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CEE 498 Homework 1 DATE OF SUBMISSION: 14 SEP 2017 ANUPAMA MOHANLAL

1. India is my home country and has an estimated population of approximately 1.15 billion. It is a developing country and has more than 50% of people below poverty line. Hence, implementing a proper public health system for the entire country is a challenging process due to this vast population and lack of education and awareness. The central government funds the state public health facilities in India similar to most other countries. The Ministry of Health and Family Welfare takes care of the interests of the central government in the public health system. All India Institute of Medical Sciences (AIIMS) is fully funded by the central government. Also, there are multiple hospitals in every Indian state funded by the state governments. Further, the state and central governments jointly fund the Regional Cancer Centers in India. The Indian government organizes the responsibilities of public health as follows: a. Health system strengthening: The National Rural Health Mission (NRHM) launched by the government of India is a leap forward in establishing effective integration and convergence of health services and affecting architectural correction in health care delivery system in India. b. Health Information system: The Integrated Disease Surveillance Project was set up to establish a dedicated highway of information relating to disease occurrence required for prevention and containment at the community level. Public health laboratories have a good capacity to support the government's diagnostic and research activities on health risks and threats, but are not being utilized efficiently. Mechanisms to monitor epidemiological challenges like mental health, occupational health and other environment risks are yet to be put in place. c. Health Research System: There is a need for strengthening research infrastructure in the departments of community medicine in various institutes and to foster their partnership with state health services. 2. a.

AF=

R−Ro R ¿

pR 1+(1− p ) Ro− Ro pR 1+( 1− p ) Ro

RR=

R1 Ro

Hence , AF= ¿

p . RR+ 1− p−1 p . RR +1− p

AF= b.

p . RR . Ro+ ( 1− p ) Ro− Ro p . RR . Ro+( 1− p ) Ro

p (RR−1) p . RR +1− p

AF=

0.05 ( 1.5−1 ) 0.05 ×1.5+( 1−0.05 )

AF=0.0244=2.44 %

c.

The propotion of cases of lung cancer due ¿ occupationaldiesel fumes exposure=150,000 × 0.0244=3660 Hence the percentage of these deaths due to occupational diesel fumes exposure is

3660 ×100=2.44 % 150,000 1(1.05−1) AF= =0.048 d. 1 ×1.05+0 ¿

The propotion of cases of lung cancer due ¿ environmentaldiesel fumes exposure=150,000× 0.048=72 Hence the percentage of these deaths due to environmental diesel fumes exposure is

¿

7200 ×100=4.8 % . 150,000

3. Malaria is caused due to mosquito bites and is diagnosed mainly by carrying out blood tests. Mosquitoes usually thrive in tropical and humid weather. They breed in stagnant water and ponds. According to the question, global warming has increased the spread of malaria due to increase in land area where mosquitoes can live and breed. Also, global warming could dry up ponds and reduce rain which could result in a decrease in mosquito population. A few models which can be used to study the co-relation between the increase in global temperature and malaria spread are as follows. a. Precipitation is required for mosquitoes to breed and global warming could reduce the precipitation of an area and hence reduce the mosquitoes in that area and hence spread of malaria. b. Good health care facilities in that area can curb malaria spread to a large extend even if global temperature changes. c. A malaria outbreak could be severe in economically backward countries due to lack of good health care facilities. Comparing a developed country and an under-developed country on the spread of malaria could easily help us understand if global temperature or lack of good health services is the main cause of malaria spread. Hence, we can perform a retrospective study by collecting the past 10 years’ temperature data and malaria cases reported during that time period in that area in an economically backward and a developed country. If we observe that the cases of malaria reported increased with each passing year in the backward country, we can say that the lack of good health care services may outweigh the increasing global temperature with regard to malaria spread.

No, it is not very early to conduct such a study. I feel that such a study now could help in implementing the strategies to curb malaria in the future years. We need the population of area to be studied (both exposed and unexposed), economic status, land used details, drainage and sewer collection and disposal information, meteorological data of the area, information about the health care system, existing malaria prevalence data and movement of malaria affected people to unaffected areas. 4. An alternative association could be that the person diagnosed already has a low GFR and hence is not excreting the PFOA properly. This could have resulted in the accumulation of PFOA in the kidney further slowing down the GFR. So when a person is tested for poor kidney performance, more PFOA may be detected in the kidney. This problem can be analyzed further by determining the average GFR a normal person should have for proper kidney functioning. Also, a control group with less PFOA exposure could be compare with people who are exposed to PFOA to study the effect of PFOA on kidney function. 5. The data can be tabulated as shown below.

Diagnosed with breast cancer No breast cancer Total

R 1= ¿

No: of women with negative mammograms 20 99,980 100,000

Number of women with+ ve mammogramsdiagonised with cancer Number of women with+ve mammograms

10 =0.1 100

R 2= ¿

No: of women with positive mammograms 10 90 100

Number of women with−ve mammograms diagonised withcancer Number of women with−ve mammograms

20 =0.0002 100,000

Relative Risk , RR=

0.1 R1 = =500 ≫ 1 R 2 0.0002

Since RR>>1, we can say that women with positive mammograms have 500 times higher risk of getting diagnosed with breast cancer in 2 years as compared to women with negative mammograms. Also, the total risk of getting diagnosed with breast cancer in the next 2 years is

R=( R 1× p 1)+( R 2 × p 2) Where, p1 = Probability of getting a positive mammogram test

p2 = Probability of getting a negative mammogram test

p1=

100 =9.99× 10−4 100,100

p2=

100,000 =9.99× 10−1 100,100

∴ R=0.1 × 9.99 × 10−4 +0.0002 × 9.99 ×10−1 0.0003 Hence, it is proved that 3 women in every 10,000 women are at a total risk of getting diagnosed with breast cancer....