scholarly journals Morbidity profiles in Europe and Israel: international comparisons from 20 countries using biopsychosocial indicators of health via latent class analysis

Author(s):  
Johannes Beller

Abstract Aim I examined health/morbidity profiles across 20 countries, determined their associated demographic characteristics and risk factors and compared the distribution of these health/morbidity profiles across countries. Subject and methods I used population-based data drawn from the European Social Survey (N = 20092, 52% female, ages 40+) covering 20 mostly European countries (Austria, Belgium, Czechia, Denmark, Finland, France, Germany, Great Britain, Hungary, Ireland, Israel, Lithuania, Netherlands, Norway, Poland, Portugal, Slovenia, Spain, Sweden and Switzerland) from 2014. Diverse indicators of health/morbidity were used, including self-rated health, self-rated disability, self-reported health problems and mental health symptoms using the CES-D. Latent class analysis was conducted to determine health/morbidity profiles across countries. Results I found that four distinct health profiles best describe overall health/morbidity status in the international sample, each associated with specific demographic and behavioural risk factors: ‘healthy’ profile (62% of participants), ‘unhappy but healthy’ profile (14%), ‘high morbidity, mostly physical’ profile (16%) and ‘high morbidity, mostly psychological’ profile (8%). With few exceptions, participants from Northern Europe and Western Europe were more likely to belong to the ‘healthy’ and the ‘unhappy but healthy’ profiles, whereas participants from Eastern Europe were more likely to belong to the ‘high morbidity, mostly physical’ profile. Distribution of the ‘high morbidity, mostly psychological’ profile appeared to be more uniform across regions. Conclusions Distinct morbidity/health profiles could be identified across countries, and countries varied regarding the relative distribution of these profiles. Specific prevention and treatment consequences associated with each profile are discussed. Future studies should further investigate the patterns of overall health and morbidity in Europe’s populations.

Public Health ◽  
2021 ◽  
Vol 198 ◽  
pp. 180-186
Author(s):  
R.S. Mkuu ◽  
T.D. Gilreath ◽  
A.E. Barry ◽  
F.M. Nafukho ◽  
J. Rahman ◽  
...  

2019 ◽  
Vol 243 ◽  
pp. 360-365 ◽  
Author(s):  
Hongguang Chen ◽  
Xiao Wang ◽  
Yueqin Huang ◽  
Guohua Li ◽  
Zhaorui Liu ◽  
...  

2021 ◽  
Vol 8 ◽  
Author(s):  
Leila Jahangiry ◽  
Mahdieh Abbasalizad Farhangi ◽  
Mahdi Najafi ◽  
Parvin Sarbakhsh

Background: Coronary heart disease (CHD) is the major cause of mortality in the world with a significant impact on the younger population. The aim of this study was to identify prematurity among patients with coronary artery bypass graft surgery (CABG) based on the clustering of CHD risk factors.Methods: Patients were recruited from an existing cohort of candidates for CABG surgery named Tehran Heart Center Coronary Outcome Measurement (THC-COM). A latent class analysis (LCA) model was formed using 11 potential risk factors as binary variables: cigarette smoking, obesity, diabetes, family history of CHD, alcohol use, opium addiction, hypertension, history of stroke, history of myocardial infarction (MI), peripheral vascular disease (PVD), and hyperlipidemia (HLP). We analyzed our data to figure out how the patients are going to be clustered based on their risk factors.Results: For 566 patients who were studied, the mean age (SD) and BMI of patients were 59.1 (8.9) and 27.3 (4.1), respectively. The LCA model fit with two latent classes was statistically significant (G2 = 824.87, df = 21, p < 0.0001). The mean (SD) age of patients for Class I and Class II was 55.66 (8.55) and 60.87 (8.66), respectively. Class I (premature) was characterized by a high probability of smoking, alcohol consumption, opium addiction, and a history of MI (P < 0.05), and class II by a high probability of obesity, diabetes, and hypertension.Conclusion: Latent class analysis calculated two groups of severe CHD with distinct risk markers. The younger group, which is characterized by smoking, addiction, and the history of MI, can be regarded as representative of premature CHD.


2021 ◽  
pp. 0095327X2110469
Author(s):  
Scott D. Landes ◽  
Janet M. Wilmoth ◽  
Andrew S. London ◽  
Ann T. Landes

Military suicide prevention efforts would benefit from population-based research documenting patterns in risk factors among service members who die from suicide. We use latent class analysis to analyze patterns in identified risk factors among the population of 2660 active-duty military service members that the Department of Defense Suicide Event Report (DoDSER) system indicates died by suicide between 2008 and 2017. The largest of five empirically derived latent classes was primarily characterized by the dissolution of an intimate relationship in the past year. Relationship dissolution was common in the other four latent classes, but those classes were also characterized by job, administrative, or legal problems, or mental health factors. Distinct demographic and military-status differences were apparent across the latent classes. Results point to the need to increase awareness among mental health service providers and others that suicide among military service members often involves a constellation of potentially interrelated risk factors.


2019 ◽  
Vol 33 (10) ◽  
pp. 1272-1281 ◽  
Author(s):  
Lan Luo ◽  
Wei Du ◽  
Shanley Chong ◽  
Huibo Ji ◽  
Nicholas Glasgow

Background: At the end of life, cancer survivors often experience exacerbations of complex comorbidities requiring acute hospital care. Few studies consider comorbidity patterns in cancer survivors receiving palliative care. Aim: To identify patterns of comorbidities in cancer patients receiving palliative care and factors associated with in-hospital mortality risk. Design, Setting/Participants: New South Wales Admitted Patient Data Collection data were used for this retrospective cohort study with 47,265 cancer patients receiving palliative care during the period financial year 2001–2013. A latent class analysis was used to identify complex comorbidity patterns. A regression mixture model was used to identify risk factors in relation to in-hospital mortality in different latent classes. Results: Five comorbidity patterns were identified: ‘multiple comorbidities and symptoms’ (comprising 9.1% of the study population), ‘more symptoms’ (27.1%), ‘few comorbidities’ (39.4%), ‘genitourinary and infection’ (8.7%), and ‘circulatory and endocrine’ (15.6%). In-hospital mortality was the highest for ‘few comorbidities’ group and the lowest for ‘more symptoms’ group. Severe comorbidities were associated with elevated mortality in patients from ‘multiple comorbidities and symptoms’, ‘more symptoms’, and ‘genitourinary and infection’ groups. Intensive care was associated with a 37% increased risk of in-hospital deaths in those presenting with more ‘multiple comorbidities and symptoms’, but with a 22% risk reduction in those presenting with ‘more symptoms’. Conclusion: Identification of comorbidity patterns and risk factors for in-hospital deaths in cancer patients provides an avenue to further develop appropriate palliative care strategies aimed at improving outcomes in cancer survivors.


2016 ◽  
Vol 71 (12) ◽  
pp. 1653-1660 ◽  
Author(s):  
Emilie Ferrat ◽  
Etienne Audureau ◽  
Elena Paillaud ◽  
Evelyne Liuu ◽  
Christophe Tournigand ◽  
...  

PLoS ONE ◽  
2015 ◽  
Vol 10 (11) ◽  
pp. e0143184 ◽  
Author(s):  
Marianna Virtanen ◽  
Jussi Vahtera ◽  
Jenny Head ◽  
Rosemary Dray-Spira ◽  
Annaleena Okuloff ◽  
...  

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