Epidemiology, Maastricht University, Maastricht, the Netherlands.
Data Science Institute, I-BioStat, Hasselt University, Hasselt, Belgium.
BMC Public Health. 2022 Sep 7;22(1):1699. doi: 10.1186/s12889-022-14067-y.
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.
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.
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.
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.
吸烟是全球可预防死亡和发病的主要原因之一，与其他世卫组织区域相比，欧洲区域成年人的吸烟率最高。比利时健康访谈调查（BHIS）提供了全国和区域吸烟流行率的可靠来源；然而，目前在比利时，在更小的地理分辨率（例如市一级）没有估计数。这阻碍了在较小的地理尺度（即归因于烟草的死亡人数）上估算吸烟归因死亡率的空间分布。本研究的目的是使用 BHIS 获得比利时每个市的吸烟流行率估计数，并计算市一级的吸烟归因死亡率。
从 2018 年 BHIS 中获取了年龄在 15 岁及以上的参与者的吸烟行为、年龄、性别、教育程度和居住市的数据。使用贝叶斯层次贝赛格-约克-莫利（BYM）模型对基于设计的霍维茨-汤普森直接流行率估计的对数变换进行建模。模型中使用了从比利时统计局（Statbel）获得的市一级变量作为辅助变量。使用集成嵌套拉普拉斯逼近（INLA）估计模型参数。计算了偏差信息准则（DIC）和条件预测有序（CPO）以评估模型拟合度。使用在比利时 589 个市中的每个市的吸烟流行率的估计值和从已发表的荟萃分析中获得的相对风险计算了人群归因分数（PAF）。通过将 PAF 与每个市的年龄-性别标准化和分层死亡人数相乘来计算吸烟归因死亡率。
BHIS 2018 数据包括来自 154 个市的 7829 名受访者。每个市的当前吸烟率的平滑估计值在 11%[可信区间 3；23]和 27%[21；34]之间，而前吸烟者的吸烟率在 4%[0；14]和 34%[21；47]之间。吸烟归因死亡率的估计值占每个市总死亡人数的 10%[7；15]至 47%[34；59]。
观察到国内吸烟和吸烟归因死亡率的差异。计算得出的估计数应告知地方公共卫生预防运动，并有助于解释死亡率的区域差异。
