Contribution of chronic conditions to smoking differences in life expectancy with and without disability in Belgium

How Does Environmental and Occupational Exposure Contribute to Carcinogenesis in Genitourinary and Lung Cancers?

Environmental and occupational exposures have been associated with an increased risk of different types of cancers, although the exact mechanisms of higher carcinogenesis risk are not always well understood. Lung cancer is the leading cause of global cancer mortality, and, also, genitourinary neoplasms are among the main causes of cancer-related deaths in Western countries. The purpose of this review is to describe the main environmental and occupational factors that increase the risk of developing lung and genitourinary cancers and to investigate carcinogenesis mechanisms that link these agents to cancer onset. Further objectives are to identify methods for the prevention or the early detection of carcinogenic agents and, therefore, to reduce the risk of developing these cancers or to detect them at earlier stages.

Individuals with long-term illness, disability or infirmity are more likely to smoke than healthy controls: An instrumental variable analysis

Despite the prevalence of smoking cessation programs and public health campaigns, individuals with long-term illness, disability, or infirmity have been found to smoke more often than those without such conditions, leading to worsening health. However, the available literature has mainly focused on the association between long-term illness and smoking, which might suffer from the possible bidirectional influence, while few studies have examined the potential causal effect of long-term illness on smoking. This gap in knowledge can be addressed using an instrumental variable analysis that uses a third variable as an instrument between the endogenous independent and dependent variables and allows the identification of the direction of causality under the discussed assumptions. Our study analyzes the UK General Household Survey in 2006, covering a nationally representative 13,585 households. We exploited the number of vehicles as the instrumental variable for long-term illness, disability, or infirmity as vehicle numbers may be related to illness based on the notion that these individuals are less likely to drive, but that vehicle number may have no relationship to the likelihood of smoking. Our results suggested that chronic illness status causes a significantly 28% higher probability of smoking. The findings have wide implications for public health policymakers to design a more accessible campaign around smoking and for psychologists and doctors to take targeted care for the welfare of individuals with long-term illnesses.

Mixed evidence for the compression of morbidity hypothesis for smoking elimination-a systematic literature review

BACKGROUND

There is debate around the composition of life years gained from smoking elimination. The aim of this study was to conduct a systematic review of the literature to synthesize existing evidence on the effect of smoking status on health expectancy and to examine whether smoking elimination leads to compression of morbidity.

METHODS

Five databases were systematically searched for peer-reviewed articles. Studies that presented quantitative estimates of health expectancy for smokers and non-/never-smokers were eligible for inclusion. Studies were searched, selected and reviewed by two reviewers who extracted the relevant data and assessed the risk of bias of the included articles independently.

RESULTS

The search identified 2491 unique records, whereof 20 articles were eligible for inclusion (including 26 cohorts). The indicators used to measure health included disability/activity limitations (n=9), health-related quality of life (EQ-5D) (n=2), weighted disabilities (n=1), self-rated health (n=9), chronic diseases (n=6), cardiovascular diseases (n=4) and cognitive impairment (n=1). Available evidence showed consistently that non-/never-smokers experience more healthy life years throughout their lives than smokers. Findings were inconsistent on the effect of smoking on the absolute number of unhealthy life years. Findings concerning the time proportionally spent unhealthy were less heterogeneous: nearly all included articles reported that non-/never-smokers experience relatively less unhealthy life years (e.g. relative compression of morbidity).

CONCLUSIONS

Support for the relative compression of morbidity due to smoking elimination was evident. Further research is needed into the absolute compression of morbidity hypothesis since current evidence is mixed, and methodology of studies needs to be harmonized.

Environmental exposures and health behavior in association with mental health: a study design

BACKGROUND

Air pollution, green space and smoking are known to affect human health. However, less is known about their underlying biological mechanisms. One of these mechanisms could be biological aging. In this study, we explore the mediation of biomarkers of exposure and biological aging to explain the associations between environmental exposures, health behavior and mental health.

METHODS

The study population of this cross-sectional study (n = 1168) is a subsample of the Belgian 2018 Health Interview Survey (BHIS). Mental health indicators including psychological and severe psychological distress, life satisfaction, vitality, eating disorders, suicidal ideation, subjective health and depressive and anxiety disorders, demographics and health behavior such as smoking are derived from the BHIS. Urine and blood samples are collected to measure respectively the biomarkers of exposure (urinary black carbon (BC) and (hydroxy)cotinine) and the biomarkers of biological aging (mitochondrial DNA content (mtDNAc) and telomere length (TL)). Recent and chronic exposure (μg/m3) to nitrogen dioxide (NO2), particulate matter ≤2.5 μm (PM2.5) and ≤ 10 μm (PM10) and BC at the participants' residence are modelled using a high resolution spatial temporal interpolation model. Residential green space is defined in buffers of different size (50 m - 5000 m) using land cover data in ArcGIS 10 software. For the statistical analysis multivariate linear and logistic regressions as well as mediation analyses are used taking into account a priori selected covariates and confounders.

RESULTS

As this study combined data of BHIS and laboratory analyses, not all data is available for all participants. Therefore, data analyses will be conducted on different subsets. Data on air pollution and green space exposure is available for all BHIS participants. Questions on smoking and mental health were answered by respectively 7829 and 7213 BHIS participants. For biomarker assessment, (hydroxy) cotinine, urinary BC and the biomarkers of biological aging are measured for respectively 1130, 1120 and 985 participants.

CONCLUSION

By use of personal markers of air pollution and smoking, as well as biological aging, we will gain knowledge about the association between environmental exposures, health behavior, and the mental health status. The results of the study can provide insights on the health of the Belgian population, making it a nationwide interesting study.

Potential impact of reduced tobacco use on life and health expectancies in Belgium

OBJECTIVES

We investigated the potential impact of reduced tobacco use scenarios on total life expectancy and health expectancies, i.e., healthy life years and unhealthy life years.

METHODS

Data from the Belgian Health Interview Survey 2013 were used to estimate smoking and disability prevalence. Disability was based on the Global Activity Limitation Indicator. We used DYNAMO-HIA to quantify the impacts of risk factor changes and to compare the "business-as-usual" with alternative scenarios.

RESULTS

The "business-as-usual" scenario estimated that in 2028 the 15-year-old men/women would live additional 50/52 years without disability and 14/17 years with disability. The "smoking-free population" scenario added 3.4/2.8 healthy life years and reduced unhealthy life years by 0.79/1.9. Scenarios combining the prevention of smoking initiation with smoking cessation programs are the most effective, yielding the largest increase in healthy life years (1.9/1.7) and the largest decrease in unhealthy life years (- 0.80/- 1.47).

CONCLUSIONS

Health impact assessment tools provide different scenarios for evidence-informed public health actions. New anti-smoking strategies or stricter enforcement of existing policies potentially gain more healthy life years and reduce unhealthy life years in Belgium.