Identifying Geographic Disparities in Diabetes Prevalence Among Adults and Children Using Emergency Claims Data

Insights Into Eye Care Accessibility: Geospatial Distribution of Eye Care Providers and Socioeconomic Factors by ZIP Code

Purpose

In the United States, the ZIP Code has long been used to collect geospatial data revealing disparities in social determinants of health. This cross-sectional study examines the distribution of eye care access in association with local socioeconomic factors at a ZIP Code level.

Methods

Data from the 2020 Centers of Medicare and Medicaid Services and American Community Survey were used to examine locations of 47,949 providers (17,631 ophthalmologists and 30,318 optometrists) and corresponding local socioeconomic variables (education, employment, and income). Multivariable zero-inflated negative binomial regression was used to model eye care provider count per capita in each ZIP Code area with socioeconomic factors as independent covariates.

Results

For every 1% increase in percentage of population over 25 years with a bachelor's degree or higher, the expected number of providers increases by 4.4% (incidence rate ratio [IRR] = 1.044; 95% confidence interval [CI], 1.041-1.046; P < 0.001). For every 1% increase in percentage unemployment, the expected number of providers decreases by 2.7% (IRR = 0.973; 95% CI, 0.964-0.983; P < 0.001). However, for every $1000 increase in median household income, the expected number of providers decreases by 1.6% (IRR = 0.984; 95% CI, 0.983-0.986; P < 0.001).

Conclusions

Disparities in access exist in areas of lower employment and educational attainment, as both have positive correlations with eye care provider access. Conversely, areas of greater median household income have lower access to providers.

Translational Relevance

This research contributes to a greater field studying social determinants of health and may inform public health strategies on allocation of providers to improve equitable access to vision care.

Association between the number of convenience stores and caries status in permanent teeth among elementary school children: results from the A-CHILD population-based longitudinal cohort study

Background

In Japan, people have rich access to 24-h convenience stores where they can buy sweets, juice and fluoride hygiene products, among others. However, the association between the number of convenience stores in a neighbourhood and caries in elementary school children status has not been studied. This study aimed to investigate this particular association.

Methods

Data were derived from a population-based longitudinal cohort study (A-CHILD study) of elementary school children from first-grade to fourth-grade in Adachi City, Tokyo. Caregivers were asked to complete a questionnaire in 2015, 2016, and 2018. A total of 3,136 caregivers provided a valid response. We analysed the association using multilevel Poisson regression.

Results

The mean number of caries among children in school districts with low, middle, and high number of convenience stores was 0.31 (SD: 0.81), 0.21 (SD: 0.69), and 0.16 (SD: 0.58). After covariate adjustment, children in the school districts with high and middle number of convenience stores had 44% (mean ratio 0.56, 95% CI: 0.31, 0.998) and 31% (mean ratio 0.69, 95% CI: 0.42, 1.13) fewer caries in their permanent teeth, respectively, than children in the school districts with low number of convenience stores. We also found dose-response relationship (p for trend: 0.042).

Conclusion

Higher number of convenience stores in a school district was associated with fewer caries in permanent teeth among elementary school children. Further study elucidating the mechanism on this protective association is warranted.

Analysis of Cause-of-Death Mortality in Children and Young Adults with Diabetes: A Nationwide 10-Year Follow-Up Cohort Study

We examined the associations of clinical characteristics and cause-of-death patterns with mortality in children and young adults (<30 years) with diabetes. We analyzed a nationwide cohort sample from the KNHIS database using propensity score matching from a sample of 1 million people from 2002 to 2013. There were 10,006 individuals in the diabetes mellitus (DM) group and 10,006 in the control (no DM) group. The numbers of deaths were 77 in the DM group and 20 in the control group. The deaths of patients in the DM Group were 3.74 (95% confidence interval (CI) = 2.25-6.21) times higher than in the control group. Type 1 DM, type 2 DM and unspecified DM were 4.52 (95% CI = 1.89-10.82) times, 3.25 (95% CI = 1.95-5.43) times and 10.20 (95% CI = 5.24-20.18) times higher, respectively. Mental disorders were 2.08 times higher in the risk of death (95% CI = 1.27-3.40). Mortality rates have increased in children and young adults with diabetes alone. Therefore, in the future, it is necessary to identify the cause of the increased mortality rate among young diabetic people and select vulnerable groups among them so that early prevention can be achieved.

Categorizing community type for epidemiologic evaluation of community factors and chronic disease across the United States

Existing classifications of community type do not differentiate urban cores from surrounding non-rural areas, an important distinction for analyses of community features and their impact on health. Inappropriately classified community types can introduce serious methodologic flaws in epidemiologic studies and invalid inferences from findings. To address this, we evaluate a modification of the United States Department of Agriculture's Rural Urban Commuting Area codes at the census tract, propose a four-level categorization of community type, and compare this with existing classifications for epidemiologic analyses. Compared to existing classifications, our method resulted in clearer geographic delineations of community types within urban areas.

Neighborhood-Level Chronic Disease Surveillance: Utility of Primary Care Electronic Health Records and Emergency Department Claims Data

CONTEXT

Disease burden may vary substantively across neighborhoods in an urban setting. Yet, data available for monitoring chronic conditions at the neighborhood level are scarce. Large health care data sets have potential to complement population health surveillance. Few studies have examined the utility of health care data for neighborhood-level surveillance.

OBJECTIVE

We examined the use of primary care electronic health records (EHRs) and emergency department (ED) claims for identifying neighborhoods with higher chronic disease burden and neighborhood-level prevalence estimation.

DESIGN

Comparison of hypertension and diabetes estimates from EHRs and ED claims with survey-based estimates.

SETTING

Forty-two United Hospital Fund neighborhoods in New York City.

PARTICIPANTS

The EHR sample comprised 708 452 patients from the Hub Population Health System (the Hub) in 2015, and the ED claim sample comprised 1 567 870 patients from the Statewide Planning and Research Cooperative System in 2015. We derived survey-based estimates from 2012 to 2016 Community Health Survey (n = 44 189).

MAIN OUTCOME MEASURE

We calculated hypertension and diabetes prevalence estimates by neighborhood from each data source. We obtained Pearson correlation and absolute difference between EHR-based or claims-based estimates and survey-based estimates.

RESULTS

Both EHR-based and claims-based estimates correlated strongly with survey-based estimates for hypertension (0.91 and 0.72, respectively) and diabetes (0.83 and 0.82, respectively) and identified similar neighborhoods of higher burden. For hypertension, 10 and 17 neighborhoods from the EHRs and ED claims, respectively, had an absolute difference of more than 5 percentage points from the survey-based estimate. For diabetes, 15 and 4 neighborhoods from the EHRs and ED claims, respectively, differed from the survey-based estimate by more than 5 percentage points.

CONCLUSIONS

Both EHRs and ED claims data are useful for identifying neighborhoods with greater disease burden and have potential for monitoring chronic conditions at the neighborhood level.

The Relationship Between Estimated Median Household Income and Critical Care Length of Stay in Children With Diabetic Ketoacidosis

Diabetic ketoacidosis (DKA) is an important diagnosis in the pediatric intensive care unit (PICU) and is associated with significant morbidity. We hypothesized children with DKA living in poorer communities would have unfavorable outcomes while critically ill. This single-center retrospective study included children with DKA admitted to a PICU over a 27-month period. Patients were classified as low-income if they lived in a ZIP code where the median household income was estimated to be less than 200% of the federal poverty threshold, or $48 016 for a family of 4. In this study, living in a low-income ZIP code was not associated with increased severity of illness, longer PICU length of stay (LOS), or readmission.

Concordance and Discordance in the Geographic Distribution of Childhood Obesity and Pediatric Type 2 Diabetes in New York City

OBJECTIVE

As rates of childhood obesity and pediatric type 2 diabetes (T2D) increase, a better understanding is needed of how these 2 conditions relate and which subgroups of children are more likely to develop diabetes with and without obesity.

METHODS

To compare hotspots of childhood obesity and pediatric T2D in New York City, we performed geospatial clustering analyses on obesity estimates obtained from surveys of school-aged children and diabetes estimates obtained from health care claims data, from 2009 to 2013. Analyses were performed at the Census tract level. We then used multivariable regression analysis to identify sociodemographic and environmental factors associated with these hotspots.

RESULTS

We identified obesity hotspots in Census tracts with a higher proportion of Black or Hispanic residents, with low median household income, or located in a food swamp. Total 51.1% of pediatric T2D hotspots overlapped with obesity hotspots. For pediatric T2D, hotspots were identified in Census tracts with a higher proportion of Black residents and a lower proportion of Hispanic residents.

CONCLUSIONS

Non-Hispanic Black neighborhoods had a higher probability of being hotspots of both childhood obesity and pediatric T2D. However, we identified a discordance between hotspots of childhood obesity and pediatric diabetes in Hispanic neighborhoods, suggesting either under-detection or under-diagnosis of diabetes, or that obesity may influence diabetes risk differently in these 2 populations. These findings warrant further investigation of the relationship between childhood obesity and pediatric diabetes among different racial and ethnic groups, and may help guide pediatric public health interventions to specific neighborhoods.

Projected geographic disparities in healthcare worker absenteeism from COVID-19 school closures and the economic feasibility of child care subsidies: a simulation study

BACKGROUND

School closures have been enacted as a measure of mitigation during the ongoing coronavirus disease 2019 (COVID-19) pandemic. It has been shown that school closures could cause absenteeism among healthcare workers with dependent children, but there remains a need for spatially granular analyses of the relationship between school closures and healthcare worker absenteeism to inform local community preparedness.

METHODS

We provide national- and county-level simulations of school closures and unmet child care needs across the USA. We develop individual simulations using county-level demographic and occupational data, and model school closure effectiveness with age-structured compartmental models. We perform multivariate quasi-Poisson ecological regressions to find associations between unmet child care needs and COVID-19 vulnerability factors.

RESULTS

At the national level, we estimate the projected rate of unmet child care needs for healthcare worker households to range from 7.4 to 8.7%, and the effectiveness of school closures as a 7.6% and 8.4% reduction in fewer hospital and intensive care unit (ICU) beds, respectively, at peak demand when varying across initial reproduction number estimates by state. At the county level, we find substantial variations of projected unmet child care needs and school closure effects, 9.5% (interquartile range (IQR) 8.2-10.9%) of healthcare worker households and 5.2% (IQR 4.1-6.5%) and 6.8% (IQR 4.8-8.8%) reduction in fewer hospital and ICU beds, respectively, at peak demand. We find significant positive associations between estimated levels of unmet child care needs and diabetes prevalence, county rurality, and race (p<0.05). We estimate costs of absenteeism and child care and observe from our models that an estimated 76.3 to 96.8% of counties would find it less expensive to provide child care to all healthcare workers with children than to bear the costs of healthcare worker absenteeism during school closures.

CONCLUSIONS

School closures are projected to reduce peak ICU and hospital demand, but could disrupt healthcare systems through absenteeism, especially in counties that are already particularly vulnerable to COVID-19. Child care subsidies could help circumvent the ostensible trade-off between school closures and healthcare worker absenteeism.

A Pantheoretical Framework to Optimize Adherence to Healthy Lifestyle Behaviors and Medication Adherence: The Use of Personalized Approaches to Overcome Barriers and Optimize Facilitators to Achieve Adherence

Patient nonadherence to healthy lifestyle behaviors and medical treatments (like medication adherence) accounts for a significant portion of chronic disease burden. Despite the plethora of behavioral interventions to overcome key modifiable/nonmodifiable barriers and enable facilitators to adherence, short- and long-term adherence to healthy lifestyle behaviors and medical treatments is still poor. To optimize adherence, we aimed to provide a novel mobile health solution steeped in precision and personalized population health and a pantheoretical approach that increases the likelihood of adherence. We have described the stages of a pantheoretical approach utilizing tailoring, clustering/profiling, personalizing, and optimizing interventions/strategies to obtain adherence and highlight the minimal engineering needed to build such a solution.

Projected geographic disparities in healthcare worker absenteeism from COVID-19 school closures and the economic feasibility of child care subsidies: a simulation study

Background

School closures have been enacted as a measure of mitigation during the ongoing COVID-19 pandemic. It has been shown that school closures could cause absenteeism amongst healthcare workers with dependent children, but there remains a need for spatially granular analyses of the relationship between school closures and healthcare worker absenteeism to inform local community preparedness.

Methods

We provide national- and county-level simulations of school closures and unmet child care needs across the United States. We develop individual simulations using county-level demographic and occupational data, and model school closure effectiveness with age-structured compartmental models. We perform multivariate quasi-Poisson ecological regressions to find associations between unmet child care needs and COVID-19 vulnerability factors.

Results

At the national level, we estimate the projected rate of unmet child care needs for healthcare worker households to range from 7.5% to 8.6%, and the effectiveness of school closures to range from 3.2% (R0 = 4) to 7.2% (R0 = 2) reduction in fewer ICU beds at peak demand. At the county-level, we find substantial variations of projected unmet child care needs and school closure effects, ranging from 1.9% to 18.3% of healthcare worker households and 5.7% to 8.8% reduction in fewer ICU beds at peak demand (R0 = 2). We find significant positive associations between estimated levels of unmet child care needs and diabetes prevalence, county rurality, and race (p < 0.05). We estimate costs of absenteeism and child care and observe from our models that an estimated 71.1% to 98.8% of counties would find it less expensive to provide child care to all healthcare workers with children than to bear the costs of healthcare worker absenteeism during school closures.

Conclusions

School closures are projected to reduce peak ICU bed demand, but could disrupt healthcare systems through absenteeism, especially in counties that are already particularly vulnerable to COVID-19. Child care subsidies could help circumvent the ostensible tradeoff between school closures and healthcare worker absenteeism.

Heart rate variability for determining autonomic nervous system effects of lifestyle behaviors in early life: A systematic review

BACKGROUND

An unhealthy lifestyle negatively alters autonomic nervous system (ANS) activity as reflected by decreased heart rate variability (HRV), increasing cardiovascular disease (CVD) risk. Research investigating the effect of modifiable lifestyle factors on ANS activity in young children is limited. Early identification of these risk factors is vital to improving long-term individual and public health outcomes. A systematic review was conducted to assess the effect of maternal or child modifiable lifestyle factors on child ANS activity.

METHODS

Following the 2009 PRISMA guidelines, three electronic databases were searched from February 2018 - July 2019 for articles describing human trials between 1996 - 2019. Included studies examined ANS activity of children between 28 weeks gestational age - 6 years in relation to modifiable lifestyle CVD risk factors.

RESULTS

Twenty-six studies fulfilled inclusion criteria. Sixteen studies reported that modifiable lifestyle factors significantly influenced the HRV of children. Increased HRV was significantly associated with higher maternal zinc and omega-3 fatty acid intake, regular maternal aerobic exercise and a non-smoking environment. Child diet and body composition demonstrated some support for an association between these modifiable lifestyle factors and child HRV.

CONCLUSION

There is cross-sectional evidence supporting an association between maternal lifestyle factors and child HRV. Evidence is less supportive of a relationship between child modifiable lifestyle factors and child HRV. Monitoring the effects of lifestyle interventions on the ANS via HRV measurements of both mother and child may identify child CVD risk.

U.S. county "food swamp" severity and hospitalization rates among adults with diabetes: A nonlinear relationship

The relationship between food environments and diabetes morbidity is vastly understudied, despite the well-recognized linkage between dietary quality and diabetes complications. Further, literature demonstrates that attributes of places can have nonlinear relationships with health outcomes. This study examines the extent to which "food swamps" are associated with greater rates of hospitalizations for complications among adults with diabetes over time as well as the linearity of this relationship. We conduct a longitudinal county-level analysis of 832 counties across 16 U.S. states in 2010, 2012, and 2014 using data from the USDA Food Environment Atlas and the AHRQ Health Care Cost and Utilization Project State Inpatient Databases. Food swamp severity is measured as the percentage of food outlets in a county that sell primarily unhealthy foods. Hierarchical linear mixed models with county random intercepts are estimated, controlling for area-level covariates and state and year fixed effects. Curvilinear relationships are explored by additively incorporating quadratic terms. We find that, over the study period, mean food swamp severity remained relatively stable. Mean hospitalization rates decreased from 296.72 to 262.82 hospitalizations per 1000 diabetic adults (p < 0.001). In adjusted models, greater food swamp severity was associated with higher hospitalization rates in a curvilinear manner (severity: β = 2.181, p = 0.02; severity2: β = -0.017, p = 0.04), plateauing at approximately 64% unhealthy outlets, a saturation point observed in 17% of observations. Policies that limit saturation of the environment with unhealthy outlets may help in the prevention of diabetic complications, but more saturated counties will likely require more extensive intervention.

Age Disparities Among Patients With Type 2 Diabetes and Associated Rates of Hospital Use and Diabetic Complications

Introduction

Although screening for diabetes is recommended at age 45, some populations may be at greater risk at earlier ages. Our objective was to quantify age disparities among patients with type 2 diabetes in New York City.

Methods

Using all-payer hospital claims data for New York City, we performed a cross-sectional analysis of patients with type 2 diabetes identified from emergency department visits during the 5-year period 2011–2015. We estimated type 2 diabetes prevalence at each year of life, the age distribution of patients stratified by decade, and the average age of patients by sex, race/ethnicity, and geographic location.

Results

We identified 576,306 unique patients with type 2 diabetes. These patients represented more than half of all people with type 2 diabetes in New York City. Patients in racial/ethnic minority groups were on average 5.5 to 8.4 years younger than non-Hispanic white patients. At age 45, type 2 diabetes prevalence was 10.9% among non-Hispanic black patients and 5.2% among non-Hispanic white patients. In our geospatial analyses, patients with type 2 diabetes were on average 6 years younger in hotspots of diabetes-related emergency department use and inpatient hospitalizations. The average age of patients with type 2 diabetes was also 1 to 2 years younger in hotspots of microvascular diabetic complications.

Conclusion

We identified profound age disparities among patients with type 2 diabetes in racial/ethnic minority groups and in neighborhoods with poor health outcomes. The younger age of these patients may be due to earlier onset of diabetes and/or earlier death from diabetic complications. Our findings demonstrate the need for geographically targeted interventions that promote earlier diagnosis and better glycemic control.

Geographic Differences in the Dietary Quality of Food Purchases among Participants in the Nationally Representative Food Acquisition and Purchase Survey (FoodAPS)

Objective grocery transactions may reflect diet, but it is unclear whether the diet quality of grocery purchases mirrors geographic and racial/ethnic disparities in diet-related diseases. This cross-sectional analysis of 3961 households in the nationally representative Food Acquisition and Purchase Survey evaluated geographic and racial/ethnic disparities in grocery purchase quality. Respondents self-reported demographics and recorded purchases over 7 days; the Healthy Eating Index (HEI) 2015 assessed diet quality. Survey-weighted multivariable-adjusted regression determined whether there were geographic and racial/ethnic differences in HEI-15 scores. Respondents were, on average, 50.6 years, non-Hispanic white (NHW) (70.3%), female (70.2%), and had attended some college (57.8%). HEI-15 scores differed across geographic region (p < 0.05), with the highest scores in the West (57.0 ± 0.8) and lowest scores in the South (53.1 ± 0.8), and there was effect modification by race/ethnicity (p-interaction = 0.02). Regionally, there were diet disparities among NHW and non-Hispanic black (NHB) households; NHWs in the South had HEI-15 scores 3.2 points lower than NHWs in the West (p = 0.003). Southern NHB households had HEI-15 scores 8.1 points lower than Western NHB households (p = 0.013). Racial/ethnic disparities in total HEI-15 by region existed in the Midwest and South, where Hispanic households in the Midwest and South had significantly lower diet quality than NHW households. Heterogeneous disparities in the diet quality of grocery purchases by region and race/ethnicity necessitate tailored approaches to reduce diet-related disease.

Feasibility and Acceptability of a Culturally Tailored Website to Increase Fruit and Vegetable Intake and Physical Activity Levels in African American Mother-Child Dyads: Observational Study

BACKGROUND

African American youth (aged 8-14 years) do not adhere to national dietary and physical activity guidelines. Nonadherence to these recommendations contributes to disproportionate rates of obesity compared with their white counterparts. Culturally tailored electronic health (eHealth) solutions are needed to communicate nutrition and physical activity messages that resonate with this target population.

OBJECTIVE

This study aimed to identify the impact of exposure to a website hosting culturally tailored cartoons to inspire fruit and vegetable uptake and physical activity levels in African American mother-child dyads.

METHODS

Statistical analysis included paired sample t tests to evaluate knowledge gains, self-efficacy, and readiness to change. Adapted items from Prochaska's Stages of Change toward the following 4 behaviors were assessed with pre- and posttest surveys: (1) fruit and vegetable selection on my plate, (2) meal preparation, (3) fruit and vegetable selection outside of home, and (4) physical activity. Open-ended comments on videos from mother-child dyads were used to determine user acceptance. Observations of repeated responses during content analysis informed coding and development of key themes.

RESULTS

A final sample size of 93 mother-child dyads completed the study. Mothers reported significant improvement from precontemplation or contemplation stages to preparation or action stages for (1) fruit and vegetable selection on her plate (P=.03), (2) meal preparation for her family (P=.01), (3) fruit and vegetable selection outside the home (P<.001), and (4) physical activity (P<.001). Significant improvements were found in knowledge, stage of change, and self-efficacy for the 4 target behaviors of interest (P<.001). Children's open-ended commentary reported vicarious learning and positive character identification with brown-skinned cartoons exhibiting healthful food and exercise behaviors. Mothers commented on the lack of accessible produce in their neighborhoods not depicted in the cartoon videos.

CONCLUSIONS

Culturally adapted cartoons that incorporate tailored preferences by African American families, such as race or demography, may help increase adherence to target health behaviors when developing eHealth behavior solutions.