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Module 2 Introduction
Context
Content Area: Hypothesis Generation
Essential Question (Generic): What hypotheses might explain the distribution of healthrelated events or states?
Essential Question (Drug Abuse Specific): What hypotheses might explain drug abuse?
Enduring Epidemiological Understanding: Clues for formulating hypotheses can be found
by observing the way a health-related condition or behavior is distributed in a population.
Synopsis:
In Module 2, students explore how descriptive epidemiological information on person,
place, and time (PPT) are used to generate hypotheses to explain “why” a health-related
event or state has occurred. Students begin to uncover and develop the following
epidemiological concepts and skills: evaluating PPT information, developing hypotheses to
explain that distribution, understanding that there may be more than one credible
hypothesis, recognizing when a particular hypothesis does NOT explain the PPT
information.
Lessons:
Lesson 2-1:
Lesson 2-2:
Lesson 2-3:
Lesson 2-4:
Lesson 2-5:
What’s My Hypothesis? AIDS, etc
In the News
Drug Abuse by “Person” Race / Ethnicity
Drug Abuse by “Place” States in USA
Drug Abuse by “Time” Boundary Effect
DrugEpi 2-5 Time – Boundary Effect
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Module 2 - Hypothesis Generation
Lesson 2-5 Drug Abuse by “Time”
Boundary Effect
Content
•
•
•
•
Brief review of descriptive epidemiology factors of person, place, and time
“Time” trends in the Monitoring the Future data 1976-2006
“Time” trends in admissions to treatment
An investigation of the effect of “week of the month” as a “time” variable,
regarding deaths in the USA
• Discussion of hypotheses that are generated from “time” information
Big Ideas
• “Time” information can generate hypotheses
• Cyclical time trends in drug use over the past 30 years suggest hypotheses
about time-related fluctuations in attitudes about drug use, extent of active
prevention programs, and types of illicit substances that are available.
• Some causes of death are more common in the first week of the month;
this suggests hypotheses about relationships between death and availability
of money to purchase illicit substances.
This project is supported by a Science Education Drug Abuse Partnership Award, Grant Number 1R24DA016357-01,
from the National Institute on Drug Abuse, National Institutes of Health.
DrugEpi 2-5 Time – Boundary Effect
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Where are we?
Essential Questions
Enduring Understandings
1.
How is this disease
distributed?
Health-related conditions and behaviors are not distributed uniformly in a
population. They have unique distributions that can be described by how
they are distributed in terms of person, place, and time.
2.
What hypotheses might
explain the distribution of
disease?
Clues for formulating hypotheses can be found by observing the way a
health-related condition or behavior is distributed in a population.
3.
Is there an association
between the hypothesized
cause and the disease?
Causal hypotheses can be tested by observing exposures and diseases
of people as they go about their daily lives. Information from these
observational studies can be used to make and compare rates and
identify associations.
4.
Is the association causal?
Causation is only one explanation for finding an association between an
exposure and a disease. Because observational studies are flawed,
other explanations must also be considered.
5.
What should be done
when preventable causes
of disease are found?
Individual and societal health-related decisions are based on more than
scientific evidence. Because of competing values, social, economic, and
political factors must also be considered.
6.
Did the disease
prevention strategy
work?
The effectiveness of a strategy can be evaluated by making and
comparing rates of disease in populations of people who were and were
not exposed to the strategy. Costs, trade-offs and alternative strategies
must also be considered.
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Descriptive Epidemiology
Epidemiological Factors
Person (who?)
Place (where?)
Time (When?)
Sex
Residence
Year
Occupation
Events
Season
Age
Anatomical Site
Day, etc.
SES
Geographic Site
Onset
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Descriptive Epidemiology - Time
Epidemiological Factors
Person
Place
Time
Sex
Residence
Year
Occupation
Events
Season
Age
Anatomical Site
Day, etc.
SES
Geographic Site
Onset
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Descriptive Epidemiology - Time
“Time” Can Mean “Years”
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Any Illicit Drug:
Trends in
Annual
Prevalence
by Gender
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Hypotheses about Time Trends?
•
•
•
•
•
•
•
Perceived Risk
Disapproval
Public Attention
News Coverage / Advertisements
Drug-free campaigns and programs
Emergence of new, “attractive” substances
“Generational Forgetting”
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Marijuana:
Both
Genders,
8th, 10th, and
12th Grade
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Time Trends by Type of Substance
Change in Illicit Drug Use by 8tth, 10th, and 12th Graders Since 2001
Percent Reporting Past Month Use
2001
2007
Change as % of 2001
Any Illicit Drug
19.4
14.8
-24
Marijuana
16.6
12.4
-25
MDMA (Ecstasy)
2.4
1.1
-54
LSD
1.5
0.6
-60
Amphetamines
4.7
3.2
-32
Inhalants
2.8
2.6
-7
Methamphetamine
1.4
0.5
-64
Steroids
0.9
0.6
-33
Cocaine
1.5
1.4
-7
Heroin
0.4
0.4
0
Alcohol
35.5
30.1
-15
Cigarettes
20.2
13.6
-33
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Admissions by Location - Age 12 and Older
As recent findings from the National Survey on Drug Use and Health (NSDUH) show,
substance abuse varies across States. Admissions to substance abuse treatment
also demonstrate geographic differences, and admissions for various substances of
abuse show specific geographic concentrations and patterns. These patterns also
change over time.
Admissions to substance abuse treatment by State can be monitored with the
Treatment Episode Data Set (TEDS), an annual compilation of data on the
demographic characteristics and substance abuse problems of those admitted to
substance abuse treatment, primarily at facilities that receive some public funding.
TEDS records represent admissions rather than individuals, as a person may be
admitted to treatment more than once during a single year.
Among the six primary substances of abuse that dominate TEDS admissions, the
rates of substance abuse treatment admissions in the Nation as a whole increased
for three (marijuana, methamphetamine/amphetamine, and opiates other than
heroin) and decreased for three (alcohol, cocaine, and heroin). This report focuses
on trends in admission rates for methamphetamine/ amphetamine and marijuana,
which have the largest number of admissions among the substances with increased
admission rates and, therefore, have the greatest impact on the treatment system.
DrugEpi 2-5 Time – Boundary Effect
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Admissions - Comparison Between 1995 and 2005
Methamphetamine / Amphetamine
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Admissions - Comparison Between 1995 and 2005
Methamphetamine / Amphetamine
Source: 2005 SAHSA Treatment Episode Data Set (TEDS).
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Admissions - Comparison Between 1995 and 2005
Marijuana
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Admissions - Comparison Between 1995 and 2005
Marijuana
Source: 2005 SAHSA Treatment Episode Data Set (TEDS).
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Descriptive Epidemiology - Time
“Time” Can Mean “Week in the Month”
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Actual Study of “Week of the Month”
“… the Number of Deaths in the United States
… (by) Week of the Month”
Does week of the month make a difference?
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Study Method
Number of Deaths
in the United States by Week of the Month
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How Results are Presented
Number of Deaths
in the United States by Week of the Month
Hidden
Data
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How Results are Presented
Number of Deaths
in the United States by Week of the Month
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Results
“Over the course of the average year, there were 4,320 more deaths
in the first week of every month than in the last week of the preceding month.”
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Boundary Effect
“Over the course of the average year, there were 4,320 more deaths
in the first week of every month than in the last week of the preceding month.”
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Hypotheses Generation
“Over the course of the average year, there were 4,320 more deaths
in the first week of every month than in the last week of the preceding month.”
What
hypotheses
might explain
this
distribution?
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Hypotheses
• New Medical Personnel
• “Hanging On”
• Federal Benefits
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New Medical Personnel?
• New Medical Personnel
• “Hanging On”
• Federal Benefits
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New
NewMedical
MedicalPersonnel?
Personnel
What
hypotheses
might explain
this
distribution?
“If so, the boundary effect would be smaller for people
who were dead on arrival at the medical facility
than for those who died while hospitalized.”
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New
NewMedical
MedicalPersonnel?
Personnel
“In fact, … the boundary effect was larger
for those who were dead on arrival
than for those who died while hospitalized.”
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Hanging On?
• New Medical Personnel
• “Hanging On”
• Federal Benefits
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“Hanging
Hanging On?
On”
What
hypotheses
might explain
this
distribution?
“… some persons who might otherwise have died at the end of the month
‘held on’ until the beginning of the next month
so that their families would receive one last Social security check.”
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Hanging On?
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Hanging On?
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Hanging On?
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Federal Benefits?
• New Medical Personnel
• “Hanging On”
• Federal Benefits
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Federal
FederalBenefits?
Benefits
What
hypotheses
might explain
this
distribution?
What causes of death would be
related to receiving money (federal benefits)?
DrugEpi 2-5 Time – Boundary Effect
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Federal
FederalBenefits?
Benefits
What
hypotheses
might explain
this
distribution?
What causes of death would be
related to receiving money (federal benefits)?
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A List of Causes of Death
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
Complications of pregnancy/childbirth
Congenital anomalies
Disorders of blood or blood-forming organs
Disorders of musculoskeletal system or connective tissue
Disorders of nervous system
Genitourinary disorders
Infectious and parasitic diseases
Mental disorders, excluding substance abuse
Motor vehicle accidents
Liver disease with mention of alcohol
Liver disease without mention of alcohol
Neoplasms (tumors - cancer and non-cancer)
Respiratory disorders
Circulatory disorders
Substance abuse
Suicide
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Calculating the Boundary Effect
Boundary Effect
# of Deaths in 1st Week
X 100
# of Deaths in Last Week
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Significant Boundary Effect?
Hidden Causes of Death
What causes of death would be
related to receiving money (federal benefits)?
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Significant Boundary Effect
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No Significant Boundary Effect?
Hidden Causes of Death
What causes of death would not be
related to receiving money (federal benefits)?
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No Significant Boundary Effect
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Study Abstract
AN INCREASE IN THE NUMBER OF DEATHS IN THE UNITED STATES
IN THE FIRST WEEK OF THE MONTH
An Association with Substance Abuse and Other Causes of Death
David P. Phillips, PhD, Nicholas Christenfeld, PhD, Natalie M. Ryan, B.A.
(New England Journal of Medicine 1999;341_93-8)
ABSTRACT
Background and Methods . . . Previous research has shown that among persons with
schizophrenia, the rates of cocaine use and hospital admissions increase at the beginning of the
month, after receipt of disability payments. . . Using computerized data from all death certificates
in the US between 1973 and 1988, we compared the number of deaths in the first week of the
month with the number of deaths in the last week of the preceding month .
Results: . . . Between 1983 and 1988, for deaths involving substance abuse and an external
cause (such as suicides, accidents and homicides, there were 114.2 deaths . . in the first week of
the month for every 100 in the last week of the preceding month . . .
Conclusions . . . In the United States, the number of deaths is higher in the first week of the
month than in the last week of the preceding month. The increase at the beginning of the
month is associated with substance abuse and other causes of death.
DrugEpi 2-5 Time – Boundary Effect
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Re-Cap
Big Ideas in this Lesson (2-5)
•
•
•
“Time” information can generate hypotheses
Cyclical time trends in drug use over the past 30 years
suggest hypotheses about time-related fluctuations in
attitudes about drug use, extent of active prevention
programs, and types of illicit substances that are available.
Some causes of death are more common in the first week
of the month; this suggests hypotheses about relationships
between death and availability of money to purchase illicit
substances.
This project is supported by a Science Education Drug Abuse Partnership Award, Grant Number 1R24DA016357-01,
from the National Institute on Drug Abuse, National Institutes of Health.
DrugEpi 2-5 Time – Boundary Effect
43
Next Lesson
Hypothesis
about
associations
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