Essay On Application And Interpretation Of Public Health Data

It has been considered over the ages that the youths are the future of a generation. If they are not nurtured clearly then it might be downfall for the nation and it might deal a fatal effect to the future generation too. As a part a program Youth Risk Behaviour Survey has been created to do the analysis whether there are any significant bad or good thing that can affect their health or their mind. Statical analysis can lead to that part if any necessary process can be included which would help for the betterment of youth. There are so may objectives that can be considerable in this case. There must be education issues, health issues, nutrition and obviously the activities which will make the good metal state for the youths. As they are in the verge of the adulation period so there must be some physical and mental issues that would make a trouble to them. In order to make their life better YRBS has been launched so that it can count on their daily routines or activities. But our main objective is to look after the 9th standard girls about their status of living or how they are being benefited by the program. So, we are interested about the weight gain. Obese or weight gaining is common issue for the new generation youths as many of them are interested more in the indoor activities more than physical work outs. So, we choose physical work out more than the other activities.  Obesity effects a person’s body and eventually it effects their daily life and later the mental health also decays by. The main thing is to look after their physical activities over ages and gender so that how the weight gain affects their daily life. So, the thing is to find how the physical activities are effective for the weight gain or loss which is better for them. 

 

The measure that has been taken to gather the sample is the simple questionnaire process. Some early projected questions have been asked to the students which has might be taken in a school session. There was so many questions asking about what they are fond and how are they doing different activities. The questions were not in the straightforward way as some of them may not give the truthful answer. The data that has been gathered most are in categorical form which has been converted to numbers as in the nominal form just for the sake of the analysis purpose. At first to go through the data set we have run some through the descriptive analysis of the data set and then gone through the inferential analysis like regression and the chi-square test to find some convenient relationship between the variables that has been chosen as the study of interest.

 

The data has been collected by the questionnaire method. As most of the answers was not in the numerical forms so for the analysis purpose it was needed to convert those variables into the numerical format. So, after the conversion it was in a nominal form. Variables that are chosen out of interest as per the context of the analysis are Age, Sex, Weight and Days of physical activities. Here Age, Sex and Physical Activities are in nominal form and Weight is the only continuous variable that has been used for. First of all, a descriptive measure has been found to see their whereabouts and outlines for the variables that has been mentioned above. Some of the visual representation has also been evaluated through different charts which will portray a clear display for the variables that has been used. Later for more information some inferential measures have also been done. Chi square test has been used to identify if there is any kind of relationship between the nominal variables. And a regression model has been evaluated to see how the Weight variable is related to others. SPSS software has been used for the analysis purpose.

 

 

First, we will look upon the descriptive measures. There are 158 observations for each of the variables. Age variables has been categorised into five categories 14 years old, 15 years old, 16 years old, 17 years old and 18 years old or older. There are 15 respondents under 14 years old category. There are 44 respondents under 15 years old category. There are 40 respondents under 16 years old category. There are 35 respondents under 17 years old category. There are 24 respondents under 18 years old or older category. Sex variable has been categorised by male and female. There are 74 females and 84 males under this variable. Next comes the Weight variable which is a continuous random variable. Minimum value for this variable is 40.82 and maximum value is 136.08. Average of this variable is 66.85. Standard Deviation for this variable is 17.85. Skewness coefficient for this variable is 1.23 which means this variable is positively skewed. Kurtosis coefficient is -2.01 which means the variable is platykurtic. Physical Activity is the variable which has eight categories. There are 19 respondents under 0-day category. There are 14 respondents under 1 day category. There are 5 respondents under 2 days category. There are 22 respondents under 3 days category. There are 20 respondents under 4 days category. There are 18 respondents under 5 days category. There are 16 respondents under 6 days category. There are 44 respondents under 7 days category. 

 

 

Chi square test has been done for Age and Physical Activities. It has been found that there is no significant measure of association between these two categories as p value for this test is greater than 0.05. Chi square test has been done for Sex and Physical Activities. It has been found that there is a significant measure of association between these two categories as p value for this test is less than 0.05. Chi square test has been done for Age and Sex. It has been found that there is no significant measure of association between these two categories as p value for this test is greater than 0.05.

 

 

Then a regression analysis has been done to find out whether the weight variable is related to rest three variables and how they are good to be included into that linear model. The linear regression equation that has been found for Weight variable is as follows,

 

 

Weight = 40.69 + (0.744*Age) + (14.524*Sex) + (0.03*Physical Activity)

 

 

The model is quite significant as the p value for the f test is less than 0.05

 

From the above study it has been found that there is a significant relation between Weight and the Age group, Sex and Physical Activities. However, it has been found that the Weight distribution is quite positively related with the Physical Activities as the number of samples are not quite in a required number so that the result that has been found, would be so relevant to the actual scenario. These type of program like YRBS would become quite helpful to prevent the youths from different adulation problems. Through healthy lifestyle and with perfect nutrition as well as with proper effective routine this type of critical adulation problem could be minimized. This will also help the future generations to adapt a proper way to lead their life also. This will become an  healthy practice if different policies are implemented in the schools which will be helpful to the youths. 

 

Centers for Disease Control and Prevention (CDC. (2019). Youth risk behavior survey (YRBS).

 

 

Sabia, J. J., Nguyen, T. T., & Rosenberg, O. (2017). High school physical education requirements and youth body weight: New evidence from the YRBS. Health Economics, 26(10), 1291-1306.

 

 

Underwood, J. M., Brener, N., Thornton, J., Harris, W. A., Bryan, L. N., Shanklin, S. L., ... & Dittus, P. (2020). Overview and methods for the youth risk behavior surveillance system—United States, 2019. MMWR supplements, 69(1), 1.

 

 

Wheaton, A. G., Jones, S. E., Cooper, A. C., & Croft, J. B. (2018). Short sleep duration among middle school and high school students—United States, 2015. Morbidity and Mortality Weekly Report, 67(3), 85.

 

 

Centers for Disease Control and Prevention. (2020). Youth risk behavior survey data summary & trends report 2007–2017.