Epi Final Practice Quiz

The relative risk and odds ratio are indeed important measures of the strength of an association. They are commonly used in epidemiology and research studies to assess the relationship between exposures and outcomes. The relative risk compares the risk of an outcome in one group to the risk in another group, while the odds ratio compares the odds of an outcome in one group to the odds in another group. These measures help quantify the strength of the association and determine the level of risk or likelihood of an outcome occurring.

Biases in a study occur when there are flaws or shortcomings in the way the study was designed or conducted. These biases can have a significant impact on the validity of the findings, meaning that the results may not accurately represent the true nature of the phenomenon being studied. Therefore, it is true that biases can reflect inadequacies in the study and can undermine the validity of the findings.

In a cohort study, the relative risk can be calculated directly because this type of study involves following a group of individuals over a period of time to observe the development of a particular outcome. By comparing the incidence of the outcome in those exposed to a risk factor versus those not exposed, the relative risk can be calculated as the ratio of the two incidence rates. This allows researchers to determine the strength of the association between the risk factor and the outcome. Therefore, the statement that the relative risk can be calculated directly in a cohort study is true.

The statement is true because attributable risk measures the proportion of disease cases that can be attributed to a specific exposure. By calculating the attributable risk, we can determine the potential impact of eliminating the exposure on reducing the occurrence of the disease. Therefore, the attributable risk is indeed useful in answering the question of how much disease can be prevented if the exposure were eliminated.

A causal pathway refers to the sequence of events or factors that lead to a particular outcome. It can be either direct, where there is a clear and immediate cause-effect relationship, or indirect, where there are intermediate steps or factors involved. Therefore, the statement that a causal pathway can be either direct or indirect is true.

Meta-analysis is indeed the statistical analysis of a large collection of analysis results from individual studies. It is used to integrate the findings from these studies and provide an overall perspective, especially when there is disagreement among the individual studies. By combining the data from multiple studies, meta-analysis increases statistical power and allows for more robust conclusions to be drawn. Therefore, the statement "Meta-analysis is the statistical analysis of a large collection of analysis results from individual studies for the purpose of integrating the findings" is true.

This statement suggests that if there is a connection between the occurrence of a disease and a specific genetically determined characteristic, it indicates that genetic factors play a role in the development of the disease. In other words, the presence of a genetic association implies that genetics contribute to at least some instances of the disease. Therefore, the answer is true.

The statement is false because not everyone who is exposed to an environmental exposure will develop a disease. The development of a disease depends on various factors such as the individual's immune system, genetics, overall health, and the specific nature and intensity of the exposure. While exposure to certain environmental factors may increase the risk of developing a disease, it does not guarantee that everyone exposed will develop it.

Confounding is indeed one of the most important problems in observational epidemiologic studies. Confounding occurs when an extraneous factor is associated with both the exposure and the outcome, leading to a distortion of the true relationship between the two. This can result in inaccurate conclusions and misleading associations. Therefore, it is crucial for researchers to identify and control for potential confounders in order to obtain valid and reliable results in observational epidemiologic studies.

Epidemiologic study investigators have an obligation to balance the rights of the individual and the welfare of society. This is because epidemiological studies involve collecting and analyzing data on large populations to understand patterns and causes of diseases. In order to protect the rights of individuals, investigators must ensure that the data collected is confidential and that participants' privacy is respected. At the same time, they have a responsibility to society to use the data to identify and implement measures that can improve public health and prevent diseases. Therefore, it is true that epidemiologic study investigators must balance the rights of the individual and the welfare of society.

Most chronic diseases are caused by a combination of multiple factors or exposures. It is rare for a single exposure to be the sole cause of a chronic disease. Multiple exposures can interact and contribute to the development of a disease over a long period of time. Therefore, it is true that most causal relationships in chronic diseases involve multiple exposures.

The correct answer is "all of the above" because all four steps mentioned - hazard identification, dose-response assessment, exposure assessment, and risk characterization - are indeed part of the risk assessment process. Hazard identification involves identifying potential hazards or dangers. Dose-response assessment determines the relationship between the amount of exposure to a hazard and the resulting health effects. Exposure assessment measures the extent of exposure to the hazard. Finally, risk characterization combines the information from the previous steps to estimate the risk posed by the hazard.

odds ratio = (a/b) / (c/d) = ad/bc

Epidemiologists play a crucial role in the process of policy making by generating and interpreting data related to public health issues. They collect and analyze data on the prevalence and incidence of diseases, as well as the risk factors associated with them. This data helps inform policy decisions by providing evidence-based information on the impact of different policy options. Additionally, epidemiologists are involved in presenting specific policy options to policymakers, highlighting the potential benefits and drawbacks of each. They also contribute to the development of specific policy proposals, using their expertise to recommend effective strategies for addressing public health concerns. Therefore, the correct answer is "all of the above".

The use of medical records in epidemiologic studies serves multiple purposes. Firstly, it allows researchers to generate aggregate data without having to directly contact patients, which can be time-consuming and costly. Secondly, medical records can be used to validate information obtained through other means, ensuring the accuracy and reliability of the data. Lastly, medical records are useful in identifying patients for subsequent follow-up, enabling researchers to track the progress of diseases or conditions over time. Therefore, the correct answer is "All of the above."

The guidelines for judging whether an association is causal include the strength of an association, which refers to the magnitude of the relationship between the exposure and the outcome. The temporal relationship is also important, as the exposure should precede the outcome in time. Additionally, a dose-response relationship is considered, meaning that as the exposure increases, the likelihood of the outcome also increases. Therefore, all of these factors are taken into account when determining if an association is causal.

Confounding refers to a non-causal association between an exposure and outcome that is influenced by a third variable. In other words, when studying the relationship between an exposure and an outcome, confounding occurs when there is another factor that is related to both the exposure and the outcome, and it distorts the true relationship between them. This third variable creates a spurious association, making it appear as if the exposure is causing the outcome when in reality it is not. Confounding is an important concept to consider in research studies to ensure accurate and valid results.

Confounding occurs when the relationship between an exposure and an outcome is distorted by the presence of a third variable, known as a confounder. To control for confounding, various methods can be used. Stratifying by levels of the confounder involves analyzing the data separately for each level of the confounder, allowing for a more accurate assessment of the exposure-outcome relationship within each stratum. Direct and indirect adjustment involves adjusting the analysis for the confounder through statistical methods such as standardization or regression. Multivariate regression analyses allow for the simultaneous adjustment of multiple confounders. Therefore, all of the mentioned methods (stratifying, direct and indirect adjustment, and multivariate regression analyses) can be used to control for confounding.

This statement is true because exposure to a disease-causing agent must occur before the disease can develop. In order for someone to contract a disease, they need to come into contact with the pathogen or risk factor that causes it. Without exposure to the disease-causing agent, there would be no opportunity for the disease to develop. Therefore, exposure always precedes the occurrence of the disease.

The measure of risk for a study design refers to the method used to determine the likelihood of an outcome occurring. In this case, the correct answer is "cohort" because a cohort study design is commonly used to assess the relative risk of developing a certain outcome or disease. In a cohort study, a group of individuals who share a common characteristic or exposure are followed over time to determine the incidence of the outcome in question. This design allows researchers to calculate the relative risk by comparing the incidence of the outcome between those exposed to a risk factor and those not exposed.

All of the options listed could cause selection bias. The use of volunteers and paid participation may lead to a non-representative sample, as these individuals may differ from the general population in various ways. Data from hospitals and clinics may also introduce bias, as it may only capture information from individuals seeking medical care and exclude those who do not visit healthcare facilities. Similarly, using working populations as a sample may not accurately represent the entire population, as it excludes individuals who are not employed. Therefore, all of these factors can potentially introduce selection bias.

The correct answer is False. A major difference between a case-control study and a cohort study is that in a case-control study, the study starts with individuals who have the disease (cases) and compares them to individuals without the disease (controls). On the other hand, in a cohort study, the study starts with individuals who are exposed to a particular risk factor and compares them to individuals who are not exposed.

If the relative risk is equal to 1, it means that the risk in the exposed group is the same as the risk in the nonexposed group. This suggests that there is no difference in risk between the two groups, indicating that the exposure being studied does not have an impact on the outcome being measured.

Using group data in Outcomes Research can introduce a potential bias due to self-selection. Self-selection refers to the fact that individuals or groups may choose to participate in a study or intervention based on certain characteristics or preferences. This can lead to a non-representative sample, as those who self-select may differ in important ways from the general population. Therefore, the results obtained from this group may not be applicable or generalizable to the larger population, introducing bias into the research findings.

Selection bias occurs when there are differences between the study subjects in a sample and the population they are supposed to represent. This can happen if the selection process is not random or if certain groups are over- or under-represented in the sample. It can lead to inaccurate or misleading results, as the sample may not accurately reflect the characteristics of the population.

The statement is true because the incidence of heart disease among non-smokers is indeed 17.4 per 1,000. This can be calculated by dividing the number of non-smokers who developed heart disease (87) by the total number of non-smokers in the study (5,000), and then multiplying by 1,000 to get the incidence per 1,000. The calculation is (87/5,000) * 1,000 = 17.4.

In a retrospective cohort design, researchers look back at past data and analyze the relationship between exposure and disease. However, since the data is collected after the disease has occurred, it can be challenging to establish a clear temporal relationship between exposure to a certain factor and the development of the disease. This is because there may be other confounding factors or biases that could influence the results. Therefore, it is true that it can be difficult to establish a temporal relationship between exposure and disease in a retrospective cohort design.

If a factor is necessary but not sufficient to cause a disease, it means that the factor is required for the disease to occur, but it is not the only factor responsible for causing the disease. In other words, the presence of the factor alone is not enough to cause the disease, but without it, the disease cannot occur. Therefore, the statement is true.

Relative risk cannot be calculated directly in a case control study. That is why relative odds or odd ratio is used to estimate reslative risk.

The relative risk can be calculated directly when using the cohort study design. It is the ratio of incidence rates in the exposed and in the non-exposed, which is represented by the formula a/a+b divided by c/c+d. Therefore, all of the above statements are correct.

Genetic diseases typically manifest at an earlier age compared to non-genetic diseases. This is because genetic diseases are caused by abnormalities or mutations in an individual's genes, which are present from birth or inherited from parents. These genetic abnormalities can result in the malfunctioning of certain biological processes, leading to the development of diseases. In contrast, non-genetic diseases may be caused by various factors such as environmental factors, lifestyle choices, or infections, which can occur at any age. Therefore, the statement that genetic diseases have a pattern of early age onset is true.

The correct answer is a and b. This means that both proposals with privacy and confidentiality are included. The first proposal suggests that patient consent should be required before investigators can access medical records, ensuring that individuals have control over who can access their personal information. The second proposal states that data from medical records should be made available to investigators, but without any information that would identify an individual, thus maintaining anonymity and protecting privacy. Both of these proposals prioritize privacy and confidentiality in accessing medical records.

The statement is false because the relative risk is actually the incidence in the exposed divided by the incidence in the nonexposed. This measure is used to compare the risk of an event or outcome between two groups, typically an exposed group and a nonexposed group. By comparing the incidence rates, we can determine the relative risk and assess the association between the exposure and the outcome.

relative risk = incidence in exposed/ incidence in nonexposed

They are examples of systematic error. They both favor a particular result.

In order for screening to be beneficial, two necessary conditions must be present. Firstly, all or most clinical cases of a disease should first go through a detectable preclinical phase. This means that there is a period of time before symptoms appear where the disease can be detected through screening tests. Secondly, in the absence of intervention, all or most cases in the preclinical phase should progress to a clinical phase. This means that if left untreated, the disease will eventually progress to a stage where symptoms become apparent. Therefore, both conditions (a and b) need to be met for screening to be effective.

The predictive value is not an important criterion when making causal inferences. When determining causality, it is crucial to consider factors such as consistency with existing knowledge, dose-response relationship, consistency of association in several studies, and strength of association. The predictive value, on the other hand, focuses on the ability to predict future outcomes, which may not necessarily be directly related to causality. Therefore, it is not a key criterion in making causal inferences.

The statement is true because the incidence of a disease in a population refers to the number of new cases of that disease occurring within a specific time period. Absolute risk is a measure of the actual risk or probability of developing a disease, and it is often expressed as a percentage. Therefore, it is correct to say that the incidence of a disease in a population is termed the absolute risk.

The statement "The incidence among the exposed is 17.4 per 1,000" is false. The incidence among the exposed, which refers to the smokers in this case, is calculated by dividing the number of smokers who developed heart disease (84) by the total number of smokers (3,000) and multiplying by 1,000. This gives an incidence rate of 28 per 1,000, not 17.4 per 1,000. Therefore, the correct answer is false.

Ecologic fallacy refers to the mistake of attributing the characteristics of a group to every individual within that group. This means assuming that all individuals within a group share the same characteristics or behaviors, which is not necessarily true. It is important to recognize that individuals within a group can have variations in their characteristics or responses, and generalizing the characteristics of a group to every individual can lead to incorrect conclusions or assumptions.

The correct answer is "all of the above" because interaction refers to the combined effect of multiple factors in causing disease. It involves the modification of the effect of one factor by another, which is known as effect modification. Confounding, on the other hand, occurs when the association between an exposure and an outcome is distorted by the presence of a third factor. Therefore, all three options (interaction, effect modification, and confounding) are valid explanations for the given statement.

Efficacy refers to the extent to which a specific health care treatment, service, or intervention produces a beneficial result under ideal controlled conditions. It focuses on determining whether the intervention works in a controlled setting, such as a clinical trial, where variables are carefully controlled and monitored. Efficacy does not take into account real-world factors or the effectiveness of the intervention in a broader population or diverse settings.

The statement "Causal guidelines prove an association is causal" is false. Causal guidelines can provide evidence or support for a causal relationship, but they do not definitively prove causality. Causal relationships require rigorous scientific research and evidence from controlled experiments or observational studies to establish causation. Guidelines can help researchers understand the criteria or factors that may suggest causality, but they do not serve as conclusive proof.

i still don't really understand this question... look at page 218...

The US passed the Public Health Service Act of 1985 to prevent unethical research. This act established the creation of Institutional Review Boards (IRBs). IRBs are now required to approve all research involving human subjects. These boards are responsible for ensuring that the rights and welfare of the subjects are protected, and that the research is conducted ethically and in accordance with applicable regulations and guidelines.

The statement is false because the issues that arise in the evaluation of health services are not completely different from those in etiologic studies. Both types of studies involve analyzing and understanding health-related issues, although they may focus on different aspects. Evaluations of health services often involve assessing the effectiveness, accessibility, and quality of healthcare, while etiologic studies aim to identify the causes and risk factors for diseases. However, there can be overlap in the methods and approaches used in both types of studies, and they can inform each other to improve healthcare practices.

In this match-paired case-control study, the authors identified 7 concordant pairs and 7 discordant pairs. Among the concordant pairs, there were 3 pairs where both the case and control were exposed, and 4 pairs where neither the case nor the control was exposed. Among the discordant pairs, there were 5 pairs where the case was exposed but the control was not exposed, and 2 pairs where the control was exposed but the case was not exposed. The matched-pairs odds ratio is calculated by dividing the odds of exposure in the discordant pairs (5/2) by the odds of exposure in the concordant pairs (3/4), which equals 2.5.

The statement is false because the Health Insurance Portability and Accountability Act of 1996 (HIPAA) actually gives patients more control over their own medical records, rather than investigators. HIPAA provides patients with the right to access and control their medical information, including the ability to request copies of their records and to determine who can access their records.

The 5 criteria published in 1964 by the US Surgeon General for establishing causality are strength of association, time sequence, consistency upon repetition, specificity, and coherence of explanation. These criteria are used to determine if there is a causal relationship between an exposure and a health outcome. The strength of association refers to the magnitude of the relationship between the exposure and outcome. Time sequence means that the exposure must precede the outcome. Consistency upon repetition means that the relationship is observed consistently in different studies. Specificity suggests that the exposure is specifically related to the outcome. Coherence of explanation means that the causal relationship is supported by existing knowledge and theories.

The statement is false because the definition of confounding provided is incorrect. Confounding refers to a situation where the association between an exposure and an outcome is distorted due to the presence of a third variable that is related to both the exposure and the outcome. It does not specifically involve the incidence rate of disease or the presence of multiple risk factors.