1. Introduction
Population-based cancer registries (PBCRs) play a crucial role in cancer epidemiology by serving as a foundation for tracking cancer incidence over time and across different populations or subgroups of people defined by sex, age, or diagnosis [ 1 ]. By systematically collecting and analyzing data on cancer cases, these registries provide essential insights into the patterns and causes of cancer, which can then be used to design and implement health policies, support research activities, improve patient management, and reallocate resources, such as diagnostic and treatment tools [ 2 , 3 ]. The ability of a cancer registry to provide support to such actions strongly relies on the quality of the data fed into it [ 4 ]. In fact, the engagement of registries in such doings, with the active participation of clinicians and researchers in routine data collection and analysis, along with improved registration procedures, significantly enhance their quality [ 5 ].
Cancer registries should be evaluated based on three quality dimensions: comparability, validity, and completeness [ 6 ]. To evaluate the accuracy of a registry in estimating the true cancer incidence within a population, assessing its completeness is the most relevant dimension. It measures the degree to which all incident cancers occurring in a population are captured in the registry’s database [ 7 ].
Methods for completeness evaluations can be either qualitative (or semi-quantitative), when comparing completeness relative to other registries or over time, or quantitative, when providing numerical estimates of case registration for all eligible cases [ 8 ]. While all current methods have drawbacks and biases, ongoing efforts have been made to refine them, improving their feasibility [ 9 ]. Knowing this, PBCR worldwide struggle to find the most suitable method to assess completeness. To our knowledge, no systematic review has evaluated the methods used by population-based cancer registries to assess completeness. This study aims to identify these methods and explore challenges faced by cancer registries in data handling, how available data determine which method to use, and unveil new methods and differences in completeness assessments across different registries.
2. Materials and Methods
2.1. Search Strategy and Data Sources
The protocol for this review was registered in the International Prospective Register of Systematic Reviews (PROSPERO) on 9 July 2024 ( www.crd.york.ac.uk/PROSPERO/ , accessed on 7 July 2024, registration number 563033).
This study was conducted according to Cochrane Handbook for Systematic Reviews of Interventions and adheres the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) directives to ensure transparency and reproducibility [ 10 , 11 ]. To locate relevant studies, an electronic literature search in the databases Web of Science https://www.webofscience.com/ (accessed on 1 July 2024), Scopus https://www.scopus.com/ (accessed on 1 July 2024), and PubMed http://www.ncbi.nlm.nih.gov/pubmed/ (accessed on 1 July 2024) was conducted. A Boolean search was conducted using the following keywords: “cancer registry” OR “cancer registries” OR “cancer registration” OR “oncologic registry”) AND “completeness”. The search was conducted between July 2024 and December 2024 as the databases continued to notify about new articles. The search was limited to recent studies published between January 2004 and up to December 2024. In addition, due to translation restrictions, only English/Portuguese/Spanish-language studies were eligible. The search strategy applied in the systematic review is presented in Table S1 . A cross-reference search of all studies that met the inclusion criteria was carried out to ensure study inclusion was as complete as possible. For this, we manually examined the reference lists of the included articles.
2.2. Exclusion Criteria
Duplicates were identified and removed using the Desktop Mendeley software 1.19.5 version. Papers that outlined methods to assess the completeness of cancer registries were included in this study. We excluded articles that were (1) written in languages other than English, Portuguese, or Spanish; (2) repetitive or duplicated; (3) reviews, opinions, book chapters, or conference proceedings; (4) not conducted in adult population-based cancer registries; or (5) did not assess cancer registry completeness. Childhood Cancer Registries (0–14 age) were excluded as the completeness of these is usually assessed by comparing the observed age-specific rates with an expected range of values, as described in the Volume VIII of Cancer Incidence in Five Continents, by Parkin and Plummer [ 12 ]. This way, no new methods were expected to be found.
Two researchers (MPS and TM) scrutinized, independently, the titles and abstracts of the papers as of potential relevance according to the eligibility criteria. Articles included by title and abstract were reviewed in full text by the two reviewers to determine their final inclusion. A third reviewer (MJB) was asked to resolve disagreements between the two researchers during the title and abstract screening, as well as the full-text screening. The number of articles included and excluded was registered in a PRISMA flowchart— Figure S1 .
2.3. Data Extraction and Quality Assessment
From each included study, information about the country, name of the cancer registry, years under study, and method used for the completeness assessment was gathered in Table 1 , ordered by publication year. If studies reported insufficient information, we attempted to contact study authors to obtain further information, when possible; otherwise, assumptions were made based on contextual information (e.g., registry reports or methodological descriptions). All reported completeness assessment methods were collected along with the corresponding years under study, ensuring that each method was analyzed within its respective timeframe.
The risk of bias for each study was independently appraised by two review authors (MPS and TM), using the Joanna Briggs Institute’s appraisal tool specifically designed for Case Series (2020 version) [ 96 ]. To enhance the tool’s suitability to analyze the nature of the articles under review, we added other bullet points to the existing checklist, to scrutinize more effectively eventual differences between them. The following criteria were added to the tool: (1) whether the goal of the article was clearly stated; (2) if the type of registry (population-based) was clearly specified; (3) if all the cancer data sources (e.g., hospitals, death certificates, pathology reports) were explicitly mentioned and comprehensive; (4) if the completeness evaluation method was appropriate for the available data (considering some drawbacks like poor quality mortality data); and (5) if the article acknowledges any limitations in the completeness assessment methods and the generalizability of its findings. The final checklist is presented in Table S2 . Studies were scored for each checklist item: “YES” (2 points), “UNCLEAR” (1 point), and “NO” (0 points), to a total of 28 points. Results were converted in a percentage, and good quality studies were those scored above 75% (21 points). The registries with worse quotation failed to report demographic information about the population under study in the methods section, providing it later in the results and discussion, so the article could be compared with other populations. In addition, there are also some articles that used population cohorts instead of the whole population, assessed completeness for a single year, or were unable to state any limitation regarding the method used for the completeness assessment and the results obtained. No attempts were made to contact study authors for additional information. All data were extracted as reported in the original publications.
3. Results
The initial search yielded 1619 results, of which 817 duplicates were removed prior to screening ( Figure S1 ). From the combined 802 records, 660 additional records were excluded during screening based on the language, article type, studies not conducted in adult-population cancer registries, and not using any method to assess completeness of registries. From the remaining 142 studies, three were not retrieved leading to 139 articles for full text examinations. Upon review, 58 more results were excluded as they met the exclusion criteria. More so, one of the articles was excluded as a repetitive publication and another for being a short-version article, further substituted by its full version through cross-referencing. Overall, two articles were added through cross-referencing [ 13 , 16 ], which resulted in 83 studies included in this review [ 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 ].
This review comprises articles published between 2004 and December 2024 that assessed the completeness of population-based registries between 1953 and 2018.
Most of the evaluated PBCRs belong to European countries (n = 32) with the highest percentage belonging to Nordic countries [ 18 , 24 , 26 , 27 , 29 , 30 , 32 , 36 , 38 , 39 , 45 , 48 , 49 , 53 , 57 , 59 , 66 , 70 , 72 , 76 , 77 , 80 , 82 , 85 , 87 ] (25 evaluations) and Central Europe [ 17 , 22 , 25 , 28 , 31 , 43 , 68 , 73 , 89 , 92 ] (7 evaluations). The set of studies includes the evaluation performed using data from 30 European countries collected in the first ENCR-JCR data call, in 2015 [ 88 ]. Among European countries, Sweden had the highest number of completeness assessments (n = 14), which started being published, periodically, since 2007 [ 18 , 24 , 27 , 36 , 48 , 49 , 53 , 57 , 70 , 72 , 76 , 77 , 80 , 85 ]. Following Europe, other regions assessing completeness are North America (n = 8) [ 14 , 19 , 42 , 46 , 54 , 56 , 74 , 79 ], the Middle East (n = 9) [ 51 , 55 , 58 , 60 , 63 , 69 , 81 , 84 , 95 ], Southeast Asia (n = 3) [ 34 , 35 , 62 ], and Oceania [ 16 , 21 , 33 ] (n = 3). For the period under study, completeness assessment studies were conducted in three countries in Africa [ 44 , 50 , 86 ], four in India [ 65 , 83 , 91 , 93 ], two in Australia [ 16 , 33 ], two in South America [ 40 , 94 ], and one each in New Zealand [ 21 ], Russia [ 90 ], and China [ 67 ]. The most common method to assess completeness among registries was an independent case ascertainment, being used in nearly half of the entries (n = 39) [ 16 , 18 , 20 , 21 , 23 , 24 , 26 , 27 , 30 , 32 , 33 , 36 , 37 , 38 , 39 , 45 , 47 , 48 , 49 , 53 , 54 , 56 , 57 , 59 , 61 , 66 , 72 , 74 , 76 , 77 , 78 , 79 , 80 , 82 , 84 , 85 , 86 , 87 , 91 ]. Other methods employed included the Mortality-to-Incidence ratio (n = 21) [ 13 , 15 , 25 , 28 , 29 , 31 , 35 , 39 , 40 , 52 , 62 , 65 , 66 , 67 , 68 , 73 , 83 , 88 , 90 , 92 , 94 ], historical data (n = 18) [ 29 , 31 , 39 , 44 , 46 , 50 , 52 , 62 , 66 , 67 , 71 , 73 , 83 , 90 , 92 , 93 , 94 , 95 ], capture–recapture (n = 15) [ 14 , 22 , 34 , 41 , 44 , 51 , 52 , 55 , 58 , 60 , 62 , 63 , 69 , 70 , 75 , 81 ], the flow method (n = 9) [ 17 , 29 , 31 , 41 , 43 , 47 , 62 , 64 , 68 ], death-certificate methods other than the flow method (n = 11) [ 13 , 22 , 34 , 41 , 42 , 47 , 52 , 65 , 68 , 90 , 95 ], number of notifications/sources (n = 6) [ 29 , 39 , 44 , 62 , 66 , 83 ], and the percentage of morphologically verified cases (n = 8) [ 13 , 31 , 34 , 42 , 68 , 88 , 94 , 95 ]. Additional methods exposed in this review include the age-standardized sex and cancer-site-specific incidence-to-mortality rate (IMRR) method, used by the North American Association of Central Cancer Registries (NAACCR) [ 19 , 42 ], and the mortality-to-incidence ratio method used by the Robert-Kock Institute to calculate the expected incidence [ 28 , 89 ].
Most PBCRs used a single method to assess the completeness of their registries (n = 58), while eight registries used two methods [ 22 , 65 , 67 , 71 , 73 , 88 , 92 , 93 ]; ten registries used three methods [ 13 , 34 , 41 , 42 , 44 , 47 , 83 , 90 , 94 , 95 ], five registries used four methods [ 19 , 35 , 40 , 42 , 85 ], and two registries used five methods [ 29 , 62 ].
The registries analyzed a diversity of neoplasms with 27 registries focusing on malignant neoplasms [ 13 , 17 , 20 , 29 , 39 , 43 , 47 , 49 , 52 , 55 , 62 , 64 , 65 , 66 , 67 , 68 , 71 , 73 , 75 , 83 , 86 , 88 , 90 , 91 , 93 , 94 , 95 ]. Fourteen of these studies excluded non-melanoma skin cancer [ 13 , 43 , 47 , 49 , 52 , 64 , 67 , 68 , 71 , 73 , 88 , 93 , 94 , 95 ], and three included benign or in situ neoplasms [ 39 , 49 , 88 ].
The majority of registries (n = 39) [ 14 , 18 , 20 , 21 , 24 , 27 , 28 , 29 , 30 , 35 , 36 , 37 , 39 , 42 , 44 , 45 , 46 , 47 , 48 , 49 , 53 , 57 , 59 , 64 , 65 , 66 , 68 , 69 , 70 , 71 , 73 , 75 , 76 , 79 , 87 , 89 , 90 , 92 , 95 ] report their neoplasms using the International Classification of Diseases (ICD7-10) [ 97 ], predominantly the 10th version (n = 32) [ 18 , 21 , 27 , 28 , 29 , 30 , 35 , 36 , 37 , 39 , 42 , 44 , 45 , 47 , 49 , 53 , 57 , 59 , 64 , 65 , 66 , 68 , 70 , 71 , 73 , 75 , 76 , 79 , 87 , 89 , 90 , 92 , 95 ]. Others opted for the International Classification of Diseases for Oncology (ICD-O 1st to 3rd edition n = 18) [ 31 , 34 , 38 , 40 , 50 , 55 , 56 , 61 , 62 , 74 , 78 , 80 , 83 , 84 , 85 , 86 , 88 , 94 ], recommended for tumor coding [ 98 ]. Some used both ICD9-10 and ICD-O [ 25 , 54 , 91 , 93 ] or the concomitant use of ICD-O only for morphology classifications [ 52 , 67 ]. In addition to this, there is also a mention of the use of SNOMED codes [ 82 ].
In the quality assessment, only one study received a score below 75%. Overall, the articles met high-quality standards, aligning with the established inclusion criteria. The observed high quality of the studies included may be partly due to the exclusion of gray literature.
The table compiling the results is next presented— Table 1 .
4. Discussion
Accurate and comprehensive registry data are essential for deriving reliable cancer incidence rates, tracking trends, and guiding public health interventions. Evaluating registry completeness in adult populations is vital for developing effective cancer-control strategies and addressing the unique challenges associated with diverse cancer types and risk factors [ 8 , 12 ].
Independent case ascertainment has been employed since the early years of cancer registry development and continues to be used universally across different countries, irrespective of their economic status or geographical region— Table 1 .
Most registries assess unusual patterns in incidence rates by comparing their data with independent cancer case datasets. Independent case ascertainment is widely used in Nordic countries [ 39 , 53 , 66 ], North America [ 54 , 79 ], and Scotland [ 61 ].
This method either involves re-screening previously registered cases or comparing registry data against external sources of cancer cases [ 8 ]. For instance, O’Brien et al. [ 47 ] used “case-finding audits” to estimate rectal cancer cases in the Irish National Cancer Registry, while Stevens et al. [ 21 ] applied similar audits to evaluate registry completeness. Although initially used to assess the completeness of specific facilities, such as hospitals, independent case ascertainment has been since adapted for population-based cancer registries [ 12 , 47 ]. Registry completeness is often estimated by re-examining cancer case records over a specific timeframe and identifying under-reported cases [ 8 , 99 ]. Lam et al. (2020) demonstrated this approach by reviewing SEER-Medicare data for Chronic Myeloid Leukemia and Bladder Cancer, using claims-based algorithms to detect missing cases through diagnosis or treatment codes [ 79 ].
Similarly, comparisons between registry data and independent sources—such as multi-center clinical trials [ 23 ], hospital discharge registries [ 27 , 39 ], national screening programs [ 47 ], and other cancer registries [ 45 , 53 ]—provide quantitative measures of completeness. Although reliable, traditional case ascertainment methods are increasingly challenged by the growing interdependence of data.
Advances in electronic health records (EHRs), as seen in the Indiana State Cancer Registry, enable direct data linkage using probabilistic and deterministic models, effectively integrating heterogeneous healthcare records [ 56 ].
Choosing the right data source for comparisons is crucial when assessing completeness. For instance, the Swedish Colorectal Cancer Registry reported over 98% completeness using the Swedish Cancer Registry as a gold standard [ 72 ]. However, since the completeness of this gold standard is not 100%, the true number of cases may have been underestimated. Similarly, Palliative Care Registries can introduce biases, as comparisons are limited to end-of-life populations [ 49 ]. The dataset size also plays a key role, as small datasets can lead to the overestimation of completeness [ 32 ].
Death certificates (DCs) are valuable for identifying cancer cases missed during life, with metrics like DCN%, DCI%, DCO%, or the DCN/M:I ratio used to assess registry completeness [ 13 , 22 , 34 , 41 , 42 , 47 , 52 , 65 , 68 , 90 ]. However, errors in death certificates and discrepancies between cancer diagnoses and causes of death on DCs limit their reliability, particularly for less well-defined cancers like those of the biliary tract [ 48 , 100 ]. Similarly, vulvar and vaginal premalignant lesions were less completely registered than tumors at the same sites, due to limited data from DCs [ 38 ].
When a death certificate cannot be matched to a registered case, it is categorized as a death certificate only (DCO). DCOs, often resulting from errors in DCs, are typically excluded as incident cases, contributing to underreporting [ 47 ]. Neither the DCO% nor DCN% accurately estimate completeness. For example, Lorez et al. demonstrated underreporting for hepatic and pancreatic cancers using DCN%, which was disproved by other methods [ 68 ]. Instead, DCO% is better interpreted as a validity measure reflecting the case-finding efficiency [ 95 ]. Despite its limitations, six studies used DCO% (n = 5) or DCN% to assess completeness but relied on complementary methods for accuracy [ 13 , 35 , 42 , 65 , 68 ]. Sharma et al. used the DCO% to evaluate the Guwahati Cancer Registry’s validity but depended on the mortality-to-incidence (M:I) ratio for a completeness assessment [ 65 ].
Another DC method calculates the proportion of cases first reported through other means than DCs. This requires identifying DCI cases to estimate unregistered but living cancer cases using M:I ratios [ 8 ]. Parkin et al. [ 101 ] introduced a formula for this purpose, later adapted by Ajiki et al. for registries with higher DCI proportions [ 102 ]. Of the five studies applying this method, four used Ajiki’s formula [ 41 , 47 , 52 , 90 ], while one used Parkin’s [ 22 , 41 ]. Schmidtmann et al. used the Lincoln–Peterson estimator, approximating Ajiki’s formula [ 22 ], while Castro et al. found comparable results using both strategies [ 41 ]. Barchuk et al. further adapted Ajiki’s index to a DCI/M:I format [ 90 ].
This method conceptually overlaps with capture–recapture strategies, which also use multiple data sources to assess completeness. Most studies using capture–recapture methods employed a three-source log-linear model to address interdependency and heterogeneity issues [ 20 , 34 , 47 , 55 , 57 , 58 , 60 , 61 , 88 ]. Some used Crocetti et al.’s method to evaluate dependencies [ 103 ], grouping the two most dependent sources and applying a two-source capture–recapture model with a third source [ 47 , 56 , 65 , 68 , 85 , 87 ]. However, this can underestimate completeness [ 14 ], as shown by Törner et al. for the Swedish Cancer Registry [ 70 ]. Schmidtmann et al. compared various capture–recapture estimators, including DCN methods, finding that all except a four-source log-linear model with second-order interactions underestimated completeness, albeit with a larger mean square error [ 22 ]. Among all methods, Parkin’s formula yielded the most accurate estimates when all cancer deaths were included.
The aforementioned methods cannot detect unreported cases outside routine notifications, besides lacking statistical power for the early detection of incompleteness [ 104 ]. In contrast, the flow method addresses these gaps. It considers cancer registration as a post-diagnosis time-sensitive event, following a probabilistic framework to improve the completeness assessment [ 104 ].
For all cancer registries, completeness increased logarithmically during the first three years and stabilizes thereafter, reflecting the natural data registration process, where most cases are captured shortly after diagnosis [ 6 , 17 , 29 , 31 , 41 , 43 , 47 , 62 , 64 , 68 ]. However, registration delays can impact completeness assessments, particularly for tumors diagnosed in outpatient settings [ 68 ]. Nonparametric models have been proposed to address this challenge [ 105 ]. The flow method, while effective in delayed registration scenarios, is susceptible to the overestimation bias caused by registration delays [ 17 ]. To mitigate this, Bullard et al. modified the flow method adjusting it for delayed registrations [ 104 ], a strategy later applied by Fung et al. in the Singapore Cancer Registry [ 62 ]. Compared to capture–recapture methods, the flow method generally provides higher completeness estimates.
Despite its advantages, the flow method has notable limitations. It underestimates survival times, leading to an overestimation of completeness, and requires the long-term follow-up of vital status [ 29 , 43 ]. Additionally, it assumes stable incidence and mortality rates, and the lack of cause-of-death information on death certificates can introduce bias [ 47 , 68 ]. Hackl et al. criticized the method, arguing that it inadequately accounts for delayed registrations, effectively setting these cases to zero without modeling their effects [ 43 ].
Comparing death-certificate methods, O’Brien et al. found that Ajiki’s formula provides lower completeness estimates than the flow method [ 47 ], while Silcocks et al. demonstrated that the flow method is more accurate but biased for cancers with good survival rates [ 106 ]. Similarly, Schmidtmann et al. advised against using the Lincoln–Petersen estimator, an approximation of Ajiki’s formula, due to its limitations [ 22 ].
DCN methods utilizing the M:I ratio, offer a straightforward metric for a semi-quantitative completeness estimation. Of the 22 articles employing the M:I ratio, eleven exclusively relied on it [ 13 , 15 , 25 , 28 , 65 , 67 , 73 , 83 , 88 , 89 , 94 ]. For instance, Hübner et al. applied the Robert-Koch Institute methodology, comparing observed cases to expected cases based on M:I ratios from high-quality reference regions [ 89 ]. However, this method assumes consistent M:I ratios, which may not hold due to variations in screening practices, tumor stage distributions, or subtype prevalences, leading to potential inaccuracies [ 89 , 107 ].
Over time, the M:I ratio evolved into complementary metrics, such as 1-M:I, proposed in 1970 as both an indicator of completeness and a proxy for 5-year survival probability [ 107 ]. While useful for registries lacking survival data, its statistical validity for registry comparisons remains debated [ 108 , 109 ]. When mortality data are reliable and incidence and survival are stable, 1-M:I can approximate completeness [ 8 , 35 ]. Registries often plot the M:I ratio as a function of the 1–5-year survival rates to evaluate coherence between the variables, where deviations may suggest underreporting [ 29 , 31 , 35 , 39 , 40 , 52 , 62 , 66 , 68 , 71 , 90 , 92 ]. However, these interpretations depend on tumor reporting on death certificates, as well as stable incidence and mortality trends [ 8 ]. While 1-M:I has been shown to approximate 5-year survival for many tumor types, its validity as a survival proxy is questioned [ 109 , 110 ].
The incidence-to-mortality rate ratio (IMRR), used by NAACCR, defines completeness as the ratio of observed-to-expected incidence rates. Expected cases are derived by multiplying a registry’s mortality rate by the national IMRR, stratified by sex, race, and the cancer site. This assumes a relatively constant incidence-to-mortality ratio (I:M) across geographic areas [ 9 ]. However, Das et al. identified limitations in this assumption, proposing an enhanced model incorporating additional covariates [ 19 ], later confirmed in a Canadian study [ 42 ]. Notably, NAACCR’s indicator often produces results identical to the I:M ratio, suggesting that the latter as a simpler alternative [ 42 ].
Historical data methods are frequently used to assess registry completeness. These methods compare incidence rates among similar populations, attributing differences to local risk factors or screening practices [ 29 , 31 , 39 , 44 , 46 , 50 , 52 , 62 , 66 , 67 , 71 , 73 , 83 , 90 , 92 , 93 , 94 , 95 ]. Inconsistent patterns across cancer types may indicate case ascertainment issues, which can also be evaluated using age-specific incidence curves [ 44 , 52 , 62 , 71 , 90 ]. Since the incidence typically increases logarithmically with age, deviations or “drop-offs” may signal under-ascertainment [ 111 , 112 ]. Such patterns may also reflect socio-economic disparities or population risk factors [ 71 ].
Despite their utility in identifying trends and anomalies, historical data methods cannot provide quantitative completeness estimates, limiting their attractiveness. They are, however, useful in settings with poor-quality mortality data.
Bashar et al. compared historic methods and the M:I ratio for four Indian PBCRs in 2013, finding that completeness was underestimated by the M:I ratio at 0.27 and 0.43 times, for females and males, respectively [ 83 ]. The authors acknowledge the lack of reliable mortality data, time, and resources as reasons for using only qualitative methods, similar to Shivashankar et al. [ 93 ]. In the Icelandic Cancer Registry and Cancer Registry of Norway, incidence rates and age-specific curves were used to estimate solely the previous year of registration, also due to the unreliable mortality data [ 29 , 39 ].
Another metric to assess registry completeness is the percentage of cases morphologically verified (MV%), which should be compared to regional or national standards [ 6 ]. A high MV% indicates reliance on pathology laboratories and the potential inability to capture cases through alternative methods [ 8 ]. This was observed in the Basel Cancer Registry where an MV% for hepatic cancer of 94.4% was detected, against 56.7% for a Swiss PBCR [ 68 ]. This strategy strongly relies on local regional circumstances where registries are situated, being discouraged in places with inadequate or insufficient tissue processing [ 113 ]. Similar to the DCO%, this method can assess the validity and completeness at once, as demonstrated in a study on 130 European PBCRs [ 88 ].
Finally, the number of sources/notifications per case were found for six articles, as it primarily indicates how good data flow is rather than completeness [ 29 , 39 , 44 , 62 , 66 , 83 ]. It is interesting to notice how notification numbers vary across registries, as seen for multiple myeloma, which was low in the Norwegian registry but high in the Icelandic registry [ 29 , 39 ]. These variations highlight the influence of local data quality and methodological preferences on the choice of evaluation strategies.
Overall, a completeness assessment is particularly challenging for specific cancer sites, especially those with poor reporting of low morphologically verified cancers, such as those of the central nervous system, hematological malignancies, soft tissue, and pancreas [ 29 , 62 , 66 , 73 , 114 ]. Furthermore, this is more likely to occur among the elderly, for tumors with poor survival, such as lung, pancreas, or liver [ 27 , 36 , 49 , 115 ]. This underreporting contributes to an increasing reliance on notifications by death certificate only (DCO%) [ 66 , 73 ]. This highlights the importance of accurate coding on death certificates for the completeness of registries. In addition, overreporting errors may arise from the incorrect coding of sites prone to metastasis, such as the brain or liver, as primary sites [ 35 , 40 ], although less common in developed countries, due to improvements in diagnostic tools. Additionally, registration errors based on misclassifying close anatomical sites may also occur, as described for ureteral and bladder cancers [ 24 ].
Improved data linkages through both probabilistic and deterministic models can help fill gaps in data integration, while methods like machine learning and claims-based algorithms have the potential to discover missed cases. More so, techniques that reduce bias and delays within capture–recapture and flow techniques may increase accuracy. Reliability can be further enhanced by standardizing metrics, such as the M:I ratio, across registries and by conducting periodic audits.
The quality evaluation of studies included in this analysis highlights the robustness of the methodologies employed. Only one study received a score below 75%, reflecting overall high quality across the articles reviewed. This is likely attributable to strict inclusion criteria and the exclusion of grey literature, which often lacks rigorous peer review. Most studies met established quality standards, clearly outlining registry types, data sources, and completeness evaluation methods. However, articles with lower scores often failed to report demographic information about the study population in the methods section or assessed completeness for only a single year. Some studies did not acknowledge limitations in their methodologies, such as the use of population cohorts instead of the entire population or reliance on poor-quality mortality data. Interestingly, when looking at the four highest quality scores (25–28), of the 51 articles, almost half (23) used more than one method when assessing completeness [ 13 , 14 , 22 , 29 , 31 , 39 , 41 , 42 , 44 , 52 , 62 , 65 , 66 , 67 , 68 , 71 , 73 , 88 , 90 , 91 , 92 , 93 , 94 ]. Regarding the rest, only one method was used, with 22 articles opting for case ascertainment [ 18 , 20 , 21 , 23 , 26 , 36 , 37 , 38 , 48 , 49 , 53 , 54 , 59 , 61 , 74 , 76 , 77 , 78 , 79 , 82 , 86 , 87 ]. In one hand, this highlights the importance of using several methods when assessing the quality of cancer registries. On the other hand, it is clear how cancer registries find reliable independent case ascertainment, by opting for it when using only one strategy.
Study Limitations
This systematic review has several limitations that should be considered when interpreting the findings. Excluding gray literature may have introduced publication bias, overrepresenting positive findings and underestimating the challenges associated with data completeness assessments. By restricting to English, Portuguese, or Spanish language publications and major databases (Web of Science, Scopus, and PubMed), relevant studies might have been missed, particularly impacting representation from non-European regions. The heterogeneity of included studies, despite the quality assessment, posed challenges in synthesizing the findings. The focus on what methods are used, rather than how well, limits the understanding of practical implementation. The predominantly European studies limit generalizability, especially to lower-resource settings.
Future research should address these limitations by including gray literature, exploring additional language publications, and investigating the practical implementation and effectiveness of different completeness assessment methods across diverse settings.
5. Conclusions
Several statistical methods exist for evaluating cancer registry data completeness, each with its strengths and weaknesses. Common approaches include independent case ascertainment, capture–recapture analysis, death certificate-only case analysis, and comparisons with historical incidence rates. Quantitative methods are generally preferred over qualitative ones for their ability to provide precise numerical estimates, though they often require greater resources and robust data systems. The choice of most adequate method depends on specific registry’s characteristics, such as data sources and data quality, as well as the type of tumor. Enhancing data completeness requires advanced strategies and modern technologies. Crucially, the development of effective and adaptable systems for comprehensive cancer monitoring must be achieved through global cooperation among registries.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
Supplementary Materials
The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/cancers17071123/s1 , Table S1: Search Strategy; Figure S1: Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flowchart of study selection, with search strategy; Table S2: Articles quality assessment using the Joanna Briggs Institute’s appraisal tool for Case Series with additional inquiries.
Author Contributions
M.P.S.: Conceptualization, Methodology, Validation, Investigation, Writing—original draft, Visualization. T.M.: Methodology, Validation, Investigation, Writing—original draft, Visualization. C.C.S.: Formal analysis, Writing—review and editing. L.L.: Formal analysis, Writing—review and editing. M.J.B.: Conceptualization, Methodology, Validation, Formal analysis, Resources, Writing—review and editing, Visualization Supervision, Project administration. All authors have read and agreed to the published version of the manuscript.
Institutional Review Board Statement
Ethical review and approval were waived for this study due to it not involving humans as a primary source of data and it being a systematic review of published articles.
Informed Consent Statement
Not applicable.
Data Availability Statement
No new data were created or analyzed in this study. Data sharing is not applicable to this article.
Conflicts of Interest
The authors declare no conflicts of interest.
Abbreviations
The following abbreviations are used in this manuscript: DCI% Percentage of Death Certificate Initiated DCN% Percentage of Death Certificate Notification I:M ratio Incidence-to-Mortality ratio IMRR Incidence-to-mortality rate ratio M:I ratio Mortality-to-incidence ratio MV% Percentage of Microscopically Verified Tumors PBCR Population-based Cancer Registry
References
1. Brenner H. Stegmaier C. Ziegler H. Estimating Completeness of Cancer Registration: An Empirical Evaluation of the Two Source Capture-Recapture Approach in Germany J. Epidemiol. Community Health 1995 49 426 430 10.1136/jech.49.4.426 7650468 PMC1060133
2. Parkin D.M. The Role of Cancer Registries in Cancer Control Int. J. Clin. Oncol. 2008 13 102 111 10.1007/s10147-008-0762-6 18463952
3. Lee B. Gately L. Lok S.W. Tran B. Lee M. Wong R. Markman B. Dunn K. Wong V. Loft M. Leveraging Comprehensive Cancer Registry Data to Enable a Broad Range of Research, Audit and Patient Support Activities Cancers 2022 14 4131 10.3390/cancers14174131 36077668 PMC9454529
4. Storm H.H. Cancer Registries in Epidemiologic Research Cancer Causes Control 1996 7 299 301 10.1007/BF00052933 8734821
5. Asare E.A. Gress D.M. Greene F.L. Winchester D.P. Cancer Registry Data: Engaging the Clinician to Improve Quality Cancer 2015 121 2866 2867 10.1002/cncr.29483 26018328
6. Bray F. Parkin D.M. Evaluation of Data Quality in the Cancer Registry: Principles and Methods. Part I: Comparability, Validity and Timeliness Eur. J. Cancer 2009 45 747 755 10.1016/j.ejca.2008.11.032 19117750
7. Shin H.R. Curado M.P. Ferlay J. Heanue M. Edwards B. Storm H. Chapter 5: Comparability and Quality of Data IARC Lyon, France 2004 67 94
8. Parkin D.M. Bray F. Evaluation of Data Quality in the Cancer Registry: Principles and Methods Part II. Completeness Eur. J. Cancer 2009 45 756 764 10.1016/j.ejca.2008.11.033 19128954
9. The North American Association of Central Cancer Registries Identify and Implement Best Practices for Cancer Registry Operations The North American Association of Central Cancer Registries Springfield, IL, USA 2020 3 8
10. Higgins J.P.T. Thomas J. Chandler J. Cumpston M. Li T. Page M.J. Welch V.A. Cochrane Handbook for Systematic Reviews of Interventions Version 6.5 (Updated August 2024) Cochrane Chichester, UK 2024
11. Page M.J. McKenzie J.E. Bossuyt P.M. Boutron I. Hoffmann T.C. Mulrow C.D. Shamseer L. Tetzlaff J.M. Akl E.A. Brennan S.E. The PRISMA 2020 Statement: An Updated Guideline for Reporting Systematic Reviews BMJ 2021 372 n71 10.1136/bmj.n71 33782057 PMC8005924
12. Parkin D.M. Whelan S.L. Ferlay J. Teppo L. Thomas D.B. Cancer Incidence in Five Continents IARC Scientific Publications Lyon, France 1976 Volume VIII 1 583
13. Crocetti E. Capocaccia R. Casella C. Ferretti S. Guzzinati S. Rosso S. Sacchettini C. Spitale A. Stracci F. Tumino R. Cancer Trends in Italy: Figures from the Cancer Registries (1986–1997) Epidemiol. Prev. 2004 28 1 6 15281598
14. McClish D. Penberthy L. Using Medicare Data to Estimate the Number of Cases Missed by a Cancer Registry—A 3-Source Capture-Recapture Model Med. Care 2004 42 1111 1116 10.1097/00005650-200411000-00010 15586838
15. Mori M. Sakauchi F. Washio M. Ozasa K. Watanabe Y. Yoshimura T. Tamakoshi A. Survey for Incidence of Cancer as a Measure of Outcome in the JACC Study J. Epidemiol. 2005 15 (Suppl. S1) S80 S85 10.2188/jea.15.s80 15881199 PMC8565871
16. Burgess J.R. Tucker P. Incidence Trends for Papillary Thyroid Carcinoma and Their Correlation with Thyroid Surgery and Thyroid Fine-Needle Aspirate Cytology Thyroid 2006 16 47 53 10.1089/thy.2006.16.47 16487013
17. Montanaro F. Robinson D. Bordoni A. Lutz J.-M. A Modification to the Flow Method to Estimate Completeness in Cancer Registries with Delayed Registration J. Public Health 2006 28 274 277 10.1093/pubmed/fdl046 16849689
18. Turesson I. Linet M.S. Björkholm M. Kristinsson S.Y. Goldin L.R. Caporaso N.E. Landgren O. Ascertainment and Diagnostic Accuracy for Hematopoietic Lymphoproliferative Malignancies in Sweden 1964–2003 Int. J. Cancer 2007 121 2260 2266 10.1002/ijc.22912 17583571
19. Das B. Clegg L.X. Feuer E.J. Pickle L.W. A New Method to Evaluate the Completeness of Case Ascertainment by a Cancer Registry Cancer Causes Control 2008 19 515 525 10.1007/s10552-008-9114-0 18270798 PMC2668648
20. Contiero P. Tittarelli A. Maghini A. Fabiano S. Frassoldi E. Costa E. Gada D. Codazzi T. Crosignani P. Tessandori R. Comparison with Manual Registration Reveals Satisfactory Completeness and Efficiency of a Computerized Cancer Registration System J. Biomed. Inform. 2008 41 24 32 10.1016/j.jbi.2007.03.003 17452020
21. Stevens W. Stevens G. Kolbe J. Cox B. Comparison of New Zealand Cancer Registry Data with an Independent Lung Cancer Audit N. Z. Med. J. 2008 121 29 41 18574507
22. Schmidtmann I. Estimating Completeness in Cancer Registries—Comparing Capture-Recapture Methods in a Simulation Study Biom. J. 2008 50 1077 1092 10.1002/bimj.200810483 19067337
23. Brewster D.H. Stockton D.L. Ascertainment of Breast Cancer by the Scottish Cancer Registry: An Assessment Based on Comparison with Five Independent Breast Cancer Trials Databases Breast 2008 17 104 106 10.1016/j.breast.2007.07.044 17855094
24. Holmäng S. Amsler-Nordin S. Carlson K. Holmberg E. Johansson S.L. Completeness and Correctness of Registration of Renal Pelvic and Ureteral Cancer in the Swedish Cancer Registry Scand. J. Urol. Nephrol. 2008 42 12 17 10.1080/00365590701520024 17853011
25. Radespiel-Tröger M. Geiss K. Gärtig-Daugs A. Meyer M. Registration Completeness and Tumour-Node-Metastases Category Distribution in Prostatic and Testicular Cancer in Bavaria Eur. J. Cancer Prev. Off. J. Eur. Cancer Prev. Organ. 2008 17 230 237 10.1097/CEJ.0b013e3282f0c03d 18414194
26. Larjavaara S. Haapasalo H. Sankila R. Helén P. Auvinen A. Is the Incidence of Meningiomas Underestimated? A Regional Survey Br. J. Cancer 2008 99 182 184 10.1038/sj.bjc.6604438 18577989 PMC2453036
27. Barlow L. Westergren K. Holmberg L. Talbäck M. The Completeness of the Swedish Cancer Register: A Sample Survey for Year 1998 Acta Oncol. 2009 48 27 33 10.1080/02841860802247664 18767000
28. Nennecke A.L. Hentschel S. Reintjes R. Cancer Survival Analysis in Hamburg 1995–2003: Assessing the Data Quality within a Population-Based Registry Acta Oncol. 2009 48 34 43 10.1080/02841860802199808 18607873
29. Larsen I.K. Småstuen M. Johannesen T.B. Langmark F. Parkin D.M. Bray F. Møller B. Data Quality at the Cancer Registry of Norway: An Overview of Comparability, Completeness, Validity and Timeliness Eur. J. Cancer 2009 45 1218 1231 10.1016/j.ejca.2008.10.037 19091545
30. Petri A.L. Kjaer S.K. Christensen I.J. Blaakaer J. Hogdall E. Jeppesen U. Mosgaard B.J. Pagel J.D. Stilling L. Thranov I. Validation of Epithelial Ovarian Cancer and Fallopian Tube Cancer and Ovarian Borderline Tumor Data in the Danish Gynecological Cancer Database Acta Obstet. Gynecol. Scand. 2009 88 536 542 10.1080/00016340902846064 19308749
31. Oberaigner W. Siebert U. Are Survival Rates for Tyrol Published in the Eurocare Studies Biased? Acta Oncol. 2009 48 984 991 10.1080/02841860903188635 19714522
32. Bilet E.F. Langseth H. Thoresen S.Ø. Bray F. Completeness of Invasive Cervical Cancer at the Cancer Registry of Norway Acta Oncol. 2009 48 1070 1073 10.1080/02841860902862529 19353339
33. Field K. Kosmider S. Johns J. Farrugia H. Hastie I. Croxford M. Chapman M. Harold M. Murigu N. Gibbs P. Linking Data from Hospital and Cancer Registry Databases: Should This Be Standard Practice? Intern. Med. J. 2010 40 566 573 10.1111/j.1445-5994.2009.01984.x 19460066
34. Suwanrungruang K. Sriplung H. Attasara P. Temiyasathit S. Buasom R. Waisri N. Daoprasert K. Kamsa-ard S. Tasanapitak C. Quality of Case Ascertainment in Cancer Registries: A Proposal for a Virtual Three-Source Capture-Recapture Technique Asian Pac. J. Cancer Prev. 2011 12 173 178 21517253
35. Suwanrungruang K. Sriplung H. Temiyasathit S. Waisri N. Daoprasert K. Kamsa-Ard S. Tasanapitak C. McNeil E. Appropriateness of the Standard Mortality/Incidence Ratio in Evaluation of Completeness of Population-Based Cancer Registry Data Asian Pac. J. Cancer Prev. 2011 12 3283 3288 22471467
36. Lambe M. Eloranta S. Wigertz A. Blomqvist P. Pancreatic Cancer; Reporting and Long-Term Survival in Sweden Acta Oncol. 2011 50 1220 1227 10.3109/0284186X.2011.599338 21812626
37. Møller H. Richards S. Hanchett N. Riaz S.P. Lüchtenborg M. Holmberg L. Robinson D. Moller H. Richards S. Hanchett N. Completeness of Case Ascertainment and Survival Time Error in English Cancer Registries: Impact on 1-Year Survival Estimates Br. J. Cancer 2011 105 170 176 10.1038/bjc.2011.168 21559016 PMC3137398
38. Enerly E. Bray F. Mellem C. Hansen B.T. Kjølberg G. Dahl T. Johannesen T.B. Nygård M. Quality Assessment of the Registration of Vulvar and Vaginal Premalignant Lesions at the Cancer Registry of Norway Acta Oncol. 2012 51 45 50 10.3109/0284186X.2011.624545 22047060 PMC3251004
39. Sigurdardottir L.G. Jonasson J.G. Stefansdottir S. Jonsdottir A. Olafsdottir G.H. Olafsdottir E.J. Tryggvadottir L. Data Quality at the Icelandic Cancer Registry: Comparability, Validity, Timeliness and Completeness Acta Oncol. 2012 51 880 889 10.3109/0284186X.2012.698751 22974093
40. Cendales R. Pardo C. Uribe C. López G. Yepez M.C. Bravo L.E. Data Quality at Population-Based Cancer Registries in Colombia Biomedica 2012 32 536 544 10.7705/biomedica.v32i4.756 23715229
41. Castro C. Bento M.J. Lunet N. Campos P. Assessing the Completeness of Cancer Registration Using Suboptimal Death Certificate Information Eur. J. Cancer Prev. Off. J. Eur. Cancer Prev. Organ. 2012 21 478 479 10.1097/CEJ.0b013e32834f811c 22193365
42. Zakaria D. An Examination of the NAACCR Method of Assessing Completeness of Case Ascertainment Using the Canadian Cancer Registry Health Rep. 2013 24 3 13 24258322
43. Hackl M. Waldhoer T. Estimation of Completeness of Case Ascertainment of Austrian Cancer Incidence Data Using the Flow Method Eur. J. Public Health 2013 23 889 893 10.1093/eurpub/cks125 23002230
44. Shimakawa Y. Bah E. Wild C.P. Hall A.J. Evaluation of Data Quality at the Gambia National Cancer Registry Int. J. Cancer 2013 132 658 665 10.1002/ijc.27646 22618962
45. Londero S.C. Mathiesen J.S. Krogdahl A. Bastholt L. Overgaard J. Bentsen J. Hahn C.H. Schytte S. Pedersen H.B. Christiansen P. Completeness and Validity in a National Clinical Thyroid Cancer Database: DATHYRCA Cancer Epidemiol. 2014 38 633 637 10.1016/j.canep.2014.07.009 25132423
46. Lai S.-M. Keighley J. Garimella S. Role of Outpatient Physicians in Reporting of Hematopoietic Malignancies for Statewide Surveillance J. Regist. Manag. 2014 41 139 145 25419608
47. O’Brien K. Comber H. Sharp L. Completeness of Case Ascertainment at the Irish National Cancer Registry Ir. J. Med. Sci. 2014 183 219 224 10.1007/s11845-013-0993-z 23955644
48. Kilander C. Mattsson F. Ljung R. Lagergren J. Sadr-Azodi O. Systematic Underreporting of the Population-Based Incidence of Pancreatic and Biliary Tract Cancers Acta Oncol. 2014 53 822 829 10.3109/0284186X.2013.857429 24341732
49. Nilsson M. Tavelin B. Axelsson B. A Study of Patients Not Registered in the Swedish Cancer Register but Reported to the Swedish Register of Palliative Care 2009 as Deceased Due to Cancer Acta Oncol. 2014 53 414 419 10.3109/0284186X.2013.819115 23964659
50. Al-Haddad B.J.S. Jedy-Agba E. Oga E. Ezeome E.R. Obiorah C.C. Okobia M. Ogunbiyi J.O. Ukah C.O. Omonisi A. Nwofor A.M.E. Comparability, Diagnostic Validity and Completeness of Nigerian Cancer Registries Cancer Epidemiol. 2015 39 456 464 10.1016/j.canep.2015.03.010 25863982 PMC4446152
51. Khodadost M. Yavari P. Babaei M. Mosavi-Jarrahi A. Sarvi F. Mansori K. Khodadost B. Estimating the Completeness of Gastric Cancer Registration in Ardabil/Iran by a Capture-Recapture Method Using Population-Based Cancer Registry Data Asian Pac. J. Cancer Prev. 2015 16 1981 1986 10.7314/APJCP.2015.16.5.1981 25773798
52. Dimitrova N. Parkin D.M. Data Quality at the Bulgarian National Cancer Registry: An Overview of Comparability, Completeness, Validity and Timeliness Cancer Epidemiol. 2015 39 405 413 10.1016/j.canep.2015.03.015 25914137
53. Tomic K. Sandin F. Wigertz A. Robinson D. Lambe M. Stattin P. Evaluation of Data Quality in the National Prostate Cancer Register of Sweden Eur. J. Cancer 2015 51 101 111 10.1016/j.ejca.2014.10.025 25465187
54. Tran J.M. Schwartz R. Fung K. Rochon P. Chan A.-W. Comprehensive Capture of Cutaneous Melanoma by the Ontario Cancer Registry: Validation Study Using Community Pathology Reports Cancer Causes Control 2016 27 137 142 10.1007/s10552-015-0690-5 26537120
55. Mohammadi G. Akbari M.E. Mehrabi Y. Motlagh A.G. Quality Assessment of the National Cancer Registry in Iran: Completeness and Validity Iran. J. CANCER Prev. 2016 in press 10.17795/ijcp-8479
56. Ruppert L.P. He J. Martin J. Eckert G. Ouyang F. Church A. Dexter P. Hui S. Haggstrom D. Linkage of Indiana State Cancer Registry and Indiana Network for Patient Care Data J. Regist. Manag. 2016 43 174 178 29595920
57. Linder G. Lindblad M. Djerf P. Elbe P. Johansson J. Lundell L. Hedberg J. Validation of Data Quality in the Swedish National Register for Oesophageal and Gastric Cancer Br. J. Surg. 2016 103 1326 1335 10.1002/bjs.10234 27467590
58. Khodadost M. Mosavi-Jarrahi A. Hashemian S.S. Sarvi F. Maajani K. Moradpour F. Khatibi S.R. Amini H. Estimating the Completeness of Lung Cancer Registry in Ardabil, Iran with a Three-Source Capture-Recapture Method Asian Pac. J. Cancer Prev. 2016 17 225 229 10.7314/APJCP.2016.17.S3.225 27165230
59. Hansen S. Nielsen J. Laursen R.J. Rasmussen B.K. Nørgård B.M. Gradel K.O. Guldberg R. The Danish Neuro-Oncology Registry: Establishment, Completeness and Validity BMC Res. Notes 2016 9 425 10.1186/s13104-016-2233-x 27576510 PMC5006621
60. Mohammadi G. Akbari M.E. Mehrabi Y. Ghanbari Motlagh A. Partovi Pour E. Roshandel G. Khosravi A. Estimating Completeness of Cancer Registration in Iran with Capture-Recapture Methods Asian Pac. J. Cancer Prev. 2016 17 93 99 10.7314/apjcp.2016.17.s3.93 27165215
61. Morling J.R. Grant R. Brewster D.H. Completeness of Primary Intracranial Tumour Recording in the Scottish Cancer Registry 2011–2012 Public Health 2016 140 278 281 10.1016/j.puhe.2016.05.024 27370699
62. Fung J.W.M. Lim S.B.L. Zheng H. Ho W.Y.T. Lee B.G. Chow K.Y. Lee H.P. Data Quality at the Singapore Cancer Registry: An Overview of Comparability, Completeness, Validity and Timeliness Cancer Epidemiol. 2016 43 76 86 10.1016/j.canep.2016.06.006 27399312
63. Khodadost M. Yavari P. Khodadost B. Babaei M. Sarvi F. Khatibi S.R. Barzegari S. Estimating the Esophagus Cancer Incidence Rate in Ardabil, Iran: A Capture-Recapture Method Iran. J. Cancer Prev. 2016 9 e3972 10.17795/ijcp-3972 27413513 PMC4934015
64. Donnelly C. Cairnduff V. Chen J.J. Kearney T. Fitzpatrick D. Fox C. Gavin A. The Completeness and Timeliness of Cancer Registration and the Implications for Measuring Cancer Burden Cancer Epidemiol. 2017 49 101 107 10.1016/j.canep.2017.05.007 28600946
65. Sharma A. Sharma J.D. Kataki A.C. Barman D. Lahon R. Deka B.R. Misra C. Kalita M. Quality Assessment and Improvement of Cancer Registration System in Kamrup Urban District: A Report Indian J. Cancer 2017 54 560 565 10.4103/ijc.IJC_372_17 29798959
66. Leinonen M.K. Miettinen J. Heikkinen S. Pitkäniemi J. Malila N. Quality Measures of the Population-Based Finnish Cancer Registry Indicate Sound Data Quality for Solid Malignant Tumours Eur. J. Cancer 2017 77 31 39 10.1016/j.ejca.2017.02.017 28350996
67. Liu H. Lin G. Li K. Ding H. Xu H. Li Y. Dong H. Song S. Evolution of Cancer Registration Combining Online Reporting with Follow-up in the Community: Practices in Guangzhou, China Asian Pac. J. Cancer Prev. 2017 18 639 646 10.22034/APJCP.2017.18.3.639 28440969 PMC5464478
68. Lorez M. Bordoni A. Bouchardy C. Bulliard J.-L. Camey B. Dehler S. Frick H. Konzelmann I. Maspoli M. Mousavi S.M. Evaluation of Completeness of Case Ascertainment in Swiss Cancer Registration Eur. J. Cancer Prev. Off. J. Eur. Cancer Prev. Organ. 2017 26 S139 S146 10.1097/CEJ.0000000000000380 28574868
69. Fararouei M. Marzban M. Shahraki G. Completeness of Cancer Registry Data in a Small Iranian Province: A Capture-Recapture Approach Health Inf. Manag. J. Health Inf. Manag. Assoc. Aust. 2017 46 96 100 10.1177/1833358316668605 27702791
70. Törner A. Stokkeland K. Svensson Å. Dickman P.W. Hultcrantz R. Montgomery S. Duberg A.-S. The Underreporting of Hepatocellular Carcinoma to the Cancer Register and a Log-Linear Model to Estimate a More Correct Incidence Hepatology 2017 65 885 892 10.1002/hep.28775 27533761
71. Ryzhov A. Bray F. Ferlay J. Fedorenko Z. Goulak L. Gorokh Y. Soumkina O. Znaor A. Evaluation of Data Quality at the National Cancer Registry of Ukraine Cancer Epidemiol. 2018 53 156 165 10.1016/j.canep.2018.02.002 29459256
72. Moberger P. Sköldberg F. Birgisson H. Evaluation of the Swedish Colorectal Cancer Registry: An Overview of Completeness, Timeliness, Comparability and Validity Acta Oncol. 2018 57 1611 1621 10.1080/0284186X.2018.1529425 30477372
73. Wanner M. Matthes K.L. 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 10.1155/2018/7656197 30009174 PMC6020656
74. Eckstrand A. Shack L. Pham T.-M. Davis F. The Impact of Hospital Discharge Linkage on Case Ascertainment of Brain Tumors in the Alberta Cancer Registry, 2010–2015 J. Regist. Manag. 2018 45 109 116 31017880
75. Plouvier S.D. Bernillon P. Ligier K. Theis D. Miquel P.-H. Pasquier D. Rivest L.-P. Completeness of a Newly Implemented General Cancer Registry in Northern France: Application of a Three-Source Capture-Recapture Method Rev. Epidemiol. Sante Publique 2019 67 239 245 10.1016/j.respe.2019.04.053 31146902
76. Tettamanti G. Ljung R. Ahlbom A. Talbäck M. Lannering B. Mathiesen T. Segerlind J.P. Feychting M. Central Nervous System Tumor Registration in the Swedish Cancer Register and Inpatient Register between 1990 and 2014 Clin. Epidemiol. 2019 11 81 92 10.2147/CLEP.S177683 30655707 PMC6324922
77. Löfgren L. Eloranta S. Krawiec K. Asterkvist A. Lönnqvist C. Sandelin K. Validation of Data Quality in the Swedish National Register for Breast Cancer BMC Public Health 2019 19 495 10.1186/s12889-019-6846-6 31046737 PMC6498669
78. van der Willik K.D. Ruiter R. van Rooij F.J.A. Verkroost-van Heemst J. Hogewoning S.J. Timmermans K.C.A.A. Visser O. Schagen S.B. Ikram M.A. Stricker B.H.C. Ascertainment of Cancer in Longitudinal Research: The Concordance between the Rotterdam Study and the Netherlands Cancer Registry Int. J. Cancer 2020 147 633 640 10.1002/ijc.32750 31642518 PMC7317466
79. Lam C.J.K. Warren J.L. Nielsen M. Smith A. Boyd E. Barrett M.J. Mariotto A.B. Using the SEER-Medicare Data to Assess Incident Chronic Myeloid Leukemia and Bladder Cancer Cases Missed by Cancer Registries J. Natl. Cancer Inst. Monogr. 2020 2020 31 38 10.1093/jncimonographs/lgz033 32412074 PMC7868038
80. Lambe M. Wigertz A. Sandin F. Holmberg E. Amsler-Nordin S. Andersson T.M.-L. Pettersson D. Estimates of Lung and Pancreatic Cancer Survival in Sweden with and without Inclusion of Death Certificate Initiated (DCI) Cases Acta Oncol. 2020 59 1322 1328 10.1080/0284186X.2020.1826572 33063588
81. Etemad K. Mohammadi M.J. Nazari S.S.H. Amiri H. Talaiezadeh A. Estimation of Lung and Bronchial Cancer Registry Completeness via Capture-Recapture Method Using Population-Based Cancer Registry in Khuzestan Province in 2011 Clin. Epidemiol. Glob. Health 2020 8 884 889 10.1016/j.cegh.2020.02.017
82. Danckert B. Horsbøl T.A. Andersen O. Dalton S.O. Christensen J. Rasted M. Petersen A. Nørgaard M. Azawi N. Lund L. Registrations of Patients with Renal Cell Carcinoma in the Nationwide Danish Renal Cancer Database versus the Danish Cancer Registry: Data Quality, Completeness and Survival (DaRenCa Study-3) Clin. Epidemiol. 2020 12 807 814 10.2147/CLEP.S258755 32801918 PMC7394510
83. Bashar M.A. Thakur J.S. Budukh A. Evaluation of Data Quality of Four New Population Based Cancer Registries (PBCRs) in Chandigarh and Punjab, North India- A Quality Control Study Asian Pac. J. Cancer Prev. 2021 22 1421 1433 10.31557/APJCP.2021.22.5.1421 34048170 PMC8408406
84. Moore E. Silverman B.G. Fishler Y. Ben-Adiva E. Davidov O. Dichtiar R. Edri H. Zatlawi M. Keinan-Boker L. An Assessment of the Completeness and Timeliness of the Israel National Cancer Registry Isr. Med. Assoc. J. 2021 23 23 27 33443338
85. Landberg A. Bruce D. Lindblad P. Ljungberg B. Lundstam S. Thorstenson A. Sundqvist P. Validation of Data Quality in the National Swedish Kidney Cancer Register Scand. J. Urol. 2021 55 142 148 10.1080/21681805.2021.1885485 33599561
86. Somdyala N.I. Mbuthini L. Müller B. Sithole N. Ncinitwa A. Bradshaw D. Active Case-Finding Method Improves Completeness and Accuracy of Data Reported to the Rural Eastern Cape Cancer Registry in South Africa Ecancermedicalscience 2021 15 1251 34267807 10.3332/ecancer.2021.1251 PMC8241446
87. Maharjan U. Kauppila J.H. Gastric Cancer Completeness in Finnish Cancer Registry and Finnish Patient Registry: A Population-Based Nationwide Retrospective Cohort Study BMJ Open 2022 12 e056320 10.1136/bmjopen-2021-056320 PMC9020284 35440454
88. Giusti F. Martos C. Negrão Carvalho R. Van Eycken L. Visser O. Bettio M. Quality Indicators: Completeness, Validity and Timeliness of Cancer Registry Data Contributing to the European Cancer Information System Front. Oncol. 2023 13 1219128 10.3389/fonc.2023.1219128 37576881 PMC10421659
89. Hübner J. Baltus H. Eisemann N. Rohr M. Schumann L. Augustin J. Hagenström K. Wolf S. Garbe C. Augustin M. Evaluation of Early Skin Cancer Detection in Germany with Cancer Registry Data—Challenges, Solutions and Current Trends J. Der Dtsch. Dermatol. Ges. J. Ger. Soc. Dermatol. JDDG 2023 21 (Suppl. S5) 13 20 10.1111/ddg.15171 38063276
90. Barchuk A. Tursun-Zade R. Nazarova E. Komarov Y. Tyurina E. Tumanova Y. Belyaev A. Znaor A. Completeness of Regional Cancer Registry Data in Northwest Russia 2008–2017 BMC Cancer 2023 23 994 10.1186/s12885-023-11492-z 37853404 PMC10585853
91. Swaminathan R. Esmy P.O. Selvakumaran R. Sampath P. Sankaranarayanan R. Assessment of Registry-Based Surveillance Statistics Used for Cancer Control in the Dindigul District in South India J. Regist. Manag. 2023 50 26 33 PMC10414206 37577285
92. Wéber A. Mery L. Nagy P. Polgár C. Bray F. Kenessey I. Weber A. Mery L. Nagy P. Polgar C. Evaluation of Data Quality at the Hungarian National Cancer Registry, 2000–2019 Cancer Epidemiol. 2023 82 102306 10.1016/j.canep.2022.102306 36521336
93. Shivshankar S. Sarade M. Bhojane S. Kolekar S. Patil S. Budukh A. Quality Assessment of a Rural Population-Based Cancer Registry (PBCR) at Ratnagiri, Maharashtra, India for the Years 2017–18 Ecancermedicalscience 2024 18 1672 10.3332/ecancer.2024.1672 38439807 PMC10911674
94. E Silva D.R.M. de Oliveira M.M. Curado M.P. Data Quality in Brazilian Population-Based Cancer Registries for Gastrointestinal Cancers BMC Cancer 2024 24 870 10.1186/s12885-024-12477-2 39030476 PMC11264711
95. Lotfi F. Ravankhah Z. Rashidian H. Teimouri F. Maracy M.R. Javanmard S.H. Nahvijou A. Motlagh A.G. Roshandel G. Zendehdel K. The Cancer Incidence Pattern in Isfahan Province: An Industrial Region in the Central Part of Iran J. Cancer Epidemiol. 2024 2024 5592802 10.1155/2024/5592802 39574941 PMC11581794
96. Munn Z. Barker T.H. Moola S. Tufanaru C. Stern C. McArthur A. Stephenson M. Aromataris E. Methodological Quality of Case Series Studies: An Introduction to the JBI Critical Appraisal Tool JBI Evid. Synth. 2020 18 2127 2133 10.11124/JBIES-20-00142 33038125
97. World Health Organization International Classification of Diseases (ICD) Available online: https://www.who.int/standards/classifications/classification-of-diseases (accessed on 4 October 2024)
98. World Health Organization International Classification of Diseases for Oncology Available online: https://www.who.int/standards/classifications/other-classifications/international-classification-of-diseases-for-oncology (accessed on 4 October 2024)
99. Cardoso de Castro C.S.F. Estimating Completeness of Cancer Registration—An Application to Gastric Cancer in the North Region Cancer Registry of Portugal Master’s Thesis Universidade do Porto Porto, Portugal 2010
100. Turano L.M. Laudico A.V. Esteban D.B. Pisani P. Parkin D.M. Reduction of Death Certificate Only (DCO) Registrations by Active Follow Back Asian Pac. J. Cancer Prev. 2002 3 133 135 12718591
101. Parkin D.M. Muir C.S. Cancer Incidence in Five Continents Comparability and Quality of Data IARC Scientific Publications Lyon, France 1992 45 173 1284606
102. Ajiki W. Tsukuma H. Oshima A. Index for evaluating completeness of registration in population-based cancer registries and estimation of registration rate at the Osaka Cancer Registry between 1966 and 1992 using this index Nihon. Koshu Eisei Zasshi. 1998 45 1011 1017 9893469
103. Crocetti E. Miccinesi G. Paci E. Zappa M. An Application of the Two-Source Capture–Recapture Method to Estimate the Completeness of the Tuscany Cancer Registry, Italy Eur. J. Cancer Prev. 2001 10 417 423 11711756 10.1097/00008469-200110000-00005
104. Bullard J. Coleman M.P. Robinson D. Lutz J. Bell J. Peto J. Completeness of Cancer Registration: A New Method for Routine Br. J. Cancer 2000 82 1111 1116 10737395 10.1054/bjoc.1999.1048 PMC2374436
105. Midthune D.N. Fay M.P. Clegg L.X. Feuer E.J. Modeling Reporting Delays and Reporting Corrections in Cancer Registry Data J. Am. Stat. Assoc. 2005 100 61 70 10.1198/016214504000001899
106. Silcocks P.B.S. Robinson D. Simulation Modelling to Validate the Flow Method for Estimating Completeness of Case Ascertainment by Cancer Registries J. Public Health 2007 29 455 462 10.1093/pubmed/fdm053 17827194
107. Robert Koch Institute Cancer in Germany 2019/2020 Robert Koch Institute Berlin, Germany 2024 6 13
108. ParkiD D.M. Parkin M. Hakulinenn T. Cancer Registration: Principles and Methods IARD Scientific Publications London, UK 1991 1894319
109. Vostakolaei F.A. Karim-kos H.E. The Validity of the Mortality to Incidence Ratio as a Proxy for Site-Specific The Validity of the Mortality to Incidence Ratio as a Proxy for Site-Specific Cancer Survival Eur. J. Public Health 2011 21 573 577 10.1093/eurpub/ckq120 20813895
110. Ellis L. Belot A. Rachet B. Coleman M.P. The Mortality-to-Incidence Ratio Is Not a Valid Proxy for Cancer Survival J. Glob. Oncol. 2019 5 1 9 10.1200/JGO.19.00038 PMC6550058 31070980
111. Frank S.A. Dynamics of Cancer: Incidence, Inheritance, and Evolution Available online: https://www.ncbi.nlm.nih.gov/books/NBK1559/ (accessed on 26 June 2024)
112. Bray F. Ferlay J. Laversanne M. Brewster D.H. Gombe Mbalawa C. Kohler B. Piñeros M. Steliarova-Foucher E. Swaminathan R. Antoni S. Cancer Incidence in Five Continents: Inclusion Criteria, Highlights from Volume X and the Global Status of Cancer Registration Int. J. Cancer 2015 137 2060 2071 10.1002/ijc.29670 26135522
113. Raza S.A. Jawed I. Zoorob R.J. Salemi J.L. Completeness of Cancer Case Ascertainment in International Cancer Registries: Exploring the Issue of Gender Disparities Front. Oncol. 2020 10 1148 10.3389/fonc.2020.01148 32766152 PMC7378680
114. Tumino R. Ferretti S. Quality and Completeness Indices Epidemiol. Prev. 2004 28 17 21 15281601
115. Pukkala E. Engholm G. Højsgaard Schmidt L.K. Storm H. Khan S. Lambe M. Pettersson D. Ólafsdóttir E. Tryggvadóttir L. Hakanen T. Nordic Cancer Registries—An Overview of Their Procedures and Data Comparability Acta Oncol. 2018 57 440 455 10.1080/0284186X.2017.1407039 29226751