Workbook for Designing, Implementing, and Evaluating a Sharps Injury Prevention Program

Contents:

OPERATIONAL PROCESSES

Analyze Sharps Injury Data

Introduction

Sharps injury data must be compiled and analyzed if it is to be used for prevention planning. This section describes:

• How to compile data from injury and hazard reports.
• How to perform simple and complex analyses.

Compiling Sharps Injury Data

Data on sharps injuries can be compiled by hand or with a computerized database. The latter facilitates multiple types of analyses (e.g., line lists, frequency lists, cross-tabulations). In small healthcare organizations (e.g., private medical or dental offices) or those where fewer than 10 injuries are reported in a given year, a computerized system might not be practical. Alternatively, these facilities might participate in a professional organization's regional or state data collection network that allows several facilities to contribute descriptive data (with confidential individual identifiers removed) on injuries. (Although such networks are not known to be available, it is possible that they will be developed in the future.) The advantage of having small organizations of similar purpose (e.g., medical or dental offices) contribute to a larger data collection pool is so that aggregate data can enhance the understanding of the frequency of sharps injuries and identify unique injury risks associated with these work sites.

Injury data can be analyzed with very simple statistical tools, such as frequency distributions and cross-tabulation. Large databases can perform more sophisticated analyses (e.g., multivariate analysis).

Analyzing Sharps Injury Data

The first step in the analysis of data is to generate simple frequency lists, by hand or computer, on the variables that make up the following data elements:

• Occupations of personnel reporting injuries;
• Work locations (e.g., patient units, operating room, procedure room) where reported injuries occur;
• Types of devices (e.g., hypodermic needles, suture needles) involved in reported injuries;
• Types of procedures (e.g., phlebotomy, giving an injection, suturing) during which injuries occur;
• Timing of occurrence of injuries (e.g., during use, after use/before disposal, during/after disposal); and
• Circumstances of injuries (e.g., during use of the device in a patient, while cleaning up after a procedure, as a result of improper disposal of a device).

Once frequencies are tabulated, a cross-tabulation of variables provides a more detailed picture of how injuries occur. This is most easily performed in a computerized database, but it can be done by hand. For example, simple cross-tabulations using occupation and device variables might reveal differences in the types of devices involved in injuries among persons in different occupations. Cross-tabulations can also assess whether certain procedures or devices are more often associated with injuries.  The example below shows that nurses are more frequently injured by hypodermic needles and physicians by winged steel needles.  Nurses and phlebotomists report the same number of injuries from phlebotomy needles. Armed with this information, it is then possible to seek additional information that might explain these differences in injuries for each occupation.

Hypothetical example, using a grid with one variable (e.g., occupation) in the horizontal axis and another variable (e.g., device) in the vertical axis shows differences in occupational injuries by type of device. Other variables (e.g., procedure, injury circumstances, etc.) can be cross-tabulated to better understand injury risks.

Calculating Injury Incidence Rates

Injury incidence rates provide information on the occurrence of selected events over a given period of time or other basis of measurement. The calculation of injury incidence rates for specific occupations, devices, or procedures can be useful for measuring performance improvement.

However, many factors, including improved reporting of injuries, can influence changes in incidence rates. Depending on the denominator(s) used, a facility may be viewed favorably or negatively. A recent report compared sharps injury rates in 10 Midwestern facilities that differed in size and scope of operation. It found considerable variation depending on the selection of the denominator (110). Therefore, the calculation of injury rates should be considered as one of many tools available to monitor sharps injury trends within a facility, but should be carefully used for inter-facility comparisons.

Calculating injury incidence rates requires reliable and appropriate numerators and denominators. Numerators derive from information collected on the injury report form; denominators must be obtained from other sources (e.g., human resources figures, purchasing records, cost center data). The numerator and denominator must reflect a common opportunity for exposure. For example, when calculating injury incidence rates among nursing personnel, the denominator should ideally reflect only those nurses whose job responsibilities expose or potentially expose them to sharp devices.

Selecting Denominators for Calculating Occupation-specific Injury Rates. Denominators sometimes used to calculate occupation-specific incidence rates include:

• Number of hours worked
• Number of FTE positions
• Number of healthcare personnel

Of these, "number of hours" worked is probably the most accurate and easiest to obtain, especially if part-time and per diem staff are included. Human resources and/or financial departments should be able to provide these numbers. For some complex healthcare organizations (e.g. university teaching centers) and for some occupations (e.g., attending physicians, radiologists, and anaesthesiologists provided through contract), obtaining denominators might be more difficult. If the analysis does not use the same denominator to calculate occupation-specific rates, comparisons among occupational groups are invalid.

Adjusting Occupation-specific Injury Rates for Underreporting. Although rates can be adjusted for underreporting, this step is not essential, nor is it necessarily useful, particularly for small facilities. For facilities that are interested in adjusting, the most reliable source of information is data from a survey of healthcare personnel in the facility (Appendix A-3). For example, if the survey finds considerable disparities in reporting among occupational groups (e.g., phlebotomists reporting 95% of their injuries and physicians only 10%), then adjustment of occupation-specific rates is appropriate to accurately reflect differences among occupational groups. Guidance for performing these calculations is included in the Toolkit.

Toolkit Resource for This Activity:

Occupation-Specific Rate-Adjustment Calculation Worksheet (see Appendix A-10)

Calculating Procedure- and Device-specific Injury Rates. Procedure- and device-specific injury rates are also useful for defining injury risks and measuring the impact of interventions. Although the frequency of injuries is often higher with some procedures or devices, a calculation of rates can yield a different picture. For example, a 1988 study by Jagger et al. (52) found that, although the highest proportion of injuries involved the hypodermic needle/syringe, this type of device was also the most frequently used. When injury rates were calculated based on the number of devices purchased, results show that needles attached to IV tubing had the highest rate of injury, followed by phlebotomy needles, IV stylets, and winged steel needles.

Ideally, the denominators for calculating procedure- and device-specific rates are based on the actual number of procedures performed or devices used. However, it is often difficult to obtain this information. For calculating device-specific injuries, the number of devices purchased or stocked may be used as a surrogate.

Using Control Charts for Measuring Performance Improvement

Control charts are graphical statistical tools that monitor changes in a particular set of observations over time and in real time. They are now used by many healthcare organizations as a quality improvement tool for a variety of patient-care activities and events, including healthcare-associated infections, and they can be applied to the observation of sharps injuries in healthcare personnel. In concept, control charts indicate whether certain events are an exception. Over a period of time, they can also demonstrate performance improvement.

This tool is applicable and useful only to healthcare organizations with a large amount of data on sharps injuries. A minimum of 25 data points is generally needed before it is possible to make a reliable interpretation. A discussion of methods for creating and interpreting control charts is beyond the scope of this workbook. The following Website and references are provided for those who are interested in pursuing this statistical technique: http://www.isixsigma.com/st/control_charts/ (111,112).

Calculating Institutional Injury Rates

In several published studies, investigators calculate institution-wide rates of sharps injuries using a variety of denominators (e.g. number of occupied beds, number of inpatient days, number of admissions). Facility-wide information can help calculate national estimates of injuries among healthcare personnel (1). But at the institutional level, this information has limited use and is difficult to interpret. It indicates only whether a rate is changing, not why. Also, safety improvements may be masked by improved reporting. For purposes of measuring performance improvement, the basic calculations described above will prove most reliable.

Benchmarking

Benchmarking compares an institution's performance with that of similar organizations. At the present time, there is limited information for sharps injuries benchmarking. Benchmarking data from NaSH and EPINet are not yet available. As the prevention of sharps injuries in healthcare personnel is an important public health priority, and increasing numbers of facilities are collecting and reporting data on sharps injuries, resources for benchmarking will likely emerge soon.

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