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Using Indirect Measures to Identify Geographic Hot Spots of Poor Glycemic Control
TAKE-HOME MESSAGE
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The authors sought to identify areas of poor glycemic control in New York City (NYC) using indirect measures. Using the Getis-Ord Gi* statistic, they analyzed hot spots identified through the NYC A1C Registry data. A total of 42.8% of 2085 NYC census tracts contained hot spots of diabetes-related hospitalizations, emergency visits, and age-adjusted prevalence that accurately predicted poor glycemic control (88.9%, 89.6%, and 89.5%, respectively). Other indirect measures were less accurate.
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The authors suggest that healthcare utilization measures are an accurate predictor of hot spots of poor glycemic control and could provide actionable evidence of the need for intervention in areas with no population-based HbA1c registry.
abstract
This abstract is available on the publisher's site.
Access this abstract nowOBJECTIVE
Focusing health interventions in places with suboptimal glycemic control can help direct resources to neighborhoods with poor diabetes-related outcomes, but finding these areas can be difficult. Our objective was to use indirect measures versus a gold standard, population-based A1C registry to identify areas of poor glycemic control.
RESEARCH DESIGN AND METHODS
Census tracts in New York City were characterized by race, ethnicity, income, poverty, education, diabetes-related emergency visits, inpatient hospitalizations, and proportion of adults with diabetes having poor glycemic control, based on A1C >9.0% (75 mmol/mol). Hot spot analyses were then performed, using the Getis-Ord Gi* statistic for all measures. We then calculated the sensitivity, specificity, positive and negative predictive values, and accuracy of using the indirect measures to identify hot spots of poor glycemic control found using the A1C Registry data.
RESULTS
Using A1C Registry data, we identified hot spots in 42.8% of 2,085 NYC census tracts analyzed. Hot spots of diabetes-specific inpatient hospitalizations, diabetes-specific emergency visits, and age-adjusted diabetes prevalence estimated from emergency department data, respectively, had 88.9, 89.6, and 89.5% accuracy for identifying the same hot spots of poor glycemic control found using A1C Registry data. No other indirect measure tested had accuracy >80% except for the proportion of minority residents, which was 86.2%.
CONCLUSIONS
Compared with demographic and socioeconomic factors, health care utilization measures more accurately identified hot spots of poor glycemic control. In places without a population-based A1C Registry, mapping diabetes-specific health care utilization may provide actionable evidence for targeting health interventions in areas with the highest burden of uncontrolled diabetes.
Additional Info
Disclosure statements are available on the authors' profiles:
Using Indirect Measures to Identify Geographic Hot Spots of Poor Glycemic Control: Cross-Sectional Comparisons With an A1C Registry
Diabetes Care 2018 Apr 24;[EPub Ahead of Print], DC Lee, Q Jiang, BP Tabaei, B Elbel, CA Koziatek, KJ Konty, WY WuFrom MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.
As the incidence of diabetes continues to rise, it is important to improve the efficiency of public health interventions. Geographic information systems (GIS) and geospatial analysis are powerful tools that can help pinpoint clustering patterns or “hotspots” of poor glycemic control, perhaps allowing for better allocation of services. New York City maintains a patient-level registry of A1c reports, allowing for the identification of hotspots. However, most jurisdictions have not invested in such sophisticated population health tools.
Lee and co-authors used data from insurance claims and the American Community Survey (ACS) data to identify census tracts with poor glycemic control and compared the results using the A1c registry as the gold standard. Various indicators from the claims data had good sensitivity and specificity. However, the simple strategy of targeting census tracts with a high proportion of minority residents performed remarkably well. Although use of population-wide claims data or laboratory results may be marginally better, publicly available data from the Census Bureau can be used nearly anywhere in the US at little cost and without concerns about protected health information and confidentiality.
Besides providing yet more evidence that diabetes is heavily determined by social factors, this finding has huge value for public health authorities, ACOs, and others invested in population-wide outcomes. Unfortunately, Lee et al failed to provide the exact thresholds for various indicators (how high a proportion of minority residents, for instance, suggests a diabetes hotspot), making the results difficult to apply.