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Thyroid Cancer in Kazakhstan: Component Analysis of Incidence Dynamics.

Nurbek Saginbekovich IgissinovSaken KozhahmetovMarzhan ZhantubetovaGulnur IgissinovaZarina BilyalovaGulnur AkpolatovaVladimir LyustSarsembi KoblandinDulat TurebayevKairat AdaibayevArdak OmarbekovDinara TarzhanovaAkmaral ZhantureyevaAskar Esseyev
Published in: Asian Pacific journal of cancer prevention : APJCP (2019)
Background: The International Agency for Research on Cancer (IARC) reports that 567,000 new cases of thyroid cancer (TC) were registered in the world in 2018, and the age-standardized incidence rate was 6.7 per 100,000. The Global Cancer Observation forecasts a 35% growth in the number of new cases worldwide by 2040. The number of patients with TC in Kazakhstan is also increasing steadily. This investigation was the first epidemiological study of TC trends by component analysis among the population of Kazakhstan. This paper presents the results of the component analysis of TC incidence trends in Kazakhstan. Methods: The study covers primary data of TC cases (ICD 10 – C73) registered throughout Kazakhstan from 2009 to 2018. TC incidence trends were evaluated using component analysis according to the methodological recommendations. Results: 5,559 new TC cases were registered during the 10-year study period. The average age of patients was 52.0±0.2 years, the average annual age-standardized rate in 2009-2018 was 3.3±0.20/0000, with a constant upward trend (Т=+6.6%). According to the component analysis results, the increase in incidence was mainly due to the combined effect of the two factors: the increased disease risk (ΔR=+61.7%), and the population growth (ΔP=+15.4%). Conclusion: The noted increase in incidence was mainly caused by the changes in risk factors, such as the worsening environmental aspects and the increase in detection of clinically non-manifesting cases. The results of the study shall be taken into account when planning anticancer activities for TC.
Keyphrases
  • risk factors
  • papillary thyroid
  • emergency department
  • squamous cell carcinoma
  • machine learning
  • climate change
  • big data
  • adverse drug
  • quantum dots