Calculating State-Level Estimates of Upcoming Older Adult Health Needs
Bibliographic record
Abstract
OBJECTIVES: Census demographers have provided projections of the increased numbers of older adults in upcoming decades, but it is less clear whether they will also be any more or less healthy than current seniors. This is critical information for state planners, as the majority of older adults will need assistance with activities of daily living to remain in their homes. Previous longitudinal and cohort studies have yielded national estimates, but those more costly sources are generally beyond the resources of state public health agencies. We provide a more practicable model for assessing state-level changes in health-related quality of life (HRQOL) among middle-aged versus older adults as a guide to probable upcoming home- and community-based service needs. METHODS: We used 2 sets of state Behavioral Risk Factor Surveillance System data 15 years apart to calculate and compare adjusted odds ratios of 8 poor HRQOL measures for middle-aged and older adults. RESULTS: Compared with their peers only 15 years earlier, recent middle-aged adults had higher odds of poor outcomes across all HRQOL measures, whereas adults 65-74 years had higher odds of poor outcomes for far fewer of the measures. Among adults 75 years and older, odds were higher compared with 15 years ago for only 1 measure (multiple days of poor mental health). CONCLUSIONS: Compared with older adults, the health profile of middle-aged adults in this state appears to have worsened much more rapidly in the past 15 years, indicating that these adults will have many more health-related needs when they become seniors. While this model is less sophisticated than others using longitudinal data, it provides the state-level data that are often more compelling to state policy makers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".