The completeness and timeliness of cancer registration and the implications for measuring cancer burden

Development and quality appraisal of a new English breast screening linked data set as part of the age, test threshold, and frequency of mammography screening (ATHENA-M) study

OBJECTIVES

To build a data set capturing the whole breast cancer screening journey from individual breast cancer screening records to outcomes and assess data quality.

METHODS

Routine screening records (invitation, attendance, test results) from all 79 English NHS breast screening centres between January 1, 1988 and March 31, 2018 were linked to cancer registry (cancer characteristics and treatment) and national mortality data. Data quality was assessed using comparability, validity, timeliness, and completeness.

RESULTS

Screening records were extracted from 76/79 English breast screening centres, 3/79 were not possible due to software issues. Data linkage was successful from 1997 after introduction of a universal identifier for women (NHS number). Prior to 1997 outcome data are incomplete due to linkage issues, reducing validity. Between January 1, 1997 and March 31, 2018, a total of 11 262 730 women were offered screening of whom 9 371 973 attended at least one appointment, with 139 million person-years of follow-up (a median of 12.4 person years for each woman included) with 73 810 breast cancer deaths and 1 111 139 any-cause deaths. Comparability to reference data sets and internal validity were demonstrated. Data completeness was high for core screening variables (>99%) and main cancer outcomes (>95%).

CONCLUSIONS

The ATHENA-M project has created a large high-quality and representative data set of individual women's screening trajectories and outcomes in England from 1997 to 2018, data before 1997 are lower quality.

ADVANCES IN KNOWLEDGE

This is the most complete data set of English breast screening records and outcomes constructed to date, which can be used to evaluate and optimize screening.

Cancer survival: left truncation and comparison of results from hospital-based cancer registry and population-based cancer registry

Background

Cancer survival is an important indicator for evaluating cancer prognosis and cancer care outcomes. The incidence dates used in calculating survival differ between population-based registries and hospital-based registries. Studies examining the effects of the left truncation of incidence dates and delayed reporting on survival estimates are scarce in real-world applications.

Methods

Cancer cases hospitalized at Nantong Tumor Hospital during the years 2002-2017 were traced with their records registered in the Qidong Cancer Registry. Survival was calculated using the life table method for cancer patients with the first visit dates recorded in the hospital-based cancer registry (HBR) as the diagnosis date (OSH), those with the registered dates of population-based cancer (PBR) registered as the incidence date (OSP), and those with corrected dates when the delayed report dates were calibrated (OSC).

Results

Among 2,636 cases, 1,307 had incidence dates registered in PBR prior to the diagnosis dates of the first hospitalization registered in HBR, while 667 cases with incidence dates registered in PBR were later than the diagnosis dates registered in HBR. The 5-year OSH, OSP, and OSC were 36.1%, 37.4%, and 39.0%, respectively. The "lost" proportion of 5-year survival due to the left truncation for HBR data was estimated to be between 3.5% and 7.4%, and the "delayed-report" proportion of 5-year survival for PBR data was found to be 4.1%.

Conclusion

Left truncation of survival in HBR cases was demonstrated. The pseudo-left truncation in PBR should be reduced by controlling delayed reporting and maximizing completeness. Our study provides practical references and suggestions for evaluating the survival of cancer patients with HBR and PBR.

The Intervention of Data Mining in the Allocation Efficiency of Multiple Intelligent Devices in Intelligent Pharmacy

With the wide application of artificial intelligence and big data technology in the medical field, the problems of high cost and low efficiency of traditional pharmacy management were becoming more and more obvious. Therefore, this paper proposed to use data mining technology to design and develop the dispensing process and equipment of intelligent pharmacy. Firstly, it summarized the existing data mining technology and association rule methods and expounded its application value in the related fields. Secondly, the data standard and integration platform of dispensing in intelligent pharmacy were established. Web service technology was used to design the interactive interface and call it to the intelligent device of pharmacy. Finally, an intelligent pharmacy management system based on association rule mining was constructed through the data mining of intelligent pharmacy equipment, in order to improve the intelligence and informatization of modern pharmacy management. For the emergency dispensing process of intelligent equipment failure, data mining was used to optimize the intelligent pharmacy equipment and dispensing process and change the pharmacy management from traditional prescription to patient drug treatment, so as to improve the dispensing efficiency of intelligent pharmacy equipment. Through the systematic test and analysis, the results showed that through the real-time risk prevention and control, the formula accuracy and operation speed of the intelligent dispensing machine were improved and the dispensing time was shortened. Through intelligent drug delivery, the unreasonable drug use of patients was reduced, the safety and effectiveness of clinical drug use were ensured, and the contradiction between doctors and patients was reduced. This study can not only optimize the medical experience of patients and provide patients with more high-quality and humanized pharmaceutical technical services but also provide some support for the intelligent management of modern hospitals.

Research Strategies for Low-Survival Cancers

While substantial progress has been made to improve the diagnosis, prognosis, and survivorship of patients with cancer, certain cancer types, along with metastatic and refractory disease, remain clinical challenges. To improve patient outcomes, ultimately, the cancer research community must meet and overcome these challenges, leading to improved approaches to treat the most difficult cancers. Here, we discuss research progress aimed at gaining a better understanding of the molecular and cellular changes in tumor cells and the surrounding stroma, presented at the 56th Irish Association for Cancer Research (IACR) Annual Conference. With a focus on poor prognosis cancers, such as esophageal and chemo-resistant colorectal cancers, we highlight how detailed molecular knowledge of tumor and stromal biology can provide windows of opportunity for biomarker discovery and therapeutic targets. Even with previously characterized targets, such as phosphoinositide 3-kinase (PI3K), one of the most altered proteins in all human cancers, new insights into how this protein may be more effectively inhibited through novel combination therapies is presented.

Conceptual Framework to Guide Early Diagnosis Programs for Symptomatic Cancer as Part of Global Cancer Control

Diagnosing cancer earlier can enable timely treatment and optimize outcomes. Worldwide, national cancer control plans increasingly encompass early diagnosis programs for symptomatic patients, commonly comprising awareness campaigns to encourage prompt help-seeking for possible cancer symptoms and health system policies to support prompt diagnostic assessment and access to treatment. By their nature, early diagnosis programs involve complex public health interventions aiming to address unmet health needs by acting on patient, clinical, and system factors. However, there is uncertainty regarding how to optimize the design and evaluation of such interventions. We propose that decisions about early diagnosis programs should consider four interrelated components: first, the conduct of a needs assessment (based on cancer-site-specific statistics) to identify the cancers that may benefit most from early diagnosis in the target population; second, the consideration of symptom epidemiology to inform prioritization within an intervention; third, the identification of factors influencing prompt help-seeking at individual and system level to support the design and evaluation of interventions; and finally, the evaluation of factors influencing the health systems' capacity to promptly assess patients. This conceptual framework can be used by public health researchers and policy makers to identify the greatest evidence gaps and guide the design and evaluation of local early diagnosis programs as part of broader cancer control strategies.

Disclosing progress in cancer survival with less delay

Cancer registration plays a key role in monitoring the burden of cancer. However, cancer registry (CR) data are usually made available with substantial delay to ensure best possible completeness of case ascertainment. Here, we investigate empirically with routinely available data whether such a delay is mandatory for survival analyses or whether data can be used earlier to provide more up-to-date survival estimates. We compared distributions of prognostic factors and period relative survival estimates for three population-based CRs in Germany (Schleswig-Holstein (SH), Rhineland-Palatinate (RP), Saarland (SA)) computed on datasets extracted one (DY+1) to 5 years after the year of diagnosis (DY+5; reference). Analyses were conducted for seven cancer sites and various survival analyses scenarios. The proportion of patients registered in the datasets at a given time varied strongly across registries with 57% (SH), 2% (RP) and 26% (SA) registered in DY+1 and >93% in all registries in DY+3. Five-year survival estimates for the most recent three-year period were comparable to estimates from the reference dataset already in DY+1 (mean absolute deviations = 0.2-0.6% units). Deviations >1% units were only observed for pancreatic and lung cancer in RP and leukemia in SA (all ≤1.5% units). For estimates of 1-year survival based on the most recent 1-year period only, slightly longer delays were required, but reasonable estimates were still obtained after 1-2 years, depending on the CR and cancer site. Thus, progress in cancer survival could be disclosed in a more timely manner than commonly practiced despite delays in completeness of registration.

Indicators of Data Quality at the Cancer Registry Zurich and Zug in Switzerland

Data quality is an important issue in cancer registration. This paper provides a comprehensive overview of the four main data quality indicators (comparability, validity, timeliness, and completeness) for the Cancer Registry Zurich and Zug (Switzerland). We extracted all malignant cancer cases (excluding non-melanoma skin cancer) diagnosed between 1980 and 2014 in the canton of Zurich. Methods included the proportion of morphologically verified cases (MV%), the proportion of DCN and DCO cases (2009-2014), cases with primary site uncertain (PSU%), the stability of incidence rates over time, age-specific incidence rates for childhood cancer, and mortality:incidence (MI) ratios. The DCO rate decreased from 6.4% in 1997 to 0.8% in 2014 and was <5% since 2000. MV% was 95.5% in 2014. PSU% was <3% over the whole period. The incidence rate of all tumours increased over time with site-specific fluctuations. The overall M:I ratio decreased from 0.58 in 1980 to 0.37 in 2014. Overall, data quality of the Cancer Registry Zurich and Zug was acceptable according to the methods presented in this review. Most indicators improved over time with low DCO rates, high MV%, low PSU%, relatively low M:I ratios and age-specific incidence of childhood cancer within reference ranges.