children participating in some unhealthy behaviour patterns.

a. Based on this information calculate the cumulative incidence of osteoporosis among this population and interpret your answer (4 marks). b. Based on this information, calculate the incidence rate of osteoporosis and interpret your answer (8 marks) c. Of the two measures calculated, which is the most appropriate measure of risk and why?

QUESTIONS
1. Read the following and answer the questions below (14 marks).
Wheaton N, Millar L, Allender S, Nichols M. (2015). The stability of weight status through the
early to middle childhood years in Australia: a longitudinal study. BMJ Open. Vol. 5, Issue 4, 9p.
Abstract
Objectives: To investigate the sociodemographic and behavioural factors associated with
incidence, persistence or remission of obesity in a longitudinal sample of Australian children
aged 4–10 years.
Study population: Data used in this analysis were from the Longitudinal Study of Australian
Children (LSAC), which is a continuing nationally representative longitudinal survey study that
aims to observe Australian children’s development and well-being and examine links with social,
economic and cultural aspects of their environment. The children were identified through the
Medicare Australia enrolments database which contains the majority of Australian children’s
details. In 2004, >18000 children were sampled from this database (0–1 years, the Birth or ‘B’
cohort; 4–5 years, Kinder or ‘K’ cohort). The data were collected using a two stage clustered
design; first selecting postcodes and then children. Stratification was used to ensure that
proportionate numbers of children were selected to represent states and territories as well as
representation from cities as well as rural and remote areas. Data are collected in a series of
‘waves’ that are collected biennially, as well as mail-out data collections that occur between
waves. The sample for this analysis included all children in the K-cohort (aged 4–5 years at wave
1) who participated in all four waves of LSAC (wave 1, 2004, aged 4–5 years; wave 2, 2006,
aged 6–7 years; wave 3, 2008, aged 8–9 years and wave 4, 2010, aged 10–11 years). Of the 4983
children who participated in the baseline (wave 1) survey, 4169 (83.7%) completed all four
waves of data collection; of these children, 223 had some missing data at random for at least one
wave.
Primary and secondary outcome measures: Movement of children between weight status
categories over time and individual-level predictors of weight status change (sociodemographic
characteristics, selected dietary and activity behaviours).
Results: The study found tracking of weight status across this period of childhood. There was an
inverse association observed between socioeconomic position and persistence of
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overweight/obesity. Sugar sweetened beverages (SSB) and fruit and vegetable intake and screen
time appeared to be important predictors of stronger tracking.
Of the 4169 participants, 51.3% were male and the mean age of children at wave 1 was 4.8±0.2
years. Almost all children were born in Australia (95.8%), spoke English at home (87.5%) and
the majority had parents who were born in Australia. Furthermore, 61.4% of children lived in
metropolitan areas and the remainder lived in non-metropolitan areas.
The mean height and weight of the children increased over waves (with age), but the BMI zscores
did not. Overall, the mean BMI z-scores at waves 2 (−0.15; p<0.001), 3 (−0.06; p=0.012)
and 4 (−0.07; p=0.006) were significantly lower than the mean BMI z-score at wave 1. For males,
the mean BMI z-scores at waves 2 (−0.22, p<0.001) and 3 (−0.12, p=0.001) were significantly
lower than at wave 1; however, the mean BMI z-score at wave 4 (−0.06, p=0.09) was not
different from baseline. For females, the mean BMI z-scores at waves 2 (−0.09, p=0.01) and 4
(−0.08, p=0.02) were significantly lower than at wave 1; however, the mean BMI z-score at wave
3 (−0.002, p=0.96) was not different from baseline. From wave 1 to 4, the number of children
who were classified as obese increased significantly (8.9– 12.9%, p<0.001). Males had a
significantly higher prevalence of overweight and obesity compared with females at all waves.
Children’s weight status categories at each wave of data collection were cross-tabulated to
examine the proportion of children who changed category between waves of data collection (each
2 years apart). As expected, over short time periods, weight status was relatively stable, while
over 6 years (from wave 1 to 4), there was a greater degree of change, particularly among those
who were overweight at wave 1. Among the children who were normal weight at wave 1, the vast
majority remained in the same weight status category at wave 4, while a small percentage moved
into the overweight category. Among those who were overweight at wave 1, however, over 40%
were classified as normal weight 6 years later, while nearly 20% had become obese. Almost twothirds
of those who were obese at wave 1 remained obese at wave 4.
An unadjusted multinomial regression analysis with weight status being predicted by fruit and
vegetable intake, high fat food intake, SSB intake, computer use during the week and weekend
and television use during the week and weekend found that the relative risk of being in the
overweight–obese category compared with the normal weight status category increased
significantly with increased television viewing and consumption of SSB at all waves. Further,
compared with children who remained in the normal weight status category from wave 1 to 2,
those who were in the overweight–obese category at wave 1 and remained so at wave 2 were
more likely to drink SSB and watch television on weekends (p<0.05).
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Over 2, 4 and 6 years, the relative risk of moving into or remaining in the overweight–obese
category compared with the normal weight status category increased with increased fruit and
vegetable consumption (p<0.05) in the adjusted model. Over 6 years (wave 1–4), compared with
children who remained in the normal weight status category, those remaining in the overweight–
obese category were more likely to consume SSB (p<0.05). Moreover, from wave 1 to 3, the
relative risk of progressing into the overweight–obese category compared with remaining in the
normal weight status category increased with increased television viewing on weekends (p<0.05).
From wave 1 to 2, the relative risk of remaining in the overweight/obese category compared with
the normal weight status category was more than two times greater for the most disadvantaged
compared with the least disadvantaged. Between waves 3 and 4, the relative risk of resolving
from overweight/obese to normal compared with remaining in the normal weight status category
decreased for the most disadvantaged compared with the least disadvantaged. Furthermore, this
was particularly evident among females. Analysis of changes between waves 1–3 and waves 1–4
(4 and 6 years, respectively) found that the relative risk of moving from normal to
overweight/obese and of remaining overweight/obese compared with remaining in the normal
weight status category was greater for the most disadvantaged compared with the least
disadvantaged. This was significant among girls but not boys.
Conclusions: Overweight and obesity established early in childhood tracks strongly to the middle
childhood years in Australia, particularly among children of lower socioeconomic position and
children participating in some unhealthy behaviour patterns.
a. What is the population of interest in this study (1 mark)?
b. Describe the sample used for this study (3 marks).
c. How representative do you think the sample is of the population of interest? Give reasons for
your answer (4 marks).
d. Epidemiologic studies often have both descriptive and analytic characteristics. State three
ways in which this study is descriptive and three ways in which it is analytic (6 marks).
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2. Using the key below, classify each of the following ten descriptions of epidemiologic
studies according to (40 marks):
a. Study type (1 mark each)
• Descriptive Cross-sectional Study
• Analytic Ecological Study
• Analytic Cross-Sectional Study
• Case-Control Study
• Retrospective Cohort Study
• Prospective Cohort Study
b. Give reasons for your choice of study type (2 marks each).
c. Name the measure of frequency (incidence or prevalence including type of prevalence
(point or period) and type of incidence (cumulative or rate)) or the measure of
association (rate ratio, odds ratio etc.) that would be appropriate to use for that study
type (1 mark each).
Questions
i. A study investigated whether maternal vaccination was protective against pertussis
infection in infants. 117 infants who had confirmed pertussis infection were recruited
and matched with 117 randomly selected infants without infection. Data was collected
on maternal pertussis vaccination. Maternal vaccine effectiveness was not found to be
significantly protective of pertussis infection for infants < 6 months old.
ii. A study of the health of truck drivers was conducted over a 20 year period to investigate
whether the amount of time spent driving per week was associated with heart disease.
33,014 individuals without a history of heart disease were classified on their average
driving time as either high (≥ 40 hrs/week), medium (30-40 hrs/week) or low (≤ 30
hrs/week), and followed every five years from 1979 to 1999 to assess their health. 826
new cases of coronary heart disease were identified.
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iii. In 2013 a study investigated the association between health knowledge in 2005 and later
stroke incidence (from 2005 to 2013) in patients with type 2 diabetes. Using 2005
Taiwan National Health Interview Survey (NHIS) to assess health knowledge, and
National Health Insurance claims data, 597 individuals were selected who were
diagnosed with type 2 diabetes. 65 new stroke cases were identified between 2005-2013.
Compared to the group of low health knowledge level, the risk of stroke was
significantly lower for those with moderate (adjusted hazard ratio [AHR] = 0.63; 95%
CI, 0.33–1.19; p-value = 0.15) and high level of health knowledge (AHR = 0.43; 95%
CI, 0.22–0.86; p-value = 0.02), with a significant linear trend (p-value = 0.02).
iv. A study examined the relationship between the delivery of preventive primary care and
cervical cancer mortality rates in Brazil from 2002 to 2012. Brazilian states and the
federal district were the unit of analysis (N = 27). Results suggest that primary health
care has contributed to reducing cervical cancer mortality rates in Brazil.
v. A study investigated whether living in a built up environment compared to living near
open spaces increased the risk of being diagnosed with depression. 110 participants
diagnosed with depression were recruited and matched with 110 participants from the
same population who were not diagnosed with depression. Data on living environments
were collected from all participants. Results showed that people living in a built up area
were more likely to be clinically diagnosed with depression compared to people who
lived near open spaces.
vi. A study investigating whether there is a relationship between drinking coffee and hay
fever, randomly selected a sample of 20,043 adults aged 18-35 in 2017. Data was
collected by completing a health questionnaire. No significant associations were found
between the amount or frequency of drinking coffee and whether a person suffered from
hay fever.
vii. A study was conducted to investigate an association between working with insulation
and later death from lung cancer. In 1975, 825 insulation employees were identified
from personnel records who had worked in insulation manufacturing plants in south
eastern between 1941 and 1944. Between 1941 and 1975, 26 of these workers died from
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lung cancer. During the same time period, six lung cancer deaths were reported among a
comparable group of 700 co-workers who did not work with insulation. Study results
suggest that insulation exposure increases the risk of lung cancer.
viii. Congenital birth defect data was collected from all babies who were born in a large
maternity hospital in Dhaka in 2015. Of the 10,720 babies born, 608 were found to have
a congenital defect.
ix. A large UK study investigated whether alcohol consumption is associated with mental
health over time. 6,330 participants were recruited and assessed as low-level or heavy
alcohol consumers, defined by the number of units of alcohol consumed on a weekly
basis. Participants were followed over a 10 year period and were assessed on their
mental health using a 36 mental health component score.
x. In 2012, 750 women aged 41-50 and 750 women aged 5-60 with no history of breast
cancer underwent annual breast screening over a period of 5 years. After one year, 11
women aged 41-50 and 25 women aged 51-60 were diagnosed with breast cancer.
Note: You may find it helpful to present your answers to question 2 in a table like the one below.
Question Study type Reason Relevant measure of
frequency OR
association
i.
ii.
iii.
iv.
v.
vi.
vii.
viii.
ix.
x.
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3. A longitudinal study of the onset of osteoporosis followed 1,800 initially healthy women,
65-75 years of age, over several years. Every two years the investigators interviewed the
women to see if they had had a diagnosis of osteoporosis since the previous interview.
The results were as follows:
• 5 new osteoporosis cases were diagnosed at the first interview,
• 6 new osteoporosis cases were diagnosed at the second interview,
• 10 new osteoporosis cases were diagnosed at the third interview, and
• 15 new osteoporosis cases were diagnosed at the fourth interview.
In addition, the investigators noted that:
• 12 women withdrew at the second interview
• 15 women withdrew at the third interview
Show all workings and show your final answer correct to 2 decimal places (15 marks).
a. Based on this information calculate the cumulative incidence of osteoporosis among this
population and interpret your answer (4 marks).
b. Based on this information, calculate the incidence rate of osteoporosis and interpret your
answer (8 marks)
c. Of the two measures calculated, which is the most appropriate measure of risk and why?
(3 marks)
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4. Researchers were interested in investigating the effects of smoking on stomach cancer.
They recruited 1,900 people age 18 years who then underwent a health check and 15
were found to have stomach cancer. Ten years later (at age 28) all 1,900 people attended
a second health check and another 57 people had developed stomach cancer. The 15
people initially identified with stomach cancer have received ongoing treatment for their
stomach cancer.
Show all workings and show your final answer correct to 2 decimal places (15 marks).
a. What was the prevalence of stomach cancer among the sample at age 18? (3 marks)
State your results in words (1 mark)
b. How many people were at risk of developing stomach cancer at the start of the 10 year
period? (1 mark)
c. What was the incidence of stomach cancer among this sample during the study period?
(4 marks)
d. Is this a measure of cumulative incidence or incidence rate? (1 mark)
e. On the basis of these findings, the researchers decided to test the hypothesis that
smoking is related to the risk of stomach cancer. A case-control study was conducted in
six medical centres between 2010 and 2014 using a structured questionnaire. A total of
418 men and women aged 18 to 80 years with stomach cancer and 418 matched controls
without stomach cancer were recruited into the study. The table below presents the study
findings.
Outcome (stomach cancer)
Yes No
Exposure
(smoking)
Yes 103 51
No 315 367
418 418
i. Name the measure of association that would be most appropriate to see if
there is an association between smoking and stomach cancer (1 mark).
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ii. Calculate the appropriate measure of association (3 marks).
iii. Interpret your result (1 mark).
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5. In an industry employing 30,000 people, 9,200 were employed in areas where they were
exposed to asbestos, while the remaining 20,800 were not exposed. At the beginning of
the study, all employees were free from disease. The entire population of 30,000 was
followed for 15 years to determine whether exposure to asbestos increased the risk of
developing asbestosis. The findings are provided in the table below.
Show all workings and show your final answer correct to 2 decimal places (16 marks).
Outcome (asbestosis)
Yes No Total
Exposure to
asbestos
Yes 2,315 6,885 9,200
No 995 19,805 20,800
3,310 26,690 30,000
a. Calculate the appropriate measure of frequency of asbestosis for (8 marks):
i. The exposed workers (3 marks)
ii. The unexposed workers (2 marks)
iii. All the workers (2 marks)
iv. Compare the results between the exposed and unexposed groups in words (1 mark)
b. Calculate the appropriate measure of association and interpret your answer (4 marks)
c. How much disease in the exposed workers could be due to their asbestos exposure? Interpret
your answer (4 marks).

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