Health Sciences Centre, University of Canterbury, Christchurch, New Zealand.
Intern Med J. 2012 Nov;42(11):1207-12. doi: 10.1111/j.1445-5994.2012.02754.x.
Identifying eligible individuals for a prevalence survey is difficult in the absence of a disease register or a national population register.
To develop a method to identify and invite eligible individuals to participate in a national prevalence survey while maintaining confidentiality and complying with privacy legislation.
A unique identifier (based on date of birth, sex and initials) was developed so that database holders could identify eligible individuals, notify us and invite them on our behalf to participate in a national multiple sclerosis prevalence survey while maintaining confidentiality and complying with privacy legislation.
Several organisations (including central government, health and non-governmental organisations) used the method described to assign unique identifiers to individuals listed on their databases and to forward invitations and consent forms to them. The use of a unique identifier allowed us to recognise and record all the sources of identification for each individual. This prevented double counting or approaching the same individual more than once and facilitated the use of capture-recapture methods to improve the prevalence estimate. Capture-recapture analysis estimated that the method identified over 96% of eligible individuals in this prevalence survey.
This method was developed and used successfully in a national prevalence survey of multiple sclerosis in New Zealand. The method may be useful for prevalence surveys of other diseases in New Zealand and for prevalence surveys in other countries with similar privacy legislation and lack of disease registers and population registers.
在缺乏疾病登记册或国家人口登记册的情况下,难以确定患病率调查的合格人选。
制定一种方法,在保持机密性并遵守隐私法规的同时,识别和邀请合格人员参加全国患病率调查。
开发了一种独特的标识符(基于出生日期、性别和首字母),以便数据库持有者能够识别合格人员,通知我们,并代表我们邀请他们参加全国多发性硬化症患病率调查,同时保持机密性并遵守隐私法规。
包括中央政府、卫生和非政府组织在内的几个组织使用所描述的方法为其数据库中的个人分配独特的标识符,并向他们发送邀请和同意书。使用独特的标识符使我们能够识别和记录每个个体的所有识别来源。这防止了重复计数或对同一个人进行多次接触,并促进了使用捕获-再捕获方法来提高患病率估计。捕获-再捕获分析估计,该方法在新西兰的多发性硬化症全国患病率调查中识别了超过 96%的合格人员。
该方法在新西兰的多发性硬化症全国患病率调查中得到了成功的开发和应用。该方法可能对新西兰其他疾病的患病率调查以及其他具有类似隐私法规和缺乏疾病登记册和人口登记册的国家的患病率调查有用。