Predictors of changing patterns of adherence to containment measures during the early stage of COVID-19 pandemic: an international longitudinal study.

BACKGROUND: Identifying common factors that affect public adherence to COVID-19 containment measures can directly inform the development of official public health communication strategies. The present international longitudinal study aimed to examine whether prosociality, together with other theoretically derived motivating factors (self-efficacy, perceived susceptibility and severity of COVID-19, perceived social support) predict the change in adherence to COVID-19 containment strategies. METHOD: In wave 1 of data collection, adults from eight geographical regions completed online surveys beginning in April 2020, and wave 2 began in June and ended in September 2020. Hypothesized predictors included prosociality, self-efficacy in following COVID-19 containment measures, perceived susceptibility to COVID-19, perceived severity of COVID-19 and perceived social support. Baseline covariates included age, sex, history of COVID-19 infection and geographical regions. Participants who reported adhering to specific containment measures, including physical distancing, avoidance of non-essential travel and hand hygiene, were classified as adherence. The dependent variable was the category of adherence, which was constructed based on changes in adherence across the survey period and included four categories: non-adherence, less adherence, greater adherence and sustained adherence (which was designated as the reference category). RESULTS: In total, 2189 adult participants (82% female, 57.2% aged 31-59 years) from East Asia (217 [9.7%]), West Asia (246 [11.2%]), North and South America (131 [6.0%]), Northern Europe (600 [27.4%]), Western Europe (322 [14.7%]), Southern Europe (433 [19.8%]), Eastern Europe (148 [6.8%]) and other regions (96 [4.4%]) were analyzed. Adjusted multinomial logistic regression analyses showed that prosociality, self-efficacy, perceived susceptibility and severity of COVID-19 were significant factors affecting adherence. Participants with greater self-efficacy at wave 1 were less likely to become non-adherence at wave 2 by 26% (adjusted odds ratio [aOR], 0.74; 95% CI, 0.71 to 0.77; P < .001), while those with greater prosociality at wave 1 were less likely to become less adherence at wave 2 by 23% (aOR, 0.77; 95% CI, 0.75 to 0.79; P = .04). CONCLUSIONS: This study provides evidence that in addition to emphasizing the potential severity of COVID-19 and the potential susceptibility to contact with the virus, fostering self-efficacy in following containment strategies and prosociality appears to be a viable public health education or communication strategy to combat COVID-19.

Profiling Hesitancy to COVID-19 Vaccinations in Six European Countries: Behavioral, Attitudinal and Demographic Determinants.

Vaccination hesitancy is an important barrier for the effective control of the COVID-19 pandemic. Identifying determinants of COVID-19 vaccination hesitancy is essential in order to reduce mortality rates. Further, given the variability of the factors and the different recommendations used in each country, it is important to conduct cross-country research to profile individuals who are hesitant toward COVID-19 vaccinations. This cross-sectional study aimed to examine cross-country differences and the behavioral, attitudinal and demographic characteristics of vaccine hesitant individuals. Adults living in six European countries (Cyprus, France, Germany, Italy, Poland, and Spain) were eligible to participate. A total of 832 individuals completed the online survey, with 17.9% reporting being hesitant to COVID-19 vaccination. Vaccine accepters were significantly older (M = 38.9, SD = 14.3), more educated (master/postgraduate studies) and lived in a place with a higher number of residents (>500,000 people) compared to those hesitant to COVID-19 vaccination. Discriminant analysis confirmed that the hesitant profile includes a person of younger age, living alone in smaller communities, and without children. Additionally, hesitant participants reported COVID-19-specific characteristics such as lower institutional trust, less adherence to COVID-19 protective behaviors and higher pandemic fatigue. When tackling COVID-19 vaccination hesitancy both socio-demographic and behavioral/attitudinal aspects should be taken into account. Stakeholders are advised to implement targeted vaccination programs while at the same time building trust with population illness cognitions addressed in order to reduce hesitancy rates. Further, stakeholders and public health authorities in each country are suggested to target interventions according to different population characteristics as behavioral and attitudinal determinants of COVID-19 vaccination hesitancy differed between countries.

Assessing the Role of Social Bots During the COVID-19 Pandemic: Infodemic, Disagreement, and Criticism.

BACKGROUND: Social media has changed the way we live and communicate, as well as offering unprecedented opportunities to improve many aspects of our lives, including health promotion and disease prevention. However, there is also a darker side to social media that is not always as evident as its possible benefits. In fact, social media has also opened the door to new social and health risks that are linked to health misinformation. OBJECTIVE: This study aimed to study the role of social media bots during the COVID-19 outbreak. METHODS: The Twitter streaming API was used to collect tweets regarding COVID-19 during the early stages of the outbreak. The Botometer tool was then used to obtain the likelihood of whether each account is a bot or not. Bot classification and topic-modeling techniques were used to interpret the Twitter conversation. Finally, the sentiment associated with the tweets was compared depending on the source of the tweet. RESULTS: Regarding the conversation topics, there were notable differences between the different accounts. The content of nonbot accounts was associated with the evolution of the pandemic, support, and advice. On the other hand, in the case of self-declared bots, the content consisted mainly of news, such as the existence of diagnostic tests, the evolution of the pandemic, and scientific findings. Finally, in the case of bots, the content was mostly political. Above all, there was a general overriding tone of criticism and disagreement. In relation to the sentiment analysis, the main differences were associated with the tone of the conversation. In the case of self-declared bots, this tended to be neutral, whereas the conversation of normal users scored positively. In contrast, bots tended to score negatively. CONCLUSIONS: By classifying the accounts according to their likelihood of being bots and performing topic modeling, we were able to segment the Twitter conversation regarding COVID-19. Bot accounts tended to criticize the measures imposed to curb the pandemic, express disagreement with politicians, or question the veracity of the information shared on social media.

Prevalence of Health Misinformation on Social Media: Systematic Review.

BACKGROUND: Although at present there is broad agreement among researchers, health professionals, and policy makers on the need to control and combat health misinformation, the magnitude of this problem is still unknown. Consequently, it is fundamental to discover both the most prevalent health topics and the social media platforms from which these topics are initially framed and subsequently disseminated. OBJECTIVE: This systematic review aimed to identify the main health misinformation topics and their prevalence on different social media platforms, focusing on methodological quality and the diverse solutions that are being implemented to address this public health concern. METHODS: We searched PubMed, MEDLINE, Scopus, and Web of Science for articles published in English before March 2019, with a focus on the study of health misinformation in social media. We defined health misinformation as a health-related claim that is based on anecdotal evidence, false, or misleading owing to the lack of existing scientific knowledge. We included (1) articles that focused on health misinformation in social media, including those in which the authors discussed the consequences or purposes of health misinformation and (2) studies that described empirical findings regarding the measurement of health misinformation on these platforms. RESULTS: A total of 69 studies were identified as eligible, and they covered a wide range of health topics and social media platforms. The topics were articulated around the following six principal categories: vaccines (32%), drugs or smoking (22%), noncommunicable diseases (19%), pandemics (10%), eating disorders (9%), and medical treatments (7%). Studies were mainly based on the following five methodological approaches: social network analysis (28%), evaluating content (26%), evaluating quality (24%), content/text analysis (16%), and sentiment analysis (6%). Health misinformation was most prevalent in studies related to smoking products and drugs such as opioids and marijuana. Posts with misinformation reached 87% in some studies. Health misinformation about vaccines was also very common (43%), with the human papilloma virus vaccine being the most affected. Health misinformation related to diets or pro-eating disorder arguments were moderate in comparison to the aforementioned topics (36%). Studies focused on diseases (ie, noncommunicable diseases and pandemics) also reported moderate misinformation rates (40%), especially in the case of cancer. Finally, the lowest levels of health misinformation were related to medical treatments (30%). CONCLUSIONS: The prevalence of health misinformation was the highest on Twitter and on issues related to smoking products and drugs. However, misinformation on major public health issues, such as vaccines and diseases, was also high. Our study offers a comprehensive characterization of the dominant health misinformation topics and a comprehensive description of their prevalence on different social media platforms, which can guide future studies and help in the development of evidence-based digital policy action plans.

Socioeconomic Inequalities in Health Through Lifestyles: Analysing Gender and Age Differences in Andalusia, Spain.

This research aims to analyse the association between socioeconomic status (SES) and self-rated health (SRH) through its relationship with lifestyle factors, as well as examining these effects stratified by gender and age groups. A cross-sectional study was performed with a representative sample from Andalusia (Southern Spain) composed of 1200 adults. The study analysed several variables related to lifestyle (healthy diet, physical activity and tobacco and alcohol consumption) and SES (education and income), along with SRH. The joint effects of SRH and the set of variables were studied by using a hierarchical linear regression analysis. Later a path analysis was carried out to test the impact of gender and age group on these associations. The results show that a better SRH was reported by men with lower age, higher SES, more frequent physical activity, healthy diet, and lower alcohol use compared with their counterparts. Income was directly related to better SRH through more physical activity, a healthier diet and less alcohol consumption. SES had a greater positive relationship with women's health than with men's health. In addition, a greater explained variance in SRH was observed in older women based on SES and lifestyle factors. Our findings provide some cross-sectional evidence of the social inequalities in health mediated through lifestyle factors, with gender and age differences. Therefore, socioeconomic inequalities should be addressed by public and social policies considering the gender and age gaps.

The impact of financial crisis and austerity policies in Andalusia, Spain: disentangling the mechanisms of social inequalities in health through the perceptions and experiences of experts and the general population.

BACKGROUND: Andalusia has been one of the regions most damaged by the economic crisis in Spain. A qualitative study of the effects of the economic crisis and austerity policies in this region has been conducted within the framework of the IMPACT-A project. This research seeks to analyse the perceived impact of the crisis upon the health of the Andalusian population through the first-hand discourses of professionals from the health and social sectors on the one hand, and citizens of different socioeconomic status (SES) on the other. METHODS: A total of five focus groups and ten semi-structured interviews were conducted and analysed following an inductive process based on Grounded Theory (GT). RESULTS: Our results show a general perception among professionals: the financial crisis has either directly or indirectly affected population health in Andalusia, though mostly impacting low-income individuals who were already at risk of social exclusion. Professionals' perceptions have been confirmed through the discourses of citizens of a lower SES, which differ from those of middle and upper SES. CONCLUSION: Findings reveal some of the most salient consequences on the socially vulnerable groups and people at risk of social exclusion. In particular, our study highlights the importance of addressing three areas of priority action: mental health, unmet (basic and medical) needs, and decline in the health system.

Measuring the impact of multiple discrimination on depression in Europe.

BACKGROUND: The study of the health effects of perceived discrimination based on ethnic and social traits has a long-standing and widespread tradition in epidemiological research, but less attention has been paid to the study of multiple discrimination, particularly its effects on mental health. The present work aims to analyse the association between multiple discrimination and depressive symptoms in Europe, and the impact of contextual socioeconomic circumstances on this relationship. METHODS: In this study, data from the 7th Round of the European Social Survey was used. Given that the outcome variable, CES-D8, is a depression scale from 0 to 24 possible values and the hierarchical organisation of individuals (level-1 units) clustered within countries (level-2 units), a linear multilevel model was carried out. RESULTS: Our findings suggest that multiple discrimination increases our risk of suffering depressive disorder, but in addition this work provides an important step forward to explain and understand how the relationship between multiple discrimination and depression might vary depending the socioeconomic context. In particular, we can observe that differences in the prevalence of depressive symptoms along multiple discrimination levels decrease as GDP per capita increases among European countries. CONCLUSION: This study is relevant since provides new evidence on how the association between multiple discrimination and depression operates at the micro and macro-level context, which is fundamental to understand how macro-economic fluctuations of countries may determine depressive disorders through the effect of single and combined forms of discrimination.

Methodological barriers to studying the association between the economic crisis and suicide in Spain.

BACKGROUND: The hypothetical relationship between economic recession and the increase in suicides in Spain is subject to various arguments. In addition to the inherent complexity of capturing and explaining the underlining mechanisms that could describe this causal link, different points of contention have been be identified. The period of this association and its possible starting points, the socioeconomic determinants that may explain the variation in suicide rate, and the data sources available are the main focus of controversy. The present study aims to identify the phases of association between different periods of economic recession and suicide rates, and compare the effect of different social determinants of health that have been mentioned in previous studies. METHODS: We have used interrupted time series analyses to assess the impact of economic recession on national rates of suicide mortality provided by the Spanish Statistical Office (1980-2014). In an attempt to consider the factors that have affected the study of suicide in Spain, different data sources/periods, predictors, and regions in Spain were analysed. RESULTS: The analysis revealed a positive and significant relationship between the Great Recession and suicide rates during the second period of economic recession (2011-2014), while appeared to decrease during the first recession period. However, the first decreasing trend was not statistically significant in the global analysis of the evolution of monthly suicide rates for the entire country. Both unemployment and per capita GDP were positively related to suicide trends. Finally, the regional analysis demonstrates a similar pattern in different Spanish areas. CONCLUSION: Although previous studies have mentioned the double-dip in the suicide rate associated with the corresponding period of double recession, our study only identify a positive relationship during the second recession period. These results points out that the major impact of economic problems might have had a delayed effect due to initial protection policies.

Discovering complex interrelationships between socioeconomic status and health in Europe: A case study applying Bayesian Networks.

Studies assume that socioeconomic status determines individuals' states of health, but how does health determine socioeconomic status? And how does this association vary depending on contextual differences? To answer this question, our study uses an additive Bayesian Networks model to explain the interrelationships between health and socioeconomic determinants using complex and messy data. This model has been used to find the most probable structure in a network to describe the interdependence of these factors in five European welfare state regimes. The advantage of this study is that it offers a specific picture to describe the complex interrelationship between socioeconomic determinants and health, producing a network that is controlled by socio-demographic factors such as gender and age. The present work provides a general framework to describe and understand the complex association between socioeconomic determinants and health.

The Impact of Specialist Care on Teenage and Young Adult Patient-Reported Outcomes in England: A BRIGHTLIGHT Study.

Purpose: In England, health care policy promotes specialized age-appropriate cancer services for teenagers and young adults (TYA), for those aged 13-24 years at diagnosis. Specialist Principal Treatment Centers (PTCs) provide enhanced age-specific care for TYA, although many still receive all or some of their care in adult or children's cancer services. Our aim was to determine the patient-reported outcomes associated with TYA-PTC based care. Methods: We conducted a multicenter cohort study, recruiting 1114 TYA aged 13-24 years at diagnosis. Data collection involved a bespoke survey at 6,12,18, 24, and 36 months after diagnosis. Confounder adjusted analyses of perceived social support, illness perception, anxiety and depression, and health status, compared patients receiving NO-TYA-PTC care with those receiving ALL-TYA-PTC and SOME-TYA-PTC care. Results: Eight hundred and thirty completed the first survey. There was no difference in perceived social support, anxiety, or depression between the three categories of care. Significantly higher illness perception was observed in the ALL-TYA-PTC and SOME-TYA-PTC group compared to the NO-TYA-PTC group, (adjusted difference in mean (ADM) score on Brief Illness Perception scale 2.28 (95% confidence intervals [CI] 0.48-4.09) and 2.93 [1.27-4.59], respectively, p = 0.002). Similarly, health status was significantly better in the NO-TYA-PTC (ALL-TYA-PTC: ADM -0.011 [95%CI -0.046 to 0.024] and SOME-TYA-PTC: -0.054 [-0.086 to -0.023]; p = 0.006). Conclusion: The reason for the difference in perceived health status is unclear. TYA who accessed a TYA-PTC (all or some care) had higher perceived illness. This may reflect greater education and promotion of self-care by health care professionals in TYA units.

Evaluation of an integrated system for classification, assessment and comparison of services for long-term care in Europe: the eDESDE-LTC study.

BACKGROUND: The harmonization of European health systems brings with it a need for tools to allow the standardized collection of information about medical care. A common coding system and standards for the description of services are needed to allow local data to be incorporated into evidence-informed policy, and to permit equity and mobility to be assessed. The aim of this project has been to design such a classification and a related tool for the coding of services for Long Term Care (DESDE-LTC), based on the European Service Mapping Schedule (ESMS). METHODS: The development of DESDE-LTC followed an iterative process using nominal groups in 6 European countries. 54 researchers and stakeholders in health and social services contributed to this process. In order to classify services, we use the minimal organization unit or "Basic Stable Input of Care" (BSIC), coded by its principal function or "Main Type of Care" (MTC). The evaluation of the tool included an analysis of feasibility, consistency, ontology, inter-rater reliability, Boolean Factor Analysis, and a preliminary impact analysis (screening, scoping and appraisal). RESULTS: DESDE-LTC includes an alpha-numerical coding system, a glossary and an assessment instrument for mapping and counting LTC. It shows high feasibility, consistency, inter-rater reliability and face, content and construct validity. DESDE-LTC is ontologically consistent. It is regarded by experts as useful and relevant for evidence-informed decision making. CONCLUSION: DESDE-LTC contributes to establishing a common terminology, taxonomy and coding of LTC services in a European context, and a standard procedure for data collection and international comparison.

Perspectives: the mental health care gap in intellectual disabilities in Spain: impact analysis and knowledge-to-action plan.

BACKGROUND: Intellectual developmental disorder or Intellectual disability (ID) is a prevalent condition with a high impact along the life-span particularly when associated to other mental disorders (MD). SPECIFIC AIM: To estimate the unmet needs and to design a knowledge to action plan to reduce the care gap in ID-MD in Spain. METHOD: We followed a 5-step `maxi' impact assessment and a mixed qualitative/quantitative design including expert panels, secondary analysis of databases and a prospective survey in the 17 regions in Spain. Schizophrenia was used as comparator due to similar prevalence rates and burden. RESULTS: Persons with ID-MD had ten times less outpatient contacts and hospital admissions than patients with schizophrenia. The outpatient case load was 2.31% in ID and 14.6% in schizophrenia. ID had the lowest hospitalization rate amongst all mental disorders but the highest length of stay. The expert panel estimated that half of persons with ID-MD are not adequately assessed and 95% do not receive the required care in Spain. Basic care needs include 6.5 beds and an ID-MD outpatient service per 1 million population. At least 134 specialized psychiatrists and psychologists and 277 beds are needed to reach the minimum standards in Spain. CONCLUSION: This study quantifies the ID-MD care gap in Spain and the basic specialized services needed. In spite of the societal and health implications of ID-MD the knowledge-to-action plan had a modest impact limited at the regions where ID-MD programmes were already implemented. IMPLICATIONS FOR HEALTH POLICY: Specific priority setting on ID-MH should be incorporated to mental health strategy at the Ministry of Health within a broader health and ID plan. National and regional policies should incorporate an integrative care approach through the life cycle. The development of excellence centers on ID-MD and a national observatory on this topic should be encouraged.

Discovery and characterisation of socially polarised communities on social media.

Social polarisation processes have become a central phenomenon for the explanation of population behavioural dynamics in today's societies. Although recent works offer solutions for the detection of polarised political communities in social media, there is still a lack of works that allow an adequate characterization of the specific topics on which these divides between social groups are articulated. Our study aims to discover and characterise antagonistic communities on Twitter based on a method that combines the identification of authorities and textual classifiers around three public debates that have recently produced major controversies: (1) vaccination; (2) climate change; and (3) abortion. The proposed method allows the capture of polarised communities with little effort, requiring only the selection of some terms that characterise the topic and some initial authorities. Our findings show that the processes of social polarisation can vary considerably depending on the subject on which the debates are articulated. Specifically, polarisation manifests more prominently in the realms of vaccination and abortion, whereas this divide is less apparent in the context of climate change.

Epidemiology, mortality, and health service use of local-level multimorbidity patterns in South Spain.

Multimorbidity -understood as the occurrence of chronic diseases together- represents a major challenge for healthcare systems due to its impact on disability, quality of life, increased use of services and mortality. However, despite the global need to address this health problem, evidence is still needed to advance our understanding of its clinical and social implications. Our study aims to characterise multimorbidity patterns in a dataset of 1,375,068 patients residing in southern Spain. Combining LCA techniques and geographic information, together with service use, mortality, and socioeconomic data, 25 chronicity profiles were identified and subsequently characterised by sex and age. The present study has led us to several findings that take a step forward in this field of knowledge. Specifically, we contribute to the identification of an extensive range of at-risk groups. Moreover, our study reveals that the complexity of multimorbidity patterns escalates at a faster rate and is associated with a poorer prognosis in local areas characterised by lower socioeconomic status. These results emphasize the persistence of social inequalities in multimorbidity, highlighting the need for targeted interventions to mitigate the impact on patients' quality of life, healthcare utilisation, and mortality rates.

Social determinants of multimorbidity patterns: A systematic review.

Social determinants of multimorbidity are poorly understood in clinical practice. This review aims to characterize the different multimorbidity patterns described in the literature while identifying the social and behavioral determinants that may affect their emergence and subsequent evolution. We searched PubMed, Embase, Scopus, Web of Science, Ovid MEDLINE, CINAHL Complete, PsycINFO and Google Scholar. In total, 97 studies were chosen from the 48,044 identified. Cardiometabolic, musculoskeletal, mental, and respiratory patterns were the most prevalent. Cardiometabolic multimorbidity profiles were common among men with low socioeconomic status, while musculoskeletal, mental and complex patterns were found to be more prevalent among women. Alcohol consumption and smoking increased the risk of multimorbidity, especially in men. While the association of multimorbidity with lower socioeconomic status is evident, patterns of mild multimorbidity, mental and respiratory related to middle and high socioeconomic status are also observed. The findings of the present review point to the need for further studies addressing the impact of multimorbidity and its social determinants in population groups where this problem remains invisible (e.g., women, children, adolescents and young adults, ethnic groups, disabled population, older people living alone and/or with few social relations), as well as further work with more heterogeneous samples (i.e., not only focusing on older people) and using more robust methodologies for better classification and subsequent understanding of multimorbidity patterns. Besides, more studies focusing on the social determinants of multimorbidity and its inequalities are urgently needed in low- and middle-income countries, where this problem is currently understudied.

The impact of government actions and risk perception on the promotion of self-protective behaviors during the COVID-19 pandemic.

INTRODUCTION: We aim to understand the factors that drive citizens of different countries to adhere to recommended self-protective behaviors during the COVID-19 pandemic. METHODS: Survey data was obtained through the COVID-19 Impact project. We selected countries that presented a sufficiently complete time series and a statistically relevant sample for running the analysis: Cyprus, Germany, Greece, Ireland, Latvia, Spain, Switzerland, the United Kingdom, and the United States of America. To identify country-specific differences in self-protective behaviors, we used previous evidence and change-point detection analysis to establish variations across participating countries whose effect was then assessed by means of interrupted series analysis. RESULTS: A high level of compliance with health and governmental authorities' recommendations were generally observed in all included countries. The level of stress decreased near the period when countries such as Cyprus, Greece or the United Kingdom relaxed their prevention behavior recommendations. However, this relaxation of behaviors did not occur in countries such as Germany, Ireland, or the United States. As observed in the change-point detection analysis, when the daily number of recorded COVID-19 cases decreased, people relaxed their protective behaviors (Cyprus, Greece, Ireland), although the opposite trend was observed in Switzerland. DISCUSSION: COVID-19 self-protective behaviors were heterogeneous across countries examined. Our findings show that there is probably no single winning strategy for exiting future health crises, as similar interventions, aimed to promote self-protective behaviors, may be received differently depending on the specific population groups and on the particular geographical context in which they are implemented.

Corrigendum: Illness perceptions of COVID-19 in Europe: predictors, impacts and temporal evolution.

[This corrects the article DOI: 10.3389/fpsyg.2021.640955.].

Discovery and classification of complex multimorbidity patterns: unravelling chronicity networks and their social profiles.

Multimorbidity can be defined as the presence of two or more chronic diseases in an individual. This condition is associated with reduced quality of life, increased disability, greater functional impairment, increased health care utilisation, greater fragmentation of care and complexity of treatment, and increased mortality. Thus, understanding its epidemiology and inherent complexity is essential to improve the quality of life of patients and to reduce the costs associated with multi-pathology. In this paper, using data from the European Health Survey, we explore the application of Mixed Graphical Models and its combination with social network analysis techniques for the discovery and classification of complex multimorbidity patterns. The results obtained show the usefulness and versatility of this approach for the study of multimorbidity based on the use of graphs, which offer the researcher a holistic view of the relational structure of data with variables of different types and high dimensionality.

Social inequalities in multimorbidity patterns in Europe: A multilevel latent class analysis using the European Social Survey (ESS).

Multimorbidity is associated with lower quality of life, greater disability and higher use of health services and is one of the main challenges facing governments in Europe. There is a need to identify and characterize patterns of chronic conditions and analyse their association with social determinants not only from an individual point of view but also from a collective point of view. This paper aims to respond to this knowledge gap by detecting patterns of chronic conditions and their social determinants in 19 European countries from a multilevel perspective. We used data from the ESS round 7. The final sample consisted of 18,933 individuals over 18 years of age, and patterns of multimorbidity from 14 chronic conditions were detected through Multilevel Latent Class Analysis, which also allows detecting similarities between countries. Gender, Age, Housing Location, Income Level and Educational Level were used as individual covariates to determine possible associations with social inequalities. The goodness-of-fit indices derived in a model with six multimorbidity patterns and five countries clusters. The six patterns were "Back, Digestive and Headaches", "Allergies and Respiratory", "Complex Multimorbidity", "Cancer and Cardiovascular", "Musculoskeletal" and "Cardiovascular"; the five clusters could be associated with some geographical areas or welfare states. Patterns showed significant differences in the covariates of interest, with differences in education and income being of particular interest. Some significant differences were found among patterns and the country groupings. Our findings show that chronic diseases tend to appear in a combined and interactive way, and socioeconomic differences in the occurrence of patterns are not only of the individual but also of group importance, emphasising how the welfare states in each country can influence in the health of their inhabitants.

Multimorbidity Patterns and Their Association with Social Determinants, Mental and Physical Health during the COVID-19 Pandemic.

BACKGROUND: The challenge posed by multimorbidity makes it necessary to look at new forms of prevention, a fact that has become heightened in the context of the pandemic. We designed a questionnaire to detect multimorbidity patterns in people over 50 and to associate these patterns with mental and physical health, COVID-19, and possible social inequalities. METHODS: This was an observational study conducted through a telephone interview. The sample size was 1592 individuals with multimorbidity. We use Latent Class Analysis to detect patterns and SF-12 scale to measure mental and physical quality-of-life health. We introduced the two dimensions of health and other social determinants in a multinomial regression model. RESULTS: We obtained a model with five patterns (entropy = 0.727): 'Relative Healthy', 'Cardiometabolic', 'Musculoskeletal', 'Musculoskeletal and Mental', and 'Complex Multimorbidity'. We found some differences in mental and physical health among patterns and COVID-19 diagnoses, and some social determinants were significant in the multinomial regression. CONCLUSIONS: We identified that prevention requires the location of certain inequalities associated with the multimorbidity patterns and how physical and mental health have been affected not only by the patterns but also by COVID-19. These findings may be critical in future interventions by health services and governments.