Near universal childhood vaccination rates in Rwanda: how was this achieved and can it be duplicated?
Bibliographic record
Abstract
BackgroundAccording to data from the WHO Vaccine-Preventable Disease Monitoring System, Rwanda has achieved near universal childhood vaccination rates, with an overall national coverage rate for childhood immunisation of 98% in 2015. These rates are striking given the country's status as a post-conflict, post-genocide, and low-income country. In this study, we aimed to determine factors that contributed to the success of Rwanda's childhood immunisation programme.MethodsWe used primary and secondary sources to identify such factors. Primary research was conducted in August, 2017, in Eastern Province, Northern Province, and the city of Kigali, Rwanda. We used snow-ball sampling to recruit interviewees. Semi-structured interviews were conducted with government, multilateral organisations, and non-governmental staff members involved in the planning and delivery of Rwanda's immunisation programme. Secondary sources included review of primary databases, grey literature, and peer-reviewed literature that was identified through searches in Google Scholar and PubMed for articles written in English and published since Jan 1, 2000, using combinations of the search terms “Rwanda”, “vaccination”, “immunisation”, and “programme”.Findings24 interviews were conducted and secondary data were analysed. Several factors have contributed to Rwanda's vaccination success. First, at the local level, an engaged cadre of community health workers sensitises communities on the importance of vaccinations and performs health surveillance duties. Second, an integrated health management information system guides vaccination procurement and distribution to support vaccine delivery at the local level. Third, at the governmental level, the vaccination programme is driven by strong political will to prioritise health. Fourth, implementation is sufficiently decentralised to the district and village level to tailor appropriate approaches for the local population. Fifth, the uniquely Rwandan practice of imihigo, which involves leaders at all levels of government (centrally and locally) signing performance contracts to achieve certain targets, enhances accountability and ownership. Finally, the Rwandan health system benefits from strong relationships with development partners and cross-over effects from global health initiatives, particularly in developing capacity for supply chain and cold chain management.InterpretationAlthough cultural factors such as imihigo differentiate Rwanda from demographically comparable countries, the success of the Rwandan vaccine programme is multifactorial. These factors include strong, high-level political will, multilevel accountability, effective use of funding, partnership with development partners, integrated health information, and community-level data collection. Countries aiming to improve coverage may wish to study and emulate these factors.FundingMastercard Center for Inclusive Growth.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".