360 terms

Pandemic

" . . . an epidemic on a worldwide scale; during a pandemic, large numbers of persons may be affected and a disease may cross international borders." An example is a flu pandemic.

Epidemic

"The occurrence in a community or region of cases of an illness (or an outbreak) clearly in excess of expectancy..." Relative to usual frequency of the disease Not confined to just infectious diseases Examples of non-infectious epidemics: Love Canal Red spots among airline flight attendants Toxic Shock Syndrome doesn't necessarily mean a LARGE number—it can be one or two cases more than what usually happens. When a disease has disappeared and a single case reappears, that represents an epidemic! Epidemics are not confined to just infectious diseases—epidemics can occur due to environmental causes and non-infectious causes (exposures—think red dots on airline workers and TSS). Usual frequency: disease's typical occurrence at the same time, within the same population, and in the same geographic area. Communicable disease An illness caused by an infectious agent that can be transmitted from one person to another. Infectious disease A synonym for a communicable disease Outbreak A localized disease epidemic, e.g., in a town or health care facility

Framingham Heart Study

(ongoing since 1948) investigates coronary heart disease risk factors. Smoking and lung cancer; e.g., Doll and Peto's study of British doctors' smoking AIDS, chemical spills, breast cancer screening, second-hand cigarette smoke Association between HPV and cervical cancer The Framingham Heart Study is still ongoing!! Sir Richard Doll used a large "Case-Control study" to demonstrate a connection between smoking and lung cancer—case-control studies use 2 groups: 1 group that has the outcome being studied (Cases—in this study, outcome was lung cancer) and 1 group that does NOT have the outcome (Controls). Doll's case-control study compared the smoking history of a group of hospitalized patients with lung cancer with the smoking history of a similar group without lung cancer. Opponents argued (correctly) for many years that this type of study cannot prove causation, but the eventual results of cohort studies confirmed the causal link which the case-control studies suggested, and it is now accepted that tobacco smoking is the cause of about 87% of all lung cancer mortality in the US. Epidemiological research has been done on AIDS, chemical spills, breast cancer screening, 2nd hand smoke—and it helped uncover the association between HPV and cervical cancer. Conducted in Framingham, Massachusetts Ongoing study of CHD initiated in 1948 Used a random sample of 6,500 from targeted age range of 30 to 59 years

Alexander Fleming

1928—discovered the antimicrobial properties of the mold: developed penicillin; it became available toward the end of WWII

Modern Concepts of Causality:

1964 Surgeon General's Report 1. Strength of association: Rates of morbidity and mortality must be higher for the exposed group than for the non-exposed group (risk of heart disease is higher in smokers than in non-smokers) 2. Time Sequence: Demonstration of correct temporal sequence: Exposure to the causal factor must occur before the effect, or the disease. 3. Consistency with other studies: Varying types of studies in other populations must observe similar associations. 4. Specificity of the association: The exposure variable must be necessary and sufficient to cause disease; there is only one causal factor. **This one is not as important today since diseases have multifactorial origins. 5. Coherence of explanation: the association must not seriously conflict with what is already known about the natural history and biology of the disease. Sir Austin Bradford Hill 1. Biological gradient (Dose-response relationship): An increased exposure to the risk factor causes a concomitant increase in disease (risk for heart disease is higher in heavy smokers compared to light smokers) 2. Biological plausibility: The data must make biological sense and represent a coherent explanation for the relationship. It must be credible on the basis of existing biomedical knowledge—but what if the biologic knowledge of the day is wrong, not up-to-date, etc.? 3. Experimental evidence: experimental designs provide the strongest epidemiologic evidence for causal associations, but they are not feasible or ethical to conduct for many risk factors—disease association. However, "natural experiments" that happen (remember Snow) may shed light on the subject! Think about the observation that dental caries decreased in populations with fluoride added to the water supply! 4. Analogy: Thalidomide and Rubella in pregnancy—caused great problems with the baby during pregnancy (birth defects [both], stillbirths, miscarriages), so considering this, "we would surely be ready to accept slighter but similar evidence with another drug or viral infection during pregnancy." Absolute causality is rarely established.... Some non-smokers get heart disease but most all smokers have heart disease....Smoking doesn't give you heart disease but puts you at greater risk for it. ***Keep in mind that there are multiple causes of disease in most cases—this is referred to as multiple causality.

Determinants

1st key aspect in the definition of Epidemiology ² Factors or events that are capable of bringing about a change in health. ² Examples: • Biologic agents—bacteria • Chemical agents—carcinogens • Less specific factors—stress, drinking, sedentary lifestyle, or high-fat diet § Search for Determinants • Outbreak of Fear—Ebola in Kikwit, Zaire • Fear on Seventh Ave.—Legionnaires' disease in New York City • Red Spots on Airline Flight Attendants—dye from life vests • Bioterrorism-Associated Anthrax Cases

Distribution:

2nd key elemnt ency of the disease occurrence—which is the number of cases of the disease and the relationship of that number to the size of the population. It also examines how/why some people get sick and others do not; it examines time (annual, seasonal, weekly, daily, hourly), place (geographic location, urban vs. rural, location of school/work), and person (individual characteristics, such as age, sex, marital status, and socioeconomic status, as well as behaviors and environmental exposures). For example, in 2006, death rates from CHD and stroke were higher among African-Americans than among American Indians/Alaskan natives, Asian/Pacific Islanders, or whites. Coronary heart disease occurrence differs between Hispanics and non-Hispanics—Hispanics have lower mortality rates from CHD than non-Hispanics.

Predictions

A population pyramid represents the age and sex composition of the population of an area or country at a point in time. By examining the distribution of a population by age and sex, one may view the impact of mortality from acute and chronic conditions.

The Black Death

A Pandemic! Occurred between 1346-1352. Claimed one-quarter to one-third of population of Europe—20 to 30 million people! Bubonic plaque—caused by Yersinia Pestis—a germ transmitted by fleas; rats carried the disease (reservoir), fleas bit the rats, then bit humans. No cure at that time—people died within a few days of developing the buboes (boils). In modern times, antibiotics are effective against Bubonic plague. Mortality: 20 - 30 million in Europe!! To put this into perspective, the state of Texas has 26.5 million people!

Cohort

A cohort is defined as a population group, or subset thereof, that is followed over a period of time. The term cohort is said to originate from the Latin cohors, which referred to one of ten divisions of an ancient Roman legion. Cohort group members experience a common exposure associated with a specific setting (e.g., an occupational cohort or a school cohort) or they share a non-specific exposure associated with a general classification (e.g., a birth cohort—being born in the same year or era). The tabulation and analysis of morbidity or mortality rates in relationship to the ages of a specific group of people (cohort) identified at a particular period of time and followed as they pass through different ages during part or all of their life span. cohort effect- The influence of membership in a particular cohort. Example: Tobacco use in the U.S. Fewer than 5% of population smoked around the early 1900s. Free cigarettes for WWI troops increased prevalence of smoking in the population. During WWI, age of onset varied greatly; then people began smoking earlier in life. One net effect was a shift in the distribution of the age of onset of lung cancer. Cohort Studies- Include at least two observation points: one to determine exposure status and eligibility and a second (or more) to determine the number of incident cases This permits the calculation of disease/incidence rates. Cohort studies measure incidence directly. Not only is incidence measured, but mortality, health status (morbidity), and certain biological parameters as well. Going from cause to effect: Exposure of interest (cause) is determined for each member of the cohort at the start of the study—group is followed through time to document the incidence of an outcome (effect) among exposed and non-exposed members. The individual forms the unit of observation and the unit of analysis. Involve the collection of primary data, although secondary data sources are used sometimes for both exposure and disease assessment Population-Based Cohort Studies- The cohort includes either an entire population or a representative sample of the population. Population-based cohorts have been used in studies of coronary heart disease. Exposures unknown until the first period of observation when exposure information is collected Examples: After administration of questionnaires, collection of biologic samples, and clinical examinations, there can be two or more levels of exposure. The Alameda County Study Studied factors associated with health and mortality Involved residents of Alameda County, CA, ages 16-94 years Data collected through mailed questionnaires; telephone interviews or home interviews of non-respondents Follow-up with same procedures at years 9, 18, and 29 Honolulu Heart Program Studied coronary heart disease and stroke in men of Japanese ancestry Involved men of Japanese ancestry living on Oahu, HI, ages 45-65 years Data were collected through mailed questionnaires, interviews, and clinic examinations. Nurses' Health Study Originally studied oral contraceptive use; expanded to women's health Married female R.N.s ages 30-55 years Data collected through mailed questionnaires Follow-up every 2 years; toenail sample at year 6 and blood sample at year 13 Permit direct determination of risk. Because you start with disease-free subjects, it permits direct determination of risk. Time sequencing of exposure and outcome. These studies provide evidence about lag time between exposure and disease occurrence (the time from exposure to development of the disease). Can study multiple outcomes. If they are properly designed and executed, they allow examination of multiple outcomes. Can study rare exposures. Cohort studies can increase the efficiency for rare exposure studies through selection of cohorts with known exposures (such as certain occupational groups). Subjects lost to follow-up—either because they dropped out of the study or died. Can be a significant problem if the loss is too high. Questions concerning the reliability and validity of the results can arise due to this.

Survival Curves

A method for portraying survival times In order to construct a survival curve, the following information is required: Time of entry into the study Time of death or other outcome Status of patient at time of outcome, e.g., dead or censored (patient is lost to follow-up)

Geographic Information Systems (GIS)

A method to provide a spatial perspective on the geographic distribution of health conditions A GIS produces a chloropleth map shows variations in disease rates by different degrees of shading. (Pg. 216) This is new software! Using this technology, maps can be made of locations that have higher disease occurrence or mortality risk than other areas. This helps with targeting where interventions are needed. This technology is essentially a computerized version of John Snow's "cluster map".

Nested Case-Control Studies

A nested case-control study is defined as a type of case-control study ". . . in which cases and controls are drawn from the population in a cohort study." Example: nested case-control breast cancer study Controls are a subset of the source population for the cohort study of breast cancer. Cases of breast cancer identified from the cohort study would comprise the cases. Example: a nested case-control breast cancer study—the population of this cohort would involve both exposed (women who used birth control pills) and the non-exposed (women who do not use birth control pills)---just like the cohort study of children with autism. That group would have contained children who had been exposed to immunizations and children who had NOT been exposed to immunizations! Provide a degree of control over confounding factors. Reduce cost because exposure information is collected from a subset of the cohort only. An example is an investigation of suicide among electric utility workers. Suicide among electric utility workers—this study examined the association between exposure to very low-frequency magnetic fields and suicide. Cases (536 suicides) and controls (n=5248) were selected from a cohort of 138,905 male utility workers. Findings supported the hypothesis—there was association found. Permit direct determination of risk. Because you start with disease-free subjects, it permits direct determination of risk. Time sequencing of exposure and outcome. These studies provide evidence about lag time between exposure and disease occurrence (the time from exposure to development of the disease). Can study multiple outcomes. If they are properly designed and executed, they allow examination of multiple outcomes. Can study rare exposures. Cohort studies can increase the efficiency for rare exposure studies through selection of cohorts with known exposures (such as certain occupational groups).

Method for Calculation of Age-Specific Death Rates

A rate for a specified age group. The numerator and denominator refer to the same age group. The population has to be subdivided into age groups (with total numbers for each age group). In this calculation, you're simply getting the age-specific death rate—what percentage died in each age group. It is not specific to any one disease.

Simple Sex Ratio Calculation

A ratio may be expressed as = X/Y Simple sex ratio (data from textbook) Of 1,000 motorcycle fatalities, 950 victims are men and 50 are women. (#of make cases)/ (#of female cases) = 950/50 = 19:1 male to female

Rate

A ratio that consists of a numerator and a denominator but is different from a proportion because the denominator involves a measure of time. Contains the following elements: disease frequency unit size of population time period during which an event occurs When you think rate, think heart rate, pulse rate, respiratory rate.....what is this telling you? The number of beats or breaths per a unit of time! The numerator consists of the frequency of a disease over a specified period of time, and the denominator is a unit size of population. CRITICAL: to calculate a rate, remember that TWO periods of time are involved—the beginning of the period and the end of the period. Rates improve the ability to make comparisons, but they also have limitations: rates of mortality and morbidity for a specific disease reduce that standard of comparison to a common denominator, the unit size of population. So if the crude death rates for diseases of the heart is 188.9 per 100,000 in Texas and 288.0 per 100,000 in New York, it looks like the death rate is higher in New York but there is no way to tell if there are important differences in population composition (such as age differences between populations) that would affect mortality experience. Crude death rate = [Number of deaths in a given year/ Reference population (during the midpoint of the year] x 100,000 Example: Number of deaths in the United States during 2007 = 2,423,712 Population of the U.S. as of July 1, 2007 = 301,621,157 2,423,712 301,621,157 Rates can be expressed in any form that is convenient—per 1,000, per 100,000, or per 1,000,000. Many of the most commonly used rates are expressed in particular conventions: cancer rates are typically expressed per 100,000 population, infant mortality is expressed per 1,000 live births. This is done to prevent the answer from being a very small number—for example, rather than saying 0.04 per 1,000, say 4.2 per 100,000

Proportion

A type of ratio in which the numerator is part of the denominator. A measure that states a count relative to the size of the group. May be expressed as a percentage. For a count to be descriptive of a group, it usually should be seen relative to the size of the group. Can demonstrate the magnitude of a problem. Example: 10 dormitory students develop hepatitis. How important is this problem? If only 20 students live in the dorm, 50% are ill. If 500 students live in the dorm, 2% are ill.

Primary Prevention: Active and Passive

Active Necessitates behavior change on the part of the subject Examples: Vaccinations and wearing protective devices Passive Does not require any behavior change Examples: Fluoridation of public water and vitamin fortifications of milk and bread products

follow-up

Active Follow-up- The investigator, through direct contact with the cohort, must obtain data on subsequent incidence of the outcome (disease, change in risk factor, change in biological marker). Accomplished through follow-up mailings, phone calls, or written invitations to return to study sites/centers. Example: Minnesota Breast Cancer Family Study Mailed survey A reminder postcard 30 days later A second survey A telephone call to non-responders Answering machines, caller ID, telemarketing, and cell phones are making this even more labor intensive and frustrating. Passive Follow-up- Does not require direct contact with cohort members. Possible when databases containing the outcomes of interest are collected and maintained by organizations outside the investigative team. Example: Used in the Iowa Women's Health Study. Epidemiologists are able to achieve record linkage between databases and study cohorts.

Multivariate Techniques

Advantages: Continuous variables do not need to be converted to categorical variables. Allow for simultaneous control of several exposure variables in a single analysis. Disadvantages: Potential for misuse.

Attack Rate (AR)

Alternative form of incidence rate. Used for diseases observed in a population for a short time period. Not a true rate because time dimension often uncertain. Example: Salmonella gastroenteritis outbreak Formula: ill AR= ill + Well x 100 (during a time period) Used when the population is observed for a short period of time, usually as a result of a specific exposure. In this case, the numerator is the number of people who got sick, the denominator is all people involved—both the ill and the well. So let's use the example from your book: 87 people attended a holiday dinner and ate roast turkey—63 became ill, the remainder did not. So---- AR=63/(63 + 24) X 100 = 72.4% The AR is not a true rate because the time dimension is uncertain or arbitrarily specified. Incidence is the number of new cases of illness/health problems—it flows into Prevalence, which is the number of existing cases! Sooooo.....incidence becomes a part of Prevalence, but can still stand alone!

Intervention Studies

An investigation involving intentional change in some aspect of the status of subjects Introduction of a preventive or therapeutic regimen or an intervention to test a hypothesized relationship. Used to test efficacy of preventive or therapeutic measures Manipulation of the study factor and randomization of study subjects Efficacy - the power to produce a desired result or effect Two categories: Clinical trials (focus on the individual) Community trial or community intervention (focus on the group or community. NOTE: Controlled clinical trials may be conducted both at the individual and community levels. Key difference—focus is on the individual in clinical trials and on the group or community in community trials!

Surveillance

Analysis and interpretation of these data. Dissemination of disease-related information Common activities include monitoring food born disease outbreaks and tracking influenza. Surveillance is performed—data is collected pertaining to the occurrence of the specific disease or outbreak; then data is compared to the usual occurrence of the disease to see if there is an increase.

What the Individual Data Show

From the individual data, one observes that 100% of persons (4) who did not wear hats were sunburned. Among persons who wore hats (6), only 50% were sunburned. This conclusion reverses the conclusion from the ecologic data, i.e., that wearing hats affords little protection from sunburn.

Crossover Designs

Any change of treatment for a patient in a clinical trial involving a switch of study treatments In planned crossovers, a protocol is developed in advance; the patient may serve as his or her own control. Unplanned crossovers exist for various reasons, such as patient's request to change treatment. Unplanned—Example: patients in a coronary artery bypass grafting treatment may have misgivings about this invasive surgical procedure, or patients in a medical care group may require surgery because of deterioration of their condition.

William Farr

Appointed compiler of abstracts in England, 1839 Provided foundation for classification of diseases (ICD system) Used data such as census reports to study occupational mortality in England Examined linkage between mortality rates and population density This guy developed a more sophisticated system for codifying medical conditions than was previously in use. This system that he developed actually laid the foundation for the present day ICD (International Classification of Diseases)-9 (or 10) system!! He also used census reports to study occupational mortality in England Lastly, he examined the link between mortality rates and population density---he found that the average number of births and deaths per 1,000 people increased with population density (the number of people per square mile). More people=more crowding, more dust, more easily passing on of contagions, etc.

randomization

Attempts to ensure equal distributions of the confounding variable in each exposure category. Advantages: Convenient, inexpensive; permits straightforward data analysis. Disadvantages: Need control over the exposure and the ability to assign subjects to study groups. Need large sample sizes.

Practical Considerations Regarding Cohort Studies

Availability of exposure data Size and cost of the cohort used Data collection and data management Follow-up issues Sufficiency of scientific justification

Phases of Clinical Trials

Before a vaccine, drug, or treatment can be licensed for general use, it must go through several stages of development. This lengthy process requires balance to: protect the public from a potentially deleterious vaccine satisfy the urgent needs for new vaccines

Ethical Aspects of Human Experimentation

Benefits must outweigh risks. Ethical issues: Informed consent Withholding treatment known to be effective Protecting the interests of the individual patient Monitoring for side effects & toxicity Deciding when to withdraw a patient from the study Certain bad side effects that could've been avoided if the person had not participated AND withholding medication from people who may benefit from it are the 2 main ethical concerns in clinical trials. To avoid this, experimenters use what is called a sequential design—as soon as the results are statistically significant and either confirm or reject a positive outcome for the drug being tested, the trial is stopped. If the drug produces improvement in the patients' conditions, it then becomes available for use by the control group as well—or the drug is discontinued if it is found to produce adverse effects. Sometimes, experimental drugs are used only on those for whom all other treatments have failed.

Clustering

Case clustering--refers to an unusual aggregation of health events grouped together in space and time Temporal clustering (Time): e.g., post-vaccination reactions, postpartum depression Spatial clustering (Space): concentration of disease in a specific geographic area, e.g., Hodgkin's disease

Study of Risks to Individuals

Case control studies: A type of design that compares persons who have a disease (cases) with those who are free from the disease (controls). This design explores whether differences between cases and controls result from exposures to risk factors. the study begins after the health outcome has already occurred. People are selected from a population because they are known to have the outcome of interest (cases) or they are known NOT to have the outcome of interest (controls). Do the people with the outcome of interest (cases) have the exposure characteristic (or history of exposure) more frequently that those without the outcome (controls)? Cohort Design A group of people free from a disease is assembled according to a variety of exposures. The group (cohort) is followed over a period of time for development of disease. A cohort is a group of people who share a common characteristic or experience within a defined period and are followed over time to observe some health outcome (e.g., are born, leave school, lose their virginity, are exposed to a drug or a vaccine) Example: the reports of a relationship with autistic children and the MMR immunization Parents noted there were behavioral problems after their child was immunized with the second MMR. They stated that 8 out 12 children receiving the MMR vaccination displayed early signs of autism which sparked a detailed analytical study by the government to disprove the theory. Eventually, it was a combination of a case-control and cohort study that proved the MMR does not cause autism!

Descriptive Epidemiology Approaches

Case reports: counts—helpful for describing diseases. Astute observations of unusual cases could spur more investigation to determine whether larger numbers of these types of cases exist. Case reports are a starting point for exploring the causes of diseases and for introducing preventive interventions. Example: a single case of methylene chloride poisoning. Case series: One case report may not be enough to draw firm conclusions, so an observation may need to be expanded to a series of cases. Typical features are generated from a set of observations. An example from CDC is one that involved 5 cases of Hantavirus pulmonary syndrome in 5 pediatric patients. These 5 cases happened from May 2009 until November 2009. Cross-sectional studies: a cross-section of a population or group; information is collected on current health status, personal characteristics, and potential risk factors/exposures—and it is collected all at once. It is a FAST study! This study is efficient at identifying association, but may have trouble deciding cause and effect. With data at only one time point, you don't know whether the chicken or the egg came first, so these are often conducted at different points in time. This way, they are effective in detecting trends in prevalence of disease or risk factors. Example: National Health Interview Survey Persons: Age- One of the most important factors to consider when describing the occurrence of any disease or illness Age-specific disease rates usually show greater variation than rates defined by almost any other personal attribute. This is why age-specific rates are used when comparing the disease burden among populations. Childhood to early adolescence Leading cause of death, ages 1-14 years—unintentional injuries Infants—mortality from developmental problems, e.g., congenital birth defects Childhood—occurrence of infectious diseases & communicable diseases Meningococcal disease, otitis media, measles, mumps, and chicken pox. Teenage years (15-24 years) Leading causes of death—unintentional injuries, homicide, and suicide Other issues—unplanned pregnancy, tobacco use, substance abuse, and binge drinking Alcohol, marijuana, tobacco are the drugs of choice among 12-17 y.o.—also Rx. drugs. Lifestyle choices: obesity d/t sedentary hobbies (video games, computers); sugary & high-fat foods too available. Adults: also homicide and HIV disease (even though death rates have declined by 50% since the 1990's). Lung, brain, and colon cancers were the most common causes of cancer deaths among men; leading among women were breast, lung, and cervical cancers. Older adults: Age-specific rates of cancer incidence increase with age deaths from chronic diseases (e.g., cancer & heart disease) dominate after age 45. leading causes of death Unintentional injuries Cancer Heart disease Suicide (3X higher in men than women) Elderly: (65+) 5 leading causes of death in 2007 were heart, malignant neoplasms, Cerebrovascular disease, chronic lower respiratory disease, and Alzheimer's. Some of the elderly have problems caring for themselves and may not be able to live on their own. deaths from chronic diseases and limitations in activities of daily living A few reasons for age affects on mortality are: Validity of diagnoses across the life span: exact cause of death of elderly people may be hard to determine as many have multiple co-morbidities Latency effects: long latency period between environmental exposures and the development of certain diseases—example: the passage of many years between the initial exposure to a potential carcinogen and the subsequent appearance of cancer later in life. Action of the "human biologic clock": as we age, we get more vulnerable to disease—the immune system may wane, producing increased tissue susceptibility to disease. Life cycle and behavioral phenomena: causes of death can be influenced by factors such as personal behavior and risk-taking; lifestyle influences the occurrence of diabetes and other chronic diseases. Age-specific rates of cancer incidence increase with age with apparent declines late in life—this is deceptive, though, because the 80-84 age group is few in number when compared to other age groups, hence the numerators and denominators for the very elderly categories are smaller. The data is not reliable due to this. Sex Marital Status Race and ethnicity Nativity and migration Religion Socioeconomic status Men- All-cause age-specific mortality rates is higher for men than for women. May be due to social factors May have biological basis Men often develop severe forms of chronic disease. Hearing impairment, smoking-associated conditions, & cardiovascular disease are more common among men. Men affected by the same chronic diseases as women (lung cancer, cardiovascular disease, and diabetes) are more likely to develop severe forms of these conditions and die from them. female paradox- Reports from the 1970s indicated female age-standardized morbidity rates for many acute and chronic conditions were higher than rates for males, even though mortality was higher among males. Higher female rates for: Pain Asthma Some lung difficulties Depression This phenomenon is known as the Female Paradox—females have higher morbidity rates than males for acute and chronic conditions even though the mortality rate was higher among males. Cancer- Cancer of the lung and bronchus is leading cause of cancer death for both men and women in the U.S. Increases among women are related to changes in lifestyle and risk behavior, e.g., smoking. With women adopting more and more of the same behaviors and habits as men (smoking, etc.), diseases such as lung cancer have increased among women much faster than men. Males have a greater frequency of smoking, a greater prevalence of the coronary-prone behavior patterns, higher suicide and motor vehicle accident rates, as well as risky behavioral patterns that are expected of and condoned among men. While women don't have as high of a mortality rate for lung & bronchus cancer YET, women's roles in society have changed and many have been smoking more and more since the late 1960's.....Consequently, when women started smoking more, low and behold, lung cancer increased much faster in women than in men between the years of 1975 and 1990—"you've come a long way baby"!! This supports the view that certain behavioral and lifestyle variables (smoking behavior, etc.) may relate to male/female lung cancer morality differences. CHD is the leading cause of mortality among women and men, but sex differences in mortality rates of CHD exist between women and men even when both have high-risk factor status for serum cholesterol, blood pressure, and smoking. Women tend to be protected from CHD in the pre-menopause stage but cardiac disease increases in the post-menopausal phase (after age 60). Minority Women in Economically Disadvantaged U.S. Areas In Los Angeles County, some have higher rates of diabetes and hypertension than men. A large percentage are physically inactive. High rates of obesity among Latinas and African Americans. Some women may be unaware of the fact that they may be at high risk of cardiac disease—consequently, they may not be alert for symptoms of CHD—this can cause a delay in seeking treatment. Many women resist the lifestyle changes (increased activity level and consumption of low-fat food). Minority women who live in Los Angeles County have higher rates of diabetes, HTN, and elevated cholesterol. In 2005, about ½ of these women reported little physical activity and the frequency of obesity was high, especially among Latinas (25% or 1 out of 4 women) & African Americans (33% or 1 out of 3 women). Marital Status- In general, married people, especially men, tend to have lower rates of morbidity and mortality and tend to be healthier than adults in other marital status categories—REGARDLESS of population subgroup (age, sex, race, education, income, or nativity) or health indicator (fair or poor health, limitations in activities, low back pain, headaches, serious psychological distress, smoking, or leisure-time physical inactivity). This is true for chronic diseases (CHD & cancer), some infectious diseases, suicides, and accidents. Among older women, divorce and separation are linked with adverse health outcomes such as physical impairments. Men had higher mortality risks than women if divorced or widowed. Married women have been found to have a reduced risk of breast cancer mortality when compared with single women. Never married adults (especially men) less likely to be overweight whereas being married was associated with obesity, especially among men (this was the one exception to being healthier for married people). Single people (40-79 years) experienced a higher mortality risk than married persons, according to a 2007 Japanese study. Protective hypothesis: marriage makes a positive impact to health by influencing lifestyle factors, providing mutual psychological and social support, and increasing available financial resources. The marital environment and factors associated with marriage apparently reduce the risk of death and therefore, should be considered as a possible source of differences in disease rates. Marital Selection Model: physically attractive people are more likely to be successful in competing for a mate and are healthier than those persons who never marry; also, this model proposes that less healthy individuals, if married, are more predisposed than healthy persons to gravitate to non-married status. Suicide tends to be higher in widowed young men in comparison with married young men in the same age group (20-34 years). Depressed mood more common among widowed individuals than among persons who were married, living together, never married, or divorced/separated. Married people had the lowest percentage of depressed mood. Race—refers to a person's physical characteristics (bone structure, skin, hair, eye color) Ethnicity—refers to cultural factors, including nationality, regional culture, ancestry, and language. However, this being said, some scientists propose that race is primarily a social and cultural construct—take a look at the 2000 Census. Census 2000 used five categories of race—white, black/AA, American Indian & Alaska Native, Asian, Native Hawaiian & Other Pacific Islander. Also, Census 2000 changed the race category by allowing respondents to choose one or more race categories. The 2 words, race and ethnicity, tend to be used interchangeably because using physical characteristics to assign someone to a particular race may be difficult at best, as in the case of persons of mixed race parentage. So as far as the "Who" (Person) goes in descriptive epidemiology, we will be briefly discussing the characteristics of 4 races to demonstrate how the prevalence and the causes of morbidity/mortality may vary from one race to another. Heart disease is the leading cause of death in white, black, and American Indians/Alaskan Natives. Stroke (CVA) is the 3rd most common cause of death in blacks and Asian/Pacific Islanders AA: In 2007, age-adjusted death rate for African Americans was 1.3 times rate for whites. Included in the 10 leading causes of death in non-Hispanic blacks were homicide, HIV, and septicemia—these are not among the top 10 causes of death in non-Hispanic whites. AAs have more problems with HTN: causes? Possible influence of stress or diet (decreased fruits/veggies), decreased social support, increased rates of obesity, lack of participation in cardiovascular risk reduction programs. Cancer is high—breast CA higher in black women than in 4 of the 5 races. Are any of these things due to lack of access? Are services being offered but not utilized? Lack of education on risky behaviors, diet, resources available, etc.? Differences in life expectancy—black men don't live as long as white men; black women don't live as long as white women. Why? Is lack of access the ONLY factor? What if we compared blacks with other races?! What would the life expectancy be when comparing blacks with Native Americans?! Notice that most of these statistics compared blacks to ONLY whites—the next slide on American Indians compares them to the general US population. Heart disease is the leading cause of death in white, black, and American Indians/Alaskan Natives. AI: High rates of chronic diseases, adverse birth outcomes, and some infectious diseases (TB & Hepatitis A) Diabetes—11.9 times the rate for all races in US Cirrhosis—6.5 times the rate for all races in US Prevalence of TB is 2 times that of US Population—7 times that of non-Hispanic white population. Accidents—5.9 times the rate for all races in US Homicide—7.4 times the rate for all races in US Suicide—4.3 times the rate for all races in US Life expectancy—decreased compared to general US Population Notice that this group of individuals was compared to the general US population and/or to all races and not to just whites. Asian Japanese: lower mortality rates than whites Lower rates of CHD and cancer Attributed to low-fat diet & stress-reducing strategies Some Asian groups (Cambodians) have high smoking rates 70% smoking rates TB rates highest among Asian/Pacific Islander group Almost 25 times higher than Non-Hispanic whites. Stroke (CVA) is the 3rd most common cause of death in blacks and Asian/Pacific Islanders Cancer is the leading cause of death in Asian/Pacific Islanders. Japanese culture seems to protect them—low rates of CHD d/t diet and stress-reducing strategies; however, Japanese moving to the US and becoming acculturated lost this protection over time. Studies show a link to environmental and behavioral factors on chronic diseases. Acculturation—modifications that individuals or groups undergo when they come into contact with another country (migrate, move, etc.). Studying those who have immigrated provides evidence of the influence of environmental and behavioral factors on chronic disease. Example: Japanese migrants experience a shift in rates of chronic disease toward those of the host country. Stroke (CVA) is the 3rd most common cause of death in blacks and Asian/Pacific Islanders Cancer is the leading cause of death in Asian/Pacific Islanders. HHANES—found out that there is much diversity between Hispanic groups (Cubans, Puerto Ricans, etc.) and that they need to be studied individually. Low rates of CHD in Mexican Americans—this may be d/t culture—diet preferences, social support in large and extended families; Puerto Ricans also had a low prevalence of CHD. San Antonio Heart Study Hispanic Mortality Paradox: Despite having a high prevalence of Diabetes and other risk factors for chronic disease, Hispanics/Latinos in the US have a lower mortality rate than non-Hispanic whites (28.5% lower--and 44.7% lower than non-Hispanic blacks!) Could this be d/t underreporting of deaths among Hispanics? Do many of them return back to their native country to die (salmon bias effect)? It was found that Cubans and Puerto Ricans do NOT return home to die......and this paradox occurs with them, too. This difference has not been fully explained. Hispanic/Latino HHANES—Hispanic Health and Nutrition Examination Survey 1st special population survey of Hispanics in the US Examined health & nutrition status of major Hispanic/Latino population in the US San Antonio Heart Study Found high rates of obesity and diabetes among Mexican Americans Hispanic mortality paradox (text box) Nativity—where they or parents were born. Categories are foreign born and native born—these are common subdivisions used in epidemiology. Nativity is tied to migration because foreign-born persons have immigrated to their host country. Natural experiment—migration meets the criteria for this, in which the effects of change from one environment can be studied—various health dimensions (stress, acculturation, chronic and infectious diseases) can be studied as far as the effect of moving from one environment to another (remember the Japanese?) Studies on admission to mental hospitals in New York—admission rates were higher among foreign-born than native-born persons, suggesting that foreign-born individuals may experience stresses associated with migration to a new environment.....but WHAT else may it suggest? Impact of migration: Importation of "Third World" disease by immigrants from developing countries Leprosy during 1980s Programmatic needs resulting from migration: Specialized screening programs (tuberculosis and nutrition) Familiarization of doctors with formerly uncommon (in U.S.) tropical diseases In the 1980's, it was leprosy. Currently, with people coming across our southern border unchecked, who knows what they are carrying with them?! Some of the diseases they bring with them are diseases that people in the U.S. have never been exposed to—think about the diseases that are "endemic" to their country! These are things to which they may have natural immunity but we don't because we've never seen them before. Also, some migrants are inadequately immunized with respect to measles and other vaccine-preventable diseases (remember that not all nations have the immunization program that the U.S. does)—this has hampered efforts of health officials to eliminate these diseases from the U.S. Observation that healthier, younger persons usually form the majority of migrants Often difficult to separate environmental influences in the host country from selective factors operative among those who choose to migrate Certain religions prescribe lifestyles that may influence rates of morbidity and mortality. Example: Seventh Day Adventists Follow vegetarian diet and abstain from alcohol and tobacco use Have lower rates of CHD, reduced cancer risk, and lower blood pressure Similar findings for Mormons SES: Low social class is related to excess mortality, morbidity, and disability rates. Factors that negatively effect health include: Poor housing Crowded conditions Racial disadvantage Low income Poor education Unemployment Relationship between SES and health has been demonstrated for a wide range of health outcomes and confirmed by a massive body of evidence. Variables include: Prestige of occupation or social position Educational attainment Income Combined indices of two or more of the above variables But there are some problems with each of these..... Many epidemiologic terms come from Sociology, such as the measurement of social class, which is a means to measure the economic position. The measurement of social class includes all of these variables, but it is also related to ethnicity, race, religion, & nativity. Occupation/social position: the problem with relying on this one is that there may be 2 workers in the same family who are at different levels of occupational prestige, but even those with the same level of occupational prestige may or may not make the same money. Education: higher levels of education, in contrast to income or occupation, appear to be the strongest and most important predictor of positive health status! (1992 study cited) Income: problem with using income as a measure of social class: 1. Some people may not know the income of the entire family or may not disclose it. 2. Two workers in a lower SES family may actually make more combined than 1 worker in a higher SES family.

Enlargement of the Clinical Picture of Disease

Cases of new disease often more dramatic: because it's new and not been seen before, it may appear scarier than it really is. Initially, it may look like disease has a high mortality rate, but with epidemiologic research, it may be shown to be a very mild disease. To develop a full clinical picture, you may need to survey a complete population—perform much more thorough studies on the entire population! Legionnaires' disease: 1976—seemed highly virulent at first; after epidemiologic research, only 15% died. It was also discovered that it had occurred in other areas of the country, but had gone unnoticed—no one knew what it was.

Cause-Specific Rate equation

Cause-specific mortality rate (age group 25-34) due to HIV in 2003 = 1,588/39,872,598 = 4.0 per 100,000 Specifies events, such as deaths according to their cause. Here the example is the cause-specific mortality rate of people aged 25-34 due to HIV in 2003—this number would be divided by the population at midyear 2003.

Trends

Chronic diseases have replaced acute infectious diseases as the major causes of morbidity and mortality. In 2009, the leading causes of U.S. deaths were heart disease, cancer, and chronic lower respiratory disease. Increases were reported for Alzheimer's disease, kidney disease, and hypertension. Dramatic changes have occurred: Chronic conditions have replaced acute infectious diseases as the major causes of morbidity & mortality The next slide will compare mortality causes in 1900 with those in 2009—leading causes of deaths in the U.S. in 2009 were heart disease, cancer, and chronic lower respiratory disease Recently, death rates for Alzheimer's disease, kidney disease, and hypertension have increased while death rates for heart disease, cancer, and stroke have been declining since the 1960's.Disappearing: Conditions that were once common but are no longer present in epidemic form. Examples include smallpox, poliomyelitis, and measles. Brought under control by immunizations, improvement in sanitary conditions, and the use of antibiotics and other medications; led to eradication of smallpox and the elimination of the other 2 diseases in certain areas of the world. Residual: Contributing factors are largely known, but methods of control have not been implemented effectively Examples: STDs Perinatal and infant mortality among low SES persons Problems associated with alcohol and tobacco use Persisting: No effective method of prevention or cure has been discovered so they remain common. Examples: certain types of cancer and mental disorders New epidemic: diseases that are increasing markedly in frequency in comparison with previous time periods. Examples: Lung cancer, AIDS, Obesity, Type 2 diabetes The emergence of new epidemics of diseases may be a result of increased life expectancy of the population, new environmental exposures, or changes in lifestyle, diet, and other practices.

Comparison (Non-Exposed Group)

Cohort studies involve the comparison of disease rates between exposed and non-exposed groups. The comparison group is similar in demographics and geography to the exposed group, but lacks the exposure. In an occupational setting, several categories of exposure may exist.

The 2 by 2 Table Association Between Exposure & Disease Status

Columns represent disease status or outcome (Y or N) and the rows represent exposure (Y or N). 1st column should always refer to those with the disease and the first row should always refer to those with the exposure. Total number of those with the disease is A + C and the total number free from disease is B + D. Total number exposed is A + B while the total number not exposed is C + D. The 4 totals are referred to as the marginal totals This table can also explain and keep track of the different observational study designs— Cross-sectional study: Enter a sample number (N), determine each subject's exposure and disease status. Enter into the 4 cells and total marginal totals after the study is complete. Cohort study: start with the marginal totals of exposed (A + B) and non-exposed (C + D) and follow them for the development of the disease—the interior cells would be filled at the end of the study. Case-Control study: start with the column totals A + C (disease) and B + D (no disease) and determine the exposures to complete the interior cell totals. In all of these study designs, information is known about each subject's exposure and disease status—so filling in the interior cells can be done. The interior cell information is not known with ecologic studies.

Community trials

Community intervention trials determine the potential benefit of new policies and programs Intervention: Any program or other planned effort designed to produce changes in a target population Community refers to a defined unit, e.g., a county, state, or school district trial—an "experiment in which the unit of allocation to receive a preventive, therapeutic, or social intervention is an entire community or political subdivision." Example—fluoridation of drinking water. Start by determining eligible communities and their willingness to participate Collect baseline measures of the problem to be addressed in the intervention and control communities Use a variety of measures, e.g., disease rates, knowledge, attitudes, and practices Permission to enroll the community is usually given by someone in charge—the mayor, governor, school board, etc. Measures to use as baselines may include: disease prevalence or incidence, knowledge/attitudes/practices, purchase of lean relative to fatty cuts of meat, etc. Communities are randomized and followed over time Outcomes of interest are measured After baseline information is obtained, communities are randomized to receive or not receive the intervention. These randomized community trials are also called cluster randomized trials. North Karelia Project Minnesota Heart Health Program Stanford Five-City Project Pawtucket Heart Health Program Community Intervention Trial for Smoking Cessation (COMMIT) Project Respect They represent the only way to estimate directly the impact of change in behavior or modifiable exposure on the incidence of disease. They are inferior to clinical trials with respect to: ability to control entrance into study, delivery of the intervention, and monitoring of outcomes. Fewer study units are capable of being randomized, which affects comparability. They are affected by population dynamics, secular trends, and nonintervention influences. Affected by population dynamics, secular trends, and nonintervention influences. For example, the fluoridation of the community water—these studies take place over long periods of time, so people come into the community and move out of the community. This will have some effect on the results—it can negatively impact the results by making them look worse than they really would have been or it can positively impact the results by making them look better than they would have been.

Primordial Prevention

Concerned with minimizing health hazards in general Inhibits the emergence & establishment of processes & factors, which are known to increase risk of disease: Economic conditions Social conditions Behavioral conditions Cultural patterns of living Concerned with minimizing health hazards in general whereas Primary prevention seeks to lower the occurrence of disease. Achieved in part through health promotion, which includes health education programs in general, marriage counseling, sex education, and provision of adequate housing.

Tecumseh Study

Conducted in Tecumseh, Michigan A total community cohort study Examined the contribution of environmental and constitutional factors to the maintenance of health and origins of illness Started in 1959-1960 and enrolled 8,641 (88% of the community)

Epidemiologic Measures

Count: The simplest and most frequently performed quantitative measure in epidemiology. Refers to just the number of cases of a disease or other health phenomenon being studied Examples of Counts: Cases of influenza reported in Westchester County, New York, during January of a particular year. Traffic fatalities in Manhattan in a 24-hour time period College dorm students who had mono Foreign-born stomach cancer patients It sounds like counts are not that important, but just a single case report may be significant, depending upon what the infectious agent is. For example, if a single case of smallpox or Ebola was reported, the size of the denominator (or the size of the population—which is normally represented by the denominator) would not matter. **Counts are limited by themselves, though. Without involving other measures, they are just the number of cases. Ratio: The value obtained by dividing one quantity by another. Consists of a numerator and a denominator. The most general form has no specified relationship between numerator and denominator. Rates, proportions, and percentages are also ratios.

Crude Rates, Measures of Natality

Crude birth rate Fertility rate General Total Infant mortality rate Neonatal mortality rate Maternal mortality rate Crude rates are summary rates based on the actual number of events in a population over a given time period. An example is the Crude Death Rate—this approximates the proportion of a population that dies during a time period of interest.

Time

Cyclical patterns of disease. One common type of cyclical variation is the seasonal fluctuation seen in a number of infectious illnesses—like the flu. Point epidemics Long-term patterns of morbidity or mortality rates (i.e., over years or decades) are called secular trends. Secular trends may reflect changes in social behavior or health practices. Clustering: an unusual aggregation of health events grouped together in space or time. An outbreak of Cholera in England in the late 1800's—John Snow found clusters of illness in certain areas of the town. More recently, the outbreak of Hantavirus Pulmonary Syndrome in Yosemite National Park....8 exposed, 3 dead. Periodic changes in the frequency of diseases and health conditions over time Examples: Birth rates (increase in early summer) Higher heart disease mortality in winter Influenza Unintentional injuries (accidents—maybe summer recreation, etc.) Meningococcal disease (peaks in winter; declines in late summer) Rotavirus infections (stomach—also seasonal)

Bioterrorism-Associated Anthrax Cases:

Index case reported in Florida Additional cases, including fatal cases, reported in New York, New Jersey, Connecticut Contaminated mail linked to some of the cases

Retrospective Cohort Study

Despite substantial benefits of prospective cohort studies, investigators have to wait for cases to accrue while conducting a prospective cohort study. Several years could go by before any meaningful analysis is done. Retrospective cohort studies make use of historical data to determine exposure level at some baseline in the past. Follow-up for subsequent occurrences of disease between baseline in past and the present is completed. A significant amount of follow-up may be accrued in a relatively short period of time. The amount of exposure data collected can be quite extensive and available to the investigator at minimal cost. See examples on page 343— Difference between case-control and retrospective cohort: Case-control: subjects chosen on basis of disease status—data is then collected regarding exposures prior to disease. Only 1 point of observation, so disease rates can't be computed. Uses identified cases and controls only. Retrospective: begins with exposure in the past. Subsequent occurrence of disease (maybe with additional assessments) is the primary focus of research. Uses the entire group

Diseases Treated in Special Clinics and Hospitals

Data cannot be generalized because patients are a highly selected group. Case-control studies can be done with unusual and rare diseases. However, it is not possible to determine incidence and prevalence rates without knowing the size of the denominator. Data from these sources are not very useful because of these problems—however....... An exception to the rule about special clinics and hospitals is......The Mayo Clinic in Rochester, Minnesota. The Rochester Epidemiology Project, housed at the Mayo Clinic, uses a medical records-linkage system that has afforded access to details of medical care provided to the residents of Rochester and Olmsted County, Minnesota, since the early 1900's!! It has worked so well because this area is geographically isolated from other urban centers. This Project links all data about a specific patient to a unique Mayo identification number. The records of more than 5 million patients are kept in a central repository and tracked by computer bar codes. This project has successfully provided data and facilities to complete over 1,000 reports on the epidemiology of acute and chronic diseases. The unique traits that make the Mayo Clinic a natural laboratory for population-based studies are the following: Mayo has always provided primary, secondary, and tertiary care for the residents in Rochester, Minnesota— They also offer care in every medical and surgical specialty and sub-specialty, so local residents don't have to leave the area for those services. This has created a unique environment from which to study population-based disease causes and outcomes unlike any other area in the U.S. or world!

Prevalence

Definition: The number of existing cases of a disease or health condition in a population at some designated time. Prevalence is used to: Describe the burden of a health problem in a population. Estimate the frequency of an exposure. Determine allocation of health resources such as facilities and personnel. Provides an indication of the extent of a health problem. Example 1: Prevalence of diarrhea in a children's camp on July 13 was 15. Example 2: prevalence of obesity among women aged 55-69 years was 367 per 1,000. Prevalence can be reported as a number, a percentage, or a number of cases per unit size of population. A designated time can be specified or unspecified. When the time period is not specified, it usually refers to a particular point in time....this is called point prevalence. **Prevalence studies are not as helpful as other types of epidemiologic research designs for studies of etiology, mainly because of the possible influence of differential survival. For a case to be included in a prevalence study, he or she would have had to survive the disease long enough to participate. Cases that died before participation would be missed and affect the study.

Incidence

Definition: The number of new cases of a disease that occur in a group during a certain time period. Example of Incidence measured as frequency: Number of new cases of HIV infection diagnosed in a population in a given year: a total of 164 HIV diagnoses were reported among American Indians or Alaska natives in the U.S. during 2009. The number of new health-related events in a defined population within a specified time period. It can be measured as a frequency count, a rate, or a proportion. It is a measure of the risk of a specified health-related event. Incidence rate: Describes the rate of development of a disease in a group over a certain time period. Contains three elements: Numerator = the number of new cases. Denominator = the population at risk. Time = the period during which the cases occur. Here it is used as a rate! It describes the rate of development of a disease in a group over a certain time period—this time period is included in the denominator. The number of new cases: uses the frequency of new cases in the numerator—so individuals who have a history of the disease are not included (they are existing cases—prevalence!) The population at risk: people who have already developed the disease or who are not capable of developing it should be excluded. For example, if looking at the risk for ovarian cancer, you would not include women who have already developed the disease and women who have had their ovaries removed—they can't develop it. Specification of a time period: a date of onset for the condition or disease during the time period—this is easily done with acute illnesses (stroke, MI) but other illnesses, such as cancer, may be defined by the date of diagnosis. Here it is used as a rate! It describes the rate of development of a disease in a group over a certain time period—this time period is included in the denominator. The number of new cases: uses the frequency of new cases in the numerator—so individuals who have a history of the disease are not included (they are existing cases—prevalence!) The population at risk: people who have already developed the disease or who are not capable of developing it should be excluded. For example, if looking at the risk for ovarian cancer, you would not include women who have already developed the disease and women who have had their ovaries removed—they can't develop it. Specification of a time period: a date of onset for the condition or disease during the time period—this is easily done with acute illnesses (stroke, MI) but other illnesses, such as cancer, may be defined by the date of diagnosis. Here it is used as a rate! It describes the rate of development of a disease in a group over a certain time period—this time period is included in the denominator. The number of new cases: uses the frequency of new cases in the numerator—so individuals who have a history of the disease are not included (they are existing cases—prevalence!) The population at risk: people who have already developed the disease or who are not capable of developing it should be excluded. For example, if looking at the risk for ovarian cancer, you would not include women who have already developed the disease and women who have had their ovaries removed—they can't develop it. Specification of a time period: a date of onset for the condition or disease during the time period—this is easily done with acute illnesses (stroke, MI) but other illnesses, such as cancer, may be defined by the date of diagnosis. Incidence rate= [# of new cases over a period of time/ total population at risk during the same time period] x multi[lier (e.g. 100,000) Number of new cases= 1085 pop at risk= 37,105 Incidence rate= 1085/37105= = 0.02924/8 (8 year total)—divide this number by 8 for an annual total of 0.003655; multiply by 100,000 = = 365.5 cases per 100,000 women per year

Limitations of Other Study Designs

Demonstrating temporality is a difficulty of most observational studies. Cross-sectional and case-control study designs are based on exposure and disease information that is collected at the same time. Advantage: Efficient for generating and testing hypotheses. Disadvantage: Leads to challenges regarding interpretation of results. Cross-sectional studies: Present difficulties in distinguishing the exposures from the outcomes of the disease, especially if the outcome marker is a biological or physiological parameter. Case-control studies: Raise concerns that recall of past exposures differs between cases and controls. There has been no actual lapse of time between measurement of exposure and disease. None of the previous study designs is well suited for uncommon exposures.

Population Dynamics

Denotes changes in the demographic structure of populations associated with such factors as births and deaths and immigration and emigration Two types of populations Fixed populations Dynamic populations Fixed population: one distinguished by a specific happening and consequently adds no new members; therefore, the population decreases in size as a result of deaths only. No change in this population except through death of members! Dynamic population: This population CHANGES—with the addition of new members through immigration and births or the loss of members through emigration and deaths. Three major factors affect the sizes of population births, deaths, and migration: Population in equilibrium or a steady state Population increasing in size Population decreasing in size Three major factors affect the sizes of population births, deaths, and migration: Population in equilibrium or a steady state The 3 factors do not contribute to net increases or decreases in the number of persons—meaning that the number of members exiting for various reasons equals the number entering. Population increasing in size The number of persons immigrating plus the number of births exceeds the number of persons emigrating plus the number of deaths. Population decreasing in size The number of persons emigrating plus the number of deaths exceeds the number of persons immigrating plus the number of births. Demographic Transition Shift from high birth and death rates found in agrarian societies to lower birth and death rates found in developed countries. Decline in death rate: attributed to improvement in general hygienic & social conditions Decline in birth rate: attributed to industrialization and urbanization Industrialization and urbanization, while they have led to a decline in the birth rate, both have led to environmental contamination, concentration of social and health problems in the urban core areas of the US, and out-migration of inner city residents to the suburbs. Demographic and Social Variables: Age and sex distribution: How old is the population? Young? Old? If old, you'll see health problems r/t aging—chronic diseases, heart attacks, CVAs. Also, in an older community, women tend to live longer than men, so you'll see more of women's issues: osteoporosis, screenings needed for various things. With younger people, the focus will be on immunizations for children and teens, education on STDs, etc. for Teens as well as health promotional education on smoking, substance use, and prevention of unintentional deaths/injuries. Socioeconomic status (SES): income level, educational level, types of occupation—insurance? Health care? Educational level influences diet and exercise—people with lower educational levels may be less aware of importance. Family structure: Marital status, single moms,/dads, number of children in single parent homes, number of single parent families. Racial, ethnic, and religious composition: Some health outcomes are more common in one race or ethic group than another. Example: Sickle cell anemia, diabetes, Tay-Sachs. Some religions restrict dietary items, may abstain from ETOH, may not smoke, may eat foods low in fat and avoid high fat foods! They should be REALLY healthy, right?!!

Health Status and Health Services

Describing the occurrence of disease in the community Planning for allocation of resources Public health practitioners Administrators Policy evaluation Evaluating programs, e.g., public health service programs 1.The rise and fall of diseases and changes in their characters: Illnesses and causes of morbidity have changed over time. Chronic conditions have generally replaced acute infectious disease as a major cause of morbidity and mortality. 2.Describe the health of the community: can provide a key to the types of problems that require attention and also accentuate the need for specific health services—this is helpful to public health practitioners and administrators as it helps them plan for allocation of resources. 3. Epidemiology and policy evaluation: Epidemiologic research helps lawmakers develop evidence-based laws to safeguard the public. 4. Examine the working of health services: to help improve them! Operations research: assessing what is available, what the community expects and what it needs. Program evaluation: the success of services delivered and the effects on community health have to be appraised

*

Descriptive studies: cross-sectional surveys (hypothesis generation) Analytic studies: ecologic, case-control, and cohort (hypothesis testing)

Developing vs Developed

Developing countries In 1950 and 1990, countries had a triangular population distribution, which is associated with high death rates from infections, high birth rates, and other conditions that take a heavy toll during childhood years—things associated with poverty and deprivation: such as poor nutrition, lack of potable water, lack of access to vaccines and antibiotics, and unavailability of sewage treatment. By 2030, improvements in health are likely to result in greater survival of younger persons, causing a projected change in the shape of the population distribution. Developed countries Manifest a rectangular population distribution—it was consistent for 1950 and 1990, and is projected for 2030 with a few exceptions. Infections take a smaller toll and thus cause a greater proportion of children to survive into old age Residents enjoy greater life expectancy The population of developed countries will grow increasingly older due to continuing advances in medical care

Place comparisons

Disease patterns are due to unique environmental or social conditions found in particular area of interest. Examples include: Fluorosis: associated with naturally occurring fluoride deposits in water. Goiter: iodine deficiency formerly found in land-locked areas of U.S. Fluorosis—mottled teeth. Common in areas where fluoride is naturally high in the water—or where there is too large a consumption of fluoride from any source. Goiter—thyroid disease—decreased iodine in diet. Iodine is normally found in seafood/shellfish, therefore, a decrease (which causes goiter) is found in land-locked areas. A solution is to make sure you are using iodized salt if you live in these areas—or don't like seafood. Other areas of the country have their own localized problems— Some Ohio communities have exposure to increased levels of Radon—Cancer incidence is high.

Risks

Due to the uncertainty of "causal" factors the term risk factor is used. Definition: exposure that is associated with a disease Example of a risk factor: smoking. Three Criteria for Risk Factors Frequency of disease varies by category or value of the factor: Heavy smokers more likely to have cancer/emphysema/chronic disease than light smokers. The risk factor precedes onset of the disease: If someone starts smoking after they are diagnosed with lung cancer, then smoking didn't cause it. The observation must not be due to error: In research, errors can occur in selection of study groups, measurement of exposure and disease, and data analysis.

Within-Country Variations in Rates of Disease

Due to variations in climate, geology, latitude, pollution, and ethnic and racial concentrations In U.S., comparisons can be made by region, state, and/or county. Examples include: higher rates of leukemia in Midwest; state by state variations in infectious, vector-borne, parasitic diseases Geographic variations: (within countries) State to state, county to county, and region to region are compared in U.S. Example: Cancer—leukemias appear to be concentrated in upper Midwest; Louisiana, however, had increased death rates for Leukemia in 2008. Malignant melanoma mortality showed a relationship with latitude—more people in southern latitudes affected (sun—UV index higher!). Infections, vector-borne, and parasitic diseases may vary from state to state. HIV tends to be concentrated in NE and SE U.S.

Causality in Epidemiologic Research

Epidemiologic research is the subject of criticism. Many conflicting studies Henle-Koch postulates are not relevant to many contemporary diseases. Multivariate causality Many studies are contradictory and conflict with others. Consider red food coloring and cancer scare; hormones/birth control pills—different things you've heard! We studied Koch's postulates in Chapter 1—they aren't relevant, however to many diseases today! They were a good starting point back in his time, but let's look at Cardiovascular Disease—is an agent present in every case like the postulate states needs to occur? CVD patients are overweight, physically inactive, smoke, have high BP, high Cholesterol---but are these present in EVERY patient with CVD?! NO!! Multivariate causality—Koch also said "1 agent, 1 disease"—but is CVD the ONLY disease associated with smoking? NO!

Disease Etiology

Epidemiologists continue to search for clues as to the nature of disease. Knowledge that is acquired may be helpful in efforts to prevent the occurrence. 1. Looking at the entire group, what are the individual risks on the average of disease, accident and defect, and the chances of avoiding them? 2. Identify syndromes by describing the distribution and association of clinical phenomena in the population. 3. Include in due proportion all kinds of patients, wherever they present, together with the undemanding and the symptomless cases who do not present but whose needs may be as great; by following the course of remission and relapse, adjustment and disability in defined populations. 4. By computing the experience of groups defined by their composition, inheritance and experience, their behavior and environments.

Health Phenomena

Epidemiology investigates many different kinds of health outcomes: Infectious diseases Chronic diseases Disability, injury, limitation of activity Mortality Active life expectancy Mental illness, suicide, drug addiction The 4th key element of the definition of Epidemiology: Health Phenomena—many different kinds of health outcomes! Not only the "negative" health outcomes, but the "positive" health outcomes are researched—for example, active life expectancy among geriatric populations—research to determine factors associated with optimal mental health and physical functioning as well as enhanced quality of life. This is done in an effort to hopefully limit disability later in life.

Multiple Causality

Establishing causality: Diseases have not one, but multiple causes Determining the relationships between the different causes of disease is important to PH practitioners who seek to prevent, diagnose, and treat disease. Screening: Surveillance: Passive vs. Active surveillance Also referred to as multifactorial etiology--"...requirement that more than one factor be present for disease to develop..." It is often discovered that not one but many things cause a disease or event or a chain of causes occur....producing a "Chain of Causality" Screening: Key component in many secondary prevention interventions Used to identify risk factors and diseases in their earliest stages. Surveillance: the systematic collection, analysis, and interpretation of data related to the occurrence of disease and the health status of a given population. Passive surveillance: health care providers in the community report cases of notifiable diseases to public health authorities through the use of standardized reports. Inexpensive, but limited—information depends on providers reporting practices. Active surveillance: purposeful, ongoing search for new cases of disease by public health personnel, through personal or telephone contacts or review of laboratory reports or hospital and/or clinic records. Costly—so it's only used in cases of emergence of new diseases, severe diseases, or re-emergence of a previously eradicated disease. Epidemiologic triangle Web of causation, e.g., in avian influenza A two-dimensional causal web that considers multiple levels of factors that affect health and disease Looks like a spider web What might be the "spider"? This concept is based upon the fact that there are various factors interacting, sometimes in subtle ways, to increase the risk for illness or to decrease it. Krieger (1994) has suggested that in addition to research on the relationships within the web, we need to look for "the spider," that is, focus on those larger factors and contexts that influence or create the causal web itself. More on this later..... Krieger (1994) has suggested that in addition to research on the relationships within the web, we need to look for "the spider," that is, focus on those larger factors and contexts that influence or create the causal web itself. The concept of multiple causation is comparable to that of the chain of disease transmission. It reflects the complex relationships between many different factors that interact to either increase or decrease the risk of disease. This model helps to explain how diseases have multifactorial etiology. Some of these may be both positive factors (or protective factors) that may protect someone from diabetes and negative (predictive) factors, depending upon existence. -Think of this as a concept map! Just as we know that the body is made up of many systems, and we need to address them all in care, there is no single cause of disease; -Most of the causes of diseases interact like this, too, & this illustrates the interconnectedness of possible contributing factors-- **Also, remember that not everyone who smokes gets lung cancer and not everyone who has an MI has high cholesterol or ate high-fat foods all of their lives! Wheel model, e.g., childhood lead poisoning Pie model, e.g., lung cancer

Evaluating Epidemiologic Associations

Five key questions to be asked: Could the association have been observed by chance? Determined through the use of statistical tests. Could the association be due to bias? Bias refers to systematic errors, i.e., how samples were selected or how data was analyzed. Could the association have been observed by chance? The P-value indicates the probability that the findings observed could have occurred by chance alone. A small P-value (a highly significant result) for an observed association should provide some assurance that the results were not obtained simply by chance. However, a very small P-value doesn't imply that the association is real. Could association be d/t bias? Errors at any of the following stages may lead to results that are not valid: +How study groups selected +How information about exposure and disease was collected +How data were analyzed Confounding—refers to the masking of an association between an exposure and an outcome because of the influence of a 3rd variable that wasn't considered in the design or analysis. To whom does this apply? Population-based samples are important and the sampling procedures used enhance the likelihood of generalizability (ability to apply results to other populations not involved), but this doesn't mean that you'll get this outcome (generalizability). Representativeness of sample: a lot can be learned from an unrepresentative study sample! It's easier to generalize from one group to another if groups are similar! (Ex. White women aged 55 to 69 who live in Iowa could be generalized to other white women of the same age who live in the Midwest). Participation Rates: % of a sample that completes the data collection phase of a study. Some people think this needs to be high to generalizability, but high participation rates don't necessarily ensure generalizability. Generalizability can be high even with low participation rates. Cause-and-effect relationship? Strength of association---more on this later in the presentation.

Four Stages of Evaluation

Formative: Will all plans and procedures work as conceived? Process: Is the program serving the target group as planned? Is number being served more or less than expected Impact: Has the program produced any changes among the target group? Outcome: Did the program accomplish its ultimate goal? Formative—begin this evaluation as soon as the idea for a program is conceived. (We do formative testing of you during the semester) Process—begin this step as soon as the program goes into operation Impact—Informs program planners whether they are making progress toward goals. Outcome—after the program is finished

Life Table Methods

Give estimates for survival during time intervals and present the cumulative survival probability at the end of the interval. Example: Life tables can be constructed to portray the survival times of patients in clinical trials. There are two life table methods: Cohort Life Table Shows the mortality experience of all persons born during a particular year, such as 1900. Period (Current) Life Table Enables us to project the future life expectancy of persons born during the year as well as the remaining life expectancy of persons who have attained a certain age.

Epidemiological uses

Health Status and Health Services Study history of the health of populations Diagnose the health of the community Examine the working of health services Disease Etiology Estimate the individual risks and chances Identify syndromes Complete the clinical picture Search for causes

Availability of Exposure Data

High quality historical exposure data are absolutely essential for retrospective cohort studies. Need to trade off between a retrospective study design (with the benefits of more immediate follow-up time) and collection of primary exposure data in a prospective cohort design.

Environment

Hippocrates wrote On Airs, Waters, and Places in 400 BC. He suggested that disease might be associated with the physical environment. This represented a movement away from supernatural explanations of disease causation. The Greeks attributed epidemics to the wrath of the gods, the breakdown of religious beliefs and morality, the influence of weather, and "bad" air—that is, until Hippocrates, who theorized that disease may be attributed to the physical environment and climate!

Women's Health Initiative

Hormone Replacement Therapy (HRT) This was a very LARGE clinical trial (in early 2000's)!! It was part of the motivation for the Women's Health Initiative. Pg. 369 - 371 gives the results of this study.....including the fact that they had the women stop taking the estrogen-plus-progestin pills during the study! The risk, especially with this drug combination, was too high! Epidemiologic studies had shown that HRT use had significant benefits against coronary heart disease. Clinical trials had failed to demonstrate any benefit. Large body of epidemiologic research had observed that women who took HRT had elevated risks of breast cancer. Why experimental studies are important! Remember that we talked about women taking hormones and needed breast biopsies?!! The results of this study was the reason why use of HRT declined by 40 - 80% after the trial was stopped! To resolve the question of risks versus benefits of HRT, a clinical trial was conducted. Demonstrated that: the epidemiologic findings on cancer were generally accurate the benefits on cardiovascular disease had been overestimated Results Use of HRT decreased 40%-80% after the trial was stopped

Interrelationship Between Prevalence and Incidence

If duration of disease is short and incidence is high, prevalence becomes similar to incidence. Short duration—cases recover rapidly or are fatal. Example: common cold Prevalence becomes similar to incidence if the disease duration is short and incidence is high. So in other words, there are a whole lot of NEW cases of something (like a cold—short duration), but the people recover quickly so they don't get added to the prevalence numbers. If cases recover quickly or are fatal, this eliminates the buildup of prevalent cases! If duration of disease is long and incidence is low, prevalence increases greatly relative to incidence. Example: HIV/AIDS prevalence On the other hand, when you have a disease where the duration is LONG (HIV/AIDS), but the incidence is LOW, then prevalence goes up while incidence may stay the same or maybe even decrease.

Clinical Trials

In 1537, Ambroise Paré applied experimental treatment for battlefield wounds. East India Shipping Company (1600) found that lemon juice protected against scurvy. James Lind (1747) used the concurrently treated control group method. Pare—turpentine, rose oil, egg yolks concoction to treat battlefield wounds—was better than boiling oil at treating wounds! Lemon juice saved sailors in 1600—this was discovered when the East India Shipping Company compared sailors from ships with lemons and sailors from ships without. 1747—citrus fruits discovered to be the treatment for scurvy. 12 sailors suffering from scurvy were fed 6 different types of diets—those who ate citrus fruits recovered the best. Edward Jenner's efforts to develop a smallpox vaccine in the late 18th century Most recent historical developments include the use of multicenter trials. Instrumental in the development of treatments for infectious diseases and recently in chronic diseases that are of noninfectious origin Jenner—while the early experiments were carried out without a control group or a comparison group, subsequent studies contributed to the development of control treatments and randomization. Multicenter trials—recruitment of participants is extended across several to hundreds of accrual sites with data sent to a coordinating center for analysis. A research activity that involves the administration of a test regimen to humans to evaluate its efficacy and safety Wide variation in usage: The first use of the term was for studies in humans without any control treatment Now denotes a rigorously designed and executed experiment involving RANDOM ALLOCATION of test and control treatments Pg. 368—NIH Definition: A prospective biomedical or behavioral research study of human subjects that is designed to answer specific questions about biomedical or behavioral interventions (drugs, treatments, devices, or new ways of using known drugs, treatments, or devices). Clinical Trials are used to determine whether new biomedical or behavioral interventions are safe, efficacious, and effective. The key point is that clinical trials enroll individual subjects and enable randomization of subjects to either receive or not receive the intervention. Carefully designed and rigidly enforced protocol Tightly controlled in terms of eligibility, delivery of the intervention, and monitoring of outcomes Duration ranges from days to years Participation is generally restricted to a highly selected group of individuals. Participation is generally restricted to a highly selected group of individuals—mainly people who have been diagnosed with a disease, who are screened subjects at high risk for disease, or just interested people. Once subjects agree to participate, they are randomly assigned to one of the study groups, e.g., intervention or control (placebo) Eligibility of potential subjects is determined first. Eligibility rules must be carefully defined and rigidly enforced. Criteria for inclusion will vary by the type and nature of the intervention proposed. Once eligible subjects agree to participate, they are randomly assigned to a study group—either intervention or control Strengths: Provide the greatest control over: the amount of exposure (drug dosage) the timing and frequency of exposure the period of observation for end points Ability to randomize reduces the likelihood that groups will differ significantly. Less likelihood of variables influencing the outcome Limitations: Artificial setting Limited scope of potential impact Adherence to protocol is difficult to enforce Especially if treatment produces undesirable side effects &/or a significant burden to the subjects. Ethical dilemmas Withholding a potentially beneficial treatment from the control group Artificial setting—treatments may not work as well in a setting other than the controlled clinical area.

Case-Control Studies

In a case-control study with two groups, one group has the disease of interest (cases) and a comparable group is free from the disease (controls). The case-control study identifies possible causes of disease by finding out how the two groups differ with respect to exposure to some factor. A single point of observation Unit of observation and the unit of analysis are the individual Exposure is determined retrospectively Does not directly provide incidence data Data collection typically involves a combination of both primary and secondary sources. A mainstay of epidemiologic research! Usually going from the "effect" to the "cause" as these studies are done retrospectively—by looking into the history of the subject's exposure after the disease has already occurred. Data is usually collected by researchers, but valuable information can also be obtained from medical, school, and employee records. Selection of Cases Two tasks are involved in case selection: Defining a case conceptually Identifying a case operationally Sources Need to define a case conceptually Ideally, identify and enroll all incident cases in a defined population in a specified time period A tumor registry or vital statistics bureau may provide a complete listing of all cases Medical facilities also may be a source of cases, but not always incident cases Lack of suitable disease registries may mean that you're left with only medical facilities that may receive only the most severe cases, so this can skew your data. Also, medical facilities don't always treat just incident cases..... Selection of controls- The ideal controls should have the same characteristics as the cases (except for the exposure of interest). If the controls were equal to the cases in all respects other than disease and the hypothesized risk factor, one would be in a stronger position to ascribe differences in disease status to the exposure of interest. If the controls differ in demographic factors such as age or socioeconomic status, these factors could operate as a rival explanation to account for the observed outcomes. Number of controls—usually it is a one-to-one ratio—one control for each case, but researchers can also accept up to a 4:1 controls to cases ratio. This is the maximum. Population-based controls—Obtain a list that contains names and addresses of most residents in the same geographic area as the cases. A driver's license list would include most people between the ages of 16 and 65. Tax lists, voting lists, and telephone directories Patients from the same hospital as the cases Relatives of cases They should come from the same population at risk for the disease or condition as the cases being studied. Ex. In a study of ovarian cancer, women who have had their ovaries removed would not be eligible to be controls as they no longer have the risk of contracting ovarian cancer. Measures of Association This just shows you how to link association with an illness.... The OR represents the odds that an outcome will occur given a particular exposure, compared to the odds of the outcome occurring in the absence of that exposure. Odds ratios are most commonly used in case-control studies, however they can also be used in cross-sectional On the association between chili pepper consumption and gastric cancer risk: a population-based case-control study conducted in Mexico City Source: Lopez-Carillo, et al. Am J Epidemiol. 1994;139:263-71. Calc. Odds Among Cases = 204/9 = 22.67 Odds Among Controls = 552/145 = 3.81 The OR (unadjusted for age and sex) is: AD = (204)(145) = 5.95 BC (552)(9) The study involved 220 incident cases and 752 controls who were randomly selected from the general population. The CASES had or have had gastric cancer; the CONTROLS do NOT or have NOT had gastric cancer. So using the data from this study, we will plug in the numbers to calculate the OR. Out of the Cases, 204 had been diagnosed with gastric cancer who had a history of eating chili peppers (this is A); 9 got gastric cancer who did NOT eat chili peppers (this is C). Out of the Controls (People alike in age, gender, and location but without Cancer!), 552 had a history of pepper intake (this is B) and 145 did NOT (this is D). So setting up the calculation as shown......we get 5.95 for our OR The OR = 5.95 suggests that cases were nearly six times more likely to eat chili peppers than controls. • The OR = 5.95 also suggests that the odds of gastric cancer is nearly six-fold higher in those consuming chili peppers than non-consumers. • These results suggest that eating chili peppers may be a risk factor for the development of gastric cancer. • Chili peppers contain the hot spice capsaicin which irritates the gastric mucosa. • Chronic consumption of chili peppers may therefore increase the risk of developing gastric carcinoma. Young women's cancers resulting from utero exposure to diethylstilbestrol Green tea consumption and lung cancer Maternal anesthesia and development of fetal birth defects Passive smoking at home and risk of acute myocardial infarction Household antibiotic use and antibiotic resistant pneumococcal infection Tend to use smaller sample sizes than surveys or prospective studies Quick and easy to complete Cost effective Useful for studies of rare diseases Smaller—hundreds to thousands vs. thousands to tens of thousands! Increases the likelihood that a case-control study will be repeated—this is desirable, as it provides consistency in epidemiological research. Much better for studying rare diseases than cohorts. Unclear temporal relationships between exposures and diseases Use of indirect estimate of risk Representativeness of cases and controls often unknown

Infectious diseases

Infectious diseases SARS, pandemic influenza 2009 H1N1, use of epidemiologic methods to attempt to eradicate, when possible, polio, measles, smallpox, and other communicable diseases. As mentioned earlier, the ONLY one that has been ERADICATED has been smallpox. The rest may have been somewhat eliminated in certain areas of the world, but they are still around. Outbreaks of infectious diseases, such as H5N1, Ebola, etc. will also be investigated. Environmental health: human health may be affected by toxic chemicals used in industry, air pollutants, contaminated drinking water, unsafe homes, etc. Both occupational and environmental epidemiology address this. Chronic Disease, lifestyle, and health promotion: Epidemiology in this area has looked at connections between Lifestyle choices and poor health—exercise, diet, smoking, ETOH consumption—can play a part in obesity, coronary heart disease, arthritis, diabetes, and cancer. Epidemiology has also looked at connections between the environment and exercise—does the environment dissuade exercise? How much do cultural practices affect behaviors linked to health and disease? Psychological and Social Factors: Stress, social support, socioeconomic status affect the occurrence and outcomes of mental and physical health. What is the relationship between psychological and illnesses such as arthritis, some GI conditions, and essential hypertension?! What about personality factors and disease? Are some personalities more prone to heart disease? Psychosocial determinants: social, cultural, demographic factors (socioeconomic status, gender, employment, marital status, and race) are demonstrated correlates of mental and physical health status. This type of epidemiology looks at all of this. Molecular and genetic epidemiology: use of DNA typing (genetic and molecular markers) to determine behavioral outcomes and host susceptibility to disease. Genetic epidemiology looks at things such as inherited susceptibility to breast and ovarian cancer as well as to alcohol use disorders!

Foundations of Epidemiology

Interdisciplinary Methods and procedures—quantification Use of special vocabulary Epidemic frequency of disease These 3 Aspects characterize the epidemiologic approach: Interdisciplinary: draws from biostatistics, social and behavioral sciences, and medically related fields such as toxicology, pathology, virology, genetics, microbiology, and clinical medicine. Quantification: Quantify—what does that mean?! Think quantity.....counts, numbers. We'll talk about this on the next slide..... Use of special vocabulary: Words such as epidemic and pandemic are used to describe the frequency and occurrence of disease. More on these two words and other special vocabulary words later.

Cohort Studies:Sample Calculation

Is there an association between child abuse and suicide attempts among chemically dependent adolescents? Source: Deykin EY, Buka SL. Am J Public Health. 1994;84:634-639.

Interrelationships Between Reliability and Validity

It is possible for a measure to be highly reliable but invalid. It is not possible for a measure to be valid but unreliable.

Edward Jenner

Jenner conducted an experiment to create a smallpox vaccine. He developed a method for smallpox vaccination. In 1978 smallpox was finally eradicated worldwide. Since 1972, routine vaccination of the nonmilitary population of the U.S. has been discontinued. His experiment: Dairymaids with Cowpox (vaccination—vaca=cow) were immune to smallpox. He used scabs from cowpox lesions on the arm of a dairymaid to create the vaccine, then vaccinated an 8 year old boy with it. It worked! He then vaccinated his own children. Smallpox was eradicated worldwide, thanks to the smallpox vaccine, in 1978. Before his vaccine, there was a procedure referred to as variolation—dried scabs from patients with smallpox were introduced into the noses of those who were not yet ill but vulnerable—it produced a milder version of the disease—sort of like vaccinating people with live but weakened viruses (old flu shot)! This method was first tested in Europe among abandoned children and prisoners!! When it was declared safe, members of the English royal family were inoculated.

The 1918 Influenza Pandemic

Known as "The Mother of All Pandemics" and Spanish Flu Occurred between 1918 and 1919 Killed 50 to 100 million persons worldwide 2.5% case-fatality rate versus 0.1% for other influenza pandemics Deaths most frequent among 20- to 40-year-olds Unlike earlier pandemics and seasonal flu outbreaks, the 1918 pandemic flu saw high mortality rates among healthy adults. In fact, the illness and mortality rates were highest among adults 20 to 50 years old. The reasons for this remain unknown. Just to give you a little reference: The population of New York City is 8 million.......the population of California is 39 million.... What if it happened again? It's very possible that it could! Look at the 2009 outbreak of H1N1— Will our hospitals be able to handle the influx of victims?!! Will we need to use measures such as "social distancing"? How will essential services be maintained?

Data Collection and Data Management

Larger studies are more demanding than smaller ones; challenges due to data collection and data management. Explicit protocols for quality control should be considered in the design and implementation stage. (e.g., double entry of data and scannable forms)

Mort causes in 1900 vs 2009

Leading causes of deaths in the U.S. in 2009 were heart disease, cancer, and chronic lower respiratory disease. What were they in 1900?! All of these things are important when assessing the health of the community!

Observational vs. Experimental Approaches

Manipulation of study factor Was exposure of interest controlled by investigator? Randomization of study subjects Was there use of a random process to determine exposure of study subjects? If a study involves both Manipulation (M) and Randomization (R), it is an Experimental study (Clinical trials). If a study involves only M, but not R, it is a quasi-experimental study (Community trials). If a study involves neither M nor R, it is an Observational Study.

matching

Matches subjects in the study groups according to the value of the suspected or known confounding variable to ensure equal distributions. Frequency matching—the number of cases with particular match characteristics is tabulated. Individual matching—the pairing of one or more controls to each case based on similarity in sex, race, or other variables. Advantages: Fewer subjects are required than in unmatched studies of the same hypothesis. May enhance the validity of a follow-up study. Disadvantages: Costly because extensive searching and recordkeeping are required to find matches.

Restriction

May prohibit variation of the confounder in the study groups. For example, restricting participants to a narrow age category can eliminate age as a confounder. Provides complete control of known confounders. Unlike randomization, cannot control for unknown confounders. Stratification—analyses performed to evaluate the effect of an exposure within strata (levels) of the confounder. Multivariate techniques—use computers to construct mathematical models that describe simultaneously the influence of exposure and other factors that may be confounding the effect.

Publication Bias

Occurs because of the influence of study results on the chance of publication. Studies with positive results are more likely to be published than studies with negative results. May result in a preponderance of false-positive results in the literature. Bias is compounded when published studies are subjected to meta-analysis.

Sources of Unreliability and Invalidity

Measurement bias--constant errors that are introduced by a faulty measuring device and tend to reduce the reliability of measurements. Example: A miscalibrated blood pressure manometer. Halo effect—the influence upon an observation of the observer's perception of the characteristics of the individual observed. The influence of the observer's recollection or knowledge of findings on a previous occasion. Example: a health care provider's tendency to rate a patient's sexual behavior use in a particular manner, based on a general opinion about a patient's characteristics without obtaining specific information about past sexual behavior. Social desirability effects - - Respondent answers questions in a manner that agrees with desirable social norms. Example: Teenage boys might respond to a screening interview about sexual behavior by exaggerating their frequency of sexual activities because these behaviors might be perceived as socially desirable among some male peer groups.

Ex of Clinical Trials

Medical Research Council Vitamin Study—studied role of folic acid in preventing neural tube defects. South Bronx, NY, STD Program—evaluated effectiveness of education efforts to prevent spread of sexually transmitted diseases (STDs). Page 380 - 381 Neural tube defects—anencephaly, spina bifida, encephalocele....congenital malformations. Folic acid (a B-vitamin) has been shown (thanks to this study and others) to prevent these defects! The supplements used in this study were Folic acid or a mixture of 7 other vitamins (A. D, B1, B2, B6, C, and nicotinamide). Eligible subjects were women planning a pregnancy, had had a previous child with NTD, and/or were NOT already taking vitamin supplements. Rate of NTDs among women taking Folic acid (alone or with other vitamins) was 1%!! The other group not taking Folic acid (vitamins only) had a rate of 3.5%--it was only slightly lower than the group in which the women were taking nothing at all. Effectiveness of STD education—read about this on page 381

Randomization

Method of choice for assigning subjects to the treatment or control conditions of a clinical trial. Non-random assignment may cause mixing of the effects of the intervention with differences (e.g., demographic) among the participants of the trial. A simple flip of the coin is the simplest form of randomization. To be fully effective, a reasonably large number of subjects must be enrolled in order to obtain equal distribution of demographic and other variables.

Ignaz Semmelweis

Mid-19th century, Viennese hospital Clinical assistant in obstetrics and gynecology Observed higher mortality rate among the women on the teaching wards for medical students and physicians than on the teaching wards for midwives Postulated that medical students and physicians had contaminated their hands during autopsies Introduced the practice of hand washing About this same time, Florence Nightingale was drawing the conclusion that fewer men died in hospitals (following injuries in the Crimean War) when conditions were more sanitary!!

Secondary Prevention

Occurs during pathogenesis phase Designed to reduce the progress of disease Examples include: screening programs for cancer and diabetes. Breast exams, mammograms, PSAs, testicular exams. S for screening

Surveys

Morbidity Surveys of the General Population Morbidity surveys collect data on the health status of a population group. Ones authorized by US govt. obtain more comprehensive information than would be available from routinely collected data Example: National Health Interview Survey Remember that Morbidity means illness/condition These surveys use a scientifically designed representative sample of the population. Their purpose: to determine the frequency of chronic and acute diseases and disability, collect measurements of bodily characteristics, conduct physical exams and laboratory tests, and explore other health-related problems that may be of concern to whoever is sponsoring the survey. National Health Survey—established by The National Health Survey Act of 1956 in order to obtain information about the overall health status of the US population. National Health Survey Refers generically to a group of surveys and not a single survey. In response to the Act, the National Center for Health Statistics (NCHS) conducts three separate and distinct programs. National Health Interview Survey (NHIS) Health Examination Survey (HES) Various surveys of health resources National Hospital Discharge Survey National Ambulatory Medical Care Survey National Health Interview Survey (NHIS) General household health survey of the U.S. civilian non-institutionalized population Studies a comprehensive range of conditions such as diseases, injuries, disabilities, and impairments Health Examination Survey (HES) Provides direct information about morbidity through examinations, measurements, and clinical tests Identifies conditions previously unreported or undiagnosed Provides information not previously available for a defined population Now known as the Health and Nutrition Examination Survey (HANES) http://wonder.cdc.gov/datasets.html Other healthcare surveys conducted as part of the National Health Survey include: National Hospital Discharge Survey, National Nursing Home Survey, National Ambulatory Medical Care Survey. In addition, there are also these vital statistics surveys: National Natality Survey, National Fetal Mortality Survey, National Mortality Followback Survey. The CDC is the center for the data collected from these surveys and other sources—CDC uses the CDC WONDER online databases to release this data. The link is listed above. Behavioral Risk Factor Surveillance System (BRFSS) Collects data on behaviorally related phenomena Behavioral risks for chronic diseases Preventive activities Healthcare utilization The largest telephone survey in the world Established in 1984 by CDC, BRFSS collects data from all 50 states plus DC, Puerto Rico, U.S. Virgin Islands, and Guam. Used for evaluating public health policies/activities, and for providing quantitative support for state-based legislative initiatives. California Health Interview Survey (CHIS) Provides information on the health and demographic characteristics of California residents Uses telephone survey methods Topics include Physical and mental health conditions Health behaviors Health insurance coverage and utilization Conducted on a continuing basis http://www.chis.ucla.edu/about.html Begun in 2001, CHIS has surveyed 42,000 to 56,000 randomly selected households in each wave of collection. A full data collection cycle takes 2 years to complete! It is conducted on a continuing basis so that annual estimates can be made. Data is available for public use by accessing the AskCHIS option on the website.

John Graunt

Mortality counts 1662, published Natural and Political Observations Made upon the Bills of Mortality. Recorded seasonal variations in births and deaths Showed excess male over female differences in mortality Known as the "Columbus" of biostatistics See Yearly Mortality Bill for 1632: The 10 Leading Causes of Mortality in Graunt's Time.

Statistics

Mortality data are nearly complete Most deaths in the U.S. and other developed countries are unlikely to be unreported. Death certificates include demographic information about the deceased, cause of death (immediate cause & contributing factors), as well as attending doctor & date of death. Medical Examiner or Coroner will complete if death is due to accident, suicide, or homicide or if attending is unavailable. Certification of cause of death: Diabetes may not be given as the cause of death—may be heart failure or pneumonia, which could be complications of Diabetes. There can be errors in coding the deaths—a nosologist (a person who classifies diseases) reviews the death certificate and codes it. Errors are minimized by standardized training and routine audits. Codes change over time—we are using ICD-10, but it was the 6th edition in 1948 when WHO took over coding. ICD-10 is the International Statistical Classification of Diseases & Related Health Problems. Sudden increases or decreases in a particular cause of death may be due to changes in coding. **To avoid this error, be very careful when using data that spans more than one version of the ICD—interpret results carefully because codes and groupings of diseases may have changed. Doctors may be reluctant to put AIDS or Alcoholism as the cause of death, especially if they are friends of the deceased. Lack of standardization of diagnostic criteria. Stigma associated with certain diseases AIDS or alcoholism, may lead to inaccurate reporting. Birth Statistics: Certificates of Birth and of Fetal Death Birth certificate includes information that may affect the neonate, such as congenital malformations, birth weight, and length of gestation. Sources of unreliability: Mothers' recall of events during pregnancy may be inaccurate. Conditions that affect neonate may not be present at birth. Needed to calculate birth rates Also collects information about the conditions listed on the slide. Keep in mind that some of this data collected may not be reliable due to the mom's recall of events....plus some conditions may not be present at birth. Varying state requirements for fetal death certificates. Both types of certificates have been used in studies of environmental influences upon congenital malformations. Have also been used successfully to search for cluster of birth defects where chemical/biological exposures have occurred. Varying state requirements can also reduce the usefulness of fetal death certificates. The same things that affect accuracy of birth certificates (mom's recall, etc.) can affect accuracy of certificates of fetal death certificates. Reportable Disease Statistics Federal and state statutes require health care providers to report those cases of diseases classified as reportable and notifiable. Include infectious and communicable diseases that endanger a population, e.g., STDs, measles, foodborne illness. Certain diseases and conditions are notifiable/reportable to health agencies at the local, state, and federal level. This is public health surveillance!! Notice the difference in time frames of required reporting from Class A to Class B, then from Class B to Class C, etc. Reporting chain is Local health department, State, CDC, then WHO (for international diseases for which quarantine is needed). CDC collects information from 4 sources: labs across the US, flu data from 121 cities, sentinel physicians (150 Family Practice Docs), and state epidemiologists. Sentinel physicians—doctors who raise the alarm about unusual or untimely cases of illnesses, disease, disability, or deaths whose occurrences may be a warning of potential outbreaks. Limitations of Reportable Disease Statistics Possible incompleteness of population coverage. For example, not everyone will seek treatment, esp. if little to no symptoms. Failure of physician to fill out required forms. Unwillingness to report cases that carry a social stigma (HIV, AIDS, etc.). Failure of physician to fill out required forms. Docs, NPs, have to keep current with reporting requirements. Also, things that are widespread but less dramatic than some of the others on the list sometimes go unreported. The more severe—at least with symptoms that are severe—will usually be reported.

True Experimental Studies

Most convincing for conferring evidence of associations between risk factors and outcomes Manipulation of study factor and randomization of subjects An example is a randomized clinical trial. This study design is the most convincing for conferring evidence of associations between risk factors and outcomes! An investigator has complete control over exposure and can determine issues such as timing of exposure, intensity, and duration....so factors CAN be manipulated by the investigators and subjects can be randomized. So this enables assigning a definitive relationship between cause and effect. Care has to be taken when the study involves humans! Complete control over exposures is not possible, especially if the exposure is harmful!

Quality and Utility of Epidemiologic Data

Nature of the data Availability of the data Completeness of population coverage Representativeness Generalizability (external validity) Thoroughness Strengths versus limitations

Interpretation of an Odds Ratio (OR)

OR = 1 implies no association. Assuming statistical significance: OR = 2 suggests cases were twice as likely as controls to be exposed. OR<1 suggests a protective factor. The OR measures the odds of exposure to a given disease. An OR of 1 implies that the odds of exposure are equal among the cases and controls—a particular exposure is NOT a risk factor for the disease An OR provides a good approximation of risk when: Controls are representative of a target population. Cases are representative of all cases. The frequency of disease in the population is small. These are the conditions under which an OR is reliable— In the study of chili peppers caution needs to be taken in interpreting these results because: •Other studies have not been able to replicate the high level of risk observed in the Mexico City study. •Furthermore, these studies have not observed a dose response relationship of increasing risk of gastric cancer development with increasing frequency of eating chili peppers. •The conflicting evidence from human studies may be explained by selective recall bias, confounding with other factors (e.g., H. Pylorus infection), sampling bias, and difficulties in dietary assessment.

The Ecologic Fallacy

Observations made at the group level may not represent the exposure-disease relationship at the individual level. The ecologic fallacy occurs when incorrect inferences about the individual are made from group level data. Implications: The bias that may occur because an association observed between variables on an aggregate (group) level does not necessarily represent the association that exists at an individual level. Example: mortality rates for emphysema lower in central LA, CA (highly industrialized area) than in Palm Springs, CA (less industrialized)—this looks like areas with lower pollution rates have higher rates of emphysema—until you get into the individual exposure data and discover that after spending years working in LA, some people move to Palm Springs to retire, as do people from other highly industrialized areas of the country (north). An ecologic study examines 10 individuals who go into the sun. The study finds that 7 persons (70%) have sunburned foreheads although 6 persons (60%) wore hats. The expected number of sunburned foreheads is 4 (the number who did not wear hats). The media report that wearing hats will not protect you from sunburn. This is another example of an ecologic "fallacy": from this study, it looks like wearing hats in the sun won't protect you from sunburn!

Primary Prevention

Occurs during prepathogenesis phase Includes health promotion and specific protection against diseases Examples: Utilization of specific dietary supplements Immunizations Includes interventions aimed at preventing the occurrence of disease, injury, or disability: health promotion activities (nutrition education, promote exercise), environmental protection (basic sanitation and food safety), immunizations, use of seat belts and car seats, fluoridation of water. Involves specific protection against disease-causing hazards Remember—at this level, people are susceptible but not sick/injured/disabled!

Online Sources of Epidemiologic Data

Online bibliographic databases include MEDLINE, TOXLINE, and commercial databases. National Library of Medicine's PubMed® MEDLINE is the main part of PubMed® Premier source of health-related literature TOXLINE—keyed to toxicology and includes information on drugs and chemicals Medline is a part of PubMed, which is a bibliographic search engine available from National Institutes of Health (NIH)-National Library of Medicine. Medline focuses on biomedicine—over 21 million articles from life sciences; indexed according to Medical Subject Headings (MESH). This means that you can enter either a search topic or author(s) name(s). Toxline—effects of drugs and other chemicals. Toxicology Google ProQuest Dialog—sociological abstracts and/or retrieval of entire articles PsycINFO—mental health articles American Public Health Association—http://www.apha.org Centers for Disease Control and Prevention—http://www.cdc.gov PubMed®—http://www.ncbi.nlm.nih.gov/sites/entrez

Working of Health Services

Operations research (OR) Coordination of programs for the developmentally disabled Studies of health care utilization Residential care facilities The development of research designs, analytic techniques, and measurement procedures by epidemiologists to study the placement and optimum utilization of health services in a community. Services need to be coordinated and integrated to optimize the use of funds and services. Operations research strives to answer the following kinds of questions: What health services are not being supplied in the community? Are services duplicated in the community? What segments are primary utilizers of services and what segments are being underserved? What is the most efficient organizational and staff power configuration? What characteristics of the community, providers, patients affects service delivery and outcome? What procedures could be used to assess, match, and refer patients to service facilities? Program evaluation Uses epidemiologic tools to determine how well a health program meets certain stated goals So whatever your goals are, the evaluation would center around them. Example: if goal was to provide equal access to health services, an evaluation of the program should include utilization by socioeconomic status variables.

Population

Our 3rd key element of the definition of Epidemiology—population. Population—groups, not individuals! The epidemiologic description—example: TSS— The clinical description for TSS in an individual would focus on symptoms—high fever, headache, malaise, and other more dramatic symptoms, such as vomiting and profuse watery diarrhea. The epidemiologic description would focus on which age groups would be most likely affected, time trends, geographic trends, and other variables that affect the distribution of TSS. Epidemiology examines disease occurrence among population groups, not individuals—therefore, it is often referred to as "population medicine". Epidemiologic and clinical descriptions of a disease are quite different The epidemiologic description indicates variation by age groups, time, geographic location, and other variables.

Proportional Mortality Ratio (PMR) %

PMR (%) for HIV among the 25- to 34-year-old group = 1,588/41,300 = 3.8% Indicates relative importance of a specific cause of death; not a measure of the risk of dying of a particular cause. The number of deaths within a population due to a specific disease or cause divided by the total number of deaths in the population. Indicates relative importance of a specific cause of death; not a measure of the risk of dying of a particular cause—it merely indicates the relative importance of a specific cause of death. This PMR should be used with caution when comparisons are made across populations, especially those that have different rates of total mortality. Let's say that 2 countries have identical death rates from cardiovascular disease and that each country has exactly 1 million inhabitants—however, they have different rates of total mortality—Country A has 30 deaths per year per 100,000 and Country B has only 10. Expected number of deaths in Country A would be 300 and would be 100 in Country B. Looking at this, the proportion of deaths from cardiovascular disease is higher in Country B (0.50) than in Country A (0.17). For a health administrator, such information may be useful to determine priorities and planning.

Common Source Epidemic

Point Epidemic: The response of a group of people circumscribed in place and time to a common source of infection, contamination, or other etiologic factor to which they were exposed almost simultaneously. (A group of people in the same place and time exposed to a common source of infection/toxic substance at the same time.) Examples: foodborne illness; responses to toxic substances; infectious diseases. Continuous Common Source Epidemic: When an outbreak lasts longer than the time span of a single incubation period and is caused by a common source of exposure. Example: Cholera outbreak in London 1854

Stages in the Development of A Vaccination Program

Pre-licensing evaluation of vaccine Phase I trials: Safety of adult volunteers testing of new vaccine in fewer than 100 adult volunteers Phase II trials: Immunogenicity and reactogenicity in the target population. if Phase I is successful, this phase expands the number of adult volunteers to between 100 and 200—it's looking for antibody responses and clinical reactions Phase III trials: protective efficacy Post-licensing evaluation Safety and efficacy of vaccine Disease surveillance Serologic surveillance Measurement of vaccine coverage the main test of the vaccine. Vaccine efficacy refers to the reduction in incidence rate of a disease in a vaccinated population vs. in an unvaccinated population. It is only now that a license to manufacture the vaccine may be granted. Phase IV Trials There can be more than three phases in a clinical trial. Phase IV trials involve post-marketing research to gather more information about risks and benefits of a drug. Clinical trials address only short-term efficacy—so Phase IV is used to detect any possible long-term complications or side effects.

Natural History of a Disease

Prepathogenesis—before agent reacts with host. Susceptibility Stage: disease is not present and individuals have not been exposed; Primary Prevention stage Pathogenesis—after agent reacts with host Subclinical Stage—NO symptoms—individuals are exposed but have not started showing symptoms; this is where incubation period takes place. The organism multiples and grows to sufficient numbers to produce a host reaction and clinical symptoms. Secondary Prevention Stage Clinical Stage- The beginning of symptoms—early stages of clinical stage may show signs in lab tests or other exams. Like TB lesions on a chest x-ray or cervical diseases in a PAP smear. Late stages acute symptoms are clearly visible like with food poisoning. Later stages include development of active signs and symptoms. Clinical end points are: recovery, disability, or death. Resolution Stage- Symptoms may vary from mild to severe, residual or chronic forms of the disease that ends either with disabling limitations or death. The condition or disease has caused enough bodily harm/changes that recognizable S/S occur. Also known as the advanced disease stage because it may have completed its course, or it may even conclude with a return to health. Tertiary Prevention Stage *The Goal of epidemiology is to identify and understand the causes and mechanism of disease, disability, and injury so that effective interventions can be created & implemented to prevent these things before they occur or before they get any worse. **Recognizing these different stages of the evolution of disease, one can determine the most effective application of the Levels of Prevention—a framework commonly used in public health.

Confidentiality

Privacy Act of 1974 Prohibits the release of confidential data (by a fed govt. agency or its contractor) without the consent of the individual Freedom of Information Act Mandates the release of government information to the public, except for personal and medical files The Public Health Service Act Protects confidentiality of information collected by some federal agencies, such as the National Center for Health Statistics (NCHS) Privacy and confidentiality of certain information is legally protected. Personally identifiable information includes information that a person has not given permission for release to public. Also covers information on whether a person participated in a study and any information that may identify a deceased person. The HIPAA Privacy Rule Refers to the Health Insurance Portability and Accountability Act of 1996 Sections of HIPAA "...require the Secretary of HHS to publicize standards for the electronic exchange, privacy and security of health information..." Categories of protected health information pertain to individually identifiable data re: The individual's physical and mental health Provision of health care to the individual Payment for provision of health care HIPAA.......NOT HIPPA!! Protects individually identifiable health information, among other things (insurance portability—transfer of insurance from one job to another, etc.). PHI—Protected Health Information pertains to individually identifiable data. Many common identifiers such as name, address, date of birth, social security number. This information is typically demographic data related to: -individual's past, present, or future mental or physical health -provision of health care to the individual -past, present, or future payment for provision of health care. Data Sharing The voluntary release of information by one investigator or institution to another for the purpose of scientific research. Can enhance data quality and increase knowledge from research. Key issue The primary investigator's potential loss of control over information The linkage of large data sets and the pooling of multiple studies that combine results from several research projects in order to enhance the quality of data and to increase knowledge from research. Key issue: The primary investigator's potential loss of control over information—but because of the benefit to society, many researchers are willing to make their non-confidential data available to the research community. Record Linkage Joining data from two or more sources, Employment records and mortality data. Applications include genetic research, planning of health services, and chronic disease tracking. Sources of Epidemiologic data Table 5-1, pages 248 - 250 Sources include a range of information from vital statistics to absenteeism from work and/or school

Prophylactic and Therapeutic Trials

Prophylactic trials are used to evaluate preventive measures such as vaccines, vitamin supplements, or patient education. Therapeutic trials are used to assess new treatment methods such as curative drugs or a new surgical procedure. Controls in these trials would receive the standard of care (for a surgery or drug trial), a placebo (for a vitamin trial), or no intervention (for patient education trial).

School Health Programs

Provide information about immunizations, physical exams, and self-reports of previous illness Have been used in studies of intelligence, mental retardation, and disease etiology Paffenbarger, et al. used information from health records of college students to track causes of chronic diseases. These are usually detailed records that can be extremely valuable in the study of disease etiology. (Paffenbarger)

U.S. Bureau of the Census

Provides information on the general, social, and economic characteristics of the U.S. population U.S. Census is administered every 10 years. Attempts to account for every person and his or her residence Characterizes population according to sex, age, family relationships, and other demographic variables The Census provides information on the general, social, and economic characteristics of the U.S. Population. Is administered every 10 years to entire nation. It's an attempt to account for every person and his/her residence and to characterize the population according to sex, age, family relationships, and other demographic variables.

Prospective Cohort Study

Purely prospective in nature; characterized by determination of exposure levels at baseline (the present), and follow-up for occurrence of disease at some time in the future Enable the investigator to collect data on exposures; the most direct and specific test of the study hypothesis The size of the cohort is under greater control by the investigators Biological and physiological assays can be performed with decreased concern that the outcome will be affected by the underlying disease process. Direct measures of the environment can be made. (e.g., indoor radon levels, electromagnetic field radiation, cigarette smoke concentration)

Quantification

Quantification is a central activity of epidemiology. Epidemiologic measures often require counting the number of cases of disease. So the number of cases are counted and disease distributions are examined according to demographic variables such as age, sex, race, and other variables, such as exposure category and clinical features. Epidemiologists may use graphs or charts to demonstrate the quantification.

Evaluation of Screening Programs

Randomized control trials Subjects randomly receive either the new screening test or usual care. Ecologic time trend studies Compare geographic regions with screening programs to those without. Case-control studies Cases--fatal cases of the disease. Controls--nonfatal cases. Exposure--screening program. Lead time bias The perception that the screen-detected case has longer survival because the disease was identified early. Length bias Particularly relevant to cancer screening. Tumors identified by screening are slower growing and have a better prognosis. Selection bias Motivated participants have a different probability of disease than do those who refuse to participate.

Elimination:

Reduction to zero (or a very low defined target rate) of new cases of an infectious disease in a defined geographical area as a result of deliberate efforts.

Secular Time Trends

Refer to gradual changes in the frequency of a disease over long time periods. Example is the decline of heart disease mortality in the U.S. May reflect impact of public health programs, dietary improvements, better treatment, or unknown factors.

Outcomes of Clinical Trials

Referred to as clinical end points May include rates of disease, death, or recovery The outcome of interest is measured in the intervention and control arms of the trial to evaluate efficacy—these must be measured in a comparable manner.

Data

Refers to the source of data, e.g., vital statistics, case registries, physicians' records, surveys of the general population, or hospital and clinic cases. The nature of the data will affect the types of statistical analyses and inferences that are possible—so it will affect the usefulness. Are the sources vital statistics (death, birth, etc.), case registries, doctors' records, general population surveys, or cases from hospitals/clinics? Refers to investigator's access to data. For example, medical records and other data with personal identifiers may not be used without patients' consent. (HIPAA) Data Perturbation If the data has been stripped of all identifying characteristics, it can be used. The process of modifying the identifying characteristics is called "data perturbation". Insurance Data Sources include: Social Security--provides data on disability benefits and Medicare. Health insurance--provides data on those who receive care through a prepaid medical program. Life insurance--provides information on causes of mortality; also provides results of physical examinations. All of these are examples of insurance data that have been widely used in epidemiologic studies. Limitation of Insurance Data: Data may not be representative of entire population, as the uninsured are excluded. Clinical Data Sources Hospital data Diseases treated in special clinics and hospitals Data from physicians' practices Results from clinical laboratories affiliated with clinical sites. Remember that hospital cancer registries forward information to the SEER database (Conducted by the National Cancer Institute (NCI)). Using clinical data presents challenges because of the confidential nature of most of it, a lack of standardized recordkeeping and diagnostic procedures, etc. Hospital Data Consists of both inpatient and outpatient data Deficiencies of data: Not representative of any specific population Different information collected on each patient Settings may differ according to social class of patients; e.g., specialized clinics, emergency rooms Deficiencies: Individuals do not represent any specific population (the denominator is undefined)—they may come from all over a large metro area or even from other countries. The lack of standardization in recordkeeping and in diagnostic procedures can cause problem with this data as well. Socioeconomic data may be useless, too, because you may have different levels of socioeconomic status being seen in different areas—specialized clinics and renowned hospitals in urban areas vs. hospital ERs and outpatient clinics (low SES will use these more often as their primary source of medical care). A Positive: Electronic Health Records should facilitate the sharing of information among providers and enhance data standardization. Data from Physicians' Practices Limited application due to: Confidentiality of patient data Highly selected group of patients Lack of standardization of information collected Useful for the purposes of: Verification of self-reports Source of exposure data Need written informed consent from patients to release confidential information. Highly selected group of patients: Most patients at private doctor's offices are unrepresentative of the entire population because they generally have insurance or the means by which to pay for the services. Because of the lack of standardization, the records are likely to be highly idiosyncratic documents that cannot be linked readily to other data sources! Data from doctors' offices may be useful in analytic studies—such as finding a population of women at risk for breast cancer.....you could verify self-reports on questionnaires by using the medical records to help you exclude the women who had developed cancer previously. These records could also be used as a source of data for exposure to breast cancer agents, such as birth control pills—more complete data on age at first use, duration of use, and strength, etc. can be obtained reliably from medical records. Absenteeism Data Records of absenteeism from work or school Possible deficiencies: Data omits people who neither work nor attend school. Not all people who are ill take time off. Those absent are not necessarily ill. Useful for the study of rapidly spreading conditions Subject to a host of possible deficiencies! Useful for the study of rapidly spreading conditions, such as respiratory disease outbreaks & epidemics of flus, etc. Morbidity Data from the Armed Forces Reports from physicals, hospitalizations, and selective service examinations Data have been used for: Studies of disease etiology. Study of twins serving in Korean War or WWII to determine influence of "nature and nurture" on cause of disease. Studies investigating genetic factors in obesity Information on reported morbidity from active armed forces personnel and veterans. Since the draft has been abolished, there are no more "selective service exams"---these physical exams are now done selectively to volunteers for military service—this makes the information from these exams less useful than before. Overall, this type of data is useful for studying disease etiology. With the study of obesity in twins, results suggested that the identical twins were more alike in various measures of obesity than the non-identical twins. This expectation was consistent with genetic influences.

Neonatal Mortality Rate equation

Reflects events happening after birth, primarily: Congenital malformations Prematurity (birth before gestation week 28) Measures the risk of dying among newborn infants who are under the age of 28 days for a given year. Neonatal deaths (1st 28d of life)/1000 live birth

Maternal Mortality Rate

Reflects health care access and socioeconomic factors; it includes maternal deaths resulting from causes associated with puerperium (period after childbirth), eclampsia, and hemorrhage. The number of maternal deaths ascribed to childbirth per 10,000 or 100,000 live births. Factors that affect maternal mortality include maternal age, socioeconomic status, nutritional status, and healthcare access. Leading causes of maternal mortality include: complications related to the puerperium (the period of about six weeks after childbirth during which the mother's reproductive organs return to their original nonpregnant condition); eclampsia and pre-eclampsia; hemorrhage of pregnancy, childbirth, and placenta previa; pregnancy with abortive outcome; other causes.

Validity (Accuracy)

The ability of a measuring instrument to give a true measure. Can be evaluated only if an accepted and independent method for confirming the test measurement exists. Content validity--the degree to which the measurement incorporates the domain of the phenomenon under study. Criterion-referenced validity--found by correlating a measure with an external criterion of the entity being assessed. Two types of criterion-referenced validity: Predictive validity--denotes the ability of a measure to predict some attribute or characteristic in the future. Concurrent validity--obtained by correlating a measure with an alternative measure of the same phenomenon taken at the same point in time. Construct Validity--degree to which the measurement agrees with the theoretical concept being investigated.

Disease Registries

Registry--a centralized database for collection of data about a disease Coding algorithms are used to maintain patient confidentiality. Noteworthy uses of registries: Patient tracking Identification of trends in rates of disease Case-control studies Example: SEER program Has provided unique and valuable data on cancer survival, incidence, & treatment. Examples of Registries: Tumor registries, mental disabilities, strokes, unintentional injuries, and diabetes. Collection of information is dependent upon cooperation of agencies/medical facilities. Coding algorithms: names/personal identifiers HAVE to do with the medical records so that patients can be contacted by the registry but information is "coded" to help maintain confidentiality. SEER (Surveillance, Epidemiology, and End Results) Program: -Conducted by the National Cancer Institute (NCI) -Collects cancer data from different cancer registries across the U.S. -Provides information about trends in cancer incidence, mortality, and survival

Relative Risk

Relative risk = Incidence rate in the exposed/ Incidence rate in the non-exposed

Cohort Studies:Measures of Effect

Relative risk is the ratio of the risk of disease or death among the exposed to the risk among the unexposed.

Reasons for Place Variation in Disease

Religious/ethnic groups—7th Day Adventists—vegetarian diet—decreased rates for CHD in the part of Los Angeles where they live. Genetic/environment—relation between a genetic trait and a noxious environment. Increased rates of sickle cell in people who live in Africa; also increase rate in malaria, too. Tay-Sachs: Jews in Eastern Europe (or of Eastern Europe descent). Climate factors—temperature and humidity. Some climates provide a good habitat for pathogens. The tsetse fly lives in tropical areas and carries "African Sleeping Sickness". Hansen's (Leprosy) found primarily in the tropics. So a person's response to noxious influences in the environment is influenced by their genetic makeup! Responses are determined by combination of geographical, cultural, and socioeconomic influences (type & quantity of food).

Prevention of Disease

Research is applied to identify where in a disease's natural history effective intervention might be implemented. The natural history of disease refers to the course of disease from its beginning to its final clinical end points.

Cross-Sectional Study

Sample of subjects (N) is selected first, then exposure and level of disease is determined—however, some studies may focus on just the level of disease while others focus on just the level/distribution of exposure. Individual level Single period of observation—both histories (exposure & history of disease) collected simultaneously. They are basically descriptive in nature—they yield quantitative estimates of the magnitude of a problem but do not measure cause and effect. Sample design: Probability and non-probability sampling is used— Probability sample: every element in the population has a nonzero probability of being included in the sample. Ex. Simple random samples (everyone has same chance of being selected), systematic samples ( some simple, systematic rule—all first names start with C), stratified samples (population divided by age, sex, race, etc.) Non-probability sample: based on a sampling plan that does not have this feature. Ex. Quota samples (certain number of samples must be completed) and Judgmental samples (subjects selected on the basis of investigator's perception that they represent the population as a whole). Surveys of smokeless tobacco use among high school students Prevalence surveys of the number of vasectomies performed Prevalence surveys of cigarette smoking among Cambodian Americans in Long Beach, California Prevalence surveys are helpful for identifying resource needs for health interventions. Read about these studies and more on Pages 296-300 Hypothesis generation Intervention planning Planning health services and administering medical care facilities Estimation of the magnitude and distribution of a health problem Examine trends in disease or risk factors that can vary over time Limited usefulness for inferring disease etiology Do not provide incidence data Cannot study low prevalence diseases Cannot determine temporality of exposure and disease Cannot study low prevalence diseases—prevalence is proportional to the incidence of the disease times its duration. A large survey may have many cases of diseases with short duration. Which came first, the chicken or the egg? They are done so quickly, with exposure and disease histories being taken at the same time, it is hard to determine whether the exposure came first or the disease. This makes it hard to determine cause and effect.

Screening

Screening--the presumptive identification of unrecognized disease or defects by the application of tests, examinations, or other procedures that can be applied rapidly. Positive screening results are followed by diagnostic tests to confirm actual disease. Example: phenylalanine loading test in children positive on PKU screening Surveys Conducted on an ad hoc basis to identify individuals who may have infectious or chronic diseases. Examples: breast cancer screenings, health fairs. Clientele are highly selected. Individuals who participate are concerned about the particular health issue. Ad hoc—a particular group of people Test and procedures to identify unrecognized/suspected cases of disease. Results need to be sent to PCP for follow-up!! multiphasic- Defined as the use of two or more screening tests together among large groups of people. Information obtained on risk factor status, history of illness, and physiologic and health measurements. Commonly used by employers and health maintenance organizations. Administration of 2 or more screening tests during a single screening program Ongoing screening programs often are carried out at worksites. Potential biases from worker attrition Data can be useful for research on occupational health problems. Data may not contain etiologic information. Mass screening--screening on a large scale of total population groups regardless of risk status. Selective screening--screens subsets of the population at high risk for disease. More economical, and likely to yield more true cases. Example: Screening high-risk persons for Tay-Sachs disease. Mass Health Examinations- Population or epidemiologic surveys--purpose is to gain knowledge regarding the distribution and determinants of diseases in selected populations. No benefit to the participant is implied. Epidemiologic surveillance--aims at the protection of community health through case detection and intervention (e.g., tuberculosis control). Case finding (opportunistic screening)--the utilization of screening tests for detection of conditions unrelated to the patient's chief complaint. social- The health problem should be important for the individual and the community. Diagnostic follow-up and intervention should be available to all who require them. There should be a favorable cost-benefit ratio. Public acceptance must be high. scientific- Natural history of the condition should be adequately understood. This knowledge permits identification of early stages of disease and appropriate biologic markers of progression. A knowledge base exists for the efficacy of prevention and the occurrence of side effects. Prevalence of the disease or condition is high. Ethical- The program can alter the natural history of the condition in a significant proportion of those screened. Suitable, acceptable tests for screening and diagnosis of the condition as well as acceptable, effective methods of prevention are available. Characteristics of a Good Screening Test- Simple--easy to learn and perform. Rapid--quick to administer; results available rapidly. Inexpensive--good cost-benefit ratio. Safe--no harm to participants. Acceptable--to target group. Evaluation of Screening Tests- Reliability types Repeated measurements Internal consistency Interjudge Validity types Content Criterion-referenced Predictive Concurrent Construct

Measures of the Validity of Screening Tests

Sensitivity--the ability of the test to identify correctly all screened individuals who actually have the disease (a/a+c). Specificity--the ability of the test to identify only nondiseased individuals who actually do not have the disease (d/b+d). Predictive value (+)—the proportion of individuals screened positive by the test who actually have the disease (a/a+b). Predictive value (-)—the proportion of individuals screened negative by the test who do not have the disease (d/c+d).

Epidemiologic Transition

Shift in the pattern of morbidity and mortality from infectious and communicable diseases to chronic, degenerative diseases. Accompanies demographic transition

Mental health and Social Class

Social causation: environment/conditions actually caused the mental illness. Ask yourself, however—Which came first? The chicken or the egg? Are people economically disenfranchised because they are mentally ill and cannot hold a job or are they economically disenfranchised because their SES caused the mental illness? Downward drift hypothesis: clustering of psychosis was due to drift of schizophrenics to poor areas of a city (they make up a large portion of the homeless!) and not due to lower SES. Other Correlates of Low Social Class Higher rate of infectious disease- TB, rheumatic fever, flu, pneumonia, and other respiratory diseases linked to: -overcrowding -increased exposure -lack of medical care -nutritional deficiencies -poor sanitary conditions Higher infant mortality rate and overall mortality rates (1967 study) Lower life expectancy Larger proportion of cancers with poor prognosis (England & Wales study) May be due to delay in seeking health care Low self-perceived health status education and family income both affected this—the higher the educational level and income, the better health they perceived to be in.

Specific Rates

Specific rates refer to a particular subgroup of the population defined in terms of race, age, sex, or single cause of death or illness. A type of rate based on a particular subgroup of the population defined, for example, in terms of race, age, or sex---or they may refer to the entire population but be specific for some single cause of death or illness.

Hollingshead and Redlich

Studied association of socioeconomic status and mental illness Classified New Haven, Connecticut, into five social classes based on occupational prestige, education, and address Strong inverse association between social class and likelihood of being a mental patient under treatment. As social class increased, severity of mental illness decreased. Type of treatment varied by social class. As social class increased, severity of mental illness decreased: (1958 study) Higher SES patients tended to be more Neurotic (less severe form of mental illness) whereas lower SES patients tended to be more Psychotic (more severe form of mental illness), such as schizophrenic. In 1958, it was more common for the "upper class" to receive treatment from a psychiatrist (private) whereas lower SES patients were treated in state hospitals—usually with shock treatment.

Adjusted Rates

Summary measures of the rate of morbidity and mortality in a population in which statistical procedures have been applied to remove the effect of differences in composition of various populations. So what this is saying in plain terms is that when we have morbidity and mortality rates, ALL we have is the number of illnesses and deaths! We don't know what may have caused them, etc.----or what variables in the population may be influencing the numbers. Age adjustment of morbidity and mortality is a very common adjustment to make—it's probably one of the most important variables in risk of morbidity and mortality! Age plays a big part in death---older populations have a higher risk of dying as opposed to younger populations! Age also plays a part in developing some illnesses (stroke, MI, etc.). So when we see a mortality rate and want to take out the variable of age so that we can see just how many people died from other causes besides aging, there is a method in which to do this. The rate can be "adjusted" to remove the variable of age.....rates can also be adjusted to remove the impact of other variables, too. We are not going to get into calculating age adjustments---just wanted you to know that there are methods in which rates can be adjusted for different variables.

Ascertainment of Epidemics

Surveillance The systematic collection of data pertaining to the occurrence of specific diseases. Epidemic threshold The minimum number of cases (or deaths) that would support the conclusion than an epidemic was underway. This is based on statistical projections.

Tertiary Prevention

Takes place during late pathogenesis Designed to limit disability from disease Also directed at restoring optimal functioning (rehabilitation) Examples include: physical therapy for stroke patients, halfway houses for alcohol abuse recovery, and fitness programs for heart attack patients.

Temporality

Temporality refers to the timing of information about cause and effect. Did the information about cause and effect refer to the same point in time? Or, was the information about the cause garnered before or after the information about the effect?

Reporting the Results of Clinical Trials

The CONSORT Statement is a protocol that guides the reporting of randomized trials by providing a 22-item checklist and a flowchart. Consolidated Standards of Reporting Trials--this just helps authors optimize the quality of their reports on simple RCTs. Organizational and administrative burdens are increased when there are multiple levels of data collection (such as phone interviews, mailed questionnaires, consent forms to access medical records) at multiple time periods. Managing data from cohort studies can be very challenging....this needs to be carefully considered when staffing needs are being defined.

Reliability (Precision)

The ability of a measuring instrument to give consistent results on repeated trials. Repeated measurement reliability--the degree of consistency among repeated measurements of the same individual on more than one occasion. Internal consistency reliability—evaluates the degree of agreement or homogeneity within a questionnaire measure of an attitude, personal characteristic, or psychological attribute. Interjudge reliability—reliability assessments derived from agreement among trained experts.

Descriptive vs. Analytic Epidemiology

The basic premise of epidemiology is that disease does not occur randomly—it occurs in patterns that reflect the operation of underlying factors. This chapter will discuss methods used to describe those patterns—which generally fall into one or more categories: Person, Place, or Time Descriptive studies focus on Person, Place, or Time.....They describe the amount and distribution of the disease. Descriptive studies AND Analytic studies are both types of observational studies (there are no interventions/treatments by the people performing the study). Descriptive studies are performed to gather information and then come up with a hypothesis or hypotheses as to what the epidemiologist thinks is happening. Analytic studies follow the descriptive studies—they explore the determinants of disease—variables such as infectious agents, environmental exposures, and risky behaviors. They ask How and Why! How is the disease/condition being transmitted or spread and Why are some people getting sick and others not? They take the hypothesis/hypotheses developed by the descriptive studies and use them to find the cause of the health problem.

Eradication

The complete and permanent worldwide reduction to zero new cases of an infectious disease through deliberate efforts; no further control measures are required.

Validity of Study Designs

The degree to which the inference drawn from a study, is warranted when account it taken of the study, methods, the representativeness of the study sample, and the nature of the population from which it is drawn. internal validity: A study is said to have internal validity when there have been proper selection of study groups and a lack of error in measurement. Concerned with the appropriate measurement of exposure, outcome, and association between exposure and disease. external validity: External validity implies the ability to generalize beyond a set of observations to some universal statement. A study is externally valid, or generalizable, if it allows unbiased inferences regarding some other target population beyond the subjects in the study.

How Study Designs Differ

The easiest studies to start with when not much is known about the topic of study are studies that can use existing data, that are quick and easy to conduct, and that are economical. As knowledge increases, the complexity of the research questions increases....this will require more rigorous studies at that time. Number of observations made: some studies require that observations be made at just one time while others may do observations at 2 or more points in time. Directionality of exposure: varies because it is relative to disease; the investigator may go back in time (retrospective) and ask subjects about the disease they currently have or may elect to start with a group who does NOT have the disease and follow them prospectively for the development of the disease. Data collection methods: Some methods require use of existing data while some require collection of new data. Timing of data collection: data should be collected and used quickly or questions might be raised about the quality and applicability of the data. Ex. if there was a long time between the measurement of exposure and disease. Unit of observation: Some studies use groups while others use individuals Availability of subjects: certain classes of subjects may be off-limits due to a number of considerations, including ethical issues. Experimental Studies: maintain the greatest control over the research setting; investigator both manipulates variables in the study (drug or placebo—blind, double-blind to eliminate bias) and randomly assigns subjects to exposed and non-exposed groups. One example of an experimental study is a clinical trial—usually a new medication, surgical procedure, etc. They may be used to test hypotheses developed from the observational studies. Usually participants are randomly assigned. Quasi-experimental studies: Manipulation of variables in the study but no randomization of participants—could be thought of as a natural experiment. Ex. Before federal seat belt laws, some states had laws and some did not. Residents didn't determine their "exposure" (seat belt), their politicians did (manipulation)—so a study of the traffic fatalities in states with laws vs. those without is an example of a quasi-experimental study. Another type of quasi-experimental study is a Community trial (or intervention)—most are oriented towards education and behavior modification. Ex. Fluoridation of community's water supply, smoking cessation, control of ETOH use, weight loss, establishing healthy eating behaviors, encouraging increased activity, etc. May also focus on people at high risk for a certain disease within the community and may even help shape public health policy, such as mandatory seat belt usage. Data from these studies can also be used to evaluate programs, to compare programs to determine why something worked or why it failed, to compare costs, and to suggest changes in current health policies. Observational: Descriptive: Person, Place, & Time studies—case reports, case series, cross-sectional surveys. Also, estimates disease frequency and time trends. Mostly used to generate hypotheses for further studies. Analytic: ecologic studies, case-control studies, and cohort studies. Used to test the hypotheses generated by descriptive studies, to generate more hypotheses, and to suggest mechanisms of causation.

Issues in the Classification of Morbidity and Mortality

The nomenclature and classification of disease are central to the reliable measurement of the outcome variable in epidemiologic research. Nomenclature--a highly specific set of terms for describing and recording clinical or pathologic diagnoses to classify ill persons into groups. Classification--the statistical compilation of groups of cases of disease by arranging disease entities into categories that share similar features. Two types of criteria used for the classification of ill persons: Causal (e.g., tuberculosis or syphilis) Manifestational (e.g., affected anatomic site: hepatitis or breast cancer)

How Results Impact Clinical Decisions

The following considerations determine a study's influence: Criteria of causality Relevance to each patient Size of the risk Public health implications Individual vs. population Criteria of causality: Do they meet the criteria of causality we talked about? How large the effect? How consistent with previous research is it? Biologic plausibility? Relevance to each patient: Studies are done in GROUPS—be careful trying to apply conclusions from observations of groups to an individual (can't really tell an individual that he/she is 20 times more likely to get lung CA because he/she smokes—but you can say that GROUPS of individuals who smoke are 20 times more likely to get lung CA.) Size of the risk: there is only a slight risk of mortality from CVD with high serum cholesterol—the risk is small so you may not want to do anything. Public health implications: a risk factor that may not be important for an individual may be important when multiplied over the population as a whole!

History Behind Epidemiology

The history of epidemiology began with the Greeks and Romans!! Environment and disease The Black Death Use of mortality counts Smallpox vaccination Use of natural experiments William Farr Identification of specific agents of disease The 1918 influenza pandemic Uses: Refers to the study of past and future trends in health and illness For example: Secular trends—changes in disease frequency over time

Size and Cost of the Cohort

The larger the size of the cohort, the greater the opportunity to obtain findings in a timely manner. Resource constraints typically influence design decisions. As the size of the study increases, so does the cost! Cost has a big influence on the design decisions. Book talks about some ways to reduce costs on page 345

Koch's Postulates

The microorganism must be observed in every case of the disease The microorganism must be isolated and grown in pure culture The pure culture must, when inoculated into a susceptible animal, reproduce the disease The microorganism must be observed in, and recovered from, the experimentally diseased animal In the late 1800's, Robert Koch actually proved that a human disease was caused by a specific living organism—he was experimenting with Tuberculosis. His research made possible the greater refinement of the classification of disease by specific causal organisms—he basically demonstrated, for the first time, a strict relation between a micro-organism and a disease. This helped provide definite criterion for identifying a specific disease. Before this, they were grouped in such a manner that it had actually hampered their epidemiologic study. His Postulates are shown here.....so for a specific disease to be the cause of an observed infection/disease, these 4 things must be met!

Method of Analogy(MacMahon and Pugh)

The mode of transmission and symptoms of a disease of unknown etiology bear a pattern similar to that of a known disease. This information suggests similar etiologies for both diseases. The distribution of a disease of unknown etiology bears a pattern similar to that of a known disease, which epidemiologists have investigated more thoroughly. Example: diseases of unknown origin that spread during the summer and that are confined to certain geographic areas could be disseminated by a vector (something tha can easily carry it and spread it unnoticed-mosquito). Legionnaires' disease was uncovered using this method—the symptoms were similar to those produced by another infectious respiratory disease. They have, however, made mistakes using this methodology—the hypothesized relationship between smoking and tuberculosis based on the relationship between smoking and lung cancer. Another is the hypothesis of an infectious agent for Multiple Sclerosis because of the similarity of the geographic distribution of MS with that of polio.

Temporal Differences in Cohort Designs

There are several variations in cohort designs that depend on the timing of data collection. These variations are: prospective cohort studies retrospective cohort studies

Causality of Disease

The relationship between an event (the cause) and a second event (the effect), where the second event is a consequence of the first. Why is an event or a disease occurring? What aspect of "environment" (broadly defined) if removed/reduced/controlled would reduce outcome/burden of disease? Is there an association between a disease and other variables? Statistical Associations—Does a statistical association exist between some factor and a health outcome? If the probability of disease seems unaffected by the presence or level of the factor, then there is no association. But if probability of disease DOES vary according to whether the factor is present, then there is a statistical association. Bias is a systematic error resulting from the study, design, execution, or confounding; information (classification) bias: r/t how information is collected, including information that subjects supply or how subjects are classified. Selection bias: bias attributable to the way subjects enter a study. Assessing for Causality: just because statistical association exists doesn't mean that there is a causal relationship or that causality is present. The association could be d/t bias (flaws in study design or execution) or may be a chance event. Some epidemiologists use criteria for causality to evaluate the link between an infectious agent and disease—the next slide will cover these criteria Used to identify etiological factors of diseases; it is a very useful tool in epidemiology to determine the most effective primary prevention activities and develop treatment modalities. Strength of association: Rates of morbidity and mortality must be higher for the exposed group than for the non-exposed group (risk of heart disease is higher in smokers than in non-smokers) Consistency with other studies: Varying types of studies in other populations must observe similar associations. Biological plausibility: The data must make biological sense and represent a coherent explanation for the relationship. Demonstration of correct temporal sequence: Exposure to the causal factor must occur before the effect, or the disease. Dose-response relationship: An increased exposure to the risk factor causes a concomitant increase in disease (risk for heart disease is higher in heavy smokers compared to light smokers) Specificity of the association: The exposure variable must be necessary and sufficient to cause disease; there is only one causal factor. This one is not as important today since diseases have multifactorial origins. Experimental evidence: experimental designs provide the strongest epidemiologic evidence for causal associations, but they are not feasible or ethical to conduct for many risk factors—disease association. Absolute causality is rarely established.... Some non-smokers get heart disease but most all smokers have heart disease....Smoking doesn't give you heart disease but puts you at greater risk for it. ***Keep in mind that there are multiple causes of disease in most cases—this is referred to as multiple causality or causation. Multiple causality (or causation) results in what is known as "web of causation" or "chain of causation" which is very common for noncommunicable/chronic diseases (cancer, cardiovascular, etc.)

Sex Ratio at Birth Calculation

The sex ratio at birth is defined as: (the number of male births divided by the number of female births) multiplied by 1,000. male births/ female births x 1,000 Here it is multiplied by 1000 because you may end up with a very small number if this is not done. 1000 was used because the sex ratio has exceeded 1000 in 1940 and 2002.

Ecologic Studies

The unit of analysis is the group, not the individual. They can be used for generating hypotheses. The level of exposure for each individual in the unit being studied is unknown. Generally makes use of secondary data. Advantageous with cost and duration. With ecologic studies, the marginal numbers are usually known or inferred; the interior cells are unknown. Cheaper because they make use of available data. Ecologic comparison study—involves an assessment of the correlation between exposure rates and disease rates among different groups over the same time period. Ecologic trend study—involves correlation of changes in exposure with changes in disease within the same community, country, or other aggregate unit. used to ascertain trends—Ex. the consistent downward trend in the incidence of and mortality from CHD. Is it really happening? What are the reasons? If it is due to doctors working hard to achieve this result, then they need to claim responsibility for it....using this type of study, ecologic correlation data could be generated that can support the claim that there was an increase in BP meds being prescribed or an increase in CABG surgeries. Example of an Ecologic Correlation study. The association between breast cancer and dietary fat for 39 countries. High intakes of dietary fats associated with high rates of breast cancer mortality. Another example is the study of childhood lead poisoning in Massachusetts. More than 200,000 children screened in doctors' offices, hospitals, state-funded screening sites, through nutritional programs, and door-to-door screening in high-risk areas (houses, daycares, schools older than 1978). There are a few more examples of ecologic studies in your textbook....read about them from page291-292. Is the ranking of cities by air pollution levels associated with the ranking of cities by mortality from cardiovascular disease, adjusting for differences in average age, percent of the population below poverty level, and occupational structure? What are long-term trends (1950-1995) for mortality from the major cancers in the US, Canada, and Mexico? The effect of fluoridation of the water supply on hip fractures The association of naturally occurring fluoride levels and cancer incidence rates The relationship between neighborhood or local area social characteristics and health outcomes Advantages—When little is known about the association between an exposure and a disease, an ecologic study is a good place to start—it's quick, simple, and inexpensive. When a disease etiology is unknown, ecologic studies are a good approach for generating hypotheses. Disadvantages— ecological fallacy and imprecise measurement of exposure and disease makes accurate quantification of this exposure/disease relationship difficult.

Strengths vs Limitations

The utility of the data for various types of epidemiologic research. Factors inherent in the data may limit their usefulness. Incomplete diagnostic information. Case duplication. How useful is the data for various types of epidemiologic research? Investigations of mortality, detection of outbreak of infectious disease, studies of the incidence of chronic disease. Example—death certificates may define the cause(s) of death but not the etiology. Are there limiting factors in data? Can be due to incomplete diagnostic information and case duplication.

Sufficiency of Scientific Justification

There should be considerable scientific rationale for a cohort study. Additional justification for cohort studies may come from laboratory experiments or animal studies. Cohort studies are the only observational study design that permits examination of multiple outcomes. Since cohort studies are so costly in time, money, and energy, you need lots of scientific rationale to conduct one. May find some cohort studies to support your need to conduct another cohort study from laboratory experiments or animal studies. Cohort studies are the only observational study design that permits examination of multiple outcomes—remember that case-control studies can study only ONE outcome at a time.

Mill's Canons of Inductive Reasoning

These are processes for deriving or creating hypotheses: The method of difference: basically, it's stating that when looking at 2 or more domains (areas), everything is the same except for one factor. The variation in the frequency of disease between the 2 domains is due to a variation in the single causative factor (the factor that is different). The method of agreement: A single factor is common to a variety of settings, so it is hypothesized that this single factor is the cause of the disease. (air pollution—where it exists, the prevalence of chronic respiratory diseases, such as asthma and emphysema, tends to increase.) The method of concomitant variation: the frequency of disease varies according to the potency of a factor. A factor is the causative agent of the disease. Example: the direct relationship (confirmed by numerous studies) between the incidence of lung disease (lung cancer, bronchitis, emphysema) and the number of cigarettes smoked: The more cigarettes smoked, the greater the risk of incurring lung cancer and other lung diseases. The method of residues: subtracting potential causal factors to determine which factor(s) have the greatest impact. Example: Coronary Heart Disease (CHD)—the risk factors—one's hereditary, stress level, diet, amount of exercise, and blood lipid level are quantified to determine which factor has the greatest impact.

Exposure-Based Cohort Studies

These studies overcome limitations of population-based cohort studies, which are not efficient for rare exposures. Certain groups, such as occupational groups, may have higher exposures than the general population to specific hazards. An exposure-based cohort is made up of subjects with a common exposure. Examples: Workers exposed to lead during battery production Childhood cancer survivors Veterans College Graduates

Factors Affecting Reliability of Observed Changes

Things may not always be what they seem!! When we are looking for the reasons for these trends, we have to take into account certain conditions that may affect the reliability of the observed changes. These things can be variations in diagnosis, reporting, case fatality, or something else.....let's look at these in a bit more detail: Lack of comparability over time due to altered diagnostic criteria: the diagnostic criteria used in a later time reflect new knowledge about a disease and therefore may be more precise (considerable information has been obtained over ¾ of a century about chronic diseases!); some categories of disease may be omitted altogether. Aging of the general population: due to reduced impact of infectious diseases, better medical care, and a decline in the death rate; also the cause of death may be wrongly attributed to a disease in elderly with multiple co-morbidities—multiple organ systems may fail simultaneously. Changes in the fatal course of the condition: may be seen over the long run of a disease—decreases in the number of people who actually die of it.

Age-Specific Rate (Ri)

This is an example of making it specific to a certain disease/condition. The above example shows an example of an age-specific cancer mortality rate for 5-14 year olds. **Specific rates are a much better indicator of risk than crude rates, especially for rates specific to defined subsets of the population (age, sex, race specific). **A disadvantage of specific rates is the difficulty in visualizing the "big picture" in those situations where specific rates for several factors are presented in complex tables. It can be difficult to synthesize the data from complex tables such as Table 3-5 on page 141. R1= # of deaths among those aged 5-14/ # of person who aged 5-14 years (during time period) x 100,000

Hierarchy of Study Designs

This just shows that the experimental study design is the best for identifying a relationship between cause and effect. All of the observational studies are less powerful. The experimental study is the most scientifically rigorous method of hypothesis testing available involving human participants. The emphasis is on rigor and not feasibility. Experimental studies help us to overcome some deficiencies in observational studies! By exercising control over who will receive the exposure as well as the level of exposure, the investigator in an experimental study more confidently may attribute cause and effect to associations than in observational designs! This doesn't mean that experimental designs are always the most appropriate design for investigating the causes of disease—experimental designs don't work well in occupational or environmental health. For example—you may want to examine the contribution of smoking and radon exposure to lung cancer among uranium miners. For ethical reasons....you can't do this because it would involve deliberate exposure of subjects to agents suspected of being harmful. An observational study would be the only answer here. However, you could always use a prospective experimental study to test the hypothesis that elimination of exposure to smoking and radon reduces lung cancer risk!

Total Fertility Rate

This rate is "[t]he average number of children that would be born if all women lived to the end of their childbearing years and bore children according to a given set of age-specific fertility rates." In the United States, the total fertility rate was 2.06 in 2012. This rate is close to the replacement fertility rate of 2.1—the rate at which the number of births is equivalent to the number of deaths. The 2.06 total fertility rate is close to the replacement fertility rate of 2.1—when the U.S. total fertility rate reaches 2.1, it will no longer have a net population gain due to births!

Demographic Sex Ratio Calculation

This ratio refers to the number of males per 100 females. In the U.S., the sex ratio in 2010 for the entire population was 96.7, indicating more females than males. male/female x 100

Aims and Levels of Epidemiology

To describe the health status of populations To explain the etiology of disease To predict the occurrence of disease To control the occurrence of disease Epidemiology is concerned with efforts to describe, explain, predict, and control disease causes. There is a hierarchy to these aims—they are leveled: description of the occurrence of disease is less demanding than explaining the causes of a disease and predicting and controlling it; therefore, it is ranked lower than the other 3. Importance increases as you go "up" the list: explain is higher than describe, predict is higher than explain, control is the highest!

Relationship Between Sensitivity and Specificity

To improve sensitivity, the cut point used to classify individuals as diseased should be moved farther in the range of the nondiseased (normals). To improve specificity, the cut point should be moved farther in the range typically associated with the disease. Retrain screeners--reduces the amount of misclassification in tests that require human assessment. Recalibrate screening instrument--reduces the amount of imprecision. Utilize a different test. Utilize more than one test.

Blinding (Masking)

To maintain the integrity of a study and reduce the potential for bias, the investigator may utilize one of two popular approaches: Single-blind design: subject unaware of group assignment, but investigators aware Double-blind design: Neither subject nor experimenter is aware of group assignment

Coverage

Two sub-criteria of Completeness of Population Coverage are Representativeness and Thoroughness. Generalizability is a related concept of representativeness. Representativeness: have major subdivisions of the population been omitted from the data? Is the population base clearly defined or is there an unspecified mixture of different populations? Generalizability: (also called external validity) can the findings of this study be taken and applied to people who didn't participate in the study? If the population selection was too narrow, it would be inappropriate to apply findings to other groups. (Example: Black medical students compared to white medical students instead of entire population of medical students). Thoroughness: care taken to identify all cases of a given disease. Are only the severe cases represented in the data—the ones that have come to attention of health care? Are there likely to be substantial numbers of unreported cases?

Place

Types of place comparisons: International WHO & OECD Geographic (within-country) variations Urban/rural differences Localized occurrence of disease This is on page 203 in your textbook. WHO—The World Health Organization—a major source of information on international variations in rates of disease. As countries/regions become more developed, the "diseases of affluence" (CHD, HTN, CVA, & DM) begin to become more common. Cardiovascular diseases are the leading cause of death worldwide! Cancer is the 2nd cause—and increasing worldwide. Different parts of the world have different infectious diseases (remember migration). Africa & some sections of Latin America have blood flukes (worms), but U.S. does not unless it's imported (infected rivers/lakes). Malaria is rampant in some parts of Africa but we have very few cases here. Communicable diseases vary in incidence in countries. AIDS/HIV, Zoonotic diseases such as rabies, Mad Cow disease (was only in UK first, then spread to Europe, Japan, and U.S). The OECD is the Organization for Economic Cooperation and Development. It computes international comparisons in life expectancy, which varies greatly from one country to another.

Census Bureau Sources Relevant to Epidemiologic Studies

U.S. Bureau of the Census publications: Statistical Abstract of the United States County and City Data Book Decennial Censuses of Population and Housing Historical Statistics of the United States, Colonial Time to 1970 http://www.census.gov/

Urban/Rural Differences in Disease Rates

Urban Diseases and mortality associated with crowding, increased person-to-person contact, pollution, & poverty Example: lead poisoning in inner cities; heart disease, TB, cirrhosis, & most cancers increased in urban vs. rural Increased homicide rates in central cities Rural Mortality (among all age groups) increases with decreasing urbanization. Health risk behaviors higher in rural South More smoking, poorer, & more physically inactive Remember that there are many different variables that we need to consider when we are looking at statistics! Variations in these two areas reflect, in part, differences in demographic, economic, social, and environmental characteristics between rural and urban areas. Also we need to consider the availability and nature of health care resources.

Crude Rates

Use crude rates with caution when comparing disease frequencies between populations. Observed differences in crude rates may be the result of systematic factors (e.g., sex or age distributions) within the population rather than true variation in rates.

General Fertility Rate

Used for comparisons of fertility among age, racial, and socioeconomic groups. General fertility rate [# of live births within a year/ # of women aged 15-44 yrs. during the midpoint of the year] X 1,000 women aged 15-44 Sample calculation: During 2009, there were 61,948,144 women aged 15 to 44 in the U.S. There were 4,130,665 live births. The general fertility rate was 4,130,665/61,948,144 = 66.7 per 1,000 women aged 15 to 44. Used for comparisons of fertility among age, racial, and socioeconomic groups. This rate consists of the number of live births reported in an area during a given time interval divided by the number of women of childbearing age (15 - 44 or 15 - 49 in some instances).

Infant Mortality Rate

Used for international comparisons; a high rate indicates unmet health needs and poor environmental conditions. Obtained by dividing the number of infant deaths during a calendar year by the number of live births reported in the same year. This measures the risk of dying during the first year of life among infants born alive. **Note that not all infants who die in a certain year are born in that year (baby born in one year and dies in the next year). This is a source of error, but usually evens out. Infant mortality rates are highest among the least developed countries. # of infant deaths among infants aged 0-365 during the year/ # of live births during the year Used for international comparisons; a high rate indicates unmet health needs and poor environmental conditions. Obtained by dividing the number of infant deaths during a calendar year by the number of live births reported in the same year. This measures the risk of dying during the first year of life among infants born alive. **Note that not all infants who die in a certain year are born in that year (baby born in one year and dies in the next year). This is a source of error, but usually evens out. Infant mortality rates are highest among the least developed countries.

MSA

Used to distinguish between metropolitan and nonmetropolitan areas Metropolitan area large population nucleus together with adjacent communities that have a high degree of economic & social integration with the nucleus Six urban-classification levels used by the National Center for Health Statistics (refer to text, Pg. 212). How does the census determine between Rural and Urban? MSAs (Metropolitan Statistical Areas) are used to provide a distinction between metropolitan and non-metropolitan areas by type of residence, industrial concentration, and population concentration. Pg. 212 differentiates between small, medium, and large metropolitans.

Crude Birth Rate

Used to project population changes; it is affected by the number and age composition of women of childbearing age [Number of live births within a given period/ Population size at the middle of that period] X 1,000 population Sample calculation: 4,130,665 babies were born in the U.S. during 2009, when the U.S. population was 307,006,550. The birth rate was 4,130,665/307,006,550 = 13.5 per 1,000. Used to project population changes; it is affected by the number and age composition of women of childbearing age. It is a useful measure of population growth and is an index for comparing developed and developing countries. It is the number of live births during a specified time period (like one calendar year) per the resident population during the midpoint of the time period (expressed as rate per 1,000). It is generally higher in less developed areas than in more developed areas of the world.

Epidemiology and Policy Evaluation

Using epidemiologic methodologies to evaluate public health policies Examples: tobacco control (smoke-free bars, restaurants, malls, etc.), needle distribution programs, ban on plastic bags, printing of nutritional content on restaurant menus, removal of high fat and high sugar content foods from vending machines in schools, fluoridation of water, helmet laws for motorcycles, seatbelt laws, and prohibition of drivers' use of cell phones

Effects of Disease Prevalence on the Predictive Value of a Screening Test

When the prevalence of a disease falls, the predictive value (+) falls, and the predictive value (-) rises. Sensitivity and Specificity are stable properties of screening tests—they are unaffected by the prevalence of a disease. Predictive value, however, is very much affected by the prevalence of the condition being screened. So when the prevalence of a disease falls, the predictive value + falls, and the predictive value - rises! (look back at slide 24 for explanation on what predictive value + means and what predictive value - means. ) This is on page 480; information about predictive value + and predictive value - can be found on page 478

HIV Incidence & Prevalence

Why do you think prevalence is going up (increasing) and new cases (Incidence) went down?! If I had HIV/AIDS or someone I loved did, this would be great news---people are living LONGER with HIV/AIDS and this is why the prevalence is increasing!! The medications are working! At the same time, new infections appear to have decreased in 1991 and look to be remaining at the same level---maybe our health promotion education/prevention strategies are working?! **While prevalence data is helpful for determining the extent of a disease in the community, it is not as helpful as incidence data when we are looking at the causality and/or the risk of contracting a disease. As shown a few slides back, the cumulative incidence rate can be helpful in estimating the risk of developing the disease.

International Comparisons of Disease Frequency

World Health Organization (WHO) tracks international variations in rates of disease. Infectious and chronic diseases show great variation across countries. Variations are attributable to climate, cultural factors, dietary habits, and health care access. The U.S. fell in the bottom half of OECD countries for both male and female life expectancy; Japan was highest. BIG variation in infectious and chronic illness from one country to another! Possible reasons: climate, cultural factors, national dietary habits, and access to health care. The U.S. fell in the bottom half of life expectancy for both male and female (males 75.2 years and females 80.4 years) while Japan had the highest life expectancy with males living to 78.6 years and females to 85.6. Russian Federation was the lowest with men living 59.1 years and females living 72.4.

Describing the Mortality Experience of the Population

Years of Potential Life Lost (YPLL) Computed for each individual in a population by subtracting that person's life span from the average life expectancy of the population Disability-adjusted life years (DALYs) Adds the time a person has a disability to the time lost to early death

Stratification

adv. Performing analyses within strata is a direct and logical strategy. Minimum assumptions must be satisfied for the analysis to be appropriate. The computational procedure is straightforward. dis. Small numbers of observations in some strata. A variety of ways to form strata with continuous variables. Difficulty in interpretation when several confounding factors must be evaluated. Categorization results in loss of information.

Epidemiologic methods

are used to describe the Health of the Community. Epidemiologic methods and studies also provide a key to the types of problems that require attention and also determine the need for specific health services

Descriptive

used to identify a health problem that may exist. Characterizes the amount and distribution of disease Descriptive studies focus on Person, Place, or Time.....They describe the amount and distribution of the disease. Descriptive studies AND Analytic studies are both types of observational studies (there are no interventions/treatments by the people performing the study). Descriptive studies are performed to gather information and then come up with a hypothesis or hypotheses as to what the epidemiologist thinks is happening.

Joseph Goldberger

cure for pellagra—a nutritional deficiency disease characterized by the 3 D's: dermatitis, diarrhea, and dementia.

Epidemiology

derives from "epidemic," a term which provides an immediate clue to its subject matter. • originates from the Greek words, epi (upon) + demos (people) + logy (study of). • Epidemiology is concerned with the distribution and determinants of health and diseases, morbidity, injuries, disability, and mortality in populations. • Epidemiologic studies are applied to the control of health problems in populations

Morbidity

designates illness

Legionnaire's

determinant? Bacteria in stagnant water (transmitted through the cooling system of a hotel—1978)

Alexander Langmuir

established CDC's Epidemic Intelligence Service. 1949—established the epidemiology section (EIS) of the CDC

Analytic studies

follow descriptive studies, and are used to identify the cause of the health problem Analytic studies follow the descriptive studies—they explore the determinants of disease—variables such as infectious agents, environmental exposures, and risky behaviors. They ask How and Why! How is the disease/condition being transmitted or spread and Why are some people getting sick and others not? They take the hypothesis/hypotheses developed by the descriptive studies and use them to find the cause of the health problem. Person Place Time Secular trends Cyclical patterns Event-related clusters Descriptive epidemiology describes the distribution of disease, death, and other health outcomes in the population according to person, place, and time, providing a picture of how things are or have been—the who, where, and when of disease patterns. The variables of person, place, and time directly or indirectly relate to the occurrence of illnesses by affecting a wide range of exposures associated with lifestyle, behavioral patterns, healthcare access, and exposure to environmental hazards....just to name a few. In this chapter, we will identify descriptive characteristics (age, sex, race, etc—all Person characteristics) that help to delineate patterns of disease and generate hypotheses regarding their underlying causes. We will also look at Place and Time characteristics. To get your thinking going in the right direction, let's look at a few characteristics of Person, Place, and Time! Personal characteristics of interest in epidemiology include race, ethnicity, sex, age, education, occupation, income (and related socioeconomic status), and marital status. The most important predictor of overall mortality is age. The combination of variables, such as age and sex ,is noteworthy, too. When we think of the distribution of a disease, geographical patterns come to mind: does the rate of disease differ from place to place (e.g., with local environment)? In relation to time, epidemiologists ask these questions: Is there an increase or decrease in the frequency of the disease over time? Are other temporal (and spatial) patterns evident? Temporal patterns of interest to epidemiologists include secular trends, point epidemic, cyclical patterns, and event-related clusters. 3 Broad Objectives of Descriptive Epidemiology: To evaluate and compare trends in health and disease: this includes monitoring known diseases as well as identifying emerging problems To provide a basis for planning, provision, and evaluation of health services: data needed for efficient allocation of resources often come from descriptive epidemiologic studies To identify problems for analytic studies (creation of hypotheses) and suggest areas that may be fruitful for investigation. Hypotheses—remember high school science?!! 3 types: Positive declaration (research hypothesis): The infant mortality rate is higher in one area than another. Negative declaration (null hypothesis): there is no difference between the infant mortality rates of two regions Implicit question: To study the association between infant mortality and geographic region of residence. Hypotheses should be made as explicit as possible and not left as implicit!

Health Disparities

is defined as differences in health outcomes that are closely linked with social, economic, and environmental disadvantage. Healthy People 2010, Goal 2 " . . . To eliminate health disparities among segments of the population, including differences that occur by gender, race, or ethnicity, . . ." Healthy People 2020 ". . .To achieve health equity, eliminate disparities, and improve the health of all groups. . ." 6 Areas of focus of the U.S. Department of Health and Human Services: Infant Mortality Cancer screening and management Cardiovascular disease Diabetes HIV/AIDS Immunizations Infant Mortality: U.S. is 27th in the world when compared to other developed nations; however, in African Americans, infant mortality is 2.45 times higher than in whites—epidemiology can identify geographic areas with increased rates of infant mortality and help find risk factors! Gini Index: a common measure of income inequality Range: 0 - 1 The closer it gets to 1, the greater level of inequality. In 2007, the U.S. was 0.46 Association between the Gini index and health inequality is reported using the number of healthy days for a state. The higher that number, the lower the health inequality. The 3 states with the lowest health inequality: Utah, Connecticut, North Dakota The 3 states with the highest health inequality: Tennessee, Kentucky, and West Virginia

Natural Experiments

naturally occurring circumstances in which people in a population have different levels of exposure to a supposed causal factor in a situation resembling an actual experiment, a situation in which human subjects are randomly allocated to groups— In most experiments, the presence of a person in a particular group is typically nonrandom!

Red Spots

on Airline Flight Attendants—dye from life vests was found to be the determinant.

Error

random: Reflect fluctuations around a true value of a parameter because of sampling variability. Factors That Contribute to Random Error- Poor precision Occurs when the factor being measured is not measured sharply. Analogous to aiming a rifle at a target that is not in focus. Precision can be increased by increasing sample size or the number of measurements. Example: Bogalusa Heart Study Sampling error Arises when obtained sample values (statistics) differ from the values (parameters) of the parent population. Although there is no way to prevent a non-representative sample from occurring, increasing the sample size can reduce the likelihood of its happening. Variability in measurement The lack of agreement in results from time to time reflects random error inherent in the type of measurement procedure employed. Bias (Systematic Errors) "Deviation of results or inferences from the truth, or processes leading to such deviation. Any trend in the collection, analysis, interpretation, publication, or review of data that can lead to conclusions that are systematically different from the truth." Selection bias- Refers to distortions that result from procedures used to select subjects and from factors that influence participation in the study. Arises when the relation between exposure and disease is different for those who participate and those who theoretically would be eligible for study but do not participate. Example: Respondents to the Iowa Women's Health Study were younger, weighed less, and were more likely to live in rural, less affluent counties than non-respondents. Information bias- Can be introduced as a result of measurement error in assessment of both exposure and disease. Types of information bias: Recall bias: better recall among cases than among controls. Example: Family recall bias Interviewer/abstractor bias--occurs when interviewers probe more thoroughly for an exposure in a case than in a control. Prevarication (lying) bias--occurs when participants have ulterior motives for answering a question and thus may underestimate or exaggerate an exposure Confounding The distortion of the estimate of the effect of an exposure of interest because it is mixed with the effect of an extraneous factor. Occurs when the crude and adjusted measures of effect are not equal (difference of at least 10%). Can be controlled for in the data analysis. To be a confounder, an extraneous factor must satisfy the following criteria: Be a risk factor for the disease. Be associated with the exposure. Not be an intermediate step in the causal path between exposure and disease. Simpson's paradox: means that an association in observed subgroups of a population may be reversed in the entire population. Illustrated by examining the data (% of black and gray hats) first according to two individual tables and then by combining all the hats on a single table. When the hats are on separate tables, a greater proportion of black hats than gray hats on each table fit. On table 1: 90% of black hats fit 85% of gray hats fit On table 2: 15% of black hats fit 10% of gray hats fit When the man returns the next day and all of the hats are on one table: 60% of gray hats fit (18 of 30) 40% of black hats fit (12 of 30) Note that combining all of the hats on one table is analogous to confounding. ex. Air pollution and bronchitis are positively associated. Both are influenced by crowding, a confounding variable. The association between high altitude and lower heart disease mortality also may be linked to the ethnic composition of the people in these regions. Techniques to Reduce Selection Bias- Develop an explicit (objective) case definition. Enroll all cases in a defined time and region. Strive for high participation rates. Take precautions to ensure representativeness. Ensure that all medical facilities are thoroughly canvassed. Develop an effective system for case ascertainment. Consider whether all cases require medical attention; consider possible strategies to identify where else the cases might be ascertained. Compare the prevalence of the exposure with other sources to evaluate credibility. Attempt to draw controls from a variety of sources. Use memory aids; validate exposures. Blind interviewers as to subjects' study status. Provide standardized training sessions and protocols. Use standardized data collection forms. Blind participants as to study goals and classification status. Try to ensure that questions are clearly understood through careful wording and pretesting. Prevention strategies--attempt to control confounding through the study design itself. Three types of prevention strategies: Randomization Restriction Matching Two types of analysis strategies: Stratification Multivariate techniques

Mortality

referes to deaths that occur in a population or other group

John Snow

was an English physician and anesthesiologist. Called "The Father of Epidemiology" He investigated a cholera outbreak that occurred during the mid-19th century in Broad Street, Golden Square, London. natural experiments Linked the cholera epidemic to contaminated water supplies Groundbreaking because he used many features of epidemiologic inquiry: He used powers of observation He used a spot map (Cluster map) of cases He tabulated (counted) the number of fatal attacks and deaths At this time (1849), people didn't know what caused cholera.....they didn't know it was caused by water contaminated by sewage! So John Snow's natural experiment was groundbreaking—because he used the spot map (cluster map) and counted the number of deaths and those sick—these are epidemiologic methods! Through his observations and inference, he was the first to hypothesize that water contaminated with sewage was the cause of cholera. In 1849, ALL residents in this area of London received contaminated water from 2 water companies, the Lambeth Company and the Southwark and Vauxhall Company, which both got their water from the Thames River at a point heavily contaminated with sewage. Snow's Nat Experiment: Two different water companies supplied water from the Thames River to houses in the same area. The Lambeth Company moved its source of water to a less polluted portion of the river. Snow noted that during the next cholera outbreak those served by the Lambeth Company had fewer cases of cholera. Then, after the cholera outbreak in 1849, one of the companies, the Lambeth company, relocated their water plant to a less contaminated part of the Thames river. When another cholera epidemic happened in 1854, two-thirds of London's resident population south of the Thames River was still being served by both water companies—their water lines were laid out in an interpenetrating manner, so that houses on the same street were receiving their water from different sources. This time, Snow was able to demonstrate that a disproportionate number of residents who contracted cholera in the 1854 outbreak used water from the company that was still using the highly contaminated water, in comparison with the other company, which was using relatively unpolluted water!

Wade Hampton Frost

was the first professor of epidemiology in the U.S. 1930's—advocated the use of quantitative methods to define public health problems. Popularized cohort analysis method. Arranged tuberculosis mortality rates in a table with age on one axis and year of death on the other.

Ebola:

what was the determinant that brought about changes in these people's health? Viral hemorrhagic fever