drugepi 2-5 time – boundary effect 1 module 2 introduction context content area: hypothesis...

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


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.


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.


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?)


Epidemiological Factors

Person Place Time

Sex

Occupation

Age

SES

Residence

Events

Anatomical Site

Geographic Site

Year

Season

Day, etc.

Onset

Descriptive Epidemiology - Time


“Time” Can Mean “Years”



Any Illicit Drug: Trends in

Annual Prevalence

by Gender


• Perceived Risk• Disapproval• Public Attention• News Coverage / Advertisements• Drug-free campaigns and programs• Emergence of new, “attractive” substances• “Generational Forgetting”

Hypotheses about Time Trends?


Marijuana: Both

Genders, 8th, 10th, and 12th Grade


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


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


Admissions - Comparison Between 1995 and 2005

Methamphetamine / Amphetamine

DrugEpi 2-5 Time – Boundary Effect 13Source: 2005 SAHSA Treatment Episode Data Set (TEDS).


Methamphetamine / Amphetamine



Marijuana

DrugEpi 2-5 Time – Boundary Effect 15Source: 2005 SAHSA Treatment Episode Data Set (TEDS).

Marijuana



“Time” Can Mean “Week in the Month”



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”


Number of Deaths in the United States by Week of the Month

Study Method


Hidden Data


How Results are Presented



How Results are Presented


“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



Boundary

Effect

Boundary Effect



What hypotheses

might explain this

distribution?

Boundary

Effect

Hypotheses Generation


• New Medical Personnel

• “Hanging On”

• Federal Benefits

Hypotheses





New Medical Personnel?


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

“In fact, … the boundary effect was larger for those who were dead on arrival

than for those who died while hospitalized.”






Hanging On?


“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?


Hanging On?





Federal Benefits?


Federal Benefits

Why?

What causes of death would be related to receiving money (federal benefits)?

What hypotheses

might explain this

distribution?

Federal Benefits?


Federal Benefits

Why?


What hypotheses

might explain this

distribution?

Federal Benefits?


• 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


# of Deaths in 1st Week

Boundary Effect

# of Deaths in Last Week

X 100

Calculating the Boundary Effect


Hidden Causes of Death


Significant Boundary Effect?


Significant Boundary Effect


Hidden Causes of Death

What causes of death would not be related to receiving money (federal benefits)?

No Significant Boundary Effect?


No Significant Boundary Effect


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.


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


Next Lesson

Analytical Epidemiology

Tests hypotheses

Hypothesis about

associations

Descriptive Epidemiology

Generates hypotheses

drugepi 2-5 time – boundary effect 1 module 2 introduction context content area: hypothesis...

Documents

drug use

causal hypotheses

time ppt

formulating hypotheses

timerelated fluctuations

time variable

distribution of disease

usa discussion of hypotheses