External Validation of a Population-Based Prediction Model for High Healthcare Resource Use in Adults

Characterizing high-cost healthcare users among adults with back pain in Ontario, Canada: a population-based cohort study

ABSTRACT

Some patients with back pain contribute disproportionately to high healthcare costs; however, characteristics of high-cost users with back pain are not well defined. We described high-cost healthcare users based on total costs among a population-based cohort of adults with back pain within the Ontario government's single-payer health system across sociodemographic, health, and behavioural characteristics. We conducted a population-based cohort study of Ontario adult (aged 18 years or older) respondents of the Canadian Community Health Survey (CCHS) with back pain (2003-2012), linked to administrative data (n = 36,605; weighted n = 2,076,937, representative of Ontario). Respondents were ranked based on gradients of total healthcare costs (top 1%, top 2%-5%, top 6%-50%, and bottom 50%) for 1 year following the CCHS survey, with high-cost users as top 5%. We used multinomial logistic regression to investigate characteristics associated with the 4 cost groups. Top 5% of cost users accounted for 49% ($4 billion CAD) of total healthcare spending, with inpatient hospital care as the largest contributing service type (approximately 40% of costs). Top 5% high-cost users were more likely aged 65 years or older (ORtop1% = 16.6; ORtop2-5% = 44.2), with lower income (ORtop1% = 3.6; ORtop 2-5% = 1.8), chronic disease(s) (ORtop1% = 3.8; ORtop2-5% = 1.6), Aggregated Diagnosis Groups measuring comorbidities (ORtop1% = 25.4; ORtop2-5% = 13.9), and fair/poor self-rated general health (ORtop1% = 6.7; ORtop2-5% = 4.6) compared with bottom 50% users. High-cost users tended to be current/former smokers, obese, and report fair/poor mental health. High-cost users (based on total costs) among adults with back pain account for nearly half of all healthcare spending over a 1-year period and are associated with older age, lower income, comorbidities, and fair/poor general health. Findings identify characteristics associated with a high-risk group for back pain to inform healthcare and public health strategies that target upstream determinants.

Prediction Models for Future High-Need High-Cost Healthcare Use: a Systematic Review

BACKGROUND

In an effort to improve both quality of care and cost-effectiveness, various care-management programmes have been developed for high-need high-cost (HNHC) patients. Early identification of patients at risk of becoming HNHC (i.e. case finding) is crucial to a programme's success. We aim to systematically identify prediction models predicting future HNHC healthcare use in adults, to describe their predictive performance and to assess their applicability.

METHODS

Ovid MEDLINE® All, EMBASE, CINAHL, Web of Science and Google Scholar were systematically searched from inception through January 31, 2021. Risk of bias and methodological quality assessment was performed through the Prediction model Risk Of Bias Assessment Tool (PROBAST).

RESULTS

Of 5890 studies, 60 studies met inclusion criteria. Within these studies, 313 unique models were presented using a median development cohort size of 20,248 patients (IQR 5601-174,242). Predictors were derived from a combination of data sources, most often claims data (n = 37; 62%) and patient survey data (n = 29; 48%). Most studies (n = 36; 60%) estimated patients' risk to become part of some top percentage of the cost distribution (top-1-20%) within a mean time horizon of 16 months (range 12-60). Five studies (8%) predicted HNHC persistence over multiple years. Model validation was performed in 45 studies (76%). Model performance in terms of both calibration and discrimination was reported in 14 studies (23%). Overall risk of bias was rated as 'high' in 40 studies (67%), mostly due to a 'high' risk of bias in the subdomain 'Analysis' (n = 37; 62%).

DISCUSSION

This is the first systematic review (PROSPERO CRD42020164734) of non-proprietary prognostic models predicting HNHC healthcare use. Meta-analysis was not possible due to heterogeneity. Most identified models estimated a patient's risk to incur high healthcare expenditure during the subsequent year. However, case-finding strategies for HNHC care-management programmes are best informed by a model predicting HNHC persistence. Therefore, future studies should not only focus on validating and extending existing models, but also concentrate on clinical usefulness.