Use of state sequence analysis for care pathway analysis: The example of multiple sclerosis

Jonathan Roux (1, 2) , Olivier Grimaud (1, 2) , Emmanuelle Leray (1, 2, 3)

1 EHESP - École des Hautes Études en Santé Publique [EHESP]
2 REPERES - Recherche en Pharmaco-épidémiologie et Recours aux Soins
3 CIC - Centre d'Investigation Clinique [Rennes]

The concept of care pathways is increasingly being used to enhance the quality of care and to optimize the use of resources for health care. We here propose an innovative method in epidemiology that is derived from social sciences: state sequence analysis (SSA). This method takes into account the chronology of care consumption and allows for identification of specific patterns. A process for using SSA in the health area is proposed and discussed. The main steps are: data coding, measurement of dissimilarities between sequences (focusing on optimal matching methods and the choice of related costs), and application of a clustering method to obtain a typology of sequence patterns. As an example of its use in the health area, SSA was employed to analyse care pathways of a random sample of patients with multiple sclerosis. This sample has been selected from the main French healthcare database covering the period 2007 to 2013 (n = 1 000). A five-cluster typology was obtained which allowed distinction of care consumption groups. Overall, about half of the patients had low care consumption, about one quarter had medium to high consumption, and another quarter had high consumption. We conclude that state sequence analysis is an innovative and flexible methodology that is worth considering in health care research.

Keywords: Care pathways; Multiple sclerosis; State sequence analysis; Epidemiology; Administrative data

Domains: Life Sciences [q-bio]; Santé publique et épidémiologie; Statistics [stat]; Applications [stat.AP]; Life Sciences [q-bio]; Bioengineering

Jonathan Roux, Olivier Grimaud, Emmanuelle Leray. Use of state sequence analysis for care pathway analysis: The example of multiple sclerosis. Statistical Methods in Medical Research, 2019, 28 (6), pp.1651-1663. ⟨10.1177/0962280218772068⟩. ⟨hal-01798652⟩