For the second time in less than two months, federal safety and health inspectors found a worker at a commercial laundry equipment manufacturer had suffered an amputation because a machine lacked adequate safety guarding.
Just five weeks after a 28-year-old maintenance worker lost part of his right arm in an improperly guarded bread wrapping machine at the Cincinnati-based Klosterman Baking Co., federal safety inspectors investigating the injury found another worker exposed to the same hazard.
Visit any emergency department in the United States and you may find individuals who were injured or who became ill on the job. In 2013 alone, an estimated 2.7 million workers received treatment in emergency departments for nonfatal work-related injuries and illnesses.
Individuals who are exposed to high temperatures or flames and those who must handle flammable liquids are at increased risk for burns of the hand and wrist. Burns are most frequently sustained at home (72%), whereas 5% occur in relation to motor vehicle accidents and 9% are work-related, The most common type of burn injury to the wrist and hand is a scald injury, usually from a hot water source, followed by flame burns, flash burns from explosion of flammable gases or liquids, and contact burns.
Falls remain a leading cause of unintentional injury mortality nationwide, and 43% of fatal falls in the last decade have involved a ladder.
Among workers, approximately 20% of fall injuries involve ladders. Among construction workers, an estimated 81% of fall injuries treated in U.S. emergency departments (EDs) involve a ladder.
Do you know when and where your next injury will occur? SafetyNet users do!
Attend a FREE webinar: Predicting Injuries – How to Achieve a Data Analytics Advantage in Workplace Safety on June 23, 2016 at 2 p.m. EDT.
Now On Demand According to a recent report by Deloitte, a data analytics evolution is well underway, and those who have cracked the analytics code are reaping its rewards. In the world of safety, the use of data analytics translates into predicting and preventing workplace injuries using machine-learning predictive models.