Where does it hurt? Small area estimates and inequality in the prevalence of chronic pain

Abstract

Background: Chronic pain affects up to half of UK adults, impacting quality of life and demand on local health services. Whilst local health planning is currently based on subnational prevalence estimates, associations between pain and sociodemographic characteristics suggest that inequalities in the prevalence of chronic and high-impact chronic pain between neighbourhoods within local authorities are likely. We aimed to derive lower super output area (LSOA) estimates of the prevalence of chronic and high-impact chronic pain.

Methods: Presence of self-reported chronic and high-impact chronic pain were measured in adults aged 35+ in North Staffordshire and modelled using multilevel regression as a function of demographic and geographic predictors. Multilevel model predictions were post-stratified using the North Staffordshire age-sex population structure and LSOA demographic characteristics to estimate the prevalence of chronic and high-impact chronic pain in 298 LSOAs, corrected for ethnic diversity underrepresented in the data. Confidence intervals were generated for high-impact chronic pain using bootstrapping.

Results: Data were analysed from 4162 survey respondents (2358 women, 1804 men). The estimated prevalence of chronic and high-impact chronic pain in North Staffordshire LSOAs ranged from 18.6% to 50.1% and 6.18 [1.71, 16.0]% to 33.09 [13.3, 44.7]%, respectively.

Conclusions: Prevalence of chronic and high-impact chronic pain in adults aged 35+ varies substantially between neighbourhoods within local authorities. Further insight into small-area level variation will help target resources to improve the management and prevention of chronic and high-impact chronic pain to reduce the impact on individuals, communities, workplaces, services and the economy.

Significance: Post-stratified multilevel model predictions can produce small-area estimates of pain prevalence and impact. The evidence of substantial variation indicates a need to collect local-level data on pain and its impact to understand health needs and to guide interventions.