Incorporating Community Case Management in Risk-Based Surveillance for Malaria Elimination in the Dominican Republic.

Abstract

As countries strive for malaria elimination, it is crucial to gather sufficient evidence to confirm the absence of transmission. Routine surveillance data often lack the sensitivity to detect community transmission at low levels. In the Dominican Republic, community health workers (CHWs) have been deployed in malaria foci to perform active case detection. This study aimed to assess the added value of CHWs in enhancing the health system's malaria detection capabilities. Freedom from infection (FFI) is a statistical framework designed to demonstrate the absence of malaria by using routinely collected health data. We adapted this framework to include CHW data, estimating their contribution to the health system's malaria detection ability. The model was applied to facility and CHW data from 33 facilities across nine provinces in the Dominican Republic, covering the period from January 2018 to April 2022. The likelihood that a facility's catchment population is free from malaria infection (Pfree) was achieved in 52% of facilities by using only routine data, sustained for an average of 13 months. With the addition of CHW data, 88% of facilities reached Pfree, sustained for an average of 37 months. Incorporating CHW data enhanced the precision of model estimates by over 500-fold. The study demonstrated the near absence of malaria in several facility catchment populations. It highlighted the importance of community case management in supplementing routine surveillance, thereby improving the precision of malaria transmission estimates. These findings support the further application of the FFI framework to accelerate progress toward malaria elimination in the Dominican Republic.