Research Group on Statistics, Econometrics and Health (GRECS), University of Girona, Girona, Spain.
BMC Public Health. 2012 Jul 30;12:574. doi: 10.1186/1471-2458-12-574.
In this study we propose improvements to the method of elaborating deprivation indexes. First, in the selection of the variables, we incorporated a wider range of both objective and subjective measures. Second, in the statistical methodology, we used a distance indicator instead of the standard aggregating method principal component analysis. Third, we propose another methodological improvement, which consists in the use of a more robust statistical method to assess the relationship between deprivation and health responses in ecological regressions.
We conducted an ecological small-area analysis based on the residents of the Metropolitan region of Barcelona in the period 1994-2007. Standardized mortality rates, stratified by sex, were studied for four mortality causes: tumor of the bronquial, lung and trachea, diabetes mellitus type II, breast cancer, and prostate cancer. Socioeconomic conditions were summarized using a deprivation index. Sixteen socio-demographic variables available in the Spanish Census of Population and Housing were included. The deprivation index was constructed by aggregating the above-mentioned variables using the distance indicator, DP2. For the estimation of the ecological regression we used hierarchical Bayesian models with some improvements.
At greater deprivation, there is an increased risk of dying from diabetes for both sexes and of dying from lung cancer for men. On the other hand, at greater deprivation, there is a decreased risk of dying from breast cancer and lung cancer for women. We did not find a clear relationship in the case of prostate cancer (presenting an increased risk but only in the second quintile of deprivation).
We believe our results were obtained using a more robust methodology. First off, we have built a better index that allows us to directly collect the variability of contextual variables without having to use arbitrary weights. Secondly, we have solved two major problems that are present in spatial ecological regressions, i.e. those that use spatial data and, consequently, perform a spatial adjustment in order to obtain consistent estimators.
本研究提出了改进剥夺指数编制方法的措施。首先，在变量选择方面，我们纳入了更广泛的客观和主观指标。其次，在统计方法方面，我们使用距离指标代替标准聚合方法主成分分析。第三，我们提出了另一种方法学改进，即使用更稳健的统计方法来评估生态回归中剥夺与健康反应之间的关系。
我们对 1994-2007 年巴塞罗那大都市区的居民进行了小区域生态分析。研究了四种死因（支气管、肺和气管肿瘤、二型糖尿病、乳腺癌和前列腺癌）的按性别分层的标准化死亡率。使用剥夺指数总结社会经济状况。纳入了西班牙人口和住房普查中可用的 16 个社会人口变量。剥夺指数是通过使用距离指标 DP2 聚合上述变量构建的。为了进行生态回归估计，我们使用了分层贝叶斯模型并进行了一些改进。
在更高的剥夺水平下，两性患糖尿病的死亡风险增加，男性患肺癌的死亡风险也增加。另一方面，在更高的剥夺水平下，女性患乳腺癌和肺癌的死亡风险降低。我们没有发现前列腺癌（呈现出增加的风险，但仅在剥夺的第二五分位数）的明确关系。
我们相信我们的结果是使用更稳健的方法学获得的。首先，我们构建了一个更好的指数，可以直接收集上下文变量的变异性，而无需使用任意权重。其次，我们解决了空间生态回归中存在的两个主要问题，即使用空间数据并且需要进行空间调整以获得一致的估计量。
