Improving Rural Access to Orthopaedic Care Through Visiting Consultant Clinics

Gruca, Thomas S.; Pyo, Tae‐Hyung; Nelson, Gregory C.

doi:10.2106/jbjs.15.00946

Cited by 30 publications

(24 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…RUCA codes are based on population size and density, urbanization, and primary and secondary worker commuter flow patterns to larger urban areas and clusters [31]. The 10 RUCA codes were then aggregated into a dichotomized variable: (1) Urban (i.e., population centers with 50,000 or more residents) and (2) Non-Urban (i.e., towns or small cities with population centers with less than 50,000 residents) [31, 32]. We obtained all county-level demographic statistics from the 2014 ACS via National Historical Geographic Information System [29].…”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

“…In contrast to LISA, the Gi* statistic are not related to the global statistic of spatial association [32]. Moreover, while the LISA statistic includes a diagnostic for outliers with respect to a measure of global association, the Gi* only examines associations for counties that share borders [32]. Further, the LISA analysis allows for examining local associations while accounting for the global spatial association (can be observed from the counties identified as either high-low or low-high outliers).…”

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

Mapping hot spots of breast cancer mortality in the United States: place matters for Blacks and Hispanics

Moore

Royston

Langston

et al. 2018

Cancer Causes Control

View full text Add to dashboard Cite

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Mapping hot spots of breast cancer mortality in the United States: place matters for Blacks and Hispanics

Moore

Royston

Langston

et al. 2018

Cancer Causes Control

View full text Add to dashboard Cite

show abstract

“…We obtained data on PM2.5 for years 2003 through 2011 from the US Environmental Protection Agency (EPA) and the National Aeronautics and Space Administration (NASA) by assessing the CDC “wide-ranging online data for epidemiologic research” (Wonder) system [17, 18]. Al-Hamdan et al (2009) generated continuous spatial surfaces of daily PM2.5 for the entire contiguous US for 2003 through 2011 using environmental data from the US EPA and NASA [17].…”

Section: Methodsmentioning

confidence: 99%

“…RUCA codes are based on population size and density, urbanization, and primary and secondary worker commuter flow patterns to larger urban areas and clusters [20]. The 11 RUCA codes were then aggregated into a dichotomized variable: (1) urban (i.e., population centers with ≥50,000 residents) and (2) non-urban (i.e., towns or small cities with population centers of <50,000 residents) [18, 20]. We obtained county-level demographic statistics from the 2014 ACS via the National Historical Geographic Information System (NHGIS) [15].…”

Section: Methodsmentioning

confidence: 99%

Pollution and regional variations of lung cancer mortality in the United States

Moore

Akinyemiju

Wang

2017

Cancer Epidemiology

View full text Add to dashboard Cite

Introduction The aims of this study were to identify counties in the United States (US) with high rates of lung cancer mortality, and to characterize the associated community-level factors while focusing on particulate-matter pollution. Methods We performed a descriptive analysis of lung cancer deaths in the US from 2004 through 2014. We categorized counties as “clustered” or “non-clustered” – based on whether or not they had high lung cancer mortality rates – using novel geospatial autocorrelation methods. We contrasted community characteristics between cluster categories. We performed logistic regression for the association between cluster category and particulate-matter pollution. Results Among 362 counties (11.6%) categorized as clustered, the age-adjusted lung cancer mortality rate was 99.70 deaths per 100,000 (95%CI: 99.1–100.3). Compared with non-clustered counties, clustered counties were more likely in the south (72.9% versus 42.1%, P < 0.01) and in non-urban communities (73.2% versus 57.4, P < 0.01). Clustered counties had greater particulate-matter pollution, lower education and income, higher rates of obesity and physical inactivity, less access to healthcare, and greater unemployment rates (P < 0.01). Higher levels of particulate-matter pollution (4th quartile versus 1st quartile) were associated with two-fold greater odds of being a clustered county (adjusted OR: 2.10; 95%CI: 1.23–3.59). Conclusion We observed a belt of counties with high lung mortality ranging from eastern Oklahoma through central Appalachia; these counties were characterized by higher pollution, a more rural population, lower socioeconomic status and poorer access to healthcare. To mitigate the burden of lung cancer mortality in the US, both urban and rural areas should consider minimizing air pollution.

show abstract