County-Level Characteristics Driving Malnutrition Death Rates among Older Adults in Texas
Bibliographic record
Abstract
OBJECTIVES: This study aims to identify older adult malnutrition in Texas, examine county-level characteristics associated with crude malnutrition death rates, and describe assets and opportunities available to address and improve malnutrition among the older population. DESIGN: Secondary data analysis using the Centers for Disease Control and Prevention's WONDER online database, the U.S. Census 2014-2018 American Community Survey, and the U.S. Department of Agriculture's Food Access Research Atlas data. SETTING: All 254 counties in the state of Texas. PARTICIPANTS: Individuals aged 65 years and older. MEASUREMENT: The dependent variable was the proportion of county-level malnutrition crude death rates. Independent variables included Health Provider Shortage Area designations, rurality, poverty status, food access, age, race, ethnicity, and education. RESULTS: The overall malnutrition crude death rate in Texas was 65.6 deaths per 100,000 older Texans, ranging from 0 to 414.46 deaths per 100,000 depending on the county. Higher malnutrition crude death rates were associated with non-metropolitan counties (P=0.018), lower education (P=0.047), greater household poverty (P=0.010), and low food access (P<0.001). CONCLUSION: Socioeconomic disadvantages at the county-level appear to be one of the root causes of malnutrition crude death rates in Texas.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".