Expected number of asbestos-related lung cancers in the Netherlands in the next two decades: a comparison of methods

Sjoukje van der Bij, Roel C.H. Vermeulen, Lutzen Portengen, Karel G.M. Moons, Hendrik Koffijberg

Research output: Contribution to journalArticleAcademicpeer-review

2 Citations (Scopus)

Abstract

Objectives Exposure to asbestos fibres increases the risk of mesothelioma and lung cancer. Although the vast majority of mesothelioma cases are caused by asbestos exposure, the number of asbestos-related lung cancers is less clear. This number cannot be determined directly as lung cancer causes are not clinically distinguishable but may be estimated using varying modelling methods. Methods We applied three different modelling methods to the Dutch population supplemented with uncertainty ranges (UR) due to uncertainty in model input values. The first method estimated asbestos-related lung cancer cases directly from observed and predicted mesothelioma cases in an age-period-cohort analysis. The second method used evidence on the fraction of lung cancer cases attributable (population attributable risk (PAR)) to asbestos exposure. The third method incorporated risk estimates and population exposure estimates to perform a life table analysis. Results The three methods varied substantially in incorporated evidence. Moreover, the estimated number of asbestos-related lung cancer cases in the Netherlands between 2011 and 2030 depended crucially on the actual method applied, as the mesothelioma method predicts 17 500 expected cases (UR 7000–57 000), the PAR method predicts 12 150 cases (UR 6700–19 000), and the life table analysis predicts 6800 cases (UR 6800–33 850). Conclusions The three different methods described resulted in absolute estimates varying by a factor of ∼2.5. These results show that accurate estimation of the impact of asbestos exposure on the lung cancer burden remains a challenge
Original languageEnglish
Pages (from-to)342-349
JournalOccupational and environmental medicine
Volume73
DOIs
Publication statusPublished - 2016

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Asbestos
Netherlands
Lung Neoplasms
Uncertainty
Mesothelioma
Life Tables
Population
Cohort Studies
Demography

Keywords

  • IR-101960
  • METIS-318677

Cite this

van der Bij, Sjoukje ; Vermeulen, Roel C.H. ; Portengen, Lutzen ; Moons, Karel G.M. ; Koffijberg, Hendrik. / Expected number of asbestos-related lung cancers in the Netherlands in the next two decades: a comparison of methods. In: Occupational and environmental medicine. 2016 ; Vol. 73. pp. 342-349.
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abstract = "Objectives Exposure to asbestos fibres increases the risk of mesothelioma and lung cancer. Although the vast majority of mesothelioma cases are caused by asbestos exposure, the number of asbestos-related lung cancers is less clear. This number cannot be determined directly as lung cancer causes are not clinically distinguishable but may be estimated using varying modelling methods. Methods We applied three different modelling methods to the Dutch population supplemented with uncertainty ranges (UR) due to uncertainty in model input values. The first method estimated asbestos-related lung cancer cases directly from observed and predicted mesothelioma cases in an age-period-cohort analysis. The second method used evidence on the fraction of lung cancer cases attributable (population attributable risk (PAR)) to asbestos exposure. The third method incorporated risk estimates and population exposure estimates to perform a life table analysis. Results The three methods varied substantially in incorporated evidence. Moreover, the estimated number of asbestos-related lung cancer cases in the Netherlands between 2011 and 2030 depended crucially on the actual method applied, as the mesothelioma method predicts 17 500 expected cases (UR 7000–57 000), the PAR method predicts 12 150 cases (UR 6700–19 000), and the life table analysis predicts 6800 cases (UR 6800–33 850). Conclusions The three different methods described resulted in absolute estimates varying by a factor of ∼2.5. These results show that accurate estimation of the impact of asbestos exposure on the lung cancer burden remains a challenge",
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author = "{van der Bij}, Sjoukje and Vermeulen, {Roel C.H.} and Lutzen Portengen and Moons, {Karel G.M.} and Hendrik Koffijberg",
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Expected number of asbestos-related lung cancers in the Netherlands in the next two decades: a comparison of methods. / van der Bij, Sjoukje; Vermeulen, Roel C.H.; Portengen, Lutzen; Moons, Karel G.M.; Koffijberg, Hendrik.

In: Occupational and environmental medicine, Vol. 73, 2016, p. 342-349.

Research output: Contribution to journalArticleAcademicpeer-review

TY - JOUR

T1 - Expected number of asbestos-related lung cancers in the Netherlands in the next two decades: a comparison of methods

AU - van der Bij, Sjoukje

AU - Vermeulen, Roel C.H.

AU - Portengen, Lutzen

AU - Moons, Karel G.M.

AU - Koffijberg, Hendrik

PY - 2016

Y1 - 2016

N2 - Objectives Exposure to asbestos fibres increases the risk of mesothelioma and lung cancer. Although the vast majority of mesothelioma cases are caused by asbestos exposure, the number of asbestos-related lung cancers is less clear. This number cannot be determined directly as lung cancer causes are not clinically distinguishable but may be estimated using varying modelling methods. Methods We applied three different modelling methods to the Dutch population supplemented with uncertainty ranges (UR) due to uncertainty in model input values. The first method estimated asbestos-related lung cancer cases directly from observed and predicted mesothelioma cases in an age-period-cohort analysis. The second method used evidence on the fraction of lung cancer cases attributable (population attributable risk (PAR)) to asbestos exposure. The third method incorporated risk estimates and population exposure estimates to perform a life table analysis. Results The three methods varied substantially in incorporated evidence. Moreover, the estimated number of asbestos-related lung cancer cases in the Netherlands between 2011 and 2030 depended crucially on the actual method applied, as the mesothelioma method predicts 17 500 expected cases (UR 7000–57 000), the PAR method predicts 12 150 cases (UR 6700–19 000), and the life table analysis predicts 6800 cases (UR 6800–33 850). Conclusions The three different methods described resulted in absolute estimates varying by a factor of ∼2.5. These results show that accurate estimation of the impact of asbestos exposure on the lung cancer burden remains a challenge

AB - Objectives Exposure to asbestos fibres increases the risk of mesothelioma and lung cancer. Although the vast majority of mesothelioma cases are caused by asbestos exposure, the number of asbestos-related lung cancers is less clear. This number cannot be determined directly as lung cancer causes are not clinically distinguishable but may be estimated using varying modelling methods. Methods We applied three different modelling methods to the Dutch population supplemented with uncertainty ranges (UR) due to uncertainty in model input values. The first method estimated asbestos-related lung cancer cases directly from observed and predicted mesothelioma cases in an age-period-cohort analysis. The second method used evidence on the fraction of lung cancer cases attributable (population attributable risk (PAR)) to asbestos exposure. The third method incorporated risk estimates and population exposure estimates to perform a life table analysis. Results The three methods varied substantially in incorporated evidence. Moreover, the estimated number of asbestos-related lung cancer cases in the Netherlands between 2011 and 2030 depended crucially on the actual method applied, as the mesothelioma method predicts 17 500 expected cases (UR 7000–57 000), the PAR method predicts 12 150 cases (UR 6700–19 000), and the life table analysis predicts 6800 cases (UR 6800–33 850). Conclusions The three different methods described resulted in absolute estimates varying by a factor of ∼2.5. These results show that accurate estimation of the impact of asbestos exposure on the lung cancer burden remains a challenge

KW - IR-101960

KW - METIS-318677

U2 - 10.1136/oemed-2014-102614

DO - 10.1136/oemed-2014-102614

M3 - Article

VL - 73

SP - 342

EP - 349

JO - Occupational and environmental medicine

JF - Occupational and environmental medicine

SN - 1351-0711

ER -