Aim: To determine whether neighborhood factors have direct or indirect effects, via self-care behaviors on glycemic control.
Methods: Adult patients with type 2 diabetes were recruited from an academic medical center and Veterans Affairs Medical Center in the southeastern United States. Confirmatory factor analysis was used to create latent variables for neighborhood factors and diabetes self-care behavior. Structural equation modeling was used to test direct and indirect effects between neighborhood factors and glycemic control as assessed by HbA1c levels.
Results: CFA yielded four latent variables for neighborhood factors (neighborhood violence, access to healthy food, social support, and neighborhood aesthetics) and one latent variable diabetes self-care. We found that social support (β=0.28, z=4.86, p<0.001) and access to healthy foods (β=-0.17, z=-2.95, p=0.003) had direct effects on self-care; self-care (β=-0.15, z=-2.48, p=0.013) and neighborhood aesthetics (β=0.12, z=2.19, p=0.03) had direct effects on glycemic control; while social support (β=-0.04, z=-2.26, p=0.02) had an indirect effect on glycemic control via self-care.
Conclusion: This study showed that self-care behaviors and neighborhood aesthetics have direct effects on glycemic control, social support and access to health foods had direct effects on self-care, and social support had an indirect effect on glycemic control via self-care.
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http://dx.doi.org/10.1016/j.jdiacomp.2014.10.008 | DOI Listing |
Cancer Epidemiol Biomarkers Prev
January 2025
University of Kentucky, Lexington, KY, United States.
Background: Kentucky is within the top five leading states for breast mortality nationwide. This study investigates the association between neighborhood socioeconomic disadvantage and breast cancer outcomes, including surgical treatment, radiation therapy, chemotherapy, and survival, and how associations vary by race and ethnicity in Kentucky.
Methods: We conducted a retrospective cohort analysis using data from the Kentucky Cancer Registry (KCR) for breast cancer patients diagnosed between 2010 and 2017, with follow-up through December 31, 2022.
Breastfeed Med
January 2025
Slone Epidemiology Center, Boston University, Boston, Massachusetts, USA.
Social determinants of health account for racial inequities in breastfeeding rates in the United States. There is a gap in the role of neighborhood socioeconomic status (NSES) as it relates to breastfeeding disparities. Using longitudinal data from the Black Women's Health Study, we assessed associations of NSES with breastfeeding initiation and duration in a cohort of primiparous U.
View Article and Find Full Text PDFSwiss Med Wkly
November 2024
Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland.
Background And Aims: Despite a well-funded healthcare system with universal insurance coverage, Switzerland has one of the highest neonatal and infant mortality rates among high-income countries. Identifying avoidable risk factors targeted by evidence-based policies is a public health priority. We describe neonatal and infant mortality in Switzerland from 2011 to 2018 and explore associations with neonatal- and pregnancy-related variables, parental sociodemographic information, regional factors and socioeconomic position (SEP) using data from a long-term nationwide cohort study.
View Article and Find Full Text PDFFront Digit Health
January 2025
Key Laboratory of Sports Trauma and Rehabilitation of General Administration of Sport of the People's Republic of China, Beijing, China.
Introduction: The aim of this study is to compare the injury patterns of female water polo players before and after the implementation of the Male-Assisted Female Training (MAFT) program. The study seeks to identify key factors influencing these changes and propose corresponding injury prevention measures.
Methods: We utilized pattern analysis and classification techniques to explore the injury data.
Infect Dis Model
June 2025
Department of Statistics, IME, Federal University of Bahia, Salvador, BA, Brazil.
This paper presents an investigation into the spatio-temporal dynamics of Severe Acute Respiratory Syndrome (SARS) across the diverse health regions of Brazil from 2016 to 2024. Leveraging extensive datasets that include SARS cases, climate data, hospitalization records, and COVID-19 vaccination information, our study employs a Bayesian spatio-temporal generalized linear model to capture the intricate dependencies inherent in the dataset. The analysis reveals significant variations in the incidence of SARS cases over time, particularly during and between the distinct eras of pre-COVID-19, during, and post-COVID-19.
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