{"id":5977,"date":"2026-07-14T22:31:39","date_gmt":"2026-07-14T22:31:39","guid":{"rendered":"https:\/\/www.tarleton.edu\/tieuc\/?page_id=5977"},"modified":"2026-07-16T21:12:44","modified_gmt":"2026-07-16T21:12:44","slug":"protein-energy-malnutrition-mortality-in-texas","status":"publish","type":"page","link":"https:\/\/www.tarleton.edu\/tieuc\/dashboards\/protein-energy-malnutrition-mortality-in-texas\/","title":{"rendered":"Protein-Energy Malnutrition Mortality in Texas"},"content":{"rendered":"\n

County & Demographic Patterns, 2013\u20132019<\/h4>\n\n\n\n

24-County North & Central Texas Region | Source: IHME U.S. County-Level Mortality Estimates<\/em><\/p>\n\n\n\n

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Executive Summary<\/h4>\n\n\n\n

This brief examines mortality attributable to protein-energy malnutrition (PEM) across 24 counties in North and Central Texas \u2014 including Dallas, Tarrant, Collin, and Denton \u2014 using age-standardized death rate estimates from the Institute for Health Metrics and Evaluation (IHME), covering 2013 through 2019. All rates are expressed per 100,000 population and age-standardized to allow fair comparison across counties, ethnicity groups, and time periods.<\/p>\n\n\n\n

Three findings stand out:<\/p>\n\n\n\n

    \n
  • Rates more than doubled region-wide<\/strong>, rising 104% from 2.26 to 4.61 deaths per 100,000 between 2013 and 2019 \u2014 a steady, uninterrupted year-over-year climb.<\/li>\n\n\n\n
  • Racial disparities are pronounced and widening.<\/strong> Black residents face the highest burden (5.78 per 100,000 in 2019), more than double the rate for AIAN residents (2.53).<\/li>\n\n\n\n
  • Risk is heavily concentrated among adults 85 and older<\/strong>, whose raw death rate (108.4 per 100,000) is nearly four times that of the next-highest age group (80\u201384, at 27.5).<\/li>\n<\/ul>\n\n\n\n
    \n\n\n\n

    Data & Methods<\/h4>\n\n\n\n
    Attribute<\/th>Detail<\/th><\/tr>
    Source<\/td>IHME U.S. County-Level Mortality Estimates, 2000\u20132019<\/td><\/tr>
    Geography<\/td>24 counties, North & Central Texas<\/td><\/tr>
    Time period<\/td>2013\u20132019 (7 years)<\/td><\/tr>
    Cause<\/td>Protein-energy malnutrition (Cause ID 387)<\/td><\/tr>
    Demographic breakdowns<\/td>Ethnicity (5 groups + Total), male\/female, 21 age bands + age-standardized<\/td><\/tr>
    Metric used in this brief<\/td>Age-standardized death rate per 100,000 population<\/td><\/tr>
    Total records<\/td>63,504 rows<\/td><\/tr>
    Data completeness<\/td>~67% of ethnicity\/age\/county\/year cells populated; remainder suppressed for small cell counts, standard practice for rare-cause mortality data<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

    Note on methodology:<\/strong> age-standardized rates (rather than crude or summed rates) are used throughout so that comparisons across counties and demographic groups are not distorted by differences in age structure. An earlier dashboard draft summed rates across all age bands, which inflated figures \u2014 that issue has been corrected in this analysis.<\/p>\n\n\n\n

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    Regional Trend, 2013\u20132019<\/h4>\n\n\n\n

    The age-standardized death rate for the total population rose every single year across the study period, with no reversals:<\/p>\n\n\n\n

    Year<\/th>Rate per 100,000<\/th><\/tr>
    2013<\/td>2.26<\/td><\/tr>
    2014<\/td>2.51<\/td><\/tr>
    2015<\/td>2.90<\/td><\/tr>
    2016<\/td>3.28<\/td><\/tr>
    2017<\/td>3.74<\/td><\/tr>
    2018<\/td>4.21<\/td><\/tr>
    2019<\/td>4.61<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

    This represents a 104% increase over seven years, roughly a 12\u201313% year-over-year compounding rise. The consistency of the upward trend (no year shows a decline) suggests a sustained regional pattern rather than a single anomalous year \u2014 worth flagging to Dr. Ogundari as a candidate for further investigation into contributing factors (e.g., reporting\/coding changes at IHME vs. genuine incidence increase, aging population shifts, or healthcare access trends).<\/p>\n\n\n\n

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    Demographic Analysis<\/h4>\n\n\n\n

    By Ethnicity<\/strong><\/p>\n\n\n\n

    Every racial group saw rates climb over the study period, but the gap between the highest- and lowest-rate groups widened rather than narrowed:<\/p>\n\n\n\n

    ETHNICITY<\/th>2013<\/th>2016<\/th>2019<\/th>% Change<\/th><\/tr>
    Black<\/td>2.76<\/td>3.86<\/td>5.78<\/td>+109%<\/td><\/tr>
    White<\/td>2.27<\/td>3.31<\/td>4.66<\/td>+105%<\/td><\/tr>
    Latino<\/td>1.95<\/td>2.81<\/td>3.97<\/td>+104%<\/td><\/tr>
    Asian<\/td>1.76<\/td>2.58<\/td>3.49<\/td>+99%<\/td><\/tr>
    AIAN<\/td>2.00<\/td>2.26<\/td>2.53<\/td>+27%<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

    Black residents consistently show the highest age-standardized rate throughout the period, and the gap versus White residents grew from 0.49 (2013) to 1.12 per 100,000 (2019) \u2014 the disparity is not static, it is widening. AIAN residents show comparatively flat growth (+27%), the slowest of any group, which may reflect smaller underlying case counts and warrants a data-completeness caveat rather than a substantive conclusion.<\/p>\n\n\n\n

    By Age Group<\/strong><\/p>\n\n\n\n

    Mortality risk is overwhelmingly concentrated in the oldest population segment:<\/p>\n\n\n\n

    Age Group<\/th>Rate per 100,000 (avg. 2013\u20132019)<\/th><\/tr>
    85+<\/td>108.37<\/td><\/tr>
    80\u201384<\/td>27.49<\/td><\/tr>
    75\u201379<\/td>12.99<\/td><\/tr>
    70\u201374<\/td>6.18<\/td><\/tr>
    65\u201369<\/td>3.25<\/td><\/tr>
    60\u201364<\/td>1.91<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

    The 85+ group’s rate is nearly 4x the next age band and roughly 57x the 60\u201364 rate \u2014 consistent with clinical literature linking PEM mortality to frailty, comorbidity, and end-of-life decline in the oldest-old population. This suggests any intervention framing should center long-term care and elder-nutrition programs.<\/p>\n\n\n\n

    By Male\/Female<\/strong><\/p>\n\n\n\n

    Age-standardized rates are modestly higher for women than men across the full period: 3.62 per 100,000 for women versus 2.88 for men. This gap is smaller than the racial or age disparities observed and is consistent with women’s greater representation in the oldest age brackets, where PEM mortality risk concentrates.<\/p>\n\n\n\n

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    County-Level Analysis<\/h4>\n\n\n\n

    2019 age-standardized rates ranged from 3.29 to 6.89 per 100,000 across the 24 counties \u2014 more than a two-fold spread.<\/p>\n\n\n\n

    Highest-Rate Counties (2019)<\/strong><\/p>\n\n\n\n

      \n
    1. Somervell County \u2014 6.89<\/li>\n\n\n\n
    2. Ellis County \u2014 6.23<\/li>\n\n\n\n
    3. Navarro County \u2014 5.94<\/li>\n\n\n\n
    4. Tarrant County \u2014 5.93<\/li>\n\n\n\n
    5. Johnson County \u2014 5.88<\/li>\n<\/ol>\n\n\n\n

      Lowest-Rate Counties (2019)<\/strong><\/p>\n\n\n\n

        \n
      1. Collin County \u2014 3.29<\/li>\n\n\n\n
      2. Wise County \u2014 3.38<\/li>\n\n\n\n
      3. Hunt County \u2014 3.39<\/li>\n\n\n\n
      4. Jack County \u2014 3.41<\/li>\n\n\n\n
      5. Brown County \u2014 3.49<\/li>\n<\/ol>\n\n\n\n

        Notably, Dallas County (a major urban center) sits mid-range at 4.66 per 100,000 \u2014 close to the regional average \u2014 while several smaller, more rural counties (Somervell, Ellis, Navarro, Hill) occupy the top tier. This pattern is worth flagging to Dr. Ogundari: it may point to rural healthcare access, nursing-home\/elder-care density, or age-structure differences (rural counties often skew older) as drivers, rather than the urban-poverty factors more commonly hypothesized for malnutrition-related mortality.<\/p>\n\n\n\n

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        Limitations & Caveats<\/h4>\n\n\n\n
          \n
        • Roughly a third of ethnicity\/age\/county\/year combinations are suppressed due to small cell counts \u2014 a standard IHME practice for rare-cause mortality, but it limits granularity for the smallest counties and rarest ethnicity\/age intersections.<\/li>\n\n\n\n
        • This is mortality data only; it does not capture prevalence, hospitalization, or near-miss malnutrition cases, so it likely understates the true burden.<\/li>\n\n\n\n
        • Rate increases could partly reflect changes in death-certificate coding practices for PEM over 2013\u20132019 rather than purely a rise in true incidence \u2014 a common confound in cause-specific mortality trend data, worth discussing with Dr. Ogundari before drawing causal conclusions.<\/li>\n\n\n\n
        • County-level rankings are based on modeled IHME estimates, not raw vital records, and carry uncertainty intervals (upper\/lower bounds are available in the source data but not shown in this summary).<\/li>\n<\/ul>\n\n\n\n
          \n\n\n\n

          Appendix: Full Ethnicity Trend, 2013\u20132019 (per 100,000)<\/h4>\n\n\n\n
          Year<\/th>AIAN<\/th>Asian<\/th>Black<\/th>Latino<\/th>White<\/th><\/tr>
          2013<\/td>2.00<\/td>1.76<\/td>2.76<\/td>1.95<\/td>2.27<\/td><\/tr>
          2014<\/td>2.05<\/td>2.05<\/td>3.02<\/td>2.10<\/td>2.53<\/td><\/tr>
          2015<\/td>2.15<\/td>2.21<\/td>3.43<\/td>2.48<\/td>2.93<\/td><\/tr>
          2016<\/td>2.26<\/td>2.58<\/td>3.86<\/td>2.81<\/td>3.31<\/td><\/tr>
          2017<\/td>2.31<\/td>3.02<\/td>4.59<\/td>3.27<\/td>3.77<\/td><\/tr>
          2018<\/td>2.39<\/td>3.26<\/td>5.30<\/td>3.68<\/td>4.25<\/td><\/tr>
          2019<\/td>2.53<\/td>3.49<\/td>5.78<\/td>3.97<\/td>4.66<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
          <\/div>\n\n\n\n