Correlation Between Fasting and Blood Sugar

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Research Problem

There is an increase in the prevalence of cardiovascular disorders around the world and especially in developing and transition economies. The increased prevalence and incidence of such disorders is caused by high- risk lifestyles among other factors (Sharathkumar et al., 2011). Aghasadeghi et al. (2011) point out that Coronary Artery Disease (also referred to as CAD) accounts for about 50 percent of the deaths linked to cardiovascular disorders in the country each year. According to Aghasadeghi et al. (2011), several risk factors are associated with the severity and prevalence of CAD in the society. Some of these factors include dyslipidaemia, obesity, hypertension, and diabetes mellitus among others (Khan & Shah, 2012). In order to determine the relationship between these risk factors, Aghasadeghi et al. (2011) endeavoured to establish the correlation between fasting blood sugar and resting blood pressure among teachers living in Shiraz.

Data Source and Collection

The process of collecting, compiling, and interpreting data was carried out between February 2009 and December 2009. According to Aghasadeghi et al. (2011), data was collected from 2848 teachers aged between 21 and 80 years. At the time of data collection, all informants were working in various education institutions in Shiraz, Iran. It is noted that 59.1% of the informants were female while 40.9% were male.

Data was collected using semi-structured questionnaires. The nursing staff interviewed the participants to obtain the information required. The data collected contained demographic information such as the gender, age, hypertension and diabetes mellitus history, and the current usage of prescribed medication among the participants (Aghasadeghi et al., 2011). The researchers also collected anthropometric data. This included height, weight, and body mass index (BMI) of the participants. The trained nursing staff obtained the blood pressure of the patients and collected blood sample for FBS analysis (Aghasadeghi et al., 2011).

Independent and Dependent Variables

There were three independent variables in the study. These were BMI, age, and sex. The dependent variables that were measured against the three independent variables were blood pressure and fasting blood sugar. Informants with a hypertension reading of ≤90mmHg were regarded as ‘hypertensive’. On the other hand, those whose blood pressure readings ranged between 80 and 89mmHg were regarded as ‘pre-hypertensive’ (Aghasadeghi et al., 2011). Those participants whose blood pressure was lower than 80mmHg were regarded as normal. Participants with FBS of more than 126mg/dl were considered to have diabetes mellitus. Those participants with FBS readings ranging between 100 and 125mg/dl were considered to be ‘pre-diabetic’. Finally, those with FBS lower than 100mg/dl were regarded to be ‘normal’ (Aghasadeghi et al, 2011).

Sample Size Estimation

As mentioned earlier in this paper, the sample size for this research was 2848 teachers. In most cases, researchers find it unnecessary to assess the entire population. This is because of obvious impediments such as time, cost, and unwillingness of potential participants to be included in study (Motl, McAuley & Suh, 2010). As a result of this, researchers settle for a sample that is representative of the whole population. The researchers will then compute a hypothesis that can be tested statistically and which will help them gauge the characteristics of the population they are interested in. Studies are associated with several errors whose margins can be significantly reduced if the sample size is large enough. Consequently, a larger sample size translates into increased accuracy in the findings of the study (Khan & Shah, 2012). In this study, the sample size that the researchers settled for was large enough to provide correlational information using the chi-square test.

‘Statistical power’ depends largely on the statistical method used by the researchers, the size of the sample (Motl et al., 2010), size of experimental effects, and error margins (Khan & Shah 2012). In this study, the null hypothesis read:

“There is no significant correlation between fasting blood sugar and resting blood pressure in teachers residing in Shiraz,” (Aghasadeghi et al., 2011: p. 23).

It is noted that this hypothesis was proved wrong. This implies that Aghasadeghi et al. (2011) conducted a power analysis and the sample size was right for the statistical method used.

Statistics Used

As already mentioned in this paper, Aghasadeghi et al. (2011) used the chi-square test to analyse data. The chi-square is preferred over other statistical methods given that it is easier to compute. This statistical test is also preferred over others because the data is measured using a categorical or nominal scale. Additionally, the method is used to determine the differences among participants in a study. Lastly, the method, unlike others, makes no assumptions on population distribution (Khan & Shah 2012).

Assumptions

There are two main assumptions made in Chi-square test. The two have to be met by the data to be analysed. The first is that the sample is supposed to have being selected randomly from the study population. The second is that the sample size should be large enough such that the expected ‘count per cell’ is at least 5 (Khan & Shah, 2012). In the process of collecting data, both assumptions have to be met if the researcher is to use the Chi-square test for analysis.

The fact that data was collected from teachers working in different education institutions implies that Aghasadeghi et al. (2011) randomly selected their sample. However, they do not state this categorically in the article. The second assumption was met because the lowest value in the data collected was 8.

Level of Measurement

Aghasadeghi et al. (2011) used an interval level of measurement where the patients were classified as normal, pre-diabetic/pre-hypertensive, or diabetic/hypertensive. The interval level of measurement was appropriate for this study. This is given that it allowed the researchers to determine both the correlation and the number of patients who should undergo treatment or those who should change their lifestyle.

Data Displays

The results of data analysis were presented in one table. The table contained the estimated prevalence of hypertension and diabetes mellitus on the basis of BMI, age, and sex. The table also contained Chi-square p-values used to determine statistical significance. Additionally, it contained data on the correlation between blood pressure and FBS. This form of presentation was appropriate for the study because interval data is best presented in tables. However, Aghasadeghi et al. (2011) could have improved their presentation if they had divided the table into two. One section should have presented the prevalence data and the other correlation data.

What the Data Analysis Showed

The study found that the prevalence of hypertension among men was significantly higher than that of women. It also found that resting blood pressure rate was higher among persons of more than 40 years in comparison to their younger counterparts. It was also found that there was a correlation between FBS and blood pressure on the one hand and BMI on the other (Aghasadeghi et al., 2011).

Conclusions

Aghasadeghi et al. (2011) concluded that there was a correlation between resting blood pressure and FBS among pre-hypertensive and hypertensive teachers in Shiraz. However, there was no such correlation among teachers with normal blood pressure. They also made conclusions touching on the need for regular screening for diabetes mellitus. This is together with hypertension among individuals in the population. This is as a result of the undiagnosed cases. I agree with these conclusions because they were based on statistical analysis of data. The sample size was also significantly large.

Limitations

Aghasadeghi et al. (2011) point out that, although they were able to establish a correlation between resting blood pressure and FBS, the scope of their study did not permit for an investigation of the underlying mechanisms. Otherwise, the researchers did not discuss other study limitations.

Methods

Aghasadeghi et al. (2011) used a very effective method to collect data. The method made it possible to collect data from all the participants targeted for the study, with exception of 267 participants who had received treatment for either diabetes mellitus or hypertension. In general, the methods were excellent with the exception of using one table instead of two. This means that the findings made by the scholars can be trusted.

Understanding the Data

In the light of increasing prevalence of ailments associated with lifestyle, and especially CAD, the findings of this study are very useful in the nursing profession. They can be used to inform the formulation of policies with regard to the management of hypertension and diabetes. My nursing peers may raise concerns as to why the study failed to provide information on persons who have diabetes but who do not suffer from- or are not vulnerable to- hypertension. However, it should be noted that the recommendations made by Aghasadeghi et al. (2011)- that there should be regular screening because of neglected hypertension and diabetes mellitus cases- is important and can inform practice.

References

Aghasadeghi, K., et al. (2011). Correlation between fasting blood sugar and resting blood pressure in teachers residing in Shiraz, Iran 2009. Iran Cardiovasc Res J, 5(1), 14-18.

Khan, A., & Shah, I. A. (2012). Distributional properties of order statistics and record statistics. Pakistan Journal of Statistics & Operation Research, 8(3), 573-581.

McAuley, E., et al. (2005). Measuring disability and function in older women: Psychometric properties of the late-life function and disability instrument. Journals of Gerontology Series A: Biological Sciences & Medical Sciences, 60A(7), 901-909.

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