Barriers to healthcare access and healthcare seeking for childhood illnesses among childbearing women in sub-Saharan Africa: A multilevel modelling of Demographic and Health Surveys
Bibliographic record
Abstract
INTRODUCTION: The success of current policies and interventions on providing effective access to treatment for childhood illnesses hinges on families' decisions relating to healthcare access. In sub-Saharan Africa (SSA), there is an uneven distribution of child healthcare services. We investigated the role played by barriers to healthcare accessibility in healthcare seeking for childhood illnesses among childbearing women in SSA. MATERIALS AND METHODS: Data on 223,184 children under five were extracted from Demographic and Health Surveys of 29 sub-Saharan African countries, conducted between 2010 and 2018. The outcome variable for the study was healthcare seeking for childhood illnesses. The data were analyzed using Stata version 14.2 for windows. Chi-square test of independence and a two-level multivariable multilevel modelling were carried out to generate the results. Statistical significance was pegged at p<0.05. We relied on 'Strengthening the Reporting of Observational Studies in Epidemiology' (STROBE) statement in writing the manuscript. RESULTS: Eighty-five percent (85.5%) of women in SSA sought healthcare for childhood illnesses, with the highest and lowest prevalence in Gabon (75.0%) and Zambia (92.6%) respectively. In terms of the barriers to healthcare access, we found that women who perceived getting money for medical care for self as a big problem [AOR = 0.81 CI = 0.78-0.83] and considered going for medical care alone as a big problem [AOR = 0.94, CI = 0.91-0.97] had lower odds of seeking healthcare for their children, compared to those who considered these as not a big problem. Other factors that predicted healthcare seeking for childhood illnesses were size of the child at birth, birth order, age, level of community literacy, community socio-economic status, place of residence, household head, and decision-maker for healthcare. CONCLUSION: The study revealed a relationship between barriers to healthcare access and healthcare seeking for childhood illnesses in sub-Saharan Africa. Other individual and community level factors also predicted healthcare seeking for childhood illnesses in sub-Saharan Africa. This suggests that interventions aimed at improving child healthcare in sub-Saharan Africa need to focus on these factors.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.007 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".