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DrugEpi 2-5 Time – Boundary Effect 1 Module 2 Introduction Context Content Area: Hypothesis Generation Essential Question (Generic): What hypotheses might.

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Presentation on theme: "DrugEpi 2-5 Time – Boundary Effect 1 Module 2 Introduction Context Content Area: Hypothesis Generation Essential Question (Generic): What hypotheses might."— Presentation transcript:

1 DrugEpi 2-5 Time – Boundary Effect 1 Module 2 Introduction Context Content Area: Hypothesis Generation Essential 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, etc Lesson 2-2: In the News Lesson 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

2 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.

3 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 QuestionsEnduring 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.

4 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?)

5 DrugEpi 2-5 Time – Boundary Effect 5 Epidemiological Factors PersonPlaceTime Sex Occupation Age SES Residence Events Anatomical Site Geographic Site Year Season Day, etc. Onset Descriptive Epidemiology - Time

6 DrugEpi 2-5 Time – Boundary Effect 6 “Time” Can Mean “Years” Descriptive Epidemiology - Time

7 DrugEpi 2-5 Time – Boundary Effect 7 Any Illicit Drug: Trends in Annual Prevalence by Gender

8 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?

9 DrugEpi 2-5 Time – Boundary Effect 9 Marijuana: Both Genders, 8 th, 10 th, and 12 th Grade

10 DrugEpi 2-5 Time – Boundary Effect 10 Time Trends by Type of Substance 20012007Change as % of 2001 Any Illicit Drug19.414.8-24 Marijuana16.612.4-25 MDMA (Ecstasy)2.41.1-54 LSD1.50.6-60 Amphetamines4.73.2-32 Inhalants2.82.6-7 Methamphetamine1.40.5-64 Steroids0.90.6-33 Cocaine1.51.4-7 Heroin0.4 0 Alcohol35.530.1-15 Cigarettes20.213.6-33 Change in Illicit Drug Use by 8 tth, 10 th, and 12 th Graders Since 2001 Percent Reporting Past Month Use

11 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

12 DrugEpi 2-5 Time – Boundary Effect 12 Admissions - Comparison Between 1995 and 2005 Methamphetamine / Amphetamine

13 DrugEpi 2-5 Time – Boundary Effect 13 Source: 2005 SAHSA Treatment Episode Data Set (TEDS). Admissions - Comparison Between 1995 and 2005 Methamphetamine / Amphetamine

14 DrugEpi 2-5 Time – Boundary Effect 14 Admissions - Comparison Between 1995 and 2005 Marijuana

15 DrugEpi 2-5 Time – Boundary Effect 15 Source: 2005 SAHSA Treatment Episode Data Set (TEDS). Marijuana Admissions - Comparison Between 1995 and 2005

16 DrugEpi 2-5 Time – Boundary Effect 16 “Time” Can Mean “Week in the Month” Descriptive Epidemiology - Time

17 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”

18 DrugEpi 2-5 Time – Boundary Effect 18 Number of Deaths in the United States by Week of the Month Study Method

19 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

20 DrugEpi 2-5 Time – Boundary Effect 20 Number of Deaths in the United States by Week of the Month How Results are Presented

21 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

22 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

23 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

24 DrugEpi 2-5 Time – Boundary Effect 24 New Medical Personnel “Hanging On” Federal Benefits Hypotheses

25 DrugEpi 2-5 Time – Boundary Effect 25 New Medical Personnel “Hanging On” Federal Benefits New Medical Personnel?

26 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?

27 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?

28 DrugEpi 2-5 Time – Boundary Effect 28 New Medical Personnel “Hanging On” Federal Benefits Hanging On?

29 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?

30 DrugEpi 2-5 Time – Boundary Effect 30 Hanging On?

31 DrugEpi 2-5 Time – Boundary Effect 31 Hanging On?

32 DrugEpi 2-5 Time – Boundary Effect 32 Hanging On?

33 DrugEpi 2-5 Time – Boundary Effect 33 New Medical Personnel “Hanging On” Federal Benefits Federal Benefits?

34 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?

35 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?

36 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

37 DrugEpi 2-5 Time – Boundary Effect 37 # of Deaths in 1 st Week Boundary Effect # of Deaths in Last Week X 100 Calculating the Boundary Effect

38 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?

39 DrugEpi 2-5 Time – Boundary Effect 39 Significant Boundary Effect

40 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?

41 DrugEpi 2-5 Time – Boundary Effect 41 No Significant Boundary Effect

42 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.

43 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

44 DrugEpi 2-5 Time – Boundary Effect 44 Next Lesson Analytical Epidemiology Tests hypotheses Hypothesis about associations Descriptive Epidemiology Generates hypotheses

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