The adherence paradox : guideline deviations contribute to the increased 5-year survival of breast cancer patients

Background: In German breast cancer care, the S1-guidelines of the 1990s were substituted by national S3-guidelines in 2003. The application of guidelines became mandatory for certified breast cancer centers. The aim of the study was to assess guideline adherence according to time intervals and its impact on survival. Methods: Women with primary breast cancer treated in three rural hospitals of one German geographical district were included. A cohort study design encompassed women from 1996–97 (N = 389) and from 2003–04 (N = 488). Quality indicators were defined along inpatient therapy sequences for each time interval and distinguished as guideline-adherent and guideline-divergent medical decisions. Based on all of the quality indicators, a binary overall adherence index was defined and served as a group indicator in multivariate Cox-regression models. A corrected group analysis estimated adjusted 5-year survival curves. Results: From a total of 877 patients, 743 (85 %) and 504 (58 %) were included to assess 104 developed quality indicators and the resuming binary overall adherence index. The latter significantly increased from 13–15 % (1996–97) up to 33–35 % (2003–04). Within each time interval, no significant survival differences of guideline-adherent and -divergent treated patients were detected. Across time intervals and within the group of guideline-adherent treated patients only, survival increased but did not significantly differ between time intervals. Across time intervals and within the group of guideline-divergent treated patients only, survival increased and significantly differed between time intervals. Conclusions: Infrastructural efforts contributed to the increase of process quality of the examined certified breast cancer center. Paradoxically, a systematic impact on 5-year survival has been observed for patients treated divergently from the guideline recommendations. This is an indicator for the appropriate application of guidelines. A maximization of guideline-based decisions instead of the ubiquitous demand of guideline adherence maximization is advocated

Valid comparisons and decisions based on clinical registers and population based cohort studies: assessing the accuracy, completeness and epidemiological relevance of a breast cancer query database

Abstract Background Data accuracy and completeness are crucial for ensuring both the correctness and epidemiological relevance of a given data set. In this study we evaluated a clinical register in the administrative district of Marburg-Biedenkopf, Germany, for these criteria. Methods The register contained data gathered from a comprehensive integrated breast-cancer network from three hospitals that treated all included incident cases of malignant breast cancer in two distinct time periods from 1996–97 (N=389) and 2003–04 (N=488). To assess the accuracy of this data, we compared distributions of risk, prognostic, and predictive factors with distributions from established secondary databases to detect any deviations from these “true” population parameters. To evaluate data completeness, we calculated epidemiological standard measures as well as incidence-mortality-ratios (IMRs). Results In total, 12% (13 of 109) of the variables exhibited inaccuracies: 9% (5 out of 56) in 1996–97 and 15% (8 out of 53) in 2003–04. In contrast to raw, unstandardized incidence rates, (in-) directly age-standardized incidence rates showed no systematic deviations. Our final completeness estimates were IMR=36% (1996–97) and IMR=43% (2003–04). Conclusion Overall, the register contained accurate, complete, and correct data. Regional differences accounted for detected inaccuracies. Demographic shifts occurred. Age-standardized measures indicate an acceptable degree of completeness. The IMR method of measuring completeness was inappropriate for incidence-based data registers. For the rising number of population-based health-care networks, further methodological advancements are necessary. Correct and epidemiologically relevant data are crucial for clinical and health-policy decision-making.</p

Valid comparisons and decisions based on clinical registers and population based cohort studies: assessing the accuracy, completeness and epidemiological relevance of a breast cancer query database

BACKGROUND: Data accuracy and completeness are crucial for ensuring both the correctness and epidemiological relevance of a given data set. In this study we evaluated a clinical register in the administrative district of Marburg-Biedenkopf, Germany, for these criteria. METHODS: The register contained data gathered from a comprehensive integrated breast-cancer network from three hospitals that treated all included incident cases of malignant breast cancer in two distinct time periods from 1996–97 (N=389) and 2003–04 (N=488). To assess the accuracy of this data, we compared distributions of risk, prognostic, and predictive factors with distributions from established secondary databases to detect any deviations from these “true” population parameters. To evaluate data completeness, we calculated epidemiological standard measures as well as incidence-mortality-ratios (IMRs). RESULTS: In total, 12% (13 of 109) of the variables exhibited inaccuracies: 9% (5 out of 56) in 1996–97 and 15% (8 out of 53) in 2003–04. In contrast to raw, unstandardized incidence rates, (in-) directly age-standardized incidence rates showed no systematic deviations. Our final completeness estimates were IMR=36% (1996–97) and IMR=43% (2003–04). CONCLUSION: Overall, the register contained accurate, complete, and correct data. Regional differences accounted for detected inaccuracies. Demographic shifts occurred. Age-standardized measures indicate an acceptable degree of completeness. The IMR method of measuring completeness was inappropriate for incidence-based data registers. For the rising number of population-based health-care networks, further methodological advancements are necessary. Correct and epidemiologically relevant data are crucial for clinical and health-policy decision-making