Comparison of Conditional and Marginal Models in Assessing a Child Nutrition Intervention in Armenia
Arin A Balalian1, Sharon Daniel2, Hambardzum Simonyan3
1Mailman School of Public Health, Columbia University, 722 West 168th St., New York, NY, USA. arin.balalian@columbia.edu.
Introduction:
Child malnutrition is a major issue in conflict zones. Evidence-based interventions and their thorough evaluation could help to eliminate malnutrition. We aimed to assess the causal effect of a community-based multidisciplinary nutrition program for children in a chronic conflict zone near the northeastern border of Armenia on two main outcomes: stunting and anemia. We further compared the interpretations and public health relevance of the obtained effect estimates.
Methods:
In 2016, the study measured hemoglobin and anthropometric measures and collected data from the children's caregivers. We used propensity score matching analyses, inverse probability weighting, and overlap weighting methods to examine the average treatment effects among treated population (ATT), and among population with overlapping weights (ATO).
Results:
The ATT for stunting among children who participated in the intervention program estimated by propensity score matching analyses (PSM-ATT) was (1.95; 95%CI 1.15-3.28). Nevertheless, children who took part in the program had a lower risk of anemia (0.28; 95%CI 0.19-0.42). The ATT, estimated by inverse probability weighting (IPTW-ATT), was slightly lower for stunting (1.82; 95%CI 1.16-2.86) while similar for anemia (0.33; 95%CI 0.23-0.46) compared to PSM-ATT. Compared to the IPTW-ATT and PSM-ATT the ATO was lower for stunting (1.75; 95%CI 1.14-2.68) and similar for anemia (0.31; 95%CI 0.22-0.43).
Discussion:
Marginal models could be used in similar quasi-experimental settings to identify the causal effect of interventions in specific populations of interest. Nonetheless, these methods do not eliminate threats to internal validity. Thorough study design and accurate data collection are necessary to improve the efficiency of marginal models.
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