Association between non-medical cannabis legalization and emergency department visits for cannabis-induced psychosis

A major public health concern of cannabis legalization is that it may result in an increase in psychotic disorders. We examined changes in emergency department (ED) visits for cannabis-induced psychosis following the legalization and subsequent commercialization (removal of restrictions on retail stores and product types) of non-medical cannabis in Ontario, Canada (population of 14.3 million). We used health administrative data containing the cause of all ED visits to examine changes over three periods; 1) pre-legalization (January 2014–September 2018); 2) legalization with restrictions (October 2018 – February 2020); and 3) commercialization (March 2020 – September 2021). We considered subgroups stratified by age and sex and examined cocaine- and methamphetamine-induced psychosis ED visits as controls. During our study, there were 6300 ED visits for cannabis-induced psychosis. The restricted legalization period was not associated with changes in rates of ED visits for cannabis-induced psychosis relative to pre-legalization. The commercialization period was associated with an immediate increase in rates of ED visits for cannabis-induced psychosis (IRR 1.30, 95% CI 1.02–1.66) and no gradual monthly change; immediate increases were seen only for youth above (IRR 1.63, 1.27–2.08, ages 19–24) but not below (IRR 0.73 95%CI 0.42–1.28 ages, 15–18) the legal age of purchase, and similar for men and women. Commercialization was not associated with changes in rates of ED visits for cocaine- or methamphetamine-induced psychosis. This suggests that legalization with store and product restrictions does not increase ED visits for cannabis-induced psychosis. In contrast, cannabis commercialization may increase cannabis-induced psychosis presentations highlighting the importance of preventive measures in regions considering legalization

Using routinely collected data to develop and evaluate a clinical tool for early identification of palliative care needs in long-term care: The RESPECT Project.

Objectives Prognostication tools reporting personalized mortality risk and survival can improve advance care planning and discussions about end-of-life care. We developed, validated, and implemented a mortality risk algorithm for older adults with diverse care needs in long-term care (LTC) homes, called the Risk Evaluation for Support: Predictions for Elder-Life in the Community Tool for LTC (RESPECT-LTC). Approach RESPECT-LTC was developed using routinely-collected health information on residents in LTC homes in Ontario, Canada. Model development used a cohort of LTC residents aged 50 years or older with at least 1 Resident Assessment Instrument—Minimum Data Set (RAI-MDS) record between January 2010 and December 2016. The primary outcome was mortality 6 months after a RAI-MDS assessment. We used proportional hazards regression with robust standard errors to account for clustering by the individual. We validated this algorithm, temporally, in a cohort of LTC residents who were assessed between January and December 2017. We constructed 37 risk bins based on incremental increases in estimated median survival of ~3 weeks among residents at high risk of death and 3 months among residents with lower mortality risk. We implemented and are evaluating the use of RESPECT-LTC for early identification of palliative care needs in LTC homes across Ontario. Results Development and validation cohorts included 2,228,176 and 328,204 RAI-MDS assessments, respectively. Mean predicted 6-month mortality risk ranged from 1.38% (95% CI 0.63%-1.61%) in the lowest to 91.97% (95% CI 81.47%-99.9%) in the highest risk group. Estimated median survival spanned from 42 days (15 to 128 d at the 25th and 75th percentiles) in the highest risk group to over 8 years (2,066 to 3,428 d) in the lowest risk group. The algorithm had a c-statistic of 0.730 (95% CI 0.726–0.736) in our validation cohort. Conclusion RESPECT-LTC makes use of routinely-collected information to improve the identification of palliative and end-of-life care needs in LTC. Ongoing evaluation will assess its impact on referrals to palliative care, hospitalization at the end of life, and location of death

Using Large Date to Present Uncertainty for Risk Prediction in the Era of Precision Medicine: The RESPECT Algorithm for Predicted Death at End-of-Life

Introduction In Ontario, only 52% of people received palliative care in their last year of life, with only 20\% of those receiving it at home, which can improve the dying experience. Existing algorithms identifying people at end-of-life can potentially improve access to palliative care but are difficult for patients to understand. Objectives and Approach To predict and communicate risk of death for community dwelling older adults using a pre-specified and published approach (Trial registration NCT02779309). All assessments from community-dwelling Ontarians (N = 488,636) who received at least one home care assessment from the residential assessment instrument – home care (RAI HC) from 2007 to 2013 (N=1,331,273) were included. The algorithm used a two-step approach by rank ordering participants into 61 groups based on six-month probability of death (from Cox-proportional hazard models) and generated Kaplan-Meier five-year survival curves for each group. Median Survival time is reported with uncertainties expressed with 25th to 75th percentiles. Results The median predicted probability of death within six-months was 0.1095 (0.1093-0.1097, 95% CI). Risk varied among the 61 groups from 0.0158 (0.0158-0.0159) to 0.9820 (0.9810-0.9830). Median observed survival time varied from 27 days (10 to 81 days, 25th and 75th percentile) in the highest risk group to 10 years (3655 days (2111 to >3655 days)) in the lowest risk group. Discrimination and calibration were satisfactory between the derivation (2007-2012 assessments) and validation (2013 assessments) cohorts, with a C statistics of 0.77 and discrimination plot intercept 0.094, slope 0.914. The Kaplan-Meier five-year survival curves for each of the 61 groups will be visually represented in six different ways displaying the risk and uncertainty, and can be altered to yield information of interest specific to each patient/caregiver. Conclusion/Implications RESPECT is adaptive and personalized, with instantaneous feedback as the user provides a response to each question. We will present RESPECT’s development and implementation processes and set up an interactive presentation of the calculator, demonstrating RESPECT’s ability to deliver patient-comprehensible end-of-life prognoses with uncertainty to patients and their caregivers

A Data Science Approach to Predictive Analytic Research and Knowledge Translation

Introduction Current approaches to the development and application of predictive studies is inefficient and difficult to reproduce. Thousands of predictive health algorithms have been developed; however, less than 2\% have been assessed outside their original setting and even fewer have been applied and evaluated in practice. Objectives and Approach Objective: To develop a standardized workflow for algorithm development, dissemination and implementation. Existing predictive analytics workflow and open standards were adapted and expanded for health research and health care settings. The approach was designed to work within multidisciplinary teams and to improve research transparency, reproducibility, quality, efficiency and application. Key components include standardized algorithm description files, documentation and code libraries. All libraries and programming packages, which were created for/with open-source software, can be used for a wide range of statistical and machine learning models. Publicly-available repositories contain the algorithms, validation data, R code and other supporting infrastructure. Results Algorithm development involves variable pre-specification and documentation of model variables, followed by creation of data preprocessing code to generate model variables from the study dataset. Preprocessing uses algorithm specification documentation and a function library, building upon and integrating with existing algorithms when possible to preventing code duplication. Models are output as a Predictive Modelling Markup Language (PMML) file, a portable industry standard for describing and scoring predictive models. A separate scoring "engine" is used to implement PMML-described algorithms in a range of settings, including algorithm validation at other research institutions. Algorithm applications currently include the Project Big Life (www.projectbiglife.ca) online calculators, population, health services and public health planning uses and an algorithm visualization tool. An API permits use of the calculator engine by other organizations. Conclusion/Implications Barriers to the implementation of predictive analytics in real-world settings—such as within electronic medical records or decision aid applications—can be mitigated with well described algorithms that are easy to replicate and implement, especially as access to big health data increases and algorithms become increasingly complex

Care trajectory in homes care users across mortality-risk profiles: an observational study.

Objectives RESPECT is a prognostic tool, developed using linked population-based data, to predict 6-month mortality in community-dwelling older adults. RESPECT is implemented and openly accessible as a web-based tool on ProjectBigLife.ca, where over 700,000 calculations have been performed to date. Our objective was to describe healthcare utilization patterns among home care (HC) users across mortality risk profiles generated from RESPECT to inform care planning for older persons who have varying mortality risks and levels of care needs as they decline. Approach We conducted a retrospective cohort study examining healthcare use among HC users in Ontario, Canada, who received at least one interRAI HC assessment between April 2018 and September 2019.  Using linked health administrative data at the individual level, we examined the use of acute care (hospitalizations and emergency department (ED) visits), long-term care (LTC), and palliative home care within 6-months of each assessment and prognostication using RESPECT. Mortality risk profiles from RESPECT were created based on the median survival. Results The cohort comprised 247,377 community-dwelling older adults; 14.3% died within 6-months of an assessment. Among decedents, half (51.51%) of HC users with a predicted median survival of less than 3-months received at least one palliative care home visit; 39.17%, 34.82% and 13.84% visited the ED, were hospitalized, or were admitted to LTC, respectively. The proportion of assessments that received at least one palliative HC visit declined to 43.11% and 30.28% of assessments with a median survival between 3- and 6-months and those between 6-months and 12-months, respectively.  The proportion of assessments with an acute care use increases with increasing median survival. Conclusion A considerable proportion of people at the end-of-life do not receive any palliative home care and continued to be institutionalized. This may be indication that the reduced life expectancies and palliative care needs of many older adults are not being recognized, thus demonstrating the value of prognostic models like RESPECT to inform care planning  for individuals in their final years of life

Protocol: Health, social care and technological interventions to improve functional ability of older adults: Evidence and gap map

This is the final version. Available on open access from Wiley via the DOI in this frecordThis is a protocol for a Campbell Evidence and Gap Map. The objectives are to identify and assess the available evidence on health, social care and technological interventions to improve functional ability among older adults

A Multi-Stage Process to Develop Quality Indicators for Community-Based Palliative Care Using interRAI Data

Background: Individuals receiving palliative care (PC) are generally thought to prefer to receive care and die in their homes, yet little research has assessed the quality of home- and community-based PC. This project developed a set of valid and reliable quality indicators (QIs) that can be generated using data that are already gathered with interRAI assessments-an internationally validated set of tools commonly used in North America for home care clients. The QIs can serve as decision-support measures to assist providers and decision makers in delivering optimal care to individuals and their families. Methods: The development efforts took part in multiple stages, between 2017-2021, including a workshop with clinicians and decision-makers working in PC, qualitative interviews with individuals receiving PC, families and decision makers and a modified Delphi panel, based on the RAND/ULCA appropriateness method. Results: Based on the workshop results, and qualitative interviews, a set of 27 candidate QIs were defined. They capture issues such as caregiver burden, pain, breathlessness, falls, constipation, nausea/vomiting and loneliness. These QIs were further evaluated by clinicians/decision makers working in PC, through the modified Delphi panel, and five were removed from further consideration, resulting in 22 QIs. Conclusions: Through in-depth and multiple-stakeholder consultations we developed a set of QIs generated with data already collected with interRAI assessments. These indicators provide a feasible basis for quality benchmarking and improvement systems for care providers aiming to optimize PC to individuals and their families

The healthcare costs of heart failure during the last five years of life: : A retrospective cohort study

Background Evidence on the economic impact of heart failure (HF) is vital in order to predict the cost-effectiveness of novel interventions. We estimate the health system costs of HF during the last five years of life. Methods We used linked primary care and mortality data accessed through the Clinical Practice Research Datalink (CPRD) to identify 1555 adults in England who died with HF in 2012/13. We used CPRD and linked Hospital Episode Statistics to estimate the cost of medications, primary and hospital healthcare. Using GLS regression we estimated the relationship between costs, HF diagnosis, proximity to death and patient characteristics. Results In the last 3 months of life, healthcare costs were £8827 (95% CI £8357 to £9296) per patient, more than 90% of which were for inpatient or critical care. In the last 3 months, patients spent on average 17.8 (95% CI 16.8 to 18.8) days in hospital and had 8.8 (95% CI 8.4 to 9.1) primary care consultations. Most (931/1555; 59.9%) patients were in hospital on the day of death. Mean quarterly healthcare costs in quarters after HF diagnosis were higher (£1439; [95% CI £1260 to £1619]) than in quarters preceding diagnosis. Older patients and patients with lower comorbidity scores had lower costs. Conclusions Healthcare costs increase sharply at the end of life and are dominated by hospital care. There is potential to save money by implementation and evaluation of interventions that are known to reduce hospitalisations for HF, particularly at the end of life

Potentially inappropriate prescribing in long-term care residents and its association with probable delirium.

Objective: Medications can increase the risk of delirium due to drug toxicities, polypharmacy, and drug interactions. This study examined potentially inappropriate prescribing (PIP) of medication and its association with probable delirium among long-term care residents. Approach: We conducted a cross-sectional study of long-term care residents in Ontario, Canada between January 1, 2016 and December 31, 2019. Routinely collected long-term care resident assessment data from the Resident Assessment Instrument – Minimum Dataset (RAI-MDS) was linked to prescription claims data to ascertain probable delirium and medication use in the two weeks preceding the index assessment. PIP was measured via the STOPP/START criteria and Beers criteria, with residents classified as having 0, 1, 2, or 3+ PIPs. Associations between PIP and probable delirium was assessed via bivariate and multivariable logistic regression models. Results The study population included 171,190 long-term care residents. The mean age was 84.5 years, 66.8% were female, and 62.9% had dementia. Probable delirium was documented on 3.7% of resident assessments. Over half (51.8%) of residents had 1+ PIP and 21% had 3+ PIPs according to the STOPP/START criteria. The odds of probable delirium increased as the number of PIPs increased. Probable delirium was 1.86 times more likely (95% confidence interval 1.74-1.98) in residents with 3+ PIPs compared to those with no PIPs after confounder adjustment. Similar findings were observed when PIP was evaluated using the Beers criteria. Conclusion This population-based study highlighted that potentially inappropriate medication prescribing was highly prevalent and was significantly associated with the increased likelihood of probable delirium among long-term care residents

Potentially non-beneficial interventions in the last 100 days of life of patients with cancer: A population-based retrospective cohort study.

Objectives The objective of this study was to describe the receipt of potentially non-beneficial interventions in the last 100 days of life of cancer patients and to examine variations in these interventions according to patient characteristics and cancer site. Approach We conducted a population-based retrospective cohort study of all adults age 18+ who died of cancer in Ontario, Canada between January 1, 2013 and December 31, 2017 using linked administrative health data held at ICES. Potentially non-beneficial interventions were captured via hospital discharge records and included chemotherapy, major surgery, intensive care unit admission, cardiopulmonary resuscitation, defibrillation, dialysis, percutaneous coronary intervention, mechanical ventilation, feeding tube placement, blood transfusion and bronchoscopy. We used bivariate analyses and multivariable Poisson regression to examine associations between the receipt of interventions and decedent age, sex, rurality, area-level income, and cancer site. Results Among the 125,755 decedents, the most common intervention was blood transfusion (18.1%) and major surgery (12.8%); 23.8% received no interventions, while 14% of decedents received 3+ interventions. Lower intervention rates were observed in older patients (adjusted rate ratio (RR) 0.46, 95% confidence interval (CI) 0.44-0.49 for age 95+ vs. 19-44), females (RR 0.93, 95% CI 0.92-0.94), and individuals living in higher income areas (RR 0.96, 95% CI 0.95-0.98 for highest vs. lowest income quintile). Higher intervention rates were observed in rural patients (RR 1.13, 95% CI 1.11-1.14). Patients with pancreatic cancer had the highest intervention rate (RR 1.13, 95% CI 1.10-1.16), while breast cancer patients had the lowest intervention rate (RR 0.86, 95% CI 0.84-0.89) compared to colorectal cancer patients. Conclusion Potentially non-beneficial interventions were common in the last 100 days of life of patients with cancer. Variations in interventions across patient demographics and cancer site may reflect differences in healthcare access, end-of-life care preferences, patients’ prognostic awareness, and disease factors