EPID 403

Epi –

Demos –

Logos –

Upon, on top of

people or population

study of

Epidemiology
The study of distribution and determinants of health-related states and events in specified populations, and the application of this study to control health problems
Assumptions in epidemiology

-disease and ill health are not randomly distributed

-factors that determine this distribution are knowable and mostly modifiable

-modification = prevention and control of disease

Descriptive vs Analytic

D – concerned with distribution of disease by persons, places, and times.  Without a hypothesis – hypothesis generating

 

A – Concerned with identifying disease determinants.  Hypothesis testing.  Casual inference is ultimate goal.  

Epi study design

Hypothesis (the link)

Description

Analysis

Surveillance

Describes changing patterns of community health problems

 

Provides clues

 

Health services planning

 

Public health policy

Epi – Demos – Logos
Upon, on top of
people or population
study of
Primary prevention
prevent initial development of disease (risk factor reduction, immunization)
Secondary prevention
Early detection of existing disease to reduce morbidity (screening for cancer, high blood pressure, etc.)
Tertiary prevention
Reduce the impact of acute worsening of disease
Epi Triad of infectious disease
Host – Susceptible
Agent – infectious or non-infectious
Environment – promotes exposure
Middle – vehicle

*or vector organism for indirect transmission

Triad of Non-infectious disease
Host – genetics, stress, psychological factors
Agent – behavior, diet, tobacco
Environment – neighborhood, parks, food outlets
Modes of disease transmission
1. escape of the agent from a source or reservoir
2. Conveyance
3. entry into host
Direct transmission
person-to-person
Indirect transmission
1. common vehicle
a. single exposure (potato salad)
b. multiple exposures
c. continuous exposure (telephone receiver)
2. vector (organism)
Definition of “carrier” status
A host “harbors” the infectious agent but appears not infected and there are no detectable signs or symptoms
effect of non-apparent infection on disease control and disease statistics
when the proportion with non-apparent infection is high, statistics will underestimate rates of infection and overstate overall disease severity
endemic
habitual presence of a disease in a geographical area or population
epidemic
occurrence of a disease in a geographical area that is unusually frequent
pandemic
worldwide epidemic
primary case
the first disease case that appears in the population
index case
the disease case that brings the group or population to the attention of public health personnel
incubation
time between infection and appearance of signs or symptoms
herd immunity
resistance of group or population to invasion and spread of infectious agent based on the immunity of a high proportion of the group
point source
from a single brief and essentially simultaneous source of exposure

– all of the cases usually result in a single incubation period

propagated (“progressive”)
transmission from one individual to another

– epidemic extends over many incubation periods

general approach to outbreak investigation
– verify the diagnosis – perform clinical studies
– verify existence of epidemic – compare with past levels
– person, place and time – calc. attack rates
attack rate
new cases rapidly occuring in a well-defined population over a short period of time
——————————————
total number of people at risk during that same period of time
secondary attack rate
*All times 100

new cases among contacts of initial cases occurring during a short period of time
—————————————
(total number of people at risk during same period of time) – (initial cases)

morbidity
1. a diseased state or symptom
2. the incidence of disease: the rate of sickness
Rate
a ratio where time is included in the denominator
incidence rate
-special type of proportion that includes time
– represents the probability of disease in a defined population
– the basic measure of disease occurence
incidence rate formula
number of new cases of disease occurring during a defined period of time
————————————-
number of people at risk for disease during defined period of time
cumulative incidence
all observed throughout a defined period of time
– closed cohort
– denominator = sum of all people at risk
incidence density
all NOT observed for the full defined period of time
– person-time units
– denominator = sum of person observation time for each person at risk
cumulative incidence formula
number of new cases of disease occurring during a defined period of time
————————————–
initial population at risk of the disease
incidence density formula
number of new cases of disease occurring during a defined period of time
————————————–
person-time at risk for the disease
Incidence rate based on group level data
number of new cases of disease occurring during a defined period of time
—————————————–
number of people in population at risk at the mid-point of the period
ICD
International Statistical Classification of Diseases and related health problems
– used for coding and classifying mortality data from death certificates
ICD-CM
International Statistical Classification of Diseases – Clinical Modification
– Used to code and classify disease morbidity data from inpatient and outpatient records
problems determining who goes into the numerator
1. what is the best case definition
2. detection (under or over diagnosis)
3. prevalent or incident (existing cases or new cases)
problems determining who goes into the denominator
1. under-counting of certain population subgroups
2. not everyone in the denominator is at risk for disease
3. population studied may have limited external applicability
Limitations of hospital data
1. admissions are selective
2. Hospital records are not designed for research
3. Populations at risk are not generally well defined
point prevalence
number of existing cases AT a point in time
———————————–
number of people in the popualtion AT that point in time
Period prevalence
Number of existing cases DURING a defined period of time
————————————————
number of people in the population DURING that defined period of time
Relationship between incidence and prevalence
P = I*D
Crude Rate
number of all new events during a period of time
—————————————-
number of people at risk for these events during same period
Problems with crude rates
Crude rates can obscure the fact that subgroups in the population at risk exhibit significant differences in risk. Thus, rates may not be comparable across populations unless there is adjustment for population-specific subgroup composition.
Why look at mortality quantitatively?
– can indicate disease severity and determine if treatment has become more effective over time
-serve as a surrogate for incidence when the disease is severe
– pinpoint difference in the risk of dying from a disease between population subgroups
Mortality rate formula
number of people who die during a period of time
—————————————-
size of population during midpoint of the period
all-cause annual mortality formula
total number of people who die from any cause during 1 year
————————————————–
population at midpoint of that year
disease specific annual mortaity
number of people who die of a specific disease during 1 year
————————————————
population at risk at midpoint of that year
Case fatality rate
* proportion, so a %

number of people who die of a disease
——————————–
number of people with the disease

proportionate mortality
*proportion, so a %

number of people who die of a disease
—————————–
total deaths

Age-adjusted rates
1. age is associated with the disease or health state of interest
2. age distribution structure of the populations being compared are different
Direct method for age-adjustment
compute the overall mortality rate that would be expected if each population had the same age structure as the standard population
Age-adjustment pros
– holds age constant
– gives a more accurate picture of differences in rates across populations
Age-adjustment cons
– fictional rates are calculated
– suppresses details of how subgroups differ across the variable used for adjustment
Indirect age-adjustment
– used when age group-specific rates are not available
prognosis
probability of death vs probability of survival
Case fatality rate formula
*rate, so a %

number of people who die of a disease
——————————-
number of people with the disease

case fatality rate
– good index of severity of a short-term, acute disease
– can measure benefit of a new therapy
survival rate
percentage of people with a disease who are alive at the end of a specified time interval
5-year survival rate formula
*rate, so a %

number of cases of the disease who are alive 5 years later
——————————————–
number of cases of the disease

limitations of 5-year survival rate
– assumes the probability of surviving is equal throughout
– does not use actual observed survival
– cannot account for survival affect
creator of the first life table
edmond halley
limitations of classic life tables
– assumes the probability of surviving is equal throughout the year or fixed interval
Numerators in Kaplan-Meier life tables
– an event is instantaneous
– numerator = the number of people who died at that instant
– denominator = the number of people alive up to that instant, including those who died
– minus withdrawals that occurred before that instant
Kaplan-Meier life table assumptions
– no secular(temporal) changes in effectiveness of treatment or tendency toward survival over time
– censoring must be independent of the probability of survival
median survival
the length of time that half of the study population survives
mean survival
the sum of the individual survival times divided by the total number of people in the study population
relative survival
observed survival in people with the disease
————————————-
expected survival without the disease
cumulative incidence in an open cohort or closed cohort with incomplete follow-up
number of cases of disease occurring during a defined period of time
————————————————-
initial population at risk of the disease
risk
the probability of an event occurring
– difficult to measure over time
– applies to a population, not individuals
– values range from 0-1, unit-less
– also expressed as a %
– defined for a certain time period
Rate
– special form of proportion that includes a specification of time
– probability of disease in a defined population over time
– the basic measure of disease occurrence
– a risk in disguise
– the risk of acquiring a disease, and death from a disease is the best approx. by a measure of its incidence
Which measures of disease occurrence below gives you the most accurate information about risk?
incidence density
approximate formula
Risk = (incidence rate * time)
exact formula
Risk = 1 – [exp ^-(rate * time)]
Canons of Inductive Reasoning
– Method of Difference
examines differences among groups for clues as to why disease rates or other health problems vary across groups
Canons of Inductive Reasoning
– Method of Agreement
looks for commonality in groups that manifest the same health problem
Canons of Inductive Reasoning
– Method of Concomitant Variation
traces how exposure to a hazard varies in relation to disease or other health problem
Relative Risk
A/(A+B)
——–
C/(C+D)
Odds Ratio
A*D
—–
B*C
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