Health care data allow for the study and surveillance of chronic diseases such as diabetes. The objective of this study was to identify and validate optimal algorithms for diabetes cases within health care administrative databases for different research purposes, populations, and data sources. We linked health care administrative databases from Ontario, Canada to a reference standard of primary care electronic medical records (EMRs). We then identified and calculated the performance characteristics of multiple adult diabetes case definitions, using combinations of data sources and time windows. The best algorithm to identify diabetes cases was the presence at any time of one hospitalization or physician claim for diabetes AND either one prescription for an anti-diabetic medication or one physician claim with a diabetes-specific fee code [sensitivity 84.2%, specificity 99.2%, positive predictive value (PPV) 92.5%]. Use of physician claims alone performed almost as well: three physician claims for diabetes within one year was highly specific (sensitivity 79.9%, specificity 99.1%, PPV 91.4%) and one physician claim at any time was highly sensitive (sensitivity 93.6%, specificity 91.9%, PPV 58.5%). This study identifies validated algorithms to capture diabetes cases within health care administrative databases for a range of purposes, populations and data availability. These findings are useful to study trends and outcomes of diabetes using routinely-collected health care data.

The burden of hypereosinophilic syndrome (HES) in Europe is not well characterized. To evaluate real-world patient characteristics, treatment patterns, clinical manifestations, and healthcare resource utilization for patients with HES from France, Germany, Italy, Spain, and the United Kingdom. In this retrospective, noninterventional study, data for patients with a physician-confirmed diagnosis of HES were abstracted from medical chart reviews. Patients were aged 6 years or older at the time of HES diagnosis and had 1 or more years of follow-up from the index date (first clinic visit between January 2015 and December 2019). Data on treatment patterns, comorbidities, clinical manifestations, clinical outcomes, and healthcare resource utilization were collected from diagnosis or index date to end of follow-up. Data for 280 patients were abstracted from medical charts by 121 physicians treating HES, with multiple specialties. Most patients (55%) had idiopathic HES, and 24% had myeloid HES; the median number (interquartile range [IQR]) of diagnostic tests per patient was 10 (6-12). The most common comorbidities were asthma (45%) and anxiety or depression (36%). Most patients (89%) used oral corticosteroids; 64% used immunosuppressants or cytotoxic agents, and 44% used biologics. Patients had a median (IQR) of 3 clinical manifestations (1-5), most commonly constitutional (63%), lung (49%), and skin (48%). Twenty-three percent of patients experienced a flare, and 40% had a complete treatment response. Some patients (30%) were hospitalized with a median (IQR) stay of 9 days (5-15) for HES-related issues. Patients with HES across 5 European countries had a substantial disease burden despite extensive oral corticosteroids treatment, highlighting the need for additional targeted therapies.

To determine whether childhood infections were associated with the development of childhood acute lymphoblastic leukaemia (ALL). We included studies that assessed any infection in childhood prior to the diagnosis of ALL in children aged 0–19 years compared to children without cancer. The primary analysis synthesised any infection against the odds of ALL, and secondary analyses assessed the frequency, severity, timing of infections, and specific infectious agents against the odds of ALL. Subgroup analyses by data source were investigated. In our primary analysis of 12 496 children with ALL and 2 356 288 children without ALL from 38 studies, we found that any infection was not associated with ALL (odds ratio (OR)=1.10, 95% CI: 0.95–1.28). Among studies with laboratory-confirmed infections, the presence of infections increased the odds of ALL by 2.4-fold (OR=2.42, 95% CI: 1.54–3.82). Frequency, severity, and timing of infection were not associated with ALL. The hypothesis put forward by Greaves and others about an infectious aetiology are neither confirmed nor refuted and the overall evidence remains inadequate for good judgement. The qualitative difference in the subgroup effects require further study, and future research will need to address the challenges in measuring infectious exposures.

While medical records have detailed information, they are limited in reach to the availability and accessibility of those records. On the other hand, administrative data while limited in scope, have a much further reach in coverage of an entire population. However, few studies have validated the use of administrative data for identifying infections in pediatric populations. Pediatric patients from Ontario, Canada aged <18 years were randomly sampled from the Electronic Medical Record Administrative data Linked Database (EMRALD). Using physician diagnoses from the electronic medical record (EMR) as the reference standard, we determined the criterion validity of physician billing claims in administrative data for identifying infectious disease syndromes from 2012 to 2014. Diagnosis codes were assessed by infection category (respiratory, skin and soft tissue, gastrointestinal, urinary tract and otitis externa) and for all infections combined. Sensitivity analyses assessed the performance if patients had more than one reason to visit the physician. We analysed 2,139 patients and found 33.3% of all visits were for an infection, and respiratory infections accounted for 67.6% of the infections. When we combined all infection categories, sensitivity was 0.74 (95% CI 0.70–0.77), specificity was 0.95 (95% CI 0.93–0.96), positive predictive value (PPV) was 0.87 (95% CI 0.84–0.90), and negative predictive value (NPV) was 0.88 (95% CI 0.86–0.89). For respiratory infections, sensitivity was 0.77 (95% CI 0.73–0.81), specificity was 0.96 (95% CI 0.95–0.97), PPV was 0.85 (95% CI 0.81–0.88), and NPV was 0.94 (95% CI 0.92–0.95). Similar performance was observed for skin and soft tissue, gastrointestinal, urinary tract, and otitis externa infections, but with lower sensitivity. Performance measures were highest when the patient visited the physician with only one health complaint. We found when using linked EMR data as the reference standard, administrative billing codes are reasonably accurate in identifying infections in a pediatric population.