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## Analyzing Health Statistics: Insights from Top Experts

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### Introduction - Applied Statistics And Data Analysis For Public Health

The term statistics of health is the summarized data that is related to the health of the patient in a particular healthcare center. The researchers collect the data from the healthcare organization and use this data for learning about the health of the public and the health care of the patients. The analysis of the data in public healthcare helps in understanding the factors that are associated with the outcomes of the desired interest (Bachner, 2021). It is also used for determining the extent to which the different kinds of interventions may be effective. This model will help in the development of the knowledge of the statistics, designing of the statistical data appropriate execution, and planning for addressing the question related to the health of the public. The methods that have been used for analyzing the statistics data of public health are mean, median T-test standard deviation, and many more for the data analysis. There should be employment of formats of graphs like pie charts, histograms, and line graphs for presenting the data epidemiologically (Ott, 2018). In the report, the data obtained was a randomized trial of control that was conducted to evaluate the education intervention of lifestyle health. The proposed intervention is delivered for 12 weeks and the main aim of this intervention would be the promotion of a healthy lifestyle amongst the students of the university and also increasing the literacy related to health and improving healthy weights in participants of the study.

### Preliminary analyses and investigations

The analysis of the preliminary that is discussed on the screening of the data and the statistics of the description like the mean, standard deviation, frequencies like percentages and counts and medians, charts, and graphs would also be used in summarizing the variables of these assessments of public health care.

### Question 1

Here the difference between the healthy literacy of the two campuses of Compus A and Campus B has been reflected through the “independent T-test” in the SPSS.

Group Statistics

Asthma diagnosed N “Mean Std. Deviation Std. Error Mean”

“Health literacy at baseline” No asthma 69 58.4811 15.66407 1.88573

Asthma 12 58.2318 12.37569 3.57255

Health literacy after intervention No asthma 69 58.9262 15.80211 1.90235

Asthma 12 58.6599 12.59182 3.63494

“Independent Samples Test”

“Levene's Test for Equality of Variances” “t-test for Equality of Means”

F Sig. t df Sig. (2-tailed) Mean Difference Std. Error Difference “95% Confidence Interval of the Difference”

Lower Upper

“Health literacy at baseline” “Equal variances assumed” 1.366 .246 .052 79 .958 .24929 4.76938 -9.24392 9.74250

Equal variances not assumed .062 17.760 .951 .24929 4.03969 -8.24601 8.74459

Health literacy after intervention Equal variances assumed 1.247 .267 .055 79 .956 .26629 4.81520 -9.31814 9.85071

Equal variances not assumed .065 17.637 .949 .26629 4.10265 -8.36580 8.89838

The difference has been concluded based on the Asthma variable and comparing the two variables together, that is baseline health literacy and the literacy of the health after the intervention (Peng, 2018). In the baseline health literacy, the no of people using smoking are the No asthma of 69 and there are 12 similarly literacy of health after the intervention is the same. The P values of both campuses are 0.246 and 0.267 respectively. The T-statistics and the P value of the test have been reflected in the valve table (Baek et al. 2018). The test is performed using the “non-parametric equivalent” technique for comparing the dependent variable on the two interventions in the “Mann-Whitney test”. Here the grouping variable is Asthma. “Mann-Whitney test indicates that there is not any difference of significance in the test between the literacy and health of the two campuses. (U= 407 and 408 respectively)

### Question 2

#### Body mass index -1

“Group Statistics”

“Intervention N Mean Std. Deviation Std. Error Mean”

“Body_mass_index_1 Control group 47” 227512.1697 32330.66698 4715.91246

Intervention group 34 254355.2581 46664.93009 8002.96949

“Independent Samples Test”

“Levene's Test for Equality of Variances” “t-test for Equality of Means”

F Sig. t df Sig. (2-tailed) Mean Difference Std. Error Difference 95% Confidence Interval of the Difference

Lower Upper

“Body_mass_index_1 Equal variances assumed” 3.626 .061 -3.060 79 .003 -26843.08843 8772.62439 -44304.56050 -9381.61637

Equal variances not assumed -2.890 55.128 .006 -26843.08843 9289.09850 -45457.88467 -8228.29220

As per the “Body mass index” the evaluation of the effect of the intervention of the “body mass index” has been conducted by using the statistical T-test of the independent variable in the SPSS software, at first there should be the understanding of the T-test, this is used for the finding out the difference between the two groups mean and how they are related with each other. This is used for the test of the hypothesis and the values of the T-test and determining the significant statistic (Bahariniya and Madadizadeh, 2021). From the above table, it can be seen that the values of the means of the two groups are in the Body mass index one the sample size in the control group is 47 and in the intervention group it is 34 the mean values are 227512 and 254355 respectively and the standard deviation is 32330 and 46664 respectively (Rahman, 2020). P values of the T-test are 0.003 and 0.006 and the T values are -3.060 and -2.890 respectively and there is a 95% difference in confidence.

#### Body mass index -2

“Group Statistics”

“Intervention N Mean Std. Deviation Std. Error Mean”

“Body_mass_index_2 Control group 47” 228673.2373 31962.15678 4662.15973

Intervention group 34 246787.1870 44987.64505 7715.31748

“Independent Samples Test”

“Levene's Test for Equality of Variances” “t-test for Equality of Means”

F Sig. t df Sig. (2-tailed) Mean Difference Std. Error “Difference 95% Confidence Interval of the Difference”

Lower Upper

“Body_mass_index_2 Equal variances assumed” 3.536 .064 -2.120 79 .037 -18113.94970 8544.27829 -35120.91023 -1106.98918

Equal variances not assumed -2.009 56.131 .049 -18113.94970 9014.53588 -36171.30761 -56.59180

Here it can be seen that the values of the mean of the control group and intervention group are 2286.73 and 246787 respectively (Saglimbene et al. 2020). Standard deviations are 31962 and 44987 for the two groups respectively. Here the P values of the T test are 0.037 and 0.049 respectively and the values of T are -2.120 and -2.009 respectively.

Correlations

“Descriptive Statistics”

“Mean Std. Deviation N”

“Body_mass_index_1” 238779.6389 40951.06134 81

“Body_mass_index_2” 236276.6236 38770.82538 81

Intervention 1.42 .497 81

Correlations

Body_mass_index_1 Body_mass_index_2 Intervention

Body_mass_index_1 Pearson Correlation 1 .990** .326**

Sig. (2-tailed) .000 .003

N 81 81 81

Body_mass_index_2 Pearson Correlation .990** 1 .232*

Sig. (2-tailed) .000 .037

N 81 81 81

Intervention Pearson Correlation .326** .232* 1

Sig. (2-tailed) .003 .037

N 81 81 81

** “Correlation is significant at the 0.01 level (2-tailed)”.

* “Correlation is significant at the 0.05 level (2-tailed)”.

It can be said that the optimal value of the correlation in the Spss of the variables is -1. There has been a significant correlation between all the specified variables (Vinceti and Filippini, 2021). The correlations are significant at the level of 0.01(2-tailed).

### Question 3

 “Case Processing Summary” Cases Valid Missing Total N Percent N Percent N Percent “Asthma diagnosed * Intervention” 81 100.0% 0 0.0% 81 100.0%
 “Asthma diagnosed * Intervention Cross tabulation” “Intervention” Total “Control group’ “Intervention group” “Asthma diagnosed” No asthma Count 40 29 69 % within Asthma diagnosed 58.0% 42.0% 100.0% Asthma Count 7 5 12 % within Asthma diagnosed 58.3% 41.7% 100.0% Total Count 47 34 81 % within Asthma diagnosed 58.0% 42.0% 100.0%

The use of cross-tabulation has been done to conduct the answer to this question. The use of this table the significant statistical difference of the cases of Asthma and the intervention group and the control group (Benke and Benke, 2018). There has been a total of 81 of the people in the control group who had 40 cases without Asthma Asthma it is 7 persons and the total percentage is 58% for the group of control (Wing et al. 2018). In the intervention group, the no of no Asthma cases was 29 and those with Asthma was 5 and there total no of participants in the control group was 47 and in the intervention group were 34 whole total it was 69 no people without Asthma and 12 propels are with Asthma.

“Chi-square test”

If the value of the calculated “Chi-square” is higher than the critical value of the chi-square there is a chance of rejecting the hypothesis of null (Berman, 2018). This signifies that the how much differences are there between the count's observation and the no of the counts that have been expected, the value of the “Chi-square” that is calculated is 0.001 and the value of p is 0.981, the results can be said to be significant as the value is less and equal to the designated level of the alpha that is normally is 0.05 (Wong et al. 2019). Here the “P value” is greater than the “standard value” of alpha so we reject the “hypothesis of null”.

 Chi-Square Tests Value df Asymp. Sig. (2-sided) Exact Sig. (2-sided) Exact Sig. (1-sided) “Pearson Chi-Square” .001a 1 .981 “Continuity Correction” .000 1 1.000 “Likelihood Ratio” .001 1 .981 “Fisher's Exact Test” 1.000 .619 “Linear-by-Linear Association” .001 1 .981 “N of Valid Cases” 81 a. “0 cells (0.0%) have an expected count less than 5. The minimum expected count is 5.04”. b. “Computed only for a 2x2 table”

### Question 4

In question 4 the determination should be made on the difference between the baseline health literacy between the participants of the age groups of the “≤20 years, 21-25 years and ≥26 years”. For this, the test has used cross-tabulation to make out the difference according to the age criteria. There is the total number of the participants of the 8 (Chen, 2022). As per the calculation has been made based on the health literacy baseline it can clearly show that none of the people have come on the criteria of the range. In the below table, it can be seen that the no of participants in the age group of less than 20 is 29 which is the highest no of participants in the three age group criteria (Zhao et al. 2021). In the age group of 21 to 25 years, the no of participants is 19 and in the last age group, the age of more than 26 is 27. The total no of participants in all the age groups is 72.

 Health literacy at baseline ≤20 years Between 21 to 25 years ≥26 years total 25.57 1 1 27.56 1 1 28.57 1 1 2 35.71 3 3 35.72 1 1 35.8 1 1 42.84 1 1 42.86 1 1 49.85 1 1 50 4 2 2 8 55.14 1 1 56.14 1 1 57 1 1 57.14 2 3 7 12 62.29 1 1 63.12 1 1 63.3 1 1 64 1 1 64.29 4 1 4 9 64.52 1 1 70.4 1 1 70.41 1 1 71.43 4 4 1 9 71.5 1 1 76.41 1 1 76.42 1 1 78.57 2 1 1 4 83.42 1 1 84.62 1 1 85.71 1 1 90.8 1 1 92.86 1 1 Total 29 16 27 72

Chi-square test

 Chi-Square Tests Value df Asymp. Sig. (2-sided) “Pearson Chi-Square” 796.146a 651 .000 “Likelihood Ratio” 249.884 651 1.000 “Linear-by-Linear Association” .321 1 .571 “N of Valid Cases” 81 a. “704 cells (100.0%) have expected count less than 5. The minimum expected count is .01”.

The “chi-square test” of the difference of the health line literacy based on the age criteria is reflected. The value of the personal Chi-square is 796.146 and the value of the “df” is 650.

### Question 5

For the conduction of the analysis and investigation of the baseline literacy of health, sex, and age for predicting the body mass index of the post-intervention amongst the participants. For this investigation, the test of the “Univariate Analysis of Variance” Has been used for the calculation (Escolà-Gascón, 2022). This tells us the analysis of the Univariate of each variable of the set of the data. It will look at the value range and the value's central tendency, describe the response pattern of the variables, and describe each variable in its own way.

“Health literacy at baseline and post-intervention body mass index”

The highest no of participants in the literacy of health in the criteria of 57.14 that is 12 participants.

“Age and post-intervention body mass index”

The age range of the starting from 18 years to 44 years the no of participants in the age range of 20 age, this has the participants of 14 people in it 19 years is the second highest age range.

“Sex and post-intervention body mass index”

As per the criteria of the sex, the no of participants in the male is 26, and for females is 55, the number of female participants is highest.

The descriptive statistics of having the dependent variable of Body mass index of 2 with the factors of “Health literacy at baseline”, age, and sex has been determined (Hernán et al. 2018). The highest mean values of the male and female are 376917 and 376917 respectively for male and female. This comes in the baseline of the health literacy of 41. The total value of the mean for males is 250504 and for females, it is 229550 in total it is 236276.

 “Levene's Test of Equality of Error Variances” Dependent Variable: Body_mass_index_2 F df1 df2 Sig. 1.204 69 11 .390 “Tests the null hypothesis that the error variance of the dependent variable is equal across groups”. a. “Design: Intercept” + HealthLiteracy1 + Age + Sex + HealthLiteracy1 * Age + HealthLiteracy1 * Sex + Age * Sex + HealthLiteracy1 * Age * Sex
 “Tests of Between-Subjects Effects” “Dependent Variable: Body_mass_index_2” Source “Type III Sum of Squares” df “Mean Square” F Sig. Partial Eta Squared Consent. Parameter Observed Power Corrected Model 115396180218.325a 69 1672408408.961 3.787 .010 .960 261.294 .962 Intercept 2520555580569.784 1 2520555580569.784 5707.343 .000 .998 5707.343 1.000 HealthLiteracy1 30614103838.475 20 1530705191.924 3.466 .019 .863 69.320 .896 Age 30385707840.807 16 1899106740.050 4.300 .009 .862 68.803 .945 Sex 4272086817.556 1 4272086817.556 9.673 .010 .468 9.673 .808 HealthLiteracy1 * Age 5270511529.461 10 527051152.946 1.193 .386 .520 11.934 .336 HealthLiteracy1 * Sex 76748229.464 2 38374114.732 .087 .917 .016 .174 .060 Age * Sex .000 0 . . . .000 .000 . HealthLiteracy1 * Age * Sex .000 0 . . . .000 .000 . Error 4857971829.802 11 441633802.709 Total 4642212222917.494 81 Corrected Total 120254152048.127 80 a. R Squared = .960 (Adjusted R Squared = .706) b. Computed using alpha = .05

The test between the effect of the subject of the age, sex, and health literacy based on the body mass index 2 tests the ability of the variations in the variable of the dependent, here the “dependent variable” is “body mass index”.

 3. Sex Dependent Variable: Body_mass_index_2 Sex Mean Std. Error 95% Confidence Interval Lower Bound Upper Bound male 253014.244a 4307.376 243533.773 262494.714 female 230893.995a 2942.381 224417.857 237370.132 a. “Based on modified population marginal mean”.

Here the dependent variable of the body mass index and the sex criteria show the mean value of both genders with 95% interval confidence (Mooney and Pejaver, 2018). The mean value is 253014 and 230893 for male and female respectively.

#### Profile plots

The plot of the profile is the analysis of the graphical data for analyzing the behavior of relatives. Thai consists of a sequence of vertical spokes representing the different kinds of variables in the data set of the multivariate (McClure et al. 2020). Here the profile plots show the data of graphical of the age, sex, and the baseline of the literacy of health based on the body index of 2.

Figure 1: profit plots

(Source: Self-created in SPSS)

Conclusion

This report summary concludes that the different tasks have been done based on the different tests and different formulas of the tasks. In task 1 the conclusion is made by applying the T-test and a comparison is made based on the mean of the variables for examining the difference between the baseline of health literacy between the different campuses of campus And campus B. In the second task, the evaluation is done on the intervention and the BMI of the participants, the body mass index is calculated by using the formula and then the evaluation is done on the intervention groups and control groups. In the third, the examination of the significant statically of the no of Asthma cases between the control and intervention groups has been done. This reflects the number of participants in every group having Asthma or not. In the fourth task the determination of the significant difference in the statistics of the baseline of health literacy between the participants of the age criteria of “≤20 years, 21-25 years, and ≥26 years' '. For this, the test has used cross-tabulation to make out the difference according to the age criteria. At last for task 5 the investigation of how the baseline literacy of health, sex, and age can be used for the prediction of the body mass index of the post-intervention between the participants.

References

Journals

Bachner, J., 2021. Pedagogical Recommendations for Applied Statistics Courses. In The Palgrave Handbook of Political Research Pedagogy (pp. 311-321). Palgrave Macmillan, Cham.

Baek, H., Cho, M., Kim, S., Hwang, H., Song, M. and Yoo, S., 2018. Analysis of length of hospital stay using electronic health records: A statistical and data mining approach. PloS one13(4), p.e0195901.

Bahariniya, S. and Madadizadeh, F., 2021. Review of the Statistical Methods Used in Original Articles Published in Iranian Journal of Public Health from 2015–2019: A Review Article. Iranian Journal of Public Health50(8), p.1577.

Benke, K. and Benke, G., 2018. Artificial intelligence and big data in public health. International journal of environmental research and public health15(12), p.2796.

Berman, A., 2018, November. General topics in applied public health statistics. In APHA's 2018 Annual Meeting & Expo (Nov. 10-Nov. 14). APHA.

Chen, X.W., 2022. Public health. In Network Science Models for Data Analytics Automation (pp. 35-47). Springer, Cham.

Escolà-Gascón, Á., 2022. Statistical indicators of compliance with anti-COVID-19 public health measures at European airports. International Journal of Disaster Risk Reduction68, p.102720.

Hernán, M.A., Hsu, J. and Healy, B., 2019. A second chance to get causal inference right: a classification of data science tasks. Chance32(1), pp.42-49.

McClure, E.S., Vasudevan, P., Bailey, Z., Patel, S. and Robinson, W.R., 2020. Racial capitalism within public health—how occupational settings drive COVID-19 disparities. American Journal of Epidemiology189(11), pp.1244-1253.

Mooney, S.J. and Pejaver, V., 2018. Big data in public health: terminology, machine learning, and privacy. Annual review of public health39, pp.95-112.

Ott, W.R., 2018. Environmental statistics and data analysis. Routledge.

Peng, L., 2018, November. Invited Session in Applied Public Health Statistics. In APHA's 2018 Annual Meeting & Expo (Nov. 10-Nov. 14). APHA.

.Rahman, A. ed., 2020. Statistics for data science and policy analysis. Springer Nature.

Saglimbene, V., Strippoli, G., Craig, J.C. and Wong, G., 2020. Statistics and data analyses—a new educational series for nephrologists. Kidney International97(2), pp.233-235.

Vinceti, S.R. and Filippini, T., 2021. Towards the dismissal of null hypothesis/statistical significance testing in public health, public law and toxicology.

Wing, C., Simon, K. and Bello-Gomez, R.A., 2018. Designing difference in difference studies: best practices for public health policy research. Annual review of public health39.

Wong, Z.S., Zhou, J. and Zhang, Q., 2019. Artificial intelligence for infectious disease big data analytics. Infection, disease & health24(1), pp.44-48.

Zhao, Y. and Chen, D.G. eds., 2021. Modern Statistical Methods for Health Research. Springer.

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