Department of Epidemiology, Geisel School of Medicine at Dartmouth, Dartmouth College, Hanover, NH, 03756, USA.
Bielefeld School of Public Health and Department of Economy and Business Administration, Bielefeld University, Bielefeld, Germany.
BMC Public Health. 2024 Sep 28;24(1):2655. doi: 10.1186/s12889-024-20075-x.
A major challenge in epidemiology is knowing when an exposure effect is large enough to be clinically important, in particular how to interpret a difference in mean outcome in unexposed/exposed groups. Where it can be calculated, the proportion/percentage beyond a suitable cut-point is useful in defining individuals at high risk to give a more meaningful outcome. In this simulation study we compute differences in outcome means and proportions that arise from hypothetical small effects in vulnerable sub-populations.
Data from over 28,000 mother/child pairs belonging to the Environmental influences on Child Health Outcomes Program were used to examine the impact of hypothetical environmental exposures on mean birthweight, and low birthweight (LBW) (birthweight < 2500g). We computed mean birthweight in unexposed/exposed groups by sociodemographic categories (maternal education, health insurance, race, ethnicity) using a range of hypothetical exposure effect sizes. We compared the difference in mean birthweight and the percentage LBW, calculated using a distributional approach.
When the hypothetical mean exposure effect was fixed (at 50, 125, 167 or 250g), the absolute difference in % LBW (risk difference) was not constant but varied by socioeconomic categories. The risk differences were greater in sub-populations with the highest baseline percentages LBW: ranging from 3.1-5.3 percentage points for exposure effect of 125g. Similar patterns were seen for other mean exposure sizes simulated.
Vulnerable sub-populations with greater baseline percentages at high risk fare worse when exposed to a small insult compared to the general population. This illustrates another facet of health disparity in vulnerable individuals.
流行病学中的一个主要挑战是了解暴露效应何时足够大而具有临床重要性，特别是如何解释未暴露/暴露组之间的平均结果差异。在可以计算的情况下，超过合适临界点的比例/百分比对于定义高风险个体以获得更有意义的结果非常有用。在这项模拟研究中，我们计算了来自易受伤害亚人群的假设小效应引起的结果平均值和比例差异。
使用来自超过 28000 对母婴对的环境影响儿童健康结果计划的数据，研究了假设环境暴露对平均出生体重和低出生体重（LBW）（出生体重<2500g）的影响。我们通过社会人口统计学类别（母亲教育、健康保险、种族、族裔）计算了未暴露/暴露组的平均出生体重，并使用一系列假设的暴露效应大小进行了计算。我们比较了使用分布方法计算的平均出生体重和 LBW 的百分比差异。
当假设的平均暴露效应固定（在 50、125、167 或 250g 时），LBW 的百分比差异（风险差异）的绝对值不是恒定的，而是因社会经济类别而异。在 LBW 的基线百分比最高的亚人群中，风险差异更大：对于 125g 的暴露效应，范围从 3.1%到 5.3%。对于模拟的其他平均暴露大小，也出现了类似的模式。
与一般人群相比，基线 LBW 百分比较高且处于高风险的脆弱亚人群在暴露于小刺激时情况更糟。这说明了脆弱个体中健康差异的另一个方面。
