This is a cross-sectional descriptive epidemiological study. All data were obtained from the NEISS database, which is de-identified, and therefore, no institutional review board approval is required.
The CPCS NEISS database is a probability sample of 100 emergency departments in the USA. These hospitals are selected and weighted to represent the average US population by accounting for geographic location, department size, and children’s hospitals. Each year, a sampling frame based on all emergency department visits in the USA is used to determine statistical weights for the current NEISS hospital sample that provide estimates of the entire US population [6
]. A NEISS query for a specific injury provides both the number of cases presenting to the 100 emergency departments in the probability sample as well as the estimate for the entire US population. Based on these estimates, we calculated incidence rates using estimates of the US population from the US Census Bureau, Population division [16
At participating emergency departments, a case record is made of each injury that includes treatment date, age, sex, race, diagnostic category, body region (e.g., wrist, shoulder), patient disposition (e.g., treated and released, treated and admitted, etc.), location of injury, and two descriptive narrative fields. These data are directly keyed in computer systems by staff at each participating hospitals [7
In this study, the NEISS database was queried for all injuries in the upper extremity between 01 January 2009 and 31 December 2009. The NEISS database uses a body part diagram. The upper extremity includes the following regions: finger, wrist, lower arm, elbow, upper arm, and shoulder. In the NEISS estimates query builder, three parameters are entered: treatment dates, product codes, and other parameters. Treatment date was between 01 January 2009 and 31 December 2009. The product code section (each diagnosis is coded) was not filled in, and the section with other parameters was queried for each body part of the upper extremity separately in the Body Part section and then put in a common database. The specific diagnostic categories used in the NEISS database are amputation, contusion/abrasion, crush, dislocation, presence of a foreign body, fracture, hematoma, laceration, nerve damage, puncture, strain or sprain, hemorrhage, avulsion, dermatitis, burns, and other/not stated. Burns can be divided into electrical, scald, chemical, radiation, thermal, and burns not specified, but we considered them as a single group because they were relatively uncommon.
We analyzed the data in several ways. First, we calculated the percentage of total upper extremity injuries that occurred in each body region. Next, for each body region, we calculated the distribution of injury types as a percentage of the total number of injuries for that region. Next, for each injury type, we calculated the distribution among the body regions as a percentage of the total number of that type of injury. Next, we calculated the distribution of the circumstances/location where the injury occurred as a percentage of the total number of injuries and as a percentage of the total number of injuries for each body region. Finally, we calculated incidence rates for each specific type of injury in a specific body part. For the ten most common injuries, we used the Spearman correlation test to see whether age correlated with incidence. To calculate age-specific incidences, we used 5 year age groups to age 105 years.
For the five most common injuries with high incidence rates, we analyzed annual incidence rates from 2000 to 2009 (Fig. ). Linear regression was used to analyze if there was an increase or decrease in incidence with time.
Incidence rates per 100,000 persons per year from 2000–2009