Background: The objective of this paper is to describe the numbers, characteristics, and trends in the migration to the United States of physicians trained in sub-Saharan Africa.

Purpose To characterize the validity of algorithms to identify AF from electronic health data through a systematic review of the literature, and to identify gaps needing further research. Methods Two reviewers examined publications during 1997–2008 that identified patients with AF from electronic health data and provided validation information. We abstracted information including algorithm sensitivity, specificity, and positive predictive value (PPV). Results We reviewed 544 abstracts and 281 full-text articles, of which 18 provided validation information from 16 unique studies. Most used data from before 2000, and 10 of 16 used only inpatient data. Three studies incorporated electronic ECG data for case identification or validation. A large proportion of prevalent AF cases identified by ICD-9 code 427.31 were valid (PPV 70–96%, median 89%). Seven studies reported algorithm sensitivity (range, 57–95%; median 79%). One study validated an algorithm for incident AF and reported a PPV of 77%. Conclusions The ICD-9 code 427.31 performed relatively well, but conclusions about algorithm validity are hindered by few recent data, use of nonrepresentative populations, and a disproportionate focus on inpatient data. An optimal contemporary algorithm would likely draw on inpatient and outpatient codes and electronic ECG data. Additional research is needed in representative, contemporary populations regarding algorithms that identify incident AF and incorporate electronic ECG data.

Defining complexity in terms of the misalignment between patient needs and services offers new insights in how to research and develop solutions to patient care needs.

This review demonstrates that dietary supplement use was common among Canadian athletes at both the Atlanta and Sydney Olympic Games. There was a slight increase in total dietary supplement use at the Sydney Games. Widespread use of supplements, combined with an absence of evidence of their efficacy and a concern for the possibility of "inadvertent" doping, underscore the need for appropriately focused educational initiatives in this area.

Purpose To validate an algorithm that uses delivery date and diagnosis codes to define gestational age at birth in electronic health plan databases. Methods Using data from 225,384 live born deliveries among women aged 15–45 years in 2001–2007 within 8 of the 11 health plans participating in the Medication Exposure in Pregnancy Risk Evaluation Program, we compared 1) the algorithm-derived gestational age versus the “gold-standard” gestational age obtained from the infant birth certificate files; and 2) the prenatal exposure status of two antidepressants (fluoxetine and sertraline) and two antibiotics (amoxicillin and azithromycin) as determined by the algorithm-derived versus the gold-standard gestational age. Results The mean algorithm-derived gestational age at birth was lower than the mean obtained from the birth certificate files among singleton deliveries (267.9 versus 273.5 days) but not among multiple-gestation deliveries (253.9 versus 252.6 days). The algorithm-derived prenatal exposure to the antidepressants had a sensitivity and a positive predictive value (PPV) of ≥95%, and a specificity and a negative predictive value (NPV) of almost 100%. Sensitivity and PPV were both ≥90%, and specificity and NPV were both >99% for the antibiotics. Conclusions A gestational age algorithm based upon electronic health plan data correctly classified medication exposure status in most live born deliveries, but misclassification may be higher for drugs typically used for short durations.