The role of smoking in changes in the survival curve: an empirical study in 10 European countries

Janssen, Fanny; Rousson, Valentin; Paccaud, Fred

doi:10.1016/j.annepidem.2015.01.007

Cited by 25 publications

(16 citation statements)

References 37 publications

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“…This finding of postponement of mortality having had a much bigger effect than compression is in line with the recent results by Janssen et al [45], who performed a decomposition of life expectancy at age 50 into the effect of compression versus delay in nine European countries between 1950 and 2009, using the decomposition method by Rossi et al [20]. Bergeron-Boucher et al – very recently – found for Swedish females as well that since the mid-1960s more than 70% of the increase in life expectancy at birth was caused by delay [21].…”

Section: Discussionsupporting

confidence: 93%

A new parametric model to assess delay and compression of mortality

Beer

Janssen

2016

Popul Health Metrics

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BackgroundA decrease in mortality across all ages causes a shift of the age pattern of mortality, or mortality delay, while differences in the rate of decrease across ages cause a change in the shape of the age-at-death distribution, mortality compression or expansion. Evidence exists for both compression and delay of mortality. Existing parametric models to describe the full age pattern of mortality are not able to capture mortality delay versus mortality compression. More recent models that assess delay versus compression mostly focused on the adult or old ages alone and did not distinguish mortality compression below and above the modal age at death, although they represent different mechanisms.MethodsThis paper presents a new parametric model that describes the full age pattern of mortality and assesses compression – at different stages of life – and delay of mortality: the CoDe model. The model includes 10 parameters, of which five are constant over time. The five time-varying parameters reflect delay of mortality and compression of mortality in infancy, adolescence, young adulthood, late adulthood, and old age. The model describes infant and background mortality by two simple functions, uses a mixed logistic model with different slopes in adult, middle, and old age, and includes the modal age at death as a parameter to account for the delay in mortality.ResultsApplying the CoDe model to age-specific probabilities of death for Japanese, French, American, and Danish men and women between 1950 and 2010 showed a very good fit of the full age pattern of mortality. Delay of mortality explained about two-thirds of the increase in life expectancy at birth, whereas compression of mortality due to mortality declines in young age explained about one-third. No strong compression of mortality in late adulthood age was observed. Mortality compression in old age has had a small negative impact on life expectancy.ConclusionsThe CoDe model proved a valid instrument for describing the full age pattern of mortality and for disentangling the effects of mortality delay and compression – at different stages of life – on the increase in life expectancy.

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Section: Discussionsupporting

confidence: 93%

A new parametric model to assess delay and compression of mortality

Beer

Janssen

2016

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“…Lung cancer is one of the most preventable cancers 35 . Experience from the developed countries show marked decrease in smoking rates and lung cancer occurrence after comprehensive tobacco control program 36 . Tobacco control can also reduce risk of cardiovascular diseases 37 .…”

Section: Discussionmentioning

confidence: 99%

Non-communicable diseases are key to further narrow gender gap in life expectancy in Shanghai, China

Chen

Zhou

Sun

et al. 2020

Preprint

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Background: To address change in gender gap of life expectancy (GGLE) in Shanghai from 1973 to 2018, and to identify the major causes of death and age groups associated with the change overtime.Methods: Retrospective demographic analysis with application of Joinpoint regression to evaluate the temporal trend in GGLE. Causes of death were coded in accordance with International Classification of Diseases and mapped with the Global Burden of Disease (GBD) cause list. Life table technique and decomposition method was used to express changes in GGLE.Results: Trend of GGLE in Shanghai experienced two phases ie., a decrease from 8.4 to 4.2 years in the descent phase (1973-1999) and a fluctuation between 4.0 and 4.9 years in the plateau phase (1999-2018). The reduced age-specific mortality rates tended to concentrate to a narrower age range, from age 0-9 and above 30 years in the descent phase to age above 55 years in the plateau phase. Gastroesophageal and liver cancer, communicable, chronic respiratory and digestive diseases were once the major contributors to narrow GGLE in the descent phase. While importance should be attached to a widening effect on GGLE by lung cancer, cardiovascular diseases, other neoplasms like colorectal and pancreatic cancer and diabetes in recent plateau phase.Conclusions: Non-communicable diseases (NCDs) have made GGLE enter a plateau phase from a descent phase in Shanghai China. Public efforts to reduce excess mortalities for male NCDs, cancers, cardiovascular diseases, chronic respiratory diseases and diabetes in particular and health policies focused on the middle-aged and elderly population might further narrow GGLE and ensure improvement in health and health equity in Shanghai China.

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“…Lung cancer is one of the most preventable cancers 37 . Experience from the developed countries show marked decrease in smoking rates and lung cancer occurrence after comprehensive tobacco control program 38 . Tobacco control can also reduce risk of cardiovascular diseases 39 .…”

Section: Discussionmentioning

confidence: 99%

Non-communicable diseases are key to further narrow gender gap in life expectancy in Shanghai, China

Chen

Zhou

Sun

et al. 2020

Preprint

View full text Add to dashboard Cite

Background To address change in gender gap of life expectancy (GGLE) in Shanghai from 1973 to 2018, and to identify the major causes of death and age groups associated with the change overtime.Methods Retrospective demographic analysis with application of Joinpoint regression to evaluate the temporal trend in GGLE. Causes of death were coded in accordance with International Classification of Diseases and mapped with the Global Burden of Disease (GBD) cause list. Life table technique and decomposition method was used to express changes in GGLE.Results Trend of GGLE in Shanghai experienced two phases ie., a decrease from 8.4 to 4.2 years in the descent phase (1973-1999) and a fluctuation between 4.0 and 4.9 years in the plateau phase (1999-2018). The reduced age-specific mortality rates tended to concentrate to a narrower age range, from age 0-9 and above 30 years in the descent phase to age above 55 years in the plateau phase. Gastroesophageal and liver cancer, communicable, chronic respiratory and digestive diseases were once the major contributors to narrow GGLE in the descent phase. While importance should be attached to a widening effect on GGLE by lung cancer, cardiovascular diseases, other neoplasms like colorectal and pancreatic cancer and diabetes in recent plateau phase.Conclusions Non-communicable diseases (NCDs) have made GGLE enter a plateau phase from a descent phase in Shanghai China. Public efforts to reduce excess mortalities for male NCDs, cancers, cardiovascular diseases, chronic respiratory diseases and diabetes in particular and health policies focused on the middle-aged and elderly population might further narrow GGLE and ensure improvement in health and health equity in Shanghai China.

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The role of smoking in changes in the survival curve: an empirical study in 10 European countries

Cited by 25 publications

References 37 publications

A new parametric model to assess delay and compression of mortality

A new parametric model to assess delay and compression of mortality

Non-communicable diseases are key to further narrow gender gap in life expectancy in Shanghai, China

Non-communicable diseases are key to further narrow gender gap in life expectancy in Shanghai, China

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