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了解乌干达医疗保健部门的天气和住院模式,为适应气候变化战略提供信息。

Understanding Weather and Hospital Admissions Patterns to Inform Climate Change Adaptation Strategies in the Healthcare Sector in Uganda.

机构信息

Department of Population Medicine, University of Guelph, Guelph, ON N1G 2W1, Canada.

Indigenous Health Adaptation to Climate Change Research Team: Cesar Carcamo, Edmonton, AB T6G 2R3, Canada.

出版信息

Int J Environ Res Public Health. 2018 Oct 29;15(11):2402. doi: 10.3390/ijerph15112402.

Abstract

Season and weather are associated with many health outcomes, which can influence hospital admission rates. We examined associations between hospital admissions (all diagnoses) and local meteorological parameters in Southwestern Uganda, with the aim of supporting hospital planning and preparedness in the context of climate change. : Hospital admissions data and meteorological data were collected from Bwindi Community Hospital and a satellite database of weather conditions, respectively (2011 to 2014). Descriptive statistics were used to describe admission patterns. A mixed-effects Poisson regression model was fitted to investigate associations between hospital admissions and season, precipitation, and temperature. Admission counts were highest for acute respiratory infections, malaria, and acute gastrointestinal illness, which are climate-sensitive diseases. Hospital admissions were 1.16 (95% CI: 1.04, 1.31; = 0.008) times higher during extreme high temperatures (i.e., >95th percentile) on the day of admission. Hospital admissions association with season depended on year; admissions were higher in the dry season than the rainy season every year, except for 2014. : Effective adaptation strategy characteristics include being low-cost and quick and practical to implement at local scales. Herein, we illustrate how analyzing hospital data alongside meteorological parameters may inform climate-health planning in low-resource contexts.

摘要

季节和天气与许多健康结果有关,这些结果可能会影响医院的入院率。我们研究了在乌干达西南部,医院入院率(所有诊断)与当地气象参数之间的关系,目的是在气候变化背景下为医院规划和准备提供支持。

:从布温迪社区医院和天气条件卫星数据库分别收集了入院数据和气象数据(2011 年至 2014 年)。使用描述性统计数据来描述入院模式。拟合了混合效应泊松回归模型,以调查医院入院率与季节、降水和温度之间的关系。急性呼吸道感染、疟疾和急性胃肠道疾病的入院人数最高,这些都是对气候敏感的疾病。在入院当天,极端高温(即>第 95 个百分位数)时,入院人数增加 1.16 倍(95%置信区间:1.04,1.31;P=0.008)。医院入院与季节的关系取决于年份;除 2014 年外,每年旱季的入院人数都高于雨季。

:有效的适应战略特征包括低成本、快速以及在地方规模上实施的实用性。在这里,我们说明了如何分析医院数据和气象参数,以便为资源匮乏的环境中的气候健康规划提供信息。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b591/6265697/fa41d814cf5e/ijerph-15-02402-g002.jpg

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