Modelling Factors that Predict Differences in Childhood Mortality in Lagos Communities Using Prognostic Logistic and Poisson Regression Models
Bibliographic record
Abstract
Lagos State is a city with one of the largest concentration of people in the world with heterogenous behaviour and cultural beliefs. There are different prognostic models in the medical sciences, yet their real life application, especially to childhood mortality is limited. There are variations in childhood mortality rate across different communities in Lagos State. Childhood mortality is a subject of interest to World Health Organization (WHO) and one of the major Millennium Development Goals. In 2014, the Special Adviser to the Lagos State Governor on Public Health, Dr. Yewande Adeshina said that under-5 and infant mortality rates in Lagos state have reduced due to various health interventions being implemented in the State. However, the truth of the matter is that childhood mortality is still high and this is an indication that we still have lots of work to do in this regard. In this paper, prognostic models were used in modelling factors that predict the differences in childhood mortality in Lagos communities. Six models, two each from logistic regression, linear regression and Poisson regression models were used. Primary data were collected from mothers that fall in the age bracket (15-49), who reside in any of the 5 divisions of Lagos State, namely Ikorodu, Badagry, Lagos Mainland/Ikeja, Lagos Island and Epe. Five variables were identified as covariates. The prognostic multi-variable models were employed. The binary logistic regression model with 5 covariates was selected as the best model for the binary response variable, while the Poisson regression model with 4 covariates was selected as the best model for the count response variable. At the end of the research, Ikorodu, Badagry and Epe communities have higher than expected childhood mortality rates. Also, we estimated childhood mortality rate in Lagos State and measured the variations in childhood mortality across Lagos communities. The factors that predict these variations were detected and control measures were recommended to reduce the difference in childhood mortality rate in Lagos State.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".