Geographic variations in risk: adjusting for unmeasured confounders through joint modeling of multiple diseases.
Geographic variations in risk: adjusting for unmeasured confounders through joint modeling of multiple diseases.
复制标题
风险的地理变化：通过多种疾病的联合建模来调整未测量的混杂因素。
DOI：
10.1097/ede.0b013e31819d90f9
复制
发表时间：
2009-05
期刊：
影响因子：
5.4
通讯作者：
Hansell, Anna Louise
中科院分区：
文献类型：
--
作者：
Best, Nicky;Hansell, Anna Louise
慢性阻塞性肺病 (COPD) 是导致死亡的重要原因，在英国，其地域差异明显。除了吸烟之外，其他因素也可能会影响这些变化，但目前还很难获得有关各地区吸烟情况的直接信息。我们比较了对慢性阻塞性肺病和肺癌死亡率空间分布进行联合建模的方法，并使用后者作为吸烟的替代指标，以确定吸烟以外的危险因素可能很重要的领域。我们获得了 1981 年至 1999 年英国 45 岁及以上男性的区级死亡率和人口数据。比较了三个模型：使用观察到的（模型 1）或空间平滑（模型 2）肺癌标准化死亡率（SMR）作为吸烟代理的贝叶斯生态回归，以及将吸烟视为两种疾病共有的空间潜在变量的双变量回归（模型 3）。模型选择标准倾向于模型 2 和 3，而不是模型 1。据估计，COPD 死亡率的空间变异中有 9%（模型 3）至 25%（模型 2）与吸烟无关。将肺癌作为吸烟的替代因素进行调整后，两个模型都显示出大城市和矿区慢性阻塞性肺病死亡率较高的相似地理模式，这些地区历史上与重工业和较高的空气污染水平有关。多种疾病的联合建模可用于研究风险的地理差异。这些模型揭示了针对没有直接数据的共享区域级风险因素的影响进行调整的模式。
Chronic obstructive pulmonary disease (COPD) is an important cause of mortality with marked geographic variations in Great Britain. Additional factors beyond cigarette smoking are likely to influence these variations, but direct information on smoking by area is not readily available. We compared methods of jointly modeling the spatial distribution of mortality from COPD and lung cancer, using the latter as a proxy for smoking, to identify areas in which risk factors other than smoking may be important. We obtained district-level mortality and population data for men aged 45 years or older in 1981–1999 in Great Britain. Three models were compared: Bayesian ecological regression using observed (model 1) or spatially smoothed (model 2) lung cancer standardized mortality ratio (SMR) as a smoking proxy, and bivariate regression (model 3) treating smoking as a spatial latent variable common to both diseases. Model selection criteria favored models 2 and 3 over model 1. Between 9% (model 3) and 25% (model 2) of spatial variation in COPD mortality was estimated to be unrelated to smoking. After adjustment for lung cancer as a proxy for smoking, both models showed similar geographic patterns of higher COPD mortality in conurbation and mining areas, historically associated with heavy industry and higher air pollution levels. Joint modeling of multiple diseases can be used to investigate geographic variations in risk. These models reveal patterns that are adjusted for the effects of shared area-level risk factors for which no direct data are available.