BackgroundThe 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.MethodsWe 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.ResultsOf 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).ConclusionsWe efficiently determined rates and factors associated with exclusive breastfeeding using data from a large EMR database.Electronic supplementary materialThe online version of this article (10.1186/s12884-017-1633-9) contains supplementary material, which is available to authorized users.

Algorithms developed from health administrative data are sensitive and specific for identifying older adults with AD-RD.

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.

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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.