Modeling and Forecasting Subnational Mortality in the Presence of Aggregated Data
Bibliographic record
Abstract
This study proposes a new approach to modeling subnational mortality that relies on individual features (e.g., sex, geographical region, socioeconomic status) instead of dealing directly with subpopulations. Our strategy leads to more parsimonious models because fewer parameters are needed to explain mortality. Also, data providers might aggregate data over privacy concerns, and our framework allows for the use of such data, unlike the common subnational mortality modeling approach. A general one-step Bayesian estimation methodology that works well with most age–period–cohort mortality models proposed thus far in the literature is presented; it uses Markov chain Monte Carlo techniques by combining deterministic filtering with adaptive Metropolis steps and is well-suited for high-dimensional cases like the one investigated in this article. In a case study using real data, the framework is applied to Canadian mortality data from three datasets that encompass three features: sex, geographic region, and socioeconomic status. We show that the proposed approach combined with a reasonable mortality model provides realistic, coherent, and plausible mortality projections and that it fits the data reasonably.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".