Dinâmica da transmissão de chikungunya e associação com indicadores de vulnerabilidade social no Brasil

Abstract

Introduction: In Brazil, Chikungunya epidemics have been recurrent since the virus was introduced into the country in 2014. Environmental, social, economic and political factors influence the transmission dynamics and emergence of the epidemic. The use of advanced technologies, such as geoprocessing and spatial analysis, plays a crucial role in identifying areas at risk for the incidence of the disease and in planning surveillance strategies and implementing control actions. Objective: To analyze the spatial and temporal dynamics of the incidence of Chikungunya and the association with indicators of social vulnerability in Brazil. Materials and methods: Ecological, exploratory and analytical study, which was conducted in Brazil. Records of cases reported and confirmed by clinical and clinical-epidemiological criteria of Chikungunya between the years 2017 and 2023 obtained from SINAN were used. Data relating to social vulnerability indicators were collected on the official platform of the United Nations Development Program and the Social Vulnerability Atlas. Data analysis occurred in stages: 1) characterization of the epidemiological profile of Chikungunya incidence; 2) prediction of new cases and an interrupted time series using ARIMA considering the Covid-19 pandemic; 3) temporal trends using the Prais-Winsten regression model and determination of annual percentage change; 4) spatial distribution using the local empirical Bayesian method, global and local Moran index; and 5) association between social vulnerability indicators and the incidence of Chikungunya applying global spatial regression and multiscale geographically weighted regression. Results: The analyzes showed a total of 487,775 cases of Chikungunya diagnosed in Brazil between 2017 and 2023 with an incidence of 232.1/100 thousand inhabitants, with a higher incidence in the northeast region (58.8/100 thousand inhabitants). The time series demonstrated the impact of seasonality on the incidence of the disease with an average of 2,000 cases monthly/year. With the Covid-19 pandemic in 2020, the interrupted time series demonstrated a reduction in notification. In the first ARIMA model, it was not possible to predict new cases. In the second model, it was observed that without the influence of the pandemic, there would be an increase in cases. The temporal trend showed that in 2017, the incidence increased by 1.90% and there was a decreasing trend in the period from 2020 to 2021 with a reduction of -0.93% of cases. The spatial distribution was heterogeneous and showed positive spatial autocorrelation (I: 0.71; p <0.001). The highest risk areas were concentrated in the northeast region. The factors associated with the incidence of Chikungunya in Brazil were: the social vulnerability index (R2: -1.28; p=0.003), IVS income and work (R2: 1.93; p=<0.001), percentage of people in households with inadequate water supply and sanitation (R2: 0.80; p=0.002), percentage of people aged 15 to 24 who do not study, do not work and have a per capita household income equal to or less than half the minimum wage ( R2: 0.16; p=0.003), percentage of people living in households with a per capita income of less than half the minimum wage and who spend more than one hour commuting to work (R2: 0.94; p<0.001), percentage of the population in households with density > 2 (R2: 0.58; p <0.001). The local regression pointed to the northeast region with the highest association values (t: 0.45; p <0.001), with the percentage of people in households with inadequate water supply and sewage disposal (R2: 0.80; p=0.002) and percentage of the population in households with density > 2 (R2: 0.58; p <0.001) the most impactful factors. Conclusion: The study allowed an integrated analysis of the transmission dynamics of Chikungunya in Brazil and the associated factors. The integration of technologies and multifaceted analyzes allows the mapping of risk areas and evidence for the development of public health strategies.