Introduction

The prevalence of chronic health conditions has been on the rise in recent times [1,2] leaving many people dependant on long-term management. Over the last few decades, the presence of more than one chronic health condition in a single individual–a phenomenon known as multimorbidity–has become common in our society and in primary care [3]. Multimorbidity is associated with significant increases in mortality, poor quality of life, and reduced functional status [4]. This has presented major challenges to healthcare systems globally and is considered a public health concern due to its association with the aging population, frailty, healthcare, and social care support systems [5,6].

In 2021, the global prevalence of multimorbidity was estimated at 37.2%, and was significantly higher in North America (43.1%) [7]. Several recent studies have estimated the prevalence in Canada to be 10–25% [8–11]. Discrepancy in the estimated prevalence may be due to differences in classification systems used to identify chronic health conditions, including the choice of conditions, and variations in study population [12]. This variation in prevalence estimates makes it difficult to compare the results from different studies and accurately quantify the specific burden of the most common multimorbidity that affect majority of the Canadian adult population. To address this challenge, there is the need for a more consistent approach to assessing multimorbidity. Prevalence studies of the most common multimorbidity, which employ data from a nationally represented sample, will serve as valuable sources of evidence that could be used to inform policymakers and public health experts in their decisions to develop and implement policies that focus on the allocation of resources in the management, treatment, and prevention of multimorbidity.

Multimorbidity patterns commonly identified include cardiovascular diseases, musculoskeletal disorders, metabolic disorders, and mental health disorders [13,14]. Due to the different sociodemographic factors and disease patterns, the prevalence and patterns of multimorbidity among Canadian adults may be different from those of other countries [15]. Although previous studies have investigated the burden of multimorbidity in Canada [8–11], the focus has been on describing the trend in terms of the number and disease patterns of the multiple chronic conditions among the Canadian population.

Therefore, the current study will focus on identifying the most common multimorbidity (MCM), and associated risk factors in a sample of middle-aged and older Canadians using data from the Canadian Longitudinal Study on Aging (CLSA). The CLSA is a national research initiative designed to explore the aging process and the factors that influence healthy aging. The study collects extensive data on health, lifestyle, social, and economic factors, which allow for a holistic understanding of aging and the factors that influence it. Particularly, the CLSA provides a robust and valuable dataset that can be used to explore the health dynamics and burden of multimorbidity among Canadian adults.

We believe that the outcome of this investigation will: a) help in the understanding of how chronic conditions coexist in middle-aged and older Canadians; b) inform public health experts on the necessary preventive measures needed in the growing fight against multimorbidity by targeting those modifiable factors identified by this study; c) help increase awareness of multimorbidity in Canada; and d) influence the creation and implementation of health policies that target the training and recruitment of specialized health professionals with expertise in the management of multimorbidity.

Method

Results

Table 1 displays the weighted results of the characteristics of our study population. The proportion of females was 52.4%, and the average age of participants was 59.7 years. Most participants were highly educated with more than 60% having a post secondary education. A sizable proportion were in a marital or common-law relationship, but only a few were single or never married. Furthermore, 44.8% considered themselves either completely or partly retired, and more than 89% of study participants were concentrated in the urban core regions of Canada. The results also show that majority of the population were homeowners, and approximately 62.6% of households earn less than $100,000 annually with less than one-fifth making $150,000 or more.

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Table 1. Characteristics of study population (weighted sample size, n = 3,812,085)^a.

https://doi.org/10.1371/journal.pone.0317688.t001

According to Table 1 approximately 90% of participants self-rated their current health as being at least good. However, almost 27% of the population were not satisfied with the quality of sleep they were getting at night. More than 65% of the population did not engage in any physical activity in the past seven days, and the proportion of overweight and obese participants, as determined by standard BMI values, was high (approximately 70%). A significant number of subjects were classified as regular consumers of alcohol, and more than 56% of the population had a smoking history (i.e., either former or current smoker).

The eastern provinces (i.e., Newfoundland and Labrador, and Nova Scotia) accounted for the lowest proportion of recruits for this study (about 5.8%), and individuals of white ancestry were higher than any other race. The MCM was osteoarthritis-high blood pressure with a prevalence of 16.5% among middle-aged and older Canadians.

The results of the univariate analysis are summarized in Table 2. S1 Table is the supplementary information for Table 2. Age, sex, education level, marital status, retirement status, urban-rural settlement, total annual household income, BMI, self-rated sleep quality and general health, homeownership, drinking habit, physical activity, province at recruitment, cultural background, and smoking status were significantly associated with the odds of the MCM.

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Table 2. Univariate analysis of sociodemographic factors and the MCM among Canadian adults (weighted sample size, n = 18564.89)^a.

https://doi.org/10.1371/journal.pone.0317688.t002

As seen from Table 2, while the likelihood of having the MCM increases with age by about 10%, the association with sex is significantly higher among females compared to males. Moreover, participants with less than secondary education were about three times more likely than those with post-secondary education of developing the two conditions. A similar association is seen with the population of retirees (either partly or completely retired) versus non-retired (Retired vs Not Retired; OR: 4.85, CI = (4.28–5.48)). The results further show that an economic indicator, such as total annual income, is significantly associated with multimorbidity. Compared with the most affluent population, those who make the least annual income have the highest likelihood of having multimorbidity. The relationship between physical activity and multimorbidity in Table 2 indicates that those who reported to have never engaged in any physical activity for the past seven days were about 2.32 times more likely of having the MCM compared with their active counterparts.

The multivariate analysis in Table 3 showed a significant interaction between age and sex (p<0.001). S2 Table is the supplementary data for Table 3. S3 Table provides the main effect without the interaction term for the final model. The odds of the MCM associated with increasing age was significantly different between Females and Males: it was significantly higher in Females (OR = 1.12, 95% CI = 1.11–1.13) than Males (OR = 1.08, 95% CI = 1.07–1.10).

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Table 3. Multivariate analysis of risk factors of the MCM among Canadian adults (weighted sample size, n = 18392.76)^a.

https://doi.org/10.1371/journal.pone.0317688.t003

The participants who reported feeling unhealthy were at increased odds of multimorbidity compared to those who reported feeling healthier. The odds for those who reported feeling poor about their health was nearly twice as high as those who reported feeling good. Furthermore, a decrease in BMI tended to be associated with a reduced likelihood of multimorbidity. Also, retirement from work conferred a higher likelihood of multimorbidity on participants. Participants reporting dissatisfaction with sleep quality were 1.33 times more likely of having the MCM than those neutral respondents. Urban-rural settlement was significantly associated with the risk of MCM. In comparison with participants living in urban core areas, participants living in rural settlements were not significantly associated with increased odds of having the MCM, however, those living in other urban centres were significantly associated with lower odds of the MCM (OR = 0.69, 95% CI = 0.53–0.89, p<0.01). Living in Atlantic Canada (i.e., Newfoundland and Labrador, and Nova Scotia) was also significantly associated with increased likelihood of the MCM than those in British Columbia.

Discussion

This study found that the co-occurrence of osteoarthritis and high blood pressure was the MCM among middle-aged and older Canadian adults. The estimated prevalence was found to be 16.5%. A total of seven sociodemographic factors were significantly associated with the MCM: age, retirement status, self-reported general health, self-reported sleep quality, body-mass index, urban-rural settlement, and province at recruitment. The odds of the MCM associated with increasing age was significantly higher in Females than in Males.

Given that multimorbidity is a combination of chronic conditions, the identification of osteoarthritis and high blood pressure as the MCM among Canadian adults merits a brief overview of these conditions. According to Coppola et al., osteoarthritis is a bone and joint condition and is characterized by progressive degradation of articular cartilage and remodelling of the underlying bone due to active response of chondrocytes [24]. High blood pressure is a common condition of arteries, and it is characterized by consistently elevated blood pressure against the artery walls. Although osteoarthritis and high blood pressure are two distinct diseases and the mechanism of pathogenesis of the two diseases is not clear, it is generally believed that many sociodemographic factors and genetic predisposition are the driving force behind development of the two diseases [25]. The identified sociodemographic risk factors of the co-occurrence of osteoarthritis and hypertension by this study shed light on the etiology of the two diseases.

Our results are consistent with a recent systematic review, which reported that the overall odds of having osteoarthritis was significantly increased in the people with hypertension compared to the normotensive ones (OR = 1.60, 95% CI = 1.33–1.94) [26]. Moreover, the association was significantly stronger in women when adjusted for BMI [26], a finding which harmonizes well with the results of this study.

This study reported that increasing age was significantly associated with greater odds of the MCM, and the association was significantly higher in Females than in Males (OR = 1.12 vs. OR = 1.08, p<0.001).

This may be partly explained by the fact that women are, in general, more health conscious and tend to visit healthcare providers more often and are therefore more likely to be diagnosed of hypertension than men as shown in some studies [27,28]. In Canada, hypertension has been found to be more prevalent in women than men [27].

Another explanation for the observed significant difference may be due to the role played by sex hormones. Since estrogens play a crucial role in cartilage protection, inflammation modulation, and bone metabolism promotion, hormonal fluctuations in females during menstrual cycles, and most notably postmenopausal changes, significantly increase disease incidence in the mid-forties age range [29,30].

Furthermore, sex-specific differences in joint alignment have been found to increase the incidence of osteoarthritis in females. Females generally exhibit higher quadriceps angle, lower arch height index, and a broader range of internal and external rotation in the hip joint, resulting in prolonged stress on the hip and knee joints over time [31]. Additionally, weight gain and the prolonged hormonal fluctuation in pregnancy weakens joints, particularly the lower joints and spine increasing the risk of osteoarthritis in females [32].

On the other hand aging is a gradual, continuous process of biological, physiological and psychological changes across all organ systems, and it has long been established as a common risk factor for many chronic diseases [33–39], including osteoarthritis and high blood pressure [24,40]. Many studies have shown that the most common risk factors for osteoarthritis are aging and obesity [41].

Telomere attrition is a feature of normal aging and has been found to be far greater in individuals with chronic diseases such as cardiovascular disease and diabetes [42,43]. Since high blood pressure is a precursor to many cardiovascular diseases, telomere attrition may also be involved in the shared pathway of disease development. Aside telomere attrition, there are many other potential mechanisms at cellular level that link aging with increased risk of multimorbidity.

Cellular senescence, for example, is a state of irreversible cell cycle arrest which occurs in response to cellular stressors such as DNA damage, and oxidative stress [37]. Cells enter senescence when DNA damage repairs are no longer effectively achieved and are subsequently programmed to die by apoptosis. New cells are eventually generated to replace damaged ones. Senescence is beneficial to cells as it maintains genomic stability and protects against the development of tumours. Chronic conditions of accelerated aging have been found to be associated with enhanced cellular senescence (possibly due to oxidative stress pathway), which–unlike apoptotic cells–remain metabolically active and may induce senescence in other neighbouring cells via senescence-associated secretory phenotype (SASP). SASP produces proinflammatory agents which mediate the development of complex chronic conditions such as chronic obstructive pulmonary disease (COPD), and cardiovascular diseases [37]. Senescent cells have been found to accumulate in conditions such as diabetes and cardiovascular diseases including hypertension [44]. Enhanced cellular senescence associated with chronic conditions and multimorbidity suggest a possible causal relationship which needs further exploration. More research is needed to establish a firm association between cellular senescence and the development of multimorbidity, especially with specific combinations such as osteoarthritis and high blood pressure.

A study on mice demonstrated that mice with T cells that are deficient in mitochondrial DNA-stabilizing protein have features associated with abnormal neurological, metabolic, muscular and cardiovascular functions and that these changes produce similar effects to inflammaging [45]. The study suggested that mitochondrial dysfunction can be used to predict multimorbidity via an important mediating factor (i.e., mitochondrial transcription factor A) which has been found to be associated with inflammaging.

Stem cells are responsible for replacing old worn-out and damaged cells with new and better ones. As cells age there is increased difficulty to function effectively resulting in impaired cell replacement and recovery [44]. Thus, stem cell exhaustion is a state of impaired stem cell function due to factors such as aging. Stem cell exhaustion is closely linked to cellular senescence, and as such may have synergistic effect which may help explain the progressive nature of multimorbidity especially among the aged.

These foregoing factors may help explain the significant sex/gender differences in the association between age and risk of the MCM.

Moving away from age and gender, participants who were partly or completely retired were at increased odds of developing the MCM compared to those in the workforce (OR = 1.43, 95% CI = 1.19–1.71, p<0.001) even after controlling for age. A similar result was reported in another cross-sectional study examining the prevalence of multiple chronic conditions among adults in Portugal attending primary care [46]. This current study suggests that the social engagements and daily routines at the workplace may offer protection against the development of the MCM identified (i.e., osteoarthritis and high blood pressure), and that individuals deprived of this psychosocial factor are more susceptible to developing the MCM. An alternative explanation would be that the development of multimorbidity leads to increased likelihood of retiring.

The association between self-reported general health and multimorbidity has been studied [47]. As expected, participants of the study with a poor/fair assessment of health were significantly more likely to be diagnosed with the MCM compared with those with excellent health reports (Table 3). Self-rated health has been reported in previous population-based studies to be significantly associated with multimorbidity and a good predictor of disease outcome [48,49]. These studies consistently show that persons who are diagnosed with more than one chronic condition, with at least one cardiovascular condition, are more likely to report poor self-rated health.

Our study showed participants’ sleep quality was significantly associated with risk of the MCM. There is evidence to suggest that insufficient sleep disrupts pathways related to cardio-metabolic, immune and inflammation [50], causing the release of stress hormones such as cortisol [51]. Furthermore, pain and discomfort associated with osteoarthritis, as well as the treatments and medications used for disease management, may lead to accumulation of sleep problems in patients with multimorbidity [52–54]. These may help explain the observed negative association between sleep satisfaction and the MCM. In a study investigating the association between sleep problems and multimorbidity patterns in older adults, participants with poor quality sleep were at greater odds of presenting with vascular-metabolic, cardiopulmonary, musculoskeletal, and coexisting conditions [55]. Other authors of a systematic review also found comparable results linking poor sleep quality and satisfaction with increasing odds of multimorbidity, including a previous study exploring sleep behaviors and multimorbidity occurrence in middle-aged and older adults using data from the CLSA [50,52].

Obese individuals are more at risk of developing hypertension and other related cardiovascular events [56] than those with normal BMI values. It is also important to note that increasing weight puts pressure on bones, particularly on the joints, and may cause or worsen conditions associated with the skeletal system (e.g., osteoarthritis). Our study suggests that obese individuals are approximately six times more likely to develop multimorbidity than those with normal BMI values (OR = 6.11, 95% CI = 5.16–7.24, p<0.0001). It is also worth noting that a lesser degree of excess weight significantly reduces the odds to less than half, suggesting that maintaining a healthy weight has a strong positive impact on reducing the risk of hypertension and osteoarthritis. In two systematic reviews and a meta-analysis to assess the impact of BMI on both knee and hip osteoarthritis, Jiang et al. found that a 5-unit increase in BMI was associated with a 35% increased risk of knee osteoarthritis (RR = 1.35, 95% CI = 1.21–1.51). This relationship was found, by the authors, to be significantly stronger in females than males (men, RR = 1.22; 95% CI = 1.19–1.25; women, RR = 1.38; 95% CI = 1.23–1.54; p = 0.04) [57,58]. A similar study exploring the risk factors of hypertension among Canadian adults aged 20 to 79 years found that being overweight or obese was one of the leading risk factors for hypertension among the Canadian population [59].

These findings, together with those of this study, underscore the need for increased public awareness of the health dangers posed by excessive weight gain. It should also be noted that although the underweight category seems to offer protection from multimorbidity when compared with those of normal weight from our study results (Table 3), such interpretations must be done cautiously especially due to the potential of survival bias, which could underestimate the health risk in this category and lead to wrong conclusions.

Urban-rural dwelling residence also affects the development of many health outcomes. Our study suggested that individuals in rural areas have relatively lower odds of having MCM compared with those in urban core areas. However, the association was not statistically significant (OR = 0.87, 95% CI = 0.70–1.08; p = 0.1915). In general, participants residing in other urban centres have significantly lower odds of MCM than those in urban core areas, and this should be a concern for policy makers and public health authorities since more than 89% of the middle-aged and older Canadian adults in this study live in these urban core regions. In a study examining the excess burden of osteoarthritis in the largest province of Canada (Ontario), Tarride et al. reported that almost 80% of study patients with osteoarthritis were living in urban areas, suggesting a greater concentration of the disease in urban areas of Canada [60]. Similarly, a population-based study conducted to estimate the prevalence and incidence of hypertension from 1995–2005 found that the percentage of patients diagnosed with high blood pressure was consistently higher in those living in urban areas [61]. The findings of Foguet-Boreu et al. in their study examining the impact of urban-rural settlement on multimorbidity in southern Europe, also suggest that living in rural areas, compared with urban areas, is significantly associated with a lower probability of multimorbidity [62] which supports the results of this study. Furthermore, a Scottish study describing the prevalence of multimorbidity among 41,545 hospitalized patients found that multimorbidity prevalence was 28.8% (95% CI = 28.1–29.5) in large urban versus 22.0% (95% CI = 20.9–23.3) in remote rural areas [63].

Factors influencing this observed disparity may include lifestyle choices, dietary habits, social support systems, and healthcare access. Rural dwellers typically engage in regular and more physical activities than urban dwellers due to their lifestyle, which can help maintain healthy weight and lower blood pressure. Also, the consumption of more traditional diets, whole foods, fruits and vegetables may contribute to the better overall health among rural dwellers. The strong social ties and support systems that exist in rural communities may further contribute to lower stress levels and better mental health, both of which are beneficial for promoting health. Moreover, the relatively reduced access to healthcare facilities in rural areas may lead to lower rates of diagnosis, as many remain undiagnosed, which can skew the data.

Participants residing in the eastern provinces of Canada (i.e., Newfoundland and Labrador, and Nova Scotia) at the time of recruitment were found to be significantly associated with increased odds of the MCM compared with those from British Columbia (OR = 1.35, 95% CI = 1.10–1.65, p = 0.0041). In a study exploring the regional variation in self-reported heart disease prevalence in Canada the authors found wide variations across provinces, with Nova Scotia and Newfoundland recording the highest [64]. The results of a large population survey, the Canadian Community Health Survey, also reported the proportion of high blood pressure among residents in eastern provinces was significantly higher than those in western provinces [65]. Similar regional variation in the prevalence of osteoarthritis was also reported by Statistics Canada, which showed that the prevalence in Atlantic provinces was higher than Canadian average, however, the prevalence in Alberta and BC was lower than Canadian average [66]. Similar studies also attribute increased prevalence of heart diseases and coexisting patterns to living in the Atlantic provinces [27]. This pattern is a replication of the east-to-west gradient of cardiometabolic health outcomes and represents a greater burden of chronic disease and multimorbidity on Atlantic Canada.

This observed regional variation in the prevalence of MCM is not fully understood. However, it could be attributed to many factors, such as lifestyle choices, socioeconomic factors, healthcare access, and demographic characteristics of residents in the eastern provinces. It is well established that a sedentary lifestyle and lower consumption of fruits and vegetables are associated with increased risk of both cardiovascular and musculoskeletal diseases. Also, economic challenges in some eastern provinces can lead to stress and limited access to healthy food options, which are essential for maintaining healthy lifestyle. This can exacerbate health issues including hypertension and osteoarthritis. Access to healthcare services is more limited in rural areas of eastern Canada, affecting the diagnosis and management of chronic health conditions, including hypertension and osteoarthritis. The recent pandemic further complicated healthcare access, leading to increased concerns about awareness and treatment of hypertension and other chronic health conditions. Finally, the population of eastern Canada may have a higher proportion of older people than other provinces, and thus may help explain the observed risk.

Conclusion

Our study identified the co-occurrence of osteoarthritis and high blood pressure as the MCM among the Canadian adult population aged 45 to 85 years. The identified risk factors for the MCM provide insights into the etiology of MCM, which has implications for researchers, policy makers and public health authorities. Finally, the results of our study provide important information for future epidemiological and genetic-epidemiological studies of MCM in middle-aged and older Canadian adults.

Supporting information