Multimorbidity Patterns in the General Population: Results from the EpiChron Cohort Study.
Ignatios Ioakeim-SkoufaBeatriz Poblador-PlouJonás Carmona-PírezJesús Díez ManglanoRokas NavickasLuis Andrés Gimeno-FeliúFrancisca González-RubioElena JurevičienėLaimis DambrauskasAlexandra Prados-TorresAntonio Gimeno-MiguelPublished in: International journal of environmental research and public health (2020)
The correct management of patients with multimorbidity remains one of the main challenges for healthcare systems worldwide. In this study, we analyze the existence of multimorbidity patterns in the general population based on gender and age. We conducted a cross-sectional study of individuals of all ages from the EpiChron Cohort, Spain (1,253,292 subjects), and analyzed the presence of systematic associations among chronic disease diagnoses using exploratory factor analysis. We identified and clinically described a total of 14 different multimorbidity patterns (12 in women and 12 in men), with some relevant differences in the functions of age and gender. The number and complexity of the patterns was shown to increase with age in both genders. We identified associations of circulatory diseases with respiratory disorders, chronic musculoskeletal diseases with depression and anxiety, and a very consistent pattern of conditions whose co-occurrence is known as metabolic syndrome (hypertension, diabetes, obesity, and dyslipidaemia), among others. Our results demonstrate the potential of using real-world data to conduct large-scale epidemiological studies to assess the complex interactions among chronic conditions. This could be useful in designing clinical interventions for patients with multimorbidity, as well as recommendations for healthcare professionals on how to handle these types of patients in clinical practice.
Keyphrases
- metabolic syndrome
- clinical practice
- healthcare
- type diabetes
- insulin resistance
- end stage renal disease
- newly diagnosed
- blood pressure
- cardiovascular disease
- mental health
- weight loss
- chronic kidney disease
- prognostic factors
- electronic health record
- big data
- machine learning
- body mass index
- uric acid
- risk factors
- human health
- patient reported outcomes
- glycemic control
- artificial intelligence
- middle aged
- cardiovascular risk factors
- health information
- high fat diet induced