scholarly journals Using sparse dose-response data for wildlife risk assessment

2013 ◽  
Vol 10 (1) ◽  
pp. 3-11 ◽  
Author(s):  
Ryan A Hill ◽  
Brian J Pyper ◽  
Gary S Lawrence ◽  
Gary S Mann ◽  
Patrick Allard ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Dose Response ◽  
Response Data

Framework for Identification and Collection of Data Useful for Risk Assessments of Microbial Foodborne or Waterborne Hazards: A Report from the International Life Sciences Institute Research Foundation Advisory Committee on Data Collection for Microbial Risk Assessment

2007 ◽  
Vol 70 (7) ◽  
pp. 1744-1751 ◽  
Author(s):  
ISABEL WALLS
Keyword(s):  
Risk Assessment ◽  
Decision Making ◽  
Risk Management ◽  
Data Collection ◽  
Dose Response ◽  
Management Decision ◽  
Microbial Risk Assessment ◽  
Microbial Risk ◽  
Response Data ◽  
Management Decision Making

A microbial risk assessment (MRA) can provide the scientific basis for risk management decision making. Much data are needed to complete an MRA, including quantitative data for pathogens in foods. The purpose of this document was to provide information on data needs and data collection approaches for MRAs that will be useful for national governments, particularly in developing countries. A framework was developed, which included the following activities: (i) identify the purpose of data collection—this should include stating the specific question(s) to be addressed; (ii) identify and gather existing data—this should include a determination of whether the data are sufficient to answer questions to be addressed; (iii) develop and implement a data collection strategy; (iv) analyze data and draw conclusions; and (v) use data to answer questions identified at the start of the process. The key data needs identified for an MRA were as follows: (i) burden of foodborne or waterborne disease; (ii) microbial contamination of foods; and (iii) consumption patterns. In addition, dose-response data may be necessary, if existing dose-response data cannot be used to estimate dose response for the population of interest. Data should be collected with a view to its use in risk management decision making. Standard sampling and analysis methods should be used to ensure representative samples are tested, and care should be taken to avoid bias when selecting data sets. A number of barriers to data collection were identified, including a lack of clear understanding of the type of data needed to undertake an MRA, which is addressed in this document.

Analysis of Transcriptomic Dose-Response Data for Toxicology and Risk Assessment

2011 ◽  
pp. 237-250 ◽  
Author(s):  
Russell S. Thomas ◽  
Longlong Yang ◽  
Harvey J. Clewell ◽  
Melvin E. Andersen
Keyword(s):  
Risk Assessment ◽  
Dose Response ◽  
Response Data

First Generation of a New Science: Risk Assessment in Transition3

1989 ◽  
Vol 5 (5) ◽  
pp. 621-627
Author(s):  
Jerry F. Stara
Keyword(s):  
Risk Assessment ◽  
Dose Response ◽  
Public Awareness ◽  
Rapid Change ◽  
Chemical Pollution ◽  
Human Populations ◽  
Environmental Media ◽  
Response Data ◽  
Theoretical Approaches ◽  
Health Need

The greatest challenge facing human populations today is that of extraordinary rapid change. Such a change in the society is illus trated by the increasing public awareness of environmental issues, accompanied by continuously expanding scientific investigations of chemical pollution. Our industrial civilization has developed and introduced into the various environmental media many compounds affecting human health individually and as a society. The science of toxicology is the evaluation of the effects of chemical and physical agents in various biological systems. Most chemical compounds cannot be tested in man due to their possible carcinogenic, muta genic, teratogenic, or other long-term toxic potential. Therefore, carefully designed toxicologic studies in other species, especially mammalian, are conducted to provide biological dose-response data, which can be used to predict human response. Toxicologists have the responsibility of providing accurate scientific dose- response data based on experiments employing, among others, "practical" concentrations of pollutants or toxicants. When the toxic effects are considered, the action of these agents in the atmosphere, water, and other environmental vehicles should be considered. There are always interacting events that co-exist in the environment. Multiple causality as a factor of a disease is well established but frequently overlooked. The various issues in envi ronmental health need to be tied together in order to be understood by scientists who are not intimately familiar with risk assessment procedures as they relate to the implementation of environmental laws. Much effort is needed both in the area of improved risk assessment methodology as well as in the area of toxicologic testing and validation of the theoretical approaches. The U.S. EPA is making every reasonable effort to improve its risk assessment approach.

scholarly journals Weighted mixed-effects dose–response models for tables of correlated contrasts

2021 ◽  
Vol 21 (2) ◽  
pp. 320-347
Author(s):  
Nicola Orsini
Keyword(s):  
Dose Response ◽  
Response Pattern ◽  
Mixed Effects ◽  
Data Generation ◽  
Measures Of Association ◽  
Response Models ◽  
Response Data ◽  
Hazard Ratios ◽  
Health Related ◽  
Generation Mechanisms

Recognizing a dose–response pattern based on heterogeneous tables of contrasts is hard. Specification of a statistical model that can consider the possible dose–response data-generating mechanism, including its variation across studies, is crucial for statistical inference. The aim of this article is to increase the understanding of mixed-effects dose–response models suitable for tables of correlated estimates. One can use the command drmeta with additive (mean difference) and multiplicative (odds ratios, hazard ratios) measures of association. The postestimation command drmeta_graph greatly facilitates the visualization of predicted average and study-specific dose–response relationships. I illustrate applications of the drmeta command with regression splines in experimental and observational data based on nonlinear and random-effects data-generation mechanisms that can be encountered in health-related sciences.

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