Evaluation of Completeness of Cancer Data Variables on Data Quality in Hospital-Based Cancer Registration Activities at Dharmais Cancer Hospital
Grace Shalmont(1*), Pradnya Sri Rahayu(2), Susanna Hilda Hutajulu(3), Lutfan Lazuardi(4)
(1) National Cancer Center, Dharmais Cancer Hospital
(2) National Cancer Centre, Dharmais Cancer Hospital, Jakarta
(3) Universitas Gadjah Mada, Yogyakarta
(4) Universitas Gadjah Mada, Yogyakarta
(*) Corresponding Author
Abstract
Background: Cancer is a major burden of disease worldwide, including in Indonesia. As an effort to control the burden of cancer, WHO established National Cancer Control Programs (NCCP) where cancer registration is one of the key points. Dharmais Cancer Center Hospital, appointed as the national cancer center, has the responsibility to conduct a cancer registry in Indonesia. The good quality data of cancer registry according to international standards is beneficial to describe the cancer burden in the country. In Dharmais Cancer Center Hospital, microscopic verification is one of the variables that has not been qualified. Therefore, it is important to evaluate the completeness of cancer data variables toward data cancer quality on hospital-based cancer registry of Dharmais Cancer Center Hospital. To assess the quality of cancer data based on microscopic verification, to evaluate the completeness of hospital-based cancer registry variables and the quality of data based on microscopic verification between complete and incomplete variable groups. Materials and Methods: This quantitative research is an observational study (non-experimental) with cross-sectional study design. It utilizes secondary data from hospital-based cancer registry of Dharmais Cancer Center Hospital for incidence year 2013-2017.
Results: Data quality of microscopic verification that assessed on a complete data group is 87,8% and for overall cancer cases is 62%. Among social variables, identity numbers are the most incomplete variable, which is 39%. While among tumor data variables, stage is also the most incomplete variable with 82% data. There are differences between the quality of data based on microscopic verification with the completeness of data, especially among social data variables and tumor data variables. Conclusion: The quality data based on microscopic verification that is assessed on a complete variable group is better than microscopic verification on overall cancer cases. The incomplete variables among social variables are identity number, date of birth, address, and district/province. Whereas on tumor variables, the incomplete variables are stage, treatment, metastasis, and laterality. The completeness of cancer data has an important
role on data quality based on microscopic verification mainly on social and tumor variables. Improvement and strengthening particularly on management and technical aspects of cancer registration are indispensable
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1. Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA: A Cancer Journal for Clinicians. 2021;71(3):209–49.
2. World Health Organization. National Cancer Control Programmes [Internet]. 2022. Available from: https://www.who.int/publications-detail-redirect/national-cancer-control-programmes
3. Bray F, Parkin DM. Evaluation of data quality in the cancer registry: principles and methods. Part I: comparability, validity and timeliness. Eur J Cancer. 2009 Mar;45(5):747–55.
4. Kementerian Kesehatan RI. Pedoman Sistem Registrasi Kanker di Indonesia, Jakarta. Kementerian Kesehatan RI; 2018.
5. Mohammadzadeh Z, Ghazisaeedi M, Nahvijou A, Kalhori SRN, Davoodi S, Zendehdel K. Systematic Review of Hospital Based Cancer Registries (HBCRs): Necessary Tool to Improve Quality of Care in Cancer Patients. Asian Pac J Cancer Prev. 2017;18(8):2027–33.
6. Bernal-Delgado EE, Martos C, Martínez N, Chirlaque MD, Márquez M, Navarro C, et al. Is hospital discharge administrative data an appropriate source of information for cancer registries purposes? Some insights from four Spanish registries. BMC Health Serv Res. 2010 Jan 8;10:9.
7. National Cancer Institute. Cancer Incidence Rates Adjusted for Reporting Delay [Internet]. 2022 [cited 2022 Jan 10]. Available from: https://surveillance.cancer.gov/delay/
8. Sirirungreung A, Buasom R, Jiraphongsa C, Sangrajrang S. Data Reliability and Coding Completeness of Cancer Registry Information Using Reabstracting Method in the National Cancer Institute: Thailand, 2012 to 2014. J Glob Oncol. 2018 Sep;4:1–9.
9. Yang DX, Khera R, Miccio JA, Jairam V, Chang E, Yu JB, et al. Prevalence of Missing Data in the National Cancer Database and Association With Overall Survival. JAMA Netw Open. 2021 Mar 23;4(3):e211793.
10. Swaminathan S, Katoch V. Consolidated Report of Hospital Based Cancer Registries 2012-2014. Indian Council of Medical Research; 2016.
11. Insamran W, Sangrajrang S. National Cancer Control Program of Thailand. Asian Pac J Cancer Prev. 2020 Mar;21(3):577–82.
12. Miguel F, Bento MJ, de Lacerda GF, Weiderpass E, Santos LL. A hospital-based cancer registry in Luanda, Angola: the Instituto Angolano de Controlo do Cancer (IACC) Cancer registry. Infect Agent Cancer. 2019;14:35.
13. Plichta JK, Rushing CN, Lewis HC, Blazer DG, Hyslop T, Greenup RA. Missing data in breast cancer: Relationship with survival in national databases. JCO. 2020 May 20;38(15_suppl):e19114–e19114.
14. Eisemann N, Waldmann A, Katalinic A. Imputation of missing values of tumour stage in population-based cancer registration. BMC Med Res Methodol. 2011 Sep 19;11:129.
15. Elbasmi AA, Fayaz MS, Al-Mohanadi S, Al-Nesf Y, Al-Awadi A. Reliability of the Kuwait Cancer Registry: a comparison between breast cancer data collected by clinical oncologists and registry staff. Asian Pac J Cancer Prev. 2010 Jan 1;11(3):735–8.
16. Pollock AM, Vickers N. Reducing DCO registrations through electronic matching of cancer registry data and routine hospital data. Br J Cancer. 2000 Feb;82(3):712–7.
17. Wanner M, Matthes KL, Korol D, Dehler S, Rohrmann S. Indicators of Data Quality at the Cancer Registry Zurich and Zug in Switzerland. Biomed Res Int. 2018;2018:7656197.
18. Brackley ME, Penning MJ, Lesperance ML. In the absence of cancer registry data, is it sensible to assess incidence using hospital separation records? Int J Equity Health. 2006 Oct 6;5:12.
DOI: https://doi.org/10.22146/ahj.v5i1.80443
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