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Waiting times for medical services such as specialist visits and surgery continue to be an issue in most countries with publicly funded healthcare systems, particularly in Canada, where timely access to healthcare services is at the top of the health policy agenda (Romanow 2002). In this country, long waits are routinely identified as the leading barrier to care (Sanmartin et al. 2002). Waiting for care, however, is only problematic when patients consider their waiting times unacceptable (Martin et al. 2003). To address the issue of unacceptable waits for care, it is important to understand the factors contributing to patients’ assessment of the acceptability of their waiting times. Much of the evidence to date focuses on the duration of the waiting time as the principal determinant of wait time acceptability, often with little to no regard for other factors that may influence patients’ views on waiting times (Sanmartin 2001; Llewellyn-Thomas et al. 1998; Conner-Spady et al. 2004). In Ontario, Ho and colleagues (1994) asked patients who underwent knee replacement about the acceptability of their waiting times. Patients who considered their waiting times acceptable reported a median waiting time of 8 weeks from consultation to surgery as compared with the median of 32 weeks reported by those who considered their waits unacceptable (Ho et al. 1994). In a subsequent study, the duration of the wait was identified as a primary determinant of wait time acceptability (Coyte et al. 1994). A similar approach was used by Dunn and associates (1997) among cataract patients in Manitoba, where the majority of patients agreed that 3 months or less was a reasonable wait for surgery (Dunn et al. 1997). Recent results from a national study on access to care in Canada revealed that individuals who considered their waiting times unacceptable waited an average of between 9 weeks (for specialist visits) and 13 weeks (for non-emergency surgery) for specialized services, which was three to four times longer than the wait for those who did not consider their wait unacceptable (Figure ​(Figure1).1). These individuals were also more likely to report that waiting for care affected their lives (Sanmartin et al. 2004). FIGURE 1. Median waiting times for specialized services by reported acceptability, Canada, 2003 Although current evidence provides some insight regarding patients’ experiences waiting for care, considerably less information exists about other factors that may affect wait time acceptability, such as patient demographics and socio-economic status. The purpose of this study, therefore, was to analyze national data on waiting for care to identify the key determinants associated with unacceptable waiting times. Specifically, the study focuses on three specialized services: specialist visits, non-emergency (or elective) surgery and diagnostic tests.

Background The co-morbidity of health conditions is becoming a significant health issue, particularly as populations age, and presents important methodological challenges for population health research. For example, the calculation of summary measures of population health (SMPH) can be compromised if co-morbidity is not taken into account. One popular co-morbidity adjustment used in SMPH computations relies on a straightforward multiplicative combination of the severity weights for the individual conditions involved. While the convenience and simplicity of the multiplicative model are attractive, its appropriateness has yet to be formally tested. The primary objective of the current study was therefore to examine the empirical evidence in support of this approach. Methods The present study drew on information on the prevalence of chronic conditions and a utility-based measure of health-related quality of life (HRQoL), namely the Health Utilities Index Mark 3 (HUI3), available from Cycle 1.1 of the Canadian Community Health Survey (CCHS; 2000–01). Average HUI3 scores were computed for both single and co-morbid conditions, and were also purified by statistically removing the loss of functional health due to health problems other than the chronic conditions reported. The co-morbidity rule was specified as a multiplicative combination of the purified average observed HUI3 utility scores for the individual conditions involved, with the addition of a synergy coefficient s for capturing any interaction between the conditions not explained by the product of their utilities. The fit of the model to the purified average observed utilities for the co-morbid conditions was optimized using ordinary least squares regression to estimate s. Replicability of the results was assessed by applying the method to triple co-morbidities from the CCHS cycle 1.1 database, as well as to double and triple co-morbidities from cycle 2.1 of the CCHS (2003–04). Results Model fit was optimized at s = .99 (i.e., essentially a straightforward multiplicative model). These results were closely replicated with triple co-morbidities reported on CCHS 2000–01, as well as with double and triple co-morbidities reported on CCHS 2003–04. Conclusion The findings support the simple multiplicative model for computing utilities for co-morbid conditions from the utilities for the individual conditions involved. Future work using a wider variety of conditions and data sources could serve to further evaluate and refine the approach.