Understanding the Uses and Limitations of Risk Assessment
Many organizations work under pressure because the working environment is full of uncertainty and risk. The uncertainty and risks might include aspects such as natural calamities, human act, environmental failures, and financial turmoil. For this reasons, scientists are carrying out research in various disciplines to unveil answers to such mysteries. International Programme on Chemical Safety (IPCS) harmonization program has worked tirelessly to develop a consistent risk assessment that would easily be acceptable across the world. Risk assessment involves the estimation and prediction of results of a given entry of exotic disease agents which can affect the animal. In other words, risk assessment examines the effect of exotic disease agents on the living organisms. Although the administrators and managers have the responsibility of implementing the findings of the scientific research, they have the obligation of reviewing some of the alternative solutions. Alternatively, the management should not concentrate on the positive impact the research would create, but also examine its social and economic consequences. World Health Organization (2004) states that, a decision maker should examine the scientific limitations while making judgments to avoid future risks. In other words, the management require scientific basis to synthesize the findings and the models applied to ascertain the meaning. Exposure assessment could be complex or simple depending on the expected outcome. The assessment is based on measurement, assumptions and models. Generally, the assessment focuses on the media, individual chemicals and sources. World Health Organization (2004) defines exposure assessment as the analysis of characterizing human behavior to chemicals presence in the environment.
Importance of Mathematical Models during Assessment
In the past, the assessment was done through advance analytical chemistry. Therefore, during the estimation of exposure of the population to chemical, it was important to make various hypothetical assumptions. However, Crump (2003) explains that with the emergent of improved technology such as lapel badges and mini-pumps, it has permitted accurate and easy measurement. As such, many scientific researches contain mathematical models as explanation for the connection between various events. Therefore, the decision makers should not dispute the fact that, the model makes it easy to interpret the findings. Research shows that, models combine assumptions and measurements to produce an estimate (Lawrence, 2007). For example, the rate of environmental agent intake in a given environment includes air, soil and animals. On the other hand, in a huge population, mathematical models assist in producing a precise and accurate result which applies to the past, present, and future alternative outcomes. Moreover, outcomes are easy to monitor thus facilitate quick decision making.
The Validity of Models Used In Exposure Assessment
Mathematical modeling has been used in many cases during the risk assessment processes to arrive at strategies to be employed in a given circumstances. Lawrence (2007) asserts that, a model has been a tool for evaluating and attaining answers for the alternative hypothesis. Although the model is used to assess risks and evaluate hypothesis, it is also used to carry exposure risk assessment for human health. For example, to determine the chemical concentration in a given environment, samples required receptor’s level of hazard and safe consumption rate of dietary items. To find the result, it is necessary if the variables are combined to make mathematical sense and facilitate the estimate. However, models could not be used in all aspects of assessment because some cases are critical to management’s decision making. For example, the model that was applied to address the phenomena about the shot ingestion among birds proved futile (Lawrence, 2007). This is because during the experiment, the underlying assumptions made were too weak to hold the explanation.
The model becomes useful if the parameters or variables and constraints are included. The constraint variables and constant factor would assume any barriers encountered during the data collection and error. For example, bad weather, inaccurate answers by he respondents, and lack of certain information about population. In the case of biological phenomena, the model should captures and identifies the biological health effect that agrees with the observable phenomenon on the ground. Otherwise, the conclusion and interpretation would not demonstrate the actual result of the case study. For instance, the model on toxicological effect of doses proved difficult because in most parts, it had not been translated to quantitative measures. Although it was a biological model, it did not incorporate detailed information on the reliable estimate of dose risk as well as biological mechanisms required. Crump (2003) found out that, in the field of PBPK (physiologically based pharmacokinetic) modeling has provided adequate measures that allows the dosage of targeted organs, exposure routine and dosage regimens. This reflects that in some cases, models could give accurate outcomes, which assist the medical practitioners to address a given disease within a population.
The Determination of Assumption Made About Population Exposure
Exposure is necessary because it specifies which people are exposed to what substance in a given physical boundaries. Although measuring one member of the population gives an accurate assumption, several questions concerning the whole population remains unanswered. That is, every organism or individuals differ from one another in many aspects. For example, susceptibility to diseases, resistance to diseases, and life expectancy. In order to determine if the assumption made about the population’s exposure is valid, it is essential to use a multiple LOEs. For instance the chemical measurement, toxicity data, bioaccumulation and biomagnifications data, and community structure assessment. Wharfe et al. (2007) explains that, the logical application of the LOEs is the basis of situ methods used to ascertain the validity of assumptions made about population. This process commences by screening the data used during the assessment. This is because the data collected in the field is insufficient to make statutory decision. On the other hand, the data may contain unlimited information for initial LOEs. Therefore, the LOEs assist in the reduction of uncertainty during the completion of WOE assessment. Wharfe et al. (2007) asserts that, LOEs allow the integration and evaluation of multiple frameworks that assist in the decision making. As such, in the quest to ascertain the validity of the assumption the following procedure would be followed.
The selection of LOEs depends on the alternative assessment tools that are available. For example, in the risk assessment about stressors and receptors in a population, LOEs provide a strong linkage between the assumptions and the outcome. Thus, it becomes appropriate for this assessment. Therefore, the first stage for one to achieve the objective is the selection of LOEs. The second method of determining the validity of assumption is the examination of data quality. Data could be incompatible with each other because it involves collecting and maintaining information using different methods hence reducing the quality of hypothesis (Department of Health and Human Services (DHHS), 2002). As such, the data could lead the researcher to make the wrong assumption. Therefore, carrying out quality assurance would act as an integral part in the identification of the validity of exposure assumptions. Furthermore, data quality assessment would involve the review of model specification, oversight of measurement criteria, and auditing the result.
Conclusion
The development of programs such as IPCS has been critical in the facilitation and harmonization of universally accepted principles and methods for risk assessment. However, among the issues that challenges such program is the choice and applicability of mathematical model used in the measurement of population exposure. The management should utilize the information released by the scientific researchers to make decisions about issues affecting the citizens. For example, the information on the impact of air pollution could assist to prevent air pollution and ensure there is clean air to people. In other words, exposure assessments are important aspects of management and assessment; they assist many researchers to improve the model selection. On the other hand, the validity of assumption could be ascertained through the LOEs and the conducting the data quality assessment. However, this is possible if appropriate models are used to estimate the outcomes.
References
Crump, S. K. (2003). Quantitative risk assessment since the red book: where have we come and where should we be going? Human and Ecological Risk Assessment, 9(5), 1105-1112.
Department of Health and Human Services (2002). Exposure assessment methods: research needs and priorities. Available at <www.cdc.gov/niosh.>
Lawrence, T. V. (2007). And so we model: the ineffective use of mathematical model in ecological risk assessments. Integrated Environmental Assessment and Management, 4733(4), 473-475.
World Health Organization (2004). Principles of characterizing and applying human exposure models. Web. 14 Jan 2014. <http://www.who.int/ipcs/methods/harmonization/en/ipcs_exposure_modelling_peer_review_version.pdf>
Wharfe et al. (2007). In situ measures. Integrated Environmental Assessment and Management, 3, 270-274.
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