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Additional file 1: Figure S1. Flowchart of sampling selection for independent variable BMI. Figure S2. Flowchart of sampling selection for independent variable tobacco consumption. Figure S3. Flowchart of sampling selection for independent variable physical activity. Figure S4. Flowchart of sampling selection for independent variable ageing. Table S1. List of variables for 2014 IFLS analysis. Table S2. Sample characteristics stratified by age groups, before matching. Table S3. Sample characteristics stratified by tobacco consumption groups, before matching. Table S4. Sample characteristics stratified by BMI groups, before matching. Table S5. Sample characteristics stratified by physical activity (PA) groups, before matching. Table S6. Mean biases of covariates after matching using individual t-test (age group 50-59 vs 40-49). Table S7. Mean biases of covariates after matching using individual t-test (age group 60-69 vs 40-49). Table S8. Mean biases of covariates after matching using individual t-test (age group 70+ vs 40-49). Table S9. Mean biases of covariates after matching using individual t-test (former vs never use tobacco). Table S10. Mean biases of covariates after matching using individual t-test (light user vs never use tobacco). Table S11. Mean biases of covariates after matching using individual t-test (moderate user vs never use tobacco). Table S12. Mean biases of covariates after matching using individual t-test (heavy user vs never use tobacco). Table S13. Mean biases of covariates after matching using individual t-test (overweight vs normal BMI). Table S14. Mean biases of covariates after matching using individual t-test (obesity vs normal BMI). Table S15. Mean biases of covariates after matching using individual t-test (underweight vs normal BMI). Table S16. Mean biases of covariates after matching using individual t-test (low vs high physical activity. Table S17. Mean biases of covariates after matching using individual t-test (moderate vs high physical activity). Table S18. The ATT of the number of chronic conditions across different matching algorithms. Table S19. The ATT of multimorbidity presence across different matching algorithms. Table S20. The ATT of the number of outpatient visits across different matching algorithms. Table S21. The ATT of the number of inpatient visits across different matching algorithms. Table S22. The ATT of CHE >25% of total household expenditure across different matching algorithms. Table S23. The ATT of CHE >40% of total non-food expenditure across different matching algorithms. Table S24. The ATT of labour participation across different matching algorithms. Table S25. The ATT of the number of days primary activity missed across different matching algorithms. Table S26. Logistic and ZINB regression for the number of chronic condition and presence of multimorbidity. Table S27. Logistic and ZINB regression for the number of outpatient and inpatient visits. Table S28. Logistic regression for CHE >25% and 40%. Table S29. Logistic and ZINB regression for the productivity loss.

Additional file 3: Table S2. Joint associations of physical activity and sleep with multimorbidity.

Additional file 3. Statistic summary of disability, work productivity and social participation by physical and mental conditions and gender. Table A2 presents the unadjusted mean or proportion of disability and productivity outcomes by type of multimorbidity.

Additional file 2. Statistic summary of sample characteristics. Table A1 presents the sample characteristics of the analytical sample in this study.

Additional file 5. Sensitivity analyses on the association between multimorbidity and productivity loss among different age groups. Table A4 presents the results of the sensitivity analyses on the association between multimorbidity and productivity loss using different samples. We repeated the analysis using a sample of respondents aged under 55 years, which is the mandatory retirement age for female white-collar workers. In addition, we used a cut-off at 65 years, given the possibilities that people may continue working after the mandatory retirement age.

Additional file 4. Association between multimorbidity and disability, work productivity and social participation, models including interactions between physical conditions and depression. Table A3 presents the results from the model that included interactions between number of physical chronic conditions and depression.

Additional file 4: Table S3. Unadjusted results of regression analyses.

Additional file 1: Appendix 1. Sample flowchart. Appendix 2. List of variables for 2014 IFLS-5 analysis. Appendix 3. Sample characteristics of respondents with multimorbidity. Appendix 4. The incremental outpatient visits by population quantile. Appendix 5. The incremental inpatient visits by population quantile. Appendix 6. The incremental out-of-pocket expenditure by population quantile. Appendix 7. Predictive four-weekly OOPE by number of NCDs. Appendix 8. Average number of outpatient visit, inpatient visits, and four-weekly OOPE by population quantile.

Additional file 2: Table S1. Characteristics of participants in different physical activity and sleep group.

Additional file 1: Figure S1. Flowchart showing the selection of the subjects who were included in the final analysis, 2015.

Additional file 1: Table S2. Indicators for health system performance in rural and urban China. Figure S1. Sample flowchart for all outcome variables