Automated selection of relevant information for notification of incident cancer cases within a multisource cancer registry

Socio-demographic inequalities in stage at diagnosis of lung cancer: A French population-based study

BACKGROUND

Diagnosing patients at a non-advanced stage has become a mainstay of lung cancer prevention and control strategies. Understanding socio-demographic inequalities in stage at diagnosis may improve the targeting of interventions on patients at higher risk. This study aimed to identify these socio-demographic determinants in a large-scale French population-based cancer registry.

METHODS

All incident lung cancers diagnosed between 2008 and 2019 identified from the Poitou-Charentes Cancer Registry (south-west France) were included. Stage at diagnosis was categorised as advanced/non-advanced (TNM III/IV vs I/II) according to the 8th TNM edition, the objective being to ensure a consistent level of prognosis over time. Socio-demographic variables included age, sex, the French European Deprivation Index (EDI) and patient's place of residence. Their impact on stage at diagnosis was quantified by multivariate logistic regression models with subgroup analyses by histological subtype.

RESULTS

Out of the 15,487 included patients, 75% were diagnosed at an advanced stage (66% to 95% depending on the histological subtype), 17% at a non-advanced stage and 10% at a non-specified stage. Multivariate analysis showed different patterns according to histological subtypes. In patients with adenocarcinoma, a higher risk of advanced stage was found for younger and older patients (u-shape), those most deprived, and those living in rural areas. The same effect of age was reported for squamous cell carcinomas, while no association was found for small-cell lung carcinomas.

CONCLUSIONS

This study highlighted substantial socio-demographic inequalities in stage at diagnosis, specifically for adenocarcinoma patients. Diagnosis strategies could be refined and strengthened in the non-smoker population, in which adenocarcinomas are mainly reported.

Indicators of guideline-concordant care in lung cancer defined with a modified Delphi method and piloted in a cohort of over 5,800 cases

BACKGROUND

To identify indicators of guideline-concordant care in lung cancer, to implement such indicators with cancer registry data linked to health databases, and to pilot them in a cohort of patients from the cancer registry of the Milan Province.

METHODS

Thirty-four indicators were selected by revision of main guidelines by cancer epidemiologists, and then evaluated by a multidisciplinary panel of clinicians involved in lung cancer care and working on the pathway of lung cancer diagnosis and treatment in the Lombardy region, Italy. With a modified Delphi method, they assessed for each indicator the content validity as a quality measure of the care pathway, the degree of modifiability from the health professional, and the relevance to the health professional. Feasibility was assessed using the cancer registry and the routine health records of the Lombardy region. Feasible indicators were then calculated in the cohort of lung cancer patients diagnosed in 2007-2012 derived from the cancer registry of the Milan Province. Criterion validity was assessed reviewing clinical records of a random sample of 114 patients (threshold for acceptable discordance ≤20%). Finally, reliability was evaluated at the provider level.

RESULTS

Initially, 34 indicators were proposed for evaluation in the first Delphi round. Of the finally 22 selected indicators, 3 were not feasible because the required information was actually not available. The remaining 19 were calculated on the pilot cohort. After assessment of criterion validity (3 eliminated), 16 indicators were retained in the final set and evaluated for reliability.

CONCLUSION

The developed and piloted set of indicators is now available to implement and monitor, over time, quality initiatives for lung cancer care in the studied health system.

Building a model for disease classification integration in oncology, an approach based on the national cancer institute thesaurus

BACKGROUND

Identifying incident cancer cases within a population remains essential for scientific research in oncology. Data produced within electronic health records can be useful for this purpose. Due to the multiplicity of providers, heterogeneous terminologies such as ICD-10 and ICD-O-3 are used for oncology diagnosis recording purpose. To enable disease identification based on these diagnoses, there is a need for integrating disease classifications in oncology. Our aim was to build a model integrating concepts involved in two disease classifications, namely ICD-10 (diagnosis) and ICD-O-3 (topography and morphology), despite their structural heterogeneity. Based on the NCIt, a "derivative" model for linking diagnosis and topography-morphology combinations was defined and built. ICD-O-3 and ICD-10 codes were then used to instantiate classes of the "derivative" model. Links between terminologies obtained through the model were then compared to mappings provided by the Surveillance, Epidemiology, and End Results (SEER) program.

RESULTS

The model integrated 42% of neoplasm ICD-10 codes (excluding metastasis), 98% of ICD-O-3 morphology codes (excluding metastasis) and 68% of ICD-O-3 topography codes. For every codes instantiating at least a class in the "derivative" model, comparison with SEER mappings reveals that all mappings were actually available in the model as a link between the corresponding codes.

CONCLUSIONS

We have proposed a method to automatically build a model for integrating ICD-10 and ICD-O-3 based on the NCIt. The resulting "derivative" model is a machine understandable resource that enables an integrated view of these heterogeneous terminologies. The NCIt structure and the available relationships can help to bridge disease classifications taking into account their structural and granular heterogeneities. However, (i) inconsistencies exist within the NCIt leading to misclassifications in the "derivative" model, (ii) the "derivative" model only integrates a part of ICD-10 and ICD-O-3. The NCIt is not sufficient for integration purpose and further work based on other termino-ontological resources is needed in order to enrich the model and avoid identified inconsistencies.

Surveillance of waiting times for access to treatment: a registry-based computed approach in breast cancer care

The current study set out to automatically generate waiting times for access to surgery, chemotherapy and radiotherapy, and to analyse their determinants for non-metastatic breast cancer patients. We used data from the Poitou-Charentes regional cancer registry of women diagnosed with stages I-III breast carcinoma between 2008 and 2010. Waiting times were automatically computed from a previously validated algorithm modelling the care trajectory and then compared with national guidelines. The population of this study included 1082 patients. The compliance with guidelines ranged from 52.4% (access to adjuvant chemotherapy) to 89.2% (access to adjuvant radiotherapy). Younger age, a higher TNM stage, a lower grade, having a triple negative tumour, being the subject of multidisciplinary meetings and being a patient at a public hospital were associated with longer waiting times. The main result was the significant heterogeneity between geographical areas of treatment for all waiting times studied. The original, reproducible use of a registry-based automated algorithm to generate waiting times will help to follow these indicators routinely and efficiently.

Missing semantic annotation in databases. The root cause for data integration and migration problems in information systems

Data integration is a well-known grand challenge in information systems. It is highly relevant in medicine because of the multitude of patient data sources. Semantic annotations of data items regarding concept and value domain, based on comprehensive terminologies can facilitate data integration and migration. Therefore it should be implemented in databases from the very beginning.

Temporal representation of care trajectories of cancer patients using data from a regional information system: an application in breast cancer

Background

Ensuring that all cancer patients have access to the appropriate treatment within an appropriate time is a strategic priority in many countries. There is in particular a need to describe and analyse cancer care trajectories and to produce waiting time indicators. We developed an algorithm for extracting temporally represented care trajectories from coded information collected routinely by the general cancer Registry in Poitou-Charentes region, France. The present work aimed to assess the performance of this algorithm on real-life patient data in the setting of non-metastatic breast cancer, using measures of similarity.

Methods

Care trajectories were modeled as ordered dated events aggregated into states, the granularity of which was defined from standard care guidelines. The algorithm generates each state from the aggregation over a period of tracer events characterised on the basis of diagnoses and medical procedures. The sequences are presented in simple form showing presence and order of the states, and in an extended form that integrates the duration of the states. The similarity of the sequences, which are represented in the form of chains of characters, was calculated using a generalised Levenshtein distance.

Results

The evaluation was performed on a sample of 159 female patients whose itineraries were also calculated manually from medical records using the same aggregation rules and dating system as the algorithm. Ninety-eight per cent of the trajectories were correctly reconstructed with respect to the ordering of states. When the duration of states was taken into account, 94% of the trajectories matched reality within three days. Dissimilarities between sequences were mainly due to the absence of certain pathology reports and to coding anomalies in hospitalisation data.

Conclusions

These results show the ability of an integrated regional information system to formalise care trajectories and automatically produce indicators for time-lapse to care instatement, of interest in the planning of care in cancer. The next step will consist in evaluating this approach and extending it to more complex trajectories (metastasis, relapse) and to other cancer localisations.