Kawasaki disease (KD), also known as mucocutaneous lymph node syndrome, is a pathological condition that causes acute early childhood vasculitis of certain unknown etiology. It is regarded as one of the common cause behind the acquired paediatric heart disease in the industrialised countries (Newburger and Kato 2015) The common symptoms include high temperature of the body, skin rashes, and swollen glands in the neck, red eyes and finger tips along with dry cracked lips (Son and Newburger 2013). The following assignment aims to critically analyse the paper published by Saundankar et al. (2014) which is based on the epidemiology and clinical features of the Kawasaki disease in Australia. While critiquing the paper, the researcher will mainly emphasize over the methodology and the overall research design of the paper along with the importance of the elucidated findings.
According to the reports published by ABC News (2012), rapid industrialization in the countries which are gradually moving from rural to the industrial economy has high rate of occurrence of Kawasaki disease. Since Australia is experiencing a sea change in the domain of industrialization, the rate of occurrence of Kawasaki disease among the paediatric population is also rising steeply. Moreover, the research conducted by Bronstein et al. (2000) reported that KD is an infectious disease that is influenced via the genetic and environmental factors. Thus KD has a direct link with the social epidemiology. ABC News (2012) highlighted that though there is a direct relation between rapid industrialization and KD, the causative agent of the disease (bacteria or virus) is still unknown. Studying the epidemiology and the clinical features of the disease will help to get a detailed insight about the skewed ethnic distribution and the seasonality of the disease. This information will help in the characterisation of the disease further which will in turn promote stringent development of the therapy and the care plan.
The aim of the paper Saundankar et al. (2014) is to deduce the epidemiology and the clinical features associated with KD in Australian population.
The researcher, Saundankar et al. (2014) mainly studied reviewed the demographic, clinical laboratory and echocardiographic data from the individual discharge diagnosis of hospitalized patients residing in Western Australia during the tenure of 1979 to 2009. The diagnosis was done via using the standard criteria.
All the pediatric patients of Western Australia (WA) who are discharged from the hospital with an International Classification of Diseases coding for KD were selected for the study via Data Linkage Unit of WA Health Department. The duration of the patient population selection was 1989 onwards (Saundankar et al. 2014). According to Chow et al. (2017), adequate sample size is important for conducting a well-designed epidemiologic study. Chow et al. (2017) is of the opinion that adequate sample sizes are crucial to ensure sufficient statistical power in order to elucidate the differences. It also helps in highlight the lack of significant differences between the groups. The research conducted by Saks and Allsop (2007) used a large sample size in order to elucidate the neglected area of health support work in United Kingdom under the context of the recent social policies. The use of large sample size helped Saks and Allsop (2007) to navigate their research towards the proper findings
Button et al. (2013) is of the opinion that small sample size in comparison to the ideal increases the chances of assuming false or true premise. This increases the chances of getting false positive results and the situation becomes even worse if the research employs public funding (Faber and Fonseca 2014).
Selection of large sample size is also associated with certain sort of limitations. Faber and Fonseca (2014) stated that using very large sample size that exceeds the overall value estimated by the required sample size calculations generates different hurdles. First hurdle in case of the epidemiological research study as adopted by Saundankar et al. (2014) is financial and human resource constraints. Recruitment of large sample size demands more access of data, which demands more human resource availability along with more funding. Faber and Fonseca (2014) also highlighted that use of very large sample size substantially increase the statistical analysis power and this in turn causes exaggerated tendency to reject the null hypothesis with clinically negligible differences. Thus, what is insignificant becomes significant further leading to the generation of the false positive results. Marshall et al. (2013) stated that only way to use a proper sample size without compromising with the quality of the obtained results is the appropriate removal of the duplicates from the sample size. Saundankar et al. (2014) cross-referenced, which helped in the removal of the duplicate data like duplicate admission and thereby helping to retain the reliability of the research.
In order to retrieve the patient’s data, the researcher obtained the ethical approval from PMH Institutional Ethics Committee and the Chief Executive Officer of the Human Research Ethics Committees of the concerned hospitals. According to Newson and Lipworth (2016) accessing public data due to health related information is subject to the ethical approval from the concerned hospital authority. This goes in sync with the Data Protection Act of 1988. The health care data is strictly confidential and the researcher can only access the hospital data upon the ethical approval from the concerned hospital authority. However, the research needs to ensure that no private information of the patients like name of contact details will be revealed or used in the research. Thus, the research conducted by Saundankar et al. (2014) followed the desired ethical guidelines along with strict maintenance of confidentiality, which helped to improved the acceptability of the research.
Poisson distribution is a tool that helps to predict the probability of certain events from occurring when the rate of occurrence of events is known. Poisson distribution helps in the generation of probability of a given number of events happening under a fixed interval of time (Bagheri et al. 2014). On the other hand, confidence interval is the measure of uncertainty (Hopkins 2017). Thus, calculation of the confidence interval with the poisson distribution helps the research to get accurate overview of the uncertainty and this help to increase the validity of the statistical calculations along with the generation of reliable P-value which further aids in accurate statistical outcome (Cocks and Torgerson 2013).
Saundankar et al. (2014) used Mann – Whitney U test as a non-parametric alternative test to the independent sample t-test. It helps to compare the mean values of two different samples that is extracted from the same populations and is mainly used to analyze whether two sample means are equal or not. Using Mann- Whitney U test helped the researcher to compare and contrast and thereby helping to get a detailed epidemiological overview of the selected population in KD study (Statistics 2015). David et al. (2014) also employed Mann – Whitney U test in order compare the placebo group and the test group to elucidate the effect of diet in gut micro biome.
Single Reference to disease definition
The definition of KD was based on definition famed by the American Heart Association and this single parameter of defining the vital signs of disease might lead to inappropriate classification of the disease. In U.S National Library of Medicine (2017), the definition of the KD is based on inheritance pattern, frequency of occurrence, genetic changes. They termed acute febrile mucocutaneous lymph node syndrome however, American Heart Association defines it as mucocutaneous lymph node syndrome.
Single Reference of Symptom Definition Number
Coronary artery (CA) dilatation was defined via taking reference from Japanese Ministry of Health and Welfare criteria. Citing disease symptoms definition via citing single governmental body may help in the generation of biased disease classification (Ogata et al. 2013).
Through the population size was large but it was concentrated only on the Western part of Australia. Thus the epidemiological data obtained can be biased as the target group of population for this study is not population of Australia as a whole but only the central part of Australia (Chow et al. 2017).
The pattern and the points that are required to be covered in the discharge diagnosis can changed drastically in the Australian health care system. Such that the some important points which are now included in the discharge summary in the Australian healthcare system like the patients, condition and functional status on discharge, follow up arrangement and patients psychological and emotional responses were not used before (Mahfouz et al. 2017). This discrepancy in the format of the discharge diagnosis might lead to error while comparing discharge summary.
The elucidate findings help to get a detailed epidemiological overview of KD on the basis of age, clinical features, demographics and diagnostic criteria.
Conclusion
Thus from the above discussion, it can be concluded that the epidemiological study conducted by Saundankar et al. (2014) in spite of having certain strengths in the domain of sample size and ethical approval, has some minor limitations in the domain of study design, methodology used. However, results highlighted in the paper helped to get an overview of KD from the perspective of epidemiology and clinical features.
References
ABC News. (2012). Kawasaki Disease. Access date: 25th June 2018. Retrieved from: https://www.abc.net.au/catalyst/stories/3505144.htm
Bagheri, S.F., Alizadeh, M., Baloui Jamkhaneh, E. and Nadarajah, S., 2014. Evaluation and comparison of estimations in the generalized exponential-Poisson distribution. Journal of Statistical Computation and Simulation, 84(11), pp.2345-2360.
Bronstein, D.E., Dille, A.N., Austin, J.P., Williams, C.M., Palinkas, L.A. and Burns, J.C., 2000. Relationship of climate, ethnicity and socioeconomic status to Kawasaki disease in San Diego County, 1994 through 1998. The Pediatric infectious disease journal, 19(11), pp.1087-1091.
Button, K.S., Ioannidis, J.P., Mokrysz, C., Nosek, B.A., Flint, J., Robinson, E.S. and Munafò, M.R., 2013. Power failure: why small sample size undermines the reliability of neuroscience. Nature Reviews Neuroscience, 14(5), p.365.
Chow, S.C., Shao, J., Wang, H. and Lokhnygina, Y., 2017. Sample size calculations in clinical research. Chapman and Hall/CRC.
Cocks, K. and Torgerson, D.J., 2013. Sample size calculations for pilot randomized trials: a confidence interval approach. Journal of clinical epidemiology, 66(2), pp.197-201.
David, L.A., Maurice, C.F., Carmody, R.N., Gootenberg, D.B., Button, J.E., Wolfe, B.E., Ling, A.V., Devlin, A.S., Varma, Y., Fischbach, M.A. and Biddinger, S.B., 2014. Diet rapidly and reproducibly alters the human gut microbiome. Nature, 505(7484), p.559.
Derntl, M., 2014. Basics of research paper writing and publishing. International Journal of Technology Enhanced Learning, 6(2), pp.105-123.
Efford, M.G. and Fewster, R.M., 2013. Estimating population size by spatially explicit capture–recapture. Oikos, 122(6), pp.918-928.
Faber, J. and Fonseca, L.M., 2014. How sample size influences research outcomes. Dental press journal of orthodontics, 19(4), pp.27-29.
Good, P., 2013. Permutation tests: a practical guide to resampling methods for testing hypotheses. Springer Science & Business Media.
Hopkins, W.G., 2017. A Spreadsheet for Deriving a Confidence Interval, Mechanistic Inference and Clinical Inference from a P Value. Sportscience, 21.
Lewis, S., 2015. Qualitative inquiry and research design: Choosing among five approaches. Health promotion practice, 16(4), pp.473-475.
Mahfouz, C., Bonney, A.D., Mullan, J. and Rich, W.C., 2017. An Australian discharge summary quality assessment tool: A pilot study.
Marshall, B., Cardon, P., Poddar, A. and Fontenot, R., 2013. Does sample size matter in qualitative research?: A review of qualitative interviews in IS research. Journal of Computer Information Systems, 54(1), pp.11-22.
Newburger, J.W. and Kato, H., 2015. Kawasaki disease. In Coronary Artery Disease (pp. 581-595). Springer, London.
Newson, A.J. and Lipworth, W., 2016. Why should ethics approval be required prior to publication of health promotion research?. Health Promotion Journal of Australia, 26(3), pp.170-175.
Ogata, S., Tremoulet, A.H., Sato, Y., Ueda, K., Shimizu, C., Sun, X., Jain, S., Silverstein, L., Baker, A.L., Tanaka, N. and Ogihara, Y., 2013. Coronary artery outcomes among children with Kawasaki disease in the United States and Japan. International journal of cardiology, 168(4), pp.3825-3828.
Saks, M. and Allsop, J., 2007. Social policy, professional regulation and health support work in the United Kingdom. Social Policy and Society, 6(2), pp.165-177.
Saundankar, J., Yim, D., Itotoh, B., Payne, R., Maslin, K., Jape, G., Ramsay, J., Kothari, D., Cheng, A. and Burgner, D., 2014. The epidemiology and clinical features of Kawasaki disease in Australia. Pediatrics, 133(4), pp.e1009-e1014.
Son, M.B. and Newburger, J.W., 2013. Kawasaki disease. Pediatr Rev, 34(4), pp.151-62.
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