Your browser doesn't support javascript.
loading
Mostrar: 20 | 50 | 100
Resultados 1 - 2 de 2
Filtrar
Mais filtros











Base de dados
Intervalo de ano de publicação
1.
Biostatistics ; 23(1): 1-17, 2022 01 13.
Artigo em Inglês | MEDLINE | ID: mdl-32118253

RESUMO

Infectious disease models can be of great use for understanding the underlying mechanisms that influence the spread of diseases and predicting future disease progression. Modeling has been increasingly used to evaluate the potential impact of different control measures and to guide public health policy decisions. In recent years, there has been rapid progress in developing spatio-temporal modeling of infectious diseases and an example of such recent developments is the discrete-time individual-level models (ILMs). These models are well developed and provide a common framework for modeling many disease systems; however, they assume the probability of disease transmission between two individuals depends only on their spatial separation and not on their spatial locations. In cases where spatial location itself is important for understanding the spread of emerging infectious diseases and identifying their causes, it would be beneficial to incorporate the effect of spatial location in the model. In this study, we thus generalize the ILMs to a new class of geographically dependent ILMs, to allow for the evaluation of the effect of spatially varying risk factors (e.g., education, social deprivation, environmental), as well as unobserved spatial structure, upon the transmission of infectious disease. Specifically, we consider a conditional autoregressive (CAR) model to capture the effects of unobserved spatially structured latent covariates or measurement error. This results in flexible infectious disease models that can be used for formulating etiological hypotheses and identifying geographical regions of unusually high risk to formulate preventive action. The reliability of these models is investigated on a combination of simulated epidemic data and Alberta seasonal influenza outbreak data ($2009$). This new class of models is fitted to data within a Bayesian statistical framework using Markov chain Monte Carlo methods.


Assuntos
Doenças Transmissíveis , Teorema de Bayes , Doenças Transmissíveis/epidemiologia , Humanos , Cadeias de Markov , Modelos Estatísticos , Método de Monte Carlo , Reprodutibilidade dos Testes
2.
Int J Epidemiol ; 51(1): 166-178, 2022 02 18.
Artigo em Inglês | MEDLINE | ID: mdl-34561694

RESUMO

BACKGROUND: We studied the impact of fine particulate matter (PM2.5) exposure due to a remote wildfire event in the Pacific Northwest on daily outpatient respiratory and cardiovascular physician visits during wildfire (24-31 August, 2015) and post-wildfire period (1-30 September, 2015) relative to the pre-wildfire period (1-23 August, 2015) in the city of Calgary, Canada. METHODS: A quasi-Poisson regression model was used for modelling daily counts of physician visits due to PM2.5 while adjusting for day of the week (weekday versus weekend or public holiday), wildfire exposure period (before, during, after), methane, relative humidity, and wind direction. A subgroup analysis of those with pre-existing diabetes or hypertension was performed. RESULTS: An elevated risk of respiratory disease morbidity of 33% (relative risk: RR) [95% confidence interval (CI): 10%-59%] and 55% (95% CI: 42%-69%) was observed per 10µg/m3 increase in PM2.5 level during and after wildfire, respectively, relative to the pre-wildfire time period. Increased risk was observed for children aged 0-9 years during (RR = 1.57, 95% CI: 1.21-2.02) and after the wildfire (RR = 2.11, 95% CI: 1.86-2.40) especially for asthma, acute bronchitis and acute respiratory infection. The risk of physician visits among seniors increased by 11% (95% CI: 3%-21%), and 19% (95% CI: 7%-33%) post-wildfire for congestive heart failure and ischaemic heart disease, respectively. Individuals with pre-existing diabetes had an increased risk of both respiratory and cardiovascular morbidity in the post-wildfire period (RR = 1.35, 95% CI: 1.09-1.67; RR = 1.22, 95% CI: 1.01-1.46, respectively). CONCLUSIONS: Wildfire-related PM2.5 exposure led to increased respiratory condition-related outpatient physician visits during and after wildfires, particularly for children. An increased risk of physician visits for congestive heart failure and ischaemic heart disease among seniors in the post-wildfire period was also observed.


Assuntos
Poluentes Atmosféricos , Doenças Cardiovasculares , Médicos , Incêndios Florestais , Poluentes Atmosféricos/efeitos adversos , Poluentes Atmosféricos/análise , Canadá , Doenças Cardiovasculares/epidemiologia , Doenças Cardiovasculares/etiologia , Criança , Pré-Escolar , Exposição Ambiental/efeitos adversos , Exposição Ambiental/análise , Humanos , Lactente , Recém-Nascido , Material Particulado/efeitos adversos , Material Particulado/análise , Fumaça/efeitos adversos , Fumaça/análise
SELEÇÃO DE REFERÊNCIAS
DETALHE DA PESQUISA