scholarly journals Rural-to-urban migration, discrimination experience, and health in China: Evidence from propensity score analysis

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0244441
Author(s):  
Zihong Deng ◽  
Yik Wa Law
Keyword(s):  
Propensity Score ◽  
Urban Areas ◽  
Propensity Score Analysis ◽  
Urban Migration ◽  
Rural Residents ◽  
Mediation Analyses ◽  
Urban Migrants ◽  
Score Analysis ◽  
Rural To Urban Migration ◽  
Using Data

This research examines how rural-to-urban migration influences health through discrimination experience in China after considering migration selection bias. We conducted propensity score matching (PSM) to obtain a matched group of rural residents and rural-to-urban migrants with a similar probability of migrating from rural to urban areas using data from the 2014 China Family Panel Studies (CFPS). Regression and mediation analyses were performed after PSM. The results of regression analysis after PSM indicated that rural-to-urban migrants reported more discrimination experience than rural residents, and those of mediation analysis revealed discrimination experience to exert negative indirect effects on the associations between rural-to-urban migration and three measures of health: self-reported health, psychological distress, and physical discomfort. Sensitivity analysis using different calipers yielded similar results. Relevant policies and practices are required to respond to the unfair treatment and discrimination experienced by this migrant population.

scholarly journals Assessing covariate balance when using the generalized propensity score with quantitative or continuous exposures

2018 ◽  
Vol 28 (5) ◽  
pp. 1365-1377 ◽  
Author(s):  
Peter C Austin
Keyword(s):  
Propensity Score ◽  
Propensity Score Analysis ◽  
Balance Assessment ◽  
Propensity Score Method ◽  
Propensity Score Methods ◽  
Covariate Balance ◽  
Crucial Component ◽  
Score Analysis ◽  
Generalized Propensity Score ◽  
Using Data

Propensity score methods are increasingly being used to estimate the effects of treatments and exposures when using observational data. The propensity score was initially developed for use with binary exposures (e.g., active treatment vs. control). The generalized propensity score is an extension of the propensity score for use with quantitative exposures (e.g., dose or quantity of medication, income, years of education). A crucial component of any propensity score analysis is that of balance assessment. This entails assessing the degree to which conditioning on the propensity score (via matching, weighting, or stratification) has balanced measured baseline covariates between exposure groups. Methods for balance assessment have been well described and are frequently implemented when using the propensity score with binary exposures. However, there is a paucity of information on how to assess baseline covariate balance when using the generalized propensity score. We describe how methods based on the standardized difference can be adapted for use with quantitative exposures when using the generalized propensity score. We also describe a method based on assessing the correlation between the quantitative exposure and each covariate in the sample when weighted using generalized propensity score -based weights. We conducted a series of Monte Carlo simulations to evaluate the performance of these methods. We also compared two different methods of estimating the generalized propensity score: ordinary least squared regression and the covariate balancing propensity score method. We illustrate the application of these methods using data on patients hospitalized with a heart attack with the quantitative exposure being creatinine level.

scholarly journals Parental Incarceration and Child Well-being: Conceptual and Practical Concerns Regarding the Use of Propensity Scores

2018 ◽  
Vol 4 ◽  
pp. 237802311877930 ◽  
Author(s):  
Jennifer E. Copp ◽  
Peggy C. Giordano ◽  
Wendy D. Manning ◽  
Monica A. Longmore
Keyword(s):  
Propensity Score ◽  
Problem Behaviors ◽  
Propensity Scores ◽  
Propensity Score Analysis ◽  
Well Being ◽  
Propensity Score Methods ◽  
Score Analysis ◽  
Data Limitations ◽  
Using Data ◽  
And Control

The aim of the current investigation was to examine the appropriateness of propensity score methods for the study of incarceration effects on children by directing attention to a range of conceptual and practical concerns, including the exclusion of theoretically meaningful covariates, the comparability of treatment and control groups, and potential ambiguities resulting from researcher-driven analytic decisions. Using data from the Fragile Families and Child Wellbeing Study, we examined the effects of maternal and paternal incarceration on a range of child well-being outcomes, including internalizing and externalizing problem behaviors, Peabody Picture Vocabulary Test scores, and early juvenile delinquency. Our findings suggested that propensity scores and treatment effect estimates are highly sensitive to a number of decisions made by the researcher, including aspects where little consensus exists. In light of the conceptual underpinnings of propensity score analysis and existing data limitations, we suggest the potential utility of different identification methods and specialized data collection efforts.

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