To our knowledge, this is the first, most comprehensive study on risk factors for Anopheles larvae presence and malaria transmission across regions of Madagascar with distinct ecologies, climates, and transmission patterns. The goals of this study were two-fold: (1) to understand potential ecological drivers of Anopheles mosquito presence in peri-domicile habitats across rural Madagascar and (2) to identify socioeconomic, demographic, ecological, and geographic risk factors associated with malaria infection. We found that Anopheles larvae were more commonly found in peri-domicile aquatic agriculture as compared to other types of peri-domicile habitat, but that no other ecological variables predicted peri-domicile Anopheles larvae presence. We found differences in risk factors between study regions, with some regions demonstrating more complex suites of risk factors than others. For example, Anopheles presence in peri-domicile habitat predicted an increased risk of malaria in the SE and SW but was not in the WC. Current vector control efforts in Madagascar target adult mosquitoes, however, our data suggest that locally managed control of peri-domicile larval sources could be an effective, novel intervention in SE and SW Madagascar.
This study has limitations, namely diagnosis by RDT as discussed above, and the scope of larval sampling. More accurate testing methods, like PCR of malaria parasites, may reduce bias introduced by RDT sensistivity/specificity. Additionally, most sampling of larvae was limited to habitats within 25m of households. Malaria prevalence in some communities with few peridomicile habitats was high compared to regional averages (17% in site SW.3, few peridomicile habitats but bordered by valley of rice fields ~0.5km away). Anopheles gambiae adults have been observed to have maximum flight distances of 0.2 to 6.4km (47). Larval sources more distant from households may be responsible for risk but little evidence exists (46). In support, in the SE, hamlet ownership increased malaria risk. We observed that household proximity to larval sources did not predict malaria risk in the WC region, which had the highest mean prevalence. As such, vector management may need to account for more distant larval sources.
Anopheles species composition varied by site, region, habitat preference, and relative abundance (Figure 2). Within the Anopheles gambiae species complex, Anopheles gambiae and Anopheles arabiensis oviposit in freshwater sites, while Anopheles merus targets brackish habitats (42). These three species are known as major malaria vectors in Madagascar (42). Anopheles mascarensis is a secondary major vector in Madagascar, and breeds in fresh and brackish habitats (48). Recent findings showed Anopheles coustani was a competent malaria vector in Madagascar for both P. vivax and P. falciparum (49). Anopheles coustani oviposit in freshwater and brackish habitats (30, 43). Anopheles squamosus is suspected to mainly transmit malaria in the HP region due to high abundance (48). The results of our modelling of larval habitat preference align with other studies in Madagascar. Marrama et al. (1995) showed rice fields had the highest percentage of mosquito larvae and provide half of all positive larval breeding habitats (50). Zohdy et al. (2016) demonstrated that Anopheles species are the dominant mosquitoes in agricultural and village settings, and are the minority in in forest settings (28).
According to the national Malaria Indicator Survey conducted between May and July 2016, countrywide prevalence of malaria infection in children under 5 is about 6%, and regional prevalence ranges from 1%-15% (51). Though our data are not nationally representative, our site prevalence estimates indicate high heterogeneity within regions, and that regional/national statistics should be interpreted cautiously. Our data demonstrate high variation between sites and alarmingly high prevalence in the WC and SE. In the WC, drivers of increased malaria risk were higher urbanicity, living in a household with a less-educated head, and not using a bed net. In the SE, drivers of increased malaria were the presence of vector larvae in peridomicile habitat and lower urbanicity. Bed net usage significantly reduced an individual’s risk for malaria in the WC. However, coverage in the WC was significantly lower than coverage in the SE and SW (χ2-test for independence, p<0.001). Our data suggest that interventions focused on improving bed net coverage in the WC, as well as education and behavior change campaigns, are high priority.
Different risk factors are associated with malaria infection in each region (Table 2, Figure 3). Each region varies in the site-, household-, and individual-level factors that are most important for predicting infection and determination of suitable control strategies. In the SE and the SW, the presence of Anopheles larvae was associated with an increased risk of malaria infection. The SW region is dry, with average annual rainfall below 400mm (52). This value is low compared to other study regions and accompanies a shift in dominant agriculture from flooded rice farming to dry-land farming. One out of the six SW sites had any form of aquatic agriculture, however, Anopheles larvae were found in three of the six surveyed sites. In sites without rice fields, larvae were found in small, man-made ponds. Additionally, in both sites, children under 5 had malaria infections, indicative of local transmission as children are less likely to travel outside the community (12). Anopheles larvae presence in these two sites was confined to a few small, easily identifiable locations that may be driving sustained transmission at the community level. In the SW region, application of larvicide to these few, identifiable Anopheles habitats may reduce malaria transmission burden significantly. Since water in these habitats is used for human consumption, a non-toxic larvicide is recommended (53).
The relationship between rurality and malaria risk also demonstrates regional heterogeneity. In the SE region, individuals living closer to urban areas were at lower risk of malaria infection. This may reflect a relationship between less remote sites and more distant Anopheles breeding habitats, more accessible healthcare, cash income from markets, and wage labor access which can support prevention or treatment (54).The opposite was found in the WC region. Here, the urban center is coastal and near a gradually widening river estuary area. Communities near this center sit closer to slow-moving wate, some of which has been diverted to flooding rice fields. Increases in wetland areas around communities proximate to the coastal urban center may increase Anopheles breeding habitats near less remote sites. Anopheles mascarensis and Anopheles coustani have been found in brackish water, and both were present five of six sites in the WC. In the SW and HP, rurality had no significant effect on malaria prevalence. Of note, more remote and less remote sites generally had better road access in the SW and HP than in the SE and WC. As such, living in more remote versus less remote sites in the SW and HP may not be as different in ecological, occupational, and accessibility factors as in the SE and WC.
In the SE region, individuals were at higher risk of having malaria if they lived in a household owning a hamlet. Individuals in hamlets may be at higher risk of malaria infection because hamlets may be in locations with higher vector concentrations (i.e., near flooded rice paddies), have less protective household structures, or have fewer bed nets. Households owning hamlets may also be most dependent on seasonal agriculture, which may bring people into closer, more frequent contact with Anopheles-dense habitats. In contrast, households owning hamlets in the WC were protected against malaria infection, which may be explained by local ecology. In both regions, hamlets are located away from communities and other hamlets, but in the SE region, they are located in ecology rife with mosquito habitats. Hamlets in the WC are in drier habitats where mosquito density may be lower than the center of communities, often located near water sources. Our findings indicate the need for future studies on vector exposure risk for hamlet owners and agricultural activity in the SE and WC of Madagascar.
In the WC, heads of households with a high school education or higher imparted lower malaria infection risk for all household members. Other studies demonstrate that individuals living in households with more educated household leaders have improved awareness of family health (55). The observed association in the WC may indicate the need to increase awareness of symptoms and prevention mechanisms, such as bed net use.
In the SE and WC regions, individuals 5+ years old and males were more susceptible to malaria infection compared to women and children under five. Women and children under five may spend more time nearer to households where transmission is less severe. Furthermore, individuals not using a bed net were not at different risk than individuals using a bed net. However, bed net use in the SE was 95.1%, so sample size of those not using bed nets was limited. These relationships show that transmission is likely occurring outside households/community bounds. Bed nets remain effective in reducing malaria burden overall, but additional control measures may be needed in these areas to reduce transmission, such as better testing and treatment for high-risk individuals in the SE. Contrarily, bed net use in the WC protected against malaria infection. This association demonstrate that transmission of malaria may occur in households and communities proper. Therefore, distribution of bed nets in the WC region to ensure universal coverage may be an effective control strategy.
The results of this study demonstrate the need for spatially targeted malaria control strategies in Madagascar. Each geographical context should be considered independently prior to employing interventions. Malaria stratification, the process of classifying regions based on malaria risk to aid in resource distribution, has existed since the 1940s (56). However, modern techniques like remote sensing, coupled with a growing case study literature, have amplified effectiveness of this stratification (57, 58). Though the national malaria control program of Madagascar stratifies by broad geographical zones, more granular stratification of control measures is needed. Our results help clarify risk factors to augment and tailor current malaria control programs according to socioeconomics, ecology, and geography. These data suggest the potential merits of sub-national, sub-regional stratification of malaria control efforts, specifically: (i) applying larvicide to peri-domicile habitats in the SE and SW, (ii) additional effort in distributing bed nets and prevention education in all regions but most heavily in the WC, and (iii) targeting all family members, but most acutely males over the age of 5, for active case detection in the SE and WC.