Epidemiological Study Designs Muhammad Tahir, MPH,MSc Epi & Bio Acknowledgment: Ms. Tazeen Saeed Ali Assistant Prof. AKUSON

Objectives By the end of the session the participants will be able Examine the purposes, structure, strengths and weaknesses of the different types of research designs. Compare difference between these approaches Review the definition of epidemiology, objectives, and history. Compare the different phases of natural history of disease transmission. Explain the integration of epidemiological designs in to community health nursing practice.

Background Rationale and Characteristics of epidemiological Research Need to understand disease causation Need to describe disease occurrence Used to generate and test hypothesis, and evaluate health interventions Design Characteristics Specific goals and objectives Methodology

Background Goals of Epidemiology and Public Health Scientific research is the process of proposing and testing postulates and hypothesis in a specific field that may be or not related to public health Evaluation research is concerned with the decision making process to implement, continue or adopt a new program that may be or not in the public health field Epidemiological studies can be and has been used in both research approaches

Applications of Epidemiological Designs Clinical Trials testing new drugs or clinical management approaches determining prognosis Cohort natural history of disease etiology of disease - test hypothesis on causation

Applications of Epidemiological Designs Case-Control natural history of disease etiology - test hypothesis on causation Case report, case series, X-sectional, describe disease occurrence generate hypothesis

Research Designs in the Health Field Observational Designs Analytic Cohort studies Case-control studies Cross-sectional studies Descriptive Case report Case series Ecological or correlation studies

Research Designs in the Health Field Experimental Designs Laboratory experiments Clinical Trials Field Trials Intervention Trials Quasi-Experimental Field

Epidemiological Designs Descriptive objective is to describe the patterns and trends help to generate hypothesis help to plan programs measure frequency of disease or other health outcome (occurrence) measures determinants (risk factors) and effects on health outcomes risk factors and effects may be measured over time

Case Report What? the profile of a single patient is reported in detail by one or more clinicians Example In 1961, a published case report of a 40 year-old women who developed pulmonary embolism after beginning use oral contraceptive

Case Series What? An individual case report that has been expanded to include a number of patients with a given disease Example In Los Angeles, five young homosexuals men, previously healthy, were diagnosed with pneumocyst cariini pneumonia in a 6-month period

Case Series Clinical case-series: usually a coherent and consecutive set of cases of a disease (or similar problem) which derive from either the practice of one or more health care professionals or a defined health care setting, e.g. a hospital or family practice. A case-series is, effectively, a register of cases. Analyse cases together to learn about the disease. Clinical case-series are of value in epidemiology for: Studying symptoms and signs Creating case definitions Clinical education, audit and research

Cross-sectional often interest is to describe frequency and pattern of either disease or health-related outcome occurrence. existing traits, be it disease or health-related outcome, are measured at same time. Usually data collected in a survey. door to door, mail or telephone interview and measurement. neither cases nor comparison group, if exist, are pre-selected (post hoc selection).

Example - X-sectional Prevalence of Pap Smear

Cross-Sectional Study In special circumstances can be analytic neither cases nor comparison group are pre-selected. post hoc selection. existing traits, be it exposure or health outcome, are measured at same time. therefore, assessment of temporality in found association is not possible. There are exceptions.

Cross-Sectional Study

Cross-Sectional Study

Cross-Sectional Study

Cross-Sectional Study

Prevalence Proportion of individuals in a population with disease at a specific point of time Provides estimate of the probability or risk at one will be ill at a point in time Provides an idea of how severe a problem may be Useful for planning health services (facilities, staff)

Formula for prevalence: Number of existing cases of disease P = at a given point in time Total population at risk 2176 subjects with asthma encounter P = = .07 31005 subjects = 7 asthmatics per 100 subjects = 7 %

Types of Prevalence Point prevalence: number of cases that exist at a given point in time Lifetime prevalence: proportion of the population that has a history of a given disorder at some point in time Period prevalence: number of cases that exist in a population during a specified period of time

Ecological or Correlation Ecological Studies whole population is the unit of analysis relationship between exposure and outcome at the individual level is missing (incomplete design) ecological fallacy Correlation Studies same as ecological aim to show strength of the ecological association

Ecological fallacy: example Imagine a study of the rate of coronary heart disease in the capital cities of the world relating the rate to average income. Within the cities studied, coronary heart disease is higher in the richer cities than in the poorer ones. We might predict from such a finding that being rich increases your risk of heart disease. In the industrialised world the opposite is the case - within cities such as London, Washington and Stockholm, poor people have higher CHD rates than rich ones. The ecological fallacy is usually interpreted as a major weakness of ecological analyses. Ecological analyses, however, informs us about forces which act on whole populations.

Epidemiological Designs Analytic main objective is to test hypothesis of relationship between exposure to a risk factor and disease or other health outcome a measure of association is estimated the magnitude, precision and statistical significance of the association is determined

Case Control Study select population of cases and controls that are comparable using historical data, determinant (exposure or risk factor) is measured retrospectively among case and controls exposure and level of exposure measurement results compared between cases and controls to test a-priori hypothesis

Case-Control Study

Case-Control Study

Case-Control Study

Case-Control Study

Odds Ratio Breast No Breast Cancer Cancer Alcohol 70 100 No alcohol 50 140 a x d (70) (140) b x c (50) (100) * Used for case control studies because persons are selected based on disease status so you can’t calculate risk of getting disease OR = = = 2.0

RR or OR RR = 1 Risk in exposed is equal to risk in non exposed RR > 1 Risk in exposed is greater than risk in non exposed RR < 1 Risk in exposed is less than risk in non exposed

Cohort or Follow-up Study determinant (risk factor) is measured in pre-selected cohort to identify exposed and non-exposed cohort is followed-up effect or health outcome (disease) is measured over time at the end of study period results are compared between exposed and non-exposed to test a-priori hypothesis

follow-up period

end of follow-up

Cohort study exposed not exposed

Incidence among non-exposed Cohort study exposed Incidence among exposed RR not exposed Incidence among non-exposed

Cohort Study

Cohort Study

Cohort Design Study begins here disease Factor present no disease population free of disease disease Factor absent no disease Cohort Design present future time Study begins here

Cohort Study

Cohort Study

Relative Risk Measure of association between incidence of disease and factor being investigated Ratio of incidence rate for persons exposed to incidence rate for those not exposed Incidence rate among exposed RR = Incidence rate among unexposed Estimate of magnitude of association between exposure and disease

Formula for relative risk: Incidence rate among exposed RR = Incidence rate among unexposed a / (a + b) RR = c / (c+ d)

Difference Measures Attributable risk # of cases among the exposed that could be eliminated if the exposure were removed = Incidence in exposed - Incidence in unexposed Population attributable risk percent Proportion of disease in the study population that could be eliminated if exposure were removed Incidence in total population - Incidence in unexposed incidence in total population =

Attributable risk Relative risk: RR

RR= 60 /100 40 /100 = 60/100 x 100/40= 1.5

The rate (proportion) of a disease or other outcome in exposed individuals that can be attributed to the exposure Attribute: one of the feature of a disease Or cause of a disease

Formula for Attributable Risk Incidence in exposed group - incidence in non exposed group Incidence in exposed group (60/100) - (40/100) (60/100) = = = 0.33 = 33 % Attributable risk indicates the prevention or cessation of smoking or risk factor facilitates the decrease in the burden of lunge cancer

Research Designs Controlled experiments (Experimental) determinant or intervention (treatment) is planned control (non-treatment) group is free from the intervention subjects selected into intervention (treatment) and control (non-treatment) groups by randomization

Examples Laboratory experiments Clinical trials Field trials effects of anti-cancer drugs in mice Clinical trials effect of anti-cancer drugs in humans ( volunteers) Field trials a-priori hypothesis about effect of intervention is assessed subjects of study not patients. usually healthy individuals in the community because probability of disease is small, a large number of subjects are needed Salk and Sabin vaccine trials Intervention Trials similar to field trial however, intervention is available at a group or community level. fluoridation of tap water

Experimental Design time outcome Intervention no outcome Study RANDOMIZATION Intervention no outcome Study population outcome Control no outcome Experimental Design baseline future time Study begins here (baseline point)

Experimental Advantages Best evidence study design No selection bias (using blinding) Controlling for possible confounders Comparable Groups (using randomization)

Quasi-Experimental Does not meet all of the requirements necessary for controlling the influence of extraneous variables. Most common criteria not met is random assignment. The operative word is plausible. With a well-designed quasi experiment, other interpretations, while possible, are highly implausible. Sometimes there are ethical or logistical constraints that make it impossible for you to conduct a true experimental study. Thus, the quasi-experimental design can be your best alternative. Generally, a quasi-experiment exists whenever causal conclusions cannot be drawn because of one or more rival hypothesis for the observed effects.

Questions