Epidemiology, a discipline dedicated to analyzing the distribution of health-related conditions and events within populations, along with discerning their determinants, stands as a cornerstone of public health. The pivotal role of epidemiological study designs becomes apparent in unraveling the origins and patterns of diseases, subsequently informing the development of public health strategies and healthcare interventions. In this discussion, our goal is to provide a thorough understanding of the fundamental methodologies that form the basis of epidemiological research. We aim to illuminate the nuanced aspects of study designs and highlight critical considerations necessary for conducting scientifically rigorous investigations.
What is covered on this page:
Develop a comprehensive understanding of epidemiological study designs, including analytical, descriptive, and evaluative approaches.
Differentiate between analytical study types (e.g., case-control, cohort) to grasp their applications and limitations in exploring associations and causal relationships.
Investigate descriptive study methods and how they contribute to characterizing the distribution of health-related phenomena in populations.
Evaluate evaluative study designs for assessing the effectiveness and impact of public health interventions (overview provided on this page, click here to view detailed content).
Conduct an in-depth analysis of observational designs (e.g., cross-sectional, case-control, cohort) uncovering patterns in health-related conditions.
Highlight critical considerations essential for conducting scientifically rigorous epidemiological investigations, ensuring research reliability and validity.
Types of Research
Basic research, also known as pure or fundamental research, is conducted primarily to enhance scientific knowledge and understanding. Researchers in basic research explore theoretical concepts and principles without a specific practical application in mind. The primary goal is to expand our understanding of the natural world, uncover fundamental laws and theories, and answer fundamental scientific questions. Basic research often forms the foundation for applied research and technological advancements but doesn’t have immediate or direct practical applications. Scientists in basic research may investigate phenomena, conduct experiments, or develop theories, contributing valuable knowledge to their respective fields.
Applied research aims to solve specific, practical problems or address practical questions. It involves the application of scientific principles and theories to real-world issues. Researchers in applied research focus on developing solutions, products, or interventions that have direct relevance and usefulness in various fields, including medicine, engineering, technology, and social sciences. Applied research is goal-oriented and often conducted in specific contexts, with the intention of solving immediate problems, improving processes, or creating new technologies. The remaining content on this page will focus on applied research.
Basic research and applied research are two fundamental types of scientific inquiry, each serving distinct purposes and methodologies. The key difference between basic and applied research lies in their objectives and outcomes. Basic research seeks to expand knowledge and theories, often without immediate practical applications, while applied research aims to address specific problems and create tangible solutions that can be directly applied to real-world situations. While both types of research are valuable, they serve different purposes in the scientific community, contributing to the overall progress of knowledge and the betterment of society. The remaining content on this page will focus on applied research. Types of Research were covered here.
The Research Design
A meticulous approach to defining the study population is paramount. This involves detailing the demographic specifics and sociocultural characteristics of the individuals under scrutiny. Simultaneously, estimating the population size aids in selecting an appropriate sample size, a critical factor in ensuring the study’s statistical power. The sampling technique is methodically described, outlining the methodology used for participant selection. Additionally, a robust rationale for the chosen sample size is provided, considering factors such as confidence levels and margin of error. These elements collectively form the foundational structure of the research design, ensuring overall credibility, alignment with research goals, and facilitating accurate data collection and analysis.
The research design encompasses various methodologies, with surveys being essential tools for collecting data in health and social science research. Cross-sectional surveys offer a snapshot of specific populations at one point in time, while longitudinal surveys track the same participants over multiple time points, capturing changes and trends. Panel surveys focus on a consistent group of individuals, allowing researchers to observe developments over time. Cross-sequential surveys combine cross-sectional and longitudinal elements, studying different age cohorts at various time intervals. Within longitudinal designs, cohort studies follow a specific group, retrospective studies examine past events’ impact, prospective studies observe real-time events, and case-control studies compare individuals with specific outcomes to those without, providing valuable insights into various social phenomena.
The flow diagram below offers a concise overview of research methodologies, categorizing them into fundamental branches: basic research and applied research. Within these broad categories, various subtypes are meticulously classified, providing insights into the diverse avenues of exploration. Additionally, the diagram meticulously outlines distinct research designs, elucidating the methodologies employed in scientific inquiry. This visual representation serves as an invaluable guide, offering researchers a clear roadmap to navigate the intricate terrain of academic investigation. By succinctly capturing the essence of different research types and designs, the flow diagram stands as a foundational resource for both novice and seasoned researchers, enhancing their understanding of the multifaceted world of research.
Types of Epidemiological Studies
Analytical research is a methodological approach centered on understanding cause-and-effect relationships. Its primary objective is to quantify the connections between exposures and outcomes while rigorously testing hypotheses concerning causal relationships. Analytical research can take either experimental or observational forms. In observational studies, researchers examine the distribution or determinants of outcomes without manipulating the variables that might influence them. There is no interference by the investigator, allowing for natural observation of the phenomena. These observational studies can be individual-based or population-based, taking the form of longitudinal studies, case-control studies, cross-sectional studies, or ecological studies. Analytical research endeavors to answer the fundamental question: Why do certain phenomena occur?
In contrast, experimental research involves the deliberate manipulation of conditions to investigate cause-and-effect relationships. Here, the researcher manipulates an independent variable and observes its impact on a dependent variable, ensuring strict control over other potentially influential factors (extraneous variables). To establish a causal link, researchers must carefully manage these variables. In cases where controlling extraneous variables becomes challenging, researchers may incorporate them into the study design or develop methods to account for their impact. Experimental studies, often involving hypothesis testing, frequently employ techniques like clinical trials or Randomized Controlled Trials (RCTs), wherein exposures or interventions are actively allocated to specific groups by the investigator, whether individuals or communities.
Descriptive research serves as an invaluable approach when the objective is to accurately define and measure known concepts, terms, or phenomena. Precision and accuracy are of paramount concern within this research paradigm. Descriptive research methodologies encompass diagnostic analyses, program reviews, case reports, and case series, as well as comprehensive surveys, facilitating the exploration and depiction of general disease patterns within a population. These studies are particularly instrumental in determining the frequency of diseases, identifying when and where they occur, and predominantly rely on hospital records as their primary data source.
A striking example of descriptive research is exemplified by a census, where the goal is to provide a precise and comprehensive description of various characteristics within the Jamaican population. Additionally, cross-sectional or prevalence studies, where individuals are assessed at a single point in time, enable the collection of data within a defined period, yielding prevalence rates. These studies are instrumental in health service planning, elucidating the natural history of diseases, categorizing diseases, and forming the basis for hypotheses. Descriptive research proves particularly useful for estimating the burden of diseases, be it in terms of prevalence or incidence, a vital resource for effective public health planning. For instance, data on the prevalence of cataract in a city can guide the government in determining the requisite number of ophthalmologic facilities, while incidence data for diseases like dengue can expedite the identification of outbreaks and the formulation of appropriate healthcare responses.
A crucial endeavor aimed at assessing the value and effectiveness of specific practices within a given context. It involves a methodical process that yields a reliable account of the actions taken and the reasons behind them. Through a comprehensive examination of program outcomes, evaluation research seeks to address key questions such as “What actions were undertaken?” “For whom and how?” and “What results were achieved?” A well-structured evaluation enables us to extract meaningful insights from the data, ultimately shedding light on the significance of these outcomes.
When selecting measures of outcome, several essential guidelines should be followed. These measures ought to be readily quantifiable, allowing for clear expression in percentages or rates. Furthermore, they should be relatively easy to define and diagnose, fostering a standardized approach for research purposes. In terms of data sources, various options are available, including mortality records, hospital records, personal interviews, health insurance companies, and disease registries. These sources provide diverse and valuable information to support the comprehensive evaluation of the practices under scrutiny.
Additional information on the uses of evaluation research is available here.
Observational Study Designs
Epidemiological study designs can be classified based on the dichotomous nature of disease outcomes and the unit of analysis, which is the individual. This classification relies on two main criteria: the type of outcome (incidence or prevalence) and whether there is sampling based on the outcome. The resulting four basic study designs are incidence studies, incidence case–control studies, prevalence studies, and prevalence case–control studies. Incidence studies measure exposures, confounders, and outcome times for the entire population, often referred to as cohort studies, and can include descriptive studies of national death rates. Incidence case–control studies are more efficient versions of incidence studies, utilizing a sample of controls. Prevalence studies focus on disease prevalence at a specific time, while prevalence case–control studies efficiently obtain findings similar to full prevalence studies by sampling prevalent cases and controls. These designs are essential for understanding the causes of diseases, each offering distinct advantages in terms of efficiency and resource utilization.
The nature of various methodological approaches, the types of questions they can answer, and their respective strengths and weaknesses are pivotal aspects of research analysis. In this exploration, we will delve into several study designs, namely ecological, cross-sectional, case-control, cohort, and intervention studies. By scrutinizing these designs, we can comprehensively understand their applications and limitations, thereby enhancing our research discernment.
Case-control studies, a cornerstone of epidemiological research, categorize participants based on their outcomes, not their exposures. Cases exhibit the outcome of interest, while controls do not. By retrospectively examining their prior exposure to specific risk factors, researchers evaluate differences. Unlike cohort studies, case-control designs cannot determine outcome frequency (prevalence, risk, odds, or incidence rate) in the general population. Instead, these studies focus on discerning past exposures among individuals with and without the outcome of interest. Data on outcomes are collected at the study’s outset, followed by retrospective exploration of exposure data derived from interviews, questionnaires, medical and employment records, and biological samples. While case-control studies can’t estimate outcome prevalence, they can measure the odds ratio, indicating the strength of the association between exposure and outcome (Carneiro & Howard, 2011a; PHAST, 2020a).
Study Design and Data Sources
For a case-control study to be valid, it requires a well-defined research hypothesis, explicit case criteria, precise inclusion/exclusion criteria, and a control group resembling cases, except for the outcome. The individuals, selected based on outcome, must represent the target population. Data on outcomes are collected initially, followed by retrospective exposure data collection. Despite their limitations, case-control studies offer cost-effectiveness and rapidity, especially for studying rare outcomes or those with long latency periods. They enable the examination of associations between outcomes and multiple exposures. However, they are susceptible to biases, such as selection, observer, and reporting biases, which can impact the study’s validity. Case-control studies are confined to a single health outcome and require thorough tracing of potential locations for treating or servicing patients (Carneiro & Howard, 2011a; PHAST, 2020a).
Strengths
Faster than cohort studies due to no prolonged follow-up requirement.
Enables the exploration of multiple exposures’ associations with the outcome.
Appropriate for studying rare outcomes or those with extended latent periods.
Quantifies the strength of association between exposure and outcome.
Weaknesses
Susceptible to selection, observer, and reporting biases, which can skew results.
Limited to investigating a single health outcome per study.
Unable to calculate outcome prevalence or incidence unless the study covers the entire population.
Difficulties in locating and including all cases in studies involving rare outcomes.
Cohort studies, a cornerstone of epidemiological research, center around a cohort—a group of individuals sharing a defining characteristic—followed to measure outcome incidence. This characteristic might be workers from a factory, children born in the same year, or individuals at risk of a specific outcome. Exposure data are collected at the study’s outset and updated if necessary. Unlike other observational designs, cohort studies often establish the temporality criterion for causality. These studies compare the incidence of outcomes in exposed and unexposed cohorts to discern any observable differences, unveiling the relationships between exposures and outcomes (PHAST, 2020b).
Study Design and Data Sources
Cohort studies can be descriptive or analytical, with analytical studies further categorized as prospective or retrospective. Descriptive cohorts provide baseline data, while analytical cohorts measure the association between exposure and outcome. Prospective cohorts, starting with participants free of the outcome, follow them over time, capturing exposure before the outcome manifests. In contrast, retrospective cohorts utilize existing data and avoid longitudinal follow-up. Data sources vary, including medical records, interviews, biological samples for prospective studies, and hospital records, death registries, and workplace documents for retrospective ones (Carneiro & Howard, 2011b).
Strengths
Enables investigation of multiple exposures or outcomes, fostering a nuanced understanding.
Ideal for common outcomes; retrospective cohorts are effective for diseases with lengthy development periods.
Prospective cohorts can capture data on confounding factors, enhancing precision.
Prospective studies establish the sequence of exposure and outcome, aiding causal inference.
Provides avenues to measure both incidence and prevalence.
Suitable for studying changing exposures due to repeated measurements.
Weaknesses
Prospective studies demand significant time and resources due to extended follow-up periods.
Large sample sizes are essential, especially for rare outcomes, making them resource-intensive.
Prone to selection and observer biases; loss to follow-up affects prospective cohorts, while data completeness impacts retrospective ones.
Comparisons between cohorts with diverse demographics require meticulous data standardization.
Cross-sectional studies examine the existing relationship between exposure and health outcome to provide a snapshot of the relationship between these variables within a specific population at a particular point or period in time. These studies simultaneously measure the prevalence of both outcomes and exposures, offering valuable insights into the health status and characteristics of a population at a given moment. Researchers collect data through surveys, questionnaires, interviews, and diagnostic tests to understand the existing associations between various factors.
Advantages
Cross-sectional studies are relatively quick and economical to conduct, enabling researchers to gather data from a large number of participants efficiently.
These studies allow researchers to collect data on multiple outcomes, exposures, risk factors, and potential confounders, providing a comprehensive overview of the population’s health.
The descriptive data obtained from cross-sectional studies can be used not only for analytical studies but also to generate research hypotheses and plan health services effectively.
Disadvantages
Due to the concurrent measurement of exposure and outcome, cross-sectional studies cannot establish causality or determine the temporal sequence of events.
Cross-sectional studies focus on prevalence rather than incidence, providing a static view of the population’s health status without capturing dynamic changes.
A representative sample of the overall population is necessary for meaningful results, requiring careful consideration during participant selection.
These studies are susceptible to biases, including recall bias (pertaining to questions about past experiences), information bias (related to non-responders), and interviewer bias, which can impact the accuracy of the collected data.
Ecological studies, a valuable research approach, examine health-related outcomes within specific geographical regions over time, analyzing entire communities and exploring correlations between outcomes and exposures at the population or group level, often united by common attributes like geography or socio-economic status (Carneiro & Howard, 2011d). Researchers aggregate data from sources such as demographic records, censuses, and surveys, enabling a comprehensive overview of localized health patterns. By examining trends over time and employing statistical methods, these studies offer unique insights into societal factors’ impact on public health, often sparking hypotheses for further research and contributing significantly to the field of public health knowledge.
Advantages
Accessible data from routine sources make ecological studies practical and budget-friendly (PHAST, 2020d).
Ideal when data is available only at the group level or when significant variations exist between different populations or areas (Carneiro & Howard, 2011d).
Useful for evaluating the effects of interventions, programs, or health policies on specific groups (Carneiro & Howard, 2011d).
Provides a spatiotemporal framework for diseases and exposures, serving as a basis for further investigative hypotheses (PHAST, 2020d).
Disadvantages
Limitation lies in drawing individual conclusions from group-level data, hindering the ability to infer direct causal relationships between exposure and outcome (Carneiro & Howard, 2011d; PHAST, 2020d).
Potential biases can arise due to variances in data collection methods, and confounding factors might not always be fully accounted for in demographic and group data (Carneiro & Howard, 2011d; PHAST, 2020d).
To facilitate accurate comparisons, data must be standardized to adjust for demographic differences between various population groups or geographic areas.
Experimental Study Designs
Experimental study designs stand as the gold standard in determining causality in epidemiology, employing deliberate interventions to assess outcomes. Unlike observational studies, these designs involve active manipulation of variables to observe their effects, providing stronger evidence for causal relationships. Key experimental designs include interventional studies and randomized controlled trials (RCTs), each offering unique insights into the impact of interventions on health outcomes.
Interventional studies are meticulously designed experiments aimed at implementing planned interventions or exposures to assess their effects on specific outcomes. These studies involve active introduction of treatments, preventive measures, or therapies to a group of participants, enabling scientists to explore the efficacy of specific interventions and their impact on health outcomes. These interventions can vary widely, from preventive measures targeting those at risk of a specific outcome (like using insecticide-treated mosquito nets to combat malaria) to treatments reducing mortality or severity in individuals already affected (such as interferon treatment for chronic Hepatitis B, reducing progression to liver cancer). Interventions can also involve removing or reducing assumed risk factors (e.g., health education programs to reduce smoking) or introducing protective factors (e.g., vaccination against poliovirus).
Study Design and Ethical Considerations
Intervention studies are experimental, enabling researchers to intervene and manipulate exposures deliberately. Ethical approval from national bodies is paramount, ensuring the study addresses critical public health issues. Participants, representative of the population of interest, are selected based on clearly defined inclusion and exclusion criteria, emphasizing informed consent. These studies employ rigorous designs such as cross-over trials, where participants switch between intervention and control groups, or randomized controlled trials (RCTs), where participants are randomly assigned to either the intervention or control group. RCTs, in particular, provide strong evidence for causal relationships as they ensure randomization, reducing selection bias and confounding variables.
Intervention studies, crucial for understanding the impact of specific interventions on public health, require ethical approval from national and institutional bodies before initiation. Ethical considerations are paramount, guiding study design decisions, especially since many designs involve withholding potentially beneficial interventions from the control group. The ethical judgment of a study can vary across countries and time, influenced by evolving scientific knowledge and cultural norms. These studies must address significant public health questions, justifying their implementation. Ultimately, the general population benefits from these studies, with participants being the first to enjoy the protective intervention’s advantages once the trial concludes.
Strengths and Weaknesses
Interventional studies offer crucial evidence on the efficacy, effectiveness, and safety of interventions, making them essential for public health decision-making. RCTs, in particular, elucidate causal relationships due to their clear temporal sequence. These studies minimize bias and confounding, enhancing the validity of results. However, they are subject to ethical constraints and can be resource-intensive, time-consuming, and costly. Furthermore, they may not be suitable for studying rare or long-term outcomes. Selection bias can occur if there are differences in follow-up or compliance between the intervention and control groups, highlighting the importance of meticulous study design and implementation.
The randomized controlled trial (RCT) stands as the gold standard in intervention study designs, effectively mitigating errors stemming from bias and confounding variables. Ethical considerations sometimes necessitate alternative designs, but RCTs remain the pinnacle of experimental research. In these trials, participants are randomly allocated to the intervention group, receiving the treatment, or the control group, maintaining the current standard or receiving a placebo. Randomization guarantees an equal chance for each participant to be in either group, thereby minimizing selection bias and enabling a robust comparison between the intervention and control cohorts. RCTs hold unmatched credibility in establishing causal relationships between interventions and outcomes, forming the bedrock of evidence-based medicine. Through meticulous randomization protocols, RCTs yield reliable results, shaping the course of medical practices and policies.
Key Components of RCTs
Purpose and Advantages
RCTs establish a cause-and-effect relationship between the intervention and the outcomes, providing robust evidence of efficacy.
RCTs enable direct comparisons between the intervention and control groups, allowing researchers to determine the intervention’s superiority or equivalence to existing treatments.
Randomization and blinding techniques reduce various biases, such as selection bias and observer bias, enhancing the study’s internal validity.
RCTs generate high-quality evidence that informs clinical practice, healthcare policies, and treatment guidelines.
RCTs are conducted in multiple phases, starting from Phase I (safety and dosage) to Phase IV (post-marketing surveillance), ensuring a comprehensive evaluation of the intervention.
Challenges and Considerations
Ethical approval is crucial, and informed consent from participants is mandatory. Ethical concerns may arise when a potentially effective intervention is withheld from the control group.
RCTs require significant resources, including funding, time, skilled personnel, and infrastructure.
Ensuring participant adherence to the intervention and follow-up protocols is essential for the study’s validity.
RCTs may not capture the intervention’s long-term effects, necessitating additional post-marketing surveillance and real-world studies.
Validity and Reliability in Epidemiological Study Designs
In the realm of scientific inquiry, validity and reliability provide the bedrock for the credibility and trustworthiness of research findings. Epidemiological studies, which play a pivotal role in shaping public health policies and interventions, rely heavily on the concepts of validity and reliability to produce meaningful and impactful results.
Research Validity
At its core, research validity refers to the degree to which a study accurately measures or assesses what it claims to measure. In epidemiology, where the goal is often to uncover patterns and associations in health-related phenomena within populations, achieving validity is paramount. A valid study in epidemiology ensures that the conclusions drawn from the data are a true representation of the real-world scenario.
Validity in epidemiological studies is multifaceted. Internal validity addresses the extent to which the observed associations can be attributed to the studied variables, excluding alternative explanations. External validity, on the other hand, concerns the generalizability of the findings beyond the study population, making them applicable to broader contexts.
Reliability in Research
Reliability, on the other hand, focuses on the consistency and stability of research findings. A study is considered reliable if it produces consistent results when conducted under similar conditions. In epidemiology, reliability is essential for ensuring that the study’s outcomes are not mere chance occurrences but rather replicable and dependable.
To achieve reliability, epidemiologists employ rigorous methodologies and standardized data collection tools. Consistency in measurement, sampling, and data analysis contributes to the reliability of the study, reinforcing the confidence in the derived conclusions.
Importance of Validity and Reliability in Epidemiological Study Designs
Epidemiological studies serve as critical instruments in shaping public health policies and interventions. These studies provide evidence that informs decision-making processes at various levels, from local health departments to national policymakers. The consequences of these decisions can have far-reaching impacts on the quality of life of entire populations.
Policy Formulation- Valid and reliable epidemiological studies provide the evidence base for policymakers to formulate effective health policies. For instance, studies demonstrating a link between smoking and lung cancer played a pivotal role in the implementation of anti-smoking campaigns and regulations.
Intervention Design and Evaluation- Epidemiological studies guide the design and evaluation of health interventions. Reliable data on the prevalence and risk factors of a disease help in developing targeted interventions, while valid studies ensure that the interventions are addressing the actual health issues at hand.
Resource Allocation- Policymakers often face challenges in allocating limited resources efficiently. Valid and reliable epidemiological data aid in prioritizing health needs, directing resources where they are most needed to achieve the maximum impact on population health.
Public Health Surveillance- Surveillance systems built on valid and reliable epidemiological data enable the early detection of outbreaks, the monitoring of disease trends, and the timely implementation of preventive measures.
Factors influencing Validity and Reliability in Epidemiological Study Designs
While validity and reliability are paramount, achieving them in epidemiological studies is not without challenges. Several factors can influence the validity and reliability of research findings:
Placebo Effect
The placebo effect refers to the phenomenon where a person experiences a perceived improvement in their condition or symptoms after receiving a placebo, which is an inactive substance or treatment that has no therapeutic effect. This improvement occurs solely because the individual believes they are receiving a real treatment. The placebo effect can influence study outcomes by confounding the results, especially in clinical trials, where participants’ expectations and beliefs about the treatment can affect their response, leading to an overestimation of the treatment’s effectiveness.
In clinical studies, participants receiving placebos often report significant improvements [30 – 50%] in various symptoms, such as depression scores, pain scores, or hot flash scores, simply due to their belief in the efficacy of the treatment, even though the substance itself has no therapeutic properties.
To overcome this in clinical trials, include a control group receiving a placebo to compare the treatment group’s outcomes with those who receive the inactive substance. Implement double-blind or triple-blind designs to ensure participants and researchers are unaware of who is receiving the active treatment and who is receiving the placebo. Clearly explain the possibility of receiving a placebo and the concept of the placebo effect to participants to manage their expectations.
Hawthorne Effect
The Hawthorne effect is a behavioral phenomenon where individuals modify their behavior or performance in response to being observed or knowing they are part of a study. Participants may change their behavior, become more motivated, or work harder simply because they are aware of being studied. This effect can impact study outcomes by altering participants’ responses and actions, leading to results that may not accurately represent their natural behavior or condition.
To overcome this reduce the visibility of observers or researchers as much as possible to minimize the awareness of being observed. In surveys or observational studies, assure participants of their anonymity to reduce the likelihood of altered behavior due to the awareness of being studied. Conduct observations over an extended period, allowing participants to acclimate to the presence of observers, potentially minimizing the Hawthorne effect.
View the slide show below on other factors impacting validity and reliability. You may also review Measures of Risks and Bias in Research for Mitigation Strategies. Additionally, detailed content on validity and reliability concepts can be found here, Health Science Research Methods.
Summary
Understanding epidemiological study designs is pivotal in unraveling the complex patterns of health-related phenomena within populations. Observational studies, including cohort and case-control designs, illuminate natural associations between variables, shedding light on risk factors and disease trends. Interventional studies, notably Randomized Controlled Trials (RCTs), provide valuable insights into the efficacy of health interventions, offering a basis for evidence-based practices. These study designs serve as cornerstones for health policy formation, guiding decision-makers in allocating resources, designing interventions, and safeguarding public health.
Ethical considerations are paramount in epidemiological research, ensuring the protection of participants’ rights and well-being. Researchers must navigate issues of informed consent, confidentiality, and potential risks, upholding the ethical standards that underpin the integrity of their studies.
Furthermore, a keen understanding of validity and reliability is imperative. Validity ensures that study findings accurately represent reality, while reliability ensures consistent and replicable results. This ensures that the evidence derived from epidemiological studies is not only robust but also trustworthy, contributing to the advancement of public health knowledge and the enhancement of health outcomes for entire populations.
References
Carneiro, I., & Howard, N. (2011a). Case-control studies. In L. Bailey, K. Vardulaki, J. Langham & D. Chandramohan (Eds.), Introduction to Epidemiology (2nd ed., pp. 110-117). Maidenhead, England: Open University Press.
Carneiro, I., & Howard, N. (2011b). Cohort studies. In L. Bailey, K. Vardulaki, J. Langham & D. Chandramohan (Eds.), Introduction to Epidemiology (2nd ed., pp. 100-109). Maidenhead, England: Open University Press.
Carneiro, I., & Howard, N. (2011c). Cross-sectional studies. In L. Bailey, K. Vardulaki, J. Langham & D. Chandramohan (Eds.), Introduction to Epidemiology (2nd ed., pp. 94-99). Maidenhead, England: Open University Press.
Carneiro, I., & Howard, N. (2011d). Ecological studies. In L. Bailey, K. Vardulaki, J. Langham & D. Chandramohan (Eds.), Introduction to Epidemiology (2nd ed., pp. 75-92). Maidenhead, England: Open University Press.
Carneiro, I., & Howard, N. (2011e). Intervention studies. In L. Bailey, K. Vardulaki, J. Langham & D. Chandramohan (Eds.), Introduction to Epidemiology (2nd ed., pp. 118-134). Maidenhead, England: Open University Press.
Pearce, N. (2012) Classification of Epidemiological Study Designs. International Journal of Epidemiology 2012(41) 393–397
doi:10.1093/ije/dys049
PHAST. (2020a). Introduction to study designs – case-control studies. https://www.healthknowledge.org.uk/e-learning/epidemiology/practitioners/introduction- study-design-ccs
PHAST. (2020b). Introduction to study designs – cohort studies. https://www.healthknowledge.org.uk/e-learning/epidemiology/practitioners/introduction- study-design-cs
PHAST. (2020c). Introduction to study designs – cross-sectional studies. https://www.healthknowledge.org.uk/e- learning/epidemiology/practitioners/introduction-study-design-css
PHAST. (2020d). Introduction to study designs – geographical studies. https://www.healthknowledge.org.uk/e-learning/epidemiology/practitioners/introduction- study-design-gs.
PHAST. (2020e). Introduction to study designs – intervention studies and randomized controlled trials. https://www.healthknowledge.org.uk/e- learning/epidemiology/practitioners/introduction-study-design-is-rct
D. L. Baker (BPharm, MBA, MPH, Dip.Ed.)
Published: 2023- Oct- 10; Last updated: 2024- Jan- 14