title: Estimating lifetime healthcare costs with morbidity data.
abstract: BACKGROUND
  In many developed countries, the economic crisis started in 2008 producing a serious contraction of the financial resources spent on healthcare. Identifying which individuals will require more resources and the moment in their lives these resources have to be allocated becomes essential. It is well known that a small number of individuals with complex healthcare needs consume a high percentage of health expenditures. Conversely, little is known on how morbidity evolves throughout life. The aim of this study is to introduce a longitudinal perspective to chronic disease management.
  
  
  METHODS
  Data used relate to the population of the county of Baix Empordà in Catalonia for the period 2004-2007 (average population was N = 88,858). The database included individual information on morbidity, resource consumption, costs and activity records. The population was classified using the Clinical Risk Groups (CRG) model. Future morbidity evolution was simulated under different assumptions using a stationary Markov chain. We obtained morbidity patterns for the lifetime and the distribution function of the random variable lifetime costs. Individual information on acute episodes, chronic conditions and multimorbidity patterns were included in the model.
  
  
  RESULTS
  The probability of having a specific health status in the future (healthy, acute process or different combinations of chronic illness) and the distribution function of healthcare costs for the individual lifetime were obtained for the sample population. The mean lifetime cost for women was €111,936, a third higher than for men, at €81,566 (all amounts calculated in 2007 Euros). Healthy life expectancy at birth for females was 46.99, lower than for males (50.22). Females also spent 28.41 years of life suffering from some type of chronic disease, a longer period than men (21.9).
  
  
  CONCLUSIONS
  Future morbidity and whole population costs can be reasonably predicted, combining stochastic microsimulation with a morbidity classification system. Potential ways of efficiency arose by introducing a time perspective to chronic disease management.
authors.affiliation: GRESSiRES, Research group on health services and health outcomes, Serveis de Salut Integrats Baix Empordà, Hospital 27, Palamós, 17230, Spain. marc.carrerasp@udg.edu.
authors.forename: Marc
authors.initials: M
authors.lastname: Carreras
authors.forename: Pere
authors.initials: P
authors.lastname: Ibern
authors.forename: Jordi
authors.initials: J
authors.lastname: Coderch
authors.forename: Inma
authors.initials: I
authors.lastname: Sánchez
authors.forename: Jose M
authors.initials: JM
authors.lastname: Inoriza
mesh_terms: D000293:Adolescent; D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D002648:Child; D002675:Child, Preschool; D002908:Chronic Disease / Q000191:economics / Q000453:epidemiology; D005260:Female; D017048:Health Care Costs / Q000706:statistics & numerical data*; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D008017:Life Expectancy; D008297:Male; D008390:Markov Chains; D008875:Middle Aged; D015233:Models, Statistical; D009010:Monte Carlo Method; D009017:Morbidity; D012737:Sex Factors; D013030:Spain / Q000453:epidemiology; D055815:Young Adult