title: Multilevel temporal Bayesian networks can model longitudinal change in multimorbidity.
abstract: OBJECTIVE
  Although the course of single diseases can be studied using traditional epidemiologic techniques, these methods cannot capture the complex joint evolutionary course of multiple disorders. In this study, multilevel temporal Bayesian networks were adopted to study the course of multimorbidity in the expectation that this would yield new clinical insight.
  
  
  METHODS
  Clinical data of patients were extracted from 90 general practice registries in the Netherlands. One and half million patient-years were used for analysis. The simultaneous progression of six chronic cardiovascular conditions was investigated, correcting for both patient and practice-related variables.
  
  
  RESULTS
  Cumulative incidence rates of one or more new morbidities rapidly increase with the number of morbidities present at baseline, ranging up to 47% and 76% for 3- and 5-year follow-ups, respectively. Hypertension and lipid disorders, as health risk factors, increase the cumulative incidence rates of both individual and multiple disorders. Moreover, in their presence, the observed cumulative incidence rates of combinations of cardiovascular disorders, that is, multimorbidity differs significantly from the expected rates.
  
  
  CONCLUSIONS
  There are clear synergies between health risks and chronic diseases when multimorbidity within a patient progresses over time. The method used here supports a more comprehensive analysis of such synergies compared with what can be obtained by traditional statistics.
authors.affiliation: Institute for Computing and Information Sciences, Radboud University Nijmegen, Nijmegen, The Netherlands. Electronic address: mlappens@cs.ru.nl.
authors.forename: Martijn
authors.initials: M
authors.lastname: Lappenschaar
authors.forename: Arjen
authors.initials: A
authors.lastname: Hommersom
authors.forename: Peter J F
authors.initials: PJ
authors.lastname: Lucas
authors.forename: Joep
authors.initials: J
authors.lastname: Lagro
authors.forename: Stefan
authors.initials: S
authors.lastname: Visscher
authors.forename: Joke C
authors.initials: JC
authors.lastname: Korevaar
authors.forename: François G
authors.initials: FG
authors.lastname: Schellevis
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D001499:Bayes Theorem; D002318:Cardiovascular Diseases / Q000453:epidemiology*; D002908:Chronic Disease; D015897:Comorbidity; D005260:Female; D005500:Follow-Up Studies; D006801:Humans; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D055361:Multilevel Analysis; D009426:Netherlands / Q000453:epidemiology; D012042:Registries; D012307:Risk Factors; D013997:Time Factors
keywords: Bayesian networks; Cardiovascular disease; Interpractice variation; Multilevel analysis; Multimorbidity; Synergy