Development of Short-Form Versions of the Screener and Opioid Assessment for Patients with Pain-Revised (SOAPP-R): A Proof-of-Principle Study

Pain Management in Oncology Patients Amidst the Opioid Epidemic: How To Minimize Non-Medical Opioid Use

The opioid epidemic continues to be a significant public health concern. On the surface, it appears difficult to understate the profound consequences and unnecessary loss of life at the hands of the opioid crisis throughout the 2010s. This reality should not dissuade rigorous attention toward those who have suffered unnecessarily due to an overreaching backlash toward the opioid crisis. Oncology patients have been significantly impacted on both ends of the opioid crisis. Like other populations, cancer patients were first affected during the initial surge of opioid availability, prescription, and use at the beginning of the crisis, where opioid abuse and overdose negatively impacted cancer patient populations at similar rates as the general population. Yet, cancer patients were perhaps even more heavily affected during secondary events after the initial crisis, as opioid restrictions and the stigmatization, undoubtedly beneficial in many spaces, of opioid use became prevalent across the American society. During this second period of the opioid crisis (loosely from 2013 to the present day after the Veterans Health Administration Opioid Safety Initiative started), restrictions on opioids have significantly decreased the use and access of opioids for cancer patients. Management of pain, in general, is a complex topic, and cancer pain is no exception. Cancer patients may experience pain related to the disease itself, its treatment, or other comorbidities. This review aims to clarify the impact of reducing opioid use on cancer patients over the past eight years. We summarize the challenges facing providers as they attempt to manage cancer-related pain. Additionally, we propose tools for best practices to reduce the unnecessary suffering of cancer patients and protect against the overuse and abuse of opioids.

Techniques to Shorten a Screening Tool for Emergency Department Patients

Introduction

Screening of patients for opioid risk has been recommended prior to opioid prescribing. Opioids are prescribed frequently in the emergency department (ED) setting, but screening tools are often of significant length and therefore limited in their utility. We describe and evaluate three approaches to shortening a screening tool: creation of a short form; curtailment; and stochastic curtailment.

Methods

To demonstrate the various shortening techniques, this retrospective study used data from two studies of ED patients for whom the provider was considering providing an opioid prescription and who completed the Screener and Opioid Assessment for Patients with Pain-Revised, a 24-item assessment. High-risk criteria from patients’ prescription drug monitoring program data were used as an endpoint. Using real-data simulation, we determined the sensitivity, specificity, and test length of each shortening technique.

Results

We included data from 188 ED patients. The original screener had a test length of 24 questions, a sensitivity of 44% and a specificity of 76%. The 12-question short form had a sensitivity of 41% and specificity of 75%. Curtailment and stochastic curtailment reduced the question length (mean test length ranging from 8.1–19.7 questions) with no reduction in sensitivity or specificity.

Conclusion

In an ED population completing computer-based screening, the techniques of curtailment and stochastic curtailment markedly reduced the screening tool’s length but had no effect on test characteristics. These techniques can be applied to improve efficiency of screening patients in the busy ED environment without sacrificing sensitivity or specificity.

Measurement versus prediction in the construction of patient-reported outcome questionnaires: can we have our cake and eat it?

BACKGROUND

Two important goals when using questionnaires are (a) measurement: the questionnaire is constructed to assign numerical values that accurately represent the test taker's attribute, and (b) prediction: the questionnaire is constructed to give an accurate forecast of an external criterion. Construction methods aimed at measurement prescribe that items should be reliable. In practice, this leads to questionnaires with high inter-item correlations. By contrast, construction methods aimed at prediction typically prescribe that items have a high correlation with the criterion and low inter-item correlations. The latter approach has often been said to produce a paradox concerning the relation between reliability and validity [1-3], because it is often assumed that good measurement is a prerequisite of good prediction.

OBJECTIVE

To answer four questions: (1) Why are measurement-based methods suboptimal for questionnaires that are used for prediction? (2) How should one construct a questionnaire that is used for prediction? (3) Do questionnaire-construction methods that optimize measurement and prediction lead to the selection of different items in the questionnaire? (4) Is it possible to construct a questionnaire that can be used for both measurement and prediction?

ILLUSTRATIVE EXAMPLE

An empirical data set consisting of scores of 242 respondents on questionnaire items measuring mental health is used to select items by means of two methods: a method that optimizes the predictive value of the scale (i.e., forecast a clinical diagnosis), and a method that optimizes the reliability of the scale. We show that for the two scales different sets of items are selected and that a scale constructed to meet the one goal does not show optimal performance with reference to the other goal.

DISCUSSION

The answers are as follows: (1) Because measurement-based methods tend to maximize inter-item correlations by which predictive validity reduces. (2) Through selecting items that correlate highly with the criterion and lowly with the remaining items. (3) Yes, these methods may lead to different item selections. (4) For a single questionnaire: Yes, but it is problematic because reliability cannot be estimated accurately. For a test battery: Yes, but it is very costly. Implications for the construction of patient-reported outcome questionnaires are discussed.