Do individual or area-level measurements of social determinants of health perform better at predicting chronic pain?
1
School of Medicine, Keele University, Staffordshire, United Kingdom
Popul. Med. 2026;8(Supplement Supplement 1):A1313
ABSTRACT
ABSTRACT:
Social risk factors are strongly linked to poor health, 1,2. Their measurement is crucial for efficiently guiding services and resources that improve patient and population health. The absence of individual social risk measures in electronic health records (EHRs) has led to the routine use of area-based measures as proxies for individual measures. However, this may misclassify individual social circumstances, 3,4. This study aims to assess the agreement between area- and individual-level social risk measures and to compare their predictive performance of chronic pain (CP) and high-impact chronic pain (HICP). Using the MIDAS population survey of adults aged 35 and over in North Staffordshire, England, we compared several area-based measures (IMDs) and individual indicators (income adequacy, education, food poverty, food bank usage, housing instability, poor housing quality, utility need, transport poverty, emotional support, loneliness, community participation and a derived composite SDoH index). Their agreement was assessed using Gwet inter-rater agreement. Multilevel models of area- and individual-level SDoH measures were compared. Model performance was assessed using AUC for discrimination and the Brier Skill Score for overall predictive accuracy. Of a sample of 2519 respondents, the prevalence of CP and HICP was 41.2% and 22.6%, respectively. The agreement between the IMD measures and individual measures was generally low. Individual composite SDoH index has better discriminative ability of CP than IMDs. Income adequacy and the composite SDoH index improved discrimination for HICP compared with the IMD measures. Overall predictive performance based on the Barier Skill Score, individual composite SDoH index, income adequacy, education, loneliness, transport poverty, and food poverty improved CP and HICP prediction compared with IMD measures. This study suggests a cautious use of area measures as proxies in the absence of individual-level measures. The study emphasises the need for individual measures in EHRs to improve disparity research and patient risk stratification.
Social risk factors are strongly linked to poor health, 1,2. Their measurement is crucial for efficiently guiding services and resources that improve patient and population health. The absence of individual social risk measures in electronic health records (EHRs) has led to the routine use of area-based measures as proxies for individual measures. However, this may misclassify individual social circumstances, 3,4. This study aims to assess the agreement between area- and individual-level social risk measures and to compare their predictive performance of chronic pain (CP) and high-impact chronic pain (HICP). Using the MIDAS population survey of adults aged 35 and over in North Staffordshire, England, we compared several area-based measures (IMDs) and individual indicators (income adequacy, education, food poverty, food bank usage, housing instability, poor housing quality, utility need, transport poverty, emotional support, loneliness, community participation and a derived composite SDoH index). Their agreement was assessed using Gwet inter-rater agreement. Multilevel models of area- and individual-level SDoH measures were compared. Model performance was assessed using AUC for discrimination and the Brier Skill Score for overall predictive accuracy. Of a sample of 2519 respondents, the prevalence of CP and HICP was 41.2% and 22.6%, respectively. The agreement between the IMD measures and individual measures was generally low. Individual composite SDoH index has better discriminative ability of CP than IMDs. Income adequacy and the composite SDoH index improved discrimination for HICP compared with the IMD measures. Overall predictive performance based on the Barier Skill Score, individual composite SDoH index, income adequacy, education, loneliness, transport poverty, and food poverty improved CP and HICP prediction compared with IMD measures. This study suggests a cautious use of area measures as proxies in the absence of individual-level measures. The study emphasises the need for individual measures in EHRs to improve disparity research and patient risk stratification.
| eISSN: | 2654-1459 |
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