title: A cross-sectional study on health differences between rural and non-rural U.S. counties using the County Health Rankings.
abstract: BACKGROUND
  By examining 2013 County Health Rankings and Roadmaps data from the University of Wisconsin and the Robert Wood Johnson Foundation, this paper seeks to add to the available literature on health variances between United States residents living in rural and non-rural areas. We believe this is the first study to use the Rankings data to measure rural and urban health differences across the United States and therefore highlights the national need to address shortfalls in rural healthcare and overall health. The data indicates that U.S. residents living in rural counties are generally in poorer health than their urban counterparts.
  
  
  METHODS
  We used 2013 County Health Rankings data to evaluate differences across the six domains of interest (mortality, morbidity, health behaviors, clinical care, social and economic factors, and physical environment) for rural and non-rural U.S. counties. This is a cross-sectional study employing chi-square analysis and logit regression.
  
  
  RESULTS
  We found that residents living in rural U.S. counties are more likely to have poorer health outcomes along a variety of measurements that comprise the County Health Rankings' indexed domains of health quality. These populations have statistically significantly (p ≤ 0.05) lower scores in such areas as health behavior, morbidity factors, clinical care, and the physical environment. We attribute the differences to a variety of factors including limitations in infrastructure, socioeconomic differences, insurance coverage deficiencies, and higher rates of traffic fatalities and accidents.
  
  
  CONCLUSIONS
  The largest differences between rural and non-rural counties were in the indexed domains of mortality and clinical care.
  
  
  CONCLUSIONS
  Our analysis revealed differences in health outcomes in the County Health Rankings' indexed domains between rural and non-rural U.S. counties. We also describe limitations and offer commentary on the need for more uniform measurements in the classification of the terms rural and non-rural. These results can influence practitioners and policy makers in guiding future research and when deciding on funding allocation.
authors.affiliation: Essentia Institute of Rural Health, 502 E. 2nd Street, Duluth, MN, 55805, USA. tanderson@eirh.org.
authors.forename: Timothy J
authors.initials: TJ
authors.lastname: Anderson
authors.affiliation: Essentia Institute of Rural Health, 502 E. 2nd Street, Duluth, MN, 55805, USA. Dsaman@eirh.org.
authors.forename: Daniel M
authors.initials: DM
authors.lastname: Saman
authors.affiliation: Roseman University of Health Sciences, 10920 S. River Front Parkway, South Jordan, UT, 84095, USA. mlipsky@roseman.edu.
authors.forename: Martin S
authors.initials: MS
authors.lastname: Lipsky
authors.affiliation: National Center for Interprofessional Practice and Education, University of Minnesota-Twin Cities Campus, MMC 501 Mayo, 420 Delaware St SE, Minneapolis, MN, 55455, USA. nlutfiyy@umn.edu.
authors.forename: M Nawal
authors.initials: MN
authors.lastname: Lutfiyya
mesh_terms: D003430:Cross-Sectional Studies; D003695:Delivery of Health Care / Q000592:standards / Q000706:statistics & numerical data; D004651:Employment / Q000706:statistics & numerical data; D004779:Environment Design; D015438:Health Behavior; D054624:Health Status Disparities; D006801:Humans; D019458:Insurance Coverage / Q000706:statistics & numerical data; D009017:Morbidity; D061213:Mortality, Premature; D011787:Quality of Health Care / Q000706:statistics & numerical data; D012959:Socioeconomic Factors; D014481:United States / Q000453:epidemiology; D014504:Urban Health / Q000706:statistics & numerical data