Community effects on public health in India: A hierarchical model

Police-related deaths are a public health problem; however, their effects on community-level reproductive health outcomes remain largely unexplored. Among US counties with populations >100,000 (N = 580), we examined the longitudinal association between police-related deaths and rates of low birthweight (LBW) (<2,500 grams) deliveries, with counties stratified by quartiles of poverty. In the two lowest poverty quartiles, two or more police-related deaths were associated with a 5% (incidence rate ratio [IRR]: 1.05, 95% confidence interval [CI]: 1.02, 1.09) and 10% (IRR 1.10, 95% CI: 1.05, 1.15) higher rate of LBW births, respectively. The effect was not significant in the two highest poverty quartiles. No counties are “immune” from the important effects of police-related deaths and therefore the public health importance of police violence is a ubiquitous one regardless of social class or position.

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A Bayesian Approach to Real-Time Monitoring and Forecasting of Chinese Foodborne Diseases

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph15081740 ◽

2018 ◽

Vol 15 (8) ◽

pp. 1740 ◽

Cited By ~ 4

Author(s):

Xueli Wang ◽

Moqin Zhou ◽

Jinzhu Jia ◽

Zhi Geng ◽

Gexin Xiao

Keyword(s):

Public Health ◽

Hierarchical Model ◽

Bayesian Hierarchical Model ◽

Hunan Province ◽

Foodborne Diseases ◽

Foodborne Disease ◽

Bayesian Hierarchical ◽

True Number ◽

Number Of Patients ◽

Reporting Delays

Foodborne diseases have a big impact on public health and are often underreported. This is because a lot of patients delay treatment when they suffer from foodborne diseases. In Hunan Province (China), a total of 21,226 confirmed foodborne disease cases were reported from 1 March 2015 to 28 February 2016 by the Foodborne Surveillance Database (FSD) of the China National Centre for Food Safety Risk Assessment (CFSA). The purpose of this study was to make use of the daily number of visiting patients to forecast the daily true number of patients. Our main contribution is that we take the reporting delays into consideration and apply a Bayesian hierarchical model for this forecast problem. The data shows that there were 21,226 confirmed cases reported among 21,866 visiting patients, a proportion as high as 97%. Given this observation, the Bayesian hierarchical model was established to predict the daily true number of patients using the number of visiting patients. We propose several scoring rules to assess the performance of different nowcasting procedures. We conclude that Bayesian nowcasting with consideration of right truncation of the reporting delays has a good performance for short-term forecasting, and could effectively predict the epidemic trends of foodborne diseases. Meanwhile, this approach could provide a methodological basis for future foodborne disease monitoring and control strategies, which are crucial for public health.

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