1499Health behaviours as risk factors of COVID-19 incidence in South Korea

Background Personal health behaviours, which rely on community characteristics, could affect individual vulnerability on disease infection. Due to insufficient study to examine health behaviours as risk factors of COVID-19 infection, we conducted municipal level spatial analysis to investigate association between health behaviours and COVID-19 incidence. Methods We extracted cumulative COVID-19 incidence data from January 20th 2020 to February 25th 2021, health behaviours, health condition, socio-economic factors, and covariates in municipal level from publicly available dataset. We chose variables, which were standardized, considering multicollinearity (VIF<10). Further, we employed bayesian hierarchical negative binomial model with intrinsic conditional autoregressive (iCAR) and Besag, York and Mollié (BYM) model, and used deviance information criterion (DIC) for final model selection. Results The mean cumulative COVID-19 incidence per 10,000 population among 229 municipality was 13.73 (Standard deviation=11.43). iCAR model (DIC=2,825.3) outperformed BYM model (DIC=14,009.4). The results of iCAR model highlighted that incidence was associated with dental hygiene practice (incidence risk ratios [IRR]=0.92, 95% Credible Interval [CI]=0.85–1.00), whether tried to be thin (IRR=1.10, 95% CI = 1.00–1.20), proportion of medical personnel (IRR=1.09, 95% CI = 1.01–1.17), and volume of public transportation (IRR=1.19, 95% CI = 1.05–1.35), even after adjusting for various confounding factors. Conclusions Municipality with lower cumulative incidence was likely to have more people who practiced to keep dental hygiene and less people who tried to be thin. Key messages Municipal level spatial analysis resulted that health behaviours were associated with COVID-19 incidence in South Korea.

Temporal and geographic analysis of human trichinellosis incidence in Chile with risk assessment

Trichinellosis is a foodborne disease caused by several Trichinella species around the world. In Chile, the domestic cycle was fairly well-studied in previous decades, but has been neglected in recent years. The aims of this study were to analyze, geographically, the incidence of trichinellosis in Chile to assess the relative risk, as well as to analyze the temporal fluctuation in the incidence rates in the last decades. Using temporal data spanning 1964–2019, as well as geographical data from 2010–2019, the time series of cases was analyzed with ARIMA models to explore trends and periodicity. The Dickey–Fuller test was used to study trends, and the Portmanteau test was used to study white noise in the model residuals. The Besag–York–Mollie (BYM) model was used to create Bayesian maps of the level of risk relative to that expected by the overall population. The association of the relative risk with the number of farmed swine was assessed with Spearman’s correlation. The number of annual cases varied between 5 and 220 (mean: 65.13); the annual rate of reported cases varied between 0.03 and 1.9 cases per 105 inhabitants (mean: 0.53). The cases of trichinellosis in Chile showed a downward trend that has become more evident since the 1980s. No periodicities were detected via the autocorrelation function. Communes (the smallest geographical administrative subdivision) with high incidence rates and high relative risk were mostly observed in the Araucanía region. The relative risk of the commune was significantly associated with the number of farmed pigs and boar (Sus scrofa Linnaeus, 1758). The results allowed us to state that trichinellosis is not an (re)emerging disease in Chile, but local conditions must be further studied to identify the factors favoring the presence of outbreaks in some communes, particularly in Araucanía.

Mapping the relative risk of weight disorders in children and adolescents across provinces of Iran: the CASPIAN-V study

Background: This study aimed to find possible spatial variation in children’s weight disorders and in predicting the spatial distribution. Methods: The study population of this ecological study consisted of 7-18-year-old students living in 30 provinces of Iran. We used Besag, York and Mollie (BYM) model, a Bayesian model, to study the relative risk (RR) of underweight and excess weight (overweight and obese). The model was fitted to data using OpenBUGS (3.2.1) software. Results: The highest RR of underweight was found in southeastern provinces. Whereas, the highest RR of excess weight was documented in northern, northwestern and capital provinces.Sistan-Balouchestan (RR=1.973; Bayesian confidence interval [BCI]: 1.682, 2.289), Hormozgan(RR=1.482; BCI: 1.239, 1.749), South Khorasan (RR=1.422; BCI: 1.18, 1.687) and Kerman(RR=1.413; BCI: 1.18, 1.669) had the highest RR of underweight. Mazandaran (RR=1.366; BCI:1.172,1.581), Gilan (RR=1.346; BCI: 1.15,1.562), Tehran (RR=1.271; BCI: 1.086,1.472) and Alborz (RR=1.268; BCI: 1.079,1.475) provinces are high risk regions for excess weight. Conclusion: The significant variations in geographical distribution of weight disorders are because of various sociodemographic and ethnic differences. The current findings should be considered in health policy making in different regions of the country.

Bayesian Spatial Joint Model for Disease Mapping of Zero-Inflated Data with R-INLA: A Simulation Study and an Application to Male Breast Cancer in Iran

Hierarchical Bayesian log-linear models for Poisson-distributed response data, especially Besag, York and Mollié (BYM) model, are widely used for disease mapping. In some cases, due to the high proportion of zero, Bayesian zero-inflated Poisson models are applied for disease mapping. This study proposes a Bayesian spatial joint model of Bernoulli distribution and Poisson distribution to map disease count data with excessive zeros. Here, the spatial random effect is simultaneously considered into both logistic and log-linear models in a Bayesian hierarchical framework. In addition, we focus on the BYM2 model, a re-parameterization of the common BYM model, with penalized complexity priors for the latent level modeling in the joint model and zero-inflated Poisson models with different type of zeros. To avoid model fitting and convergence issues, Bayesian inferences are implemented using the integrated nested Laplace approximation (INLA) method. The models are compared according to the deviance information criterion and the logarithmic scoring. A simulation study with different proportions of zero exhibits INLA ability in running the models and also shows slight differences between the popular BYM and BYM2 models in terms of model choice criteria. In an application, we apply the fitting models on male breast cancer data in Iran at county level in 2014.