Transcript Epi 712 – Intermediate Epidemilogy

Epi 712 – Intermediate
Epidemiology
Patty Kissinger, Ph.D. (Prof)
Jeff Kopicko, MSPH (TA)
Meg O’Brien, MPH (TA)
Objectives
• To discuss course logistics
• To review the basics of Epidemiology
• To describe the difference between
descriptive and analytic epidemiology
• To discuss the basic study designs
• To discuss criteria for causality
• Readings: Szklo and Nieto Chapter 1
Definition of Epidemiology
• The study of the distribution and
determinants of health-related states or
events in specified populations and the
application of this study to control
health problems.
Classifications of Epidemiology
• Descriptive –
– Used to describe person, place and time
– Used to generate hypotheses
• Analytic
– Used to test hypotheses
Uses of Epidemiology
• Determine etiologic or causal factors
• Describe factors associate with adverse
conditions
• Community diagnosis of distribution of
diseases
• Predicting disease occurrence, impact
and distribution
• Estimating individuals risk of disease
Uses of Epidemiology (con’t)
• Evaluating therapeutic and intervention
activities
• Measurement of efficacy of health
measures
• Studying of historical disease
• Identifying disease syndromes
• Planning for current health needs
• Predicting future needs.
Person, Place, Time
• Person - age, sex, race/ethnicity, martial
status, occupation, education, socioeconomic status
• Place - hospital-based, communitybased, regional
• Time - seasonal, secular, am/pm/noc
Examples of descriptive
epidemiologic findings
Gonorrhea (cases per 100,000)
The Hidden Epidemic, Institute of Medicine, 1997
Sweden
Germany
Denmark
Autstralia
Canada
England
U.S.
0
50
100
150
200
Possible interpretations
• May be confounded by detection bias
(i.e. More people get tested in U.S and
policy in Europe is to just treat
syndromically and not test)
• May be reporting bias (gonorrhea is not
reported as well in countries other than
the U.S.)
• The U.S. people are more likely to have
unprotected sex.
Abnormal Pap results by age
(U.S., 1991-1993, NBCCEDP)
14
12
10
8
6
4
2
0
< 30
30-39
40-49
50-59
60-69
>69
Possible interpretations
• Interpretation is difficult because we don’t
know if it is a rate or just cases reported
• If it is cases, then it is not interpretable
without know the population distribution (i.e.
denominator information)
• If it is rates, then women < 30 are at highest
risk (maybe due to Human Papillomavirus)
By 12th grade, nearly 70 percent of high school
students have had sexual intercourse (YRBS,
1993)
80
70
60
50
40
30
20
10
0
68
58
46
38
9th
10th
11th
12th
Possible Interpretation
• Again, we don’t know if it is rates or
cases
• It is prevalence data, and we don’t know
when the first sexual act occurred, but
we know that 38% of the 9th graders
already had sex so, sex education
starting at high school is too late.
Definitions
• Endemic - a persistent level of occurrence
with low to moderate disease level
• Hyper-endemic - persistently high level of
occurrence
• Sporadic - irregular
• Epidemic (outbreak)- occurrence of disease
is in excess of expected levels
• Pandemic - epidemic spreads over several
countries or continents
Role of an Epidemiologist
•
•
•
•
•
Surveillance
Outbreak investigation
Hypothesis testing
Evaluation
Communication
Surveillance
The importance of surveillance for
Management
Purposes of surveillance
• setting of priorities
• planning and allocating resources for service
• defining population subgroups and risky behaviors for
targeted interventions
• directing public health policy
• informing diagnostic and therapeutic practice
• evaluation of interventions
• stimulating further research
Characteristics of
Surveillance
• Purpose is to monitor trends
of disease
• Usually large data sets
• Can be used to identify
persons at risk for a disease
Possible Interpretations
• This is a better slide because we know
it is rates.
• We can probably draw a conclusion that
the risk of breast cancer increases with
age.
Possible interpretations
• We know that the rates of diabetes is
going up, we assume it is type II or adult
onset.
• We don’t what factors are related but
we could hypothesis that obesity is
increasing or there is an aging of the
cohort since these are not age adjusted
Possible interpretations
• If you look at the last two slides and
imagine them being super-imposed
upon one another you can see that
physical inactivity and cardiovascular
disease co-vary.
• You could hypothesis that physical
inactivity may be associated with cardiovascular disease
Possible interpretations
• There does seem to be a trend for an
increase in smoking among teenagers
• A statistical test, such as a Chi-square
test for trends would better help you to
decide.
Possible interpretations
• We should probably be most worried
about the heterosexual community
because it is steadily increasing and it is
a broader base of the population (~90%
of the population)
• This does not tell us anything about
incidence, it is only a description of
prevalence.
Confounding
E
D
C
E=exposure
C=confounder
D=disease
Birth Cohorts
• Age is a strong risk factor for many
health outcomes
• For many diseases, exposures have a
cumulative effect that are expressed
over long periods of time.
Definitions
• Age effect – Change in the rate of a condition
according to age irrespective of birth cohort
and calendar time.
• Cohort effect – Change in the rate of a
condition according to year of birth,
irrespective of age and calendar time.
• Period effect – Change in the rate of a
condition affecting an entire population at
some point in time, irrespective of age and
cohort effect
Surveillance Systems
• Are active or passive
• Can be for a whole population
or for a selected group
(sentinel)
Outbreak Investigation
John Snow (The Father of Field Epidemiology)
Cholera Outbreak Investigation 1854
Possible Interpretations
• For both the community water sources
(representing the poorer community)
and individual home water sources
(representing the higher socio-economic
groups), Southwark rates were greater.
• Intervention should be started there.
Outbreak investigations
• A good idea to plot out the cases (better
rates) and then determine when
interventions were implemented to see
if any helped.
Criteria for Causality
• Strength of association - larger ratio between
diseased and non diseased greater the likelihood of
causation
• Biological credibility – the association has to make
sense biologically
• Consistency of findings - association found in one
study is found in others.
• Dose-response - with increasing levels of exposure
to the factor, a corresponding rise in occurrence of
disease occurs
• Specificity - The extent to which the occurrence of a
variable can be used to predict the occurrence of an
outcome.
• Time sequence- exposure precedes the disease
Study Designs
Descriptive
Analytic
correlational
case control
case report/
case series
cohort
cross-sectional
Experimental
clinical trial
community trial