Sociodemographic Links With Mortality and Survival in the Mexican Older Adult Population: Impact of Survey Attrition in the Mexican Health and Aging Study 2001-2015
Bibliographic record
Abstract
Abstract Most studies on old age mortality and survival focus on high-income countries, leaving limited knowledge about these trajectories in low- and middle-income countries. We use the longest-run longitudinal study of aging in Latin America, the Mexican Health and Aging Study (MHAS), to assess mortality and survival in the Mexican older adult population. We examine the likely impact of survey attrition and missing date of death on estimates of age-specific death rates, life expectancy, and the link between sociodemographic characteristics and mortality risk among Mexican older adults. Results show attrition of less than 6% of the baseline sample in MHAS from 2001 to 2015. Being lost to follow-up (LFU) is associated with age, education, and place of residence. Age-specific death rates and life expectancy estimates in MHAS align with vital statistics suggesting minimal impact of survey attrition in these estimates at older ages. However, ignoring sample attrition produces statistically significant educational gradients in mortality among males (but not among females), but imputing attrition and/or death date deaths reverses this pattern. Thus, we recommend imputing vital status by, for example, assuming attrited respondents survived to the midpoint of their LFU interval and assessing mortality determinants, including imputed cases. We also found sizeable sex differences in life expectancy at age 50 favoring women with larger sex differences in more populous places. We conclude that MHAS reliably supports the study of older age mortality and survival in Mexico, offering a unique chance to enhance knowledge in a middle-income country in the Americas.
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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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".