Russian Brief Pain Inventory: Validation and Application in Cancer Pain

Vol. 35 No. 1 January 2008

Journal of Pain and Symptom Management

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Original Article

Russian Brief Pain Inventory: Validation and Application in Cancer Pain Svetlana A. Kalyadina, MD, PhD, Tatyana I. Ionova, PhD, Maria O. Ivanova, MD, Olga S. Uspenskaya, MD, Anton V. Kishtovich, PhD, Tito R. Mendoza, PhD, Hong Guo, MS, Andrei Novik, MD, PhD, Charles S. Cleeland, PhD, and Xin S. Wang, MD, MPH National Cancer Research and Treatment Center (S.A.K., T.I.I., M.O.I., O.S.U., A.V.K.), St. Petersburg, Russia; Department of Symptom Research (T.R.M., H.G., C.S.C., X.S.W.), The University of Texas M. D. Anderson Cancer Center, Houston, Texas, USA; and National Pirogov Medical Surgical Center (A.N.), Moscow, Russia

Abstract To validate the Russian version of the Brief Pain Inventory (BPI-R) and to examine predictors of inadequate pain management, 221 Russian patients with advanced-stage hematological malignancies or solid tumors completed the BPI-R and a Russian-language Medical Outcomes Study 36-Item Short-Form Health Survey (SF-36-R). Factor analysis of the BPI-R found two underlying constructs, pain severity and pain interference, with Cronbach alphas of 0.93 and 0.95, respectively. Concurrent validity was established by comparing BPI-R items with SF-36-R scales. The BPI-R detected significant differences in pain severity and interference levels by Eastern Cooperative Oncology Group (ECOG) performance status, supporting known-group validity. Determination of the Pain Management Index revealed that 68% of the patients were inadequately treated by World Health Organization standards. Having advanced-stage disease and not receiving chemotherapy predicted inadequate pain management in a multivariate logistic regression model. The Russian version of the BPI is psychometrically sound in its reliability and validity. J Pain Symptom Manage 2008;35:95e102. Ó 2008 U.S. Cancer Pain Relief Committee. Published by Elsevier Inc. All rights reserved. Key Words Russia, BPI, validation, Pain Management Index

Introduction Pain is one of the most persistent and incapacitating symptoms of cancer and its treatment. The authors gratefully acknowledge the Hawn Foundation, Dallas, Texas, for its support of this project. Address correspondence to: (in the U.S.) Xin Shelley Wang, MD, MPH, The University of Texas M. D. Anderson Cancer Center, Department of Symptom Research, Box 221, 1515 Holcombe Boulevard, Houston, TX 77030, USA. E-mail: xswang@ Ó 2008 U.S. Cancer Pain Relief Committee Published by Elsevier Inc. All rights reserved.

A large proportion of patients with metastatic cancer have pain long before reaching the terminal stage of their disease.1 Pain severity,

mdanderson.org, and (in Russia) Tatyana Ionova, PhD, National Cancer Research and Treatment Center, 154 Fontanka emb., 198103 St. Petersburg, Russia. E-mail: [email protected] Accepted for publication: February 28, 2007.

0885-3924/08/$esee front matter doi:10.1016/j.jpainsymman.2007.02.042

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which can be quantified through the use of such assessment tools as the Brief Pain Inventory (BPI), has been shown to interfere with various components of a patient’s quality of life, including mood, sleep, and activity.2 Despite estimates that cancer pain can be controlled through relatively simple means for approximately 90% of patients,3,4 health care providers and patients agree that cancer-related pain is nonetheless poorly managed and remains a most frequent and devastating symptom. The Russian Federation has a high cancer morbidity rate that is increasing annually. For example, in St. Petersburg (the second largest city in Russia, with a population of about five million people), cancer is diagnosed in more than 17,000 new patients each year, one of the highest rates in Russia.5 Most of these patients have advanced disease at diagnosis, putting them at much higher risk for pain and other symptoms than those with earlier-stage disease.6 However, Russian national policies regulating the use of opioids for cancer treatment are quite conservative, imposing strict limits on dose, schedule, and distribution of narcotic analgesics. In addition, adherence to the World Health Organization (WHO) analgesic ladder is not the standard approach to pain management. An International Narcotics Control Board report confirms that opioid drug consumption in Russia from 1994 to 1998 was significantly lower than that in most other European countries.7 Although some Russian guidelines are in line with WHO recommendations for cancer pain management,5 restrictions on opioid use make full application of these guidelines infrequent, and under-management of cancer pain may be widespread. Evaluating pain severity, understanding its relationship to the status and adequacy of cancer care, and studying current approaches and barriers to effective pain management are relatively new concepts in Russia. No validated pain assessment tool has heretofore been routinely used in Russia, and an extensive search of the current literature from Russian resources and from the U.S. National Library of Medicine’s PubMed database found no citations that described the status of pain control (prevalence, severity, or treatment) among Russian cancer patients. Translating new concepts into practice will require the availability of a simple measurement tool that a)

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adequately reflects the severity and impact of cancer pain, b) is sensitive to the effect of interventions, c) is easy to use for both patients and investigators, and d) is suitable for the study of pain across cultures. The BPI meets these requirements.8,9 The BPI is a patient-reported outcome assessment tool that measures the intensity of pain (sensory dimension) and the interference of pain in the patient’s life (reactive dimension). The BPI’s 0e10 pain severity scales ask patients to rate their pain at the time of responding to the questionnaire and also at its worst, least, and average during the past 24 hours. BPI data obtained from different countries show that it is a psychometrically sound and culturally sensitive tool for assessing pain severity and interference across languages.9e21 The goal of this study was to validate a Russian-language version of the BPI (BPI-R), to examine the status of cancer pain management in a sample of Russian cancer patients, and to seek out possible predictors of inadequate cancer pain management.

Methods Subjects Two hundred twenty-one patients with cancer from four St. Petersburg hospitals were enrolled in the study. These hospitals included the City Oncological Center (surgery, radiotherapy, and chemotherapy departments), the Russian Military Medical Academy (surgery, hematology, and clinical immunology departments), District Hospice No. 3, and the City Hospital No. 15, Kirovsky District (hematology unit). The study, which was conducted from December 2001 to May 2002, included both inpatients and outpatients who met the following inclusion criteria: spoke Russian, were 18 years or older, had a confirmed pathological diagnosis of cancer, had evidence of cancer metastasis or advanced-stage hematological malignancy, and gave informed consent to participate. Patients were excluded from the study if they did not want to participate in the investigation or did not understand its intent. Only three patients among the 224 approached refused to take part.

Instruments Used For this study, subjects were asked to complete the BPI-R and a Russian-language version

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of the Medical Outcomes Study 36-Item ShortForm Health Survey (SF-36-R).

current pain treatment, were obtained from medical charts.

The BPI-R: Linguistic Adaptation. The BPI, described above, was translated into Russian using a standard translation/back-translation procedure. The English-language items were initially translated into Russian by two native Russians who spoke fluent English. Their goal was to preserve the original meanings while using the Russian language as simply as possible, with no idiomatic expressions, to minimize the influence of the various dialects spoken in the region. A committee of bilingually fluent experts evaluated and approved these item translations. The items were then back translated into English by bilingual translators in the United States who had not seen the original English version. The English back translations and the originals were compared. If the Russian and English items were not deemed to be congruent, the process was repeated until the translation was acceptable. The resulting questionnaire was pilot tested among patients with cancer who subsequently were interviewed about the instrument’s clarity and ease of use. On the basis of results from the pilot test, we made further translation refinements and finalized the tool.

Statistical Analysis

SF-36-R. The SF-36-R is a comprehensive tool for assessing a patients’ health-related quality of life and is suitable for use in the general population.22 The SF-36-R has been established and validated in the Russian population, with Cronbach alpha coefficients greater than 0.7 for all SF-36-R scales except General Health and Social Functioning (0.6), indicating good reliability. Known-group and construct validity were also determined.22

Data Collection Most of the assessment tools were completed by patients themselves, although the research staff was permitted to read the questionnaires aloud and to assist with the completion of surveys for patients who were too ill to read the questions or to write. At the same time, clinical checklist and demographic data, disease status, and treatment information, including cancer diagnosis, stage, Eastern Cooperative Oncology Group (ECOG) performance status, and

Descriptive statistics (percentage, mean, standard deviation [SD], and 95% confidence limits [CL]) are used to present this study sample’s demographic and disease-related characteristics and the patients’ ratings of pain severity and interference with function. Construct validity was determined by confirmatory factor analysis. Principal axis factoring with oblimin rotation was used to extract the factors. We hypothesized a two-factor (severity and interference) structure for the BPI-R, based on the structure of the BPI English version. We judged the adequacy of this model, which would need to fit well and be clinically interpretable, by examining the size and distribution of the residual correlations. According to Harman,23 the SD of the residuals should be equal to or less than the reciprocal of the square root of the sample size (standard error of the correlation coefficient). Internal consistency reliability was assessed using Cronbach alpha coefficients, where values closer to 1 indicate less measurement error. We also examined the BPI-R’s possible boundaries, or cut-points, for defining mild, moderate, and severe pain, based on the patient’s rating of ‘‘pain worst.’’ This method has been used in previous severity categorization studies.2 On the basis of these previous studies, we proposed four models for grading pain severity at its worst during the last 24 hours. Multivariate analysis of variance (MANOVA) was used to calculate F-values. The three test criteria used were Pillai’s trace, Wilks’ lambda, and Hotelling’s trace. Larger F-statistics indicated a better cut-point for discriminating between levels of pain severity. To evaluate how adequately subjects’ cancer pain was being managed, we calculated the Pain Management Index (PMI).6 Logistic regression analysis was used to examine the predictors of negative PMI (inadequate pain management). Univariate analysis, Chi-square tests, and t-tests were used to screen for possible candidates for multivariate analysis. Independent variables included demographic characteristics and disease characteristics. Variables that had a large standard error and a wide CI for odds ratio (OR) were excluded from the

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multivariate analysis. To evaluate model fit, standard residual diagnostics were performed.24

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Table 2 Descriptive Statistics for BPI-R Items Item

Results Descriptive Results Table 1 describes certain demographic and disease-related characteristics of the study sample. Other demographic data include marital status (66% were married, 15% were widowed, 16% were single, and 4% were divorced or separated), education level (32% had a high school education or less, 37% attended a special technical school, and 31% had college or graduate education), and employment status (33% were employed, 39% were retired, 23% were disabled, and 5% were unemployed). Most of the patients answered all of the BPIR items, with 272 of 2,873 aggregate data points missing (a missing-data rate of 9%), most often for the item ‘‘percentage of pain relief.’’ Table 2 presents descriptive statistics for the BPI-R items. Patients rated pain’s interference with general activity as the most severe, Table 1 Demographic and Disease-Related Characteristics (n ¼ 221) Characteristic Patients’ age Sex Female/male

%

Mean

95% CL

n

Severity items ‘‘Pain worst’’ ‘‘Pain average’’ Pain now Pain least Mean of four severity items

3.7 2.6 2.2 1.2 2.4

(2.9) (2.2) (2.2) (1.5) (2.0)

3.3, 2.3, 1.9, 1.0, 2.1,

4.1 2.9 2.5 1.4 2.7

206 202 207 207 207

Interference items General activity Work Sleep Mood Walking Enjoyment of life Relations with others Mean of seven interference items

3.0 2.9 2.8 2.7 2.4 2.4 1.5 2.5

(2.8) (3.0) (3.0) (2.7) (2.7) (2.8) (2.2) (2.4)

2.6, 2.5, 2.4, 2.3, 2.0, 2.0, 1.2, 2.2,

3.4 3.3 3.2 3.1 2.8 2.8 1.8 2.8

200 192 199 200 200 200 200 201

followed by interference with work, sleep, and mood. There was no significant difference in pain severity by sex. Pain severity had a weak positive correlation with age (r ¼ 0.19, P < 0.007). ‘‘Pain worst’’ ratings differed significantly (P < 0.01) by cancer diagnosis (see Table 6). Post hoc analysis with Bonferroni correction showed that those with gastrointestinal cancer reported significantly higher ‘‘pain worst’’ than did those with either nonHodgkin’s lymphoma or leukemia.

n

Mean ¼ 62; median ¼ 64; SD ¼ 14.1; range (18e92) 62/38

136/85

Cancer diagnosis Gynecologic Lymphoma or myeloma Leukemia Gastrointestinal Breast Lung Others

20 20 19 17 14 4 6

45 44 41 38 31 9 13

ECOG Performance Status 0e1 (good) 2e4 (poor)

52 48

113 106

Cancer stage I or II III IV Recurrent disease

7 52 35 6

15 110 74 13

Type of cancer pain Abdominal visceral pain Bone pain Postoperative pain Neuropathic pain Pleuritic pain Other

24 16 13 11 2 9

53 35 28 25 4 20

Construct Validity, Known-Group Validity, and Concurrent Validity of the BPI-R To establish construct validity, we performed confirmatory principal axis factor analysis with direct oblimin rotation, which extracted two factors, pain interference and pain severity (Table 3). Initial eigenvalues were 8.09 and 0.72 for the two factors that accounted for 80% (73.5% and 6.5%, respectively) of common total variance. Although a meaningful factor should be associated with an eigenvalue greater than 1.0,23,25 two factors were more clinically interpretable than a one-factor solution. We tested the adequacy of model fitting for the two-factor solution by examining the differences between the observed correlations and the reproduced correlations based on the two-factor solution. In the present study, the SD of the residuals is 0.043, which is less than 0.074 (the reciprocal of the square root of the sample size 185), indicating that our two-factor solution had an acceptable fit.

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Russian Brief Pain Inventory Validation

Table 3 Factor Analysis for the BPI-R (Extract Two Factors) (n ¼ 185) Pain Item Enjoyment of life Mood Sleep Work Relations with others General activity Walking ability ‘‘Pain average’’ ‘‘Pain worst’’ Pain least Pain now

Factor 1

Factor 2

0.90 0.87 0.86 0.85 0.79 0.64 0.48 0.12 0.13 0.08 0.32

0.05 0.04 0.003 0.02 0.03 0.32 0.34 L1.10 L0.84 L0.72 L0.59

Numbers in bold indicate the factor on which the pain item loaded, i.e., the pain interference factor (Factor 1) and the pain severity factor (Factor 2).

Known-group validity was examined by comparing the composite scores of pain severity (mean of four BPI-R severity items) and pain interference (mean of seven BPI-R interference items), stratified by good or poor ECOG performance status. Results indicated that, as expected, patients with poor performance status had greater pain severity than patients with good performance status (3.2, SD 2.1 vs. 1.7, SD 1.7, respectively; P < 0.001) and more severe pain-related interference (3.6, SD 2.5 vs. 1.5, SD 1.8, respectively; P < 0.001).

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items contributes to the underlying constructs. These data indicate that the BPI-R is a reliable instrument with little measurement error.

Defining Pain Severity Categories To define pain severity categories among Russian cancer patients, we used MANOVA (n ¼ 148) to examine several possible sets of pain-intensity cut-points (Table 5). Model 1, which used the cut-points published by Serlin et al.,2 achieved the highest F-score according to two criteria (Pillai’s trace and Wilks’ Lambda). A higher F-score indicates that the model discriminated pain severity more optimally than models with a lower F-score. According to these methods, the Russian sample had severity boundaries of 1e4 for mild pain, 5e6 for moderate pain, and 7e10 for severe pain (Model 1), although analysis using Hotelling’s trace gave Model 1 the second highest F-score. In conclusion, the optimum cut-points for the BPI-R are consistent with those found for the English BPI and for many of the other BPI language versions, including Chinese and French.15,20,26 Categorized by these cut-points, 17% of patients had severe pain, 24.8% had moderate pain, 30.1% had mild pain, and 28.2% had no pain.

Adequacy of Cancer Pain Management Reliability of the BPI-R Cronbach coefficient alphas for the two factors, pain severity and pain interference, are presented in Table 4. Coefficient alphas of 0.927 for the pain severity items and 0.948 for the pain interference items indicate good internal consistency. Table 4 also shows that the values for alpha, if items are deleted, are comparable to the overall alpha value of each of the two factors, and thus that each of the Table 4 Reliability Analysis for the BPI-R (Two Factors) Pain Severity Item (a ¼ 0.927) n ¼ 201 ‘‘Pain worst’’ Pain least ‘‘Pain average’’ Pain now

a if Item Deleted

0.902 0.942 0.868 0.900

Pain Interference Item

a if Item Deleted

(a ¼ 0.948) n ¼ 189 General activity Mood Walking ability Work Relations with others Sleep Enjoyment of life

0.934 0.935 0.946 0.936 0.946 0.941 0.939

The PMI which evaluates the adequacy of pain relief6 considers analgesic prescription appropriate when there is congruence between pain severity and the potency of the prescribed analgesic as outlined by the WHO guidelines for cancer pain management.5 A PMI score of 0 or higher indicates adequate pain management. The 120 patients included in the analysis had pain that was not caused by recent surgery and had taken pain medication. Sixty-eight percent had a negative PMI value, indicating inadequate pain management. Univariate analysis was used to screen for possible candidates for multivariate analysis for predictors of negative PMI. Two variables, age and disease stage, were identified as possible candidates, but were not significantly related to negative PMI. Of 10 dichotomous or categorical variables, seven were identified as possible candidates by Chi-square and Fisher’s exact tests (Table 6). Two predictors of negative PMI (inadequate management), as determined by both forward conditional

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Table 5 Four Models’ F-Statistics for ‘‘Pain Worst’’ in Multivariate Analyses of Variance (n ¼ 148) Possible Boundary Models Model

Mild

1 2 3 4

F-statistics in MANOVA

Moderate

Severe

Pillai’s trace

Wilks’ Lambda

Hotelling’s trace

5e6 4e6 5e7 4e7

7e10 7e10 8e10 8e10

7.22 6.79 6.89 6.31

8.35 8.20 7.90 7.43

9.52 9.66 8.95 8.58

1e4 1e3 1e4 1e3

and stepwise methods in the logistic regression analysis (Table 7), included advanced disease (OR ¼ 2.7, CI ¼ 1.1, 6.9, P ¼ 0.034) and nonactive chemotherapy status (OR ¼ 6.5, CL ¼ 2.5, 17.1, P ¼ 0.001). Standard residual diagnostics supported good model fit.24

Discussion The current study demonstrated the excellent psychometric properties of the BPI-R as a reliable measure of pain and its impact on Russian cancer patients. The validity of the BPI-R was supported by the similarity of its factor structure to the factor structures of other validated language versions of the BPI, such as the original English version9 and the French,26 Italian,11 Chinese,20 and Japanese19

versions. The BPI-R can serve as a valuable instrument in clinical practice and in research on pain management in Russia. Its ‘‘pain worst’’ and ‘‘pain average’’ scores can be used in the clinic to screen for pain severity. The mean of the BPI-R’s interference items could provide a useful global measure of pain’s impact on function. A unique area of investigation involves efforts to quantify what is typically meant by mild, moderate, and severe pain on the 0e10 scale used by the BPI and other tools. Serlin et al. found that the optimum cut-points for categorizing pain severity for the ‘‘pain worst’’ score are 0 for none, 1e4 for mild, 5e6 for moderate, and 7e10 for severe.2 This categorization is quite consistent across patients from different cultures using various language versions of the BPI, including this Russian sample.2,27

Table 6 Univariate Analysis of Relationships Between Biomedical Variables and Negative PMI (Inadequate Pain Management) Dichotomous and Categorical Variables

Variable Coding

%Negative PMI (n/N )

Sex Female Male

75 (54/72) 58 (28/48)

Hematological Solid tumors

41 (15/37) 81 (67/83)

No evidence of disease or local Advanced

59 (32/54) 80 (49/61)

Poor (2e4) Good (0e1)

60 (37/62) 78 (45/58)

No Yes

55 (38/69) 86 (44/51)

No Yes

85 (56/66) 48 (26/54)

Cancer diagnosis

Disease status

ECOG performance status

If having radiotherapy

If having chemotherapy

Physician discrepancy in rating interference with activity Adequately estimated Underestimated

57 (36/63) 83 (44/53)

P

N

0.05

120

<0.001

120

0.013

115

0.035

120

<0.001

120

<0.001

120

0.003

116

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Table 7 Predictors of Negative PMI (Inadequate Pain Management) by Logistic Regression Model (n ¼ 108) Predictors Disease status Receiving chemotherapy

%Negative PMI

Coefficient (Standard Error)

P

OR

95% CL of OR

Advanced vs. local/NED Without vs. with chemo

1.007 (0.475) 1.88 (0.489)

0.034 <0.001

2.7 6.55

1.08, 6.94 2.51, 17.08

In this model, Cox & Snell R-square was 0.186 (Nagelkerke R-square was 0.265), and the overall classification rate was 78%; the rate for predicting negative PMI was 90%. NED ¼ No evidence of disease.

Established cut-points for moderate (5 or greater) and severe (7 or greater) pain can facilitate meaningful pain intervention when they are incorporated into clinical practice guidelines for cancer pain control, which are lacking in Russia. In addition, categorizing pain severity similarly across countries facilitates crosscultural comparisons of pain prevalence and severity and makes possible the use of international pain management guidelines. Before this study, no quantitative pain assessment data existed in Russia. That nearly half of the advanced-cancer patients in this study experienced moderate to severe pain demonstrates the need for educating Russian health care professionals about active pain assessment and management to improve health-related quality of life. The negative PMI values calculated for 68% of this St. Petersburg study sample suggest that pain was inadequately managed in this patient sample. In the clinic, patients who were being monitored carefully, such as those receiving chemotherapy, and patients with earlier-stage disease had a better chance of receiving good pain management, whereas follow-up patients who were off treatment were more likely to have inadequate pain control. Increased attention from oncologists is thus warranted for patients in groups at risk for poor pain control. Further, good pain management will not be achieved when there is great discrepancy in pain ratings between patients and clinicians, thus highlighting the importance of taking seriously the patient’s own report of symptoms. The predictors of negative PMI found in this sample of Russian patients mirror the results from an earlier pain study conducted in the United States,28 indicating that professionals’ attitudes and knowledge about pain control are universal factors in optimizing pain management practice. The study had at least two limitations. First, because of the cross-sectional design of the

study, we did not examine the sensitivity of the BPI-R. Second, the sample was drawn solely from an urban population in one city which cannot represent the pain epidemiology and practice status of cancer patients living in other cities or in rural areas. In taking the initiative to develop this cancer pain assessment tool, we hope to stimulate the beginning steps needed to achieve practice change in Russia, such as developing treatment guidelines, training health care professionals in the use of strong analgesics, and stimulating a greater interest in pain clinical researchdall of which will bring great benefit to the people in Russia who suffer from cancer.

Acknowledgments The authors acknowledge with appreciation the assistance of the physicians of City Oncological Center (Departments of Surgery, Radiotherapy, and Chemotherapy), Russian Military Medical Academy (Departments of Surgery, Hematology, and Clinical Immunology), District Hospice No. 3, and City Hospital No. 15, Kirovsky district (Hematology Unit). In particular, they would like to acknowledge Dr. G. Manikhas, Dr. S. Chekrizov, Dr. M. Fridman, Dr. E. Karyagina, Dr. L. Sokolova, and Dr. G. Lisichnikova for their contributions. The authors also acknowledge with appreciation the editorial assistance of Ms. Jeanie F. Woodruff in the Department of Symptom Research at M. D. Anderson Cancer Center.

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