OBJECTIVE: To evaluate the occurrence and factors associated with multimorbidity among Brazilians aged 50 years and over. METHODS: This is a cross-sectional study in a nation-based cohort of the non-institutionalized population in Brazil. Data were collected between 2015 and 2016. Multimorbidity was assessed from a list of 19 morbidities, which were categorized into ≥ 2 and ≥ 3 diseases. The analysis included the calculation of frequencies and the most frequent 10 pairs and triplets of combinations of diseases. The crude and adjusted analyses evaluated the demographic, socioeconomic, behavioral, and contextual variables (area of residence, geopolitical region, and coverage of the Family Health Strategy) using Poisson regression. RESULTS: From the total of 9,412 individuals, 67.8% (95%CI 65.6–69.9) and 47.1% (95%CI 44.8–49.4) showed ≥ 2 and ≥ 3 diseases, respectively. In the adjusted analysis, women, older persons, and those who did not consume alcohol had increased multimorbidity. There were no associations with race, area of residence, geopolitical region, and coverage of the Family Health Strategy. The 10 pairs (frequencies observed between 11.6% and 23.2%) and the 10 triplets (frequencies observed between 4.9% and 9.5%) of the most frequent diseases mostly included back problems (15 times) and systemic arterial hypertension (11 times). All combinations were statistically higher than expected by chance. CONCLUSIONS: The occurrence of multimorbidity was high even among younger individuals (50 to 59 years). Approximately two in three (≥ 2 diseases) and one in two (≥ 3 diseases) individuals aged 50 years and over presented multimorbidity, which represents 26 and 18 million persons in Brazil, respectively. We observed high frequencies of combinations of morbidities.

Objectives The study aims to evaluate the magnitude of multimorbidity in Brazilian adults, as well to measure their association with individual and contextual factors stratified by Brazilian states and regions. Methods A national-based cross-sectional study was carried out in 2013 with Brazilian adults. Multimorbidity was evaluated by a list of 22 physical and mental morbidities (based on self-reported medical diagnosis and Patient Health Questionnaire-9 for depression). The outcome was analysed taking ≥2 and ≥3 diseases as cut-off points. Factor analysis (FA) was used to identify disease patterns and multilevel models were used to test association with individual and contextual variables. Results The sample comprised 60 202 individuals. Multimorbidity frequency was 22.2% (95% CI 21.5 to 22.9) for ≥2 morbidities and 10.2% (95% CI 9.7 to 10.7) for ≥3 morbidities. In the multilevel adjusted models, females, older people, those living with a partner and having less schooling presented more multiple diseases. No linear association was found according to wealth index but greater outcome frequency was found in individuals with midrange wealth index. Living in states with higher levels of education and wealthier states was associated with greater multimorbidity. Two patterns of morbidities (cardiometabolic problems and respiratory/mental/muscle–skeletal disorders) explained 92% of total variance. The relationship of disease patterns with individual and contextual variables was similar to the overall multimorbidity, with differences among Brazilian regions. Conclusions In Brazil, at least 19 million adults had multimorbidity. Frequency is similar to that found in other Low and and Middle Income Countries. Contextual and individual social inequalities were observed.

Multimorbidity is highly prevalent in older adults and can lead to hospitalisation. We investigate the prevalence, associated factors, and multimorbidity pattern associated to hospitalisation, readmission, and length of stay in the population aged 50 years and older. We analysed baseline data (2015–2016) from the ELSI-Brazil cohort, a representative sample of non-institutionalised Brazilians aged ≥ 50 years. In total, 8807 individuals aged ≥ 50 years were included. Poisson regression with robust variance adjusted for confounders was used to verify the associations with hospitalisation. Multiple linear regression was used to analyse the associations with readmission and length of stay. Network analysis was conducted using 19 morbidities and the outcome variables. In 8807 participants, the prevalence of hospitalisation was 10.0% (95% CI 9.1, 11), mean readmissions was 1.55 ± 1.191, and mean length of stay was 6.43 ± 10.46 days. Hospitalisation was positively associated with male gender, not living with a partner, not having ingested alcoholic beverages in the last month, and multimorbidity. For hospital readmission, only multimorbidity ≥ 3 chronic conditions showed a statistically significant association. Regarding the length of stay, the risk was positive for males and negative for living in rural areas. Five disease groups connected to hospitalisation, readmission and length of stay were identified. To conclude, sociodemographic variables, such as gender, age group, and living in urban areas, and multimorbidity increased the risk of hospitalisation, mean number of readmissions, and mean length of stay. Through network analysis, we identified the groups of diseases that increased the risk of hospitalisation, readmissions, and length of stay.

O objetivo deste trabalho foi medir a ocorrência de multimorbidade e estimar o número de indivíduos na população brasileira com 50 anos ou mais em risco para COVID-19 grave. Estudo transversal de base nacional com dados do Estudo Longitudinal da Saúde dos Idosos Brasileiros (ELSI-Brasil), conduzido em 2015-2016, com 9.412 indivíduos com 50 anos ou mais. A multimorbidade foi caracterizada como ≥ 2 condições crônicas com base em uma lista de 15 morbidades consideradas de risco para COVID-19 grave. As análises incluíram cálculo de prevalência e estimativa do número absoluto de pessoas na população em risco. Autoavaliação do estado de saúde, fragilidade e atividades básicas da vida diária foram utilizadas como marcadores da situação de saúde. Sexo, idade, região geopolítica e escolaridade foram usados como covariáveis. Cerca de 80% dos indivíduos da amostra apresentaram pelo menos alguma das morbidades avaliadas, o que representa cerca de 34 milhões de indivíduos; a multimorbidade foi referida por 52% da população em estudo, com maior proporção nas regiões Centro-oeste, Sudeste e Sul. Doenças cardiovasculares e obesidade foram as condições crônicas mais frequentes. Estima-se que 2,4 milhões de brasileiros estejam em risco grave de saúde. Desigualdades segundo a escolaridade foram observadas. O número de pessoas com 50 anos ou mais que apresentam morbidades de risco para COVID-19 grave é elevado tanto em termos relativos quanto absolutos. A estimativa apresentada é importante para planejar as estratégias de monitoramento das pessoas com morbidades crônicas e de prevenção no enfrentamento do novo coronavírus.

Background: Multimorbidity is the co-occurrence of two or more diseases in the same individual. One method to identify this condition at an early stage is the use of specific markers for various combinations of morbidities. Nonetheless, evidence related to physiological markers in multimorbidity is limited. Objective: The aim was to perform a systematic review to identify physiological markers associated with multimorbidity. Design: Articles available on PubMed, Register of Controlled Trials, Academic Search Premier, CINAHL, Scopus, SocINDEX, Web of Science, LILACS, and SciELO, from their inception to May 2018, were systematically searched and reviewed. The project was registered in PROSPERO under the number CRD42017055522. Results: The systematic search identified 922 papers. After evaluation, 18 articles were included in the full review reporting at least one physiological marker in coexisting diseases or which are strongly associated with the presence of multimorbidity in the future. Only five of these studies examined multimorbidity in general, identifying five physiological markers associated with multimorbidity, namely, dehydroepiandrosterone sulfate (DHEAS), interleukin 6 (IL-6), C-reactive protein (CRP), lipoprotein (Lp), and cystatin C (Cyst-C). Conclusions: There is a paucity of studies related to physiological markers in multimorbidity. DHEAS, IL-6, CRP, Lp, and Cyst-C could be the initial focus for further investigation of physiological markers related to multimorbidity.