Comparison of Methods to Define High Use of Inpatient Services Using Population-Based Data

Journal of Hospital Medicine 12 (8). 2017 August;596-602 | 10.12788/jhm.2778

July 31, 2017|Journal of Hospital Medicine

James P. Wick, MSc;Brenda R. Hemmelgarn, MD, PhD;Braden J. Manns, MD, MSc;Marcello Tonelli, MD, MS;Hude Quan, MD, PhD;Richard Lewanczuk MD, PhD;Paul E. Ronksley, PhD

BACKGROUND: A variety of methods have been proposed to define “high users” of inpatient services, which may have implications for targeting subgroups for intervention.

OBJECTIVE: To compare 3 common definitions of high inpatient service use and their influence on patient capture, outcomes, and inpatient burden.

DESIGN, SETTING, AND PATIENTS: Cross-sectional population-level study of 219,106 adults in Alberta, Canada, with ≥1 hospitalization from April 1, 2012, to March 31, 2013.

MEASUREMENTS: We defined “high use” based on the upper 5th percentile of the population by 3 definitions: (1) number of inpatient episodes (≥3 hospitalizations/year), (2) cumulative length of stay (≥56 days in hospital/year), and (3) cumulative cost based on hospitalization resource intensity weights (≥ $63,597 Canadian dollars/year). Clinical characteristics, health outcomes, and overall health burden were compared across definitions and stratified by age.

RESULTS: Of that population, 10.3% of individuals were common to all definitions. High users based on number of inpatient episodes were more likely to be admitted for acute conditions, with most high users based on length of stay admitted for mental health-related conditions, while those based on costs were more likely to have hospitalizations resulting in death (9.3%). High-episode individuals accounted for 16.6% of all inpatient episodes, high-length of stay individuals for 46.4% of all hospital days, and high-cost individuals for 38.9% of total cost.

CONCLUSIONS: Three definitions of high users of inpatient services captured significantly different groups of patients. This has implications for targeting subgroups for intervention and highlights important considerations for selecting the most suitable definition for a given objective.

Demographic, Clinical, and Encounter Characteristics

Individual characteristics were measured using a combination of provincial administrative data sources. All measures were recorded as of the admission date of the first eligible hospitalization. Demographic characteristics included age, sex, First Nations status, urban/rural status (based on the individual’s residential postal code), and median neighborhood income quintile. Clinical characteristics included 28 comorbid conditions defined based on separate validated International Statistical Classification of Disease and Health Related Problems, Tenth Revision, Canada (ICD-10-CA) coding algorithms reported individually and cumulatively (categorized as 0, 1, 2–3, and 4+).¹⁹ Primary care attachment was defined as the percentage of all outpatient primary care visits made to a single practitioner in the 2-year period prior to their first hospitalization (among those with ≥3 visits). Attachment was categorized as 75%-100% (good attachment), 50%-74% (moderate attachment), or <50% (low attachment).^20,21

We also identified hospital encounter-level characteristics. These included the most responsible diagnosis, admission category (elective or urgent/emergent), and discharge disposition for each hospital episode. Reported health outcomes included the proportion of patients with in-hospital mortality and those with at least one 30-day, all-cause readmission to hospital.

Analysis

Patient characteristics were described using proportions and means (standard deviation) as appropriate for high users and nonhigh users within and across each definition. Encounter characteristics were also described and stratified by age category (18-64 or 65+ years). Comparison of patient capture was then analyzed among patients who were high use by at least 1 definition. The overlap and agreement of the 3 definitions were compared using a Venn diagram and kappa statistic. The 10 most responsible diagnoses (based on frequency) were also compared across definitions and stratified by age.

Finally, the percentage of system burden accounted for by each measure was calculated as the amount used by high users divided by the total amount used by the entire study population (x 100). To assess the potential modifying effect of age, results were stratified by age category for each definition.

All analyses were conducted using Stata 11.2 (StataCorp LP, College Station, TX).²² The Conjoint Health Research Ethics Board of the University of Calgary approved this study and granted waiver of patient consent. This manuscript is written in accordance with reporting guidelines for studies conducted using observational routinely collected health data (RECORD statement).²³

RESULTS

Comparison of Patient and Encounter-level Characterist

ics

A total of 219,106 adults had 283,204 inpatient episodes of care within the study timeframe. There were 12,707 (5.8%), 11,095 (5.1%), and 10,956 (5.0%) patients defined as high users based on number of inpatient episodes, length of stay, and cost, respectively (supplementary Figure 1). Regardless of definition, when compared to their non–high use counterparts, patients classified as high use were more likely to be male, older, in a lower median neighborhood income quintile, and have a higher level of comorbidity. Comparing across definitions of high use, those defined by number of inpatient episodes were more likely to be younger, live in rural areas, have better primary care attachment, and have fewer comorbidities, compared to the other definitions. High users by length of stay were more likely to be older and had a higher proportion of mental health–related comorbidities, including dementia and depression, as compared with the other definitions. Results were largely similar for those defined by cost (Table 1).

Encounter-level analyses

showed that high users were more likely to die within hospital (range 3.6%-9.3%) or be discharged to a long-term care setting (range 4.2%-15.2%) ,compared with nonhigh users. High users were also more likely to be readmitted within 30 days during the study period. Comparing across definitions, those defined by number of inpatient episodes were more often discharged home. High users defined by length of stay were more likely to have been discharged to a long-term care facility, while those defined by cost were more likely to have died in hospital (Table 2). Similar trends were observed across definitions when stratified by age with proportions increasing with advancing age (supplementary Table 1).

Comparison of Patient Capture and Inpatient Burden

Of the 22,691 individuals who were defined as high use by at least 1 definition, 2,331 (10.3%) were consistently high use across all 3 definitions (kappa = 0.38; Figure 1). Of the 13,682 individuals classified as high use by at least 1 of length of stay or cost, 8369 (61.2%) were defined as high use by both definitions (kappa = 0.75). However, of the 12,707 defined as high use by the number of inpatient episodes, only 3698 (29.1%) were also defined as high use by another definition. Exploration of the most responsible diagnoses across definitions showed that congestive heart failure (2.8%-3.5%), chronic obstructive pulmonary disease (1.6%-3.2%), and dementia (0.6%-2.2%) were the most frequent. Acute medical conditions (eg, pneumonia [1.8%] or gastroenteritis [0.7%]) that may result in multiple shorter hospitalizations were observed at higher frequencies among high users defined by inpatient episodes, while conditions commonly requiring rehabilitation (eg, fracture [1.8%] and stroke [1.7%]) were more common among high users defined by length of stay and cost (supplementary Table 2). Stratification by age showed marked differences in the diagnoses across high-use definitions. Among hi

gh users defined by inpatient episodes, patients aged 18-64 years had a wide range of medical diagnoses, including several for complications of childbirth. Major diagnoses among high users by length of stay aged 18-64 years were dominated by mental health–related conditions. Diagnoses among older adults (65+) were often related to degenerative neurological conditions (dementia and Alzheimer’s disease). Diagnoses among high users by cost showed similar trends to length of stay (supplementary Table 3).