Week 2

What is epidemiology?  Key science of public health  Focuses on examining the distribution of disease across the population  Use of largely quantitative methods to study diseases, inform prevention and control

Epidemiology: “ the study of the distribution and determinants of health-related states or events in specific populations And the application of this study to the prevention and control of health problems”

Epidemiology: uses  Causation of disease: association between risk factors and outcome  Natural History of disease : course and outcome of disease in groups/ individuals  Health status of populations: disease burden (mortality, morbidity, disability etc)  Evaluating interventions: effectiveness/efficiency

PART I: Measuring Health & Disease  Defining presence or absence of health state/disease  Case Definition Need to be clear about what definition you are using

Case Definition What is and is not a case?  Need to be clear, easy to use  Standardised for use by others Thinking of your research project- what is your case definition? Epidemiologcal case not necessarily the same as clinical case

Measuring disease frequency What is population at risk?Need correct estimation of population you are studying (denominator), if you want an accurate rate

Population at risk Total population Women in population Women aged years

What is the population at risk in your study?

Measures of Health & Disease Measures of disease/ health indicator frequency Incidence- rate of new cases in a given time period in a specific population Prevalence – frequency of existing cases in a defined population at a given time

Incidence vs. prevalence IncidencePrevalence NumeratorNo. NEW cases of disease in a specific time frame No. EXISTING cases of disease at specific time DenominatorPopulation at risk UsesExpresses risk of becoming illEstimates the probability of the population being ill at the period of time being studied Acute conditions Useful for causation Useful in study of burden of chronic diseases and implication for health services PREVALENCE = INCIDENCE X DURATION

Calculating prevalence Prevalence = No. people with disease or condition at specific time No. people in population at risk in specified time  Often expressed as cases per 100 (%) or per 1,000

Prevalence  Period Prevalence: Total No. of cases at any time during a specific period Population at risk midway through the period  Point prevalence: data collected at one specific point in time  Lifetime prevalence Total no. persons known to have had the disease for at least some part of their lives

Factors influencing prevalence  Severity of illness (if severe with high fatality, prevalence reduces)  Duration of illness (if short, prevalence lower than if long)  Number of new cases (if many people develop a disease, its prevalence is higher than if fewer people do)  In migration & out-migration (of healthy or susceptible population)  Diagnosis and cure rates

Incidence  The rate at which new events occur in population.  Incorporates the variable time periods during which individuals are disease free – or ‘at risk’ of developing disease

Incidence Incidence = No. new events in specific period No. persons exposed to risk during period  Strictly new cases  Refers to specific time period e.g. year, lifetime.

Example of Incidence 6184 cases of OCD in a blantyre district population of 150,000 = 6184/150,000 = = 41.2 per 1,000 population or 4.12%

Cumulative incidence (or risk) The proportion of persons in population initially disease free who develop the condition during a specified time interval = No new cases of disease in time period No. disease- free persons at the beginning of that time period Rate as cases per 1000 population

Cumulative Incidence  Can be interpreted as the probability, or risk, that an individual will develop a disease during a specified time frame  Assumes the entire population at risk at the beginning has been followed up for specified time period  Measures denominator only at beginning of the study

Other measures of incidence  Incidence Rate Takes into consideration that some participants will be lost to follow up – and that length of follow up varies: calculates time at risk: the sum of the time that each person remained at risk of becoming a case

Incidence Rate No. new cases in given time period Total person-time at risk during that period

Example of incidence rate No. cases of schizophrenia in BT district = 150 Population Mid-2006 = 22,554 Estimated total person years at risk= 5 (years) x 22,554 = 112,770 person years Mean annual incidence rate of schizophrenia = 150/112,700 = 1.3 per 1,000 person-years

Other measures of incidence Odds of Disease  The odds of disease to non-disease = No. new cases in given time period No. persons who did not become a case in that time period Denominator is all people who are NOT cases

Example of odds of disease 10 women at HIV clinic – all tested 3 test positive 7 test negative Odds = 3/7 = 0.43

Incidence rate  For each individual in the population the time of observation is the period that the person remains disease-free: person- time incidence rate  Denominator: sum of all disease-free person-time periods during the period of observation of population at risk  As can be hard to define disease-free periods: often approximate- : average size of study population x time period

Incidence, prevalence and duration of disease Steady state population: where no. people with/without disease remain stable Point ≈ Incidence rate + mean duration of prevalencedisease Provided prevalence is <0.1 Useful rule of thumb for estimating prevalence in steady state populations

Risks vs. Odds  Assume entire population has been followed over a specific time – are cumulative  Rates- more accurate measure of disease over time Account for ‘at risk’ time which may be variable

Measures of exposure effect Measure of association between risk factor (exposure) and effect (outcome) This comparison can be summarised by measures of relative risk Examines the likelihood of developing outcome in the exposed individuals relative to those unexposed or the difference between the two

Relative measures of exposure effect 3 types of relative measures Risk ratio = risk(cumulative incidence) in exposed risk (cumulative incidence in unexposed Rate ratio = Incidence rate in exposed group Incidence rate in unexposed group Odds ratio = Odds of disease in exposed group Odds of disease in unexposed group

Relative Risk  Measures aetiological strength Value of 1 : exposed = unexposed  Incidence of disease in exposed and unexposed is the same  No association between exposure and outcome Value > 1: positive association between risk fx and outcome (increased risk) Value <1 :negative association (decreased risk/protective)

Relative Risk  Risk ratio of 2: exposed group twice as likely as unexposed group to get outcome  Risk ratio of 0.5 = exposed group is 50% less likely (half as likely) than unexposed group to get outcome

 If disease is rare and studies appropriately designed, then  Odds Ratio ≈ Risk Ratio ≈ Rate Ratio Odds ratios and rate ratios – used most often in epidemiology

Making absolute comparisons Risk difference Excess risk – the difference in rates of occurance between exposed and unexposed groups = risk in exposed - risk in unexposed Rate difference = rate in exposed - rate in unexposed Nb must be comparable populations

Attributable risks  The proportion of all cases that can be attributed to the exposure Attributable risk = Risk difference Incidence amongst exposed population The proportion of disease in the specific population that would be eliminated if the exposure were eliminated Useful for assessing priorities for public health action

Population attributable risk  The incidence of a disease in a population that is associated with exposure to a particular risk factor  The proportion by which incidence rate of outcome in whole population would be reduced if exposure were eliminated = Incidence (population) – Incidence(unexposed) Incidence (unexposed) group

Which measure for which study? Cross sectional studies: Prevalence ratio and odds ratio of prevalent cases in different groups Cohort studies/ Longitudinal intervention studies Risk ratio, rate ratio and disease odds ratio Case control studies Cannot calculate incidence, can calculate odds of exposure in cases vs controls: Odds ratio of exposure, which is equivalent to odds ratio of disease

Other measures

Case Fatality  Measure of disease severity  Defined as proportion of cases with a specific condition who die within a specified time. (%) Case Fatality (%) = No. deaths of diagnosed cases in a given period No. diagnosed cases in same period X 100

Adult mortality rates  The probability of dying between ages of 15 and 60 years per 1,000 population  Enables comparison between countries Crude mortality rate = No deaths during specific period No. persons at risk of dying during same period

Mortality Rates  ICD-10 classification of causes  Verbal autopsy- indirect way of ascertaining cuase of death from information on signs, symptoms and circumstances preceding death.  Used mainly in demographic surveillance and sample registration systems.

Challenges with mortality rates  Biases in diagnosis  Incorrect or incomplete death certificates  Misinterpretation of ICD-10 rules for selection of underlying cause  Variation in use of coding categories for unknown and ill-defined causes

Mortality recording  Certificates may not be complete  Poorer segments of population may not be covered  Death may not be reported for cultural/religious reasons  Age at death may not be accurate  Late registration  Missing data  Errors in reporting or classifying cause of death

What are challenges of examining suicide rates? Thinking about article from journal club, what are the potential biases in suicide rate estimation?

Standardisation death rates Age- standardised death rate (age adjusted rate) – summary measure of death rate that a population would have if it had a standard age structure  Enables comparison between populations that have different age structures.  Can be done for other variables apart from age/death – enables comparison where there are different basic characteristics that can influence outcome (age, gender, SES)

Standardisation methods  Direct and indirect methods Direct standardization:  Applies disease rate of population studied to a standard population (e.g. WHO standard population)  Provides the number of cases that would be expected if the age-specific rates in the standard population were true for the study population

Using standardisation  Eliminates the influence of different age distributions (or other fx) on the morbidity or mortality rates being compared. CountryCrude death rateAge standardised rate Brazil79118 Finland USA176105

CAUSALITY A cause is an event/condition/characteristic which plays an important role in producing the health outcome

Causality: 9 Bradford Hill Critera 1. Temporal relation : cause must precedes effect 2. Plausability – is association consistent with knowledge 3. Strength – What is strength of association between cause and effect (relative risk) 4. Dose- Response Is increased exposure associated with increased effect 5. Specificity 6. Analogy - with effect of similar factors

Causality 7. Reversibility – does removal of cause reduce risk 8. Consistency – several studies give same result 9. Study design quality: experimental evidence Does inevitable involve (subjective) judgement of the evidence

References Basic epidemiology (2 nd Edition): R Bonita; R Beaglehole; T Kjellstrom. World Health Organisation (2006) – available in library  Chapter 2: measuring health and disease