In this study, we have used an extensive range of clinically measured and self-reported chronic conditions and two complementary analytical approaches to describe the prevalence and patterns of multimorbidity in a community cohort of middle-aged adults. Our first approach, using single disease counts of pairs and triplets of conditions, identified that multimorbidity is prevalent and that some combinations of conditions occur more frequently than expected by chance. Our second approach, using LCA, identified four unbiased classes of multimorbidity, with differences in membership of each class characterised by both higher probability of the number of chronic conditions and a clinically-distinct groupings of conditions. The predictors of chronic disease load (number of conditions) differed from those of patterns of multimorbidity.
Using single disease counts, we showed that, although the majority (73%) of adults in this cohort had two or more chronic conditions, the overall prevalence of specific combinations of disease was relatively low. The most prevalent pair (atopy and arthritis) had a prevalence of just over 13% and the most prevalent triplet (arthritis, bowel disease and atopy) was observed in under 5% of the cohort. By calculating O/E prevalence ratios, we were able to identify combinations of disease that occurred more frequently than expected by chance. Some of the combinations identified in this cohort have obvious pathological links or are well known to co-exist clinically. Asthma and COPD either as a pair, or along with atopy in a triplet, were combinations with high O/E ratios and point towards well-documented inflammatory links between allergy and respiratory function. Although we mainly used spirometry to define COPD, the appearance of this condition alongside asthma may point towards overlap of symptoms or diagnostic mislabelling and highlights the need for further research into how these conditions might be better characterised [37].
Arthritis, bowel disease and low back pain were the most common conditions appearing in highest ranked O/E combinations alongside depression-anxiety. Arthritis featured in 60% of the highest-ranking triplets of conditions and bowel disease and depression-anxiety featured in 40%. Depression-anxiety featured in over one-third of all triplets identified and was highly prevalent among LCA Class 4 with the highest condition load. Depression is a common feature of multimorbidity [38] and has been associated with a range of chronic conditions including cardiac disease, stroke, loss of hearing, loss of vision, and chronic lung diseases such as asthma and COPD [39, 40]. In our study, depression-anxiety often featured in combination with arthritis, back pain and bowel disease. Previous studies have shown high prevalence of depression and anxiety in inflammatory bowel disorders such as ulcerative colitis and Crohn’s disease [41, 42]. Depression is also a common comorbid feature in different arthritis subtypes [43] and can worsen both the severity of and disability caused by chronic low back pain [44]. As depression is a treatable illness, a rational approach may be to screen for and treat comorbid depression in patients presenting with these patterns of multimorbidity.
Other combinations of disease identified in our study may point towards shared aetiologies. The high co-occurrence of arthritis with bowel disease has been described in previous studies and was a common combination identified in both our disease counts (7/27 triplets) and occurred in high prevalence in LCA Classes 3 and 4 [45, 46]. Previous studies have suggested changes in gut bacteria as potentially implicated in the development of joint inflammation, or alternatively recruitment of gut lymphocytes or activated macrophages in joint inflammation [47].
Common risk factors such as obesity, tobacco smoking, physical inactivity, and family history may underlie some of these patterns, yet predictors of latent class membership showed distinct differences. Our analysis of the lifestyle factors underlying class membership or the number of chronic conditions did not find alcohol consumption or diet variables (data not shown) to be significant variables.
LCA patterns
The largest disease burden was evident in Classes 3 and 4, which together comprised around 20% of the cohort. Individuals assigned to Class 3 multi-morbid non-cardiometabolic had an average of over 4 chronic conditions and showed a higher probability of musculoskeletal conditions (arthritis, low back pain and osteoporosis); conditions resulting in sensory deficits (hearing impairment and eye conditions); and non-cardiovascular conditions including bowel, thyroid and kidney diseases. Individuals in this category were more likely to be female (73%) and older than those in classes 1 and 2. While only 5% of the cohort was assigned to Class 4, this class was 64% male and had the highest disease load with an average of six co-occurring chronic conditions. Cardiometabolic conditions including heart disease, diabetes, stroke, and peripheral artery disease along with liver disease and sleep apnoea were highly prevalent in this group. Individuals in Class 4 were much more likely to be male, older and obese.
Similar to the findings for common pairs and triplets, Class 2 was characterised by above-average prevalence of respiratory and allergy conditions but did not significantly differ from Class 1 (relatively healthy) in terms of age, obesity or smoking status. This is interesting because this class shows a distinct clustering of multimorbidity that was not associated with increased age (the average age was lower in this group compared with Classes 3 and 4) or with behavioural risk factors related to poor lung function, such as smoking and excess abdominal fat (compared with Class 1). Family history of asthma was a significant predictor of membership of this group, perhaps highlighting a strong genetic and early life component underlying this pattern of multimorbidity.
Comparing our findings with previous studies that identify multimorbidity patterns is difficult due to the heterogeneity in the populations and age-ranges sampled, disease type and number of conditions included, as well as variation in data sources and analytical methods. Nonetheless a systematic review of 14 studies on non-random patterns of multimorbidity provides support for three broad categories consisting of cardiovascular and metabolic diseases, musculoskeletal disorders and mental health disorders [27]. Using disease counts, similar patterns have been described in Australian samples. Britt et al. found that arthritis, chronic back pain and depression-anxiety conditions were the most prevalent combinations of conditions, along with vascular disease, in older primary-care patients [20]. Holden et al. used exploratory factor analysis to describe patterns and identified a dominant group with arthritis and irritable bowel disease [17].
Relatively few published studies have employed LCA to describe multimorbidity classes in general population samples. An Australian study of over 4500 adults with mean age of 69 years included 11 self-reported disease conditions resulting in four multimorbidity LCA classes [18]. Similar to our findings, the majority of the study sample (> 55%) were assigned to a minimally diseased (‘healthy’) group. The remainder were assigned to groups characterised by dominant presence of either: arthritis and asthma and depression–anxiety; high blood pressure and diabetes, or; cancer and heart disease and stroke. The clustering of conditions in that study was largely consistent across the multiple analytical techniques employed, which included pair/triplet disease combinations and cluster analyses. While we identified broadly similar combinations of conditions and clusters in our study sample, it is likely that inclusion of more conditions in our analyses resulted in a distinct grouping of cardio-metabolic conditions that included diabetes, heart disease and stroke into a single class (Class 4). A study using 15 self-reported conditions in over 160,000 Danish citizens with mean age 47 years, identified seven classes of multimorbidity, three of which were similar to those identified in our analyses [21]. These include a distinct “musculoskeletal class” characterised by high probability of arthritis, osteoporosis and back injuries; a “complex cardio-metabolic disorders” class with high probability of diabetes, heart disease and stroke, similar to our Class 4; and a third class termed “asthma–allergy”, similar to our Class 2 but which did not include COPD.
A US study of over 14,000 adults aged over 65 years used 13 self-reported conditions identified six latent multimorbidity classes [36]. Only one-third were assigned to a “Minimal disease” group and the remainder spread between groups characterised by vascular, non-vascular, cardio-stroke-cancer or neurological disease, and a ‘Very sick’ group with above average prevalence of all conditions. These findings differ from our study in that we found distinct differences between predictors of class membership and number of diseases, and that the majority of our sample (70%) fell within a relatively healthy (minimal disease) group, the latter of which may be explained by the overall younger age of our cohort.
Comparing the patterns of multimorbidity identified in our study and others conducted in high income countries, with patterns identified in low-middle income countries is difficult given the different nature of disease burden and the limited availability of studies. Nonetheless a recent LCA analysis in cohorts from both low and high income countries identified three classes, two of which labelled “cardio-metabolic” and “healthy” were similar to those found in our study and others [27, 48]. However the proportion of individuals in each of the multimorbidity classes differed across regions highlighting the difficulties in comparing multimorbidity burden and patterns across different socio-economic and epidemiological settings.
Strengths
The strengths of our study include a high participation and completion rate in a moderately large community-dwelling cohort, assessed at an age where chronic disease load often begins to manifest. By using a large number of conditions (over 50% of which were defined by physical or biochemical clinical measure or validated instrument score) and including those conditions that are current and chronic (long-lasting symptoms and likely to impact on daily activities) we have captured a comprehensive range of conditions of ageing that have not been able to be included in previous studies of multimorbidity.
Limitations
Our sample was drawn from a regional urban community; however the cohort was similar on a number of key health indicators to the general Australian population. Current medication use, although available, was not included in the definition or criteria for any of the conditions so as to avoid potential misclassification errors that may arise from off-label prescribing or under-reporting. Without an objective clinical marker for arthritis (similar to other studies), we have relied on self-reported doctor diagnosis, but we used validated instrument scores to capture musculoskeletal conditions including low back pain and a wide range of gastrointestinal disorders in our definition of bowel disease, some of which have not often been included in previous studies of multimorbidity patterns [32].
When defining multimorbidity, “health conditions” may include diseases, risk factors or symptoms. The present analysis has included predominantly conditions that are diseases or symptoms. We therefore did not include conditions such as obesity or hypertension. While hypertension can be considered a cardiovascular condition, it was not included in this analysis because of its very strong risk factor association with stroke, heart disease, kidney disease and diabetes and is thus a somewhat redundant aspect of the cardio-metabolic profile and patterns identified. Decisions on what to include when defining multimorbidity depend on the purpose of the definition, e.g. assessing effects on perceived future risk, or effects on current function and disability, or effects on health care costs [48]. The present study aimed at examining how diseases and symptoms co-occurred and might be predicted.
