A new method to evaluate the completeness of case ascertainment by a cancer registry.
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ABSTRACT:
SUBMITTER: Das B
PROVIDER: S-EPMC2668648 | biostudies-other | 2008 Jun
REPOSITORIES: biostudies-other
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Project description:BackgroundThere is a trend to increasing use of routinely collected health data to ascertain outcome measures in trials. We report on the completeness and accuracy of national ovarian cancer and death registration in the United Kingdom Collaborative Trial of Ovarian Cancer Screening (UKCTOCS).MethodsOf the 202,638 participants, 202,632 were successfully linked and followed through national cancer and death registries of Northern Ireland, Wales and England. Women with registrations of any of 19 pre-defined ICD-10 codes suggestive of tubo-ovarian cancer or notification of ovarian/tubal/peritoneal cancer from hospital episode statistics or trial sites were identified. Copies of hospital and primary care notes were retrieved and reviewed by an independent outcomes review committee. National registration of site and cause of death as ovarian/tubal/peritoneal cancer (C56/C57/C48) obtained up to 3 months after trial censorship was compared to that assigned by outcomes review (reference standard).ResultsOutcome review was undertaken in 3110 women on whom notification was received between 2001 and 2014. Ovarian cancer was confirmed in 1324 of whom 1125 had a relevant cancer registration. Sensitivity and specificity of ovarian/tubal/peritoneal cancer registration were 85.0% (1125/1324; 95% CI 83.7-86.2%) and 94.0% (1679/1786; 95% CI 93.2-94.8%), respectively. Of 2041 death registrations reviewed, 681 were confirmed to have a tubo-ovarian cancer of whom 605 had relevant death registration. Sensitivity and specificity were 88.8% (605/681; 95% CI 86.4-91.2%) and 96.7% (1482/1533, 95% CI 95.8-97.6%), respectively. When multiple electronic health record sources were considered, sensitivity for cancer site increased to 91.1% (1206/1324, 95% CI 89.4-92.5%) and for cause of death 94.0% (640/681, 95% CI 91.9-95.5%). Of 1232 with cancer registration, 8.7% (107/1232) were wrongly designated as ovarian/tubal/peritoneal cancers by the registry and 4.0% (47/1172) of confirmed tubo-ovarian cancers were mis-registered. In 656 with death registrations, 7.8% (51/656) were wrongly assigned as due to ovarian/tubal/peritoneal cancers while 6.2% (40/645) of confirmed tubo-ovarian cancer deaths were mis-registered.ConclusionFollow-up of trial participants for tubo-ovarian cancer using national registry data will result in incomplete ascertainment, particularly of the site due in part to the latency of registration. This can be reduced by using other routinely collected data such as hospital episode statistics. Central adjudication by experts though resource intensive adds value by improving the accuracy of diagnoses.Trial registrationISRCTN: ISRCTN22488978 . Registered on 6 April 2000.
| S-EPMC7831170 | biostudies-literature
Project description:Background:Triclustering has shown to be a valuable tool for the analysis of microarray data since its appearance as an improvement of classical clustering and biclustering techniques. The standard for validation of triclustering is based on three different measures: correlation, graphic similarity of the patterns and functional annotations for the genes extracted from the Gene Ontology project (GO). Results:We propose TRIQ, a single evaluation measure that combines the three measures previously described: correlation, graphic validation and functional annotation, providing a single value as result of the validation of a tricluster solution and therefore simplifying the steps inherent to research of comparison and selection of solutions. TRIQ has been applied to three datasets already studied and evaluated with single measures based on correlation, graphic similarity and GO terms. Triclusters have been extracted from this three datasets using two different algorithms: TriGen and OPTricluster. Conclusions:TRIQ has successfully provided the same results as a the three single evaluation measures. Furthermore, we have applied TRIQ to results from another algorithm, OPTRicluster, and we have shown how TRIQ has been a valid tool to compare results from different algorithms in a quantitative straightforward manner. Therefore, it appears as a valid measure to represent and summarize the quality of tricluster solutions. It is also feasible for evaluation of non biological triclusters, due to the parametrization of each component of TRIQ.
| S-EPMC6091209 | biostudies-literature
Project description:BackgroundData from population-based cancer registries are often used to compare cancer survival between countries or regions. The ICBP SURVMARK-2 study is an international partnership aiming to quantify and explore the reasons behind survival differences across high-income countries. However, the magnitude and relevance of differences in cancer survival between countries have been questioned, as it is argued that observed survival variations may be explained, at least in part, by differences in cancer registration practice, completeness and the availability and quality of the respective data sources.MethodsAs part of the ICBP SURVMARK-2 study, we used a simulation approach to better understand how differences in completeness, the characteristics of those missed and inclusion of cases found from death certificates can impact on cancer survival estimates.ResultsBias in 1- and 5-year net survival estimates for 216 simulated scenarios is presented. Out of the investigated factors, the proportion of cases not registered through sources other than death certificates, had the largest impact on survival estimates.ConclusionOur results show that the differences in registration practice between participating countries could in our most extreme scenarios explain only a part of the largest observed differences in cancer survival.
| S-EPMC7921088 | biostudies-literature
Project description:Complete and accurate registration of cancer is needed to provide reliable data on cancer incidence and to investigate aetiology. Such data can be derived from national cancer registries, but also from large population-based cohort studies. Yet, the concordance and discordance between these two data sources remain unknown. We evaluated completeness and accuracy of cancer registration by studying the concordance between the population-based Rotterdam Study (RS) and the Netherlands Cancer Registry (NCR) between 1989 and 2012 using the independent case ascertainment method. We compared all incident cancers in participants of the RS (aged ?45?years) to registered cancers in the NCR in the same persons based on the date of diagnosis and the International Classification of Diseases (ICD) code. In total, 2,977 unique incident cancers among 2,685 persons were registered. Two hundred eighty-eight cancers (9.7%) were coded by the RS that were not present in the NCR. These were mostly nonpathology-confirmed lung and haematological cancers. Furthermore, 116 cancers were coded by the NCR, but not by the RS (3.9%), of which 20.7% were breast cancers. Regarding pathology-confirmed cancer diagnoses, completeness was >95% in both registries. Eighty per cent of the cancers registered in both registries were coded with the same date of diagnosis and ICD code. Of the remaining cancers, 344 (14.5%) were misclassified with regard to date of diagnosis and 72 (3.0%) with regard to ICD code. Our findings indicate that multiple sources on cancer are complementary and should be combined to ensure reliable data on cancer incidence.
| S-EPMC7317466 | biostudies-literature
Project description:Population-based Cancer Registries (PBCRs) are tasked with collecting high-quality data, important for monitoring cancer burden and its trends, planning and evaluating cancer control activities, clinical and epidemiological research and development of health policies. The main indicators to measure data quality are validity, completeness, comparability and timeliness. The aim of this article is to evaluate the quality of PBCRs data collected in the first ENCR-JRC data call, dated 2015.MethodsAll malignant tumours, except skin non-melanoma, and in situ and uncertain behaviour of bladder were obtained from 130 European general PBCRs for patients older than 19 years. Proportion of cases with death certificate only (DCO%), proportion of cases with unknown primary site (PSU%), proportion of microscopically verified cases (MV%), mortality to incidence (M:I) ratio, proportion of cases with unspecified morphology (UM%) and the median of the difference between the registration date and the incidence date were computed by sex, age group, cancer site, period and PBCR.ResultsA total of 28,776,562 cases from 130 PBCRs, operating in 30 European countries were included in the analysis. The quality of incidence data reported by PBCRs has been improving across the study period. Data quality is worse for the oldest age groups and for cancer sites with poor survival. No differences were found between males and females. High variability in data quality was detected across European PBCRs.Conclusionthe results reported in this paper are to be interpreted as the baseline for monitoring PBCRs data quality indicators in Europe along time.
| S-EPMC10421659 | biostudies-literature
Project description:BackgroundThe FeverApp registry uses ecological momentary assessment (EMA) to collect parental data on pediatric fever for scientific research. The mobile app FeverApp educates parents on safe fever management and serves as a fever diary.ObjectiveThe focus of this study was to evaluate the completeness and concordance of the EMA-based FeverApp registry with regard to its data quality from a multilevel perspective.MethodsStructured descriptions of fever episodes by health care professionals from an office were used as reference. The number of children, their sociodemographic data, and agreement of fever episodes, with maximum temperature, intake of antipyretics and antibiotics, and physician visits, were compared with the entries in the corresponding physician's reference records. The data quality indicators for completeness, meaning the extent to which the necessary data for the registry has actually been submitted, and concordance, which is the correspondence of the value of a data element with a reference source, were chosen to analyze whether EMA may be a suitable method for this kind of registry.ResultsIn both data sources, 1012 children were available for comparison over 16 months. The completeness of gender (1012/1012, 100%) and date of birth (1004/1012, 99.2%) information was high, and the mismatches were 0.69% (7/1012) and 1.19% (12/1012), respectively, between the sources. Of these 1012 children, 668 (66%) registered fever episodes in FeverApp. They relate to 534 families with 953 fever episodes in the reference records and 1452 episodes in the FeverApp registry. Of the 534 families, 183 (34.3%) refrained from visiting the office during fever episodes but nevertheless documented them in FeverApp. Largest part (766/1452, 52.75%) episodes were recorded exclusively in the FeverApp registry by 371 (371/534, 69.5%) families. The remaining 686 (47.2%) episodes of 391 (58.5%) children from 351 (65.7%) families were comparable with the reference data source in terms of physician visits, medication, and temperature. The completeness ranged, depending on the kind of variable, from 11.5% to 65% in the registry and from 7.6% to 42.6% in the office. The 953 fever episodes reported by the reference office consisted of 681 (71.5%) acute and 272 (28.5%) past episodes. In FeverApp, most past (262/272, 96.3%) but less acute (424/681, 62.3%) episodes have been entered. The concordance rates were varied: 90.2% for antibiotic use, 66.6% for antipyretic use, 61.7% for physician visits, and 16% for the highest temperature during the fever episode.ConclusionsBoth sources delivered only partial data, and the rates of completeness and concordance depended on the kind of variable. However, the FeverApp registry showed higher documentation and precision rates than professional records for all considered variables. Therefore, EMA may play a unique supplement for research in ambulatory care. FeverApp could support pediatric offices, especially during the pandemic.
| S-EPMC9669892 | biostudies-literature
Project description:ObjectivesTo compare registry and electronic health record (EHR) data mining approaches for cohort ascertainment in patients with pediatric pulmonary hypertension (PH) in an effort to overcome some of the limitations of registry enrollment alone in identifying patients with particular disease phenotypes.Study designThis study was a single-center retrospective analysis of EHR and registry data at Boston Children's Hospital. The local Informatics for Integrating Biology and the Bedside (i2b2) data warehouse was queried for billing codes, prescriptions, and narrative data related to pediatric PH. Computable phenotype algorithms were developed by fitting penalized logistic regression models to a physician-annotated training set. Algorithms were applied to a candidate patient cohort, and performance was evaluated using a separate set of 136 records and 179 registry patients. We compared clinical and demographic characteristics of patients identified by computable phenotype and the registry.ResultsThe computable phenotype had an area under the receiver operating characteristics curve of 90% (95% CI, 85%-95%), a positive predictive value of 85% (95% CI, 77%-93%), and identified 413 patients (an additional 231%) with pediatric PH who were not enrolled in the registry. Patients identified by the computable phenotype were clinically distinct from registry patients, with a greater prevalence of diagnoses related to perinatal distress and left heart disease.ConclusionsMining of EHRs using computable phenotypes identified a large cohort of patients not recruited using a classic registry. Fusion of EHR and registry data can improve cohort ascertainment for the study of rare diseases.Trial registrationClinicalTrials.gov: NCT02249923.
| S-EPMC5572538 | biostudies-literature
Project description:IntroductionMany national and subnational governments need to routinely measure the completeness of death registration for monitoring and statistical purposes. Existing methods, such as death distribution and capture-recapture methods, have a number of limitations such as inaccuracy and complexity that prevent widespread application. This paper presents a novel empirical method to estimate completeness of death registration at the national and subnational level.MethodsRandom-effects models to predict the logit of death registration completeness were developed from 2,451 country-years in 110 countries from 1970-2015 using the Global Burden of Disease 2015 database. Predictors include the registered crude death rate, under-five mortality rate, population age structure and under-five death registration completeness. Models were developed separately for males, females and both sexes.FindingsAll variables are highly significant and reliably predict completeness of registration across a wide range of registered crude death rates (R-squared 0.85). Mean error is highest at medium levels of observed completeness. The models show quite close agreement between predicted and observed completeness for populations outside the dataset. There is high concordance with the Hybrid death distribution method in Brazilian states. Uncertainty in the under-five mortality rate, assessed using the dataset and in Colombian departmentos, has minimal impact on national level predicted completeness, but a larger effect at the subnational level.ConclusionsThe method demonstrates sufficient flexibility to predict a wide range of completeness levels at a given registered crude death rate. The method can be applied utilising data readily available at the subnational level, and can be used to assess completeness of deaths reported from health facilities, censuses and surveys. Its utility is diminished where the adult mortality rate is unusually high for a given under-five mortality rate. The method overcomes the considerable limitations of existing methods and has considerable potential for widespread application by national and subnational governments.
| S-EPMC5976169 | biostudies-literature
Project description:Mixed models have become the tool of choice for genetic association studies; however, standard mixed model methods may be poorly calibrated or underpowered under family sampling bias and/or case-control ascertainment. Previously, we introduced a liability threshold-based mixed model association statistic (LTMLM) to address case-control ascertainment in unrelated samples. Here, we consider family-biased case-control ascertainment, where case and control subjects are ascertained non-randomly with respect to family relatedness. Previous work has shown that this type of ascertainment can severely bias heritability estimates; we show here that it also impacts mixed model association statistics. We introduce a family-based association statistic (LT-Fam) that is robust to this problem. Similar to LTMLM, LT-Fam is computed from posterior mean liabilities (PML) under a liability threshold model; however, LT-Fam uses published narrow-sense heritability estimates to avoid the problem of biased heritability estimation, enabling correct calibration. In simulations with family-biased case-control ascertainment, LT-Fam was correctly calibrated (average ?2 = 1.00-1.02 for null SNPs), whereas the Armitage trend test (ATT), standard mixed model association (MLM), and case-control retrospective association test (CARAT) were mis-calibrated (e.g., average ?2 = 0.50-1.22 for MLM, 0.89-2.65 for CARAT). LT-Fam also attained higher power than other methods in some settings. In 1,259 type 2 diabetes-affected case subjects and 5,765 control subjects from the CARe cohort, downsampled to induce family-biased ascertainment, LT-Fam was correctly calibrated whereas ATT, MLM, and CARAT were again mis-calibrated. Our results highlight the importance of modeling family sampling bias in case-control datasets with related samples.
| S-EPMC5223022 | biostudies-literature
Project description:BackgroundIn epidemiologic studies where physician-based case adjudication is not feasible, Parkinson's disease (PD) case ascertainment is often limited to self-reports which may not be accurate. We evaluated strategies to identify PD cases in the Agricultural Health Study (AHS).MethodsDoctor-diagnosed PD was self-reported on all cohort-wide surveys; potential cases were also identified from death certificates. Follow-up surveys asked about PD-related motor and non-motor symptoms. For PD confirmation, we collected additional diagnosis, symptom, and treatment data from 510 potential PD cases or their proxy (65% of those contacted) in a supplemental screener and obtained medical records for a subset (n = 65). We classified PD cases using established criteria and screener data.ResultsOf 510 potential PD cases, 75% were considered "probable" or "possible"; this proportion increased among participants diagnosed by a specialist (81.2%), taking PD medication (85.2%), or reporting ≥5 motor symptoms (86.8%) in a regular AHS survey. Of those with medical records, 93% (57 of 61) of probable or possible PD was confirmed. Never-smoking and non-motor and motor symptoms reported in prior AHS surveys were more common with probable/possible PD than unconfirmed PD.ConclusionIn this retrospective PD case ascertainment effort, we found that PD self-report with information on motor symptoms or medications may be a reasonable alternative for identifying PD cases when physician exam is not feasible. Because of intervening mortality, screeners could not be obtained from about one-third of those contacted. Thus, findings warrant replication.
| S-EPMC8133399 | biostudies-literature