{"id":5953,"date":"2026-06-12T16:52:25","date_gmt":"2026-06-12T16:52:25","guid":{"rendered":"https:\/\/www.tarleton.edu\/tieuc\/?page_id=5953"},"modified":"2026-06-15T19:03:32","modified_gmt":"2026-06-15T19:03:32","slug":"diabetes-burden-in-nct","status":"publish","type":"page","link":"https:\/\/www.tarleton.edu\/tieuc\/dashboards\/diabetes-burden-in-nct\/","title":{"rendered":"Diabetes Burden in NCT"},"content":{"rendered":"\n

Diabetes Burden in NCT<\/h3>\n\n\n\n

Overview \u2014 What the Data Measures<\/strong><\/p>\n\n\n\n

YLLs (Years of Life Lost) measure premature mortality \u2014 years a person would have lived had they not died early from diabetes. Expressed as a rate per 100,000 population across 24 Texas counties, 2000\u20132019.<\/p>\n\n\n\n

Metric<\/th>Value<\/th><\/tr>
Avg YLL Rate<\/td>423 per 100K<\/td><\/tr>
Peak Rate<\/td>870 \u2014 Brown County, 2005<\/td><\/tr>
Lowest Rate<\/td>112 \u2014 Collin County<\/td><\/tr>
Disparity Ratio<\/td>7.75\u00d7 highest vs. lowest county (2019)<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

Key Finding:<\/strong> Non-Latino Black residents bear nearly 4\u00d7 the YLL burden of Non-Latino Asian\/Pacific Islander residents \u2014 the starkest racial disparity across all 24 counties in 2019 (507 vs. 129 per 100,000).<\/p>\n\n\n\n

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20-Year Trend: Rise, Peak & Partial Recovery<\/strong><\/p>\n\n\n\n

Year<\/th>Avg Rate<\/th>Event<\/th><\/tr>
2000<\/td>450.9<\/td>Baseline \u2014 burden already high in rural counties<\/td><\/tr>
2005<\/td>489.8<\/td>Peak<\/strong> \u2014 Brown County hits 870, rural crisis<\/td><\/tr>
2013<\/td>348.1<\/td>Best<\/strong> \u2014 29% drop, ACA + better medication access<\/td><\/tr>
2019<\/td>396.9<\/td>Rebound \u2014 +14% from low, obesity + aging pressure<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

The 2005\u20132013 decline reflects expanded healthcare access and improved diabetes medications. Texas’s decision not to expand Medicaid under the ACA likely contributed to the post-2013 rebound.<\/p>\n\n\n\n

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Racial Disparities (2019, Adults 20+)<\/strong><\/p>\n\n\n\n

Ethnicity<\/th>YLL Rate<\/th><\/tr>
Non-Latino, Black<\/td>507<\/td><\/tr>
Non-Latino, White<\/td>424<\/td><\/tr>
Total (All ethnicities)<\/td>397<\/td><\/tr>
Latino, Any ethnicity<\/td>259<\/td><\/tr>
Am. Indian \/ Alaska Native<\/td>234<\/td><\/tr>
Asian \/ Pacific Islander<\/td>129<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

The Latino Paradox:<\/strong> Despite socioeconomic disadvantages, Latino residents show lower rates than Non-Latino White and Black populations \u2014 possibly due to stronger social networks, dietary practices, or selective migration effects.<\/p>\n\n\n\n

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Geography: Rural vs. Urban (2019)<\/strong><\/p>\n\n\n\n

Rural avg: 564 YLLs<\/strong> vs Urban avg: 231 YLLs<\/strong> \u2014 a 2.4\u00d7 gap<\/strong><\/p>\n\n\n\n

County<\/th>YLL Rate<\/th>Tier<\/th><\/tr>
Brown County<\/td>701<\/td> High<\/td><\/tr>
Eastland County<\/td>593<\/td> High<\/td><\/tr>
Navarro County<\/td>565<\/td> High<\/td><\/tr>
Dallas County<\/td>278<\/td> Moderate<\/td><\/tr>
Denton County<\/td>202<\/td> Low<\/td><\/tr>
Rockwall County<\/td>174<\/td> Low<\/td><\/tr>
Collin County<\/td>134<\/td> Lowest<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
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Age Distribution (2019)<\/strong><\/p>\n\n\n\n

Burden rises sharply with age, peaking in the 75\u201379 bracket, with early warning signs emerging in the 40\u201354 group.<\/p>\n\n\n\n

Age Group<\/th>YLL Rate<\/th><\/tr>
75\u201379 years<\/td>878<\/td><\/tr>
70\u201374 years<\/td>856<\/td><\/tr>
65\u201369 years<\/td>784<\/td><\/tr>
55\u201359 years<\/td>560<\/td><\/tr>
45\u201349 years<\/td>302<\/td><\/tr>
40\u201344 years<\/td>117<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
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Strengths & Limitations<\/strong><\/p>\n\n\n\n

Strengths<\/strong><\/p>\n\n\n\n

    \n
  • 20-year longitudinal data \u2014 rare at county level<\/li>\n\n\n\n
  • Ethnicity-stratified data exposes systemic health disparities<\/li>\n\n\n\n
  • YLL metric captures years of productive life lost<\/li>\n\n\n\n
  • FIPS-coded geography enables precise Tableau mapping<\/li>\n\n\n\n
  • Spans both urban (DFW metro) and rural counties<\/li>\n\n\n\n
  • Confidence intervals provided for statistical rigor<\/li>\n<\/ul>\n\n\n\n

    Limitations<\/strong><\/p>\n\n\n\n

      \n
    • Only combined genders (Both) \u2014 no male vs. female breakdown<\/li>\n\n\n\n
    • Single cause only \u2014 comorbidities like obesity not captured<\/li>\n\n\n\n
    • Ends at 2019 \u2014 COVID-19 impact not reflected<\/li>\n\n\n\n
    • Small rural populations create wide confidence intervals<\/li>\n\n\n\n
    • No socioeconomic variables to contextualize racial disparities<\/li>\n\n\n\n
    • County-level too coarse for neighborhood analysis<\/li>\n<\/ul>\n\n\n\n
      \n\n\n\n

      Policy Recommendations<\/strong><\/p>\n\n\n\n

      Target Rural Counties First<\/strong> \u2014 Brown, Eastland, Navarro show 2\u20133\u00d7 metro rates. Mobile screening clinics and telehealth endocrinology yield highest ROI.<\/p>\n\n\n\n

      Address Racial Fairness<\/strong> \u2014 Non-Latino Black communities need culturally tailored interventions addressing social determinants of health.<\/p>\n\n\n\n

      Prioritize Aging Population<\/strong> \u2014 65\u201379 age group accounts for disproportionate YLLs. Expand Medicare-enrolled diabetes management programs<\/p>\n\n\n\n