Abstract

Malaria is still ravaging millions of Cameroonians. Despite significant efforts have been made to end it challenges remain in assessing malaria policy and interventions, more specific due to routine data quality issues. Use thess routine collected data, remains a challenge in Cameroon to assess their malaria intervention. We are proposing suitable statistical methods that handle spatial structure and uncertainty on the relative risk that is relevant to National Malaria Control Program. In this study, we used a spatio-temporal model to estimate malaria incidence and assess effectiveness of malaria interventions on the trend of malaria incidence. Bayesian spatio-temporal model was developed using monthly routine data from health facilities for the period from 2011 to 2022 in Cameroon. The fitted model was used to generate relative risk (RR) using some control knew confounding factors: meteorological factors Minimum temperature, maximum temperature and rainfall), reporting, testing, use of Insected Treated Net, Poverty Rate and addition accounting for spatial and temporal autocorrelation. Our results showed that one additional year of Seasonal Malaria Chemoprevention (SMC) implementation in Cameroon reduced the clinical malaria incidence by 10.0% (95% CrI (5.0–14.0%). Conversely, the adoption of malaria free of charge health care in Country (RR=2.90, 95% CrI 1.85–4.53) was associated to an increase in clinical malaria incidence. This study offers a quick and efficient way to predict clinical malaria incidence and to monitor malaria burden within the country and it reinforce the importance to strengthen surveillance, monitoring and evaluation system. It shows that using routine collected data would be beneficial for setting priorities and timely preventive actions in specific area or health district and then accelerate reaching the common goal of malaria elimination towards the country.