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

Álvarez‐Gálvez, Javier; Carretero-Bravo, Jesús; Suárez-Lledó, Víctor; Ortega-Martín, Esther; Ramos-Fiol, Begoña; Lagares‐Franco, Carolina; González, Cristina O'Ferrall; Barrios, José Almenara; González-Caballero, Juan Luis

doi:10.1016/j.ssmph.2022.101268

Cited by 5 publications

(1 citation statement)

References 65 publications

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“…Finally, the ‘Complex Multimorbidity’ pattern is associated with older and less educated people compared to the other patterns. It is a pattern related to older age in other studies, in addition to low-income levels [ 29 ] and low education [ 61 , 79 ], thus higher education was significantly associated with a decreased risk of general multimorbidity, being the pattern with the highest number of chronic conditions. Given the problem of ageing in Spain, with several regions having the highest life expectancy rates in Europe, finding the characteristics that lead to this type of pattern is essential to address the challenge of multimorbidity.…”

Section: Discussionmentioning

confidence: 91%

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

Carretero-Bravo

Ramos-Fiol

Ortega-Martín

et al. 2022

IJERPH

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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.

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Section: Discussionmentioning

confidence: 91%

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

Carretero-Bravo

Ramos-Fiol

Ortega-Martín

et al. 2022

IJERPH

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

Alvarez-Galvez,

Ortega-Martin,

Ramos-Fiol

et al. 2023

Nat Commun

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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.

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What can death records tell us about multimorbidity?

Baneshi¹,

Eynstone-Hinkins²,

McElwee³

et al. 2023

J Epidemiol Community Health

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BackgroundMultimorbidity has been measured from many data sources which show that prevalence increases with age and is usually greater among women than men and in more recent periods. Analyses of multiple cause of death data have shown different patterns of multimorbidity associated with demographic and other characteristics.MethodsDeaths in Australia among over 1.7 million decedents aged 55+ were stratified into three types: medically certified deaths, coroner-referred deaths with natural underlying causes and coroner-referred deaths with external underlying causes. Multimorbidity was measured by prevalence of ≥2 causes and analysed over three periods based on administrative changes: 2006–2012, 2013–2016 and 2017–2018. Poisson regression was used to examine the influence of gender, age and period.ResultsThe prevalence of deaths with multimorbidity was 81.0% for medically certified deaths, 61.1% for coroner-referred deaths with natural underlying causes and 82.4% for coroner-referred deaths with external underlying causes. For medically certified deaths, multimorbidity increased with age: incidence rate ratio (IRR 1.070, 95% CI 1.068, 1.072) was lower for women than men (0.954, 95% CI 0.952, 0.956) and changed little over time. For coroner-referred deaths with natural underlying causes, multimorbidity showed the expected pattern increasing with age (1.066, 95% CI 1.062, 1.070) and being higher for women than men (1.025, 95% CI 1.015, 1.035) and in more recent periods. For coroner-referred deaths with external underlying causes, there were marked increases over time that differed by age group due to changes in coding processes.ConclusionDeath records can be used to examine multimorbidity in national populations but, like other data sources, how the data were collected and coded impacts the conclusions.

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

Cited by 5 publications

References 65 publications

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

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

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

What can death records tell us about multimorbidity?

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