Spatial and temporal projections of the prevalence of active tuberculosis in Cambodia
Spatial and temporal projections of the prevalence of active tuberculosis in Cambodia
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DOI：
10.1136/bmjgh-2018-001083
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发表时间：
2019-02-01
影响因子：
8.1
通讯作者：
Cook, Alex R.
中科院分区：
文献类型：
--
作者：
Prem, Kiesha;Pheng, Sok Heng;Cook, Alex R.
柬埔寨是结核病负担最重的30个国家之一。利用代表该国城市、农村和偏远地区的具有全国代表性的多阶段抽样对活动性结核病流行率进行了估计，但未抽样的社区的流行率仍然未知。这项研究使用地理空间贝叶斯统计来估计柬埔寨各地的点患病率，并使用人口模型来考虑生育率、死亡率、城市化和患病率的长期趋势，以预测活动性结核病的未来负担。方法采用贝叶斯分层模型对2011年全国结核病患病率调查数据进行分析，估计年龄、性别和地理阶层对活动性结核病患病率的差异影响；然后将这些估计值与高分辨率地理信息系统层相结合，以估算柬埔寨各地的患病率。然后，通过将这些估计值与基于个人的人口统计模型相结合，得出在不同情景下的未来结核病预测。结果年龄和性别之间的巨大风险差异，以及地理上不同的人口结构，产生了第一个1公里尺度的估计患病率图。得出了每个现有政府保健设施的集水区内预计的活动性结核病病例数，以及到2030年在三种情况下的预测：未来没有改善、继续减少和国内生产总值预测。综合卫生和地理数据可以以高分辨率绘制可能的发病率地图，以促进资源规划，而人口模型可以预测各种情况，表明有必要加快控制工作，以对柬埔寨未来的结核病负担产生实质性影响。
Introduction Cambodia is among the 30 highest burden of tuberculosis (TB) countries. Active TB prevalence has been estimated using nationally representative multistage sampling that represents urban, rural and remote parts of the country, but the prevalence in non-sampled communes remains unknown. This study uses geospatial Bayesian statistics to estimate point prevalence across Cambodia, and demographic modelling that accounts for secular trends in fertility, mortality, urbanisation and prevalence rates to project the future burden of active TB.Methods A Bayesian hierarchical model was developed for the 2011 National Tuberculosis Prevalence survey to estimate the differential effect of age, sex and geographic stratum on active TB prevalence; these estimates were then married with high-resolution geographic information system layers to project prevalence across Cambodia. Future TB projections under alternative scenarios were then derived by interfacing these estimates with an individual-based demographic model.Results Strong differences in risk by age and sex, together with geographically varying population structures, yielded the first estimated prevalence map at a 1 km scale. The projected number of active TB cases within the catchment area of each existing government healthcare facility was derived, together with projections to the year 2030 under three scenarios: no future improvement, continual reduction and GDP projection.Conclusion Synthesis of health and geographic data allows likely disease rates to be mapped at a high resolution to facilitate resource planning, while demographic modelling allows scenarios to be projected, demonstrating the need for the acceleration of control efforts to achieve a substantive impact on the future burden of TB in Cambodia.