Healthcare utilization after a first hospitalization for COPD: a new approach of State Sequence Analysis based on the '6W' multidimensional model of care trajectories

Abstract

Background: Published methods to describe and visualize Care Trajectories (CTs) as patterns of healthcare use are very sparse, often incomplete, and not intuitive for non-experts. Our objectives are to propose a typology of CTs one year after a first hospitalization for Chronic Obstructive Pulmonary Disease (COPD), and describe CT types and compare patients' characteristics for each CT type.

Methods: This is an observational cohort study extracted from Quebec's medico-administrative data of patients aged 40 to 84 years hospitalized for COPD in 2013 (index date). The cohort included patients hospitalized for the first time over a 3-year period before the index date and who survived over the follow-up period. The CTs consisted of sequences of healthcare use (e.g. ED-hospital-home-GP-respiratory therapists, etc.) over a one-year period. The main variable was a CT typology, which was generated by a 'tailored' multidimensional State Sequence Analysis, based on the "6W" model of Care Trajectories. Three dimensions were considered: the care setting ("where"), the reason for consultation ("why"), and the speciality of care providers ("which"). Patients were grouped into specific CT types, which were compared in terms of care use attributes and patients' characteristics using the usual descriptive statistics.

Results: The 2581 patients were grouped into five distinct and homogeneous CT types: Type 1 (n = 1351, 52.3%) and Type 2 (n = 748, 29.0%) with low healthcare and moderate healthcare use respectively; Type 3 (n = 216, 8.4%) with high healthcare use, mainly for respiratory reasons, with the highest number of urgent in-hospital days, seen by pulmonologists and respiratory therapists at primary care settings; Type 4 (n = 100, 3.9%) with high healthcare use, mainly cardiovascular, high ED visits, and mostly seen by nurses in community-based primary care; Type 5 (n = 166, 6.4%) with high healthcare use, high ED visits and non-urgent hospitalisations, and with consultations at outpatient clinics and primary care settings, mainly for other reasons than respiratory or cardiovascular. Patients in the 3 highest utilization CT types were older, and had more comorbidities and more severe condition at index hospitalization.

Conclusions: The proposed method allows for a better representation of the sequences of healthcare use in the real world, supporting data-driven decision making.

Keywords: (3–10) State sequence analysis; COPD; Care Trajectories; Data visualization; Healthcare utilization; Observational study; Optimal matching; TraMineR; Typology.

Fig. 1

Study cohort flow diagram

Fig. 2

Three-dimensional state sequence analysis diagram: example of week as time unit. The main steps of this multidimensional modified version of SSA were to: Step 1) define the time unit for state sequences analysis (e.g. weeks); Step 2) for each of the three dimensions, select categorical states, specify their priorities and measure the state sequences to generate patient-sequences; Step 3) for each of the three dimensions, calculate the distance between each pair of patient-sequences using an appropriate dissimilarity measure method, resulting in three distance matrices; Step 4) calculate a pooled distance matrix by summing the three dimension-specific matrices; Step 5) based on the pooled distance matrix calculated in step 4, choose and apply a classification method resulting in groups of distinct patient-sequences; and finally 6) display results by visual representations offered by SSA for interpretation

Fig. 3.

Hierarchical cluster analysis (HCA) - Dendrogram (a) and Intertia jump curve (b) for state sequences by week. a Patients with similar sum of dimension-specific distances were classified in the same group. In HCA, each patient starts in its own cluster, and then pairs of clusters are merged as one moves up the hierarchy, until all patients are combined in a unique group. The Ward’s linkage criterion was chosen to find the pair of clusters that leads to minimum increase in total within-cluster variance after merging. b The choice of the optimal number of groups or clusters was guided on statistical criteria (sum of squares or inertia)

Fig. 4

State Distribution Plots of CT typology by dimension (where, why and which). State Distribution Plots show the distribution of states for each time unit point (52 weeks)

Fig. 5

Sequence Index Plots of CT typology by dimension (where, why and which). In Sequence Index Plots, each line represents an individual’s CT sequence

Fig. 6

Median (quartiles) number of days spent in each care setting of consultation by CT typology

Fig. 7

Hospital readmissions in the year following index date by cause and CT typology