Introduction: Rural populations face numerous barriers to health, including poorer health care infrastructure, access to care, and other sociodemographic factors largely associated with rurality. Multiple measures of rurality used in the biomedical and public health literature can help assess rural-urban health disparities and may impact the observed associations between rurality and health. Furthermore, understanding what makes a place truly "rural" versus "urban" may vary from region to region in the US.
Purpose: The objectives of this study are to compare and contrast five common measures of rurality and determine how well-correlated these measures are at the national, regional, and divisional level, as well as to assess patterns in the correlations between the prevalence of obesity in the population aged 60+ and each of the five measures of rurality at the regional and divisional level.
Methods: Five measures of rurality were abstracted from the US Census and US Department of Agriculture (USDA) to characterize US counties. Obesity data in the population aged 60+ were abstracted from the Behavioral Risk Factor Surveillance System (BRFSS). Spearman's rank correlations were used to quantify the associations among the five rurality measurements at the national, regional, and divisional level, as defined by the US Census Bureau. Geographic information systems were used to visually illustrate temporal, spatial, and regional variability.
Results: Overall, Spearman's rank correlations among the five measures ranged from 0.521 (percent urban-urban influence code) to 0.917 (rural-urban continuum code-urban influence code). Notable discrepancies existed in these associations by Census region and by division. The associations between measures of rurality and obesity in the 60+ population varied by rurality measure used and by region.
Conclusion: This study is among the first to systematically assess the spatial, temporal, and regional differences and similarities among five commonly used measures of rurality in the US. There are important, quantifiable distinctions in defining what it means to be a rural county depending on both the geographic region and the measurement used. These findings highlight the importance of developing and selecting an appropriate rurality metric in health research.
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http://dx.doi.org/10.3389/fpubh.2015.00267 | DOI Listing |
Perspect Med Educ
December 2024
Griffith University Rural Clinical School, Toowoomba, Australia.
Introduction: Medical students learn to reflect to gain new insights into self and practice; however, allowing for reflection within a busy curriculum is challenging. In this study we embedded reflective writing prompts (RWP) into an existing assessment item, Online Submission of Case Reports (OSCAR), to investigate whether this minimalistic scaffolding intervention could develop students' reflective capacity and increase their exposure to rural social determinants of health.
Methods: This study is framed by ontological realism and informed by an interpretivist stance.
Objectives: To evaluate whether recorded cases of oral cancer in India align with actual prevalence, identify gaps in screening efforts, and propose strategies for effective nationwide screening and surveying initiatives.
Study Design: A comprehensive review of secondary data, including global and national surveys, government statistics, and published studies, to analyze the prevalence of oral cancer and tobacco use and identify barriers to screening.
Methods: Data from GLOBOCAN 2022, National Family Health Survey-5 (NFHS-5), Global Adult Tobacco Survey-2 (GATS-2), and related studies were analyzed to assess oral cancer prevalence, tobacco usage, and screening participation.
Sci Rep
December 2024
Institute of Agriculture and Life Science, Gyeongsang National University, Jinju, 52828, Republic of Korea.
Heat stress (HS) is an impactful condition in ruminants that negatively affects their physiological and rumen microbial composition. However, a fundamental understanding of metabolomic and metataxonomic mechanisms in goats under HS conditions is lacking. Here, we analyzed the rumen metabolomics, metataxonomics, and serum metabolomics of goats (n = 10, body weight: 41.
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December 2024
Key Laboratory of Biology and Genetic Improvement of Oil Crops, Ministry of Agriculture and Rural Affairs, Oil Crops Research Institute, Chinese Academy of Agricultural Science, Wuhan, 430062, China.
The photosynthetic mechanism responsible for the differences in yield between different rapeseed varieties remains unclear, and there have been no consensus and definite conclusions about the relationship between photosynthesis and yield. Representation of the whole plant by measuring the photosynthetic performance at a single site may lead to biased results. In this study, we comprehensively analyzed the main photosynthetic organs of four high-yielding rapeseed varieties at the seedling, bud, flowering, and podding stages.
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December 2024
Department of Biomedical Engineering, University of Los Andes, Bogotá, Colombia.
Agriculture 4.0 technologies continue to see low adoption among small and medium-sized farmers, primarily because these solutions often fail to account for the specific challenges of rural areas. In this work, we propose and implement a design methodology to develop a Precision Agriculture solution aimed at assisting farmers in managing water stress in Hass avocado crops.
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