Background Governments and healthcare systems are facing multimorbidity (MM) as a major challenge due to the difficulties related to its proper identification and clinical management. Despite growing research on MM, its epidemiology is poorly understood due to the great complexity of underlying patterns of chronicity. The present review aims to identify the most frequent MM profiles and their social determinants. Methods A systematic review following the PRISMA statement was conducted. The search strategy was performed by combining three sets of keywords (MM, inequalities and patterns) that were searched in Pubmed, Scopus, Web of Science, OVID, CINAHL Complete, and PsycINFO. Primary studies analysing MM patterns and their relationship with social determinants were included. The quality of the studies was assessed using the Axis tool quality assessment. Results After the review process, 96 studies were selected from the 46,726 identified. The main methods used to identify MM patterns fell into five categories: latent class analysis (38.54%), cluster techniques (23.96%), factor analysis (19.79%), and machine learning (10.42%), and expert knowledge (7.29%). Latent class analysis was widely used, although in recent years the use of techniques based on machine learning has increased. The main patterns were cardiometabolic, cardiovascular, mental, musculoskeletal, complex MM, and respiratory diseases. Some MM profiles were more prevalent among lower-SES groups. In particular, patterns of mental multimorbidity were more prevalent among women and complex patterns were associated with low income. Conclusions Results show different disease combinations among disparate social determinants such as gender, age, education, and socio-economic status. Our results suggest that more and better designed studies are needed to improve clinical practice and health policies with the aim of enhancing the quality of patients with MM and their relationship to health system use and care. Key messages

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Background Multimorbidity (MM) is associated with lower quality of life, greater disability, and higher use of health services. It is a complex problem that is difficult to capture due to the broad spectrum of concurrent chronic diseases involved. There is a need to identify and characterize patterns of chronic conditions in the local context of specific population groups. The DEMMOCAD project aims to respond to this knowledge gap by detecting patterns of MM and their inequalities in the province of Cadiz (Spain). Methods A cross-sectional study was carried out by means of telephone interviews with people over 50 years of age. The final sample was 1592 individuals with MM. A latent class analysis was carried out to identify patterns of MM from 31 chronic conditions. First, the appropriate number of classes was established, considering model fit indices, class size, and clinical interpretability. Subsequently, covariates were introduced into the model using the three-step approach, a technique that minimizes biases in the multinomial regression model. Results Preliminary analyses of the goodness-of-fit indices of the model derived five MM patterns (entropy = 0.727): (C1) mild MM; (C2) cardiovascular; (C3) musculoskeletal; (C4) musculoskeletal plus mental; and (C5) complex MM. Compared with class C1, persons in class C5 were significantly older and less educated, class C4 had a lower income, class C3 was smokers and disabled, and class C2 was characteristic among older males. All four classes also showed lower scores on mental and physical dimensions of the SF12 scale compared to class C1. Conclusions In addition to providing an adjusted characterization of the population of the area analyzed, these initial findings highlight the existence of social inequalities in multimorbidity at the local level that should be addressed by implementing policies targeting the most vulnerable groups in Cadiz (low socioeconomic status groups, people with disabilities, and the elderly). Key messages

Background Along with the Covid-19 pandemic we need to fight an ‘infodemic'. Some of the most widespread social media platforms such as Facebook, Instagram and Twitter have implemented policies to combat the spread of misinformation about Covid. However, the online ecosystem is still full of health myths, hoaxes, and fake news that-either consciously or unconsciously-is propagated by social media users with different purposes, messages that can lead to attitudinal and behavioral changes which might result in inadequate health decision making Methods We use Twitter Stream API to collect tweets about Covid-19 during the early outbreak. Then we filtered those tweets with hashtags related to three infodemic topics: 5g, bill gates, UV and hydroxychloroquine. Then, we use Botometer to obtain the probability that each account is a bot or not. We use bot classification along with network analysis (Louvain community detection) to delve into the subtopics and the use of hashtags. Results The resulting data collection contains ∼14M tweets from ∼285K of different Twitter accounts. We selected only tweets written in English. Regarding 5G, the most important communities link China with the virus, are about “democratshateamerica” or conspiracy theories. Tweets about Bill Gates contain hashtags about Trump, America, or mention the batflu. Communities related with UV are about Trump disinfectant, or pointing out that tv channels spread fake news. Those tweets that mention hydroxychloroquine mostly contain hashtags that mention qanon or maga content. Conclusions In this paper, we analyze the use of hashtags by accounts classified as bots. Using Louvain community detection we identify co-occurring hashtags. Using social network analysis we identify which hashtags are the most important within the conversation. Key messages • We identify several communities around most important infodemic topics. Bots activity in most of the cases is about political content than spreading health misinformation. • This method allows to find subtopics based on the use of hashtags. Which allow public health policies to prevent the spread of infodemics.