Search Results (72)

Objective: To evaluate population-level temporal relationships between modifiable lifestyle factors and rising breast, colorectal and uterine cancer incidence rates among females under 50 years old. Methods: This retrospective ecological study utilized data from the United States Cancer Statistics (USCS) for cancer incidence, the National Health and Nutrition Examination Survey (NHANES) for health-related behaviors, and the Behavioral Risk Factor Surveillance System (BRFSS) for physical activity. Modifiable lifestyle factors analyzed included obesity (BMI ≥ 30 kg/m²), smoking, alcohol use, fiber and saturated fat intake, caloric intake, and physical activity. Trends were assessed using average annual percent change (AAPC), and population-level correlations between cancer incidence and lifestyle factors were evaluated using Pearson correlation coefficients. Results: Between 2001 and 2018, 914,659 breast, 144,130 colorectal, and 124,399 uterine cancer cases were identified. The largest increases in cancer incidence occurred in age groups under 30 years old. Colorectal cancer increased by 6.9%, followed by uterine cancer at 4.8% and breast cancer at 1.7%, all p < 0.001. When examining this age group by race, colorectal cancer increased by 8.0% (p < 0.001) annually in White women aged 20–24 years, while uterine cancer rose 4.8% (p < 0.001) in Hispanic women in the 20–24 and 25–29 year age groups. Breast cancer also increased by 2.0% (p < 0.001) per year in White women 25–29 years old. Smoking rates decreased, and alcohol consumption and obesity rates increased. No significant correlation was found between cancer incidence and smoking, caloric intake, saturated fat, or physical activity. A moderate positive correlation was identified between alcohol use and cancer risk (r = 0.55–0.67, p < 0.05). Obesity prevalence showed strong population-level temporal correlation with cancer incidence for all three cancers with stratified analysis demonstrating the strongest correlations in patients with class III obesity. Conclusions: From 2001 to 2018, the incidence of breast, colorectal, and uterine cancers increased most sharply among women under 30 years of age. Over the same period, obesity prevalence in this population also increased. These population-level observations are hypothesis-generating and require confirmation in individual-level, prospective studies to determine whether and how obesity and other lifestyle factors influence early-onset cancer risk. Full article

Background: Diabetes disproportionately affects U.S. subgroups, yet non-Hispanic multiracial adults are underrepresented in epidemiologic studies. This study aimed to examine behavioral and sociodemographic predictors of diabetes in this population. Methods: We analyzed data from the 2023 Behavioral Risk Factor Surveillance System (BRFSS) using a cross-sectional design that incorporated survey weights, strata, and primary sampling units. Binary logistic regression was employed to identify predictors of diabetes, including variables with p ≤ 0.20 from bivariate models in the multivariable analysis. Adjusted odds ratios (AORs) with 95% confidence intervals (CIs) were reported. Results: The study included a total of 6429 participants. Obesity (AOR = 4.16; 95% CI: 3.33, 33.23), being overweight (AOR = 2.05; 1.62, 2.60), poor general health (AOR = 2.82; 2.38, 38.35), age ≥ 65 years (AOR = 3.08; 2.60, 3.65), male sex (AOR = 1.34; 1.15, 1.58), and health insurance (AOR = 2.14; 1.35, 3.61) were associated with higher odds of diabetes. Physical activity (AOR = 0.76; 0.64, 0.90) and alcohol consumption (AOR = 0.55; 0.47, 47.65) were linked to lower odds of diabetes. Smoking status showed no significant association after adjustment. Conclusions: In non-Hispanic multiracial adults, factors such as adiposity and older age increased the risk of diabetes, while physical activity and alcohol consumption offered protective benefits. These findings indicate that current diabetes prevention strategies are applicable to this subgroup, and public health initiatives should prioritize their inclusion in outreach, screening, and intervention efforts. Full article

Background: Chronic diseases are a significant and escalating public health concern in the United States (U.S.) and globally. Chronic co-morbidities such as heart disease, stroke, diabetes, other cardiovascular diseases, and asthma are major risk factors for death and disability. Behavioral factors such as smoking, alcohol use, sedentary lifestyle, and poor dietary habits are among the major risk factors leading to these chronic diseases. Purpose: This study aims to investigate how combinations of unhealthy behaviors are associated with the risk of cardiovascular diseases in various populations. Methods: Using data from the 2023 Behavioral Risk Factor Surveillance System (BRFSS), we computed multivariable logistic regression models to assess the association of unhealthy behaviors with the risk of chronic diseases. Results: Our results show that compounded score of risky health behaviors such as smoking, alcohol use, and physical inactivity, as well as other covariates such as older age, being male, previously married, living in a rented house, unemployed, living in non-metropolitan counties, having high blood pressure, and high cholesterol, were associated with experiencing a heart attack, coronary heart disease, and stroke. Conclusions: Our results highlight the need for behavior-focused population health interventions to lower morbidity and health inequities by showing that unhealthy behaviors and sociodemographic disparities significantly raise the risk of cardiovascular diseases. Full article

Background: Little is known about women’s contraceptive use in the United States during the novel coronavirus (“COVID-19”) pandemic and the risk of unintended pregnancy. Methods: We compared the weighted response rates on contraception use for female respondents aged 18–44 from the Behavioral Risk Factor Surveillance System (BRFSS) between 2019 and 2022. Results: Our study reveals a significant increase of 16.1% (CI = 0.145, 0.177) in the proportion of women using contraception in 2022 as compared to 2019. The largest increase in the use of non-reversible contraception was seen in the proportion of female sterilization, at 3.0% (CI = 0.017, 0.043), mostly attributed to non-Hispanic Black individuals with a 12% increase (CI = 0.046, 0.198). The largest decrease was seen in the use of condoms, at 7.4% (CI = −0.094, −0.055). This was driven by both non-Hispanic Black and multiracial groups, each experiencing a 19% decrease (CI = −0.251, −0.127; CI = −0.304, −0.068, respectively). The proportion of women at risk of unintended pregnancy increased by 3.7% (CI = 0.010, 0.063). These increases were observed among those with an income of less than USD 15k, showing a 14.9% increase (CI = 0.037, 0.262). Older females and those with Medicaid insurance were more likely to use female sterilization. Hispanics, college graduates, and those with Medicaid insurance were more likely to use condoms. Non-White females and those without annual checkups were more at risk of unintended pregnancy. Conclusions: Contraceptive methods shifted among females with slightly increased sterilization in the years 2019 to 2022. Full article

Short sleep duration (≤6 h) is a public health concern linked to cardiometabolic disease and premature mortality. However, persistent disparities across sociodemographic, psychosocial, and structural domains remain underexplored in recent nationally representative samples. We analyzed 2022 Behavioral Risk Factor Surveillance System (BRFSS) data, including 228,463 adults (weighted N ≈ 122 million). Sleep duration was dichotomized as short (≤6 h) versus adequate (≥7 h). Complex samples logistic regression estimated associations between sociodemographic, psychosocial, behavioral, and structural determinants and short sleep, accounting for survey design. The weighted prevalence of short sleep was 33.2%. Non-Hispanic Black (AOR = 1.56, 95% CI: 1.46–1.65) and American Indian/Alaska Native adults (AOR = 1.46, 95% CI: 1.29–1.65) were disproportionately affected compared with non-Hispanic White adults. Psychosocial factors contributed strongly: life dissatisfaction, limited emotional support, and low social connectedness increased odds, whereas high connectedness was protective. Food insecurity and smoking were significant structural and behavioral risks, while binge drinking and urbanicity were not. One-third of U.S. adults report short sleep, with marked disparities across demographic, socioeconomic status, psychosocial stressors, and structural barriers. Findings highlight the multifactorial nature of sleep health inequities and the need for multilevel interventions addressing both individual behaviors and upstream determinants. Full article

Dental care is a well-established marker of both oral and systemic health, driven by behavioral, socioeconomic, and geographic factors. This study aimed to develop and evaluate machine learning models to classify the presence and severity of permanent tooth loss in U.S. adults using the 2022 Behavioral Risk Factor Surveillance System (BRFSS) dataset. We analyzed responses from 365,803 adults after recoding demographic, behavioral, socioeconomic, and access variables. Ten supervised classifiers were trained and evaluated using stratified 80/20 train–test splits, with ANOVA-based selection for the binary task and Pearson correlation plus engineered features for the multiclass task. Performance was assessed by accuracy, AUC, precision, recall, and specificity. For binary classification (any loss vs. none), XGBoost achieved the highest performance (AUC = 0.786; accuracy = 71.4%), with CatBoost close behind (AUC = 0.711). For multiclass severity (none, 1–5, 6+, all teeth removed), an ensemble of gradient-boosting models achieved strong discrimination (macro-AUC = 0.752). Key predictors included age, smoking, education, income, and general health. These findings demonstrate that large-scale survey–based ML models can support oral health surveillance by identifying high-risk groups and informing targeted prevention strategies. Full article

Despite growing recognition of the role that social determinants of health (SDOHs) and health-related social needs (HRSNs) play in chronic disease, limited research has examined their associations with Chronic Obstructive Pulmonary Disease (COPD) in population-based studies. This cross-sectional study analyzed 2022 Behavioral Risk Factor Surveillance System (BRFSS) data from 37 U.S. states and territories to determine how financial hardship, food insecurity, employment loss, healthcare access barriers, and psychosocial stressors influence the prevalence of COPD. Weighted logistic regression models were used to assess the associations between COPD and specific SDOHs and HRSNs. Several individual SDOH and HRSN factors were significantly associated with COPD prevalence, with financial strain emerging as a particularly strong predictor. In models examining specific SDOH factors, economic hardships like inability to afford medical care were strongly linked to higher COPD odds. Psychosocial HRSN risks, such as experiencing mental stress, also showed moderate associations with increased COPD prevalence. These findings suggest that addressing both structural and individual-level social risks may be critical for reducing the prevalence of COPD in populations experiencing financial challenges. Full article

Large language models (LLMs) are increasingly being applied to clinical tasks, but it remains unclear whether medical-specific models consistently outperform smaller, generalpurpose ones. This study investigates that assumption in the context of cardiovascular disease (CVD) risk assessment. We fine-tuned eight LLMs—both general-purpose and medical-specific—using textualized data from the Behavioral Risk Factor Surveillance System (BRFSS) to classify individuals as “High Risk” or “Low Risk”. To provide actionable insights, we integrated a Retrieval-Augmented Generation (RAG) framework for personalized recommendation generation and deployed the system within an interactive chatbot interface. Notably, Gemma2, a compact 2B-parameter general-purpose model, achieved a high recall (0.907) and F1-score (0.770), performing on par with larger or medical-specialized models such as Med42 and BioBERT. These findings challenge the common assumption that larger or specialized models always yield superior results, and highlight the potential of lightweight, efficiently fine-tuned LLMs for clinical decision support—especially in resource-constrained settings. Overall, our results demonstrate that general-purpose models, when fine-tuned appropriately, can offer interpretable, high-performing, and accessible solutions for CVD risk assessment and personalized healthcare delivery. Full article

Mississippi has long been one of the most obese states in the U.S., with its obesity rates consistently exceeding the national average. The state’s severe obesity rate is also among the highest in the nation. This study utilized the 2011 to 2021 data from the Mississippi Behavioral Risk Factor Surveillance System (BRFSS) to conduct a comprehensive analysis of obesity and severe obesity trends in Mississippi by sex, age, and race and ethnicity. The data set included a BMI variable calculated by using self-reported height and weight, which the authors categorized into two obesity classification groups—obesity (BMI: 30.00 to 39.99) and severe obesity (BMI: 40.00 or greater)—and demographic characteristics such as sex, age, race and ethnicity. The data were analyzed using SAS 9.4 software to account for the complex design. Weighted prevalence estimates and associated standard errors (SEs) for obesity and severe obesity were calculated. Changes in the prevalence over time were assessed using logistic regression models. The prevalence estimates and SEs were exported to Joinpoint software to calculate the annual percentage change (APC) and associated 95% confidence intervals (CIs) and p-values for the trends. Our analysis of the data revealed a consistent increase in severe obesity, regardless of age, sex, or race. A concerning trend exists where individuals are moving from the obese category to the severely obese category, indicating a worsening trend in overall weight status. This is likely to accelerate the development of chronic disease and, hence, place additional strain on an economically disadvantaged state. Future research should explore the underlying drivers of this shift, including biological, behavioral, and socioeconomic factors, while also evaluating the effectiveness of existing obesity prevention and treatment programs. Full article

This study aims to examine the mediating effects of health literacy on the relationship between frailty and medical costs among community-dwelling older adults. Methods: This study conducted a secondary data analysis of the research data that were constructed by linking the Korean Frailty and Aging Cohort Data (KFACD) and the National Health Insurance Database (NHID). Frailty was measured using the Modified Fried Phenotype. Medical costs were calculated using insurance-covered medical costs, including both inpatient and outpatient medical costs, from January 1 to December 31 of the year when the participants were enrolled in the Korean Frailty and Aging Cohort Study. Health literacy was assessed using questions from the Behavioral Risk Factor Surveillance System (BRFSS) conducted by the US Centers for Disease Control and Prevention. To examine the mediating role of health literacy in the relationship between frailty and medical costs, Baron and Kenny’s method was used. Linear regression was applied to estimate the association between frailty and health literacy, and Poisson regression was used to model the relationship between frailty, health literacy, and medical costs. Results: Frailty showed a negative correlation with health literacy (r = −0.27, p < 0.001) and a positive correlation with medical costs (r = 0.15, p < 0.001). Health literacy had a negative correlation with medical costs (r = −0.07, p = 0.008). We verified that health literacy played a partial mediating role in the relationship between frailty and medical costs. Conclusions: To reduce medical costs in older adults, intervention measures to improve health literacy as well as prevention and management measures for frailty should be considered simultaneously. In addition, primary medical institutions’ active participation in such projects is needed. Full article

Social determinants of health (SDoH) are considered significant determinants of mental health. This study examines the association between SDoH and mental health status in the United States. We analyzed 2023 Behavioral Risk Factor Surveillance System (BRFSS) data from 183,318 U.S. adults using multinomial logistic regression. Several SDoH were significantly linked to the frequency of poor mental health days. After adjusting for all covariates, individuals facing difficulty paying utility bills had lower odds of experiencing episodic (vs. chronic) poor mental health (AOR = 0.47, p = 0.031). Transportation challenges were associated with lower odds of episodic distress rather than chronic mental health issues (AOR = 0.35, p = 0.026). Individuals who were unable to afford a doctor or who experienced employment loss had significantly lower odds of reporting no poor mental health days compared to reporting chronic poor mental health, with adjusted odds ratios of 0.37 and 0.84, respectively. Non-Hispanic Whites and males were more likely to report chronic poor mental health. Policies that prioritize economic stability and job security, reliable transportation, and equal access to education and healthcare are crucial for promoting mental health equity across diverse populations. Full article

The role of smoking in the development of periodontal disease has been well explored. However, this study aims to explore the relationship between intensity of smoking history and permanent tooth removal. We utilized the 2022 Behavioral Risk Factor Surveillance System (BRFSS), a nationally representative sample of 107,859 US adults, to explore this association. Smoking history intensity was a BRFSS-derived measure of pack-year smoking history. Permanent tooth removal was binarized as the presence or absence of a history of permanent tooth removal. A binary logistic regression was conducted to analyze this association after adjusting for a variety of sociodemographic, health, and substance-use covariates. There was a dose-dependent relationship in which increasing smoking history intensity was associated with increased odds for removal of one or more permanent teeth. For example, those who reported a pack-year history of 30 or more years had a 6.4 times significantly higher odds of reporting a history of permanent tooth removal when compared to those with a 0 pack-year history (adjusted odds ratio = 6.37, 95% CI = 3.80–10.69, p < 0.001). These findings can be used to promote smoking reduction or cessation as a means of decreasing risk of permanent tooth removal due to tooth decay or gum disease. Full article

Probability calibration and decision threshold selection are fundamental aspects of risk prediction and classification, respectively. A strictly proper loss function is used in clinical risk prediction applications to encourage a model to predict calibrated class-posterior probabilities or risks. Recent studies have shown that training with focal loss can improve the discriminatory power of gradient-boosted decision trees (GBDT) for classification tasks with an imbalanced or skewed class distribution. However, the focal loss function is not a strictly proper loss function. Therefore, the output of GBDT trained using focal loss is not an accurate estimate of the true class-posterior probability. This study aims to address the issue of poor calibration of GBDT trained using focal loss in the context of clinical risk prediction applications. The methodology utilizes a closed-form transformation of the confidence scores of GBDT trained with focal loss to estimate calibrated risks. The closed-form transformation relates the focal loss minimizer and the true-class posterior probability. Algorithms based on Bayesian hyperparameter optimization are provided to choose the focal loss parameter that optimizes discriminatory power and calibration, as measured by the Brier score metric. We assess how the calibration of the confidence scores affects the selection of a decision threshold to optimize the balanced accuracy, defined as the arithmetic mean of sensitivity and specificity. The effectiveness of the proposed strategy was evaluated using lung transplant data extracted from the Scientific Registry of Transplant Recipients (SRTR) for predicting post-transplant cancer. The proposed strategy was also evaluated using data from the Behavioral Risk Factor Surveillance System (BRFSS) for predicting diabetes status. Probability calibration plots, calibration slope and intercept, and the Brier score show that the approach improves calibration while maintaining the same discriminatory power according to the area under the receiver operating characteristics curve (AUROC) and the H-measure. The calibrated focal-aware XGBoost achieved an AUROC, Brier score, and calibration slope of 0.700, 0.128, and 0.968 for predicting the 10-year cancer risk, respectively. The miscalibrated focal-aware XGBoost achieved equal AUROC but a worse Brier score and calibration slope (0.140 and 1.579). The proposed method compared favorably to the standard XGBoost trained using cross-entropy loss (AUROC of 0.755 versus 0.736 in predicting the 1-year risk of cancer). Comparable performance was observed with other risk prediction models in the diabetes prediction task. Full article

Cannabis use among young adults in the U.S. has nearly doubled in recent years, driven by diverse methods of consumption and evolving cannabis legalization. Multi-modal cannabis use among young adults is an emerging public health issue that remains underexplored. This study examines the prevalence, patterns, and predictors of multi-modal cannabis use, defined as the use of two or more administration methods of cannabis use (e.g., smoking, vaping, edibles, dabbing, other forms) in the past month, among U.S. young adults aged 18–34 years. Data from the 2022–2023 Behavioral Risk Factor Surveillance System (BRFSS) across 23 states (n = 7635; weighted n = 7,482,134) show that 57% of young adults reporting current cannabis use engaged in multi-modal use, with dual- and triple-mode use being the most common. Factors associated with higher odds of multi-modal use include sexual minority status, poor physical health, frequent cannabis use, and co-use of electronic cigarettes and alcohol. Recreational cannabis legalization (RCL) is significantly linked to higher odds of multi-modal use. These findings underscore the interplay between individual risk factors and cannabis policy environments in shaping multi-modal cannabis use behaviors. To mitigate potential harms, targeted prevention strategies should prioritize young adults at risk for cannabis use, addressing both personal and policy-related factors influencing multi-modal cannabis use. Full article

Background/Objectives: Colorectal cancer (CRC) is the second leading cause of cancer-related deaths globally. Screening for cancer helps to prevent comorbid conditions among individuals with chronic medical conditions, such as stroke. The gold standard for CRC screening is colonoscopy. Stroke is the fifth leading cause of death in the United States and a leading cause of long-term disability. This study examined the prevalence of screening colonoscopy among individuals who reported ever having had a stroke (stroke survivors). Methods: The 2022 Behavioral Risk Factor Surveillance System (BRFSS) data were analyzed for the weighted prevalence and odds of screening colonoscopy uptake among adults aged 45 years and older, based on having had a stroke and socioeconomic status. Results: Almost 6% (n = 16,371) of the adults included in the analysis (N = 285,329) reported having had a stroke, and the weighted prevalence of screening colonoscopy for this group was 73.3% compared to 67.8% for those without stroke. Stroke survivors were 1.3 times more likely to have had a screening colonoscopy compared to those without a history of stroke. Higher odds of screening colonoscopy uptake were observed among stroke survivors that were older than 45–49 years old, with high school or greater than high school education. Stroke survivors that were multiracial and without health insurance coverage had lower odds of screening colonoscopy uptake compared to those that were white and had health insurance coverage, respectively. Conclusions: Though adult stroke survivors, compared to those without a stroke, are more likely to report having had a screening colonoscopy, differences in screening colonoscopy uptake were observed among subgroups of this population based on sociodemographic status. Tailored interventions are needed for increasing screening colonoscopy uptake among disadvantaged subgroups. Full article