RESUMO
Zika, a viral disease transmitted to humans by Aedes mosquitoes, emerged in the Americas in 2015, causing large-scale epidemics. Colombia alone reported over 72,000 Zika cases between 2015 and 2016. Using national surveillance data from 1121 municipalities over 70 weeks, we identified sociodemographic and environmental factors associated with Zika's emergence, re-emergence, persistence, and transmission intensity in Colombia. We fitted a zero-state Markov-switching model under the Bayesian framework, assuming Zika switched between periods of presence and absence according to spatially and temporally varying probabilities of emergence/re-emergence (from absence to presence) and persistence (from presence to presence). These probabilities were assumed to follow a series of mixed multiple logistic regressions. When Zika was present, assuming that the cases follow a negative binomial distribution, we estimated the transmission intensity rate. Our results indicate that Zika emerged/re-emerged sooner and that transmission was intensified in municipalities that were more densely populated, at lower altitudes and/or with less vegetation cover. Warmer temperatures and less weekly-accumulated rain were also associated with Zika emergence. Zika cases persisted for longer in more densely populated areas with more cases reported in the previous week. Overall, population density, elevation, and temperature were identified as the main contributors to the first Zika epidemic in Colombia. We also estimated the probability of Zika presence by municipality and week, and the results suggest that the disease circulated undetected by the surveillance system on many occasions. Our results offer insights into priority areas for public health interventions against emerging and re-emerging Aedes-borne diseases.
Assuntos
Aedes , Cadeias de Markov , Infecção por Zika virus , Zika virus , Infecção por Zika virus/transmissão , Infecção por Zika virus/epidemiologia , Colômbia/epidemiologia , Humanos , Animais , Aedes/virologia , Teorema de Bayes , Mosquitos Vetores/virologia , Surtos de DoençasRESUMO
Dengue, Zika, and chikungunya are arboviral diseases (AVD) transmitted mainly by Aedes aegypti. Rio de Janeiro city, Brazil, has been endemic for dengue for over 30 years, and experienced the first joint epidemic of the three diseases between 2015-2016. They present similar symptoms and only a small proportion of cases are laboratory-confirmed. These facts lead to potential misdiagnosis and, consequently, uncertainty in the registration of the cases. We have available the number of cases of each disease for the n=160 neighborhoods of Rio de Janeiro. We propose a Poisson model for the total number of cases of Aedes-borne diseases and, conditioned on the total, we assume a multinomial model for the allocation of the number of cases of each of the diseases across the neighborhoods. This provides simultaneously the estimation of the associations of the relative risk of the total cases of AVD with environmental and socioeconomic variables; and the estimation of the probability of presence of each disease as a function of available covariates. Our findings suggest that a one standard deviation increase in the social development index decreases the relative risk of the total cases of AVD by 28%. Neighborhoods with smaller proportion of green area had greater odds of having chikungunya in comparison to dengue and Zika. A one standard deviation increase in population density decreases the odds of a neighborhood having Zika instead of dengue by 18% but increases the odds of chikungunya in comparison to dengue by 18% and by 43% in comparison to Zika.
Assuntos
Aedes , Febre de Chikungunya , Dengue , Infecção por Zika virus , Zika virus , Animais , Brasil/epidemiologia , Febre de Chikungunya/epidemiologia , Dengue/epidemiologia , Surtos de Doenças , Humanos , Infecção por Zika virus/epidemiologiaRESUMO
The spatial distribution of surveillance-reported dengue cases and severity are usually analyzed separately, assuming independence between the spatial distribution of non-severe and severe cases. Given the availability of data for the individual geo-location of surveillance-notified dengue cases, we conducted a spatial analysis to model non-severe and severe dengue simultaneously, using a hierarchical Bayesian model. We fit a joint model to the spatial pattern formed by dengue cases as well as to the severity status of the cases. Results showed that age and socioeconomic status were associated with dengue presence, and there was evidence of clustering for overall cases but not for severity. Our findings inform decision making to address the preparedness or implementation of dengue control strategies at the local level.
Assuntos
Dengue , Dengue Grave , Teorema de Bayes , Colômbia/epidemiologia , Dengue/epidemiologia , Dengue/prevenção & controle , HumanosRESUMO
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.
Assuntos
Febre de Chikungunya/epidemiologia , Classe Social , Temperatura , Aedes , Animais , Brasil/epidemiologia , Vírus Chikungunya , Cidades/epidemiologia , Surtos de Doenças , Ecossistema , Humanos , Mosquitos Vetores , Análise Espaço-TemporalRESUMO
We develop a Bayesian approach to estimate the average treatment effect on the treated in the presence of confounding. The approach builds on developments proposed by Saarela et al in the context of marginal structural models, using importance sampling weights to adjust for confounding and estimate a causal effect. The Bayesian bootstrap is adopted to approximate posterior distributions of interest and avoid the issue of feedback that arises in Bayesian causal estimation relying on a joint likelihood. We present results from simulation studies to estimate the average treatment effect on the treated, evaluating the impact of sample size and the strength of confounding on estimation. We illustrate our approach using the classic Right Heart Catheterization data set and find a negative causal effect of the exposure on 30-day survival, in accordance with previous analyses of these data. We also apply our approach to the data set of the National Center for Health Statistics Birth Data and obtain a negative effect of maternal smoking during pregnancy on birth weight.
Assuntos
Teorema de Bayes , Viés , Peso ao Nascer , Cateterismo Cardíaco/estatística & dados numéricos , Simulação por Computador , Fatores de Confusão Epidemiológicos , Feminino , Humanos , Recém-Nascido , Estudos Observacionais como Assunto , Gravidez , Pontuação de Propensão , Tamanho da Amostra , Fumar/efeitos adversos , Análise de SobrevidaRESUMO
Em muitas aplicações os dados podem ser divididos em grupos que impõem, de forma natural, uma estrutura hierárquica. Por exemplo, se o interesse fosse avaliar o desempenho acadêmico dos departamentos de uma universidade, um modelo de regressão que relacionasse a força de trabalho do departamento e a sua produção acadêmica poderia ser adotado. É evidente que este modelo não representaria adequadamente os dados se não considerasse as possíveis diferenças dentro de cada centro de pesquisa. Por outro lado, poderia se pensar em adotar um modelo para cada centro, mas neste caso a informação sobre o conjunto seria ignorada. Um modelo mais realístico deveria permitir estimar o desempenho dos departamentos, dentro de seus respectivos centros, considerando também a informação sobre o todo, o que pode ser contemplado pela formulação dos modelos hierárquicos. Esta forma de modelagem permite a obtenção de estimativas individuais mais concentradas e se utiliza da informação contextual para contrair os estimadores individuais, em direção uns aos outros, amortecendo parte da variabilidade presente nestes estimadores. Dessa forma, considerando a importância dos modelos hierárquicos ou multiníveis, no desenvolvimento de aplicações em diversas áreas do conhecimento, o principal objetivo deste minicurso é fornecer uma introdução para formulação, ajuste e avaliação destes modelos, utilizando abordagem bayesiana. Para atingir tais objetivos este minicurso abordará inferência bayesiana, estatística computacional e modelagem de problemas reais. No que se refere ao desenvolvimento das aplicações envolvendo dados reais, será explorada a utilização de modelos hierárquicos em vários domínios de aplicação da Estatística como, por exemplo, Avaliação de Desempenho, Atuária, Demografia, Amostragem de Populações Finitas e Curvas de Crescimento. A análise desses modelos, estocasticamente complexos, demanda métodos numéricos eficientes para a integração e otimização. Para isto, será utilizado o software WinBUGS (Bayesian Analysis Using Gibbs Sampler for Windows), de domínio público, capaz de ajustar uma gama enorme de modelos