Selected article for: "age group and regression model"

Author: Chu, Yanhui; Wu, Zhenyu; Ji, Jiayi; Sun, Jingyi; Sun, Xiaoyu; Qin, Guoyou; Qin, Jingning; Xiao, Zheng; Ren, Jian; Qin, Di; Zheng, Xueying; Wang, Xi-Ling
Title: Effects of school breaks on influenza-like illness incidence in a temperate Chinese region: an ecological study from 2008 to 2015
  • Document date: 2017_3_6
  • ID: r7a0orh7_19
    Snippet: We established an autocorrelated Poisson regression model to control the effect of potential confounders that may distort age-specific ILI incidence rates, such as seasonal change of ILI visits and year. We found a significant reduction in ILI incidence rates for age group of 5-14 when entering winter breaks, compared with those of 2 weeks before (IRR 0.89, 95% CI (0.81 to 0.98)). However, the reduction is less significant when comparing with the.....
    Document: We established an autocorrelated Poisson regression model to control the effect of potential confounders that may distort age-specific ILI incidence rates, such as seasonal change of ILI visits and year. We found a significant reduction in ILI incidence rates for age group of 5-14 when entering winter breaks, compared with those of 2 weeks before (IRR 0.89, 95% CI (0.81 to 0.98)). However, the reduction is less significant when comparing with the rates throughout the entire break for the adults' groups. The phenomenon that reduction of ILI incidence rates for age above 60 was larger than those for age 25-59 and close to those for age [15] [16] [17] [18] [19] [20] [21] [22] [23] [24] (table 2) could be due to age-specific health-seeking behaviour, immune system characteristics and residual confounders of seasonal ILI visits. Although a Serfling model has been adopted, our model is still relatively conservative in controlling seasonal pattern of ILI because timing of peaks of influenza activities had severe overlaps with timing of winter breaks, and the overfitting could underestimate the effect of winter breaks on decrease of IRRs. Therefore, additional analysis that considers high variability in the temporal relationship between winter breaks and weekly ILI rates across influenza seasons are needed and incorporating environmental factors or human behaviour data may better clarify the relationship between age-specific influenza activity and winter breaks.

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