ArticleBMJ open2024
Identifying pre-diabetes 'hotspots' in Northern California using geospatial analysis: opportunities to target diabetes prevention strategies and improve health equity.
Article in BMJ open, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Built Environment, Community Safety, and Diabetes in the Deep South: A County Level Geospatial Analysis.Journal of racial and ethnic health disparities · 2026Article
- Geographic Variation in Measles Vaccine Coverage Among Children Aged 7 Years in Northern California.JAMA network open · 2026Article
- Association of Glycated Hemoglobin (HbA1c) with Functional Outcomes After Endovascular Thrombectomy for Acute Ischemic Stroke.Clinical interventions in aging · 2026Article
- Ecological assessment of extreme temperature and fine particulate matter (pm2.5) impact on diabetes service and outcomes in Thailand.BMC public health · 2025Article
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7 authors.
Funding
Abstract
objectivesThe US Preventive Services Task Force recommends screening of adults aged 35-70 with a body mass index ≥25 kg/m
designA cross-sectional study using ArcGIS software to geospatially map pre-diabetes prevalence hotspots. We used health system and census data to identify pre-diabetes hotspots using a systematic five-step geoprocessing approach that made use of incremental spatial autocorrelation and Getis-Ord Gi*.
settingThis study was set in Kaiser Permanente Northern California (KPNC), an integrated health delivery system with over four million members.
participantsKPNC adults ages 35-70 who underwent a haemoglobin A1c (HbA1c) or fasting plasma glucose (FPG) screening test in 2019 were mapped to census tracts in Northern California. People were considered to have pre-diabetes with an HbA1c of 5.7%-6.4% (39-46 mmol/mol) or FPG 100-125 mg/dL. PRIMARY AND SECONDARY OUTCOME MEASURES: Individual and census-level characteristics were compared between hotspots and non-hotspots using χ
resultsA total of 760 044 adults met the study inclusion criteria and 40% had pre-diabetes. Individuals in pre-diabetes hotspots were less likely to be non-Hispanic white (33.6% vs 50.6%, RD: -17.04%, 95% CI -17.26% to -16.81%, p<0.0001) and more likely to have overweight or obesity (72.2% vs 69.2%, RD: 2.95%, 95% CI 2.73% to 3.16%, p<0.0001). Census tracts within hotspots had lower levels of household income (HL estimate: -3651.00, 95% CI -7256.00 to -25.00), per cent of adults with bachelor's degrees or higher (HL estimate: -9.08, 95% CI -10.94 to -7.24) and median home values (HL estimate: -113 200.00, 95% CI -140 600.00 to -85 700.00) and higher rates of household poverty (HL estimate: 0.96, 95% CI 0.55 to 1.37), unemployment (HL estimate: 0.39, 95% CI 0.24 to 0.54), household public assistance (HL estimate: 0.97, 95% CI 0.76 to 1.18) and per cent receiving Medicaid (HL estimate: 4.56, 95% CI 3.40 to 5.76) (p<0.05 for all).
conclusionsWe found that individual-level and census tract-level socioeconomic status, obesity prevalence and race and ethnicity categories of patients living in pre-diabetes hotspots differed from those not identified as a hotspot. Policy-makers and care providers can use this information to target diabetes prevention resources and outreach by enacting policies that provide insurance coverage for low-income populations and placing diabetes prevention programmes in communities with highest need.
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