Self-Reported Falls and Fall-Related Injuries Among Persons Aged ≥65 Years–United States, 2006

Problem: In 2005, 15,802 persons aged ≥65 years died from fall injuries. How many older adults seek outpatient treatment for minor or moderate fall injuries is unknown. Method: To estimate the percentage of older adults who fell during the preceding three months, the Centers for Disease Control and Prevention (CDC) analyzed data from two questions about falls included in the 2006 Behavioral Risk Factor Surveillance System (BRFSS) survey. Results: Approximately 5.8 million (15.9%) persons aged ≥65 years reported falling at least once during the preceding three months, and 1.8 million (31.3%) of those who fell sustained an injury that resulted in a doctor visit or restricted activity for at least one day. Discussion: This report presents the first national estimates of the number and proportion of persons reporting fall-related injuries associated with either doctor visits or restricted activity. Summary: The prevalence of falls reinforces the need for broader use of scientifically proven fall prevention interventions. Impact on industry: Falls and fall-related injuries represent an enormous burden to individuals, society, and to our health care system. Because the U.S. population is aging, this problem will increase unless we take preventive action by broadly implementing evidence-based fall prevention programs. Such programs could appreciably decrease the incidence and health care costs of fall injuries, as well as greatly improve the quality of life for older adults

Detecting Anomalies in Controlled Drug Prescription Data Using Probabilistic Models

Abstract. Opioid analgesic drugs are widely used in pain management and substance dependence treatment. However, these drugs have high potential for misuse and subsequent harm. As a result, their prescribing is monitored and controlled. In Queensland, Australia, the Medicines Regulation and Quality Unit within the state health system maintains a database of prescribing events and uses this data to identify anoma-lies and provide subsequent support for patients and prescribers. In this study, we consider this task as an unsupervised anomaly detection prob-lem. We use probability density estimation models to describe the dis-tribution of the data over a number of key attributes and use the model to identify anomalies as points with low estimated probability. The re-sults are validated against cases identified by healthcare domain experts. There was strong agreement between cases identified by the models and expert clinical assessment