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Here the authors investigate the relationship between environmental pollutants and cancer incidence rates across various geographical clusters in the United States from 2018 to 2020. By calculating Pearson correlation coefficients and t-statistics on CDC and EPA data, they demonstrated a strong correlation between specific pollutants and various cancers, offering insights that could help explain regional disparities in cancer rates and aid in preventing premature deaths.

This study examined how social determinants of health (SDH) relate to vision loss by analyzing publicly available data from 18 northern and southern U.S. states and using Bayesian correlation analysis.

The authors survey teenagers within the United States regarding the effect of social media use on sleep quality and attention span.

A significant percentage of cancer survivors develop a second primary cancer. Using data of deceased patients provided by the Peninsula Regional Medical Center, Li and Holdai conducted a retrospective statistical analysis to investigate whether the type of the first cancer affects the occurrence time and type of the second primary cancer.

Researching gun violence and mass shootings in the U.S. is difficult due to the lack of consistent data collection. Some studies have linked mass shootings to personal financial stress, but little formal research exists on the impact of broader economic conditions. This study hypothesized an inverse relationship between mass shootings and economic performance, using the S&P 500 and unemployment rate as indicators.

Parkinson’s disease (PD) is a prevalent neurodegenerative disorder in the U.S., second only to Alzheimer’s disease. Current diagnostic methods are often inefficient and dependent on clinical exams. This study explored using machine and deep learning to enhance PD diagnosis by analyzing spiral drawings affected by hand tremors, a common PD symptom.

Cross country is a popular sport in the U.S. Both athletes and coaches are interested in the factors that make runners successful. In this study, the authors explore the relationship between runners' physical attributes and their race performance.

The printing-related expenditure that is budgeted in 2014 for U.S. Federal agencies is $1.8 billion. A sample of five publically available documents produced by various federal agencies is analyzed and the cost savings arising from a change in font type are estimated. The analysis predicts that the Government’s annual savings by switching to Garamond are likely to be about $234 million with worst-case savings of $62 million and best-case savings of $394 million. Indirect benefits arising from a less detrimental impact on the environment due to lower ink production and disposal volumes are not included in these estimates. Times New Roman is not as efficient as Garamond, and the third federally-recommended font, Century Gothic, is actually worse on average than the fonts used in the sample documents.

This study used machine learning models to examine which factors most influenced U.S. household energy consumption in 2020 using data from 18,496 households.

Coronary heart disease (CHD) is the leading cause of death in the U.S., responsible for nearly 700,000 deaths in 2021, and is marked by artery clogging that can lead to heart attacks. Traditional prediction methods require expensive clinical tests, but a new study explores using machine learning on demographic, clinical, and behavioral survey data to predict CHD.