Association between the proportion of Plasmodium falciparum and Plasmodium vivax infections detected by passive surveillance and the magnitude of the asymptomatic reservoir in the community: a pooled analysis of paired health facility and community data

Abstract

Summary Background Passively collected malaria case data are the foundation for public health decision making. However, because of population-level immunity, infections might not always be sufficiently symptomatic to prompt individuals to seek care. Understanding the proportion of all Plasmodium spp infections expected to be detected by the health system becomes particularly paramount in elimination settings. The aim of this study was to determine the association between the proportion of infections detected and transmission intensity for Plasmodium falciparum and Plasmodium vivax in several global endemic settings. Methods The proportion of infections detected in routine malaria data, P(Detect), was derived from paired household cross-sectional survey and routinely collected malaria data within health facilities. P(Detect) was estimated using a Bayesian model in 431 clusters spanning the Americas, Africa, and Asia. The association between P(Detect) and malaria prevalence was assessed using log-linear regression models. Changes in P(Detect) over time were evaluated using data from 13 timepoints over 2 years from The Gambia. Findings The median estimated P(Detect) across all clusters was 12·5% (IQR 5·3–25·0) for P falciparum and 10·1% (5·0–18·3) for P vivax and decreased as the estimated log-PCR community prevalence increased (adjusted odds ratio [OR] for P falciparum 0·63, 95% CI 0·57–0·69; adjusted OR for P vivax 0·52, 0·47–0·57). Factors associated with increasing P(Detect) included smaller catchment population size, high transmission season, improved care-seeking behaviour by infected individuals, and recent increases (within the previous year) in transmission intensity. Interpretation The proportion of all infections detected within health systems increases once transmission intensity is sufficiently low. The likely explanation for P falciparum is that reduced exposure to infection leads to lower levels of protective immunity in the population, increasing the likelihood that infected individuals will become symptomatic and seek care. These factors might also be true for P vivax but a better understanding of the transmission biology is needed to attribute likely reasons for the observed trend. In low transmission and pre-elimination settings, enhancing access to care and improvements in care-seeking behaviour of infected individuals will lead to an increased proportion of infections detected in the community and might contribute to accelerating the interruption of transmission. Funding Wellcome Trust.

Sustainable Surveillance of Neglected Tropical Diseases for the Post-Elimination Era

“…Impact assessment surveys for monitoring the status of PC-NTD control programs prior to achieving elimination are usually designed as community- or school-based cluster surveys representative at the level of the implementation unit (typically an administrative or health district) or an evaluation unit representing a group of implementation units with similar transmission characteristics [ 18–20 ]. Rather than measure prevalence to a given level of precision, they are usually intended to classify units as above or below a target prevalence threshold.…”

Addressing Child Health Inequity Through Case Management of Under-Five Malaria in Nigeria: An Extended Cost-Effectiveness Analysis

“…The probability that a case is clinically treated was proxied by the proportion of febrile under-ves for whom treatment is sought, available from the 2018 Nigeria DHS. This approximation was based on the fact that fever in under-ves is a relatively good indicator of malaria in endemic countries [39]. Treated cases split into those treated with ACTs and without ACTs (e.g., chloroquine, sulphadoxinepyrimethamine), since ACT is the recommended standard rst-line treatment but not always used.…”

Estimating the hidden magnitude of the malaria community burden

“…In The Lancet Infectious Diseases, Gillian Stresman and colleagues, 4 representing a variety of malaria-endemic countries, propose a new approach to quantify, using a Bayesian model, the proportion of the total malaria infections in a given community detected through routine passive case surveillance. Such an indicator, termed P(Detect) and ranging from 0 to 100%, would use health system clinical data to derive an idea of the overall community transmission, by expressing the probability that an infection becomes symptomatic and potentially detected by the health system.…”