Epid 600 Class 3 Disease Occurence

EPID 600; Class 3 Measuring disease occurrence University of Michigan School of Public Health

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Epidemiology and measurement Measurement is central to epidemiology Fundamental observations in epidemiology are measures of the occurrence of illness

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Quantification in epidemiology 1. 

Prevalence

2. 

Risk (incidence proportion)

3. 

Incidence rate

4. 

Odds

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1. Prevalence (proportion)

Number of cases Prevalence

= Number of persons in population

at a specified time

Specified time interval can be a ‘point’ or ‘period’ of time EXAMPLES 14 adult men per 1,000 were HIV + in Tanzania in June 1995 27 adult men per 1,000 were HIV+ in Tanzania in 1995

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2. Risk (incidence proportion) In epidemiology, risk applies to an individual and refers to the probability that a person will develop a given disease In epidemiology, risk is seldom measured at the individual level but rather at the population level, hence, the risk measurement for agroup is referred to as incidence proportion

Risk =

Number of new cases of disease Number of persons followed

over a time period

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Example, risk Fixed population: 1000 people observed for 5 years When people become ill, assume all become ill on the last day of the time period

0.5 years

3 sick

1.0 years

5 sick

1.5 years

4 sick

2.0 years

3 sick

2.5 years

3 sick

3.0 years

5 sick

3.5 years

1 sick

4.0 years

2 sick

4.5 years

3 sick

5.0 years

4 sick

967 people never became ill Therefore, Risk = # sick/total pop = 33/1000=0.033

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Risk, examples Voluntary risks

Risk of death per person per year

Motorcycling

1 in 50

Smoking a pack a day

1 in 200

Taking contraceptive pills

1 in 5,000

Legal abortion, >12 weeks

1 in 5,900

Drinking 1 bottle of wine/day

1 in 13,300

Playing soccer

1 in 25,500

Canoeing

1 in 100,000

Skiing

1 in 430,000

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Different risk patterns over time Morbidity B

Annual risk of morbidity

Morbidity A

Age

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Two key notions in stating and interpreting risk Risk is meaningless without a time interval e.g., “Risk of death from cardiovascular disease among women 60 years of age is 2%”...means what? Competing risks make it difficult to use risk as the only assessment of disease occurrence in a study although this can be done in studies where follow-up is short and competing risks are low; e.g., Salk vaccine trial in children

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3. Incidence rate One solution to dealing with competing risk problem faced by incidence proportion is to consider incidence rates Incidence rates are concerned with the number of new cases during a particular person time of follow-up In some cases, e.g., national mortality rates, time period is taken as mid-point of a specific interval (i.e., mid-year)

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Incidence rate

Number of new cases Incidence Rate = Total time at risk of persons followed

EXAMPLE 20 new cases of HIV infection during 8,000,000 person years of follow-up, that is, among 8,000,000 persons followed-up for 1 year

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Understanding person-time T1

T2

T3

T4

T5

T6

T7

T8

T9

T10

T11

T12

T13

T14

T15

T16

T17

T18

T19

T20

TT

P1

P2

P3

P4

P5

P6

P7

P8

P9

P10

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A person-time example T1

T2

T3

T4

T5

T6

T7

T8

T9

T10

T11

T12

T13

T14

T15

T16

T17

T18

T19

T20

TT

P1

14

P2

20

P3

11

P4

11

P5

20

P6

20

P7

10

P8

20

P9

2

P10

10

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An example T1

T2

T3

T4

T5

T6

T7

T8

T9

T10

T11

T12

T13

T14

T15

T16

T17

T18

T19

T20

TT

P1

14

P2

20

P3

11

P4

11

P5

20

P6

20

P7

10

P8

20

P9

2

P10

10

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Therefore Assume lightning bolt is disease onset

Pr evalence at T11 =

1 7

3 Risk = new cases over a given time period 10 3 3 new cases Incidence rate = = 14 + 20 + 11 + 11 + 20 + 20 + 10 + 20 + 2 + 10 138 person time

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Example, Incidence Rate Fixed population: 1000 people observed for 5 years When people become ill, assume all become ill on the last day of the time period 0.5 years

3 sick

1.0 years

5 sick

1.5 years

4 sick

2.0 years

3 sick

2.5 years

3 sick

3.0 years

5 sick

3.5 years

1 sick

4.0 years

2 sick

4.5 years

3 sick

5.0 years

4 sick

967 people never became ill Therefore, IR= # sick/PYO = 33/[(967*5)+(3*0.5)+...etc]=33/4,921=6.7/1000 PYOs 16

Another way to think of incidence and prevalence Incidence

Prevalence

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Another way to think of incidence and prevalence Incidence

Prevalence

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Another way to think of incidence and prevalence Incidence

Prevalence factors affecting prevalence include incidence, duration of disease, death rates 19

Factors leading to increased prevalence Increased incidence rate Increased duration of disease, e.g., prolonged survival More case finding, e.g., screening Lower mortality

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HIV Prevalence and Incidence

time

number of new cases per 100,000 population per year

number of cases per 100,000 population

Comparing incidence and prevalence

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Notes about prevalence and incidence Prevalence represents the number of cases among persons of interest at a given point in time Therefore, prevalence is not a rate; the term “prevalence rate” should not exist Incidence (including both incidence proportion and incidence rate) represents number of new cases over a particular person-time of follow-up Therefore risk (incidence proportion) and incidence rate both are not meaningful without a time unit Always be as specific as possible when articulating units of measurement...what are you measuring? among who? over what time period?

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Comparing prevalence, risk (incidence proportion), and incidence rates   Risk

and prevalence range from 0 to 1

  Incidence

rate ranges from 0 to infinity

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Issues in measurement of incidence and prevalence Think of numerator and denominator How do we know something is a “case”? How do we count population “at risk”? What is a specific “time period”?

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Interrelations among measures (1), incidence proportion and incidence rate

Risk = Incidence rate x time [0-1]

[0-∞]

[0-∞]

This is only applicable where risk is low (<20%?)

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Example, incidence proportions and rates Population of 1,000 persons Mortality rate of 11 deaths per 1000 person years over a 20 year time period Therefore, Risk = [11/1000]*20 year = 0.22 So, risk is that among 1000 people there would be 220 deaths (22%) over the 20 years This ignores population change or possible addition or removal of population from follow-up

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Interrelations among measures (2), prevalence and incidence rate

IR * D =

P 1–P

This applies only in “steady state”, where incidence rates and disease duration are stable over time

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And.... P = IR * D 1− P therefore P = ( IR * D) *(1 − P) P = IR * D − IR * D * P P + IR * D * P = IR * D P(1 + IR * D) = IR * D IR * D P= 1 + IR * D 28

Therefore, prevalence and incidence rate

IR * D =

P 1–P if p <0.10, then 1-P ~1

Therefore, for low prevalence (and steady state), IR * D ≅ P

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4. Odds

p odds = 1− p

probability, risk

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Interpreting odds Odds = p/(1-p) Therefore, if probability is 0.75, odds are 0.75/(1-0.75)=0.75/0.25=3 Note that 3:1 odds means that a horse has a 75% probability of losing a race Good odds, if this is a horse race, are 1:1, that is, probability is 0.50 and odds are 0.50/(1-0.50)=1; this then means that a horse has a 50:50 chance of winning or losing

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Annual mortality rate

Annual mortality = rate

# of deaths in 1 year x 1000

# of persons in population at mid-year

Notes  

Multiplication by 1000 is by convention

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Case fatality rate

Case fatality = rate

total # of deaths x 1000

# of persons with a disease

Notes  

Multiplication by 1000 is by convention

 

The time period here is implicit as the time for persons with disease, presumably low

 

For diseases with low fatality, survival measures are better used

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Attack rate

Attack rate

total # of new persons with disease =

x 100 # of persons at risk of disease

 

Note: the time period here is implicit as the time for persons with disease, presumably low

 

For diseases with low fatality, survival measures are better used 34

Some examples Is the prevalence of osteoarthritis in the population low or high? Is the annual mortality rate from osteoarthritis low or high? Is the annual mortality rate from respiratory anthrax low or high? Is the case fatality rate from respiratory anthrax low or high?

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Measuring Tuberculosis Recap...some reasons why accurate measurement is important To define public health needs To assess how well we are doing at addressing those needs

A review of measurement issues in the context of the global TB burden was published in the Lancet in April 2008

36 Dye et al. Lancet. Measuring tuberculosis burden, trends, and the impact of control programmes. 2008; 8: 233-243.

Measuring Tuberculosis trends

37 Dye et al. Lancet. Measuring tuberculosis burden, trends, and the impact of control programmes. 2008; 8: 233-243.

Measuring Tuberculosis trends “incidence” over time by country

38 Dye et al. Lancet. Measuring tuberculosis burden, trends, and the impact of control programmes. 2008; 8: 233-243.

Measuring Tuberculosis trends Prevalence estimates

39 Dye et al. Lancet. Measuring tuberculosis burden, trends, and the impact of control programmes. 2008; 8: 233-243.

Measuring Tuberculosis trends

Problems: Plausibility of measured distribution Cases/suspects Fluctuations in reported cases 40 Dye et al. Lancet. Measuring tuberculosis burden, trends, and the impact of control programmes. 2008; 8: 233-243.

Measuring Tuberculosis trends

Problems: Plausibility of measured distribution Cases/suspects Annual fluctuations in reported cases 41 Dye et al. Lancet. Measuring tuberculosis burden, trends, and the impact of control programmes. 2008; 8: 233-243.

Final notes (1), expressing prevalence and incidence Prevalence often expressed as cases per 100,000 Incidence rate is also sometimes expressed as per 100,000 but this is wrong since needs time dimension (per year?)

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Final notes (2), other terms for measures of disease occurrence Frequently we see “period prevalence” referred to, which refers to prevalence measured during a time period (e.g., one year) Cumulative incidence is sometimes used to measure the incidence of disease during a given time period divided by population at risk; this is the same as incidence proportion Incidence density is sometimes used instead of incidence rate to refer to computations that use person-time; this is identical to incidence rate discussed here

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Summary of measures of disease frequency Prevalence

Risk

Incidence rate

Odds

Number with disease

Number with new disease

Number with new disease

Risk

Denominator

Total number of people

Total number of people at risk at baseline

Person years at risk

1-Risk

Time

At a point in time

Over a time interval

Over a time interval

Over a time interval

Numerator

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