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Objective The objective of this work was to provide easy access to reliable health information based on good quality research that will help health care professionals to learn what works best for seniors to stay as healthy as possible, manage health conditions and build supportive health systems. This will help meet the demands of our aging population that clinicians provide high quality care for older adults, that public health professionals deliver disease prevention and health promotion strategies across the life span, and that policymakers address the economic and social need to create a robust health system and a healthy society for all ages. Results The McMaster Optimal Aging Portal’s (Portal) professional bibliographic database contains high quality scientific evidence about optimal aging specifically targeted to clinicians, public health professionals and policymakers. The database content comes from three information services: McMaster Premium LiteratUre Service (MacPLUS™), Health Evidence™ and Health Systems Evidence. The Portal is continually updated, freely accessible online, easily searchable, and provides email-based alerts when new records are added. The database is being continually assessed for value, usability and use. A number of improvements are planned, including French language translation of content, increased linkages between related records within the Portal database, and inclusion of additional types of content. While this article focuses on the professional database, the Portal also houses resources for patients, caregivers and the general public, which may also be of interest to geriatric practitioners and researchers.

Information on patient symptoms can be obtained by patient self-report or medical records review. Both methods have limitations. To assess the agreement between self-report and documentation in the medical records of signs/symptoms of respiratory illness (fever, cough, runny nose, sore throat, headache, sinus problems, muscle aches, fatigue, earache, and chills). Respondents were 176 research participants in the Hutterite Influenza Prevention Study during the 2008–2009 influenza season with information about the presence or absence of signs/symptoms from both self-report and primary care medical records. Compared with medical records, lower proportions of self-reported fever, sore throat, earache, cough, and sinus problems were found. Total agreements between self-report and medical report of symptoms ranged from 61% (for sore throat) to 88% (for muscle aches and earache), with kappa estimates varying from 0.05 (for chills) to 0.41 (for cough) and 0.51 (for earache). Negative agreement was considerably higher (from 68% for sore throat to 93% for muscle aches and earache) than positive agreement (from 13% for chills to 58% for earache) for each symptom except cough where positive agreement (77%) was higher than negative agreement (64%). Agreements varied by age group. We found better agreement for earache (kappa=0.62) and lower agreements for headache, sinus problems, muscle aches, fatigue, and chills in older children (aged ≥5 years) and adults. Agreements were variable depending on the specific symptom. Contrary to research in other patient populations which suggests that clinicians report fewer symptoms than patients, we found that the medical record captured more symptoms than self-report. Symptom agreement and disagreement may be affected by the perspectives of the person experiencing them, the observer, the symptoms themselves, measurement error, the setting in which the symptoms were observed and recorded, and the broader community and cultural context of patients.

Objective: Several surveillance definitions of influenza-like illness (ILI) have been proposed, based on the presence of symptoms. Symptom data can be obtained from patients, medical records, or both. Past research has found that agreements between health record data and self-report are variable depending on the specific symptom. Therefore, we aimed to explore the implications of using data on influenza symptoms extracted from medical records, similar data collected prospectively from outpatients, and the combined data from both sources as predictors of laboratory-confirmed influenza. Methods: Using data from the Hutterite Influenza Prevention Study, we calculated: 1) the sensitivity, specificity and predictive values of individual symptoms within surveillance definitions; 2) how frequently surveillance definitions correlated to laboratory-confirmed influenza; and 3) the predictive value of surveillance definitions. Results: Of the 176 participants with reports from participants and medical records, 142 (81%) were tested for influenza and 37 (26%) were PCR positive for influenza. Fever (alone) and fever combined with cough and/or sore throat were highly correlated with being PCR positive for influenza for all data sources. ILI surveillance definitions, based on symptom data from medical records only or from both medical records and self-report, were better predictors of laboratory-confirmed influenza with higher odds ratios and positive predictive values. Discussion: The choice of data source to determine ILI will depend on the patient population, outcome of interest, availability of data source, and use for clinical decision making, research, or surveillance. Key words: Influenza; influenza-like illness; surveillance definition; data source