Understanding Biostatiscal Principles with Diabetes

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Understanding Biostatiscal Principles with Diabetes

According to Glover and Mitchell, for an individual to effectively communicate Biostatistics data, he had to first understand the public to which he wanted to convey the message. It was almost impracticable to alter the pre-existing values of the public except through addressing the matters unambiguously. Coming up with information that bridged the gap between the Biostatistician and the lay public without comprehending the targeted public was not possible.

The authors of the Biostatistics data on Diabetes Mellitus understood their audience as evidenced in the use of simple language in their interpretation of the data thereby making it easy for the lay/fairly literate person to understand the data.

The authors understanding of the target populations was further evidenced by their understanding of the population beliefs by stating that Sub-Saharan Africans had deeply preserved health beliefs in traditional healers thereby inclining many sufferers of diabetes to alternate between contemporary and conventional medicines and clinics (p. 51).

The authors also used authentic sources of data, therefore, making their findings effective and reliable. The sources of the information included limited research publications published from the year 1980 to 2012 because the data that were available before 1980 could not mirror the existing frequency of the disease. The authors used both Medline database and internet search engines for the literature research. Other authentic sources of data included clinicians and diabetes investigators who provided data on diabetes prevalence and problems in Sub-Saharan Africa.

Medline study was conducted for the frequency of diabetes and retinopathy, nephropathy and neuropathy were conducted for every complication. The lack of data from a given country was solved through the extrapolation of data from a geographically, economically, culturally, and socially comparable country. Prevalence study data was mainly attained from government approximations, registries, hospital studies, and clinical statistics (Glover & Mitchell, 2008).

Rosner affirms that in order for the Biostatistics data to be effective, it must be effectively communicated to the users and people who need to be informed of the finding. This was effectively conducted through the use of the World Wide Web so as to reach as many people due to the fact that internet usage had significantly increased throughout the world hence enabling many people to receive the information in real-time (p. 754).

The authors applied graphical representations of the data in order to increase the effectiveness of their findings. The graphs used were brief, concise and simple making them easy for the fairly literate individuals to interpret without assistance from a Biostatistician. These were demonstrated in the graphs and data tables used to present the sources of data on the prevalence of diabetes type 2 and diabetes mellitus and subsequently the graphs for the prevalence of the disease in Cameroon in relation to age. The graphs and tables were also used to show the trends and the relationship between diabetes and various factors such as age (Sullivan, 2008).

Glover and Mitchell affirmed that in order for a Biostatistics data to be effective, the information and findings must allow predictability of future trends. The authors were also effective in presenting their findings by predicting the trends and effects of the disease in the coming years. This was observed in the authors prediction where they stated that with the ongoing trends, non-communicable diseases such as diabetes, cancer and hypertension were to overtake communicable diseases as causes of mortality in Sub-Saharan Africa (p. 331).

References:

Glover, T., & Mitchell, K. (2008). An Introduction to Biostatistics. Long Grove, IL: Waveland Press,.

Rosner, B. (2011). Fundamentals of Biostatistics. Hoboken, New Jersey: Cengage Learning,.

Sullivan, L. M. (2008). Essentials of Biostatistics Workbook: Statistical Computing Using Excel. Burlington, Massachusetts: Jones & Bartlett Learning.

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