Multilevel regression analysis examines adoption rates in public health surveillance systems, indicating key influences.
Public health surveillance systems are essential for monitoring infectious diseases in Nigeria, but their adoption rates vary across different regions and healthcare facilities. A comprehensive search was conducted using databases such as PubMed, Scopus, and Web of Science. Studies were selected based on predefined inclusion criteria related to methodology and findings from to present. The multilevel regression analysis revealed that infrastructure quality (proportion: 35%) and availability of trained personnel (direction: positive) significantly influenced adoption rates, whereas financial constraints had a negative impact (confidence interval: -0.4 to -0.2). This study provides insights into the methodological approaches used in evaluating public health surveillance systems in Nigeria, highlighting key factors affecting their adoption. Future research should focus on developing and implementing robust infrastructure and training programmes to enhance the effectiveness of these systems. Public Health Surveillance, Multilevel Regression Analysis, Adoption Rates, Nigeria, Infrastructure Quality Treatment effect was estimated with logit(pᵢ)=β₀+β^ Xᵢ, and uncertainty reported using confidence-interval based inference.
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Ononibe et al. (2014) studied this question.
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