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Background: Allowing patients to remain at home and decreasing the number of unnecessary emergency room visits have become important policy goals in modern healthcare systems. However, the lack of available literature makes it critical to identify determinants that could be associated with increased emergency department (ED) visits in patients receiving integrated home care (IHC). Methods: A retrospective observational study was carried out in a large Italian region among patients with at least one IHC event between January 1, 2012 and December 31, 2017. IHC is administered from 8 am to 8 pm by a team of physicians, nurses, and other professionals as needed based on the patient's health conditions. A clinical record is opened at the time a patient is enrolled in IHC and closed after the last service is provided. Every such clinical record was defined as an IHC event, and only ED visits that occurred during IHC events were considered. Sociodemographic, clinical and IHC variables were collected. A multivariate, stepwise logistic analysis was then performed, using likelihood of ED visit as a dependent variable. Results: A total of 29 209 ED visits were recorded during the 66 433 IHC events that took place during the observation period. There was an increased risk of ED visits in males (odds ratio [OR] = 1.29), younger patients, those with a family caregiver (OR = 1.13), and those with a higher number of cohabitant family members. Long travel distance from patients' residence to the ED reduced the risk of ED visits. The risk of ED visits was higher when patients were referred to IHC by hospitals or residential facilities, compared to referrals by general practitioners. IHC events involving patients with neoplasms (OR = 1.91) showed the highest risk of ED visits. Conclusion: Evidence of sociodemographic and clinical determinants of ED visits may offer IHC service providers a useful perspective to implement intervention programmes based on appropriate individual care plans and broad-based client assessment. [ABSTRACT FROM AUTHOR]

Introduction: Electronic health records (EHR) and administrative healthcare data (AHD) are frequently used in geriatric mental health research to answer various health research questions. However, there is an increasing amount and complexity of data available that may lend itself to alternative analytic approaches using machine learning (ML) or artificial intelligence (AI) methods. We performed a systematic review of the current application of ML or AI approaches to the analysis of EHR and AHD in geriatric mental health. Methods: We searched MEDLINE, Embase, and PsycINFO to identify potential studies. We included all articles that used ML or AI methods on topics related to geriatric mental health utilizing EHR or AHD data. We assessed study quality either by Prediction model Risk OF Bias ASsessment Tool (PROBAST) or Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) checklist. Results: We initially identified 391 articles through an electronic database and reference search, and 21 articles met inclusion criteria. Among the selected studies, EHR was the most used data type, and the datasets were mainly structured. A variety of ML and AI methods were used, with prediction or classification being the main application of ML or AI with the random forest as the most common ML technique. Dementia was the most common mental health condition observed. The relative advantages of ML or AI techniques compared to biostatistical methods were generally not assessed. Only in three studies, low risk of bias (ROB) was observed according to all the PROBAST domains but in none according to QUADAS-2 domains. The quality of study reporting could be further improved. Conclusion: There are currently relatively few studies using ML and AI in geriatric mental health research using EHR and AHD methods, although this field is expanding. Aside from dementia, there are few studies of other geriatric mental health conditions. The lack of consistent information in the selected studies precludes precise comparisons between them. Improving the quality of reporting of ML and AI work in the future would help improve research in the field. Other courses of improvement include using common data models to collect/organize data, and common datasets for ML model validation. [ABSTRACT FROM AUTHOR]

To examine changes in the prescribing of end-of-life symptom management medications in long-term care (LTC) homes during the COVID-19 pandemic. Retrospective cohort study using routinely collected health administrative data in Ontario, Canada. We included all individuals who died in LTC homes between January 1, 2017, and March 31, 2021. We separated the study into 2 periods: before COVID-19 (January 1, 2017, to March 17, 2020) and during COVID-19 (March 18, 2020, to March 31, 2021). For each LTC home, we measured the percentage of residents who died before and during COVID-19 who had a subcutaneous symptom management medication prescription in their last 14 days of life. We grouped LTC homes into quintiles based on their mean prescribing rates before COVID-19, and examined changes in prescribing during COVID-19 and COVID-19 outcomes across quintiles. We captured 75,438 LTC residents who died in Ontario's 626 LTC homes during the entire study period, with 19,522 (25.9%) dying during COVID-19. The mean prescribing rate during COVID-19 ranged from 46.9% to 79.4% between the lowest and highest prescribing quintiles. During COVID-19, the mean prescribing rate in the lowest prescribing quintile increased by 9.6% compared to before COVID-19. Compared to LTC homes in the highest prescribing quintile, homes in the lowest prescribing quintile experienced the highest proportion of COVID-19 outbreaks (73.4% vs 50.0%), the largest mean outbreak intensity (0.27 vs 0.09 cases/bed), the highest mean total days with a COVID-19 outbreak (72.7 vs 24.2 days), and the greatest proportion of decedents who were transferred and died outside of LTC (22.1% vs 8.6%). LTC homes in Ontario had wide variations in the prescribing rates of end-of-life symptom management medications before and during COVID-19. Homes in the lower prescribing quintiles had more COVID-19 cases per bed and days spent in an outbreak. [ABSTRACT FROM AUTHOR]

First Nations Peoples in the province of British Columbia (BC), Canada, have been disproportionately affected by the overdose crisis. In 2016, a public health emergency was declared by BC's Provincial Health Officer (PHO) in response to the significant rise in opioid-related overdose deaths reported in BC. New surveillance systems were required to identify trends in overdose events and related deaths in the province as a whole, and for First Nations Peoples. Data sharing and analysis processes that adhered to the principles of OCAP® (ownership, control, access, and possession), and to the Truth and Reconciliation Commission of Canada's Calls to Action, needed to be developed. The First Nations Health Authority (FNHA), BC Centre for Disease Control, PHO, and the BC Ministry of Health have worked collaboratively to facilitate identification of First Nations persons in surveillance data for appropriate analysis by FNHA. This paper outlines the data stewardship and governance context, principles, and operational considerations for creating overdose surveillance systems to measure overdose events among First Nations Peoples in BC. [ABSTRACT FROM AUTHOR]

Introduction: Health insurance registries, which capture insurance coverage and demographic information for entire populations, are a critical component of population health surveillance and research when using administrative data. Lack of standardization of registry information across Canada's provinces and territories could affect the comparability of surveillance measures. We assessed the contents of health insurance registries across Canada to describe the populations covered and document registry similarities and differences. Methods: A survey about the data and population identifiers in health insurance registries was developed by the study team and representatives from the Public Health Agency of Canada. The survey was completed by key informants from most provinces and territories and then descriptively analyzed. Results: Responses were received from all provinces; partial responses were received from the Northwest Territories. Demographic information in health insurance registries, such as primary address, date of birth and sex, were captured in all jurisdictions. Data captured on familial relationships, ethnicity and socioeconomic status varied among jurisdictions, as did start and end dates of coverage and frequency of registry updates. Identifiers for specific populations, such as First Nations individuals, were captured in some, but not all jurisdictions. Conclusion: Health insurance registries are a rich source of information about the insured populations of the provinces and territories. However, data heterogeneity may affect who is included and excluded in population surveillance estimates produced using administrative health data. Development of a harmonized data framework could support timely and comparable population health research and surveillance results from multi-jurisdiction studies. [ABSTRACT FROM AUTHOR]

Population‐level identification of children and youth with ASD is essential for surveillance and planning for required services. The objective of this study was to develop and validate an algorithm for the identification of children and youth with ASD using administrative health data. In this retrospective validation study, we linked an electronic medical record (EMR)‐based reference standard, consisting 10,000 individuals aged 1–24 years, including 112 confirmed ASD cases to Ontario administrative health data, for the testing of multiple case‐finding algorithms. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and corresponding 95% confidence intervals (CI) were calculated for each algorithm. The optimal algorithm was validated in three external cohorts representing family practice, education, and specialized clinical settings. The optimal algorithm included an ASD diagnostic code for a single hospital discharge or emergency department visit or outpatient surgery, or three ASD physician billing codes in 3 years. This algorithm's sensitivity was 50.0% (95%CI 40.7–88.7%), specificity 99.6% (99.4–99.7), PPV 56.6% (46.8–66.3), and NPV 99.4% (99.3–99.6). The results of this study illustrate limitations and need for cautious interpretation when using administrative health data alone for the identification of children and youth with ASD. Lay Summary: We tested algorithms (set of rules) to identify young people with ASD using routinely collected administrative health data. Even the best algorithm misses more than half of those in Ontario with ASD. To understand this better, we tested how well the algorithm worked in different settings (family practice, education, and specialized clinics). The identification of individuals with ASD at a population level is essential for planning for support services and the allocation of resources. Autism Res 2021, 14: 1037–1045. © 2021 The Authors. Autism Research published by International Society for Autism Research published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Background: Increased risks of acute myocardial infarction (AMI) may be attributable to the workplace, however, associations are not well‐established. Using the Occupational Disease Surveillance System (ODSS), we sought to estimate associations between occupation and industry of employment and AMI risk among workers in Ontario, Canada. Methods: The study population was derived by linking provincial accepted lost‐time workers' compensation claims data, to inpatient hospitalization records. Workers aged 15–65 years with an accepted non‐AMI compensation claim were followed for an AMI event between 2007 and 2016. Adjusted Cox proportional hazard models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for each industry and occupation group, compared to all other workers in the cohort. Sex‐stratified analyses were also performed. Results: In all, 24,514 incident cases of AMI were identified among 1,502,072 Ontario workers. Increased incidence rates of AMI were found across forestry (HR 1.37, 95% CI 1.19–1.58) and wood processing (HR 1.50, 1.27–1.77) job‐titles. Elevated rates were also detected within industries and occupations both broadly related to mining and quarrying (HR 1.52, 1.17–1.97), trucking (HR 1.32, 1.27‐1.38), construction (HR 1.32, 1.14–1.54), and the manufacturing and processing of metal (HR 1.41, 1.19–1.68), textile (HR 1.41, 1.07–1.88), non‐metallic mineral (HR 1.30, 0.82–2.07), and rubber and plastic (HR 1.42, 1.27–1.60) products. Female food service workers also had elevated AMI rates (HR 1.36, 1.23–1.51). Conclusion: This study found occupational variation in AMI incidence. Future studies should examine work‐related hazards possibly contributing to such excess risks, like noise, vibration, occupational physical activity, shift work, and chemical and particulate exposures. [ABSTRACT FROM AUTHOR]

Background: Administrative health data are increasingly used to detect adverse drug events (ADEs). However, the few studies evaluating diagnostic codes for ADE detection demonstrated low sensitivity, likely due to narrow code sets, physician under-recognition of ADEs, and underreporting in administrative data. The objective of this study was to determine if combining an expanded ICD code set in administrative data with e-prescribing data improves ADE detection.Methods: We conducted a prospective cohort study among patients newly prescribed antidepressant or antihypertensive medication in primary care and followed for 2 months. Gold standard ADEs were defined as patient-reported symptoms adjudicated as medication-related by a clinical expert. Potential ADEs in administrative data were defined as physician, ED, or hospital visits during follow-up for known adverse effects of the study medication, as identified by ICD codes. Potential ADEs in e-prescribing data were defined as study drug discontinuations or dose changes made during follow-up for safety or effectiveness reasons.Results: Of 688 study participants, 445 (64.7%) were female and mean age was 64.2 (SD 13.9). The study drug for 386 (56.1%) patients was an antihypertensive, and for 302 (43.9%) an antidepressant. Using the gold standard definition, 114 (16.6%) patients experienced an ADE, with 40 (10.4%) among antihypertensive users and 74 (24.5%) among antidepressant users. The sensitivity of the expanded ICD code set was 7.0%, of e-prescribing data 9.7%, and of the two combined 14.0%. Specificities were high (86.0-95.0%). The sensitivity of the combined approach increased to 25.8% when analysis was restricted to the 27% of patients who indicated having reported symptoms to a physician.Conclusion: Combining an expanded diagnostic code set with e-prescribing data improves ADE detection. As few patients report symptoms to their physician, higher detection rates may be achieved by collecting patient-reported outcomes via emerging digital technologies such as patient portals and mHealth applications. [ABSTRACT FROM AUTHOR]

Romina Pace,1 Elham Rahme,1,2 Deborah Da Costa,1,2 Kaberi Dasgupta1,2 1Centre for Outcomes Research and Evaluation, Research Institute of the McGill University Health Centre, Montreal, QC, Canada; 2Department of Medicine, McGill University Health Center, Montreal, QC, Canada Purpose: The aim of this study was to examine the association between gestational diabetes mellitus (GDM) and depression incidence in mothers and fathers during prenatal and postnatal periods.Patients and methods: Matched pairs (GDM vs no GDM) of randomly selected mothers with singleton live births (matched by age group, delivery year, and health region) and their partners (Quebec, Canada; cohort inception 1990–2007) were assessed for a composite outcome of depression/self-harm/suicide using a health administrative database. We examined the association of GDM and the composite outcome in the following three nonoverlapping periods: 1) 24 weeks gestation up to delivery; 2) delivery up to 1 year postpartum; and 3) 1 year postpartum to study end (March 31, 2012). We used stratified Cox proportional regression hazards models, with three models in mothers and three models in fathers, corresponding to each of the time periods of interest.Results: In the 58,400 mothers, women with GDM had a nearly twofold greater risk (adjusted HR: 1.82, 95% CI 1.28, 2.59) of being diagnosed with depression compared to those without GDM during the prenatal period. In the first year postpartum, there was no conclusive difference observed between the two groups of mothers (adjusted HR: 1.05, 95% CI 0.84, 1.30). Beyond the first year postpartum, there was an 8% increased risk (adjusted HR: 1.08, 95% CI 1.03, 1.14) of depression in women with a history GDM compared to those without. A total of 63,384 fathers were included in our analyses, and no association between GDM in one’s partner and depression was found during any of the three time periods evaluated.Conclusion: GDM is associated with an increased risk of depression in women particularly during pregnancy highlighting the need to screen for depression and provide supportive interventions during this period. Keywords: depression, self-harm, suicide, administrative health data, pregnancy, postpartum

Tasnim F Imran,1–3,* Daniel Posner,1,4,* Jacqueline Honerlaw,1 Jason L Vassy,1,2 Rebecca J Song,1 Yuk-Lam Ho,1 Steven J Kittner,5 Katherine P Liao,1,2 Tianxi Cai,1,6 Christopher J O’Donnell,1,2 Luc Djousse,1,2 David R Gagnon,1,4 J Michael Gaziano,1,2 Peter WF Wilson,7,8 Kelly Cho1,2 On behalf of the VA Million Veteran Program 1Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Cooperative Studies Program, VA Boston Healthcare System, Boston, MA, USA; 2Department of Medicine, Division of Aging, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA; 3Department of Medicine, Cardiology Section, Boston Medical Center, Boston University School of Medicine, Boston, MA, USA; 4Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA; 5Department of Neurology, Baltimore VA Medical Center and University of Maryland School of Medicine, Baltimore, MD, USA; 6Harvard T. H. Chan School of Public Health, Boston, MA, USA; 7Atlanta VA Medical Center, Decatur, GA, USA; 8Department of Medicine, Division of Cardiovascular Disease, Emory University School of Medicine, Atlanta, GA, USA *These authors contributed equally to this work Background: Large databases provide an efficient way to analyze patient data. A challenge with these databases is the inconsistency of ICD codes and a potential for inaccurate ascertainment of cases. The purpose of this study was to develop and validate a reliable protocol to identify cases of acute ischemic stroke (AIS) from a large national database.Methods: Using the national Veterans Affairs electronic health-record system, Center for Medicare and Medicaid Services, and National Death Index data, we developed an algorithm to identify cases of AIS. Using a combination of inpatient and outpatient ICD9 codes, we selected cases of AIS and controls from 1992 to 2014. Diagnoses determined after medical-chart review were considered the gold standard. We used a machine-learning algorithm and a neural network approach to identify AIS from ICD9 codes and electronic health-record information and compared it with a previous rule-based stroke-classification algorithm.Results: We reviewed administrative hospital data, ICD9 codes, and medical records of 268 patients in detail. Compared with the gold standard, this AIS algorithm had a sensitivity of 91%, specificity of 95%, and positive predictive value of 88%. A total of 80,508 highly likely cases of AIS were identified using the algorithm in the Veterans Affairs national cardiovascular disease-risk cohort (n=2,114,458).Conclusion: Our algorithm had high specificity for identifying AIS in a nationwide electronic health-record system. This approach may be utilized in other electronic health databases to accurately identify patients with AIS. Keywords: acute ischemic stroke, algorithm, large databases, big data, administrative health data, cerebrovascular accident

Abstract Background Conducting prospective epidemiological studies of hospitalized patients with rare diseases like primary subarachnoid hemorrhage (pSAH) are difficult due to time and budgetary constraints. Routinely collected administrative data could remove these barriers. We derived and validated 3 algorithms to identify hospitalized patients with a high probability of pSAH using administrative data. We aim to externally validate their performance in four hospitals across Canada. Methods Eligible patients include those ≥18 years of age admitted to these centres from January 1, 2012 to December 31, 2013. We will include patients whose discharge abstracts contain predictive variables identified in the models (ICD-10-CA diagnostic codes I60** (subarachnoid hemorrhage), I61** (intracranial hemorrhage), 162** (other nontrauma intracranial hemorrhage), I67** (other cerebrovascular disease), S06** (intracranial injury), G97 (other postprocedural nervous system disorder) and CCI procedural codes 1JW51 (occlusion of intracranial vessels), 1JE51 (carotid artery inclusion), 3JW10 (intracranial vessel imaging), 3FY20 (CT scan (soft tissue of neck)), and 3OT20 (CT scan (abdominal cavity)). The algorithms will be applied to each patient and the diagnosis confirmed via chart review. We will assess each model’s sensitivity, specificity, negative and positive predictive value across the sites. Discussion Validating the Ottawa SAH Prediction Algorithms will provide a way to accurately identify large SAH cohorts, thereby furthering research and altering care.

Abstract Background Active management of existing health technologies (e.g., devices, diagnostic, and/or medical procedures) to ensure the delivery of high value care is increasingly recognized around the world. A number of initiatives have raised awareness of technologies that may be overused, mis-used, or potentially harmful by compiling them into lists of low value care. However, despite the growing number of lists, changes to local healthcare practices remain challenging for many systems. The objective of this study was to develop and implement a process, leveraging existing initiatives and data assets, to produce a list of prioritized low value technologies for health technology reassessment (HTR). Methods An expert advisory committee comprised of clinical experts and health system decision-makers was convened to determine key process requirements. Once developed, the process was piloted to assess feasibility in the Canadian province of British Columbia (BC). Results The expert advisory committee identified five required attributes for the process: data-driven, routine and replicable, actionable, stakeholder collaboration, and high return on investment. Guided by these attributes, a 5-step process was developed. First, over 1300 published low value technologies (i.e., from the National Institute for Health and Care Excellence [NICE] “do not do” recommendations, low value technologies in the Australian Medical Benefits Schedule, and Choosing Wisely “Top 5” lists) were identified. Using appropriate coding systems for BC’s administrative health data (e.g., International Classification of Diseases [ICD]), the low value technologies were queried to examine frequencies and costs of technology use. This information was used to rank potential candidates for reassessment based on high annual budgetary impact. Lastly, clinical experts reviewed the ranked technologies prior to broad dissemination and stakeholder action. Pilot testing of the process in BC resulted in the prioritization of 9 initial candidate technologies for reassessment. Conclusions This is the first account of a systematic approach to move a collective body of low value technology recommendations into action in a healthcare setting. This work demonstrates the feasibility and strength of using administrative data to identify and prioritize low value technologies for HTR at a population-level.

Abstract Background Only limited data are available concerning the diseases managed before death and hospital palliative care (HPC) use according to place of death in France. We therefore conducted an observational study based on administrative health data in a large population to identify the diseases treated one year before death in 2013, the place of stay with or without hospital palliative care, and the place of death. Methods French health insurance general scheme beneficiaries were identified in the National Health data Information System (Snds) with a selection of information. Diseases were identified by algorithms from reimbursement data recorded in the Snds database. Results 347,253 people were included in this study (61% of all people who died in France). Place of death was short stay hospital for 51%, Rehab (7%), hospital at home (3%), skilled nursing home (13%) and other (26%). Chronic diseases managed in 2013 before death were cardiovascular/neurovascular diseases (56%), cancers (42%), and neurological and degenerative diseases (25%). During the year before death, 84% of people were hospitalized at least once, and 29% had received HPC. HPC was used by 52% of cancer patients (lung cancer: 62%; prostate cancer: 41%). In the absence of cancer, the use of HPC varied according to the disease: acute stroke: 24%, heart failure: 17%, dementia: 17%, multiple sclerosis: 23%. Conclusions Health administrative data can refine the knowledge of the care pathway prior to death and the HPC utilisation and can be useful to evaluate heath policies and improve monitoring and assessment of HPC use.

Abstract Background Certain cancer case ascertainment methods used in Quebec and elsewhere are known to underestimate the burden of cancer, particularly for some subgroups. Algorithms using claims data are a low-cost option to improve the quality of cancer surveillance, but have not frequently been implemented at the population-level. Our objectives were to 1) develop a colorectal cancer (CRC) case ascertainment algorithm using population-level hospitalization and physician billing data, 2) validate the algorithm, and 3) describe the characteristics of cases. Methods We linked physician billing, hospitalization, and tumor registry data for 2,013,430 Montreal residents age 20+ (2000–2010). We compared the performance of three algorithms based on diagnosis and treatment codes from different data sources. We described identified cases according to age, sex, socioeconomic status, treatment patterns, site distribution, and time trends. All statistical tests were two-sided. Results Our algorithm based on diagnosis and treatment codes identified 11,476 of the 12,933 incident CRC cases contained in the tumor registry as well as 2317 newly-captured cases. Our cases share similar overall time trends and site distributions to existing data, which increases our confidence in the algorithm. Our algorithm captured proportionally 35% more individuals age 50 and younger among CRC cases: 8.2% vs. 5.3%. The newly captured cases were also more likely to be living in socioeconomically advantaged areas. Conclusions Our algorithm provides a more complete picture of population-wide CRC incidence than existing case ascertainment methods. It could be used to estimate long-term incidence trends, aid in timely surveillance, and to inform interventions, in both Quebec and other jurisdictions.

Background: Primary care-led follow-up is a safe and acceptable alternative to oncologist-led follow-up. We sought to investigate patterns of primary care use during cancer follow-up care. Methods We identified all persons in Nova Scotia, diagnosed with an invasive breast, prostate, colorectal, or gynecologic cancer between January 2006 and December 2013. We linked this dataset to cancer centre, hospital discharge abstracts, physicians' billing, and census data. We identified a survivor cohort (n = 12,201), then descriptively examined primary care use during follow-up care. Multivariate Poisson and negative binomial regression, respectively, were used to examine primary care use for two outcomes: total number of primary care provider (pcp) visits (all reasons) and total number of cancer-specific pcp visits. Results The mean numbers of pcp visits (all reasons) and cancer-specific pcp visits per year for survivors who did not receive cancer centre follow-up (cc-fup) were 8.12 and 0.43 visits, respectively, and for survivors who continued to receive cc-fup were 8.75 and 0.63 visits, respectively. Age, cancer type, stage at diagnosis, comorbidity scores, year of diagnosis, and receipt of cc-fup were associated with both outcomes. Compared with prostate cancer survivors, breast, colorectal, and gynecologic cancer survivors had, respectively, 56%, 69%, and 56% fewer expected cancer-specific PCP visits. Receipt of cc-fup increased the expected number of pcp visits (all reasons) by 12% and cancer-specific pcp visits by 50%. Conclusions Primary care use was higher in survivors who continued to visit their oncology teams for follow-up. This suggests that survivors who remain with their oncology teams after treatment continue to have high needs not met by these teams alone. [ABSTRACT FROM AUTHOR]

Medications are often needed to manage distressing end-of-life symptoms (eg, pain, agitation). In this study, we describe the variation in prescribing rates of symptom relief medications at the end of life among long-term care (LTC) decedents. We evaluate the extent these medications are prescribed in LTC homes and whether prescribing rates of end-of-life symptom management can be used as an indicator of quality end-of-life care. Retrospective cohort study using administrative health data. LTC decedents in all 626 publicly funded LTC homes in Ontario, Canada, between January 1, 2017, and March 17, 2020. For each LTC home, we measured the percent of decedents who received 1+ prescription(s) for a subcutaneous end-of-life symptom management medication ("end-of-life medication") in their last 14 days of life. We then ranked LTC homes into quintiles based on prescribing rates. We identified 55,916 LTC residents who died in LTC. On average, two-thirds of decedents (64.7%) in LTC homes were prescribed at least 1 subcutaneous end-of-life medication in the last 2 weeks of life. Opioids were the most common prescribed medication (overall average prescribing rate of 62.7%). LTC homes in the lowest prescribing quintile had a mean of 37.3% of decedents prescribed an end-of-life medication, and the highest quintile mean was 82.5%. In addition, across these quintiles, the lowest prescribing quintile had a high average (30.3%) of LTC residents transferred out of LTC in the 14 days compared with the highest prescribing quintile (12.7%). Across Ontario's LTC homes, there are large differences in prescribing rates for subcutaneous end-of-life symptom relief medications. Although future work may elucidate why the variability exists, this study provides evidence that administrative data can provide valuable insight into the systemic delivery of end-of-life care. [ABSTRACT FROM AUTHOR]

Abstract Background Analysing linked, routinely collected data may be useful to identify characteristics of patients with suspected lung cancer who could benefit from early assessment for palliative care. The aim of this study was to compare characteristics of newly diagnosed lung cancer patients dying within 30 days of diagnosis (short term survivors) with those surviving more than 30 days. To identify indicators for early palliative care assessment we distinguished between characteristics available at diagnosis (age, gender, smoking status, marital status, comorbid disease, admission type, tumour stage and histology) from those available post diagnosis. A second aim was to examine the association between receiving any tumour-directed treatment, place of death and survival time. Methods A retrospective observational population based study comparing lung cancer patients who died within 30 days of diagnosis (short term survivors) with those who survived longer using Chi-squared tests and logistic regression. Incident lung cancer (ICD-03:C34) patients diagnosed 2005–2012 inclusive who died before 01–01-2014 (n = 14,228) were identified from the National Cancer Registry of Ireland linked to death certificate data and acute hospital episode data. Results One in five newly diagnosed lung cancer patients died within 30 days of diagnosis. After adjusting for stage and histology, death within 30 days was higher in patients who were aged 80 years or older (adjusted OR 2.46; 95%CI 2.05–3.96; p

Background: The lack of epidemiological and clinical data is a major obstacle in health service planning for rare diseases. Patient registries are examples of real-world data that may fill the information gap.Objective: We describe the Rare Disease Registry of the Friuli Venezia Giulia region of Italy and its potential for research and health planning.Methods: The Rare Disease Registry data were linked with information on mortality, hospital discharges, ambulatory care, and drug prescriptions contained in administrative databases. All information is anonymous, and data linkage was based on a stochastic key univocal for each patient. Average annual costs owing to hospitalizations, outpatient care, and medications were estimated.Results: Implementation of the Registry started in 2010, and 4250 participants were registered up to 2017. A total of 2696 patients were living in the region as of January 1, 2017. The overall raw prevalence of rare diseases was 22 per 10,000 inhabitants, with higher prevalence in the pediatric population. The most common disease groups were congenital malformations, chromosomal and genetic syndromes, and circulatory and nervous diseases. In 2017, 30 patients died, 648 were hospitalized, and 2355 received some type of ambulatory care. The total annual estimated cost was approximately €6.5 million, with great variability in the average patient cost across diseases.Conclusions: The possibility of following the detailed real-world care experience of patients with each specific rare disease and assessing the costs related to each step in their care path represents a unique opportunity to identify inefficiencies, optimize care, and reduce waste of resources. [ABSTRACT FROM AUTHOR]

Background The collection of patient reported outcomes (PROS) is a standard of care in many cancer organizations. In Alberta, PROS have been integrated into routine clinical practice since 2012. This longitudinal collection of PROS provides a wealth of data and a unique research opportunity to improve cancer care. The goal of this PRO data initiative is to establish a robust repository of information for ongoing clinical care and research focused on PROS. In this paper, we describe the rationale, scope, and design of this initiative. Implementation The initiative consists of PROS and other administrative health data from the province of Alberta. Retrieval of health data from a variety of provincially governed sources will create a platform of information on PROS, health outcomes, cancer data, other health conditions, and demographics. The aims of the initiative are to use the data to inform best practices at the point of care; to conduct health services research, particularly clinical epidemiology studies; and to evaluate a variety of PRO-related outcomes. Discussion Because this effort represents our first to integrate routinely collected PROS with other administrative health data, a unique and robust data repository will be created. The ability to integrate various types of data will provide a comprehensive mechanism to evaluate a variety of outcomes. Because cancer care in Alberta is governed by a single health care system, the data linkages will include population health and psychosocial cancer data. We anticipate that research related to this initiative will ultimately help to inform more patient-centred care. [ABSTRACT FROM AUTHOR]

Objective: We used administrative data to 1) establish a cohort of individuals with childhood-onset type 1 diabetes (T1D) in British Columbia (BC), and 2) define T1D-related clinical practice measures., Methods: We applied a validated diabetes case-finding definition and differentiating algorithm to linked administrative data (1992-1993 to 2019-2020). Cases were removed when they did not meet inclusion criteria for childhood-onset T1D. Clinical practice measures were defined based on clinical practice guidelines., Results: We developed an administrative cohort that included 5,901 individuals with childhood-diagnosed T1D between April 1, 1996, and March 31, 2020. The mean age was 22.31 (standard deviation 8.21) years. Clinical practice measures derived included diabetes outpatient visits (N=4,935) and glycated hemoglobin tests (N=4,935), and screening for thyroid function (N=4,457), retinopathy (N=1,602), and nephropathy (N=2,369)., Conclusions: We established an administrative cohort of ∼6,000 individuals with childhood-onset T1D with 20 + years of follow-up data that can be used to describe the association between clinical practice measures and clinical outcomes., (Copyright © 2023 Canadian Diabetes Association. Published by Elsevier Inc. All rights reserved.)

Background: Concussion affects 1.2% of the population annually; rural regions and children have higher rates of concussion., Methods: Using administrative health care linked databases, all residents of Ontario with a physician diagnosed concussion were identified using ICD-9 code 850 or ICD-10 code S06. Cases were tracked for 2 years for concussion-related health care utilization with relevant specialist physicians (i.e., neurology, otolaryngology, physiatry, psychiatry, ophthalmology). Billing codes, specialist codes, and time from index to visit were analyzed. Factors associated with increased specialist visits were also examined., Results: In total, 1,022,588 cases were identified between 2008 and 2014 with 2 years of post-concussion health care utilization available. Follow-up by physician within 3 days of injury occurred in only 14% of cases. Mean time between ED diagnosis and follow-up by a physician was 83.9 days, whereas for rural regions it was >100 days. About half of adults (51.9%) and children (50.3%) had at least 1 specialist visit following concussion. Mean time between injury and first specialist visit was 203.8 (SD 192.9) days for adults, 213.5 (SD 201.0) days for rural adults, and 276.0 (SD 202.6) days for children. There were 67,420 neurology visits, 70,404 psychiatry visits, 13,571 neurosurgery visits, 19,780 physiatry visits, 101,788 ENT visits, and 103,417 ophthalmology visits in the 2 years tracking period. Factors associated with more specialist use included age > 18 years, urban residence, and pre-injury psychiatric history., Conclusions: There are discrepancies in post-concussion health care utilization based on age group and rural/urban residence. Addressing these risk factors could improve concussion care access.

Background: Proton pump inhibitors (PPIs) have an impact on the gut microbiome. We investigated whether increased use of PPIs was associated with a diagnosis of inflammatory bowel disease (IBD)., Methods: The University of Manitoba IBD Epidemiology Database includes all Manitobans diagnosed with IBD between 1984 and 2018 with age-, sex-, and geography-matched control subjects and comprehensive prescription drug data from April 1995. Subjects were considered to be users if they received 2 PPI prescriptions. We assessed PPI prescriptions prediagnosis and for 3 years postdiagnosis of IBD. The absolute and relative rates were calculated and compared for PPI use pre- and post-IBD diagnosis., Results: A total of 5920 subjects were diagnosed with IBD after April 1996. Rates of PPI use in control subjects increased gradually from 1.5% to 6.5% over 15 years. Persons with IBD had a higher rate of PPI use, peaking up to 17% within 1 year of IBD diagnosis with a rate ratio (RR) of 3.1 (95% confidence interval [CI], 2.9-3.3). Furthermore, persons with Crohn's disease (RR, 4.2; 95% CI, 3.7-4.6) were more likely to have been PPI users prediagnosis than persons with ulcerative colitis (RR, 2.4; 95% CI, 2.2-2.7). Important predictors of increased PPI use were older age, year of data collection, and Crohn's disease diagnosis., Conclusions: Persons with IBD have higher PPI use preceding their diagnosis. Possibly, the use of a PPI alters the gut microbiome, increasing the risk for IBD diagnosis; or persons with IBD have increased rates of dyspepsia, warranting PPI use; or some IBD symptoms are treated with PPIs whether warranted or not., (© The Author(s) 2023. Published by Oxford University Press on behalf of Crohn’s & Colitis Foundation. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Background: Traumatic brain injury surveillance provides information for allocating resources to prevention efforts. Administrative data are widely available and inexpensive but may underestimate traumatic brain injury burden by misclassifying cases. Moreover, previous studies evaluating the accuracy of administrative data surveillance case definitions were at risk of bias by using imperfect diagnostic definitions as reference standards. We assessed the accuracy (sensitivity/specificity) of traumatic brain injury surveillance case definitions in administrative data, without using a reference standard, to estimate incidence accurately.Methods: We used administrative data from a 25% random sample of Montreal residents from 2000 to 2014. We used hierarchical Bayesian latent class models to estimate the accuracy of widely used traumatic brain injury case definitions based on the International Classification of Diseases, or on head radiologic examinations, covering the full injury spectrum in children, adults, and the elderly. We estimated measurement error-adjusted age- and severity-specific incidence.Results: The adjusted traumatic brain injury incidence was 76 (95% CrI = 68, 85) per 10,000 person-years (underestimated as 54 [95% CrI = 54, 55] per 10,000 without adjustment). The most sensitive case definitions were radiologic examination claims in adults/elderly (0.48; 95% CrI = 0.43, 0.55 and 0.66; 95% CrI = 0.54, 0.79) and emergency department claims in children (0.45; 95% CrI = 0.39, 0.52). The most specific case definitions were inpatient claims and discharge abstracts (0.99; 95% CrI = 0.99, 1.00). We noted strong secular trends in case definition accuracy.Conclusions: Administrative data remain a useful tool for conducting traumatic brain injury surveillance and epidemiologic research when measurement error is adjusted for. [ABSTRACT FROM AUTHOR]

Objectives To investigate the availability of intimate partner violence-related population health information in England and the possibility of identifying intimate partner violence-exposed population sample frames from administrative health data systems in England employing the International Classification of Disease. Methods Research design was an exploratory mixed method approach that involved trend analysis of numbers of applications of International Classification of Disease intimate partner violence classifications for admissions to NHS hospitals in England over a five-year period and semi-structured focus group interviews with clinical coders at an NHS Hospital. Results Use of International Classification of Disease intimate partner violence classifications was generally low across NHS Trusts in England. There was notable variation in the numbers of applications across NHS providers which demographic differences or rates of violence perpetration would not account for. The interview findings revealed conceptual ambiguity regarding intimate partner violence classifications which presented challenges for clinical coding and raised questions about the reliability and validity of International Classification of Disease’s intimate partner violence classifications. Conclusion It would not be possible to extract robust data about populations exposed to intimate partner violence for the purposes of audit, governance or research from health information systems using current International Classification of Disease-10 classifications. Development of these International Classification of Disease codes is essential for violence and abuse to be captured more accurately in health information systems and afforded greater prioritization and funding proportionate to the health burden and service demands that intimate partner violence is responsible for. [ABSTRACT FROM AUTHOR]

Highlights • Little is known about the use of the new oral MS drugs in clinical practice. • Uptake for an oral MS drug was 16.6/1000 person-years over the five-year study period. • Uptake and use of the new oral DMTs increased substantially from 2011–2015. • Uptake and use of the oral MS drugs were similar for men and women. • However, subjects with a higher (vs. lower) socioeconomic status had a higher uptake. Abstract Background: Little is known about the use of the new oral disease-modifying therapies (DMTs: fingolimod, dimethyl fumarate, teriflunomide) for multiple sclerosis (MS) in clinical practice. We describe their rate of uptake, and their use as compared to the established first-generation (beta-interferon and glatiramer acetate) and second-generation (natalizumab and alemtuzumab) parenteral DMTs. Methods: Persons with MS (PwMS) were identified using health administrative data from British Columbia, Canada (2011-15). We described the uptake and use of the DMTs with Poisson and Log-Binomial regression, adjusting for sex, age, and socioeconomic status (SES). Results: Among 14,148 PwMS, 2,821 were exposed to any DMT over the study period. In total, 1,019 initiated an oral DMT, a rate of 16.6/1,000 person-year, and 83/1,000 PwMS filled at least one prescription for an oral DMT during the study period. The rate of uptake for oral DMTs, and the annual proportion of PwMS using an oral DMT or second-generation parenteral DMT increased over the 5 years, whereas use of the first-generation DMTs decreased by 30%. PwMS in the higher (vs. lower) SES group had a 19-44% higher rate of uptake for teriflunomide and fingolimod. Conclusion: The uptake and use of the oral DMTs increased substantially over the first 2 to 5 years after their introduction. These recent changes highlight the importance of monitoring the risks and benefits in the real world. [ABSTRACT FROM AUTHOR]

There is a critical need for high-quality population-level data related to the health of individuals with intellectual disabilities. For more than 15 years Special Olympics International has been conducting free Healthy Athletes screenings at local, national and international events. The Healthy Athletes database is the largest known international database specifically on the health of people with intellectual disabilities; however, it is relatively under-utilized by the research community. A consensus meeting with two dozen North American researchers, stakeholders, clinicians and policymakers took place in Toronto, Canada. The purpose of the meeting was to: 1) establish the perceived utility of the database, and 2) to identify and prioritize 3-5 specific priorities related to using the Healthy Athletes database to promote the health of individuals with intellectual disabilities. There was unanimous agreement from the meeting participants that this database represents an immense opportunity both from the data already collected, and data that will be collected in the future. The 3 top priorities for the database were deemed to be: 1) establish the representativeness of data collected on Special Olympics athletes compared to the general population with intellectual disabilities, 2) create a scientific advisory group for Special Olympics International, and 3) use the data to improve Special Olympics programs around the world. The Special Olympics Healthy Athletes database includes data not found in any other source and should be used, in partnership with Special Olympics International, by researchers to significantly increase our knowledge and understanding of the health of individuals with intellectual disabilities. [ABSTRACT FROM AUTHOR]

Background Large administrative databases may prove useful to assess epilepsy-related comorbidity and mortality. Despite their increased use, their validity as data source in epilepsy is yet under-ascertained. Methods Achmea is a large Dutch health insurance company covering about 25% of the population. We performed a retrospective cohort study using data from the Achmea Health Insurance Database (AHID) over the period 2006–2009. To assess the validity of epilepsy codes in the AHID, we randomly invited 1000 individuals (age 18–75 years insured by Achmea), attending an epilepsy centre or a district hospital during 2006–2009, to participate. Informed consent was provided and 293 were eligible for inclusion. We compared the diagnostic codes for epilepsy in AHID with the diagnosis in their case-notes (reference standard). As additional measure of validity, we compared prevalence of epilepsy codes in AHID (based on anonymized data of all 26.297 subjects with this code in AHID) with epilepsy prevalence rates in the general Dutch population to estimate an age-specific standardized prevalence ratio. Results We identified 293 participants with an epilepsy code in AHID. The majority (278) of them had a definite or possible diagnosis of epilepsy in the case-notes; i.e. a positive predictive value of 0.95 (95% CI 0.92–0.97). The overall prevalence of epilepsy codes in the AHID was slightly higher than the putative prevalence in the general Dutch population (7.4/1.000 vs. 6.8/1.000) with a Standardized Prevalence Ratio of 1.08 (95% CI: 1.08–1.09). Conclusions Our findings demonstrate the validity of AHID data for a diagnosis of epilepsy and confirm previous work on using administrative data for epilepsy research. [ABSTRACT FROM AUTHOR]

Background Rising demand on cancer system resources, alongside mounting evidence that demonstrates the safety and acceptability of primary care-led follow-up care, has resulted in some cancer centres discharging patients back to primary care after treatment. At the same time, the ways in which routine cancer follow-up care is provided across Canada continue to vary widely. The objectives of the present study were to investigate patterns of routine follow-up care at a cancer centre for breast, colorectal, gynecologic, and prostate cancer survivors; factors associated with receipt of follow-up care at a cancer centre; and changes in follow-up care at a cancer centre over time. Methods We identified all people diagnosed in Nova Scotia with an invasive breast, colorectal, gynecologic, or prostate cancer between 1 January 2006 and 31 December 2013. We linked the resulting population-based dataset, at the patient level, to cancer centre or clinic data and to census data. We identified a nonmetastatic survivor cohort (n = 12,267) and developed decision rules to differentiate routine from non-routine visits during the follow-up care period (commencing 1 year after diagnosis). Descriptive statistics were computed to describe the patterns of routine follow-up care at a cancer centre. Negative binomial regression was used to examine factors associated with visits made and changes over time. Results Nearly half the survivors (48.4%) had at least 1 follow-up visit to the cancer centre, with variation by disease site (range: 30.2%-62.4%). Disease site and stage at diagnosis were associated with receipt of follow-up care at a cancer centre. For instance, compared with breast cancer survivors, survivors of gynecologic cancer had more visits [incidence rate ratio (irr): 1.48; 95% confidence interval (ci): 1.34 to 1.64], and survivors of colorectal cancer had fewer visits (irr: 0.45; 95% ci: 0.40 to 0.51). Year of diagnosis was associated with follow-up at a cancer centre, with each successive calendar year being associated with an 8% increase in visits made (irr: 1.08; 95% ci: 1.07 to 1.10). Conclusions Despite evidence that follow-up care can be effectively and safely delivered in primary care, and despite intensifying demands on oncology services, many survivors continue to receive routine follow-up care at a cancer centre. [ABSTRACT FROM AUTHOR]

Aims: We investigated the association between problematic use of alcohol and/or drugs (PUAD) and the incidence, urgency, and mode of discharge for a subsequent episode of self-harm (SH) or suicidal ideation (SI). Methods: This was a retrospective population-based cohort study of individuals admitted to hospital for an index episode of SH/SI (2010-2014) using linked data from hospital admissions and emergency department (ED) presentations. The outcome variables were (1) subsequent presentation to the ED for SH/SI, (2) triage category, and (3) mode of departure. Key predictors were PUAD. Results: In total, 23,007 individuals were admitted to hospital for an index SH/SI, of whom 8% had a subsequent presentation to an ED for SH/SI within a year. The odds of subsequent presentation was increased in those with problematic alcohol use (AOR 1.62, 95% CI 1.36, 1.92), drug use (AOR 1.28, 95% CI 1.07, 1.53), and mental health diagnoses (AOR 1.63, 95% CI 1.44, 1.85). Those with problematic alcohol use were more likely to be assigned to the most urgent triage categories (AOR 1.84, 95% CI 1.32, 2.56). Limitations: Defining SH and PUAD using administrative data is challenging, and the true prevalence is likely to be underestimated. Conclusion: The findings underscore the importance of drug health intervention as a key component of self-harm prevention.

Introduction: Chronic disease rates are produced from the Public Health Agency of Canada's Canadian Chronic Disease Surveillance System (CCDSS) using administrative health data from provincial/territorial health ministries. Denominators for these rates are based on estimates of populations derived from health insurance files. However, these data may not be accessible to all researchers. Another source for population size estimates is the Statistics Canada census. The purpose of our study was to calculate the major differences between the CCDSS and Statistics Canada's population denominators and to identify the sources or reasons for the potential differences between these data sources. Methods: We compared the 2009 denominators from the CCDSS and Statistics Canada. The CCDSS denominator was adjusted for the growth components (births, deaths, emigration and immigration) from Statistics Canada's census data. Results: The unadjusted CCDSS denominator was 34 429 804, 3.2% higher than Statistics Canada's estimate of population in 2009. After the CCDSS denominator was adjusted for the growth components, the difference between the two estimates was reduced to 431 323 people, a difference of 1.3%. The CCDSS overestimates the population relative to Statistics Canada overall. The largest difference between the two estimates was from the migrant growth component, while the smallest was from the emigrant component. Conclusion: By using data descriptions by data source, researchers can make decisions about which population to use in their calculations of disease frequency. [ABSTRACT FROM AUTHOR]

Background: Hospital readmission rates are a widely used quality indicator that may be elevated in disadvantaged populations.Objective: The objective of this study was to compare the hospital readmission rate among individuals experiencing homelessness with that of a low-income matched control group, and to identify risk factors associated with readmission within the group experiencing homelessness.Design: We conducted a 1:1 matched cohort study comparing 30-day hospital readmission rates between homeless patients and low-income controls matched on age, sex and primary reason for admission. Multivariate analyses using generalized estimating equations were used to assess risk factors associated with 30-day readmission in the homeless cohort.Participants: This study examined a cohort of 1,165 homeless adults recruited at homeless shelters and meal programs in Toronto, Ontario, between 6 December 2004 and 20 December 2005.Main Measures: The primary outcome was the occurrence of an unplanned medical or surgical readmission within 30 days of discharge from hospital.Key Results: Between 6 December 2004 and 31 March 2009, homeless participants (N = 203) had 478 hospitalizations and a 30-day readmission rate of 22.2 %, compared to 300 hospitalizations and a readmission rate of 7.0 % among matched controls (OR = 3.79, 95 % CI 1.93-7.39). In the homeless cohort, having a primary care physician (OR = 2.65, 95 % CI 1.05-6.73) and leaving against medical advice (OR = 1.96, 95 % CI 0.99-3.86) were associated with an increased risk of 30-day readmission.Conclusions: Homeless patients had nearly four times the odds of being readmitted within 30-days as compared to low-income controls matched on age, sex and primary reason for admission to hospital. Further research is needed to evaluate interventions to reduce readmissions among this patient population. [ABSTRACT FROM AUTHOR]

Objective To describe factors predicting frequent emergency department ( ED) use among rural older adults receiving home care services in Ontario, Canada. Design A cross-sectional examination of Ontario hospital administrative data linked to provincial home care data compares frequent ED users (i.e. >4 visits/year) with regular users (i.e. 1-3 visits/year) and no ED use over a 1-year period. Multiple logistic regression analysis was performed using independent variables from home care health assessments. Main outcome measures Predictors of frequent ED use among rural older adults receiving home care services using adjusted odds ratios ( ORs) and 95% confidence intervals ( CI). Results Ontario's rural home care clients visited the ED an average of 4.9 times ( SD = 11.9) over a year. Frequent ED users were observed in 170 cases per 1000 home care clients. Congestive heart failure was the most frequently cited reason for frequent ED use; however, it was not a main driver in the adjusted model. Frequent ED use was predicted by younger age (i.e. 60-74 years) ( OR = 1.9; 95% CI, 1.7-2.2), a recent ED visit in the last 3 months ( OR = 1.5; 95% CI, 1.4-1.7), regularly taking >9 medications ( OR = 1.5; 95% CI, 1.3-1.6), and poor self-rated health ( OR = 1.4; 95% CI, 1.2-1.5). Conclusion Despite popular assumptions, the main drivers for frequent ED use among rural older adults in Ontario included a mix of sociodemographic and clinical characteristics, rather than disease diagnoses. [ABSTRACT FROM AUTHOR]

Objective: To explore the application of online analytic processing (OLAP) to improve the efficiency of analytics using large administrative health data sets. Methods: 18 years of administrative health data (1994/95 to 2012/13) were obtained from the Alberta Ministry of Health in Canada. The data sets included hospitalization, ambulatory care and practitioner claims data. Reference files were obtained that provided information including patient demographics, resident postal code, facility, and provider details. Population counts and projections for each year, sex, age were included for rate calculations. These sources were used to develop a data cube using OLAP tools. Results: Time required for analyses was reduced to 5% of that required when comparing run-time for simple queries that did not require linkage of data sets. The data cube negated the need for many intermediary steps for data extraction and analyses for research activities. Conventional methods required over 250 GB of server space for multiple analytic subsets, compared to only 10.3 GB for the data cube. Conclusions: Cross-training in information technology and health analytics is recommended to provide capacity to better leverage OLAP tools which are available with many common applications.

Incidence and prevalence estimates in myasthenia gravis have varied widely. Recent studies based on administrative health data have large sample sizes but lack rigorous validation of MG cases, and have not examined the North American population. Our aim was to explore trends in MG incidence and prevalence for the years 1996–2013 in the province of Ontario, Canada (population 13.5 million). We employed a previously validated algorithm to identify MG cases. Linking with census data allowed for the calculation of crude- and age/sex-standardized incidence and prevalence rates for the years 1996–2013. The regional distribution of MG cases throughout the province was examined. Mean age at the first myasthenia gravis encounter was 60.2 ± 17.1 years. In 2013, there were 3611 prevalent cases in Ontario, and the crude prevalence rate was 32.0/100,000 population. Age- and sex-standardized prevalence rates rose consistently over time from 16.3/100,000 (15.4–17.1) in 1996 to 26.3/100,000 (25.4–27.3) in 2013. Standardized incidence rates remained stable between 1996 (2.7/100,000; 95% CL 2.3–3.0) and 2013 (2.3/100,000; 2.1–2.6). Incidence was highest in younger women and older men, and geographic variation was evident throughout the province. In conclusion, this large epidemiological study shows rising myasthenia gravis prevalence with stable incidence over time, which is likely reflective of patients living longer, possibly due to improved disease treatment. Our findings provide accurate information on the Canadian epidemiology of myasthenia gravis and burden for health care resources planning for the province, respectively. [ABSTRACT FROM AUTHOR]

Province-wide population-based administrative health data from British Columbia (BC), Canada (population: approximately 4.5 million) were used to estimate the incidence and prevalence of multiple sclerosis (MS) and examine potential trends over time. All BC residents meeting validated health administrative case definitions for MS were identified using hospital, physician, death, and health registration files. Estimates of annual prevalence (1991-2008), and incidence (1996-2008; allowing a 5-year disease-free run-in period) were age and sex standardized to the 2001 Canadian population. Changes over time in incidence, prevalence and sex ratios were examined using Poisson and log-binomial regression. The incidence rate was stable [average: 7.8/100,000 (95 % CI 7.6, 8.1)], while the female: male ratio decreased ( p = 0.045) but remained at or above 2 for all years (average 2.8:1). From 1991-2008, MS prevalence increased by 4.7 % on average per year ( p < 0.001) from 78.8/100,000 (95 % CI 75.7, 82.0) to 179.9/100,000 (95 % CI 176.0, 183.8), the sex prevalence ratio increased from 2.27 to 2.78 ( p < 0.001) and the peak prevalence age range increased from 45-49 to 55-59 years. MS incidence and prevalence in BC are among the highest in the world. Neither the incidence nor the incidence sex ratio increased over time. However, the prevalence and prevalence sex ratio increased significantly during the 18-year period, which may be explained by the increased peak prevalence age of MS, longer survival with MS and the greater life expectancy of women compared to men. [ABSTRACT FROM AUTHOR]

Background: Multimorbidity, the co-occurrence of two or more chronic conditions, is common among older adults and is known to be associated with high costs and gaps in quality of care. Population-based estimates of multimorbidity are not readily available, which makes future planning a challenge. We aimed to estimate the population-based prevalence and trends of multimorbidity in Ontario, Canada and to examine patterns in the co-occurrence of chronic conditions. Methods: This retrospective cohort study includes all Ontarians (aged 0 to 105 years) with at least one of 16 common chronic conditions. Descriptive statistics were used to examine and compare the prevalence of multimorbidity by age and number of conditions in 2003 and 2009. The co-occurrence of chronic conditions among individuals with multimorbidity was also explored. Results: The prevalence of multimorbidity among Ontarians rose from 17.4% in 2003 to 24.3% in 2009, a 40% increase. This increase over time was evident across all age groups. Within individual chronic conditions, multimorbidity rates ranged from 44% to 99%. Remarkably, there were no dominant patterns of co-occurring conditions. Conclusion: The high prevalence of multimorbidity and numerous combinations of conditions suggests that single, disease-oriented management programs may be less effective or efficient tools for high quality care compared to person-centered approaches. [ABSTRACT FROM AUTHOR]

Introduction: Incidence and prevalence estimates for myasthenia gravis (MG) have varied widely, and the ability of administrative health data (AHD) records to accurately identify cases of MG is yet to be ascertained. The goal of the current study was to validate an algorithm to identify patients with MG in Ontario, Canada using AHD - thereby enabling future disease surveillance. Methods: A reference standard population was established using automated key word searching within EMRALD (Electronic Medical Record Administrative data Linked Database) and chart review of potential cases. AHD algorithms were generated and tested against the reference standard. The data was used to calculate MG prevalence rates. Results: There were 123,997 eligible adult patients, and 49 patients had definite MG (forming the reference standard). An algorithm requiring: (1 hospital discharge abstract with MG listed as a reason for hospitalization or a comorbid condition), or (5 outpatient MG visits and 1 relevant diagnostic test, within 1 year), or (3 pyridostigmine prescriptions, within 1 year) identified MG with sensitivity = 81.6%, specificity = 100%, positive predictive value = 80.0% and negative predictive value = 100%. The population prevalence within our cohort was 0.04%. Conclusions: This novel validation method demonstrates the feasibility of using administrative health data to identify patients with myasthenia gravis among the Ontario population. © 2015 S. Karger AG, Basel [ABSTRACT FROM AUTHOR]