Determining cancer stage at diagnosis in population-based cancer registries: A rapid scoping review

Pung, Li; Moorin, Rachael; Trevithick, Richard; Taylor, Karen; Chai, Kevin; Gewerc, Cristiana Garcia; Ha, Ngoc San; Smith, Stephanie

doi:10.3389/frhs.2023.1039266

Cited by 5 publications

(10 citation statements)

References 36 publications

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“…At the outset of the WA Cancer Staging Project, the framework was developed to provide guidance and exibility for the collection of cancer stage data. It acknowledged the diversity of stage data collection in Australia, emphasising that it is not a one-size-ts-all approach, and recognising that data restrictions are often encountered (4). The tiered framework is a set of rules for staging that incorporates different available data sources and presents an explicit hierarchy of completeness (Figure 2).…”

Section: Developing the Cancer Staging Tiered Frameworkmentioning

confidence: 99%

“…Current approaches to the routine collection of cancer stage at diagnosis in PBCRs are constrained by the absence of standardised methodologies for collecting staging data, resulting in poor quality or incomplete data, and di culties in accessing relevant data sources (4,7,8). The use of various staging classi cation systems also creates challenges in achieving harmonisation and comparisons across jurisdictions and countries (4). The collection of cancer stage at diagnosis in PBCRs has been found to encompass a variety of methods, relying on a wide range of data sources connected to routine data pipelines and collection processes -highlighting the diversity and complexity of how cancer stage information is gathered in PBCRs (4).…”

Section: Introductionmentioning

confidence: 99%

“…3 School of Population and Global Health, The University of Western Australia. 4 Cancer Network WA, North Metropolitan Health Service, Perth, WA, Australia. 5 Curtin Medical School, Curtin University, Perth, WA, Australia.…”

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confidence: 99%

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Collecting routine and timely cancer stage at diagnosis by implementing a cancer staging tiered framework: The Western Australian Cancer Registry experience

Smith,

Moorin,

Taylor

et al. 2024

Preprint

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Background Current processes collecting cancer stage data in population-based cancer registries (PBCRs) lack standardisation, resulting in difficulty utilising diverse data sources and incomplete, low-quality data. Implementing a cancer staging tiered framework aims to improve stage collection and facilitate inter-PBCR benchmarking. ObjectiveDemonstrate the application of a cancer staging tiered framework in the Western Australian Cancer Staging Project to establish a standardised method for collecting cancer stage at diagnosis data in PBCRs. Methods The tiered framework, developed in collaboration with a Project Advisory Group and based on breast, colorectal, and melanoma cancers, provides business rules for stage collection. Tier 1 represents the highest staging level, involving complete American Joint Committee on Cancer (AJCC) tumour–node–metastasis (TNM) data collection and other critical staging information. Tier 2 (registry-derived stage) relies on supplementary data, including hospital admission data, to make assumptions based on data availability. Tier 3 (pathology stage) solely uses pathology reports. Findings The tiered framework promotes flexible utilisation of staging data, recognising various levels of data completeness. While Tier 1 is suitable for all purposes, including clinical and epidemiological applications, Tiers 2 and 3 are recommended for epidemiological analysis alone. Lower tiers provide valuable insights into disease patterns, risk factors, and overall disease burden for public health planning and policy decisions. Capture of staging at each tier depends on data availability, with potential shifts to higher tiers as new data sources are acquired. ConclusionsThe tiered framework offers a dynamic approach for PBCRs to record stage at diagnosis, promoting consistency in population-level staging data and enabling practical use for benchmarking across jurisdictions, public health planning, policy development, epidemiological analyses, and assessing cancer outcomes. Evolution with staging classifications and data variable changes will futureproof the tiered framework. Its adaptability fosters continuous refinement of data collection processes and encourages improvements in data quality.

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Section: Developing the Cancer Staging Tiered Frameworkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Collecting routine and timely cancer stage at diagnosis by implementing a cancer staging tiered framework: The Western Australian Cancer Registry experience

Smith,

Moorin,

Taylor

et al. 2024

Preprint

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“…"Age at diagnosis" was calculated by subtracting the patient's date of birth from the date of cancer diagnosis, demanding accurate and consistent date recording. This calculation relied on cross-referencing the registry's date of birth and date of cancer diagnosis records (21,22). "Sex of the registered cancer patient" represented the gender, sourced from patient self-report or medical records, vital for gender-specific analyses (23).…”

Section: Study Variables and Measurementsmentioning

confidence: 99%

Quality of Data in Population-Based Cancer Registry and its Implications for Cancer Control: A Cross-Sectional Study in Northern Tanzania

Alloyce,

Amour,

Mushy

et al. 2024

Preprint

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Background: Cancer remains a significant global health challenge, especially in low-resource settings where limited data obstructs effective control measures. Reliable cancer registries are essential tools for collecting, managing, and analyzing cancer-related data. In Tanzania, as in many other sub-Saharan African countries, the need for accurate and timely cancer data is critical for informed decision-making. This study assesses the data quality of a population-based cancer registry in Tanzania to improve cancer control efforts. Methodology: This study adopted a descriptive cross-sectional study design to analyze 9617 records of cancer patients in population-based cancer registry database (Canreg5) from 2018 to 2022. Data analysis is done using Stata 17.0 and IARC validity check software. The percentage of complete, valid, and timely records was computed and compared with the required target. Comparability analysis compared age-standardized rates (ASR) and percentages of morphologically verified (MV%) against the standards in sub-Saharan Africa. Results: In the defined period from 2018 to 2022, a total of 9,617 records were included in the final analysis. The study found high data quality indicators: completeness at 94% for mandatory variables, validity at 76%, timeliness at 93%, and a high comparability with cancer data in sub-Saharan Africa at 60%. However, variations were noticed in age-standardized rates (ASR) and morphologically verified cases (MV). For comparability findings, mouth and pharynx cancer had lower age-standardized rates (ASR) for males (5.7) and females (3.4) but high morphological verification rates. Esophageal cancer had significantly higher ASR for males (22.0) and females (6.1), with moderate morphological verification rates. Stomach cancer had lower ASR but higher verification rates. Colon, rectum, and anus cancer had comparable ASR and higher verification rates. Conclusion: The data quality in the population-based cancer registry at the study site was optimal, with a high proportion of completeness, timeliness, and comparability, but with suboptimal validity of data, particularly for date format, and unlike combinations that highlight continuous monitoring and quality checks. While data quality is vital, challenges like missing and invalid records persist, necessitating reinforced protocols and validation measures.

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“…The most commonly used approach stages cancers using the TNM Staging System (8 th Edition), developed and maintained by the American Joint Committee on Cancer (AJCC) and the Union for International Cancer Control (UICC) [ 6 ]. There are iterations of the TNM staging system, such as the Condensed TNM and Essential TNM, used by cancer registries in the absence of any or all TNM data elements [ 7 ]. Another approach to staging cancer uses categories of localised, regional, or distant spread.…”

Section: Introductionmentioning

confidence: 99%

Registry-derived stage (RD-Stage) for capturing cancer stage at diagnosis for endometrial cancer

Evans,

Ivanova,

Rome

et al. 2023

BMC Cancer

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Background Capture of cancer stage at diagnosis is important yet poorly reported by health services to population-based cancer registries. In this paper we describe current completeness of stage information for endometrial cancer available in Australian cancer registries; and develop and validate a set of rules to enable cancer registry medical coders to calculate stage using data available to them (registry-derived stage or ‘RD-Stage’). Methodology Rules for deriving RD-stage (Endometrial carcinoma) were developed using the American Joint Commission on Cancer (AJCC) TNM (tumour, nodes, metastasis) Staging System (8th Edition). An expert working group comprising cancer specialists responsible for delivering cancer care, epidemiologists and medical coders reviewed and endorsed the rules. Baseline completeness of data fields required to calculate RD-Stage, and calculation of the proportion of cases for whom an RD stage could be assigned, was assessed across each Australian jurisdiction. RD-Stage (Endometrial cancer) was calculated by Victorian Cancer Registry (VCR) medical coders and compared with clinical stage recorded by the patient’s treating clinician and captured in the National Gynae-Oncology Registry (NGOR). Results The necessary data completeness level for calculating RD-Stage (Endometrial carcinoma) across various Australian jurisdictions varied from 0 to 89%. Three jurisdictions captured degree of spread of cancer, rendering RD-Stage unable to be calculated. RD-Stage (Endometrial carcinoma) could not be derived for 64/485 (13%) cases and was not captured for 44/485 (9%) cases in NGOR. At stage category level (I, II, III, IV), there was concordance between RD-Stage and NGOR captured stage in 393/410 (96%) of cases (95.8%, Kendall’s coefficient = 0.95). Conclusion A lack of consistency in data captured by, and data sources reporting to, population-based cancer registries meant that it was not possible to provide national endometrial carcinoma stage data at diagnosis. In a sample of Victorian cases, where surgical pathology was available, there was very good concordance between RD-Stage (Endometrial carcinoma) and clinician-recorded stage data available from NGOR. RD-Stage offers promise in capturing endometrial cancer stage at diagnosis for population epidemiological purposes when it is not provided by health services, but requires more extensive validation.

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Determining cancer stage at diagnosis in population-based cancer registries: A rapid scoping review

Cited by 5 publications

References 36 publications

Collecting routine and timely cancer stage at diagnosis by implementing a cancer staging tiered framework: The Western Australian Cancer Registry experience

Collecting routine and timely cancer stage at diagnosis by implementing a cancer staging tiered framework: The Western Australian Cancer Registry experience

Quality of Data in Population-Based Cancer Registry and its Implications for Cancer Control: A Cross-Sectional Study in Northern Tanzania

Registry-derived stage (RD-Stage) for capturing cancer stage at diagnosis for endometrial cancer

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