Public health surveillance systems are crucial for monitoring disease trends and public health interventions in Kenya. However, their effectiveness can vary over time, necessitating methodological evaluations to assess adoption rates. A time-series forecasting model was developed to predict adoption rates over time. The model incorporates historical data on system usage and external influences such as funding availability and policy changes. Robust standard errors were used to account for uncertainty in the predictions. The forecasted adoption rate showed a steady increase from baseline levels, indicating improved implementation with each passing year. However, fluctuations due to seasonal variations and external shocks were significant. This study provides insights into the temporal dynamics of public health surveillance system adoptions in Kenya. Future research should focus on identifying specific strategies that enhance system adoption rates and long-term sustainability. Treatment effect was estimated with logit (pᵢ) =₀+^ Xᵢ, and uncertainty reported using confidence-interval based inference.
Kinyanjui et al. (Wed,) studied this question.
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