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Introduction Tobacco smoking poses a significant risk for various diseases, including cardiovascular diseases, chronic respiratory diseases, and cancers. In Kenya, tobacco-related deaths contribute substantially to non-communicable disease mortality. This study aims to quantify the mortality attributed to tobacco smoking in Kenya from 2012 to 2021. Methods Employing a prevalence-based analysis model, the study utilized population attributable fraction (PAF) to estimate age-specific smoke attributable mortality (SAM) rates for individuals aged ≥35 years. Causes of death associated with tobacco use, including cancers, cardiovascular diseases, respiratory diseases, tuberculosis, and diabetes, were analyzed based on age, sex, and death records between 2012 and 2021. Results Over the study period, 60228 deaths were attributed to tobacco-related diseases, with an annual increase observed until 2016 and subsequent fluctuations. Respiratory diseases, diabetes mellitus, malignant cancers, tuberculosis, and cardiovascular diseases collectively accounted for 16.5% of deaths among individuals aged ≥35 years. Notable contributors were pneumonia and influenza (respiratory diseases), esophageal cancer (cancers), and cerebrovascular diseases (cardiovascular diseases). Of the observed deaths, 16.5% were attributed to smoking, with respiratory diseases (40.5%), malignant cancers (31.4%), tuberculosis (13%), cardiovascular diseases (8.9%), and diabetes mellitus (6.1%) contributing. Pneumonia and influenza, esophageal cancer, chronic airway obstruction, and tuberculosis were primary causes, comprising 70% of all SAM. Conclusions Tobacco-related mortality is a significant public health concern in Kenya. Efforts should focus on preventing tobacco use and managing associated disease burdens. Smoking cessation initiatives and comprehensive tobacco control measures are imperative to mitigate the impact on population health.

Introduction: Tobacco smoking poses a significant risk for various diseases, including cardiovascular diseases, chronic respiratory diseases, and cancers. In Kenya, tobacco-related deaths contribute substantially to non-communicable disease mortality. This study aims to quantify the mortality attributed to tobacco smoking in Kenya from 2012 to 2021. Methods: Employing a prevalence-based analysis model, the study utilized population attributable fraction (PAF) to estimate age-specific smoke attributable mortality (SAM) rates for individuals aged ≥35 years. Causes of death associated with tobacco use, including cancers, cardiovascular diseases, respiratory diseases, tuberculosis, and diabetes, were analyzed based on age, sex, and death records between 2012 and 2021. Results: Over the study period, 60228 deaths were attributed to tobacco-related diseases, with an annual increase observed until 2016 and subsequent fluctuations. Respiratory diseases, diabetes mellitus, malignant cancers, tuberculosis, and cardiovascular diseases collectively accounted for 16.5% of deaths among individuals aged ≥35 years. Notable contributors were pneumonia and influenza (respiratory diseases), esophageal cancer (cancers), and cerebrovascular diseases (cardiovascular diseases). Of the observed deaths, 16.5% were attributed to smoking, with respiratory diseases (40.5%), malignant cancers (31.4%), tuberculosis (13%), cardiovascular diseases (8.9%), and diabetes mellitus (6.1%) contributing. Pneumonia and influenza, esophageal cancer, chronic airway obstruction, and tuberculosis were primary causes, comprising 70% of all SAM. Conclusions: Tobacco-related mortality is a significant public health concern in Kenya. Efforts should focus on preventing tobacco use and managing associated disease burdens. Smoking cessation initiatives and comprehensive tobacco control measures are imperative to mitigate the impact on population health. [ABSTRACT FROM AUTHOR]

Abstract Background Smoking is one of the leading causes of preventable mortality and morbidity worldwide, with the European Region having the highest prevalence of tobacco smoking among adults compared to other WHO regions. The Belgian Health Interview Survey (BHIS) provides a reliable source of national and regional estimates of smoking prevalence; however, currently there are no estimates at a smaller geographical resolution such as the municipality scale in Belgium. This hinders the estimation of the spatial distribution of smoking attributable mortality at small geographical scale (i.e., number of deaths that can be attributed to tobacco). The objective of this study was to obtain estimates of smoking prevalence in each Belgian municipality using BHIS and calculate smoking attributable mortality at municipality level. Methods Data of participants aged 15 + on smoking behavior, age, gender, educational level and municipality of residence were obtained from the BHIS 2018. A Bayesian hierarchical Besag-York-Mollie (BYM) model was used to model the logit transformation of the design-based Horvitz-Thompson direct prevalence estimates. Municipality-level variables obtained from Statbel, the Belgian statistical office, were used as auxiliary variables in the model. Model parameters were estimated using Integrated Nested Laplace Approximation (INLA). Deviance Information Criterion (DIC) and Conditional Predictive Ordinate (CPO) were computed to assess model fit. Population attributable fractions (PAF) were computed using the estimated prevalence of smoking in each of the 589 Belgian municipalities and relative risks obtained from published meta-analyses. Smoking attributable mortality was calculated by multiplying PAF with age-gender standardized and stratified number of deaths in each municipality. Results BHIS 2018 data included 7,829 respondents from 154 municipalities. Smoothed estimates for current smoking ranged between 11% [Credible Interval 3;23] and 27% [21;34] per municipality, and for former smoking between 4% [0;14] and 34% [21;47]. Estimates of smoking attributable mortality constituted between 10% [7;15] and 47% [34;59] of total number of deaths per municipality. Conclusions Within-country variation in smoking and smoking attributable mortality was observed. Computed estimates should inform local public health prevention campaigns as well as contribute to explaining the regional differences in mortality.

Background: Smoking is one of the leading causes of preventable mortality and morbidity worldwide, with the European Region having the highest prevalence of tobacco smoking among adults compared to other WHO regions. The Belgian Health Interview Survey (BHIS) provides a reliable source of national and regional estimates of smoking prevalence; however, currently there are no estimates at a smaller geographical resolution such as the municipality scale in Belgium. This hinders the estimation of the spatial distribution of smoking attributable mortality at small geographical scale (i.e., number of deaths that can be attributed to tobacco). The objective of this study was to obtain estimates of smoking prevalence in each Belgian municipality using BHIS and calculate smoking attributable mortality at municipality level.Methods: Data of participants aged 15 + on smoking behavior, age, gender, educational level and municipality of residence were obtained from the BHIS 2018. A Bayesian hierarchical Besag-York-Mollie (BYM) model was used to model the logit transformation of the design-based Horvitz-Thompson direct prevalence estimates. Municipality-level variables obtained from Statbel, the Belgian statistical office, were used as auxiliary variables in the model. Model parameters were estimated using Integrated Nested Laplace Approximation (INLA). Deviance Information Criterion (DIC) and Conditional Predictive Ordinate (CPO) were computed to assess model fit. Population attributable fractions (PAF) were computed using the estimated prevalence of smoking in each of the 589 Belgian municipalities and relative risks obtained from published meta-analyses. Smoking attributable mortality was calculated by multiplying PAF with age-gender standardized and stratified number of deaths in each municipality.Results: BHIS 2018 data included 7,829 respondents from 154 municipalities. Smoothed estimates for current smoking ranged between 11% [Credible Interval 3;23] and 27% [21;34] per municipality, and for former smoking between 4% [0;14] and 34% [21;47]. Estimates of smoking attributable mortality constituted between 10% [7;15] and 47% [34;59] of total number of deaths per municipality.Conclusions: Within-country variation in smoking and smoking attributable mortality was observed. Computed estimates should inform local public health prevention campaigns as well as contribute to explaining the regional differences in mortality. [ABSTRACT FROM AUTHOR]