Multi-level Modelling of International Variations and Time Trends in Asthma and Allergic Diseases in Children and Adults

该项目将把对哮喘病因和时间趋势的实质性研究与开发处理复杂的多层次数据集的统计方法结合起来。主要数据来源来自三项相互关联的研究：儿童哮喘和过敏症国际研究(ISAAC)第一阶段(1992-1995年)通过问卷从56个国家155个合作中心的721,602名儿童中收集了哮喘、鼻结膜炎和湿疹的症状和危险因素的信息。ISAAC第三阶段(2001-2002年)收集了98个国家237个中心的1187 496名儿童的类似信息。全球哮喘网络(GAN)将在2016-2017年间使用Isaac方法收集类似的数据，但将延长有关父母的数据以及关于哮喘管理和环境的额外问题。到目前为止，来自34个国家的88个中心已经登记了GAN，登记正在进行中。为研究收集的数据类型包括症状和严重程度信息，以及三个不同年龄段的环境和人口数据，一个是儿童时期，一个是青少年，一个是成年人。这非常符合MRC的战略研究目标，即通过生命过程调查健康，并了解生活方式和环境如何影响健康。在每个国家内，将寻求至少一个城市中心和一个农村中心，以便探索大环境对哮喘的影响。每个中心每个年龄组至少3000人的样本规模将被用来提供足够的力量来检测哮喘严重程度的差异。在较小的岛屿人口中，将选择该年龄组的所有学生及其家长。从尚未充分利用最先进的统计建模技术的现有Isaac调查开始，然后将其与GaN调查数据相结合，将有可能对所有三项研究进行综合分析。这一复杂的多层次结构将包含3个年龄段、4个不同水平(个人、学校、中心和国家)的多重暴露，最多3个时间点。对于一些中心，将会有缺失的年龄组和(或)缺失的时间点，需要正确处理这些问题，以确保统计推断的稳健性。这与定量和跨学科技能的MRC技能优先事项很好地一致，将为大型、多水平数据集开发新的统计方法与其在哮喘和其他过敏性疾病的潜在原因或风险因素的流行病学问题上的应用相结合。该项目的结果应该对多个国家的公共卫生官员有用。了解不同水平的重要风险因素可提高通过改变卫生政策积极影响哮喘和其他过敏性疾病的患病率和严重程度的能力。各国的结果将大不相同，这项工作将有助于估计潜在政策变化的影响。此外，方法论的发展将对从事其他卫生研究领域但具有类似结构的多水平数据集的统计学家有用。

This project will combine substantive research into asthma causes and time trends with developing statistical methodologies to handle complex multi-level datasets.The main data sources are from three linked studies:Phase I (1992-1995) of the International Study of Asthma and Allergies in Childhood (ISAAC) collected information by questionnaire on symptoms and risk factors for asthma, rhinoconjunctivitis and eczema from 721,602 children in 155 collaborating centres in 56 countries. Phase III (2001-2002) of ISAAC collected similar information from 1,187,496 children in 237 centres in 98 countries. The Global Asthma Network (GAN) will use the ISAAC methodology to collect similar data during 2016-2017 but with the extension of data on parents and extra questions on asthma management and the environment. To date, 88 centres from 34 countries have registered for GAN and registration is ongoing.The type of data collected for the studies includes information on symptoms and severity along with environmental and demographic data for three different age groups, one in childhood, one teenage and one adult. This fits in well with the MRC strategic research aims of investigating health through the life course, and understanding how lifestyles and environment affect health. Within each country at least one urban and one rural centre will be sought so the influence of the general environment on asthma can be explored. A sample size of at least 3,000 people per age group per centre will be used to give sufficient power to detect differences in the severity of asthma. In smaller island populations, all pupils in the age group and their parents will be selected.Starting with the existing ISAAC surveys, which have yet to be fully exploited with state-of-the-art statistical modelling techniques, and later combining it with the GAN survey data it will be possible to run integrated analysis on all three studies. This complex multi-level structure will contain multiple exposures on 3 age groups, at 4 different levels (person, school, centre and country) across up to 3 time points. For some centres there will be missing age groups and/or missing time points which will need to be addressed correctly to ensure the statistical inference is robust. This aligns well with the MRC skills priorities of quantitative and interdisciplinary skills by combining the development of novel statistical methodologies for large, multi-level datasets with their application on epidemiological questions of potential causes or risk factors for asthma and other allergic diseases.The results of this project should be useful to public health officials within multiple countries. Understanding the important risk factors at different levels improves the ability to positively impact the prevalence and severity of asthma and other allergic diseases through changing health policies. Outcomes will vary widely from country to country and this work will help estimate the impact of potential policy changes. In addition, the methodological developments will be useful for statisticians working in other areas of health research but with similarly constructed multi-level datasets.