Modelling Adult Mortality in Nigeria: Ananalysis Based on the Lee-Carter Model
Bibliographic record
Abstract
For several decades, global public-health efforts have focused on the development and application of various programs to improve child survival in developing countries. By contrast, little emphasis has been placed on adult mortality especially in a developing country like Nigeria. In order to plan and monitor the effectiveness of public-health programs, the Government and international agencies need accurate information on the past and current level and patterns of adult mortality in the country and how they are changing with time. This study used the Lee-Carter method to model adult mortality in Nigeria (a limited data situation). The model was applied to the age-specific mortality rates for Nigeria (for both sexes) aged 15-84 years for the time periods 1990, 2000 and 2009. An evaluation of past time trends in the general pattern of adult mortality, the relative pace of change in mortality by age, the general pattern of mortality by age and forecast of future mortality index and rates from 2010-2019 was made. The model’s parameters are estimated using the approach proposed by Lee and Carter (1992) based on the singular value decomposition technique, while the mortality index is predicted using the approach developed by Nan Li et al . (2002). Our findings reflect that the model follows the mortality pattern very well for most of the ages except that the fit of the model was better for the male data than the females’. Furthermore, it is observed that presently, females have a higher mortality rate than males in Nigeria while forecast values of the mortality index show that the male folk will experience a gradual decline in mortality from 2010-2019 all things being equal. Conclusively, the Lee-Carter model can be used in the Nigerian situation provided that the earliest and latest points of the data are sufficiently far apart in time.
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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 | 0.009 | 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 | 0.001 | 0.003 |
| 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".