MBI 361 Exam 1

Differentiate between scientific findings and statements of opinion in media.

A factual statement, regardless of whether it was accurate or inaccurate. In other words, they were to choose this classification if they thought that the statement could be proved or disproved based on objective evidence. "The scientific method is important because it allows us to objectively approach the questions we are confronted with in daily life. The keys to the scientific method are striving to be objective (i.e., unbiased), and to collect data to support a conjecture, or hypothesis." An opinion statement, regardless of whether they agreed with the statement or not. In other words, they were to choose this classification if they thought that it was based on the values and beliefs of the journalist or the source making the statement, and could not definitively be proved or disproved based on objective evidence. "One thing that bothers me is when people try to counter sound research evidence with their opinion ("I don't care what the studies show, I believe that...."). The problem with that line of argument is summed up by the first rule of debate: No matter how passionate or eloquent a speech (nor how much it appeals to 'common sense,' I might add) is, it is powerless without sound evidence or information to back it up."

Identify the parts of a primary research article and become more proficient in reading research articles.

A primary research article typically follows a structured format, and understanding its different parts can indeed help you navigate and comprehend the information more effectively. Title: The title should be concise and provide a glimpse of the research topic. Abstract: This is a brief summary of the entire study, including the research question, methods, results, and conclusions. It helps readers quickly grasp the main findings. Introduction: This section outlines the background and context of the study, defines the research problem or question, and often includes a literature review to show what is already known and what gaps the study aims to fill. Methods (or Methodology): Here, the authors describe how the study was conducted. This includes details about the study design, participants or subjects, data collection methods, and statistical analyses. It should provide enough information for another researcher to replicate the study. Results: This section presents the findings of the study. It often includes text, tables, and figures to convey the data. Raw data is usually not included; instead, the authors summarize and interpret the results.

Explain how scientific investigation is conducted and how peer review works.

A study design is a formal approach of scientific or scholarly investigation. It is the program that directs the researcher along the path of systematically collecting, analyzing, and interpreting observations. Peer reviews are conducted when a paper is sent to a group of experts that evaluate the article's quality. The editors are the authors peers. Since they are working in the same research area and since they are making a judgement about the article, they are sometimes called references. So peer reviewed articles are sometimes called "referred articles". Each reviewer evaluates the article by asking questions to judge the quality and significance of the research. Questions like "what is this research about?" "Is it interesting?" "Is it important?" "Is the methodology sound?" "Are the conclusions logical?" and "Are the findings original?" Based on the answers to these questions the reviewers decide whether the article is worthy of publication in the journal. They then make a recommendation to the editor, either approve the article for publication or reject it. Even if they recommend publishing the article, they usually expect the author to make revisions. The editor, however, makes the final determination.

Describe the major public health achievements of the 20th century

After reading over the ten great public health achievements I personally do not think that I or many others, would be able to live without the achievement of prevention and control of infectious diseases. As we know from past microbiology classes, there are different levels of prevention (primary, secondary, and tertiary). These levels of prevention have been established in public health to identify health problems, establish priorities, and find health resources for preventing, protecting, and promoting the overall health of the public (Merrill, 14). Infectious diseases, as we know from historical events from our textbook, can spread rapidly. This means that a population can be taken down very quickly from an infection. Today we have the technology and understanding to prevent this from happening. We can prevent this in many ways such as quarantining, vaccinating, and promoting personal hygiene. These are all crucial prevention methods that could protect the overall well-being of a population at risk for the spread of an infectious disease. Without these measures at play, there is a great risk that there could continue to be a spread of the infectious disease, and could potentially cause a widespread illness and possibly even death in a population. As we all know from either personal experience or from watching the news, a widespread illness across a population can create chaos. This chaos can be in many places, one in particular would be healthcare systems. If there are troubles with controlling disease and an outbreak occurs. There is a possibility that healthcare systems will be hit with many patients at one time. This can be defined as a healthcare system overload. We all saw it happen when COVID hit. Hospitals were filled to the rim with patients who were so sick, some even having to be put on ventilators. When a pandemic like this hits, sometimes there isn't enough room for everyone in a hospital. Luckily, after a while of being in the red zone for COVID cases, the area I lived in calmed down and hospitals were becoming less chaotic. If we didn't have prevention and control efforts in place for infectious diseases this could happen more often. Healthcare systems would constantly be overwhelmed and over

Recognize age-adjusted data, what they represent, and why they are used.

Age-adjusted data is a statistical method used to compare and analyze data across different populations or time periods by accounting for differences in age distributions. The purpose of age adjustment is to remove the influence of age as a confounding factor, allowing for a more accurate comparison of health or disease-related outcomes. Here's why age-adjusted data is important: Population Comparisons: Different populations can have varying age structures. Comparing raw data, such as disease rates or mortality rates, without adjusting for age can lead to misleading conclusions. Age-adjustment helps in comparing populations with different age distributions more fairly. Temporal Comparisons: Over time, the age distribution of a population can change due to factors like changes in birth rates or life expectancy. Age-adjusted data allows researchers to compare data across different time periods while accounting for changes in the age composition of the population. Standardization: Age adjustment involves applying a standard population structure (often a reference population) to the populations being compared. This standardization helps in isolating the effects of age, making the comparison more accurate. Risk Assessment: When assessing risks or developing public health policies, it's crucial to understand how age influences outcomes. Age-adjusted data helps identify whether observed differences in health outcomes are primarily due to age or other factors. Example: Consider two cities with different age distributions. City A has a higher proportion of elderly individuals, while City B has a younger population. If you compare the overall rates of a specific disease, it might appear that City A has a higher incidence. However, age-adjustment would account for the fact that the higher incidence in City A might be partly explained by its older population. This allows for a more accurate comparison of disease rates between the two cities. In summary, age-adjusted data is a valuable tool in epidemiology and public health research. It helps researchers and policymakers make more meaningful comparisons and draw accurate conclusions when analyzing health-related data across different populations or time periods.

Define analytic epidemiology (observational), state how it differs from descriptive epidemiology, and name the main types of observational analytic studies.

Analytic Epidemiology (Observational): Analytic epidemiology is a branch of epidemiology that involves the study of the determinants of disease and the identification of factors that influence the occurrence and distribution of health-related events within a population. It aims to understand the causes of diseases by analyzing patterns and associations through observational studies. Differences from Descriptive Epidemiology: Descriptive Epidemiology: Describes the distribution of health-related events, such as the occurrence of diseases, by person, place, and time. It provides a snapshot of the who, what, where, and when of a particular health issue. Analytic Epidemiology: Goes beyond describing the distribution and seeks to identify and understand the causes of health-related events. It explores the "why" and "how" of disease occurrence. Main Types of Observational Analytic Studies: Cohort Studies: Participants are classified into groups (cohorts) based on exposure status, and then followed over time to observe the development of diseases. Cohort studies are useful for assessing causal relationships between exposures and outcomes. Case-Control Studies: Participants with a particular health outcome (cases) are compared with those without the outcome (controls) to identify differences in exposure history. Case-control studies are often more efficient for rare diseases and are suitable for investigating associations. Cross-Sectional Studies: Data is collected from participants at a single point in time to examine the relationship between exposures and outcomes. Cross-sectional studies provide a snapshot of the population and are useful for generating hypotheses about associations. Ecological Studies: Analyzes population-level data to explore associations between exposure variables and health outcomes. Ecological studies examine correlations between variables at the group or population level rather than at the individual level. Nested Case-Control Studies: A case-control study that is nested within a cohort study. Cases are individuals who develop the outcome of interest within the cohort, and controls are selected from the same cohort but have not developed the outcome at the time of selection. These observatio

Distinguish between associations that represent correlation and causation.

Association: Definition: An association refers to a statistical relationship or correlation between two variables. It indicates that changes in one variable are related to changes in another variable, but it does not imply a cause-and-effect relationship. Example: There may be an association between the number of ice cream sales and the number of drowning incidents. During hot summer months, both ice cream sales and drowning incidents tend to increase. However, this association does not imply that buying more ice cream causes more drownings or vice versa. Causation: Definition: Causation implies a cause-and-effect relationship between two variables. It suggests that changes in one variable directly influence or cause changes in another variable. Example: Smoking is causally linked to lung cancer. Extensive scientific evidence, including cohort and case-control studies, supports the assertion that smoking increases the risk of developing lung cancer. In this case, smoking is considered a causal factor for lung cancer.

Define bias and provide examples of several different types.

Bias in epidemiology refers to systematic errors or deviations from the truth in the design, conduct, or analysis of a study that can lead to incorrect conclusions about associations or causal relationships between exposures and outcomes. Bias can distort the observed effect and compromise the validity and generalizability of study findings. Selection Bias: Definition: Occurs when the selection of study participants is not random, leading to a sample that is not representative of the target population. Example: If cases and controls in a case-control study are selected from different sources, and the sources have different characteristics, it can introduce selection bias. Information Bias: Definition: Arises when there are errors in the measurement or classification of exposure, outcome, or confounding variables. Example: If there is differential misclassification of exposure between cases and controls in a case-control study, it can lead to information bias. Recall Bias: Definition: A specific type of information bias where there is differential recall of past exposures between cases and controls, often due to differences in the awareness of the disease. Example: In a case-control study on diet and a rare disease, cases might have heightened awareness and better recall of their dietary habits compared to controls, leading to biased results. Observer Bias (Measurement Bias): Definition: Occurs when the measurement or assessment of outcomes is influenced by knowledge of the exposure status. Example: In a clinical trial, if those assessing the outcomes are aware of the treatment assignments, their expectations or beliefs may influence the way they measure or record outcomes. Confounding Bias: Definition: Arises when an extraneous factor is associated with both the exposure and the outcome, leading to a spurious association. Example: Suppose a study finds an association between alcohol consumption and cardiovascular disease. However, this association might be confounded by smoking, as smokers are more likely to both consume alcohol and develop cardiovascular disease. Publication Bias: Definition: Occurs when there is a systematic tendency for studies with certain results to be published while studies with

Describe case studies including case reports and case series, provide examples, and discuss their uses

Case studies, including case reports and case series, are research methodologies that provide in-depth and detailed examinations of specific instances or occurrences. They are often used in medicine, psychology, and other fields to explore and document unique or unusual cases. Let's delve into the definitions, examples, and uses of case reports and case series: Case Report: Definition: A case report is a detailed and singular description of a particular patient's medical history, diagnosis, treatment, and outcome. It often highlights unique or rare aspects of the case. Example: A case report might describe an unusual presentation of a disease, an unexpected side effect of a treatment, or a novel therapeutic approach. For instance, a case report could focus on a patient who developed a rare skin reaction after taking a specific medication. Uses: Documenting rare or atypical cases that can contribute to medical knowledge. Providing insights into new diseases or manifestations of known diseases. Highlighting unexpected outcomes or complications of treatments. Case Series: Definition: A case series is an aggregation of multiple case reports, typically focusing on patients with similar characteristics or those who underwent a similar intervention. It provides a more comprehensive view of a specific phenomenon. Example: A case series might include several patients with a particular type of cancer who were treated with a new experimental therapy. The series would present the collective experiences, outcomes, and trends observed in these patients. Uses: Identifying patterns or trends in a group of patients with similar conditions. Evaluating the effectiveness and safety of a novel treatment in a small cohort. Generating hypotheses for further research. Uses and Importance: Learning and Teaching: Case reports and case series are valuable for medical education. They provide real-life examples that can be used in teaching settings to illustrate specific conditions, treatments, or diagnostic challenges. Hypothesis Generation: They serve as a starting point for generating hypotheses and ideas for further research. Unusual or unexpected findings in case reports can lead to broader investigations. Rare Events and Outcomes:

Describe case-control studies including their objective and their three separate stages: selection of participants, collection of data, analysis of data.

Case-Control Studies: Objective: The primary objective of a case-control study is to investigate the association between a specific outcome or disease (cases) and potential risk factors or exposures by comparing them to a group without the outcome (controls). These studies are particularly useful for rare diseases or conditions. Three Separate Stages: Selection of Participants: Cases: Individuals with the specific outcome or disease under investigation are identified and recruited. Cases are usually selected from a defined population or through specific criteria, such as a hospital or registry. Controls: Individuals without the outcome are selected as controls. Controls should be representative of the population from which the cases arise. They are often matched to cases based on certain characteristics, such as age, gender, or other potential confounding factors, to ensure comparability. Collection of Data: Exposure Data: Detailed information on potential risk factors or exposures is collected for both cases and controls. This information may include demographic data, lifestyle factors, medical history, and other relevant variables. The data collection process is retrospective, meaning it looks back in time to assess exposures that occurred before the development of the outcome. Selection of Controls: Controls should be selected based on the same criteria used for cases, except for the outcome of interest. This ensures that any observed differences in exposures between cases and controls are likely associated with the disease. Matching: In some case-control studies, researchers may match cases and controls based on certain characteristics to minimize the impact of confounding variables. For example, if age is considered a potential confounder, cases and controls might be matched within specific age groups. Analysis of Data: Statistical Analysis: The collected data are analyzed to assess the association between the exposure variables and the outcome. The odds ratio (OR) is a commonly used measure of association in case-control studies. It compares the odds of exposure in cases to the odds of exposure in controls. Confounding and Bias: Researchers carefully consider potential confounding variables and biases t

Identify the changing foci of 21st-century epidemiological studies.

Epidemiology in the 21st century has been evolving to address emerging health challenges and leverage advancements in technology and data. Some changing foci include: 1. Global Health and Infectious Diseases: With increased global travel and interconnectedness the focus has shifted to understanding and managing infectious disease on a global scale. For example, COVID-19 was an emerging pandemic that highlighted the need for international collaboration and data sharing. 2. Chronic Diseases and Non-Communicable Diseases: As lifestyles change and populations age, there's a growing emphasis on studying and preventing chronic conditions such as heart disease (Framington Heart Disease), diabetes, and certain cancers. 3Data Science and Technology Integration: The 21st century has seen a surge in the use of big data, artificial intelligence, and machine learning in epidemiology. These technologies aid in analyzing vast datasets, identifying patterns, and predicting disease trends. Digital health tools and wearables also contribute to real-time data collection.

Define epidemiology and explain its objectives.

Epidemiology is the study of distribution and determinants of health-related states or events in human populations and the application of this study to the prevention and control of health problems. Epidemiologic investigations involve descriptive and analytic methods that draw on statistical techniques for describing data and evaluating hypotheses, biological principles, and causal theory. Descriptive epidemiology involves characterization of the distribution of health-related states or events. Analytic epidemiology involves finding and quantifying associations, testing hypotheses, and identifying causes of health-related states or events.2

Identify the most important issues in hospital epidemiology

Hospital epidemiology, also known as infection control or healthcare epidemiology, is a critical field that focuses on preventing and controlling infections within healthcare settings. The most important issues in hospital epidemiology include: Healthcare-Associated Infections (HAIs): HAIs, also known as nosocomial infections, are infections that patients acquire during the course of receiving treatment in a healthcare facility. Preventing and controlling HAIs is a primary concern in hospital epidemiology. Antimicrobial Resistance (AMR): The emergence of antimicrobial-resistant pathogens poses a significant threat in hospital settings. Inappropriate use of antibiotics can contribute to the development of resistant strains, making infections more challenging to treat. Surveillance and Outbreak Investigations: Hospital epidemiologists must continuously monitor and analyze data to detect trends and potential outbreaks of infectious diseases. Timely identification and response to outbreaks are crucial for preventing the spread of infections within the hospital. Hand Hygiene and Infection Control Practices: Ensuring compliance with proper hand hygiene and infection control practices among healthcare workers is essential for preventing the transmission of infections within the hospital. Device-Associated Infections: Infections associated with the use of medical devices, such as catheters and ventilators, are common in healthcare settings. Hospital epidemiologists work to implement strategies to reduce the risk of these infections. Environmental Hygiene: The hospital environment can play a role in the transmission of infections. Adequate cleaning and disinfection of surfaces, equipment, and patient rooms are critical components of infection control. Vaccination Programs: Hospital epidemiologists may be involved in implementing and promoting vaccination programs, not only for patients but also for healthcare workers to prevent the spread of vaccine-preventable diseases within the hospital. Occupational Health and Safety: Protecting the health and safety of healthcare workers is paramount. This includes measures to prevent needlestick injuries, exposure to bloodborne pathogens, and other occupational hazards. Emerging

Define the terms: period effects, age effects, cohort effects

Identify representative population pyramids and discuss their implications ChatGPT Population pyramids are graphical representations of a population's age and sex structure. The shape of a population pyramid provides insights into demographic trends, such as birth rates, death rates, and overall population growth. Let's discuss a few representative population pyramid shapes and their implications: Expansive (Triangular) Population Pyramid: Implications:Broad base indicates a high birth rate.Narrowing towards the top suggests lower life expectancy.Common in developing countries with high fertility rates and lower life expectancy. Impacts:Potential for rapid population growth.Strain on resources, healthcare, and education systems. Constrictive (Inverted Triangle) Population Pyramid: Implications:Narrow base indicates declining birth rates.Widening towards the top suggests an aging population.Common in developed countries with lower fertility rates and higher life expectancy. Impacts:Challenges related to an aging workforce.Increased demand for healthcare and social services for the elderly. Stationary (Columnar) Population Pyramid: Implications:Balanced birth and death rates.Stable population over time. Impacts:Sustainable population growth.Less immediate strain on social services compared to expansive pyramids. Pyramidal (Conventional) Population Pyramid: Implications:Relatively high birth rates.Moderate life expectancy.Common in countries with ongoing population growth but also improving healthcare. Impacts:Transitioning towards demographic stability.Potential for economic development. Box-Shaped Population Pyramid: Implications:Constant birth and death rates.Stable population size. Impacts:Predictable population dynamics.Emphasis on maintaining social services at a stable level. Understanding these implications is crucial for policymakers, as it helps in planning for healthcare, education, employment, and social services. For example, countries with expansive pyramids might prioritize family planning and healthcare initiatives, while those with constrictive pyramids might need policies to support an aging population.

Discuss how "cause" can mean different things in different contexts

In epidemiology, causation is often discussed in terms of associations between variables. Establishing a statistical association between a risk factor and a health outcome is different from proving a direct cause-and-effect relationship. Epidemiologists often use criteria like strength of association, consistency, temporality, and biological plausibility to infer causation.

Define notifiable diseases, state their use, and explain the difference between state and national levels.

Notifiable diseases are those of considerable public health importance because of their seriousness. As a general rule, a disease is included on a state's list if it (1) causes serious morbidity or death, (2) has the potential to spread, and (3) can be controlled with appropriate intervention. The list of notifiable diseases varies from state to state, reflecting state-specific public health priorities. Notifiable infectious diseases that are currently reported by most states are presented in TABLE 3-10.16 Other reportable diseases or events may include Alzheimer's disease, animal bites, cancer, disorders characterized by lapses of consciousness, and pesticide exposure.

Define basic epidemiology terms

Health: "Health is a state of complete physical, mental and social well-being and not merely the absence of disease or infirmity (World Health OrganizationLinks to an external site.)." Disease: Disease may be defined as any deviation from a state of health. There are many ways to categorize disease. The textbook gives five categories of disease. Diseases are also classified as acute, chronic, infectious, and noninfectious Acute disease: A disease which develops relatively quickly and lasts a short time period Chronic disease: A disease that develops relatively slowly and is long-lasting Infectious disease: A disease that is caused by a microorganism (virus, bacteria, fungi, protozoa, or helminth) or by microbial products (e.g. bacterial or algae toxins). Some infectious disease are communicable while others are transmitted only from the environment (non-communicable) Noninfectious disease: A disease caused by means other than a microbe or its products Communicable disease: A disease that is transmissible from person to person Etiology: Cause(s) of disease Risk Factors: Host characteristics or environments that increase the chances of disease developing Population: Any group with shared characteristics Susceptible: At risk for a disease Sign: A change in the body that is measurable/observable by others (e.g. body temperature, rash, or blood pressure) Symptom: A change in the body that is not apparent to others (only felt by the person such as nausea or pain) Syndrome: A defined group of signs and/or symptoms Subclinical disease: A disease that has no apparent signs or symptoms Clinical disease: A disease with signs and/or symptoms present Case: A person that has been diagnosed with a given disease or identified as having the disease in a clinical record, screening progress, population survey, or another type of record Case definition: "A case definition is a set of standard criteria for classifying whether a person has a particular disease, syndrome, or other health condition (Center for Disease Control and Prevention)." Sporadic disease: Disease that occurs occasionally in a population Endemic disease: Disease that occurs at some constant or expected level in a population Epidemic disease:

Explain what public health surveillance is and how it might lead to causal insights

Public health surveillance is a systematic and ongoing collection, analysis, interpretation, and dissemination of data related to health conditions, with the aim of informing public health action. The primary goal is to monitor and understand the patterns and trends of diseases, injuries, and other health-related events within a population. Surveillance plays a crucial role in identifying potential public health threats, planning interventions, and evaluating the impact of health policies. Here's how public health surveillance might lead to causal insights: Early Detection of Health Events: Surveillance systems often involve the continuous monitoring of various health indicators. Early detection of unusual patterns or spikes in certain diseases or health conditions can trigger investigations into potential causes. Identification of Risk Factors: By analyzing surveillance data, researchers can identify factors associated with increased risk of diseases. This can include demographic information, lifestyle factors, environmental exposures, and more. Identifying these risk factors is a crucial step in understanding the causation of health outcomes. Temporal and Spatial Analysis: Surveillance allows for the examination of trends over time and geographical locations. Temporal and spatial patterns can provide insights into the possible causes of health events. For example, if a specific disease suddenly increases in a particular area, it might be linked to local environmental factors or changes in population behavior. Comparative Analysis: Surveillance data can be compared across different populations or regions. This comparative analysis helps identify variations in health outcomes and potential factors contributing to these variations. Differences in healthcare access, socioeconomic status, or environmental conditions may emerge as potential causal factors. Hypothesis Generation: Surveillance data often leads to the generation of hypotheses about potential causal relationships. For instance, if a certain population has a higher incidence of a disease, it might prompt researchers to investigate lifestyle factors, genetic predispositions, or environmental exposures as potential causes. Evaluation of Interventions: S

Calculate and interpret morbidity rates including incidence, attack rates, point prevalence, and period prevalence

Refer to equation sheet

Describe and apply the epidemiological triangle for infectious and noninfectious diseases

Refer to module 3: the epidemiology triangle

Describe and apply the chain of the transmission model of disease

The chain of infection explains the spread of a communicable disease from one host to another.

Define the term ecological fallacy and provide an example

The ecological fallacy is a logical error that occurs when conclusions about individuals are drawn from aggregate (group-level) data. In other words, it's assuming that relationships observed at the group level also hold true for individuals within that group. This can lead to incorrect or misleading interpretations. Example of Ecological Fallacy: Let's say researchers conduct a study on the average income and educational levels of residents in different neighborhoods of a city. They find that in Neighborhood A, where the average income is higher, the average educational level is also higher. In Neighborhood B, with a lower average income, the educational level is lower. Now, committing the ecological fallacy would involve assuming that individuals within Neighborhood A have higher incomes and education levels than individuals in Neighborhood B. This might not be accurate because individual variation within each neighborhood could be significant. It's entirely possible that some residents in Neighborhood B have high incomes and education levels, while some in Neighborhood A have lower incomes and education levels. In essence, the ecological fallacy highlights the importance of recognizing that group-level patterns don't necessarily reflect individual-level relationships. It's a cautionary note against making sweeping generalizations about individuals based solely on aggregate data.

Describe the Framingham Heart Study and explain how it has contributed to our understanding of cardiovascular disease and how it has expanded beyond the study of heart disease.

The study has not only contributed enormously to our understanding of the natural history of cardiovascular disease and stroke, it also enabled us to identify their major causal risk factors. Framingham changed the way we study and approach chronic diseases in the medical and public health spheres. Thanks in large part to Framingham, we now go beyond treating disease once it occurs by emphasizing disease prevention and addressing modifiable risk factors. Framingham was an early pioneer in the use of epidemiology to study non-infectious diseases and gave rise to innovative methods that are being put to use in countless studies across the world. The overall impact of the Framingham Heart Study is vast, and the study continues to unveil new insights into human health to this day. The study, which aimed to unravel the underlying causes of heart disease, started in 1948 with 5,209 participants in the town of Framingham, Massachusetts. Framingham is a longitudinal cohort study, a type of epidemiological study that follows a group of individuals over time to determine the natural history of certain diseases, explore the behavior of those diseases, and identify the factors that might explain their development. Part of the reason Framingham, Massachusetts was picked as the study site was because it was just big enough to provide a sufficient number of individuals for the study, while also small enough to be suited to the community approach of recruiting and effectively following participants over time.4,5 Participants underwent physical examinations, gave blood samples for laboratory tests, and provided lifestyle and medical history information at regular intervals. Now a joint project of the NHLBI and Boston University, Framingham has expanded over the years, both in geographical and population scope. Today it includes many grandchildren and spouses in three generations of participants, as well as two cohorts of minority participants (the Framingham Omni Cohorts).

List possible explanations for changes in frequency and patterns of disease in a population that are not due to changes in exposure to agents or risk factors

Changes in the frequency and patterns of disease in a population can be influenced by various factors beyond alterations in exposure to agents or risk factors. Here are some possible explanations: Improved Diagnostic Methods: Advancements in diagnostic technologies and medical practices may lead to better detection and reporting of diseases. An apparent increase in disease frequency might be due to increased accuracy in identification rather than a true rise in cases. Changes in Diagnostic Criteria: Revisions to diagnostic criteria and definitions can impact disease prevalence. If criteria become broader or more inclusive, it may result in an apparent increase in the number of diagnosed cases. Enhanced Surveillance and Reporting: Improvements in public health surveillance systems and reporting mechanisms can contribute to a more comprehensive and timely collection of data. This can lead to a better understanding of disease patterns but may not necessarily indicate a true increase in disease occurrence. Population Aging: As populations age, certain diseases become more prevalent. An increase in the proportion of elderly individuals in a population can contribute to a higher overall incidence of age-related conditions. Changes in Population Demographics: Migration, changes in birth rates, and shifts in population composition can influence disease patterns. For example, the introduction of a new demographic group may bring with it different health profiles. Natural Variability: Some diseases exhibit natural variability over time. Periodic fluctuations may occur due to factors such as climate, seasonal variations, or natural cycles in infectious disease transmission. Social and Economic Factors: Changes in socio-economic conditions, lifestyle, and access to healthcare can impact disease patterns. Economic development, urbanization, and lifestyle shifts may contribute to changes in the prevalence of certain diseases. Medical Interventions: Advances in medical treatments and interventions can lead to changes in disease outcomes. For example, the introduction of effective vaccines or improved treatment options may reduce the severity or mortality associated with certain diseases. Public Health Policies and Intervent

Explain how epidemiological studies led to the reduction in deaths due to childbed fever in the mid-1800s. Connect it to the Germ theory of disease proven by Koch in the late-1800s.

Childbed fever was a diseas ein the 1840s where there was a uterine infection, usually of the placental site, after childbirth. After the child was born, the mother would get an infection and die of childbed fever, a streptococcal disease. Many times the child would become infected and die as well. After observing the symptoms of childbed fever for many years, Semmelweis began a series of investigations. These investigations involved Semmelweis dividing the clinic into two separate clinics. From this clinic it was obvious that there was a significant difference between the two when it came to maternal mortality rates. He observed that mother become ill either immediately during birth or 24 to 36 hours after the delivery. The mother died quickly from developing rapid child bed fever. Semmelweis observed that it was not actually the actual labor that was the problem but the examination of the patients seemed to be connected to the onset of disease. Through clinical observation, retrospective study, collection and analysis of data on maternal deaths and infant deaths, and clinically controlled experimentation, he was able to ascertain that the communication of childbed fever was through germs passed from patient to patient by the physician in the process of doing pelvic examinations. Semmelweis discovered that, unlike the second clinic, the medical students would come directly from the death house after performing autopsies of infected and decaying dead bodies and then would conduct pelvic exams on the mothers ready to give birth. Hand washing or any form of infection control was not a common practice. Unclean hands with putrefied cadaver material on student doctors' hands were used to conduct the routine daily pelvic exams, and the practice was never questioned. There was no reason to be concerned about clean hands because the theory of medicine that was accepted at the time relied on the Hippocratic theory of medicine and the idea that disease developed spontaneously. Semmelweis observed that a whole row of patients became ill while patients in the adjacent row stayed healthy. The germ theory of disease is the currently accepted scientific theory for many diseases. It states that microorganisms known as pa

Define confounding and provide examples.

Confounding: Confounding is a situation in epidemiology where the association between an exposure and an outcome is distorted by the presence of an additional variable (confounder) that is related to both the exposure and the outcome. The confounder can create a false impression of a causal relationship or mask a true association. Examples of Confounding: 1. Examples: Smoking and Lung Cancer: - Exposures: smoking - Outcome: Lung Cancer - Confounder: Age - Explanation: In studies investigating the association between smoking and lung cancer, age can be a confounder because smoking is more common among older individuals, and older individuals are also more likely to develop lung cancer. If the analysis does not account for age, it may incorrectly attribute the association to smoking when age is the true culprit. 2. Example: Coffee Consumption and Heart Disease: - Exposure: Coffee Consumption - Outcome: Heart Disease - Confounder: Physical Activity - Explanation: Suppose a study finds an association between high coffee consumption and increased risk of heart disease. However, physical activity is related to both coffee consumption and heart disease. If the study does not control for physical activity, it might incorrectly suggest a causal relationship between coffee consumption and heart disease when the true association is with physical inactivity.

Differentiate between analytic and descriptive epidemiology.

Descriptive epidemiology involves racterization of the distribution of health-related states or events. Descriptive epidemiology involves observation, definitions, measurements, interpretations, and dissemination of health-related states or events by person, place, and time. A descriptive study assists us in (1) providing information about a disease or condition, (2) providing clues to identify a new disease or adverse health effect, (3) identifying the extent of the public health problem, (4) obtaining a description of the public health problem that can be easily communicated, (5) identifying the population at greatest risk, (6) assisting in planning and resource allocation, and (7) identifying avenues for future research that can provide insights about an etiologic relationship between an exposure and health outcome. Disease affected by aspects of person, time, and place Introduced the terms endemic and epidemic: - Endemic: occurs at some contact or expected level in a population. - Epidemic: occurs at a higher than expected level in a population. Analytic epidemiology involves finding and quantifying associations, testing hypotheses, and identifying causes of health-related states or events. studies that are used to examine associations to permit inferences about cause-effect relationships. For example, is there an association between fruit and vegetable intake and risk of recurrent myocardial infarction in individuals with a history of coronary heart disease? The disease has logical, not supernatural causes. Hot and cold disease Humoral theory of disease.

Analyze a timeline of the natural history of disease and describe what occurs during each stage.

Each disease has a natural history of progression if no medical intervention is taken and the disease is allowed to run its full course. There are four common stages relevant to most diseases. 1. Stage of susceptibility 2. Stage of presymptomatic disease 3. Stage of clinical disease 4. Stage of recovery, disability, or death The stage of susceptibility precedes the disease and involves the likelihood a host has of developing ill effects from an external agent. The stage of presymptomatic disease begins with exposure and subsequent pathologic changes that occur before the onset of symptoms. This is also typically called the incubation period. For chronic diseases, the time from exposure to clinical symptoms is typically called the latency period. The stage of clinical disease begins when signs and symptoms are manifest. The final stage reflects the expected prognosis. Several factors may influence these stages including early detection and effective treatment. With regard to prevention, primary prevention may occur during the stage of susceptibility. Secondary prevention may occur during the stage of presymptomatic disease or the stage of clinical disease, and tertiary prevention may occur during the stage of clinical disease or in the final stage. With an infectious disease, the natural course begins with the susceptible person's exposure to a pathogen. The pathogen propagates itself and then spreads within the host. Factors of each disease, each pathogen, and each individual host vary in the way a disease responds, spreads, and affects the body. The progress of a disease can be halted at any point, either by the strength of the response of the body's natural immune system or through intervention with antibiotics, therapeutics, or other medical interventions (FIGURE 3-1). Changes in the body are initially undetected and unfelt. As the pathogen continues to propagate, changes are experienced by the host, marked by the onset of such symptoms as fever, headache, weakness, muscle aches, malaise, and upset stomach. The disease reacts in the body in the manner peculiar to that disease. Possible outcomes are recovery, disability, or death. A generalized presentation of the natural course of disease is shown in F

Describe ecological studies and differentiate between descriptive and analytic ecological studies

Ecological studies are a fascinating branch of research that examines the interactions between organisms and their environment. These studies often focus on populations or communities of organisms in a particular ecosystem. Now, let's dive into the two main types: descriptive and analytic ecological studies. Descriptive Ecological Studies: These studies aim to observe and document the characteristics of a population or ecosystem without manipulating any variables. It's like creating a detailed snapshot of a specific point in time. For example, a descriptive study might record the biodiversity of a rainforest, detailing the types and numbers of species present. These studies provide a foundation of information, helping researchers understand the baseline conditions before any changes occur. Analytic Ecological Studies: On the other hand, analytic studies go a step further. They involve manipulating variables or comparing different groups to uncover patterns, relationships, or causation. Using the rainforest example, an analytic study might investigate the impact of a specific environmental change (like deforestation) on species diversity. These studies often involve statistical analyses to draw conclusions about the relationships between variables. In a nutshell, descriptive studies paint a detailed picture of an ecosystem or population, while analytic studies dig deeper, seeking to understand the reasons behind observed patterns. Do you find ecological studies intriguing, or is there a specific aspect you're curious about

List the strengths and weaknesses of case-control studies.

Efficient for Rare Outcomes: Case-control studies are particularly useful when investigating rare diseases or outcomes, as it allows for the efficient identification of cases. Suitable for Multiple Exposures: Case-control studies are well-suited for investigating situations where there are multiple potential exposures or risk factors. Quick and Cost-Effective: Compared to prospective cohort studies, case-control studies are often quicker and more cost-effective, making them feasible for researchers with limited resources. Useful for Hypothesis Generation: These studies are valuable for generating hypotheses and exploring associations between exposures and outcomes, providing a foundation for further research. Good for Investigating Rare Exposures: Case-control studies are effective when studying exposures that are rare in the general population. Useful for Investigating Temporal Relationships: Case-control studies are well-suited for investigating the temporal relationship between exposures and outcomes, especially when outcomes have a long latency period. Weaknesses of Case-Control Studies: Retrospective Design: Case-control studies are retrospective, relying on the recall of past exposures. This introduces the potential for recall bias, where cases may remember or report exposures differently from controls. Selection Bias: Selection bias may occur if cases and controls are not properly matched or if there are systematic differences in how cases and controls are selected. Temporal Ambiguity: Since exposure data are collected retrospectively, establishing a clear temporal relationship between exposure and outcome can be challenging. It's often difficult to determine whether the exposure preceded the development of the disease. Dependence on Matching: Matching cases and controls can introduce complexities and may limit the generalizability of the results. It might also lead to overmatching, where controls are too similar to cases, potentially obscuring associations. Cannot Establish Causation: While case-control studies are useful for identifying associations, they do not establish causation. Establishing causation requires additional evidence, such as experimental studies and consideration of alternative expl

Calculate and interpret mortality rates, ratios, and proportions

Look at equation sheet

Define mortality and morbidity

Mortality: Mortality refers to the state of being subject to death. In the context of health and demography, mortality is often used to describe the occurrence of death within a specific population or group during a particular time period. Mortality rates are commonly expressed as the number of deaths per unit of population over a given time, often standardized to allow for comparisons between different populations. Morbidity: Morbidity, on the other hand, refers to the state of being diseased or unhealthy within a population. It encompasses the incidence, prevalence, and duration of diseases and other health-related conditions. Morbidity is a broader term than mortality, as it includes not only fatal conditions but also non-fatal illnesses, injuries, and disabilities. Morbidity rates are measures of the frequency or occurrence of illnesses or health-related conditions within a specific population. In summary, mortality focuses on the occurrence of death, while morbidity encompasses the prevalence and impact of diseases and health conditions, whether fatal or non-fatal. These terms are crucial in public health and epidemiology for understanding and addressing health challenges within populations.

Define the three levels of prevention, place them on the natural history of disease timeline, and provide examples.

Three types of prevention have been established in public health: primary prevention, secondary prevention, and tertiary prevention. Primary prevention is preventing a disease or disorder before it happens. Health promotion, health education, and health protection are three main facets of primary prevention. Secondary prevention is aimed at the health screening and detection activities used to identify disease. If pathogenicity (the ability to cause disease) is discovered early, diagnosis and early treatment can prevent conditions from progressing and spreading within the population and can stop or at least slow the progress of disease, disability, disorders, or death.1 Secondary prevention aims to block the progression of disease or prevent an injury from developing into an impairment or disability The aim of the third level of prevention is to retard or block the progression of a disability, condition, or disorder in order to keep it from advancing and requiring excessive care. Tertiary prevention consists of limiting any disability by providing rehabilitation when a disease, injury, or disorder has already occurred and caused damage. At this level, the goal is to help diseased, disabled, or injured individuals avoid wasteful use of health care services and not become dependent on health care practitioners and health care institutions. Prompt diagnosis and treatment, followed by proper rehabilitation and posttreatment recovery, proper patient education, behavior changes, and lifestyle changes are all necessary so that diseases or disorders will not recur.

Explain how vaccination and herd immunity works and how they were used to eradicate smallpox

Vaccination and herd immunity are interconnected concepts that play a crucial role in preventing and controlling the spread of infectious diseases. The eradication of smallpox is a prime example of how these strategies can be effectively employed. Vaccination: Introduction of Weakened or Inactivated Pathogens: Vaccines typically contain weakened or inactivated forms of a pathogen or specific parts of it. In the case of smallpox, the vaccine used a related virus called vaccinia. Stimulation of Immune Response: When a person is vaccinated, their immune system recognizes the foreign components in the vaccine as threats. This triggers the production of antibodies and the activation of immune cells that specifically target the pathogen. Memory Immune Response: Importantly, vaccination establishes a "memory" in the immune system. If the vaccinated individual is later exposed to the actual pathogen, their immune system can rapidly recognize and mount a robust defense, preventing or mitigating the infection. Herd Immunity: Collective Immunity: Herd immunity occurs when a significant portion of a population becomes immune to a disease, either through vaccination or previous infection. This reduces the overall transmission of the pathogen in the community. Protection of Vulnerable Individuals: Herd immunity is especially vital for protecting individuals who cannot be vaccinated, such as those with certain medical conditions or allergies. It creates a barrier that limits the spread of the disease, indirectly safeguarding vulnerable members of the population. Eradication of Smallpox: Global Vaccination Campaign: The World Health Organization (WHO) led an intensive global vaccination campaign against smallpox, starting in the 1960s. The strategy involved identifying and isolating cases, vaccinating individuals in close contact with patients, and conducting mass vaccination campaigns in endemic areas. Ring Vaccination: The concept of "ring vaccination" was crucial. When a smallpox case was identified, a ring of individuals around the patient, including family members and contacts, was vaccinated to create a protective buffer and prevent further transmission. Global Collaboration: Countries around the world collaborated to

Describe the WHO and the CDC, and their jurisdictions and functions

WHO stands for the World Health Organization, which is the United Nations agency charged with spearheading international public health efforts. It is responsible for international public health. The CDC answers to all three branches of the US government: legislative, executive, and judicial. It is known as the Centers for Disease Control and Prevention, expanding its power as one of the major operating components of the Department of Health and Human Services. In this capacity, the CDC has protected public health and safety through infectious disease surveillance as well as the prevention of disease, injury, and disability, both domestically and worldwide.

Describe and understand examples of the concept of One Health and zoonoses

Zoonosis is an infectious organism in vertebrate animals (e.g., rabies virus, bacillus anthracis, Ebola virus, influenza virus) that can be transmitted to humans through direct contact, a fomite, or a vector. The World Health Organization states that zoonoses are those diseases and infections that can be naturally transmitted between vertebrate animals and humans.20 For example, the rabies virus is transmitted from an infected animal (e.g., dog, cat, skunk, raccoon, monkey, bat, coyote, wolf, fox) to a human host through saliva by biting, or through scratches. A vehicle is a nonliving intermediary such as a fomite, food, or water that conveys the infectious agent from its reservoir to a susceptible host. Describe and understand examples of the concept of One Health and zoonoses ChatGPT The concept of One Health recognizes the interconnectedness of human health, animal health, and the environment. It emphasizes that the health of humans, animals, and ecosystems are interdependent, and addressing health challenges requires a holistic and collaborative approach. Zoonoses, which are diseases that can be transmitted between animals and humans, are a key focus within the One Health framework. One Health: Interconnected Systems: One Health acknowledges that the health of humans, animals, and the environment are interconnected systems. Changes or disruptions in one of these systems can have cascading effects on the others. Collaborative Efforts: It promotes interdisciplinary collaboration among professionals in human health, veterinary medicine, environmental science, and other related fields. This collaboration is crucial for addressing complex health issues that span across species. Prevention and Control: One Health recognizes the importance of prevention and control measures at the human-animal-environment interface. This includes surveillance, early detection, and response to emerging diseases that may affect multiple species. Antimicrobial Resistance (AMR): One Health is particularly relevant in the context of antimicrobial resistance. The overuse and misuse of antibiotics in both human and animal healthcare contribute to the development of resistant strains of bacteria, posing a threat to public health. Zoono