OBSTETRICS & GYNECOLOGYLIQIAN QIU Womens Hospital Zhejiang University School of Medicine

Womens reproductive system diseases

Epidemiology for the Gynecology

What is topic ? How to learn?

Obstetrics) (Gynecology) (Family Planning) (Womens Health, ~Maternal Health)

birth attendant) Caesarean section)

Endoscopy Reproductive assistant technology Prenatal diagnosis technology Cervical cancer screening

How to learn?

Evidence based medicine) Humanistic medicine) .

Epidemiology

Definition Health events in human populations. should learn the interpretation and discussion the relevant Epi study finding.Reading literature with critical Epi principle knowledge.

What exactly did you measure (definitions)What has worked?Has bias been eliminated?Can the study be generalised to a larger population?Can the program be reproduced somewhere else? (Pilot becomes program)So what how will this change best practice? Does it have significant advantages over existing practices? Cost, culture, efficacy etcDo these results have a use by date? How long will this advice last before conditions change?

1Age-specific incidenceAttributable riskClinical trialCohort Confidence interval (CI)Confounder

2Incidence & PrevalencePredictive value of a negative testPrevalencebiasRelative risk & Odds ratio

3SensitivitySpecificityStatistical significanceYield (RR, OR..)

Ratios, proportions and rates These are combinations of two numbers:Numerator Denominator (Errors in either cause errors in final result)

Ratio a general term. A relationship between two quantities. Ratio a/b (no relationship necessary between a and b, eg beds per 1000 population) (eg SMR, Odds Ratio)

Proportion a specific ratio where numerator is included in denominator. Eg =No. Smokers/total population (which includes smokers) a/(a+b)

Rate A proportion.

A rate is a ratio with a specific relationship between n and d and time period in denominator.Eg. Incidence Lung cancer cases /population/yearCDR, age specific death rates.

Rates include cases in denominator a/a+bRatios a/b

IncidenceThe rate at which new cases occur in a population during a specified period.

No. cases measles/100,000/year

PrevalenceThe prevalence of a disease is the proportion of a population that are cases at a point in time. Point prevalencePeriod prevalence

Acute disease. High incidence, low prevalenceChronic disease. low incidence, High prevalence

Examples:URTI Diabetes(eg kwashiorkor) with chronic diseases (eg obesity).

Study Designs

Descriptive Studies: It is the basic for any study for description of general characteristics. (surveys, case studies etc)Case Report or Series

Cross-sectional StudiesIncidenceAge-specific incidencePrevalence

Analytic StudiesTest a hypothesis the relationship between exposure and no exposure. 2 types of studies: Nonexperimental: Cohort, case-control Experimental: Clinical trials

Cohort studyLongitudinal study, using survival analysis to describe mortality.Yield: evaluation the association between exposure and illness. (Fig 4.2)Strengths: Obtain both attributable and relative risk, less susceptible and recall biasWeakness: long time, high cost, lost cases

Case control studyComparing difference to exposure between cases and controls.Yield: Odds ratio, similar to relative risk. No attributable risk could gotten.Strengths: low cost, easy to do.Weaknesses: selection bias, information bias, confounding variable.

Observational Studies

Descriptive (surveys, case studies etc) Case-Control, Cohort studies (usually longitudinal, can be retrospective)

Intervention studies Laboratory StudiesCell biologyGeneticsChemistry and Biochemistry. Clinical trials

Clinic trials: prospective cohort study. Good design is sufficient number of subjects.Field and community intervention trails: vaccines, dietary intervention. Need large number participants.

TestSignificanceDegree of conflictConfidence intervalBiasConfoundingBiologic credibility

Risk: probability of an individual of experiencing a specific event, in a specific time, under specific conditions.We estimate individual risks from populations.

Risk can be negative (ie protects) or positive (ie causal).

Risk and OddsThe risk of an event happening is simply the number of those who experience the event divided by the total number of people at risk of having that event. It is usually expressed as a proportion or as a percentage.a/a+b

The odds of an event is the number of those who experience the event divided by the number of those who do not. It is expressed as a number from zero (event will never happen) to infinity (event is certain to happen).a:b

Measures of effectWe compare outcomes in two groups those who are exposed and those who are not exposed (controls)

Odds ratio = a:b/c:dRisk ratio = a/(a+b)/c(c+d)

OR and RR are different but approximate each other in rare events. RR is a better indication of risk, but requires prospective studies.

See BMJ 1998;316:989-991

Cohort Study, recruit at exposure, measure OutcomeAdvantage - Measures risk ratio (RR). Disadvantages - Time, n, cost.Cohort StudyT1T2

Cohort Study

Risk ratio (RR) = Rate exposedRate unexposedA direct measure of riskMake sure 95% CI is given(Cases included in denominator)(relative risk)

Case-Control Study, recruit on outcome, measure exposure (odds ratio OR). Advantages time, n, cost. Disadvantage no direct risk measurement.Case-Control StudyT1

Odds ratio (OR) = Odds in exposedOdds in unexposed

Case-Control Study(Cases not included in denominator)

Sensitivity - proportion ofthe true cases detected. a/a +c

Specificity-A specific test has few false positives, and this quality is measured by d/b + d.

Calculation1ProportionSurvival AnalysisLife table analysis

(Always present 95% confidence intervals)

Calculation2Crude RatesAdjusted Rates (standardised rates)

ErrorsRandom errorSystematic errors (bias)

Calculation3Life ExpectancyPerson years of life lostDisability adjusted life expectancy (DALES)BiasSample RepresentativeSizeControlsValidity

Describing DataMean Standard Deviation (SD = Z)Z Score a Z score of +1 is one standard deviation above the mean a Z score of -2 is two standard deviations below the mean

Truth

Pooled data analysis, Meta analysis of all studies, systematic reviews

Randomised Controlled Trials

Cohort

Case control

Surveys, ecological studies, case reports100

80

60

40

20

10

Studyometer: Hierarchy of studies

Minimising Confounding Impossible to be sure eliminated Match the controls to the exposed subjects so that they have a similar pattern of exposure to the confounder, or by measuring exposure to the confounder in each group and adjusting for any difference in the statistical analysis.Eg Standardisation to adjust for age and sex

Can be applied to others adjust for confounding, eg mathematical modelling techniques such as logistic regression. These assume that a person's risk of disease is a specified mathematical function of his exposure to different risk factors and confounders. They should be used with caution, however, as the mathematical assumptions in the model may not always reflect the realities of biology.

Nutrition studies always check definitions as they can vary a lot from study to study.

Eg ObesityUndernutritionBreastfeeding

Gynecologic Events Reproductive historyMenarche, Menstrual cycle, Num of pregnancies, contraception, hormone using.Contraception barrier, IUD, Oral pill, Sterilization

HRTSTDLifestyleIllness

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