Health Links are a new model of providing care coordination for high-cost, high-needs patients in Ontario. We evaluated use of hospital-related health care services among Health Links patients in the Central Local Health Integration Network (LHIN) of Ontario in the year before versus after program enrolment and compared rates of use with those among similar patients with complex needs not enrolled in the program (comparator group).We identified all patients who received a Health Links coordinated care plan before Jan. 1, 2015, using linked registry and health administrative data. We used propensity scores to match (1:1) enrollees (registry) with comparator patients (administrative data). Using a difference-in-differences approach with generalized estimating equations, we evaluated 5 measures of Health Link performance: rates of hospital admission, emergency department visits, days in acute care, 30-day readmissions and 7-day postdischarge primary care follow-up.Of the 344 enrollees in the registry, we matched 313 [91.0%] to comparator patients. All measured sociodemographic, comorbidity and health care use characteristics were balanced between the 2 groups (all standardized differences < 0.10). For enrollees, the rate of days in acute care per person-year increased by 35% (incidence rate ratio 1.35 [confidence interval 1.11-1.65]) after versus before the index date, but differences were nonsignificant for all other measures. Difference-in-differences analyses revealed greater reductions in hospital admissions, emergency department visits and acute care days after the index date in the comparator group than among enrollees.Initial implementation of the Health Link program in the Central LHIN did not reduce selected indicators of Health Link performance among enrollees. As the Health Link program evolves and standardization is implemented, future research may reveal effects from the initiative in other outcomes or with longer follow-up.
ObjectiveTo adapt the BCHSM population segmentation methodology to Ontario’s health administrative data to identify mutually exclusive segments with similar health care needs to support integrated care efforts and population health management in Ontario, Canada. To compare health system related costs across derived segments to identify opportunities for better integrated care. ApproachWe identified Ontarians alive with valid health card numbers as of April 1, 2020 (n =14,358,565) and created a matrix of prior utilization, cost and diagnoses using linked health administrative databases. Using a hierarchical technique, we assigned individuals into one of 14 BCHSM segments based on the greatest health care needs. Segments of need range from non-users (low need) to end-of-life patients (greatest need). We report the distribution of individual characteristics, average monthly costs across segments and further stratified health care costs by quintile of material deprivation within segments. ResultsThe largest segment was the healthy (low) users (43%) followed by low chronic conditions (28%) and non-users (10%). Five segments comprised <1% of the total population: end-of-life, frail in care, cancer, frail in the community and child and youth major. Average costs per month alive increased from $28 for the non-user segment to $5,100 for the end-of-life segment (0.5% of the population). Costs in the Frail with high chronic conditions segment ($2,740/mo) were 3-times higher than costs in the high chronic conditions segment ($930/mo), 6-times higher than costs in the medium chronic conditions segment ($450/mo), and 14-times higher than costs in the low chronic conditions segment ($193/mo). Results were generally more favourable in areas of low (vs high) material deprivation overall and within population segments. ConclusionUsing Ontario’s linkable health administrative data we have created an Ontario adaptation of the BCHSM needs-based population segmentation approach. Segmentation supports population health management as well as helping identify opportunities for improvement to strengthen integrated care and potential cost savings.
There is currently mixed evidence on the influence of long-term conditions and deprivation on mortality. We aimed to explore whether number of long-term conditions contribute to socioeconomic inequalities in mortality, whether the influence of number of conditions on mortality is consistent across socioeconomic groups and whether these associations vary by working age (18-64 years) and older adults (65 + years). We provide a cross-jurisdiction comparison between England and Ontario, by replicating the analysis using comparable representative datasets.Participants were randomly selected from Clinical Practice Research Datalink in England and health administrative data in Ontario. They were followed from 1 January 2015 to 31 December 2019 or death or deregistration. Number of conditions was counted at baseline. Deprivation was measured according to the participant's area of residence. Cox regression models were used to estimate hazards of mortality by number of conditions, deprivation and their interaction, with adjustment for age and sex and stratified between working age and older adults in England (N = 599,487) and Ontario (N = 594,546).There is a deprivation gradient in mortality between those living in the most deprived areas compared to the least deprived areas in England and Ontario. Number of conditions at baseline was associated with increasing mortality. The association was stronger in working age compared with older adults respectively in England (HR = 1.60, 95% CI 1.56,1.64 and HR = 1.26, 95% CI 1.25,1.27) and Ontario (HR = 1.69, 95% CI 1.66,1.72 and HR = 1.39, 95% CI 1.38,1.40). Number of conditions moderated the socioeconomic gradient in mortality: a shallower gradient was seen for persons with more long-term conditions.Number of conditions contributes to higher mortality rate and socioeconomic inequalities in mortality in England and Ontario. Current health care systems are fragmented and do not compensate for socioeconomic disadvantages, contributing to poor outcomes particularly for those managing multiple long-term conditions. Further work should identify how health systems can better support patients and clinicians who are working to prevent the development and improve the management of multiple long-term conditions, especially for individuals living in socioeconomically deprived areas.
The reliability of diagnostic coding of acute stroke and transient ischemic attack (TIA) in administrative data is uncertain. The purpose of this study is to determine the agreement between administrative data sources and chart audit for the identification of stroke type, stroke risk factors, and the use of hospital-based diagnostic procedures in patients with stroke or TIA.Medical charts for a population-based sample of patients (n = 14,508) with ischemic stroke, intracerebral hemorrhage (ICH), or TIA discharged from inpatient and emergency departments (ED) in Ontario, Canada, between April 1, 2012 and March 31, 2013, were audited by trained abstractors. Audited data were linked and compared with hospital administrative data and physician billing data. The positive predictive value (PPV) of hospital administrative data and kappa agreement for the reporting of stroke type were calculated. Kappa agreement was also determined for stroke risk factors and for select stroke-related procedures.The PPV for stroke type in inpatient administrative data ranged from 89.5% (95% CI 88.0-91.0) for TIA, 91.9% (95% CI 90.2-93.5) for ICH, and 97.3% (95% CI 96.9-97.7) for ischemic stroke. For ED administrative data, PPV varied from 78.8% (95% CI 76.3-81.2) for ischemic, 86.3% (95% CI 76.8-95.7) for ICH, and 95.3% (95% CI 94.6-96.0) for TIA. The chance-corrected agreement between the audited and administrative data was good for atrial fibrillation (k = 0.60) and very good for diabetes (k = 0.86). Hospital administrative data combined with physician billing data more than doubled the observed agreement for carotid imaging (k = 0.65) and echocardiography (k = 0.66) compared to hospital administrative data alone.Inpatient and ED administrative data were found to be reliable in the reporting of the International Classification of Diagnosis, 10th revision, Canada (ICD-10-CA)-coded ischemic stroke, ICH and TIA, and for the recording of atrial fibrillation and diabetes. The combination of physician billing data with hospital administrative data greatly improved the capture of some diagnostic services provided to inpatients.
ObjectiveTo provide an update on the key characteristics, refinements to project flow, and future opportunities for a program providing customized research evidence to health system policymakers and providers. ApproachWith a goal to inform health system decision making, the program answers research questions posed by health system requestors that can be answered with linked, population-based administrative data. Now in its 10th year of operation, the program has been refined over time to improve efficiency, usefulness of research products, and requestor satisfaction. Satisfaction surveys and individual-level engagements with requestors are used to collect feedback on the needs of an increasingly diverse group of requests. ResultsWith 508 requests, 82 Data Sharing Agreements, and 257 unique requestors since its inception, the program informs governmental decision making, evaluates intervention effectiveness, and aids grassroots organizations’ planning for services. Notable updates to the program include, inserting multiple opportunities for connection between the program and requestors through the project life cycle to understand goals and needs; assigning staff scientists to shepherd projects and promote efficiency; training coordinators to review privacy impact assessments; and (soon) accepting a broader range of projects to include data beyond the health sector e.g. education. ConclusionThis program continues to improve and is an exemplar of sustainable approaches to supporting health system requestors with evidence from administrative data in program evaluation, planning and policy change. ImplicationsBeing responsive to requestor needs has allowed the program to attract an increasingly diverse range of requesters who can obtain impactful and timely research evidence.
In this study, we investigated the incremental 1-year direct costs of health care associated with frailty among home care recipients in Ontario with and without dementia.We conducted a cohort study of 159,570 home care clients aged 50 years and older in Ontario, Canada in 2014/2015. At index home care assessment, we ascertained dementia status using a validated algorithm and frailty level (robust, prefrail, frail) based on the proportion of accumulated to potential health deficits. Clients were followed for 1-year during which we obtained direct overall and sector-specific publicly-funded health care costs (in 2015 Canadian dollars). We estimated the incremental effect of frailty level on costs using a 3-part survival- and covariate-adjusted estimator. All analyses were stratified by dementia status.Among those with dementia (n=42,828), frailty prevalence was 32.1% and the average 1-year cost was $30,472. The incremental cost of frailty (vs. robust) was $10,845 [95% confidence interval (CI): $10,112-$11,698]. Among those without dementia (n=116,742), frailty prevalence was 25.6% and the average 1-year cost was $28,969. Here, the incremental cost of frailty (vs. robust) was $12,360 (95% CI: $11,849-$12,981). Large differences in survival between frailty levels reduced incremental cost estimates, particularly for the dementia group (survival effect: -$2742; 95% CI: -$2914 to -$2554).Frailty was associated with greater 1-year health care costs for persons with and without dementia. This difference was driven by a greater intensity of health care utilization among frail clients. Mortality differences across the frailty levels mitigated the association especially among those with dementia.
Administrative data validation is essential for identifying biases and misclassification in research. The objective of this study was to determine the accuracy of diagnostic codes for acute stroke and transient ischemic attack (TIA) using the Ontario Stroke Registry (OSR) as the reference standard.We identified stroke and TIA events in inpatient and emergency department (ED) administrative data from eight regional stroke centres in Ontario, Canada, from April of 2006 through March of 2008 using ICD-10-CA codes for subarachnoid haemorrhage (I60, excluding I60.8), intracerebral haemorrhage (I61), ischemic (H34.1 and I63, excluding I63.6), unable to determine stroke (I64), and TIA (H34.0 and G45, excluding G45.4). We linked administrative data to the Ontario Stroke Registry and calculated sensitivity and positive predictive value (PPV).We identified 5,270 inpatient and 4,411 ED events from the administrative data. Inpatient administrative data had an overall sensitivity of 82.2% (95% confidence interval [CI 95%]=81.0, 83.3) and a PPV of 68.8% (CI 95%=67.5, 70.0) for the diagnosis of stroke, with notable differences observed by stroke type. Sensitivity for ischemic stroke increased from 66.5 to 79.6% with inclusion of I64. The sensitivity and PPV of ED administrative data for diagnosis of stroke were 56.8% (CI 95%=54.8, 58.7) and 59.1% (CI 95%=57.1, 61.1), respectively. For all stroke types, accuracy was greater in the inpatient data than in the ED data.The accuracy of stroke identification based on administrative data from stroke centres may be improved by including I64 in ischemic stroke type, and by considering only inpatient data.
The burden of multimorbidity is a growing clinical and health system problem that is known to be associated with socioeconomic status, yet our understanding of the underlying determinants of inequalities in multimorbidity and longitudinal trends in measured disparities remains limited.
Introduction: Previous research has shown that the socioeconomic status (SES)-health gradient also extends to high-cost patients; however, little work has examined high-cost patients with mental illness and/or addiction. The objective of this study was to examine associations between individual-, household-, and area-level SES factors and future high-cost use among these patients. Methods: We linked survey data from adult participants (ages 18 and older) of three cycles of the Canadian Community Health Survey (CCHS) to administrative health care data from Ontario, Canada. Respondents with mental illness and/or addiction were identified based on prior mental health and addiction health care use and followed for 5 years for which we ascertained health care costs covered under the public health care system. We quantified associations between SES factors and becoming a high-cost patient (i.e, transitioning into the top 5%) using logistic regression models. For ordinal SES factors, such as income, education and marginalization variables, we measured absolute and relative inequalities using the slope and relative index of inequality. Results: Among our sample, lower personal income (OR=2.11, 95% C.I. [1.54, 2.88] for $0 to $14,999), lower household income (OR=2.11, 95% C.I. [1.49, 2.99] for lowest income quintile), food insecurity (OR=1.87, 95% C.I. [1.38, 2.55]) and non-homeownership (OR=1.34, 95% C.I. [1.08, 1.66]), at the individual and household levels, respectively, and higher residential instability (OR=1.72, 95% C.I. [1.23, 2.42] for most marginalized), at the area level, were associated with higher odds of becoming a high-cost patient within a 5-year period. Moreover, the inequality analysis suggests pro-high-SES gradients in high-cost transitions.
We developed and validated a multivariable probabilistic case-detection model to detect known cases of diabetes mellitus (DM) using clinical and demographic data. We applied our method to a cohort of older adult residents of the region of Sherbrooke, Quebec. Predictors were added to a logistic regression model and internally validated using a 2:1 split sample approach. Models were compared using measures goodness of fit, discrimination and accuracy. The best model incorporated all predictors into the model: male sex, age, at least one hospitalization, physician visit and drug dispensed for diabetes.
