Environmental risk factors were associated with the probability of finding households with parasitemic residents using RCD as demonstrated in other studies in Zambia.13 In the low transmission setting of Choma District, Zambia, identifying streams located near index households to guide and direct screening has the potential to improve RCD and affect transmission by identifying households with asymptomatic infections. These findings are in line with a previous study conducted within the same study area in 2008 where it was shown that households within 1.98 km from a third-order stream were 2.8 (95% CI: 1.2 – 6.9) times more likely to have an RDT positive resident than those within 6 km.26
Although no associations were found with the other environmental risk factors such as distance to a main road, elevation, season, and number and presence of animal pens, non-parametric comparisons between positive and negative secondary households exhibited a trend of increased malaria risk for these risk factors.19,20,21,26,36,37,38 The risk associated with animal pens varies in the literature depending on vector behavior. An. arabiensis has been reported to be anthropophilic in southern Zambia but also displays zoophilic habits by feeding opportunistically on non-human blood sources.36 Other vectors besides An. arabiensis might also have an important role in transmission as P. falciparum infected An. squamosus exhibiting outdoor zoophagic feeding behavior were recently identified in the area. Early studies in Choma District, Zambia found that ownership of cattle reduced the risk of P. falciparum infection by 87% while others have found less conclusive evidence.36,37 For elevation, however, it has been clearly shown that increased elevation offers protection against malaria infection.13,20,24,26,38 Since index and secondary households in this study were located only <300 meters from each other and variation in elevation was minimal, it is unlikely that the elevation would influence malaria risk at this spatial scale. Distance from the index household marginally increased the probability of finding positive secondary households (OR: 1.24, 95% CI: 0.98 – 1.58), in contrast to other studies. Larsen et al. observed a decreased risk for neighboring households located further away, and Bulterys et al. found an adjusted OR of 0.26 (95% CI: 0.07 – 0.98) as distance between households increased. Finally, distance to the main road has often been treated as an indicator of increased malaria risk. In Chongwe District, Zambia, the odds of RDT positive households increased by 5% for every 500-meter increase in distance from the road.39 As we only looked at proximity to the main road, it is possible that constant use from vehicles, animal carts, and people prevented mosquito breeding sites from developing undisturbed, reducing this as a risk factor. Less frequently used subsidiary roads and rural paths (not included in the analysis) could provide more opportune mosquito breeding sites closer to residences as their composition allows for easier accumulation of aquatic breeding sites compared to the tarmac and concrete main road.
The use of environmental risk factors for malaria risk prediction is a common approach to malaria control and has been employed in various transmission settings around the world. For example, in Chimoio, Mozambique a GIS-based spatial model was designed to estimate areas of risk using temperature, precipitation, altitude, slope, distance to water bodies, distance to roads, normalized difference vegetation index (NDVI), land use, and land cover.40 The model identified that 4% of Chimoio was at high risk for contracting malaria, with precipitation as a key risk factor for the entire area studied.40 Another study in south Sumatra, Indonesia used ordinary least square and geographically weighted regression to show that altitude, distance to forest, and rainfall determined overall malaria incidence with considerable heterogeneity at the village level.41 These findings were consistent with other studies in Cambodia, Addis Ababa, Ethiopia, and Rondôia, Brazil.41 Despite the extensive literature on environmental risk factors for malaria, their application within the context of RCD has been limited.
Many studies evaluating the efficiency of RCD highlight its inability to halt infections in areas of low transmission due to the use of less sensitive RDTs, travel-related infections, and large budgetary requirements.2,18 A major concern for RCD-based strategies is that asymptomatic individuals will be missed if no clinical cases report to CHWs.42 A survey in coastal Kenya found that asymptomatic and symptomatic infections do not necessarily overlap spatially, and that clusters of symptomatic infections have greater temporal stability over more than ten years.42 Another issue often highlighted is the different criteria and screening radii employed by countries to define and recruit neighboring households.42 For example, RCD data from four villages in the Myanmar-Thailand border determined that RCD would only be successful at a radius of 150 meters, and any screening occurring beyond this radius would not perform better than random screening.2 Another study in Pailin Province in western Cambodia screened the nearest five households for every fifteenth index case and ten nearest households for every 30th index case. Using this approach, they predicted only 40% of infections and concluded that RCD was not recommended in a setting targeting elimination.43 However, with the shortcomings of a circular radius and the various implementation challenges, for RCD to be an effective method for malaria elimination in these low-endemic countries a tailored approach adapted to the local parasite epidemiology, vector biology, and living/working environment of the community must be considered key for it to succeed.
This study used environmental risk factors for malaria to characterize the low transmission setting to improve RCD efficiency. Previous work on enhancing RCD efficiency in Southern Province, Zambia has also shown that time-invariant measures of the environment, such as the topographic position index (TPI; measure of an area’s relative elevation to find slopes, valleys, and ridges), the convergence index (CI; measure of an area’s propensity to pool water), median enhanced vegetation index (EVI; measure of vegetation density), and the topographical wetness index (TWI; measure of water flow) were stronger predictors for identifying parasitemic individuals than demographics of incident symptomatic cases.13,26 They showed that ridges and upper slopes (at a TPI scale of 270 m) and wetter regions (TWI > 10.2) were associated with finding more parasitemic individuals during RCD.13 These findings, along with the current study, support the significance of water bodies in improving the efficiency of RCD. Third through fifth-order streams are mid-level streams that may not always be suitable for larval development; however, larvae have been collected from water at the edges of these streams (unpublished findings). During the dry season, as water accumulates into smaller pools, they become ideal larval development sites. These streams can also serve as important markers for nearby areas with similar high water table harboring larvae.13,26,38,44 And as these streams can be challenging to locate depending on size and season, spatial risk maps with topographical measures, such as CI and TWI, can offer guidance to CHWs to possibly reach clusters of asymptomatic carriers otherwise missed during regular RCD screening. Other water sources such as dams, are also important determinants for malaria transmission as was shown in Ethiopia, where reservoir water level management suppressed larval development.45
In addition to the use of streams as a screening tool, RCD efficiency could benefit from the combined use of RDTs and highly sensitive qPCR. For this study region, the overall parasite prevalence (3.7%) was mostly driven by qPCR as parasite prevalence by RDT was only 1.3%. Although costly, sensitive molecular methods such as qPCR are critical in low endemic settings to detect potential parasite-transmitting asymptomatic carriers. Even ultra-sensitive RDTs (uRDTs), such as the new AlereTM Malaria Ag P.f uRDT which was designed for low transmission settings, may not be sufficiently sensitive alternatives to SD Bioline PfHRP2 RDTs.46 The AlereTM us-RDT has a ten-fold lower limit of detection for PfHRP2 compared to regular RDTs but missed 56% of PCR-detectable P. falciparum infections in a low-endemic setting in Myanmar, and in Papua New Guinea the test missed 50% of P. falciparum infections otherwise detectable by qPCR.47
There were several limitations to this study. Restricting environmental variables within set radii raises concerns for edge-effect associations. For example, animal pens located just outside the 100-meter radius were not counted as belonging to neighboring households, thus potentially underestimating the number of animal pens associated with a household. Not all environmental risk factors important for malaria transmission were evaluated. Vegetation cover, an important indicator of available mosquito habitat, could also be a useful screening tool and has yet to be evaluated for RCD strategies.13,48,49 Finally, the risk factors shown to be associated with positive households in this low transmission setting of Choma District, Zambia may not be applicable in other endemic regions.
The effectiveness of RCD ultimately depends on the number of cases found and treated in a timely manner and the resources allocated during implementation.2 However, it is important to consider the added value of a tailored RCD approach based on demographic and ecological risk factors and more sensitive diagnostic tools to fully reap the benefits of this screening method to achieve malaria elimination.1 In Cambodia, where infection is linked to occupation and mobility, an expanded RCD approach was implemented in which individuals who were coworkers of a symptomatic index case in settings of high malaria infection, such as forests and plantations, were also screened.1 The expanded RCD had a detection rate of 3.9% compared to 0 – 2% using the classic RCD approach.1 Through this adapted RCD design, Cambodia’s National Malaria Control Program sought to identify and treat asymptomatic individuals within a discrete population whose members shared a common malaria risk through occupations such as logging, mining, and migrant labor.1 The environmental risk factors identified in this study demonstrate that, even in low transmission settings, a tailored approach is possible; however, further work is needed to fully understand how these risk factors vary across district and season and how they can be modified to guide RCD strategies nationally in Zambia.