Income inequalities in multimorbidity prevalence in Ontario, Canada: a decomposition analysis of linked survey and health administrative data

An increasing number of adults in high-income settings have been diagnosed with multiple coexisting chronic conditions [1], also known as multimorbidity. This increased burden has created significant challenges in the effective provision of clinical care and has added pressures on health systems that traditionally provide highly specialized care for a single condition. Extensive research has shown that persons with multimorbidity have a greater risk of functional decline, cognitive decline and early mortality [2‐4], are more frequent users of healthcare services, experience greater care fragmentation and longer hospital stays, and also incur higher healthcare costs [5‐12].

Multiple studies from high-income settings have shown that multimorbidity is concentrated among persons from lower socio-economic ranks [13‐17]. Multimorbidity may develop 10–15 years earlier among young and middle-aged persons in the most (vs. least) deprived areas [18]. While quantifying multimorbidity inequalities may help inform where policy action is needed, less is known about the relative contributions of determinants to socioeconomic disparities in multimorbidity [19]. These data are particularly useful to policymakers for informing future resource allocation and designing targeted interventions. Moreover, few studies have quantified longitudinal trends in measured inequalities in multimorbidity prevalence by income, education, or occupation, despite calls for action by federal agencies and professional organizations in many jurisdictions to reduce health disparities [20, 21].

To address these gaps, we undertook a comprehensive examination of inequalities in multimorbidity prevalence in Ontario, Canada. Our key research objectives were to: 1) quantify household income inequalities in multimorbidity prevalence among adults in Ontario, 2) identify the relative contribution of key determinants of multimorbidity to this inequality including socio-demographic, socio-economic, lifestyle and health system factors; and 3) assess whether these disparities were widening, and whether the drivers contributing to this inequality were changing over time.

We used (cross-sectional) survey data and linked health administrative information that is routinely collected in Ontario. The use of this data was authorized under section 45 of Ontario’s Personal Health Information Protection Act, which does not require review by a Research Ethics Board. The study is reported according to the RECORD guidelines [22].

Of 134,395 Ontario respondents across 4 cycles of linked CCHS and administrative data, 122,002 (91% of sample) were adults, and 113,627 (93% of adults) had complete information across all variables for analysis. A higher proportion of adult respondents excluded (vs. included) from analyses were age 75+ years, female, from the lowest income quintile and had prevalent multimorbidity. Table 1 describes the characteristics of respondents included in analyses for each CCHS cycle. The numbers outside and inside the brackets correspond to sample counts and population weighted proportions, respectively. Crude multimorbidity prevalence increased steadily with each cycle, from 26.4% in the 2005 survey to 33.5% in the 2011/12 survey. Crude prevalence estimates by household income quintile are shown in Fig. 1, and by determinant in the Additional file 1: Table S2. Of note, prevalence in the lowest vs. highest household income quintiles, respectively, were 34.7% vs. 20.8% in 2005 and 37.8% vs. 27.6% in 2011/12.

The measured C_Erreygers are shown in the last row of Table 1: values ranged from − 0.114 (CI: -0.132, − 0.096) in 2005 to − 0.085 (CI: -0.108, − 0.062) in 2011/12. The negative and statistically significant C_Erreygers indicates a greater concentration of multimorbidity among persons with low household income, although the smaller C_Erreygers value in the 2011/12 survey suggests that inequality was less pronounced.

Table 2 summarizes the results of the C_Erreygers decomposition of multimorbidity prevalence in the most recent (2011/12) and baseline (2005) surveys. Results from the 2007/08 and 2009/10 surveys are available in the Additional file 1: Table S3. Data include marginal effects from probit regressions, elasticities of each determinant (β_k*x̅_k/μ), the C_Erreygers of each determinant, and the relative contribution of each determinant to measured inequality. For example, for persons aged 75+ in the 2011/12 CCHS cycle: the difference in conditional probability of multimorbidity (vs. 18–34 age group) was 77.3%; this group represented 7.4% of the total population (from Table 1) but persons were concentrated in lower income levels (C_k*(4*μ) = − 0.061); and their contribution to measured income inequality was 55.6%.

Individual determinants with the largest contribution to the observed pro-rich inequality in the 2011/12 CCHS cycle included low household income (57.2 and 18.8% for quintiles 1 and 2, respectively), older age (55.6 and 29.9% for ages 75+ and 65–74 years, respectively), being divorced, separated, widowed or single (15.2%), and physical inactivity (10.8%). The relative contribution of each of these determinants to measured inequality increased from the decomposition analysis conducted using the 2005 CCHS survey data. For example, older ages (75+ years) were less concentrated among the low income (vs. 2005) but the elasticity (i.e., the % change in multimorbidity associated with a % change in the determinant) increased between cycles; for non-married persons, the elasticity increased, as did the concentration of non-married individuals among lower income groups. In contrast, variables that contributed negatively to inequality in the latter survey included being middle-aged (− 51.6% and − 12.2% for 50–64 and 35–49 years, respectively), having higher household income (− 10.5% for quintile 4), enrolment in a FHO (− 11.5%) and being born outside of Canada (− 7.8%). Of note, the growth in FHO enrolment between surveys (from 3.3 to 42.1%) corresponded with a greater concentration among higher household income (C_Erreygers from − 0.012 to 0.093); the opposite pattern was found for enrolment in a FHG (increased in enrolment from 23.6 to 27.7% and shift in C_Erreygers from 0.029 to − 0.042).

Aggregating these percentages by determinant (Fig. 2), income explained 69.0% of the measured inequality in 2011/12 (an increase of + 30.2% from 2005 CCHS survey), followed by age at 21% (− 16.5%), marital status at 15.2% (+ 8.6%), and physical activity at 10.9% (+ 8.6%). In contrast, determinants that contributed the largest to decreasing inequality were immigration at − 7.8 (− 7.2%) and primary care enrolment at − 7.4 (− 5.0%). In 2011/12, the modeled determinants explained − 0.093 of the total inequality (− 0.085), whereas in 2005 the determinants explained − 0.107 of the total inequality (− 0.114).

Table 3 shows the age-sex adjusted relative (RII) and absolute (SII) inequality measures for each survey, as well as the longitudinal trend analysis. The prevalence rate ratio (RII) in 2011/12 comparing high vs. low income quintile was 1.32 (CI: 1.19, 1.46) in 2011/12. The corresponding prevalence rate difference (SII) was 9.1% (CI: 5.7, 12.4%). In pooled regression analyses, longitudinal trends in inequality were not statistically significant over the study period on either the absolute or relative scale, suggesting no change in measured income inequality (p for interaction = 0.405 for RII and 0.443 for SII).

These findings were robust to the defined sensitivity analyses (Additional file 1: Table S3).

This study confirms a moderate, statistically significant pro-rich household income gradient in multimorbidity prevalence in Ontario, Canada. This gap was observed on both relative and absolute scales, and has persisted from 2005 to 2011/12. Our results are consistent with measured wealth and socioeconomic inequalities in chronic conditions among adults observed in low and middle-income countries [19, 56, 58]. These studies considered only a small number of conditions in defining chronic disease burden. Our findings are also consistent with a study of multimorbidity (among 52 conditions) in deprived areas in the Basque Country of Spain [59]. Although socioeconomic differences in common chronic conditions among adult populations have been previously reported [60, 61], few studies have considered an aggregated count of multiple high impact chronic conditions or measured inequality with robust methods that account for the entire socio-economic distribution.

These findings highlight the unequal distribution of multimorbidity across socio-economic ranks. An important contribution of the current study to existing literature is the measurement of the relative contributions of multimorbidity’s determinants to measured income inequalities from a high-income setting. The decomposition method is useful for policy makers who seek to reduce health inequalities by targeting determinants that contribute the most to observed inequalities. Recently, Kunna et al. [19] decomposed wealth-related inequalities in multimorbidity prevalence (defined as 2 or more of 7 chronic conditions) among adults aged 50 years and older in China (middle-income setting) and in Ghana (low-income setting). Considering a similar set of measured determinants, the authors found that largest contributors to inequality were wealth, age, and education in China and BMI, wealth and rurality in Ghana. We found that the largest contributor to household income inequality in multimorbidity prevalence was income itself. This finding has been observed for similar decomposition studies of income-related inequalities in general health status from North America [62] and Europe [63]. Our data also show that the proportion of inequality in multimorbidity due to income increased over time. The growth in the rich-poor income gap in Canada, like may comparable OECD nations, has been well documented [37].

The strong negative association between increasing age and multimorbidity prevalence we observed is well established in existing literature [16]. The positive contribution of age to measured inequality in our study decreased steadily between cycles, mainly due to changes among the 50–64 year age group, who became more concentrated among higher household incomes. Among other socio-demographic variables, the contribution of marital status to measured inequality increased. Similar to age, where the change to the elasticity was minor, the increase in contribution was driven by a greater concentration of divorced, separated, widowed or single individuals in lower household income levels.

Physical activity was the strongest lifestyle factor contributing to observed inequalities in multimorbidity. We found that prevalence of multimorbidity increased over the study period (from 29.4 to 39.5%) among inactive persons in Ontario, and that inactivity was increasingly concentrated among lower incomes. Socioeconomic gradients in inactivity have been documented in other high-income jurisdictions [64], although less is known regarding changes across the socioeconomic distribution over time. Body-mass index had only a small negative contribution to inequality overall, and obesity in particular was relatively equally distributed across income. This contrasts findings by Hajizahen et al. [65], who report a small but significant concentration of obesity among the poor in Canada. Their data, however, exclude older adults (aged 65+ years). Our findings show that heavy and former smoking were unequally distributed across income but had small (although statistically significant) regression effects and small corresponding elasticities, also resulting in a minor contribution to inequality. The variability in the contribution of lifestyle determinants to inequalities in multimorbidity from previous work from low and middle-income jurisdictions [19] suggest that interventions meant to improve equality need to be targeted to local contexts. Other unhealthy lifestyle factors that are potentially amenable to public health intervention, such as healthy diet and alcohol consumption, were not included in our multivariable decomposition analyses since their relationships with multimorbidity prevalence are less certain [40].

The contribution of health system variables to overall inequality, including health region of residence and enrolment in a primary care model, were small. All participants of the study had universal access to medically necessary care; however, enrolment into a primary care model is voluntary. The negative contributions for this determinant in particular suggest that holding all else constant, persons enrolled in these models (particularly FHNs) are less likely to be multimorbid and of higher household income, which has been previously documented [44, 45]. Lastly, across all decomposition analyses, being born outside of Canada was negatively associated with multimorbidity prevalence (to varying effect sizes), and negatively contributed to multimorbidity inequality. A healthy immigrant effect for chronic disease incidence has been previously documented in Canada, although with diminishing associations as immigrants’ time in Canada lengthened [66, 67]. The effect of time since immigrating on socioeconomic inequalities in multimorbidity (and other health outcomes) is an avenue for future research.

The results from the decomposition were robust to multiple sensitivity analyses. Other strengths of the research include the use of a large sample size, survey weights that are reflective of Ontario’s population, and high survey response rates. Our results are likely to be generalizable to other high-income jurisdictions with comparable universal health care systems. Validated algorithms were also used to ascertain disease status; linked survey and administrative data enabled this inclusion. There are limitations of this research that are worth noting, however. Variables were defined using routinely-collected health administrative and survey data which are subject to known limitations [22, 23]. For health administrative data in particular, ascertainment of chronic disease is dependent having a place of residence and on health system contact (physician visits or hospital admissions), which are less likely among vulnerable, low-SES individuals. This would underreport multimorbidity prevalence and the overall measured inequality. Each survey is cross-sectional by design, and therefore, causality cannot be inferred from the data or by the decompositions. Inequality trends were assessed over a 7-year period from biennial cross-sectional surveys; the inclusion of additional surveys could be an avenue for future research, once linked to administrative data. Importantly, interpretation of decomposition results rely on a correct model specification; Pseudo R-squared values from the multivariable probit models were 0.22 (2005 CCHS) and 0.23 (2011/12 CCHS) suggesting the variables included in the regression explain only a portion of the variance observed in the outcome. Lastly, we were only able to include program enrolment as a marker of primary care access. However, a high-performing primary care sector can support equitable health outcomes by promoting seamless access to services across the care continuum, including preventive care and ongoing chronic disease management [68‐70]. As such, other access-related determinants not measurable with our data may contribute to income inequalities in multimorbidity prevalence. Additional research that includes primary care capacities could help inform policymakers, and play a pivotal role for reducing overall disparities.

This study confirms a pro-rich income gap in multimorbidity prevalence that has persisted over time. This gap is driven primarily by income, increasing age, marital status (not being married or common-law) and physical inactivity. Additional research to uncover the root causes of co-existing chronic conditions and to develop and evaluate the effectiveness of prevention strategies are warranted to Acknowledgements.

We thank IMS Brogan Inc. for use of their Drug Information Database.