{"id":5965,"date":"2026-06-23T16:06:37","date_gmt":"2026-06-23T16:06:37","guid":{"rendered":"https:\/\/www.tarleton.edu\/tieuc\/?page_id=5965"},"modified":"2026-06-24T15:12:12","modified_gmt":"2026-06-24T15:12:12","slug":"obesity-in-nct","status":"publish","type":"page","link":"https:\/\/www.tarleton.edu\/tieuc\/dashboards\/obesity-in-nct\/","title":{"rendered":"Obesity in NCT"},"content":{"rendered":"\n

Complete Demographic & Statistical Summary<\/strong> for Texas 24-County Obesity<\/strong><\/h3>\n\n\n\n

1. Dataset Structure & Coverage<\/strong><\/p>\n\n\n\n

Dimension<\/th>Detail<\/th><\/tr>
Geography<\/strong><\/td>24 Texas counties (DFW metro + surrounding rural counties)<\/td><\/tr>
Time span<\/strong><\/td>2000\u20132019 (20 years, annual)<\/td><\/tr>
Ethnicity<\/strong><\/td>6 categories: Total, Latino (Any ethnicity), Non-Latino White, Non-Latino Black, Non-Latino American Indian\/Alaska Native, Non-Latino Asian\/Pacific Islander<\/td><\/tr>
Male or Female<\/strong><\/td>\u201cBoth\u201d only (no male\/female split in this extract)<\/td><\/tr>
Age groups<\/strong><\/td>14 five-year bands (20\u201324 through 80\u201384, plus 85+), plus 2 aggregates: \u201c20 plus\u201d and \u201c20 plus, age standardized\u201d<\/td><\/tr>
Metric<\/strong><\/td>Prevalence rate of obesity (proportion of population, e.g. 0.30 = 30%)<\/td><\/tr>
Total rows<\/strong><\/td>46,080<\/td><\/tr>
Data completeness<\/strong><\/td>67% populated (15,040 cells suppressed \u2014 typical for small-population ethnicity\/age cross-tabs, e.g. Asian\/Pacific Islander in rural counties)<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

Counties included:<\/strong> Bosque, Brown, Collin, Comanche, Dallas, Denton, Eastland, Ellis, Erath, Hamilton, Hill, Hood, Hunt, Jack, Johnson, Kaufman, Navarro, Palo Pinto, Parker, Rockwall, Somervell, Stephens, Tarrant, Wise<\/p>\n\n\n\n

\n\n\n\n

2. Statewide Trend (24-county average, age-standardized, all races)<\/strong><\/p>\n\n\n\n

Year<\/th>Obesity Prevalence<\/th><\/tr>
2000<\/td>29.96%<\/td><\/tr>
2005<\/td>35.50%<\/td><\/tr>
2010<\/td>39.65%<\/td><\/tr>
2015<\/td>42.50%<\/td><\/tr>
2019<\/td>45.39%<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
    \n
  • Absolute increase:<\/strong> +15.43 percentage points (2000\u21922019)<\/li>\n\n\n\n
  • Relative growth:<\/strong> +51.5%<\/li>\n\n\n\n
  • CAGR:<\/strong> 2.21%\/year over the full period<\/li>\n<\/ul>\n\n\n\n
    \n\n\n\n

    3. Breakdown by Ethnicity (2000 vs 2019, age-standardized)<\/strong><\/p>\n\n\n\n

    ETHNICITY<\/th>2000<\/th>2019<\/th>\u0394 pts<\/th>% growth<\/th><\/tr>
    Non-Latino, White<\/td>28.54%<\/td>43.97%<\/td>+15.43<\/td>+54.1%<\/td><\/tr>
    Total<\/td>29.96%<\/td>45.39%<\/td>+15.44<\/td>+51.5%<\/td><\/tr>
    Latino, Any ethnicity<\/td>36.93%<\/td>50.86%<\/td>+13.93<\/td>+37.7%<\/td><\/tr>
    Non-Latino, American Indian\/AK Native<\/td>31.35%<\/td>44.01%<\/td>+12.65<\/td>+40.4%<\/td><\/tr>
    Non-Latino, Black<\/td>39.73%<\/td>50.92%<\/td>+11.19<\/td>+28.2%<\/td><\/tr>
    Non-Latino, Asian\/Pacific Islander<\/td>12.59%<\/td>22.54%<\/td>+9.95<\/td>+79.1%<\/strong><\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

    Key insight:<\/strong> Black and Latino populations have the highest absolute<\/em> obesity rates throughout the period, but Asian\/Pacific Islander populations show the fastest relative growth rate<\/em> \u2014 nearly doubling, albeit from a low base.<\/p>\n\n\n\n

    \n\n\n\n

    4. Breakdown by Age Group (Total ethnicity, statewide avg)<\/strong><\/p>\n\n\n\n

    Age Band<\/th>2000<\/th>2019<\/th>\u0394 pts<\/th><\/tr>
    20\u201324<\/td>21.60%<\/td>32.21%<\/td>+10.61<\/td><\/tr>
    35\u201339<\/td>32.22%<\/td>48.75%<\/td>+16.52<\/td><\/tr>
    45\u201349 (peak)<\/strong><\/td>33.48%<\/td>50.93%<\/strong><\/td>+17.45<\/td><\/tr>
    65\u201369<\/td>32.67%<\/td>47.37%<\/td>+14.70<\/td><\/tr>
    85+<\/td>13.21%<\/td>25.42%<\/td>+12.21<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

    Obesity prevalence peaks in middle age (45\u201354)<\/strong> across both years and declines after 65, consistent with national CDC patterns.<\/p>\n\n\n\n

    \n\n\n\n

    5. County-Level Highlights (2019, age-standardized)<\/strong><\/p>\n\n\n\n

    Highest obesity prevalence:<\/strong><\/p>\n\n\n\n

      \n
    1. Navarro County \u2014 49.66%<\/li>\n\n\n\n
    2. Hill County \u2014 49.29%<\/li>\n\n\n\n
    3. Ellis County \u2014 47.63%<\/li>\n<\/ol>\n\n\n\n

      Lowest obesity prevalence:<\/strong><\/p>\n\n\n\n

        \n
      1. Collin County \u2014 35.02%<\/li>\n\n\n\n
      2. Denton County \u2014 39.60%<\/li>\n\n\n\n
      3. Rockwall County \u2014 39.68%<\/li>\n<\/ol>\n\n\n\n

        Fastest-growing counties (2000\u21922019):<\/strong><\/p>\n\n\n\n

          \n
        1. Johnson County \u2014 +16.50 pts<\/li>\n\n\n\n
        2. Stephens County \u2014 +16.41 pts<\/li>\n\n\n\n
        3. Wise County \u2014 +16.25 pts<\/li>\n<\/ol>\n\n\n\n
          \n

          Pattern: affluent, more urbanized counties near Dallas (Collin, Denton, Rockwall) consistently post the lowest rates; rural\/exurban counties (Navarro, Hill, Stephens) post the highest.<\/p>\n<\/blockquote>\n\n\n\n

          \n\n\n\n

          6. Growth Projections (Linear Trend Extrapolation)<\/strong><\/p>\n\n\n\n

          Year<\/th>Full-period trend (2000\u201319)<\/th>Recent-momentum trend (2015\u201319)<\/th><\/tr>
          2020<\/td>46.7%<\/td>46.1%<\/td><\/tr>
          2022<\/td>48.2%<\/td>47.5%<\/td><\/tr>
          2025<\/td>50.5%<\/td>49.7%<\/td><\/tr>
          2030<\/td>54.4%<\/td>53.3%<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

           Linear extrapolation only \u2014 does not account for COVID-era disruptions, policy interventions, or demographic shifts. Treat as a directional estimate, not a forecast.<\/p>\n\n\n\n

          \n\n\n\n

          7. Practical Uses of This Data<\/strong><\/p>\n\n\n\n

            \n
          • Public health resource allocation<\/strong> \u2014 target counties (Navarro, Hill, Ellis) for intervention funding<\/li>\n\n\n\n
          • Grant writing \/ research justification<\/strong> \u2014 supports Tarleton State research with quantified disparity trends by ethnicity and county<\/li>\n\n\n\n
          • Healthcare facility planning<\/strong> \u2014 bariatric\/metabolic service demand forecasting by region<\/li>\n\n\n\n
          • Policy support<\/strong> \u2014 demonstrates disparate impact across ethnicity groups for equality-focused programs<\/li>\n\n\n\n
          • Academic publication<\/strong> \u2014 20-year longitudinal dataset suitable for trend\/disparity analysis in peer-reviewed work<\/li>\n\n\n\n
          • Insurance\/employer wellness programs<\/strong> \u2014 regional risk stratification<\/li>\n<\/ul>\n\n\n\n
            \n\n\n\n

            8. Note on Dashboard Metric<\/strong><\/p>\n\n\n\n

            Your two dashboards plot SUM(Obesity)<\/strong><\/code> (summed across multiple age-group rows per county\/ethnicity\/year) \u2014 values in the 5\u20137.5 range. This is a different scale than the age-standardized prevalence rate<\/strong> (%) used in this summary. Both are valid for comparison<\/em> purposes (year-over-year, county-over-county), but the dashboard values aren\u2019t directly interpretable as \u201c% of population obese\u201d the way the age-standardized figures above are.<\/p>\n\n\n\n

            <\/div>\n\n\n\n