OP33 Contextual Factors Associated with Health Care Service Utilisation for Children in Nigeria: A Multilevel Analysis
Bibliographic record
Abstract
Background The leading causes of high under-five mortality in Nigeria are infectious diseases which could be easily prevented and treated through health care services utilisation. There is poor utilisation of these services and most studies examining its determinants have focused on individual factors. The objective of this study is to examine the independent contribution of individual-, community- and state-level factors to health care service utilisation for children in Nigeria. Methods The study was based on secondary analyses of cross-sectional population-based data from the 2013 Nigeria Demographic and Health Survey. The survey used a three-stage cluster sampling technique. The first stage involved selecting 896 clusters with a probability proportional to the size; the size being the number of households in the cluster. The second stage involved the systematic sampling of households from the selected clusters. The third stage involved the distribution of the households in each state proportionately among its urban and rural areas. A total of 40,680 households were finally sampled with 16,740 and 23,940 from urban and rural areas respectively. Data were collected by visiting households and administering questionnaires. Multilevel logistic regression models were applied to the data on 31,482 under-five children who used or did not use health care service when they were sick (level 1), nested within 896 communities (level 2) from 37 districts (level 3). All multilevel modelling were performed using MLwiN calling Stata statistical software from windows version 14. Results About one-quarter of the mothers were between 15 and 24 years old and almost half of them did not have formal education (47%). Close to 67% of the children lived in the rural area. In the fully adjusted model, mothers with higher education attainment (OR = 1.66, 95% CI 1.37–1.95), from richer households (OR = 1.32, 95% CI 1.04–1.63), with access to media (radio, television or magazine), and living in ethnic diverse communities (OR = 1.04, 95% CI 1.01–1.07) were significantly more likely to have used healthcare services for acute childhood illnesses. Conclusion Our study revealed that utilisation of healthcare service for acute childhood illnesses was influenced by not only maternal factors but also various community- and state-level factors, suggesting that public health strategies should recognise these complex web of individual composition and contextual composition factors to guide provision of healthcare services. The study was limited due to inability to measure the impact of residential changes over time. Further research should consider longitudinal study.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".