Abstract
Constructing accurate patient transfer networks between hospitals is critical for understanding the spread of healthcare associated infections through statistical and mathematical modeling, and for determining optimal screening and treatment strategies. The Healthcare Cost & Utilization Project (HCUP) State Inpatient Databases (SID) provide valuable information on patient transfers from publicly obtainable claims databases, yet often give an incomplete picture due to missingness of patient tracking identifiers. We designed a novel imputation algorithm that enabled us to estimate the true number of patient transfers between each pair of hospitals in a state over a specified time period and age group in the presence of these missing identifiers. We then validated the algorithm’s performance through a series of simulation experiments using the HCUP SID, and finally tested the algorithm on multiple states’ genuine data. Our proposed method significantly reduced the total mean squared error in predicting the true number of transfers amongst hospitals for all simulation experiments, and it also yielded epidemic simulations that more closely approximated those corresponding to the true patient transfer network.
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Availability of data and materials
The data that support the findings of this study are available from the Healthcare Cost and Utilization Project. Restrictions apply to the availability of these data, which were used under license for this study.
Code availability
A function written in the R statistical programming language to implement the EM algorithm detailed in the paper is given at https://github.com/sjustice19/UIowa/tree/master/EdgelistCorrections, along with a detailed description.
References
Assab, R., Nekkab, N., Crepey, P., Astagneau, P., Guillemot, D., Opatowski, L., Temime, L.: Mathematical models of infection transmission in healthcare settings: recent advances from the use of network structured data. Curr. Opin. Infect. Dis. 30(4), 410–418 (2017). https://doi.org/10.1097/qco.0000000000000390
Assenova, V.A.: Modeling the diffusion of complex innovations as a process of opinion formation through social networks. PLoS ONE 13(5), e0196699 (2018). https://doi.org/10.1371/journal.pone.0196699
Borracci, R.A., Giorgi, M.A.: Agent-based computational models to explore diffusion of medical innovations among cardiologists. Int. J. Med. Inform. 112, 158–165 (2018). https://doi.org/10.1016/j.ijmedinf.2018.02.008
Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the EM algorithm. J. R. Stat. Soc. Ser. B (Methodol.) 39(1), 1–22 (1977)
DiDiodato, G., McArthur, L.: Interhospital patient transfers between Ontario’s academic and large community hospitals increase the risk of Clostridium difficile infection. Am. J. Infect. Control 46(2), 191–196 (2018). https://doi.org/10.1016/j.ajic.2017.08.018
DuGoff, E.H., Fernandes-Taylor, S., Weissman, G.E., Huntley, J.H., Pollack, C.E.: A scoping review of patient-sharing network studies using administrative data. Transl. Behav. Med. 8(4), 598–625 (2018). https://doi.org/10.1093/tbm/ibx015
Fernández-Gracia, J., Onnela, J.-P., Barnett, M.L., Eguíluz, V.M., Christakis, N.A.: Influence of a patient transfer network of US inpatient facilities on the incidence of nosocomial infections. Sci. Rep. 7(1), 2930–2930 (2017). https://doi.org/10.1038/s41598-017-02245-7
Garas, G., Cingolani, I., Panzarasa, P., Darzi, A., Athanasiou, T.: Network analysis of surgical innovation: measuring value and the virality of diffusion in robotic surgery. PLoS ONE 12(8), e0183332 (2017). https://doi.org/10.1371/journal.pone.0183332
Iwashyna, T.J., Christie, J.D., Kahn, J.M., Asch, D.A.: Uncharted paths: hospital networks in critical care. Chest 135(3), 827–833 (2009a). https://doi.org/10.1378/chest.08-1052
Iwashyna, T.J., Christie, J.D., Moody, J., Kahn, J.M., Asch, D.A.: The structure of critical care transfer networks. Med. Care 47(7), 787 (2009b)
Karkada, U.H., Adamic, L.A., Kahn, J.M., Iwashyna, T.J.: Limiting the spread of highly resistant hospital-acquired microorganisms via critical care transfers: a simulation study. Intensive Care Med. 37(10), 1633 (2011). https://doi.org/10.1007/s00134-011-2341-y
Lomi, A., Mascia, D., Vu, D.Q., Pallotti, F., Conaldi, G., Iwashyna, T.J.: Quality of care and interhospital collaboration: a study of patient transfers in Italy. Med. Care 52(5), 407–414 (2014). https://doi.org/10.1097/mlr.0000000000000107
Lumpkin, S.T., Strassle, P., Chaumont, N.: Risk factors for 30-day readmission after colorectal surgery: does transfer status matter? J. Surg. Res. 231, 234–241 (2018). https://doi.org/10.1016/j.jss.2018.05.031
Mascia, D., Angeli, F., Di Vincenzo, F.: Effect of hospital referral networks on patient readmissions. Soc. Sci. Med. 132, 113–121 (2015). https://doi.org/10.1016/j.socscimed.2015.03.029
Miller, A.C., Polgreen, L.A., Polgreen, P.M.: Optimal screening strategies for healthcare associated infections in a multi-institutional setting. PLoS Comput. Biol. 10(1), e1003407 (2014). https://doi.org/10.1371/journal.pcbi.1003407
Moen, E.L., Bynum, J.P., Austin, A.M., Skinner, J.S., Chakraborti, G., O’Malley, A.J.: Assessing variation in implantable cardioverter defibrillator therapy guideline adherence with physician and hospital patient-sharing networks. Med. Care 56(4), 350–357 (2018). https://doi.org/10.1097/mlr.0000000000000883
Mueller, S., Zheng, J., Orav, E.J., Schnipper, J.L.: Inter-hospital transfer and patient outcomes: a retrospective cohort study. BMJ Qual. Saf. 28(11), e1 (2019). https://doi.org/10.1136/bmjqs-2018-008087
Nekkab, N., Astagneau, P., Temime, L., Crépey, P.: Spread of hospital-acquired infections: a comparison of healthcare networks. PLoS Comput. Biol. 13(8), e1005666 (2017). https://doi.org/10.1371/journal.pcbi.1005666
Polgreen, P.M., Segre, A.M.: Editorial commentary: network models, patient transfers, and infection control. Clin. Infect. Dis. 63(7), 894–895 (2016). https://doi.org/10.1093/cid/ciw465
R Core Team: R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna (2020)
Ray, M.J., Lin, M.Y., Weinstein, R.A., Trick, W.E.: Spread of Carbapenem-resistant Enterobacteriaceae among illinois healthcare facilities: the role of patient sharing. Clin. Infect. Dis. 63(7), 889–893 (2016). https://doi.org/10.1093/cid/ciw461
Ray, M.J., Lin, M.Y., Tang, A.S., Arwady, M.A., Lavin, M.A., Runningdeer, E., Jovanov, D., Trick, W.E.: Regional spread of an outbreak of Carbapenem-resistant Enterobacteriaceae through an ego network of healthcare facilities. Clin. Infect. Dis. 67(3), 407–410 (2018). https://doi.org/10.1093/cid/ciy084
Sewell, D.K., Simmering, J.E., Justice, S., Pemmaraju, S.V., Segre, A.M., Polgreen, P.M.: Estimating the attributable disease burden and effects of interhospital patient sharing on Clostridium difficile infections. Infect. Control Hosp. Epidemiol. 40(6), 656–661 (2019). https://doi.org/10.1017/ice.2019.73
Slayton, R.B., Toth, D., Lee, B.Y., Tanner, W., Bartsch, S.M., Khader, K., Wong, K., Brown, K., McKinnell, J.A., Ray, W., Miller, L.G., Rubin, M., Kim, D.S., Adler, F., Cao, C., Avery, L., Stone, N.T., Kallen, A., Samore, M., Huang, S.S., Fridkin, S., Jernigan, J.A.: Vital signs: estimated effects of a coordinated approach for action to reduce antibiotic-resistant infections in health care facilities—United States. MMWR Morb. Mortal. Wkly. Rep. 64(30), 826–831 (2015)
Snitkin, E.S., Won, S., Pirani, A., Lapp, Z., Weinstein, R.A., Lolans, K., Hayden, M.K.: Integrated genomic and interfacility patient-transfer data reveal the transmission pathways of multidrug-resistant Klebsiella pneumoniae in a regional outbreak. Sci. Transl. Med. 9(417), eaan0093 (2017). https://doi.org/10.1126/scitranslmed.aan0093
Teresi, J.A., Holmes, D., Bloom, H.G., Monaco, C., Rosen, S.: Factors differentiating hospital transfers from long-term care facilities with high and low transfer rates. Gerontologist 31(6), 795–806 (1991). https://doi.org/10.1093/geront/31.6.795
Trogdon, J.G., Weir, W.H., Shai, S., Mucha, P.J., Kuo, T.M., Meyer, A.M., Stitzenberg, K.B.: Comparing shared patient networks across payers. J. Gen. Intern. Med. 34(10), 2014–2020 (2019). https://doi.org/10.1007/s11606-019-04978-9
Usher, M., Sahni, N., Herrigel, D., Simon, G., Melton, G.B., Joseph, A., Olson, A.: Diagnostic discordance, health information exchange, and inter-hospital transfer outcomes: a population study. J. Gen. Intern. Med. 33(9), 1447–1453 (2018). https://doi.org/10.1007/s11606-018-4491-x
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This work was supported by the US Centers for Disease Control and Prevention (5 U01 CK000531-02, 1 U01 CK000594-01-00).
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Justice, S.A., Sewell, D.K., Miller, A.C. et al. Inferring patient transfer networks between healthcare facilities. Health Serv Outcomes Res Method 22, 1–15 (2022). https://doi.org/10.1007/s10742-021-00249-5
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DOI: https://doi.org/10.1007/s10742-021-00249-5