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DrugEpi 2-5 Time – Boundary Effect 1
Module 2 IntroductionContextContent Area: Hypothesis GenerationEssential Question (Generic): What hypotheses might explain the distribution of health-related 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: What’s My Hypothesis? AIDS, etcLesson 2-2: In the NewsLesson 2-3: Drug Abuse by “Person” Race / Ethnicity Lesson 2-4: Drug Abuse by “Place” States in USA Lesson 2-5: Drug Abuse by “Time” Boundary Effect
DrugEpi 2-5 Time – Boundary Effect 2
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 3
What hypotheses might explain the distribution of disease?
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.
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.
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.
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.
5.
6.
2.
3.
4.
Clues for formulating hypotheses can be found by observing the way a health-related condition or behavior is distributed in a population.
Where are we?Essential Questions Enduring Understandings
How is this disease distributed?
1. 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.
DrugEpi 2-5 Time – Boundary Effect 4
Epidemiological Factors
Descriptive Epidemiology
Residence
Events
Anatomical Site
Geographic Site
Year
Season
Day, etc.
Onset
Time (When?)
Sex
Occupation
Age
SES
Person (who?) Place (where?)
DrugEpi 2-5 Time – Boundary Effect 5
Epidemiological Factors
Person Place Time
Sex
Occupation
Age
SES
Residence
Events
Anatomical Site
Geographic Site
Year
Season
Day, etc.
Onset
Descriptive Epidemiology - Time
DrugEpi 2-5 Time – Boundary Effect 6
“Time” Can Mean “Years”
Descriptive Epidemiology - Time
DrugEpi 2-5 Time – Boundary Effect 7
Any Illicit Drug: Trends in
Annual Prevalence
by Gender
DrugEpi 2-5 Time – Boundary Effect 8
• Perceived Risk• Disapproval• Public Attention• News Coverage / Advertisements• Drug-free campaigns and programs• Emergence of new, “attractive” substances• “Generational Forgetting”
Hypotheses about Time Trends?
DrugEpi 2-5 Time – Boundary Effect 9
Marijuana: Both
Genders, 8th, 10th, and 12th Grade
DrugEpi 2-5 Time – Boundary Effect 10
Time Trends by Type of Substance
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
Change in Illicit Drug Use by 8tth, 10th, and 12th Graders Since 2001
Percent Reporting Past Month Use
DrugEpi 2-5 Time – Boundary Effect 11
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.
Admissions by Location - Age 12 and Older
DrugEpi 2-5 Time – Boundary Effect 12
Admissions - Comparison Between 1995 and 2005
Methamphetamine / Amphetamine
DrugEpi 2-5 Time – Boundary Effect 13Source: 2005 SAHSA Treatment Episode Data Set (TEDS).
Admissions - Comparison Between 1995 and 2005
Methamphetamine / Amphetamine
DrugEpi 2-5 Time – Boundary Effect 14
Admissions - Comparison Between 1995 and 2005
Marijuana
DrugEpi 2-5 Time – Boundary Effect 15Source: 2005 SAHSA Treatment Episode Data Set (TEDS).
Marijuana
Admissions - Comparison Between 1995 and 2005
DrugEpi 2-5 Time – Boundary Effect 16
“Time” Can Mean “Week in the Month”
Descriptive Epidemiology - Time
DrugEpi 2-5 Time – Boundary Effect 17
Actual Study of “Week of the Month”
Does week of the month make a difference?
“… the Number of Deaths in the United States … (by) Week of the Month”
DrugEpi 2-5 Time – Boundary Effect 18
Number of Deaths in the United States by Week of the Month
Study Method
DrugEpi 2-5 Time – Boundary Effect 19
Hidden Data
Number of Deaths in the United States by Week of the Month
How Results are Presented
DrugEpi 2-5 Time – Boundary Effect 20
Number of Deaths in the United States by Week of the Month
How Results are Presented
DrugEpi 2-5 Time – Boundary Effect 21
“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.”
Results
DrugEpi 2-5 Time – Boundary Effect 22
“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.”
Boundary
Effect
Boundary Effect
DrugEpi 2-5 Time – Boundary Effect 23
“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?
Boundary
Effect
Hypotheses Generation
DrugEpi 2-5 Time – Boundary Effect 24
• New Medical Personnel
• “Hanging On”
• Federal Benefits
Hypotheses
DrugEpi 2-5 Time – Boundary Effect 25
• New Medical Personnel
• “Hanging On”
• Federal Benefits
New Medical Personnel?
DrugEpi 2-5 Time – Boundary Effect 26
New Medical Personnel
Why?
“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.”
What hypotheses
might explain this
distribution?
New Medical Personnel?
DrugEpi 2-5 Time – Boundary Effect 27
New Medical Personnel
“In fact, … the boundary effect was larger for those who were dead on arrival
than for those who died while hospitalized.”
New Medical Personnel?
DrugEpi 2-5 Time – Boundary Effect 28
• New Medical Personnel
• “Hanging On”
• Federal Benefits
Hanging On?
DrugEpi 2-5 Time – Boundary Effect 29
“Hanging On”
Why?
“… 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.”
What hypotheses
might explain this
distribution?
Hanging On?
DrugEpi 2-5 Time – Boundary Effect 30
Hanging On?
DrugEpi 2-5 Time – Boundary Effect 31
Hanging On?
DrugEpi 2-5 Time – Boundary Effect 32
Hanging On?
DrugEpi 2-5 Time – Boundary Effect 33
• New Medical Personnel
• “Hanging On”
• Federal Benefits
Federal Benefits?
DrugEpi 2-5 Time – Boundary Effect 34
Federal Benefits
Why?
What causes of death would be related to receiving money (federal benefits)?
What hypotheses
might explain this
distribution?
Federal Benefits?
DrugEpi 2-5 Time – Boundary Effect 35
Federal Benefits
Why?
What causes of death would be related to receiving money (federal benefits)?
What hypotheses
might explain this
distribution?
Federal Benefits?
DrugEpi 2-5 Time – Boundary Effect 36
• 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
A List of Causes of Death
DrugEpi 2-5 Time – Boundary Effect 37
# of Deaths in 1st Week
Boundary Effect
# of Deaths in Last Week
X 100
Calculating the Boundary Effect
DrugEpi 2-5 Time – Boundary Effect 38
Hidden Causes of Death
What causes of death would be related to receiving money (federal benefits)?
Significant Boundary Effect?
DrugEpi 2-5 Time – Boundary Effect 39
Significant Boundary Effect
DrugEpi 2-5 Time – Boundary Effect 40
Hidden Causes of Death
What causes of death would not be related to receiving money (federal benefits)?
No Significant Boundary Effect?
DrugEpi 2-5 Time – Boundary Effect 41
No Significant Boundary Effect
DrugEpi 2-5 Time – Boundary Effect 42
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 43
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.
Re-Cap
DrugEpi 2-5 Time – Boundary Effect 44
Next Lesson
Analytical Epidemiology
Tests hypotheses
Hypothesis about
associations
Descriptive Epidemiology
Generates hypotheses