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Assessment 3: EpiStats Short Answer

Assessment type:Short answer (Individual)Task descriptionDetermines how well you understand fundamental epidemiological and statistical concepts and their application in public health.Instructions:Download the Word and Excel templates. Enter your findings in these files. Also download the paper you are reviewing for Question 7.Weighting:25%Minimum mark or grade:A minimum of 50% must be achieved for this assessment.Submission:Online via Moodle Turnitin. Submit BOTH the completed Word template AND your completed Excel template with calculations at the same time.

Q2

Birthweight in grams Maternal antenatal health index (MAHI) MAHI category 1 MAHI category 2 MAHI category 3

1800 1 Mean Mean Mean

1900 1 Median Median Median

2000 1

2000 1

2200 1

2300 1

2500 1

2500 1

2500 1

2600 1

2700 1

2800 1

3100 1

3200 1

3300 1

3300 1

3300 1

3500 1

4100 1

4200 1

4500 1

4500 1

4500 1

4600 1

4600 1

5100 1

5200 1

5200 1

6400 1

2000 2

2000 2

2100 2

2500 2

2500 2

2600 2

2600 2

2600 2

2600 2

2700 2

2700 2

2800 2

2900 2

3000 2

3000 2

3200 2

3300 2

3500 2

3900 2

4100 2

4100 2

4100 2

4100 2

4200 2

4300 2

4700 2

4700 2

4800 2

5500 2

1800 2

2000 3

2100 3

2500 3

2600 3

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2600 3

2600 3

2600 3

2700 3

2700 3

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2800 3

2800 3

2800 3

2800 3

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2900 3

3000 3

3000 3

3100 3

3100 3

3400 3

3400 3

3400 3

3400 3

4300 3

4300 3

4400 3

4600 3

Q4

Cardiovascular accident (CVA, stroke) at work in agricultural workers

CVA No CVA Total

No workplace adherence to Aust standard Workplace health and safety (WHS) principles 140 6,780 6,920

Workplace adherence to Aust standard Workplace health and safety (WHS) principles 650 191,000 191,650

Total 790 197,780 198,570

Q6

2×2 contingency table results of Low-dose CT screening for lung cancer in workers using high temperature insulating wools

Lung cancer No lung cancer Total

Test positive 28 29,540 29,568

Test negative 30 35,675 35,705

Total 58 65,215 65,273

PBHL20003 Social Epidemiology and Statistics Term 2 2021
Your details

Name:

Student ID:

Task description
Determines how well you understand fundamental epidemiological and statistical concepts and their application in public health.
Instructions
1 Download this Word template.
2 Answer the questions below using BOTH this Word document; and the Excel workbook for your calculations.
3
Do not just give numbers!
Respond to the questions in full sentences,
explaining what the answers mean in the context of the questions
.
4 Submit two files via Turnitin: 1) this completed Word template, and 2) the accompanying Excel workbook with your calculations.

Question

Marks

1

3

2

4

3

2

4

5

5

2

6

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5

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25

Question 1 [3 marks]
The two figures below are taken from the Australian Burden of Disease Study (2018 data)*. The first figure show DALY# rate by life stage and remoteness. The second shows DALYs by disease groups and remoteness areas.

*Australian Institute of Health and Welfare 2021. Australian Burden of Disease Study: impact and causes of illness and death in Australia 2018. Australian Burden of Disease Study series no. 23. Cat. no. BOD 29. Canberra: AIHW. This report provides estimates of the burden of disease analysis for the Australian population in 2018, using the disability-adjusted life years (DALY) measure.
# Definition of DALY: One disability adjusted life year (or 1 DALY) represents 1 year of healthy life lost, either through premature death (‘years of life lost’ or YLL) or from living with an illness or injury (‘years lived with disability’ or YLD).
a) What conclusions do you draw from Figure 8.3?

b) What conclusions do you draw from Figure 8.6?

c) (From a public health perspective), what factors may be impacting on the burden of disease as shown in the above two figures?

Question 2 [4 marks]
A study was conducted of the association of high birthweight (macrosomia) with social determinants of health (SDH) using a specially created Index. Macrosomia is defined as a birthweight of 4500 grams or more at birth. A composite Antenatal Health Index algorithm was calculated, comprising SEIFA*, access to transport facilities, access to antenatal care and extreme weather events impact to create the SDH Index. The SDH Index was classified as 1-3, with 1=low SEIFA, poorer transport access, poorer access to antenatal care, and significant impact from extreme weather events; and 3 = high SEIFA, excellent transport access, good access to antenatal care, and less impact from extreme weather events. The results are in the Excel workbook.
a) Compute the mean (Formulas > Insert Function > AVERAGE) and median (MEDIAN function) for each Antenatal Health Index category.

b) Examine the boxplots for the three Index categories. Compare the data. Discuss with reference to outliers, IQR and measures of central tendency:

PBHL20003 2022T1 Assessment 3 sample responses.docx
PBHL20003 Social Epidemiology and Statistics Term 1 2022
Notes on Assessment

Question

Marks

1

3

2

4

3

2

4

5

5

2

6

4

7

5

Total

25

Question 1 [3 marks]
The two figures below are taken from the Australian Burden of Disease Study (2018 data)*. The first figure show DALY# rate by life stage and remoteness. The second shows DALYs by disease groups and remoteness areas.

*Australian Institute of Health and Welfare 2021. Australian Burden of Disease Study: impact and causes of illness and death in Australia 2018. Australian Burden of Disease Study series no. 23. Cat. no. BOD 29. Canberra: AIHW. This report provides estimates of the burden of disease analysis for the Australian population in 2018, using the disability-adjusted life years (DALY) measure.
# Definition of DALY: One disability adjusted life year (or 1 DALY) represents 1 year of healthy life lost, either through premature death (‘years of life lost’ or YLL) or from living with an illness or injury (‘years lived with disability’ or YLD).

General comments

This question is testing your ability to read and interpret data and trends related to morbidity, burden of disease and socio-economic factors, and contextualise from a public health perspective.
The reference for the Figure and Table was given. Reviewing the discussion related to the Figure and Table in the Report (ie the section ‘Burden of disease by remoteness areas’ commencing page 111) would help give context and background (and thus guide your responses to the questions 1a-1c. The comments below are general – you may have other insights in your submission.

a) What conclusions do you draw from Figure 8.3?
The burden of DALYS (years of healthy life lost) are higher in remote areas compared to urban regions, and this increases disproportionately with age.
(Refer to the Report!) “Each remoteness area showed a similar pattern of increasing rates of burden in older age groups with Remote and very remote areas having the highest rates across all age groups (Figure 8.3). Inner regional and Outer regional areas experienced similar burden rates for all age groups.”
You would consider these findings and make your own conclusions, paraphrasing to show your understanding of what was being stated. You would reference the Figure. Many of you focused on the increasing DALY according to age group: however, this is expected – the burden of disease will always increase with age, no matter the population. They key concern here (indicated by the chart title!) is the differential increase in burden according to remoteness area, and how this compares between regions as well as age groups.
b) What conclusions do you draw from Table 8.6?
This table reports a lot of interesting information related to burden of disease according to disease group, across levels of remoteness. Look at the rate ratios and rate differences between reg

PBHL20003 Social Epidemiology and Statistics Term 1 2022
Notes on Assessment

Question

Marks

1

3

2

4

3

2

4

5

5

2

6

4

7

5

Total

25

Question 1 [3 marks]
The two figures below are taken from the Australian Burden of Disease Study (2018 data)*. The first figure show DALY# rate by life stage and remoteness. The second shows DALYs by disease groups and remoteness areas.

*Australian Institute of Health and Welfare 2021. Australian Burden of Disease Study: impact and causes of illness and death in Australia 2018. Australian Burden of Disease Study series no. 23. Cat. no. BOD 29. Canberra: AIHW. This report provides estimates of the burden of disease analysis for the Australian population in 2018, using the disability-adjusted life years (DALY) measure.
# Definition of DALY: One disability adjusted life year (or 1 DALY) represents 1 year of healthy life lost, either through premature death (‘years of life lost’ or YLL) or from living with an illness or injury (‘years lived with disability’ or YLD).

General comments

This question is testing your ability to read and interpret data and trends related to morbidity, burden of disease and socio-economic factors, and contextualise from a public health perspective.
The reference for the Figure and Table was given. Reviewing the discussion related to the Figure and Table in the Report (ie the section ‘Burden of disease by remoteness areas’ commencing page 111) would help give context and background (and thus guide your responses to the questions 1a-1c. The comments below are general – you may have other insights in your submission.

a) What conclusions do you draw from Figure 8.3?
The burden of DALYS (years of healthy life lost) are higher in remote areas compared to urban regions, and this increases disproportionately with age.
(Refer to the Report!) “Each remoteness area showed a similar pattern of increasing rates of burden in older age groups with Remote and very remote areas having the highest rates across all age groups (Figure 8.3). Inner regional and Outer regional areas experienced similar burden rates for all age groups.”
You would consider these findings and make your own conclusions, paraphrasing to show your understanding of what was being stated. You would reference the Figure. Many of you focused on the increasing DALY according to age group: however, this is expected – the burden of disease will always increase with age, no matter the population. They key concern here (indicated by the chart title!) is the differential increase in burden according to remoteness area, and how this compares between regions as well as age groups.
b) What conclusions do you draw from Table 8.6?
This table reports a lot of interesting information related to burden of disease according to disease group, across levels of remoteness. Look at the rate ratios and rate differences between regions in particular.
Its main message is …’ For most di

RESEARCH ARTICLE

Clinical, financial and social impacts of COVID-

19 and their associations with mental health

for mothers and children experiencing

adversity in Australia

Hannah Bryson
1,2

, Fiona Mensah
2,3

, Anna Price
1,2,3

, Lisa Gold
4
, Shalika

Bohingamu Mudiyanselage
4
, Bridget Kenny

1,2
, Penelope Dakin

5
, Tracey Bruce

6
,

Kristy Noble
5
, Lynn Kemp

6
, Sharon GoldfeldID

1,2,3*

1 Centre for Community Child Health, The Royal Children’s Hospital, Parkville, VIC, Australia, 2 Population

Health, Murdoch Children’s Research Institute, Parkville, VIC, Australia, 3 Department of Paediatrics,

University of Melbourne, Parkville, VIC, Australia, 4 School of Health and Social Development, Deakin

University, Burwood, VIC, Australia, 5 Australian Research Alliance for Children and Youth, Canberra City,

ACT, Australia, 6 Ingham Institute, Western Sydney University, NSW, Australia

* sharon.goldfeld@rch.org.au

Abstract

Background

Australia has maintained low rates of SARS-COV-2 (COVID-19) infection, due to geo-

graphic location and strict public health restrictions. However, the financial and social

impacts of these restrictions can negatively affect parents’ and children’s mental health. In

an existing cohort of mothers recruited for their experience of adversity, this study exam-

ined: 1) families’ experiences of the COVID-19 pandemic and public health restrictions in

terms of clinical exposure, financial hardship family stress, and family resilience (termed

‘COVID-19 impacts’); and 2) associations between COVID-19 impacts and maternal and

child mental health.

Methods

Participants were mothers recruited during pregnancy (2013–14) across two Australian

states (Victoria and Tasmania) for the ‘right@home’ trial. A COVID-19 survey was con-

ducted from May-December 2020, when children were 5.9–7.2 years old. Mothers reported

COVID-19 impacts, their own mental health (Depression, Anxiety, Stress Scales short-

form) and their child’s mental health (CoRonavIruS Health and Impact Survey subscale).

Associations between COVID-19 impacts and mental health were examined using regres-

sion models controlling for pre-COVID-19 characteristics.

Results

319/406 (79%) mothers completed the COVID-19 survey. Only one reported having had

COVID-19. Rates of self-quarantine (20%), job or income loss (27%) and family stress (e.g.,

difficulty managing children’s at-home learning (40%)) were high. Many mothers also

PLOS ONE

PLOS ONE | https://doi.org/10.1371/journal.pone.0257357 September 13, 2021 1 / 18

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OPEN ACCESS

Citation: Bryson H, Mensah F, Price A, Gold L,

Mudiyanselage SB, Kenny B, et al. (2021) Clinical,

financial and social impacts of COVID-19 and their

associations with mental health for mothers and

children experiencing adversity in Australia. PLoS

ONE 16(9): e0257357. https://doi.org/10.1371/

journal.pone.0257357

Editor: Livio Provenzi, Fondazi

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