Month: June 2011

In my ongoing series that reviews epidemiological concepts for the comps, I continue to blog about Introductory Epidemiology (EPI 810) concepts. Today, I am going to use Gordis text Epidemiology and review important concepts chapter-by-chapter. I’ll start with Ch 2 and only try to cover those concepts that either I don’t remember or bear repeating.

Chapter 2 – The Dynamics of Disease Transmission

• Disease severity represents a broad spectrum. Clinical disease is the common stage of disease that shows signs and symptoms. With a nonclinical (inapparent) disease state, the disease is not clinically apparent (i.e., no signs or symptoms) and may never arise to become clinically significant, but it is often diagnosed by a biologic test. Persistent (chronic) disease is one in which the symptoms continue over a long period of time. Latent disease is usually characterized by a viral infection that lies dormant (doesn’t replicate) and does not produce disease.
• Herd immunity is a concept that essentially means immunity conferred to a group of people once a certain proportion of the population is immune (through natural immunity, vaccination, cured). A susceptible person is unlikely to contract the disease if he/she is only coming into contact (or is more likely to come into contact) with immune individuals.
• The incubation period is defined as the time between the contraction of a disease and the onset of clinical illness (i.e., signs and symptoms).
• An attack rate is the number at risk in whom a certain illness develops divided by the total number of people at risk. Time is not explicit, but implicit.
• The secondary attack rate is the attack rate in susceptible people who have been exposed to the primary case.

Chapter 3 – Measuring the Occurrence of Disease: I. Morbidity

• Remember that an incidence rate is the number of new cases of a disease occurring during a specific time period divided by the number of persons at risk of developing the disease during the same time period. Understand the concept of person-time (the sum of the time periods of observation for each person).
• The difference between active and passive surveillance. Active surveillance is where study staff go out to sites to identify new cases. Passive surveillance is where new cases are found through either available data or reported by health care providers.

Chapter 4 – Measuring the Occurrence of Disease: II. Mortality

• The annual mortality rate is the total no. of deaths divided by the total population at midyear.
• The case fatality rate is the no. of people dying of a disease during a specific period of time divided by the total no. of individuals with that disease.
• Know how to do direct- and indirect- age adjustment. With the direct-adjustment you are basically applying the population-specific mortality rate(s) to determine the expected number of deaths, and then dividing the total expected deaths by a ‘standard’ population. See Table 4-11 (pp.76) for a good illustration.
• For indirect-age adjustment one can calculate the standardized mortality ratio (SMR), which is the ratio of the observed no. of deaths per year / expected no. of deaths per year. See Table 4-13 (pp. 78) for a good illustration.

Chapter 5 – Assessing the Validity and Reliability of Diagnostic and Screening Tests

• Validity of a test is the ability of the test to distinguish between who has the disease or not. It has two components: sensitivity and specificity.
• Sensitivity is the ability of the test to correctly identify true cases of the disease. It is the ratio of the true positives (people with the disease that tested positive) divided by the total number of people with the disease.
• Specificity is the ability of the test to correctly identify true non-cases of the disease. It is the ratio of the true negatives divided by the total number of people without the disease.
• When tests are used sequentially, net sensitivity decreases, while net specificity increases.
• When tests are used simultaneously, net sensitivity increases, while net specificity decreases.
• Remember that the positive predictive value of a test asks, “If we screened the population, what proportion of people who have the disease will be correctly identified?”. It is the number of true positives divided by the total number of positives.
• Conversely, the negative predictive value of a test is the number of true negatives divided by the total number of negatives.
• What is the relationship between prevalence and the predictive value of a test? The higher the prevalence, the higher the predictive value.
• Specificity has a bigger impact on the positive predictive value than sensitivity. As specificity increases, the PPV increases.
• Reliability of a test is whether it is repeatable. There are different types of variation in reliability for tests. Intrasubject variation within the individual (e.g., blood pressure). Intraobserver variation is differences in observation by the same observer. Interobserver variation is differences between two observers.
• Kappa is the (% agreement observed – % agreement by chance) / (100% – % agreement by chance). See Figure 5-17 (pp. 105) for an illustration.

Chapter 6 – The Natural History of Disease: Ways of Expression Prognosis

• The two main points to understand about this chapter is calculating a life table using the standard and the Kaplan-Meier method, and then understanding a few assumptions of the life table.
• I will return to this post and create an example for each at a later date.

I am going to stop here for today.

I am going to take a slightly different approach to my study of Historical Roots. Instead of reviewing every article, I am instead going to discuss important topics that will be covered on the comps. I will use the readings as references to the ideas and points I will try to make. My approach will be that of someone who is trying to explain the topic to a novice.

The first topic is Miasma Theory and Cholera, in which I will discuss the miasma theory of disease within the historical context, the competing theory(ies), and how cholera fits within the picture. I will try to emphasize the important events and characters that shaped this topic.

The miasma theory of disease was one in which diseases (such as cholera) was the result of polluted air caused by foul water, waste, filth, and decomposition of living matter. Removal of waste, improved sanitary conditions, and better ventilation were thought to prevent or cure the disease. It was not an illogical connection given the association of poor sanitation and disease prevalence. It was a popular theory of disease through the Middle Ages and was only later supplanted by the germ theory of disease supported by such figures as John Snow and Robert Koch.

Sir Edwin Chadwick was a 19th social reformer whose primary contribution was to the improvement of sanitary conditions in England. Perhaps his most influential written work 1842 report titled,<!–[if !mso]> st1\:*{behavior:url(#ieooui) } <![endif]–> On an Inquiry into the Sanitary Condition of the Labouring Population of Great Britain. An excerpt of this report is part of the required reading. In brief, the report comes to a number of conclusions about the relationship between sanitary conditions and the presence of bodily and moral diseases in the population. It is interesting that not only does Sir Chadwick champion better public sanitation infrastructure for the laboring classes in order to prevent disease, but that the miasma also contributes to the moral evils of these unsanitary areas.

John Simon was also a 19th sanitation reformer and supporter of the miasma theory. He along with others, including Dr. William Farr, a influential proponent of the miasma theory, published Report of the Committee for Scientific Inquiries in Relation to the Cholera-Epidemic of 1854, which is another of the required readings. In the excerpt, it appears Dr. Simon is discussing environmental factors affecting atmospheric conditions and thus the miasma. Such factors include electrical discharge (lightning in the formation of ozone from oxygen), solar light, atmospheric pressure, temperature, presence of fog, mist, or haze, and lack of air circulation. 

He attempts to note that in a previous outbreak of cholera, (1) that it was characterized by atmospheric conditions (of those mentioned above) that render the air less “pure”, and (2) these factors are more apparent in the areas of London most afflicted with cholera. Then follows a detailed account of examinations of the air and water around London for the presence of organic and chemical products in places where cholera was most frequent and less frequent or absent. While microscopy techniques were available at the time, perhaps it was not developed to the point of identifying something as small as the cholera bacterium.With respect to water purity, they could find no reason to suspect some mineral contaminant and it appears that a parasitic organism was ruled out on the basis that other known parasites do not cause symptoms similar to cholera. However, they seem to not completely rule out that some organic contaminant as a result of some “decaying animal product within or without the body” may be a danger for cholera or like diseases.

Dr. Thomas Southwood Smith was a contemporary and collaborator of Dr. John Simon. He wrote A Treatise on Fever in 1830 within the context of the miasma theory of disease. In the causes of fever, he first distinguishes between immediate causes (which he calls ‘exciting’ causes) and secondary causes that brings the body into the condition to be affected by the primary causes (‘predisposing causes’). He explicitly states the ‘exciting’ cause of fever: “[A] poison formed by the corruption or the decomposition of organic matter”. However, he does note that the specific agent is unknown – only its origin is known. Then he discusses the conditions that support putrification of organic matter – namely, heat and moisture. He then uses an example of where a military battalion with troops stationed in a low area became sick with disease while troops of a higher elevation did not, with the result attributed to free and dry air. Several additional examples follow.

In yet a new first part of an ongoing series of posts, I will try to blog about concepts of epidemiology that will be covered on the comprehensive examinations (hereto referred to as “comps”). In the interest of efficiency and time, I’ll only briefly cover concepts and ideas I feel that I do not quite remember well or understand. For example, if asked to recall the five rubrics of epidemiology (quantity, location, causes, mechanisms, prevention and control), it would not be difficult, thus I should not waste my time blogging about them.

Today, I will cover concepts from EPI 810 – Introductory Epidemiology. In subsequent posts I may continue to discuss issues of EPI 810, or jump around to other courses.

• Epidemiology comes from Greek, ‘epi’ meaning ‘upon, among’, ‘demos’ meaning ‘people’, and ‘ology’ or ‘the study of’. It literally means ‘what falls upon the people’.
• Epidemiology is defined by Last as “the study of the distribution and determinants of health related states and events in the population and the application of this study to control of health problems“. It is important to note that this definition emphasizes epidemiology’s concern with quantity/location, causes of disease, a focus on the population, and the importance of prevention/control.
• It is important to remember the difference between a vector and a vehicle in the transmission of infectious disease. A vector is an animate carrier, such as a mosquito, fly, or even human being. A vehicle is an inanimate carrier.
• It is likely that JCA will ask about Geoffrey Rose’s distinction between “causes of cases” and “causes of incidence“. Basically, the cause of disease in an individual (cause of a case) may not be the same as the cause of disease in a population (cause of incidence). They address different question and may have different answers.

• Another important point in the Rose article worth remembering are the pros and cons of the ‘high-risk strategy‘ of disease prevention/control and the ‘population strategy‘. I may return to this idea in the future, but for now, remember that the high-risk strategy has a large benefit for those individuals, but low for the population, and does little to impact the root cause. Conversely, the population strategy can have the largest impact on the population, but may have very little individual benefit.
• Also remember Rose’s observation that “a large number of people at a small risk may give rise to more cases of a disease than the small number who are at a high risk.”
• Recall that the four major methods of person-to-person transmission are:
• Direct contact – <3' without an intermediate object
• Indirect contact via a vehicle (e.g., bacteria on a tooth brush)
• Indirect contact via airborne dust or droplet (>3′)
• Indirect contact via a vector
• Remember that a contagion is ‘a mechanism of disease transmission that must be identified as an after-effect of a prior effect‘.

Unfortunately, I have to end this post prematurely. The PowerPoint slides from JCA on EPI 810 were removed from the Angel site, and I have no other source of information for the content.

One of my goals this summer is to familiarize myself with the concepts and methods of survival analysis. Not only will this add another “tool” to my epidemiological “toolbox”, but it will help me prepare for the comprehensive examination this August. Each week I intend to read and blog about one chapter of the text, Survival Analysis: A Self-Learning Text, Second Edition by David G. Kleinbaum and Mitchel Klein. Again, I will only want to highlight the main points, especially the important concepts to be covered on the comps.

Chapter 1 – Introduction to Survival Analysis

• The problem that survival analysis solves is analyzing the time to an event as the main outcome.
• There is time, which can be continuous time or discrete time, such as age.
• There is the event, which is some outcome of interest such as death, onset of morbidity, or remission. Usually the event is a dichotomous variable.
• When more than one outcome event is of interest, then a more complicated competing risk is needed.
• The event is also sometimes called the failure.
• Censoring is an important concept in survival analysis. It is when an individual’s survival time is unknown due to the failure event not being observed. This can be due to the individual not experiencing the event before the end of the study, withdrawal from the study, or the person is lost to follow-up.
• This type of censoring is right-censored.

• Survival analysis can be described mathematically in relation to two related functions: the survival function and the hazard function.
• The survival function, S(t), is the probability that a person’s survival time (T, a random variable) is greater than some specified time (t). S(t) = P(T>t)
• There are some important properties of the survival function that should be noted:
• 1) Since it is a probability, S(t) has a range between 0 ant 1.
• 2) S(t) is always non-increasing. At time 0 the S(t) is 1 and decreases as time increases. At infinity, S(t) is zero.
• In theory, S(t) represents a smooth curve from 1 to 0, but in reality it is a step function that may not reach zero if the study ends before all subjects have experienced the event.
• The other important function is the hazard function, h(t), which represents the instantaneous risk per unit of time of the event occurring given survival to some time (t). 

• The key points about the hazard function is that it is a rate and not a probability, so it can take values between 0 and infinity.
• When the hazard function is constant (a straight line), then it is called exponential.
• The survival and hazard functions are mathematically related, such that knowing the survival function allows one to derive the hazard function, and vice verse (see equations below).

• There are three main goals of survival analysis:
1. Estimate and interpret the survival and/or hazard function for survival data.
2. Compare survival/hazard functions.
3. Assess the relationship between explanatory variables and survivor time.
• One way of laying out survival data is to assign each row to an individual observation with each column variable subscripted by subject with the following column variables: survival time (t), failure status (whether the subject experienced the event, d), and an array of explanatory variables (X). This is likely the form of the data needed for analysis by computer.
• Another data layout that facilitates survival analysis arranges the data where each row of the data exists for each unique value of uncensored survival time, beginning with t=0 and in ascending order. For each row, there is a column for survival time, # of failures between prior survival time up to and including that row’s survival time, # of censored observations between prior survival time up to and including that row’s survival time, and the total obs in the risk set.

• The average survival time (T-bar) is simply the sum of the survival times for all subjects (censored and non-censored) divided by the number of subjects.
• The average hazard rate (h-bar) is the number of failures divided by the sum of the survival times (censored and non-censored).
• Median survival time may also be a useful descriptive statistic of survival curves. It is the time (t) at which 50% of the subjects have still survived. 

This will be the first part in a series of posts on material from the text, Drugs, Society, & Human Behavior, 13th Edition, by Hart, Ksir, and Ray. While my research focus is on drug use and dependence, there has been little in the epidemiology curriculum that teaches specifically about drugs. This text is a good introduction to that topic, and to that end, my goal is to summarize the information. I will not follow the book sequentially by chapter, but rather jump around to that which interests me.

For the first post, I will write a little about opioids (Ch. 13). To keep it short, I’ll just highlight important points in bullet form, taking a lead from the chapter objectives at the beginning of each chapter.

• Opium comes from the plant Papaver somniferum
• It likely originated thousands of years ago in the Middle East.
• Opium can only be harvested in a few days of the plant’s life, by making shallow cuts into the unripe seedpods, allowing the opium resin to ooze out.
• Its use has been known throughout antiquity, including references in Homer’s Odyssey, Greek physician Galen, and Arabic physicians such as Biruni and Avicenna, the latter of which probably first described opium dependence.
• Laudanum was a popular opium formulation popularized by such medical luminaries as Paracelsus and Dr. Thomas Syndenham

• Thomas De Quincey wrote an influential article about opium called “The Confessions of an English Opium-Eater”
• The Opium Wars (1839) was between the British who wanted to continue to import opium into China, and the Chinese government who wanted to ban it because of its evils.
• Oddly, it was the earlier banning to tobacco in China that led to the rise in popularity of smoking opium
• In 1806, a German scientist, Frederich Serturner, first isolated the primary psychoactive ingredient of opium and called it morphium (morphine), after Morpheus, the god of dreams.
• Later, another important opioid alkaloid, codeine, was isolated.
• With the development of the hypodermic syringe for injecting morphine, it became a popular medicine during the wars of the latter 19th century.
• In 1874, it was discovered that adding two acetyl groups to morphine, creating diacetylmorphine (heroin), increased morphine’s potency.
• Opium was likely introduced in the United States by Chinese immigrants in the 1850s.
• It was a popular ingredient in patent medicines at the time (potions, elixirs, soft drinks)
• As opium became illegal, heroin rose to be the popular choice due to its potency.
• Black tar is a form of Mexican heroin, called such because of the brown or black color.
• The majority of the world’s heroin comes from Afghanistan, though Colombia and Mexico supplies most of the US.
• Opioid pain relievers are another important source of opioid dependence, with such medications as Vicodin, Lortab, and Oxycontin.
• Raw opium is about 10% morphine and a smaller fraction codeine.
• Heroin is more potent because the two additional acetyl groups make morphine more lipophilic and so can more readily cross the blood-brain barrier.
• Heroin is two to three times more potent than morphine.
• Medical chemists have created opioids even more potent than heroin, such as fentanyl, which is about 100 times more potent than morphine.
• There are also opioid antagonists, such as naloxone and naltrexone.
• Starting in the 1970s, scientists discovered endogenous morphine-like substances that activate opioid receptors: enkephalins and endorphins.
• These substances appear to have roles in pain perceptions in the brain and spinal cord.
• Medical uses for opioids include pain relief and gastrointestinal uses, such as diarrheal dysentery.
• Repeated use of opioids can result in tolerance, physical dependence, and psychological dependence.
• Withdrawal symptoms are flu-like, such as nausea, vomiting, diarrhea, aches, pains, and general malaise.
• Methadone is a synthetic opioid agonist that produces less severe withdrawal symptoms and is used as a treatment for heroin dependence.
• Clonidine can also reduce withdrawal symptom severity and does not have a narcotic effect.
• Acute toxicity is marked by depressed respiration.
• Opioid overdose: opioid triad – coma, depressed respiration, pinpoint pupils.
• Sharing needles among heroin users is accompanied by a high risk of hepatitis and HIV
• Heroin users can start to feel withdrawal after as little as four hours after their last use.
• It is often sold in ‘dime’ bags of $10, but the quality of heroin may vary greatly. It is a habit that may run between $30 to $100 per day.
• Heroin is typically dissolved in water and heated to speed the process, then injected intravenously (also called ‘banging’)
• There are several misconceptions about heroin and users. First, not everyone experiences intense euphoria the first time, but may feel nausea and discomfort at first. Second, withdrawal may not be as excruciating as portrayed. Though very subjective, it is often similar to persistent flu. Third, it is untrue that you are ‘hooked’ after the first time.
• Occasional heroin users are sometimes called chippers.

Although the class does not start until the end of June. We did meet briefly last week to discuss a few administrative issues and a brief overview lecture on cancer statistics. I am just going to highlight some of the key points from each slide of this lecture.

• In 2010, cancer mortality is significant. About 600,000 people die of cancer with roughly equal numbers between men and women.
• For both men and women, lung & bronchus cancer is the most common (26-29%) of cancers, followed by prostate (11% men) and breast (15% women), colon/rectum (9%), and pancreas (6-7%)
• Cancer ranks second on the list of largest cause of mortality in the US, just behind heart disease.
• The rate of cancer deaths has historically increase dramatically since the 1930s, but in more recent decades has leveled off and perhaps dropped in recent years.
• Men and women show very different profiles of cancer rates over time by type of cancer. For example, rates of lung cancer has been in decline for men, but increasing or plateauing for women.

• By race, African-Americans in both sexes show higher mortality rates due to cancer.
• For African-American men, death rates for specific cancers may be as much as twice the rate as white men (2x for prostate, 1.3x for lung). African-American women are also higher than white women, though the disparity is not quite so prominent.
• For cancer incidence, prostate and breast cancers are the most common, followed by lung, colon, and urinary/uterine.
• Incidence rates over time have been relatively stable, though there is a small uptick during the 1990s for men, possibly due to better prostate cancer screening.
• Again, African-American men are more likely to get cancer.
• For lifetime probability of getting cancer, the overall odds in men is 1 in 2, 1 in 6 for prostate, 1 in 13 for lung, and 1 in 19 for colon. For women, overall is 1 in 3, 1 in 8 for breast, 1 in 16 for lung, and 1 in 20 for colon.
• Breast and prostate cancer have the highest survival rates (90% and 100%), while pancreatic and lung cancer have the lowest (6% and 16%)
• Among children, leukemia and brain cancer is the most common (5% and 3%) with the highest mortality.
• Modifiable factors, such as tobacco smoking, consumption of fruits & vegetables, and obesity may affect cancer risk.
• Screening such as yearly mammograms for breast cancer, examinations for cervical cancer, and pap smear tests are important tools for the early detection of cancer in women.

I have two courses this summer: Epidemiology and Behavioral Health and Cancer Epidemiology. The former is currently ongoing during the first half of the summer session, and the latter will begin formally at the end of June. As a way of studying and retaining the knowledge presented in the course, I plan on blogging about each class session. It might be a short narrative or just an outline. The hope is that it will boost my retention.