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Background: There is limited population-based data on Neurofibromatosis type 1 (NF1) in North America. We aimed to develop and validate algorithms using administrative health data and electronic medical records (EMRs) to identify individuals with NF1 in Ontario, Canada.Methods: We conducted an electronic free-text search of 15 commonly-used terms related to NF1 in the Electronic Medical Records Primary Care Database. Records were reviewed by two trained abstractors who classified them as confirmed, possible, and not NF1. An investigator with clinical expertise performed final NF1 classification. Patients were classified as confirmed if there was a documented diagnosis, meeting NIH criteria. Patients were classified as possible if (1) NF1 was recorded in the cumulative patient profile, but no clinical information to support the diagnosis; (2) only one criterion for diagnosis (e.g. child of confirmed case) but no further data to confirm or rule out. We tested different combinations of outpatient and inpatient billing codes, and applied a free-text search algorithm to identify NF1 cases in administrative data and EMRs, respectively.Results: Of 273,440 eligible patients, 2,058 had one or more NF1 terms in their medical records. The terms "NF", "café-au-lait", or "sheath tumour" were constrained to appear in combination with another NF1 term. This resulted in 837 patients: 37 with possible and 71 with confirmed NF1. The population prevalence ranged from 1 in 3851 (confirmed NF1) to 1 in 2532 (possible and confirmed NF1). Billing code algorithms had poor performance, with overall low PPV (highest being 71%). The accuracy of the free-text EMR algorithm in identifying patients with NF1 was: sensitivity 85% (95% CI 74-92%), specificity 100% (95% CI 100-100%), positive predictive value 80% (95% CI 69-88%), negative predictive value 100% (95% CI 100-100%), and false positive rate 20% (95% CI 11-33%). Of false positives, 53% were possible NF1.Conclusions: A free-text search algorithm within the EMR had high sensitivity, specificity and predictive values. Algorithms using billing codes had poor performance, likely due to the lack of NF-specific codes for outpatient visits. While NF1 ICD-9 and 10 codes are used for hospital admissions, only ~ 30% of confirmed NF1 cases had a hospitalization associated with an NF1 code. [ABSTRACT FROM AUTHOR]

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]

Importance: There are limited data regarding COVID-19 outcomes and vaccine uptake and safety among people with myasthenia gravis (MG)., Objective: To investigate COVID-19-related outcomes and vaccine uptake among a population-based sample of adults with MG., Design, Setting, and Participants: This population-based, matched cohort study in Ontario, Canada, used administrative health data from January 15, 2020, and August 31, 2021. Adults with MG were identified using a validated algorithm. Each patient was matched by age, sex, and geographic area of residence to 5 controls from the general population and from a cohort of individuals with rheumatoid arthritis (RA)., Exposure: Patients with MG and matched controls., Main Outcomes and Measures: Main outcomes were COVID-19 infection and related hospitalizations, intensive care unit admissions, and 30-day mortality among patients with MG vs controls. Secondary outcomes were uptake of COVID-19 vaccination among patients with MG vs controls., Results: Among 11 365 233 eligible Ontario residents, 4411 patients with MG (mean [SD] age, 67.7 [15.6] years; 2274 women [51.6%]) were matched to 22 055 general population controls (mean [SD] age, 67.7 [15.6] years; 11 370 women [51.6%]) and 22 055 controls with RA (mean [SD] age, 67.7 [15.6] years; 11 370 women [51.6%]). In the matched cohort, 38 861 of 44 110 individuals (88.1%) were urban residents; in the MG cohort, 3901 (88.4%) were urban residents. Between January 15, 2020, and May 17, 2021, 164 patients with MG (3.7%), 669 general population controls (3.0%), and 668 controls with RA (3.0%) contracted COVID-19. Compared with general population controls and controls with RA, patients with MG had higher rates of COVID-19-associated emergency department visits (36.6% [60 of 164] vs 24.4% [163 of 669] vs 29.9% [200 of 668]), hospital admissions (30.5% [50 of 164] vs 15.1% [101 of 669] vs 20.7% [138 of 668]), and 30-day mortality (14.6% [24 of 164] vs 8.5% [57 of 669] vs 9.9% [66 of 668]). By August 2021, 3540 patients with MG (80.3%) vs 17 913 general population controls (81.2%) had received 2 COVID-19 vaccine doses, and 137 (3.1%) vs 628 (2.8%), respectively had received 1 dose. Of 3461 first vaccine doses for patients with MG, fewer than 6 individuals were hospitalized for MG worsening within 30 days of vaccination. Vaccinated patients with MG had a lower risk than unvaccinated patients with MG of contracting COVID-19 (hazard ratio, 0.43; 95% CI, 0.30-0.60)., Conclusions and Relevance: This study suggests that adults with MG who contracted COVID-19 had a higher risk of hospitalization and death compared with matched controls. Vaccine uptake was high, with negligible risk of severe MG exacerbations after vaccination, as well as evidence of effectiveness. The findings support public health policies prioritizing people with MG for vaccination and new COVID-19 therapeutics.

Background: Prednisone is a common treatment for myasthenia gravis (MG), and osteoporosis is a known potential risk of chronic prednisone therapy., Objective: Our aim was to evaluate the risk of serious fractures in a population-based cohort of MG patients., Methods: An inception cohort of patients with MG was identified from administrative health data in Ontario, Canada between April 1, 2002 and December 31, 2015. For each MG patient, we matched 4 general population comparators based on age, sex, and region of residence. Fractures were identified through emergency department and hospitalization data. Crude overall rates and sex-specific rates of fractures were calculated for the MG and comparator groups, as well as rates of specific fractures. Adjusted hazard ratios (HRs) and 95% confidence intervals (CIs) were estimated using Cox regression., Results: Among 3,823 incident MG patients (followed for a mean of 5 years), 188 (4.9%) experienced a fracture compared with 741 (4.8%) fractures amongst 15,292 matched comparators. Crude fracture rates were not different between the MG cohort and matched comparators (8.71 vs. 7.98 per 1000 patient years), overall and in men and women separately. After controlling for multiple covariates, MG patients had a significantly lower risk of fracture than comparators (HR 0.74, 95% CI 0.63-0.88)., Conclusions: In this large, population-based cohort of incident MG patients, MG patients were at lower risk of a major fracture than comparators. The reasons for this finding are unclear but may highlight the importance osteoporosis prevention.

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.)