Factors influencing canine rabies vaccination among dog-owning households in Nigeria
Bibliographic record
Abstract
Rabies perpetuates in Nigeria despite initiatives like the Regional Disease Surveillance System Enhancement Project, with evidence indicating suboptimal canine vaccination rates as a contributing factor. To inform effective planning of mass dog vaccination campaigns, it is crucial to understand the factors associated with variation in canine vaccination rates. We conducted a cross-sectional study in 2022 to understand factors associated with canine vaccination. We used stratified random sampling of the streets and dog-owning households to survey 4162 households from three states and the Federal Capital Territory (FCT). We then built a joint probabilistic model to understand factors associated with dog vaccination and non-vaccination. First, we modelled rabies knowledge as a latent variable indirectly measured with several targeted survey questions. This method allowed a respondent's unobserved understanding of rabies to be estimated using their responses to a collection of survey questions that targeted different aspects of rabies epidemiology and took various possible response distributions (i.e., ordinal, categorical, binary). Second, we modelled factors influencing pet owners' decisions to vaccinate their dogs against rabies and barriers to dog vaccination among dog owners whose dogs were not vaccinated against rabies. Posterior distributions revealed that the probability of dog vaccination was positively associated with the owner's latent knowledge of rabies, civil servant service employment, residence in the FCT, ownership of a single dog, providing care to dogs, and a preference for contemporary treatment following a dog bite. Conversely, non-vaccination was positively associated with private employment, residing in Anambra and Enugu states, owning multiple dogs, allowing dogs to search for leftovers, and a preference for traditional treatment after a dog bite. Cost was the primary barrier against vaccination for dog owners in Anambra and Enugu, while mistrust posed a major challenge for those in the FCT. Owners in areas with veterinary establishments cited cost as a barrier, while those without a veterinary establishment cited access as the primary barrier. Our study underscores the need to enhance rabies knowledge, tailor vaccination campaigns to specific demographics, address financial and access barriers, and combat hesitancy to improve rabies vaccination rates in Nigeria.
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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 | 0.000 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".