Measuring the prevalence of chronic diseases using population surveys by pooling self-reported symptoms, diagnosis and treatments: results from the World Health Survey of 2003 for South Asia
Measuring the prevalence of chronic diseases using population surveys by pooling self-reported symptoms, diagnosis and treatments: results from the World Health Survey of 2003 for South Asia
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DOI：
10.1007/s00038-013-0446-5
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发表时间：
2013-06-01
影响因子：
4.6
通讯作者：
Mishra, S.
中科院分区：
文献类型：
--
作者：
Levesque, J. -F.;Mukherjee, S.;Mishra, S.
在信息系统不发达的国家，衡量疾病患病率面临着挑战。收集自我报告诊断信息的人口调查能力也有限，因为它们受到信息和回忆偏差的影响。我们的目标是提出一种通过结合自我报告的诊断、自我报告的治疗和高度提示性症状的信息来评估慢性病患病率的方法。利用来自孟加拉国、印度和斯里兰卡的世界健康调查的数据开发了一种扩展的患病率衡量标准。为六种慢性病构建了算法。慢性病的测量范围扩大增加了患病率估计值。患病率因年龄、教育程度、社会经济地位 (SES) 和国家等社会人口特征而异。最后，当考虑到高度暗示的疾病症状时，慢性病状况与不良自评健康描述之间的关联（也是危险因素）会显着增加。我们对慢性病的扩展测量可以为卫生信息系统不发达的国家的慢性病监测奠定基础。它代表了与常规监测数据相关的偏差和成本之间的有趣权衡。
Measuring disease prevalence poses challenges in countries where information systems are poorly developed. Population surveys soliciting information on self-reported diagnosis also have limited capacity since they are influenced by informational and recall biases. Our aim is to propose a method to assess the prevalence of chronic disease by combining information on self-reported diagnosis, self-reported treatment and highly suggestive symptoms.An expanded measure of prevalence was developed using data from the World Health Survey for Bangladesh, India and Sri Lanka. Algorithms were constructed for six chronic diseases.The expanded measures of chronic disease increase the prevalence estimates. Prevalence varies across socio-demographic characteristics, such as age, education, socioeconomic status (SES), and country. Finally, the association, as also risk factor, between chronic disease status and poor self-rated health descriptions increases significantly when one takes into account highly suggestive symptoms of diseases.Our expanded measure of chronic disease could form a basis for surveillance of chronic diseases in countries where health information systems have been poorly developed. It represents an interesting trade-off between the bias associated with usual surveillance data and costs.