The recent success in decreasing malaria burden brings new challenges for measuring the disease burden, particularly in low transmission areas. Measuring transmission in these situations requires more sensitive tools, and more efficient strategies for conducting surveillance. We aimed to evaluate a health facility-based sampling strategy utilising existing surveillance systems to better understand malaria transmission dynamics in a pre-elimination setting.
We did health facility-based cross-sectional surveys involving all clinical attendees and accompanying people in 8 health facilities in Kulon Progo Regency, Indonesia. Blood smears and filter paper bloodspots were collected by trained staff. Standard microscopy tests were performed, and bead-based assays were used to measure antibody responses to 3 P.falciparum antigens (Pf.SEA, ETRAMP5, GEXP18) which elicit short-term responses, as a proxy for recent infection. Epidemiological data and household coordinates were recorded. We assessed the representativeness of samples, calculated the participation rates, proportion of parasitaemia and seroprevalence, and determined risk factors for exposure.
Approximately 2300 samples were taken during surveys in June-July 2017. Participation rates were >90% across all facilities. Age distribution of samples were consistent that of the general population. No microscopy positive cases identified. Seroprevalence to Pf.SEA, ETRAMP5 and GEXP18 were 5%, 0.7% and 0.8%, respectively. Seroprevalence ranged from 4.7% to 9.1% by facility, but was similar between patients and accompanying people (6.2% and 6.0%, respectively). A risk factor for exposure was age >15 years old (OR 3.96, 95%CI: 1.3-12.6). Moreover, exposure tended to be clustered and close to province border areas.
Health facility-based surveys involving patients and accompanying people could improve surveillance capacity to capture malaria transmission in communities. This method provides an alternative approach for quickly obtaining parasitological, serological and epidemiological data to better understand disease transmission dynamics. Further implementation research is needed to enable integration of these methods with existing surveillance systems.