High-cost health care users in Ontario, Canada: demographic, socio-economic, and health status characteristics

Laura C Rosella, Tiffany Fitzpatrick, Walter P Wodchis, Andrew Calzavara, Heather Manson, Vivek Goel, Laura C Rosella, Tiffany Fitzpatrick, Walter P Wodchis, Andrew Calzavara, Heather Manson, Vivek Goel

Abstract

Background: Health care spending is overwhelmingly concentrated within a very small proportion of the population, referred to as the high-cost users (HCU). To date, research on HCU has been limited in scope, focusing mostly on those characteristics available through administrative databases, which have been largely clinical in nature, or have relied on ecological measures of socio-demographics. This study links population health surveys to administrative data, allowing for the investigation of a broad range of individual-level characteristics and provides a more thorough characterization of community-dwelling HCU across demographic, social, behavioral and clinical characteristics.

Methods: We linked three cycles of the Canadian Community Health Survey (CCHS) to medical claim data for the years 2003-2008 for Ontario, Canada. Participants were ranked according to gradients of cost (Top 1%, Top 2-5%, Top 6-50% and Bottom 50%) and multinomial logistic regression was used to investigate a wide range of factors, including health behaviors and socio-demographics, likely associated with HCU status.

Results: Using a total sample of 91,223 adults (18 and older), we found that HCU status was strongly associated with being older, having multiple chronic conditions, and reporting poorer self-perceived health. Specifically, in the fully-adjusted model, poor self-rated health (vs. good) was associated with a 26-fold increase in odds of becoming a Top 1% HCU (vs. Bottom 50% user) [95% CI: (18.9, 36.9)]. Further, HCU tended to be of lower socio-economic status, former daily smokers, physically inactive, current non-drinkers, and obese.

Conclusions: The results of this study have provided valuable insights into the broader characteristics of community-dwelling HCU, including unique demographic and behavioral characteristics. Additionally, strong associations with self-reported clinical variables, such as self-rated general and mental health, highlight the importance of the patient perspective for HCU. These findings have the potential to inform policies for health care and public health, particularly in light of increasing decision-maker attention in the sustainability of the health care system, improving patient outcomes and, more generally, in order to achieve the common goal of improving population health outcomes.

Figures

Figure 1
Figure 1
Distribution of Health Care Spending. The proportion of total health care spending incurred by each user group (a) and average (per person) spending across health care sectors for the overall weighted population (b) and by user group (c).

References

    1. Wodchis WP: The Concentration of Health Care Spending: Little Ado (yet) About Much (money). Presented at the Canadian Association for Health Services and Policy Research (CASHPR) 2012 Conference. Accessed: Oct 23, 2014
    1. Lemstra M, Mackenbach J, Neudorf C, Nannapaneni U. High Health Care Utilization and Costs Associated with Lower Socio-Economic Status: results from a Linked Dataset. Can J Public Health. 2009;100:180–183.
    1. Kephart G, Thomas V, MacLean D. Socioeconomic differences in the use of physician services in Nova Scotia. Am J Public Health. 1998;88:800–803. doi: 10.2105/AJPH.88.5.800.
    1. Calver J, Brameld K, Preen D, Alexia SJ, Boldy DP, McCaul KA. High-cost users of hospital beds in Western Australia: a population-based record linkage study. Med J Aust. 2006;184:393–397.
    1. Deber, RB and Lam KCK. Handling the High Spenders: Implications of the Distribution of Health Expenditures for Financing Health Care. APSA 2009 Toronto Meeting Paper. Accessed Oct 23, 2014.
    1. Berk ML, Monheit A. The concentration of health expenditures: an update. Health Aff (Millwood) 1992;11:145–149. doi: 10.1377/hlthaff.11.4.145.
    1. Berk ML, Monheit A. The concentration of health care expenditures, revisited. Health Aff (Millwood) 2001;20:9–18. doi: 10.1377/hlthaff.20.2.9.
    1. Conwell LJ, Cohen J. Characteristics of Persons with High Medical Expenditures in the U.S. Civilian Noninstitutionalized Population, 2002. AHRQ, 2005
    1. Heslop L, Athan D, Gardner B, Diers D, Poh BC. An analysis of high-cost users at an Australian public health service organization. Health Serv Manage Res. 2005;18:232–243. doi: 10.1258/095148405774518633.
    1. Radcliff TA, Cote M, Duncan R. The identification of high-cost patients. Hosp Top. 2005;83:17–24. doi: 10.3200/HTPS.83.3.17-24.
    1. Roos NP, Burchill C, Carriere K. Who are the high hospital users? A Canadian case study. J Health Serv Res Policy. 2013;8:5–10. doi: 10.1258/13558190360468164.
    1. Reid R, Evan R, Barer M, Sheps S, Kerluke K, McGrail K, Hertzman C, Pagliccia N. Conspicuous consumption: characterizing high users of physician services in one Canadian province. J Health Serv Res Policy. 2003;8:215–224. doi: 10.1258/135581903322403281.
    1. Rais S, Nazerian A, Ardal S, Chechulin Y, Bains N, Malikov K. High-cost users of Ontario’s Healthcare services. Healthc Policy. 2013;9:44–51.
    1. Droomers M, Westert G. Do lower socioeconomic groups use more health services, because they suffer from more illnesses? Eur J Public Health. 2004;14:311–313. doi: 10.1093/eurpub/14.3.311.
    1. Dunlop S, Coyte P, McIssac W. Socio-economic status and the utilisation of physicians’ services: results from the Canadian National Population Health Survey. Soc Sci Med. 2000;51:123–133. doi: 10.1016/S0277-9536(99)00424-4.
    1. Naessens JM, Baird M, Van Houten H, Vanness DJ, Campbell CR. Predicting persistently high primary care use. Ann Fam Med. 2005;3:324–330. doi: 10.1370/afm.352.
    1. Wodchis WP, Coralio A, Ma X, Simeonov D, Stamplecoski M, White P, Purdy I, Jeffs L, Iron K, de Nobrega P, McGillis Hall L. Health Outcomes for Better Information and Care (HOBIC): Acute Care in Ontario 2012. Toronto: The Institute for Clinical Evaluative Sciences (ICES); 2013.
    1. Billings J, Mijanovich T. Improving the management of care for high-cost medicaid patients. Health Aff (Millwood) 2007;26:1643–1654. doi: 10.1377/hlthaff.26.6.1643.
    1. Morgan MW, Zamora N, Hindmarsh M. An inconvenient truth: a sustainable healthcare system requires chronic disease prevention and management transformation. Healthc Pap. 2007;7:6–23. doi: 10.12927/hcpap.2007.18992.
    1. Statistics Canada. Canadian Community Health Survey - Annual Component (CCHS). . Accessed: Nov 29, 2013
    1. Wodchis WP, Bushmeneva K, Nikitovic M, McKillop I. Guidelines on Person-level Costing Using Administrative Databases in Ontario. Working Paper Series. Vol 1. Toronto: Health System Performance Research Network; 2013.
    1. Austin PC, van Walraven C, Wodchis WP, Newman A, Anderson GM. Using the Johns Hopkins Aggregated Diagnosis Groups (ADGs) to predict mortality in a general adult population cohort in Ontario, Canada. Med Care. 2011;49(10):932–939. doi: 10.1097/MLR.0b013e318215d5e2.
    1. Brant R. Assessing proportionality in the proportional odds model for ordinal logistic regression. Biometrics. 1990;46(4):1171–1178. doi: 10.2307/2532457.
    1. Thomas S, Wannell B. Combining cycles of the Canadian Community Health Survey. Stat Can Health Rep. 2009;20(1):55–60.
    1. Manuel DG, Perez R, Bennett C, Rosella L, Taljaard M, Roberts M, Sanderson R, Meltem T, Tanuseputro P, Manson H. Seven More Years: The Impact of Smoking, Alcohol, Diet, Physical Activity and Stress on Health and Life Expectancy in Ontario. An ICES/ PHO Report. Toronto: Institute for Clinical Evaluative Sciences and Public Health Ontario Report; 2012.
    1. Wodchis, WP. Driving Value with a Patient-Centered Health System. Presented at: OACCAC Knowledge and Inspiration 2012 Conference. Accessed: Aug 5, 2013
    1. Rotermann M, Nadeau C. Evaluation of the coverage of the linked Canadian Community Health Survey and hospital inpatient records. Health Rep. 2009;20(1):45–51.
    1. Horsman J, Furlong W, Feeny D, Torrance G. The Health Utilities Index (HUI®): concepts, measurement properties and applications. Health Qual Life Outcomes. 2003;1:54. doi: 10.1186/1477-7525-1-54.
    1. Lawlor DA, Hart CL, Hole DJ, Smith GD. Reverse causality and confounding and the Associations of Overweight and Obesity with Mortality. Obesity. 2012;14(12):2294–2304. doi: 10.1038/oby.2006.269.

Source: PubMed

Подписаться