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Measurements and Calculations in Epidemiology

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Upendra Raj Dhakal
Upendra Raj Dhakal

Purbanchal University, Basic Epidemiollogy, Unit 3, Calculations in Epidemiology

Health & Medicine
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Unit 3: Measurements and Calculations in Epidemiology (10 Hours) Draft Version 1.3 Upendra Raj Dhakal Lecturer: Valley College of Technical Sciences 9849110689 urdhakal@gmail.com
Counts β€’ Counts are the total number of events that occurs in a defined period of time. β€’ It is also known as frequencies. β€’ In epidemiology, only frequency have less meaning. β€’ Eg. Total number of Salmonellosis reported in Houston during 2000 would be an example of a count or frequency: - In 2000, 227 cases of Salmonellosis were reported. β€’ In this example, 227 cases are the frequencies. Draft Version 1.3 (Feedback Welcomed)
Rate β€’ Rates are the basic tool of epidemiological practice. β€’ Ideally, the numerator and denominator must have different units in rate. eg. Price of 1 piece of shirt = NRs. 1000/piece; kilometers per liter. β€’ A rate is a measure of the frequency with which an event occurs in a defined population in a defined time (e.g. Number of deaths per hundred thousand Nepalese in one year). Draft Version 1.3 (Feedback Welcomed)
Contd … β€’ In epidemiology, RATE is many times deviated from its ideal definition. Many times rate, ratio and proportions are used interchangeably which should not have happened. β€’ In Epidemiology, numerator contains the frequency of cases and denominator is a risk population. All cases and risk population should be within a same time and geographic frame. It can be expressed per 100, 1000 or even 1,00,000. Draft Version 1.3 (Feedback Welcomed)
Rate in epidemiology Must contain: β€’ Health issue in numerator β€’ Unit size of population β€’ Turing period during which an event occurs β€’ Time is always a part of denominator Rate = π‘π‘’π‘šπ‘π‘’π‘Ÿ π‘œπ‘“ 𝒄𝒂𝒔𝒆𝒔 𝑻𝒐𝒕𝒂𝒍 π‘·π’π’‘π’–π’π’‚π’•π’Šπ’π’ π‘€π‘–π‘‘β„Žπ‘–π‘› π‘‘β„Žπ‘’ 𝑔𝑖𝑣𝑒𝑛 π‘Žπ‘Ÿπ‘’π‘Ž 𝑖𝑛 π‘‘β„Žπ‘’ 𝑠𝑝𝑒𝑐𝑖𝑓𝑖𝑐 π‘π‘’π‘Ÿπ‘–π‘œπ‘‘ π‘œπ‘“ π‘‘π‘–π‘šπ‘’ π‘₯ 10 𝑛 Draft Version 1.3 (Feedback Welcomed)
Types of Rate (CR, AR and SR) β€’ Crude Rate: Calculated without paying regard to specific sections of the population. CR = 𝑻𝒐𝒕𝒂𝒍 π‘›π‘’π‘šπ‘π‘’π‘Ÿ π‘œπ‘“ 𝒆𝒗𝒆𝒏𝒕𝒔 π‘‘β„Žπ‘Žπ‘‘ π‘œπ‘π‘π‘’π‘Ÿπ‘’π‘‘ 𝑖𝑛 π‘Ž 𝑔𝑖𝑣𝑒𝑛 π‘”π‘’π‘œπ‘”π‘Ÿπ‘Žπ‘β„Žπ‘–π‘π‘Žπ‘™ π‘Žπ‘Ÿπ‘’π‘Ž π‘‘π‘’π‘Ÿπ‘–π‘›π‘” π‘Ž 𝑔𝑖𝑣𝑒𝑛 π‘¦π‘’π‘Žπ‘Ÿ π‘΄π’Šπ’… βˆ’ 𝒀𝒆𝒂𝒓 π‘·π’π’‘π’–π’π’‚π’•π’Šπ’π’ π‘œπ‘“ π‘‘β„Žπ‘’ π‘”π‘’π‘œπ‘”π‘Ÿπ‘Žπ‘β„Žπ‘–π‘π‘Žπ‘™ π‘Žπ‘Ÿπ‘’π‘Žπ‘  π‘“π‘œπ‘Ÿ π‘‘β„Žπ‘’ π‘ π‘Žπ‘šπ‘’ π‘π‘’π‘Ÿπ‘–π‘œπ‘‘ π‘œπ‘“ π‘‘π‘–π‘šπ‘’ π‘₯ 1,000 Draft Version 1.3 (Feedback Welcomed)
Types of Rate (CR, AR and SR) β€’ Adjusted Rate: it is done to remove the effect of variable, such as age or sex, to permit unbiased comparison in crude rate. Generally, standard population distribution is used to adjust CR. AR = π‘π‘’π‘šπ‘π‘’π‘Ÿ π‘œπ‘“ 𝒆𝒙𝒑𝒆𝒄𝒕𝒆𝒅 𝑒𝑣𝑒𝑛𝑑𝑠 π‘‡π‘œπ‘‘π‘Žπ‘™ 𝑺𝒕𝒂𝒏𝒅𝒂𝒓𝒅 π‘ƒπ‘œπ‘π‘’π‘™π‘Žπ‘‘π‘–π‘œπ‘› π‘₯ 1,00,000 Draft Version 1.3 (Feedback Welcomed)
Types of Rate (CR, AR and SR) β€’ Specific Rate: It is calculated for the specific group of population such as for a particular age, sex, marital status, occupation, etc.. SR = π‘π‘’π‘šπ‘π‘’π‘Ÿ π‘œπ‘“ 𝒆𝒗𝒆𝒏𝒕𝒔 π‘œπ‘π‘π‘’π‘Ÿπ‘’π‘‘ π‘Žπ‘šπ‘œπ‘›π‘” π‘Ž π’”π’‘π’†π’„π’Šπ’‡π’Šπ’„ π’ˆπ’“π’π’–π’‘ π‘œπ‘“ π‘π‘œπ‘π‘’π‘™π‘Žπ‘‘π‘–π‘œπ‘› π‘œπ‘“ π‘Ž 𝑔𝑖𝑣𝑒𝑛 π‘”π‘’π‘œπ‘”π‘Ÿπ‘Žπ‘β„Žπ‘–π‘ π‘Žπ‘Ÿπ‘’π‘Ž π‘‘π‘’π‘Ÿπ‘–π‘›π‘” π‘Ž 𝑔𝑖𝑣𝑒𝑛 π‘¦π‘’π‘Žπ‘Ÿ π‘΄π’Šπ’… βˆ’ π’šπ’†π’‚π’“ π’‘π’π’‘π’–π’π’‚π’•π’Šπ’π’ 𝒐𝒇 𝒕𝒉𝒆 π’”π’‘π’†π’„π’Šπ’‡π’Šπ’„ π’ˆπ’“π’π’–π’‘ π‘œπ‘“ π‘‘β„Žπ‘’ π‘π‘œπ‘π‘’π‘™π‘Žπ‘‘π‘–π‘œπ‘› 𝑖𝑛 π‘‘β„Žπ‘’ π‘ π‘Žπ‘šπ‘’ π‘”π‘’π‘œπ‘”π‘Ÿπ‘Žπ‘β„Žπ‘–π‘ π‘Žπ‘Ÿπ‘’π‘Ž π‘‘π‘’π‘Ÿπ‘–π‘›π‘” π‘‘β„Žπ‘’ π‘ π‘Žπ‘šπ‘’ π‘π‘’π‘Ÿπ‘–π‘œπ‘‘ π‘₯ 1,000 Draft Version 1.3 (Feedback Welcomed)
Ratio β€’ Ratio is a fractional relationship between two variables indicating how many times the numerator contains to denominator. β€’ In ratio there is no specific relationship between numerator and denominator like in rate, with same units. β€’ Unlike rate, it is not necessary Time to be a part of denominator. Draft Version 1.3 (Feedback Welcomed)
Contd … β€’ Since numerator and denominator have same units or even unitless, ratio is dimensionless. β€’ Numerator is not a part of denominator in ratio. β€’ Eg. Among 100 population, 58 are female and 42 are male. When it is expressed in terms of ratio, we can write it as F:M = 58:42 or 1.4:1 Draft Version 1.3 (Feedback Welcomed)
Difference MM Rate and MM Ratio MM Rate = π‘΅π’–π’Žπ’ƒπ’†π’“ 𝒐𝒇 π’Žπ’‚π’•π’†π’“π’π’‚π’ 𝒅𝒆𝒂𝒕𝒉𝒔 π‘œπ‘π‘π‘’π‘Ÿπ‘–π‘›π‘” 𝑖𝑛 π‘Ÿπ‘’π‘“π‘’π‘Ÿπ‘’π‘›π‘π‘’ π‘π‘’π‘Ÿπ‘–π‘œπ‘‘ (π‘’π‘ π‘’π‘Žπ‘™π‘™π‘¦ 1 π‘¦π‘’π‘Žπ‘Ÿ) π‘π‘’π‘šπ‘π‘’π‘Ÿ π‘œπ‘“ 𝑾𝑢𝑹𝑨 𝑖𝑛 π‘π‘œπ‘π‘’π‘™π‘Žπ‘‘π‘–π‘œπ‘› 𝑖𝑛 π‘Ÿπ‘’π‘“π‘’π‘Ÿπ‘’π‘›π‘π‘’ π‘π‘’π‘Ÿπ‘–π‘œπ‘‘ π‘₯ 𝟏, 𝟎𝟎𝟎 MM Ratio = π‘΅π’–π’Žπ’ƒπ’†π’“ 𝒐𝒇 π’Žπ’‚π’•π’†π’“π’π’‚π’ 𝒅𝒆𝒂𝒕𝒉𝒔 π‘œπ‘π‘π‘’π‘Ÿπ‘–π‘›π‘” 𝑖𝑛 π‘Ÿπ‘’π‘“π‘’π‘Ÿπ‘’π‘›π‘π‘’ π‘π‘’π‘Ÿπ‘–π‘œπ‘‘ (π‘’π‘ π‘’π‘Žπ‘™π‘™π‘¦ 1 π‘¦π‘’π‘Žπ‘Ÿ) π‘π‘’π‘šπ‘π‘’π‘Ÿ π‘œπ‘“ π’π’Šπ’—π’† π’ƒπ’Šπ’“π’•π’‰π’” π‘œπ‘π‘π‘’π‘Ÿπ‘–π‘›π‘” π‘€π‘–π‘‘β„Žπ‘–π‘› π‘‘β„Žπ‘’ π‘Ÿπ‘’π‘“π‘’π‘Ÿπ‘’π‘›π‘π‘’ π‘π‘’π‘Ÿπ‘–π‘œπ‘‘ π‘₯ 𝟏, 𝟎𝟎, 𝟎𝟎𝟎 Draft Version 1.3 (Feedback Welcomed)
Proportion β€’ Proportion is nothing more than a ratio in which, numerator is always a part of a denominator. β€’ When proportion is expressed in total number of population (population at risk), it becomes rate. β€’ Eg. In a population of 100 women, 58 had premarital sex. The proportion is 58/100 = 0.58 = 58%. β€’ Fetal Death Ratio, though is written ratio, it is a proportion. Fetal Death Ratio = π‘΅π’–π’Žπ’ƒπ’†π’“ π‘œπ‘“ π‘“π‘’π‘‘π‘Žπ‘™ π‘‘π‘’π‘Žπ‘‘β„Žπ‘  𝑻𝒐𝒕𝒂𝒍 π’π’–π’Žπ’ƒπ’†π’“ π‘œπ‘“ 𝑙𝑖𝑣𝑒 π‘π‘–π‘Ÿπ‘‘β„Žπ‘  Draft Version 1.3 (Feedback Welcomed)
Incidence β€’ It is also known as Measure of Morbidity for new cases. β€’ It is the rate of the development of new cases of a disease that occur during a specified period of time in previously disease free or condition free individuals β€’ It is not influenced by the duration of the disease β€’ Many times, the denominator in formula uses mid – interval/year population at risk, instead of Estimated number of people in population (risk population) Draft Version 1.3 (Feedback Welcomed)
Contd … β€’ Statistically, 𝐼𝑛𝑐𝑖𝑑𝑒𝑛𝑐𝑒 π‘…π‘Žπ‘‘π‘’ = π‘π‘’π‘šπ‘π‘’π‘Ÿ π‘œπ‘“ 𝑛𝑒𝑀 π‘π‘Žπ‘ π‘’π‘  𝑖𝑛 π‘Ž 𝑠𝑝𝑒𝑐𝑖𝑓𝑖𝑐 π‘π‘’π‘Ÿπ‘–π‘œπ‘‘ π‘œπ‘“ π‘‘π‘–π‘šπ‘’ πΈπ‘ π‘‘π‘–π‘šπ‘Žπ‘‘π‘’π‘‘ π‘π‘’π‘œπ‘π‘™π‘’ 𝑖𝑛 π‘‘β„Žπ‘’ π‘π‘œπ‘π‘’π‘™π‘Žπ‘‘π‘–π‘œπ‘› π‘Žπ‘‘ π‘‘β„Žπ‘’ π‘ π‘Žπ‘šπ‘’ π‘π‘’π‘Ÿπ‘–π‘œπ‘‘ π‘œπ‘“ π‘‘π‘–π‘šπ‘’ π‘₯ 100 β€’ There are two fundamental approaches to measure incidence rate: β€’ Cumulative Incidence Rate, and β€’ Incidence Density Rate Draft Version 1.3 (Feedback Welcomed)
Cumulative incidence rate (Incidence Proportion) β€’ Number of new cases of disease occurring over a specified period of time in a population at risk at the beginning of the interval. CIR= π‘π‘’π‘šπ‘π‘’π‘Ÿ π‘œπ‘“ 𝑛𝑒𝑀 π‘π‘Žπ‘ π‘’π‘  π‘‘π‘’π‘Ÿπ‘–π‘›π‘” π‘‘β„Žπ‘’ π’”π’‘π’†π’„π’Šπ’‡π’Šπ’„ π’‘π’†π’“π’Šπ’π’… π‘œπ‘“ π‘‘π‘–π‘šπ‘’ π‘π‘’π‘šπ‘π‘’π‘Ÿ π‘œπ‘“ π‘π‘œπ‘π‘’π‘™π‘Žπ‘‘π‘–π‘œπ‘› π‘Žπ‘‘ π‘‘β„Žπ‘’ 𝑏𝑒𝑔𝑖𝑛𝑛𝑖𝑛𝑔 π‘œπ‘“ π‘‘π‘–π‘šπ‘’ π‘₯ 1,000 β€’ CIR is generally expressed in decimal per thousand per specific time. β€’ If it is measured for entire life, it is known as Lifetime risk. Draft Version 1.3 (Feedback Welcomed)
Incidence density rate (Person Time Incidence) β€’ Number of new cases of disease occurring over a specified period of time in a population at risk throughout the interval. β€’ We need to add up period of time each individual was present in the population, and was at risk of becoming a new case of disease. β€’ Denominator uses person – time at risk. 𝐼𝐷 = π‘΅π’–π’Žπ’ƒπ’†π’“ 𝒐𝒇 π’π’†π’˜ 𝒄𝒂𝒔𝒆𝒔 π‘‘π‘’π‘Ÿπ‘–π‘›π‘” π‘‘β„Žπ‘’ 𝑠𝑝𝑒𝑐𝑖𝑓𝑖𝑐 π‘π‘’π‘Ÿπ‘–π‘œπ‘‘ π‘œπ‘“ π‘‘π‘–π‘šπ‘’ π‘‡π‘œπ‘‘π‘Žπ‘™ π‘π‘’π‘Ÿπ‘ π‘œπ‘› βˆ’ π‘‘π‘–π‘šπ‘’ π‘Žπ‘‘ π‘Ÿπ‘–π‘ π‘˜ π‘‘π‘’π‘Ÿπ‘–π‘›π‘” π‘‘β„Žπ‘’ π‘–π‘›π‘‘π‘’π‘Ÿπ‘£π‘Žπ‘™ π‘₯ 1,000 β€’ It is calculated in an open population (with migration), and every person contributes different duration of β€œat risk” interval Draft Version 1.3 (Feedback Welcomed)
Relation between CI and ID β€’ The numerator does not differ between the two types of incidence. β€’ However, the denominator can differ in incidence density from cumulative incidence because it takes account of: β€’ Cases who left during the defined year β€’ Cases who died β€’ Cases who had disease once and will not have it again in the same season β€’ Cases who entered in between in the defined year. Draft Version 1.3 (Feedback Welcomed)
Prevalence β€’ Prevalence is a measurement of all individuals affected by the disease at a particular time. It is also known as Measure of Morbidity for new and old cases. β€’ All current cases (New + old) existing in a given point in time or over a period of time in a given population. β€’ It is of three types: β€’ Point prevalence β€’ Period prevalence, and β€’ Lifetime prevalence Draft Version 1.3 (Feedback Welcomed)
Point - Prevalence β€’ Point prevalence is a measure of the proportion of people in a population who have disease or condition at a particular time (generally a month or less or even a day) β€’ It is like a snapshot of the disease in time and is expressed in percentage. β€’ It is used to study chronic diseases β€’ It can be used to calculate specifically for age, gender, etc. β€’ When type of prevalence is not specified, we should consider it is point prevalence 𝑃𝑑. 𝑃 = π‘π‘’π‘šπ‘π‘’π‘Ÿ π‘œπ‘“ 𝑒π‘₯𝑖𝑠𝑑𝑖𝑛𝑔 π‘π‘Žπ‘ π‘’π‘  π‘΅π’†π’˜ + 𝑢𝒍𝒅 π‘œπ‘“ π‘Ž 𝑠𝑝𝑒𝑐𝑖𝑓𝑖𝑒𝑑 π‘‘π‘–π‘ π‘’π‘Žπ‘ π‘’ π‘œπ‘› π‘Ž 𝑠𝑝𝑒𝑐𝑖𝑓𝑖𝑐 π‘‘π‘Žπ‘‘π‘’ π‘¬π’”π’•π’Šπ’Žπ’‚π’•π’†π’… 𝒑𝒆𝒐𝒑𝒍𝒆 𝑖𝑛 π‘‘β„Žπ‘’ π‘π‘œπ‘π‘’π‘™π‘Žπ‘‘π‘–π‘œπ‘› π‘Žπ‘‘ π‘‘β„Žπ‘’ π‘ π‘Žπ‘šπ‘’ π‘π‘œπ‘–π‘›π‘‘ π‘œπ‘“ π‘‘π‘–π‘šπ‘’ π‘₯ 100 Draft Version 1.3 (Feedback Welcomed)
Period Prevalence β€’ It is the proportion of the population with a given disease or condition over a specific period of time. β€’ Simply, it answers like: How many people in a population suffered from common cold in 2018. β€’ It is expressed in percentage. 𝑃𝑑. 𝑃 = π‘π‘’π‘šπ‘π‘’π‘Ÿ π‘œπ‘“ 𝑒π‘₯𝑖𝑠𝑑𝑖𝑛𝑔 π‘π‘Žπ‘ π‘’π‘  π‘΅π’†π’˜ + 𝑢𝒍𝒅 π‘œπ‘“ π‘Ž 𝑠𝑝𝑒𝑐𝑖𝑓𝑖𝑒𝑑 π‘‘π‘–π‘ π‘’π‘Žπ‘ π‘’ π‘œπ‘› π‘Ž 𝑠𝑝𝑒𝑐𝑖𝑓𝑖𝑐 π‘‘π‘–π‘šπ‘’ π‘–π‘›π‘‘π‘’π‘Ÿπ‘£π‘Žπ‘™ π‘¬π’”π’•π’Šπ’Žπ’‚π’•π’†π’… 𝒑𝒆𝒐𝒑𝒍𝒆 𝑖𝑛 π‘‘β„Žπ‘’ π‘π‘œπ‘π‘’π‘™π‘Žπ‘‘π‘–π‘œπ‘› π‘Žπ‘‘ π‘‘β„Žπ‘’ π‘ π‘Žπ‘šπ‘’ π‘π‘’π‘Ÿπ‘–π‘œπ‘‘ π‘œπ‘“ π‘‘π‘–π‘šπ‘’ π‘₯ 100 Draft Version 1.3 (Feedback Welcomed)
Relationship between Prevalence and Incidence β€’ Prevalence = Incidence x Mean duration of illness P = I X D Draft Version 1.3 (Feedback Welcomed)
Lifetime Prevalence – Not in syllabus β€’ It is the proportion of individuals in population that at some point in their life during the time of assessment, has experienced a case. β€’ The cases might have experienced a diseased condition, accident, crime, harassment, … β€’ It is also known as cumulative prevalence Draft Version 1.3 (Feedback Welcomed)
Risk β€’ Probability of developing an effect of certain risk factors. Types of risk measurement β€’ Relative risk β€’ Odds Ratio (Case Control and Cross sectional/Prevalancestudies), and β€’ Risk ratio (Cohort/Incidence Studies) β€’ Attributable risk β€’ Comparative risk (Not in syllabus) β€’ Absolute risk (Not in syllabus) Draft Version 1.3 (Feedback Welcomed)
Relative Risk β€’ It is the ratio of incidence in exposed persons to incidence in non exposed person. β€’ It is the most commonly reported result in studies of risk, partially because of conceptual convenience but also because of a common metric in studies of similar risk factors but with different baseline incident rates. β€’ Because it indicates the strength of association between exposure and disease, it is useful measure of effect for studies of disease etiology. β€’ Generally, Risk Ratio is calculated when Relative Risk is stated. Draft Version 1.3 (Feedback Welcomed)
Odds Ratio β€’ Odds = Chance or likelihood of something to happen or not to happen β€’ OR is a statistics that quantifies the strength of the association between two events (disease and exposure). It is the ratio of two odds β€’ OR is the ratio of odds that disease will occur among individuals who have been exposed to the risk factors to odds that the disease will occur among individuals who has not been exposed to risk factors. (ratio between event and non event of disease, and exposure) β€’ If the events are A (Lung Cancer) and B (Smoking), OR can be defined as a ratio of the chance of A in the presence of B, and the chance of A in the absence of B. Draft Version 1.3 (Feedback Welcomed)
Contd … β€’ Interpretation: OR > 1 : Strong association between disease and exposure to risk factors OR = 1: No association between disease and exposure to risk factors OR < 1 : Negative association between disease and exposure to the risk factors β€’ We cannot conclude our result (of any statistical calculation) only depending on OR, and without knowing Confidence Interval (CI – range of values we are fairly sure our true values lies in) and Significance value (p – probability of rejecting the null hypothesis when it is true). Draft Version 1.3 (Feedback Welcomed)
Contd … β€’ Statistically, 𝑂𝑅 = π‘‡β„Žπ‘’ π‘β„Žπ‘Žπ‘›π‘π‘’ π‘œπ‘“ π’”π’π’Žπ’†π’•π’‰π’Šπ’π’ˆ π‘‘π‘œ β„Žπ‘Žπ‘π‘π‘’π‘› π‘‡β„Žπ‘’ π‘β„Žπ‘Žπ‘›π‘π‘’ π‘œπ‘“ π’π’π’•π’‰π’Šπ’π’ˆ β„Žπ‘Žπ‘π‘π‘’π‘›π‘›π‘–π‘›π‘” or 𝑂𝑅 = π·π‘–π‘ π‘’π‘Žπ‘ π‘’ π‘Žπ‘›π‘‘ 𝑒π‘₯π‘π‘œπ‘ π‘’π‘‘ / π»π‘’π‘Žπ‘™π‘‘β„Žπ‘¦ π‘Žπ‘›π‘‘ 𝑒π‘₯π‘π‘œπ‘ π‘’π‘‘ π·π‘–π‘ π‘’π‘Žπ‘ π‘’π‘‘ π‘Žπ‘›π‘‘ π‘π‘œπ‘› βˆ’ 𝐸π‘₯π‘π‘œπ‘ π‘’π‘‘ / π»π‘’π‘Žπ‘™π‘‘β„Žπ‘¦ π‘Žπ‘›π‘‘ π‘›π‘œπ‘› 𝑒π‘₯π‘π‘œπ‘ π‘’π‘‘ or 𝑂𝑅 = π·π‘–π‘ π‘’π‘Žπ‘ π‘’ π‘Žπ‘›π‘‘ 𝑒π‘₯π‘π‘œπ‘ π‘’π‘‘ / π·π‘–π‘ π‘’π‘Žπ‘ π‘’π‘‘ π‘Žπ‘›π‘‘ π‘›π‘œπ‘› 𝑒π‘₯π‘π‘œπ‘ π‘’π‘‘ π»π‘’π‘Žπ‘™π‘‘β„Žπ‘¦ π‘Žπ‘›π‘‘ 𝐸π‘₯π‘π‘œπ‘ π‘’π‘‘ / π»π‘’π‘Žπ‘™π‘‘β„Žπ‘¦ π‘Žπ‘›π‘‘ π‘›π‘œπ‘› 𝑒π‘₯π‘π‘œπ‘ π‘’π‘‘ (all give the same result) Draft Version 1.3 (Feedback Welcomed)
Contd … β€’ Above formula can be understood from the table as: β€’ OR = (a/b)/(c/d) = ad/bc [ Statistically, we can write (a/c)/(b/d), but during interpretation, it may be defined wrongly] β€’ Interpretation: The odds of having cancer is …. times higher (no chance or lower) for smokers than the odd of having cancer to non smokers. Draft Version 1.3 (Feedback Welcomed) Disease (lung cancer) + (Cases) - (Non Cases) Exposure (Smoking) + Diseased and exposed (a) Healthy and exposed (b) - Diseased and non – exposed (c) Healthy and non – exposed (d)
Risk Ratio β€’ RR is the ratio of incidence of the outcome among exposed individuals to the incidence among non exposed individuals. β€’ It compares the probability of an outcome among exposed to a risk factor to the probability of that outcome among individuals who are not exposed to the risk. β€’ Interpretation: RR > 1 : Strong association between disease and exposure to the risk factor RR = 1 : No association between disease and exposure to the risk factor RR < 1 : Negative association between disease and exposure to the risk factor Draft Version 1.3 (Feedback Welcomed)
Contd … β€’ Statistically, 𝑅𝑅 = π‘‡β„Žπ‘’ π‘β„Žπ‘Žπ‘›π‘π‘’ π‘œπ‘“ π’”π’π’Žπ’†π’•π’‰π’Šπ’π’ˆ π‘‘π‘œ β„Žπ‘Žπ‘π‘π‘’π‘› π‘‡β„Žπ‘’ π‘β„Žπ‘Žπ‘›π‘π‘’ π‘œπ‘“ 𝒂𝒍𝒍 π’•π’‰π’Šπ’π’ˆπ’” 𝒕𝒐 β„Žπ‘Žπ‘π‘π‘’π‘› β€’ Above formula can be understood from the table as: β€’ RR = (a/a+b)/(c/c+d) = a(c+d)/b(a+b) β€’ Interpretation: The risk of having cancer is …. times higher (no chance or lower) for smokers than the risk of happening cancer to non smokers. Disease (lung cancer) + - Exposure (Smoking) + Diseased and exposed (a) Healthy and exposed (b) - Diseased and non – exposed (c) Healthy and non – exposed (d) Draft Version 1.3 (Feedback Welcomed)
RRR, ARR and No. needed to treat (not in syllabus) β€’ RR = (a/a+b)/(c/c+d) = a(c+d)/b(a+b) (already done in previous slide) β€’ Relative Risk Reduction (RRR) = 1 – Relative Risk β€’ Absolute Risk Reduction (ARR) = 𝑐 𝑐+𝑑 βˆ’ π‘Ž π‘Ž+𝑏 β€’ Number needed to treat = 1/ARR Draft Version 1.3 (Feedback Welcomed)
Concluding OR and RR (just for reference, will understand later) β€’ OR and RR are interpreted in similar way, the only difference is the use of word odds Ratio and Risk Ratio/Relative scale. β€’ Both OR, and RR gives the same information, but in different scale. Thus, they are not equivalent, though the result interpreted are similar. β€’ OR and RR are used for rare diseases. β€’ If diseases are more common, like hypertension OR overestimates RR. i.e. OR > RR. Thus, RR should be preferred in such case instead of OR β€’ RR is preferred when there is absolute incidence and prevalence. Incidence is used in cohort study, whereas prevalence is used in cross – sectional studies. β€’ If meaningful incidence or prevalence is absent, we use OR instead of RR Draft Version 1.3 (Feedback Welcomed)
Concluding OR and RR (revision of definition) β€’ OR is the ratio of odds that disease will occur among individuals who have been exposed to the risk factors to odds that the disease will occur among individuals who has not been exposed to risk factors. (ratio between event and non event of disease, and exposure) β€’ RR is the ratio of incidence of the outcome among exposed individuals to the incidence among non exposed individuals. Draft Version 1.3 (Feedback Welcomed)
Attributable Risk β€’ Attribute = Quality of a characteristics β€’ It can be understood as excess risk or risk difference β€’ In Statistics, Attribute is a Qualitative type of characteristics calculated using quantative measurements. It is done in cohort study design. β€’ It is just a difference of incidence of disease among exposed and incidence of disease among unexposed. If expressed in percent, we multiply the difference by 100 and so on. AR = Incidence of disease among exposed – Incidence of disease among unexposed Draft Version 1.3 (Feedback Welcomed)
Population Attributable Risk (not in syllabus) β€’ AR and PAR are different β€’ PAR is the amount of proportion of disease incidence that can be attributed to a specific exposure β€’ It indicates to what extent the disease under study can be attributed to the exposure. β€’ It is expressed in per 10 𝑛 population when calculated in proportion PAR = 𝐼𝑛𝑐𝑖𝑑𝑒𝑛𝑐𝑒 π‘œπ‘“ π‘‘π‘–π‘ π‘’π‘Žπ‘ π‘’ π‘Žπ‘šπ‘œπ‘›π‘” 𝑒π‘₯π‘π‘œπ‘ π‘’π‘‘ βˆ’ 𝐼𝑛𝑐𝑖𝑑𝑒𝑛𝑐𝑒 π‘œπ‘“ π‘‘π‘–π‘ π‘’π‘Žπ‘ π‘’ π‘Žπ‘šπ‘œπ‘›π‘” π‘›π‘œπ‘› 𝑒π‘₯π‘π‘œπ‘ π‘’π‘‘ 𝐼𝑛𝑐𝑖𝑑𝑒𝑛𝑐𝑒 π‘œπ‘“ π‘‘π‘–π‘ π‘’π‘Žπ‘ π‘’ π‘Žπ‘šπ‘œπ‘›π‘” 𝑒π‘₯π‘π‘œπ‘ π‘’π‘‘ π‘₯ 10 𝑛 Draft Version 1.3 (Feedback Welcomed)
Attack Rate β€’ For acute cases or during epidemic situation, Incidence rate is also known as Attack rate β€’ Attack rate is used when the occurrence of disease among population increases dramatically over a short period of time β€’ There are two types of attack rate: β€’ Primary attack rate, and β€’ Secondary attack rate Draft Version 1.3 (Feedback Welcomed)
Primary Attack Rate β€’ Primary attack rate is based on the number of person who contracted the disease directly from the source. β€’ Instead of primary attack rate, we are more focused in calculating secondary attack rate 𝑃𝐴𝑅 = π‘π‘œ.π‘œπ‘“ π‘π‘Ÿπ‘–π‘šπ‘Žπ‘Ÿπ‘¦ π‘π‘Žπ‘ π‘’π‘  π‘‘π‘’π‘Ÿπ‘–π‘›π‘” π‘‘β„Žπ‘’ 𝑠𝑝𝑒𝑐𝑖𝑓𝑖𝑐 π‘π‘’π‘Ÿπ‘–π‘œπ‘‘ π‘œπ‘“ π‘‘π‘–π‘šπ‘’ π‘‡π‘œπ‘‘π‘Žπ‘™ π‘π‘œπ‘π‘’π‘™π‘Žπ‘‘π‘–π‘œπ‘› π‘Žπ‘‘ π‘Ÿπ‘–π‘ π‘˜ π‘₯ 100 Draft Version 1.3 (Feedback Welcomed)
Secondary attack β€’ It is defined as the probability that infection occurs among susceptible persons within a reasonable incubation period following known contact with an infectious person or an infectious source. β€’ Primary cases are excluded from both numerator and denominator. S𝐴𝑅 = π‘π‘œ.π‘œπ‘“ π‘π‘œπ‘›π‘‘π‘Žπ‘π‘‘ π‘π‘’π‘Ÿπ‘ π‘œπ‘› π‘€β„Žπ‘œ π‘‘π‘’π‘£π‘’π‘™π‘œπ‘π‘’π‘‘ π‘‘β„Žπ‘’ π‘‘π‘–π‘ π‘’π‘Žπ‘ π‘’ π‘€π‘–π‘‘β„Žπ‘–π‘› π‘‘β„Žπ‘’ π‘–π‘›π‘π‘’π‘π‘Žπ‘‘π‘–π‘œπ‘› π‘π‘’π‘Ÿπ‘–π‘œπ‘‘ π‘π‘’π‘šπ‘π‘’π‘Ÿ π‘œπ‘“ 𝑠𝑒𝑠𝑐𝑒𝑝𝑑𝑖𝑏𝑙𝑒 π‘π‘’π‘Ÿπ‘ π‘œπ‘›π‘  π‘€β„Žπ‘œ π‘π‘œπ‘›π‘‘π‘Ÿπ‘Žπ‘π‘‘π‘’π‘‘ π‘€π‘–π‘‘β„Ž π‘π‘Ÿπ‘–π‘šπ‘Žπ‘Ÿπ‘¦ π‘π‘Žπ‘ π‘’π‘  π‘₯ 100 Draft Version 1.3 (Feedback Welcomed)
What do you understand? Disease (lung cancer) + - Exposure(Smoking) + Diseased and exposed (a) Healthy and exposed (b) - Diseased and non – exposed (c) Healthy and non – exposed (d) Compare When test says you have and actually you have it (a) When the test says you don’t have it but you actually have it (d) When the test says you don’t have it and you actually don’t have it (c) When the test says you have it but you actually don’t have it (b) Draft Version 1.3 (Feedback Welcomed)
Validity and reliability testing β€’ Validity measures the accuracy i.e. measuring what was aimed to measure. We can measure internal validity and external validity. β€’ Validity is tested using the formula: β€’ Accuracy testing [Accuracy = {(a + d)/(a + b + c + d)} x 100] (from 2 X 2 table) Draft Version 1.3 (Feedback Welcomed)
Contd … β€’ Reliability measures the precision of test by considering confidence interval (CI) for the measure of accuracy. It sees consistency of data. β€’ Reliability/Repeatability is tested using β€’ Positive predictable values β€’ Negative predictable values β€’ Eg. 1 Kg Mass is same regardless of altitude whereas 1 Newton weight differs with altitude taking into account the use of beam balance or spring balance. Draft Version 1.3 (Feedback Welcomed)
Sensitivity (Not in syllabus) β€’ It refers to the proportion of subjects with the disease condition giving positive test result. β€’ It is also known as true positive rate/recall/probability of detection and indicates the probability a person has a positive test result β€’ It generally measures the ability of a test to detect the disease condition. Sensitivity = π‘π‘’π‘šπ‘π‘’π‘Ÿ π‘œπ‘“ π‘‘π‘Ÿπ‘’π‘’ π‘π‘œπ‘ π‘–π‘‘π‘–π‘£π‘’π‘  π‘π‘œ. π‘œπ‘“ π‘‘π‘Ÿπ‘’π‘’ π‘π‘œπ‘ π‘–π‘‘π‘–π‘£π‘’+π‘π‘œ.π‘œπ‘“ π‘“π‘Žπ‘™π‘ π‘’ π‘›π‘’π‘”π‘Žπ‘‘π‘–π‘£π‘’ π‘₯ 100 = π‘π‘’π‘šπ‘π‘’π‘Ÿ π‘œπ‘“ π‘‘π‘Ÿπ‘’π‘’ π‘π‘œπ‘ π‘–π‘‘π‘–π‘£π‘’π‘  π‘‡π‘œπ‘‘π‘Žπ‘™ π‘›π‘œ.π‘œπ‘“ π‘ π‘–π‘π‘˜ π‘–π‘›π‘‘π‘–π‘£π‘–π‘‘π‘’π‘Žπ‘™π‘  𝑖𝑛 π‘π‘œπ‘π‘’π‘™π‘Žπ‘‘π‘–π‘œπ‘› = Probability of positive test given that the patient has a disease = {a/(a+c)}x 100 (from 2 X 2 table) Draft Version 1.3 (Feedback Welcomed)
Specificity (Not is syllabus) β€’ It refers to the proportion of subjects without the disease condition giving negative test result. β€’ It is also known as true negative and indicates the probability a person has a negative test result β€’ It generally measures the ability of a test to detect the non disease condition. Specificity = π‘π‘’π‘šπ‘π‘’π‘Ÿ π‘œπ‘“ π‘‘π‘Ÿπ‘’π‘’ π‘›π‘’π‘”π‘Žπ‘‘π‘–π‘£π‘’π‘  π‘π‘œ. π‘œπ‘“ π‘‘π‘Ÿπ‘’π‘’ π‘›π‘’π‘”π‘Žπ‘‘π‘–π‘£π‘’π‘ +π‘π‘œ.π‘œπ‘“ π‘“π‘Žπ‘™π‘ π‘’ π‘π‘œπ‘ π‘–π‘‘π‘–π‘£π‘’π‘  π‘₯ 100 = π‘π‘’π‘šπ‘π‘’π‘Ÿ π‘œπ‘“ π‘‘π‘Ÿπ‘’π‘’ π‘›π‘’π‘”π‘Žπ‘‘π‘–π‘£π‘’π‘  π‘‡π‘œπ‘‘π‘Žπ‘™ π‘›π‘œ.π‘œπ‘“ 𝑀𝑒𝑙𝑙 π‘–π‘›π‘‘π‘–π‘£π‘–π‘‘π‘’π‘Žπ‘™π‘  𝑖𝑛 π‘π‘œπ‘π‘’π‘™π‘Žπ‘‘π‘–π‘œπ‘› = Probability of negative test given that the patient well = {d/(b+d)} x 100 (from 2 X 2 table) Draft Version 1.3 (Feedback Welcomed)
Interpretation (Not in syllabus) β€’ Sensitivity: The probability that a patient with the disease will have a positive test result is …. β€’ Specificity: The probability that a patient without the disease will have a negative test result is …. β€’ Accuracy/Validity: The probability that the results of a test will accurately predict presence (or absence) of disease is … Draft Version 1.3 (Feedback Welcomed)
Predictable value β€’ There are two versions of Predictable value β€’ Positive Predictable value, and β€’ Negative Predictable value Draft Version 1.3 (Feedback Welcomed)
Positive Predictable Value β€’ Positive predictable value (PPV) gives the proportion of positive result in statistics and diagnosis tests that are true positive. 𝑃𝑃𝑉 = π‘π‘’π‘šπ‘π‘’π‘Ÿ π‘œπ‘“ π‘‘π‘Ÿπ‘’π‘’ π‘π‘œπ‘ π‘–π‘‘π‘–π‘£π‘’π‘  (π‘Ž) π‘π‘’π‘šπ‘π‘’π‘Ÿ π‘œπ‘“ π‘‘π‘Ÿπ‘’π‘’ π‘π‘œπ‘ π‘–π‘‘π‘–π‘£π‘’π‘  π‘Ž + π‘π‘’π‘šπ‘π‘’π‘Ÿ π‘œπ‘“ π‘“π‘Žπ‘™π‘ π‘’ π‘π‘œπ‘ π‘–π‘‘π‘–π‘£π‘’π‘  (𝑏) π‘₯ 100 β€’ It can also be calculated from sensitivity, specificity and prevalence. 𝑃𝑃𝑉 = 𝑠𝑒𝑛𝑠𝑖𝑑𝑖𝑣𝑖𝑑𝑦 π‘₯ π‘π‘Ÿπ‘’π‘£π‘Žπ‘™π‘’π‘›π‘π‘’ 𝑠𝑒𝑛𝑠𝑖𝑑𝑖𝑣𝑖𝑑𝑦 π‘₯ π‘π‘Ÿπ‘’π‘£π‘Žπ‘™π‘’π‘›π‘π‘’+ 1 βˆ’π‘ π‘π‘’π‘π‘–π‘“π‘–π‘π‘–π‘‘π‘¦ π‘₯ (1 βˆ’π‘π‘Ÿπ‘’π‘£π‘Žπ‘™π‘’π‘›π‘π‘’) β€’ Also from 2 X 2 table as: PPV = {a(a + b)} x 100 Draft Version 1.3 (Feedback Welcomed)
Negative Predictable Value β€’ Negative predictable value (NPV) gives the proportion of negative result in statistics and diagnosis tests that are true negative. 𝑁𝑃𝑉 = π‘π‘’π‘šπ‘π‘’π‘Ÿ π‘œπ‘“ π‘‘π‘Ÿπ‘’π‘’ π‘›π‘’π‘”π‘Žπ‘‘π‘–π‘£π‘’π‘  (𝑑) π‘π‘’π‘šπ‘π‘’π‘Ÿ π‘œπ‘“ π‘‘π‘Ÿπ‘’π‘’ π‘›π‘’π‘”π‘Žπ‘‘π‘–π‘£π‘’ 𝑐 + π‘π‘’π‘šπ‘π‘’π‘Ÿ π‘œπ‘“ π‘“π‘Žπ‘™π‘ π‘’ π‘›π‘’π‘”π‘Žπ‘‘π‘–π‘£π‘’ (𝑑) π‘₯ 100 β€’ It can also be calculated from sensitivity, specificity and prevalence. 𝑁𝑃𝑉 = 𝑠𝑝𝑒𝑐𝑖𝑓𝑖𝑐𝑖𝑑𝑦 π‘₯ (1 βˆ’ π‘π‘Ÿπ‘’π‘£π‘Žπ‘™π‘’π‘›π‘π‘’) (1 βˆ’ 𝑠𝑒𝑛𝑠𝑖𝑑𝑖𝑣𝑖𝑑𝑦) π‘₯ π‘π‘Ÿπ‘’π‘£π‘Žπ‘™π‘’π‘›π‘π‘’+ 1 βˆ’π‘ π‘π‘’π‘π‘–π‘“π‘–π‘π‘–π‘‘π‘¦ π‘₯ (1 βˆ’π‘π‘Ÿπ‘’π‘£π‘Žπ‘™π‘’π‘›π‘π‘’) β€’ Also from 2 X 2 table as: NPV = {d(c + d)} x 100 Draft Version 1.3 (Feedback Welcomed)
Likelihood Ratio β€’ Sensitivity, Specificity and Predictable values are influenced by prevalence of the disease. β€’ Instead, likelihood ratio are not influenced by the prevalence of the disease. β€’ Thus, we prefer Likelihood ratio many times instead of sensitivity, specificity and predictable values β€’ Likelihood ratio is the ratio of any two specified likelihoods β€’ The Likelihood Ratio (LR) is the likelihood that a given test result would be expected in a patient with the target disorder compared to the likelihood that that same result would be expected in a patient without the target disorder. Draft Version 1.3 (Feedback Welcomed)
Contd … β€’ Like Predictable value, Likelihood Ratio are of two types β€’ Positive Likelihood Ratio LR (+ve) = 𝑆𝑒𝑛𝑠𝑖𝑑𝑖𝑣𝑖𝑑𝑦 1 βˆ’ 𝑠𝑝𝑒𝑐𝑖𝑓𝑖𝑐𝑖𝑑𝑦 = {a/(a+c)}/{b/(b+d)} ( 2 X 2 table) and β€’ Negative Likelihood Ratio LR(βˆ’π‘£π‘’) = 1 βˆ’ 𝑆𝑒𝑛𝑠𝑖𝑑𝑖𝑣𝑖𝑑𝑦 𝑠𝑝𝑒𝑐𝑖𝑓𝑖𝑐𝑖𝑑𝑦 = {c/(a+c)}/{d/(b+d)} ( 2 X 2 table) Draft Version 1.3 (Feedback Welcomed)
Interpretation of likelihood ratio Scale Qualitative Strength LR (+ ve) LR ( - ve) Excellent >10 <0.1 Very Good 6 0.2 Fair 2 0.5 Useless 1 1 Draft Version 1.3 (Feedback Welcomed) In real world β€’ The increase in the odds of having the disease after a positive test result is ….. β€’ The decrease in the odds of having the disease after a negative test result is ….
Use of Incidence and Prevalence in disease control β€’ Prevalence measures the burden of disease in a population in specified time. β€’ Prevalence is used to compare disease burden across locations over time period. β€’ Prevalence helps to control endemic disease, and help in plans and policies. β€’ Incidence measures the number of new cases of disease among the number of susceptable person in a given location in a specified time β€’ Incidence measures the load of disease etiology and pathogenesis. β€’ Incidence measures the rate variation among different subgroups or different exposures. β€’ Incidence helps to take action to control disease, esp during epidemics and emergency. β€’ Incidence helps in making plans for mobilizing RRT, … Draft Version 1.3 (Feedback Welcomed)
Measurement of Burden of disease (DALY, HALE, QALY) β€’ DALY: Disability Adjusted Life Year. β€’ HALE: Health Adjusted Life Expectancy. β€’ QALY: Quality Adjusted Life Year. Draft Version 1.3 (Feedback Welcomed)
Disability Adjusted Life Year (DALY) β€’ DALY is a measure of overall disease burden, expressed as the number of years lost due to ill – health, disability or early death. Draft Version 1.3 (Feedback Welcomed)
Life Expectancy (LE) β€’ Life Expectancy (LE): Life expectancy at birth is the average number of years that would be lived by babies born in a given time period if mortality levels at each age remain constant. β€’ Similarly, life expectancy at age 65 is the average number of remaining years of life that a man or woman aged 65 will have if mortality levels at each age over 65 remain constant. Draft Version 1.3 (Feedback Welcomed)
Health Adjusted Life Expectancy (HALE) β€’ Health Adjusted Life Expectancy (HALE)/Healthy Life Expectancy (HLE) at birth is an estimate of the average number of years babies born this year would live in a state of β€˜good’ general health if mortality levels at each age, and the level of good health at each age, remain constant in the future. Similarly, healthy life expectancy at age 65 is the average number of remaining years a man or woman aged 65 will live in β€˜good general health’ if mortality levels and the level of good health at each age beyond 65 remain constant in the future. β€’ It measures the quality of living Draft Version 1.3 (Feedback Welcomed)
Contd … β€’ It can be calculated simply, as HALE = Life Expectancy – Number of years living in unhealthy status HALE = A - fB, where β€’ A = Years lived healthily β€’ B = Years lived with disability or illness β€’ A + B = Life Expectancy β€’ A – fB = Healthy life expectancy, where f is a weighing to reflect disability/illness level. Draft Version 1.3 (Feedback Welcomed)
Healthy Life Expectancy (HALE) in Graph Draft Version 1.3 (Feedback Welcomed) Male LE = 79.7 years - 7.3 yrs in an unhealthy state HALE 72.4 HALE Female LE = 84.2 years -9.2 yrs in an unhealthy state HALE75 HALE
Contd … (not needed) β€’ HALE at age x is the sum of YWDi from i = x to w (the last open – ended age interval in the life table) divided by lx (survivors at age x): 𝐻𝐴𝐿𝐸 π‘₯ = 𝐴 = (Οƒ 𝑖=π‘₯ 𝑀 π‘Œπ‘Šπ· 𝑖) 𝐿π‘₯ , where β€’ YWDx = Lx(1 – Dx) = Years of healthy life lived between ages i (x and x + 5) β€’ Ydx = Lx X Dx = Lost years of healthy life between ages i (x and x + 5) β€’ Lx = Total years lived by the life table population between age i (x and x + 5) β€’ Dx = Severly weighed prevalence of helth states or disability ages i (x and x + 5) β€’ Also, HALE = LHEx - (Οƒ 𝑖=π‘₯ 𝑀 π‘Œπ· 𝑖) 𝐿π‘₯ , where β€’ LHEx = Healthy Years of Lost Life β€’ YDi = Years of healthy life lived between age i (x and x + 5) Draft Version 1.3 (Feedback Welcomed)
Quality Adjusted Life Year (QALY) β€’ QALY is a generic measure of disease burden including both the quality and quantity of life lived. β€’ It is used in economic evaluation to access the value of money of medical interventions, like cost utility analysis, cost effective analysis, increment cost calculation, and for allocating health care resources. β€’ Its measurement unit us QALY, and graph is necessary to explain Draft Version 1.3 (Feedback Welcomed)
Example of QALY Draft Version 1.3 (Feedback Welcomed)

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Measurements and Calculations in Epidemiology

  • 1. Unit 3: Measurements and Calculations in Epidemiology (10 Hours) Draft Version 1.3 Upendra Raj Dhakal Lecturer: Valley College of Technical Sciences 9849110689 urdhakal@gmail.com
  • 2. Counts β€’ Counts are the total number of events that occurs in a defined period of time. β€’ It is also known as frequencies. β€’ In epidemiology, only frequency have less meaning. β€’ Eg. Total number of Salmonellosis reported in Houston during 2000 would be an example of a count or frequency: - In 2000, 227 cases of Salmonellosis were reported. β€’ In this example, 227 cases are the frequencies. Draft Version 1.3 (Feedback Welcomed)
  • 3. Rate β€’ Rates are the basic tool of epidemiological practice. β€’ Ideally, the numerator and denominator must have different units in rate. eg. Price of 1 piece of shirt = NRs. 1000/piece; kilometers per liter. β€’ A rate is a measure of the frequency with which an event occurs in a defined population in a defined time (e.g. Number of deaths per hundred thousand Nepalese in one year). Draft Version 1.3 (Feedback Welcomed)
  • 4. Contd … β€’ In epidemiology, RATE is many times deviated from its ideal definition. Many times rate, ratio and proportions are used interchangeably which should not have happened. β€’ In Epidemiology, numerator contains the frequency of cases and denominator is a risk population. All cases and risk population should be within a same time and geographic frame. It can be expressed per 100, 1000 or even 1,00,000. Draft Version 1.3 (Feedback Welcomed)
  • 5. Rate in epidemiology Must contain: β€’ Health issue in numerator β€’ Unit size of population β€’ Turing period during which an event occurs β€’ Time is always a part of denominator Rate = π‘π‘’π‘šπ‘π‘’π‘Ÿ π‘œπ‘“ 𝒄𝒂𝒔𝒆𝒔 𝑻𝒐𝒕𝒂𝒍 π‘·π’π’‘π’–π’π’‚π’•π’Šπ’π’ π‘€π‘–π‘‘β„Žπ‘–π‘› π‘‘β„Žπ‘’ 𝑔𝑖𝑣𝑒𝑛 π‘Žπ‘Ÿπ‘’π‘Ž 𝑖𝑛 π‘‘β„Žπ‘’ 𝑠𝑝𝑒𝑐𝑖𝑓𝑖𝑐 π‘π‘’π‘Ÿπ‘–π‘œπ‘‘ π‘œπ‘“ π‘‘π‘–π‘šπ‘’ π‘₯ 10 𝑛 Draft Version 1.3 (Feedback Welcomed)
  • 6. Types of Rate (CR, AR and SR) β€’ Crude Rate: Calculated without paying regard to specific sections of the population. CR = 𝑻𝒐𝒕𝒂𝒍 π‘›π‘’π‘šπ‘π‘’π‘Ÿ π‘œπ‘“ 𝒆𝒗𝒆𝒏𝒕𝒔 π‘‘β„Žπ‘Žπ‘‘ π‘œπ‘π‘π‘’π‘Ÿπ‘’π‘‘ 𝑖𝑛 π‘Ž 𝑔𝑖𝑣𝑒𝑛 π‘”π‘’π‘œπ‘”π‘Ÿπ‘Žπ‘β„Žπ‘–π‘π‘Žπ‘™ π‘Žπ‘Ÿπ‘’π‘Ž π‘‘π‘’π‘Ÿπ‘–π‘›π‘” π‘Ž 𝑔𝑖𝑣𝑒𝑛 π‘¦π‘’π‘Žπ‘Ÿ π‘΄π’Šπ’… βˆ’ 𝒀𝒆𝒂𝒓 π‘·π’π’‘π’–π’π’‚π’•π’Šπ’π’ π‘œπ‘“ π‘‘β„Žπ‘’ π‘”π‘’π‘œπ‘”π‘Ÿπ‘Žπ‘β„Žπ‘–π‘π‘Žπ‘™ π‘Žπ‘Ÿπ‘’π‘Žπ‘  π‘“π‘œπ‘Ÿ π‘‘β„Žπ‘’ π‘ π‘Žπ‘šπ‘’ π‘π‘’π‘Ÿπ‘–π‘œπ‘‘ π‘œπ‘“ π‘‘π‘–π‘šπ‘’ π‘₯ 1,000 Draft Version 1.3 (Feedback Welcomed)
  • 7. Types of Rate (CR, AR and SR) β€’ Adjusted Rate: it is done to remove the effect of variable, such as age or sex, to permit unbiased comparison in crude rate. Generally, standard population distribution is used to adjust CR. AR = π‘π‘’π‘šπ‘π‘’π‘Ÿ π‘œπ‘“ 𝒆𝒙𝒑𝒆𝒄𝒕𝒆𝒅 𝑒𝑣𝑒𝑛𝑑𝑠 π‘‡π‘œπ‘‘π‘Žπ‘™ 𝑺𝒕𝒂𝒏𝒅𝒂𝒓𝒅 π‘ƒπ‘œπ‘π‘’π‘™π‘Žπ‘‘π‘–π‘œπ‘› π‘₯ 1,00,000 Draft Version 1.3 (Feedback Welcomed)
  • 8. Types of Rate (CR, AR and SR) β€’ Specific Rate: It is calculated for the specific group of population such as for a particular age, sex, marital status, occupation, etc.. SR = π‘π‘’π‘šπ‘π‘’π‘Ÿ π‘œπ‘“ 𝒆𝒗𝒆𝒏𝒕𝒔 π‘œπ‘π‘π‘’π‘Ÿπ‘’π‘‘ π‘Žπ‘šπ‘œπ‘›π‘” π‘Ž π’”π’‘π’†π’„π’Šπ’‡π’Šπ’„ π’ˆπ’“π’π’–π’‘ π‘œπ‘“ π‘π‘œπ‘π‘’π‘™π‘Žπ‘‘π‘–π‘œπ‘› π‘œπ‘“ π‘Ž 𝑔𝑖𝑣𝑒𝑛 π‘”π‘’π‘œπ‘”π‘Ÿπ‘Žπ‘β„Žπ‘–π‘ π‘Žπ‘Ÿπ‘’π‘Ž π‘‘π‘’π‘Ÿπ‘–π‘›π‘” π‘Ž 𝑔𝑖𝑣𝑒𝑛 π‘¦π‘’π‘Žπ‘Ÿ π‘΄π’Šπ’… βˆ’ π’šπ’†π’‚π’“ π’‘π’π’‘π’–π’π’‚π’•π’Šπ’π’ 𝒐𝒇 𝒕𝒉𝒆 π’”π’‘π’†π’„π’Šπ’‡π’Šπ’„ π’ˆπ’“π’π’–π’‘ π‘œπ‘“ π‘‘β„Žπ‘’ π‘π‘œπ‘π‘’π‘™π‘Žπ‘‘π‘–π‘œπ‘› 𝑖𝑛 π‘‘β„Žπ‘’ π‘ π‘Žπ‘šπ‘’ π‘”π‘’π‘œπ‘”π‘Ÿπ‘Žπ‘β„Žπ‘–π‘ π‘Žπ‘Ÿπ‘’π‘Ž π‘‘π‘’π‘Ÿπ‘–π‘›π‘” π‘‘β„Žπ‘’ π‘ π‘Žπ‘šπ‘’ π‘π‘’π‘Ÿπ‘–π‘œπ‘‘ π‘₯ 1,000 Draft Version 1.3 (Feedback Welcomed)
  • 9. Ratio β€’ Ratio is a fractional relationship between two variables indicating how many times the numerator contains to denominator. β€’ In ratio there is no specific relationship between numerator and denominator like in rate, with same units. β€’ Unlike rate, it is not necessary Time to be a part of denominator. Draft Version 1.3 (Feedback Welcomed)
  • 10. Contd … β€’ Since numerator and denominator have same units or even unitless, ratio is dimensionless. β€’ Numerator is not a part of denominator in ratio. β€’ Eg. Among 100 population, 58 are female and 42 are male. When it is expressed in terms of ratio, we can write it as F:M = 58:42 or 1.4:1 Draft Version 1.3 (Feedback Welcomed)
  • 11. Difference MM Rate and MM Ratio MM Rate = π‘΅π’–π’Žπ’ƒπ’†π’“ 𝒐𝒇 π’Žπ’‚π’•π’†π’“π’π’‚π’ 𝒅𝒆𝒂𝒕𝒉𝒔 π‘œπ‘π‘π‘’π‘Ÿπ‘–π‘›π‘” 𝑖𝑛 π‘Ÿπ‘’π‘“π‘’π‘Ÿπ‘’π‘›π‘π‘’ π‘π‘’π‘Ÿπ‘–π‘œπ‘‘ (π‘’π‘ π‘’π‘Žπ‘™π‘™π‘¦ 1 π‘¦π‘’π‘Žπ‘Ÿ) π‘π‘’π‘šπ‘π‘’π‘Ÿ π‘œπ‘“ 𝑾𝑢𝑹𝑨 𝑖𝑛 π‘π‘œπ‘π‘’π‘™π‘Žπ‘‘π‘–π‘œπ‘› 𝑖𝑛 π‘Ÿπ‘’π‘“π‘’π‘Ÿπ‘’π‘›π‘π‘’ π‘π‘’π‘Ÿπ‘–π‘œπ‘‘ π‘₯ 𝟏, 𝟎𝟎𝟎 MM Ratio = π‘΅π’–π’Žπ’ƒπ’†π’“ 𝒐𝒇 π’Žπ’‚π’•π’†π’“π’π’‚π’ 𝒅𝒆𝒂𝒕𝒉𝒔 π‘œπ‘π‘π‘’π‘Ÿπ‘–π‘›π‘” 𝑖𝑛 π‘Ÿπ‘’π‘“π‘’π‘Ÿπ‘’π‘›π‘π‘’ π‘π‘’π‘Ÿπ‘–π‘œπ‘‘ (π‘’π‘ π‘’π‘Žπ‘™π‘™π‘¦ 1 π‘¦π‘’π‘Žπ‘Ÿ) π‘π‘’π‘šπ‘π‘’π‘Ÿ π‘œπ‘“ π’π’Šπ’—π’† π’ƒπ’Šπ’“π’•π’‰π’” π‘œπ‘π‘π‘’π‘Ÿπ‘–π‘›π‘” π‘€π‘–π‘‘β„Žπ‘–π‘› π‘‘β„Žπ‘’ π‘Ÿπ‘’π‘“π‘’π‘Ÿπ‘’π‘›π‘π‘’ π‘π‘’π‘Ÿπ‘–π‘œπ‘‘ π‘₯ 𝟏, 𝟎𝟎, 𝟎𝟎𝟎 Draft Version 1.3 (Feedback Welcomed)
  • 12. Proportion β€’ Proportion is nothing more than a ratio in which, numerator is always a part of a denominator. β€’ When proportion is expressed in total number of population (population at risk), it becomes rate. β€’ Eg. In a population of 100 women, 58 had premarital sex. The proportion is 58/100 = 0.58 = 58%. β€’ Fetal Death Ratio, though is written ratio, it is a proportion. Fetal Death Ratio = π‘΅π’–π’Žπ’ƒπ’†π’“ π‘œπ‘“ π‘“π‘’π‘‘π‘Žπ‘™ π‘‘π‘’π‘Žπ‘‘β„Žπ‘  𝑻𝒐𝒕𝒂𝒍 π’π’–π’Žπ’ƒπ’†π’“ π‘œπ‘“ 𝑙𝑖𝑣𝑒 π‘π‘–π‘Ÿπ‘‘β„Žπ‘  Draft Version 1.3 (Feedback Welcomed)
  • 13. Incidence β€’ It is also known as Measure of Morbidity for new cases. β€’ It is the rate of the development of new cases of a disease that occur during a specified period of time in previously disease free or condition free individuals β€’ It is not influenced by the duration of the disease β€’ Many times, the denominator in formula uses mid – interval/year population at risk, instead of Estimated number of people in population (risk population) Draft Version 1.3 (Feedback Welcomed)
  • 14. Contd … β€’ Statistically, 𝐼𝑛𝑐𝑖𝑑𝑒𝑛𝑐𝑒 π‘…π‘Žπ‘‘π‘’ = π‘π‘’π‘šπ‘π‘’π‘Ÿ π‘œπ‘“ 𝑛𝑒𝑀 π‘π‘Žπ‘ π‘’π‘  𝑖𝑛 π‘Ž 𝑠𝑝𝑒𝑐𝑖𝑓𝑖𝑐 π‘π‘’π‘Ÿπ‘–π‘œπ‘‘ π‘œπ‘“ π‘‘π‘–π‘šπ‘’ πΈπ‘ π‘‘π‘–π‘šπ‘Žπ‘‘π‘’π‘‘ π‘π‘’π‘œπ‘π‘™π‘’ 𝑖𝑛 π‘‘β„Žπ‘’ π‘π‘œπ‘π‘’π‘™π‘Žπ‘‘π‘–π‘œπ‘› π‘Žπ‘‘ π‘‘β„Žπ‘’ π‘ π‘Žπ‘šπ‘’ π‘π‘’π‘Ÿπ‘–π‘œπ‘‘ π‘œπ‘“ π‘‘π‘–π‘šπ‘’ π‘₯ 100 β€’ There are two fundamental approaches to measure incidence rate: β€’ Cumulative Incidence Rate, and β€’ Incidence Density Rate Draft Version 1.3 (Feedback Welcomed)
  • 15. Cumulative incidence rate (Incidence Proportion) β€’ Number of new cases of disease occurring over a specified period of time in a population at risk at the beginning of the interval. CIR= π‘π‘’π‘šπ‘π‘’π‘Ÿ π‘œπ‘“ 𝑛𝑒𝑀 π‘π‘Žπ‘ π‘’π‘  π‘‘π‘’π‘Ÿπ‘–π‘›π‘” π‘‘β„Žπ‘’ π’”π’‘π’†π’„π’Šπ’‡π’Šπ’„ π’‘π’†π’“π’Šπ’π’… π‘œπ‘“ π‘‘π‘–π‘šπ‘’ π‘π‘’π‘šπ‘π‘’π‘Ÿ π‘œπ‘“ π‘π‘œπ‘π‘’π‘™π‘Žπ‘‘π‘–π‘œπ‘› π‘Žπ‘‘ π‘‘β„Žπ‘’ 𝑏𝑒𝑔𝑖𝑛𝑛𝑖𝑛𝑔 π‘œπ‘“ π‘‘π‘–π‘šπ‘’ π‘₯ 1,000 β€’ CIR is generally expressed in decimal per thousand per specific time. β€’ If it is measured for entire life, it is known as Lifetime risk. Draft Version 1.3 (Feedback Welcomed)
  • 16. Incidence density rate (Person Time Incidence) β€’ Number of new cases of disease occurring over a specified period of time in a population at risk throughout the interval. β€’ We need to add up period of time each individual was present in the population, and was at risk of becoming a new case of disease. β€’ Denominator uses person – time at risk. 𝐼𝐷 = π‘΅π’–π’Žπ’ƒπ’†π’“ 𝒐𝒇 π’π’†π’˜ 𝒄𝒂𝒔𝒆𝒔 π‘‘π‘’π‘Ÿπ‘–π‘›π‘” π‘‘β„Žπ‘’ 𝑠𝑝𝑒𝑐𝑖𝑓𝑖𝑐 π‘π‘’π‘Ÿπ‘–π‘œπ‘‘ π‘œπ‘“ π‘‘π‘–π‘šπ‘’ π‘‡π‘œπ‘‘π‘Žπ‘™ π‘π‘’π‘Ÿπ‘ π‘œπ‘› βˆ’ π‘‘π‘–π‘šπ‘’ π‘Žπ‘‘ π‘Ÿπ‘–π‘ π‘˜ π‘‘π‘’π‘Ÿπ‘–π‘›π‘” π‘‘β„Žπ‘’ π‘–π‘›π‘‘π‘’π‘Ÿπ‘£π‘Žπ‘™ π‘₯ 1,000 β€’ It is calculated in an open population (with migration), and every person contributes different duration of β€œat risk” interval Draft Version 1.3 (Feedback Welcomed)
  • 17. Relation between CI and ID β€’ The numerator does not differ between the two types of incidence. β€’ However, the denominator can differ in incidence density from cumulative incidence because it takes account of: β€’ Cases who left during the defined year β€’ Cases who died β€’ Cases who had disease once and will not have it again in the same season β€’ Cases who entered in between in the defined year. Draft Version 1.3 (Feedback Welcomed)
  • 18. Prevalence β€’ Prevalence is a measurement of all individuals affected by the disease at a particular time. It is also known as Measure of Morbidity for new and old cases. β€’ All current cases (New + old) existing in a given point in time or over a period of time in a given population. β€’ It is of three types: β€’ Point prevalence β€’ Period prevalence, and β€’ Lifetime prevalence Draft Version 1.3 (Feedback Welcomed)
  • 19. Point - Prevalence β€’ Point prevalence is a measure of the proportion of people in a population who have disease or condition at a particular time (generally a month or less or even a day) β€’ It is like a snapshot of the disease in time and is expressed in percentage. β€’ It is used to study chronic diseases β€’ It can be used to calculate specifically for age, gender, etc. β€’ When type of prevalence is not specified, we should consider it is point prevalence 𝑃𝑑. 𝑃 = π‘π‘’π‘šπ‘π‘’π‘Ÿ π‘œπ‘“ 𝑒π‘₯𝑖𝑠𝑑𝑖𝑛𝑔 π‘π‘Žπ‘ π‘’π‘  π‘΅π’†π’˜ + 𝑢𝒍𝒅 π‘œπ‘“ π‘Ž 𝑠𝑝𝑒𝑐𝑖𝑓𝑖𝑒𝑑 π‘‘π‘–π‘ π‘’π‘Žπ‘ π‘’ π‘œπ‘› π‘Ž 𝑠𝑝𝑒𝑐𝑖𝑓𝑖𝑐 π‘‘π‘Žπ‘‘π‘’ π‘¬π’”π’•π’Šπ’Žπ’‚π’•π’†π’… 𝒑𝒆𝒐𝒑𝒍𝒆 𝑖𝑛 π‘‘β„Žπ‘’ π‘π‘œπ‘π‘’π‘™π‘Žπ‘‘π‘–π‘œπ‘› π‘Žπ‘‘ π‘‘β„Žπ‘’ π‘ π‘Žπ‘šπ‘’ π‘π‘œπ‘–π‘›π‘‘ π‘œπ‘“ π‘‘π‘–π‘šπ‘’ π‘₯ 100 Draft Version 1.3 (Feedback Welcomed)
  • 20. Period Prevalence β€’ It is the proportion of the population with a given disease or condition over a specific period of time. β€’ Simply, it answers like: How many people in a population suffered from common cold in 2018. β€’ It is expressed in percentage. 𝑃𝑑. 𝑃 = π‘π‘’π‘šπ‘π‘’π‘Ÿ π‘œπ‘“ 𝑒π‘₯𝑖𝑠𝑑𝑖𝑛𝑔 π‘π‘Žπ‘ π‘’π‘  π‘΅π’†π’˜ + 𝑢𝒍𝒅 π‘œπ‘“ π‘Ž 𝑠𝑝𝑒𝑐𝑖𝑓𝑖𝑒𝑑 π‘‘π‘–π‘ π‘’π‘Žπ‘ π‘’ π‘œπ‘› π‘Ž 𝑠𝑝𝑒𝑐𝑖𝑓𝑖𝑐 π‘‘π‘–π‘šπ‘’ π‘–π‘›π‘‘π‘’π‘Ÿπ‘£π‘Žπ‘™ π‘¬π’”π’•π’Šπ’Žπ’‚π’•π’†π’… 𝒑𝒆𝒐𝒑𝒍𝒆 𝑖𝑛 π‘‘β„Žπ‘’ π‘π‘œπ‘π‘’π‘™π‘Žπ‘‘π‘–π‘œπ‘› π‘Žπ‘‘ π‘‘β„Žπ‘’ π‘ π‘Žπ‘šπ‘’ π‘π‘’π‘Ÿπ‘–π‘œπ‘‘ π‘œπ‘“ π‘‘π‘–π‘šπ‘’ π‘₯ 100 Draft Version 1.3 (Feedback Welcomed)
  • 21. Relationship between Prevalence and Incidence β€’ Prevalence = Incidence x Mean duration of illness P = I X D Draft Version 1.3 (Feedback Welcomed)
  • 22. Lifetime Prevalence – Not in syllabus β€’ It is the proportion of individuals in population that at some point in their life during the time of assessment, has experienced a case. β€’ The cases might have experienced a diseased condition, accident, crime, harassment, … β€’ It is also known as cumulative prevalence Draft Version 1.3 (Feedback Welcomed)
  • 23. Risk β€’ Probability of developing an effect of certain risk factors. Types of risk measurement β€’ Relative risk β€’ Odds Ratio (Case Control and Cross sectional/Prevalancestudies), and β€’ Risk ratio (Cohort/Incidence Studies) β€’ Attributable risk β€’ Comparative risk (Not in syllabus) β€’ Absolute risk (Not in syllabus) Draft Version 1.3 (Feedback Welcomed)
  • 24. Relative Risk β€’ It is the ratio of incidence in exposed persons to incidence in non exposed person. β€’ It is the most commonly reported result in studies of risk, partially because of conceptual convenience but also because of a common metric in studies of similar risk factors but with different baseline incident rates. β€’ Because it indicates the strength of association between exposure and disease, it is useful measure of effect for studies of disease etiology. β€’ Generally, Risk Ratio is calculated when Relative Risk is stated. Draft Version 1.3 (Feedback Welcomed)
  • 25. Odds Ratio β€’ Odds = Chance or likelihood of something to happen or not to happen β€’ OR is a statistics that quantifies the strength of the association between two events (disease and exposure). It is the ratio of two odds β€’ OR is the ratio of odds that disease will occur among individuals who have been exposed to the risk factors to odds that the disease will occur among individuals who has not been exposed to risk factors. (ratio between event and non event of disease, and exposure) β€’ If the events are A (Lung Cancer) and B (Smoking), OR can be defined as a ratio of the chance of A in the presence of B, and the chance of A in the absence of B. Draft Version 1.3 (Feedback Welcomed)
  • 26. Contd … β€’ Interpretation: OR > 1 : Strong association between disease and exposure to risk factors OR = 1: No association between disease and exposure to risk factors OR < 1 : Negative association between disease and exposure to the risk factors β€’ We cannot conclude our result (of any statistical calculation) only depending on OR, and without knowing Confidence Interval (CI – range of values we are fairly sure our true values lies in) and Significance value (p – probability of rejecting the null hypothesis when it is true). Draft Version 1.3 (Feedback Welcomed)
  • 27. Contd … β€’ Statistically, 𝑂𝑅 = π‘‡β„Žπ‘’ π‘β„Žπ‘Žπ‘›π‘π‘’ π‘œπ‘“ π’”π’π’Žπ’†π’•π’‰π’Šπ’π’ˆ π‘‘π‘œ β„Žπ‘Žπ‘π‘π‘’π‘› π‘‡β„Žπ‘’ π‘β„Žπ‘Žπ‘›π‘π‘’ π‘œπ‘“ π’π’π’•π’‰π’Šπ’π’ˆ β„Žπ‘Žπ‘π‘π‘’π‘›π‘›π‘–π‘›π‘” or 𝑂𝑅 = π·π‘–π‘ π‘’π‘Žπ‘ π‘’ π‘Žπ‘›π‘‘ 𝑒π‘₯π‘π‘œπ‘ π‘’π‘‘ / π»π‘’π‘Žπ‘™π‘‘β„Žπ‘¦ π‘Žπ‘›π‘‘ 𝑒π‘₯π‘π‘œπ‘ π‘’π‘‘ π·π‘–π‘ π‘’π‘Žπ‘ π‘’π‘‘ π‘Žπ‘›π‘‘ π‘π‘œπ‘› βˆ’ 𝐸π‘₯π‘π‘œπ‘ π‘’π‘‘ / π»π‘’π‘Žπ‘™π‘‘β„Žπ‘¦ π‘Žπ‘›π‘‘ π‘›π‘œπ‘› 𝑒π‘₯π‘π‘œπ‘ π‘’π‘‘ or 𝑂𝑅 = π·π‘–π‘ π‘’π‘Žπ‘ π‘’ π‘Žπ‘›π‘‘ 𝑒π‘₯π‘π‘œπ‘ π‘’π‘‘ / π·π‘–π‘ π‘’π‘Žπ‘ π‘’π‘‘ π‘Žπ‘›π‘‘ π‘›π‘œπ‘› 𝑒π‘₯π‘π‘œπ‘ π‘’π‘‘ π»π‘’π‘Žπ‘™π‘‘β„Žπ‘¦ π‘Žπ‘›π‘‘ 𝐸π‘₯π‘π‘œπ‘ π‘’π‘‘ / π»π‘’π‘Žπ‘™π‘‘β„Žπ‘¦ π‘Žπ‘›π‘‘ π‘›π‘œπ‘› 𝑒π‘₯π‘π‘œπ‘ π‘’π‘‘ (all give the same result) Draft Version 1.3 (Feedback Welcomed)
  • 28. Contd … β€’ Above formula can be understood from the table as: β€’ OR = (a/b)/(c/d) = ad/bc [ Statistically, we can write (a/c)/(b/d), but during interpretation, it may be defined wrongly] β€’ Interpretation: The odds of having cancer is …. times higher (no chance or lower) for smokers than the odd of having cancer to non smokers. Draft Version 1.3 (Feedback Welcomed) Disease (lung cancer) + (Cases) - (Non Cases) Exposure (Smoking) + Diseased and exposed (a) Healthy and exposed (b) - Diseased and non – exposed (c) Healthy and non – exposed (d)
  • 29. Risk Ratio β€’ RR is the ratio of incidence of the outcome among exposed individuals to the incidence among non exposed individuals. β€’ It compares the probability of an outcome among exposed to a risk factor to the probability of that outcome among individuals who are not exposed to the risk. β€’ Interpretation: RR > 1 : Strong association between disease and exposure to the risk factor RR = 1 : No association between disease and exposure to the risk factor RR < 1 : Negative association between disease and exposure to the risk factor Draft Version 1.3 (Feedback Welcomed)
  • 30. Contd … β€’ Statistically, 𝑅𝑅 = π‘‡β„Žπ‘’ π‘β„Žπ‘Žπ‘›π‘π‘’ π‘œπ‘“ π’”π’π’Žπ’†π’•π’‰π’Šπ’π’ˆ π‘‘π‘œ β„Žπ‘Žπ‘π‘π‘’π‘› π‘‡β„Žπ‘’ π‘β„Žπ‘Žπ‘›π‘π‘’ π‘œπ‘“ 𝒂𝒍𝒍 π’•π’‰π’Šπ’π’ˆπ’” 𝒕𝒐 β„Žπ‘Žπ‘π‘π‘’π‘› β€’ Above formula can be understood from the table as: β€’ RR = (a/a+b)/(c/c+d) = a(c+d)/b(a+b) β€’ Interpretation: The risk of having cancer is …. times higher (no chance or lower) for smokers than the risk of happening cancer to non smokers. Disease (lung cancer) + - Exposure (Smoking) + Diseased and exposed (a) Healthy and exposed (b) - Diseased and non – exposed (c) Healthy and non – exposed (d) Draft Version 1.3 (Feedback Welcomed)
  • 31. RRR, ARR and No. needed to treat (not in syllabus) β€’ RR = (a/a+b)/(c/c+d) = a(c+d)/b(a+b) (already done in previous slide) β€’ Relative Risk Reduction (RRR) = 1 – Relative Risk β€’ Absolute Risk Reduction (ARR) = 𝑐 𝑐+𝑑 βˆ’ π‘Ž π‘Ž+𝑏 β€’ Number needed to treat = 1/ARR Draft Version 1.3 (Feedback Welcomed)
  • 32. Concluding OR and RR (just for reference, will understand later) β€’ OR and RR are interpreted in similar way, the only difference is the use of word odds Ratio and Risk Ratio/Relative scale. β€’ Both OR, and RR gives the same information, but in different scale. Thus, they are not equivalent, though the result interpreted are similar. β€’ OR and RR are used for rare diseases. β€’ If diseases are more common, like hypertension OR overestimates RR. i.e. OR > RR. Thus, RR should be preferred in such case instead of OR β€’ RR is preferred when there is absolute incidence and prevalence. Incidence is used in cohort study, whereas prevalence is used in cross – sectional studies. β€’ If meaningful incidence or prevalence is absent, we use OR instead of RR Draft Version 1.3 (Feedback Welcomed)
  • 33. Concluding OR and RR (revision of definition) β€’ OR is the ratio of odds that disease will occur among individuals who have been exposed to the risk factors to odds that the disease will occur among individuals who has not been exposed to risk factors. (ratio between event and non event of disease, and exposure) β€’ RR is the ratio of incidence of the outcome among exposed individuals to the incidence among non exposed individuals. Draft Version 1.3 (Feedback Welcomed)
  • 34. Attributable Risk β€’ Attribute = Quality of a characteristics β€’ It can be understood as excess risk or risk difference β€’ In Statistics, Attribute is a Qualitative type of characteristics calculated using quantative measurements. It is done in cohort study design. β€’ It is just a difference of incidence of disease among exposed and incidence of disease among unexposed. If expressed in percent, we multiply the difference by 100 and so on. AR = Incidence of disease among exposed – Incidence of disease among unexposed Draft Version 1.3 (Feedback Welcomed)
  • 35. Population Attributable Risk (not in syllabus) β€’ AR and PAR are different β€’ PAR is the amount of proportion of disease incidence that can be attributed to a specific exposure β€’ It indicates to what extent the disease under study can be attributed to the exposure. β€’ It is expressed in per 10 𝑛 population when calculated in proportion PAR = 𝐼𝑛𝑐𝑖𝑑𝑒𝑛𝑐𝑒 π‘œπ‘“ π‘‘π‘–π‘ π‘’π‘Žπ‘ π‘’ π‘Žπ‘šπ‘œπ‘›π‘” 𝑒π‘₯π‘π‘œπ‘ π‘’π‘‘ βˆ’ 𝐼𝑛𝑐𝑖𝑑𝑒𝑛𝑐𝑒 π‘œπ‘“ π‘‘π‘–π‘ π‘’π‘Žπ‘ π‘’ π‘Žπ‘šπ‘œπ‘›π‘” π‘›π‘œπ‘› 𝑒π‘₯π‘π‘œπ‘ π‘’π‘‘ 𝐼𝑛𝑐𝑖𝑑𝑒𝑛𝑐𝑒 π‘œπ‘“ π‘‘π‘–π‘ π‘’π‘Žπ‘ π‘’ π‘Žπ‘šπ‘œπ‘›π‘” 𝑒π‘₯π‘π‘œπ‘ π‘’π‘‘ π‘₯ 10 𝑛 Draft Version 1.3 (Feedback Welcomed)
  • 36. Attack Rate β€’ For acute cases or during epidemic situation, Incidence rate is also known as Attack rate β€’ Attack rate is used when the occurrence of disease among population increases dramatically over a short period of time β€’ There are two types of attack rate: β€’ Primary attack rate, and β€’ Secondary attack rate Draft Version 1.3 (Feedback Welcomed)
  • 37. Primary Attack Rate β€’ Primary attack rate is based on the number of person who contracted the disease directly from the source. β€’ Instead of primary attack rate, we are more focused in calculating secondary attack rate 𝑃𝐴𝑅 = π‘π‘œ.π‘œπ‘“ π‘π‘Ÿπ‘–π‘šπ‘Žπ‘Ÿπ‘¦ π‘π‘Žπ‘ π‘’π‘  π‘‘π‘’π‘Ÿπ‘–π‘›π‘” π‘‘β„Žπ‘’ 𝑠𝑝𝑒𝑐𝑖𝑓𝑖𝑐 π‘π‘’π‘Ÿπ‘–π‘œπ‘‘ π‘œπ‘“ π‘‘π‘–π‘šπ‘’ π‘‡π‘œπ‘‘π‘Žπ‘™ π‘π‘œπ‘π‘’π‘™π‘Žπ‘‘π‘–π‘œπ‘› π‘Žπ‘‘ π‘Ÿπ‘–π‘ π‘˜ π‘₯ 100 Draft Version 1.3 (Feedback Welcomed)
  • 38. Secondary attack β€’ It is defined as the probability that infection occurs among susceptible persons within a reasonable incubation period following known contact with an infectious person or an infectious source. β€’ Primary cases are excluded from both numerator and denominator. S𝐴𝑅 = π‘π‘œ.π‘œπ‘“ π‘π‘œπ‘›π‘‘π‘Žπ‘π‘‘ π‘π‘’π‘Ÿπ‘ π‘œπ‘› π‘€β„Žπ‘œ π‘‘π‘’π‘£π‘’π‘™π‘œπ‘π‘’π‘‘ π‘‘β„Žπ‘’ π‘‘π‘–π‘ π‘’π‘Žπ‘ π‘’ π‘€π‘–π‘‘β„Žπ‘–π‘› π‘‘β„Žπ‘’ π‘–π‘›π‘π‘’π‘π‘Žπ‘‘π‘–π‘œπ‘› π‘π‘’π‘Ÿπ‘–π‘œπ‘‘ π‘π‘’π‘šπ‘π‘’π‘Ÿ π‘œπ‘“ 𝑠𝑒𝑠𝑐𝑒𝑝𝑑𝑖𝑏𝑙𝑒 π‘π‘’π‘Ÿπ‘ π‘œπ‘›π‘  π‘€β„Žπ‘œ π‘π‘œπ‘›π‘‘π‘Ÿπ‘Žπ‘π‘‘π‘’π‘‘ π‘€π‘–π‘‘β„Ž π‘π‘Ÿπ‘–π‘šπ‘Žπ‘Ÿπ‘¦ π‘π‘Žπ‘ π‘’π‘  π‘₯ 100 Draft Version 1.3 (Feedback Welcomed)
  • 39. What do you understand? Disease (lung cancer) + - Exposure(Smoking) + Diseased and exposed (a) Healthy and exposed (b) - Diseased and non – exposed (c) Healthy and non – exposed (d) Compare When test says you have and actually you have it (a) When the test says you don’t have it but you actually have it (d) When the test says you don’t have it and you actually don’t have it (c) When the test says you have it but you actually don’t have it (b) Draft Version 1.3 (Feedback Welcomed)
  • 40. Validity and reliability testing β€’ Validity measures the accuracy i.e. measuring what was aimed to measure. We can measure internal validity and external validity. β€’ Validity is tested using the formula: β€’ Accuracy testing [Accuracy = {(a + d)/(a + b + c + d)} x 100] (from 2 X 2 table) Draft Version 1.3 (Feedback Welcomed)
  • 41. Contd … β€’ Reliability measures the precision of test by considering confidence interval (CI) for the measure of accuracy. It sees consistency of data. β€’ Reliability/Repeatability is tested using β€’ Positive predictable values β€’ Negative predictable values β€’ Eg. 1 Kg Mass is same regardless of altitude whereas 1 Newton weight differs with altitude taking into account the use of beam balance or spring balance. Draft Version 1.3 (Feedback Welcomed)
  • 42. Sensitivity (Not in syllabus) β€’ It refers to the proportion of subjects with the disease condition giving positive test result. β€’ It is also known as true positive rate/recall/probability of detection and indicates the probability a person has a positive test result β€’ It generally measures the ability of a test to detect the disease condition. Sensitivity = π‘π‘’π‘šπ‘π‘’π‘Ÿ π‘œπ‘“ π‘‘π‘Ÿπ‘’π‘’ π‘π‘œπ‘ π‘–π‘‘π‘–π‘£π‘’π‘  π‘π‘œ. π‘œπ‘“ π‘‘π‘Ÿπ‘’π‘’ π‘π‘œπ‘ π‘–π‘‘π‘–π‘£π‘’+π‘π‘œ.π‘œπ‘“ π‘“π‘Žπ‘™π‘ π‘’ π‘›π‘’π‘”π‘Žπ‘‘π‘–π‘£π‘’ π‘₯ 100 = π‘π‘’π‘šπ‘π‘’π‘Ÿ π‘œπ‘“ π‘‘π‘Ÿπ‘’π‘’ π‘π‘œπ‘ π‘–π‘‘π‘–π‘£π‘’π‘  π‘‡π‘œπ‘‘π‘Žπ‘™ π‘›π‘œ.π‘œπ‘“ π‘ π‘–π‘π‘˜ π‘–π‘›π‘‘π‘–π‘£π‘–π‘‘π‘’π‘Žπ‘™π‘  𝑖𝑛 π‘π‘œπ‘π‘’π‘™π‘Žπ‘‘π‘–π‘œπ‘› = Probability of positive test given that the patient has a disease = {a/(a+c)}x 100 (from 2 X 2 table) Draft Version 1.3 (Feedback Welcomed)
  • 43. Specificity (Not is syllabus) β€’ It refers to the proportion of subjects without the disease condition giving negative test result. β€’ It is also known as true negative and indicates the probability a person has a negative test result β€’ It generally measures the ability of a test to detect the non disease condition. Specificity = π‘π‘’π‘šπ‘π‘’π‘Ÿ π‘œπ‘“ π‘‘π‘Ÿπ‘’π‘’ π‘›π‘’π‘”π‘Žπ‘‘π‘–π‘£π‘’π‘  π‘π‘œ. π‘œπ‘“ π‘‘π‘Ÿπ‘’π‘’ π‘›π‘’π‘”π‘Žπ‘‘π‘–π‘£π‘’π‘ +π‘π‘œ.π‘œπ‘“ π‘“π‘Žπ‘™π‘ π‘’ π‘π‘œπ‘ π‘–π‘‘π‘–π‘£π‘’π‘  π‘₯ 100 = π‘π‘’π‘šπ‘π‘’π‘Ÿ π‘œπ‘“ π‘‘π‘Ÿπ‘’π‘’ π‘›π‘’π‘”π‘Žπ‘‘π‘–π‘£π‘’π‘  π‘‡π‘œπ‘‘π‘Žπ‘™ π‘›π‘œ.π‘œπ‘“ 𝑀𝑒𝑙𝑙 π‘–π‘›π‘‘π‘–π‘£π‘–π‘‘π‘’π‘Žπ‘™π‘  𝑖𝑛 π‘π‘œπ‘π‘’π‘™π‘Žπ‘‘π‘–π‘œπ‘› = Probability of negative test given that the patient well = {d/(b+d)} x 100 (from 2 X 2 table) Draft Version 1.3 (Feedback Welcomed)
  • 44. Interpretation (Not in syllabus) β€’ Sensitivity: The probability that a patient with the disease will have a positive test result is …. β€’ Specificity: The probability that a patient without the disease will have a negative test result is …. β€’ Accuracy/Validity: The probability that the results of a test will accurately predict presence (or absence) of disease is … Draft Version 1.3 (Feedback Welcomed)
  • 45. Predictable value β€’ There are two versions of Predictable value β€’ Positive Predictable value, and β€’ Negative Predictable value Draft Version 1.3 (Feedback Welcomed)
  • 46. Positive Predictable Value β€’ Positive predictable value (PPV) gives the proportion of positive result in statistics and diagnosis tests that are true positive. 𝑃𝑃𝑉 = π‘π‘’π‘šπ‘π‘’π‘Ÿ π‘œπ‘“ π‘‘π‘Ÿπ‘’π‘’ π‘π‘œπ‘ π‘–π‘‘π‘–π‘£π‘’π‘  (π‘Ž) π‘π‘’π‘šπ‘π‘’π‘Ÿ π‘œπ‘“ π‘‘π‘Ÿπ‘’π‘’ π‘π‘œπ‘ π‘–π‘‘π‘–π‘£π‘’π‘  π‘Ž + π‘π‘’π‘šπ‘π‘’π‘Ÿ π‘œπ‘“ π‘“π‘Žπ‘™π‘ π‘’ π‘π‘œπ‘ π‘–π‘‘π‘–π‘£π‘’π‘  (𝑏) π‘₯ 100 β€’ It can also be calculated from sensitivity, specificity and prevalence. 𝑃𝑃𝑉 = 𝑠𝑒𝑛𝑠𝑖𝑑𝑖𝑣𝑖𝑑𝑦 π‘₯ π‘π‘Ÿπ‘’π‘£π‘Žπ‘™π‘’π‘›π‘π‘’ 𝑠𝑒𝑛𝑠𝑖𝑑𝑖𝑣𝑖𝑑𝑦 π‘₯ π‘π‘Ÿπ‘’π‘£π‘Žπ‘™π‘’π‘›π‘π‘’+ 1 βˆ’π‘ π‘π‘’π‘π‘–π‘“π‘–π‘π‘–π‘‘π‘¦ π‘₯ (1 βˆ’π‘π‘Ÿπ‘’π‘£π‘Žπ‘™π‘’π‘›π‘π‘’) β€’ Also from 2 X 2 table as: PPV = {a(a + b)} x 100 Draft Version 1.3 (Feedback Welcomed)
  • 47. Negative Predictable Value β€’ Negative predictable value (NPV) gives the proportion of negative result in statistics and diagnosis tests that are true negative. 𝑁𝑃𝑉 = π‘π‘’π‘šπ‘π‘’π‘Ÿ π‘œπ‘“ π‘‘π‘Ÿπ‘’π‘’ π‘›π‘’π‘”π‘Žπ‘‘π‘–π‘£π‘’π‘  (𝑑) π‘π‘’π‘šπ‘π‘’π‘Ÿ π‘œπ‘“ π‘‘π‘Ÿπ‘’π‘’ π‘›π‘’π‘”π‘Žπ‘‘π‘–π‘£π‘’ 𝑐 + π‘π‘’π‘šπ‘π‘’π‘Ÿ π‘œπ‘“ π‘“π‘Žπ‘™π‘ π‘’ π‘›π‘’π‘”π‘Žπ‘‘π‘–π‘£π‘’ (𝑑) π‘₯ 100 β€’ It can also be calculated from sensitivity, specificity and prevalence. 𝑁𝑃𝑉 = 𝑠𝑝𝑒𝑐𝑖𝑓𝑖𝑐𝑖𝑑𝑦 π‘₯ (1 βˆ’ π‘π‘Ÿπ‘’π‘£π‘Žπ‘™π‘’π‘›π‘π‘’) (1 βˆ’ 𝑠𝑒𝑛𝑠𝑖𝑑𝑖𝑣𝑖𝑑𝑦) π‘₯ π‘π‘Ÿπ‘’π‘£π‘Žπ‘™π‘’π‘›π‘π‘’+ 1 βˆ’π‘ π‘π‘’π‘π‘–π‘“π‘–π‘π‘–π‘‘π‘¦ π‘₯ (1 βˆ’π‘π‘Ÿπ‘’π‘£π‘Žπ‘™π‘’π‘›π‘π‘’) β€’ Also from 2 X 2 table as: NPV = {d(c + d)} x 100 Draft Version 1.3 (Feedback Welcomed)
  • 48. Likelihood Ratio β€’ Sensitivity, Specificity and Predictable values are influenced by prevalence of the disease. β€’ Instead, likelihood ratio are not influenced by the prevalence of the disease. β€’ Thus, we prefer Likelihood ratio many times instead of sensitivity, specificity and predictable values β€’ Likelihood ratio is the ratio of any two specified likelihoods β€’ The Likelihood Ratio (LR) is the likelihood that a given test result would be expected in a patient with the target disorder compared to the likelihood that that same result would be expected in a patient without the target disorder. Draft Version 1.3 (Feedback Welcomed)
  • 49. Contd … β€’ Like Predictable value, Likelihood Ratio are of two types β€’ Positive Likelihood Ratio LR (+ve) = 𝑆𝑒𝑛𝑠𝑖𝑑𝑖𝑣𝑖𝑑𝑦 1 βˆ’ 𝑠𝑝𝑒𝑐𝑖𝑓𝑖𝑐𝑖𝑑𝑦 = {a/(a+c)}/{b/(b+d)} ( 2 X 2 table) and β€’ Negative Likelihood Ratio LR(βˆ’π‘£π‘’) = 1 βˆ’ 𝑆𝑒𝑛𝑠𝑖𝑑𝑖𝑣𝑖𝑑𝑦 𝑠𝑝𝑒𝑐𝑖𝑓𝑖𝑐𝑖𝑑𝑦 = {c/(a+c)}/{d/(b+d)} ( 2 X 2 table) Draft Version 1.3 (Feedback Welcomed)
  • 50. Interpretation of likelihood ratio Scale Qualitative Strength LR (+ ve) LR ( - ve) Excellent >10 <0.1 Very Good 6 0.2 Fair 2 0.5 Useless 1 1 Draft Version 1.3 (Feedback Welcomed) In real world β€’ The increase in the odds of having the disease after a positive test result is ….. β€’ The decrease in the odds of having the disease after a negative test result is ….
  • 51. Use of Incidence and Prevalence in disease control β€’ Prevalence measures the burden of disease in a population in specified time. β€’ Prevalence is used to compare disease burden across locations over time period. β€’ Prevalence helps to control endemic disease, and help in plans and policies. β€’ Incidence measures the number of new cases of disease among the number of susceptable person in a given location in a specified time β€’ Incidence measures the load of disease etiology and pathogenesis. β€’ Incidence measures the rate variation among different subgroups or different exposures. β€’ Incidence helps to take action to control disease, esp during epidemics and emergency. β€’ Incidence helps in making plans for mobilizing RRT, … Draft Version 1.3 (Feedback Welcomed)
  • 52. Measurement of Burden of disease (DALY, HALE, QALY) β€’ DALY: Disability Adjusted Life Year. β€’ HALE: Health Adjusted Life Expectancy. β€’ QALY: Quality Adjusted Life Year. Draft Version 1.3 (Feedback Welcomed)
  • 53. Disability Adjusted Life Year (DALY) β€’ DALY is a measure of overall disease burden, expressed as the number of years lost due to ill – health, disability or early death. Draft Version 1.3 (Feedback Welcomed)
  • 54. Life Expectancy (LE) β€’ Life Expectancy (LE): Life expectancy at birth is the average number of years that would be lived by babies born in a given time period if mortality levels at each age remain constant. β€’ Similarly, life expectancy at age 65 is the average number of remaining years of life that a man or woman aged 65 will have if mortality levels at each age over 65 remain constant. Draft Version 1.3 (Feedback Welcomed)
  • 55. Health Adjusted Life Expectancy (HALE) β€’ Health Adjusted Life Expectancy (HALE)/Healthy Life Expectancy (HLE) at birth is an estimate of the average number of years babies born this year would live in a state of β€˜good’ general health if mortality levels at each age, and the level of good health at each age, remain constant in the future. Similarly, healthy life expectancy at age 65 is the average number of remaining years a man or woman aged 65 will live in β€˜good general health’ if mortality levels and the level of good health at each age beyond 65 remain constant in the future. β€’ It measures the quality of living Draft Version 1.3 (Feedback Welcomed)
  • 56. Contd … β€’ It can be calculated simply, as HALE = Life Expectancy – Number of years living in unhealthy status HALE = A - fB, where β€’ A = Years lived healthily β€’ B = Years lived with disability or illness β€’ A + B = Life Expectancy β€’ A – fB = Healthy life expectancy, where f is a weighing to reflect disability/illness level. Draft Version 1.3 (Feedback Welcomed)
  • 57. Healthy Life Expectancy (HALE) in Graph Draft Version 1.3 (Feedback Welcomed) Male LE = 79.7 years - 7.3 yrs in an unhealthy state HALE 72.4 HALE Female LE = 84.2 years -9.2 yrs in an unhealthy state HALE75 HALE
  • 58. Contd … (not needed) β€’ HALE at age x is the sum of YWDi from i = x to w (the last open – ended age interval in the life table) divided by lx (survivors at age x): 𝐻𝐴𝐿𝐸 π‘₯ = 𝐴 = (Οƒ 𝑖=π‘₯ 𝑀 π‘Œπ‘Šπ· 𝑖) 𝐿π‘₯ , where β€’ YWDx = Lx(1 – Dx) = Years of healthy life lived between ages i (x and x + 5) β€’ Ydx = Lx X Dx = Lost years of healthy life between ages i (x and x + 5) β€’ Lx = Total years lived by the life table population between age i (x and x + 5) β€’ Dx = Severly weighed prevalence of helth states or disability ages i (x and x + 5) β€’ Also, HALE = LHEx - (Οƒ 𝑖=π‘₯ 𝑀 π‘Œπ· 𝑖) 𝐿π‘₯ , where β€’ LHEx = Healthy Years of Lost Life β€’ YDi = Years of healthy life lived between age i (x and x + 5) Draft Version 1.3 (Feedback Welcomed)
  • 59. Quality Adjusted Life Year (QALY) β€’ QALY is a generic measure of disease burden including both the quality and quantity of life lived. β€’ It is used in economic evaluation to access the value of money of medical interventions, like cost utility analysis, cost effective analysis, increment cost calculation, and for allocating health care resources. β€’ Its measurement unit us QALY, and graph is necessary to explain Draft Version 1.3 (Feedback Welcomed)
  • 60. Example of QALY Draft Version 1.3 (Feedback Welcomed)