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 findings suggest that APAP therapy is effective in controlling the majority of OSA symptoms beyond sleepiness and snoring.

A measles outbreak has been occurring in a healthcare setting in Porto, Portugal, since early March 2018, posing public health challenges for a central hospital and the community. Up to 22 April, 96 cases were confirmed, 67 in vaccinated healthcare workers, mostly between 18-39 years old. Following identification of the first cases, control measures were rapidly implemented. Concomitantly, other measles cases were notified in the Northern Region of the country. No common epidemiological link was identified.

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 GBDdefined 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%,). 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.