The Journal of Clinical Endocrinology and Metabolism Journal Article

Precision Diabetes Outreach

September 14, 2020
 

Ashby F Walker, Hui Hu, Nicolas Cuttriss, Claudia Anez-Zabala, Katarina Yabut, Michael J Haller, David M Maahs
The Journal of Clinical Endocrinology & Metabolism, Volume 105, Issue 9, September 2020, dgaa462
https://doi.org/10.1210/clinem/dgaa462

Abstract

Aims

The purpose of this work is to find the gut microbial fingerprinting of pediatric patients with type 1 diabetes.

Methods

The microbiome of 31 children with type 1 diabetes at onset and of 25 healthy children was determined using multiple polymorphic regions of the 16S ribosomal RNA. We performed machine-learning analyses and metagenome functional analysis to identify significant taxa and their metabolic pathways content.

Results

Purpose

In designing a Project ECHO™ type 1 diabetes (T1D) program in Florida and California, the Neighborhood Deprivation Index (NDI) was used in conjunction with geocoding of primary care providers (PCPs) and endocrinologists in each state to concurrently identify areas with low endocrinology provider density and high health risk/poverty areas. The NDI measures many aspects of poverty proven to be critical indicators of health outcomes.

Methods

The data from the 2013-2017 American Community Survey (ACS) 5-year estimates were used to create NDI maps for California and Florida. In addition, geocoding and 30-minute drive-time buffers were performed using publicly available provider directories for PCPs and endocrinologists in both states by Google Geocoding API and the TravelTime Search Application Programming Interface (API).

Results

Based on these findings, we defined high-need catchment areas as areas with (1) more than a 30-minute drive to the nearest endocrinologist but within a 30-minute drive to the nearest PCP; (2) an NDI in the highest quartile; and (3) a population above the median (5199 for census tracts, and 1394 for census block groups). Out of the 12 181 census tracts and 34 490 census block groups in California and Florida, we identified 57 tracts and 215 block groups meeting these criteria as high-need catchment areas.

Conclusion

Geospatial analysis provides an important initial methodologic step to effectively focus outreach efforts in diabetes program development. The integration of the NDI with geocoded provider directories enables more cost-effective and targeted interventions to reach the most vulnerable populations living with T1D.

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