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. 2017 Jul 10;7(1):4948.
doi: 10.1038/s41598-017-04708-3.

Climate Classification Is an Important Factor in Assessing Quality-of-Care Across Hospitals

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Free PMC article

Climate Classification Is an Important Factor in Assessing Quality-of-Care Across Hospitals

Mary Regina Boland et al. Sci Rep. .
Free PMC article

Abstract

Climate is a known modulator of disease, but its impact on hospital performance metrics remains unstudied. We assess the relationship between Köppen-Geiger climate classification and hospital performance metrics, specifically 30-day mortality, as reported in Hospital Compare, and collected for the period July 2013 through June 2014 (7/1/2013-06/30/2014). A hospital-level multivariate linear regression analysis was performed while controlling for known socioeconomic factors to explore the relationship between all-cause mortality and climate. Hospital performance scores were obtained from 4,524 hospitals belonging to 15 distinct Köppen-Geiger climates and 2,373 unique counties. Model results revealed that hospital performance metrics for mortality showed significant climate dependence (p < 0.001) after adjusting for socioeconomic factors. Climate is a significant factor in evaluating hospital 30-day mortality rates. These results demonstrate that climate classification is an important factor when comparing hospital performance across the United States.

Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Figure 1
Figure 1
Hospital Compare Data By County with Major Climate Designations: A Map of the United States Showing Hospital Compare Data Mapped to Köppen-Geiger Climate Classifications. Map of the United States was generated in R, using the following libraries: choroplethr (version: ‘3.5.2’, https://cran.r-project.org/web/packages/choroplethr/index.html), ggplot2 (version: ‘2.1.0’, https://cran.r-project.org/web/packages/ggplot2/index.html), noncensus (version: ‘0.1’, https://cran.r-project.org/web/packages/noncensus/index.html), zipcode (version: ‘1.0’, https://cran.r-project.org/web/packages/zipcode/index.html), grid (version: ‘3.3.0’, https://stat.ethz.ch/R-manual/R-devel/library/grid/html/00Index.html) and gridExtra (version: ‘2.2.1’, https://cran.r-project.org/web/packages/gridExtra/index.html). The map itself utilized the choroplethr library version 3.5.2.
Figure 2
Figure 2
Climate’s Impact on Hospital Performance Mortality Statistics After Adjustment for Confounders: Map of the United States of America. Model coefficients for climate’s impact on 30-day mortality are displayed by climate classification in Fig. 2A. A map of the USA illustrating the results of the model is shown in Fig. 2B. Map of the United States was generated in R, using the following libraries: choroplethr (version: ‘3.5.2’, https://cran.r-project.org/web/packages/choroplethr/index.html), and ggplot2 (version: ‘2.1.0’, https://cran.r-project.org/web/packages/ggplot2/index.html). The map itself utilized the choroplethr library version 3.5.2.

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