Prevalence and correlates of tetanus toxoid uptake among women in sub-Saharan Africa: Multilevel analysis of demographic and health survey data
Bibliographic record
Abstract
BACKGROUND: Tetanus toxoid vaccination is one of the most effective and protective measures against tetanus deaths among mothers and their newborns. We examined the prevalence and correlates of tetanus toxoid uptake among women in sub-Saharan African (SSA). MATERIALS AND METHODS: We analysed pooled data from the Demographic and Health Surveys (DHS) of 32 countries in SSA conducted from 2010 to 2020. We included 223,594 women with a history of childbirth before the survey. Percentages were used to present the prevalence of tetanus toxoid vaccine uptake among the women. We examined the correlates of tetanus toxoid uptake using a multilevel binary logistic regression. RESULTS: The overall prevalence of tetanus toxoid uptake was 51.5%, which ranged from 27.5% in Zambia to 79.2% in Liberia. Women age, education level, current working status, parity, antenatal care visits, mass media exposure, wealth index, and place of residence were the factors associated with the uptake of tetanus toxoid among the women. CONCLUSION: Uptake of tetanus toxoid vaccination among the women in SSA was low. Maternal age, education, current working status, parity, antenatal care visits, exposure to mass media, and wealth status influence tetanus toxoid uptake among women. Our findings suggest that health sector stakeholders in SSA must implement interventions that encourage pregnant women to have at least four antenatal care visits. Also, health policymakers in SSA could ensure that the tetanus toxoid vaccine is free or covered under national health insurance to make it easier for women from poorer households to have access to it when necessary.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".