POPULATION TRENDS IN HEALTHCARE USE BY MEXICAN ADULTS AGED 60 AND OLDER WITH AND WITHOUT COGNITIVE IMPAIRMENT
Bibliographic record
Abstract
Abstract Government policies that have greatly expanded health insurance coverage in Mexico have taken place in the context of rapid population aging and an increasing number of older adults living with cognitive impairment. We used data from the Mexican Health and Aging Study to investigate population-level trends in self-reported healthcare use by cognitive status in 2001, 2012, 2015, and 2018. Healthcare measures included having an outpatient procedure, any doctor visits, staying >1 nights in the hospital, and screenings for high cholesterol, diabetes, and hypertension. All outcomes were dichotomized as yes/no. The sample sizes included 6179 (2001), 8924 (2012), 9429 (2015), and 8916 (2018) participants aged 60 and older who completed a direct interview (total N=33,448). Participants with cognitive impairment were identified using five cognitive assessments (2001 n=1000; 2012 n=1273; 2015 n=1467; 2018 n=1372). Generalized estimating equations that adjusted for demographic characteristics and self-reported health conditions were used. The adjusted odds of having spent >1 night in the hospital, outpatient procedures, any doctor visits, and preventive screenings were significantly higher in 2012, 2015, and 2018 than in 2001 regardless of cognitive status. Overall, participants with cognitive impairment had significantly higher adjusted odds for >1 nights in the hospital (OR=1.31, 95% CI=1.20-1.42), but significantly lower odds for any doctor visits (OR=0.81, 95% CI=0.75-0.88), outpatient procedures (OR=0.70, 95% CI=0.57-0.85), and preventive screenings for high cholesterol (OR=0.75, 95% CI=-.70-0.81), diabetes (OR=0.78, 95% CI=0.72-0.85), and hypertension (OR=0.76, 95% CI=0.70-0.82). These results are important to understanding the healthcare needs of Mexico’s growing older adult population.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".