Quality measures of the population-based Finnish Cancer Registry indicate sound data quality for solid malignant tumours

2017 ◽  
Vol 77 ◽  
pp. 31-39 ◽  
Author(s):  
Maarit K. Leinonen ◽  
Joonas Miettinen ◽  
Sanna Heikkinen ◽  
Janne Pitkäniemi ◽  
Nea Malila
Keyword(s):  
Data Quality ◽  
Cancer Registry ◽  
Quality Measures ◽  
Population Based ◽  
Malignant Tumours ◽  
Sound Data

scholarly journals Time Trends Analysis of Cervical Cancer Incidence in Cluj County, Romania, Using Data from a Population-Based Cancer Registry

2021 ◽  
Vol 28 (3) ◽  
pp. 1706-1717
Author(s):  
Radu-Mihai Ignat ◽  
Daniela Coza ◽  
Patricia Ignat ◽  
Radu-Ion Badea ◽  
Ofelia Șuteu
Keyword(s):  
Cervical Cancer ◽  
Data Quality ◽  
Cancer Registry ◽  
Cancer Incidence ◽  
Time Trends ◽  
Screening Program ◽  
Population Based ◽  
Incidence Rates ◽  
Cervical Cancer Incidence ◽  
Annual Percent Change

(1) Background: Romania has one of the highest cervical cancer incidence rates in Europe. In Cluj County, the first screening program was initiated in 1998. We aimed to investigate the time trends of cervical cancer incidence in women from Cluj County and to evaluate the data quality at the Cancer Registry. (2) Methods: We calculated time trends of standardized incidence rates in the period 1998–2014 and the Annual Percent Change (APC%). To assess data quality, we used the indicators: mortality/incidence ratio (M/I), percentage of cases declared only at death (DOD%), and percentage of cases with pathological confirmation (PC%). (3) Results: The standardized incidence rate increased steadily, from 23.74 cases/100,000 in 1998, to 32/100,000 in 2014, with an APC% of 2.49% (p < 0.05). The rise in incidence affected both squamous cell carcinoma (APC% 2.49%) (p < 0.05) and cervical adenocarcinoma (APC% 10.54%) (p < 0.05). The M/I ratio was 0.29, DOD% 2.66%, and MC% 94.8%. The last two parameters are within the silver standard concerning data quality. (4) Conclusions. Our study revealed an ascending trend of cervical cancer incidence, more consistent for adenocarcinoma, in the context of a newly introduced screening program and partially due to the improvement of the quality of case reporting at the Cancer Registry from Cluj.

Data Quality and Quality Control of a Population-Based Cancer Registry: Experience in Finland

1994 ◽  
Vol 33 (4) ◽  
pp. 365-369 ◽  
Author(s):  
Lyly Teppo ◽  
Eero Pukkala ◽  
Maria Lehtonen
Keyword(s):  
Quality Control ◽  
Data Quality ◽  
Cancer Registry ◽  
Population Based

scholarly journals Cancer in the Azores: initial results from a recently established population-based cancer registry

2012 ◽  
Vol 15 (2) ◽  
pp. 285-297
Author(s):  
Gonçalo F. Lacerda ◽  
Paulo S. Pinheiro ◽  
José M. Cabral ◽  
Jorge G. Câmara ◽  
Vítor L. Rodrigues
Keyword(s):  
Cancer Registry ◽  
Cancer Survival ◽  
Poisson Approximation ◽  
Cancer Registries ◽  
Population Based ◽  
Malignant Tumours ◽  
Azores Archipelago ◽  
Incidence Data ◽  
Mainland Portugal ◽  
Established Population

INTRODUCTION: The Azores archipelago has long been the Portuguese region that presents the highest mortality rates for certain cancers. Lack of incidence data has prevented the evaluation of the actual burden of this disease in the Azorean population. METHODS: Malignant tumours (ICD-O 5th Digit /3) initially diagnosed between the January 1st 2000 and December 31st 2002 were retrieved from the database of the recently established population-based cancer registry. Crude, age-specific and age-standardized rates were calculated and confidence intervals were estimated using Poisson approximation. Relative risks of developing cancer in the Azores when compared to mainland Portugal have been represented by standardized ratios. Quality indicators, including Mortality:Incidence (M:I) ratios, were also assessed. RESULTS: Overall, the data shows a high incidence rate for some malignant diseases, specifically in men. Compared to those living in mainland Portugal, both Azorean men (RR 1.412; 99% CI 1.407-1.416) and women (1.127; 1.125-1.129) presented a significantly higher risk of developing cancer, all sites combined. When compared with other cancer registries, a less favourable cancer survival pattern is reported in the Azores, as emphasized by higher M:I ratios for several cancer sites. CONCLUSIONS: A preliminary analysis of the results suggests the presence of some major risk factors in the Azorean population, namely tobacco smoking in men. Higher M:I ratios would also point to survival disparities between the Azores archipelago and the continent, which should be further studied.

A Review of Failure Handling Mechanisms for Data Quality Measures

2012 ◽  
Author(s):  
Nurul A. Emran ◽  
Noraswaliza Abdullah ◽  
Nuzaimah Mustafa
Keyword(s):  
Data Quality ◽  
Quality Measures ◽  
Failure Handling

scholarly journals Does access to care play a role in liver cancer survival? The ten-year (2006–2015) experience from a population-based cancer registry in Southern Italy

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Walter Mazzucco ◽  
Francesco Vitale ◽  
Sergio Mazzola ◽  
Rosalba Amodio ◽  
Maurizio Zarcone ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Access To Care ◽  
Cancer Registry ◽  
Health Care Services ◽  
Cancer Survival ◽  
Developed Countries ◽  
Population Based ◽  
Easy Access ◽  
Net Survival ◽  
Poor Access

Abstract Background Hepatocellular carcinoma (HCC) is the most frequent primary invasive cancer of the liver. During the last decade, the epidemiology of HCC has been continuously changing in developed countries, due to more effective primary prevention and to successful treatment of virus-related liver diseases. The study aims to examine survival by level of access to care in patients with HCC, for all patients combined and by age. Methods We included 2018 adult patients (15–99 years) diagnosed with a primary liver tumour, registered in the Palermo Province Cancer Registry during 2006–2015, and followed-up to 30 October 2019. We obtained a proxy measure of access to care by linking each record to the Hospital Discharge Records and the Ambulatory Discharge Records. We estimated net survival up to 5 years after diagnosis by access to care (“easy access to care” versus “poor access to care”), using the Pohar-Perme estimator. Estimates were age-standardised using International Cancer Survival Standard (ICSS) weights. We also examined survival by access to care and age (15–64, 65–74 and ≥ 75 years). Results Among the 2018 patients, 62.4% were morphologically verified and 37.6% clinically diagnosed. Morphologically verified tumours were more frequent in patients aged 65–74 years (41.6%), while tumours diagnosed clinically were more frequent in patients aged 75 years or over (50.2%). During 2006–2015, age-standardised net survival was higher among HCC patients with “easy access to care” than in those with “poor access to care” (68% vs. 48% at 1 year, 29% vs. 11% at 5 years; p < 0.0001). Net survival up to 5 years was higher for patients with “easy access to care” in each age group (p < 0.0001). Moreover, survival increased slightly for patients with easier access to care, while it remained relatively stable for patients with poor access to care. Conclusions During 2006–2015, 5-year survival was higher for HCC patients with easier access to care, probably reflecting progressive improvement in the effectiveness of health care services offered to these patients. Our linkage algorithm could provide valuable evidence to support healthcare decision-making in the context of the evolving epidemiology of hepatocellular carcinoma.

scholarly journals Data Quality Measures and Efficient Evaluation Algorithms for Large-Scale High-Dimensional Data

2021 ◽  
Vol 11 (2) ◽  
pp. 472
Author(s):  
Hyeongmin Cho ◽  
Sangkyun Lee
Keyword(s):  
Machine Learning ◽  
Data Quality ◽  
Large Scale ◽  
High Dimensional Data ◽  
Quality Measures ◽  
Training Data ◽  
Measure Data ◽  
High Dimensional ◽  
Small Scale ◽  
Class Separability

Machine learning has been proven to be effective in various application areas, such as object and speech recognition on mobile systems. Since a critical key to machine learning success is the availability of large training data, many datasets are being disclosed and published online. From a data consumer or manager point of view, measuring data quality is an important first step in the learning process. We need to determine which datasets to use, update, and maintain. However, not many practical ways to measure data quality are available today, especially when it comes to large-scale high-dimensional data, such as images and videos. This paper proposes two data quality measures that can compute class separability and in-class variability, the two important aspects of data quality, for a given dataset. Classical data quality measures tend to focus only on class separability; however, we suggest that in-class variability is another important data quality factor. We provide efficient algorithms to compute our quality measures based on random projections and bootstrapping with statistical benefits on large-scale high-dimensional data. In experiments, we show that our measures are compatible with classical measures on small-scale data and can be computed much more efficiently on large-scale high-dimensional datasets.

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