Methods
Study Design
Our data referred to the cross sectional baseline analysis of a 12-month cohort study that identified risk factors and protective factors of pain generalization in primary care CLBP patients. A detailed study protocol has been published elsewhere.
This project is part of the research consortium LOGIN "Localized and Generalized Musculoskeletal Pain: Psychobiological Mechanisms and Implications for Treatment", funded by the German Federal Ministry of Education and Research.
Study Population
During a 5-month period, fifty-eight general practitioners (evenly distributed in the northern region of Hessen in Germany) consecutively enrolled all eligible patients consulting for CLBP as a primary or secondary consulting reason (inclusion criteria). The symptom "chronic low back pain" was defined as back pain below the costal margin and above the inferior gluteal folds (with or without pain radiation), which had started at least three months prior and continued during most days (i.e., more than 50%) in the last three months. Patients under 18 years, pregnant women, and persons with an insufficient understanding of the German language or severe cognitive impairments (e.g., dementia) were excluded from the study.
Patients who gave their informed consent were asked to complete a questionnaire directly after the consultation or at home. During the recruitment period, trained clinical monitors conducted two random quality control audits of the GPs' performance.
Measurements
To explore descriptive characteristics, the questionnaire included the following physical and psychological parameters (for detailed information please see Viniol et al.).
Pain Characteristics and Sociodemographic Data To evaluate the number of different pain areas, we measured pain localization with the "body pain drawing model" proposed by Pfau et al.. Pain anamnesis and sociodemographic data were determined with the "German Pain Questionnaire", the official pain questionnaire of the German Association for the Study of Pain. We used the following DGSS modules: duration, characteristics, course of pain, sociodemographic data, health care utilization, and medication.
We used the three-item social support subscale from the West Haven-Yale Multidimensional Pain Inventory (WHYMPI) to explore the partner's reaction in response to patient's pain (internal consistency of the subscales: α = 0.63–0.90).
The severity of chronic pain was measured by the German translation of von Korff's Graded Chronic Pain questionnaire (GCP). Severity is computed from "pain intensity" and "pain-related disability" (internal consistency of subscales: α = 0.68–0.88).
Comorbidities Using the Self-Administered Comorbidity Questionnaire (SACQ), we asked the patients about 14 common medical conditions: high blood pressure, heart disease, asthma, chronic obstructive pulmonary disease, ulcer/stomach disease, diabetes, high blood lipid level, kidney disease, osteoarthritis/degenerative arthritis, rheumatoid arthritis, osteoporosis, cancer disease, depression, other psychiatric diseases.
Psychological Parameters and Patient Resources Psychosomatic symptoms were measured with the somatization subscale of the Symptom Checklist 90-Revised (SCL-90-R), a commonly used psychological status symptom inventory for psychopathology (internal consistency: α = 0.81).
Screening for anxiety disorders and depression was done by the Hospital Anxiety and Depression Scale (HADS) (internal consistency anxiety α = 0.80; depression α = 0.81).
Coping resources for back pain were evaluated by the FBR (Fragebogen zu Bewältigungsressourcen bei Rückenschmerzen) questionnaire from Tamcam et al..
We used the resilience scale RS-11, a shortened and validated German form of the Wagnild & Young questionnaire, to assess the resilience (internal consistency α = 0.91).
Statistical Analyses
We performed k-means cluster analyses generalized to all scales of measurement with squared euclidean distances. The k-means procedure identifies relatively homogenous groups while maximizing the variability between clusters. Variables with mixed scaling can be handled in cluster analysis. Calculations were done with ALMO 15 (http://www.almo-statistik.de), which includes a k-means algorithm that is able to handle the different scalings of our variables and the large sample size. This program provides statistical measures to evaluate the appropriateness of a cluster solution (F-value, eta).
Cluster analysis is an iterative process looking for the most relevant variables adding to an interpretable solution. Therefore, we ran several analyses for selection of variables, based on a variable-specific eta < 0.05. Accordingly, the following variables were excluded: gender, living with a partner, applied for pension, pain distribution, medication, WHYMPI – social support scale, education level, kind of job, time of pain. We attached special importance to certain individual variables (number of pain areas, therapeutic strategies, consultations, operations) so that they were included irrespective of their eta.
Ethics Statement
The study was approved by the local ethics commission of Philipps University of Marburg, Germany (Ethik: 11.06.2010, AZ 88/10) and is in accordance with the Declaration of Helsinki.