Our study of arbovirus vector dynamics in three Latin American countries, chosen to reflect different eco-epidemiological settings, identified substantial variations in household-level Aedes mosquito density, both within and among study sites, and over time. Importantly, we identified several relevant determinants of vector density across study sites, while other factors were more important in certain local contexts. These findings can help to inform disease mitigation strategies by identifying modifiable risk factors that can be targeted for disease prevention and control, recognizing that local tailoring of solutions will be required.
We found considerable socio-economic differences across the three study sites when we applied a common measure of asset-based household wealth. Interestingly, we identified a complex relationship between household-level wealth and neighborhood-level socio-economic profile in terms of their interactive effect on Aedes density, wherein the highest arbovirus vector density was observed in lowest wealth households from neighborhoods of high socio-economic profile. This suggest that residents of poor households may differ in their exposure to Aedes mosquitoes compared with residents of wealthier households in the same neighborhood. In wealthier neighborhoods (i.e. low proportion of lowest wealth households), Aedes density decreased as household wealth increased, while in poorer neighborhoods (i.e. high proportion of lowest wealth households), Aedes density increased with increasing household wealth. In contrast to the highest Aedes density being observed in lowest wealth households situated in wealthier neighborhoods, lowest wealth households in poorer neighborhoods displayed the lowest Aedes density. A combination of factors could explain these results: in wealthier neighborhoods, lower household wealth, possibly accompanied with lower arbovirus knowledge, poorer household structure and weaker water and waste management [24, 26, 27, 39–41], are potentially accompanied with unequal mosquito control by municipal authorities, which is usually targeted to neighborhoods with higher disease incidence . Wealthier households may also contribute to Aedes breeding, potentially through landscape and vegetation elements in and around the household, such as managed water containers for decorative plants and green areas around the household . This could potentially support our results suggesting larger Aedes density in wealthiest households, compared to lowest wealth households, in poorer neighborhoods. These effects should be investigated further to discriminate the main determinants of Aedes breeding and identify appropriate control methods. Nonetheless, these results illustrate the importance of considering equity at the household level when targeting mosquito control interventions, which are usually delivered at the neighborhood level.
Our study results indicate that meteorological, climatic and socio-economic variation have all likely contributed to shaping conditions conducive to Aedes density in our study across the three sites. This is reflected by the six-fold increase in R2 when accounting for temporal and spatial random-effects (i.e. conditional R2 compared to marginal R2) parameters in our multivariable regression analyses across study sites. In addition to these spatial and temporal effects, other fine-scale processes are likely to have played a role in shaping individual household responses to dealing with Aedes mosquitoes. This is reflected by the differences we observed in the predictive power of variables that we investigated among and across the three study sites. Analyses of households of Ibagué, Colombia, suggested a major role of variables associated with household occupants’ characteristics, such as crowding, wealth index, and respondent arbovirus knowledge, but also indoor and outdoor household components. Number of occupants in a household, number of floors, and distance between households all affected the observed densities of Aedes mosquitoes, with a higher degree of crowding associated with higher Aedes density. Indeed, an increase in unplanned urbanization, which is typically the result of a rapid increases in human population density, is associated with the creation of new suitable habitats for Aedes mosquitoes [17, 25, 43]. We found that wealthier households where occupants displayed higher knowledge of arboviruses were associated with lower Aedes density. Household wealth is widely known to affect mosquito vector density, potentially through better access to mosquito control methods [22, 26, 39], and the effect of knowledge about mosquito vectors and arboviruses on household mosquito vector density and arboviral disease risk has also been the subject of many studies [27, 44, 45]. We also found major effects of presence of decorative vegetation and landscape elements around the household. Presence of decorative vegetation and green areas in and around the household, and absence of water bodies near the household, all led to higher Aedes densities. These effects can be explained by the ecology of Aedes mosquitoes, which frequently use discarded containers filled with exposed and shady standing water as breeding sites, and use water bodies less frequently, especially those with flowing water and/or situated more than 25 meters away from the household . In a study in the United States, managed container habitats in higher wealth neighborhoods, such as those used for decorative plants, and vegetation around households with high abandonment were both associated with higher Aedes density .
In contrast, analyses of households in Manta, Ecuador, suggested a major role of variables associated with water and waste management, and humidity. Presence of a large water container in or around the household was significantly associated with lower Aedes density. On the other hand, humidity, difficulty in obtaining water and weekly waste collection frequency were all significantly associated with larger Aedes density. Efficient water management, such as a storage dedicated container [40, 41, 47], and frequent waste management, which prevents formation of mosquito breeding sites [24, 48], are typically associated with better mosquito control.
Analyses of households in Posadas, Argentina, and across the three sites, suggested a major role of a mixture of these effects. In Posadas, households that were owned and that did not display structural points of entry for mosquitoes, displayed lower Aedes density, while unpredictable access to water by occupants and storing water were associated to higher Aedes density. In analyses across the three study sites, fewer household occupants, higher knowledge of arboviruses by occupants, living fewer years in the household, wealthier households, and absence of structural points of entry for mosquitoes into the household, were all associated with lower Aedes density. Also, across study sites, reporting that water containers were regularly emptied by occupants, and absence of decorative vegetation, led to lower Aedes density. Interestingly, using pesticides in Manta, Ecuador, and using bed nets across study sites showed a positive association with Aedes density. These results may either reflect that use of such mosquito control methods is driven by need, e.g. when mosquitoes are present in high numbers, or that their effect on diurnal mosquitoes is limited. Altogether, our results point to modifiable risk factors related to water and waste management that should be the target of future interventions. These interventions should be prioritized in lower wealth households, regardless of the neighborhood’s overall socioeconomic status, and include an educational component to raise awareness of risks associated with Aedes mosquito presence and potential control methods.
Several logistical challenges arose during our study, which led to a delay in the start of the study timeline for two of our study sites. As a result, the study timeline differed across the three sites and the entire timeline for the study lasted 21 months, instead of 12 months. However, we achieved some temporal overlap in study timeline among the three sites for comparability. In addition, two study sites did not have the expected total number of households per neighborhood for high socio-economic areas. For this reason, we sampled households in more than two neighborhoods of high socio-economic status in these study sites. This could have led to a difference in spatial resolution between neighborhoods of high versus low socio-economic status. However, all neighborhoods were located within close proximity to each other. Our regression analyses incorporated random effects for the month of sampling, nested in sampled neighborhood, nested in study site, therefore results of these analyses are unlikely to have been affected by potential temporal and spatial biases.