Shall we take turns to drive our vehicles?

There were a quarter billion registered vehicles on highway in 2008. [1] And according to the US Census, there are three quarter of US population own a car and American drives 33.4 miles per day. [2] This is a huge number on vehicle usage and a big difference compare to several decades before. Nowadays, people rely on driving motor vehicles more and more as it is convenient. Not to mention how it changes people’s daily life and contribute to such huge prevalence of obesity rate because of lack of walking for daily exercise. Let’s take a look at other side: how using motor vehicles could impact our environment and affect our health.

Do you know what vehicle exhaust emissions are? In the car exhaust fumes, there are a lot of dangerous chemicals, such as carbon monoxide, nitrogen dioxide, sulphur dioxide etc. Those harmful chemicals could pollute air. And once the polluted air get into human body and transport in the bloodstream to all major organs, it could potentially cause respiratory conditions, such as asthma or bronchitis. [3] Those respiratory diseases are still on the list of top ten leading causes of death, according to the statistical report form CDC. [4] There are also some researches showing that the air pollution caused by driving a car could not only affect human body, but also have regional/global effects. [5] Therefore, this is a huge epidemic problem.

So, how to improve our air quality? There were several regulations established since 1947 regarding to US environmental and occupational health, such as 1947 - Los Angeles Air Pollution Control District, 1959 - California Motor Vehicle Pollution Control Board, 1990 - Clean Air Act Amendments of 1990, etc. [6] Most of those regulations focus on how to improve automobile emission system and set new automobile emissions standards. Besides these, what can we do more to protect our population, especially for our next generation? I think we can propose a new regulation to reduce car usage and eventually reduce air pollution. The regulation would be: people who drive cars with odd number on their plates should only drive on Monday, Wednesday and Friday; People who drive cars with even number on their plates should only drive on Tuesday, Thursday and Saturday; there is no restriction on Sunday.

As an epidemiologist, I think the first thing we could do is establishing a cohort study regarding to test the relationship between car usage and incidence of respiratory disease, such as asthma. [7] For example, we could compare incident rate of asthma between population from small town with little traffic and population from big city with heavy traffic. In order to make it more validate, we should try to balance our sample population in terms of different characteristics, such as socio-economic level, education level, age, gender, ethnicity etc. Then, we could provide evidence-based association to the policy maker to push the policy establishment by ruling out possible confounders.

There are several challenges for conducting such study from epidemiology aspect. Firstly, it is hard to select sample population in order to avoiding selection bias and misclassification. Secondly, it is hard to balance two sample populations in all possible characteristics. Therefore, this study would possible contain some unmeasured confounders. Thirdly, in order to track the incidence of asthma, this should be a cohort study, which is hard to get funded for long research period.

Even though there are several challenges for developing such policy, we still can use this study as a pilot one in order to show the potential benefit to policy makers. And push to establish the policy eventually by providing this evidence-based result. If we could get evidence-based result showing there is a valid association between car usage and asthma, we could suggest policy makers to try this policy in one/two cities and let epidemiologists keep tracking those cities to further evaluate the association. If the incidence of asthma in those cities were reduced, as epidemiologists, we could further push the policy makers to apply the policy to other regions. Once we have this policy, the potential positive epidemiologic outcome would be decreasing incidence of asthma and other respiratory diseases.

There are some challenges from other aspects. For example, some people would think this policy against human right as it restrict when people could drive their cars. This could raise the same reaction as mandate insurance from Health Reform. For example, Missouri passed Proposition C which encourages people to have their freedom to choose insured or uninsured even though it is against mandate insurance. But I think this oppose reaction could reduced after certain time period just like everyone should have a car insurance before driving. People will be used to it. And it is good for our next generation.

Beside the benefit for reducing incidence of respiratory diseases, it could also potentially reduce prevalence of obesity rate. Because when people are restricted to certain days on driving, people would choose other ways for transportation, such as public transportation or bike. Therefore, people would be able to do more exercise than now, if they choose bike or even with public transportation, people need to walk to the station. And from environmental aspect, we could save more energy and protect our earth. By applying this policy, we could also solve traffic jam problem by reducing daily loading, especially in big cities.

Therefore, the benefits for developing this policy would weigh heavier than the challenges. Also, some countries already had similar policy for some big cities with heavy traffic, such as China. If there is no enough funding to conduct a cohort study in US, we could conduct a retrospective cohort study by using data from other countries that already applied this kind of policy and provide evidence-based relevant result to policy makers as epidemiologists. Hopefully, we could push this policy into our real life.

Reference

1. Bureau of Transportation Statistics. http://www.bts.gov/publications/national_transportation_statistics/html/table_01_11.html
2. http://greenanswers.com/q/53278/transportation/infrastructure/how-many-people-drive-every-day-america
3. Exhaust emissions: what are they? http://www.bbc.co.uk/health/physical_health/conditions/exhaust_emissions.shtm
4. Leading Causes of Death. http://www.cdc.gov/nchs/fastats/lcod.htm
5. Cars, trucks, air pollution and health. http://www.nutramed.com/environment/cars.htm
6. Timeline of major U.S. environmental and occupational health regulation. http://en.wikipedia.org/wiki/Timeline_of_major_U.S._environmental_and_occupational_health_regulation
7. Gordis, L. (2009). Epidemiology. Philadelphia, PA: Saunders.

What are the challenges of a cohort study on following breast cancer survivors for five years?

Cohort study is one type of study design that is normally used to address risk factors/incidence of a certain disease. This type of study usually starts at certain time point with baseline measurement of participants and divides them into different groups, and then follows participants for a long time period to see whether those participants develop a certain disease. In this way, it is easy to address the predictor/factors that would cause/influence this disease/outcome.

As I said in my previous post, my colleagues and I designed a cohort study regarding to test whether people’s clothing preferences would influence people’s weight status. Also, my current research project is to discover predictors for breast cancer-related lymphedema. So, what is lymphedema? Lymphedema is a chronic progressive disease often caused by cancer treatment, especially in patients who require surgical removal of or radiation to lymph nodes.  Breast cancer survivors are at life-time risk of developing lymphedema their cancer treatment likely included surgery or radiation treatment, which may adversely affect the lymphatic system. Therefore, it is a big population based chronic disease.

In this study, we followed breast cancer patients for five years starting from pre-operation as baseline measurement, which means the time period before the patients having breast cancer surgery. We then follow them based on the timeline as shown in Figure 1 [1] and see whether they develop lymphedema afterwards. At every lab visit, we interviewed patients for their symptoms and measured their BMI, etc. The ultimate goal for this study is to discover risk factors of developing lymphedema among all different potential exposures (such as a certain symptom or combination of some symptoms, BMI, etc.) for breast cancer survivors.

Figure 1. Timeline for data collection, where Ti represents the i^th visit of a patient. [1]

This study started in 2007, we’ve been following our patients (n=316) for 4 years. The main challenge that I found is follow-up bias. It is really hard to follow people for such a long time period. Even though we did call them several times before their next visit, patients might still not come. Also, even if they did come, they might not obey the timeline that we expect. In addition, patients might exit study or be lost contact. This study is only five year study which is considered as a very short time period for a cohort study, regarding to what Gordis suggested, which is twenty years. [2] Also, there is a funding issue. For this five year study, we got to apply for funding every two/three years. Normally, funding won’t have such a long period. It is hard to continue if it is not funded. And we need to report exciting findings in our annual report in order to keep funding…

Even though I think it is a wonderful and promising research project which could increase awareness of lymphedema, it is hard to get funding and follow participants over time. And there must be unmeasured confounders in our study design, because it is not practical to cover all possible confounders. Additionally, we didn’t have a hypothesis at the beginning which makes the study even harder to measure, because we want to discover as many as possible risk factors of developing lymphedema. Therefore, we could publish a guideline for practitioners of how to manage and control lymphedema. Although, this study has some drawbacks, we could still learn from the findings of this cohort study.

Reference

1. Xu S, Shyu CR. Efficient selection of association rules from lymphedema symptoms data using a graph structure. AMIA Annu Symp Proc. 2010 Nov 13; 2010:912-6.

2. Gordis, L. (2009). Epidemiology. Philadelphia, PA: Saunders.

How does preference of clothing style affect your weight status?

Do you know your weight status? What is your clothing preference? Please feel free to answer the poll questions above.

Do you think clothing preferences would affect your weight status? Some of my colleagues and I designed a study on how clothing preference influence weight status. Please click here to read more about our study.

Doctors Can Influence Patients to Lose Weight. Really?

There is an interesting health news on the MSN website from a recent study reporting findings on how doctors can influence patients to lose weight. [1] The study addressed that people would be more likely to try to lose weight if their doctors told them that they are overweight or obese. 


Based on the information it provided, I searched for the original article for this study. This article published in the Feb. 28 issue of Archives of Internal Medicine. [2] This study used 2005-2008 National Health and Nutrition Examination Survey (NHANES) data on 20-64 years old adults with greater than or equal to Body Mass Index (BMI) of 25.0. Since the data are snapshots without any temporal information, it is a cross-sectional study based on the definition from textbook. [3] The author aimed to find the influence of physician acknowledgment of patient' weight status on patient perceptions of overweight or obesity.


Here, the exposure would be whether the patients reported that their physician told them they were overweight, and the disease would be patient perceptions of overweight or obesity. They included 7790 people aged between 20-64 years from 2005-2008 NHANES data as a sample. Since they narrowed their sample within certain age range, it is a restriction sample. Because it is very hard to sample huge population, they weighted the data, so the 7790 people represent 174 million people.


They did have some confounding variables in their logistic regression model, such as age, sex, race, poverty to income ratio, marital status, education, whether the patient has a routine source of health care, and the number of physician visits in the last 12 months. But I think there might be other confounders, such as whether they have health insurance, and their occupations. I think those would also influence their perception of overweight or obesity. For example, uninsured people might not go to the doctor regularly, so the chance of being told their weight status by their physicians would be lower. Also, some job needs higher muscular-level work which might cause higher BMI (greater than 25), but people might not think they are overweight or obese, even they've been told so. 


In the end of this articles, it points out its limitations: 1. no cause and effect association can be made; 2. no information about the extent of the physician intervention; 3. Using BMI standard to define overweight would be wrong to muscular people; 4. no information about the reason for people to see a doctor.


The authors concluded that patient-reported physician acknowledgment of patients' weight status is highly associated with patient perceptions of overweight and obesity based on the odds ratios for different groups. In this case, physicians need to tell more overweight and obese patients about their weight status so that it could help encourage them to change their behaviors to lose weight and lower their risk for many diseases.


I think this is a good article based on very solid foundation. The conclusion that author addressed is responsible as they know what limitations are. So they address the conclusion very carefully without suggesting any causality relationship. I think it could alarm physicians to pay more attention on telling overweight or obese patients their weight status, since most of patients would listen to their physicians and start to change their behaviors for weight loss. 

References:

1. Amanda Gardner. Doctors Can Influence patient to Lose Weight: Studies. 2011 HealthDay. http://health.msn.com/healthy-living/articlepage.aspx?cp-documentid=100270168

2. Robert E. Post, Arch G. Mainous III, et al. The Influence of Physician Acknowledgment of Patients' Weight Status on Patient Perceptions of Overweight and Obesity in the United States. http://archinte.ama-assn.org/cgi/content/full/171/4/316


3. Gordis L. Epidemiology. 4th ed. Elsevier. 2008. ISBN: 9781416040026

How to design an epidemiology-related study?

Based on what I learned from the textbook, there are several different study designs, such as case-control study, case-cohort study, case-crossover design, and cross-sectional study. [1] let me briefly introduce them one by one. 

Firstly, what is a case-control study? Basically, we need two groups of people, one group of people with the disease (called cases), and the other don't have the disease (called controls). Then we compare these two groups and try to figure out the possible relation between an exposure and a certain disease. The major limitation of this design is recall bias between people who have the disease and try to connect the disease to the exposure intensionally.

Secondly, what is a case-cohort study? In this type of study, we basically select a certain population and follow them over certain time period. This type of study is a good fit for finding risk factors for a certain disease without recall bias, since people were followed from baseline.

Thirdly, what is a case-crossover design? Normally, this is used for studying the acute outcomes with short exposure. Each people in this study represents his/her own control. Recall bias is still the major problem for this type of study.

Fourthly, what is cross-sectional study? The main feature for this type of study is both exposure and disease are examined simultaneously for each people at one time point. This is also called prevalence study, since the determined cases of disease represents the prevalence of this disease at one time point. The major problem for this type of study is missing temporal information. This means we don't have any time series information to know whether exposure happens before the disease or the other way around. So, we could only address the association between exposure and disease, but not the causality. 

Reference:

1. Gordis L. Epidemiology. 4th ed. Elsevier. 2008. ISBN: 9781416040026

Morbidity vs. Mortality

"Morbidity" and "Mortality" are the two most important and basic concepts in epidemiology, because both of them could be used for measuring the occurrence of disease.

Firstly, what is morbidity measurement? Normally, we use incidence rate and prevalence to express the extent of a disease in populations. Here, 

Incidence rate per 1,000 =                                             

  No. of NEW cases of a disease occurring                  

              in the population during                                    

            a specified period of time               * 1,000        

       No. of persons who are at risk of                                        

          developing the disease during

                   that period of time

Prevalence per 1,000 =

 No. of cases of a disease present

in the population at a specified time * 1,000

   No. of persons in the population

           at that specified time

The relationship between incidence and prevalence could be demonstrate further by figure 1. [1] Assume that the flask is a community, and the beads in the flask represent the prevalent cases of a disease in this community. The first pic shows the baseline of prevalence, then the three pics below show three different ways that incidence could affect prevalence.

Figure 1. Relationship between incidence and prevalence.

Secondly, what is mortality measurement? Normally, mortality could be used for measurement of disease severity and how effective a treatment for a diseases is over time. There are two different types of mortality rates:

Annual mortality rate for all causes                                 

(per 1.000 population) =                                                    

  Total # of deaths from all causes in 1 year  * 1000    

  # of persons in the population at midyear                    

Disease-specific/cause-specific rate

(per 1,000 population) =

     # of deaths from a disease in 1 year     * 1000

  # of persons in the population at midyear

Case-Fatality rate (percent) =                                       

    # of individuals hying during a                                     

specified period of time after disease                                      

             onset or diagnosis                          * 100        

# of individuals with the specified disease                      

                                                                                             

Proportionate mortality (percent) =

   # of deaths from certain disease

             in a population during

              certain time period              * 100

  Total # of deaths in the population

      during the same time period

Those are some basic concepts and formulas on how to measure the occurrence of a disease that I learned from the textbook. After knowing those formulas, I think you would have more sense on how epidemiological event is measured.

Reference:

1. Gordis L. Epidemiology. 4th ed. Elsevier. 2008. ISBN: 9781416040026

Big epidemiology event - SARS

Let me share my own experience with SARS (Severe Acute Repiratory Syndrome) when I was in China. It started in 2002 when I was a freshman in college.

SARS is a very big one time epidemiology event from 2002 to 2003. According to the fact sheet from CDC, it is a fatal respiratory disease caused by a coronavirus in human. Patient would have high fever at the beginning, then pneumonia mostly. It could spread by close person-to-person contact, such as hugging, kissing. [1] There are  8,098 SARS cases from 29 countries were reported to the World Health Organization (WHO) in total with 9.6% case-fatality rate. [2] It was a very scary epidemiology event.

As I remembered that was my first year in college, I was so excited about being a college student and finally be away from my parents. :) Suddenly, SARS started without any early signs.  At the very beginning, the media in China blocked this bad news, no one knows about it and no one knows how serious it is. Especially, when SARS began, it was around Chinese New Year. A lot of people were travelling to their homes or going shopping for their New Year party and every public area became very crowd, such as airport, train station, bus station, as well as all shopping malls and markets. But no one knows a fatal disease is spreading. I think this is the most pathetic thing. 

Until SARS became uncontrollable, the media start to report this disease. And Chinese government began to report to CDC and WHO. Because of the delay reporting, we all think this happens to fast and became extremely dangerous too quickly. Because this infectious disease is very severe and can be infect person-to-person based on CDC and WHO's announcement, everyone began to panic and was afraid to go out and talk in public. 

Meanwhile, there were a lot of legislations passed. For example, no any activities that require more than 10 people presenting at the same time. Every single people needs to measure their own body temperature every day and report to their community. Anyone whose temperature excess normal temperature must report to their local SARS control center and quarantine for at least seven days.  every college had to close and forbid any student go out or come in, including my college. 

So, at that time, I couldn't go home or any where... I have to stay at my college without any classes, everybody should stay at their own apartment and minimize the chance of getting infection. We had been lock at school for a whole semester. As a student should feel so good to have a semester without any classes, but none of my classmates are happy. I wish the media in China could report SARS earlier and the Chinese government should report it to CDC and WHO earlier. If we could control it at first place instead of letting it spread, it won't become so serious and it won't destroy so many lives...

This is the most serious epidemic event that I've been lived with. Below is the picture of a school in China showing a professional spraying disinfectant for every classroom.

References:

1. Severe Acute Repiratory Syndrome (SARS) fact sheet, CDC. http://www.cdc.gov/ncidod/sars/factsheet.htm
2. Revised US Surverllance Case Definition for Severe Acute Repiratory Syndrome (SARS) and Update on SARS Cases -- United States and Worldwide, December 2003. MMWR, CDC. http://www.cdc.gov/mmwr/preview/mmwrhtml/mm5249a2.htm


The picture is cited from http://www.sdshiyan.sd.cn/shiyan/sch_news/2003/0421/news2.htm

What is epidemiology?

As I said that this blog is going to share with you what I learned in epidemiology, so the first topic would be the very basic concept: what is epidemiology, exactly?

What is your answer to the survey question at the very beginning of my blog? The answer should be all of them. Did you get it right? I have to be honest with you. Before taking this course, I thought epidemiology included only the first two areas: study of distribution and determinants. Based on the text book, it also includes applications. [1 (page3)] 

Additionally, before learning this course, I thought epidemiology was a very light job, because I only thought of epidemiology when some outbreak of certain disease happened. After learning the basic concepts of epidemiology, I realize that there are three different types of prevention, which include primary prevention even before something happens, such as immunization. [1 (page6)] Therefore, epidemiology is actually an underestimated job, because if no outbreak happens, their efforts are easy to be ignored by population; while if outbreak happens, people always blame them...

Based on the epidemiology triangle (Figure 1) below from the textbook, any epidemiology events are caused by interaction of the host (mostly means "who" gets the disease), an infectious agent (mainly means "what" causes the disease), and the environment factor (normally means "where" the disease occurs). The vector is a media which transmit the disease. [1 (page19)] Let's use tuberculosis as an example, the host would be human, the agent would be bacteria that causes TB, and the environment would be unsanitary environment. The vector should be air, because it spread by air when TB patients cough or sneeze. [2]

Figure 1. Epidemiology triangle 

Also, I learned how to determine and investigate a outbreak. Firstly, if a disease exceeds its normal frequency of occurrence, we consider it as a epidemic disease. Secondly, we need to figure out how long its incubation period is by interviewing people and figure out the specific group. Thirdly, calculate the attach rate in order to get an idea of what determinants are. Lastly, come up some recommendations for controlling and preventing this disease. [1 (chapter2)]

But I am still confused about whether epidemiology is only dealing with infectious diseases. Does it also include other non-infectious diseases, such as diabetes?

Reference:

1. Gordis L. Epidemiology. 4th ed. Elsevier. 2008. ISBN: 9781416040026
2. Konstantinos, A (2010). Testing for tuberculosis, Australian Prescriber. 33 (1): 12-18.

Welcome to my blog!

Welcome to my blog for epidemiology. As a new comer in this interesting area, I will post any topics after I learn them. If you are also a new comer, I wish we could learn it together. If you are an expert, please correct me if I made any mistake. :)