Presentation on theme: "SADC Course in Statistics Basic summaries for epidemiological studies (Session 04)"— Presentation transcript:
SADC Course in Statistics Basic summaries for epidemiological studies (Session 04)
To put your footer here go to View > Header and Footer 2 Learning Objectives At the end of this session, you will be able to correctly distinguish and use ideas of prevalence and incidence explain the concepts of risk in relation to health outcomes, and of what may be causal factors use the concepts of relative risk and odds ratio in relation to simple epidemiological studies
To put your footer here go to View > Header and Footer 3 Attribute Data An attribute is an ascertainable characteristic either present or absent in an individual, so that the measurement on an individual can be represented as either 1 or 0. Many measures in epidemiology are of this type e.g. a test for HIV seropositivity yields such a 0/1 response. This may still involve expert interpretation & judgment, with possibility of false positives and false negatives.
To put your footer here go to View > Header and Footer 4 Point Prevalence Prevalence concerns the number of instances of attribute in the pop n, usually at a point in time, relative to the number at risk, i.e. expressed as a proportion, a percentage, per 1000 or even per million where +s are rare. So point prevalence (as a %age) is No. individuals with + attribute at time point No. of indiv.s in population at risk at time point X 100
To put your footer here go to View > Header and Footer 5 Period Prevalence This refers to number of cases known to have been prevalent during a period e.g. a year. Numerator above wd be replaced by sum of (1) no. of prevalent cases at start of year, and (2) no. of new cases arising during the period. Denominator usually then a mid-year figure for population at risk.
To put your footer here go to View > Header and Footer 6 Prevalence: notes Occasionally prevalence is used for absolute number of cases/instances – best not to call this prevalence! Both point and period prevalence are snapshot figures. They are NOT rates. Period prevalence sensible for short-duration condition where numbers can rise/fall fast. No. at risk needs thought e.g. males only for prostate conditions. Prevalences can be age-specific.
To put your footer here go to View > Header and Footer 7 Incidence Incidence (always a rate ~ a flow statistic) as a population measure is normally on a yearly rate basis. As a proportion:- No. of new cases arising in a period of 1 yr. Mid-yr. population at risk As with prevalence, often put as %, etc Watch out for non-experts confusing or misusing the terms prevalence & incidence!
To put your footer here go to View > Header and Footer 8 Relationship of prevalence & incidence When prevalence P is relatively small and condition is of limited duration (say averaging time T) and population is in a steady state, then approximately:- P = I x T where I = incidence. Exercise ~ try to express in words a rough justification for the above expression.
To put your footer here go to View > Header and Footer 9 Probability, risk or cumulative incidence Sometimes a study population is relatively small, or a sample can be followed up. Then we can calculate risk or cumulative incidence as:- No. new cases arising in one year No. healthy individuals in pop n at start of yr This is then an estimated probability; note the mortality rate of session 14 is an example of this.
To put your footer here go to View > Header and Footer 10 Sources of risk: 1 Much of epidemiology concerns risk factors that may be causes of the disease. There are logical difficulties in proving causation, & often a complex set of pre- disposing and influencing factors. In simplest case, consider just one risk factor e.g. cigarette smoking, and reduce the risk factor ~ as well as disease attribute ~ to present/absent. Discuss what might be more realistic model!
To put your footer here go to View > Header and Footer 11 Sources of risk: 2 With one Yes/No attribute and one present/absent risk factor a 2x2 table of frequencies could be:- Diseased Not diseased Total Risk factor present aba + b Risk factor absent cdc + d Total a + cb + d a+b+c+d = n
To put your footer here go to View > Header and Footer 12 Cohort study This involves selecting, & following through a period of time, individuals some with risk factor present, some absent. Outcome observation = no. with disease at endpoint. In a general population cohort study only n is fixed. If low general exposure to risk, ( a + b ) will be small relative to n ~ costly, so where possible ( a + b ), ( c + d ) often selected e.g. to be equal sample sizes.
To put your footer here go to View > Header and Footer 13 Cohort study relative risk: 1 With observed frequencies a, b, c, d as above the disease risk (over the study duration) among:- the risk-factor + group is: a / ( a + b ) the risk-factor – group is: c / ( c + d ) The relative risk is the ratio of these two risks:- a. ( c + d ) ( a + b ). d RR =
To put your footer here go to View > Header and Footer 14 Cohort study relative risk: 2 The relative risk is the ratio of these two risks:- a. ( c + d ) ( a + b ). c Often disease rates are relatively low, so a / ( a + b ) a / b ; c / ( c + d ) c / d and then RR a.d / b.c – described as the odds ratio or approximate relative risk, with a / b being odds of getting disease, having the exposure, c / d odds not having the exposure. RR =
To put your footer here go to View > Header and Footer 15 Cohort study relative risk: 3 Example ~ population of miners RR = (58/430)/(27/370) = 1.85 Odds ratio = (58/372)/(27/343) = 1.98 Similar representations of extra risk factor due to occupational asbestos exposure. AsbestosLung cancer +No LC i.e. –Total Exposured Not exposed Total
To put your footer here go to View > Header and Footer 16 Case-control relative risk In a case-control study (module I1, sess. 05) numbers of lung cancer positive cases and lung cancer negative controls would be fixed by design. RR cannot be calculated, but the same odds ratio can, & is used as approximation to relative risk. Odds ratios are statistically modelled by professional epidemiologists to account for numerous complicating factors.
To put your footer here go to View > Header and Footer 17 Confounding: 1 Counfounders are nuisance variables that make over-simple conclusions misleading! Example ~ suppose in a study population the TRUE average figures are as below, so tea/coffee drinking adds 4 mg Hg to diastolic blood pressure:- Average diastolic BPOverweightNot overweight Tea/coffee drinker9474 Non-drinker of tea/coffee9070
To put your footer here go to View > Header and Footer 18 Confounding: 2 Now say numbers of individuals in study are:- If study ignores obesity and calculates simple averages, it could expect diastolic BPs as follows:- Drinkers: [(94 x 300) + (74 x 100)]/( ) = 89; non-drinkers: [(90 x 50) + (70 x 150)]/( ) = 75. Misleading 14 mg difference. Confounders only corrected if someone thinks of them! Numbers of individualsOverweightNot overweight Tea/coffee drinker Non-drinker of tea/coffee50150
To put your footer here go to View > Header and Footer 19 Practical work follows to ensure learning objectives are achieved…