Opposite trends in incidence of breast cancer in young and old female cohorts in Hungary and the impact of the Covid-19 pandemic: a nationwide study between 2011-2020.

Background: This nationwide study examined breast cancer (BC) incidence and mortality rates in Hungary between 2011-2019, and the impact of the Covid-19 pandemic on the incidence and mortality rates in 2020 using the databases of the National Health Insurance Fund (NHIF) and Central Statistical Office (CSO) of Hungary. Methods: Our nationwide, retrospective study included patients who were newly diagnosed with breast cancer (International Codes of Diseases ICD)-10 C50) between Jan 1, 2011 and Dec 31, 2020. Age-standardized incidence and mortality rates (ASRs) were calculated using European Standard Populations (ESP). Results: 7,729 to 8,233 new breast cancer cases were recorded in the NHIF database annually, and 3,550 to 4,909 all-cause deaths occurred within BC population per year during 2011-2019 period, while 2,096 to 2,223 breast cancer cause-specific death was recorded (CSO). Age-standardized incidence rates varied between 116.73 and 106.16/100,000 PYs, showing a mean annual change of -0.7% (95% CI: -1.21%-0.16%) and a total change of -5.41% (95% CI: -9.24 to -1.32). Age-standardized mortality rates varied between 26.65-24.97/100,000 PYs (mean annual change: -0.58%; 95% CI: -1.31-0.27%; p=0.101; total change: -5.98%; 95% CI: -13.36-2.66). Age-specific incidence rates significantly decreased between 2011 and 2019 in women aged 50-59, 60-69, 80-89, and ≥90 years (-8.22%, -14.28%, -9.14%, and -36.22%, respectively), while it increased in young females by 30.02% (95%CI 17,01%- 51,97%) during the same period. From 2019 to 2020 (in first COVID-19 pandemic year), breast cancer incidence nominally decreased by 12% (incidence rate ratio [RR]: 0.88; 95% CI: 0.69-1.13; 2020 vs. 2019), all-cause mortality nominally increased by 6% (RR: 1.06; 95% CI: 0.79-1.43) among breast cancer patients, and cause-specific mortality did not change (RR: 1.00; 95%CI: 0.86-1.15). Conclusion: The incidence of breast cancer significantly decreased in older age groups (≥50 years), oppositely increased among young females between 2011 and 2019, while cause-specific mortality in breast cancer patients showed a non-significant decrease. In 2020, the Covid-19 pandemic resulted in a nominal, but not statistically significant, 12% decrease in breast cancer incidence, with no significant increase in cause-specific breast cancer mortality observed during 2020.

Underlying reasons for post-mortem diagnosed lung cancer cases - A robust retrospective comparative study from Hungary (HULC study).

Objective: The Hungarian Undiagnosed Lung Cancer (HULC) study aimed to explore the potential reasons for missed LC (lung cancer) diagnosis by comparing healthcare and socio-economic data among patients with post-mortem diagnosed LC with those who were diagnosed with LC during their lives. Methods: This nationwide, retrospective study used the databases of the Hungarian Central Statistical Office (HCSO) and National Health Insurance Fund (NHIF) to identify patients who died between January 1, 2019 and December 31, 2019 and were diagnosed with lung cancer post-mortem (population A) or during their lifetime (population B). Patient characteristics, socio-economic factors, and healthcare resource utilization (HCRU) data were compared between the diagnosed and undiagnosed patient population. Results: During the study period, 8,435 patients were identified from the HCSO database with LC as the cause of death, of whom 1,203 (14.24%) had no LC-related ICD (International Classification of Diseases) code records in the NHIF database during their lives (post-mortem diagnosed LC population). Post-mortem diagnosed LC patients were significantly older than patients diagnosed while still alive (mean age 71.20 vs. 68.69 years, p<0.001), with a more pronounced age difference among female patients (difference: 4.57 years, p<0.001), and had significantly fewer GP (General Practitioner) and specialist visits, X-ray and CT scans within 7 to 24 months and 6 months before death, although the differences in GP and specialist visits within 7-24 months did not seem clinically relevant. Patients diagnosed with LC while still alive were more likely to be married (47.62% vs. 33.49%), had higher educational attainment, and had more children, than patients diagnosed with LC post-mortem. Conclusions: Post-mortem diagnosed lung cancer accounts for 14.24% of total lung cancer mortality in Hungary. This study provides valuable insights into patient characteristics, socio-economic factors, and HCRU data potentially associated with a high risk of lung cancer misdiagnosis.

GALEN based formal representation of ICD10.

OBJECTIVES: The main objective is to create a knowledge-intensive coding support tool for the International Classification of Diseases (ICD10), which is based on formal representation of ICD10 categories. Beyond this task the resulting ontology could be reused in various ways. Decidability is an important issue for computer-assisted coding; consequently the ontology should be represented in description logic. METHODS: The meaning of the ICD10 categories is represented using the GALEN Core Reference Model. Due to the deficiencies of its representation language (GRAIL) the ontology is transformed to the quasi-standard OWL. A test system which extracts disease concepts and classifies them to ICD10 categories has been implemented in Prolog to verify the feasibility of the approach. RESULTS: The formal representation of the first two chapters of ICD10 (infectious diseases and neoplasms) has been almost completed. The constructed ontology has been converted to OWL DL. The test system successfully identified diseases in medical records from gastrointestinal oncology (84% recall, however precision is only 45%). The classifier module is still under development. Due to the experiences gained during the modelling, in the future work FMA is going to be used as anatomical reference ontology.

Non Aristotelian categories in medicine.

This paper discusses the representation of medical categories that can not be defined in Aristotelian sense. Two kinds of these categories are mentioned: the prototype and the family resemblance categories. Such categories obviously do exist in medical domain. Search on the Net was performed for free text definition for some commonly used medical categories, like 'autism', 'Burkitt lymphoma' and 'disease'. Most of the found often contradicting definitions do not follow the Aristotelian rules of definition. Many definitions describe statistical properties of the category that are often useless in individual cases. A simple way is suggested that makes possible to represent such categories in biomedical ontologies and treat them separate from better formed categories. This makes possible to revise these categories at any later stage of ontology development.

GALEN Based Formal Representation of ICD10.

The authors present a formal representation of ICD10 based on GALEN CRM. The goal of the work is to create a coding support tool for coding clinical diagnoses to ICD10. The formal representation of the first two chapters of ICD10 has been almost completed. The paper presents the main aspects of the modelling, and the experienced problems. The constructed ontology has been converted to OWL, and a test system has been implemented in Prolog to verify the feasibility of the approach. The system successfully identified diseases in medical records from gastrointestinal oncology. The classifier module is still under development.