Do I Smell Smoke??

Source: www.mynewplace.com

In a recent news report, Dr. Tim McAfee, director of the CDC's Office on Smoking and Health, predicted that by 2020, nearly every state will have adopted anti-smoking laws¹. As it stands, 25 states have already adopted some form of indoor smoking regulation. If the trend continues on the same pace, it is feasible that all states will ban indoor smoking within the next nine years. Notably, Missouri does not have a comprehensive indoor smoking ban, nor does the government have plans to implement such legislation in the near future. Missouri is a smoking friendly state, with the lowest cigarette taxes in the entire country². Across the state, county and city governments have tackled this contentious issue on their own, in many cases allowing voters to decide whether or not indoor smoking regulations should be enacted. The debate for and against this legislation is based upon data and statistics; the kind that epidemiologists faithfully provide.

Is comprehensive smoking legislation needed?

Many people consider secondhand smoke a disgusting nuisance, but this reasoning alone is not enough to justify legislation that prohibits smoke in public places. Instead, we can look toward the large body of research that demonstrates the harmful effects of secondhand smoke. The U.S. Surgeon General estimated in a 2006 report that approximately 65,000 people per year die in the United States as a result of exposure to secondhand smoke³. Secondhand smoke increases the risk of lung cancer and heart disease in non-smokers. In children, inhalation of secondhand smoke increases the risk of asthma, middle ear infections, and lower respiratory infections. It also increases the risk of Sudden Infant Death Syndrome (SIDS)⁴. Clearly, proponents of anti-smoking legislation consider secondhand smoke to be a public health risk and believe that the government has a responsibility to protect citizens from this risk.

Are we better off without comprehensive smoking legislation?

The other side of the argument contends it is a personal right to smoke and that business owners should be able to decide for themselves whether or not to allow smoking in their establishment. Those opposed to anti-smoking legislation will point to economic indicators that show businesses will lose money if forced to comply with the law. Additionally, research to show health outcomes before and after smoking bans is limited. Prospective cohort studies have demonstrated the causality between secondhand smoke and lung and heart diseases, but more research is needed to confirm that locations with comprehensive smoking ban have a decreased incidence of these diseases. Furthermore, it could be argued that a large portion of the risk of secondhand smoke occurs in private homes or vehicles. It can be hard to determine what proportion of secondhand smoke is attributed public spaces and what is attributed to private places. If most of the exposure occurs in the home, a law that bans smoking in public will have little effect on overall health.

Who would this legislation impact?

In short, everyone. The general public, who patronizes restaurants or other public facilities, as well as employees of these establishments, will be impacted by indoor smoking regulations. Vulnerable populations, such as children and those susceptible to lung diseases, have the most to gain from such regulation as they are the most affected secondhand smoke. Business owners have a special interest in this legislation as it may have an impact on their economic well being. Tobacco companies are also stakeholders in this battle, as anti-smoking policy could cause cigarette sales to decline. Health care systems, who treat patients suffering from conditions related to secondhand smoke exposure, have an interest in decreasing this exposure. Law enforcement and public health agencies are also involved as they will be tasked with enforcing the legislation.

How do we justify policy?

When voters or a governing body approve legislation, the debate surrounding an issue does not rest. Once we move into the implementation stage of policy, interested parties will follow to see if the anticipated outcomes occur. Evaluation is a very important part of policy. A positive evaluation will serve as a justification for the legislation, whereas a poor evaluation may lead to proposals for change. An important step in justifying the anti-smoking legislation is in proving that a decrease in secondhand smoke actually did occur. Additionally, stakeholders in this important issue want to know, with certainty, that the smoking ban has brought about the health benefits they anticipated. If we expect that a decrease in exposure to secondhand smoke will result in a decrease of the aforementioned health conditions, we must be able to empirically show this change.

What do I think?

Source: Palmbeachpost.com

As a public health student, you can probably guess where I stand in this debate. I live in a city and county that has not adopted any type of indoor clean air act, and frankly I find that kind of embarrassing. When study after study supports clearly demonstrates the link between secondhand smoke and an array of health conditions, choosing to ignore that evidence only shows ignorance. I get that businesses have to protect their interests, but if your livelihood depends upon catering to smokers, it's time to re-examine your business model. As a consumer, I can choose where I spend my money. As an employee, that choice is not always so cut and dry. I am thinking specifically of casinos, which provide hundreds of well paying jobs, many of which require employees to endure constant contact with smoke. Employees, perhaps most of all, deserve to be protected.

Another aspect of anti-smoking legislation to be considered is the possibility that it may decrease smoking rates altogether. We know with certainty that smoking is harmful for the smoker, and it seems as though we should capitalize on any opportunity that promises to reduce smoking rates. I am encouraged by a recent study that found smoking rates among pregnant women were decreased as a result of a citywide ban prohibiting smoking in public places. Preterm birth rates were also decreased by 23 percent⁵. Studies such as this one, that examine health outcomes before and after implementation of a smoking ban will be essential to demonstrating effectiveness of this legislation. Though I think existing evidence makes a pretty tight case for comprehensive indoor smoking legislation, opponents don't see it that way. They see the limitations and confounding variables in a study and turn them into reasons why the study isn't truly valid or why it can't be generalized to other populations. For that reason, I think we need more research to document the before-and-after results of anti-smoking policy. Such research is in the works, and I am hopeful that soon it will be hard to deny the hard facts provided by epidemiology. Otherwise, this legislation could go up in smoke.

Source: Naagtag.com

1http://www.npr.org/templates/story/story.php?storyId=135602895

2http://www.time.com/time/nation/article/0,8599,2039611,00.html

3U.S. Department of Health and Human Services. The Health Consequences of Involuntary Exposure to Tobacco Smoke: A Report of the Surgeon General. Rockville, MD: U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, Coordinating Center for Health Promotion, National Center for Chronic Disease Prevention and Health Promotion, Office on Smoking and Health, 2006.

4http://www.epa.gov/smokefree/healtheffects.html

5Currie, Donya. (2011). Study finds city-wide smoking ban reduces risks of preterm birth. Nation's Health, 40(10), 12.

Epidemiology in Food Safety

Epidemiology plays an important role in the safety of our food supply. The policies that dictate how our food is produced and regulated are dependent upon epidemiological data. A recent study in the journal of Clinical Infectious Diseases highlights the need for stronger regulation in regards to food production. The study investigated the prevalence of Staphylococcus aureus bacteria in meat products. Disturbingly, researchers found 47% of the samples to be contaminated with these drug-resistant bacteria. While the study sample size was small, data was collected from 5 cities across the country; encompassing meats produced under 80 brand names and from 26 grocery stores[1]. Though we can’t make the generalization that 47% of meat in grocery stores around the country is contaminated with S. aureus, these findings warrant the need for further research. Critics of this study would say that it is possible the results were found by chance alone, but I would contend that these findings are likely indicative of typical contamination rates. To be noted, we don’t yet know the human health implications of consuming drug-resistant meat. Such implications may take years to manifest, and may not even be discernable by way of longitudinal study. Can we expect that lawmakers or government agencies will implement and enforce regulation when there is no concrete data or information to support such regulation?

            Another article of interest involves an E. coli outbreak. In this case, health officials used a traditional epidemiological approach where interviews were conducted with infected persons and analysis of this information revealed hazelnuts were to blame for the outbreak[2]. What makes this particular case newsworthy is that a hazelnut packer possibly connected to the outbreak refused to cooperate with the investigation on the basis there was no solid proof that the hazelnuts were contaminated. Though this case was solved, it brings about an interesting debate. I am usually not one to agree with food manufacturers who have possibly contaminated consumers with deadly bacteria, but I do understand the business side of the argument. Without conclusive, biological evidence, is it fair to hold manufacturers responsible? In a court of law, we would have a hard time convicting a criminal of a crime for which there is no DNA evidence. The field of epidemiology is changing and is increasingly incorporating technology. Technology was able find a genetic link between the infected people and the suspected food source in the aforementioned case. Our society has become dependent upon large-scale food suppliers, which means outbreaks are no longer contained to a particular geographic region. Is investigation of these nationwide outbreaks by traditional means too time consuming and costly? Should we develop more technological methods of conducting disease investigations that don’t rely on victim accounts? How do we ensure epidemiology keeps up with the changing landscape of food production?

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[1] Waters, E., Contente-Cuomo, T., Buchhagen, J., Lui, C.M., Watson, L., Pearce, K., Foster, J.T., Bowers, J., Driebe, E.M., Engelthaler, D.M., Keim, P.S., & Price, L.B. Multidrug-resistant Staphylococcus aureus in US meat and poultry. (2011). Clinical Infectious Diseases, 52(10), 1-4. doi:10.1093/cid/cir181. Retrieved from: http://cid.oxfordjournals.org/content/early/2011/04/14/cid.cir181.full#sec-11

[2] Anderson, R. (2011).  Making the case through epidemiology. Food Safety News. Retrieved from:http://www.foodsafetynews.com/2011/04/making-the-case-through-epidemiology/

An Apple A Day...



Source: http://www.wrensoft.com/

Keeps the doctor away, right? I trust you are familiar with this old saying, though more appropriate advice might be “Eat plenty of fruits and vegetables and you will be less likely to get sick.” But let’s just pretend that originators of this adage were convinced that eating apples alone would lead to fewer doctors’ visits. If we find that eating apples, while making no other changes, decreases healthcare utilization, what would this mean for the healthcare system in general? Health care costs are increasing at exorbitant rates. We are facing a shortage of primary care physicians. As our population ages, the demand for physicians will only increase. There are no quick solutions to this problem, but what we do know is that healthy people visit doctors less often. A magic solution to this public health dilemma is one that prevents illness and thus decreases healthcare utilization overall. This is the concept behind most prevention programs.

The Apple-a-day prevention program is based on the hypothesis that individuals who eat one apple, every day, will have fewer physician encounters. There are several ways to test this theory, though some are better than others. Basically what we need to examine is the exposure, in this case apples, and the outcome, which will be the number doctor appointments. What we want to determine here is causation. Do apples cause doctors appointments? Well, hopefully not. More accurately, what we want to test is whether or not apple eating is protective again doctor appointments. Simply put, does the risk of a seeing the doctor go down as apple eating behaviors increase? We could study this concept using several different approaches, with the most practical being as follows:

• Cross-sectional study - outcome and exposure are determined at the same time. To accomplish this study, we could survey patient’s, post-doctors appointment, to determine their exposure to apples. The benefit of this survey type is that data could be collected quickly at a single point in time. The downside is that we would probably see a participation bias towards people that already see the doctor more regularly, independent of their apple eating habits.

• Case-Control Study – Here we would identify two groups; one group of cases comprised of people known to have visited the doctor recently, and a control group of people who have not seen the doctor lately. We would then determine which of the cases and controls have been exposed to apples and the frequency of that exposure.

• Randomized control trial – this type of study applies a treatment or intervention and then measures the effect of the treatment. Study participants are divided into two groups and one group receives the treatment/intervention while the other group serves as a control, receiving no treatment.

Taking the approach that apple eating is potentially beneficial, I believe the best approach would be to conduct an intervention. Using a randomized control trial, we would conduct an experiment whereby one group would receive the treatment, and a control group would receive no treatment. The treatment group will be supplied with, and required to eat, a minimum of one apple per day. The control group will not be supplied with apples and will receive no instructions regarding apple intake. They will instead be supplied with a pamphlet on healthy habits.

The survey method will be identical for both the treatment and control group. To measure both exposure and disease, study participants will conduct a web-based survey, once per month, for the duration of one year. The survey tool to be used is a questionnaire developed by scientists Gladys Block, Christina Gillespie, Ernest Rosenbaum and Christopher Jenson, known as the Food Screener. The Food Screener is a validated one-page, self-report survey that provides a quick and reliable measure of nutrient intake¹. This survey will measure fruit and vegetable intake for both study groups. This method is being used to collect accurate data of comprehensive fruit and vegetable consumption, rather than just apple consumption. Focusing solely on apple consumption might have the unintended effect of swaying the control group to eat apples. It may also conceal the potential benefits to be gained from eating a wide variety of produce. An additional survey will ask respondents to report how many times they have visited a physician during the previous one-month period. They will be prompted to select which type of facility was visited; physician office, outpatient hospital, or emergency department. The data gained from these two surveys will tell us how many apples a participant consumed during a given month and the number of physician visits they made. A comparison will be made between the number of physician visits and the quantity of apple consumption. We will examine frequency of both measurements, as it is believe that higher apple consumption will lead to fewer physician visits.

Methods

The study will be conducted in three locations across the United States, so as to account for regional differences in produce consumption and healthcare utilization. The program sites include St. Louis, MO., Atlanta, GA., and San Francisco, CA. The recruitment goal is 1,000 participants from each city, for a total of 3,000 participants.

Inclusion criteria must be met prior to enrollment in the study. Potentials participants must be 18 years or older to participate. Participants are excluded if they report a chronic medical condition whereby routine medical care is required. Additionally, potential participants are excluded if they report an allergy to any fruit or vegetable. Participants must give written conformed consent, whereby they agree to comply with the assigned treatment. All participants are notified that intervention treatment (apples for intervention group and pamphlet from controls) will be paid for by the research study. The costs associated with receiving care from a physician are the responsibility of the participant. Each participant will receive compensation in the amount of $10 per month, to be awarded after the completion of each monthly survey.

Assignment into the control or intervention group will be accomplished using computer generated allocation. 500 participants from each city will be assigned to the treatment group, while 500 will be assigned as controls. Participants will be blinded as to which group they are assigned. The healthy eating habits pamphlet distributed to controls in intended to make these participants feel that are receiving some type of treatment. They will not be aware of the actual treatment procedures.

Confounders

Recognizing that healthcare utilization is heavily dependent upon many factors besides diet, the study will take these variables into consideration. Possible confounders include age, sex, health insurance status, and income level. The data from both groups will be stratified based upon these factors so that we can determine if there are outcome differences. For example, participants in the older age groups may, as a whole, visit the doctor more often.

Challenges

The biggest hurdle in this study will be compliance of participants. In order to entice participants to complete each survey, compensation will be distributed on a month-to-month basis, rather than at the end. The survey method was chosen in order to boost compliance. It can be completed within 7 minutes and since it will be web-based, users can access it the location of their choice. Even when participants are willing to report their monthly data, it can not be guaranteed that people in the intervention group will comply with the treatment of eating one apple per day. Supplying participants with a continuous supply of fresh apples should encourage compliance, but it is by no means is an assurance this will happen.

Strengths and Weaknesses

Though the study will attempt to overcome the aforementioned challenges, there are certain aspects of the study design that can not be manipulated for our benefit. There will be inherent weaknesses within this design. The first, and perhaps most significant weakness is the length of the study. It is hoped that a period of one year will be able to capture the relationship between the treatment and the anticipated outcome. Certainly, a longer time period will give even more insight into this relationship, but budgetary restraints of this study do not allow for a longitudinal study. We must consider, as a limitation, that the protective benefits of apple consumption could be over exaggerated. Yet-to-be-diagnosed health conditions could develop, without participant knowledge, during the study period. A longer study might be able to reveal such health conditions that would warrant additional physician care. Conversely, participants may be diagnosed, during the study period, with health conditions that existed before the study began. Development of these conditions may not be dependent upon apple consumption. Randomization will equalize these two circumstances.

The intervention portion of this study does not attempt to control the eating regimen of participants. By not placing participants on a diet, we feel compliance will be higher. We also feel results will be more realistic as participants who state they have followed the treatment will have had fewer challenges associated with doing so. Participants are not asked to make drastic behavior changes or give up a beloved food item. On the flip side, not controlling what all participants eat opens up the door for an un-planned cross-over. Since controls will have access to apples, as well as other produce, it is possible they will also engage in high-apple eating frequency. We have allowed the possibility of a such a cross-over because we feel it reflects the eating habits of the general population. Restricting apple intake may not provide an accurate picture of what a normal person, who gives no thought to apple consumption, is likely to consume.

It is also possible the the behavior of apple eating is associated with other healthy behaviors that are not being measured, such as physical activity. The participants who comply with the treatment program may be inherently more likely to engage in healthy behaviors as a whole. The fact that the study measurements will be self-reported opens up the possibility for inaccuracy. However, it should be noted that we feel the self-reported data will be a strength, rather than a weakness. Because surveys will be completed on computer, without the prompts of a researcher, it is hoped that respondents will answer honestly. We believe responders will report accurate and truthful answers, rather than the answers they think researchers want to hear.

Study Implications
If this intervention successfully proves that eating an apple a day can reduce the frequency of physician utilization, this would have important ramifications for future public health practice. Rather than focusing on complicated interventions that involve nutritional data and physician recommendations, this solution will be easy to implement. Interventions using this tactic will likely have higher success rates, as asking participants to change one behavior, instead of several, is more practical.
Success of this intervention could also have important policy implications. USDA recommendations suggest eating a wide variety of fruits and vegetables. If apples are determined to be a super fruit, their status within the food pyramid may be upgraded. It may be recommended that individuals eat a wide variety of fruits and vegetables, but that this variety should include at least one apple per day. School lunch programs, which rely on USDA guidelines, may require that all children receive an apple every day with their lunch.
There may also be implications for the pratice of medicine. Physicians, rather than prescribing medication, may advise patients to consume a certain number of apples and to schedule an appointment only if symtoms persist. Routine physical screenings and preventive medicine may include questioning that pertains to apple consumption. Doctors will likely suggest that their patients eat as many apples as possible.

Additional Thoughts

When designing a study, it is very evident that there are many, many different ways to assess a certain exposure or disease. I can certainly attest to the fact that every design aspect will be planned and re-planned several times over. In the end, I settled on the study and methods described here largely because it feels realistic. If often read studies where I feel researchers aimed too high by trying to measure too many variables, or perhaps asked too much of study participants. In this study, the commitment from participants is minimal and the compensation is fair, but not overly coercive. I can imagine volunteers committing to this study with full intent of complying, and then doing so honestly. The data collected here could easily be collected retrospectively by asking participants to report their exposure to apples and their use of physician services, within a specified time period. Admittedly, it may be more cost and time-effective to do. The concerns of recall bias and temporality are too large in a retrospective study, and for me, not worth the savings. Within reason, I'd rather undergo the added expense and effort of a randomized trial in order to be assured the study results are legitimate and lead to a worthwhile conclusion.

If by some strange chance this study were conducted, and we did find a link between apple consumption and physician utilization, I would like to see this study developed into a long-term study, where we could dig even deeper into this link. A longitudinal study would help us determine exactly what disease outcomes are affected by apple consumption, which might give insight into why apple consumption keeps people healthy.

On a final note, I urge you to look toward the top right hand side of this page where you will see voting buttons. I have created a very unscientific poll where you can answer two questions. These questions, in a very basic way, will assess the relationship between apple consumption and physician utilization. Please take a quick second to complete the poll and we can see if a relationship exists within our own responses.

1Block, G., Gillespie, C. Rosenbaum, E.H., & Jenson, C. (2000). A rapid food screener to assess fat and fruit and vegetable intake. American Journal of Preventive Medicine, 14(4), 284-288. doi:10.1016/S0749-3797(00)00119-7

Check Your Sources

Welcome back, readers. As I demonstrated in my last post, epidemiology is everywhere. It smacks you in the face whenever you open up a newspaper or log onto your favorite news outlet. While the publicity of epidemiological studies is a good thing, often times important details can get lost in translation. When you read a newspaper or magazine article, you have to consider that Mr. or Mrs. Reporter did not come up with the study or it’s findings all on his or her own. Rather, the reporter has likely been tasked with writing about ANOTHER article, originally published in a scholarly journal unfamiliar to the average American. The reporter takes the information, polishes it up, and presents it to the laymen in a sparkly article with a catchy headline. Call it sensationalism, call it recycled journalism, but whatever you call it, make sure the condensed version of the story does not get the final word.

If you are thinking that you don’t have any interest in reading an article from The New England Journal of Medicine or The Journal of the American Medical Association, I get it. They use big complicated words and scary charts and graphs. Sure, it would be useful to have a background in statistics or the biological sciences, but it not necessary to understanding a published research study. Today, we will examine an article found on MSNBC, as well as the companion journal article published by National Cancer Institute. We are going to discuss specific attributes of the articles to give to an idea of what you should be looking for when you evaluate these news findings yourself.

Source: DrSoram.com

First off, the MSNBC headline of ‘Having more kids may increase breast cancer risk’ caught my eye. I immediately thought, “Wait! This goes against everything we’ve ever been told! I must read this!” 2 points to the reporter for the attention-grabbing headline. I was suckered in immediately, as was intended, only to learn there is a whole lot more to the story. For future reference, that is almost ALWAYS the case. The article quickly clarifies that the discussion involves ‘triple-negative’ breast cancer and the reference to an increased risk of breast cancer applies only to this type of cancer, rather than the entire spectrum of breast cancers. While the research findings do not reverse Everything We Thought We Knew, they are still significant nonetheless. ‘Triple-negative’ is a rare subtype of the disease that tends to very aggressive when found. The article is in response to a study examining the risk factors associated with ‘triple-negative’ breast cancer. The study, soon to be published in the Journal of the National Cancer Institute, found that the more times a women gives birth, the higher her risk of developing this type of cancer. It also found that a woman who has never given birth is 40% less likely to develop triple-negative breast cancer.

Now that you have the synopsis, we’ll break it down a bit by answering a few basic questions.

What exposures are being assessed?
When you begin reading any article, be it a scholarly journal or a magazine, you want to ask yourself two basic questions. What is the exposure, or the variable that increases the risk of disease? Secondly, and usually most obvious, what is the disease in question? Every study is different and sometimes both the disease and the exposures are known up front. Other times, the end result, disease, is known and researchers are trying to measure exposure to determine if there is an association. What every study wants to answer is whether or not the disease and exposure are related, and sometimes evidence can go so far as to prove one causes the other. In the example case study, we know that breast cancer is the disease, and we can tell from the article that researchers are studying several possible exposures for this disease. Although the newsworthy association gleamed from this study is reproductive history and triple-negative breast cancer, the study also examined associations between this type of cancer and menstrual history, breastfeeding, and use of oral contraceptives. Additionally, these same factors were measured for women with other types of breast cancer. The researchers describe these exposures as “hormonally mediated risk factors[1].”

Are there possible confounders?

For those of you new to the epidemiology game, a confounder is an additional variable that may account for the effect of exposure on disease. Researchers must account for the possibility that any observed association may be due to differences that exist between study groups, not including the exposures being studied. Leon Gordis notes that a confounder is risk factor for both the disease and the exposure in question[2].  Accounting for confounders is important because they may inflate or deflate the findings of the study. In this particular study, hormones are believed to influence breast cancer development. I am therefore concerned about outside variables that may influence hormone levels. Diet, physical activity, and age may have an impact on hormone levels. The researchers clearly accounted for age in this study, but were less clear in their measurements of diet and exercise. While they mentioned these measurements were collected at baseline, it is unclear how this data figured into the final analysis, if at all. Given the known information about hormone levels in our food supply, especially in dairy and meat products, I am interested to know if this would influence overall hormone levels as they relate to breast cancer.

What is the Study Design?
There are several study design types and the design used can influence the success or failure of the study. Rather than trying to teach you the details of each design, I'll give you a tip that I find most useful. When reading a journal article, ask yourself if the researchers were truely able to answer their research question. Sometimes you may find that they were not able to give conclusive answers. In this situation, ask yourself what could have been done differently in order to gleam the most information out of the data collected. Researchers often do what they can with the resources available, and unfortunately this can lead to a study that tells us a whole lot of nothing.

This data for this study came from the Women’s Health Initiative (WHI), which detailed the reproductive history of 155,723 postmenopausal women. The WHI was a longitudinal study where participants were followed over a long period of time, in this case a median of 7.9 years. The study can also be called a cohort study, as a group of participants, known as a cohort, is followed to see if a particular disease develops. There are several other common study designs, though none would have been applicable for studying the development of breast cancer over time. Other common designs can be seen in the text box below.

Here we are dealing only with a cohort study. The study was further broken down into two components; an observational study of 93,676 women and randomized clinical trials with 68,132 women. 6085 women were excluded from the study for reasons that included history of breast cancer or mastectomy or lack of follow-up information. 307 of these women developed triple-negative breast cancer and 2610 developed other types of breast cancer.

What are the strengths and weaknesses of the Cohort study design?

The strength of this study is increased by large a sample size and the considerable length of time in which the cohort was observed. I personally favor this type of study because I think it tends to show real results as opposed possible associations. If I'm going to spend time reading about a study, I like to know a firm conclusion can be drawn from the results. Certainly, some studies are more of a starting point for further research, which is great too, but I like something more concrete. In the example, the study period of around 8 years gave me the confidence that the researchers really did find what they say they did. Another advantage to this design is the elimination of recall bias. Recall bias is a bias that occurs because people may remember past events or exposures differently depending upon their current disease status. Because participants in a cohort study are observed in real time, they do not have to recount exposures that may or may not have existed. Accuracy is much improved with this design. This study design also allows researchers to study the effects of more than one exposure and to compare/contrast different levels of exposures. As we see in the example study, researchers did not just examine whether a women had children, but the number of children she had as well as several other exposures. The researchers were concerned with a dose-response relationship, meaning as the exposure increases, the risk of disease increases as well[1]. They were also able to study the different stages of the disease. The researchers themselves note that that the analysis is strengthened by completeness of follow-up and exposure information.

The cohort study design has disadvantages as well, the greatest being the difficulty in executing such a study. It is very costly in terms of resources, manpower and time. Additionally, because of the length of time required in such a study, it may be difficult to track participants throughout the entire length of the study. It can also be difficult to control for and measure extraneous factors outside the realm of the study variables.

My Take

I believe the implications of this study are fairly significant. The results are consistent with previous findings and also improve upon existing evidence. Additionally, the findings warrant further research into the exposures associated with ‘triple-negative’ breast cancer.  One limitation of the study, which was addressed by the research team, is that the study was restricted to postmenopausal women, which may mean the results are not generalizeable to younger women. The findings related to oral contraceptive use and breastfeeding may not be accurate due to the population studied and results may have been markedly different had a younger demographic been included.

The journal article may have more implications for medical and research communities than for the general public. While technology is being developed to help women understand their genetic risk for certain types of cancer, this is not available, or even desired, for all women. Even if a woman did know her genetic risk, this information alone may not be enough to influence her reproductive choices. A women who is prone to ‘triple-negative’ breast cancer is not, in my opinion, likely to use this risk factor as a basis for deciding how many children she should have. Therefore, this information in this study will be more pertinent to professionals that will work to further understand these risk factors and to eliminate them.

If you would like to read the on-line article, the link is:

http://www.msnbc.msn.com/id/41828959/ns/health-cancer/

If you would like to read the entire journal article, the citation is as follows:

            Phipps, A. I., Chlebowski, R.T., Prentice,  R., McTiernan, A., Wactawski-Wende, J., Kuller,  L.H., Adams-Campbell, L.L., Lane, D., Stefanick, M.L., Vitolins, M., Kabat, G.C., Rohan, T.E.,  and Li, C.I. (2011). Reproductive history and oral contraceptive use in relation to risk of triple-negative breast cancer. Journal of the National Cancer Institute, 103, 1-8. doi: 10.1093/jnci/djr030.

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[1] Phipps, A. I., Chlebowski, R.T., Prentice,  R., McTiernan, A., Wactawski-Wende, J., Kuller,  L.H., Adams-Campbell, L.L., Lane, D., Stefanick, M.L., Vitolins, M., Kabat, G.C., Rohan, T.E.,  and Li, C.I. (2011). Reproductive history and oral contraceptive use in relation to risk of triple-negative breast cancer. Journal of the National Cancer Institute, 103, 1-8. doi: 10.1093/jnci/djr030.

[2] Gordis, Leon. (2009). Epidemiology (4th ed.). Philadelphia, PA: Saunders Elsevier

What is epidemiology?

Epidemiology is considered to be the backbone of public health; with many suggesting that public health itself is merely applied epidemiology[1].  Epidemiology, when paired with biostatistics, contributes much of the science behind public health. It also has important implications for clinical medicine. Leon Gordis defines epidemiology as “the study of how disease is distributed in populations and the factors that influence or determine this distribution[2].”  Understanding disease distribution is key to managing diseases and stopping their spread.

An additional question to consider is: Why is epidemiology important? There are several reasons, actually. The practices of public health and medicine depend upon epidemiology, and much of everyday life revolves around it. Gordis notes that epidemiology is significant because it:

• Identifies the cause of a disease or disease risk factors
• Determines extent of disease or disease burden
• Studies the natural history and prognosis of a disease
• Evaluates both existing and new developed preventive therapeutic measures and modes of health care delivery
• Provides the foundation for developing public policy relating to environmental problems, genetic issues, and other considerations regarding disease prevention and health promotion
• Determines if there is an association between a disease and an exposure2

You likely hear about epidemiology every day, perhaps without knowing. In short, epidemiology is what keeps you from getting sick, and if you do get sick, it aims to know who, what, when, where, why, and how.  Through the news media, epidemiology alerts you to risk factors and attempts to educate on ways to protect yourself. Epidemiology may not be a word you commonly hear, but the practice and teaching of epidemiology is all around you. Once you begin to notice epidemiology, it is hard NOT to notice it.  I certainly find myself unable to open the newspaper or turn on the television without being grabbed by an epidemiological anecdote. In just a short time you will see that epidemiology is….

Delicious

Source: Inquistr.com

I don’t know about you, but I tend to think that clean, bacteria-free food is much tastier than the alternative.  Epidemiology helps keep our food supply safe. When a foodborne illness is suspected or the food supply is compromised, epidemiologists come to the rescue. They determine the source of contamination, and if applicable, ensure that the contaminated source is cut off from consumers. Unfortunately, epidemiologists have been in overdrive as of late, and we are all too familiar with food recalls. You may remember the largest egg recall in U.S. history, occurring in 2010. The recall was prompted by an epidemiological data showing that eggs infected with the salmonella bacteria caused illness in more than 1,500 people[3]. It is possible that you yourself have been involved in the investigation of a food borne illness. If you have ever reported a suspected or known case of such illness to your local health department, you know how miserable such an event can be. I am thankful there are people out there trying to ensure food outbreaks do not occur and that their impact is limited.

Sexy

Source: blog.therealestatehomeguide.com

Well, sometimes, at least. An outbreak of respiratory illness recently brought epidemiologists to the Playboy Mansion for investigation. While you might suspect Hugh Hefner’s pad to be a breeding ground for a certain kind of disease, the illness in question here is a form of Legionnaire’s disease. Legionnaire’s is a fairly common infection, with the CDC estimating that between 8,00 and 18,000 people are hospitalized in the U.S. with the condition each year. [4] The bacteria that causes Legionnaire’s, known as Legionella, thrives in warm water. For this reason, the suspected sources of contamination at the Hefner Home include suspects include both a hot tub and a fog machine. Epidemiologists on this scene of this investigation will be responsible for determining the actual exposure of all persons infected with the disease, along with testing the suspected sources for bacteria. It’s a tough job, but somebody has to keep Hef and all his ladies safe from harm.

To read more about the investigation at the Playboy Mansion, click here:

http://www.chicagotribune.com/news/ktla-playboy-mansion-outbreak,0,961582.story?track=rss

Controversial

Source: Medicexchange.com

When an epidemiological study reveals new groundbreaking information, the information isn’t necessarily met with open arms. While new data is exciting, it isn’t always well received or taken as the definitive word on a subject. Take for example, the new mammography guidelines issued in 2009 by the U.S. Preventive Services Task Force. This information developed by the taskforce was intended to make decisions regarding mammograms easier. Instead, the report unleashed a myriad of opinions on the subject, including the American Cancer Society, who disagreed with the findings. Epidemiologic research is intended to aid the public and medical professionals alike, but unfortunate circumstances exists where the data merely complicates the situation.  In this situation, we see well-intentioned organizations and professionals pitted against each other, claiming that the evidence on which they rely is the most, well, reliable. And so, research will continue, as each study builds upon the last, until an undeniable conclusion can be reached. Until then, controversy remains.

Epidemiology is all around you, and you may have even contributed to the study of epidemiology. It is constantly in action, and constantly spurring action.  It is an ongoing adventure.

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[1] Turncock, Bernard. (2007). Essentials of Public Health. Sudbury, Massachusetts: Jones and Bartlett Publishers.

[2] Gordis, Leon. (2009). Epidemiology (4th ed.). Philadelphia, PA: Saunders Elsevier.

[3] NY Times. (2010, Sept 23). Egg contamination and recalls. The New York Times. Retrieved from: http://topics.nytimes.com/top/reference/timestopics/subjects/e/eggs/contamination_and_recalls/index.html

[4] Centers for Disease Control and Prevention, National Center for Immunization and Respiratory Diseases (2008). Patient facts: learn more about legionnaires’ disease.  Retrieved from: http://www.cdc.gov/legionella/patient_facts.htm

Welcome

Hello, and welcome to my new blog. As a graduate student of public health, I am interested in helping people living better lives. I believe that in order to be happy, a person must first be healthy. The particular focus of this blog will be epidemiology, and how the principles of epidemiology contribute to the overall health and wellness of society. Please follow along as I chronicle my journey through this exciting subject.