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Additional file 1: Supplementary Table 1. PATH wave 4 youth assessment of covariates used in this study. PATH wave 4 youth assessment of covariates used in this study.

Additional file 2: Supplementary Table 2. Fit indices of structural equation models. Fit indices of structural equation models.

The Duke University Religion Index (DUREL) is a widely-used 5-item scale assessing religiosity.Assess the internal consistency, reliability, and factor structure of the revised Chinese version of DUREL.Using probability proportionate to size (PPS) methods we randomly identified 3981 households with eligible occupants in 20 primary sampling sites in Ningxia Hui Autonomous Region, a province in northwest China in which 34% of the population are Muslims of the Hui ethnic group. In 3054 households a screening interview was completed and an adult family member was randomly selected; 2425 respondents completed the survey (including the DUREL) and 188 randomly selected individuals repeated the survey an average of 2.5 days later.The internal consistency (Cronbach's α) of the 5 items in the full sample was 0.90; it ranged from 0.70 to 0.90 in various subgroups of subjects stratified by ethnicity, urban versus rural residence, and above versus below median education. The test-retest reliability (intraclass correlation coefficient) for the total score in the full sample was 0.87; it ranged from 0.63 to 0.90 in the different subgroups of subjects. Exploratory factor analysis in a random half of the sample identified a single factor (eigen value=4.21) that explained 84% of the total variance. Confirmatory factor analysis in the second half of the sample confirmed the unidimensional model; the model fit measures of the one-factor model using the 5 item scores as observed variables were acceptable (comparative fit index [CFI] and Tucker-Lewis index [TLI]0.99; root mean square error of approximation [RMSEA]=0.105; χ (2) =70.49, df=5), but the model fit improved after adding the correlation between items 1 and 2 (that assess organized and personal religious activities, respectively) as a sixth observed variable(CFI and TLI0.99; RMSEA=0.046; χ (2) =14.32, df=4).The Chinese version of the DUREL is a reliable and valid measure of religiosity that can be used to assess the relationship of religiosity/spirituality to physical and psychological wellbeing in Chinese respondents. As suggested by other authors, our factor analysis results indicate that the overall score is the best measure derived from the scale, not the three dimensional scores recommended by the original authors.背景：杜克大学宗教指数量表（DUREL）是一个应用广 泛的宗教性评估量表，包含五个条目。评估DUREL中文修订版的内部一致性、信度和因子结构。宁夏回族自治区是中国西北部的一个省，其人 口的34%是信奉伊斯兰教的回族。我们利用概率比例 规模的抽样方法，在20个基本抽样单位中随机选出了 有合适居民的3981户。3054户完成了筛查访谈并随 机选出了一名成年家庭成员；2425名受访者完成了调 查（包括DUREL），平均在2.5天后，188名随机选中 的被试再次完成了该调查。全体样本中五个条目的内部一致性（Cronbach's α）为0.90；在依据民族、城市与农村居住地、和文化 程度中位数之上下分层的被试亚组中，其值为0.70到 0.90。全样本中总分的重测信度（组内相关系数）为 0.87；其值在不同被试亚组中为0.63到0.90。在随机 的一半样本中，探索性因子分析确定为单一因子（特 征根=4.21），解释总变异的84%。在另外一半的样本中， 验证性因子分析证实了单维模型；使用五条目计分的 一个因子模型的模型拟合指标是可以接受的（比较拟 合指数[CFI]和塔克-刘易斯指数[TLI]0.99；近似误差 均方根[RMSEA]=0.105；中文版DUREL是一个评估宗教性的可信有效措 施，可以用于评估中国受访者的宗教性/信仰与身心 健康之间的关系。正如其他研究者建议，我们的因子 分析结果表明总分是该量表提取的最好指标，而不是 原作者建议的三个维度的评分。

The secondary analysis of existing data has become an increasingly popular method of enhancing the overall efficiency of the health research enterprise. But this effort depends on governments, funding agencies, and researchers making the data collected in primary research studies and in health-related registry systems available to qualified researchers who were not involved in the original research or in the creation and maintenance of the registry systems. The benefits of doing this are clear but the barriers are many, so the effort of increasing access to such material has been slow, particularly in low- and middleincome countries. This article introduces the rationale and concept of the secondary analysis of existing data, describes several sources of publicly available datasets, provides general guidelines for conducting secondary analyses of existing data, and discusses the advantages and disadvantages of analyzing existing data.现有数据的二次分析已成为提升卫生研究机构 整体效率的一种日益流行的方法。该工作取决于政府、 资助机构以及研究者，取决于他们能不能让没有参与 原始研究、没有参与创建和维护登记系统的其他合格 研究人员获得原始研究数据或登记系统的数据。二次 分析的好处是显而易见的，但面临的障碍很多。因此 提高这些数据可获得性的工作进展缓慢，在低收入和 中等收入国家尤为如此。本文介绍了现有数据二次分 析的基本原理和概念，描述了若干个可公开获得的数 据库，为现有数据的二次分析提供一般准则，并讨论 了现有数据分析的优势和不足。