Epidemiology Principles and Methods Prof dr Bhisma Murti

Epidemiology: Principles and Methods Prof. dr. Bhisma Murti, MPH, MSc, Ph. D Department of Public Health, Faculty of Medicine, Universitas Sebelas Maret

Definitions in Epidemiology 1. 2. 3. Definition and aims of epidemiology Study designs used in epidemiology Measures of Disease Frequency – Incidence (Cumulative Incidence and Incidence Density) – Prevalence 4. 5. 6. 7. 8. Measures of Association Bias Confounding Chance Causal Inference

Epidemiology • A study of the distribution of disease frequency in human population and the determinants of that distribution • Epidemiologists are not concerned with an individual’s disease as clinicians do, but with a population’ distribution of the disease • Distribution of disease by person, place, time • Assumption: – Disease does not occur randomly – Disease has identifiable causes • which can be altered and therefore • prevent disease from developing

Definition of Epidemiology • The study of the distribution and determinants of health-related states or events in specified population, and the application of this study to control of health problems. [source: Last (ed. ) Dictionary of Epidemiology, 1995] • Determinants: physical, biological, social, cultural, and behavioral factors that influence health. • Health-related states or events: health status, diseases, death, other implications of disease such as disability, residual dysfunction, complication, recurrence, but also causes of death, behavior, provision and use of health services.

Aims of Epidemiologic Research 1. Describe the health status of a population 2. To assess the public health importance of diseases 3. To describe the natural history of disease, 4. Explain the etiology of disease 5. Predict the disease occurrence 6. To evaluate the prevention and control of disease 7. Control the disease distribution Descriptive epidemiology Analytic epidemiology Applied epidemiology

Descriptive and Analytical Epidemiology 1. Descriptive epidemiology • Describes the occurrence of disease (crosssectional) 2. Analytic epidemiology: • • Observational (cohort, case control, crosssectional, ecologic study) – researcher observes association between exposure and disease, estimates and tests it Experimental (RCT, quasi experiment) – researcher assigns intervention (treatment), and estimates and tests its effect on health outcome

Epidemiologic Study Designs

Study Design and Its Strength of Evidence 1. 2. 3. Systematic review, meta-analysis: secondary data analysis Randomized Controlled Trials (RCT) Cohort: prospective or retrospective Strongest evidence Quasi experiment 4. 5. 6. Case control: prospective or retrospective Cross sectional Case Reports / Case Series Weakest evidence

Which Disease if More Important to Public Health? Measure of Disease Occurence Hypothetical Data Measles Chickenpox Rubella Children exposed Children ill 251 201 238 172 218 82 Attack rate 0. 80 0. 72 0. 38 Attack rate = Number of Ill persons (new cases) Population at risk exposed • Attack rate is a Cumulative Incidence; it shows the risk (probability) of disease to occur in a population • In regard to risk, measles is the most important disease to public health while rubella being the least

Description of Disease Distribution in the Population Disease affects mostly people under five years of age Disease affects people living alongside the river Disease reaches its peak in frequency in Week 6

Natural History of Disease

Transmission Cases v Index – the first case identified v Primary – the case that brings the infection into a population v Secondary – infected by a primary case v Tertiary – infected by a secondary case T S Susceptible Immune Sub-clinical Clinical P S S T

Dynamics of infectiousness Susceptible Infection Timeline of Infectiousness Latent period Infectious period Non-infectious Dynamics of disease Susceptible Infection Time Incubation period Symptomatic period Non-diseased Time

Measure of Disease Frequency 1. 2. Cumulative Incidence (Incidence, Risk, I, R)= Number of new case over a time period Population at risk at the outset - Indicates the risk for the disease to occur in population at risk over a time period. Value from 0 to 1. Incidence Density (Incidence Rate, ID, IR)= Number of new case over a time period Person time at risk Indicates the velocity (speed) of the disease to occur in population over a time period. Value from 0 to infinity 3. Prevalence (Point Prevalence): Number of new and old cases at a point of time Population Indicates burden of disease. Value from 0 to 1.

Number of Cases of a Disease Endemic vs. Epidemic Endemic Time Epidemic

Levels of Disease Occurence Sporadic level: occasional cases occurring at irregular intervals Endemic level: persistent occurrence with a low to moderate level Hyperendemic level: persistently high level of occurrence Epidemic or outbreak: occurrence clearly in excess of the expected level for a given time period Pandemic: epidemic spread over several countries or continents, affecting a large number of people

Factors Influencing Disease Transmission Agent Environment • Infectivity • Weather • Pathogenicity • Housing • Virulence • Geography • Immunogenicity • Occupational setting • Antigenic stability • Air quality • Survival • Food Host • Age • Sex • Genotype • Behaviour • Nutritional status • Health status

Measures of Infectivity, Pathogenecity, Mortality • Infectivity (ability to infect) (number infected / number susceptible) x 100 • Pathogenicity (ability to cause disease) (number with clinical disease / number infected) x 100 • Virulence (ability to cause death) (number of deaths / number with disease) x 100 All are dependent on host factors

Preventable Causes of Disease “BEINGS” • Biological factors and Behavioral Factors • Environmental factors • Immunologic factors • Nutritional factors • Genetic factors • Services, Social factors, and Spiritual factors [JF Jekel, Epidemiology, Biostatistics, and Preventive Medicine, 1996] Types of Cause: • Necessary cause: Mycobacterium tuberculosis • Sufficient cause: HIV • Contributory cause: Sufficient-Component Cause

Causal Model of Risk Factors for CVD Morbidity and Mortality (Stroke, MI) Biological Risk Factors (Hypertension, Blood Lipids, Homocysteine) Genetic Risk Factors (Family History) Behavioral Risk Factors (Cigarette, Diet, Exercise) Environmental Factors (Socioeconomic Status, Work Environment) Disease Proximate cause Intermedi ate cause Distal cause

To Study Disease Etiology Kuartil asupan buah dan sayur

To Study Prognosis (Survival)

Validity of Estimated Association and Causation Smoking Lung Cancer OR = 7. 3 True association causal non-causal 24 Bias? Confounding? Chance?

The Role of Bias, Confounding, and Chance in The Estimated Association ? present Selection Bias and Information Bias? absent present False association likely Confounding ? unlikely Chance ? unlikely 25 True association

BIAS • Systematic errors in selection of study subjects, collecting or interpreting data such that there is deviation of results or inferences from the truth. • Selection bias: noncomparable procedure used to select study subjects leading to noncamparable study groups in their distribution of risk factors. Example: Healthy worker bias • Information bias: bias resulting from measurement error/ error in data collection (e. g. faulty instrument, differential or non-differential misclassification of disease and/ or exposure status. Example: interviewer bias, recall bias)

Confounding 1. A mixing of effects • • between the exposure, the disease, and a third factor associated with both the exposure and the disease such that the effect of exposure on the disease is distorted by the association between the exposure and the third factor 2. This third factor is so called confounding factor

Confounding Observed (but spurious) association, presumed causation Birth Order Down’s syndrome Unobserved association True association Maternal age

Apakah Ada Hubungan antara Urutan Kelahiran dan Risiko Sindroma Down?

Confounding [Biomedical Bestiary: Michael, Boyce & Wilcox, Little Brown. 1984] Observed (but spurious) association, presumed causation Gambling Unobserved association Cancer Smoking, Alcohol, other Factors True association

Hill’s Criteria for Causation 1. 2. 3. 4. 5. 6. 7. 8. 9. Strength of association Specificity Temporal sequence Biologic gradient (dose-response relationship) Biologic plausibility Consistency Coherence Experimental study Analogy