Public health priority setting for environmental metals mixtures and birth defects

出生缺陷是已知的导致婴儿死亡的主要原因，也是导致婴儿死亡的 潜在寿命损失年数的来源。事实上，在美国，每33个活产婴儿中就有一个 国家会导致出生缺陷。缺乏已知的、可改变的相关环境因素 出生缺陷仍然是取得进展的一个重大障碍。在之前的工作中，我们的团队检查了 接触砷、锰、镉和铅与出生缺陷有关。我们 已经证明，通过私人井水接触有毒金属混合物是 与出生缺陷风险增加有关。具体来说，在砷和 锰共存时，我们观察到出生缺陷的患病率高于预期。当前 过滤技术足以减少对许多有毒金属的暴露。 不幸的是，由于成本的原因，广泛采用过滤是不可行的，而且有一个 在如何最好地进行干预的知识方面存在很大差距。理想情况下，水的受控实验 过滤将被用来指导对哪里进行干预的决定，但关键的是，获得 这样的数据贵得令人望而却步。因此，公共卫生将得到很好的服务 现有的观察性数据，以评估此类干预的效果。这是一项创新 项目将使用最先进的统计方法直接量化 对有毒金属混合物暴露的潜在干预措施，作为减少 出生缺陷的风险。这种方法可以使用不同程度的复杂程度来适应 当地的公共卫生需求。这一提议的首要假设是，我们可以常规地使用 收集监测数据以确定北卡罗来纳州出生缺陷造成的公共卫生负担 由于有毒金属暴露，以及确定干预措施，以最大限度地减少 在现实的成本和可行性约束下的负担。我们将在三个月内检验这一假说 明确的目标。在目标1中，我们将估计可归因于有毒物质的出生缺陷的风险 金属混合物使用井水污染和120万新生儿的数据 2003-2013北卡罗来纳州卫生与公众服务部和北卡罗来纳州出生 缺陷监控计划。我们将应用结合贝叶斯的尖端框架 具有因果推理框架的方法估计高度可归因风险 相互关联的暴露。在目标2中，我们应用我们的框架来估计 潜在干预措施下出生缺陷的归因风险，包括过滤或改变 水源。我们将对比针对以下地区的干预措施下的出生缺陷风险 关注，例如高度暴露的地区，作为最大限度减少出生缺陷的一种方式。在《目标3》中， 我们将进行成本效益分析，以优化现有资源，以减少 先天缺陷。这项工作是环境混合物如何 被称呼为。结果将为利益相关者提供有效决策的数据 制作。重要的是，我们分析环境混合物的新方法提供了一个 用于确定优先暴露、区域或群体以最大限度地提高公共健康效益的模板 关于在资源有限的情况下接触混合物的政策。

Birth defects are the leading known cause of infant mortality and one of the leading sources of years of potential life lost. In fact, one out of every 33 live births in the United States results in a birth defect. The lack of known, modifiable environmental factors associated with birth defects remains a significant barrier to progress. In prior work, our team examined exposure to arsenic, manganese, cadmium, and lead in relation to birth defects. We have demonstrated that exposure to a toxic metals mixture through private well-water is associated with increased risk for birth defects. Specifically, in areas where arsenic and manganese co-occur, we observed higher-than-expected prevalence of birth defects. Current filtration technology is sufficient to reduce exposures to many toxic metals. Unfortunately, widespread adoption of filtration is infeasible due to cost, and there is a substantial gap in knowledge about how to best intervene. Ideally, controlled experiments of water filtration would be used to guide decisions about where to intervene, but, critically, obtaining such data is prohibitively expensive. Thus, public health would be well served by using existing, observational data to estimate the effects of such interventions. This innovative project will use state-of-the-art statistical methods to directly quantify the impact of potential interventions on toxic metal mixtures exposure as a strategy for reducing the risk of birth defects. This approach can be used with varying levels of sophistication to adapt to local public health needs. The overarching hypothesis of this proposal is that we can use routinely collected surveillance data to identify the public health burden of birth defects in North Carolina due to toxic metals exposures, as well as identify interventions to maximize reductions in this burden under realistic constraints on cost and feasibility. We will test this hypothesis in three specific aims. In Aim 1, we will estimate the risk of birth defects attributable to toxic metal mixtures using data on well water contamination and 1.2 million NC births from 2003-2013 from the NC Department of Health and Human Services and the NC Birth Defects Monitoring Program. We will apply a cutting-edge framework that combines Bayesian methodology with a causal inference framework to estimate attributable risks from highly correlated exposures. In Aim 2, we apply our framework to estimate the reductions in the attributable risk of birth defects under potential interventions including filtration or changing water sources. We will contrast birth defects risks under interventions that target areas of concern, such as highly exposed areas, as a way to maximize reductions in birth defects. In Aim 3, we will conduct a cost-effectiveness analysis in order to optimize available resources to reduce birth defects. This work is a paradigm shift in how environmental mixtures can be addressed. The results will provide stakeholders with data for effective decision making. Importantly, our new approach to the analysis of environmental mixtures provides a template for identifying priority exposures, areas, or groups to maximize the public health benefit of policies on exposure mixtures in resource-limited settings.