Risk and Detect New Health Threats in a Community

Running head: STATISTICS
Statistics
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1STATISTICS
Task 1
Statistics is an important analytic tool in the field of public health that aids in health
assessment of a population, identify population at risk and detect new health threats in a
community. Application of statistics has played a crucial role in promoting the translation of data
into causal effect data and identifying risk factors that needs to be addressed by government or
health agency of any specific country (Gleich et al. 2014). Hence, by the identification of
statistics and interpretation of correlation between risk and exposure, it has become possible to
control and manage disease in different settings. This paper will explain the use of statistics in
public health by giving examples of four quantitative peer reviewed articles that explores about
rising incidence of hypertension and its correlation with several risk factors.
Hypertension is a significant public health issue in England as it affects more than one in
England and it is third biggest risk factor for premature death. Preventing hypertension has been
prioritized in UK as it is one of the risk factors of cardiovascular disease and at least one in half
of all heart attacks and strokes have been linked to hypertension. Thus, in order to combat the
burden of hypertension, several researchers have explored the affect or benefit of diet patterns or
lifestyle changes on influencing the rate of hypertension (Public Health England, 2017). The
quantitative study by Lelong et al. (2015) investigated about the relation between nutrition and
blood pressure using a cross-sectional study design. As worldwide guidelines recommend
improvement in lifestyle behaviours for prevention of hypertension, the study aimed to analyse
the influence of dietary intake on blood pressure outcomes (BP). It was a cohort study done with
participants aged above 18 years and data was collected regarding demographics details and
daily dietary intake. The participants had to complete three 24 hours dietary records on three
random days and nutrient intake was estimated using the NutriNet-Santé food composition table.

2STATISTICS
Based on the diet taken, total energy intake was calculated and their dietary questionnaire
provided update on sodium intake from foods. In addition, anthropometrics and blood pressure
measurement was done by trained medical staffs. The significance of the data collection method
is the quality and completeness of dietary investigation which can give high quality estimated on
different nutritional factors and BP.
The significance of the quantitative investigation by Lelong et al. (2015) it used statistical
tools to conclude that salt comsumption has positive relationship with high BP and negative
relationship between fruit and vegetable consumption in preventing hypertension. The study also
reported positive relation between alcohol consumption and hypertension. This conclusion was
possible by the use of descriptive analyses and calculation of Pearson correlation coefficient.
The utility of descriptive analyses is that it helps to calculate standard deviations for each
quantitative variable, thus allowing for simpler interpretation of the data. In addition, Pearson
correlation coefficient is that gave good estimate about the effect size estimate between BP and
different types of food items (Baffoe-Djan and Smith 2019). Thus, statistical analyses helped to
explore correlation between the disease (hypertension) and risk factor (dietary intake) and played
a role in estimating the food item that is most beneficial or harmful for patient with diabetes. As
significant relation between dietary sodium to potassium ratio and BP was found, this study
confirmed that restricting diet intake can reduce or control hypertension.
The study by Aktar (2014) used statistics to explore relation between dietary sodium and
hypertension status based on older adult’s food purchasing and consumption behaviour. The
study was conducted using quantitative survey method and data was collected from 30
community dwelling older adults regarding daily sodium purchased, consumed, DASH score and
DASH dietary pattern. DASH diet is recommended for people with increased risk of developing

3STATISTICS
hypertension. Apart from interview, food receipts and 24 hour recall was also used for data
collection. The quantitative data was analyzed by calculation of frequencies and percentage and
use of independent t-test. The significance of this statistical tool in data analysis was that it
helped to identify statistically significant differences between the groups (Choudhary 2018). All
the statistical analyses were performed in Minitab15 data analysis software which further
enhances the accuracy of the data. From this data analysis, it was found that knowledge about
sodium had no meaningful relationship between among of sodium consumed. Thus, this finding
suggested the need for greater public effort to address intake of sodium in older adults. This
evidence shows how statistics can help to identify the type of public health promotion needed in
response to any disease too.
Two other quantitative research papers have been found that explores relation between
physical activity and hypertension. The study by Xu et al. (2014) explored joint association
between physical activity and hypertension on type 2 diabetes. Two community based
prospective cohort design was used to collect data from adults living in Nanjing, China. For each
participants, physical activity (PA) level and blood pressure status was assessed at baseline and
three years of follow-up and descriptive statistics was used to analyse data for the two studies.
The interaction between PA and hypertension was done using multivariate logistic regression
models. This statistical model is useful in interpreting relation between two or more independent
variables (Faraway 2016). Thus, this study revealed how statistics can be used to explore relation
between multiple variables. Through this form of analysis, the study concluded that sufficient PA
and hypertension can lower the risk of diabetes. In contrast, Moker et al. (2014) utilized statistics
to examine relationship between BP response and exercise training. The study subjects
completed exercise training progeam and the effect of this training on BP response was analyzed