Pain epidemiology insights

Fiona M. Blyth^1,2

¹University of Sydney Pain Management research Institute at Royal North Shore Hospital, Sydney, Australia;
²School of Public Health, University of Sydney, Australia

Models of care, in a perfect world, would be delivered to well-defined groups of patients with clearly identified potential to benefit from effective, evidence-based interventions. Epidemiological studies of pain have descriptive and/or analytic functions that can help inform models of care for different populations. Using examples and concepts from the pain epidemiology and wider epidemiological literature, I will outline some of the major issues that remain in the quest to improve models of care.

In a population setting, epidemiological data of chronic pain quantifies the extent of the problem (how much?) and its distribution within populations (who gets it?), the levels of unmet need (are there people who need help who don't receive it?) and poorly controlled pain, and other impacts of chronic pain at a population level, such as which health care resources get used. These are probably thought of as worthy but not particularly exciting. However, this information is essential to attempts to wrest funding dollars from better-entrenched health priority areas. Population-based studies also help identify certain models of care (self-care and use of complementary and alternative care) that are sometimes poorly characterised.

Risk factor identification, another core function of epidemiology, can also help inform models of care by (i) identifying those at high risk of poor outcomes and (ii) narrowing down a range of potential intervention targets. Modern software makes it easy to calculate measures of excess risk, but use of these is next to meaningless when disconnected from an understanding of pain (for example, having an underpinning conceptual model - currently the biopsychosocial model), the complex and murky reality of multifactorial risk, and some basic principles of population science ^(1,2,3). For these and other reasons, efforts to develop prediction algorithms or identify high risk groups tend to fall short of their early promise. New approaches may be needed ⁽⁴⁾

Models of care for chronic pain often (if not always) involve complex interventions. These interventions themselves often have a patchy evidence base, partly reflecting a disconnect between clinical trial methods developed for monotherapies and the increasing need for complex interventions for chronic diseases. The origins of modern clinical trials can be traced back over half a century ⁽⁵⁾ but it is only recently that the unique problems of evaluating complex interventions have drawn attention ^(6,7).

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3. Rose G. Int J Epidemiol 1985;14:32-38.
4. Smith BH, Macfarlane GJ, Torrance N. Pain 2007;127:5-10.
5. Doll R. BMJ 1998; 317: 1217-20.
6. Campbell M, Fitzpatrick R, Haines A et al.BMJ 2000;321:694-6.
7. Hawe P, Shiell A, Riley T. BMJ 2004;328:1561-3.