2020 (41.02% (95% CI: 37.60–44.45)). Conclusions: Females with diabetes are screening with HPV tests less frequently than females without diabetes, and females living in the South, particularly states in the Deep South, report the lowest rates of HPV testing.

Psoriasis Severity, Comorbidities, and Treatment Response Differ among Geographic Regions in the United States

Little is known about how psoriatic disease characteristics and treatment outcomes differ geographically in the United States. Our aim was to explore real-world, geographic variations in the use of biologic classes and outcomes within the Corrona Psoriasis Registry. Patient demographics and disease characteristics were assessed at biologic initiation and at 6 months. Logistic regressions were conducted to evaluate the odds of achieving targeted outcomes for seven United States geographic regions. We examined 737 biologic initiations among 717 patients. IL-17 inhibitors were used most frequently (45%), followed by IL-12‒IL-23 and IL-23 inhibitors (38%) and TNF inhibitors (17%). The proportions of patients with obesity (body mass index > 30) and very severe psoriasis (body surface area > 20) were greatest in the East South Central and West South Central regions. After adjusting for age, sex, race, body mass index, and baseline body surface area, decreased odds of achieving 75% improvement in PASI at 6 months were observed among patients in the East South Central (OR = 0.47, 95% confidence interval = 0.28–0.79, P = 0.004), West South Central (OR = 0.43, 95% confidence interval = 0.22–0.87, P = 0.019), and Pacific (OR = 0.49, 95% confidence interval = 0.28–0.84, P = 0.010) regions compared with those observed among patients in the Northeast. The East South Central and West South Central regions may have the greatest frequencies of very severe disease burden and, along with the Pacific region, may be less likely to achieve targeted response within 6 months of initiating biologic therapy.

Lifecourse socioeconomic position and diabetes incidence in the REasons for Geographic and Racial Differences in Stroke (REGARDS) study, 2003 to 2016

Low socioeconomic position (SEP) across the lifecourse is associated with Type 2 diabetes (T2DM). We examined whether these economic disparities differ by race and sex. We included 5448 African American (AA) and white participants aged ≥45 years from the national (United States) REasons for Geographic and Racial Differences in Stroke (REGARDS) cohort without T2DM at baseline (2003-07). Incident T2DM was defined by fasting glucose ≥126 mg/dL, random glucose ≥200 mg/dL, or using T2DM medications at follow-up (2013-16). Derived SEP scores in childhood (CSEP) and adulthood (ASEP) were used to calculate a cumulative (CumSEP) score. Social mobility was defined as change in SEP. We fitted race-stratified logistic regression models to estimate the association between each lifecourse SEP indicator and T2DM, adjusting for covariates; additionally, we tested SEP-sex interactions. Over a median of 9.0 (range 7-14) years of follow-up, T2DM incidence was 167.1 per 1000 persons among AA and 89.9 per 1000 persons among white participants. Low CSEP was associated with T2DM incidence among AA (OR = 1.61; 95%CI 1.05-2.46) but not white (1.06; 0.74-2.33) participants; this was attenuated after adjustment for ASEP. In contrast, low CumSEP was associated with T2DM incidence for both racial groups. T2DM risk was similar for stable low SEP and increased for downward mobility when compared with stable high SEP in both groups, whereas upward mobility increased T2DM risk among AAs only. No differences by sex were observed. Among AAs, low CSEP was not independently associated with T2DM, but CSEP may shape later-life experiences and health risks.

Evaluating efficiency of counties in providing diabetes preventive care using data envelopment analysis

For patients with diabetes, annual preventive care is essential to reduce the risk of complications. Local healthcare resources affect the utilization of diabetes preventive care. Our objectives were to evaluate the relative efficiency of counties in providing diabetes preventive care and explore potential to improve efficiencies. The study setting is public and private healthcare providers in US counties with available data. County-level demographics were extracted from the Area Health Resources File using data from 2010 to 2013, and individual-level information of diabetes preventive service use was obtained from the 2010 Behavioral Risk Factor Surveillance System. 1112 US counties were analyzed. Cluster analysis was used to place counties into three similar groups in terms of economic wellbeing and population characteristics. Group 1 consisted of metropolitan counties with prosperous or comfortable economic levels. Group 2 mostly consisted of non-metropolitan areas between distress and mid-tier levels, while Group 3 were mostly prosperous or comfortable counties in metropolitan areas. We used data enveopement analysis to assess efficiencies within each group. The majority of counties had modest efficiency in providing diabetes preventive care; 36 counties (57.1%), 345 counties (61.1%), and 263 counties (54.3%) were inefficient (efficiency scores < 1) in Group 1, Group 2, and Group 3, respectively. For inefficient counties, foot and eye exams were often identified as sources of inefficiency. Available health professionals in some counties were not fully utilized to provide diabetes preventive care. Identifying benchmarking targets from counties with similar resources can help counties and policy makers develop actionable strategies to improve performance.

Spatiotemporal Analysis of Overall Health in the United States Between 2010 and 2018

Background

Although many previous studies have documented spatial heterogeneity in health outcomes across the United States at different geographic scales, spatiotemporal analyses to understand overall health are scant.

Methodology

We used the County Health Rankings (CHR) data to analyze the three types of health outcomes, viz., overall health, length of life, and quality of life for 2010-2018 in the contiguous United States employing hierarchal Bayesian methods. Composite scores were created to proxy these outcomes utilizing predefined weights of several variables as recommended by CHR. Our methods assumed a convolution of spatially structured and unstructured errors to model the overall spatial error. Spatial effects were modeled using conditional autoregressive distribution.

Results

The substantial disparity in these health outcomes was evident, with counties having poorer health outcomes mostly concentrated in the southeastern United States. Models that incorporated county-level demographic and socioeconomic characteristics partially explained the observed spatial heterogeneity in health outcomes. Interestingly, there was no time effect in any of the outcomes suggesting a perpetuation of health disparity over the years.

Conclusions

County-specific health policy interventions that take into account the contextual factors might be beneficial in improving population health and breaking the perpetuation of health disparity.

Socioeconomic Predictors of Diabetes Mortality in Japan: An Ecological Study Using Municipality-specific Data

Objectives

The aim of this study was to examine the geographic distribution of diabetes mortality in Japan and identify socioeconomic factors affecting differences in municipality-specific diabetes mortality.

Methods

Diabetes mortality data by year and municipality from 2013 to 2017 were extracted from Japanese Vital Statistics, and the socioeconomic characteristics of municipalities were obtained from government statistics. We calculated the standardized mortality ratio (SMR) of diabetes for each municipality using the empirical Bayes method and represented geographic differences in SMRs in a map of Japan. Multiple linear regression was conducted to identify the socioeconomic factors affecting differences in SMR. Statistically significant socioeconomic factors were further assessed by calculating the relative risk of mortality of quintiles of municipalities classified according to the degree of each socioeconomic factor using Poisson regression analysis.

Results

The geographic distribution of diabetes mortality differed by gender. Of the municipality-specific socioeconomic factors, high rates of single-person households and unemployment and a high number of hospital beds were associated with a high SMR for men. High rates of fatherless households and blue-collar workers were associated with a high SMR for women, while high taxable income per-capita income and total population were associated with low SMR for women. Quintile analysis revealed a complex relationship between taxable income and mortality for women. The mortality risk of quintiles with the highest and lowest taxable per-capita income was significantly lower than that of the middle-income quintile.

Conclusions

Socioeconomic factors of municipalities in Japan were found to affect geographic differences in diabetes mortality.