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7312MED Epidemiology: Pinciples And Practices

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  • Course Code: 7312MED
  • University: Griffith University
  • Country: Australia

Questions:

Measures of Disease Frequency and Association, Types of Epi Study Design

Question 1 

The following table presents hypothetical data of cumulative incidence of prostate cancer in men living in two different cities (City A and City B).

 

City A

City B

Standard population

Age group

(years)

Male population in mid 2010

Number of prostate cancers in 2010

Male population in mid 2010

Number of prostate cancers in 2010

0-44

500,000

50

1,500,000

80

74,000

45-64

75,000

250

600,000

1000

19,000

65+

25,000

350

400,000

2500

7,000

Total

600,000

650

2,500,000

3580

100,000

 

  1. Calculateand compare the crude and age-specific incidence rates of prostate cancer per 100,000 men between the two cities. (4 marks)
  2. According to your results in Q1-a, is the comparison of crude rates valid in considerationof the age distributions in the two cities? Why or Why not? (Hint: calculate and compare the age structures of populations in the two cities)
  3. Computeand compare direct standardised incidence rates (per 100,000 men) of prostate cancer in City A and in City B using the given standard population (the shaded column of the table). Discuss the differences in the results of crude and standardised rates between the two cities.
  4. How does the risk of developing prostate cancervary by age within each of the two cities? (Calculate appropriate measures to quantify the variation). Based on your results, is age a risk factor of prostate cancer for both cities? Interpret your results briefly 

Question 2

In 2000, 5,458 women were screened for cervical cancer and 92 were found to have CIN 3 stage or above (diagnostic of carcinoma of the cervix or carcinoma-in-situ). The women who did not have cervical cancer were re-screened in 2005 and another 60 cases were diagnosed.

  1. a) What was the prevalence of cervical cancer in 2000and 2005?
  2. b) Calculate the incidence of cervical cancer in these women. Is this a measure of cumulative incidence or estimated incidence rate?Why?
  3. c) Assume that, on average, each of the 60 women who were diagnosed with cervical carcinoma was followed up half-way through during the five-year period. What is the incidence rateof cervical cancer in these womenduring the five years? 

Question 3

A researcher and colleagues reported results from a study of cataracts. All persons born before 1943 and residing in the Blue Mountains region near Sydney, Australia, were invited to make a single visit to a study clinic to undergo a detailed eye examination. Over a two-year period, 3654people did so. Lens photographs were used to detect and classify cataracts. During each person’s visit to the study clinic, he/she was also asked about current use of inhaled corticosteroids, often used to treat asthma. Subcapsular cataracts proved to be 2.6 times more common among current users of these drugs than among non-users

  1. What is the exposure factor? 
  2. What is the outcome??
  3. How would you classify the study design?
  4. What limitation does this type of study design have for inferring whether use of inhaled corticosteroids causes cataracts?

Question 4

  • Download“WHO Statistical Profiles” for 3 selected countries: Australia, India and Zimbabwe from WHO’s databank (in “Country Statistics”: http://www.who.int/gho/countries/en/  and complete the following table (2 marks). Compare the population health data among the three selected countries and summarise their public health implications briefly.

Table 4.1 WHO health statistical profiles for Australia, India and Zimbabwe

Data in 2013

Australia

India

Zimbabwe

1. Population (thousands)

 

 

 

2. Number of deaths (thousands)

 

 

 

3. Crude mortality (per 100,000)

 

 

 

4. Population proportion

 

 

 

Under 15 (%)

 

 

 

Over 60 (%)

 

 

 

5. Under 5 mortality rate (per 1000 live births)

 

 

 

6. Maternal mortality ratio (per 100,000 live births)

 

 

 

7. Life expectancy (years), 2012

 

 

 

   At birth

 

 

 

   At age 60

 

 

 

8. Probability of dying, 2012 (before age 70)

 

 

 

Male

 

 

 

Female

 

 

 

 

 

Answer:

1 (a):

City A:

Crude incidence rate per 100,000 men = (650/600,000) X 100,000 = 108.33

Incidence rate per 100,000 men for the age group of 0 – 44,

= (50/500,000) X 100,000 = 10

Incidence rate per 100,000 men for the age group of 45 – 64,

= (250/75,000) X 100,000 = 333.33

Incidence rate per 100,000 men for the age group of 65+,

= (350/25,000) X 100,000 = 1400

City B:

Crude incidence rate per 100,000 men = (3580/2,500,000) X 100,000 = 143.2

Incidence rate per 100,000 men for the age group of 0 – 44,

= (80/1,500,000) X 100,000 = 5.33

Incidence rate per 100,000 men for the age group of 45 – 64,

= (1000/600,000) X 100,000 = 166.67

Incidence rate per 100,000 men for the age group of 65+,

= (2500/400,000) X 100,000 = 625

Comparison of crude and age specific incidence rates of City A and B are provided in the Table below:

Table 1.1 Crude and age specific incidence rates of City A and B

Incidence Rate

City A

City B

0-44 age group

10

5.33

45-64 age group

333.33

166.67

65+ age group

1400

625

Crude

108.33

143.2

 

From the above table, it can be observed that the age-specific incidence rates are higher in case of City A in comparison with City B. On the contrary, the crude incidence rate is higher in case of City B in comparison with City A. This reason behind this incident is that the City B has higher number of older population which might have affected the overall crude incidence rate.

1 (b):

The age structures of the two cities are unequal. City B have a higher percentage (16%) of people who belongs to the age group 65+ in comparison with City A. City A have only 4.16% people who belong to the same category. Similarly, City A has higher percentage of people (83.33%) that belongs to the age group 0 – 44 years in comparison with City B. City B have 60% people who belong to the same category. This disparity is noticeable between the two cities in the age group of 45 – 64 years as well. Hence, the age structure of these two city are unequal. Due to this unequal distribution, the comparison result of the two cities will be distorted and the comparison of crude results will not be valid. The higher crude incidence rate of City B might be because City B has higher percentage older population.

1 (c):

Direct standardized incidence rate for City A:

Observed cases in City A = 650

Expected cases in City A with respect to standard population = (650/600,000) X 100,000 = 108.33

Hence, Standardized Incidence rate = (650/108.33) X 100 = 600.01

Direct standardized incidence rate for City B:

Observed cases in City B = 3580

Expected cases in City B with respect to standard population = (3580/2,500,000) X 100,000 = 143.2

Hence, Standardized Incidence rate = (3580/143.2) X 100 = 2500

From the above data, it can be seen that the Standardized Incidence Rate (SIR) is higher with respect to Crude Incidence Rate for both the City A and City B. Therefore, it can be concluded that the occurrence of prostate cancer in these two towns are much higher than the expected incidence.

 

1 (d):

Percentage of affected individual in each age group is provided for both the city in the below table:

Table 1.2Percentage of affected individual in each age group is provided for City A and City B

 

Percentage of people affected by  prostate cancer

Age Group

City A

City B

0 – 44

0.01 %

0.005 %

45 – 64

0.33 %

0.17 %

65 +

1.4 %

0.625 %

 

From the above the table, it is clear the percentage of affected people rapidly increased with the increase of age in the population. In case of City A, the percentage of affected individual increased more than 100 times (140) from the age group of 0 – 44 to 65 and above. Similar trend can be noticed in case of City B. In this case, the increase is 125 times. Hence, the occurrence of cancer increases with the age. Therefore, it can be concluded that the age is a risk factor for prostate cancer for both cities.

2 (a):

In the year 2000, the risk population was = 5,458 women

Cases of cervical cancer = 92 women

Therefore, the prevalence for the year 2000 is = (92/5458) X 100 = 1.68 %

In the year 2000, the risk population was = 5,366women

Cases of cervical cancer = 60 women

Therefore, the prevalence for the year 2005 is = (60/5366) X 100 = 1.12 %

2 (b):

Number of total population at risk = 5,458 women

Cases of CIN = (92+60) = 152

Therefore, incidence of cervical cancer in these women = (152/5458) X 100 = 2.78%

The above measure of incidence is a ‘cumulative incidence measurement’ as it considered the total number incident during a particular period of time and does not take into account when the incidents occurred (Dobler, 2017).

2 (c):

Number of risk population = 60

Period duration = 5 year

Time of onset = 3 year; therefore disease free year = 2

Hence, Incidence rate of these women is = (60/120) person-year = 0.5 /person- year.

3 (a):

The exposure factor for this study is the use of inhaled corticosteroids.

3 (b):

The outcome of this study is that the subcapsular cataracts are 2.6 times more common among the user of inhaled corticosteroids.

3 (c):

This study is an observational study and has been conducted over a period of two year. Also, sample population has been divided into two groups: exposed and unexposed. Therefore, design of this study can be classified as a ‘Cohort Study’ (CASP Checklists, 2019).

3 (d):

The investigators did not consider the fact that the outcome might be interfered with other substances or medication used by the sample population. They did not take any precaution to negate the fact.

4 (a):

Table 4.1WHO health statistical profiles for Australia, India and Zimbabwe

Data in 2013

Australia

India

Zimbabwe

1. Population (thousands)

23343

1252140

14150

2. Number of deaths (thousands)

150.0

9944.9

138.2

3. Crude mortality (per 100,000)

642.59

794.23

976.68

4. Population proportion

 

 

 

Under 15 (%)

19

29

40

Over 60 (%)

20

8

6

5. Under 5 mortality rate (per 1000 live births)

4

53

89

6. Maternal mortality ratio(per 100,000 live births)

6

190

470

7. Life expectancy (years), 2012

 

 

 

At birth

83

66

58

At age 60

25

17

18

8. Probability of dying, 2012 (before age 70)

 

 

 

Male

26 %

69%

74%

Female

17%

60%

65%

9. Citation

(WHO: Australia, 2015)

(WHO: India, 2015)

(WHO: Zimbabwe, 2015)

Note: Crude mortality= total number of deaths/ total population

From the above table, it is quite clear that the health condition most advanced and progressive in Australia, followed by India and Zimbabwe. The data from this table also justifies the country status of Australia, India and Zimbabwe which are First World, Developing and Third World country respectively (World Health Organization, 2016). Total population is highest in case of India and it is much higher than the Australia and Zimbabwe combined. Amongst the three country, the crude mortality rate per 100,000 population is highest in Zimbabwe (~977), even though the population in Zimbabwe is lowest among the three country. The percentage of population over 60 is highest in Australia (20 %) and this signifies the strength and advancement of Australia’s medical compared to the other two countries. Not only this, but Australia is stands out in the parameters of under-five and maternity mortality rate, life expectancy and probability of dying before the age of 70. These data signifies that the overall development, infrastructure and medical healthcare far more advanced and strong compared to India and Zimbabwe. With regard to India and Zimbabwe, the situation of Zimbabwe is particularly bleak. Zimbabwe has the lowest life expectancy at birth and highest under-five and maternity mortality rate, and probability of dying before the age of 70. Maternal mortality rate in Zimbabwe (470) particularly stands out as it is more than double of the next highest. In a nutshell, it can be concluded that Australia has the best infrastructure and quality of life followed by India and Zimbabwe. However, India needs to improve with regard to infrastructure to reach the standard of Australia.

 

References:

WHO: Australia (2015). Australia: WHO statistical profile. Retrieved from https://www.who.int/gho/countries/aus.pdf

CASP Checklists (2019). CASP - Critical Appraisal Skills Programme. Retrieved from https://casp-uk.net/casp-tools-checklists/

Dobler, C. C. (2017). Cumulative Incidence and Incidence Rate Ratio for Estimation of Risk of Tuberculosis in Patients With Cancer. Clinical Infectious Diseases, 65(8), 1423-1423. Doi: https://doi.org/10.1093/cid/cix516

WHO: India. (2015). India: WHO statistical profile. Retrieved from https://www.who.int/gho/countries/ind.pdf

World Health Organization. (2016). World health statistics 2016: monitoring health for the SDGs sustainable development goals. World Health Organization. ISBN-10: 9241565268.

WHO: Zimbabwe. (2015). Zimbabwe: WHO statistical profile. Retrieved from https://www.who.int/gho/countries/zwe.pdf

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