The aim of this study was to quantify the population impact of rheumatic and musculoskeletal diseases (RMDs) with other non-communicable diseases (NCDs), using two complementary strategies: standard multivariate models based on global burden of disease (GBD)-defined groups vs. empirical mutually exclusive patterns of NCDs. We used cross-sectional data from the Portuguese Fourth National Health Survey (n = 23,752). Six GBD-defined groups were included: RMDs, chronic obstructive pulmonary disease or asthma, cancer, depression, diabetes or renal failure, and stroke or myocardial infarction. The empirical approach comprised the patterns "low disease probability", "cardiometabolic conditions", "respiratory conditions" and "RMDs and depression". As recommended by the outcome measures in rheumatology (OMERACT) initiative, health outcomes included life impact, pathophysiological manifestations, and resource use indicators. Population attributable fractions (PAF) were computed for each outcome and bootstrap confidence intervals (95% CI) were estimated. Among GBD-defined groups, RMDs had the highest impact across all the adverse health outcomes, from frequent healthcare utilization (PAF 7.8%, 95% CI 6.2-9.3) to negative self-rated health (PAF 18.1%, 95% CI 15.4-20.6). In the empirical approach, patterns "cardiometabolic conditions" and "RMDs and depression" had similar PAF estimates across all adverse health outcomes, but "RMDs and depression" showed significantly higher impact on chronic pain (PAF 8.9%, 95% CI 7.6-10.3) than the remaining multimorbidity patterns. RMDs revealed the greatest population impact across all adverse health outcomes tested, using both approaches. Empirical patterns are particularly interesting to evaluate the impact of RMDs in the context of their co-occurrence with other NCDs.
Keywords: Health impact assessment; Multimorbidity; Musculoskeletal diseases; Non-communicable diseases; Public health; Rheumatic diseases.