Basic of Epidemiology in Ophthalmology Rajiv Khandekar Presented in the 2nd Series of the MEACO Live Scientific Lectures 11 August 2014 Riyadh, KSA
Basics of Epidemiology in Ophthalmology Dr Rajiv Khandekar Head of ophthalmic epidemiology & Low Vision services Research Department, King Khaled Eye Specialist Hospital Riyadh, Saudi Arabia
How it is useful? Evidence based information Selecting and reading article in journal Planning your own research Manuscript writing Interaction with researchers & biostatisticians Influence decision makers Good teacher Academic growth
Epidemiology How much, what, when, why, how, so what Magnitude of health problem Causes/ risk factors of a health condition Extent of health issue in the community Natural history and progress & prognosis Evaluate intervention Develop public health policy/ clinical approach
Ophthalmic epidemiology means work in community and work related to prevention of blindness only Research in health institutes, clinical, related to clients, care providers, health administrators, educators also apply basic principles of epidemiology
Epidemiology of eye diseases Infective diseases: Conjunctivitis, Chronic diseases: Cataract, glaucoma Genetic diseases: Dystrophies, Keratoconus Screening: DR, Refractive error Health interventions: Vit A x CO Impact assessment: VQL, reduced blindness Health economics: cost of RB survival, DALYs
Common words in epidemiology Prevalence: (old + new) / population Incidence: (new/ population) Exposure: risk factor, independent variable Outcome: Disease, success rate (VA, blindness) Association: linked, relationship
Incident cataract Backlog Un-operated patients died Cataract operated Prevalence and situation of Cataract Prevalence = Incidence x duration of disease
Type of studies Observational study Case Reporting - Case Series Ecological study - Cross sectional study Analytical study Case control study - Cohort study Intervention studies Clinical trials - Preventive intervention Meta-analysis Other studies
Case reporting & Case series Example: Rare syndromic eye disease, Rubella cataract in Australia Purpose to let other clinicians know & tell them to keep eyes open. 1 st step of postulation. No causality.
Ecological study Example: GDP of countries & blindness rate. Postulation No temporal relation possible No individual association so no causality Poor but good for further studies Environmental studies air pollution x eye OPD attendance
Cross-sectional study Example: National survey for Blindness Blindness and causes in sample at one point/ fixed duration are studied Information of exposure & outcome both collected at a time. Usually gives prevalence not incidence. Useful for public health planning
Case -control Exposed Not exposed Exposed Not exposed Disease + Disease - Planning of a research Is glaucoma associated with diabetes type II?
Cohort study Outcome + Study population Without exposure or outcome Prospective cohort Exposure + Exposure - Outcome - Outcome + Outcome - Study planned exposure Outcome Historical cohort Exposure Study planned Outcome
Fast Case -control Good for rare disease Many exposures could be studies at a time Temporal relation cannot be established Causal relation interpretation is not correct Odd s ratio is the parameter Example: Dry eye and trachoma exposure in past
Cohort study Powerful Good for rare exposure Many outcome could be studies at a time Causal relation possible Time consuming Relative risk is the parameter Example; smoking and AMD
Clinical/ public health trial Intervention can be controlled Disease + Group A Exposed Disease - Group B Not exposed Disease + Disease -
Clinical trial Very powerful Sample size very important Ethical issue Cross over When to stop Interpretation: Relative risk, additional risk, cure rate
Common pitfalls in a study Systematic error Bias Confounder
Systematic error Element of chance in the observation Sample size: examined and population How to address Take help Use previous evidence Use p value and 95% confidence interval in validating the results Study the effect of systematic error before generalisation
Bias Error that can affect association of exposure and outcome Be careful & minimize bias in planning Once Bias is introduced It is difficult to assess its presence and impact on research outcomes
How to avoid bias Types of bias Selection of study participants Selection of controls Sample size calculation Randomization Masking Quality assurance procedures Piloting - Training - Monitoring Inter-observer variation testing Standardization of equipment Methodology manual Observer selection
Confounder EXPOSURE OUTCOME Universal confounders Age Sex Area of residence literacy Confounding Factor Remedy Know them Collect their information Analyse their effect: By stratified analysis Interpret their effect on association
Useful reading material & websites Ophthalmic Epidemiology S West Epidemiology Leon Gordis Openepi free downloadable software for statistical calculation Epidata 3.1 free downloadable software for data entry of studies
Adopt better study design to increase weightage of evidence collected. - less case reports and case series!