Spatio-temporal modelling of the first Chikungunya epidemic in an intra-urban setting: The role of socioeconomic status, environment and temperature

Abstract

Three key elements are the drivers of Aedes-borne disease: mosquito infestation, virus circulating, and susceptible human population. However, information on these aspects is not easily available in low- and middle-income countries. We analysed data on factors that influence one or more of those elements to study the first chikungunya epidemic in Rio de Janeiro city in 2016. Using spatio-temporal models, under the Bayesian framework, we estimated the association of those factors with chikungunya reported cases by neighbourhood and week. To estimate the minimum temperature effect in a non-linear fashion, we used a transfer function considering an instantaneous effect and propagation of a proportion of such effect to future times. The sociodevelopment index and the proportion of green areas (areas with agriculture, swamps and shoals, tree and shrub cover, and woody-grass cover) were included in the model with time-varying coefficients, allowing us to explore how their associations with the number of cases change throughout the epidemic. There were 13627 chikungunya cases in the study period. The sociodevelopment index presented the strongest association, inversely related to the risk of cases. Such association was more pronounced in the first weeks, indicating that socioeconomically vulnerable neighbourhoods were affected first and hardest by the epidemic. The proportion of green areas effect was null for most weeks. The temperature was directly associated with the risk of chikungunya for most neighbourhoods, with different decaying patterns. The temperature effect persisted longer where the epidemic was concentrated. In such locations, interventions should be designed to be continuous and to work in the long term. We observed that the role of the covariates changes over time. Therefore, time-varying coefficients should be widely incorporated when modelling Aedes-borne diseases. Our model contributed to the understanding of the spatio-temporal dynamics of an urban Aedes-borne disease introduction in a tropical metropolitan city.

A Bayesian hierarchical model for disease mapping that accounts for scaling and heavy-tailed latent effects

“…All five models find that there is a fairly strong negative association between the socio-development index and the number of cases, meaning that richer districts have lower disease risks. This finding is consistent with previous studies conducted in Rio de Janeiro, one investigating the first chikungunya epidemic in the city 8 and another also investigating Zika, but using a different methodological approach 31 . These studies, including ours, indicate that improving sanitary conditions and reducing socio-economic disparities are of paramount importance to fight Aedes-borne diseases.…”

“…In Rio de Janeiro, a very unequal city, the socio-development index ranges from 0.282 (in Grumari, a neighbourhood in the West region) to 0.819 (in Lagoa, South region) 9 . In a previous study from our group, the socio-development index presented a strong inverse association with the spatio-temporal distribution of chikungunya cases in Rio de Janeiro 8 .…”

“…The diversity of the territory of Rio de Janeiro is possibly an important factor influencing this. Rio's territory is heterogeneous in terms of demographic, socio-economic, and environmental characteristics that are involved in the distribution of Aedes-borne diseases 8 .…”

Revisiting Transfer Functions: Learning About a Lagged Exposure-Outcome Association in Time-Series Data

“…We described TF modeling to estimate lagged associations when lag length is unknown a priori. Previous applications of TFs include environmental time-series analysis to capture decaying associations between arbovirus incidence and temperature [23] and interrupted time-series analysis to capture the persistent effect of interventions [11,24]. TF modeling requires pre-specification of the shape of a lag structure from investigators' prior knowledge followed by their selection based on model fit.…”

Transfer functions: learning about a lagged exposure-outcome association in time-series data

“…The transfer function approach is particularly useful when lag length may vary across many food categories and/or store-neighborhood socio-economic status, the latter being particularly relevant when investigating the role of food promotions in driving geographic and socio-economic disparities of diets. The transfer function can also be used in climate-related environmental time-series analysis to investigate spatially heterogeneous persistence of lagged association between temperature and arbovirus incidence 17 . The relatively simple Koyck lag function may also be used to capture the monotonically decaying lagged association of high ambient temperature with mortality and morbidity, although there may be a need to account for mortality displacement in some geographic regions 6 .…”