Subgroups of High-Cost Patients and Their Preventable Inpatient Cost in Rural China

Document Type : Original Article

Authors

1 School of Medicine and Health Management, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China

2 Research Centre for Rural Health Service, Key Research Institute of Humanities & Social Sciences of Hubei Provincial Department of Education, Wuhan, China

3 Amsterdam Public Health Research Institute, Department of Public and Occupational Health, University of Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands

Abstract

Background 
High-cost patients account for most healthcare costs and are highly heterogeneous. This study aims to classify high-cost patients into clinically homogeneous subgroups, describe healthcare utilization patterns of subgroups, and identify subgroups with relatively high preventable inpatient cost (PIC) in rural China.
 
Methods 
A population-based retrospective study was performed using claims data in Xi county, Henan province. 32 108 high-cost patients, representing the top 10% of individuals with the highest total spending, were identified. A densitybased clustering algorithm combined with expert opinions were used to group high-cost patients. Healthcare utilization (including admissions, length of stay, and outpatient visits) and spending characteristics (including total spending, and the proportion of PIC, inpatient and out-of-pocket spending on total spending) were described among subgroups. PIC was calculated based on potentially preventable hospitalizations (PPHs) which were identified according to the Agency for Healthcare Research and Quality Prevention Quality Indicators algorithm.
 
Results 
High-cost patients were more likely to be older (Mean = 51.87, SD = 22.28), male (49.03%) and from povertystricken families (37.67%) than non-high-cost patients, with 2.49 (SD = 2.47) admissions and 3.25 (SD = 4.52) outpatient visits annually. Fourteen subgroups of high-cost patients were identified: chronic disease, non-trauma diseases which need surgery, female disease, cancer, eye disease, respiratory infection/inflammation, skin disease, fracture, liver disease, vertigo syndrome and cerebral infarction, mental disease, arthritis, renal failure, and other neurological disorders. The annual admissions ranged from 1.83 (SD = 1.23, fracture) to 12.21 (SD = 9.26, renal failure), and the average length of stay ranged from 6.61 (SD = 10.00, eye disease) to 32.11 (SD = 28.78, mental disease) days among subgroups. The chronic disease subgroup showed the largest proportion of PIC on total spending (10.57%).
 
Conclusion 
High-cost patients were classified into 14 clinically distinct subgroups which had different healthcare utilization and spending characteristics. Different targeted strategies may be needed for subgroups to reduce preventable hospitalizations. Priority should be given to high-cost patients with chronic diseases.

Keywords

References

  1. Global Burden of Disease 2020 Health Financing Collaborator Network. Tracking development assistance for health and for COVID-19: a review of development assistance, government, out-of-pocket, and other private spending on health for 204 countries and territories, 1990-2050. Lancet. 2021;398(10308):1317-1343. doi:1016/s0140-6736(21)01258-7
  2. World Health Organization (WHO). Global Spending on Health: Weathering the Storm. https://www.who.int/publications-detail-redirect/9789240017788. Accessed December 29, 2022.
  3. McKee M, Stuckler D. If the world fails to protect the economy, COVID-19 will damage health not just now but also in the future. Nat Med. 2020;26(5):640-642. doi:1038/s41591-020-0863-y
  4. Hayes SL, Salzberg CA, McCarthy D, et al. High-need, high-cost patients: who are they and how do they use health care? A population-based comparison of demographics, health care use, and expenditures. Issue Brief (Commonw Fund). 2016;26:1-14.
  5. Blumenthal D, Chernof B, Fulmer T, Lumpkin J, Selberg J. Caring for high-need, high-cost patients - an urgent priority. N Engl J Med. 2016;375(10):909-911. doi:1056/NEJMp1608511
  6. Joynt KE, Gawande AA, Orav EJ, Jha AK. Contribution of preventable acute care spending to total spending for high-cost Medicare patients. JAMA. 2013;309(24):2572-2578. doi:1001/jama.2013.7103
  7. Wammes JJG, van der Wees PJ, Tanke MAC, Westert GP, Jeurissen PPT. Systematic review of high-cost patients' characteristics and healthcare utilisation. BMJ Open. 2018;8(9):e023113. doi:1136/bmjopen-2018-023113
  8. Zook CJ, Moore FD. High-cost users of medical care. N Engl J Med. 1980;302(18):996-1002. doi:1056/nejm198005013021804
  9. Zhang Y, Lu S, Niu Y, Zhang L. Medical expenditure clustering and determinants of the annual medical expenditures of residents: a population-based retrospective study from rural China. BMJ Open. 2018;8(6):e022721. doi:1136/bmjopen-2018-022721
  10. Lu S, Zhang Y, Zhang L, Klazinga NS, Kringos DS. Characterizing potentially preventable hospitalizations of high-cost patients in rural China. Front Public Health. 2022;10:804734. doi:3389/fpubh.2022.804734
  11. Figueroa JF, Joynt Maddox KE, Beaulieu N, Wild RC, Jha AK. Concentration of potentially preventable spending among high-cost Medicare subpopulations: an observational study. Ann Intern Med. 2017;167(10):706-713. doi:7326/m17-0767
  12. Graven PF, Meath TH, Mendelson A, Chan BK, Dorr DA, McConnell KJ. Preventable acute care spending for high-cost patients across payer types. J Health Care Finance. 2016;42(3):1-22.
  13. Joynt KE, Figueroa JF, Beaulieu N, Wild RC, Orav EJ, Jha AK. Segmenting high-cost Medicare patients into potentially actionable cohorts. Healthc (Amst). 2017;5(1-2):62-67. doi:1016/j.hjdsi.2016.11.002
  14. Powers BW, Yan J, Zhu J, et al. Subgroups of high-cost Medicare advantage patients: an observational study. J Gen Intern Med. 2019;34(2):218-225. doi:1007/s11606-018-4759-1
  15. Zhang Y, Grinspan Z, Khullar D, et al. Developing an actionable patient taxonomy to understand and characterize high-cost Medicare patients. Healthc (Amst). 2020;8(1):100406. doi:1016/j.hjdsi.2019.100406
  16. Lee NS, Whitman N, Vakharia N, Taksler GB, Rothberg MB. High-cost patients: hot-spotters don't explain the half of it. J Gen Intern Med. 2017;32(1):28-34. doi:1007/s11606-016-3790-3
  17. Khullar D, Zhang Y, Kaushal R. Potentially preventable spending among high-cost Medicare patients: implications for healthcare delivery. J Gen Intern Med. 2020;35(10):2845-2852. doi:1007/s11606-020-05691-8
  18. Yan J, Linn KA, Powers BW, et al. Applying machine learning algorithms to segment high-cost patient populations. J Gen Intern Med. 2019;34(2):211-217. doi:1007/s11606-018-4760-8
  19. Agency for Healthcare Research and Quality. Prevention Quality Indicators Overview. https://www.qualityindicators.ahrq.gov/Modules/pqi_resources.aspx. Accessed January 5, 2021.
  20. Chen T, Pan J. The effect of spatial access to primary care on potentially avoidable hospitalizations of the elderly: evidence from Chishui city, China. Soc Indic Res. 2022;160(2):645-665. doi:1007/s11205-020-02413-9
  21. Rosella LC, Fitzpatrick T, Wodchis WP, Calzavara A, Manson H, Goel V. High-cost health care users in Ontario, Canada: demographic, socio-economic, and health status characteristics. BMC Health Serv Res. 2014;14:532. doi:1186/s12913-014-0532-2
  22. Wammes JJG, Tanke M, Jonkers W, Westert GP, Van der Wees P, Jeurissen PP. Characteristics and healthcare utilisation patterns of high-cost beneficiaries in the Netherlands: a cross-sectional claims database study. BMJ Open. 2017;7(11):e017775. doi:1136/bmjopen-2017-017775
  23. Zhang L, Han R. Does poverty reduce the life expectancy of the elderly: research on multistate life table. Chin J Public Health. 2020;13(9):8-14.
  24. Xie F, Zhu Z. Resources and utilizations of medical services in poverty-stricken areas in China. Chin J Public Health. 2018;34(7):1013-1016. doi:11847/zgggws1116404
  25. Fitzpatrick T, Rosella LC, Calzavara A, et al. Looking beyond income and education: socioeconomic status gradients among future high-cost users of health care. Am J Prev Med. 2015;49(2):161-171. doi:1016/j.amepre.2015.02.018
  26. Long P, Abrams M, Milstein A, Andersen G, Apton K, Dahlberg M. NAM Special Publication. Effective Care for High-Need Patients: Opportunities for Improving Outcomes, Value and Health. Washington, DC: National Academy of Medicine; 2017.
  27. Clough JD, Riley GF, Cohen M, et al. Patterns of care for clinically distinct segments of high-cost Medicare beneficiaries. Healthc (Amst). 2016;4(3):160-165. doi:1016/j.hjdsi.2015.09.005
  28. Johnson TL, Brewer D, Estacio R, et al. Augmenting predictive modeling tools with clinical insights for care coordination program design and implementation. EGEMS (Wash DC). 2015;3(1):1181. doi:13063/2327-9214.1181
  29. National Health Commission, State Administration of Traditional Chinese Medicine. Notice of the National Health Commission and the State Administration of Traditional Chinese Medicine on Carrying out the “Delivering Qualified Services at Primary Care Facilities” Campaign. http://www.gov.cn/zhengce/zhengceku/2018-12/31/content_5435449.htm. Accessed April 23, 2023.
  30. Li Z, Yang J, Wu Y, et al. Challenges for the surgical capacity building of township hospitals among the Central China: a retrospective study. Int J Equity Health. 2018;17(1):55. doi:1186/s12939-018-0766-4
  31. Lu S, Li Y, Gao H, Zhang Y. Difference in bypass for inpatient care and its determinants between rural and urban residents in China. Int J Equity Health. 2022;21(1):132. doi:1186/s12939-022-01734-0
  32. Wang Q, Wang Y, Yang C, et al. Trends of urolithiasis in China: a national study based on hospitalized patients from 2013 to 2018. Kidney Dis (Basel). 2023;9(1):49-57. doi:1159/000527967
  33. Su Z, Gong Y, Liang Z. Prevalence of gallstone in Mainland China: a meta-analysis of cross-sectional studies. Clin Res Hepatol Gastroenterol. 2020;44(4):e69-e71. doi:1016/j.clinre.2020.04.015
  34. Cao J. Reasons for the high incidence of reproductive system diseases among rural women and their prevention and control strategies. Contemp Med. 2010;16(3):48-49.
  35. Wang Y, Li Y, Wu Y, Feng Y, Du J. Analysis of screening results for reproductive system and breast diseases in 12000 women of reproductive age in Chengde city. Hebei Medicine. 2019;25(3):695-697.
  36. Yang LR, Zhao H, Wang HP, et al. Improving ability of married women to prevent reproductive tract infections in rural western China. Environ Health Prev Med. 2006;11(5):233-240. doi:1007/bf02898012
  37. Su F, Wang J, Zhang L, Lv Y. Central dialysate supply system+medical alliance to promote the development of community dialysis centers. Journal of China-Japan Friendship Hospital. 2021;35(6):361-362.
  38. Li Y, Wang M. The challenge and measures after dialysis therapy entered in rural medical insurance of serious diseases. Chin Health Econ. 2013;32(10):12-14.
  39. Babalola O, Gormez V, Alwan NA, Johnstone P, Sampson S. Length of hospitalisation for people with severe mental illness. Cochrane Database Syst Rev. 2014;2014(1):CD000384. doi:1002/14651858.CD000384.pub3
  40. Weeks SM, Giles K. Length of stay in hospital for people with severe mental illness. Int J Evid Based Healthc. 2015;13(4):273-274. doi:1097/xeb.0000000000000032
  41. Rubin G, Berendsen A, Crawford SM, et al. The expanding role of primary care in cancer control. Lancet Oncol. 2015;16(12):1231-1272. doi:1016/s1470-2045(15)00205-3
  42. Usher-Smith J, Emery J, Hamilton W, Griffin SJ, Walter FM. Risk prediction tools for cancer in primary care. Br J Cancer. 2015;113(12):1645-1650. doi:1038/bjc.2015.409

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History

  • Receive Date: 06 June 2023
  • Revise Date: 12 December 2023
  • Accept Date: 13 February 2024
  • First Publish Date: 17 February 2024

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