Research

Objective

To examine a 2018 rule change allowing pediatric providers to bill the child’s Medicaid ID for post-partum depression (PPD) screening of mothers conducted during well-child visits, and document its relationship with PPD treatment and infant hospitalizations.

Study setting and design

Screening rates during well-child visits are calculated at the zip code level and used in linear probability and Instrumental Variable (IV) models to examine increases in screening after the policy change and relate them to PPD treatment and infant hospitalizations.

Data sources and analytic sample

Individual-level Medicaid claims were used to compute PPD screening rates and measures of PPD treatment and infant hospitalization.

Principal findings

The policy was associated with increases in screening rates, although take up was uneven and overall screening rates remained low at 8.8%. There was little overall increase in treatment, although in zip codes in the top third of screening rates, higher screening was associated with 10.1% higher probability of maternal treatment. Zip codes with high fractions in poverty and/or minority had low screening rates, but screening was more likely to be associated with increases in treatment in these areas. There are no effects in the full sample of children, but among children above the poverty line, the observed increases in screening reduced the probability of infant hospitalization in the first six months by 7.7%.

Conclusions

The policy change had only limited success increasing screening, but increased screening could lead to more maternal PPD treatment and lower infant hospitalization rates if accompanied by expanded access to PPD treatment.

This paper examines how over-the-counter drug labels influence consumer perceptions of efficacy, distort decision-making, and shape equilibrium outcomes under counterfactual regulatory scenarios. It addresses a key identification challenge—the unobservability of perceived efficacy under different information structures—by conducting a randomized controlled trial and integrating its findings into a structural model. Using data from a control group and three treatment arms, I construct product-level measures of perceived efficacy beliefs based on pairwise product comparisons. Leveraging control group data supplemented with NielsenIQ data, I estimate a structural demand model that isolates the role of efficacy beliefs while accounting for heterogeneous preferences. I then incorporate updated beliefs to assess equilibrium effects under each information treatment. In equilibrium, the most effective intervention—emphasizing equivalent efficacy—increases substitution between biologically equivalent products by 26%, reduces consumer spending by 12%, but also introduces second-degree price discrimination driven by symptom-label preferences.

This case-control study evaluates the impact of a hospitalization on credit scores for Medicaid beneficiaries in Louisiana stratified by sex, race, and ethnicity.

Hospitalizations can impose financial hardships on families,1,2 contributing to lower credit scores3 and reducing access to credit.1 Non-Hispanic Black and Hispanic patients may struggle more with hospitalization costs due to lower wealth compared with non-Hispanic White patients.

We evaluated the impact of a hospitalization on credit scores for Medicaid beneficiaries in Louisiana, examining effects by sex, race, and ethnicity. Louisiana is a setting well-suited for this research given that it is a Medicaid expansion state, has relatively high levels of personal debt (which inform credit scores), and has a relatively diverse population.