Application of systematic evidence mapping to assess the impact of new research when updating health reference values: A case example using acrolein.
Application of systematic evidence mapping to assess the impact of new research when updating health reference values: A case example using acrolein.
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应用系统证据图谱评估更新健康参考值时新研究的影响：以丙烯醛为例
DOI：
10.1016/j.envint.2020.105956
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发表时间：
2020-10
影响因子：
11.8
通讯作者：
Persad AS
中科院分区：
文献类型：
--
作者：
Keshava C;Davis JA;Stanek J;Thayer KA;Galizia A;Keshava N;Gift J;Vulimiri SV;Woodall G;Gigot C;Garcia K;Greenhalgh A;Schulz B;Volkoff S;Camargo K;Persad AS
The environmental health community needs transparent, methodologically rigorous, and rapid approaches for updating human health risk assessments. These assessments often contain reference values for cancer and/or noncancer effects. Increasingly, the use of systematic review methods are preferred when developing these assessments. Systematic evidence maps are a type of analysis that has the potential to be very helpful in the update process, especially when combined with machine-learning software advances designed to expedite the process of conducting a review. To evaluate the applicability of evidence mapping to determine whether new evidence is likely to result in a change to an existing health reference value, using inhalation exposure to the air pollutant acrolein as a case example. New literature published since the 2008 California Environmental Protection Agency’s Office of Environmental Health Hazard Assessment (OEHHA) Reference Exposure Level (REL) for acrolein was assessed. Systematic review methods were used to search the literature and screening included the use of machine-learning software. The Populations, Exposures, Comparators and Outcomes (PECO) criteria were kept broad to identify studies that characterized acute and chronic exposure and could be informative for hazard characterization. Studies that met the PECO criteria after full-text review were briefly summarized before their suitability for chronic point of departure (POD) derivation and calculation of a reference value was considered. Studies considered potentially suitable underwent a targeted evaluation to determine their suitability for use in dose–response analysis. Over 15,000 studies were identified from scientific databases. Both machine-learning and manual screening processes were used to identify 60 studies considered PECO-relevant after full-text review. Most of these PECO-relevant studies were short-term exposure animal studies (acute or less than 1 month of exposure) and considered less suitable for deriving a chronic reference value when compared to the subchronic study in rats used in the 2008 OEHHA assessment. Thirteen epidemiological studies were identified but had limitations in the exposure assessment that made them less suitable for dose–response compared to the subchronic rat study. Among the 13 studies, there were four controlled trial studies that have the potential to be informative for future acute reference value derivation. Thus, the 2008 subchronic rat study used by OEHHA appears to still be the most appropriate study for chronic reference value derivation. In addition, advances in dosimetric modeling for gases, including new evidence pertinent to acrolein, could be considered when updating existing acrolein toxicity values. Evidence mapping is a very useful tool to assess the need for updating an assessment based on understanding the potential impact of new studies on revising an existing health reference value. In this case example, the focus was to identify studies suitable for chronic exposure dose–response analysis, while also identifying studies that may be important to consider for acute exposure scenarios, hazard identification, or for future research. This allows the evidence map to be a useful resource for a range of decision-making contexts. Specialized systematic review software increased the efficiency of the process in terms of human resources and time to conduct the analysis.
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影响因子：
4.3
作者：
Claeson, Anna-Sara;Lind, Nina
通讯作者：
Lind, Nina
影响因子：
3.8
作者：
Corley, Richard A.;Kabilan, Senthil;Einstein, Daniel R.
通讯作者：
Einstein, Daniel R.
影响因子：
10
作者：
Annesi-Maesano I;Hulin M;Lavaud F;Raherison C;Kopferschmitt C;de Blay F;Charpin DA;Denis C
通讯作者：
Denis C
影响因子：
3.7
作者：
Howard BE;Phillips J;Miller K;Tandon A;Mav D;Shah MR;Holmgren S;Pelch KE;Walker V;Rooney AA;Macleod M;Shah RR;Thayer K
通讯作者：
Thayer K
影响因子：
3.8
作者：
Conklin, Daniel J.;Malovichko, Marina V.;Srivastava, Sanjay
通讯作者：
Srivastava, Sanjay