2. 2
Mark A. Kaelin, Ed.D.
kaelinm@mail.montclair.edu
Science of Public Health: Epidemiology
Orientation and Expectations
January 17, 2017
3. 3
1. What is epidemiology?
2. Enduring Epidemiological Understandings
3. Syllabus
4. Assigned Readings
Science of Public Health: Epidemiology
Orientation and Expectations
January 17, 2018
5. 5
A particular or detached incident or fact
of an interesting nature; a biographical incident or fragment;
a single passage of private life.
Anecdote
What is epidemiology?
11. 11
Detectives
Investigate crimes
Look for clues at a
crime scene
Judge quality of
evidence
Form hypotheses
Identify suspects
Present evidence
in court
Help control crime
Investigate health-related events
Look for clues in the
populations
Judge quality of
evidence
Form hypotheses
Identify suspected causes
Present evidence in scientific
journals and at scientific meetings
Help control disease
Epidemiologists
What is epidemiology?
12. 12
The term epidemiology is derived from the Greek:
epi : on, upon
demos : the people
logos : theory, source, the study of
(Webster's Unabridged Dictionary)
What is epidemiology?
14. 14
... the study of the distribution and determinants of
health-related states or events in specified
populations and the application of this study to the
control of health problems.
(A Dictionary of Epidemiology)
What is epidemiology?
15. 15
... the study of the distribution and determinants of
health-related states or events in specified
populations and the application of this study to the
control of health problems.
(A Dictionary of Epidemiology)
What is epidemiology?
16. 16
... the study of the distribution and determinants of
health-related states or events in specified
populations and the application of this study to the
control of health problems.
(A Dictionary of Epidemiology)
What is epidemiology?
17. 17
... the study of the distribution and determinants of
health-related states or events in specified
populations and the application of this study to the
control of health problems.
(A Dictionary of Epidemiology)
What is epidemiology?
18. 18
... the study of the distribution and determinants of
health-related states or events in specified
populations and the application of this study to the
control of health problems.
(A Dictionary of Epidemiology)
What is epidemiology?
19. 19
... the study of the distribution and determinants of
health-related states or events in specified
populations and the application of this study to the
control of health problems.
(A Dictionary of Epidemiology)
What is epidemiology?
20. 20
1. What is epidemiology?
2. Enduring Epidemiological Understandings
3. Syllabus
4. Assigned Readings
Science of Public Health: Epidemiology
Orientation and Expectations
January 17, 2018
21. This course was developed by following curriculum design principles advocated
by Grant Wiggins and Jay McTighe in their text Understanding by Design. Their
contention is that effective curricula are created by identifying enduring
understandings and essential questions.
Enduring understandings are the big ideas that reside at the heart of a
discipline and have lasting value outside the classroom.
The essential questions are the questions that, when answered, create the
enduring understanding in the first place.
The teacher’s challenge is to create experiences that develop students' abilities
to answer the essential questions and, in doing so, develop the their own
enduring understandings.
Essential Questions and Enduring Epidemiological Understandings
22. 22
How often does the health-related outcome occur?
How is the the health-related outcome distributed?
What hypotheses might explain the distribution of the health-related
outcome?
Health-related outcomes are not distributed haphazardly in a
population. There are patterns to their occurrence. These patterns can
be identified through the surveillance of populations. Examining these
patterns can help formulate hypotheses about the possible causes of
these outcomes.
1.
Essential Questions and Enduring Epidemiological Understandings
23. 23
How can hypotheses be tested?
Is there an association between the hypothesized cause and the
health-related outcome?
A hypothesis can be tested by comparing the frequency of health-
related outcome in selected groups of people with and without an
exposure to determine if the exposure and the outcome are associated.
When an exposure is hypothesized to have a beneficial effect, studies
can be designed in which a group of people is intentionally exposed to
the hypothesized cause and compared to a group that is not exposed.
When an exposure is hypothesized to have a detrimental effect, it is not
ethical to intentionally expose a group of people. In these
circumstances, studies can be designed that observe groups of free-
living people with and without the exposure.
2.
Essential Questions and Enduring Epidemiological Understandings
24. 24
Why did the association occur?
One possible explanation for finding an association is that the exposure
causes the outcome. Because studies are complicated by factors not
controlled by the investigator, other explanations also must be
considered.
3.
Essential Questions and Enduring Epidemiological Understandings
25. 25
Is the association causal?
Judgments about whether an exposure causes a health-related
outcome are developed by examining a body of epidemiologic evidence
as well as evidence from other scientific disciplines.
4.
Essential Questions and Enduring Epidemiological Understandings
26. 26
What should be done
when a cause of a health-related outcome is found?
Individual and societal decisions about what should be done to improve
health and prevent disease are based on more than scientific evidence.
Social, economic, ethical, environmental, cultural, and / or political
factors are also considered in decision-making.
5.
Essential Questions and Enduring Epidemiological Understandings
27. 27
Did what was done work?
The effectiveness of a health-promoting strategy can be evaluated by
comparing the frequency of a health-related outcome in selected
groups of people who were and were not exposed to the strategy.
Costs, trade-offs, and alternative solutions must also be considered in
evaluating the strategy.
6.
Essential Questions and Enduring Epidemiological Understandings
28. 28
Can epidemiological thinking be helpful
in exploring a non-health-related outcome?
An understanding of non-health related phenomena can be developed
through epidemiologic thinking, by identifying their patterns in
populations, formulating causal hypotheses, and testing those
hypotheses by making group comparisons.
7.
Essential Questions and Enduring Epidemiological Understandings
29. 29
How can epidemiology contribute to exploring public health issues?
The causes of health are discoverable by systematically and rigorously
identifying their patterns in populations, formulating causal hypotheses,
and testing those hypotheses by making group comparisons. These
methods lie at the core of the science of epidemiology. Epidemiology is
the basic science of public health, a discipline responsible for improving
health and preventing disease in populations.
8.
Essential Questions and Enduring Epidemiological Understandings
30. 30
“Learning with understanding is facilitated
when new and existing knowledge is structured around
the major concepts and principles of a discipline.”
National Research Council, (2002), Learning and Understanding.
Washington, D.C.: National Academy Press.
Enduring Epidemiological Understandings
31. 31
Learners “… presented with vast amounts of content knowledge
that is not organized into meaningful patterns
are likely to forget what they have learned
and to be unable to apply the knowledge
to new problems or unfamiliar contexts.”
National Research Council , Learning and Understanding
Enduring Epidemiological Understandings
32. 32
Ken Bain, What the Best College Teachers Do
“… they can distinguish between foundational concepts
and elaborations or illustrations of those ideas.”
Enduring Epidemiological Understandings
33. 33
“… to see past the surface
features of any problem to
the deeper, more
fundamental principles of
the discipline.”
National Research Council
Learning and Understanding
Enduring Epidemiological Understandings
34. 34
To understand something
as a specific instance
of a more general case
… is to have learned
not only a specific thing
but also a model for
understanding other things like
it that one may encounter.”
Jerome Bruner,
The Process of Education, 1960
Enduring Epidemiological Understandings
35. To understand something as a specific instance of a more general case
… is to have learned not only a specific thing but also a model
for understanding other things like it that one may encounter.
Jerome Bruner, The Process of Education, 1960
Enduring Epidemiological Understandings
Students Shown Dangers of Texting while Driving
36. To understand something as a specific instance of a more general case
… is to have learned not only a specific thing but also a model
for understanding other things like it that one may encounter.
Jerome Bruner, The Process of Education, 1960
Enduring Epidemiological Understandings
What Science Says about Marijuana
37. To understand something as a specific instance of a more general case
… is to have learned not only a specific thing but also a model
for understanding other things like it that one may encounter.
Jerome Bruner, The Process of Education, 1960
Enduring Epidemiological Understandings
38. To understand something as a specific instance of a more general case
… is to have learned not only a specific thing but also a model
for understanding other things like it that one may encounter.
Jerome Bruner, The Process of Education, 1960
Enduring Epidemiological Understandings
40. 40
Grant Wiggins and Jay McTighe, authors of Understanding by Design,
call the major concepts and principles of a discipline enduring understandings.
• Enduring, big ideas, having lasting value outside the classroom
• Big ideas and core processes at the heart of the discipline
• Abstract, counterintuitive, and often misunderstood ideas
• Big ideas embedded in facts, skills, and activities
Enduring Understandings
42. 42
1. What is epidemiology?
2. Enduring Epidemiological Understandings
3. Syllabus
4. Assigned Readings
Science of Public Health: Epidemiology
Orientation and Expectations
January 17, 2018
47. 47
• Definition or Description / Relationship
• Selected PowerPoint Slides
• Form a Hypothesis
• Assigned Readings, Videos and Podcasts
Exam 1 Preparation
48. Explain the relationship between the article, XXXXXXXXXX, and the content
of this course to date.
• Adult Use of Prescription Opioid Pain Medications — Utah, 2008
(MMWR)
• Colombia’s Data-Driven Fight Against Crime (NYT)
• Community Outbreak of HIV Infection Linked to Injection Drug Use
of Oxymorphone — Indiana, 2015 (MMWR)
• Gun Violence Archive - General Methodology
(http://www.gunviolencearchive.org/methodology)
• Measles Outbreak — California, December 2014–February 2015
(MMWR)
• Unintentional Strangulation Deaths from the "Choking Game"
Among Youths Aged 6--19 Years - United States, 1995-2007
(MMWR)
49. 49
• Definition or Description / Relationship
• Selected PowerPoint Slides
• Form a Hypothesis
• Assigned Readings, Videos and Podcasts
• New Article
• Calculations
Exam 1 Preparation
50. 50
How often does the health-related outcome occur?
How is the the health-related outcome distributed?
What hypotheses might explain the distribution of the health-related
outcome?
Health-related outcomes are not distributed haphazardly in a
population. There are patterns to their occurrence. These patterns can
be identified through the surveillance of populations. Examining these
patterns can help formulate hypotheses about the possible causes of
these outcomes.
1.
Essential Questions and Enduring Epidemiological Understandings
51. 51
How often does the health-related outcome occur?
How is the the health-related outcome distributed?
What hypotheses might explain the distribution of the health-related
outcome?
Health-related outcomes are not distributed haphazardly in a
population. There are patterns to their occurrence. These patterns
can be identified through the surveillance of populations. Examining
these patterns can help formulate hypotheses about the possible
causes of these outcomes.
1.
Essential Questions and Enduring Epidemiological Understandings
52. 1. a form or model proposed for imitation
2. something designed or used as a model for making things
(a dressmaker's pattern)
3. an artistic, musical, literary, or mechanical design or form
4. a natural or chance configuration (frost patterns, the
pattern of events)
5. a length of fabric sufficient for an article (as of clothing)
6. the distribution of shrapnel, bombs on a target, or shot from
a shotgun / the grouping made on a target by bullets
7. a reliable sample of traits, acts, tendencies, or other
observable characteristics of a person, group, or institution
(a behavior pattern, spending patterns)
Pattern
Merriam-Webster Online
Patterns of Health and Disease
53. 1. a form or model proposed for imitation
2. something designed or used as a model for making things
(a dressmaker's pattern)
3. an artistic, musical, literary, or mechanical design or form
4. a natural or chance configuration (frost patterns, the
pattern of events)
5. a length of fabric sufficient for an article (as of clothing)
6. the distribution of shrapnel, bombs on a target, or shot from
a shotgun / the grouping made on a target by bullets
7. a reliable sample of traits, acts, tendencies, or other
observable characteristics of a person, group, or institution
(a behavior pattern, spending patterns)
Merriam-Webster Online
Patterns of Health and Disease
Pattern
54. Identify:
1. Primary health-related outcome the article is about
2. Statements that describe the pattern of the particular health-
related event
3. Statements that attempt to explain the reason for the pattern
4. Statements that describe the methodology that generated the
data from which the pattern was identified
Patterns of Health-Related Outcomes
56. 56
1. Logistics
2. Review
3. Identifying Patterns and Formulating Hypotheses
Science of Public Health: Epidemiology
Surveillance, Patterns and Hypotheses
January 22, 2018
57. 57
1. Autism Rates Have Stabilized in US Children
2. The Census and Right-Wing Hysteria
3. Refusing Vaccinations
4. Why Are White Death Rates Rising
5. One Simple Way to Reduce Some Suicides by 90%
6. Fueled by Drug Crisis, US Life Expectancy Declines for Second Straight Year
7. Pregnancy Boom at Gloucester High
8. CDC Reports a Record Jump in Overdose Deaths
9. Rise in US Traffic Deaths Reported for a Second Year
10.Deadliest Counties in the US
11.What Explains US Mass Shootings?
12.Teenage Suicides Bewilder an Island and the Experts
Attendance Sheet
58. 58
1. Logistics
2. Review
3. Identifying Patterns and Formulating Hypotheses
Science of Public Health: Epidemiology
Surveillance, Patterns and Hypotheses
January 22, 2018
59. 59
How often does the health-related outcome occur?
How is the the health-related outcome distributed?
What hypotheses might explain the distribution of the health-related
outcome?
Health-related outcomes are not distributed haphazardly in a
population. There are patterns to their occurrence. These patterns can
be identified through the surveillance of populations. Examining these
patterns can help formulate hypotheses about the possible causes of
these outcomes.
1.
Essential Questions and Enduring Epidemiological Understandings
60. 1. a form or model proposed for imitation
2. something designed or used as a model for making things
(a dressmaker's pattern)
3. an artistic, musical, literary, or mechanical design or form
4. a natural or chance configuration (frost patterns, the
pattern of events)
5. a length of fabric sufficient for an article (as of clothing)
6. the distribution of shrapnel, bombs on a target, or shot from
a shotgun / the grouping made on a target by bullets
7. a reliable sample of traits, acts, tendencies, or other
observable characteristics of a person, group, or institution
(a behavior pattern, spending patterns)
Merriam-Webster Online
Patterns of Health and Disease
Pattern
61. 61
The aim of the course is to create a more scientifically literate person,
someone who:
… can ask, find, or determine answers to questions derived from
curiosity about everyday experiences. … has the ability to describe,
explain, and predict natural phenomenon. … is able to read with
understanding articles about science in the popular press and to
engage in social conversation about the validity of their
conclusions. … can identify scientific issues underlying national and
local decisions and express positions that are scientifically and
technologically informed. … (is) able to evaluate the quality of
scientific information on the basis of its source and the methods used
to generate it. … (has) the capacity to pose and evaluate arguments
based on evidence and to apply conclusions from such arguments
appropriately.
National Research Council, National Science Education Standards. Washington, DC: National Academy Press, 1996.
Scientific Literacy
62. Identify:
1. Primary health-related outcome the article is about
2. Statements that describe the pattern of the particular health-
related event
3. Statements that attempt to explain the reason for the pattern
4. Statements that describe the methodology that generated the
data from which the pattern was identified
Identifying Patterns and Formulating Hypotheses
63. The criteria used to establish a specific diagnosis
Identifying Patterns and Formulating Hypotheses
Case Definition
64. Identify:
1. Primary health-related outcome the article is about
2. Statements that describe the pattern of the particular
health-related event
3. Statements that attempt to explain the reason for the pattern
4. Statements that describe the methodology that generated the
data from which the pattern was identified
Identifying Patterns and Formulating Hypotheses
65. Characterizes the amount and distribution
of health and disease within a population
Identifying Patterns and Formulating Hypotheses
Descriptive Epidemiology
66. Identify:
1. Primary health-related outcome the article is about
2. Statements that describe the pattern of the particular health-
related event
3. Statements that attempt to explain the reason for the
pattern
4. Statements that describe the methodology that generated the
data from which the pattern was identified
Identifying Patterns and Formulating Hypotheses
67. A supposition,
arrived at from observation or reflection,
that leads to refutable predictions
Any conjecture
cast in a form that will allow it to be tested and refuted
Identifying Patterns and Formulating Hypotheses
Hypothesis
68. Identify:
1. Primary health-related outcome the article is about
2. Statements that describe the pattern of the particular health-
related event
3. Statements that attempt to explain the reason for the pattern
4. Statements that describe the methodology that
generated the data from which the pattern was identified
Identifying Patterns and Formulating Hypotheses
69. 69
The ongoing systematic
collection, analysis, and
interpretation of outcome-
specific data
for use in planning,
implementation, and
evaluation of public health
practice
closely integrated with the
timely dissemination of
these data to those who
need to know.
Identifying Patterns and Formulating Hypotheses
Public Health Surveillance
70. Identify:
1. Primary health-related outcome the article is about
2. Statements that describe the pattern of the particular health-
related event
3. Statements that attempt to explain the reason for the pattern
4. Statements that describe the methodology that generated the
data from which the pattern was identified
Identifying Patterns and Formulating Hypotheses
71. 1. Primary health-related outcome the article is about
Autism Rates Have Stabilized in US Children
The Census and Right-Wing Hysteria
Refusing Vaccinations
Why Are White Death Rates Rising?
One Simple Way to Reduce Some Suicides by 90%
Fueled by Drug Crisis, US Life Expectancy Declines for Second Straight Year
Pregnancy Boom at Gloucester High
CDC Reports a Record Jump in Overdose Deaths
Rise in US Traffic Deaths Reported for a Second Year
Deadliest Counties in the US
What Explains US Mass Shootings?
Teenage Suicides Bewilder an Island and the Experts
Identifying Patterns and Formulating Hypotheses
72. A set of diagnostic criteria that must be fulfilled
in order to identify a person as a case of a particular disease.
Identifying Patterns and Formulating Hypotheses
Case Definition
74. The Number of ‘Mass Shootings’ in the U.S.
Depends on How You Count
https://www.washingtonpost.com/graphics/business/wonkblog/mass-shooting-definition/
Identifying Patterns and Formulating Hypotheses
75. Identify:
1. Primary health-related outcome the article is about
2. Statements that describe the pattern of the particular
health-related event
3. Statements that attempt to explain the reason for the pattern
4. Statements that describe the methodology that generated the
data from which the pattern was identified
Identifying Patterns and Formulating Hypotheses
76. Characterizes the amount and distribution
of health and disease within a population
Identifying Patterns and Formulating Hypotheses
Descriptive Epidemiology
77. 2. Statements that describe
the pattern of the particular health-related event
Identifying Patterns and Formulating Hypotheses
81. 81
Descriptive Epidemiological Factors
Person Place Time
Sex
Occupation
Age
SES
Residence
Events
Anatomical Site
Geographic Site
Year
Season
Day, etc.
Onset
Identifying Patterns and Formulating Hypotheses
82. Characterizes the amount and distribution
of health and disease within a population
Identifying Patterns and Formulating Hypotheses
Descriptive Epidemiology
84. 84
1. Review
2. Identifying Patterns and Formulating Hypotheses
3. Reading and Simulation Assignments
Science of Public Health: Epidemiology
Surveillance, Patterns and Hypotheses
January 24, 2018
86. 86
1. Review
2. Identifying Patterns and Formulating Hypotheses
3. Reading and Simulation Assignments
Science of Public Health: Epidemiology
Surveillance, Patterns and Hypotheses
January 24, 2018
87. Characterizes the amount and distribution
of health and disease within a population
Identifying Patterns and Formulating Hypotheses
Descriptive Epidemiology
88. A supposition,
arrived at from observation or reflection,
that leads to refutable predictions
Identifying Patterns and Formulating Hypotheses
Hypothesis
89. 89
The ongoing systematic
collection, analysis, and
interpretation of outcome-
specific data
for use in planning,
implementation, and
evaluation of public health
practice
closely integrated with the
timely dissemination of
these data to those who
need to know.
Identifying Patterns and Formulating Hypotheses
Public Health Surveillance
93. 93
1. Review
2. Identifying Patterns and Formulating Hypotheses
3. Reading and Simulation Assignments
Science of Public Health: Epidemiology
Surveillance, Patterns and Hypotheses
January 24, 2018
94. 1.
Autism Rates Have Stabilized in US Children
Identifying Patterns and Formulating Hypotheses
Identify:
1. Primary health-related outcome the article is about
95. 1.
Autism Rates Have Stabilized in US Children
Identifying Patterns and Formulating Hypotheses
Identify:
2. Statements that attempt to explain the reason for the pattern
96. 1.
Autism Rates Have Stabilized in US Children
Identifying Patterns and Formulating Hypotheses
Identify:
3. Statements that attempt to explain the reason for the pattern
97. 1.
Autism Rates Have Stabilized in US Children
Identifying Patterns and Formulating Hypotheses
Identify:
4. Methodology that generated the data
99. The number of events,
that is, instances of a given disease or other condition,
in a given population at a designated time
Identifying Patterns and Formulating Hypotheses
Prevalence
100. The number of new events of a disease,
in a given population within a specified period of time
Identifying Patterns and Formulating Hypotheses
Incidence
104. • Increase in Occurrence of New Cases
• Immigration of Ill Cases
• Immigration of Susceptible Cases
• Emigration of Healthy Cases
• Prolongation of Life of Cases without Cure
Identifying Patterns and Formulating Hypotheses
Factors that Increase Prevalence
105. When you can measure what you are speaking about, and express
it in numbers, you know something about it.
But when you cannot measure it, when you cannot express it in
numbers, your knowledge is of a meager and unsatisfactory kind.
Lord Kelvin
Identifying Patterns and Formulating Hypotheses
106. Not everything that counts can be counted;
And not everything that can be counted counts.
Albert Einstein
Identifying Patterns and Formulating Hypotheses
107. 5. The incidence rate of a disease is five times greater in women than
in men, but the prevalence rates show no sex difference. The best
explanation is that:
a. The crude all-cause mortality rate is greater in women
b. The case-fatality rate for this disease is greater in women
c. The case-fatality rate for this disease is lower in women
d. The duration of this disease is shorter in men
e. Risk factors for the disease are more common in women
5 x
108. 1. At an initial examination in Oxford, Mass., migraine headache was
found in 5 of 1,000 men aged 30 to 35 years and in 10 of 1,000
women aged 30 to 35 years. The inference that women have a two
times greater risk of developing migraine headache than do men in
this age group is:
a. correct
b. incorrect, because a ratio has been used to compare male and
female rates
c. incorrect, because of failure to recognize the effect of age in the
two groups
d. incorrect, because no data for a comparison or control group
are given
e. incorrect, because of failure to distinguish between incidence
and prevalence
109. 4. The mortality rate from disease X in city A is 75/100,000 in persons
65 to 69 years old. The mortality rate from the same disease in city
B is 150/100,000 in persons 65 to 69 years old. The inference that
disease X is two times more prevalent in persons 65 to 69 years old
in city B than it is in persons 65 to 69 years old in city A is:
a. Correct
b. Incorrect, because of failure to distinguish between prevalence
and mortality
c. Incorrect, because of failure to adjust for differences in age
distributions
d. Incorrect, because of failure to distinguish between period and
point prevalence
e. Incorrect, because a proportion is used when a rate is required
to support the inference
A B
110. 10. For a disease such as pancreatic cancer, which is highly fatal and
of short duration:
a. Incidence rates and mortality rates will be similar
b. Mortality rates will be much higher than incidence rates
c. Incidence rates will be much higher than mortality rates
d. Incidence rates will be unrelated to mortality rates
e. None of the above
111. Identifying Patterns and Formulating Hypotheses
Identify:
1. Primary health-related outcome the article is about
2.
The Census and Right-Wing Hysteria
112. Identifying Patterns and Formulating Hypotheses
Identify:
2. Statements that attempt to explain the reason for the pattern
2.
The Census and Right-Wing Hysteria
113. Identifying Patterns and Formulating Hypotheses
Identify:
3. Statements that attempt to explain the reason for the pattern
2.
The Census and Right-Wing Hysteria
114. Identifying Patterns and Formulating Hypotheses
Identify:
4. Methodology that generated the data
2.
The Census and Right-Wing Hysteria
115. Identifying Patterns and Formulating Hypotheses
Identify:
1. Primary health-related outcome the article is about
3.
Refusing Vaccinations
116. Identifying Patterns and Formulating Hypotheses
Identify:
2. Statements that attempt to explain the reason for the pattern
3.
Refusing Vaccinations
117. Identifying Patterns and Formulating Hypotheses
Identify:
3. Statements that attempt to explain the reason for the pattern
3.
Refusing Vaccinations
118. Identifying Patterns and Formulating Hypotheses
Identify:
4. Methodology that generated the data
3.
Refusing Vaccinations
119. The resistance of a group
to invasion and spread of an infectious agent
based on the resistance to infection
of a high proportion of individual members of the group.
Identifying Patterns and Formulating Hypotheses
Herd Immunity
123. Doctors Group Urges Measles Shots as Disneyland Outbreak Spreads
http://ti.me/1BW2GU4
124.
125.
126.
127.
128. 128
... the study of the distribution and determinants of
health-related states or events in specified
populations and the application of this study to the
control of health problems.
(A Dictionary of Epidemiology)
What is epidemiology?
133. 4.
Why Are White Death Rates Rising?
Identifying Patterns and Formulating Hypotheses
Identify:
1. Primary health-related outcome the article is about
134. 4.
Why Are White Death Rates Rising?
Identifying Patterns and Formulating Hypotheses
Identify:
2. Statements that attempt to explain the reason for the pattern
135. 4.
Why Are White Death Rates Rising?
Identifying Patterns and Formulating Hypotheses
Identify:
3. Statements that attempt to explain the reason for the pattern
136. 4.
Why Are White Death Rates Rising?
Identifying Patterns and Formulating Hypotheses
Identify:
4. Methodology that generated the data
139. 139
Reference Group Theory
In the fourth quarter of 2015, the
median weekly earnings of white men
aged 25 to 54 were $950, well above
the same figure for black men ($703)
and Hispanic men ($701). But for
some whites — perhaps the ones who
account for the increasing death rate
— that may be beside the point. Their
main reference group is their parents’
generation, and by that standard they
have little to look forward to and a lot
to lament.
140. 5.
One Simple Way to Reduce Some Suicides by 90%
Identifying Patterns and Formulating Hypotheses
Identify:
1. Primary health-related outcome the article is about
141. 5.
One Simple Way to Reduce Some Suicides by 90%
Identifying Patterns and Formulating Hypotheses
Identify:
2. Statements that attempt to explain the reason for the pattern
142. 5.
One Simple Way to Reduce Some Suicides by 90%
Identifying Patterns and Formulating Hypotheses
Identify:
3. Statements that attempt to explain the reason for the pattern
143. 5.
One Simple Way to Reduce Some Suicides by 90%
Identifying Patterns and Formulating Hypotheses
Identify:
4. Methodology that generated the data
144. 144
... the study of the distribution and determinants of
health-related states or events in specified
populations and the application of this study to the
control of health problems.
(A Dictionary of Epidemiology)
What is epidemiology?
145. 145
Three Interventions to Deter Suicides at Hotspots
1. Putting up barriers and structural interventions around
the site
2. Encouraging suicidal people to seek help, by putting
up signs or phone lines for suicide crisis services
3. Putting cameras or trained staff in hotspots to
increase the likelihood that a third party would
intervene before a suicide happened
Identifying Patterns and Formulating Hypotheses
146. 6.
Fueled by Drug Crisis, US Life Expectancy Declines for Second Straight Year
Identifying Patterns and Formulating Hypotheses
Identify:
1. Primary health-related outcome the article is about
147. Identifying Patterns and Formulating Hypotheses
Identify:
2. Statements that attempt to explain the reason for the pattern
6.
Fueled by Drug Crisis, US Life Expectancy Declines for Second Straight Year
148. Identifying Patterns and Formulating Hypotheses
Identify:
3. Statements that attempt to explain the reason for the pattern
6.
Fueled by Drug Crisis, US Life Expectancy Declines for Second Straight Year
149. Identifying Patterns and Formulating Hypotheses
Identify:
4. Methodology that generated the data
6.
Fueled by Drug Crisis, US Life Expectancy Declines for Second Straight Year
150. 7.
Pregnancy Boom at Gloucester High
Identifying Patterns and Formulating Hypotheses
Identify:
1. Primary health-related outcome the article is about
151. 7.
Pregnancy Boom at Gloucester High
Identifying Patterns and Formulating Hypotheses
Identify:
2. Statements that attempt to explain the reason for the pattern
152. 7.
Pregnancy Boom at Gloucester High
Identifying Patterns and Formulating Hypotheses
Identify:
3. Statements that attempt to explain the reason for the pattern
153. 7.
Pregnancy Boom at Gloucester High
Identifying Patterns and Formulating Hypotheses
Identify:
4. Methodology that generated the data
154. 8.
CDC Reports a Record Jump in Overdose Deaths
Identifying Patterns and Formulating Hypotheses
Identify:
1. Primary health-related outcome the article is about
155. 8.
CDC Reports a Record Jump in Overdose Deaths
Identifying Patterns and Formulating Hypotheses
Identify:
2. Statements that attempt to explain the reason for the pattern
156. 8.
CDC Reports a Record Jump in Overdose Deaths
Identifying Patterns and Formulating Hypotheses
Identify:
3. Statements that attempt to explain the reason for the pattern
157. 8.
CDC Reports a Record Jump in Overdose Deaths
Identifying Patterns and Formulating Hypotheses
Identify:
4. Methodology that generated the data
158. 9.
Rise in US Traffic Deaths Reported for a Second Year
Identifying Patterns and Formulating Hypotheses
Identify:
1. Primary health-related outcome the article is about
159. 9.
Rise in US Traffic Deaths Reported for a Second Year
Identifying Patterns and Formulating Hypotheses
Identify:
2. Statements that attempt to explain the reason for the pattern
160. 9.
Rise in US Traffic Deaths Reported for a Second Year
Identifying Patterns and Formulating Hypotheses
Identify:
3. Statements that attempt to explain the reason for the pattern
161. 9.
Rise in US Traffic Deaths Reported for a Second Year
Identifying Patterns and Formulating Hypotheses
Identify:
4. Methodology that generated the data
162. 10.
Deadliest Counties in the US
Identifying Patterns and Formulating Hypotheses
Identify:
1. Primary health-related outcome the article is about
163. 10.
Deadliest Counties in the US
Identifying Patterns and Formulating Hypotheses
Identify:
2. Statements that attempt to explain the reason for the pattern
164. 10.
Deadliest Counties in the US
Identifying Patterns and Formulating Hypotheses
Identify:
3. Statements that attempt to explain the reason for the pattern
165. 10.
Deadliest Counties in the US
Identifying Patterns and Formulating Hypotheses
Identify:
4. Methodology that generated the data
169. 169
“The annual health rankings use a measure called
‘premature age-adjusted mortality’ from the Centers for
Disease Control and Prevention as one of their main
indicators of overall health. This factor uses statistical
methods to adjust for the overall distribution of ages in a
county, so that one can compare mortality in any two
counties independent of whether one has an overall
younger population than the other.”
170. A measure of the effect of diseases and injuries
in reducing the life span
below national or a hypothetical ideal life expectancy.
Identifying Patterns and Formulating Hypotheses
Potential Years of Life Lost
171. A procedure for adjusting rates designed to
minimize the effects of differences in age composition
when comparing rates from different populations.
Identifying Patterns and Formulating Hypotheses
Age Adjustment / Age Standardization
172. 3. Age-adjusted death rates are used to:
a. Correct death rates for errors in the statement of age
b. Determine the actual number of deaths that occurred in
specific age groups in a population
c. Correct death rates for missing age information
d. Compare deaths in persons of the same age group
e. Eliminate the effects of differences in the age distributions of
populations in comparing death rates
173. Calculate the age-adjusted death rate for disease Z in communities X
and Y by the direct method, using the total of both communities as the
standard population.
174. Community X Community Y
Age
Age
Age
#
People
#
Z
Deaths
#
People
#
Z
Deaths
Young 8,000 69 5,000 48
Old 11,000 115 3,000 60
What is the age-adjusted death rate from disease Z in community X?
175. Community X Community Y
Age
Age
Age
#
People
#
Z
Deaths
#
People
#
Z
Deaths
Young 8,000 69 5,000 48
Old 11,000 115 3,000 60
Total 19,000 184 8,000 108
What is the age-adjusted death rate from disease Z in community X?
176. Community X Community Y
Age
Age
Age
#
People
#
Z
Deaths
Death
Rate
per
1,000
#
People
#
Z
Deaths
Death
Rate
per
1,000
Young 8,000 69 5,000 48
Old 11,000 115 3,000 60
Total 19,000 184 9.7 8,000 108 13.5
What is the age-adjusted death rate from disease Z in community X?
177. Community X Community Y
Age
Age
Age
#
People
#
Z
Deaths
Death
Rate
per
1,000
#
People
#
Z
Deaths
Death
Rate
per
1,000
Young 8,000 69 8.6 5,000 48 9.6
Old 11,000 115 10.5 3,000 60 20.0
Total 19,000 184 9.7 8,000 108 13.5
What is the age-adjusted death rate from disease Z in community X?
178. Community X
Age
Age Standard
Population
Un-Adjusted
Death Rate
per 1,000
Expected
#
of Deaths
Adjusted
Death Rate
per 1,000
Young 13,000
Old 14,000
Total 27,000
What is the age-adjusted death rate from disease Z in community X?
179. Community X
Age
Age Standard
Population
Un-Adjusted
Death Rate
per 1,000
Expected
#
of Deaths
Adjusted
Death Rate
per 1,000
Young 13,000 8.6
Old 14,000 10.5
Total 27,000
What is the age-adjusted death rate from disease Z in community X?
180. Community X
Age
Age Standard
Population
Un-Adjusted
Death Rate
per 1,000
Expected
#
of Deaths
Adjusted
Death Rate
per 1,000
Young 13,000 8.6 111.8
Old 14,000 10.5 147.0
Total 27,000 258.8
What is the age-adjusted death rate from disease Z in community X?
181. Community X
Age
Age Standard
Population
Un-Adjusted
Death Rate
per 1,000
Expected
#
of Deaths
Adjusted
Death Rate
per 1,000
Young 13,000 8.6 111.8
Old 14,000 10.5 147.0
Total 27,000 258.8 9.6
What is the age-adjusted death rate from disease Z in community X?
182. Community Y
Age
Age Standard
Population
Un-Adjusted
Death Rate
per 1,000
Expected
#
of Deaths
Adjusted
Death Rate
per 1,000
Young 13,000
Old 14,000
Total 27,000
What is the age-adjusted death rate from disease Z in community X?
183. Community Y
Age
Age Standard
Population
Un-Adjusted
Death Rate
per 1,000
Expected
#
of Deaths
Adjusted
Death Rate
per 1,000
Young 13,000 9.6
Old 14,000 20.0
Total 27,000
What is the age-adjusted death rate from disease Z in community X?
184. Community Y
Age
Age Standard
Population
Un-Adjusted
Death Rate
per 1,000
Expected
#
of Deaths
Adjusted
Death Rate
per 1,000
Young 13,000 9.6 124.8
Old 14,000 20.0 280.0
Total 27,000 404.8
What is the age-adjusted death rate from disease Z in community X?
185. Community Y
Age
Age Standard
Population
Un-Adjusted
Death Rate
per 1,000
Expected
#
of Deaths
Adjusted
Death Rate
per 1,000
Young 13,000 9.6 124.8
Old 14,000 20.0 280.0
Total 27,000 404.8 15.0
What is the age-adjusted death rate from disease Z in community X?
187. Calculate the age-adjusted death rate for disease Z in communities X
and Y by the direct method, using the total of both communities as the
standard population.
The age-adjusted death rate from disease Z for community X is:
9.6 / 1,000
190. 11.
What Explains US Mass Shootings?
Identifying Patterns and Formulating Hypotheses
Identify:
1. Primary health-related outcome the article is about
191. 11.
What Explains US Mass Shootings?
Identifying Patterns and Formulating Hypotheses
Identify:
2. Statements that attempt to explain the reason for the pattern
192. 11.
What Explains US Mass Shootings?
Identifying Patterns and Formulating Hypotheses
Identify:
3. Statements that attempt to explain the reason for the pattern
193. 11.
What Explains US Mass Shootings?
Identifying Patterns and Formulating Hypotheses
Identify:
4. Methodology that generated the data
199. 12.
Teenage Suicides Bewilder an Island and the Experts
Identifying Patterns and Formulating Hypotheses
Identify:
1. Primary health-related outcome the article is about
200. 12.
Teenage Suicides Bewilder an Island and the Experts
Identifying Patterns and Formulating Hypotheses
Identify:
2. Statements that attempt to explain the reason for the pattern
201. 12.
Teenage Suicides Bewilder an Island and the Experts
Identifying Patterns and Formulating Hypotheses
Identify:
3. Statements that attempt to explain the reason for the pattern
202. 12.
Teenage Suicides Bewilder an Island and the Experts
Identifying Patterns and Formulating Hypotheses
Identify:
4. Methodology that generated the data
203. 203
... the study of the distribution and determinants of
health-related states or events in specified
populations and the application of this study to the
control of health problems.
(A Dictionary of Epidemiology)
What is epidemiology?
204.
205.
206.
207. 207
... the study of the distribution and determinants of
health-related states or events in specified
populations and the application of this study to the
control of health problems.
(Gordis)
What is epidemiology?
209. 209
How often does the health-related outcome occur?
How is the the health-related outcome distributed?
What hypotheses might explain the distribution of the health-related
outcome?
Health-related outcomes are not distributed haphazardly in a
population. There are patterns to their occurrence. These patterns can
be identified through the surveillance of populations. Examining these
patterns can help formulate hypotheses about the possible causes of
these outcomes.
1.
Essential Questions and Enduring Epidemiological Understandings
210. 210
The aim of the course is to create a more scientifically literate person,
someone who:
… can ask, find, or determine answers to questions derived from
curiosity about everyday experiences. … has the ability to describe,
explain, and predict natural phenomenon. … is able to read with
understanding articles about science in the popular press and to
engage in social conversation about the validity of their
conclusions. … can identify scientific issues underlying national and
local decisions and express positions that are scientifically and
technologically informed. … (is) able to evaluate the quality of
scientific information on the basis of its source and the methods used
to generate it. … (has) the capacity to pose and evaluate arguments
based on evidence and to apply conclusions from such arguments
appropriately.
National Research Council, National Science Education Standards. Washington, DC: National Academy Press, 1996.
Scientific Literacy
211. 211
1. Review
2. Identifying Patterns and Formulating Hypotheses
3. Reading and Simulation Assignments
Science of Public Health: Epidemiology
Surveillance, Patterns and Hypotheses
January 24, 2018
212. Identify:
1. Primary health-related outcome the article is about
2. Statements that describe the pattern of the particular health-
related event
3. Statements that attempt to explain the reason for the pattern
4. Statements that describe the methodology that generated the
data from which the pattern was identified
Identifying Patterns and Formulating Hypotheses
215. 215
1. Logistics
2. Review
3. Surveillance, Patterns and Hypotheses
• Pneumocystis Pneumonia - Los Angeles
• Whistles
• Adult Obesity
4. Exam 1 Preparation - 2/12
5. PPT Assignment
Science of Public Health: Epidemiology
Surveillance, Patterns and Hypotheses
January 29, 2018
216. To me, that summed up the whole problem of dealing with AIDS in the media.
Obviously, the reason I covered AIDS from the start was that...
it was never something that happened to those other people.
Randy Shilts in 1983
Identifying Patterns and Formulating Hypotheses
220. Pneumocystis Pneumonia - Los Angeles
Identifying Patterns and Formulating Hypotheses
Identify:
1. Primary health-related outcome the article is about
221. Identifying Patterns and Formulating Hypotheses
Identify:
2. Statements that attempt to explain the reason for the pattern
Pneumocystis Pneumonia - Los Angeles
223. Identifying Patterns and Formulating Hypotheses
Identify:
3. Statements that attempt to explain the reason for the pattern
Pneumocystis Pneumonia - Los Angeles
224. Identifying Patterns and Formulating Hypotheses
Identify:
4. Methodology that generated the data
Pneumocystis Pneumonia - Los Angeles
225. 225
How often does the health-related outcome occur?
How is the the health-related outcome distributed?
What hypotheses might explain the distribution of the health-related
outcome?
Health-related outcomes are not distributed haphazardly in a
population. There are patterns to their occurrence. These patterns can
be identified through the surveillance of populations. Examining these
patterns can help formulate hypotheses about the possible causes of
these outcomes.
1.
Essential Questions and Enduring Epidemiological Understandings
226. 226
1. Logistics
2. Review
3. Surveillance, Patterns and Hypotheses
• Pneumocystis Pneumonia - Los Angeles
• Whistles
• Adult Obesity
4. Exam 1 Preparation - 2/12
5. PPT Assignment
Science of Public Health: Epidemiology
Surveillance, Patterns and Hypotheses
January 29, 2018
227. Explain the relationship between the article, XXXXXXXXXX, and the content of this course to
date.
Epidemiology - A Science for the People (Lancet) (Canvas)
Colombia’s Data-Driven Fight Against Crime (Canvas)
Adult Use of Prescription Opioid Pain Medications — Utah, 2008 (MMWR) (pages 153-157)
(Canvas)
Gun Violence Archive (Online)
• Home (http://www.gunviolencearchive.org/)
• About Us (http://www.gunviolencearchive.org/about)
• General Methodology (http://www.gunviolencearchive.org/methodology)
• Last 72 Hours (http://www.gunviolencearchive.org/last-72-hours
• Charts and Maps (http://www.gunviolencearchive.org/charts-and-maps)
Oregon’s Death with Dignity Act: The First Year’s Experience (pages 1-3 and 7-10) (Canvas)
Oregon Death with Dignity Act Data summary 2016 (pages 3-7) (Canvas)
238. National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention
National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention
HIV Surveillance –
Adolescents and Young Adults
Division of HIV/AIDS Prevention
239. Diagnoses of HIV Infection among Adolescents and Young Adults Aged 13–24 years,
by Race/Ethnicity, 2010–2015—United States and 6 Dependent Areas
Note. Data include persons with a diagnosis of HIV infection regardless of stage of disease at diagnosis.
a Hispanics/Latinos can be of any race.
240. Diagnoses of HIV Infection among Adolescents and Young Adults Aged 13–24 Years,
by Transmission Category, 2010–2015—United States and 6 Dependent Areas
Note. Data have been statistically adjusted to account for missing transmission category. “Other” transmission category not displayed as it comprises less
than 1% of cases.
a Heterosexual contact with a person known to have, or to be at high risk for, HIV infection.
241. Adolescents and Young Adults Aged 13–24 Years Living with Diagnosed HIV Infection,
by Sex and Race/Ethnicity, Year-end 2015—United States and 6 Dependent Areas
a Includes Asian/Pacific Islander legacy cases.
b Hispanics/Latinos can be of any race.
242. Adolescents and Young Adults Aged 13–24 Years Living with Diagnosed HIV Infection
by Sex and Transmission Category, Year-end 2015—United States
and 6 Dependent Areas
Note. Data have been statistically adjusted to account for missing transmission category. “Other” transmission category not displayed as it comprises 1% or less
of cases.
a Heterosexual contact with a person known to have, or to be at high risk for, HIV infection.
b Includes hemophilia, blood transfusion, and risk factor not reported or not identified.
243. Rates of Adolescents Aged 13–19 Years Living with Diagnosed HIV Infection
Year-end 2015—United States and 6 Dependent Areas
N = 5,753 Total Rate = 19.4
American Samoa
Guam
Northern Mariana Islands
Puerto Rico
Republic of Palau
U.S. Virgin Islands
0.0
0.0
0.0
21.0
0.0
39.8
Note. Data are based on address of residence as of December 31, 2015 (i.e., most recent known address).
244. Rates of Young Adults Aged 20–24 Years Living with Diagnosed HIV Infection
Year-end 2015—United States and 6 Dependent Areas
N = 31,208 Total Rate = 135.8
American Samoa
Guam
Northern Mariana Islands
Puerto Rico
Republic of Palau
U.S. Virgin Islands
0.0
0.0
25.3
155.2
0.0
159.6
Note. Data are based on address of residence as of December 31, 2015 (i.e., most recent known address).
245. Stage 3 (AIDS) Classifications among Adolescents Aged 13–19 Years, by Sex and Year
of Classification, 1985–2015—United States and 6 Dependent Areas
246. Stage 3 (AIDS) Classifications Among Young Adults Aged 20–24 Years, by Sex and Year
of Classification, 1985–2015—United States and 6 Dependent Areas
247. Rates of Adolescents Aged 13–19 Years Living with Diagnosed HIV Infection Ever
Classified as Stage 3 (AIDS), Year-end 2015—United States and 6 Dependent Areas
N = 1,124 Total Rate = 3.8
American Samoa
Guam
Northern Mariana Islands
Puerto Rico
Republic of Palau
U.S. Virgin Islands
0.0
0.0
0.0
5.4
0.0
26.6
Note. Data are based on address of residence as of December 31, 2015 (i.e., most recent known address).
248. Rates of Young Adults Aged 20–24 Years Living with Diagnosed HIV Infection Ever
Classified as Stage 3 (AIDS), Year-end 2015—United States and 6 Dependent Areas
N = 6,827 Total Rate = 29.7
American Samoa
Guam
Northern Mariana Islands
Puerto Rico
Republic of Palau
U.S. Virgin Islands
0.0
0.0
0.0
35.3
0.0
59.9
Note. Data are based on address of residence as of December 31, 2015 (i.e., most recent known address).
249. Diagnoses of HIV Infection among Persons Aged 13 Years and Older,
by Sex and and Age Group, 2016—United States and 6 Dependent Areas
Note. Data for the year 2015 are preliminary and based on 6 months reporting delay.
250. Diagnoses of HIV Infection Among Male Adolescents and Young Adults, by Age
Group and Transmission Category, 2016—United States and 6 Dependent Areas
Note. Data for the year 2015 are preliminary and based on 6 months reporting delay. Data have been statistically adjusted to account for missing transmission category.
a Heterosexual contact with a person known to have, or to be at high risk for, HIV infection.
b Includes hemophilia, blood transfusion, perinatal exposure, and risk factor not reported or not identified.
c Because column totals for numbers were calculated independently of the values for the subpopulations, the values in each column may not sum to the column total
251. Diagnoses of HIV Infection Among Female Adolescents and Young Adults, by Age
Group and Transmission Category, 2016—United States and 6 Dependent Areas
Note. Data for the year 2015 are preliminary and based on 6 months reporting delay. Data have been statistically adjusted to account for missing transmission category.
a Heterosexual contact with a person known to have, or to be at high risk for, HIV infection.
b Includes hemophilia, blood transfusion, perinatal exposure, and risk factor not reported or not identified.
c Because column totals for numbers were calculated independently of the values for the subpopulations, the values in each column may not sum to the column total.
252. Diagnoses of HIV Infection and Population among Adolescents
Aged 13–19 Years, by Race/Ethnicity, 2016—United States
Note. Data for the year 2015 are preliminary and based on 6 months reporting delay.
a Hispanics/Latinos can be of any race.
253. Diagnoses of HIV Infection and Population among Young Adults
Aged 20–24 Years, by Race/Ethnicity 2016—United States
Note. Data for the year 2015 are preliminary and based on 6 months reporting delay.
a Hispanics/Latinos can be of any race.
254. Rates of Diagnoses of HIV Infection among Adolescents Aged 13–19 Years
2016—United States and 6 Dependent Areas
N = 1,688 Total Rate = 5.7
American Samoa
Guam
Northern Mariana Islands
Puerto Rico
Republic of Palau
U.S. Virgin Islands
0.0
0.0
0.0
4.1
0.0
0.0
Note. Data for the year 2016 are preliminary and based on 6 months reporting delay.
255. Rates of Diagnoses of HIV Infection among Young Adults Aged 20–24 Years
2016—United States and 6 Dependent Areas
N = 6,848 Total Rate = 30.2
American Samoa
Guam
Northern Mariana Islands
Puerto Rico
Republic of Palau
U.S. Virgin Islands
0.0
7.4
0.0
27.8
53.2
65.8
Note. Data for the year 2016 are preliminary and based on 6 months reporting delay.
256. Stage 3 (AIDS) Classifications among Persons Aged 13 Years and Older
with Diagnosed HIV Infection, by Race/Ethnicity and Age Group, 2016
United States and 6 Dependent Areas
Note. Data for the year 2016 are preliminary and based on 6 months reporting delay.
a Includes Asian/Pacific Islander legacy cases
b Hispanics/Latinos can be of any race.
257. Rates of Diagnosed HIV Infection Classified as Stage 3 (AIDS) among Adolescents
Aged 13–19 Years, 2016—United States and 6 Dependent Areas
N = 224 Total Rate = 0.8
American Samoa
Guam
Northern Mariana Islands
Puerto Rico
Republic of Palau
U.S. Virgin Islands
0.0
0.6
0.0
0.0
0.0
0.0
Note. Data for the year 2016 are preliminary and based on 6 months reporting delay.
258. Rates of Diagnosed HIV Infection Classified as Stage 3 (AIDS) among Young Adults
Aged 20–24 Years, 2016—United States and 6 Dependent Areas
N = 1,266 Total Rate = 5.6
American Samoa
Guam
Northern Mariana Islands
Puerto Rico
Republic of Palau
U.S. Virgin Islands
0.0
7.4
0.0
5.4
0.0
0.0
Note. Data for the year 2016 are preliminary and based on 6 months reporting delay.
261. • Frequency
• Severity
• Cost
• Preventability
• Communicability
• Public Interest
Does the health problem justify surveillance?
Identifying Patterns and Formulating Hypotheses
262. In December 2010, the Department of Health and Human Services
launched Healthy People 2020, which has four overarching goals:
1.Attain high-quality, longer lives free of preventable disease,
disability, injury, and premature death;
2.Achieve health equity, eliminate disparities, and improve the
health of all groups;
3.Create social and physical environments that promote good
health for all; and
4.Promote quality of life, healthy development, and healthy
behaviors across all life stages.
Identifying Patterns and Formulating Hypotheses
263. Jay Z: 'The War on Drugs Is an Epic Fail'
https://www.nytimes.com/video/opinion/100000004642370/jay-z-the-war-on-drugs-is-an-epic-fail.html
Identifying Patterns and Formulating Hypotheses
264. Identifying Patterns and Formulating Hypotheses
Active Surveillance
Denotes a system in which project staff are recruited to carry out a surveillance
program. It may involve interviewing physicians and patients, reviewing medical
records, and, in developing countries, surveying villages …. Surveillance is
generally more accurate when active … because (it) is conducted by individuals
who have been specifically employed to carry out this responsibility.
265. Denotes surveillance in which available data on a reportable disease are used,
or in which disease reporting is mandated or requested, with the responsibility for
the reporting often falling on the health care provider or district officer.
The completeness and quality of the data reported
thus largely on this individual and his or her staff,
who often take on the role without additional funds or resources.
Identifying Patterns and Formulating Hypotheses
Passive Surveillance
273. 273
Hypotheses for Card
Card / Group Hypotheses
1
2
3
4
5
6
7
8
9
10
Alcoholic beverages
Alcohol
Alcohol / Food poisoning / Drug-smoking
Music
Alcohol / Drinks / Food / Music
XXXXXXXXXXX
2
274. 274
How often does the health-related outcome occur?
How is the the health-related outcome distributed?
What hypotheses might explain the distribution
of the health-related outcome?
Health-related outcomes are not distributed haphazardly in a
population. There are patterns to their occurrence. These patterns can
be identified through the surveillance of populations. Examining these
patterns can help formulate hypotheses about the possible causes of
these outcomes.
1.
Essential Questions and Enduring Epidemiological Understandings
275. 275
1. Review / Exam 1 Preparation - 2/12
2. Surveillance, Patterns and Hypotheses
• Pneumocystis Pneumonia - Los Angeles
• Whistles
• Adult Obesity
• Pregnancy Boom
3. PPT Assignment
Science of Public Health: Epidemiology
Surveillance, Patterns and Hypotheses
January 31, 2018
276. 276
“… major behavioral risks among adults
associated with premature morbidity and mortality.”
Behavioral Risk Factor Surveillance System
(BRFSS)
Identifying Patterns and Formulating Hypotheses
277. Definitions:
• Obesity: Body Mass Index (BMI) of 30 or higher.
• Body Mass Index (BMI): A measure of an adult’s
weight in relation to his or her height, specifically the
adult’s weight in kilograms divided by the square of
his or her height in meters.
Obesity Trends Among U.S. Adults
between 1985 and 2009
278. Obesity Trends Among U.S. Adults
between 1985 and 2009
Source of the data:
• The data shown in these maps were collected
through CDC’s Behavioral Risk Factor Surveillance
System (BRFSS). Each year, state health
departments use standard procedures to collect data
through a series of landline telephone interviews with
U.S. adults.
• Prevalence estimates generated for the maps may
vary slightly from those generated for the states by
BRFSS (http://aps.nccd.cdc.gov/brfss) as slightly
different analytic methods are used.
279. Source: Behavioral Risk Factor Surveillance System, CDC.
Obesity Trends* Among U.S. Adults
BRFSS, 1985
(*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data <10% 10%–14%
280. Source: Behavioral Risk Factor Surveillance System, CDC.
Obesity Trends* Among U.S. Adults
BRFSS, 1986
(*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data <10% 10%–14%
281. Source: Behavioral Risk Factor Surveillance System, CDC.
Obesity Trends* Among U.S. Adults
BRFSS, 1987
(*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4”
person)
No Data <10% 10%–14%
282. Source: Behavioral Risk Factor Surveillance System, CDC.
Obesity Trends* Among U.S. Adults
BRFSS, 1988
(*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data <10% 10%–14%
283. Source: Behavioral Risk Factor Surveillance System, CDC.
Obesity Trends* Among U.S. Adults
BRFSS, 1989
(*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data <10% 10%–14%
284. Source: Behavioral Risk Factor Surveillance System, CDC.
Obesity Trends* Among U.S. Adults
BRFSS, 1990
(*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data <10% 10%–14%
285. Source: Behavioral Risk Factor Surveillance System, CDC.
Obesity Trends* Among U.S. Adults
BRFSS, 1991
(*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data <10% 10%–14% 15%–19%
286. Source: Behavioral Risk Factor Surveillance System, CDC.
Obesity Trends* Among U.S. Adults
BRFSS, 1992
(*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data <10% 10%–14% 15%–19%
287. Source: Behavioral Risk Factor Surveillance System, CDC.
Obesity Trends* Among U.S. Adults
BRFSS, 1993
(*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data <10% 10%–14% 15%–19%
288. Source: Behavioral Risk Factor Surveillance System, CDC.
Obesity Trends* Among U.S. Adults
BRFSS, 1994
(*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data <10% 10%–14% 15%–19%
289. Source: Behavioral Risk Factor Surveillance System, CDC.
Obesity Trends* Among U.S. Adults
BRFSS, 1995
(*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data <10% 10%–14% 15%–19%
290. Source: Behavioral Risk Factor Surveillance System, CDC.
Obesity Trends* Among U.S. Adults
BRFSS, 1996
(*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data <10% 10%–14% 15%–19%
291. Source: Behavioral Risk Factor Surveillance System, CDC.
Obesity Trends* Among U.S. Adults
BRFSS, 1997
(*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data <10% 10%–14% 15%–19% ≥20%
292. Source: Behavioral Risk Factor Surveillance System, CDC.
Obesity Trends* Among U.S. Adults
BRFSS, 1998
(*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data <10% 10%–14% 15%–19% ≥20%
293. Source: Behavioral Risk Factor Surveillance System, CDC.
Obesity Trends* Among U.S. Adults
BRFSS, 1999
(*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data <10% 10%–14% 15%–19% ≥20%
294. Source: Behavioral Risk Factor Surveillance System, CDC.
Obesity Trends* Among U.S. Adults
BRFSS, 2000
(*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data <10% 10%–14% 15%–19% ≥20%
295. Source: Behavioral Risk Factor Surveillance System, CDC.
Obesity Trends* Among U.S. Adults
BRFSS, 2001
(*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data <10% 10%–14% 15%–19% 20%–24% ≥25%
296. Source: Behavioral Risk Factor Surveillance System, CDC.
(*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
Obesity Trends* Among U.S. Adults
BRFSS, 2002
No Data <10% 10%–14% 15%–19% 20%–24% ≥25%
297. Source: Behavioral Risk Factor Surveillance System, CDC.
Obesity Trends* Among U.S. Adults
BRFSS, 2003
(*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data <10% 10%–14% 15%–19% 20%–24% ≥25%
298. Source: Behavioral Risk Factor Surveillance System, CDC.
Obesity Trends* Among U.S. Adults
BRFSS, 2004
(*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data <10% 10%–14% 15%–19% 20%–24% ≥25%
299. Source: Behavioral Risk Factor Surveillance System, CDC.
Obesity Trends* Among U.S. Adults
BRFSS, 2005
(*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data <10% 10%–14% 15%–19% 20%–24% 25%–29% ≥30%
300. Source: Behavioral Risk Factor Surveillance System, CDC.
Obesity Trends* Among U.S. Adults
BRFSS, 2006
(*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data <10% 10%–14% 15%–19% 20%–24% 25%–29% ≥30%
301. Source: Behavioral Risk Factor Surveillance System, CDC.
Obesity Trends* Among U.S. Adults
BRFSS, 2007
(*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data <10% 10%–14% 15%–19% 20%–24% 25%–29% ≥30%
302. Source: Behavioral Risk Factor Surveillance System, CDC.
Obesity Trends* Among U.S. Adults
BRFSS, 2008
(*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data <10% 10%–14% 15%–19% 20%–24% 25%–29% ≥30%
303. Source: Behavioral Risk Factor Surveillance System, CDC.
Obesity Trends* Among U.S. Adults
BRFSS, 2009
(*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data <10% 10%–14% 15%–19% 20%–24% 25%–29% ≥30%
304. Source: Behavioral Risk Factor Surveillance System, CDC.
1999
Obesity Trends* Among U.S. Adults
BRFSS, 1990, 1999, 2009
(*BMI 30, or about 30 lbs. overweight for 5’4” person)
2009
1990
No Data <10% 10%–14% 15%–19% 20%–24% 25%–29% ≥30%
305. 305
Prevalence estimates
reflect BRFSS methodological changes started in 2011.
These estimates should not be compared to
prevalence estimates before 2011.
306. Prevalence of Self-Reported Obesity Among
U.S. Adults by State and Territory
Definitions
Obesity: Body Mass Index (BMI) of 30 or higher.
Body Mass Index (BMI): A measure of an adult’s
weight in relation to his or her height, calculated by
using the adult’s weight in kilograms divided by the
square of his or her height in meters.
307. Prevalence of Self-Reported Obesity Among
U.S. Adults by State and Territory
Source of the Data
The data were collected through the Behavioral
Risk Factor Surveillance System (BRFSS), an
ongoing, state-based, telephone interview survey
conducted by state health departments with
assistance from CDC.
Height and weight data used in the BMI
calculations were self-reported.
308. Prevalence of Self-Reported Obesity Among
U.S. Adults by State and Territory
BRFSS Methodological Changes Started in 2011
New sampling frame that included both landline
and cell phone households.
New weighting methodology used to provide a
closer match between the sample and the
population.
309. Prevalence of Self-Reported Obesity Among
U.S. Adults by State and Territory
Exclusion Criteria Used Beginning with 2011 BRFSS Data
Records with the following were excluded:
Height: <3 feet or ≥8 feet
Weight: <50 pounds or ≥650 pounds
BMI: <12 kg/m2 or ≥100 kg/m2
Pregnant women
310. Prevalence¶ of Self-Reported Obesity Among U.S. Adults by
State and Territory, BRFSS, 2011
¶ Prevalence estimates reflect BRFSS methodological changes started in 2011. These estimates should not be compared to
prevalence estimates before 2011.
*Sample size <50 or the relative standard error (dividing the standard error by the prevalence) ≥ 30%.
311. Prevalence¶ of Self-Reported Obesity Among U.S. Adults by
State and Territory, BRFSS, 2012
¶ Prevalence estimates reflect BRFSS methodological changes started in 2011. These estimates should not be
compared to prevalence estimates before 2011.
*Sample size <50 or the relative standard error (dividing the standard error by the prevalence) ≥ 30%.
312. Prevalence¶ of Self-Reported Obesity Among U.S. Adults by
State and Territory, BRFSS, 2013
¶ Prevalence estimates reflect BRFSS methodological changes started in 2011. These estimates should not be
compared to prevalence estimates before 2011.
*Sample size <50 or the relative standard error (dividing the standard error by the prevalence) ≥ 30%.
313. Prevalence¶ of Self-Reported Obesity Among U.S. Adults by
State and Territory, BRFSS, 2014
¶ Prevalence estimates reflect BRFSS methodological changes started in 2011. These estimates should not be
compared to prevalence estimates before 2011.
*Sample size <50 or the relative standard error (dividing the standard error by the prevalence) ≥ 30%.
314. Prevalence¶ of Self-Reported Obesity Among U.S. Adults by
State and Territory, BRFSS, 2015
¶ Prevalence estimates reflect BRFSS methodological changes started in 2011. These estimates should not be
compared to prevalence estimates before 2011.
*Sample size <50 or the relative standard error (dividing the standard error by the prevalence) ≥ 30%.
315. Prevalence¶ of Self-Reported Obesity Among U.S. Adults by
State and Territory, BRFSS, 2016
¶ Prevalence estimates reflect BRFSS methodological changes started in 2011. These estimates should not be
compared to prevalence estimates before 2011.
*Sample size <50 or the relative standard error (dividing the standard error by the prevalence) ≥ 30%.
316. Prevalence¶ of Self-Reported Obesity Among U.S. Adults
by State and Territory, BRFSS, 2011
¶ Prevalence estimates reflect BRFSS methodological changes started in 2011. These estimates should not be compared to prevalence estimates
before 2011. Source: Behavioral Risk Factor Surveillance System, CDC.
State Prevalence 95% Confidence Interval
Alabama 32.0 (30.5, 33.5)
Alaska 27.4 (25.3, 29.7)
Arizona 25.1 (23.0, 27.3)
Arkansas 30.9 (28.8, 33.1)
California 23.8 (22.9, 24.7)
Colorado 20.7 (19.7, 21.8)
Connecticut 24.5 (23.0, 26.0)
Delaware 28.8 (26.9, 30.7)
District of Columbia 23.7 (21.9, 25.7)
Florida 26.6 (25.4, 27.9)
Georgia 28.0 (26.6, 29.4)
Guam 27.4 (24.8, 30.2)
Hawaii 21.8 (20.4, 23.4)
Idaho 27.0 (25.3, 28.9)
Illinois 27.1 (25.4, 28.9)
Indiana 30.8 (29.5, 32.3)
Iowa 29.0 (27.6, 30.3)
Kansas 29.6 (28.7, 30.4)
Kentucky 30.4 (28.9, 31.9)
Louisiana 33.4 (32.0, 34.9)
Maine 27.8 (26.8, 28.9)
Maryland 28.3 (26.9, 29.7)
Massachusetts 22.7 (21.8, 23.7)
Michigan 31.3 (30.0, 32.6)
Minnesota 25.7 (24.6, 26.8)
Mississippi 34.9 (33.5, 36.3)
State Prevalence 95% Confidence Interval
Missouri 30.3 (28.6, 32.0)
Montana 24.6 (23.3, 26.0)
Nebraska 28.4 (27.6, 29.2)
Nevada 24.5 (22.5, 26.6)
New Hampshire 26.2 (24.7, 27.7)
New Jersey 23.7 (22.7, 24.8)
New Mexico 26.3 (25.1, 27.6)
New York 24.5 (23.2, 25.9)
North Carolina 29.1 (27.7, 30.6)
North Dakota 27.8 (26.3, 29.4)
Ohio 29.6 (28.3, 31.0)
Oklahoma 31.1 (29.7, 32.5)
Oregon 26.7 (25.2, 28.3)
Pennsylvania 28.6 (27.3, 29.8)
Puerto Rico 26.3 (25.0, 27.7)
Rhode Island 25.4 (23.9, 27.0)
South Carolina 30.8 (29.6, 32.1)
South Dakota 28.1 (26.3, 30.1)
Tennessee 29.2 (26.8, 31.7)
Texas 30.4 (29.1, 31.8)
Utah 24.4 (23.4, 25.5)
Vermont 25.4 (24.1, 26.8)
Virginia 29.2 (27.5, 30.9)
Washington 26.5 (25.3, 27.7)
West Virginia 32.4 (30.9, 34.0)
Wisconsin 27.7 (25.8, 29.7)
Wyoming 25.0 (23.5, 26.6)
317. Prevalence¶ of Self-Reported Obesity Among U.S.
Adults by State and Territory, BRFSS, 2012
¶ Prevalence estimates reflect BRFSS methodological changes started in 2011. These estimates should not be compared to prevalence estimates
before 2011. Source: Behavioral Risk Factor Surveillance System, CDC.
State Prevalence 95% Confidence Interval
Alabama 33.0 (31.5, 34.4)
Alaska 25.7 (23.9, 27.5)
Arizona 26.0 (24.3, 27.8)
Arkansas 34.5 (32.7, 36.4)
California 25.0 (23.9, 26.0)
Colorado 20.5 (19.5, 21.4)
Connecticut 25.6 (24.3, 26.9)
Delaware 26.9 (25.2, 28.6)
District of Columbia 21.9 (19.8, 24.0)
Florida 25.2 (23.6, 26.7)
Georgia 29.1 (27.4, 30.8)
Guam 29.1 (26.3, 31.9)
Hawaii 23.6 (22.0, 25.1)
Idaho 26.8 (24.8, 28.8)
Illinois 28.1 (26.4, 29.9)
Indiana 31.4 (30.1, 32.7)
Iowa 30.4 (29.1, 31.8)
Kansas 29.9 (28.7, 31.0)
Kentucky 31.3 (29.9, 32.6)
Louisiana 34.7 (33.1, 36.4)
Maine 28.4 (27.2, 29.5)
Maryland 27.6 (26.3, 28.9)
Massachusetts 22.9 (22.0, 23.8)
Michigan 31.1 (29.8, 32.3)
Minnesota 25.7 (24.7, 26.8)
Mississippi 34.6 (33.0, 36.2)
State Prevalence 95% Confidence Interval
Missouri 29.6 (28.0, 31.2)
Montana 24.3 (23.1, 25.5)
Nebraska 28.6 (27.7, 29.6)
Nevada 26.2 (24.3, 28.1)
New Hampshire 27.3 (25.8, 28.8)
New Jersey 24.6 (23.6, 25.6)
New Mexico 27.1 (25.9, 28.3)
New York 23.6 (22.0, 25.1)
North Carolina 29.6 (28.5, 30.7)
North Dakota 29.7 (27.9, 31.4)
Ohio 30.1 (29.0, 31.2)
Oklahoma 32.2 (30.8, 33.6)
Oregon 27.3 (25.7, 29.0)
Pennsylvania 29.1 (28.1, 30.1)
Puerto Rico 28.4 (27.0, 29.7)
Rhode Island 25.7 (24.1, 27.4)
South Carolina 31.6 (30.4, 32.8)
South Dakota 28.1 (26.5, 29.8)
Tennessee 31.1 (29.6, 32.7)
Texas 29.2 (27.8, 30.5)
Utah 24.3 (23.3, 25.3)
Vermont 23.7 (22.3, 25.1)
Virginia 27.4 (26.0, 28.7)
Washington 26.8 (25.8, 27.8)
West Virginia 33.8 (32.2, 35.4)
Wisconsin 29.7 (27.8, 31.6)
Wyoming 24.6 (22.8, 26.4)
318. Prevalence¶ of Self-Reported Obesity Among U.S.
Adults by State and Territory, BRFSS, 2013
¶ Prevalence estimates reflect BRFSS methodological changes started in 2011. These estimates should not be compared to prevalence estimates before 2011.
Source: Behavioral Risk Factor Surveillance System, CDC.
State Prevalence 95% Confidence Interval
Alabama 32.4 (30.8, 34.1)
Alaska 28.4 (26.5, 30.4)
Arizona 26.8 (24.3, 29.4)
Arkansas 34.6 (32.7, 36.6)
California 24.1 (23.0, 25.3)
Colorado 21.3 (20.4, 22.2)
Connecticut 25.0 (23.5, 26.4)
Delaware 31.1 (29.3, 32.8)
District of Columbia 22.9 (21.0, 24.8)
Florida 26.4 (25.3, 27.4)
Georgia 30.3 (28.9, 31.8)
Guam 27.0 (24.4, 29.8)
Hawaii 21.8 (20.4, 23.2)
Idaho 29.6 (27.8, 31.4)
Illinois 29.4 (27.7, 31.2)
Indiana 31.8 (30.6, 33.1)
Iowa 31.3 (29.9, 32.7)
Kansas 30.0 (29.2, 30.7)
Kentucky 33.2 (31.8, 34.6)
Louisiana 33.1 (31.1, 35.2)
Maine 28.9 (27.5, 30.2)
Maryland 28.3 (27.0, 29.5)
Massachusetts 23.6 (22.5, 24.8)
Michigan 31.5 (30.4, 32.6)
Minnesota 25.5 (24.1, 26.8)
Mississippi 35.1 (33.5, 36.8)
State Prevalence 95% Confidence Interval
Missouri 30.4 (28.8, 32.1)
Montana 24.6 (23.4, 25.8)
Nebraska 29.6 (28.4, 30.7)
Nevada 26.2 (24.0, 28.6)
New Hampshire 26.7 (25.3, 28.3)
New Jersey 26.3 (25.1, 27.5)
New Mexico 26.4 (25.1, 27.7)
New York 25.4 (24.2, 26.6)
North Carolina 29.4 (28.1, 30.7)
North Dakota 31.0 (29.5, 32.5)
Ohio 30.4 (29.2, 31.6)
Oklahoma 32.5 (31.2, 33.9)
Oregon 26.5 (24.9, 28.1)
Pennsylvania 30.0 (28.9, 31.2)
Puerto Rico 27.9 (26.4, 29.5)
Rhode Island 27.3 (25.8, 28.8)
South Carolina 31.7 (30.5, 33.1)
South Dakota 29.9 (28.0, 31.8)
Tennessee 33.7 (31.9, 35.5)
Texas 30.9 (29.5, 32.3)
Utah 24.1 (23.2, 25.1)
Vermont 24.7 (23.4, 26.1)
Virginia 27.2 (25.9, 28.5)
Washington 27.2 (26.0, 28.3)
West Virginia 35.1 (33.6, 36.6)
Wisconsin 29.8 (28.0, 31.6)
Wyoming 27.8 (26.2, 29.5)
319. Prevalence¶ of Self-Reported Obesity Among U.S.
Adults by State and Territory, BRFSS, 2014
¶ Prevalence estimates reflect BRFSS methodological changes started in 2011. These estimates should not be compared to prevalence estimates before 2011.
Source: Behavioral Risk Factor Surveillance System, CDC.
.
State Prevalence 95% Confidence Interval
Alabama 33.5 (32.1, 35.0)
Alaska 29.7 (27.8, 31.7)
Arizona 28.9 (27.7, 30.2)
Arkansas 35.9 (33.8, 38.0)
California 24.7 (23.5, 25.9)
Colorado 21.3 (20.4, 22.2)
Connecticut 26.3 (24.9, 27.7)
Delaware 30.7 (28.6, 32.8)
District of Columbia 21.7 (19.5, 24.0)
Florida 26.2 (25.0, 27.5)
Georgia 30.5 (28.9, 32.1)
Guam 28.0 (25.6, 30.5)
Hawaii 22.1 (20.7, 23.5)
Idaho 28.9 (27.1, 30.8)
Illinois 29.3 (27.6, 31.1)
Indiana 32.7 (31.6, 34.0)
Iowa 30.9 (29.6, 32.3)
Kansas 31.3 (30.3, 32.2)
Kentucky 31.6 (30.2, 33.1)
Louisiana 34.9 (33.4, 36.4)
Maine 28.2 (26.9, 29.5)
Maryland 29.6 (28.1, 31.1)
Massachusetts 23.3 (22.3, 24.4)
Michigan 30.7 (29.4, 32.0)
Minnesota 27.6 (26.8, 28.5)
Mississippi 35.5 (33.4, 37.6)
State Prevalence 95% Confidence Interval
Missouri 30.2 (28.6, 31.9)
Montana 26.4 (24.9, 27.9)
Nebraska 30.2 (29.2, 31.3)
Nevada 27.7 (25.4, 30.1)
New Hampshire 27.4 (25.8, 29.1)
New Jersey 26.9 (25.7, 28.1)
New Mexico 28.4 (27.0, 30.0)
New York 27.0 (25.6, 28.5)
North Carolina 29.7 (28.4, 31.0)
North Dakota 32.2 (30.5, 34.0)
Ohio 32.6 (31.2, 34.1)
Oklahoma 33.0 (31.7, 34.3)
Oregon 27.9 (26.3, 29.6)
Pennsylvania 30.2 (28.9, 31.4)
Puerto Rico 28.3 (26.8, 29.8)
Rhode Island 27.0 (25.4, 28.6)
South Carolina 32.1 (30.9, 33.3)
South Dakota 29.8 (27.9, 31.8)
Tennessee 31.2 (29.3, 33.2)
Texas 31.9 (30.6, 33.3)
Utah 25.7 (24.9, 26.6)
Vermont 24.8 (23.5, 26.1)
Virginia 28.5 (27.2, 29.7)
Washington 27.3 (26.0, 28.5)
West Virginia 35.7 (34.2, 37.2)
Wisconsin 31.2 (29.6, 32.8)
Wyoming 29.5 (27.5, 31.5)
320. Prevalence¶ of Self-Reported Obesity Among U.S.
Adults by State and Territory, BRFSS, 2015
¶ Prevalence estimates reflect BRFSS methodological changes started in 2011. These estimates should not be compared to prevalence estimates before 2011.
Source: Behavioral Risk Factor Surveillance System, CDC.
.
State Prevalence 95% Confidence Interval
Alabama 35.6 (34.1, 37.2)
Alaska 29.8 (27.5, 32.3)
Arizona 28.4 (26.9, 30.0)
Arkansas 34.5 (32.2, 36.9)
California 24.2 (23.2, 25.2)
Colorado 20.2 (19.1, 21.3)
Connecticut 25.3 (24.1, 26.4)
Delaware 29.7 (27.6, 31.8)
District of Columbia 22.1 (19.7, 24.8)
Florida 26.8 (25.5, 28.1)
Georgia 30.7 (28.8, 32.6)
Guam 31.6 (28.2, 35.1)
Hawaii 22.7 (21.3, 24.1)
Idaho 28.6 (26.9, 30.4)
Illinois 30.8 (29.2, 32.4)
Indiana 31.3 (29.5, 33.1)
Iowa 32.1 (30.5, 33.8)
Kansas 34.2 (33.4, 35.0)
Kentucky 34.6 (32.9, 36.3)
Louisiana 36.2 (34.3, 38.1)
Maine 30.0 (28.6, 31.4)
Maryland 28.9 (27.2, 30.7)
Massachusetts 24.3 (23.0, 25.6)
Michigan 31.2 (29.9, 32.4)
Minnesota 26.1 (25.3, 27.0)
Mississippi 35.6 (33.8, 37.5)
State Prevalence 95% Confidence Interval
Missouri 32.4 (30.8, 34.0)
Montana 23.6 (22.1, 25.2)
Nebraska 31.4 (30.3, 32.5)
Nevada 26.7 (24.1, 29.5)
New Hampshire 26.3 (24.8, 27.9)
New Jersey 25.6 (24.3, 26.9)
New Mexico 28.8 (27.1, 30.6)
New York 25.0 (24.0, 26.1)
North Carolina 30.1 (28.7, 31.5)
North Dakota 31.0 (29.3, 32.8)
Ohio 29.8 (28.4, 31.2)
Oklahoma 33.9 (32.2, 35.6)
Oregon 30.1 (28.4, 31.8)
Pennsylvania 30.0 (28.4, 31.6)
Puerto Rico 29.5 (28.0, 31.1)
Rhode Island 26.0 (24.3, 27.7)
South Carolina 31.7 (30.5, 33.0)
South Dakota 30.4 (28.5, 32.3)
Tennessee 33.8 (31.9, 35.7)
Texas 32.4 (30.9, 33.9)
Utah 24.5 (23.5, 25.5)
Vermont 25.1 (23.8, 26.6)
Virginia 29.2 (27.9, 30.6)
Washington 26.4 (25.5, 27.4)
West Virginia 35.6 (34.1, 37.1)
Wisconsin 30.7 (29.0, 32.4)
Wyoming 29.0 (27.0, 31.1)
321. Prevalence¶ of Self-Reported Obesity Among U.S.
Adults by State and Territory, BRFSS, 2016
¶ Prevalence estimates reflect BRFSS methodological changes started in 2011. These estimates should not be compared to prevalence estimates before 2011.
Source: Behavioral Risk Factor Surveillance System, CDC.
.
State Prevalence 95% Confidence Interval
Alabama 35.7 (34.2, 37.3)
Alaska 31.4 (28.5, 34.4)
Arizona 29.0 (27.5, 30.6)
Arkansas 35.7 (33.3, 38.1)
California 25.0 (23.9, 26.1)
Colorado 22.3 (21.4, 23.2)
Connecticut 26.0 (24.8, 27.2)
Delaware 30.7 (28.7, 32.8)
District of Columbia 22.6 (20.9, 24.3)
Florida 27.4 (26.4, 28.5)
Georgia 31.4 (29.7, 33.2)
Guam 28.3 (25.1, 31.7)
Hawaii 23.8 (22.5, 25.2)
Idaho 27.4 (25.6, 29.3)
Illinois 31.6 (29.9, 33.3)
Indiana 32.5 (31.2, 33.8)
Iowa 32.0 (30.5, 33.4)
Kansas 31.2 (30.1, 32.3)
Kentucky 34.2 (32.7, 35.6)
Louisiana 35.5 (33.4, 37.7)
Maine 29.9 (28.5, 31.3)
Maryland 29.9 (28.9, 31.0)
Massachusetts 23.6 (22.3, 24.9)
Michigan 32.5 (31.4, 33.6)
Minnesota 27.8 (26.9, 28.6)
Mississippi 37.3 (35.4, 39.1)
Missouri 31.7 (30.0, 33.4)
State Prevalence 95% Confidence Interval
Montana 25.5 (23.9, 27.2)
Nebraska 32.0 (30.8, 33.2)
Nevada 25.8 (23.9, 27.8)
New Hampshire 26.6 (25.0, 28.2)
New Jersey 27.4 (25.7, 29.1)
New Mexico 28.3 (26.6, 30.1)
New York 25.5 (24.6, 26.5)
North Carolina 31.8 (30.4, 33.3)
North Dakota 31.9 (30.3, 33.6)
Ohio 31.5 (30.2, 32.8)
Oklahoma 32.8 (31.2, 34.3)
Oregon 28.7 (27.3, 30.3)
Pennsylvania 30.3 (28.8, 31.8)
Puerto Rico 30.7 (29.0, 32.5)
Rhode Island 26.6 (24.9, 28.4)
South Carolina 32.3 (31.0, 33.6)
South Dakota 29.6 (27.6, 31.7)
Tennessee 34.8 (33.0, 36.7)
Texas 33.7 (31.9, 35.4)
Utah 25.4 (24.2, 26.5)
Vermont 27.1 (25.5, 28.7)
Virgin Islands 32.5 (28.6, 36.6)
Virginia 29.0 (27.7, 30.3)
Washington 28.6 (27.6, 29.6)
West Virginia 37.7 (36.3, 39.0)
Wisconsin 30.7 (29.0, 32.5)
Wyoming 27.7 (25.7, 29.8)
322. Prevalence¶ of Self-Reported Obesity Among U.S.
Adults by State and Territory, BRFSS, 2016
Summary
No state had a prevalence of obesity less than 20%.
3 states and the District of Columbia had a prevalence of
obesity between 20% and <25%.
22 states and Guam had a prevalence of obesity between
25% and <30%.
20 states, Puerto Rico, and Virgin Islands had a prevalence
of obesity between 30% and <35%.
5 states (Alabama, Arkansas, Louisiana, Mississippi, and
West Virginia) had a prevalence of obesity of 35% or greater.
¶ Prevalence estimates reflect BRFSS methodological changes started in 2011. These estimates should not be compared to
prevalence estimates before 2011.
http://www.cdc.gov/obesity/data/prevalence-maps.html
323. 323
Public Health Surveillance
Identifying Patterns and Formulating Hypotheses
The ongoing and systematic
collection, analysis,
and interpretation
of outcome-specific data
for use in
planning, implementation,
and evaluation
of public health practice
closely integrated
with the timely dissemination
of these data to those who
need to know.
324. 324
The ongoing and systematic
collection, analysis,
and interpretation
of outcome-specific data
for use in
planning, implementation,
and evaluation
of public health practice
closely integrated
with the timely dissemination
of these data to those who
need to know.
Identifying Patterns and Formulating Hypotheses
Public Health Surveillance
325. 325
How often does the health-related outcome occur?
How is the the health-related outcome distributed?
What hypotheses might explain the distribution
of the health-related outcome?
Health-related outcomes are not distributed haphazardly in a
population. There are patterns to their occurrence. These patterns can
be identified through the surveillance of populations. Examining these
patterns can help formulate hypotheses about the possible causes of
these outcomes.
1.
Essential Questions and Enduring Epidemiological Understandings
326. 326
1. Review / Exam 1 Preparation - 2/12
2. Surveillance, Patterns and Hypotheses
• Pneumocystis Pneumonia - Los Angeles
• Whistles
• Adult Obesity
• Pregnancy Boom
3. PPT Assignment
Science of Public Health: Epidemiology
Surveillance, Patterns and Hypotheses
January 31, 2018
327. 327
How often does the health-related outcome occur?
How is the the health-related outcome distributed?
What hypotheses might explain
the distribution of the health-related outcome?
Health-related outcomes are not distributed haphazardly in a
population. There are patterns to their occurrence. These patterns can
be identified through the surveillance of populations. Examining these
patterns can help formulate hypotheses about the possible causes of
these outcomes.
1.
Essential Questions and Enduring Epidemiological Understandings
334. 334
“Other studies have shown, unsurprisingly, that rationality is not always a
key factor in conception. One of the most intense emotions that can be
experienced is the social component of belonging and the self assertion of
a group (also known as you’ll never walk alone). Thus, the act of coming
together can be interpreted on many levels when people feel motivated to
share their euphoria with others.”
336. 336
“Some authors have shown that circumstances are decisive influences on
human conception or other behaviours. Socioeconomic factors, wars,
epidemics, famines, migrations, and cultural and religious events can drive
or impede procreation every bit as much as candlelight with Julio Iglesias
on the stereo, which - depending on the individuals present - could either
enhance or reduce desire.”
343. 343
“Other studies have shown, unsurprisingly, that rationality is not always a
key factor in conception. One of the most intense emotions that can be
experienced is the social component of belonging and the self assertion of
a group (also known as you’ll never walk alone). Thus, the act of coming
together can be interpreted on many levels when people feel motivated to
share their euphoria with others.”
344. 344
“Some authors have shown that circumstances are decisive influences on
human conception or other behaviours. Socioeconomic factors, wars,
epidemics, famines, migrations, and cultural and religious events can drive
or impede procreation every bit as much as candlelight with Julio Iglesias
on the stereo, which - depending on the individuals present - could either
enhance or reduce desire.”
345. 345
“In summary, our results may have several different interpretations. One is
that human emotions on a large scale can profoundly affect demographic
swings in populations, that national or regional events can reduce the
weight of reason and increase the weight of passion. Validation of our
results could contribute to a better understanding of human behaviour,
improve healthcare planning, and even aid government policy makers in
stimulating or reducing birth rates. Ideally, to bridge the gap between
observational and trial data, it would help greatly if Iniesta were willing to
replicate his intervention—although the cost of such a study could be
prohibitive, not to mention harmful to the reference group (Chelsea).”
347. 347
"Whether it's the natural ebb and flow of labor and delivery or the Cubs celebration?
We can leave that up to the imagination."
A Cubs World Series Baby Boom? Some Parents and Hospitals Think So
351. 351
“Many working women, without an extended family to care for
a child, face a dilemma, as private child care is expensive.
Some also worry that their job security may be undermined by
missing workdays because of child care issues. Many
companies do not offer flexible hours for working mothers.”
352. 352
“So many young women are even asked to pre-sign a
resignation letter here, especially in small companies,” said
Teresa Potenza, a longtime women’s advocate in Naples,
referring to a practice in which some women are asked to sign
a resignation letter in case of pregnancy before they are hired.
“Even to all those women, that campaign is a punch to the
gut.”
353. “One of the most intense emotions that can be experienced
is the social component of belonging and the self assertion of a group
(also known as you’ll never walk alone).”