Browse Articles

The authors look at malnutrition in children and how socio-economic factors impact this.

Recent research suggests that the "American Dream" of income mobility may be becoming increasingly hard to obtain. Datta and Schmitz explore the role of government spending in socioeconomic opportunity by determining which state government spending components are associated with increased income mobility.

Here the authors examined the relationship between education levels and the gender wage gap (GWG) in the US and Puerto Rico from 2010 to 2022, hypothesizing that higher education would correlate with a lower GWG. Their analysis of income data revealed an inverse correlation, where higher education levels were associated with reduced gender wage disparities, suggesting that policies aimed at closing the gender gap in higher education could promote socioeconomic equality.

Here the authors aimed to understand factors influencing adolescent fentanyl exposure, hypothesizing a positive association between social media usage, socioeconomic factors, and fentanyl abuse among U.S. teens. Their analysis of the Monitoring the Future dataset revealed that a history of suspension and use of marijuana or alcohol were linked to higher fentanyl use, and while not statistically significant, a notable positive correlation between social media use and fentanyl frequency was observed.

Asthma is one of the most prevalent chronic conditions in the United States. But not all people experience asthma equally, with factors like healthcare access and environmental pollution impacting whether children are likely to be hospitalized for asthma's effects. Li, Li, and Ruffolo investigate what demographic and environmental factors are predictive of childhood asthma hospitalization rates across Pennsylvania.

The global mental health crisis has led to increased substance abuse among youth. Prescription drug abuse causes approximately 115 American deaths daily. Understanding intergenerational transmission of substance abuse is complex due to lengthy human studies and socioeconomic variables. Recent FDA guidelines mandate abuse liability testing for neuro-active drugs but overlook intergenerational transfer. Brown planaria, due to their nervous system development similarities with mammals, offer a novel model.

The authors use machine learning to develop an evidence-based detection tool for identifying human trafficking.

Over the last few decades, childhood stunting has persisted as a major global challenge. This study hypothesized that TPTO (Tree-based Pipeline Optimization Tool), an AutoML (automated machine learning) tool, would outperform all pre-existing machine learning models and reveal the positive impact of economic prosperity, strong familial traits, and resource attainability on reducing stunting risk. Feature correlation plots revealed that maternal height, wealth indicators, and parental education were universally important features for determining stunting outcomes approximately two years after birth. These results help inform future research by highlighting how demographic, familial, and socio-economic conditions influence stunting and providing medical professionals with a deployable risk assessment tool for predicting childhood stunting.

This article investigates the study methodologies, learning strategies, and motives of spelling bee participants. The authors identify several important educational implications of this work.

This study explores how different economic sectors, geographic regions, and greenhouse gas types might affect future global mean surface temperature (GMST) anomalies differently from historical patterns. Using the Finite Amplitude Impulse Response (FaIR) model and four Shared Socioeconomic Pathways (SSPs) — SSP126, SSP245, SSP370, and SSP585 — the research reveals that future contributions to GMST anomalies.