These data demonstrate that depression among medical students may be undertreated. Medical schools can assist depressed students by addressing issues such as the stigma of using mental health services, confidentiality, and documentation. Early treatment of impaired future caregivers may have far-reaching implications for the individual students, their colleagues, and their future patients.

Purpose To perform a systematic review of the validity of algorithms for identifying cerebrovascular accidents (CVAs) or transient ischemic attacks (TIAs) using administrative and claims data. Methods PubMed and Iowa Drug Information Service (IDIS) searches of the English language literature were performed to identify studies published between 1990 and 2010 that evaluated the validity of algorithms for identifying CVAs (ischemic and hemorrhagic strokes, intracranial hemorrhage and subarachnoid hemorrhage) and/or TIAs in administrative data. Two study investigators independently reviewed the abstracts and articles to determine relevant studies according to pre-specified criteria. Results A total of 35 articles met the criteria for evaluation. Of these, 26 articles provided data to evaluate the validity of stroke, 7 reported the validity of TIA, 5 reported the validity of intracranial bleeds (intracerebral hemorrhage and subarachnoid hemorrhage), and 10 studies reported the validity of algorithms to identify the composite endpoints of stroke/TIA or cerebrovascular disease. Positive predictive values (PPVs) varied depending on the specific outcomes and algorithms evaluated. Specific algorithms to evaluate the presence of stroke and intracranial bleeds were found to have high PPVs (80% or greater). Algorithms to evaluate TIAs in adult populations were generally found to have PPVs of 70% or greater. Conclusions The algorithms and definitions to identify CVAs and TIAs using administrative and claims data differ greatly in the published literature. The choice of the algorithm employed should be determined by the stroke subtype of interest.

Purpose To identify and describe the validity of algorithms used to detect heart failure (HF) using administrative and claims data sources. Methods Systematic review of PubMed and Iowa Drug Information Service (IDIS) searches of the English language were performed to identify studies published between 1990 and 2010 that evaluated the validity of algorithms for the identification of patients with HF using and claims data. Abstracts and articles were reviewed by two study investigators to determine their relevance based on predetermined criteria. Results The initial search strategy identified 887 abstracts. Of these, 499 full papers were reviewed and 35 studies included data to evaluate the validity of identifying patients with HF. Positive predictive values (PPVs) were in the acceptable to high range, with most being very high (>90%). Studies that included patients with a primary hospital discharge diagnosis of ICD-9 code 428.X had the highest PPV and specificity for HF. PPVs for this algorithm ranged from 84% - 100%. This algorithm, however, may compromise sensitivity since many HF patients are managed on an outpatient basis. The most common ‘gold standard’ for the validation of HF was the Framingham Heart Study criteria. Conclusions The algorithms and definitions employed to identify HF using administrative and claims data perform well, particularly when using a primary hospital discharge diagnosis. Attention should be paid to whether patients who are managed on an outpatient basis are included in the study sample. Including outpatient codes in the described algorithms would increase the sensitivity for identifying new cases of HF.