Child malaria vaccine uptake in Ghana: Factors influencing parents’ willingness to allow vaccination of their children under five (5) years
Bibliographic record
Abstract
BACKGROUND: Malaria is a substantial health burden in Ghana, particularly among children. Despite the availability of malaria vaccines, uptake remains low. Notwithstanding, there is a paucity of nationally representative studies on the factors driving hesitance towards the new malaria vaccine. In response, this study, guided by the Theory of Planned Behaviors (TPB), seeks to understand the determinants of child malaria vaccine uptake in Ghana to inform strategies for improving coverage. MATERIALS AND METHODS: We employed multiple regression model to examine the association between maternal awareness, socioeconomic status, ethnicity, geographical location, and vaccine uptake using data from the 2019 Ghana Malaria Indicator Survey (MIS). RESULTS: Maternal awareness of vaccine (OR = 2.200; P<0.01) significantly predicted higher likelihood of vaccine uptake. Household wealth was associated with child vaccination as parents in middle-income households (OR = 9.342; P<0.01), and those in poorest households (OR = 9.409; P<0.05) recorded higher likelihood of allowing their children to be vaccinated. With regards to ethnicity, parents from the Mande ethnic group (OR = 0.106; P<0.05) were less likely to allow their children to be vaccinated when compared to parents from the Akan ethnic group. Knowing that malaria is covered by National Health Insurance (OR = 2.407; P<0.05) was associated with higher likelihood of allowing child vaccination compared to not knowing. More so, geographical variations were observed as parents who lived in rural areas (OR = 0.254; P<0.05) were significantly less likely to allow vaccination of their children compared to those in urban areas. CONCLUSIONS: Enhancing awareness through education campaigns can improve child malaria vaccine coverage. Observing socioeconomic disparities in uptake and ensuring equitable access to vaccines are vital. Tailored strategies considering ethnic background and geographical location, can as well enhance acceptance of the vaccine. This study provides valuable insights for developing effective strategies to reduce the burden of malaria in children and improve coverage of uptake. This study underscores the need to improve parental awareness and the relevance of the vaccine in preventing child mortality.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".