Maier W, Fairburn J, Mielck A
Institut für Gesundheitsökonomie und Management im Gesundheitswesen.
Gesundheitswesen. 2012 Jul;74(7):416-25. doi: 10.1055/s-0031-1280846. Epub 2011 Oct 21.
Deprivation indices are valuable instruments for the analysis of regional effects on health. They may also be used as a surrogate when individual socioeconomic data are not available. These regional deprivation indices are integral parts of the public health discussion in the United Kingdom. In Germany, however, the discussion on this topic has just begun. Our aim was to develop a small-area based, multidimensional Index of Multiple Deprivation (IMD) for Germany, based on an established British method.
We chose the German state of Bavaria with its n=2 056 communities as a model region. We used official sociodemographic, socioeconomic and environmental data and created a Bavarian Index of Multiple Deprivation (BIMD). In order to test the applicability of the BIMD in epidemiological analyses we then performed a correlation analysis (Spearman's correlation coefficient) as well as a Poisson regression using data on premature mortality (< 65 years) and on total mortality (all age groups).
The correlation analysis showed a positive and significant association between regional deprivation and mortality. The Poisson regression showed a clear gradient, i. e., we found a stepwise increase of mortality risk with increasing regional deprivation. Compared with communities in the lowest deprivation quintile, communities in the highest deprivation quintile showed a clearly higher mortality risk, both for premature mortality [RR 1.49 (95% CI: 1.42 - 1.57)] and for total mortality [RR 1.21 (95% CI: 1.18 - 1.25)].
Using this new index, we could demonstrate for communities in Bavaria that higher regional deprivation is associated with higher mortality. This Index of Multiple Deprivation is a new and potentially useful tool for epidemiological and public health related studies in Germany.
贫困指数是分析地区对健康影响的重要工具。当无法获取个体社会经济数据时,它们也可作为替代指标。这些地区贫困指数是英国公共卫生讨论的重要组成部分。然而,在德国,关于这一主题的讨论才刚刚开始。我们的目标是基于一种既定的英国方法,为德国开发一个基于小区域的多维多重贫困指数(IMD)。
我们选择了德国巴伐利亚州及其n = 2056个社区作为模型区域。我们使用官方社会人口、社会经济和环境数据,创建了巴伐利亚多重贫困指数(BIMD)。为了测试BIMD在流行病学分析中的适用性,我们随后进行了相关性分析(斯皮尔曼相关系数)以及使用过早死亡率(<65岁)和总死亡率(所有年龄组)数据的泊松回归分析。
相关性分析显示地区贫困与死亡率之间存在正相关且具有统计学意义。泊松回归显示出明显的梯度,即我们发现随着地区贫困程度的增加,死亡风险逐步上升。与贫困程度最低的五分位数社区相比,贫困程度最高的五分位数社区的过早死亡率[相对风险(RR)1.49(95%置信区间:1.42 - 1.57)]和总死亡率[RR 1.21(95%置信区间:1.18 - 1.25)]都明显更高。
使用这个新指数,我们可以证明在巴伐利亚州的社区中,更高的地区贫困与更高的死亡率相关。这个多重贫困指数是德国流行病学和公共卫生相关研究中一种新的且可能有用的工具。