Using relative survival measures for cross-sectional and longitudinal benchmarks of countries, states, and districts: the BenchRelSurv- and BenchRelSurvPlot-macros
1 Central Institute of Mental Health, Medical Faculty Mannheim/University Heidelberg, Square J5, 68159 Mannheim, Germany
2 Department of Gynecology, Gynecological Endocrinology and Oncology, Breast Center Regio, University of Marburg, Marburg, Germany
BMC Public Health 2013, 13:34 doi:10.1186/1471-2458-13-34Published: 14 January 2013
The objective of screening programs is to discover life threatening diseases in as many patients as early as possible and to increase the chance of survival. To be able to compare aspects of health care quality, methods are needed for benchmarking that allow comparisons on various health care levels (regional, national, and international).
Applications and extensions of algorithms can be used to link the information on disease phases with relative survival rates and to consolidate them in composite measures. The application of the developed SAS-macros will give results for benchmarking of health care quality. Data examples for breast cancer care are given.
A reference scale (expected, E) must be defined at a time point at which all benchmark objects (observed, O) are measured. All indices are defined as O/E, whereby the extended standardized screening-index (eSSI), the standardized case-mix-index (SCI), the work-up-index (SWI), and the treatment-index (STI) address different health care aspects. The composite measures called overall-performance evaluation (OPE) and relative overall performance indices (ROPI) link the individual indices differently for cross-sectional or longitudinal analyses.
Algorithms allow a time point and a time interval associated comparison of the benchmark objects in the indices eSSI, SCI, SWI, STI, OPE, and ROPI. Comparisons between countries, states and districts are possible. Exemplarily comparisons between two countries are made. The success of early detection and screening programs as well as clinical health care quality for breast cancer can be demonstrated while the population’s background mortality is concerned.
If external quality assurance programs and benchmark objects are based on population-based and corresponding demographic data, information of disease phase and relative survival rates can be combined to indices which offer approaches for comparative analyses between benchmark objects. Conclusions on screening programs and health care quality are possible. The macros can be transferred to other diseases if a disease-specific phase scale of prognostic value (e.g. stage) exists.