Using GIS and secondary data to target diabetes-related public health efforts

Neighborhood-Level Risk Factors for Severe Hyperglycemia among Emergency Department Patients without a Prior Diabetes Diagnosis

A person's place of residence is a strong risk factor for important diagnosed chronic diseases such as diabetes. It is unclear whether neighborhood-level risk factors also predict the probability of undiagnosed disease. The objective of this study was to identify neighborhood-level variables associated with severe hyperglycemia among emergency department (ED) patients without a history of diabetes. We analyzed patients without previously diagnosed diabetes for whom a random serum glucose value was obtained in the ED. We defined random glucose values ≥ 200 mg/dL as severe hyperglycemia, indicating probable undiagnosed diabetes. Patient addresses were geocoded and matched with neighborhood-level socioeconomic measures from the American Community Survey and claims-based surveillance estimates of diabetes prevalence. Neighborhood-level exposure variables were standardized based on z-scores, and a series of logistic regression models were used to assess the association of selected exposures and hyperglycemia adjusting for biological and social individual-level risk factors for diabetes. Of 77,882 ED patients without a history of diabetes presenting in 2021, 1,715 (2.2%) had severe hyperglycemia. Many geospatial exposures were associated with uncontrolled hyperglycemia, even after controlling for individual-level risk factors. The most strongly associated neighborhood-level variables included lower markers of educational attainment, higher percentage of households where limited English is spoken, lower rates of white-collar employment, and higher rates of Medicaid insurance. Including these geospatial factors in risk assessment models may help identify important subgroups of patients with undiagnosed disease.

Prevalence and Associations of Type 2 Diabetes Risk and Sociodemographic Factors in Saudi Arabia: A Web-Based Cross-Sectional Survey Study

Type 2 diabetes mellitus (T2DM) is a chronic disease with ever-increasing prevalence worldwide. In our study, we evaluated the prevalence of the risk of developing T2DM in Saudi Arabia and investigated associations between that risk and various sociodemographic characteristics. To those ends, a web-based cross-sectional survey of Saudi nationals without diabetes, all enrolled using snowball sampling, was conducted from January 2021 to January 2022. The risk of developing T2DM was evaluated using a validated risk assessment questionnaire (ARABRISK), and associations of high ARABRISK scores and sociodemographic variables were explored in multivariable logistic regression modeling. Of the 4559 participants, 88.1% were 18 to 39 years old, and 67.2% held a college or university degree. High ARABRISK scores were observed in 7.5% of the sample. Residing in a midsize city versus a large city was associated with a lower ARABRISK risk score (p = 0.007), as were having private instead of governmental insurance (p = 0.005), and being unemployed versus employed (p < 0.001). By contrast, being married (p < 0.001), divorced or widowed (p < 0.001), and/or retired (p < 0.001) were each associated with a higher ARABRISK score. A large representative study is needed to calculate the risk of T2DM among Saudi nationals.

Evaluating the usability and acceptability of a geographical information system (GIS) prototype to visualise socio-economic and public health data

BACKGROUND

Understanding the impact of socio-economic inequality on health outcomes is arguably more relevant than ever before given the global repercussions of Covid-19. With limited resources, innovative methods to track disease, population needs, and current health and social service provision are essential. To best make use of currently available data, there is an increasing reliance on technology. One approach of interest is the implementation and integration of mapping software. This research aimed to determine the usability and acceptability of a methodology for mapping public health data using GIS technology.

METHODS

Prototype multi-layered interactive maps were created demonstrating relationships between socio-economic and health data (vaccination and admission rates). A semi-structured interview schedule was developed, including a validated tool known as the System Usability Scale (SUS), which assessed the usability of the mapping model with five stakeholder (SH) groups. Fifteen interviews were conducted across the 5 SH and analysed using content analysis. A Kruskal-Wallis H test was performed to determine any statistically significant difference for the SUS scores across SH. The acceptability of the model was not affected by the individual use of smart technology among SHs.

RESULTS

The mean score from the SUS for the prototype mapping models was 83.17 out of 100, indicating good usability. There was no statistically significant difference in the usability of the maps among SH (p = 0.094). Three major themes emerged with respective sub-themes from the interviews including: (1) Barriers to current use of data (2) Design strengths and improvements (3) Multiple benefits and usability of the mapping model.

CONCLUSION

Irrespective of variations in demographics or use of smart technology amongst interviewees, there was no significant difference in the usability of the model across the stakeholder groups. The average SUS score for a new system is 68. A score of 83.17 was calculated, indicative of a "good" system, as falling within the top 10% of scores. This study has provided a potential digital model for mapping public health data. Furthermore, it demonstrated the need for such a digital solution, as well as its usability and future utilisation avenues among SH.

Estimating Catchment Populations of Global Health Radiology Outreach Using Geographic Information Systems Analysis

PURPOSE

The purpose of this study was to design, develop, and test geographic information systems (GIS) analytic methods for quantifying and characterizing catchment populations across all sites served by a radiology global health organization.

METHODS

The analysis included populations served by 78 low-resource medical facilities in 32 countries partnered with radiology nonprofit organization, RAD-AID International. Three constraints were used to approximate patient catchment areas: (1) 1-hour driving time, (2) 1-hour walking time, and (3) 10-mile circular radius. GIS calculated populations within each constraint using publicly available geospatial input databases, including a global digital elevation model, population and land cover data, and road locations from OpenStreetMap. Demographic and health data from the World Health Organization were incorporated to provide further characteristics of covered populations.

RESULTS

The total populations served by all RAD-AID sites as measured by driving time, walking time, and 10-mile radius were 189,241,193 (47.8% female), 26,190,117 (48.7% female), and 110,884,095 (48.1% female), respectively. For individual locations, median population within 1-hour driving time was 1,795,977 (range: 8,742-30,630,800), with an average life expectancy of 68.4 ± 5.8 years. Median child mortality before age 5 was 3.8% (range: 0.9%-8.3%), and median prevalence of human immunodeficiency virus infection was 3.1% (range: 0.7%-10.9%).

CONCLUSION

In this study, GIS provided a robust multisite analysis for estimating the potential global population reached by an international radiology outreach organization with targeted individual site measurements. Given heightened needs to accurately characterize global outreach populations, this GIS-based approach may be useful for analysis, outreach planning, and resource allocation among global health organizations.

Identifying priority and bright spot areas for improving diabetes care: a geospatial approach

The objective of this study was to describe a novel geospatial methodology for identifying poor-performing (priority) and well-performing (bright spot) communities with respect to diabetes management at the ZIP Code Tabulation Area (ZCTA) level. This research was the first phase of a mixed-methods approach known as the focused rapid assessment process (fRAP). Using data from the Lehigh Valley Health Network in eastern Pennsylvania, geographical information systems mapping and spatial analyses were performed to identify diabetes prevalence and A1c control spatial clusters and outliers. We used a spatial empirical Bayes approach to adjust diabetes-related measures, mapped outliers and used the Local Moran's I to identify spatial clusters and outliers. Patients with diabetes were identified from the Lehigh Valley Practice and Community-Based Research Network (LVPBRN), which comprised primary care practices that included a hospital-owned practice, a regional practice association, independent small groups, clinics, solo practitioners and federally qualified health centres. Using this novel approach, we identified five priority ZCTAs and three bright spot ZCTAs in LVPBRN. Three of the priority ZCTAs were located in the urban core of Lehigh Valley and have large Hispanic populations. The other two bright spot ZCTAs have fewer patients and were located in rural areas. As the first phase of fRAP, this method of identifying high-performing and low-performing areas offers potential to mitigate health disparities related to diabetes through targeted exploration of local factors contributing to diabetes management. This novel approach to identification of populations with diabetes performing well or poor at the local community level may allow practitioners to target focused qualitative assessments where the most can be learnt to improve diabetic management of the community.

Linking the Flint Food Store Survey: Is Objective or Perceived Access to Healthy Foods Associated with Glycemic Control in Patients with Type 2 Diabetes?

Type 2 diabetes mellitus (DM-2) remains a significant public health concern, particularly in low-income neighborhoods where healthy foods may be scarcer. Despite the well-known relationship between diet and diabetes, little evidence exists on the connections among the objectively measured community and consumer food environment, perception of food access, and diabetes management or outcomes. This cross-sectional, ecological study represents the first example of combining a GIS-based, objectively measured food store audit considering quality, variety, and price of foods in stores with a clinical survey of patients with DM-2 (n = 126). In this way, we offer evidence on the relationship between healthy food access-measured more robustly than proximity to or density of certain store types-and diabetes management knowledge, medication adherence, and glycemic control. Better glycemic control was not correlated with better overall food store score, meaning that people in neighborhoods with better access to healthy foods are not necessarily more likely to manage their diabetes. While perceived healthy food access was not correlated with glycemic control, it was strongly correlated with objective healthy food access at shorter distances from home. These results have great importance both for clinical understanding of the persistence of poor diabetes management outcomes and for the understanding of the influence of the food environment on health behaviors.

2017 National Standards for Diabetes Self-Management Education and Support

PURPOSE

The purpose of this study is to review the literature for Diabetes Self-Management Education and Support (DSMES) to ensure the National Standards for DSMES (Standards) align with current evidence-based practices and utilization trends.

METHODS

The 10 Standards were divided among 20 interdisciplinary workgroup members. Members searched the current research for diabetes education and support, behavioral health, clinical, health care environment, technical, reimbursement, and business practice for the strongest evidence that guided the Standards revision.

RESULTS

Diabetes Self-Management Education and Support facilitates the knowledge, skills, and ability necessary for diabetes self-care as well as activities that assist a person in implementing and sustaining the behaviors needed to manage their condition on an ongoing basis. The evidence indicates that health care providers and people affected by diabetes are embracing technology, and this is having a positive impact of DSMES access, utilization, and outcomes.

CONCLUSION

Quality DSMES continues to be a critical element of care for all people with diabetes. The DSMES services must be individualized and guided by the concerns, preferences, and needs of the person affected by diabetes. Even with the abundance of evidence supporting the benefits of DSMES, it continues to be underutilized, but as with other health care services, technology is changing the way DSMES is delivered and utilized with positive outcomes.

Identifying Bright Spot Counties for Appropriate Diabetes Preventive Care: A Geospatial, Positive Deviance Approach

Positive deviance approaches, which have been used to identify and study high performers (bright spots) and translate their successes to poorer performers, offer great potential for chronic disease management. However, there are few examples of applying positive deviance approaches across different geographic contexts. Building on prior research that developed a new measure for appropriate diabetes preventive care (DMPrevCare) and identified priority counties for this strategy, we introduce a geospatial approach for identifying bright spot counties and case matching them to priority counties that need improvement. We used the Local Moran's I tool to identify DMPrevCare spatial outliers, which are counties with larger percentages of Medicare beneficiaries receiving appropriate diabetes preventive care (DMPrevCare) surrounded by counties with smaller percentages of Medicare beneficiaries receiving DMPrevCare. We define these spatial outliers as bright spots. The Robert Wood Johnson Foundation County Health Rankings Peer Counties tool was used to link bright spot counties to previously identified priority counties. We identified 25 bright spot counties throughout the southern and mountain western United States. Bright spot counties were linked to 45 priority counties, resulting in 23 peer (bright/priority) county groups. A geospatial approach was shown to be effective in identifying peer counties across the United States that had either poor or strong metrics related to DMPrevCare, but were otherwise similar in terms of demographics and socioeconomic characteristics. We describe a framework for the next steps in the positive deviance process, which identifies potential factors in bright spot counties that positively impact diabetes care and how they may be applied to their peer priority counties.

2017 National Standards for Diabetes Self-Management Education and Support

PURPOSE

The purpose of this study is to review the literature for Diabetes Self-Management Education and Support (DSMES) to ensure the National Standards for DSMES (Standards) align with current evidence-based practices and utilization trends.

METHODS

The 10 Standards were divided among 20 interdisciplinary workgroup members. Members searched the current research for diabetes education and support, behavioral health, clinical, health care environment, technical, reimbursement, and business practice for the strongest evidence that guided the Standards revision.

RESULTS

Diabetes Self-Management Education and Support facilitates the knowledge, skills, and ability necessary for diabetes self-care as well as activities that assist a person in implementing and sustaining the behaviors needed to manage their condition on an ongoing basis. The evidence indicates that health care providers and people affected by diabetes are embracing technology, and this is having a positive impact of DSMES access, utilization, and outcomes.

CONCLUSION

Quality DSMES continues to be a critical element of care for all people with diabetes. The DSMES services must be individualized and guided by the concerns, preferences, and needs of the person affected by diabetes. Even with the abundance of evidence supporting the benefits of DSMES, it continues to be underutilized, but as with other health care services, technology is changing the way DSMES is delivered and utilized with positive outcomes.

Spatial Analytics Based on Confidential Data for Strategic Planning in Urban Health Departments

Spatial data analytics can detect patterns of clustering of events in small geographies across an urban region. This study presents and demonstrates a robust research design to study the longitudinal stability of spatial clustering with small case numbers per census tract and assess the clustering changes over time across the urban environment to better inform public health policy making at the community level. We argue this analysis enables the greater efficiency of public health departments, while leveraging existing data and preserving citizen personal privacy. Analysis at the census tract level is conducted in Mecklenburg County, North Carolina, on hypertension during pregnancy compiled from 2011–2014 birth certificates. Data were derived from per year and per multi-year moving counts by aggregating spatially to census tracts and then assessed for clustering using global Moran’s I. With evidence of clustering, local indicators of spatial association are calculated to pinpoint hot spots, while time series data identified hot spot changes. Knowledge regarding the geographical distribution of diseases is essential in public health to define strategies that improve the health of populations and quality of life. Our findings support that spatial aggregation at the census tract level contributes to identifying the location of at-risk “hot spot” communities to refine health programs, while temporal windowing reduces random noise effects on spatial clustering patterns. With tight state budgets limiting health departments’ funds, using geographic analytics provides for a targeted and efficient approach to health resource planning.

Identifying Areas with Disproportionate Local Health Department Services Relative to Opioid Overdose, HIV and Hepatitis C Diagnosis Rates: A Study of Rural Illinois

Background: U.S. rural populations have been disproportionately affected by the syndemic of opioid-use disorder (OUD) and the associated increase in overdoses and risk of hepatitis C virus (HCV) and human immunodeficiency virus (HIV) transmission. Local health departments (LHDs) can play a critical role in the response to this syndemic. We utilized two geospatial approaches to identify areas of discordance between LHD service availability and disease burden to inform service prioritization in rural settings. Methods: We surveyed rural Illinois LHDs to assess their OUD-related services, and calculated county-level opioid overdose, HIV, and hepatitis C diagnosis rates. Bivariate choropleth maps were created to display LHD service provision relative to disease burden in rural Illinois counties. Results: Most rural LHDs provided limited OUD-related services, although many LHDs provided HIV and HCV testing. Bivariate mapping showed rural counties with limited OUD treatment and HIV services and with corresponding higher outcome/disease rates to be dispersed throughout Illinois. Additionally, rural counties with limited LHD-offered hepatitis C services and high hepatitis C diagnosis rates were geographically concentrated in southern Illinois. Conclusions: Bivariate mapping can enable geographic targeting of resources to address the opioid crisis and related infectious disease by identifying areas with low LHD services relative to high disease burden.

2017 National Standards for Diabetes Self-Management Education and Support

PURPOSE

The purpose of this study is to review the literature for Diabetes Self-Management Education and Support (DSMES) to ensure the National Standards for DSMES (Standards) align with current evidence-based practices and utilization trends.

METHODS

The 10 Standards were divided among 20 interdisciplinary workgroup members. Members searched the current research for diabetes education and support, behavioral health, clinical, health care environment, technical, reimbursement, and business practice for the strongest evidence that guided the Standards revision.

RESULTS

Diabetes Self-Management Education and Support facilitates the knowledge, skills, and ability necessary for diabetes self-care as well as activities that assist a person in implementing and sustaining the behaviors needed to manage their condition on an ongoing basis. The evidence indicates that health care providers and people affected by diabetes are embracing technology, and this is having a positive impact of DSMES access, utilization, and outcomes.

CONCLUSION

Quality DSMES continues to be a critical element of care for all people with diabetes. The DSMES services must be individualized and guided by the concerns, preferences, and needs of the person affected by diabetes. Even with the abundance of evidence supporting the benefits of DSMES, it continues to be underutilized, but as with other health care services, technology is changing the way DSMES is delivered and utilized with positive outcomes.

Spatial analysis of disparities in asthma treatment among adult asthmatics

BACKGROUND

Spatial analytic techniques can uncover important differences in asthma treatment and identify geographic areas with poor asthma management.

OBJECTIVE

To review geographical differences in asthma treatment in an adult asthmatic population.

METHODS

This was a retrospective, cross-sectional chart review study (n = 519) using Geographic Information System approaches to determine the impact of spatial access to pharmacies and other factors on inappropriate rescue and inadequate controller medication use. Statistical analyses included chi-square test for categorical variables and Kruskall-Wallis test for continuous variables. Logistic regression was used to determine unadjusted and adjusted odds of inappropriate and inadequate pharmaceutical management of asthma based upon distance to pharmacy and other factors. Choropleth maps were constructed to display zip code level variation of asthma management.

RESULTS

Inappropriate medication users lived further from their preferred pharmacy compared to appropriate users (median distances of 3.02 and 1.96 miles respectively; p = 0.01). Inappropriate and inadequate management of asthma varied by zip code, ranging from 5.5 to 17.3% and 25.0-59.6%, respectively. A statistically significant difference in appropriate use by age was found, with nearly 17% of adults age 65 and older overusing their rescue inhaler. Conversely, patients age 18-34 years, 35-49 years, and 50-64 years, used their controller medications inadequately (64.2%, 57.3%, and 48.2%, respectively) compared to 42.5% of patients aged 65 and older. Unadjusted and adjusted analyses showed that former smokers had higher odds of inadequate management of asthma.

CONCLUSION

The unadjusted findings suggest that distance to pharmacies may play a role in the pharmaceutical management of asthma, though these findings are explained by confounding factors. Future research should continue to explore the effect of spatial access to pharmacies on chronic disease management and the role that maps can play in guiding medication management interventions in a larger sample to allow for more rigorous analysis.

Tsunami evacuation simulation using geographic information systems for homecare recipients depending on electric devices

Tsunamis cause direct damage to property and destroy infrastructure. In addition, power outages can lead to death, especially for patients who rely on medical equipment requiring a power supply. Recently, Nankai Trough Earthquakes have been predicted, and much effort has been put into developing countermeasures in Japan. Kochi City on Shikoku Island is expected to suffer in the event of a large tsunami. The present study identifies individuals living in Kochi who need evacuation assistance and depend on electrical medical devices, simulates evacuation behavior and inundation during a tsunami using a geographic information system (GIS), and considers the usefulness of such a GIS. We asked caregivers, including visiting nurses, to introduce us to homecare recipients who rely on a ventilator, an endotracheal suction device, or other medical devices requiring electric power. We received introductions to 52 homecare recipients. Using a GIS, we plotted the area of predicted inundation and the locations of homecare recipients, nursing stations, and welfare evacuation shelters. We predicted evacuation routes, and then analyzed the time difference between the time required for evacuation and tsunami arrival at a welfare evacuation shelter. To measure the effects of the main parameters, we conducted both one-way and multi-way sensitivity analysis. In the event of a tsunami, eight of the homecare recipients living in the forecasted inundation areas in Kochi may face delayed evacuation. Among homecare recipients facing a high possibility of escape delay, 95.2% lived more than 1,800 m from the nearest welfare evacuation shelter. We found that individual evacuation behavior can be simulated by specifying the residence of a homecare recipient and the evacuation route using a GIS.

2017 National Standards for Diabetes Self-Management Education and Support

Purpose

The purpose of this study is to review the literature for Diabetes Self-Management Education and Support (DSMES) to ensure the National Standards for DSMES (Standards) align with current evidence-based practices and utilization trends.

Methods

The 10 Standards were divided among 20 interdisciplinary workgroup members. Members searched the current research for diabetes education and support, behavioral health, clinical, health care environment, technical, reimbursement, and business practice for the strongest evidence that guided the Standards revision.

Results

Diabetes Self-Management Education and Support facilitates the knowledge, skills, and ability necessary for diabetes self-care as well as activities that assist a person in implementing and sustaining the behaviors needed to manage their condition on an ongoing basis. The evidence indicates that health care providers and people affected by diabetes are embracing technology, and this is having a positive impact of DSMES access, utilization, and outcomes.

Conclusion

Quality DSMES continues to be a critical element of care for all people with diabetes. The DSMES services must be individualized and guided by the concerns, preferences, and needs of the person affected by diabetes. Even with the abundance of evidence supporting the benefits of DSMES, it continues to be underutilized, but as with other health care services, technology is changing the way DSMES is delivered and utilized with positive outcomes.

Assessing the association of diabetes self-management education centers with age-adjusted diabetes rates across U.S.: Aspatial cluster analysis approach

The purpose of this study is to identify regions with diabetes health-service shortage. American Diabetes Association (ADA)-accredited diabetes self-management education (DSME) is recommended for all those with diabetes. In this study, we focus on demographic patterns and geographic regionalization of the disease by including accessibility and availability of diabetes education resources as a critical component in understanding and confronting differences in diabetes prevalence, as well as addressing regional or sub-regional differences in awareness, treatment and control. We conducted an ecological county-level study utilizing publicly available secondary data on 3,109 counties in the continental U.S. We used a Bayesian spatial cluster model that enabled spatial heterogeneities across the continental U.S. to be addressed. We used the American Diabetes Association (ADA) website to identify 2012 DSME locations and national 2010 county-level diabetes rates estimated by the Centers for Disease Control and Prevention and identified regions with low DSME program availability relative to their diabetes rates and population density. Only 39.8% of the U.S. counties had at least one ADA-accredited DSME program location. Based on our 95% credible intervals, age-adjusted diabetes rates and DSME program locations were associated in only seven out of thirty five identified clusters. Out of these seven, only two clusters had a positive association. We identified clusters that were above the 75th percentile of average diabetes rates, but below the 25th percentile of average DSME location counts and found that these clusters were all located in the Southeast portion of the country. Overall, there was a lack of relationship between diabetes rates and DSME center locations in the U.S., suggesting resources could be more efficiently placed according to need. Clusters that were high in diabetes rates and low in DSME placements, all in the southeast, should particularly be considered for additional DSME programming.

From Neighborhood to Genome: Three Decades of Nutrition-Related Research from the Atherosclerosis Risk in Communities Study

For 30 years, the Atherosclerosis Risk in Communities (ARIC) cohort study has examined the etiology and progression of atherosclerosis and atherosclerotic diseases.1 This research has evaluated variation in cardiovascular disease (CVD) risk in relation to age, race, gender, location and lifestyle factors, including diet. In this commentary, we describe ARIC research that illustrates an expanded view of the relationship between diet and health and suggest ways that future cohort studies may influence the direction of nutrition and dietetics practice.

2017 National Standards for Diabetes Self-Management Education and Support

This article was copublished in Diabetes Care 2017;40:1409-1419 and The Diabetes Educator 2017;43:449-464 and is reprinted with permission. The previous version of this article, also copublished in Diabetes Care and The Diabetes Educator, can be found at Diabetes Care 2012;35:2393-2401 (https://doi.org/10.2337/dc12-1707).

Academic and Government Partnerships to Address Diabetes in the USA: a Narrative Review

PURPOSE OF REVIEW

Multi-sector partnerships are broadly considered to be of value for diabetes prevention and management. The purpose of this article is to summarize academic and government collaborations focused on diabetes prevention and management.

RECENT FINDINGS

Using a narrative review approach, we identified 17 articles describing 10 academic and government partnerships for diabetes management and surveillance. Challenges and gaps in the literature include complexity of diabetes management vis a vis current healthcare infrastructure; a paucity of racial/ethnic diversity in translational efforts; and the time/effort needed to maintain strong relationships across partner institutions. Academic and government partnerships are of value for diabetes prevention and management activities. Acknowledgment that the key priorities of government programming are often costs and feasibility is critical for collaborations to be successful. Future translational efforts of diabetes prevention and management programs should focus on the following: (1) expansion of partnerships between academia and local health departments; (2) increased utilization of implementation science for enhanced and efficient implementation and dissemination; and (3) harnessing of technological advances for data analysis, patient communication, and report generation.

Geographic Information Systems Mapping of Diabetic Retinopathy in an Ocular Telemedicine Network

Importance

Minimal information exists on the use of geographic information systems mapping for visualizing access barriers to eye care for patients with diabetes.

Objective

To use geographic information systems mapping techniques to visualize (1) the locations of patients participating in the North Carolina Diabetic Retinopathy Telemedicine Network, (2) the locations of primary care clinicians and ophthalmologists across the state, and (3) the travel times associated with traveling to the 5 primary care clinics in our study.

Design, Setting, and Participants

Cross-sectional study conducted from January 6, 2014, to November 1, 2015, at 5 Area Health Education Center primary care clinics that serve rural and underserved populations in North Carolina. In total, 1787 patients with diabetes received retinal screening photographs with remote expert interpretation to determine the presence and severity of diabetic retinopathy. Participants included patients 18 years or older with type 1 or type 2 diabetes who presented to these 5 clinics for their routine diabetes care.

Main Outcomes and Measures

Development of qualitative maps illustrating the density of patients with diabetes and their distribution around the 5 North Carolina Diabetic Retinopathy Telemedicine Network sites by zip code and the density of ophthalmologists and primary care clinicians by zip code relative to US Census Urban Areas. A travel time map was also created using road network analysis to determine all areas that can be reached by car in a user-specified amount of time.

Results

Mean (SD) age of patients was 55.4 (12.7) years. Women made up 62.7% of the study population. The study included more African American patients (55.4%) compared with white (35.5%) and Hispanic (5.8%) patients. The mean (SD) hemoglobin A1c level was 7.8% (2.4%) (to convert to proportion of total hemoglobin, multiply by 0.01), and the mean (SD) duration of diabetes was 9.2 (8.2) years. Whereas the clinics located in Greensboro, Asheville, and Fayetteville screened patients from more immediate surrounding areas, the Greenville site had the widest distribution of zip codes, suggesting that patients travel from greater distances to reach this facility. Primary care clinicians were spread somewhat uniformly across the state, whereas ophthalmologists were concentrated around urban centers. Also, the number and type of surface roads surrounding the clinics determined the distance and time patients must travel to receive care.

Conclusions and Relevance

Geographic information systems mapping is a useful technique for visualizing geographic access barriers to eye care for patients with diabetes and may help to identify underserved areas that would benefit from the expansion of retinal screening programs via telemedicine.

Geographic Information Systems to Assess External Validity in Randomized Trials

INTRODUCTION

To support claims that RCTs can reduce health disparities (i.e., are translational), it is imperative that methodologies exist to evaluate the tenability of external validity in RCTs when probabilistic sampling of participants is not employed. Typically, attempts at establishing post hoc external validity are limited to a few comparisons across convenience variables, which must be available in both sample and population. A Type 2 diabetes RCT was used as an example of a method that uses a geographic information system to assess external validity in the absence of a priori probabilistic community-wide diabetes risk sampling strategy.

METHODS

A geographic information system, 2009-2013 county death certificate records, and 2013-2014 electronic medical records were used to identify community-wide diabetes prevalence. Color-coded diabetes density maps provided visual representation of these densities. Chi-square goodness of fit statistic/analysis tested the degree to which distribution of RCT participants varied across density classes compared to what would be expected, given simple random sampling of the county population. Analyses were conducted in 2016.

RESULTS

Diabetes prevalence areas as represented by death certificate and electronic medical records were distributed similarly. The simple random sample model was not a good fit for death certificate record (chi-square, 17.63; p=0.0001) and electronic medical record data (chi-square, 28.92; p<0.0001). Generally, RCT participants were oversampled in high-diabetes density areas.

CONCLUSIONS

Location is a highly reliable "principal variable" associated with health disparities. It serves as a directly measurable proxy for high-risk underserved communities, thus offering an effective and practical approach for examining external validity of RCTs.

2017 National Standards for Diabetes Self-Management Education and Support

Purpose The purpose of this study is to review the literature for Diabetes Self-Management Education and Support (DSMES) to ensure the National Standards for DSMES (Standards) align with current evidence-based practices and utilization trends. Methods The 10 Standards were divided among 20 interdisciplinary workgroup members. Members searched the current research for diabetes education and support, behavioral health, clinical, health care environment, technical, reimbursement, and business practice for the strongest evidence that guided the Standards revision. Results Diabetes Self-Management Education and Support facilitates the knowledge, skills, and ability necessary for diabetes self-care as well as activities that assist a person in implementing and sustaining the behaviors needed to manage their condition on an ongoing basis. The evidence indicates that health care providers and people affected by diabetes are embracing technology, and this is having a positive impact of DSMES access, utilization, and outcomes. Conclusion Quality DSMES continues to be a critical element of care for all people with diabetes. The DSMES services must be individualized and guided by the concerns, preferences, and needs of the person affected by diabetes. Even with the abundance of evidence supporting the benefits of DSMES, it continues to be underutilized, but as with other health care services, technology is changing the way DSMES is delivered and utilized with positive outcomes.

A Community Resource Map to Support Clinical-Community Linkages in a Randomized Controlled Trial of Childhood Obesity, Eastern Massachusetts, 2014-2016

Background

Novel approaches to health care delivery that leverage community resources could improve outcomes for children at high risk for obesity.

Community Context

We describe the process by which we created an online interactive community resources map for use in the Connect for Health randomized controlled trial. The trial was conducted in the 6 pediatric practices that cared for the highest percentage of children with overweight or obesity within a large multi-specialty group practice in eastern Massachusetts.

Methods

By using semistructured interviews with parents and community partners and geographic information systems (GIS), we created and validated a community resource map for use in a randomized controlled trial for childhood obesity. We conducted semistructured interviews with 11 parents and received stakeholder feedback from 5 community partners, 2 pediatricians, and 3 obesity–built environment experts to identify community resources that could support behavior change. We used GIS databases to identify the location of resources. After the resources were validated, we created an online, interactive searchable map. We evaluated parent resource empowerment at baseline and follow-up, examined if the participant families went to new locations for physical activity and food shopping, and evaluated how satisfied the families were with the information they received.

Outcome

Parents, community partners, and experts identified several resources to be included in the map, including farmers markets, supermarkets, parks, and fitness centers. Parents expressed the need for affordable activities. Parent resource empowerment increased by 0.25 units (95% confidence interval, 0.21–0.30) over the 1-year intervention period; 76.2% of participants were physically active at new places, 57.1% of participant families shopped at new locations; and 71.8% reported they were very satisfied with the information they received.

Interpretation

Parents and community partners identified several community resources that could help support behavior change. Parent resource empowerment and use of community resources increased over the intervention period, suggesting that community resource mapping should inform future interventions.

Spatiotemporal Distribution of Gastrointestinal Tract Cancer through GIS over 2007-2012 in Kermanshah-Iran

BACKGROUND

Cancer is one of the common causes of disability and mortality in the world. The present study aimed to define the spatiotemporal distribution of gastrointestinal tract cancers using a geographic information system (GIS) over the time period of 2007-2012 in Kermanshah-Iran.

MATERIALS AND METHODS

The method of studying was descriptive-analytical as well as comparative with gastrointestinal tract cancer patients based in the City of Kermanshah over the time period covered. For data analysis, the GIS and SPSS 16.0 were applied.

RESULTS

According to the pathological reports within the space of 5 years, 283 cases of gastrointestinal tract cancer (157 in males, 156 in females) were reported. The performed tests in terms of spatial distribution in the environment of GIS indicated that the disease demonstrated a clustered pattern in the City of Kermanshah. More to the point, some loci of this disease have emerged in the City of Kermanshah that in the first level, 6 neighborhoods with 29-59 cases of this disease per square kilometer and in the second level, 15-29 cases.

CONCLUSIONS

Gastrointestinal tract cancer demonstrated an ascending trend within the space of 5 years of research and the spatiotemporal distribution of cancer featured a concentrated and clustered pattern in the City of Kermanshah.

Spatial data analysis and the use of maps in scientific health articles

INTRODUCTION

Despite the growing number of studies with a characteristic element of spatial analysis, the application of the techniques is not always clear and its continuity in epidemiological studies requires careful evaluation.

OBJECTIVE

To verify the spread and use of those processes in national and international scientific papers.

METHOD

An assessment was made of periodicals according to the impact index. Among 8,281 journals surveyed, four national and four international were selected, of which 1,274 articles were analyzed regarding the presence or absence of spatial analysis techniques.

RESULTS

Just over 10% of articles published in 2011 in high impact journals, both national and international, showed some element of geographical location.

CONCLUSION

Although these percentages vary greatly from one journal to another, denoting different publication profiles, we consider this percentage as an indication that location variables have become an important factor in studies of health.

Geographic clustering of diabetic lower-extremity amputations in low-income regions of California

For patients suffering from diabetes and other chronic conditions, a large body of work demonstrates income-related disparities in access to coordinated preventive care. Much less is known about associations between poverty and consequential negative health outcomes. Few studies have assessed geographic patterns that link household incomes to major preventable complications of chronic diseases. Using statewide facility discharge data for California in 2009, we identified 7,973 lower-extremity amputations in 6,828 adults with diabetes. We mapped amputations based on residential ZIP codes and used data from the Census Bureau to produce corresponding maps of poverty rates. Comparisons of the maps show amputation "hot spots" in lower-income urban and rural regions of California. Prevalence-adjusted amputation rates varied tenfold between high-income and low-income regions. Our analysis does not support detailed causal inferences. However, our method for mapping complication hot spots using public data sources may help target interventions to the communities most in need.

On the Analysis of Diseases and Their Related Geographical Data

Electronic medical records (EMRs) store data related to patients information enrolled during their stay in health structures. Data stored into EMRs span from data crawled from biological laboratories to textual description of diseases and diagnostic device results (e.g., biomedical images). Each EMR is related to a diagnosis related group (DRG) record. A DRG record is a record associated with a citizen that has been cured in a hospital. It contains a code, called major diagnostic category (MDC), which summarizes the treated disease and allows to reimburse costs related to patient treatments during his staying in health structures. DRGs are used for administrative process (e.g., costs and reimbursement management) as well as disease monitoring. Associating diagnostic codes with external information (such as environmental and geographical data) and with information filtered from EMRs (e.g., biological results or analytes values) can be useful to monitor citizens wellness status. We propose a methodology to analyze such data based on a multistep process. First, we cross reference data by using a semantics-based clustering procedure, extract information from EMRs, and then, cluster them by looking for similar patterns of diseases. Then, biological records in each disease cluster are analyzed to evaluate intracluster similarity by selecting analytes typologies and values. Finally, biological data is related to diagnosis codes and geometrically projected in areas of interest in order to map calculated outlier patients. We applied the methodology on two case studies: 1) diagnosis codes and biochemical analytes of 20 000 biological analyses about hospitalized patients during one observation year and 2) the correlation between cardiovascular diseases and water quality in a southern Italian region. Preliminary findings show the effectiveness of our method.

Linking community resources in diabetes care: a role for technology?

Designing and implementing effective lifestyle modification strategies remains one of the great challenges in diabetes care. Historically, programs have focused on individual behavior change with little or no attempt to integrate change within the broader social framework or community context. However, these contextual factors have been shown to be associated with poor diabetes outcomes, particularly in low-income minority populations. Recent evidence suggests that one way to address these disparities is to match patient needs to existing community resources. Not only does this position patients to more quickly adapt behavior in a practical way, but this also refers patients back to their local communities where a support mechanism is in place to sustain healthy behavior. Technology offers a new and promising platform for connecting patients to meaningful resources (also referred to as "assets"). This paper summarizes several noteworthy innovations that use technology as a practical bridge between healthcare and community-based resources that promote diabetes self-care.

Beyond HbA1c: Environmental Risk Factors for Diabetic Retinopathy

Diabetic retinopathy affects 4.2 million people in the United States and is the leading cause of blindness in working-aged people. As the prevalence of diabetes continues to rise, cost-effective interventions to decrease blindness from diabetic retinopathy will be paramount. While HbA_1c and duration of disease are known risk factors, they account for only 11% of the risk of developing microvascular complications from the disease. The assessment of environmental risk factors for diabetic eye disease allows for the determination of modifiable population-level challenges that may be addressed to facilitate the end of blindness from diabetes.

The geography of diabetes by census tract in a large sample of insured adults in King County, Washington, 2005-2006

INTRODUCTION

Identifying areas of high diabetes prevalence can have an impact on public health prevention and intervention programs. Local health practitioners and public health agencies lack small-area data on obesity and diabetes.

METHODS

Clinical data from the Group Health Cooperative health care system were used to estimate diabetes prevalence among 59,767 adults by census tract. Area-based measures of socioeconomic status and the Modified Retail Food Environment Index were obtained at the census-tract level in King County, Washington. Spatial analyses and regression models were used to assess the relationship between census tract-level diabetes and area-based socioeconomic status and food environment variables. The mediating effect of obesity on the geographic distribution of diabetes was also examined.

RESULTS

In this population of insured adults, diabetes was concentrated in south and southeast King County, with smoothed diabetes prevalence ranging from 6.9% to 21.2%. In spatial regression models, home value and college education were more strongly associated with diabetes than was household income. For each 50% increase in median home value, diabetes prevalence was 1.2 percentage points lower. The Modified Retail Food Environment Index was not related to diabetes at the census-tract level. The observed associations between area-based socioeconomic status and diabetes were largely mediated by obesity (home value, 58%; education, 47%).

CONCLUSION

The observed geographic disparities in diabetes among insured adults by census tract point to the importance of area socioeconomic status. Small-area studies can help health professionals design community-based programs for diabetes prevention and control.

Validation of three geolocation strategies for health-facility attendees for research and public health surveillance in a rural setting in western Kenya

Understanding the spatial distribution of disease is critical for effective disease control. Where formal address networks do not exist, tracking spatial patterns of clinical disease is difficult. Geolocation strategies were tested at rural health facilities in western Kenya. Methods included geocoding residence by head of compound, participatory mapping and recording the self-reported nearest landmark. Geocoding was able to locate 72·9% [95% confidence interval (CI) 67·7–77·6] of individuals to within 250 m of the true compound location. The participatory mapping exercise was able to correctly locate 82·0% of compounds (95% CI 78·9–84·8) to a 2 × 2·5 km area with a 500 m buffer. The self-reported nearest landmark was able to locate 78·1% (95% CI 73·8–82·1) of compounds to the correct catchment area. These strategies tested provide options for quickly obtaining spatial information on individuals presenting at health facilities.