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Frailty is a combination of factors that increase vulnerability to functional decline, dependence and/or death. Frailty cannot easily be defined by comorbidities or medical treatment alone. Accurate detection of frailty in practice and at a population level is needed. This may be achieved using a combination of data sources.
Objectives and Approach
We construct algorithms that can identify frailty using electronic medical record (EMR) and administrative data. We linked EMR data from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN) nodes and the administrative (e.g. billings, hospitalizations) from Population Data BC and the Manitoba Health Policy Centre. Frailty was defined as individuals 65+ who were receiving home services, had specific chronic conditions, received specific diagnoses, and/or had specific lab or other clinical indicators. We describe sociodemographic characteristics, risk factors, prescribed medications, use and costs of healthcare for those identified as frail.
People were identified as frail in 2014 and all analysis was completed with 2015 data. Among those who were > 65 years, who had a record in both EMR and administrative data, 5\%-8\% (n=191 of 3,553, BC; n=2,396 of 29,382, MB) were identified as frail. There was a higher likelihood of being frail with increasing age and being a woman. In BC, those identified as frail have higher contacts with primary care (n=20 vs. n=10) and more days in hospital (n=7.4 vs. n=2.0) compared to those who are not frail. Twenty two percent of those identified as frail in 2014 died in 2015, compared to a mortality rate of 2\% among those who are not frail.
Identifying and reporting on those who are frail in primary care as well as in communities could enable targeted communications with patients and families and community based resources in order to improve patient care, patients’ and caregivers’ quality of life and better use of the healthcare system.
Over the past 20 years, increases in opioid prescribing rates and average prescription volumes have been documented in both the United States and Canada. As prescription opioid use and overdose has steadily increased in North America, a dramatic rise in hospitalizations resulting from opioid poisonings has also been witnessed.
Objectives and Approach
To describe opioid utilization patterns for patients admitted to a tertiary care hospital in Montreal between 2014 and 2016, and to estimate the association between opioid use and risk of adverse health outcomes in the 90-days post discharge. Opioid utilization was measured using medication dispensing data from the provincial healthcare databases (RAMQ) while hospital/ED visits were obtained from RAMQ medical services. Patient characteristics and discharge prescriptions were obtained from the hospital chart. Time-varying utilization of opioids was modeled as: 1) current use, 2) cumulative duration of past use, and 3) duration of use of past 10 days, using Cox models.
Of the 3,308 included patients mean age was 70 (SD 14), 57% were male and 47% were discharged from surgical units. 856 (26%) patients had a history of opioid use in the 1-year prior to admission, 1528 (46%) were prescribed an opioid at discharge and 1481 (45%) filled an opioid in the 90-days post discharge. Among patients prescribed an opioid at discharge, 79% filled their prescription post discharge, where opioid naïve patients were less likely to fill their prescriptions compared to those with a history of opioid use (40% vs 81%). Our multivariable Cox models suggested that cumulative duration of opioid exposure in the past 10 days was associated with a 10% increased risk of ED visits and hospitalizations.
Patients with a history of opioid use were more likely to both receive an opioid prescription at hospital discharge and fill their prescription post-discharge. Our findings suggest that longer-term utilization patterns of these medications after hospitalization may increase risk of re-admissions and ED visits.
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