School of Public Health and Community Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
Epidemiology, Population Studies and Infrastructures (EPI@LUND), Lund University, Lund, Sweden.
PLoS One. 2022 Mar 8;17(3):e0265088. doi: 10.1371/journal.pone.0265088. eCollection 2022.
To study the value of combining individual- and neighborhood-level sociodemographic data to predict study participation and assess the effects of baseline selection on the distribution of metabolic risk factors and lifestyle factors in the Swedish CardioPulmonary bioImage Study (SCAPIS).
We linked sociodemographic register data to SCAPIS participants (n = 30,154, ages: 50-64 years) and a random sample of the study's target population (n = 59,909). We assessed the classification ability of participation models based on individual-level data, neighborhood-level data, and combinations of both. Standardized mean differences (SMD) were used to examine how reweighting the sample to match the population affected the averages of 32 cardiopulmonary risk factors at baseline. Absolute SMDs >0.10 were considered meaningful.
Combining both individual-level and neighborhood-level data gave rise to a model with better classification ability (AUC: 71.3%) than models with only individual-level (AUC: 66.9%) or neighborhood-level data (AUC: 65.5%). We observed a greater change in the distribution of risk factors when we reweighted the participants using both individual and area data. The only meaningful change was related to the (self-reported) frequency of alcohol consumption, which appears to be higher in the SCAPIS sample than in the population. The remaining risk factors did not change meaningfully.
Both individual- and neighborhood-level characteristics are informative in assessing study selection effects. Future analyses of cardiopulmonary outcomes in the SCAPIS cohort can benefit from our study, though the average impact of selection on risk factor distributions at baseline appears small.
研究结合个体和邻里社会人口统计学数据预测研究参与的价值，并评估基线选择对瑞典心肺生物影像研究（SCAPIS）中代谢危险因素和生活方式因素分布的影响。
我们将社会人口统计学登记数据与 SCAPIS 参与者（n=30154，年龄：50-64 岁）和研究目标人群的随机样本（n=59909）进行了关联。我们评估了基于个体水平数据、邻里水平数据和两者组合的参与模型的分类能力。使用标准化均数差（SMD）来检验重新加权样本以匹配人群如何影响基线时 32 个心肺风险因素的平均值。绝对值 SMD>0.10 被认为有意义。
将个体水平和邻里水平数据相结合产生的模型比仅具有个体水平（AUC：66.9%）或邻里水平数据（AUC：65.5%）的模型具有更好的分类能力（AUC：71.3%）。当我们使用个体和区域数据重新加权参与者时，我们观察到风险因素分布发生了更大的变化。唯一有意义的变化与（自我报告的）饮酒频率有关，SCAPIS 样本中的饮酒频率似乎高于人群。其余的危险因素没有发生有意义的变化。
个体和邻里水平特征在评估研究选择效果方面都是有信息的。SCAPIS 队列中心肺结局的未来分析可以从我们的研究中受益，尽管选择对基线时风险因素分布的平均影响似乎很小。
