Low back pain patient subgroups in primary care: pain characteristics, psychosocial determinants, and health care utilization.
OBJECTIVE
In industrialized countries, low back pain (LBP) is one of the leading causes for prolonged sick leave, early retirement, and high health care costs. Providing the same treatments to all patients is neither effective nor feasible, and may impede patients' recovery. Recent studies have outlined the need for subgroup-specific treatment allocation.


METHODS
This is a cross-sectional study that used baseline data from consecutively recruited patients participating in a guideline implementation trial regarding LBP in primary care. Classification variables were employment status, age, pain intensity, functional capacity (HFAQ), depression (CES-D), belief that activity causes pain (FABQ subscale), 2 scales of the SF-36 (general health, vitality), and days in pain per year. We performed k-means cluster analyses and split-half cross-validation. Subsequently, we investigated whether the resulting groups incurred different direct and indirect costs during a 6-month period before the index consultation.


RESULTS
A 4-cluster solution showed good statistical quality criteria, even after split-half cross-validation. "Elderly patients adapted to pain" (cluster 1) and "younger patients with acute pain" (cluster 4) accounted for 55% of all patients. Cluster validation showed the lowest direct and indirect costs in these groups. About 72% of total costs per patient referred to clusters 2 and 3 ("patients with chronic severe pain with comorbid depression" and "younger patients with subacute pain and emotional distress").


CONCLUSIONS
Our study adds substantially to the knowledge of LBP-related case-mix in primary care. Information on differential health care needs may be inferred from our study, enabling decision makers to allocate resources more appropriately and to reduce costs.
*Department of General Practice/Family Medicine **Institute for Medical Psychology, University of Marburg, Marburg †Institute of Medical Informatics, Biometry and Epidemiology, Ludwig-Maximilians-Universität, Munich ‡Institute of Genetic Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg §University Hospital of Düsseldorf, Institute for General Practice, Düsseldorf ∥University Hospital of Cologne, Institute for Health Economics and Clinical Epidemiology, Cologne ¶Department of General Practice, Institute for Community University Medicine Greifswald, Greifswald Departments of #General Practice/Family Medicine ‡‡Anaesthesiology, Pain Clinic, University of Göttingen, Göttingen, Germany ††Department of Public Health Sciences, University of Hawaii at Manoa, HI.
Oliver
O
Hirsch
Konstantin
K
Strauch
Heiko
H
Held
Marcus
M
Redaelli
Jean-François
JF
Chenot
Corinna
C
Leonhardt
Stefan
S
Keller
Erika
E
Baum
Michael
M
Pfingsten
Jan
J
Hildebrandt
Heinz-Dieter
HD
Basler
Michael M
MM
Kochen
Norbert
N
Donner-Banzhoff
Annette
A
Becker
D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D016000:Cluster Analysis; D004651:Employment; D005260:Female; D006801:Humans; D017116:Low Back Pain / Q000145:classification / Q000503:physiopathology* / Q000523:psychology* / Q000628:therapy*; D008297:Male; D008875:Middle Aged; D010147:Pain Measurement; D010342:Patient Acceptance of Health Care / Q000706:statistics & numerical data*; D011320:Primary Health Care; D011788:Quality of Life; D055815:Young Adult