This study offers a full characterization of the broad range of social, behavioral, and health factors associated with health care utilization for a population-based sample in the context of a universal health care system. While there has been a recent renewed interest in health care spending, sustainability, quality of care and patient outcomes, focus on the broader characterization of these individuals that would support alternative perspectives on how to best address this issue has been limited. For example, HCU interventions have been overwhelmingly focused on case-management for patients who are already HCU or frequent users of the health care system; knowledge of the upstream determinants of HCU, particularly those that are non-clinical in nature, such as SES and health behaviors, is desperately lacking. Recent reports have suggested that health programs targeting high-risk groups may play an important role in health care sustainability [17]-[19], suggesting that $1.5 billion could be saved if just a 10% reduction in spending could be achieved for Ontario’s Top 5% of spenders [1]. Understanding HCU from a broader socio-economic and cultural perspective is crucial if modifiable characteristics are to be identified and addressed from both within and outside the health care system.
The results of this study are consistent with previous research on HCU. Many studies have attempted to characterize HCU; however, these have examined only a minimal number and breadth of variables, have relied on binary definitions of HCU and have employed simplistic analytic techniques. Indeed, most characterizations of the HCU population have been limited to the information available in administrative data sources, such as age, sex, ethnicity and clinical measures; while others have included SES, most examined only income or education and relied on ecological-level measures. Only one other study has investigated HCU in Canada using health survey data linked to medical utilization records [2]. According to this 2009 study by Lemstra et al., low-income residents of Saskatoon had higher health care costs overall compared to higher-income groups. This is in agreement with the findings of our study; however, our affirmation of this association was confirmed even after controlling for several additional variables and finer categories of utilization. These findings suggest that differences in health care spending are not merely a result of differences in health-seeking behavior, but may reflect higher needs in specific groups, such as low income users, as a result of poorer health. Our results are also consistent with previous literature examining socioeconomic status (SES) and HCU [2]-[16], although none of the previous studies adjusted for the number and depth of individual-level SES variables that were included in our study [2]-[16]. In the descriptive and unadjusted multinomial analyses, our study found gradients existed across multiple dimensions of SES and were strongly and significantly associated with increased health care costs. However, adjusting for confounders resulted in mostly non-significant associations and the attenuation of the incremental relationship seen with household income. This demonstrates the importance of controlling for confounders when interpreting associations with HCU - as opposed to simply looking at descriptive characteristics, as often done in HCU studies. Unlike previous studies, which have typically described health care utilization as a binary outcome (i.e. HCU vs. non-HCU), we were able to further dissect gradients of use by applying a multinomial model. These results suggest that the HCU population is not homogenous and the finer separation is important to further understanding the associations. For instance, the associations among the extreme HCU (top 1%) are significantly stronger than, and in some cases differ from, that of the Top 2-5% HCU.
This current study is particularly novel in that we have investigated the effects of heath behaviors and health status in addition to multiple socio-demographics measures. We confirmed health status, primarily ADG score, and increased age to be strongly associated with increasing levels of health care utilization. What is unique compared to other studies is that we were able to demonstrate the significance of this association across self-reported indicators of health status, in addition to those identified through medical claims. Findings related to self-reported health (physical and mental) provide an interesting perspective on the self-perceptions of those individuals who are in the highest HCU group compared to those in the lowest. The strong associations seen, even after adjustment for multiple co-morbidities, demonstrate that every incremental utilization group above the bottom 50th percentile were more likely to classify their health as poor. These findings were particularly evident for self-reported general health, which demonstrated a stronger magnitude of effect than even the clinically-derived ADG score. The research on the patient perspective of HCU is very limited and would be an important area of further study and may have useful implications for HCU intervention and policy.
This study uniquely enhances our understanding of HCU through the investigation of health behaviors. Behavioral factors, such as physical activity, may be more amenable to change than others, particularly SES, and thus, more likely to have implications for interventions or policies targeted at enabling healthy choices. We did not find that established risky health behaviors, such as smoking and alcohol consumption [25], to be overwhelming drivers of short term HCU gradients. This finding, however, is likely an artifact of the short-term follow-up period. It is reasonable that as an individual’s health declines, medical contraindications and healthy living recommendations would affect health behavior choices. For example, HCU may be more likely to quit smoking or drinking alcohol upon recommendation by their doctor or due to contraindications of ongoing treatment. This could explain why HCU was associated with former smoker and current non-drinker status in our study. A longer follow-up examining trajectories of health care utilization is necessary to further study the health effects of such behaviors.
Limitations
This study is strengthened by the novel use of a large, linked population survey sample to more broadly characterize the non-institutionalized, community-dwelling HCU population and the use of a multinomial analysis to further dissect trends across HCU groups. However, there are some limitations that must be mentioned. Particularly, the CCHS sampling frame excludes the institutionalized, persons living on Aboriginal reserves, full-time members of the Canadian Forces and persons living in certain remote areas (approximately 2% of the Canadian population) [20]. As a result, Ontarians not living in private dwellings, individuals residing in LTC or complex continuing care facilities, mental health institutions or hospitals at the time of interview are excluded from these analyses. It is expected that a number of Ontario’s HCU reside in these facilities, and would not be represented by the CCHS. Indeed, long-term care (LTC) spending accounted for less than 5% of HCU spending in our analysis, and provincial estimates suggest this proportion to be much higher [1],[26]. This would affect external generalizability to the broader Ontario population, but not the internal validity since we ranked within the CCHS population and not within the entire population so relative cost categories are accurate within the study population. Similarly, homeless Ontarians would have been excluded from the CCHS. Given the relationship between SES and HCU, it is likely that a portion of HCU in Ontario are homeless and are not represented in the current study. While individuals residing within these institutions or who were homeless at baseline would not be captured by the CCHS, all CCHS respondents who transferred into these facilities or became homeless following CCHS interview would be captured, and thus, their health care costs also captured in this study [21].
Further, the CCHS-RPDB linkage is conducted only for those respondents who agreed to linkage and provided a valid health care number; selective agreement and low coverage rates may lead to biased linked samples. An evaluation of the CCHS Cycle 1.1 linkage observed that nearly 91% of Ontario CCHS respondents agreed to linkage, but only 70% agreed to linkage and provided a valid health card number; however, among Ontarian respondents who were hospitalized, over 91% of respondents agreed to linkage and provided a valid health card number [27]. Therefore, use of the Ontario linked CCHS cohort for health services research shows acceptable coverage, but this potential for bias should be considered when interpreting estimates from the CCHS linked file. Also, because the CCHS relies on self-reported data there is potential for reporting bias, such as recall or social desirability bias. Additionally, because not every question is asked in each CCHS cycle and certain questions are only asked in select provinces, we were limited in which variables to include. For instance, the Health Utilities Index (HUI®), a health status variable incorporating both qualitative and quantitative features to provide a summary measure of individual health, was not available for all three cycles and therefore could not be included, despite its relevance [28].
Furthermore, the time frame of the study is such that we characterize patients as HCU in the year following interview, although the nature of them being high-users may have influenced some of the self-reported information, i.e. reverse causality [29]. This may explain why a weak association between HCU and health behavior was noted in this study. However, the purpose of this study is to more fully characterize this population, and not to infer causality, so this is only a minor limitation to our study design.
Lastly, the health care expenditures included in this analysis are limited to only those covered by Ontario’s universal health insurance plan, OHIP. Except for eligible members of the adult population (e.g. those over 65, receiving government assistance or with specific diseases) OHIP coverage excludes prescription drug costs (outside of those received in hospital), allied health services (physiotherapy, registered massage therapy, etc.), dental care, eye care, and assistive devices (e.g. crutches, splints, and casts). However, compared to costs associated with acute hospital care and physician services, these represent a relatively smaller proportion of health care spending.
