Assessment of Factors Predicting Inadequate Pain Management in Chronic Pain Patients

Chronic pain is considered the world’s third-largest health problem (1) and has a great impact on many health domains including physical, emotional, and social health. It also extensively affects the quality of life (QOL) (2-6). Inadequate pain management (IPM) has been widely reported (7-10). A recent review suggested that nearly 50% of chronic pain patients did not receive adequate pain management (11-14). Several factors have been proposed to contribute to the IPM including patient-related factors (age, sex, education, social and psychological status) (15-18), healthcare provider-related factors (underestimation of pain intensity, lack of adequate training) (19-21), and disease-related factors (pain intensity, benign vs. malignant disease) (2, 3). Meanwhile, other studies proposed that inadequate pain assessment is the cornerstone of under-treatment (22, 23).

Pain management adequacy can be assessed by the patient’s satisfaction, pain intensity, the extent of pain relief, and pain interference with life. Pain management index (PMI) is a well-validated mean for assessing the adequacy of pain treatment (7) in a large variety of chronic pain conditions (24, 25). The PMI compares the patient’s reported level of pain intensity with the potency of prescribed analgesics.

A few studies have attempted to find predictors (risk factors) of IPM. Many variables such as personal characteristics, inter-individual relationships, employment status, and pain features may have significant correlations with IPM. Older patients, women, ethnic minorities, and less educated patients are at risk of IPM (15-18). However, the majority of the studies have been conducted with small sample size. Many reports lack the simultaneous evaluation of different variables in a model. In addition, there are inconsistencies in the results of these studies about the role of different factors like age and sex.

We aimed to evaluate the prevalence of IPM in a sample larger than the samples of the previous studies. Furthermore, we considered more possible confounding factors in IPM by simultaneously applying less-studied variables (such as education level, employment status, marital and household conditions). As there is a relationship between obesity and higher pain intensity (26-28), we considered a new variable, i.e. the body mass index (BMI), in logistic models to demonstrate more different factors that may independently correlate with IPM. Moreover, most of the studies focused on IPM in cancer patients (7-9, 11) whereas, we evaluated pain management in a wide variety of chronic pain conditions.

The Ethics Committee of the University approved this cross-sectional study. All eligible patients signed informed consent forms. The inclusion criteria were as follows: (1) all consecutive patients who referred to our pain clinic between 2014 and 2017; (2) patients aged 15 to 90 years; (3) patients with chronic pain (pain duration ≥ 3 months), and (4) patients with documented records indicating the type and dose of analgesics they had already received in primary and/or secondary care centers. Patients younger than 15 years, with a documented psychiatric disorder, or not willing to answer the questions were excluded. Patients who completely met the eligibility criteria were enrolled in the study. By considering the IPM prevalence lower limit of 30% and the maximum acceptable error level of 0.05, we would need at least 504 patients to detect this prevalence with a precision of 4%.

The patients filled in questionnaires. The questions were about age, sex, weight, height, employment status, education level, marriage (or being in a serious relationship), household condition, and the number of children. The patients answered the questions about pain intensity, pain duration, and the type and dosage of previous analgesics they were taking at the first visit. If any patient had difficulty to answer the questions, a trained staff was always available to give help. The staff ensured that the patients had completely understood and answered the questions. Afterward, the attending pain specialist visited the patients. During the consultation, the specialist evaluated the patients’ previous pain treatments and records including the type and dosage of previous medications. He also verified the questionnaire to be adequately completed. If there were any defects in a questionnaire, he would address the unanswered questions. The interviews were to support the validity of the data provided by self-report. We used the baseline data to assess the adequacy of pain management in the primary and secondary care centers.

Consecutive cases (576 patients) during 2014 - 2017 were interviewed for eligibility criteria and 511 were recruited. Among 65 patients who were excluded, 45 had a documented psychiatric disorder and the remaining 20 patients could not provide clear, documented, and detailed information about the analgesic therapy they had received. All of the 511 eligible patients filled in questionnaires and their data were analyzed (Figure 1). The average age of the patients was 47.9 years (SD = 14.9) and 214 patients (42%) were male. Table 1 presents the descriptive data of the patients. Cases included in the final analysis had no missing data in the main study parameters.

Figure 1.

Flow diagram of case recruitment. The diagram demonstrates the number of patients and the reason for exclusion in different stages of recruitment.

The mean values of minimum, present, maximum, and overall (average) pain intensities of the patients were 2.91, 5.35, 8.5, and 6.87, respectively. Moreover, 472 (92.3%) patients had moderate to severe pain intensity and 39 (7.6%) patients experienced mild pain. The mean duration of chronic pain was more than five years, i.e., 60.52 months (SD = 89), prior to the consultation at the pain clinic. In addition, 449 patients (88%) were prescribed with some kinds of analgesics (Table 1). An acceptable PMI, which included PMI scores of 0 and 1, was reported only in 120 (23%) patients. A negative PMI (scores of -1 to -3) was calculated in 77% of the patients. Only 10% of the patients with severe pain received a strong opioid. Table 2 shows the correlation between the mean PI and the analgesic potency with the Zelman PMI. The percentage of patients with negative PMI was significantly higher in patients with severe mean PI than in patients with mild pain (P < 0.001).

Univariable logistic analysis revealed that age (P = 0.02), sex (P = 0.007), education level (P = 0.01), and BMI (P = 0.03) were significantly correlated with IPM. No association was detected between negative PMI and employment status (P = 0.24), marital status (P = 0.71), household conditions (P = 0.89), the number of children (P = 0.57), and pain duration (P = 0.49).

Next, we developed multivariable logistic regression models to investigate parameters with independent and significant associations with negative PMI. In the first multivariable logistic model, age, sex, and education level retained significant associations with IPM (Table 3); this model was further adjusted for BMI, a clinical parameter that showed a potential association with negative PMI in univariable analysis. The second model (Table 3) demonstrated that women (odds ratio (OR) = 1.62, 95% confidence interval (CI): 1.03 - 2.54) and patients aged 45 - 65 years (OR = 2.34, 95%CI: 1.16 - 4.73) had higher odds to receive IPM than their corresponding reference groups. It also indicated that patients with higher education (a degree higher than bachelor) had significantly lower odds to have IPM than illiterate patients (OR = 0.32, 95%CI: 0.1 - 0.89). Obesity was associated with up to three-fold higher risk of IPM (OR = 3.04, 95%CI: 1.03 - 8.51). Figure 2 demonstrates the prevalence of negative PMI in different groups of patients.

Figure 2.

Prevalence of inadequate pain management in different groups of patients with chronic pain. Patients were classified based on their age, gender, education level, and body mass index.

We further studied the associations suggested by the multivariable model developed for PMI. The correlations of the mean PI and analgesic potency were separately investigated (Table 4). These associations were also adjusted for the association between the mean PI and analgesic potency. In the model developed for the analgesic potency, obese patients were almost three times less likely to receive analgesics with adequate potency (OR = 0.27, 95% CI: 0.09 - 0.84). In addition, patients aged 45 - 65 years had the highest likelihood to receive analgesics with inadequate potency compared to patients aged 30 or younger (OR = 0.40, 95% CI: 0.19 - 0.81). The sex and education level did not show any significant associations with the potency of analgesics. On the other hand, women were more likely to have severe mean PI (OR = 1.67, 95% CI: 1.08 - 2.59). Furthermore, cases with higher levels of education were less likely to report severe mean PI (OR = 0.33, 95% CI: 0.10 - 0.86). The age and BMI did not show any significant correlation with the mean PI (Figure 3).

Figure 3.

Risk factors for inadequate pain management index

About 92% of the patients in this study had moderate to severe pain while 77% of them had IPM. The IPM was significantly more in patients with severe pain. Age, sex, education, and BMI could independently correlate with IPM.

There are numerous reports about the prevalence of IPM in cancer patients (25% - 82%) (7-9, 11, 31-36) whereas a few studies have evaluated the adequacy of pain treatment in non-malignant conditions (24, 25). The high rate of negative PMI (77%) in our study is similar to other reports and shows that IPM occurs in patients experiencing malignant and non-malignant conditions.

There is a controversy about the correlation of independent factors such as age, sex, education, job, and marital status with IPM (13, 32, 34, 36). Greco et al. reviewed 46 papers about IPM (37). Only 6 reports had a sample size with more than 500 patients. The current study is the first report of independent associations of age, sex, education, and BMI with IPM in a large sample of patients (511 cases).

Our study demonstrated that age, sex, education, and BMI could predict the odds of IPM. We investigated the correlation between the mean PI and analgesic potency in the final model (Table 4). Sex and education showed significant associations with the mean PI whereas age and BMI had correlations with analgesic potency.

The relationship of sex with the adequacy of treatment is not consistent in different reports. Some studies showed no correlation between sex and IPM (32, 36, 38), while other reports demonstrated more negative scores of PMI in women (1, 7, 8, 33, 39). Our results revealed that women were 1.6 times more likely to have IPM, which was compatible with some studies (7). Some mechanisms have been proposed to explain this difference, including different pain sensitivities or different responses to analgesics between the two sexes (40-42), as well as sex bias in the physician prescription of potent opioids (33). Our final model demonstrated that the difference was due to different pain intensities reported by women.

Education is a less-studied variable for IPM. Our study revealed that a high level of education was inversely related to the negative PMI (Figure 2), which was compatible with other reports (8, 36). It can be partly due to the different attitudes of illiterate and educated patients toward opioid use and addiction (37). Our final models revealed that the correlation between education and negative PMI was mediated by the reported intensities. Furthermore, illiterate patients who are more populated in rural and less developed areas may have less access to quality pain clinics.

Age is another controversial determinant of negative PMI. Some studies did not find any correlation between age and PMI (13, 33) while the others reported the older age as a protective factor against IPM (32, 34, 36). Some studies demonstrated an association between the younger age (< 40 in some studies and < 65 in others) and better pain management (7, 8, 43). In our study, a higher percentage of negative PMI was observed in the age group of 45 - 65 years (Figure 2). Our final model demonstrated that the age was correlated with PMI via the analgesic potency rather than pain intensity (Table 4). It can be explained that opiophobia and fear of side effects of potent opioids can be a pivotal factor in IPM in older patients. Furthermore, we observed a drop in the prevalence of negative PMI in people older than 65 years old (Figure 2). The pharmacodynamics and pharmacokinetics of medications may change in favor of the reduction of the required dosage of analgesics, especially opioids in older patients. It is proposed to reduce the dose of opioids to 50% in geriatric patients (44). Therefore, older patients may need less analgesic for certain pain intensity. Consequently, a dosage that is insufficient in a younger patient can be considered overtreatment in older patients with the same weight. Hence, in old patients, the opioid requirement decreases and thus IPM can hide behind this change in opioid requirement.

The BMI as a determinant of negative PMI was assessed for the first time in this study. Obese patients were at higher risk of IPM (Figure 2). In addition, our models showed that BMI probably mediated its effect via the potency of analgesics. Previous studies demonstrated that obesity was associated with higher pain levels in patients even after adjustment for other demographic and pain-related factors (26-28). The physicians’ concern for the diverse side effects of potent opioids and using different types of non-opioid analgesics had significant impacts on prescribing analgesics for obese patients. These patients usually have more health issues including fatty liver, hypertension, insulin resistance, diabetes, depression, obstructive sleep apnea, and respiratory compromise (28, 45-47), which may limit the physicians’ decision to prescribe potent opioids for obese patients. Moreover, the volume of distribution and the rate of metabolism/elimination of analgesics are higher in obese patients due to the fatty liver-altered enzymatic activity, which can decrease the efficacy of prescribed drugs (48, 49).

There are some limitations to this study. The PMI is not a perfect indicator of IPM because it does not take into account factors including patients’ compliance, the dosage of medications, route of administration, the potential effect of adjuvant analgesics (antidepressants), and other non-pharmacological modalities. Residual pain intensity despite treatment is probably not an appropriate measure of IPM because the target of chronic pain management is not always to reduce the PI. The improvement of quality of life is also a very important factor. There is a big difference between cancer and non-cancer pain in terms of natural course and treatment strategy. Pain phenotype (neuropathic versus non-neuropathic) is a crucial factor to determine drug efficacy while we evaluated pain management in a wide variety of chronic pain conditions. Our pain clinic is a tertiary and public center; consequently, our patients are not the representatives of the general population. Our patients had non-negligible pain in spite of their prior management. Thus, we can assume that the prevalence of severe pain and IPM would be higher in our patients than in the general population.