The epidemiology studies provide crucial information necessary for improving the comprehension of the spectrum of Zika Virus (ZIKV) infection sources and promote potential approaches to minimize the effects of the illness. This paper explores the types of biases in an epidemiology study of Zika Virus.
First, information bias resulting from systematic variations in the methods of obtaining data concerning the group exposure may hinder the outcomes of the epidemiology studies. For instance, researchers may deduce a dubious conclusion resulting from incorrect disease causation to the study group. The inappropriate assigning of group category promotes unrealistic association estimates between outcomes and exposure. However, well-organized observational studies with standardized research procedures or protocols may assist in addressing systematic or informational biases (Reveiz et al., 2017). Application of imperfect procedures for detecting the effects of the ZIKV exposure in the study group may hinder effective results and the conclusion of investigations.
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Second, selection bias results from systematic variation between the representative sample of the study participants and non-participants in the investigation of ZIKV. Selection bias affects the generalizability of the ZIKV infection causations. Besides, the bias exists as a result of the difference between the study treatment and participants in the investigation of ZIKV circulation or infection (Reveiz et al., 2017). Selection bias affects the comparability between the study groups participants with or without instance, ZIKV circulation both previously or presently. The variations in the features of the study group may accrue from outcomes or exposure of the ZIKV infections.
Sample selection assists in ensuring the accuracy of investing the causation of the ZIKV infection among a given population. Sample selection error affects the internal legitimacy of research by resulting in an inaccurate approximation of the relationship between ZIKV infection variables and the population. Sample selection bias affects the causation validity of ZIKV infection because outcomes of a biased sampling may not represent the study population inclusively (Reveiz et al., 2017). Making an inference based on biased data of a study group affects the causation accuracy of the ZIKV disease, hence minimizing the authenticity of the epidemiology studies. Addressing sample bias requires proper definition and use of a corresponding sample group to the actual population.
Reference
Reveiz, L., Haby, M. M., Martínez-Vega, R., Pinzón-Flores, C. E., Elias, V., Smith, E., ... & Van Kerkhove, M. D. (2017). Risk Of Bias And Confounding Of Observational Studies Of Zika Virus Infection: A Scoping Review Of Research Protocols. PloS one , 12 (7).
