Advertisement

Network-Based Delineation of Health Service Areas: A Comparative Analysis of Community Detection Algorithms

  • Diego PinheiroEmail author
  • Ryan Hartman
  • Erick Romero
  • Ronaldo Menezes
  • Martin Cadeiras
Conference paper
Part of the Springer Proceedings in Complexity book series (SPCOM)

Abstract

A Health Service Area (HSA) is a group of geographic regions served by similar health care facilities. The delineation of HSAs plays a pivotal role in the characterization of health care services available in an area, enabling better planning and regulation of health care services. Though Dartmouth HSAs have been the standard delineation for decades, previous work has recently shown an improved HSA delineation using a network-based approach, in which HSAs are the communities extracted by the Louvain algorithm in hospital-patient discharge networks. Given the known heterogeneity of communities extracted by different community detection algorithms, a comparative analysis of community detection algorithms for optimal HSA delineation is lacking. In this work, we compared HSA delineations produced by community detection algorithms using a large-scale dataset containing different types of hospital-patient discharges spanning a 7-year period in the USA. Our results replicated the heterogeneity among community detection algorithms found in previous works, the improved HSA delineation obtained by a network-based, and suggested that Infomap may be a more suitable community detection for HSA delineation since it finds a high number of HSAs with high localization index and a low network conductance.

Keywords

Hospital-Patient Discharge Networks Community detection algorithms Health Service Area HSA delineation 

References

  1. 1.
    Blondel, V.D., Guillaume, J.-L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. J. Stat. Mech.-Theory Exp. 2008(10) (2008). https://sci-hub.tw/https://iopscience.iop.org/article/10.1088/1742-5468/2008/10/P10008/pdf CrossRefGoogle Scholar
  2. 2.
    CHHS. California Health and Human Services Agency. https://data.chhs.ca.gov. Accessed 2019 Nov 15
  3. 3.
    Everson, J., Hollingsworth, J.M., Adler-Milstein, J.: Comparing methods of grouping hospitals. Health Serv. Res. 24(4), 333–339 (2019)Google Scholar
  4. 4.
    Fortunato, S.: Community detection in graphs. Phys. Rep. 486(3–5), 75–174 (2010)ADSMathSciNetCrossRefGoogle Scholar
  5. 5.
    Fortunato, S., Hric, D.: Community detection in networks: a user guide. Phys. Rep. 659, 1–44 (2016)ADSMathSciNetCrossRefGoogle Scholar
  6. 6.
    Hartman, R., Faustino, J., Pinheiro, D., Menezes, R.: Assessing the suitability of network community detection to available meta-data using rank stability. In: WI, pp. 162–169 (2017)Google Scholar
  7. 7.
    Hu, Y., Wang, F., Xierali, I.M.: Automated delineation of hospital service areas and hospital referral regions by modularity optimization. Health Serv. Res. 53(1), 236–255 (2018)CrossRefGoogle Scholar
  8. 8.
    Peixoto, T.P.: Hierarchical block structures and high-resolution model selection in large networks. Phys. Rev. X 4(1), 011047 (2014). https://journals.aps.org/prx/abstract/10.1103/PhysRevX.4.011047
  9. 9.
    Peixoto, T.P.: Model selection and hypothesis testing for large-scale network models with overlapping groups. Phys. Rev. X 5(1), 011033 (2015). https://journals.aps.org/prx/abstract/10.1103/PhysRevX.5.011033
  10. 10.
    Rosvall, M., Bergstrom, C.T.: Maps of random walks on complex networks reveal community structure. Proc. Nat. Acad. Sci. 105(4), 1118–1123 (2008)ADSCrossRefGoogle Scholar
  11. 11.
    UDS. Uniform Data System Mapper. Zip code to ZCTA crosswalk. https://www.udsmapper.org/zcta-crosswalk.cfm. Accessed 2019 Nov 15
  12. 12.
    Wennberg, J.: The Dartmouth Atlas of Health Care. American Hospital Association, Chicago (1996)Google Scholar
  13. 13.
    Wennberg, J., Gittelsohn, A.: Small area variations in health care delivery: a population-based health information system can guide planning and regulatory decision-making. Science 182(4117), 1102–1108 (1973)ADSCrossRefGoogle Scholar
  14. 14.
    Xie, J., Szymanski, B.K., Liu, X.: SLPA: uncovering overlapping communities in social networks via a speaker-listener interaction dynamic process. CoRR, abs/1109.5720 (2011)Google Scholar

Copyright information

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Internal MedicineUniversity of CaliforniaDavisUSA
  2. 2.Department of Computer ScienceUniversity of ExeterExeterUK
  3. 3.Independent ResearcherWashington D.C.USA

Personalised recommendations