Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Geospatial and seasonal variation of bronchiolitis in England: a cohort study using hospital episode statistics
Thorax ( IF 10 ) Pub Date : 2020-01-20 , DOI: 10.1136/thoraxjnl-2019-213764 Kate Marie Lewis 1 , Bianca De Stavola 2 , Pia Hardelid 2
Thorax ( IF 10 ) Pub Date : 2020-01-20 , DOI: 10.1136/thoraxjnl-2019-213764 Kate Marie Lewis 1 , Bianca De Stavola 2 , Pia Hardelid 2
Affiliation
Background Rates of hospital admissions for bronchiolitis vary seasonally and geographically across England; however, seasonal differences by area remain unexplored. We sought to describe spatial variation in the seasonality of hospital admissions for bronchiolitis and its association with local demographic characteristics. Methods Singleton children born in English National Health Service hospitals between 2011 and 2016 (n=3 727 013) were followed up for 1 year. Poisson regression models with harmonic functions to model seasonal variations were used to calculate weekly incidence rates and peak timing of bronchiolitis admissions across English regions and clinical commissioning groups (CCGs). Linear regression was used to estimate the joint association of population density and deprivation with incidence and peak timing of bronchiolitis admissions at the CCG level. Results Bronchiolitis admission rates ranged from 30.9 per 1000 infant-years (95% CI 30.4 to 31.3) in London to 68.7 per 1000 (95% CI 67.9 to 69.5) in the North West. Across CCGs, there was a 5.3-fold variation in incidence rates and the epidemic peak ranged from week 49.3 to 52.2. Admission rates were positively associated with area-level deprivation. CCGs with earlier peak epidemics had higher population densities, and both high and low levels of deprivation were associated with earlier peak timing. Conclusions Approximately one quarter of the variation in admission rates and two-fifths of the variation in peak timing of hospital admissions for bronchiolitis were explained by local demographic characteristics. Implementation of an early warning system could help to prepare hospitals for peak activity and to time public health messages.
中文翻译:
英格兰细支气管炎的地理空间和季节性变化:使用医院事件统计的队列研究
背景 英格兰各地的毛细支气管炎住院率随季节和地域而异;然而,按地区划分的季节性差异仍未得到探索。我们试图描述细支气管炎住院季节性的空间变化及其与当地人口特征的关联。方法对2011-2016年间在英国国民健康服务医院出生的单身儿童(n=3 727 013)进行1年的随访。使用具有谐波函数的泊松回归模型来模拟季节性变化,用于计算英国地区和临床委托组 (CCG) 每周发生率和毛细支气管炎入院高峰时间。在 CCG 水平上,使用线性回归来估计人口密度和贫困与细支气管炎入院的发病率和高峰时间之间的联合关联。结果毛细支气管炎的入院率范围从伦敦的每 1000 名婴儿年 30.9 例(95% CI 30.4 至 31.3)到西北部的每 1000 名婴儿年 68.7 例(95% CI 67.9 至 69.5)。在 CCG 中,发病率有 5.3 倍的变化,流行高峰在 49.3 周到 52.2 周之间。入学率与地区层面的剥夺呈正相关。流行高峰期较早的 CCG 人口密度较高,高和低水平的剥夺都与较早的高峰时间有关。结论 大约四分之一的毛细支气管炎入院率变化和五分之二的毛细支气管炎入院高峰时间变化是由当地人口统计学特征解释的。实施预警系统可以帮助医院为高峰活动做好准备并及时发布公共卫生信息。
更新日期:2020-01-20
中文翻译:
英格兰细支气管炎的地理空间和季节性变化:使用医院事件统计的队列研究
背景 英格兰各地的毛细支气管炎住院率随季节和地域而异;然而,按地区划分的季节性差异仍未得到探索。我们试图描述细支气管炎住院季节性的空间变化及其与当地人口特征的关联。方法对2011-2016年间在英国国民健康服务医院出生的单身儿童(n=3 727 013)进行1年的随访。使用具有谐波函数的泊松回归模型来模拟季节性变化,用于计算英国地区和临床委托组 (CCG) 每周发生率和毛细支气管炎入院高峰时间。在 CCG 水平上,使用线性回归来估计人口密度和贫困与细支气管炎入院的发病率和高峰时间之间的联合关联。结果毛细支气管炎的入院率范围从伦敦的每 1000 名婴儿年 30.9 例(95% CI 30.4 至 31.3)到西北部的每 1000 名婴儿年 68.7 例(95% CI 67.9 至 69.5)。在 CCG 中,发病率有 5.3 倍的变化,流行高峰在 49.3 周到 52.2 周之间。入学率与地区层面的剥夺呈正相关。流行高峰期较早的 CCG 人口密度较高,高和低水平的剥夺都与较早的高峰时间有关。结论 大约四分之一的毛细支气管炎入院率变化和五分之二的毛细支气管炎入院高峰时间变化是由当地人口统计学特征解释的。实施预警系统可以帮助医院为高峰活动做好准备并及时发布公共卫生信息。