Linking Health and Social Data to Assess the Performance of High Dimensional Propensity Scores

Main Article Content

Naomi Hamm
Deepa Singal
Matthew Dahl
Dan Chateau
Marni Brownell

Abstract

Introduction
High dimensional propensity scores (HDPS) aim to account for unmeasured confounding. However, it is unclear to what extent HDPS are able to attain this.


Objectives and Approach
This study aimed to test how well HDPS can account for confounding due to social determinants of health when using only health data. A retrospective cohort study was used to examine the effect of exposure to prescription opioids in utero on childhood outcomes (ADHD, school readiness, NICU admission, and hospitalization within the first year of life). Administrative health and social data were linked at the individual level and HDPS for each outcome were calculated using the mothers’ health data. Exposed and unexposed mother-child dyads were then matched. Standardized differences of mothers’ social factors (history of teen birth, lowest income quintile, ever received income assistance (i.e., welfare), ever lived in social housing, history with child protection services, residential mobility, and contact with the justice system) were compared before and after matching to determine to what degree the HDPS could account for differences in social determinants of health. Additional HDPS analyses were performed with social factors included in the HDPS with the health data.


Results
Before matching, standardized differences between exposed and unexposed groups for the social factors ranged between 0.40-0.75. Income assistance and lowest income quintile consistently had the greatest and smallest standardized difference for all outcomes, respectively. After matching, using health data only, standardized differences decreased considerably, ranging from 0.05-0.27. When including social factors into the HDPS, the addition of income assistance produced the smallest standardized differences with a range of 0.01-0.13 for all outcomes.


Conclusions
Using the HDPS with health data only can reduce confounding due to social factors. If data are available, including income assistance in the HDPS may further reduce confounding for all social determinants of health.

Article Details

How to Cite
Hamm, N., Singal, D., Dahl, M., Chateau, D. and Brownell, M. (2020) “Linking Health and Social Data to Assess the Performance of High Dimensional Propensity Scores”, International Journal of Population Data Science, 5(5). doi: 10.23889/ijpds.v5i5.1449.

Most read articles by the same author(s)

1 2 3 4 5 > >>