Watt Hilary, Harris Matthew, Noyes Jane, Whitaker Rhiannon, Hoare Zoe, Edwards Rhiannon Tudor, Haines Andy
Department of Primary Care and Public Health, Imperial College London, Reynolds Building, St Dunstans Road, London, W6 8RP, , England, UK.
School of Social Sciences, Bangor University, Bangor, Wales, UK.
Trials. 2015 Mar 21;16:107. doi: 10.1186/s13063-015-0625-1.
In health services research, composite scores to measure changes in health-seeking behaviour and uptake of services do not exist. We describe the rationale and analytical considerations for a composite primary outcome for primary care research. We simulate its use in a large hypothetical population and use it to calculate sample sizes. We apply it within the context of a proposed cluster randomised controlled trial (RCT) of a Community Health Worker (CHW) intervention.
We define the outcome as the proportion of the services (immunizations, screening tests, stop-smoking clinics) received by household members, of those that they were eligible to receive. First, we simulated a population household structure (by age and sex), based on household composition data from the 2011 England and Wales census. The ratio of eligible to received services was calculated for each simulated household based on published eligibility criteria and service uptake rates, and was used to calculate sample size scenarios for a cluster RCT of a CHW intervention. We assume varying intervention percentage effects and varying levels of clustering.
Assuming no disease risk factor clustering at the household level, 11.7% of households in the hypothetical population of 20,000 households were eligible for no services, 26.4% for 1, 20.7% for 2, 15.3% for 3 and 25.8% for 4 or more. To demonstrate a small CHW intervention percentage effect (10% improvement in uptake of services out of those who would not otherwise have taken them up, and additionally assuming intra-class correlation of 0.01 between households served by different CHWs), around 4,000 households would be needed in each of the intervention and control arms. This equates to 40 CHWs (each servicing 100 households) needed in the intervention arm. If the CHWs were more effective (20%), then only 170 households would be needed in each of the intervention and control arms.
This is a useful first step towards a process-centred composite score of practical value in complex community-based interventions. Firstly, it is likely to result in increased statistical power compared with multiple outcomes. Second, it avoids over-emphasis of any single outcome from a complex intervention.
在卫生服务研究中，不存在用于衡量寻求医疗行为和服务利用变化的综合评分。我们描述了用于初级保健研究的综合主要结局的基本原理和分析考量。我们在一个大型假设人群中模拟其使用情况，并使用它来计算样本量。我们将其应用于一项拟议的社区卫生工作者（CHW）干预的整群随机对照试验（RCT）背景下。
我们将结局定义为家庭成员接受的符合条件的服务（免疫接种、筛查测试、戒烟诊所）的比例。首先，我们根据2011年英格兰和威尔士人口普查的家庭构成数据模拟了一个人群家庭结构（按年龄和性别）。根据已公布的资格标准和服务利用率，为每个模拟家庭计算符合条件与接受服务的比例，并用于计算CHW干预整群RCT的样本量方案。我们假设了不同的干预百分比效应和不同程度的聚类。
假设在家庭层面不存在疾病风险因素聚类，在20000户家庭的假设人群中，11.7%的家庭不符合任何服务条件，26.4%的家庭符合1项服务条件，20.7%的家庭符合2项服务条件，15.3%的家庭符合3项服务条件，25.8%的家庭符合4项或更多服务条件。为了证明CHW干预的微小百分比效应（在原本不会接受服务的人群中服务利用率提高10%，另外假设不同CHW服务的家庭之间的组内相关系数为0.01），干预组和对照组各需要约4000户家庭。这相当于干预组需要40名CHW（每人服务100户家庭）。如果CHW更有效（提高20%），那么干预组和对照组各只需要170户家庭。
这是朝着在复杂的社区干预中具有实用价值的以过程为中心的综合评分迈出的有益的第一步。首先，与多个结局相比，它可能会提高统计效力。其次，它避免了对复杂干预中任何单一结局的过度强调。
