Using relative survival measures for cross-sectional and longitudinal benchmarks of countries, states, and districts: the BenchRelSurv- and BenchRelSurvPlot-macros

BMC Public Health

Ute S Albert¹  Iris Reinhard²  Christian O Jacke² 

[1] Department of Gynecology, Gynecological Endocrinology and Oncology, Breast Center Regio, University of Marburg, Marburg, Germany;Central Institute of Mental Health, Medical Faculty Mannheim/University Heidelberg, Square J5, 68159 Mannheim, Germany

关键词: Breast cancer;    Registries;    Relative survival;    Outcome assessment;    control;    Prevention &;    Benchmark*;   

Others  :  1162645
DOI  :  10.1186/1471-2458-13-34

 received in 2012-07-27, accepted in 2012-12-19,  发布年份 2013

【 摘 要 】

Background

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).

Objectives

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.

Methods

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.

Results

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.

Conclusions

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.

【 授权许可】

   
2013 Jacke et al.; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

Figure 2.

【 参考文献 】

• [1]Rosenberg RD, Yankaskas BC, Abraham LA, Sickles EA, Lehman CD, Geller BM, Carney PA, Kerlikowske K, Buist DSM, Weaver DL, Barlow WE, Ballard-Barbash R: Performance benchmarks for screening mammography. Radiology 2006, 241:55-66.
• [2]Sickles EA, Miglioretti DL, Ballard-Barbash R, Geller BM, Leung JWT, Rosenberg RD, Smith-Bindman R, Yankaskas BC: Performance benchmarks for diagnostic mammography. Radiology 2005, 235:775-790.
• [3]Pierce LJ, Moughan J, White J, Winchester DP, Owen J, Wilson JF: 1998–1999 patterns of care study process survey of national practice patterns using breast-conserving surgery and radiotherapy in the management of stage I-II breast cancer. Int J Radiat Oncol Biol Phys 2005, 62:183-192.
• [4]O'Brien MER, Borthwick A, Rigg A, Leary A, Assersohn L, Last K, Tan S, Milan S, Tait D, Smith IE: Mortality within 30 days of chemotherapy: a clinical governance benchmarking issue for oncology patients. Br J Cancer 2006, 95:1632-1636.
• [5]Kerba M, Miao Q, Zhang-Salomons J, Mackillop W: Defining the need for breast cancer radiotherapy in the general population: a criterion-based benchmarking approach. Clin Oncol (R Coll Radiol) 2007, 19:481-489.
• [6]Beatty J, Rees J, Atwood M, Pugliese M, Bolejack V: Standardized evaluation of regional and institutional breast cancer outcomes. http://www.facs.org/cancer/coc/pdf/beatty_breast-outcomes.pdf?bcsi_scan__58A52892D4FFF3EB=0&bcsi_scan_filename=beatty_breast-outcomes.pdf webcite
• [7]Beatty J, Rees J, Atwood M, Pugliese M, Bolejack V: Standardized evaluation of regional and institutional breast cancer outcomes. Am J Surg 2008, 195:636-640.
• [8]Gooley TA, Leisenring W, Crowley J, Storer BA: Estimation of failure probabilities in the presence of competing risks. New representations of old estimators. Statist Med 1999, 18:695-706.
• [9]Jacke C, Kalder M, Koller M, Wagner U, Albert U: Systematic assessment and improvement of medical data quality. Bundesgesundheitsbl - Gesundheitsforsch - Gesundheitsschutz 2012, 55:1495-1503.
• [10]Stausberg J, Nonnemacher M, Weiland D, Antony G, Neuhäuser M: Management of Data Quality. Development of a Computer-Mediated Guideline. Stud Health Technol Inform 2006, 477-482.
• [11]Nonnemacher M, Weiland D, Stausberg J: Datenqualität in der medizinischen Forschung: Leitlinie zum adaptiven Management von Datenqualität in Kohortenstudien und Registern. Berlin: Med.-Wiss. Verl.-Ges; 2007.
• [12]Surveillance, Epidemiology, and End Results (SEER) Program (www.seer.cancer.gov) SEER*Stat Database: Incidence - SEER 18 Regs Research Data + Hurricane Katrina Impacted Louisiana Cases, Nov 2011 Sub (1973–2009 varying). Linked To County Attributes - Total U.S., 1969–2010 Counties, National Cancer Institute, DCCPS, Surveillance Research Program, Surveillance Systems Branch, released April 2012, based on the November 2011. submission
• [13]Cancer Registry of Norway: Cancer in Norway 2008: Cancer Incidence, Mortality, Survival and Prevalence in Norway. Oslo: Cancer Registry of Norway; 2009.
• [14]Therneau T, Grambsch P: Modeling Survival Data: Extending the Cox Model. New York: Springer; 2001.
• [15]Dickman P, Sloggett A, Hills M, Hakulinen T: Regression models for relative survival. Stat Med 2004, 23:51-64.
• [16]Pohar M, Stare J: Making relative survival analysis relatively easy. Comput Biol Med 2007, 37:1741-1749.
• [17]Hakulinen T: Cancer survival corrected for heterogeneity in patient withdrawal. Biometrics 1982, 38:933-942.
• [18]Hakulinen T, Tenkanen L: Regression analysis of relative survival rates. J R Stat Soc Ser C Appl Stat 1987, 36:309-317.
• [19]Brenner H: Long-term survival rates of cancer patients achieved by the end of the 20th century. A period analysis. Lancet 2002, 360:1131-1135.
• [20]Brenner H, Gefeller O: An alternative approach to monitoring cancer patient survival. Cancer 1996, 78:2004-2010.
• [21]Brenner H, Hakulinen T, Gefeller O: Computational realization of period analysis for monitoring cancer patient survival. Epidemiology 2002, 13:611-612.
• [22]Geiss K, Meyer M, Radespiel-Tröger M, Gefeller O: SURVSOFT-Software for nonparametric survival analysis. Comput Methods Programs Biomed 2009, 96:63-71.
• [23]Sariego J: Regional variation in breast cancer treatment throughout the United States. Am J Surg 2008, 196:572-574.
• [24]Sariego J: Patterns of breast cancer presentation in the United States: does geography matter? Am Surg 2009, 75:545-9.
• [25]Brucker SY, Schumacher C, Sohn C, Rezai M, Bamberg M, Wallwiener D: Benchmarking the quality of breast cancer care in a nationwide voluntary system: the first five-year results (2003–2007) from Germany as a proof of concept. BMC Cancer 2008, 8:358. BioMed Central Full Text
• [26]Brucker SY, Wallwiener M, Kreienberg R, Jonat W, Beckmann MW, Bamberg M, Wallwiener D, Souchon R: Optimizing the quality of breast cancer care at certified german breast centers: a benchmarking analysis for 2003–2009 with a particular focus on the interdisciplinary specialty of radiation oncology. Strahlenther Onkol 2011, 187:89-99.
• [27]Wallwiener M, Brucker SY, Wallwiener D: Multidisciplinary breast centres in Germany: a review and update of quality assurance through benchmarking and certification. Arch Gynecol Obstet 2012, 285:1671-83.
• [28]Daroui P, Gabel M, Khan AJ, Haffty BG, Goyal S: Utilization of breast conserving therapy in stages 0, I, and II breast cancer patients in New Jersey: an American College of Surgeons National Cancer Data Base (NCDB) analysis. Am J Clin Oncol 2012, 35:130-135.
• [29]Ng W, Delaney GP, Jacob S, Barton MB: Estimation of an optimal chemotherapy utilisation rate for breast cancer: setting an evidence-based benchmark for the best-quality cancer care. Eur J Cancer 2010, 46:703-712.
• [30]El-Tamer MB, Ward BM, Schifftner T, Neumayer L, Khuri S, Henderson W: Morbidity and mortality following breast cancer surgery in women. National benchmarks for standards of care. Ann Surg 2007, 245:665-671.
• [31]Dinkel R: Die Berechnung des Parameters „Relative Survival“für ein Tumorregister mit regionalem Einzugsbereich. http://www.krebsregister.uni-rostock.de/docs/Relative%20Survival.pdf webcite
• [32]Coleman M, Babb P, Damiecki P, Grosclaude P, Honjo S, Jones J, Knerer G, Pitard A, Quinn M, Sloggett A, de Stavola B: Cancer survival trends in England and Wales 1971–1995: deprivation and NHS Region. London: The Stationery Office; 1999. [Series SMPS, vol. 61]
• [33]Coleman MP, Forman D, Bryant H, Butler J, Rachet B, Maringe C, Nur U, Tracey E, Coory M, Hatcher J, McGahan CE, Turner D, Marrett L, Gjerstorff ML, Johannesen TB, Adolfsson J, Lambe M, Lawrence G, Meechan D, Morris EJ, Middleton R, Steward J, Richards MA: Cancer survival in Australia, Canada, Denmark, Norway, Sweden, and the UK, 1995–2007 (the International Cancer Benchmarking Partnership): an analysis of population-based cancer registry data. Lancet 2011, 377:127-138.
• [34]Coleman M, Quaresma M, Berrino F, Lutz J, de Angelis R, Capocaccia R, Baili P, Rachet B, Gatta G, Hakulinen T, Micheli A, Sant M, Weir H, Elwood J, Tsukuma H, Koifman S, E-Silva G, Francisci S, Santaquilani M, Verdecchia A, Storm H, Young J: Cancer survival in five continents: a worldwide population-based study (CONCORD). Lancet Oncol 2008, 9:730-756.
• [35]de Blacam C, Gray J, Boyle T, Kennedy MJ, Hollywood D, Butt J, Griffin M, Nicholson S, Dunne B, Wilson G, McDermott R, Murphy P, Short I, Rowley S, Connolly E, Reynolds JV: Breast cancer outcomes following a national initiative in Ireland to restructure delivery of services for symptomatic disease. Breast 2008, 17:412-417.
• [36]Katalinic A, Bartel C, Raspe H, Schreer I: Beyond mammography screening. Quality assurance in breast cancer diagnosis (The QuaMaDi project). BJC 2007, 96:157-161.
• [37]Ferroni E, Camilloni L, Jimenez B, Furnari G, Borgia P, Guasticchi G, Rossi PG: How to increase uptake in oncologic screening: a systematic review of studies comparing population-based screening programs and spontaneous access. Prev Med 2012. in press
• [38]Puliti D, Zappa M: Breast cancer screening: are we seeing the benefit? BMC Med 2012, 10:106. BioMed Central Full Text
• [39]Amir E, Bedard PL, Ocaña A, Seruga B: Benefits and harms of detecting clinically occult breast cancer. J Natl Cancer Inst 2012. in press
• [40]Mainz J, Hjulsager M, Og MTE, Burgaard J: National benchmarking between the Nordic countries on the quality of care. J Surg Oncol 2009, 99:505-507.
• [41]Mainz J, Bartels P, Rutberg H, Kelley E: International benchmarking. Option or illusion? Int J Qual Health Care 2009, 21:151-152.
• [42]Feinstein AR, Sosin DM, Wells CK: The Will Rogers phenomenon. Stage migration and new diagnostic techniques as a source of misleading statistics for survival in cancer. N Engl J Med 1985, 312:1604-1608.
• [43]Golder WA: Das Will-Rogers-Phänomen und seine Bedeutung für die bildgebende Diagnostik. Radiologe 2009, 49:348-354.
• [44]Spratt JS: Will Rogers phenomenon. Arch Surg 1992, 127:868.
• [45]Autier P, Boniol M: Caution needed for country-specific cancer survival. Lancet 2011, 377:99-101.
• [46]Modelmog D, Goertchen R, Steinhard K, Sinn HP, Stahr H: Vergleich der Mortalitätsstatistik einer Stadt bei unterschiedlicher Obduktionsquote (Görlitzer Studie). Pathologe 1991, 12:191-195.
• [47]Schelhase T, Weber S: Die Todesursachenstatistik in Deutschland. Probleme und Perspektiven. Bundesgesundheitsbl - Gesundheitsforsch - Gesundheitsschutz 2007, 50:969-976.
• [48]Lieffers JR, Baracos VE, Winget M, Fassbender K: A comparison of Charlson and Elixhauser comorbidity measures to predict colorectal cancer survival using administrative health data. Cancer 2011, 117:1957-1965.
• [49]Li B, Evans D, Faris P, Dean S, Quan H: Risk adjustment performance of Charlson and Elixhauser comorbidities in ICD-9 and ICD-10 administrative databases. BMC Health Serv Res 2008, 8:12. BioMed Central Full Text
• [50]Walshe K: International comparisons of the quality of health care. What do they tell us? Qual Saf Health Care 2003, 12:4-5.
• [51]Walshe K: Understanding what works-and why-in quality improvement: the need for theory-driven evaluation. Int J Qual Health Care 2007, 19:57-59.
• [52]Nolte E, McKee M: Measuring the health of nations. Analysis of mortality amenable to health care. BMJ 2003, 327:1129.
• [53]Jacke C, Kalder M, Wagner U, Albert U: High-quality data for valid comparisons and decisions. Assessing the accuracy and completeness of medical data. BMC Research Notesin press
• [54]Mainz J: Defining and classifying clinical indicators for quality improvement. Int J Qual Health Care 2003, 15:523-530.
• [55]Nonnemacher M, Weiland D, Neuhäuser M, Stausberg J: Adaptive management of data quality in cohort studies and registers. Proposal for a guideline. Acta Informatica Medica 2007, 15:225-230.
• [56]Beatty JD, Adachi M, Bonham C, Atwood M, Potts MS, Hafterson JL, Aye RW: Utilization of cancer registry data for monitoring quality of care. Am J Surg 2011, 201:645-649.
• [57]Greene FL, Gilkerson S, Tedder P, Smith K: The role of the hospital registry in achieving outcome benchmarks in cancer care. J Surg Oncol 2009, 99:497-499.