Department of Public Health, Falk College, Syracuse University, Syracuse, New York, United States of America.
Department of Biostatistics and Epidemiology, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma, United States of America.
PLoS One. 2019 Jan 7;14(1):e0209735. doi: 10.1371/journal.pone.0209735. eCollection 2019.
Failure to account for misclassification error accruing from imperfect case-finding instruments can produce biased estimates of suspected major depression disorder (MDD) risk factor associations. The objective of this study was to estimate the impact of misclassification error on the magnitude of measures of association between suspected risk factors and MDD assessed using the Center of Epidemiological Studies on Depression-Short Form during the prenatal and postnatal periods. Baseline data were collected from 520 mothers participating in two home visitation studies in Oklahoma City between 2010 and 2014. A Bayesian binomial latent class model was used to compare the prevalence proportion ratio (PPR) between suspected risk factors and MDD with and without adjustment for misclassification error and confounding by period of MDD symptom on-set. Adjustment for misclassification error and confounding by period of MDD on-set (prenatal vs postnatal) showed that the association between suspected risk factors and MDD is underestimated (-) and overestimated (+) differentially in different source populations of low-income mothers. The median bias in the magnitude of PPR estimates ranged between -.47 (95% Bayesian Credible Intervals [BCI]: -10.67, 1.90) for intimate partner violence to +.06 (95%BCI: -0.37, 0.47) for race/ethnicity among native-born US residents. Among recent Hispanic immigrants, bias ranged from -.77 (95%BCI: -15.31, 0.96) for history of childhood maltreatment to +.10 (95%BCI: -0.17, 0.39) for adequacy of family resources. Overall, the extent of bias on measures of association between maternal MDD and suspected risk factors is considerable without adjustment for misclassification error and is even higher for confounding by period of MDD assessment. Consideration of these biases in MDD prevention research is warranted.
未能考虑因不完善的病例发现工具而导致的分类错误，可能会导致对疑似重度抑郁症（MDD）风险因素关联的有偏差的估计。本研究的目的是估计在产前和产后期间使用流行病学研究抑郁量表-短式评估疑似风险因素与 MDD 之间的关联程度时，分类错误对关联度量的影响。2010 年至 2014 年期间，520 名母亲参与了俄克拉荷马城的两项家访研究，收集了基线数据。使用贝叶斯二项式潜在类别模型，比较了在未调整和调整分类错误以及 MDD 发病期混杂因素的情况下，疑似风险因素与 MDD 之间的流行比例比（PPR）。调整分类错误和 MDD 发病期（产前与产后）混杂因素后，疑似风险因素与 MDD 之间的关联在不同的低收入母亲源人群中存在低估（-）和高估（+）的差异。PPR 估计值幅度的中位数偏差范围在亲密伴侣暴力的-0.47（95%贝叶斯可信区间[BCI]：-10.67，1.90）到种族/民族的+0.06（95%BCI：-0.37，0.47）之间，其中，在美国出生的居民中为-0.47（95%BCI：-10.67，1.90），而对于最近的西班牙裔移民，偏差范围从童年虐待史的-0.77（95%BCI：-15.31，0.96）到家庭资源充足的+0.10（95%BCI：-0.17，0.39）。总体而言，在未调整分类错误的情况下，MDD 与疑似风险因素之间关联的度量值存在相当大的偏差，而对于 MDD 评估时期的混杂因素的偏差甚至更高。在 MDD 预防研究中考虑这些偏差是有必要的。
