Victorian Government Health Audit Report: Riverlands Region Analysis

This health audit report, conducted for the Victorian Government's Department of Health, examines health inequalities within the Riverlands region, focusing on sixteen subzones. The analysis compares education levels (school leavers, vocational training, and higher education participation) with psychological distress levels to identify disparities. The findings reveal a correlation between education and mental health outcomes, with subzones exhibiting higher dropout rates also showing increased psychological distress. The report highlights disparities in education and health across the subzones, suggesting the need for interventions to reduce school dropout rates and improve access to education to mitigate psychological distress and promote overall health equity. It underscores the importance of addressing these inequalities to improve the well-being of the Riverlands population.

Running head: health audit report 1
Health Audit Report
Name of Student:
Name of Institution:

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Health Audit Report
Introduction and rationale
Health Audit tool is a tool that is designed to provide a comparative analysis of the health
conditions and disparities in different regions or locations. The tool gives the disparities in terms
of the disease outcome as well as the level of education and training. The health audit tool also
outlines the disparities in terms of the ages and gender (sex) of the people in the regions.
Therefore, the information or data from the health audit tool can be used to investigate and
compare different regions or zones in a particular region (Philip, Suzanne, Mary, & Nancy,
2018).
The purpose of using a health audit tool is to identify any disparities in the health system
and to find a suitable solution for them. For example, health audit tool can used to investigate
whether there is a higher prevalence of certain disease outcome in certain regions. The other
rationale for using a health audit tool is to investigate the link between different health
conditions. A perfect example is that a health audit tool can used to find out whether there is a
link between the level of education and a disease outcome such as psychological distress
(Kleopa, Panayiotou, Kouta, & Middleton, 2018).
The purposes of this study are: (1) To apply knowledge of health inequality through use
of a tool used by public health practitioners; and (2) to make a determination about population
health and inequalities based on simulated data. The analysis will focus on Riverlands region.
The total number of subzones in Riverlands is sixteen (16). Therefore, the study focuses on
comparing the disparities among the sixteen (16) subzones in Riverlands region. The comparison
is based on the level of education and a psychological distress as the disease outcome. The
comparison of education involves analyzing the number of school leavers, the number of people

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in vocational trainings, the pass rate of vocational education and training as well as the number
of school leavers who are taking part in higher education. The comparison between education
and psychological distress was also made across the subzones.
Data analysis/application of audit tool
The data analysis was conducted for education and psychological distress as a disease
outcome. The analysis of disparities in education was conducted at different stages namely the
number of school leavers, the number of people involved in vocational training, the pass rate of
the vocational training as well as the number of school leavers involved in higher education. The
outcomes of the analysis are outlined in the sections that follow.
Education
Early School Leavers
The purpose is to compare the number of early school leavers across the sixteen subzones
in Riverlands. The number of early school leavers represents the number of people who left
school at year ten (10) or below, or did not go to school. The graph below outlines that there was
a disparity in the number of school leavers among the subzones (Tineke, et al., 2018). The results
outlines that the subzone with the highest number of school leavrers is subzone 16 on the other
hand, the suzoine with the lowest number of school leavers is subzone 1. The other subzones
with relatively higher number of school leavers are subzone 12, subzone 15, subzone 11, subzone
14 and subzone 13. On the other hand, the subzones with relatively lower number of school
leavers are subzone 8, subzone 9, subzone 10, subzone 7, subzone 6, subzone 5, subzone 4,
subzone, 3 and subzone 2.

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Secondary school
The bar graph below is a comparison of the number of people who were in full-time
secondary education at age 16 in the year 2015. The results demonstrate that the subzone with
the highest number of people in full-time secondary education is subzone 16. On the other hand,
the subzone with the lowest number of people involved in full-time secondary education is
subzone 1. The interesting revelation is that the results of the number of secondary educations is
almost similar to the number of school leavers. The subzones with significantly lower number of
full-time participations in secondary school are subzone 12, subzone 10, subzone 9, subzone 8,
subzone 7, subzone 6, subzone 5, subzone 4, subzone 3 and subzone. On the other hand, the
subzone with significantly higher number of full-time participations in secondary education are
subzone 11, subzone 13, subzone 14 and subzone 15 (Dimopoulos, garcia, Marx, & Micha,
2018).

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Vocational training
The bar graph below is a comparison of the number of people in vocational trainings in
the year 2015 across the subzones in Riverlands. The results demonstrate that subzone 16 had the
highest number of people in vocational training at under all categories of vocational trainings.
On the other hand, the subzone with the lowest number of people in vocational training is
subzone one (Zahari, et al., 2019).

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Participation in vocational education and training - Aboriginal population
Participation in vocational education and training - non-Indigenous
population
Participation in vocational education and training - Total population
0 50000 100000 150000 200000 250000
A Comparative Bar Graph of Vocational Educatioin and Training
Subzone 01 Subzone 03 Subzone 16 Subzone 07 Subzone 11 Subzone 13 Subzone 06 Subzone 09
Subzone 15 Subzone 12 Subzone 14 Subzone 04 Subzone 02 Subzone 05 Subzone 10 Subzone 08
Pass rate of vocational training
The bar graph below is a comparison of the pass rate of different vocational education
and training. From the graph, we can see that the subzone with the highest pass rate across the
vocational education and trainings is subzone 16. On the other hand, the subzone with the lowest
pass rate across the vocational education and trainings is subzone 1 (Willem, Amedeo, & Jan,
2019).

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Load Pass Rate of vocational education and training
subjects - Government-funded hours
Load Pass Rate of vocational education and training
subjects - Aboriginal students
Load Pass Rate of vocational education and training
subjects - Total students
0% 20% 40% 60% 80% 100%
A bar graph of load pass rate of vocational
education and training
Subzone 01 Subzone 03 Subzone 16 Subzone 07 Subzone 11 Subzone 13
Subzone 06 Subzone 09 Subzone 15 Subzone 12 Subzone 14 Subzone 04
Subzone 02 Subzone 05 Subzone 10 Subzone 08
School leavers participating in higher education
The last section of education was to compare the number of school leavers who are
involved in higher education. The bar graph below represents a comparison of the number of
school leavers involved in higher education across the sixteen (16) subzones. The subzone with
the highest number of school leavers involved in higher education is subzone 15. On the other
hand, the subzone with the lowest number of school leavers involved in higher education is
subzone 2. The other subzone with relatively higher number of school leavers involved in higher
education are subzone 9, subzone 11, subzone 10, subzone 14, subzone 12, subzone 13, subzone
16 and subzone 15. On the other hand, the subzone with relatively lower number of school
leavers participating in higher education are subzone 8, subzone 5, subzone 7, subzone 6,
subzone 4, subzone 3 and subzone 01 (Michael, 2017).

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Disease outcome: Psychological Distress
The bar graph below represents a comparison of the level of psychological distress across
the sixteen (16) subzones in Riverlands. The bar graph reveals that the subzone with the highest
number of people with psychological distress among the sixteen (16) subzones is siubzone 15.
On the othr hand, the subzone with the lowest number of people with psychological distress
among the sixteen (16) subzones is subzone 1. The other subzone with relatively higher number
of psychological distress among the sixteen (16) subzones are subzone 10, subzone 11, subzone
12, subzone 14, subzone 15 and subzone 13. On the other hand, the subzone with relatively low
number of psychological distress are subzone 9, subzone 8, subzone 7, subzone 6, subzone 5,
subzone 4 and subzone 2 (Narcisa, Anne, & Efstathios, 2016).

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Discussion/Framing of audit results within public health principles
The purpose of this report was to provide a health audit report of the sixteen (16)
subzones of Riverlands. The audit report is meant to identify and outline the disparities in among
the sixteen (16) subzones of Riverlands. The audit report also outlines a comparison between
education and a disease outcome (psychological distress) among the sixteen (16) subzones of
Riverlands.
In summary, the results demonstrated that the subzone with the highest number of school
leavers is subzone 16 on the other hand, the subzone with the lowest number of school leavers is
subzone 1. The subzone with the highest number of people in full-time secondary education is
subzone 16. On the other hand, the subzone with the lowest number of people involved in full-
time secondary education is subzone 1. From the results, we can say that generally, subzone 16
has a relatively higher number of people compared with the other subzones. The higher

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population in subzone 16 results in higher number school leavers as well as a higher number of
people in secondary schools. Similarly, we can say that subzone 1 has relatively better education
system and lower population. The better education system in subzone 1 is reflected in the fewest
number of school leavers (Yipeng, et al., 2019).
From the results, we also saw that subzone 16 had the highest number of people in
vocational training at under all categories of vocational trainings. On the other hand, the subzone
with the lowest number of people in vocational training is subzone one. The subzone with the
highest pass rate across the vocational education and trainings is subzone 16. On the other hand,
the subzone with the lowest pass rate across the vocational education and trainings is subzone 1.
The results from vocational education and training again reveals that subzone 16 has the best
education system. Subzone 16 has the best education system since it has the highest number of
people in vocational education and training as well as the highest pass rate among the sixteen
(16) subzones (Ruili, 2016).
Despite the fact that subzone 15 was among the subzones with the highest number of
school leavers, the results also demonstrated that the subzone with the highest number of school
leavers involved in higher education is subzone 15. On the other hand, the subzone with the
lowest number of school leavers involved in higher education is subzone 2. Therefore, we can
say that subzone 15 performed better in ensuring that the school leavers are participating in
higher education compared with the other fifteen (15) subzones.
The subzone with the highest number of people with psychological distress among the
sixteen (16) subzones is subzone 15. On the other hand, the subzone with the lowest number of
people with psychological distress among the sixteen (16) subzones is subzone 1. Subzone 15
was among the subzones with the highest number of school leavers (just after subzone 16).

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Similarly, subzone 15 had the highest number of school leavers participating in higher education.
Therefore, we can link the level of psychological distress and the number of school leavers. The
other subzones also display a similar result. We can say that school leavers are highly likely to
suffer psychological distress compared to those who proceed to higher levels of education
(Dimopoulos, garcia, Marx, & Micha, 2018).
Conclusion
The results have demonstrated that there is a significant disparity in health and education
among the sixteen (16) subzones of Riverlands. The disparity is demonstrated in across all the
regions other than Riverlaands. The general observation is that those who drop out of school are
highly likely to suffer psychological distress than those who continue with education to higher
levels. Therefore, there should be proper measures to ensure that there is a significant reduction
in the number of school leavers and hence psychological distress in the society.
The other general observation is that areas with higher populations tend to register higher
number of school leavers. Such regions or subzones also tend to register higher number of
psychological distress in the society. Therefore, extra measures should be taken to ensure that
there is a reduction in the number of school leavers in the regions with high populations.
Lastly, the number of people who proceed to secondary school reflects the number of
people in vocational education and training. The higher the number of people who proceed to
secondary school the higher the number of people in vocational education and training as well as
the pass rate of vocational education and training. Again, the bottom line of increasing the
number of people in vocational education and training centers as well as the pass rates in such
institutions is to reduces the number of school leavers.

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References
Dimopoulos, G., garcia, G. G., Marx, D., & Micha, L. Y. (2018). Data analysis, processing and
interpretation from different sources. Journal of Operation Research, 1-17.
Kleopa, D., Panayiotou, A., Kouta, C., & Middleton, N. (2018). Content validity of
neighbourhood environment audit tool: virtual ethnographic study. European Journal of
Public Health, 12-45.
Michael, J. A. (2017). Quantitative Data Analysis—In the Graduate Curriculum. Journal of
Technical Writing and Communication, 46-89.
Narcisa, M., Anne, M., & Efstathios, S. (2016). Deaths by Suicide in Ecuador: A Quantitative
Data Analysis. Jouranal of Health Research, 45-100.
Philip, A., Suzanne, K., Mary, L., & Nancy, L. C. (2018). Allied health clinicians using
translational research in action to develop a reliable stroke audit tool. Joirnal of
Evaluation in Clinical Practice, 34-78.
Ruili, H. (2016). A Quantitative High-Throughput Screening Data Analysis Pipeline for Activity
Profiling. Journal of Operation Research, 12-19.
Tineke, A., Sarah, B., Tina, C., Sonia, D., Wendy, M., & Jane, S. M. (2018). Sense Making: The
Process of Data Analysis and Interpretation. Participatory Research for Health and
Social Well-Being, 12-89.
Willem, M., Amedeo, P., & Jan, R. (2019). Quantitative Data Analysis. A Companion for
Accounting and Information Systems Research, 7-9.