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Pain, 43 (1990) 7-25 Elsevier PAIN 01682

Clinical Section Review A rticle Neglected factors in chronic pain treatment outcome studies referral patterns, failure to enter treatment, and attrition Dennis C. Turk a and Thomas

E. Rudy b

a Department of Psyckiatry and b Department ojdnestkesiologv, and Pain Eoaluation and Treatment Institute, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213 (U.S.A.) (Received 12 January 1990, revision received 29 May 1990, accepted 31 May 1990)

An increasing number of chronic pain treatment outcome studies have appeared in the literature. In general, these studies support the efficacy of multidisciplinary pain programs, as well as specific treatment modalities such as biofeedback and relaxation. Reviews of this literature have tended to be cautiously optimistic. Some concerns, however, have been raised about the methodological adequacy of these studies, particularly in terms of the lack of control groups, the brief duration of follow-up periods, and the vague criteria used for establishing the success of the therapeutic interventions. Other factors that mitigate conclusions regarding the g~er~~~ty of the favorable results reported need to be insides. In this paper 3 rarely discussed topics that are implicit within most treatment outcome studies and that need to be given greater attention are examined. These topics include: (1) referral patterns to pain clinics (who are referred to pain clinics, when, and how representative is the referred sample?); (2) failure to enter treatment (e.g., exclusion criteria, lack of available financial support to cover the cost of treatment, patient’s refusal to accept recommendations), and consequently, the representativeness of the treated sample; and (3) patient’s attrition. In this paper we discuss each of these factors as they underscore important qualifications that have to be made in evaluating treatment outcome studies.

Sum--Y

Key wools: Treatment outcome studies; Chronic pain

Introduction A proliferation of studies has appeared in the literature over the past decade that report on the efficacy of a wide range of treatment programs for chronic pain patients [for reviews see 5,22,67,71, 88,91,148]. It seems appropriate at this juncture to reflect on the encouraging outcomes that have

Correspondence to: Dennis C. Turk, Ph.D., Pain Evaluation and Treatment Institute, University of Pittsburgh School of Medicine, Baum Boulevard at Craig Street, Pittsburgh, PA 15213, U.S.A.

been reported and to examine some of the limitations inherent in the chronic pain treatment outcome literature. It is the intent of this 3 part series to examine a set of factors that are of central importance when designing and evaluating treatment outcome studies that have as their primary focus the demonstration of the relative efficacy of specific chronic pain treatments. Some topics and issues that will be covered are ‘painful’ for treatment outcome investigators and, consequently, have not received sufficient attention. In the first paper in this series we will examine: (a) who is referred to pain clinics; (b) whar inclu~on and exclusion criteria are used

0304-3959/90/%03.50 0 1990 Elsevier Science Publishers B.V. (Biomedical Division)

x in deciding to offer treatment; (c) why some patients offered treatment do not enter treatment; and (d) patient attrition once treatment has begun. In the second paper, we will consider the serious problem of non-adherence and non-compliance to recommendations following discharge from treatment as well as the durability of treatment gains. Additionally, we will examine some guidelines for facilitating treatment adherence. In the final paper, we will examine problems with the traditional criteria used to evaluate the efficacy of treatment and discuss some alternatives that might be adopted.

Who is referred to pain clinics? The first point concerns the characteristics of patients who are referred to pain clinics. A central question is, are the patients referred representative of the population of patients with long standing persistent pain? Few studies have considered this issue directly, yet there is frequently an implicit assumption that patients referred to pain clinics are typical of the larger population. But are they? Differences between patients treated at specialized pain clinics and primary care facilities are especially dramatic when prevalence of psychopathology is examined. Typically, in pain clinic populations the prevalence of depression ranges from 40 to 60% [144]. However, reports of 90% [87] and even 100% [83,146] of patients being depressed have appeared. In contrast, in a large survey of patients treated in a health maintenance organization, Von Korff et al. [150] reported that the prevalence of major depression ranged from 6 to lo%, depending on the type of pain. Reich et al. [114] reported that 98% of patients evaluated in their pain clinic had a Diagnostic and Statistical Manual-III [l] Axis I disorder. In epidemiological surveys conducted by Crook and her colleagues [25-271, chronic pain patients referred to specialty pain clinics in comparison to persistent pain sufferers in the community who were not referred to a pain clinic were more likely to: (a) suffer greater levels of emotional distress, (b) to have work-related injuries, (c) report greater health-care utilization, (d) report more constant

pain, (e) indicate more negative attitudes about the future, and (e) report greater functional impairment. Similarly, Chapman et al. [21] and Pilowsky et al. [ill] directly compared patients referred to a pain clinic and those with pain treated in a general medical practice setting. These authors noted many important differences between these patient samples, including levels of psychological distress and illness behaviors, with the clinic sample displaying significantly higher levels of affective disturbance. From the findings reviewed above. it appears that neither medical status factors (e.g., duration of pain, location of pain) nor socioeconomic and demographic factors [27] significantly discriminate clinic from community pain samples. What most distinguished the patients from the pain clinic were impairment in functioning and psychosocial difficulties. Patients who reach pain clinics go through a referral filtering process and tend to show greater psychosocial and disability problems, which arc associated with greater morbidity. It is not surprising that the patients who complain the most and who are the most frustrating to treat are more likely to be referred to a specialty clinic. Crook et al.‘s [26] study confirmed this point. They found that one of the major factors that differentiated patients referred to pain clinic from a community sample of pain sufferers were complaints of more constant pain. Consider the patient referred to a clinic because of intractable headaches. It is likely that these patients have already been treated with conventional. general practice approaches to headache. Additionally. many of these patients have already consulted a neurologist and exhausted the usual clinical consultations. Those with unremitting pain despite diverse treatments are likely to be at risk for the development of significant psychological distress. For these patients, interdisciplinary pain programs may be most appropriate as they typically address psychosocial functioning as well as physical problems. When considering the success rates of specialty pain clinic programs, it is important to take into account the fact that the population served have the most recalcitrant problems and are the least

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likely to benefit from any intervention. As such, the population of pain patients referred to specialty clinics are at higher risk for failure for any treatment approach. It is against this backdrop that pain clinic outcome studies must be assessed. A recent study reported by Deyo et al. [29] provides further support for the difference between patients referred to specialty pain clinics and those treated in primary care facilities. ,Deyo et al. compared a sample of chronic pain patients recruited through media publicity for a clinical trial with routine patients referred to a pain clinic. They found that the patients recruited from the community were better educated, were more often employed, had more positive personality profiles, and were less likely to have had surgery or to be using narcotics. Deyo et al. reported that the community sample achieved a significantly better treatment outcome. However, they speculated that the treatment effects may have been due to baseline prognostic differences rather than the uniquely efficacious treatment. The above findings underscore the point that it may be inappropriate to generalize treatment outcome for non-clinic samples to pain clinic populations. Conversely, if results of the difficulty of treating chronic pain patients and pain clinics are extrapolated to the general population of people with chronic pain, it is easy to exaggerate the difficulty. A similar point was noted by Schachter [120] concerning the efficacy of smoking cessation and weight reduction programs. The literature coming from smoking cessation and weight control programs suggests that te~nation of smoking and loss of weight is an extremely difficult process with very high levels of relapse, often 70% within the first 6 months. Schachter cites Stunkard’s [128] pessimistic conclusions regarding obesity: ‘Most obese persons will not stay in treatment for obesity. Of those who stay in treatment most will not lose weight and of those who do lose weight, most will regain it’ (p. 79). Yet, as Schachter notes, many millions of Americans have successfully ceased smoking and lost significant quantities of weight on their own with subst~tial maintenance. Although difficult, it may not be as difficult to do as the literature would lead one to believe.

The unfortunate consequence of the published smoking cessation and weight reduction treatment outcome literature may be the message it conveys to the general public concerning the difficulty in stopping and may emphasize the hopelessness of the situation that may in turn lead some individuals not to try. Schachter suggests that the available literature may have the inadvertent consequence of fostering ‘psychiatrogenics.’ Schachter’s (1201 concern may be raised about chronic pain. Specialty pain clinics treat the most difficult and perhaps most atypical cases. If recent surveys 11341are any indication, the vast majority of people with chronic pain are never seen in pain clinics and many may be adjusting reasonably well.

When are referrals made? It is also important to consider

the time at which referrals are made to pain clinics. It is plausible that patients will be referred when they perceive their pain as most severe or most distressing and thus instigate a referral to a specialty pain clinic. If this is the case, then it is possible that some positive treatment effects attributed to the therapeutic regimens may reflect ‘regression to the mean.’ That is, improvements over time (i.e., back to patients’ average levels) would be anticipated irrespective of any treatment intervention. This might particularly be true for syndromes characterized by remissions and exacerbations, such as rheumatoid arthritis or acute recurrent problems such as chronic headaches and temporomandibular pain disorders. Chronic pain patients share many characteristics, but there is important prognostic variability among them; the selection of certain patient characteristics, different recruitment methods, and entry criteria for clinical or research. Programs may influence the likelihood of achieving success, despite the unique components of the treatment provided 1291.In short, we need to be cautious and probably should not generalize from pain clinic samples to the more general population of chronic pain sufferers 1261. We also need to weigh the efficacy of outcomes by selection biases and by the difficulty and nature of the problems of the

populations treated. later in this paper.

We will return

to these points

Which patients are offered treatment? Most treatment outcome studies report on the number of patients evaluated and the number of patients entered into treatment. Often these numbers suggest that only between l/3 and 2/3 of patients evaluated enter treatment. There are many ‘gate keepers’ who influence entrance into a treatment program, including the professional staff of the clinic, third-party payers, as well as self-selection by patients themselves. A number of investigators have tried to identify the factors that predict which patients are most likely to (a) enter treatment, (b) remain in treatment, (c) have successful responses to these therapeutic programs, and (d) maintain treatment benefits achieved at the time of discharge. The influence of variables such as demographic characteristics (e.g., age, gender. marital status). personality traits (e.g., passive, hostile, hysteroid), psychopathological features (e.g., major depression, somatoform pain disorders), and conscious dissimulation (malingering) by patients have been examined. In addition, medical status variables (e.g., duration of pain, number of surgeries), employment status, compensation, and legal status have been considered. To date, despite the extent of research activity, the importance of patient variables in predicting entry into treatment, dropout from treatment. or success of treatment remains equivocal [e.g.. 32,98-1001. It is important to consider each of these factors as they will influence the population available for treatment outcome studies and the generalizability of the conclusions that can be based on the outcome results. Inclusion/exclusion

criteria

Perhaps the most common reasons offered for patients’ failure to enter treatment is rejection by the clinic based on pre-established inclusion and exclusion criteria (e.g., pending surgery, severe psychopathology). It appears that, based on a wide diversity of inclusion-exclusion criteria, from

15% [113] to 54% [2] of patients evaluated are excluded from various treatment programs. Multidisciplinary chronic pain treatment programs frequently use a diverse set of exclusion criteria to screen out patients who are viewed as inappropriate and unlikely to benefit from treatment. Clinics also vary in the stringency of the inclusion criteria from minimal, for example, pain of 6 months duration and referral by physicians. lawyer, or rehabilitation counselor, and patient willingness [e.g., 491 to extensive [e.g., 2,421 (see Table I). The exclusion criteria used can be generally grouped into 6 generic categories (see Table 1). The rationale for excluding patients is simple. Screening out patients who appear unlikely to benefit from treatment is more ethical to the patient, less frustrating for the treatment staff. and more cost-efficient for third-party payers than offering treatment to all patients [71]. Although there may be clinical justification for some of these exclusion criteria especially biomedical and severe psychopathology, the available research literature does not support the use of rigid selection criteria. Demographics

Evidence on the relationship between chronicity of pain and behavioral treatment outcome is mixed. Several studies [36,72,130] found that patients with somewhat longer histories of pain were less likely to respond successfully; conversely, Painter et al. [105] and Block et al. [lo] found that patients with longer histories of pain had better outcomes. Similarly, the effects of age on treatment outcome have been equivocal. Age has been shown to make no difference in success of treatment outcome in some studies [85,103,113,122], but did in others [4,56,61,72,119,135]. In a meta-analytic examination of behavioral treatment for recurrent tension headache, Holroyd and Penzien [61] discovered that the best predictor of treatment outcome was patients’ age, accounting for 30% of the variance, with younger patients the more likely to benefit. It is important to consider the criteria of success when looking at the impact of age on treatment

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TABLE

I

PAIN TREATMENT

EXCLUSION

CRITERIA

1. Biomedical status are pending or clearly a. Other medical or surgical treatments indicated. b. Duration of pain usually less than 3-6 months. incapable of participating in the rigorous exercise C. Physically program demanded. physical pathology d. Presence or absence of documented (some programs screen out patients who show evidence of underlying tissue pathology that might be responsible for their pain [e.g., 451, whereas other programs accept only patients who have documented pathology 1541). 2. Demographics a. Age (under 18 or over 60). b. Marital status [e.g., 1021. 3. Psychopathologv a. Suicide risk. b. Severe psychopathology (e.g., schizophrenia). brain syndrome. C. Organic d. Chemical dependency deemed primary rather ondary to pain.

than

sec-

4. a. Litigation or disability decision pending [95,118,149]. and expected that this will inhibit b. Receiving compensation active involvement and progress [e.g., 21. 5. Apparent lack of motivation a. Patient. b. Family member or significant

other [e.g., 129,137].

6. Conceptual a. Inability to identify observable and measurable pain behaviors that are likely to be responsive to reinforcers in hospital environment [42,45,118,137,149]. with life or preventing patient from b. Pain not interfering engaging in desired physical activities 1421. C. Patient not able to identify specific goals that are observable, measurable, and that are achievable within the time constraints of the treatment program [42]. d. Not previously engaged in activities that could be reestablished or approximated in the patient’s environment if treatment led to an appropriate activity level [45]. Patient unwilling to return to work [I 181. No spouse or significant other willing to work with patient in program [102,118]. No financial coverage for treatment [93,97].

outcome. For example, Fredrickson et al. [46] noted that older age predicted lower levels of return to work following treatment. However, the authors of this study did not report the number of

treated patients over age 62 for whom return to work may not have been reasonable. They did report that only 23% of those over age 50 returned to work, in comparison to 55% for those under age 50. We will return to the thorny issue of outcome criteria in the third paper in this series [143]. Another demographic variable frequently examined as a predictor of treatment success and that has produced equivocal results is employment status at the time of treatment. Some have noted that those who were employed responded more satisfactorily to pain treatment 158,931, whereas others [e.g., 641 have reported the converse, with unemployed patients responding better than employed patients to pain rehabilitation, and still others have found no effect for employment status [e.g., 1031. Psychological distress

The empirical support for psychological tests to identify psychological distress and personality characteristics associated with treatment outcome and, therefore, the development of inclusion/ exclusion criteria, remains confused and unconvincing [for reviews see 90,124]. Some studies have reported that psychological distress, as measured by standardized psychological instruments designed to measure depression, appears related to successful treatment outcome [e.g., 33,85,112]. Elevations on the Hypochondriasis (Hs) and Hysteria (Hy) scales on the Minnesota Multiphasic Personality Inventory (MMPI) have been related to poor outcome following conservative medical treatment and surgery [e.g., 8,78,94,104,109,154, 1551. Yet, pretreatment psychological factors have not consistently predicted unsuccessful treatment outcome [e.g., 4,33,46,70,79,80,89,105,127,140]. A particular emphasis of psychologically oriented studies relates to compensation. In general, these studies have not shown a consistent link between greater psychopathology or psychological distress in patients receiving compensation [e.g., 10,55,82,84,99,108,132,133]. Pelz and Merskey I1081 concluded that their data supported ‘. . . the view that many patients receiving compensation have the same pattern of emotional response as those who do not obtain financial payment because of their illness’ (p. 293).

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Fishbain et al. [38] examined psychiatric diagnoses of compensation and non-compensation patients and noted that there were several differences between compensation and non-compensation patients on psychiatric diagnoses and personality disorders. They also concluded, however, that for ‘most DSM-III [l] diagnoses are evenly distributed between compensation and non-compensation chronic pain patients’ (p. 205). Caution is needed in interpreting the DSM III differences identified in this study as the authors did not indicate whether the psychiatrist making the diagnoses was blind to the compensation status of the patient. Further, no data were presented on the inter-rater reliability of the diagnoses. This latter point is particularly important because many of the differences noted were based on personality disorders (axis 2) of the DSM-III and the reliability of diagnoses on this axis have been seriously challenged [81]. These data suggest that screening out patients based on a single psychological criterion may be much too conservative. Disahili[y status/ litigation It has been suggested that because patients’ income (e.g., worker compensation benefits) may be contingent on their reports of pain that compensation serves as a disincentive for successful pain treatment. Consequently. patients receiving compensation may be poor risks for surgical treatment and pain rehabilitation and, therefore, should be excluded from treatment. Empirical support for this assumption has been provided by several studies that have reported compensation patients are less responsive to pain rehabilitation programs in New Zealand [19], Hong Kong [85], as well as in the United States [e.g., 10,56,57.72,76,100,105]. Disability status also has been associated with poor outcome following more invasive procedures such as surgery [34,37,40,116,151], chemonucleolysis [60,65], and epidural steroid injections [153]. The effects of compensation on outcome, however, are not clear cut. For example, poorer surgical outcome for compensation cases might be mitigated by the fact that compensation patients may be subject to over-diagnosis resulting in excessive surgery. Also, compensation patients often injure themselves while performing manual labor

and they are less likely to return to jobs requiring heavy physical labor, thereby deflating return to work statistics [152]. Other studies have found little relationship between disability status and outcome [e.g., 32,92. 105,106,122,133,138]. For example, Cairns ct al. [ 151 reported that 42% of patients treated as outpatients who were receiving worker compensation disability returned to work, suggesting that receiving disability may not limit return to work. Barnes et al. [S] reported that patients who were receiving the highest amount of compensation were the most likely to be successful in their rehabilitation program. They argue that receiving higher compensation may reflect higher wages being earned and. thus, may represent a salient incentive for rehabilitation so that they may return to their higher paying jobs. Dworkin et al. [32] noted that employment status prior to treatment was more important than compensation status in predicting treatment success. Thus, studies that draw conclusions rcgarding the role of compensation need to examine both the role of employment status and compensation. For example. Javid [65] noted that 90.7% of non-compensation back pain patients were successfully treated (i.e., relief of back pain at the time of 1 year follow-up) by chemonucleolyIS, in contrast to the 54.88) success rate of 3 worker compensation sample. However. it should be noted that 77.94 of the non-compensation patients were employed at the time of treatment compared to an employment rate of 59.5% for the compensation group. It is unclear how compensation status and employment status may have interacted and contributed to treatment outcome. There is frequently a tendency to examine these predictor variables separately in a univariate fashion. It is more appropriate to take a multivariate perspective. that is. to consider simultaneous combinations of these variables using multivariate statistical approaches [e.g._ 1121. Also, different sets of predictors may be identified for subgroups of patients. For example, Dworkin et al. I.731 noted that compensation status interacted with level of depression in predicting treatment success. In depressed patients, successful response to treatment was unrelated to receiving worker compensation,

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whereas in non-depressed patients, worker compensation did predict poorer outcome. Another point to note is that some studies [e.g., 15,132] tend to combine those receiving worker compensation disability with patients who have pending litigation. Although it seems obvious that pending litigation would be a disincentive for successful pain treatment and return to work, even on this issue the results are mixed. For example, several investigators have noted that pending litigation was not associated with increased pain behavior and symptom magnification [99,100,107, 1321 or successful completion of pain rehabilitation programs [e.g., 23,56,139]. Return to work, however, might be influenced by pending litigation [e.g., 46,76,85,133,139]. Talo et al. [133] noted that worker compensation cases with completed litigation had worse outcomes following pain rehabilitation than worker compensation cases with active litigation. When the impact of litigation on treatment outcome is considered, it is particularly important to examine outcome relative to whom. Although studies have reported that patients with active litigation do not do as well as those without pending litigation, this does not mean that patients with active litigation cannot show marked improvements compared to their own pre-treatment levels [10,70]. For example, Keefe et al. [70] found that although proportionally more patients in a poor outcome group (45%) were receiving disability payments than were patients in the best outcome group (23%) there were still a subset of patients receiving disability payments who did demonstrate significant improvements. Obviously, there were patients receiving disability payments who did have good outcomes and some patients without disability compensation who had poor outcomes [see also lO,lOO]. Moreover, some studies have not found litigation to influence either symptom presentation [loo] or successful treatment outcome [e.g., 23,561. Thus, when it comes to using compensation status and litigation pendency as exclusion criteria, we need to avoid the ‘compensation-litigation patient uniformity myth.’ Not all patients receiving compensation or with active litigation will fail to achieve successful progress in pain rehabilitation programs or failure to return to work. Recently,

Walsh and Dumitru [152] have reviewed in detail issues surrounding the role of compensation and litigation in recovery from chronic back pain. Conceptual The question remains if, and if so what, criteria should be used to select patients who are appropriate candidates for specific treatment, for example, programs based on operant conditioning principles [e.g., 2,451. Although a specific criterion or rigidly held criteria are difficult to justify, it seems reasonable to focus on inclusion criteria that are related to the methods and goals of treatment [71]. For example, in the studies reported by Fordyce et al. [45], Follick et al. [42], Roberts and Reinhardt [118], and Timming et al. [137], all patients underwent a comprehensive evaluation prior to entry into the program to verify that operant conditioning mechanism might be responsible for their pain problems.

Obstacles to patients entering treatment There are many reasons that patients offered treatments at pain clinics fail to enroll in the programs. These reasons need to be clearly stated when treatment outcome data are reported. Roberts [117] suggests that almost 40% of patients accepted for inpatient pain treatment refuse the recommended program. Sturgis et al. [129] interviewed patients who did not enter the treatment program offered and identified several primary reasons patients gave for declining: (a) lack of insurance coverage, (b) opposition of spouses to further treatment, (c) unwillingness to be hospitalized, and (d) transportation difficulties; along with a general lack of interest in the focus of the program. The reasons patients reject treatment have important implications for the generalizability of treatment outcome and need to be examined. Patients’ rejection of treatment Examination of the pain clinic treatment outcome literature reveals that a significant percentage of patients decline treatment when offered. For example, Jones et al. [69] examined rates of

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treatment acceptance for low back pain patients evaluated in an emergency department of a hospital. Only 42% of the patients instructed to make an appointment scheduled referral appointments and only 39% of the 42% kept them. Similarly, Winkler and his colleagues [156] noted that 44% of headache patients referred for a self-care intervention never followed through with their physicians’ recommendations. Bradley et al. [12] noted that only 40% of patients with definite or classical rheumatoid arthritis agreed to participate in recommended treatment program and approximately 60% of patients offered treatment in pain clinics accept [e.g.. 2,20,129]. Interestingly, Turner and Clancy [147] reported that 85% of back pain patients offered treatment accepted. In this study, patients were recruited from advertisements and did not have to pay for treatment. This illustrates the differences in self-referred samples obtained via the media with those characteristics of clinic patients who are usually physician referred. Obviously. such self-selection can bias the results of any treatment outcome study or at least significantly reduce the generalizability of the findings. Considerable discussion has focused on the lack of patient motivation and secondary gains as key ingredients for pain patients’ rejection of treatment. Chronic pain patients tend to be viewed as poorly motivated, resistant. denying, and having poor prognoses. Often this lack of motivation is attributed to patient characteristics such as personality traits (psychogenic, functional. ‘atypical pain syndrome.’ or another pejorative label that implies the pain is ‘psychological’) and financial disincentives (‘compensation neurosis’). Roberts [117] implies that the majority of patients who refuse treatment do so because of the secondary gains of financial compensation or positive reinforcement from the family. Roberts suggests that, ‘these are individuals with poor job potential coupled with high levels of compensation or individuals with family members who are highly enabling of their pain behaviors and do not encourage the patients to be treated (p. 175). This view tends to blame the victim. Blumer and Heilbronn [ll] have described the traits of the ‘pain-prone personality,’ Sternbach et

al. 11251 have stigmatized the low-back ‘loser,‘ and insurance companies persist in viewing many chronic pain patients as malingerers. Clinicians need to be aware of the negative stereotypes about pain patients implicit in these labels. It is all too easy to create a self-fulfilling prophecy where behaviors consistent with the stereotype are elicited by the clinician’s behavior [145]. Mismatch between putient and clinic gods Factors that contribute to lack of patient motivation should be examined. For example. Cameron [16,17] and DeGood [28] emphasize the importance of the mismatch between the patient’s beliefs and expectancies and the treatment offered. We can begin with the somewhat rhetorical question of why do patients seek treatment? The vast majority of patients seek treatment in the hope that their pain will be eliminated. Yet, most pain clinics give the explicit message that they will help the patient ‘learn to live with pain.’ This message is incompatible with patients’ desires. Although not always as effective as they could, patients know how to live with pain [e.g., 501. They come to a pain clinic because they wish to live withour pain. Further, many pain clinics emphasize that a central goal of treatment will be elimination of unnecessary analgesic medication. Some patients may be unwilling to consider treatment in such program for fear of removal of analgesic drugs on which they feel dependent, or fear that without the medication their pain will become intolerable. Recently, several authors [28.115.121,126,142] have emphasized the importance of the patient’s acceptance of the rationale for treatment. It is plausible that patients who expect complete elimination of pain will find it difficult to accept that this is not likely to be the focus or outcome of the rehabilitation program. Failure to accept this reality may be the impetus for treatment refusal. Although patients’ lack of interest in ‘learning to live with pain’ may be taken as indicating poor motivation, this position on the part of patients does not diminish the health care provider’s responsibility to attempt to enhance motivation. Consider the message presented to patients prior to entry in an operant program. Typical treatment goals of an operant approach [43] include: (1)

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increase activity level both generally and in regard to specific exercise activity constraints; (2) reduction in pain behaviors associated with protective actions by others; (3) reduction in pain-related medication; (4) restoration or establishment of ‘well-behaviors,’ including remediation of social and interpersonal problems previously limiting the ability to be well; (5) modification of the reinforcing contingencies to pain and well-behaviors in the patient’s environment; and (6) reduction in health care utihzation related to pain problems, including fruitless diagnostic and treatment procedures. Note that not listed among the goals is pain reduction or elimination. The focus is on the patient’s pain behaviors not perception of pain per se. Patients may evaluate the therapeutic efficacy of the self-care regimen against specific outcomes. The outcome that patients want is pain reduction. However, change in behavior, symptom reduction, and health outcome are not isomorphic. The patient’s pre-existing beliefs about the nature of pain rehabilitation are often at sharp odds with his or her expectations regarding the appropriate treatment for medical problems. Patients come to treatment with the expectation that health care providers will correct the physical problems that are causing their pain and thereby eliminate pain. They may be uncomfortable when terms such as learning, self-regulation, active patient participation, and personal responsibility are discussed [28]. The importance of addressing patients’ maladaptive and dysfunctional beliefs and attitudes regarding their problems and the treatment offered and the ability for them to achieve a satisfying and productive life despite residual discomfort has been discussed by DeGood [28], Follick et al. 1411and Turk et al. [142]. Patients develop an understanding of illness through personal beliefs associated with their afflictions. These develop through assimilation of new information (e.g., diagnoses, symptoms, emotional reactions) with pre-existing meanings and action patterns held by the patient [50,86]. We need to consider how individuals think, appraise, and cope with their current state. For example, Colvin et al. [24] noted that 237 of 300 pain patients evaluated thought that their pain was

caused by something more serious than or different from what their doctors had told them, and 271 believed that they did not receive adequate explanations from physicians. It is likely that these beliefs will interfere with treatment acceptance. Patient expectations about: (a) medical treatment and the clinical encounter; (b) beliefs and misconceptions about the cause, severity, or symptoms of the illness and susceptibility to complications or exacerbations; (c) goals of treatment, (d) perceptions about the cost/risks versus benefits of treatment; (e) existing health-related knowledge, skills, and practices; (f) degree of adaptation to the disease; (g) sense of hopelessness or lack of self-efficacy; (h) learning limitations; and (i) extent of family involvement all will affect resistance to treatment. Thus, goals of patients may not match those of the treatment program and, therefore, they may be ‘unmotivated’ to enter the particular treatment program offered. Chronic pain patients are understandably most concerned about the intensity of their pain. Yet behavioral treatment approaches are most successful in modifying behavioral problems such as activity level or medication intake. Patients may prefer to seek other treatments, even if they are more drastic (surgery) or have less demonstrated scientific basis (e.g., acupuncture) because on the surface they hold a better chance of meeting their primary concern - symptom elimination. The patient and family treatment goal expectations must be addressed [71]. How is the treatment presented to the patient? Does the patient understand the rationale presented? What is the rationale provided? Does the patient believe that the intervention is appropriate to his or her problem [l&28]? Unless there is some match between the patient’s views of his or her problem and the treatment being offered there is not likely to be acceptance. For example, consider the patient with a 10 year history of back pain. How credible does a treatment program that is 2 weeks in length sound, or a migraine headache patient who has suffered for 20 years only to learn that 6 sessions of biofeedback is going to have a significant effect? Farber [35] has called our attention to an important fact, namely, ‘One thing psychologists

(health care providers) can count on is that their clients will talk, if only to themselves; and not infrequently, whether relevant or irrelevant, the things people say to themselves determine the things they do’ (p. 196). Perhaps the reasons that patients reject treatment are not poor motivation and secondary gains, as suggested by Roberts [117], but a mismatch between their views of their problem and their treatment goals, which conflict with the rationale for the treatment offered and the expressed goals of the treatment program. Recently, several investigators have developed evaluation instruments designed to assess directly patients’ treatment beliefs and attitudes and how these relate to outcome [e.g., 66,115,121]. It is possible that these instruments could be used to identify attitudes that will lead to non-acceptance of treatment and these could be addressed to reduce the number of patient refusals as well as target these attitudes during treatment.

A major reason why patients offered treatment do not enter is financial. Several studies suggest that from 20 to 50% of patients for whom a pain rehabilitation program is reco~ended will be denied approval by third-party carriers [e.g., 24,123]. The criteria that third-party payers use to deny a claim are rarely specified. Yet, the screening criteria they use may result in a treatment sample that is highly biased and unrepresentative. Additionally, it is unlikely that these groups are equivalent on other characteristics besides insurance approval. For example, the patients who enter treatment may, as suggested earlier, be more motivated to engage in a variety of appropriate behaviors to help themselves, which may have influenced insurance decisions to pay for pain rehabilitation services. It is possible also that those denied insurance coverage for treatment may not only be less motivated, but also may be less willing to report that they have improved from the pre-treatment to the follow-up assessment because they were denied treatment and may feel that reporting improvement, nonetheless, may jeopardize future disability payments. Thus, the common practice of using patients denied by insurance companies as no-treatment comparison

groups in outcome studies [20.93,94,118] is inappropriate because the treated and untreated patient groups are not selected randomly.

Comp~abiii~ of patient samples Another question regarding pain treatment outcome is the comparability of patients treated across settings. Some treatment outcome studies report on the efficacy of treatment for consecutive referrals to pain clinics, whereas others note that the treated patients were respondents to newspaper advertisements for participation in a specific treatment protocol [e.g., 14,68,147]. Although patients may have ostensibly the same diagnosis (migraine headache, low back pain), it is important to consider whether it is appropriate and to what degree to generalize across different treatment settings or patient samples. For example, Flor et al. [39] reported on the efficacy of biofeedback compared to ‘pseudofeedback’ and conventional medical treatment for chronic back pain patients. Bush et al. [14], in contrast, found no beneficial effects for biofeedback training that was designed to raise or lower electromyographic activity in the paraspinal muscle. How can such opposite results for ostensibly the same treatment modality be explained? Although there were many differences between the two studies, a notable one was the samples included in the treatment group. Flor et al. included patients who had rheumatic back pain and were being treated in a specialized rheumatology clinic. Bush et al.‘s [14] sample was recruited by newspaper advertisements. Although both studies could be viewed as examining the efficacy of biofeedback for ‘back pain,’ a symptom not a diagnosis, there were marked differences in outcome that may, at least in part, be att~butable to the method of sample recruitment. Holzman et al. [62] directly compared pain patients across 4 different settings, two Veterans Administration Hospital pain clinics, one university-based pain clinic, and one pain clinic housed in a community hospital. They concluded that pain patients treated at different settings varied on several potentially important characteristics name-

17

ly: gender, age, prior treatment by conventional (e.g., physical therapy) and unconventional therapies (e.g., acupuncture), and pain duration. Thus, comparing treatment outcome across these 4 settings might reflect demographic or pain status variables as much as direct effects of the treatment. Consider, for example, the recent influential report of Mayer et al. [94] who reported return to work statistics that seem higher than those of many other programs (87% versus more commonly 30-508). It is interesting that in the Mayer et al. study, 41% of the no-treatment comparison group were employed at 2 years post-treatment discharge. How comparable are the patients in this study to pain rehabilitation outcome programs where the percentage of patients treated or untreated who return to employment is often substantially lower? It is important to examine the demographics of the sample. The mean duration of pain for the Mayer sample was 18 months, significantly shorter than many other treatment outcome studies (often 7 years or more [e.g., 9,53,75]). Several investigators [36,70,131] have reported that duration of pain was an important predictor of treatment success. When trying to compare the efficacy of the approach advocated by Mayer et al. [94] to many other treatment programs, caution is needed because it would be erroneous to conclude that differences following treatment were solely or necessarily the result of the treatment per se. Turk et al. [144] noted that the prevalence of depression among chronic pain patients reported in the literature ranges from 10% to 100%. A very great range, indeed, and they speculated that differences in the incidences reported might be attributable to the location of the pain clinic. For example, some pain programs are housed in rehabilitation hospitals, some in departments of physical medicine and rehabilitation and orthopedics, whereas others are located in psychiatric facilities or hospital units. Turk et al. [144] speculated on the selective filtering that might be inherent simply in the department to which the pain patient was referred, for example psychiatry versus orthopedics. Some support for this suggestion was reported by

Merskey et al. [loll when they compared the levels of psychiatric morbidity identified in patients referred to anesthesiology clinics, oral medicine clinics, rural hospital pain clinics, and a psychiatric pain assessment and treatment service. The psychiatric clinic patients were significantly more depressed and showed more dysfunction than the other 3 clinic groups. Whether this represents over-diagnosis or the nature of referral patterns is unknown. However, we can at least wonder whether referral sources thought differently about the patients they referred to a psychiatry service versus a rehabilitation hospital. Chapman et al. [21] have suggested that a behavioral approach was more appropriate for the more behaviorally and emotionally affected medical center patients (i.e., learned sick role behaviors), whereas a more traditional medical focus might be more appropriate for the outpatient clinic patients. Yet, the relative efficacy of these different treatment orientations with different patient populations has not been evaluated. Comparability of patients on demographic and pain status factors is particularly important for comparing the efficacy of different treatments or the same treatment across different settings because many factors have been shown to interact with treatment success. For example, Swanson et al. [131] reported that patients who were effectively treated in their program were more likely to be male, married, had pain of shorter duration, had fewer surgeries, and lower levels of psychopathology. Painter et al. [105] noted that successfully treated patients in their center were in the age range 30-50 years old, whereas those who were less successful were either under 30 or over 50 years old. It is important that we keep in mind the differences noted in these studies when we evaluate treatment outcome studies based on pain clinic samples versus community samples. Some studies report that the patients included in the study were recruited from the community by newspaper advertisements [e.g., 1471, from college student volunteers [e.g., 3,131, or referral to psychological clinics [136]. These samples appear to be significantly different from physician referred pain clinic patients.

1X

Dutu analysis and patient heterogeneit) Perhaps the most important result of the Holzman et al. [62] study, cited above, was the observation that the covariances among the variables between the 4 pain clinics were dissimilar. The presence of covariance heterogeneity has important statistical implications for comparing treatment effectiveness from different settings. Specifically, the results suggest that a major statistical assumption. the homogeneity of covariance matrices, cannot be assumed to hold for data from different settings. The violation of this assumption can distort the results and interpretation of comparative treatment studies because statistical power is reduced when group covariances are unequal. Another consequence of covariance heterogeneity relates to the frequent use of the analysis of covariance (ANCOVA) in interpreting treatment outcome data. When groups differ on some demographic or ‘background’ variables, the conventional data analytic procedure used to control for the pre-treatment differences is ANCOVA, with the variables on which group means differ used as covariates. The results of the Holzman et al. [62] study suggest that employing ANCOVA in a comparative study across pain clinics may be inappropriate. Because the covariance patterns between variables were found to differ across samples. a major assumption of ANCOVA, the homogeneity of regression, is violated. Therefore, the statistical adjustment for pretreatment group differences by employing covariates camp+ legitimately be used to compare different pain samples. Between-setting differences in terms of certain patient characteristics may seem intuitively obvious. However, even when different patient populations show similar mean scores on selected variables, the comparison of treatment effects still may not be justified. This is because it cannot be assumed that the associational patterns (e.g., correlations) between variables for different populations are similar. In other words, the mean scores may be equivalent (e.g., pain duration, age), however, the covariation among the variables may display significant between-group differences. Thus. both mean and covariance evaluations are needed to describe adequately the similarities and differences between two populations.

The sampling of studies reported here illustrates the many difficulties in comparing samples of patients who appear quite similar in diagnosis yet demonstrate other important differences that can confound comparison of the results reported in different settings. Greater attention needs to be given to the comparability of samples when studies are described.

Patient attrition - dropping out of treatment In general, studies in psychiatric clinics indicate that 20-57s of the patients fail to return after their first visit (6.311. Phillips [llO] reviewed the data on attrition from various forms of psychotherapeutic interventions and identified a similar negatively accelerating, declining curve across delivery systems (e.g., health maintenance organizations (HMO), comprehensive mental health centers, private clinics) and across therapeutic orientations (e.g., psychodynamic, behavioral). He notes that by the third session, 70% of the patients have dropped out of treatment! In outpatient treatment of alcoholism, S2-75% drop out by the fourth session [e.g., 71. In physical rehabilitation. Carmody et al. [1X] examined dropout rates from physical exercise programs for cardiac patients and found that the patient attrition rates from physical exercise rehabilitation programs represented a downwardsloping, negatively accelerating curve. The first 3 months were critical with a substantial number leaving the program by then. Kentala [73] reported in a cardiac rehabilitation program that by 5 months only 39% were still participating and at 1 year only 13% of the original enrolled patients stayed in treatment. Pain treatment outcome studies are not immune to the problem of patient attrition. Some studies do report on the number of patients who drop out. Although dropping out may be noted. the impact of the numbers who drop out on the conclusions reached is rarely considered. Although many reviews [e.g., 71,148] have supported the relative efficacy of a host of pain treatment approaches, conclusions must be tempered by the observation that many patients drop out of treat-

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ment prematurely. It appears that from 5% [147] to 46% [48] of patients who enter treatment dropout on their own. However, Kohlberg and Cahn [77] reported a 70% drop-out from a minimal therapist contact home-based treatment for headache sufferers. High drop-out rates may vitiate the effectiveness and validity of the study. For example, recently Kerns and Haythornethwaite [74] reported that depressed pain patients show a higher treatment drop-out rate than non-depressed patients. These results have important implications for generalizing across studies and from specific treatment outcome studies to the population of chronic pain patients because, as noted, the prevalence of depression varies widely across studies. Although drop-out percentages vary widely across studies, in general they do not appear to differ for inpatient or outpatient programs, diagnosis (e.g., arthritis, low back pain), or country (United States [e.g., 531, England [122], New Zealand [19]). For example, Turner and Clancy (1471 reported a 5% drop-out rate for patients recruited from the community, Mayer et al. [93] reported that 10% dropped out of their outpatient program, Kerns et al. [75] reported 20% drop-out from an outpatient program housed in a U.S. Veterans Hospital, and Tyre and Anderson [149] and Cassisi et al. [20] reported 19% and 26% drop-out rates, respectively, from their inpatient treatment programs. Finally, Bradley et al. [12] reported that 25% of arthritics dropped out or were unavailable at follow-up from an outpatient program. In sum, treatment drop-out rates are significant problems regardless of whether treatment is provided on an inpatient or outpatient basis, whether it is conducted in a private rehabilitation hospital, university-based hospital, U.S. Veterans Hospital, different geographical locations throughout the United States, or whether the target population is heterogeneous chronic pain patients or a specific diagnostic group (e.g., arthritics [12]; low back pain patients [20]). Drop-out rates from treatment become a particular problem when two or more treatments are being compared. Although the results might suggest that one treatment is more efficacious than another, differential drop-out rates might lead to a

different conclusion as to the treatment recommended. This point and others related to evaluation of treatment outcome will be examined in greater detail in the third paper in this series [143]. There are many reasons why patients drop out. For example, Beekman and Axtell [9], who recorded one of the highest attrition rates in the pain literature (39%), specifically attempted to determine the causes of dropping out. The reasons they found for patients leaving the 4 week inpatient program without staff consent included: (a) family problems (29%), (b) disruptive or uncooperative behavior (23%), (c) financial difficulties (lo%), (d) taking a job (6%), other (13%), and unspecified (19%). Funch and Gale [48] reported that the best predictors of patients with temporomandibular disorders dropping out of treatment were social factors, specifically the significant other’s attitudes toward the patient and the patient’s complaints of pain. Interestingly, patients who perceived significant others as being least supportive were the most likely to remain in treatment. Gottlieb et al. [53] suggest that most patients drop out due to dissatisfaction with the treatment and lack of motivation to be rehabilitated. Some patients may drop out because they cannot relinquish the belief that physical exercises, the sine qua non of many pain rehabilitation programs and that often initially causes increased perception of pain, may lead to increased physical pathology [8]. That is, they continue to ascribe to the view that hurt equals harm [44]. If one holds this belief, then any exacerbation of pain connotes a worsening of the condition and should be avoided. The patient then comes into conflict with the treatment program that encourages exercise, despite any increased discomfort, and he or she may terminate participation. It is also worth considering the possibility that patients who report the greatest amount of pain at the beginning of treatment may be less willing to tolerate rigorous physical therapy regimens than patients who indicate lower initial pain severity. Thus, the patients who complete treatment in outcome studies may be those whose perceived pain intensity is already lower than other chronic pain patients [e.g., 9,30,112].

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Discrepant expectations about treatment promote dropping out [51,52,59]. The importance of giving the lower socioeconomic status (SES) patient in psychotherapy both a form of treatment that agrees with his expectations (drugs) and also rapid symptomatic relief is underscored by Dodd’s report [31] that lower SES patients who were not given medication were more likely to drop out of psychotherapy than those who were given medication. If pain patients are entered into programs that have a large psychological component without adequate justification, they may view the program as inconsistent with their needs [16,17]. This may serve as an impetus for dropping out, in the same way it contributes to non-acceptance of the treatment offered in the first place. Treatment discharge In addition, a few studies note that an additional number of patients who enter treatment are asked to leave by the treatment staff [e.g., 81. Gottlieb et al. [53] reported that of 75 patients admitted to treatment, 13 (17%) were terminated by the staff based on lack of involvement. Thus, in the Gottlieb et al. study 50 patients or 66% who originally entered treatment could be considered as completing treatment, staff discharges notwithstanding. It should be noted, however, that 71 of the patients admitted to Gottlieb et al.‘s program had moderate to severe psychopathology on psychological tests. Thus, the high drop-out rate may be related to the inclusion of patients who might well be excluded from other pain clinics. An important issue is how to treat drop-outs in evaluating treatment outcome studies. Should patients who drop out be viewed as failures or should they be excluded because they did not receive an adequate clinical trial [20]? One strategy would be to establish minimal criteria for inclusion or exclusion from outcome assessment [e.g., 951. That is, if a treatment consists of 8 sessions an arbitrary decision could be made that those patients who drop out within the first 5 sessions should not be included in evaluating treatment efficacy as they did not ‘receive’ an adequate ‘dose’ of the treatment [75]. Alternatively, it could be argued that they should be viewed as treatment failures and should be included in evaluation of outcome be-

cause a component of any rehabilitation program includes changing patients’ conceptualization of their problems to fit the treatment being offered [141]. Failure to accomplish this may be demonstrated by patients dropping out of treatment.

Conclusion When examining the efficacy of treatment outcome studies it is important to consider the limiting factors discussed in the paper, namely, characteristics of those who are referred, the types of patients offered treatment, who declines treatment and for what reasons, and who drops out of treatment. These factors will influence our interpretation of treatment outcome and may mitigate against our ability to generalize treatment results. Many treatment outcome studies result in a select sample of patients who successfully complete the treatment program. The representativeness of the program completers is, therefore, limited and treatment conclusions are strongly influenced by whom received the treatment. Those patients who finally are included in treatment outcome studies are likely to be among the most motivated. This fact should lead us to be somewhat humble when we consider the outcome results of various treatment programs. This point raises a related concern about the appropriateness of using those who do not enter treatment or who drop out of treatment as comparison groups against which to evaluate the efficacy of a specific intervention [e.g., 47,118]. In the next paper in this series we will focus on two additional problems that confound conclusions that can be drawn from treatment outcome studies. relapse and non-compliance with therapeutic recommendations. In the final paper in this series we will consider the fundamental issue of evaluation of treatment efficacy and the criteria used to determine outcome.

Acknowledgements We would like to thank Donald Meichenbaum for his incisive comments and suggestions on an earlier version of this manuscript.

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