Summary table
| Author (year) | Country/population | Study design | Population size (N) | SDH reported(significant OR (S), non-significant OR (NS), or no reported OR (NR)) | Quality score (out of 9) | Strength of association |
| Bath (2018)15 | Canada/Chronic Pain | Cross-sectional survey | 113 647 | Gender (S), race/ethnicity (S), education (S), environment (S), SES (S) | 5 | Moderate |
| Bell (2017)17 | USA/Neuro | Retrospective cohort study | 243 | Transportation (S) | 7 | Moderate |
| Bawa (2016)40 | USA/MSK | Retrospective cohort study | 244 059 | Gender (S) | 6 | Moderate |
| Belau (2018)16 | Germany/MSK | Secondary analysis of longitudinal survey data | 889 | Employment(S), environment (S) | 7 | Moderate |
| Cant (2011)18 | Australia/Chronic Pain | Retrospective cohort study | 1 880 477 | Environment (NR) | 6 | Moderate |
| Carter (2007)19 | USA/MSK | Retrospective cohort study | 18 546 | Gender (NS), race/ethnicity (S), education (S), environment(S), insurance (S) | 4 | Moderate Poor (gender only) |
| Chan (2009)20 | USA/Neuro | Retrospective cohort study | 11 119 | Gender (S), race/ethnicity (S), environment (S), SES (S) | 6 | Moderate |
| Chevan (2011)21 | USA/Neuro | Secondary analysis of longitudinal survey data | 2352 | Gender (S), race/ethnicity (NS), education (S), environment (NS), SES (S) | 6 | Moderate Poor (race/ethnicity, environment) |
| Christansen (2016) | Denmark/MSK | Register-based cohort study | 57 311 | Gender (S), education (NS), environment (S), insurance (S) | 6 | Moderate Poor (education only) |
| Chun Fat (2019)25 | USA/MSK | Secondary analysis of longitudinal survey data | 769 | Race/ethnicity (NS) | 6 | Poor |
| Cisternas (2009)26 | USA/MSK | Secondary analysis of longitudinal survey data | 9933 | Gender (S), race/ethnicity (S), education (S), environment (S), insurance (S) | 4 | Moderate |
| Dahodwala (2009)27 | USA/Neuro | Retrospective cohort study | 307 | Gender (NS), race/ethnicity (S), environment (NS) | 6 | Limited (race/ethnicity) Poor (gender, environment) |
| Dee (2019)28 | USA/ Unspecified | Cross-sectional study | 1183 | Race/ethnicity (S), environment (NS) | 5 | Moderate (race/ethnicity) Poor (environment) |
| Denktas (2009)41 | Netherlands/ Unspecified | Cross-sectional study | 3284 | Gender (S), race/ethnicity (NS), education (NS), SES(S) | 5 | Moderate (gender, SES) Poor (race/ethnicity, education) |
| Dolot (2020)22 | USA/MSK | Retrospective observational cohort study | 7244 | Gender (NS), insurance (S) | 6 | Moderate (insurance) Poor (gender) |
| Freburger, Holmes (2005)44 | USA/ Unspecified | Cross-sectional study | 20 227 | Gender (NS), race/ethnicity (NS), education (S), environment (NS), SES (S), insurance (S) | 6 | Moderate (education, SES, insurance) Poor (gender, race/ethnicity, environment) |
| Freburger, Carey, Holmes (2005)42 | USA/MSK | Retrospective cohort study | 29 049 | Gender (S), race/ethnicity (NS), education (S), environment (S) | 5 | Moderate (gender, education, environment) Poor (race/ethnicity) |
| Freburger (2011)43 | USA/MSK | Retrospective cohort study | 588 | Gender (NS), race/ethnicity (NS), education (NS), insurance (S) | 5 | Limited (insurance) Poor (gender, race/ethnicity, education) |
| Freburger (2018)29 | USA/Neuro | Retrospective cohort study | 23 413 | Race/ethnicity (S), SES(S) | 7 | Strong |
| Fullard (2017)45 | USA/Neuro | Retrospective cohort study | 174 643 | Gender (S), race/ethnicity (S) | 6 | Moderate |
| Gell (2017)46 | USA/ Unspecified | Cross-sectional study | 7487 | Gender (S), race/ethnicity (S), education (S) | 4 | Moderate |
| Goode (2013)47 | USA/MSK | Cross-sectional study | 588 | Gender (NS), race/ethnicity (S), environment (NS) | 5 | Limited (race/ethnicity) Poor (gender, environment) |
| Keeney (2017)30 | USA/ Unspecified | Secondary analysis of longitudinal survey data | 1276 | Gender (S), race/ethnicity (S), education (S), environment (NS), SES(S), insurance (S), transportation (S) | 6 | Moderate Poor (environment) |
| Lin (2008)31 | Australia/MSK | Longitudinal observational study | 92 | Gender (NS), SES (NS), insurance (NS) | 8 | No evidence |
| Machlin (2011)32 | USA/ Unspecified | Secondary analysis of longitudinal survey data | 1377 | Gender (S), race/ethnicity (NS), education (NS), environment (S), SES (NS), insurance (NS) | 5 | Moderate (gender, environment) Poor (race/ethnicity, education, SES, insurance) |
| Mbada (2019)33 | Nigeria/ Unspecified | Cross-sectional study | 336 | Gender (NS), race/ethnicity (NS), education (S), employment (NS), transportation (S) | 3 | Limited (education, transportation) Poor (gender, race/ethnicity, employment) |
| Rogers (2018)35 | USA/ Unspecified | Cross-sectional study | 139 | Insurance (S) | 3 | Limited |
| Rogers (2019)34 | USA/MSK | Cross-sectional study | 138 | Insurance (S) | 3 | Limited |
| Sandstrom (2017)36 | USA/ Unspecified | Secondary analysis of longitudinal survey data | 13.2 million | Gender (NR), race/ethnicity (NR), environment (NR), SES (NR) | 4 | Moderate |
| Sandstrom (2019)4 | USA/ Unspecified | Secondary analysis of longitudinal survey data | 58.5 million | Gender (NR), race/ethnicity (NR), environment (NR), SES (NR), insurance (S) | 4 | Moderate |
| Sandstrom, Bruns (2017)37 | USA/MSK | Secondary analysis of longitudinal survey data | 55.8 million | Race/ethnicity (S), education (S), SES (S), insurance (S) | 6 | Moderate |
| Shah (2019)14 | USA/MSK | Secondary analysis of longitudinal survey data | 716 463 | Environment (NR) | 4 | Moderate |
| Schubert (2011)38 | Germany/ Unspecified | Secondary analysis of longitudinal survey data | 19 164 | Gender (S), employment (S) | 4 | Moderate |
| Tsuchiya-Ito (2020) | Japan/unspecified | Secondary analysis of longitudinal survey data | 3770 | Gender (S) SES (S) | 6 | Moderate |
| Verhagen (2014)48 | Netherlands/MSK | Retrospective cohort study | 68 214 | Race/ethnicity (S) | 4 | Moderate |
| Washington (2011)39 | USA/MSK | Cross-sectional study | 104 | Insurance (S) | 4 | Limited |