Generalized Linear Mixed Model Analysis of Urban-Rural Differences in Social and Behavioral Factors for Colorectal Cancer Screening

Document Type: Research Articles

Authors

1 Department of Biostatistics and Epidemiology, College of Public Health, East Tennessee State University, Johnson City, TN 37614, USA.

2 Department of Systems Leadership and Effectiveness Science, School of Nursing, University of Michigan, Ann Arbor, MI 48109-5482, USA.

3 Department of Economics and Finance, College of Business and Technology, East Tennessee State University, Johnson City, TN 37614, USA.

4 Department of Health and Biomedical Sciences, College of Health Affairs, University of Texas Rio Grande Valley, Brownsville, TX 78520, USA.

5 Division of Biostatistics and Bioinformatics, Department of Environmental Health, University of Cincinnati, Cincinnati, OH 45267, USA.

6 School of Kinesiology, College of Health, University of Southern Mississippi, Hattiesburg, MS 39406, USA.

Abstract

 
Objective: Screening for colorectal cancer (CRC) can reduce disease incidence, morbidity, and mortality. However, few studies have investigated the urban-rural differences in social and behavioral factors influencing CRC screening. The objective of the study was to investigate the potential factors across urban-rural groups on the usage of CRC screening. Methods: A total of 38,505 adults (aged ≥40 years) were selected from the 2009 California Health Interview Survey (CHIS) data - the latest CHIS data on CRC screening. The weighted generalized linear mixed-model (WGLIMM) was used to deal with this hierarchical structure data. Weighted simple and multiple mixed logistic regression analyses in SAS ver. 9.4 were used to obtain the odds ratios (ORs) and their 95% confidence intervals (CIs). Results: The overall prevalence of CRC screening was 48.1% while the prevalence in four residence groups - urban, second city, suburban, and town/rural, were 45.8%, 46.9%, 53.7% and 50.1%, respectively. The results of WGLIMM analysis showed that there was residence effect (p<0.0001) and residence groups had significant interactions with gender, age group, education level, and employment status (p<0.05). Multiple logistic regression analysis revealed that age, race, marital status, education level, employment stats, binge drinking, and smoking status were associated with CRC screening (p<0.05). Stratified by residence regions, age and poverty level showed associations with CRC screening in all four residence groups. Education level was positively associated with CRC screening in second city and suburban. Infrequent binge drinking was associated with CRC screening in urban and suburban; while current smoking was a protective factor in urban and town/rural groups. Conclusions: Mixed models are useful to deal with the clustered survey data. Social factors and behavioral factors (binge drinking and smoking) were associated with CRC screening and the associations were affected by living areas such as urban and rural regions.

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