Patterns and Consequences of Multimorbidity in the General Population: There is No Chronic Disease Management Without Rheumatic Disease Management

Abstract

Objective. To identify empirical model-based patterns of multimorbidity from chronic noncommunicable diseases in the general population, with a focus on the contribution of rheumatic and musculoskeletal diseases (RMDs), and to quantify their association with adverse health outcomes. Methods. Cross-sectional data from the Portuguese Fourth National Health Survey were analyzed (n 5 23,754). Latent class analysis was used to identify patterns of coexistence of 11 chronic noncommunicable diseases (RMDs, diabetes mellitus, hypertension, chronic obstructive pulmonary disease, stroke, depression, myocardial infarction, cancer, osteoporosis, asthma, and renal failure). Based on the Outcome Measures in Rheumatology, filter 2.0, health outcomes included life impact, pathophysiologic manifestations, and resource use. We assessed the association between patterns and adverse health outcomes, through sex-, age-, and body mass index-adjusted prevalence ratios with 95% confidence intervals, obtained using Poisson regression. Results. Four patterns of chronic noncommunicable diseases co-occurrence were identified and labeled as low disease probability, cardiometabolic conditions, respiratory conditions, and RMDs and depression. RMDs were highly prevalent in patients with chronic diseases (from 38.6% in cardiometabolic conditions to 66.7% in RMDs and depression). While negative self-rated health, short-term disability, and chronic pain were more strongly associated with cardiometabolic conditions and respiratory conditions, all multimorbidity patterns were similarly associated with long-term disability, frequent health care utilization, and out-of-pocket health care expenses. Conclusion. Our study emphasizes RMDs as a major presence in multimorbidity in the general population. All multimorbidity patterns were associated with a wide set of adverse health outcomes. Management strategies for the patient with chronic cardiometabolic, respiratory, or depressive conditions should also target RMDs.

The population impact of rheumatic and musculoskeletal diseases in relation to other non-communicable disorders: comparing two estimation approaches

“…After selecting the number of latent classes (also referred to as patterns) among those with the lowest Bayesian Information Criterion (BIC), we found that multimorbidity in the Portuguese population could be summarized into four patterns of chronic non-communicable diseases co-occurrence, that were labeled according to disease frequency as "low disease probability" (reference pattern), "cardiometabolic conditions", "respiratory conditions" and "RMDs and depression". We also found that RMDs were highly prevalent across all multimorbidity patterns: 38.6% in "cardiometabolic conditions", 53.5% in "respiratory conditions" and 66.7% in "RMDs and depression", while only 7.8% in "low disease probability" pattern [15].…”

“…In this context patterns are interpreted as mutually exclusive with regard to the assignment of individuals, but the same disease may (and does) occur in more than one pattern, e.g., even though RMDs were more likely in subjects assigned to the pattern "RMDs and depression" (66.7%), an RMD probability of 38.6% still exists in the "cardiometabolic conditions" pattern. Overall, the population impact of NCDs seems to be comparatively overestimated when considering the traditional GBD-defined groups, since in the latter approach PAF interpretation assumes the complete and irreversible elimination of an NCD or group, while in the empirical approach PAF interpretation considers the change of class membership into the "low disease probability" pattern, which still has some disease probability (including a 7.8% prevalence of RMDs) [15]. Whereas, in our first approach exposure was defined deterministically, since all individuals reported who RMDs were classified as exposed, in our Collected only in one trimester, due to logistic restrictions.…”

“…In the first approach, conditions were aggregated into one of six groups according to GBD grouping: (1) RMDs, (2) COPD or asthma, (3) cancer, (4) depression, (5) diabetes or renal failure, and (6) stroke or myocardial infarction [14]. In the second approach, we applied our previously defined model-based patterns of NCD co-occurrence to define exposures [15]. In that work, we used latent class analysis to identify patterns of coexistence of 11 chronic non-communicable diseases (RMDs, diabetes, hypertension, chronic obstructive pulmonary disease, stroke, depression, myocardial infarction, cancer, osteoporosis, asthma, and renal failure).…”

“…All measures of health-related dimensions were dichotomized to define the presence or absence of an adverse health outcome, as previously described in detail [15].…”

Registries in Spondyloarthritis: What have we Learned?

“…Registers allowed the study of comorbidities in real life, whereas in clinical trials patients with major comorbidities are excluded; they elucidated the notion of multimorbidity patterns [33].…”