Hypothesis testing involves several stages that are aimed at proving whether to accept the null hypothesis or the alternate hypothesis. As such, a null hypothesis is set with the alternate hypothesis being its exact opposite. Hypothesis testing involves the establishment of test statistics that enables one to measure the level of agreement between the sample data and the formulated null hypothesis (Morton, et al. 2013). This article summarizes the statistical approach used by Chapman et al., (2019), in the evaluation of the potential association between kidney diseases and risk factors such as agrochemicals, heavy metals and heats stress in any region of the world.
Chapman et al., (2019), focuses on a number of risk factors and their association with kidney diseases. Some of the risk factors researched on include; heat stress and dehydration, heavy metals, hard water, and other exposures. This article focuses on the statistical analysis of heavy metals. The tests statistics of heavy metal is not reported. However, the research was conducted at a 95% confidence level that translates to a 0.05 significance level (Chapman et al., 2019 p. 5).
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Focusing on the effect of heavy metals and its potential association between kidney risk factors the following hypothesis was formulated as discussed by Chapman et al., (2019 p. 5). The null hypothesis was "there is an association between heavy metals as a risk factor and kidney diseases." The alternate hypothesis is “heavy metals are not a risk factor associated with kidney diseases. The null hypothesis and the alternate hypothesis were not directly stated in the article. However, it was easy to extrapolate from the background and report of the result of the research.
The study, as conducted by Chapman et al., (2019) exposes two potential areas of biasing. The first bias is the sampling of the data and the second is the data type collected. It can be noted that the data was sampled from various town in the world such as Chen Chen, Zheng, Sommar, Evans, Hsueh, and As. However, the time at which the data was collected has a great variation with some data being collected in 2009 some in 2011 and others in 2013. The variation of the year could greatly influence the results given that different years experience changes in political and economic activities. Secondly, most of the cities that the sample was obtained from are Asian cities. It makes the research a bad representation of the whole world as stated in the little of the research which states that it seeks to establish the relation between the risk factors and kidney diseases in the whole world, any time. The data type on the hand refers to the type of heavy metals included in the research. The research captures only four heavy metals that include lead, arsenic, cadmium, and mercury. Such as shallow scope of heavy metals is not a representative of the list of heavy metals posing as a potential bias for the research.
The following summary tells what the report on heavy metal indicates. In general, at a 95% confidence level, it was found out that heavy metal is not a significant risk factor for kidney diseases. Findings from a study of four heavy metals indicate that the combined effect of lead, mercury, arsenic, and cadmium do not influence the risk of getting kidney disease. However, studies on the individual heavy metals indicated that only lead posed as a significant risk factor for kidney disease. Its results were homogenous and had a confidence interval of between 0.44 and 0.98 centered at 1.38.
In summary, the researcher used the interpretation approach used in the textbook. In the abstract, the author brief indicates the objective of the research which captures an overview of what the research is all about. It also highlights the method used, the results obtained and the conclusions drawn from the research. Further development of the report indicates a detailed and systematic expansion of the highlighted items in the abstract. It can be deduced that the result was conducted to establish whether chronic kidney disease of uncertain traditional etiology is associated with heavy metals and other risk factors. Considering the specific results for heavy metal, it was found that the heavy metal is less related to chronic kidney diseases of uncertain traditional etiology. Chapman et al., (2019 p. 5), notes that at a p-value of 1.29 the confidence interval is between 0.73 and 2.28 at a 95% confidence level for all the four heavy metal. It includes heavy metals such as lead, arsenic, mercury, and cadmium. The result also shows a high degree of heterogeneity at 87%. It was however noted that when the lead is analyzed individually, it posed a significant risk factor for chronic kidney disease of uncertain traditional etiology. At a p-value of 1.38 and a 95% confidence level lead had a confidence interval of 1.01 to 1.88 with 0% heterogeneity.
Basing on the finding of the results, there is a slight correlation between heavy metal and chronic kidney diseases of uncertain traditional etiology. As such it is prudent to organize a less biased study that minimizes the biases imposed by the current research by selecting a more representative sample of the whole and defining the period the study was conducted. Time-Bound research enables the reporting to be based on the period and avoid the ambiguity posed by the current research. As it can be noted by Chapman et al., (2019 p. 1), the study was to show the correlation of the risk factors and the kidney disease all time yet it samples only three years.
References
Chapman, E., Haby, M., Illanes, E., Sanchez-Viamonte, J., Elias, V., & Reveiz, L. (2019). Risk factors for chronic kidney disease of non-traditional causes: a systematic review. Revista Panamericana De Salud Pública , 43 , 1. doi: 10.26633/rpsp.2019.35
Morton, A. P., Mengersen, K. L., Playford, G., & Whitby, M. (2013). Statistical methods for hospital monitoring with R .
URL of article: http://iris.paho.org/xmlui/handle/123456789/50508
