My hearing explained for children: exploring use of this discussion tool in clinical practice.

OBJECTIVE: To explore the clinical use of 'My Hearing Explained for Children' (MHEfC) with children aged 8-11 years, from the perspectives of the child, parents and audiologist. DESIGN: A mixed methods randomised control trial. Participants completed evaluation questionnaires after their appointment. Statistical and thematic analyses were used to examine the rating scale and open response elements of the questionnaires respectively. STUDY SAMPLE: 45 families participated, 24 randomised to MHEfC and 21 to standard care. RESULTS: Use of MHEfC increased the appointment duration by 8.2 minutes (95% CI 4.1 to 14.2 minutes), which was acceptable to parents (96%) and audiologists (67%). It promoted conversation around 'behavioural and communication tactics', 'specific listening situations' and 'listening effort' at the expense of 'anatomy/physiology/aetiology'. MHEfC positively impacted the discussion of test results (54%); ease of finding joint solutions to problems (71%); and the nature of issues and management options discussed (54%). Parental satisfaction with discussion was high on both pathways, so some parents and audiologists questioned MHEfC necessity and recommended targeted use in future. CONCLUSIONS: MHEfC was acceptable to children (70%) and parents (86%). It successfully promoted child-centred topics of discussion and can positively impact discussion outcomes.

Estimates of interaural attenuation in children and the implications for masking in clinical audiometry.

OBJECTIVE: The aim of this study was to provide estimates of interaural attenuation (IA) in children, under clinical test conditions for supra-aural and insert earphones. DESIGN: This was a retrospective review of clinical audiograms for children aged 8 months to 16 years. STUDY SAMPLE: There were between 2 and 21 subjects, depending on the transducer and stimulus frequency. RESULTS: For insert earphones, younger age groups had smaller IA estimates (mean 60 dB, minimum 40 dB) compared to older children (mean 78 dB, minimum 60 dB). The insert IA estimates for older children were similar to published adult IA data. There was no significant effect of age on the children's estimated IA for supra-aural earphones. CONCLUSIONS: Under the clinical conditions of this study, cross-hearing should be considered when the difference between the better ear and poorer ear not-masked air conduction thresholds are ≥ 40 dB for inserts with foam tips in children under 13 years. Smaller estimates of IA in younger children compared to older children may be due to difficulties achieving deep insertion of foam tips in smaller ears and less cooperative subjects under these conditions. Limitations of the study, including lack of bone conduction threshold data, are discussed.

Infant feeding practices within a large electronic medical record database.

BACKGROUND: The emerging adoption of the electronic medical record (EMR) in primary care enables clinicians and researchers to efficiently examine epidemiological trends in child health, including infant feeding practices. METHODS: We completed a population-based retrospective cohort study of 8815 singleton infants born at term in Ontario, Canada, April 2002 to March 2013. Newborn records were linked to the Electronic Medical Record Administrative data Linked Database (EMRALD™), which uses patient-level information from participating family practice EMRs across Ontario. We assessed exclusive breastfeeding patterns using an automated electronic search algorithm, with manual review of EMRs when the latter was not possible. We examined the rate of breastfeeding at visits corresponding to 2, 4 and 6 months of age, as well as sociodemographic factors associated with exclusive breastfeeding. RESULTS: Of the 8815 newborns, 1044 (11.8%) lacked breastfeeding information in their EMR. Rates of exclusive breastfeeding were 39.5% at 2 months, 32.4% at 4 months and 25.1% at 6 months. At age 6 months, exclusive breastfeeding rates were highest among mothers aged ≥40 vs. < 20 years (rate ratio [RR] 2.45, 95% confidence interval [CI] 1.62-3.68), urban vs. rural residence (RR 1.35, 95% CI 1.22-1.50), and highest vs. lowest income quintile (RR 1.18, 95% CI 1.02-1.36). Overall, immigrants had similar rates of exclusive breastfeeding as non-immigrants; yet, by age 6 months, among those residing in the lowest income quintile, immigrants were more likely to exclusively breastfeed than their non-immigrant counterparts (RR 1.43, 95% CI 1.12-1.83). CONCLUSIONS: We efficiently determined rates and factors associated with exclusive breastfeeding using data from a large EMR database.

A nurse-led model of chronic disease management in general practice: Patients' perspectives.

BACKGROUND: Evidence suggests that current models of chronic disease management within general practice are not effective in meeting the needs of the community. OBJECTIVE: The objective of this article is to examine patients' perceptions of a nurse-led collaborative model of care trialled in three general practices in Australia. METHODS: This article reports on the second phase of a mixed-methods study in which semi-structured interviews with purposively selected patients were conducted to elicit information about their perceptions of nurse-led care. RESULTS: Three themes emerged from the data - time, ambiance and dimensions of the nurse role. DISCUSSION: The results suggest that general practice nurses had a positive impact on patients' ability to manage their chronic disease. This infers that there is scope for general practice nurses to expand their role in chronic disease management to assist patients to better self-manage their chronic diseases.

Identifying individuals with multiple sclerosis in an electronic medical record.

BACKGROUND: The increasing use of electronic medical records (EMRs) presents an opportunity to efficiently evaluate and improve quality of care for individuals with MS. OBJECTIVES: We aimed to establish an algorithm to identify individuals with MS within EMRs. METHODS: We used a sample of 73,003 adult patients from 83 primary care physicians in Ontario using the Electronic Medical Record Administrative data Linked Database (EMRALD). A reference standard of 247 individuals with MS was identified through chart abstraction. The accuracy of identifying individuals with MS in an EMR was assessed using information in the cumulative patient profile (CPP), prescriptions and physician billing codes. RESULTS: An algorithm identifying MS in the CPP performed well with 91.5% sensitivity, 100% specificity, 98.7% PPV and 100% NPV. The addition of prescriptions for MS-specific medications and physician billing code 340 used four times within any 12-month timeframe slightly improved the sensitivity to 92.3% with a PPV of 97.9%. CONCLUSIONS: Data within an EMR can be used to accurately identify patients with MS. This study has positive implications for clinicians, researchers and policy makers as it provides the potential to identify cohorts of MS patients in the primary care setting to examine quality of care.

Development and validation of an administrative data algorithm to estimate the disease burden and epidemiology of multiple sclerosis in Ontario, Canada.

BACKGROUND: Few studies have assessed the accuracy of administrative data for identifying multiple sclerosis (MS) patients. OBJECTIVES: To validate administrative data algorithms for MS, and describe the burden and epidemiology over time in Ontario, Canada. METHODS: We employed a validated search strategy to identify all MS patients within electronic medical records, to identify patients with and without MS (reference standard). We then developed and validated different combinations of administrative data for algorithms. The most accurate algorithm was used to estimate the burden and epidemiology of MS over time. RESULTS: The accuracy of the algorithm of one hospitalisation or five physician billings over 2 years provided both high sensitivity (84%) and positive predictive value (86%). Application of this algorithm to provincial data demonstrated an increasing cumulative burden of MS, from 13,326 patients (0.14%) in 2000 to 24,647 patients in 2010 (0.22%). Age-and-sex standardised prevalence increased from 133.9 to 207.3 MS patients per 100,000 persons in the population, from 2000 - 2010. During this same period, age-and-sex-standardised incidence varied from 17.9 to 19.4 patients per 100,000 persons. CONCLUSIONS: MS patients can be accurately identified from administrative data. Our findings illustrated a rising prevalence of MS over time. MS incidence rates also appear to be rising since 2009.

Canadian administrative health data can identify patients with myasthenia gravis.

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.

Are family physicians comprehensively using electronic medical records such that the data can be used for secondary purposes? A Canadian perspective.

BACKGROUND: With the introduction and implementation of a variety of government programs and policies to encourage adoption of electronic medical records (EMRs), EMRs are being increasingly adopted in North America. We sought to evaluate the completeness of a variety of EMR fields to determine if family physicians were comprehensively using their EMRs and the suitability of use of the data for secondary purposes in Ontario, Canada. METHODS: We examined EMR data from a convenience sample of family physicians distributed throughout Ontario within the Electronic Medical Record Administrative data Linked Database (EMRALD) as extracted in the summer of 2012. We identified all physicians with at least one year of EMR use. Measures were developed and rates of physician documentation of clinical encounters, electronic prescriptions, laboratory tests, blood pressure and weight, referrals, consultation letters, and all fields in the cumulative patient profile were calculated as a function of physician and patient time since starting on the EMR. RESULTS: Of the 167 physicians with at least one year of EMR use, we identified 186,237 patients. Overall, the fields with the highest level of completeness were for visit documentations and prescriptions (>70%). Improvements were observed with increasing trends of completeness overtime for almost all EMR fields according to increasing physician time on EMR. Assessment of the influence of patient time on EMR demonstrated an increasing likelihood of the population of EMR fields overtime, with the largest improvements occurring between the first and second years. CONCLUSIONS: All of the data fields examined appear to be reasonably complete within the first year of adoption with the biggest increase occurring the first to second year. Using all of the basic functions of the EMR appears to be occurring in the current environment of EMR adoption in Ontario. Thus the data appears to be suitable for secondary use.

A validation study of administrative data algorithms to identify patients with Parkinsonism with prevalence and incidence trends.

BACKGROUND: Epidemiological studies for identifying patients with Parkinson's disease (PD) or Parkinsonism (PKM) have been limited by their nonrandom sampling techniques and mainly veteran populations. This reduces their use for health services planning. The purpose of this study was to validate algorithms for the case ascertainment of PKM from administrative databases using primary care patients as the reference standard. METHODS: We conducted a retrospective chart abstraction using a random sample of 73,003 adults aged ≥ 20 years from a primary care Electronic Medical Record Administrative data Linked Database (EMRALD) in Ontario, Canada. Physician diagnosis in the EMR was used as the reference standard and population-based administrative databases were used to identify patients with PKM from the derivation of algorithms. We calculated algorithm performance using sensitivity, specificity, and predictive values and then determined the population-level prevalence and incidence trends with the most accurate algorithms. RESULTS: We selected, '2 physician billing codes in 1 year' as the optimal administrative data algorithm in adults and seniors (≥ 65 years) due to its sensitivity (70.6-72.3%), specificity (99.9-99.8%), positive predictive value (79.5-82.8%), negative predictive value (99.9-99.7%), and prevalence (0.28-1.20%), respectively. CONCLUSIONS: Algorithms using administrative databases can reliably identify patients with PKM with a high degree of accuracy.

Assessing the validity of using administrative data to identify patients with epilepsy.

OBJECTIVE: Previous validation studies assessing the use of administrative data to identify patients with epilepsy have used targeted sampling or have used a reference standard of patients in the neurologist, hospital, or emergency room setting. Therefore, the validity of using administrative data to identify patients with epilepsy in the general population has not been previously assessed. The purpose of this study was to determine the validity of using administrative data to identify patients with epilepsy in the general population. METHODS: A retrospective chart abstraction study was performed using primary care physician records from 83 physicians distributed throughout Ontario and contributing data to the Electronic Medical Record Administrative data Linked Database (EMRALD) A random sample of 7,500 adult patients, from a possible 73,014 eligible, was manually chart abstracted to identify patients who had ever had epilepsy. These patients were used as a reference standard to test a variety of administrative data algorithms. RESULTS: An algorithm of three physician billing codes (separated by at least 30 days) in 2 years or one hospitalization had a sensitivity of 73.7% (95% confidence interval [CI] 64.8-82.5%), specificity of 99.8% (95% CI 99.6-99.9%), positive predictive value (PPV) of 79.5% (95% CI 71.1-88.0%), and negative predictive value (NPV) of 99.7% (95% CI 99.5-99.8%) for identifying patients who had ever had epilepsy. SIGNIFICANCE: The results of our study showed that administrative data can reasonably accurately identify patients who have ever had epilepsy, allowing for a "lifetime" population prevalence determination of epilepsy in Ontario and the rest of Canada with similar administrative databases. This will facilitate future studies on population level patterns and outcomes of care for patients living with epilepsy.

An administrative data validation study of the accuracy of algorithms for identifying rheumatoid arthritis: the influence of the reference standard on algorithm performance.

BACKGROUND: We have previously validated administrative data algorithms to identify patients with rheumatoid arthritis (RA) using rheumatology clinic records as the reference standard. Here we reassessed the accuracy of the algorithms using primary care records as the reference standard. METHODS: We performed a retrospective chart abstraction study using a random sample of 7500 adult patients under the care of 83 family physicians contributing to the Electronic Medical Record Administrative data Linked Database (EMRALD) in Ontario, Canada. Using physician-reported diagnoses as the reference standard, we computed and compared the sensitivity, specificity, and predictive values for over 100 administrative data algorithms for RA case ascertainment. RESULTS: We identified 69 patients with RA for a lifetime RA prevalence of 0.9%. All algorithms had excellent specificity (>97%). However, sensitivity varied (75-90%) among physician billing algorithms. Despite the low prevalence of RA, most algorithms had adequate positive predictive value (PPV; 51-83%). The algorithm of "[1 hospitalization RA diagnosis code] or [3 physician RA diagnosis codes with ≥1 by a specialist over 2 years]" had a sensitivity of 78% (95% CI 69-88), specificity of 100% (95% CI 100-100), PPV of 78% (95% CI 69-88) and NPV of 100% (95% CI 100-100). CONCLUSIONS: Administrative data algorithms for detecting RA patients achieved a high degree of accuracy amongst the general population. However, results varied slightly from our previous report, which can be attributed to differences in the reference standards with respect to disease prevalence, spectrum of disease, and type of comparator group.

Mouse Adaptation of Human Inflammatory Bowel Diseases Microbiota Enhances Colonization Efficiency and Alters Microbiome Aggressiveness Depending on Recipient Colonic Inflammatory Environment.

Understanding the cause vs consequence relationship of gut inflammation and microbial dysbiosis in inflammatory bowel diseases (IBD) requires a reproducible mouse model of human-microbiota-driven experimental colitis. Our study demonstrated that human fecal microbiota transplant (FMT) transfer efficiency is an underappreciated source of experimental variability in human microbiota associated (HMA) mice. Pooled human IBD patient fecal microbiota engrafted germ-free (GF) mice with low amplicon sequence variant (ASV)-level transfer efficiency, resulting in high recipient-to-recipient variation of microbiota composition and colitis severity in HMA Il-10-/- mice. In contrast, mouse-to-mouse transfer of mouse-adapted human IBD patient microbiota transferred with high efficiency and low compositional variability resulting in highly consistent and reproducible colitis phenotypes in recipient Il-10-/- mice. Human-to-mouse FMT caused a population bottleneck with reassembly of microbiota composition that was host inflammatory environment specific. Mouse-adaptation in the inflamed Il-10-/- host reassembled a more aggressive microbiota that induced more severe colitis in serial transplant to Il-10-/- mice than the distinct microbiota reassembled in non-inflamed WT hosts. Our findings support a model of IBD pathogenesis in which host inflammation promotes aggressive resident bacteria, which further drives a feed-forward process of dysbiosis exacerbated gut inflammation. This model implies that effective management of IBD requires treating both the dysregulated host immune response and aggressive inflammation-driven microbiota. We propose that our mouse-adapted human microbiota model is an optimized, reproducible, and rigorous system to study human microbiome-driven disease phenotypes, which may be generalized to mouse models of other human microbiota-modulated diseases, including metabolic syndrome/obesity, diabetes, autoimmune diseases, and cancer.

Investigating the wave-fixed and place-fixed origins of the 2f(1)-f(2) distortion product otoacoustic emission within a micromechanical cochlear model.

The 2f(1)-f(2) distortion product otoacoustic emission (DPOAE) arises within the cochlea due to the nonlinear interaction of two stimulus tones (f(1) and f(2)). It is thought to comprise contributions from a wave-fixed source and a place-fixed source. The generation and transmission of the 2f(1)-f(2) DPOAE is investigated here using quasilinear solutions to an elemental model of the human cochlea with nonlinear micromechanics. The micromechanical parameters and nonlinearity are formulated to match the measured response of the cochlea to single- and two-tone stimulation. The controlled introduction of roughness into the active micromechanics of the model allows the wave- and place-fixed contributions to the DPOAE to be studied separately. It is also possible to manipulate the types of nonlinear suppression that occur within the quasilinear model to investigate the influence of stimulus parameters on DPOAE generation. The model predicts and explains a variety of 2f(1)-f(2) DPOAE phenomena: The dependence of emission amplitude on stimulus parameters, the weakness of experiments designed to quantify cochlear amplifier gain, and the predominant mechanism which gives rise to DPOAE fine structure. In addition, the model is used to investigate the properties of the wave-fixed source and how these properties are influenced by the stimulus parameters.

HashSeq: a Simple, Scalable, and Conservative De Novo Variant Caller for 16S rRNA Gene Data Sets.

16S rRNA gene sequencing is a common and cost-effective technique for characterization of microbial communities. Recent bioinformatics methods enable high-resolution detection of sequence variants of only one nucleotide difference. In this study, we utilized a very fast HashMap-based approach to detect sequence variants in six publicly available 16S rRNA gene data sets. We then use the normal distribution combined with locally estimated scatterplot smoothing (LOESS) regression to estimate background error rates as a function of sequencing depth for individual clusters of sequences. This method is computationally efficient and produces inference that yields sets of variants that are conservative and well supported by reference databases. We argue that this approach to inference is fast, simple, and scalable to large data sets and provides a high-resolution set of sequence variants which are less likely to be the result of sequencing error. IMPORTANCE Recent bioinformatics development has enabled the detection of sequence variants with a high resolution of only one single-nucleotide difference in 16S rRNA gene sequence data. Despite this progress, there are several limitations that can be associated with variant calling pipelines, such as producing a large number of low-abundance sequence variants which need to be filtered out with arbitrary thresholds in downstream analyses or having a slow runtime. In this report, we introduce a fast and scalable algorithm which infers sequence variants based on the estimation of a normally distributed background error as a function of sequencing depth. Our pipeline has attractive performance characteristics, can be used independently or in parallel with other variant callers, and provides explicit P values for each variant evaluating the hypothesis that a variant is caused by sequencing error.

Identifying cases of spinal cord injury or disease in a primary care electronic medical record database.

OBJECTIVE: To identify cases of spinal cord injury or disease (SCI/D) in an Ontario database of primary care electronic medical records (EMR). DESIGN: A reference standard of cases of chronic SCI/D was established via manual review of EMRs; this reference standard was used to evaluate potential case identification algorithms for use in the same database. SETTING: Electronic Medical Records Primary Care (EMRPC) Database, Ontario, Canada. PARTICIPANTS: A sample of 48,000 adult patients was randomly selected from 213,887 eligible patients in the EMRPC database. INTERVENTIONS: N/A. MAIN OUTCOME MEASURE(S): Candidate algorithms were evaluated using sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and F-score. RESULTS: 126 cases of chronic SCI/D were identified, forming the reference standard. Of these, 57 were cases of traumatic spinal cord injury (TSCI), and 67 were cases of non-traumatic spinal cord injury (NTSCI). The optimal case identification algorithm used free-text keyword searches and a physician billing code, and had 70.6% sensitivity (61.9-78.4), 98.5% specificity (97.3-99.3), 89.9% PPV (82.2-95.0), 94.7% NPV (92.8-96.3), and an F-score of 79.1. CONCLUSIONS: Identifying cases of chronic SCI/D from a database of primary care EMRs using free-text entries is feasible, relying on a comprehensive case definition. Identifying a cohort of patients with SCI/D will allow for future study of the epidemiology and health service utilization of these patients.

Health Care Utilization for Musculoskeletal Issues During the Prediagnosis Period in Psoriatic Arthritis: A Population-Based Study.

OBJECTIVE: Information about the prediagnosis period in psoriatic arthritis (PsA) is limited. The present study was undertaken to compare health care utilization related to musculoskeletal issues during a 5-year period prior to the diagnosis of PsA versus that of subjects with no prior inflammatory arthritis within a primary care setting. METHODS: We conducted a population-based, matched cohort study using electronic medical records and administrative data in Ontario, Canada. Age- and sex-matched cohorts of PsA patients and comparators from the same family physicians were assembled. Comparators were not allowed to have prior spondyloarthritis, ankylosing spondylitis, or rheumatoid arthritis billing code diagnoses. The study outcomes included health care utilization and costs related to nonspecific musculoskeletal issues during a 5-year period prior to the index date. RESULTS: We studied 462 PsA patients and 2,310 matched comparators. The odds ratio (OR) related to visiting a primary care physician for nonspecific musculoskeletal issues in patients with PsA was 2.14 (95% confidence interval 1.73-2.64) in the year immediately preceding the index date and was similarly elevated up to 5 years prior. The OR related to using other musculoskeletal-related health care services, including musculoskeletal specialists visits, joint injections, joint imaging, and emergency department visits, was higher in PsA as early as 5 years preceding the index date. Total and musculoskeletal-related health care costs prior to the index date were higher for patients with PsA versus comparators. CONCLUSION: A prodromal PsA phase characterized by nonspecific musculoskeletal symptoms may exist. Further study is needed to determine if this represents a window for earlier diagnosis of PsA.

Enabling research in general practice--increasing functionality of electronic medical records.

BACKGROUND: With an estimated 80% of Australians visiting a general practitioner at least once a year, the data generated by GPs is a rich source of the overall health profile of patients. However, this data is rarely used to report on health outcomes. OBJECTIVE: This article reports on the use of remote access of electronic medical records (EMRs) for the purpose of collecting data during a collaborative research project involving the staff of three general practices and an external research team. DISCUSSION: Throughout the project numerous benefits to remotely accessing general practice EMRs were identified. However, there remain some difficulties which need to be addressed. An increased functionality of the software programs used in general practice is required, along with improvements in the utilisation of the software capabilities. Collaboration between clinicians, researchers and clinical software developers will be vital to advance this process.

Validation of a type 1 diabetes algorithm using electronic medical records and administrative healthcare data to study the population incidence and prevalence of type 1 diabetes in Ontario, Canada.

INTRODUCTION: We aimed to develop algorithms distinguishing type 1 diabetes (T1D) from type 2 diabetes in adults ≥18 years old using primary care electronic medical record (EMRPC) and administrative healthcare data from Ontario, Canada, and to estimate T1D prevalence and incidence. RESEARCH DESIGN AND METHODS: The reference population was a random sample of patients with diabetes in EMRPC whose charts were manually abstracted (n=5402). Algorithms were developed using classification trees, random forests, and rule-based methods, using electronic medical record (EMR) data, administrative data, or both. Algorithm performance was assessed in EMRPC. Administrative data algorithms were additionally evaluated using a diabetes clinic registry with endocrinologist-assigned diabetes type (n=29 371). Three algorithms were applied to the Ontario population to evaluate the minimum, moderate and maximum estimates of T1D prevalence and incidence rates between 2010 and 2017, and trends were analyzed using negative binomial regressions. RESULTS: Of 5402 individuals with diabetes in EMRPC, 195 had T1D. Sensitivity, specificity, positive predictive value and negative predictive value for the best performing algorithms were 80.6% (75.9-87.2), 99.8% (99.7-100), 94.9% (92.3-98.7), and 99.3% (99.1-99.5) for EMR, 51.3% (44.0-58.5), 99.5% (99.3-99.7), 79.4% (71.2-86.1), and 98.2% (97.8-98.5) for administrative data, and 87.2% (81.7-91.5), 99.9% (99.7-100), 96.6% (92.7-98.7) and 99.5% (99.3-99.7) for combined EMR and administrative data. Administrative data algorithms had similar sensitivity and specificity in the diabetes clinic registry. Of 11 499 711 adults in Ontario in 2017, there were 24 789 (0.22%, minimum estimate) to 102 140 (0.89%, maximum estimate) with T1D. Between 2010 and 2017, the age-standardized and sex-standardized prevalence rates per 1000 person-years increased (minimum estimate 1.7 to 2.56, maximum estimate 7.48 to 9.86, p<0.0001). In contrast, incidence rates decreased (minimum estimate 0.1 to 0.04, maximum estimate 0.47 to 0.09, p<0.0001). CONCLUSIONS: Primary care EMR and administrative data algorithms performed well in identifying T1D and demonstrated increasing T1D prevalence in Ontario. These algorithms may permit the development of large, population-based cohort studies of T1D.

Assessing the completeness of infant and childhood immunizations within a provincial registry populated by parental reporting: A study using linked databases in Ontario, Canada.

INTRODUCTION: In Ontario, Canada, parents have the responsibility to report their child's routine infant and childhood vaccines to the provincial immunization registry (the Digital Health Immunization Repository; DHIR) without healthcare provider validation. Despite its use in routine immunization coverage monitoring, no study has previously examined the completeness of immunization data within the DHIR. METHODS: We assessed the completeness of DHIR immunizations, as compared to immunizations within the Electronic Medical Records-Primary Care (EMRPC) database, also known as EMRALD, a network of family physician electronic medical records (EMRs). We linked client records from the DHIR and EMRPC to a centralized population file. To create the study cohort, we examined children born during 2005-2008 and further defined the cohort based on those rostered to an EMRPC physician, visit criteria to ensure ongoing care by an EMRPC provider, and school attendance in Ontario at age 7. We calculated up-to-date (UTD) immunization coverage at age 7 for individual vaccines and overall using data from the DHIR and EMRPC separately, and compared the estimates. RESULTS: The analytic cohort to assess DHIR data completeness included 2,657 children. Overall UTD coverage (all vaccines assessed) was 82.0% in the DHIR and 67.6% in EMRPC. UTD coverage was higher in the DHIR for all vaccines assessed individually, with the exception of meningococcal C conjugate vaccine (difference = 0.3%). After excluding two EMRPC sites with irregularities in immunization data, the difference in overall UTD coverage between systems decreased from 14.4% to 6.6% INTERPRETATION: These results validate the use of DHIR for coverage assessment but also suggest that bidirectional exchange of immunization information has the potential to increase immunization data completeness in both systems.

Identifying and Characterizing Psoriasis and Psoriatic Arthritis Patients in Ontario Administrative Data: A Population-based Study From 1991 to 2015.

OBJECTIVES: We assessed the accuracy of case definition algorithms for psoriasis and psoriatic arthritis (PsA) in health administrative data and used primary care electronic medical records (EMR) to describe disease and treatment characteristics of these patients. METHODS: We randomly sampled 30,424 adult Ontario residents from the Electronic Medical Record Primary Care database and identified 2215 patients with any possible psoriatic disease-related terms in their EMR. The relevant patient records were chart abstracted to confirm diagnoses of psoriasis or PsA. This validation set was then linked to health administrative data to assess the performance of different algorithms for physician billing diagnosis codes, hospitalization diagnosis codes, and medications for psoriatic disease. We report the performance of selected case definition algorithms and describe the disease characteristics of the validation set. RESULTS: Our reference standard identified 1028 patients with psoriasis and 77 patients with PsA, for an overall prevalence of 3.4% for psoriasis and 0.3% for PsA. Most patients with PsA (66%) had a rheumatology-confirmed diagnosis, while only 30% of the patients with psoriasis had dermatology-confirmed diagnosis. The use of systemic medications was much more common with PsA than with psoriasis. All algorithms had excellent specificity (97-100%). The sensitivity and positive predictive value were moderate and varied between different algorithms (34-72%). CONCLUSION: The accuracy of case definition algorithms for psoriasis and PsA varies widely. However, selected algorithms produced population prevalence estimates that were within the expected ranges, suggesting that they may be useful for future research purposes.