Case-mix adjustments for patient reported experience and outcome measures in primary care: an empirical approach to identify patient characteristics as case-mix adjusters based on a secondary analysis of an international survey among patients and their general practitioners in 34 countries

Journal of Patient-Reported Outcomes

Table 4 Summary of the analyses for the dependent variable Comprehensiveness of care (PREM), multilevel linear regression

Potential case-mix variable	Fixed effect significant (y/n)	Slope effect GP level important* (y/n)	Slope effect country level important (y/n)	Case-mix control (y/n)
Self-reported general health	Yes Worse self-reported health → more experienced comprehensiveness	No	No	Yes
Longstanding disease	Yes Longstanding disease → more experienced comprehensiveness	No	No	Yes
Patient’s age	Yes Older than 40 → better experienced comprehensiveness	No	No	Yes
Patient’s sex	No	No	No	No
Education	Yes Higher education → less experienced comprehensiveness	No	No	Yes
Income	Yes Higher income → less experienced comprehensiveness	No	No	Yes
Migrant status	Yes Second generation migrants → less experienced comprehensiveness	No	No	Yes
Place of living	Yes In mixed urban–rural and rural areas → more experienced comprehensiveness	No	No	Yes

^*Important means that the difference in variance between categories is more that 0.25*variance in the model with fixed effect and random intercept