Case-mix adjustment for diabetes indicators: a systematic review.

OBJECTIVES

Case-mix adjustment is generally considered indispensable for fair comparison of healthcare performance. Inaccurate results are also unfair to patients as they are ineffective for improving quality. However, little is known about what factors should be adjusted for. We reviewed case-mix factors included in adjustment models for key diabetes indicators, the rationale for their inclusion, and their impact on performance.

STUDY DESIGN

Systematic review.

METHODS

This systematic review included studies published up to June 2013 addressing case-mix factors for 6 key diabetes indicators: 2 outcomes and 2 process indicators for glycated hemoglobin (A1C), low-density lipoprotein cholesterol, and blood pressure. Factors were categorized as demographic, diabetes-related, comorbidity, generic health, geographic, or care-seeking, and were evaluated on the rationale for inclusion in the adjustment models, as well as their impact on indicator scores and ranking.

RESULTS

Thirteen studies were included, mainly addressing A1C value and measurement. Twenty-three different case-mix factors, mostly demographic and diabetes-related, were identified, and varied from 1 to 14 per adjustment model. Six studies provided selection motives for the inclusion of case-mix factors. Marital status and body mass index showed a significant impact on A1C value. For the other factors, either no or conflicting associations were reported, or too few studies (n ≤ 2) investigated this association.

CONCLUSIONS

Scientific knowledge about the relative importance of case-mix factors for diabetes indicators is emerging, especially for demographic and diabetes-related factors and indicators on A1C, but is still limited. Because arbitrary adjustment potentially results in inaccurate quality information, meaningful stratification that demonstrates inequity in care might be a better guide, as it can be a driver for quality improvement.