Clustering of subpatent infections in households with asymptomatic rapid diagnostic test-positive cases in Bioko Island, Equatorial Guinea independent of travel to regions of higher malaria endemicity: a cross-sectional study

Abstract

Background Prevalence of falciparum malaria on Bioko Island remains high despite sustained, intensive control. Progress may be hindered by high proportions of subpatent infections that are not detected by rapid diagnostic tests (RDT) but contribute to onward transmission, and by imported infections. Better understanding of the relationship between subpatent infections and RDT-detected infections, and whether this relationship is different from imported versus locally acquired infections, is imperative to better understand the sources of infection and mechanisms of transmission to tailor more effective interventions. Methods Quantitative reverse transcriptase polymerase chain reaction (qRT-PCR) was performed on a sub-set of samples from the 2015 Malaria Indicator Survey to identify subpatent infections. Households with RDT(+) individuals were matched 1:4 with households with no RDT(+) individuals. The association between living in a household with an RDT(+) individual and having a subpatent infection was evaluated using multivariate hierarchical logistic regression models with inverse probability weights for selection. To evaluate possible modification of the association by potential importation of the RDT(+) case, the analysis was repeated among strata of matched sets based on the reported eight-week travel history of the RDT(+) individual(s). Results There were 142 subpatent infections detected in 1,400 individuals (10.0%). The prevalence of subpatent infections was higher in households with versus without an RDT(+) individual (15.0 vs 9.1%). The adjusted prevalence odds of subpatent infection were 2.59-fold greater (95% CI: 1.31, 5.09) for those in a household with an RDT(+) individual compared to individuals in a household without RDT(+) individuals. When stratifying by travel history of the RDT(+) individual, the association between subpatent infections and RDT(+) infections was stronger in the strata in which the RDT(+) individual(s) had not recently travelled (adjusted prevalence odds ratio (aPOR) 2.95; 95% CI:1.17, 7.41), and attenuated in the strata in which recent travel was reported (aPOR 1.76; 95% CI: 0.54, 5.67). Conclusions There is clustering of subpatent infections around RDT(+) individual(s) when both imported and local infection are suspected. Future control strategies that aim to treat whole households in which an RDT(+) individual is found may target a substantial portion of infections that would otherwise not be detected.

Altitude, not potential larval habitat availability, explains pronounced variation in Plasmodium falciparum infection prevalence in the western Kenya highlands

“…Yet, the presence of microscopy-positive infections was not a predictor for submicroscopic infections households, and thus cannot guide control strategies towards further targeted treatment. This contrasts with observations in other sites [ 70 ] and with numerous studies where prevalence of asymptomatic and/or submicroscopic infections was higher in household members of clinical cases, a pattern exploited by reactive case detection programs [ 16 , 36 , 71 – 73 ]. The contrasting findings might be explained by differences in transmission intensity.…”

Altitude, not potential larval habitat availability, explains pronounced variation inPlasmodium falciparuminfection prevalence in the western Kenya highlands

“…Yet, the presence of microscopy-positive infections was not a predictor for submicroscopic infections households, and thus cannot guide control strategies towards further targeted treatment. This contrasts with observations in other sites [64] and with numerous studies where prevalence of asymptomatic and/or submicroscopic infections was higher in household members of clinical cases, a pattern exploited by reactive case detection programs [16,[65][66][67][68]. The contrasting findings might be explained by differences in transmission intensity.…”

Impact of a six-month COVID-19 travel moratorium on Plasmodium falciparum malaria prevalence on Bioko Island, Equatorial Guinea

“…The MIS is carried out annually on Bioko Island between August and September has been previously described. 16,17 Brie y, information on malaria risk factors, including off island travel in the previous eight weeks, is collected from selected households. Sampling units are geographically de ned enumeration areas (EAs); under this scheme all households were eligible for selection into the survey through a strati ed, single-cluster survey design.…”

“…Data on malaria infection was collected before (2019) and after (2020) the travel moratorium through a household-based malaria indicator survey (MIS) with households selected from the whole island. 16,17 Using historical reported travel from MIS between 2015 and 2018, we used the distribution of travel frequency by enumeration area (EA) to classify areas as "high travel" (in the top quartile) or "low travel" (bottom quartile). We then compared the change in malaria prevalence in high and low travel areas before and after the travel mortarium to estimate the contribution of imported malaria to prevalence in high travel areas.…”