The association between injurious falls requiring a visit to the emergency department and various classes of medications was examined in a case-control study of community living persons aged 66 years and older.Administrative databases from an urban health region provided the information used. Five controls for each case were randomly selected from community dwelling older persons who had not reported an injurious fall to one of the six regional emergency departments in the study year. Two series of analyses on medication use within 30 days of the fall were conducted using logistic regression, the first controlling for age, sex, and median income, the second controlling for co-morbid diagnoses as well.During the study year there were 2,405 falls reported by 2,278 individuals to six regional emergency departments giving a crude fall rate of 31.6 per 1,000 population per year. The initial analysis identified seven medication classes that were associated with an increased risk of an injurious fall, while controlling for age, gender and income. However, with further analyses controlling for the additional effects of co-morbid disease, narcotic pain-killers (odds ratio 1.68), anti-convulsants (odds ratio 1.51) and anti-depressants (odds ratio 1.46) were significant independent predictors of sustaining an injurious fall.These results are based on a Canadian population-based study with a large community sample. The study found that taking certain medications were independent predictors of sustaining an injurious fall in our elderly population - in addition to the risk associated with their medical condition.
Agent-based models (ABMs) are often parameterized using empirical data from the real world. For some ABMs this is not possible because the reality upon which the models are based does not exist or is not generalizable from one setting to another. In this paper we implement an online decision game to parameterize an agent-based model of pedestrian route choice decisions in a neighbourhood. Our conceptual framework is to use an experimental game to log decision-making behaviour, summarize this behaviour into a decision model, and then transfer this model to an ABM. The product of this framework is an ABM with agents informed by human decision making made within the game, rather than the real world. The results of our analysis suggest that the decision model is consistent with some general theory about pedestrian decision making, but the ABM illustrates some unique and contextually specific patterns of pedestrian flow. ABMs parameterized with game data may be useful for forecasting the effects of change on urban transportation infrastructure.
Background: The risk of stroke is elevated in the first 48 hours after TIA. Previous prognostic models suggest that diabetes mellitus, age, and clinical symptomatology predict stroke. The authors evaluated the magnitude of risk of stroke and predictors of stroke after TIA in an entire population over time. Methods: Administrative data from four different databases were used to define TIA and stroke for the entire province of Alberta for the fiscal year (April 1999–March 2000). The age-adjusted incidence of TIA was estimated using direct standardization to the 1996 Canadian population. The risk of stroke after a diagnosis of TIA in an Alberta emergency room was defined using a Kaplan-Meier survival function. Cox proportional hazards modeling was used to develop adjusted risk estimates. Risk assessment began 24 hours after presentation and therefore the risk of stroke in the first few hours after TIA is not captured by our approach. Results: TIA was reported among 2,285 patients for an emergency room diagnosed, age-adjusted incidence of 68.2 per 100,000 population (95% CI 65.3 to 70.9). The risk of stroke after TIA was 9.5% (95% CI 8.3 to 10.7) at 90 days and 14.5% (95% CI 12.8 to 16.2) at 1 year. The risk of combined stroke, myocardial infarction, or death was 21.8% (95% CI 20.0 to 23.6) at 1 year. Hypertension, diabetes mellitus, and older age predicted stroke at 1 year but not earlier. Conclusions: Although stroke is common after TIA, the early risk is not predicted by clinical and demographic factors. Validated models to identify which patients require urgent intervention are needed.
