OBJECTIVES:  In BB-type selection bias all study groups include patients. BB can result to spurious associations and results in comparative settings. In HRQoL "burden comparisons", MGPC is often based on age and gender alone, if any. Furthermore, commonly available specialist care HRQoL data may generalize poorly to primary health care setting (PHCS) and may not include baseline values for patients entering the system. Thus, real-world evidence (RWE) generation for HRQoL benchmarking is difficult. Our objectives were to develop MAIC, predict MGPC based on the MAIC, estimate PTB and assess UMN; all at individual PHCS level. METHODS:  A MAIC-based MGPC was developed using representative Finnish general population RWE (N ~5144) to estimate MGPC HRQoL ("reference value") for unselected patients participating in Effective Health Centre Study (N=511, three health centres, Pirkanmaa, Finland). Both studies used EQ-5D-3L with British preferences, and included age, sex, income, education, and comorbidities for the MAIC. PTB was estimated by subtracting the baseline EQ-5D-3L scores from the estimated MGPC scores. UMN (potential residual disutility) was estimated by subtracting the EQ-5D-3L scores at 3-month follow-up from the estimated MGPC scores. RESULTS:  Mean baseline EQ-5D-3L scores for PHCS patients and MGPC were 0.74 and 0.81, respectively, demonstrating a significant and clinically important PTB (0.07; 95%CI 0.06-0.09), "medical need". Most patient groups demonstrated significant mean PTB. The mean PTB was highest for ICPC-2 chapter L: Musculoskeletal and lowest for S: Skin. The mean scores at three months for PHCS patients and MGPC were 0.79 and 0.81, respectively, demonstrating an insignificant UMN of 0.02 (95%CI 0.00-0.04). The mean UMN was highest among patients with ICPC-2 chapter N: Neurological or for patients with asthma comorbidity. The developed MAIC-based MGPC outperformed age- and sex-matched MGPC. CONCLUSIONS: BB and all time-specific measurements and their development can be taken into account in the MAIC-based MGPC, PTB and UMN HRQoL benchmarking.