Authors | Year | Country | Aims | No. of used observations | Method of data analysis | Outcome |
---|---|---|---|---|---|---|
Andriopoulou F, et al. [19] | 2013 | Greece | Managing patients suffering from chronic conditions. | 30 | Random Forest | Framework that identifies the necessity to deliver personalized health services by specialists when they are most appropriate. |
Schäfer I, et al. [20] | 2014 | Germany | Depicting which diseases are associated with each other on person-level in multimorbid patients and which ones are responsible for the overlapping of multimorbidity clusters. | 98.619 (72.548 for replication analyses) | Analysis based on clustering techniques | Model for the association of diseases to each other. Identification of diseases that form a multimorbidity cluster as well as the identification of diseases responsible for overlapping multimorbidity clusters. |
Marx P, et al. [21] | 2015 | Hungary | Investigating a systems-based approach for the use of separated large-scale multimorbidity data to explore common latent factors of related diseases. | 117.803 (subset of the UK Biobank) | MCMC on a Bayesian network | Bayesian, multivariate, system-based approach to identify shared latent factors that could cause multi-morbid diseases without interpreting these factors. |
Boshuizen HC, et al. [22] | 2017 | Netherlands | Determining the magnitude of the difference in the burden of a risk factor with different calculation methods. | Not defined. Study based on the Global Burden of disease database. | Temporal counterfactual reasoning | Dynamic modelling with the DYNAMO-HIA Method estimates the gain in Disability Adjusted Life Years (DALYs) obtained by eliminating exposure to a risk factor more accurately than other established methods. |
Kalgotra P, et al. [23] | 2017 | USA | Addressing the co-occurrences of diseases using network analysis while putting a special focus in disparities by gender. | 22.1 million | Network analysis | Identification of different multimorbidity clusters for male and female patients with a prevalence of higher comorbidities in females than males. |
Nicholson K, et al. [24] | 2017 | Canada | Development of the Multimorbidity Cluster Analysis Tool and Toolkit to identify distinct clusters within patients living with multimorbidity. | 75.000 | Analysis based on clustering techniques | Downloadable Toolkit for analysis and description of combination and permutation of diseases in large datasets of multimorbid patients. |