| Autor: |
Roach MH; Extremity Trauma & Amputation Center of Excellence, Defense Health Agency, Falls Church, VA 22041, USA.; Department of Surgery, Uniformed Services University of the Health Sciences, Bethesda, MD 20814, USA.; Department of Clinical Investigations, Womack Army Medical Center, Fort Liberty, NC 28310, USA., Bird MB; Extremity Trauma & Amputation Center of Excellence, Defense Health Agency, Falls Church, VA 22041, USA.; Department of Clinical Investigations, Womack Army Medical Center, Fort Liberty, NC 28310, USA., Helton MS; U.S. Army, Fort Liberty, NC 28310, USA., Mauntel TC; Extremity Trauma & Amputation Center of Excellence, Defense Health Agency, Falls Church, VA 22041, USA.; Department of Surgery, Uniformed Services University of the Health Sciences, Bethesda, MD 20814, USA.; Department of Clinical Investigations, Womack Army Medical Center, Fort Liberty, NC 28310, USA. |
| Abstrakt: |
Risk factor identification is a critical first step in informing musculoskeletal injury (MSKI) risk mitigation strategies. This investigation aimed to determine if a self-reported MSKI risk assessment can accurately identify military service members at greater MSKI risk and determine whether a traffic light model can differentiate service members' MSKI risks. A retrospective cohort study was conducted using existing self-reported MSKI risk assessment data and MSKI data from the Military Health System. A total of 2520 military service members (2219 males: age 23.49 ± 5.17 y, BMI 25.11 ± 2.94 kg/m 2 ; and 301 females: age 24.23 ± 5.85 y, BMI 25.59 ± 3.20 kg/m 2 , respectively) completed the MSKI risk assessment during in-processing. The risk assessment consisted of 16 self-report items regarding demographics, general health, physical fitness, and pain experienced during movement screens. These 16 data points were converted to 11 variables of interest. For each variable, service members were dichotomized as at risk or not at risk. Nine of the 11 variables were associated with a greater MSKI risk and were thus considered as risk factors for the traffic light model. Each traffic light model included three color codes (i.e., green, amber, and red) to designate risk (i.e., low, moderate, and high). Four traffic light models were generated to examine the risk and overall precision of different cut-off values for the amber and red categories. In all four models, service members categorized as amber [hazard ratio (HR) = 1.38-1.70] or red (HR = 2.67-5.82) were at a greater MSKI risk. The traffic light model may help prioritize service members who require individualized orthopedic care and MSKI risk mitigation plans. |
