Measuring Epidemiologic Outcomes. Epidemiology (Schneider) Epidemiological Outcomes Ratio:...

Measuring

Epidemiologic

Outcomes

Epidemiology (Schneider)

Epidemiological Outcomes

Ratio: Relationship between two numbers

Example: males/females

Proportion: A ratio where the numerator is included in the denominator

Example: males/total births

Rate: A proportion with the specification of time

Example: (deaths in 1999/population in 1999) x 1,000


In epidemiology, the occurrence of a disease or condition can be measured using rates and proportions. We use these measures to express the extent of these outcomes in a community or other population.

Rates tell us how fast the disease is occurring in a population.

Proportions tell us what fraction of the population is affected.

(Gordis, 2000)


Morbidity Measures

Incidence is always calculated for a given period of time

An attack rate is an incidence rate calculated for a specific disease for a limited period of time during an epidemic

Population at riskX 1,000

Number of new events during a time period

Incidence Rate =


Morbidity Measures

Prevalence is not a rate

Point prevalence measures the frequency of all current events (old and new) at a given instant in time

Period prevalence measures the frequency of all current events (old and new) for a prescribed period of time

Population at riskX 1,000

Number of existing events, old and new

Prevalence =


Interrelationship: P ID

High prevalence may reflect: High risk Prolonged survival without cure

Low prevalence may reflect: Low risk Rapid fatal disease progression Rapid cure

Examples: Ebola, Common cold


Relationship Between Incidence and Prevalence (cont.)

Cancer of the pancreas Incidence low

Duration short

Prevalence low

Adult onset diabetes Incidence low

Duration long

Prevalence high

Roseola infantum Incidence high

Duration short

Prevalence low

Essential hypertension Incidence high

Duration long

Prevalence high


Calculation Practice

Skin Cancer on Sunny Beach:

1. Point prevalence on 9/28/1974

2. Period prevalence for year 1975

3. Incidence rate for year 1975

What information will you need?


Diagnosed cases of Skin Cancer on Sunny Beach, 9/28/1974

Point Prevalence (9/28/1974)

= (10/450)*1000

= 22 per 1000

# of existing cases = 10

Total population at risk = 450


Diagnosed cases of Skin Cancer on Sunny Beach, 1975

Average population at risk = 500

Incidence rate (year 1975)

= (5/500)*1000

= 10 per 1000

Period prevalence (year 1975)

= (15/500)*1000

= 30 per 1000

# of new cases = 5


JAN 2000 MAY JULY SEPT

DEC 2000

What is the numerator for incidence in 2000?

What is the numerator for point prevalence if a survey was done in May? July? September? December?

Number of cases of disease beginning, developing, and ending during a period of time, January 1, 2000 – December 31, 2000. Length of each line corresponds to duration of each case.


Risk Versus Rate

Risk and rate are often used

interchangeably by epidemiologists

but there are differences


Risk Versus Rate (cont.) Risk is a probability statement assuming an individual is not

removed for any other reason during a given period of time

As such, risk ranges from 0 to 1 (no chance to 100% probability of occurrence)

Risk requires a reference period and reflects the cumulative incidence of a disease over that period

Example: 1 in a million chance of developing cancer in a 70 year lifetime


Risk Versus Rate (cont.)

Rates can be used to estimate risk if the time

period is short (annual) and the incidence of

disease over the interval is relatively constant

If however, individuals are in a population for

different periods of time for any reason, then

you should estimate risk by incidence density


Incidence Density

ID =

Number of new cases during the time period

Total person-time of observation (often years)


ID Example

In the Iowa Women’s Health Study (IWHS), 37,105

women contributed 276,453 person-years of follow-up

Because there were 1,085 incident cases, the rate of

breast cancer using the incidence density method is:

1,085/276,453 = 392.5/100,000 person-years


ID Example (cont.)

If each woman had been followed for the

entire 8-year period of the study, the total

person-years would have been 296,840 and

the rate would have been lower (assuming the

number of incident cancers was the same)

The incidence density method yielded a

higher and more accurate estimate


Natality Outcomes

Natality measures are used primarily by

demographers for population projection

Estimated mid-interval total population

X 1,000

Number of live births

for a given time period (year)Crude Birth Rate =


Concerns About Crude Birth Rates

Definitions of a live birth may vary

U.S. = “any product of conception that shows any

sign of life after complete birth (pulse, heartbeat,

respiration, crying, pulsation of umbilical cord or

movement of the voluntary muscles)”

The denominator used for birth rates is inaccurate

because men are not part of the population-at-risk


Natality Outcomes (cont.)

Estimated # of women 15-44 years at mid-interval

X 1,000

Number of live births for a given time period (year)

General Fertility Rate =


Natality Outcomes (cont.)

Total fertility rate: Same as above, but use women

10-49 years and adjust for age cohorts

Gross reproductive rate: Same as TFR, but use

only live births of females in numerator

Net reproductive rate: Same as GRR, but count

only births of females who survive to

reproductive age in the numerator


Net Reproductive Rate (NRR)

If NRR = 1,000, each generation will just replace itself

If NRR < 1,000, indicates a potentially declining population

If NRR > 1,000, indicates a potential population increase


Mortality Measures Related to Natality Fetal Death Rate or Ratio: Used primarily by public

health officials to estimate the health of populations

Estimates risk of death associated with late states of gestation

Fetal deaths plus live births in that interval

X 1,000

Number of fetal deaths 20 weeks or more gestation in a given intervalFetal Death

Rate =


Mortality Measures Related to Natality (cont.)

Measures fetal loss relative to live births

Number of live births reported during the same time interval

X 1,000

Number of fetal deaths 20 weeks or more gestation in a given intervalFetal Death

Ratio =


Reflects events occurring during pregnancy and after birth

Number of fetal deaths 20 weeks or more gestation plus number of live births during the same interval

X 1,000

Number of fetal deaths 20 weeks or more gestation plus number of

neonatal deaths (28 days or less in age) during a given interval

Perinatal Mortality Rate =




Estimates events immediately after birth, primarily congenital

malformations, prematurity and low birth weight

Number of live births during the same interval

X 1,000

Number of deaths of neonates (28 days or less) in a given intervalNeonatal Mortality

Rate =



Used for international comparisons; high rates indicate

unmet public health needs and poor socioeconomic and

environmental conditions


X 1,000

Number of deaths under 1 year during a given intervalInfant Mortality

Rate =



Rates reflect health care access and socioeconomic factors


X 1,000

Number of deaths assigned to causes related to pregnancy during a given interval

Maternal Mortality Rate =


Chart of Early Life Mortality Measures


Mortality Outcomes Crude rate:

The number of events in a population over a given

period of time, usually a calendar year

Crude rates reflect the probability of an event

As the probability of death increases with age, the

crude death rate reflects the age structure of the

population


Mortality Outcomes (cont.)Example: 1980

The larger crude death rate in Florida reflects the larger population of elderly in that state.

Location Deaths PopulationCrude Death

Rate per 1,000

Florida 111,114 10,194,000 10.9

Alaska 1,830 416,000 4.4


Mortality Outcomes (cont.)

Specific rate:

Used to construct rates for specific segments of

the population so we can compare among strata

or between groups (used especially for age,

race, ethnicity, gender)

We can also construct cause-specific rates to

compare rates among causes


Mortality Outcomes (cont.)

Examples Age-specific rates

Gender-specific rates

Race-specific rates

Cause-specific rates

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