title: [Study on correction of data bias caused by different missing mechanisms in survey of medical expenditure among students enrolling in Urban Resident Basic Medical Insurance].
abstract: The study of the medical expenditure and its influencing factors among the students enrolling in Urban Resident Basic Medical Insurance (URBMI) in Taiyuan indicated that non response bias and selection bias coexist in dependent variable of the survey data. Unlike previous studies only focused on one missing mechanism, a two-stage method to deal with two missing mechanisms simultaneously was suggested in this study, combining multiple imputation with sample selection model. A total of 1 190 questionnaires were returned by the students (or their parents) selected in child care settings, schools and universities in Taiyuan by stratified cluster random sampling in 2012. In the returned questionnaires, 2.52% existed not missing at random (NMAR) of dependent variable and 7.14% existed missing at random (MAR) of dependent variable. First, multiple imputation was conducted for MAR by using completed data, then sample selection model was used to correct NMAR in multiple imputation, and a multi influencing factor analysis model was established. Based on 1 000 times resampling, the best scheme of filling the random missing values is the predictive mean matching (PMM) method under the missing proportion. With this optimal scheme, a two stage survey was conducted. Finally, it was found that the influencing factors on annual medical expenditure among the students enrolling in URBMI in Taiyuan included population group, annual household gross income, affordability of medical insurance expenditure, chronic disease, seeking medical care in hospital, seeking medical care in community health center or private clinic, hospitalization, hospitalization canceled due to certain reason, self medication and acceptable proportion of self-paid medical expenditure. The two-stage method combining multiple imputation with sample selection model can deal with non response bias and selection bias effectively in dependent variable of the survey data.
authors.affiliation: Department of Health Statistics, Shanxi Medical University, Taiyuan 030001, China.
authors.forename: Haixia
authors.initials: H
authors.lastname: Zhang
authors.affiliation: The Health Supervision Institution of Taiyuan Municipal Health Bureau.
authors.forename: Junkang
authors.initials: J
authors.lastname: Zhao
authors.affiliation: Department of Health Statistics, Shanxi Medical University, Taiyuan 030001, China.
authors.forename: Caijiao
authors.initials: C
authors.lastname: Gu
authors.affiliation: Department of Health Statistics, Shanxi Medical University, Taiyuan 030001, China.
authors.forename: Yan
authors.initials: Y
authors.lastname: Cui
authors.affiliation: Medical Insurance Office, the Second Hospital of Shanxi Medical University.
authors.forename: Huiying
authors.initials: H
authors.lastname: Rong
authors.affiliation: Department of Urban Resident Basic Medical Insurance, Medical Insurance Management Service Center.
authors.forename: Fanlong
authors.initials: F
authors.lastname: Meng
authors.affiliation: Department of Health Statistics, Shanxi Medical University, Taiyuan 030001, China; Email: wtstat1@sina.com.
authors.forename: Tong
authors.initials: T
authors.lastname: Wang
mesh_terms: D015982:Bias; D002648:Child; D002681:China; D002908:Chronic Disease; D003625:Data Collection / Q000592:standards*; D005102:Health Expenditures / Q000706:statistics & numerical data*; D006801:Humans; D007182:Income; D007348:Insurance, Health / Q000191:economics*; D013334:Students; D011795:Surveys and Questionnaires; D014505:Urban Population