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Comparisons of disease cluster patterns, prevalence and health factors in the USA, Canada, England and Ireland



Identification of those who are most at risk of developing specific patterns of disease across different populations is required for directing public health policy. Here, we contrast prevalence and patterns of cross-national disease incidence, co-occurrence and related risk factors across population samples from the U.S., Canada, England and Ireland.


Participants (n = 62,111) were drawn from the US Health and Retirement Study (n = 10,858); the Canadian Longitudinal Study on Ageing (n = 36,647); the English Longitudinal Study of Ageing (n = 7938) and The Irish Longitudinal Study on Ageing (n = 6668). Self-reported lifetime prevalence of 10 medical conditions, predominant clusters of multimorbidity and their specific risk factors were compared across countries using latent class analysis.


The U.S. had significantly higher prevalence of multimorbid disease patterns and nearly all diseases when compared to the three other countries, even after adjusting for age, sex, BMI, income, employment status, education, alcohol consumption and smoking history. For the U.S. the most at-risk group were younger on average compared to Canada, England and Ireland. Socioeconomic gradients for specific disease combinations were more pronounced for the U.S., Canada and England than they were for Ireland. The rates of obesity trends over the last 50 years align with the prevalence of eight of the 10 diseases examined. While patterns of disease clusters and the risk factors related to each of the disease clusters were similar, the probabilities of the diseases within each cluster differed across countries.


This information can be used to better understand the complex nature of multimorbidity and identify appropriate prevention and management strategies for treating multimorbidity across countries.

Peer Review reports


Multimorbidity (the presence of two or more medical conditions simultaneously [1,2,3]) is an increasingly important topic as there is growing evidence that multimorbidity is now the norm rather than the exception in ageing populations [1, 4,5,6]. Multimorbidity is strongly associated with physical and functional decline, mortality [4, 5, 7] decreased quality of life [8, 9] and increased health care usage and costs [4, 7, 10]. The Academy of Medical Sciences identified the investigation of disease clusters and their corresponding risk factors as a critical gap in our understanding of multimorbidity [3].

Our aim is to provide a cross-country comparison of disease prevalence as well as the unique patterns of multimorbidity as disease clusters and associated risk factors to uncover how differences in demographics, socio-economic status and health behaviors affect the combinations of diseases within and across four countries: United States, Canada, England and Ireland. Selection of these four countries (all ranked globally in the top 14 according to the 2018 United Nations human development index) allows for comparison across the range of public healthcare delivery systems of North America and Europe.

An abundant body of literature compares health outcomes between the U.S. and other developed countries; with several publications reporting health disadvantage and higher disease prevalence for the U.S. [11,12,13,14,15,16]. In particular, Banks et al. showed that the U.S. population when compared to England’s population had worse health and higher prevalence for seven common diseases regardless of level of socio-economic status, demographics and behavioral risk factors [12]. Although the U.S., Canada, England and Ireland all have some form of publicly funded health care for those aged over 65, the level of public care offered varies over countries. England and Canada have universal healthcare for all ages; Ireland has a mixed public and private healthcare system, with public health care for those below an identified income level, and a range of community and hospital services free of charge for all, despite income levels. In contrast, the United States has a mostly privatized system.

The United States, despite having a per capita expenditure on health care that is 1.9–2.8 times higher than Canada, Ireland or England, has the lowest life expectancy, highest mortality rate and highest number of disability-adjusted life years lost due to non-communicable and largely preventable diseases of these four countries (Additional file 1: Table 1).

Evidence required to address these health policy issues requires an understanding of the complexities of multimorbidity and related health factors. Here we have the opportunity to do so across multiple countries, yielding findings of the common trends of health and the specific patterns of multimorbidity unique to the U. S, Canada, Ireland and England.


Analysis was based on cross-sectional data from a total of 62,111 respondents aged 52–85, participating in the 2012 (wave 11) U.S. Health and Retirement Survey (HRS) (n = 10,858) [17]; 2012–2013 (wave 6) English Longitudinal Study on Ageing (ELSA) (n = 7938) [18]; 2012 (wave 2) The Irish Longitudinal Study in Ageing (TILDA) (n = 6668) [19] and 2010–2015 baseline of the Canadian Longitudinal Study on Aging (CLSA) (n = 36,647) [20]. The design of these studies has been comprehensively described elsewhere [17,18,19,20,21] but for completeness is explained in Additional file 2 Section 1.

To eliminate differences in disease prevalence and patterns due to the disparate racial structures, analysis was limited to the non-Hispanic white subpopulation. A breakdown of the cohort characteristics for all four countries can be seen in Additional file 3: Tables 1–4.

Self-reported diagnoses and risk factors

Nine self-reported medical conditions were identified as common across all four studies: hypertension, diabetes, stroke (including transient ischemic attack), angina, myocardial infarction (MI), arthritis, cancer (not including minor skin cancers), lung disease (at least one of: emphysema, chronic bronchitis or chronic obstructive pulmonary disease) and osteoporosis. A tenth condition included psychological disorders of anxiety/mood (Psych 1) (CLSA, HRS) and/or psychiatric problems (Psych 2) (TILDA, HRS, ELSA).


Covariates included age, sex and socioeconomic status (SES), characterized by education level and household income tertiles. Employment status was also included to ensure differences in household income were more reflective of permanent features of SES and not confounded by a lack of income due to temporary unemployment or retirement. Health factors controlled for were body mass index, smoking history and alcohol consumption. For detailed information on the covariates and harmonization of medical conditions across studies, see Additional file 2 Section 2.

Statistical analysis

Cross-sectional survey weights were used to report population representative disease prevalence using STATA 15. Crude population prevalence of disease was calculated using the tab command in STATA 15. Odds ratios for disease presence and risk factors were calculated using a survey-weighted logistic regression for each disease. This was implemented with the svy:logit command in STATA15. When making comparisons directly to the U.S., we pooled data across countries, ensuring cluster and strata variables across countries were accounted for; country-level weights were scaled to have a common mean and standard deviation 1 to prevent countries with weights on a larger scale dominating the analysis. Fully adjusted income, education and BMI gradients for each disease were identified and calculated with the addition of an interaction term by country. The marginal effect of each respective variable was then extracted assuming all other confounding variables were equal. This was performed using the margins command in STATA15.

Disease patterns were identified using Latent Class Analyses (LCA) which were population weighted in all cases and took into account the stratification and clustering inherent in the cohort sampling designs. LCA is a model-based clustering method for multivariate categorical data and has previously been applied in the analysis of multimorbidity [22, 23]. In the case of multimorbidity, clustering using LCA is more appropriate than standard distance-based methods, such as k-means or hierarchical clustering, since the appropriate probability distribution for the data is readily available. Furthermore, LCA allows extra flexibility for diseases to have partial membership across multiple clusters unlike other more limiting distance-based clustering methods.

Two sets of parameters underlie the model: the group probability τ and item probability θ. The group probability parameter represents the a priori probability that an observation belongs to a particular group, so that P(Group g) = τg. The item response probability represents the probability of a success for a given item, conditional on group membership, so that P(Item m = 1 | Group g) = θgm.

More formally, let X = X1, …, Xn denote M-dimensional vector-valued binary random variables, composed of G groups. The observed-data likelihood distribution for the data X can then be written: \( p\Big(X\left|\ \uptheta, \uptau \right)={\prod}_{\mathrm{i}=1}^{\mathrm{n}}{\sum}_{\mathrm{g}=1}^{\mathrm{G}}{\uptau}_{\mathrm{g}}{\prod}_{\mathrm{m}=1}^{\mathrm{M}}{\uptheta}_{\mathrm{g}\mathrm{m}}^{{\mathrm{x}}_{\mathrm{i}\mathrm{m}}}{\left(1-{\uptheta}_{\mathrm{g}\mathrm{m}}\right)}^{1-{\mathrm{X}}_{\mathrm{i}\mathrm{m}}} \).

The naïve Bayes assumption that observations are conditionally independent based on group membership has been made for this model. Direct inference using the observed-data likelihood is typically difficult and is facilitated by the introduction of latent variables Z = Z1, …, Zn. Each Zi = Zi1, …ZiG is a G-dimensional vector, representing the true cluster membership of Xi as a multinomial random variable. That is, suppose that the true group membership is known for each Xi and is denoted by Zig = 1 if observation i belongs to Group g, otherwise Zig = 0. The complete-data density for an observation (Xi, Zi) is then \( \mathrm{p}\left(\mathrm{X},\mathrm{Z}|\uptheta, \uptau \right)={\prod}_{\mathrm{i}=1}^{\mathrm{n}}{\prod}_{\mathrm{g}=1}^{\mathrm{G}}{\left\{{\uptau}_{\mathrm{g}}{\prod}_{\mathrm{m}=1}^{\mathrm{M}}{\uptheta}_{\mathrm{g}\mathrm{m}}^{{\mathrm{x}}_{\mathrm{i}\mathrm{m}}}{\left(1-{\uptheta}_{\mathrm{g}\mathrm{m}}\right)}^{1-{\mathrm{X}}_{\mathrm{i}\mathrm{m}}}\right\}}^{{\mathrm{Z}}_{\mathrm{i}\mathrm{g}}}. \) LCA thus allows the data to be summarised at a global and local level. The parameters θ and τ summarise the overall behaviour of the clusters in the data, while each variable Zi informs us of the cluster membership, and thus behaviour, of an individual observation i.

Inference for our LCA models was performed using an expectation-maximisation (EM) algorithm. This works in two steps: the E-step, where Z is estimated, based on the current values of θ and τ, and the M-step, where the complete data likelihood is maximised with respect to θ and τ based on the current value of Z. The algorithm proceeds iteratively until it has deemed to converge; that is, once parameter estimates remain more or less unchanged after successive iterations.

As the true number of groups G is not known in advance, each LCA model was run over a range of 1–10 groups. The number of clusters was then chosen using the Bayesian information criterion (BIC), where \( BIC=-2\log \mathrm{p}\left(\mathrm{X}\ \right|\uptheta, \tau \Big)+\left( GM+G-1\right)\log \left(\sum \limits_{\mathrm{i}=1}^{\mathrm{n}}{w}_i\right) \); wi is the survey weight attached to observation i and logp(X |θ, τ) is the survey weighted pseudo-loglikelihood. Here a lower value of BIC indicates a more suitable choice of model. In many practical examples as was performed in this current work a balance has to be found between model parsimony and model fit and so an “elbow” is usually identified whereby the addition of clusters has diminishing returns to model fit improvement. We applied LCA using the software package lcca in R [24]. Code to implement this analysis and BIC values for all models assessed are provided in Additional file 4.


Individual disease prevalence

Figure 1 shows the crude population weighted prevalence of the 10 medical conditions by sex and age categories. Table 1 shows the odds ratio of each condition compared to the U.S. after adjusting for confounding variables. Here, it can be seen, that the U.S. had significantly higher prevalence than England for all 10 medical conditions and for all, except diabetes, when compared to Canada, even in adjusted models. The U.S. had significantly higher prevalence for all, except osteoporosis, when compared to Ireland.

Fig. 1

Crude disease prevalence (weighted %) stratified across sex and age groups for the four countries

Table 1 Odds ratios and standard errors for each medical condition compared to the U.S. as reference

The U.S. and Canada had very similar prevalence of diabetes, regardless of age and sex (Fig. 1). The odds of having diabetes in the U.S. was approximately double that of Ireland or England (Table 1) even in adjusted models. The U.S. had a pronounced higher prevalence of hypertension, arthritis, and psychological conditions across all age and sex categories. In particular, 56.8% of the U.S. population had arthritis; significantly, higher than the other countries whose prevalence was 32.3–38.2%. Ireland had the highest prevalence of osteoporosis (13.85%) followed closely by the U.S. (13.0%) then Canada (9.8%) and England (7.5%).

Additional files 5, 6 and 7 show the fully adjusted disease prevalence across countries with respect to income, education and BMI. Here it can be seen that income and education gradients are more pronounced for the U.S., especially for lung disease, stroke (with respect to income), myocardial infarction, psychological illnesses (with respect to income), and high blood pressure. The prevalence of psychological illnesses in all cases was inversely related to income, however the opposite was true of education level. Hence those with lower income but who are higher educated are more likely to be diagnosed with a psychological illnesses. In the U.S., 60.7% of adults, aged 52–85, had two or more medical conditions. This is considerably higher than the other countries: Canada 45.3%, England 42.1% and Ireland 38.6%.

Disease cluster compositions

Five latent classes (disease clusters) were identified for all four cohorts. The item response probabilities for the clusters of each country can be seen in Fig. 2. Figures 3, 4, 5 and 6 show the odds ratios and 95% confidence intervals for risk factors associated with each specific disease pattern compared to the “low probability of disease” group for the U.S., Canada, England and Ireland respectively. The population weighted proportion of each disease pattern for the U.S., Canada, England and Ireland with respect to its risk factors can be seen in Additional files 8, 9, 10 and 11 respectively. In general, the risk factors and their direction of association for disease clusters are consistent across countries (Fig. 3, 4, 5 and 6). However, the composition of clusters across countries varies. For example, the “high probability of disease” group across all countries was associated with higher odds of being older, lower educated, lower income having a smoking history and being obese (Fig. 3, 4, 5 and 6). Regarding the composition of this “high probability of disease” group however, Canada and England had higher probability of myocardial infarction (47.9% England, 37.2% Canada, 6.32% Ireland 19.2% U.S.) and angina (67.6% England, 37.5% Canada, 15.3% Ireland, 29.9% U.S.) (see Group 1 Fig. 2a, b, c, d). For Ireland and the U.S., cardiovascular diseases were separated into a distinct cluster along with high blood pressure, diabetes and arthritis (see Fig. 2 Group 2 a, d).

Fig. 2

Item response probabilities Item Response Probabilities for identified disease clusters for the USA (a top left), Canada (b top right), England (c bottom left) and Ireland (d bottom right). Note: Definition of stroke includes transient ischemic attack; lung disease included a self-reported diagnosis or at least one of the following: emphysema, chronic bronchitis or chronic obstructive pulmonary disease; Psych1 was measured as self-reported diagnosis of any emotional, mental or psychological disorder; Psych2 included a self-reported diagnosis or at least one of: depression, anxiety disorder or bipolar disorder; Cancer excluded minor skin cancers and melanoma

Fig. 3

Odds Ratios and 95% confidence intervals of risk factors for each latent class compared to reference “Low probability of disease” class of the HRS sample. Group 1:High Probability of Disease (population weighted prevalence 8.04%, n = 934); Group 2:Metabolic, Cardiovascular, Arthritis, Cancer (population weighted prevalence 8.87%, n = 1208); Group 3:Osteoporosis,Arthritis, Hypertension, Psychological, Cancer (Female) (population weighted prevalence 19.05%, n = 2215)”; Group 4:Metabolic, Arthritis, Psychological, Cancer (population weighted prevalence 37.20%, n = 3720)

Fig. 4

Odds Ratios and 95% confidence intervals of risk factors for each latent class compared to reference “Low probability of disease” class of the CLSA sample. Group 1:High Probability of Disease (population weighted prevalence 6.85%, n = 2423); Group 2:Metabolic, Arthritis, Cancer (population weighted prevalence 18.23%, n = 7928)”; Group 3:Osteoporosis,Arthritis, Hypertension,Psychological, Cancer (Female) (population weighted prevalence 19.12%, n = 7548); Group 4:Metabolic, Arthritis, Psychological (population weighted prevalence 16.73%, n = 5949)

Fig. 5

Odds Ratios and 95% confidence intervals of risk factors for each latent class compared to reference “Low probability of disease” class of the ELSA sample. Group 1:High Probability of Disease (population weighted prevalence 8.08%, n = 633); Group 2:Metabolic, Arthritis (population weighted prevalence 21.13%, n = 1762); Group 3:Osteoporosis, Arthritis, Hypertension, Psychological (Female) (population weighted prevalence 25.0%, n = 2196); Group 4:Metabolic, High Prob. Arthritis (population weighted prevalence 13.53%, n = 1101)

Fig. 6

Odds Ratios and 95% confidence intervals of risk factors for each latent class compared to reference “Low probability of disease” class of the TILDA sample. Group 1:High Probability of Disease (population weighted prevalence 7.88%, n = 468); Group 2:Metabolic, Cardiovascular (population weighted prevalence 9.67%, n = 437); Group 3:Osteoporosis,Arthritis, Hypertension (Female) (population weighted prevalence 18.19%, n = 1387); Group 4:Metabolic, Arthritis (population weighted prevalence 28.47%, n = 2047)


Having an elevated BMI (> 25) was strongly associated with the “high probability of disease” groups across all four countries (see Group 1 Figs. 3, 4, 5 and 6). It was also associated with the two groups which were predominantly cardiometabolic and metabolic in nature across countries: Group 2 and Group 4 (see Fig. 2 (cluster patterns), Figs. 3, 4, 5 and 6 (odds ratios and 95% CI), see also Additional files 8, 9, 10 and 11). Although, older age increased the odds of all disease groups, the “high probability of disease” group for the U.S. had a high proportion of 52–64 year olds of 37.4%. The disease cluster with the highest disease burden for the other countries (Group 1) had a lower proportion of younger participants (31.6% Canada, 14.2% England, 11.4% Ireland).


The gradient and significance of socioeconomic indicators varied across countries for their respective disease patterns. In general, groups which consisted of high blood pressure, diabetes and arthritis and/or cardiovascular diseases were negatively associated with education. High education was associated with decreased odds of three disease groups for the U.S. and Canada (see Groups 1, 2, 4 Fig. 3 U.S. and Groups 1,2,4 Fig. 4 Canada). High education was also associated with two groups for England (Groups 1, 4 Fig. 5) and Ireland (Groups 1,2 Fig. 6). Across all four countries the disease cluster which predominantly consisted of high blood pressure, arthritis, osteoporosis and in some cases mental illnesses was not associated with education (Group 3 Fig. 2 and Fig. 3, 4, 5 and 6).


Canada and the U.S. had more pronounced income gradients with regards to disease patterns. For Canada, medium and high income were negatively associated with three clusters (Groups 1,3,4 Fig. 2b and Fig. 4 see also Additional file 9). For the U.S. high income significantly decreased the odds of all four clusters (Figs. 2, 3 and Additional file 8). High income was associated with decreased odds of three groups for England (Groups 1,2,3 Fig. 2c and Fig. 5 see also Additional file 10). Ireland had the least pronounced income gradient and high income was negatively associated with only two groups, both of which had high probability of high blood pressure, diabetes and arthritis and cardiovascular diseases (Groups 2 and 4 Fig. 2d and Fig. 6, see also Additional file 11).


With regards to lifestyle factors, in general having a history of smoking increased the odds of disease for the majority of disease groups. Canada and the U.S. had the most pronounced effect for smoking history where being a current smoker was positively associated with all four disease patterns for the U.S. (Fig. 2a and 3) and all but Group 2 for Canada which was predominantly metabolic, arthritis and cancer (see Group 2 Fig. 2b and Fig. 4). A similar trend was found for being a past smoker. Ireland had the least pronounced effect where being a past smoker was only associated with Group 1 “high probability of disease” and Group 2 which had high prevalence of metabolic and cardiovascular conditions (Groups 1 and 2 Fig. 2d and Fig. 6; see also Additional file 11).


Alcohol consumption was negatively associated with all disease clusters for the U.S. (Fig. 3) and for two clusters for Canada and England (Canada Group 1: High probability of disease and Group 4: Metabolic, Arthritis, Psychological groups Fig. 2b and Fig. 4; England Group 1: High probability of disease and Group 4: Metabolic, High Probability of Arthritis see Fig. 2c and Fig. 5). Ireland displayed the opposite trend, with alcohol consumption being positively associated with Group 3: Osteoporosis,Arthritis, Hypertension (Female) (population weighted prevalence 18.19%, n = 1387) and Group 4: Metabolic, Arthritis see Fig. 2d and Fig. 6.


Overall, the U.S. had significantly higher prevalence of nearly all medical conditions compared to Canada, England and Ireland. This trend persisted even after controlling for age, sex, SES, and health behaviors. This study focused solely on the non-Hispanic white sub-populations of each country to control for the disparities of health outcomes in racial structures of the U.S. and other countries.

Differences in disease prevalence

The U.S. and Ireland had marked higher prevalence of osteoporosis when compared to England. Differences in genetic susceptibility or vitamin D levels are unlikely to account for such a difference as Ireland and England have similar latitude and a very homogenous genetic structure [25]. A possible explanation lies in increased detection in the U.S. and Ireland as both countries have more of a culture of privatized healthcare and offer affordable scans to diagnose osteoporosis privately in both countries ($85–305 U.S., €80–100 Ireland). In the case of Ireland this theory is further supported by the fact that those with high income and high education (i.e. those who are more likely to have private health insurance and or more disposable income to afford doctor’s fees) had the highest probability of being diagnosed with osteoporosis.

The prevalence of diabetes and cancer were considerably higher in the U.S. and Canada. All four countries offer public screening programs for cervical, breast and bowel cancer so increased detection is unlikely to explain such discrepancies. Ireland and England have a higher cancer mortality rate [26,27,28,29] which may partially explain this difference. There were also pronounced geographical similarities in the prevalence of psychological conditions. The U.S. and Canada had much higher prevalence of psychological conditions than Ireland or England. This is likely at least partially due to differences in how psychological conditions are defined and diagnosed across countries. Practitioners in the U.S. and Canada use the Diagnostic and Statistical Manual of Mental Disorders to inform diagnoses whereas England and Ireland use the International Classification of Diseases and Related Health Problems. Previous literature has noted discrepancies in disease classification between these criteria [30,31,32]. The U.K. and Ireland also have a higher number of psychiatrists working in the mental health sector per 100,000 population (18.9 England, 17.4 Ireland, 14.7 U.S., 10.5 Canada) [33, 34] a higher number of mental health hospitals per 100,000 population (Ireland 0.62, U.S. 0.19, Canada 0.06) and higher number of mental health outpatient facilities per 100,000 population (Ireland 3.83, U.S. 0.37, Canada 0.33) [31,32,33,34]. The level of funding for mental health services has a nearly exact inverse relationship to the prevalence of psychological conditions. Although speculative, it may also be that more availability and early access to mental health services in England and Ireland is partially offsetting the onset of chronic mental health conditions in these countries [32, 35].

Disease patterns

Regarding the analysis of disease clusters and their associated risk factors, all four countries uncovered five similar groups. Knowing disease combinations for a given country can bring about a better understanding of the complex nature of multimorbidity. For example, in all four countries hypertension and arthritis were highly prevalent in three disease clusters. Non-steroidal anti-inflammatory drugs, used to treat pain in inflammatory conditions such as arthritis, can affect renal function and therefore the effectiveness of antihypertensive medications [36, 37]. This highlights the importance of treating the complex combination of diseases present in individuals and not just individual diseases [38]. A comprehensive assessment of older persons rather than specialty specific assessments is most appropriate (i.e. comprehensive geriatric assessment [39]).

In all four countries there is socioeconomic disparity across disease patterns and also with respect to individual disease prevalences. The U.S. in particular have a much more pronounced socio-economic gradient than the other three countries for conditions such as lung disease, stroke, myocardial infarction, psychological illnesses and high blood pressure in adjusted models. Socioeconomic indicators for such as education and, particularly income, are also more pronounced for the U.S., Canada and England than for Ireland with respect to disease clusters. One possible reason may be that Ireland’s distribution of wealth is more equal than the other countries with the U.S. having the least equal distribution of the four countries. OECD estimates for the Gini coefficient: a measure of income equality shows that Ireland’s index was lower (more equal) than the U.S., Canada and England (0.308, 0.396, 0.320, 0.358 respectively for 2013). The work of Marmot [40] and Pickett and Wilkinson [41] for example, suggests that high levels of country-level inequality is harmful for population health, and some recent studies have documented interesting links between country-level inequality and inflammatory markers such as CRP [42]. The work of Zaninotto et al. [17] also noted that healthy life expectancy was significantly related to socioeconomic inequality and showed similar levels of disability-free life expectancy in the U.S. and England. Nevertheless, income inequality does not wholly explain the differences in disease prevalence and patterns, as the U.S. had consistently higher prevalence of most diseases at each level of the socioeconomic gradient when compared to other countries even after adjusting for confounding covariates.

The link between alcohol consumption and disease clusters across countries at first seems counterintuitive. For the U.S., Canada and England drinking alcohol was associated with reduced odds for at least two clusters whereas the opposite trend was found for Ireland. For the U.S., Canada and England the disease clusters negatively associated with alcohol consumption were those with the highest average number of medical conditions. Conversely, the two disease clusters positively associated with alcohol consumption for Ireland had the lowest mean number of morbidities. Therefore, the negative association for the U.S., Canada and England may be due to changes in behavior after disease diagnosis.

Excess weight represents a de-facto state of increased inflammatory signalling [43] which in turn increases risk for many chronic diseases [44]. Obesity is also a known risk factor for many of the conditions included in this study such as diabetes, hypertension, heart disease, osteoarthritis and certain types of cancer [45]. Having an elevated BMI increased the odds of being in three disease clusters across countries and had a pronounced effect on the individual prevalence of all conditions except osteoporosis and cancer. Self-reported obesity was highest for the U.S. (33.3%), similar for England and Canada (26.7, 26.2% respectively) and lowest for Ireland (24.2%). Although BMI was based on self-reported weight and height, our estimations are in line with WHO and other estimates for 2012 [46,47,48,49].

Two main limitations of this work are that, our analysis was limited to 10 chronic conditions and these conditions were self-reported doctor’s diagnoses. As such, prevalence of diseases may be underestimated in cases where a participant has not yet engaged with the healthcare system to get a diagnosis, or a participant may not report a chronic disease as the condition has been managed. To counteract the latter issue we included the lifetime prevalence of all conditions and so included participants as having a condition if they had ever reported disease incidence at any previous wave and had not later disputed it. Another limitation is that although risk factors such as BMI, smoking history, and alcohol consumption, were controlled for, the length of time being overweight/obese; alcohol/smoking intensity and duration and physical activity over the life course were not. The obesity epidemic started earlier in the U.S. across all age ranges. Childhood obesity in the U.S. in 1975 (relevant to the youngest participants) was more than double that of England, Canada and Ireland (5.5% U.S., 2.7% Canada, 2.7% UK, 1% Ireland) [50]. Adult obesity rates have increased dramatically in the last 45 years in all countries but have been persistently higher in the U.S.. Between 1975 and 2016, the obesity rate in the U.S. increased to 36.2% (from 11.6%) while in Canada it increased to 29.4% (from 9.8%), in the U.K. to 27.8% (from 9.4%) and in Ireland to 25.3% (from 6.4%) [51]. For eight of the 10 conditions studied, the U.S. had the highest overall prevalence and for six conditions Ireland has the lowest prevalence with respect to BMI. These trends align with the childhood and adult obesity rates from 1975 onwards. There is a large body of evidence to suggest that childhood habits and health factors are strong determinants of disease onset in adulthood [12, 52, 53] and although anecdotal it is possible that many of the adverse health patterns and disease patterns found may be due to the fact that the U.S. were the first to experience the obesity epidemic followed by Canada, England then Ireland.


We have shown that the U.S. had significantly higher prevalence of multimorbidity and nearly all medical conditions studied compared to Canada, England and Ireland. This trend persisted even after controlling for age, sex, socio-economic and lifestyle factors.

The effect of socioeconomic status on disease patterns and individual disease prevalence was more pronounced in the U.S., Canada, and England, than for Ireland. This information can be used to better understand the complex nature of multimorbidity and identify appropriate prevention and management strategies for treating the unique disease patterns of multimorbidity in these respective countries.

The trends and patterns of disease prevalence across the four countries aligned closely with obesity trends since 1975, although anecdotal this may suggest that lifestyle habits and health behaviors over the life course may be likely drivers for the differences in later disease onset, multimorbidity and disease patterns.

Availability of data and materials

TILDA data are available from the Irish Social Science Data Archive (, Gateway to Globing Aging (, and Interuniversity Consortium for Political and Social Research ( The ELSA data were made available through the UK Data Archive. Instructions to access ELSA data can be found at Although all efforts are made to ensure the quality of the materials, neither the original data creators, depositors or copyright holders, the funders of the data collections, nor the UK Data Archive, nor the UK Data Service bear any responsibility for the accuracy or comprehensiveness of these materials.

HRS data can be accessed from CLSA does not currently have a publicly available version of their data however, access to CLSA and all studies can be provided upon request to the primary author B.H. email:


  1. 1.

    Fortin M, Bravo G, Hudon C, Vanasse A, Lapointe L. Prevalence of multimorbidity among adults seen in family practice. Ann Fam Med 2005;3:223–228. doi:, 3.

  2. 2.

    Van Den Akker M, Buntinx F, Knottnerus JA. Comorbidity or multimorbidity: What’s in a name? A review of literature. Eur J Gen Pract. 1996;2(2):65–70.

    Article  Google Scholar 

  3. 3.

    The Academy of Medical Sciences. Multimorbidity: a priority for global health research; 2018.

    Book  Google Scholar 

  4. 4.

    Schäfer I, Kaduszkiewicz H, Wagner HO, Schön G, Scherer M, Van Den Bussche H. Reducing complexity: A visualisation of multimorbidity by combining disease clusters and triads. BMC Public Health 2014;14(1). doi:

  5. 5.

    Boyd CM, Fortin M. Future of multimorbidity research: how should understanding of multimorbidity inform health system design. Public Health Rev. 2010;32(2):451–74.

    Article  Google Scholar 

  6. 6.

    Marengoni A, Angleman S, Melis R, Mangialasche F, Karp A, Garmen A, et al. Aging with multimorbidity: a systematic review of the literature. Ageing Res Rev. 2011;10(4):430–9.

    Article  PubMed  Google Scholar 

  7. 7.

    Gijsen R, Hoeymans N, Schellevis FG, Ruwaard D, Satariano WA, Van Den Bos GAM. Causes and consequences of comorbidity: a review. J Clin Epidemiol. 2001;54(7):661–74.

    CAS  Article  PubMed  Google Scholar 

  8. 8.

    Dubois M-F, Vanasse A, Fortin M, et al. Relationship between multimorbidity and health-related quality of life of patients in primary care. Qual Life Res. 2006;15(1):83–91.

    Article  PubMed  Google Scholar 

  9. 9.

    Fortin M, Lapointe L, Hudon C, Vanasse A, Ntetu AL, Maltais D. Multimorbidity and quality of life in primary care: a systematic review. Health Qual Life Outcomes 2004;2. doi:

  10. 10.

    Barnett K, Mercer SW, Norbury M, Watt G, Wyke S, Guthrie B. Epidemiology of multimorbidity and implications for health care, research, and medical education: a cross-sectional study. Lancet. 2012;380(9836):37–43.

    Article  PubMed  Google Scholar 

  11. 11.

    Avendano M, Glymour MM, Banks J, Mackenbach JP. Health disadvantage in US adults aged 50 to 74 years: a comparison of the health of rich and poor Americans with that of Europeans. Am J Public Health. 2009;99(3):540–8.

    Article  PubMed  PubMed Central  Google Scholar 

  12. 12.

    Banks J, Marmot M, Oldfield Z, Smith JP. Disease and disadvantage in the USA and in England. J Am Med Assoc. 2006;295(17):2037–45.

    CAS  Article  Google Scholar 

  13. 13.

    Organisation for Economic Co-operation and Development. Health: Health Status. OECD.Stat. Accessed 24 Feb 2020.

  14. 14.

    Institute for Health Metrics and Evaluation. Global Burden of Disease 2017: GBD Compare Viz Hub. Accessed 24 Feb 2020.

    Google Scholar 

  15. 15.

    Savva GM, Maty SC, Setti A, Feeney J. Cognitive and physical health of the older populations of England, the United States, and Ireland: international comparability of the Irish longitudinal study on ageing. J Am Geriatr Soc. 2013;61(SUPPL2):291–8.

    Article  Google Scholar 

  16. 16.

    Zaninotto P, David Batty G, Stenholm S, et al. Socioeconomic inequalities in disability-free life expectancy in older people from England and the United States: a cross-national population-based study. J Gerontol A Biol Sci Med Sci 2020:1–8. doi:

  17. 17.

    Heeringa SG, Connor JH. Technical description of the Health and Retirement Survey sample design. Tech Descr Health Retire Surv Sample Des. 1995:1–59.

  18. 18.

    Banks J, Batty G, Coughlin K, et al. English Longitudinal Study of Ageing: Waves 0–8, 1998–2017. [Data Collection]. 29th ed; 2019. (UK Data Service, ed). SN: 5050.

    Book  Google Scholar 

  19. 19.

    Whelan BJ, Savva GM. Design and methodology of the Irish longitudinal study on ageing. J Am Geriatr Soc. 2013;61(SUPPL2):S265–8.

    Article  PubMed  Google Scholar 

  20. 20.

    Raina P, Wolfson C, Kirkland S, Griffith L. The Canadian Longitudinal Study on Aging (CLSA) Report on Health and Aging in Canada: Findings from Baseline Data Collection; 2018. p. 1–210.

    Google Scholar 

  21. 21.

    Donoghue OA, McGarrigle CA, Foley M, Fagan A, Meaney J, Kenny RA. Cohort Profile Update: The Irish Longitudinal Study on Ageing (TILDA). Int J Epidemiol 2018;47(5). doi:

  22. 22.

    Larsen FB, Pedersen MH, Friis K, Gluèmer C, Lasgaard M. A latent class analysis of multimorbidity and the relationship to socio-demographic factors and health-related quality of life. A national population-based study of 162,283 Danish adults. PLoS One. 2017;12(1):1–17.

    CAS  Article  Google Scholar 

  23. 23.

    Olaya B, Moneta MV, Caballero FF, Tyrovolas S, Bayes I, Ayuso-Mateos JL, et al. Latent class analysis of multimorbidity patterns and associated outcomes in Spanish older adults: a prospective cohort study. BMC Geriatr. 2017;17(1):1–10.

    Article  Google Scholar 

  24. 24.

    Schafer JL, Kang J. LCCA package for R users’guide (Version 1.1.0). The Methodology Center, Penn State: University Park; 2013. Available from (Accessed: 16 June 2021)

    Google Scholar 

  25. 25.

    Byrne RP, Martiniano R, Cassidy LM, Carrigan M, Hellenthal G, Hardiman O, et al. Insular Celtic population structure and genomic footprints of migration. PLoS Genet. 2018;14(1):1–22.

    CAS  Article  Google Scholar 

  26. 26.

    Canadian Cancer Society, Statistics Canada. Canadian Cancer Statistics 2012; 2012.

    Google Scholar 

  27. 27.

    Office for National Statistics. Deaths Registered in England and Wales; 2012. Accessed 16 Nov 2020.

    Google Scholar 

  28. 28.

    Centers for Disease Control Prevention. United States Cancer statistics: data visualizations. 2016. Accessed 20 Sept 2006.

    Google Scholar 

  29. 29.

    Central Statistics Office Ireland. Report on vital Statistics; 2012.

    Google Scholar 

  30. 30.

    Kogan CS, Paterniti S. The true north strong and free? Opportunities for improving Canadian mental health care and education by adopting the WHO’s ICD-11 classification. Can J Psychiatr. 2017;62(10):690–6.

    Article  Google Scholar 

  31. 31.

    Kaltenboeck A, Winkler D, Kasper S. Bipolar and related disorders in DSM-5 and ICD-10. CNS Spectr. 2016;21(4):318–23.

    Article  PubMed  Google Scholar 

  32. 32.

    Tyrer P. A comparison of DSM and ICD classifications of mental disorder. Adv Psychiatr Treat. 2014;20(4):280–5.

    Article  Google Scholar 

  33. 33.

    WHO. Global Health Observatory data repository. MHHR?lang=en. Accessed 7 Jan 2020.

  34. 34.

    WHO. European Health Information Gateway. Accessed 7 Jan 2020.

  35. 35.

    Jorm AF, Patten SB, Brugha TS, Mojtabai R. Has increased provision of treatment reduced the prevalence of common mental disorders? Review of the evidence from four countries. World Psychiatry. 2017;16(1):90–9.

    Article  PubMed  PubMed Central  Google Scholar 

  36. 36.

    Zheng L, Du X. Non-steroidal anti-inflammatory drugs and hypertension. Cell Biochem Biophys. 2014;69(2):209–11.

    CAS  Article  PubMed  Google Scholar 

  37. 37.

    Fournier JP, Sommet A, Bourrel R, Oustric S, Pathak A, Lapeyre-Mestre M, et al. Non-steroidal anti-inflammatory drugs (NSAIDs) and hypertension treatment intensification: a population-based cohort study. Eur J Clin Pharmacol. 2012;68(11):1533–40.

    CAS  Article  PubMed  Google Scholar 

  38. 38.

    Banerjee S. Multimorbidity--older adults need health care that can count past one. Lancet. 2015;385(9968):587–9.

    Article  PubMed  Google Scholar 

  39. 39.

    Wieland D, Hirth V. Comprehensive geriatric assessment. Cancer Control. 2003;10(6):454–62.

    Article  PubMed  Google Scholar 

  40. 40.

    Marmot M. The health gap: the challenge of an unequal world: the argument. Int J Epidemiol. 2017;46(4):1312–8.

    Article  PubMed  PubMed Central  Google Scholar 

  41. 41.

    Pickett KE, Wilkinson RG. Income inequality and health: a causal review. Soc Sci Med. 2015;128:316–26.

    Article  PubMed  Google Scholar 

  42. 42.

    Layte R, McCrory C, Cheallaigh CN, Bourke N, Kivimaki M, Ribeiro AI, et al. A comparative analysis of the status anxiety hypothesis of socio-economic inequalities in health based on 18,349 individuals in four countries and five cohort studies. Sci Rep. 2019;9(1):1–12.

    CAS  Article  Google Scholar 

  43. 43.

    Lumeng CN, Saltiel AR. Review series inflammatory links between obesity and metabolic disease. Life Sci. 2011;121(6):2111–7.

    CAS  Article  Google Scholar 

  44. 44.

    Ferrucci L, Fabbri E. Inflammageing: chronic inflammation in ageing, cardiovascular disease, and frailty. Nat Rev Cardiol. 2018;15:505–22, 9.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  45. 45.

    Bianchini F, Kaaks R, Vainio H. Overweight, obesity, and cancer risk. Lancet Oncol. 2002;3(9):565–74.

    Article  PubMed  Google Scholar 

  46. 46.

    World Health Organzation. WHO country profile: nutrition, physical activity and obesity. Ireland: WHO; 2013.

    Google Scholar 

  47. 47.

    Baker C. Obesity Statistics; 2018. p. 3336.

    Google Scholar 

  48. 48.

    An R. Prevalence and trends of adult obesity in the US, 1999–2012. ISRN Obes. 2014;2014:1–6.

    Article  Google Scholar 

  49. 49.

    Center for Health Statistics N. NCHS Data Brief, Number 131; 2013.

    Google Scholar 

  50. 50.

    WHO. WHO Childhood Obesity Estimates. Accessed 17 Oct 2019.

  51. 51.

    WHO. WHO Adult Obesity Estimates. Accessed 17 Oct 2019.

  52. 52.

    Forrest CB, Riley AW. Childhood origins of adult health: a basis for life-course health policy. Health Aff. 2004;23(5):155–64.

    Article  Google Scholar 

  53. 53.

    Ben-Shlomo Y, Kuh D. A life course approach to chronic disease epidemiology: conceptual models, empirical challenges and interdisciplinary perspectives. Int J Epidemiol. 2002;31(2):285–93.

    Article  PubMed  Google Scholar 

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We thank the CLSA, TILDA, HRS and ELSA participants who took the time to provide the data we have used in this research.

This research was made possible using the data/biospecimens collected by the Canadian Longitudinal Study on Aging (CLSA). Funding for the Canadian Longitudinal Study on Aging (CLSA) is provided by the Government of Canada through the Canadian Institutes of Health Research (CIHR) under grant reference: LSA 94473 and the Canada Foundation for Innovation. This research has been conducted using the CLSA dataset, Baseline Tracking Dataset version 3.4 and Baseline Comprehensive Dataset version 4.0, under Application Number 180002S. The CLSA is led by Drs. Parminder Raina, Christina Wolfson, and Susan Kirkland. The opinions expressed in this manuscript are the author’s own and do not reflect the views of the Canadian Longitudinal Study on Aging.

The HRS 2009 Health and Well-Being Study is sponsored by the National Institute on Aging (grant number NIA U01AG009740) and was conducted by the University of Michigan.

This research was funded by the general research activities of the Irish Longitudinal Study on Ageing (TILDA), which is funded by the Irish Department of Health, Atlantic Philanthropies and Irish Life.

The English Longitudinal Study of Ageing was developed by a team of researchers based at the University College London, NatCen Social Research, and the Institute for Fiscal Studies. The data were collected by NatCen Social Research. The funding is currently provided by the National Institute of Aging (R01AG017644), and a consortium of UK government departments coordinated by the National Institute for Health Research.

Code availability

Code available on request from the primary author B.H. email:


This work was supported by the National Institute on Aging of the National Institutes of Health [grant numbers 1P01AG043362 and 1R01AG067621] (Integrative Analysis of Longitudinal Studies of Aging and Dementia). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Belinda Hernández, Richard Reilly and Rose Anne Kenny were funded by the Health Research Board [grant number ILP-HSR-2017-021]. Stacey Voll received financial support from the Integrative Analysis of Longitudinal Studies of Aging and Dementia [grant numbers NIH 1P01AG043362 and 1R01AG067621]. Lucy Stirland was funded by the Medical Research Foundation and Medical Research Council through the PsySTAR, Psychiatry: Scottish Training in Academic Research programme [grant number MR/J000914/1]. Cathal McCrory was funded by the Health Research Board of Ireland under an Emerging Investigator Award [grant number EIA-2017-012]. Lauren Griffith was supported by the McLaughlin Foundation Professorship in Population and Public Health.

Author information




Each author has participated substantially in the work to take public responsibility for the content. Contributors participation listed here are based upon the International Committee of Medical Journal Editors authorship criteria. Belinda Hernandez: Conception and design, preparation of data and analysis, preparation of manuscript, corresponding author. Stacey Voll: Conception and design, preparation of data and analysis, preparation of manuscript. Nathan Lewis, Cathal McCrory, Lucy Stirland, Craig Hutton: Design, analysis, preparation of manuscript. Scott Hofer, Graciela Muniz Terrera, Lauren Griffith, Susan Kirkland: Conception and design, interpretation of data, preparation of manuscript. Arthur White, Richard Reilly and Rose Anne Kenny: Interpretation of data, methodological refinement, ensuring data accuracy and outcomes, preparation of manuscript. Collaborators from the IALSA-CLSA Cross-National Analysis Workshop, held in February 2019, Victoria, BC, Canada: Conception. IALSA-CLSA Cross-National Analysis Workshop Collaborators: Gerry Mugford, Gabrilea Ilie, Dorina Cadar, Jaqueline McMillan, Zhiwei Gao, John Best, Istvan Molnar-Szakacs, Jonathan Rush, Theodore Cosco, Rumaisa Aljied, Meshari Alwashmi, Robert Stawski, Janice Ranson, Parminder Raina, Christina Wolfson. The authors read and approved the final manuscript.

Corresponding author

Correspondence to Stacey Voll.

Ethics declarations

Ethics approval and consent to participate

The TILDA study was approved by the Trinity College Faculty of Health Sciences Ethics Committee. All participants provided written, informed consent when they first participated in the study (at wave 1) and consent was repeated at wave 2 (the focus of this study), both written and verbal. Respondents in all cases were provided with copies of their signed consent forms.

Ethical approval for all the ELSA waves was granted from the National Research and Ethics Committee. ELSA Wave 6 was approved by NRES Committee South Central – Berkshire (11/SC/0374). All participants provided informed signed consent to participate.

Ethical approval for the HRS Study was obtained from the University of Michigan Institutional Review Board. Prior to each interview, participants are provided with a written informed consent information document. At the start of each interview, all respondents are read a confidentiality statement, and give oral consent by agreeing to do the interview.

The CLSA study was approved by 12 institutional Research Ethics Boards: Memorial University Health Research Ethics Authority, Dalhousie University Office of Research Ethics Administration, McGill University Institutional Review Board, CIUSS de l’Éstrie – CHUS Bureau des plaints et de la qualitè des services, Bruyère Research Ethics Board, Hamilton Integrated Research Ethics Board, University of Manitoba Research Ethics Board, University of Calgary Conjoint Health Research Ethics Board, Simon Fraser University Office of Research Ethics, University of British Columbia Office of Research Services, University of Victoria Human Research Ethics Office of Research Service, Vancouver Island Health Authority Research Ethics Office. All participants provided informed signed consent to participate.

For all four studies testing protocols conformed and were conducted with the principals of the Declaration of Helsinki.

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Not applicable.

Competing interests

The authors declare no conflict of interest.

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Hernández, B., Voll, S., Lewis, N.A. et al. Comparisons of disease cluster patterns, prevalence and health factors in the USA, Canada, England and Ireland. BMC Public Health 21, 1674 (2021).

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  • Multimorbidity
  • Disease clusters
  • Latent class analysis
  • Ageing studies