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地方性疟疾暴发的预测因素:哥伦比亚早期预警系统的开发方法。

Predictors of local malaria outbreaks: an approach to the development of an early warning system in Colombia.

机构信息

División Salud, Fundación FES Social, Cali, Colombia.

出版信息

Mem Inst Oswaldo Cruz. 2011 Aug;106 Suppl 1(Suppl 1):107-13. doi: 10.1590/s0074-02762011000900014.

Abstract

Risk factor surveillance is a complementary tool of morbidity and mortality surveillance that improves the likelihood that public health interventions are implemented in a timely fashion. The aim of this study was to identify population predictors of malaria outbreaks in endemic municipalities of Colombia with the goal of developing an early warning system for malaria outbreaks. We conducted a multiple-group, exploratory, ecological study at the municipal level. Each of the 290 municipalities with endemic malaria that we studied was classified according to the presence or absence of outbreaks. The measurement of variables was based on historic registries and logistic regression was performed to analyse the data. Altitude above sea level [odds ratio (OR) 3.65, 95% confidence interval (CI) 1.34-9.98], variability in rainfall (OR 1.85, 95% CI 1.40-2.44) and the proportion of inhabitants over 45 years of age (OR 0.17, 95% CI 0.08-0.38) were factors associated with malaria outbreaks in Colombian municipalities. The results suggest that environmental and demographic factors could have a significant ability to predict malaria outbreaks on the municipal level in Colombia. To advance the development of an early warning system, it will be necessary to adjust and standardise the collection of required data and to evaluate the accuracy of the forecast models.

摘要

风险因素监测是疾病监测和死亡率监测的补充工具,它提高了公共卫生干预措施及时实施的可能性。本研究的目的是确定哥伦比亚流行地区疟疾爆发的人口预测因素,旨在开发疟疾爆发的早期预警系统。我们在市级进行了多组探索性生态研究。我们研究的 290 个流行疟疾的市根据是否发生疫情进行了分类。变量的测量基于历史记录,使用逻辑回归分析数据。海拔高度[比值比(OR)3.65,95%置信区间(CI)1.34-9.98]、降雨量变化(OR 1.85,95% CI 1.40-2.44)和 45 岁以上居民比例(OR 0.17,95% CI 0.08-0.38)是与哥伦比亚市疟疾爆发相关的因素。结果表明,环境和人口因素可能对哥伦比亚市级疟疾爆发具有重要的预测能力。为了推进早期预警系统的发展,有必要调整和规范所需数据的收集,并评估预测模型的准确性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1d21/4830683/5a1b041ee153/nihms775571f1.jpg

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