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Using linked health and administrative data to reduce bias due to missing data and measurement error in observational research

使用关联的健康和管理数据来减少观察研究中由于缺失数据和测量误差而导致的偏差

基本信息

批准号：
MR/L012081/1
负责人：
Rosaleen Peggy Cornish
金额：
$ 25.65万
依托单位：
University of Bristol
依托单位国家：
英国
项目类别：
Fellowship
财政年份：
2014
资助国家：
英国
起止时间：
2014 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=MR%2FL012081%2F1
关键词：
Using linked health administrative data

项目摘要

The Avon Longitudinal Study of Parents and Children (ALSPAC), also known as Children of the 90s, is a health research study. Around 14,000 pregnant women joined the study in 1990-1991 and their children, born between April 1991 and December 1992, have been followed up ever since. Information about these children (and the mothers) has been collected using postal questionnaires and through clinics held at the University of Bristol. The main aim of ALSPAC is to identify factors which influence people's physical and mental health and development so that steps can be taken to prevent illness and improve the health and well-being of the population as a whole. To do this, scientists use the data collected in ALSPAC to estimate a "measure of effect", a measure which quantifies the likely extent of association between a particular factor and the outcome they are investigating. For example, in 2003 researchers found that the use of skin preparations containing peanut oil was associated with an almost seven-fold increase in the risk of developing peanut allergy. In observational studies like ALSPAC, particularly when data is collected over a very long period of time, it is unusual to have complete information on all the individuals in the study. Some people drop out of the study for various reasons; others do not complete every questionnaire or attend every clinic; in addition, some people may not answer a whole questionnaire or may not want certain measurements taken at a clinic. All of these scenarios result in missing data. When information is more likely to be missing for some people than others (for example, heavy smokers may be less likely to complete questions on smoking), the measure of effect may be distorted (biased). Questionnaire-based studies like ALSPAC are also prone to errors because people are asked about events that they may not completely remember. In addition, some topics on questionnaires may be sensitive for some people and they might not be completely honest - about how much they smoke, for example. Both of these issues result in something called misclassification, whereby some people may be wrongly classified as having (or not having) a particular condition - such as asthma, for example - or wrongly classified as being a light smoker when in fact they are a heavy smoker. This can also lead to biased measures of effect.One way of addressing these problems in studies like ALSPAC is to use comparable information from health or administrative (government) records. ALSPAC has already obtained education data from the DfE. In addition, the Project to Enhance ALSPAC through Record Linkage (PEARL) has been set up to obtain data on ALSPAC participants from the following records: health, benefits and earnings, criminal convictions and cautions, plus further and higher education. PEARL is currently investigating how to use the data obtained from these sources to enhance the existing ALSPAC data as well as looking at the feasibility of using such data to provide future information on health and other outcomes.In this project I will build on the work of PEARL by investigating particular measures - smoking, IQ, and teenage depression - in depth, investigating missing data and misclassification and devising ways in which administrative and health data can be used to overcome these issues, both in ALSPAC and in similar studies. In particular, I will look at whether linked health and education data can be used to understand whether particular people are more likely to have missing information on smoking, IQ or depression. I will also investigate whether the linked data can be used to "fill in" missing information in the ALSPAC data. In addition, by comparing self-reported smoking and depression to equivalent information in the GP records I will assess how accurate the self-reported data is likely to be and what influence this may have on results based on these measures.

雅芳家长和儿童纵向研究（ALSPAC），也称为90年代儿童，是一项健康研究。1990年至1991年期间，约有14，000名孕妇参加了这项研究，他们的孩子在1991年4月至1992年12月期间出生，此后一直接受随访。通过邮寄问卷和在布里斯托大学举办的诊所收集了关于这些儿童（和母亲）的信息。该方案的主要目的是确定影响人们身心健康和发展的因素，以便采取措施预防疾病，改善全体人民的健康和福利。为了做到这一点，科学家们使用ALSPAC收集的数据来估计“效果测量”，这是一种量化特定因素与他们正在调查的结果之间可能关联程度的测量。例如，2003年，研究人员发现，使用含有花生油的皮肤制剂与花生过敏风险增加近7倍有关。在像ALSPAC这样的观察性研究中，特别是当数据是在很长一段时间内收集的时候，获得研究中所有个体的完整信息是不寻常的。有些人由于各种原因退出研究;其他人没有完成每一份问卷或参加每一个诊所;此外，有些人可能没有回答整个问卷或可能不希望在诊所进行某些测量。所有这些情况都会导致数据缺失。当某些人的信息比其他人更有可能缺失时（例如，重度吸烟者可能不太可能完成有关吸烟的问题），效果的测量可能会扭曲（偏倚）。像ALSPAC这样的基于记忆的研究也容易出错，因为人们被问到他们可能不完全记得的事件。此外，问卷上的一些话题对某些人来说可能是敏感的，他们可能不完全诚实-例如他们吸烟多少。这两个问题都会导致所谓的错误分类，即有些人可能会被错误地归类为患有（或没有）某种特定疾病-例如哮喘-或者被错误地归类为轻度吸烟者，而实际上他们是重度吸烟者。在ALSPAC这样的研究中，解决这些问题的一种方法是使用来自健康或行政（政府）记录的可比信息。ALSPAC已经从教育部获得了教育数据。此外，还设立了通过记录联系加强ALSPAC项目，以从以下记录中获取ALSPAC参与者的数据：健康、福利和收入、刑事定罪和警告，以及继续教育和高等教育。PEARL目前正在研究如何使用从这些来源获得的数据来增强现有的ALSPAC数据，并研究使用这些数据提供未来健康和其他结果信息的可行性。在这个项目中，我将在PEARL工作的基础上，深入研究特定的测量方法-吸烟，智商和青少年抑郁症，在ALSPAC和类似研究中，调查缺失数据和错误分类，并设计如何利用行政和卫生数据克服这些问题。特别是，我将研究是否可以使用相关的健康和教育数据来了解特定的人是否更有可能在吸烟，智商或抑郁症方面缺失信息。我还将研究链接数据是否可以用来“填充”ALSPAC数据中缺失的信息。此外，通过将自我报告的吸烟和抑郁与GP记录中的等效信息进行比较，我将评估自我报告的数据可能有多准确，以及这可能对基于这些措施的结果产生什么影响。