Before the Affordable Care Act expansion in 2014, Medicaid program primarily covered pregnant women, low-income families with children, and elderly and disabled people with Supplemental Security Income (SSI). The expansion extended coverage to most of the adults up to 138% Federal Poverty Level (FPL), and it was adopted by 27 states in 2014 and by 2021, it increased to 39 states (including DC). Past literature provided evidence that this led to a significant increase in the total Medicaid coverage and consequently a decrease in the total number of the uninsured individuals in the expansion states. According to CDC, among chronic conditions, Diabetes is one of the leading causes of morbidity and mortality in the US. Access to health care is most essential to stay healthy for a diabetic person. With high cost of supplies, medication, education, and medical care to manage diabetes, affordable and adequate health insurance is imperative. This paper examines the impact of the Affordable Care Act (ACA) Medicaid Expansion on diabetes diagnoses, and its management to prevent health complications caused by diabetes. Employing a difference-in-differences framework, we exploit the variation in the eligibility caused by the ACA expansion to compare the changes in diabetes outcomes in the expansion states (treatment group) with the non-expansion states (control group).
In this paper, we study the role of peoples’ attitudes on their labor market behavior. Focusing within a household, we estimate how one’s labor market decisions are dependent on their partner’s labor market outcomes, and how these decisions are driven by their culture component. Historically, man has been associated as the primary earner in a family. We argue that culture might play a role in determining a person’s labor market outcomes as it induces an aversion to the situation of when the wife earns more than the husband. We find that husbands increase their participation as well as hours worked in the labor market if their wives earn more and this effect is even more prominent if they are from a country where people have the traditional view that man should be the primary breadwinner for the family. However, wives do not exhibit any such behavior. We argue that this irregularity is explained by the role that culture plays on forming labor market decisions.
Research on the relationship between dietary patterns and well-being has expanded rapidly in recent years. This study contributes to previous work by examining how diet relates to well-being among rural middle school students in North Carolina, with an average age of 12. Two indicators are used to measure youth well-being at an individual level: self-reported academic performance and perceived stress levels. Students’ diets are categorized as healthy, unhealthy, or neutral, and we construct a health index to capture differences across these dietary patterns.Our analysis draws on cross-sectional data from the Motivating Adolescents with Technology to Choose Health (MATCH) program, a school-based wellness initiative implemented during the 2022–2023 academic year in rural North Carolina. Using a mixed-effects model, we assess the association between diet and well-being in this rural adolescent population.Finally, we explore whether participation in MATCH moderates the effects of dietary patterns. Although findings suggest that school-based wellness programs may help buffer the negative influence of a poor diet, the moderating effect is not statistically significant, providing insights for policies that promote adolescent well-being and academic success.
Seasonality is a defining feature of many epidemiological time series, reflecting recurring patterns over time. Accurate characterization of these patterns is essential for influenza surveillance, forecasting, and public health planning.This study examines influenza incidence in Wisconsin using laboratory-confirmed weekly case data from 2021–2025 provided by the CDC. We compare four approaches for modeling seasonal time-series data: seasonal dummy variables, Fourier harmonic regression, Seasonal ARIMA models, and generalized additive models (GAMs) with spline-based smoothers. While recent studies have applied GAMs to assess meteorological effects, few have used this flexible approach to directly model the underlying seasonal structure of respiratory infection data.Results show that the spline-based GAM consistently outperforms alternative methods. It captures shifting peak timing, asymmetric seasonal curves, and multiple inflection points, the features that more rigid approaches cannot fully represent. No evidence of overfitting was observed. Findings also suggest increased variability in post-COVID influenza seasonality in Wisconsin, highlighting the need for adaptive, data-driven models.
