We collated 2 different datasets to assess clustering of
malaria infections within households: (i) demographic health survey (DHS) data, integrating household information and patent
malaria infection, recent
fever, and recent treatment status in children; and (ii) data from cross-sectional and reactive detection studies containing information on the household and
malaria infection status (patent and subpatent) of all-aged individuals. Both datasets were used to assess the odds of
infections clustering within index households, where index households were defined based on whether they contained
infections detectable through one of 3 programmatic strategies: (a) Reactive Case Detection (RACD) classifed by confirmed clinical cases, (b) Mass Screen and Treat (MSAT) classifed by febrile, symptomatic
infections, and (c) Mass Test and Treat (
MTAT) classifed by
infections detectable using routine diagnostics. Data included 59,050
infections in 208,140 children under 7 years old (median age = 2 years, minimum = 2, maximum = 7) by microscopy/rapid diagnostic test (RDT) from 57
DHSs conducted between November 2006 and December 2018 from 23 African countries. Data representing 11,349
infections across all ages (median age = 22 years, minimum = 0.5, maximum = 100) detected by molecular tools in 132,590 individuals in 43 studies published between April 2006 and May 2019 in 20 African, American, Asian, and Middle Eastern countries were obtained from the published literature. Extensive clustering was observed-overall, there was a 20.40 greater (95% credible interval [CrI] 0.35-20.45; P < 0.001) odds of patent
infections (according to the DHS data) and 5.13 greater odds (95% CI 3.85-6.84; P < 0.001) of molecularly detected
infections (from the published literature) detected within households in which a programmatically detectable
infection resides. The strongest degree of clustering identified by polymerase chain reaction (PCR)/ loop mediated isothermal amplification (LAMP) was observed using the
MTAT strategy (odds ratio [OR] = 6.79, 95% CI 4.42-10.43) but was not significantly different when compared to MSAT (OR = 5.2, 95% CI 3.22-8.37; P-difference = 0.883) and RACD (OR = 4.08, 95% CI 2.55-6.53; P-difference = 0.29). Across both datasets, clustering became more prominent when transmission was low. However, limitations to our analysis include not accounting for any
malaria control interventions in place,
malaria seasonality, or the likely heterogeneity of transmission within study sites. Clustering may thus have been underestimated.
CONCLUSIONS: