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Exploring Smoking Disparities and Sociodemographic Factors in a Peri-urban Landscape: A Census Tract-Level Analysis in McLennan County, Texas.

Jahsarah I WilliamsonKathryn M Janda-ThomteStephanie K Jones
Published in: Journal of community health (2023)
Cigarette smoking significantly contributes to preventable illness, death, and economic costs. Despite overall reduction in national smoking rates, disparities persist between demographic groups and geographic regions. While some studies have explored urban-rural differences in smoking prevalence, gaps exist in understanding localized patterns. This study focuses on examining smoking rates and related factors at the census tract level in McLennan County, Texas, a county that contains a mixture of urban, peri-urban, and rural areas. This study uses census tract level aggregate sociodemographic, smoking, and health-related data from the American Community Survey and the PLACES Project City Health Dashboard. Geospatial analyses mapped co-occurrence of high prevalence of smoking, mental and physical distress, and co-occurrence of lower routine medical check-ups, household income, and education. Multiple linear regression modeled associations between smoking and sociodemographic, and health-related factors. Geospatial analyses identified census tracts with co-occurring high prevalence of smoking, mental and physical distress, and co-occurrence of lower routine medical check-ups, household income, and education level in McLennan County. Regression analyses identified that smoking rates were positively correlated with frequent physical distress (p < 0.0001) and negatively correlated with the proportion of routine medical check-ups (p < 0.0001) and the proportion living in poverty (p = 0.0002). This study found significant variations in smoking rates, physical and mental distress, medical check-ups, and sociodemographic factors between neighboring census tracts which geospatial analyses examining larger geographic units may have overlooked. Future research should focus on obtaining individual-level and community-level data to develop more targeted interventions sensitive to specific community contexts.
Keyphrases
  • mental health
  • healthcare
  • smoking cessation
  • physical activity
  • quality improvement
  • public health
  • emergency department
  • clinical practice
  • cross sectional
  • risk factors
  • big data
  • cancer therapy
  • deep learning
  • human health