Article Text

Geospatial evaluation of trade-offs between equity in physical access to healthcare and health systems efficiency
  1. Hari S Iyer1,2,
  2. John Flanigan3,
  3. Nicholas G Wolf3,
  4. Lee Frederick Schroeder4,
  5. Susan Horton5,
  6. Marcia C Castro6,
  7. Timothy R Rebbeck1,2,3
  1. 1Division of Population Sciences, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
  2. 2Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, United States
  3. 3Zhu Family Center for Global Cancer Prevention, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA
  4. 4Department of Pathology, University of Michigan Medical School, Ann Arbor, Michigan, USA
  5. 5School of Public Health and Health Systems, University of Waterloo, Waterloo, Ontario, Canada
  6. 6Department of Global Health and Population, Harvard University T. H. Chan School of Public Health, Boston, Massachusetts, USA
  1. Correspondence to Dr Hari S Iyer; hai161{at}mail.harvard.edu

Abstract

Introduction Decisions regarding the geographical placement of healthcare services require consideration of trade-offs between equity and efficiency, but few empirical assessments are available. We applied a novel geospatial framework to study these trade-offs in four African countries.

Methods Geolocation data on population density (a surrogate for efficiency), health centres and cancer referral centres in Kenya, Malawi, Tanzania and Rwanda were obtained from online databases. Travel time to the closest facility (a surrogate for equity) was estimated with 1 km resolution using the Access Mod 5 least cost distance algorithm. We studied associations between district-level average population density and travel time to closest facility for each country using Pearson’s correlation, and spatial autocorrelation using the Global Moran’s I statistic. Geographical clusters of districts with inefficient resource allocation were identified using the bivariate local indicator of spatial autocorrelation.

Results Population density was inversely associated with travel time for all countries and levels of the health system (Pearson’s correlation range, health centres: −0.89 to −0.71; cancer referral centres: −0.92 to −0.43), favouring efficiency. For health centres, negative spatial autocorrelation (geographical clustering of dissimilar values of population density and travel time) was weaker in Rwanda (−0.310) and Tanzania (−0.292), countries with explicit policies supporting equitable access to rural healthcare, relative to Kenya (−0.579) and Malawi (−0.543). Stronger spatial autocorrelation was observed for cancer referral centres (Rwanda: −0.341; Tanzania: −0.259; Kenya: −0.595; Malawi: −0.666). Significant geographical clusters of sparsely populated districts with long travel times to care were identified across countries.

Conclusion Negative spatial correlations suggested that the geographical distribution of health services favoured efficiency over equity, but spatial autocorrelation measures revealed more equitable geographical distribution of facilities in certain countries. These findings suggest that even when prioritising efficiency, thoughtful decisions regarding geographical allocation could increase equitable physical access to services.

  • geographic information systems
  • health services research
  • health policy
  • health systems evaluation
  • public health
http://creativecommons.org/licenses/by-nc/4.0/

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.

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Footnotes

  • Handling editor Seye Abimbola

  • Twitter @hiyer_epi, @marciacastrorj

  • Contributors HSI, JF and TRR conceived the study and design. JF and TRR facilitated acquisition of the data. HSI and NGW analysed the data. HSI, NGW, MCC, SH, LFS, JF and TRR interpreted the data. HSI drafted the manuscript. HSI, JF, NGW, SH, LFS, MCC and TRR provided critical review and final approval of the manuscript.

  • Funding We are grateful for administrative support from the Zhu Family Global Center for Cancer Prevention at the Harvard T. H. Chan School of Public Health and the Department of Medical Oncology at the Dana-Farber Cancer Institute. This study grew out of related geographical analytical work as part of the Lancet Commission on Diagnostics, and we are grateful for the support of our colleagues in that group. HSI was supported by NIH T32 CA 009001 and the Harvey V. Fineberg Fellowship in Cancer Prevention. TRR was supported by NIH U01-CA184374.

  • Disclaimer This study would not have been possible without the researchers, private companies, government workers, and non-profits who have made vast amounts of satellite, demographic, and government geospatial data publicly available.

  • Competing interests LFS reports holding stocks from InheRET and personal fees from Roche Diagnostics, outside the submitted work.

  • Patient consent for publication Not required.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Data availability statement Data for preparing the scatter plots presented in this study are available in a public, open access repository. Additional data are available upon request to the corresponding author. Data used in this study include tables with health centre and cancer referral centre locations and gridded image files with travel time data (500m resolution) and population density data (100m resolution). These data were generated using publicly available datasets and so no conditions apply constraining their use. We have made selected data and code used to generate the major figures in the paper available at the following link: https://github.com/hiyer09/geopsa_paper.

  • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

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