Epidemiology Master Level Quiz

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MeasuresofMorbidityandMortalityPart1.pdf

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Measures of Morbidity and Mortality, Part 1

Jennifer Deal, PhD Johns Hopkins University

!  By the end of this lecture, you should be able to: !  Define and calculate indices of morbidity: incidence rate, prevalence, and

cumulative incidence !  Explain the relationship between incidence and prevalence !  Define and calculate indices of mortality: mortality rate, age-specific mortality

rate, and cause-specific mortality rate !  Calculate survival using a life table and identify the underlying assumptions

Learning Objectives

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Incidence and Prevalence

Section A

!  Measures of disease frequency quantify the occurrence of the endpoint with respect to time: !  New endpoints

•  Incidence rate •  Cumulative incidence (risk)

!  Existing endpoints •  Prevalence

Measures of Disease Frequency

!  Incidence = number of new cases of a disease occurring in the population during a specified period of time

Incidence

1.  The number of persons at risk observed for a defined time period if all people at risk in the population are observed for the full time period

Two Types of Denominators Used in Incidence Measures

1.  The number of persons at risk observed for a defined time period if all people at risk in the population are observed for the full time period

2.  Units of person-time at risk observed during a defined time period when different people are observed for different lengths of time !  For example: person-months or person-years

Two Types of Denominators Used in Incidence Measures

!  Incidence rate per 1,000 persons =

If All People at Risk in the Population Are Observed for the Full Time Period

Number of new cases of a disease occurring in the population during a specified period of time

Number of persons who are at risk of developing the disease during that period of time

x 1,000

What if the people at risk in the population are observed for different lengths of time?

!  Incidence rate per 1,000 observed person-years* at risk =

!  Person-years = (number of participants) x (years of follow-up per participant)

If People at Risk in the Population Are Observed for Different Lengths of Time

Number of new cases of a disease occurring in the population during a specified period of time

Observed number of person-years at risk of developing the disease during that period of time

x 1,000

!  Prevalence per 1,000 =

Prevalence

Number of cases of a disease present in the population at a specified time

Number of persons in the population at that specified time

x 1,000

Prevalence

Factors Influencing Observed Prevalence

!  Increased by: !  Increase in new cases !  Longer duration of the disease !  Prolongation of life without cure !  In-migration of cases !  Out-migration of healthy people !  Improved diagnostic capabilities or

better reporting

!  Decreased by: !  Decrease in new cases !  Shorter duration of disease !  High case-fatality !  In-migration of healthy people !  Out-migration of cases !  Improved cure rate of cases

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!  Point prevalence

!  Period prevalence

Variations

Examples of Point and Period Prevalence and Cumulative Incidence in Interview Studies of Asthma

1.  “Do your currently have asthma?”

2.  “Have you had asthma during the last ten years?”

3.  “Have you ever had asthma?”

1.  Point prevalence

2.  Period prevalence

3.  Cumulative or life-time incidence

Rates and Proportions

!  Incidence is a rate !  Prevalence is a proportion

!  Ratio !  Division of two unrelated numbers

!  Proportion !  Division of two numbers !  Numerator is subset of the denominator

!  Rate !  Division of two numbers !  Time is in the denominator

Definitions

!  A rate involves specification of: !  A numerator !  A denominator !  Time (implicit or explicit)

Rates

!  Rates: how fast is the disease occurring?

!  Proportions: what proportion of the population is affected?

Measures of Disease Occurrence

Number of events (e.g., new cases)

Population-time

Number of people affected

Total population

!  Attack “rate”

!  Prevalence “rate”

!  Case-fatality “rate”

Indices that Are Often Called Rates but Are Really Proportions

Incidence and Prevalence, Continued

Section B

!  Cumulative incidence is a proportion

!  Cumulative incidence is a measure of risk

!  Risk is the probability that an event will occur during a specified time

Cumulative Incidence

Measuring Risk: Cumulative Incidence

!  Closed cohort !  Simple calculation of a proportion

!  Open cohort !  Life-table method for discrete time

periods !  Kaplan-Meier method for known

event times

Incidence, Prevalence, and Cumulative Incidence

Determining the One-Year Incidence of Disease

!  Prevalence = incidence x duration of disease

Relationship between Incidence and Prevalence

Incidence and Prevalence of AIDS in the United States

Source: http://www.cdc.gov/nchhstp/newsroom/ images/2012-images/Incidence-fact-sheet- figure7.jpg 12

A Hypothetical Example of Chest X-Ray Screening

Screened population Number with positive x-ray

1,000 Hitown 100

1,000 Lowtown 60

A Hypothetical Example of Chest X-Ray Screening

Screened population Number with positive x-ray Point prevalence per 1,000

1,000 Hitown 100 100

1,000 Lowtown 60 60

A Hypothetical Example of Chest X-Ray Screening

Point prevalence per 1,000 =

Incidence x Average duration

1,000 Hitown 4/year 25 years

1,000 Lowtown 20/year 3 years

Mortality and Survival

Section C

Measures of Mortality

Source: http://www.theonion.com/articles/worlddeath-rate-holding-steady-at-100-percent,1670/

World Death Rate Holding Steady At 100 Percent

GENEVA, SWITZERLAND—World Health Organization officials expressed disappointment Monday at the group’s finding that, despite the enormous efforts of doctors, rescue workers, and other medical professionals worldwide, the global death rate remains constant at 100 percent.

Trends in US Cancer Deaths

Source: American Cancer Society. 3

Trends in US Population

!  Annual mortality rate from all causes (per 1,000 population) =

Crude Mortality Rate

Total number of deaths from all causes in one year

Number of persons in the population at midyear x 1,000

!  Annual mortality rate from lung cancer (per 1,000 population) =

Cause-Specific Mortality Rates

Total number of deaths from lung cancer in one year

Number of persons in the population at midyear x 1,000

!  Annual mortality rate from all causes for children under age ten (per 1,000 population) =

Age-Specific Mortality Rates

Total number of deaths from all causes in one year in children under age ten

Number of children in the population under age ten at midyear

x 1,000

!  Annual mortality rate from leukemia for children under age ten (per 1,000 population) =

Age- and Cause-Specific Mortality Rates

Number of deaths from leukemia in one year in children under age ten

Number of children in the population under age ten at midyear

x 1,000

Prognosis

1.  Describe the severity of disease so that clinical and public health priorities can be established

2.  Answer patients’ questions about prognosis

3.  Compare effectiveness of currently available therapies or other interventions

4.  Establish a baseline for evaluating new treatments

Why Do We Need to Express Prognosis in Quantitative Terms?

!  When do we start to measure survival?

!  How is the diagnosis made?

!  What endpoint is being measured?

General Issues

1.  Case fatality

Ways of Expressing Prognosis

!  CF % =

Case Fatality (CF)

Number of individuals dying during a specified period of time after disease onset or diagnosis

Number of individuals with the specified disease x 100

!  Case fatality rate is a proportion

Remember!

1.  Case fatality

2.  Five-year survival rate

Ways of Expressing Prognosis

Five-Year Survival

Biologic onset of disease Diagnosis and treatment Death

Survival 2002 2008 2010

Five-Year Survival

Biologic onset of disease Diagnosis and treatment Death

Survival 2002 2008 2010

Death

Survival

Detected by screening: diagnosis and treatment Biologic onset of disease

2002 2008 2010 2005

Five-Year Survival

1.  Case fatality

2.  Five-year survival rate

3.  Observed survival rate

Ways of Expressing Prognosis

1.  Case fatality

2.  Five-year survival rate

3.  Observed survival rate !  Person-years

Ways of Expressing Prognosis

1.  Case fatality

2.  Five-year survival rate

3.  Observed survival rate !  Person-years !  Life tables

Ways of Expressing Prognosis

Hypothetical Study of Treatment Results in Patients Treated from 2006 to 2010 and Followed to 2011

Year of treatment

Number of patients treated

Number alive on anniversary of treatment in:

2007 2008 2009 2010 2011

2006 84 44 21 13 10 8

2007 62 31 14 10 6

2008 93 50 20 13

2009 60 29 16

2010 76 43

None lost to follow-up

Rearrangement of Data Showing Survival Tabulated by Years Since Enrollment in Treatment

Year of treatment

Number of patients treated

Number alive on anniversary of treatment in:

1st 2nd 3rd 4th 5th

2006 84 44 21 13 10 8

2007 62 31 14 10 6

2008 93 50 20 13

2009 60 29 16

2010 76 43

None lost to follow-up

!  Probability of surviving first year = 197/375 = 0.53

Survival Analysis in Patients Treated from 2006–2010 and Followed to 2011

Year of treatment

Number of patients treated

Number alive on anniversary of treatment in:

1st 2nd 3rd 4th 5th

2006 84 44 21 13 10 8

2007 62 31 14 10 6

2008 93 50 20 13

2009 60 29 16

2010 76 43

375 197

!  Probability of surviving second year = 71/197—43 = 0.46

Survival Analysis in Patients Treated from 2006–2010 and Followed to 2011

Year of treatment

Number of patients treated

Number alive on anniversary of treatment in:

1st 2nd 3rd 4th 5th

2006 84 44 21 13 10 8

2007 62 31 14 10 6

2008 93 50 20 13

2009 60 29 16

2010 76 43

197 71

!  Probability of surviving third year = 36/71—16 = 0.66

Survival Analysis in Patients Treated from 2006–2010 and Followed to 2011

Year of treatment

Number of patients treated

Number alive on anniversary of treatment in:

1st 2nd 3rd 4th 5th

2006 84 44 21 13 10 8

2007 62 31 14 10 6

2008 93 50 20 13

2009 60 29 16

2010 76 43

71 36

!  Probability of surviving fourth year = 16/36—13 = 0.70

Survival Analysis in Patients Treated from 2006–2010 and Followed to 2011

Year of treatment

Number of patients treated

Number alive on anniversary of treatment in:

1st 2nd 3rd 4th 5th

2006 84 44 21 13 10 8

2007 62 31 14 10 6

2008 93 50 20 13

2009 60 29 16

2010 76 43

36 16

!  Probability of surviving fifth year = 8/16—6 = 0.80

Survival Analysis in Patients Treated from 2006–2010 and Followed to 2011

Year of treatment

Number of patients treated

Number alive on anniversary of treatment in:

1st 2nd 3rd 4th 5th

2006 84 44 21 13 10 8

2007 62 31 14 10 6

2008 93 50 20 13

2009 60 29 16

2010 76 43

16 8

Probability of Surviving

P1 = Probability of surviving the first year 197/375 = 0.53 = 53%

P2 = Probability of surviving the second year given survival to the end of the first year

71/197—43 = 0.46 = 46%

P3 = Probability of surviving the third year given survival to the end of the second year

36/71—16 = 0.66 = 66%

P4 = Probability of surviving the fourth year given survival to the end of the third year

16/36—13 = 0.70 = 70%

P5 = Probability of surviving the fifth year given survival to the end of the fourth year

8/16—6 = 0.80 = 80%

Probability of surviving all five years = 0.53 x 0.46 x 0.66 x 0.70 x 0.80 = 0.090 = 9%

Survival Curve for Hypothetical Example of Patients Treated 2006–2010 and Followed to 2011

!  No changes have occurred in survivorship over calendar time

!  Those lost to follow-up experience the same survivorship as those who are followed

Two Assumptions Made in Using Life Tables

!  Case fatality

!  Five-year survival rate

!  Observed survival rate !  Person-years !  Life tables !  Kaplan-Meier method

Ways of Expressing Prognosis

!  Define and calculate indices of morbidity !  Incidence rate !  Prevalence !  Cumulative incidence

!  Explain the relationship between incidence and prevalence

!  Define and calculate indices of mortality !  Mortality rate !  Age-specific mortality rate !  Cause-specific mortality rate

!  Calculate survival using a life table and identify the underlying assumptions

Lessons Learned

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