Shu Kay Ng1, Richard Tawiah1, Michael Sawyer2,3, Paul Scuffham1. 1. School of Medicine, Menzies Health Institute Queensland, Griffith University, Nathan, Australia. 2. Research & Evaluation Unit, Women's and Children's Hospital, North Adelaide, Australia. 3. School of Medicine, University of Adelaide, Adelaide, Australia.
Abstract
Background: The latest review of studies on multimorbidity patterns showed high heterogeneity in the methodology for identifying groups of multimorbid conditions. However, it is unclear how analytical methods used influence the identified multimorbidity patterns. Methods: We undertook a systematic review of analytical methods used to identify multimorbidity patterns in PubMed and EMBASE from their inception to January 2017. We conducted a comparison analysis to assess the effect the analytical methods had on the multimorbidity patterns identified, using the Australian National Health Survey (NHS) 2007-08 data. Results: We identified 13 194 studies and excluded 13 091 based on titles/abstracts. From the full-text reviews of the 103 remaining publications, we identified 41 studies that used five different analytical methods to identify multimorbid conditions in the studies. Thirty-seven studies (90%) adopted either the factor-analysis or hierarchical-clustering methods, but heterogeneity arises for the use of different proximity measures within each method to form clusters. Our comparison analysis showed the variation in identified groups of multimorbid conditions when applying the methods to the same NHS data. We extracted main similarities among the groupings obtained by the five methods: (i) cardiovascular and metabolic diseases, (ii) mental health problems and (iii) allergic diseases. Conclusion: We showed the extent of effects for heterogeneous analytical methods on identification of multimorbidity patterns. However, more work is needed to guide investigators for choosing the best analytical method to improve the validity and generalizability of findings. Investigators should also attempt to compare results obtained by various methods for a consensus grouping of multimorbid conditions.
Background: The latest review of studies on multimorbidity patterns showed high heterogeneity in the methodology for identifying groups of multimorbid conditions. However, it is unclear how analytical methods used influence the identified multimorbidity patterns. Methods: We undertook a systematic review of analytical methods used to identify multimorbidity patterns in PubMed and EMBASE from their inception to January 2017. We conducted a comparison analysis to assess the effect the analytical methods had on the multimorbidity patterns identified, using the Australian National Health Survey (NHS) 2007-08 data. Results: We identified 13 194 studies and excluded 13 091 based on titles/abstracts. From the full-text reviews of the 103 remaining publications, we identified 41 studies that used five different analytical methods to identify multimorbid conditions in the studies. Thirty-seven studies (90%) adopted either the factor-analysis or hierarchical-clustering methods, but heterogeneity arises for the use of different proximity measures within each method to form clusters. Our comparison analysis showed the variation in identified groups of multimorbid conditions when applying the methods to the same NHS data. We extracted main similarities among the groupings obtained by the five methods: (i) cardiovascular and metabolic diseases, (ii) mental health problems and (iii) allergic diseases. Conclusion: We showed the extent of effects for heterogeneous analytical methods on identification of multimorbidity patterns. However, more work is needed to guide investigators for choosing the best analytical method to improve the validity and generalizability of findings. Investigators should also attempt to compare results obtained by various methods for a consensus grouping of multimorbid conditions.
Authors: Søren T Skou; Frances S Mair; Martin Fortin; Bruce Guthrie; Bruno P Nunes; J Jaime Miranda; Cynthia M Boyd; Sanghamitra Pati; Sally Mtenga; Susan M Smith Journal: Nat Rev Dis Primers Date: 2022-07-14 Impact factor: 65.038
Authors: Ignatios Ioakeim-Skoufa; Beatriz Poblador-Plou; Jonás Carmona-Pírez; Jesús Díez-Manglano; Rokas Navickas; Luis Andrés Gimeno-Feliu; Francisca González-Rubio; Elena Jureviciene; Laimis Dambrauskas; Alexandra Prados-Torres; Antonio Gimeno-Miguel Journal: Int J Environ Res Public Health Date: 2020-06-14 Impact factor: 3.390