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177 Cards in this Set

  • Front
  • Back
Core Functions of Public Health
Assessment
Policy Development
Assurance
Assessment
To regularly and systematically collect,
assemble, analyze, and make available
information on the health of the community,
including statistics on health status,
community health needs, and epidemiologic
and other studies of health problems
Policy Development
To serve the public interest in the development
of comprehensive public health policies by
promoting the use of the scientific knowledge
base in decision-making about public health
and by leading in the developing public health
policy
Assurance
To assure their constituents that services
necessary to achieve agreed upon goals are
provided, either by encouraging actions by
other entities (private or public), by requiring
such action through regulation, or by
providing services directly
Primary Prevention
Strategies, tactics, and procedures
that prevent the occurrence of
disease in the first place

Examples: safe drinking water,
vaccinations
Secondary Prevention
Strategies, tactics, and procedures,
including screening tests, that detect
disease as early as possible so that its
progress can be arrested and, if
possible, the disease eradicated

Examples: Pap test for cervical cancer,
HIV test
Tertiary Prevention
Strategies, tactics, and procedures,
including interventions, that aim to
arrest the progress of established
disease

Examples: chemotherapy for
colorectal cancer, stroke rehabilitation
program
What is Epidemiology?
The STUDY of the DISTRIBUTION and DETERMINANTS
of HEALTH-RELATED STATES OR EVENTS in specified
populations, and the application of this study
to control health problems
“Study” includes:
surveillance
observation
hypothesis testing
analytic research
experiments
"Distribution” refers to analysis by:
• time
• place
• characteristics of persons
affected
“Determinants” refer to:
• physical factors
• biological factors
• social factors
• cultural factors
• behavioral factors that influence
health
“Health-related states or events” include:
• diseases
• causes of death
• behaviors (e.g., tobacco use)
• reactions to preventive regimens
• provision and use of health
services
Epidemiology as a Tool
Premised upon the assumptions…
• Patterns of disease result from specific causes,
and
• Through an orderly sequence of reasoning those
disease patterns can be elucidated,
THEN, once elucidated, this information can be
used to prevent disease and promote health.
Epidemiology as a Liberal Art
• Because epidemiology is useful for taking a first
look at a new problem, it is applicable to a broad
range of interesting phenomena.
• Epidemiology illustrates the approaches to
problems and the kinds of thinking that a liberal
education should cultivate:
- the scientific method
- analogic thinking
- deductive reasoning
- problem solving within constraints
Objectives of Epidemiology
1. To identify the etiology or cause of a disease and
the relevant risk factors
2. To determine the extent of disease occurring in the
community
3. To study the natural history and prognosis of
disease
4. To evaluate both existing and new preventive and
therapeutic measures and modes of health care
delivery
5. To provide the foundation for developing policy
regarding disease prevention and health promotion
10 leading causes of death and
frequency of deaths: US, 2008
1. Diseases of heart
2. Malignant neoplasms (i.e., cancer)
3. Chronic lower respiratory diseases
4. Cerebrovascular diseases (i.e., stroke)
5. Unintentional injuries
6. Alzheimer's disease
7. Diabetes mellitus
8. Influenza and pneumonia
9. Nephritis, nephrotic syndrome, and
nephrosis (i.e., kidney disease)
10. Suicide
Defining a Population
Person (Who is getting the disease?)
e.g., age, sex, race/ethnicity, job, SES

Place (Where is it occurring?)
e.g., geopolitical (country, state), natural
geographic features, environment (physical,
biologic, social)

Time (How is it changing over time?
e.g., calendar year, time in the life course, can
range from hours to decades
Why is the population is important?
Variations in disease by person, place, and
time provide useful information!
Understand health status of population
Formulate hypotheses regarding disease
Plan, implement, evaluation public health
programs to control and prevent disease
Definition of population
is related to question at hand
Political entities – e.g., country, state, city
Geographic areas – e.g., specific neighborhoods,
living within 10 miles of a power plant
Health service catchment population – e.g.,
Medicare enrollees, patients at a federally
qualified health center (FQHC)
Occupational group – e.g., firefighters
Exposure group – e.g., persons taking Vioxx
Population “At Risk”
• Some measures in epidemiology focus on new
cases of a disease, so denominators for these
measures should exclude people who cannot
develop the disease.

• In those cases, the denominator should only
consist of people who are “at risk” for the
disease.
Those who are not at risk include…
– people who currently have the disease
– people who lack the organ defining the disease
– people who are immune to the disease
Dynamic (i.e., open) population
– membership is based on a condition and is transitory
– e.g., Population of the US in 2012
Fixed (i.e., closed) population
– membership based on an event and is permanent
– e.g. Persons living in Chernobyl on April 25, 1986;
persons on a cruise ship during an outbreak of
norovirus
Estimating the size of
the population: dynamic population
Dynamic population: You usually assume “steady
state” conditions apply for short intervals; the
number leaving are balanced by the number
entering, e.g., use midpoint estimate
Estimating the size of
the population: fixed population
You take account of “persontime” (e.g., person-years) under observation
Person-Time” at Risk
In studies of closed (i.e., fixed)
populations, it is often important to know
not only who was at risk, but also how
long each person was at risk.
Heterogeneity within a population
A population is a group of people who share a
common characteristic
In reality, every population is heterogeneous
with regard to other characteristics
We often specify subgroups of a population
and treat each subgroup as being
homogeneous
Subgroups of a population
However, even subgroups are heterogeneous
and made up of further subgroups
How far we go in sub-grouping depends on…
–Scientific justification
–Demographic, social, political issues
–Size of subgroups
–Availability of data
comparing populations
Inherently, statistics about populations
are weighted averages over all of the
subgroups making up the population, and
this become especially important when
comparing populations
Crude rates
refer to rates calculated for the
entire population of interest without regard to
different subgroups of the population
Specific rates
are those calculated for
designated subgroups or strata of the
population
Populations differ by characteristics
that affect morbidity and mortality
• Differences in disease rates may be due to
differences in distribution of characteristics
• Populations at two time periods may differ in
distribution of these characteristics
• Adjustment techniques equalize distributions
when comparing different populations
"Secular trends” or long-term variation
Changes in disease occurrence over a
period longer than a year
Cyclic changes or periodic fluctuations
Recurrent alterations in the frequency of
disease (e.g., seasonal variation)
Characterization of relative disease burden
Endemic vs. Epidemic vs. Pandemic
Seasonal Variation
Many disease cycles occur annually and represent
seasonal variation in disease occurrence
Seasonal variation is a well-known characteristic
for many infectious diseases and is usually based
on…
- characteristics of the infectious agent itself,
- the life pattern of the vector or other animal hosts, and/or
- changes in the likelihood of person-to-person spread.
Endemic
The constant presence of a disease within a
given geographical area or population group

May also refer to the usual prevalence of a
given disease within such an area or group
Epidemic
The occurrence in a community or region of
cases of an illness, specific health-related
behavior, or other health-related events clearly
in excess of normal expectancy

Relative to usual frequency of the disease
Pandemic
An epidemic occurring worldwide, or
over a very wide area, crossing
international boundaries, and usually
affecting a large number of people
Demographic Concepts
• Population census
• Demographic equation
• Epidemiologic transition model
• Demographic transition model
• Population pyramid
Uses of Censuses in the US
Government and administrative matters
- Apportionment of Representatives to states
- Drawing of Congressional districts
- Allocation of funding

Science
- Demographic trends (e.g., population growth,
urbanization, changes in population structure)
- Current and future trends in occupations, standard
of living, education, economic and business cycles
- Sampling frames
Demographic Equation
Natural change in population size =
Births - Deaths ± Migration
The Epidemiologic Transition
Theory focuses on…
• The complex change in patterns of health
and disease
• The interactions between these patterns and
their demographic, economic and sociologic
determinants and consequences

Parallels demographic and technologic
transitions observed in developed and
developing countries
Ages in the Epidemiologic Transition
The Age of Pestilence and Famine
The Age of Receding Pandemics
The Age of Degenerative and Man-Made Diseases
The Age of Pestilence and Famine
Mortality is high
and fluctuating, low and variable life expectancy
(range 20-40 years)
The Age of Receding Pandemics
Mortality declines
with fewer epidemics, life expectancy increases (from
30 to 50 years), population growth is sustained and
begins to be exponential
The Age of Degenerative and Man-Made Diseases
Mortality continues to decline and approaches
stability at low level, chronic diseases replace
infectious diseases as the primary causes of death
Determinants of Transitions
• Complex balance between disease agents, the
level of hostility in the environment and the
resistance of the host.

• Socioeconomic, political and cultural
determinants include standards of living, health
habits and hygiene and nutrition.

• Medical and public health determinants are
specific preventive and curative measures used
to combat disease; they include improved public
sanitation, immunization and the development of
decisive therapies.
Leading Causes of Death in the US,
1900 & 2008
1900
1. Pneumonia & influenza
2. Tuberculosis
3. Diarrhea
4. Heart disease
5. Vascular lesions of CNS
6. Chronic nephritis
7. Accidents
8. Malignant neoplasms
9. Diseases of early infancy
10. Diphtheria
2008
1. Heart disease
2. Malignant neoplasms
3. Chronic lower resp. diseases
4. Cerebrovascular disease
5. Injuries
6. Alzheimer’s disease
7. Diabetes
8. Pneumonia & Influenza
9. Chronic nephritis
10. Suicide
Infectious versus Chronic Diseases
Infectious disease > communicable
• Microorganism (e.g., virus, bacteria)
• Examples: tuberculosis
malaria
measles
Chronic disease > non-communicable
• Usually no organism
• Examples: cancer
heart disease
The Demographic Transition Model
Pre-industrial → industrialized economic system
Shift from high to low levels of mortality and
fertility
Age structure changes
younger → older
Population pyramid changes shape
triangular → more rectangular
The Population Pyramid
Graphical summary of the age and sex
composition of a population
Useful tool
– Important source of clues for
• Health status
• Causes of mortality
Broad base and narrow apex indicates a high
fertility population
Numerators and Denominators
Numerators
– How do we find / identify
“cases” of disease and death
in the population? (Surveillance, vital registration, surveys, hospital records)

Denominators
– How do we measure the
population at risk over time? (Census, samples of the population, surveys)
Types of Calculations to Measure
Morbidity & Mortality
Rate
Proportion
Ratio
Rate
How fast is disease occurring?

Events (e.g., cases)/ Population-Time
Proportion
What fraction of the population is affected by disease?

Number Affected/ Total Population
Ratio
How do two quantities compare?

Estimate in Ratio population #1: Estimate in population #2

Note: Ratios are unitless (when the two estimates
are in the same units)
Incidence as a concept
The number of new cases of a disease that occur
during a specified period of time in a population at
risk for developing the disease

Incidence =
Number of NEW cases of a disease occurring in the population during a specified period of time /
Number of persons who are AT RISK of developing the disease during that same period of time
Cumulative Incidence formula
Cumulative Incidence (%) =
Number of new cases
of a disease occurring
in the population during
a specified period of time /

Number of persons at risk
for the disease at the
beginning of the period

Note: All individuals are observed for entire period
Cumulative Incidence
Cumulative incidence is a proportion
Cumulative incidence is a measure of risk
Risk is the probability that an event will occur
during a specified time
Similar to attack “rates” in outbreak
investigations
“Ever” questions on surveys (i.e., Have you
ever been diagnosed with asthma?)
Second “flavor” - Incidence Rate
Incidence expressed as a rate in units of
person-time “at risk”

Note: Some individuals are not observed for
entire period (i.e., different people are observed
for different lengths of time)

# new cases/Person-time at risk
example: 5/11.67 person-yr = 42.8 per 100 person-
Prevalence
The proportion (%) of the population affected by
the disease at a given time (i.e., point in time or
period in time); not a measure of risk

Prevalence=
(Number of existing cases of disease present in the population at a specified time/
Number of persons in the population at that specified time) x 100

Range for prevalence: [0,1] or [0%, 100%]

Prevalence = Incidence x Duration If there is a steady-state situation (i.e., rates are not changing and in-migration equals out-migration)
Factors Influencing Observed Prevalence
Increased by:
• Longer duration of the
disease
• Prolongation of life of
patient without cure
• Increase in new cases
• In-migration of cases
• Out-migration of healthy
people
• Improvements in diagnosis
(or better reporting)

Decreased by:
• Shorter duration of
disease
• High case-fatality rate
from disease
• Decrease in new cases
• In-migration of healthy
people
• Out-migration of cases
• Improved cure rate of
cases
Annual All-cause Mortality Rate
(per 1,000 population)
Annual All-cause Mortality Rate per 1,000 population=

(Total number of deaths from all causes in 1 year/
Number of persons in the population at mid-year) x1,000
Cause-specific Mortality Rate
(per 1,000 population)
AnnualMortality rate from “Disease A” per 1,000 population= Number of deaths from “Disease A” in 1 year/ Number of persons in the population at mid-year) x1,000
Proportionate Mortality
What proportion of all deaths in a given
year are due to a particular disease?

# deaths due to particular disease/ Total # deaths

Sometimes this is (incorrectly) called the
Proportionate Mortality Rate.
Case-Fatality Rate (CFR) (%)
# deaths from Disease Y/ # people with Disease Y

Note: Although usually called a “rate”, the
CFR is really a proportion. CFR is a
measure of the severity of a disease.
When is a mortality rate a good index
of an incidence rate?
When case-fatality rate is high (e.g., untreated rabies)

When the duration of the disease (i.e., survival) is short (e.g., pancreatic cancer)
Adjustment
A procedure for adjusting rates (e.g.,
death rates), designed to minimize the
effects of differences in population
composition when comparing rates for
different populations
Two methods of adjustment
Direct - weighted average of specific rates
• Uses rates of sub-groups of populations
being compared
• Need to know sub-group distributions of
events in both populations

Indirect - compares observed number of events to
expected number
• Used when sub-group distribution of events
in population of interest is not known
Direct age-adjustment
A standard population is used in order to
eliminate the effects of any difference in age
between the populations being compared
Direct age-adjustment
Steps
• Step 1 – Calculate rates for each population
overall and by age category
– Divide the number of events by the population

• Step 2 - Identify the standard population
– Most often, add populations of interest by age
categories
– Can select appropriate population with age categories
(e.g., 2010 US population)

• Step 3 – Apply all rates to standard population
by age category to obtain expected number of
events for each population

• Step 4 – Sum the expected number of events for
each population

• Step 5 – For each population, divide the sum of
expected number of events by the total standard
population to obtain the age-adjusted rate
Interpreting differences between
crude and age-adjusted rates I
When conducting direct age-adjustment,
the standard population has a different age
composition from that of population being
compared.

Another way to say this… the standard
population has different “weights” of age
categories than those of the population
being compared.
Interpreting differences between
crude and age-adjusted rates II
Assuming the rate of interest is related to
disease that increases with older age…

If the population of interest has a higher
proportion of older ages (i.e., it’s weighted
with older ages) than the standard
population, then the age-adjusted rate for
the population of interest will be lower than
the crude rate for that population.
Interpreting differences between
crude and age-adjusted rates III
Assuming the rate of interest is related to
disease that increases with older age…

If the population of interest has a lower
proportion of older ages (i.e., it’s weighted
with younger ages) than the standard
population, then the age-adjusted rate for
the population of interest will be higher
than the crude rate for that population.
Indirect age-adjustment
Comparison of expected versus observed events is
utilized in order to eliminate the effects of any
difference in age between the populations being
compared
Employed when numbers of events for each agespecific stratum are not available
Indirect age-adjustment
Steps
• Step 1 – Identify total observed events in
population of interest
• Step 2 – Identify age-specific event rates for
known population of comparison
• Step 3 – Apply age-specific event rates (from
comparison population) to age-specific strata of
population of interest to obtain expected number
of deaths for each age category
• Step 4 – Sum the expected number of events for
the population of interest
• Step 5 – Divide the number of observed events
by the number of expected events to obtain the
standardized mortality ratio (SMR)
Standardized mortality ratio
(SMR)
Observed number of deaths/ Expected number of deaths

Note: Multiplication by 100 is often done to
yield results without decimals
Things to remember regarding
adjustment
• Always examine data before adjusting
• Adjusted rates are hypothetical
Measures of Prognosis
5-year survival rate

Observed survival rates
– person-time
– life tables
– Kaplan-Meier method

Median survival time

Relative survival rate
5-Year Survival Rate
Proportion of persons in a specified group
alive at the beginning of the 5 years who
survive to the end of the 5 years
Observed survival rates
Person-time
Life tables
Two Assumptions Made
In Using Life Tables
No changes have occurred in survivorship over
calendar time.

Those lost to follow-up experience the same
survivorship as those who are followed.
Kaplan-Meier Method
requires date last observed or date outcome occurred on each individual (end of study can be the last date observed) The essence of the Kaplan-Meier (KM) method is having the date each outcome in the cohort occurred.
CALCULATING SURVIVAL USING THE
KAPLAN-MEIER METHOD
Columns
(1)Times to deaths from starting treatment (months)

(2) Number alive at each time

(3) Number who died at each time

(4) Proportion who died at that time Column(3)/ Column(2)

(5) Proportion who survived at that time 1-Column(4)

(6) Cumulative proportion who survived to that time (Cumulative Survival)
Median Survival Time
Length of time that half of the study
population survives

Why median rather than mean?
– Less affected by extreme values
– Only need to observe deaths of half of
the study group rather than the entire
group
Relative Survival Rate
Observed Survival Rate/
Expected Survival Rate
Investigating an Outbreak I
0. Determine existence of epidemic
1. Case definition
2. Identify cases and population at risk
3. Obtain information on time, place,
and person
4. Collect specimens for lab analysis
5. Analyze data
6. Develop, test, and refine hypotheses
7. Implement control measures
8. Prepare report and disseminate
findings
Determine existence of epidemic
Info from local health officials
-observed versus expected

Beware of changes!
-local reporting practices, interest in
diseases, “new kid on the block,”
diagnostic methods

May need to acquire additional data
Case definition
Clinical features and known causes

What tests to confirm diagnosis?

Start with a simple case definition, and
then refine as you go
-What happens when you change the case
definition?
Identify cases and population at risk
Identify sources for finding cases

May need more intensive efforts

Collect info likely to provide clues to determine
population at risk
-Natural history of epidemic (disease transmission)
Characteristics of the ill (opportunities for exposure)
Obtain information
Time of occurrence

Place

Characteristics of persons affected
-Demographics
-Potential exposures
Collect specimens for lab analysis
Collect from people, vectors, fomites
Serologic tests
X-rays
Etc
Analyzing Data
in Outbreak Investigations
1. Determine whether the observed number
of cases clearly exceeds the expected
number

2. Calculate attack rates
– Rate defined as the cumulative incidence of
infection in a group observed during an
epidemic
– Note, time is implicit

3. Epidemic curve - A graph of the frequency of
occurrence of cases by time of onset
Patterns:
Point source epidemic
Common source epidemic
Propagated epidemic

4. Spot map - A detailed map identifying the
location of cases in a discrete area
How to Calculate an Attack Rate
Number of people at risk in whom a certain illness develops / Total number of people at risk

• AKA cumulative incidence
• Can be designated for a given exposure,
e.g., food-specific
• Time is implicit
Characteristic Patterns
of Epidemic Curves
• Point source outbreak
• Common source outbreak
• Propagated outbreak
Point Source Outbreaks
Population is exposed briefly /
simultaneously / at a single point in time

Cases occur suddenly, and, after a brief
peak period of time, fall off in a logarithmic
fashion

One incubation period
Common Source Outbreak
Exposure is not brief / simultaneous, rather it
occurs over time

Cases arise suddenly and continue to arise
as more individuals continue to be exposed
to the source

Peaks in epidemic curve may represent two
or more incubation periods
Propagated Outbreak
Exposure can be brief or over time

Person-to-person transmission of disease

Two or more peaks in epidemic curve as
secondary cases occur from person-toperson spread
Determinants of Propagated Outbreaks
Balance between proportion of susceptible
and immune individuals in the population
-Immunity may be:
Innate (genetic) or
Acquired (previous illness, immunization)

Effective contact between agent and
susceptible host

Virulence of the organism
Herd Immunity
The relative resistance of a group to an
outbreak of a disease when a critical
proportion, but not all, of the group is
immune.

This occurs when the disease is spread by
person-to-person contact.

The greater the proportion of immune
people in the group, the less likely it is that
an infected person will encounter a
susceptible person.

If such encounters are sufficiently rare, the
disease will not propagate in the group.

The critical proportion of immune people
varies with the virulence of the disease,
e.g. for measles it is about 94%
Develop, test, and refine hypotheses
Examine distribution of cases by time and place

Look for time-place interactions

Examine possible interactions among potential
exposures

Incorporate existing knowledge, if any, about
disease

Refine hypotheses and collect additional data
that may be needed
Cross-tabulation of Attack Rates
Risk Difference = (ARexposed) – (ARnon-exposed)
Risk Ratio = (ARexposed) / (ARnon-exposed)
Incubation Period
The time interval from infection
(exposure) to appearance of first sign
or symptoms of the disease
Median Incubation Period
• 50th percentile
• observation at the middle of the distribution

Example: Time of symptom onset was known for 30 people (N=30)

Median = (30+1)/2 = 15.5 observation
Dose-response Relationship
A gradient in the attack rate in relation to the
amount of the exposure

Provides strong evidence that the exposure
is the cause of the disease
Institute control measures
Control of current outbreak

Prevention of future similar outbreaks

Directed at source
-modify environment, quarantine, treat infected
persons/animals, etc

Directed at susceptibles
-modify behavior to reduce risk to self and/or
others, administer post-exposure prophylaxis, etc
Prepare report and disseminate findings
Document for action, record of performance,
document for medical/legal issues, enhance
outbreak investigations, instrument for
teaching epi

Share with…
Public health personnel involved in policy
development and implementation
General public
Scientific community
(EIS)
Epidemic Intelligence Service
- Featured in the movie Contagion
Public Health Surveillance
Continuous, systematic collection, analysis and
interpretation of health-related data needed for
the planning, implementation, and evaluation of
public health practice.

Passive – Routine reporting of disease cases
seen in health care facilities

Active - Special search to find disease cases

Sentinel – Disease-specific reporting systems
in defined catchment areas
Public health surveillance can…
• Serve as an early warning system for
impending public health emergencies
• Document the impact of an intervention, or
track progress towards specified goals
• Monitor and clarify the epidemiology of health
problems, to allow priorities to be set and to
inform public health policy and strategies
Data Sources
• Mortality data
• Morbidity data
• Laboratory data
• Individual case reports
• Epidemic reporting
• Sentinel systems
• Knowledge of vertebrate and arthropod vector
species
• Surveys of general population, special
databases
• Demographic and environmental factors
Real-time Surveillance
• Alert public health care practitioners in early
phases of outbreak
• Promptly institute case finding and control
measures
• Improve access to treatment with the goal of
reducing morbidity and mortality
Syndromic Surveillance
Adapted CDC definition:
Surveillance using already existing healthrelated data that precede diagnosis and
signal a sufficient probability of a case or an
outbreak to warrant further public health
response

Supplements existing surveillance methods
Syndromic Surveillance
Clinical Data Sources
Emergency Department (ED) data, e.g., chief
complaint, total patient volume

Emergency medical system call type, e.g., 911

Poison control center call

Unexplained deaths

Insurance claims or billing data

Clinical laboratory or radiology ordering volume
Syndromic Surveillance
Alternative Data Sources
School absenteeism

Work absenteeism

Over-the-counter medication sales

Health-care provider database searches

Internet-based health searches by public

Animal illness or deaths
Analysis of Web Data
• Best used for surveillance of epidemics and
disease with high prevalence
• Better suited for use in developed countries
with large populations of web search users
• Pitfall - Disease publicity may increase use of
disease related search terms
Social Media Use in the US
• Rapid uptake of internet use and social media
(e.g., social networking, blogging, etc)
• Internet access- disparities exist by
race/ethnicity and health status
– Though, among people with access, these
characteristics do not affect social media use
• Growth of social media is not uniformly
distributed across age groups
Desirable Characteristics
of Real-time Data in Any Setting
• Authentic users
• Ability to tease apart “real traffic” for disease
versus chatter
– Media coverage on disease
– Sales at pharmacies
• Protection of privacy (both real and perceived
privacy concerns)
mHealth
mHealth is the use of mobile
hand-held devices, especially
text and web-enabled cell
phones, for the delivery of
health information and
messaging
mHealth

Cell phone market penetration in the developed
world is around 90 percent, and in the developing
world about one-third of that, and growing

Android operating system is open-source
Uses of mHealth
Mobile phones can provide reminders about
healthy activities, sources for disease-specific
information
Promise of mHealth
Mobile phones are also being explored as a
means of…
- Tracking patient wellbeing or compliance
with treatments
- Data collection for public health purposes
- Warning of disasters or emergencies (e.g.,
tsunami, JHU text alerts)
Challenges of mHealth
Evaluation is difficult
How to quantify effectiveness as part of an
intervention?
How to transition from pilot projects to scaledup programs?
Sustainability
No standards for mHealth platform integration
Magpi
• Creates web-based data gathering forms that
can be filled out via cell phone from the field
• Omits data collection on paper and input into
electronic databases for sharing and analysis
• GPS enabled phones allow data to be tagged
with geo-coordinates
Desirable Attributes
of a Health Measurement
• Relevant
• Valid
• Precise
• Quantitative
• Safe
• Acceptable
• Practical
• Inexpensive
Validity
An expression of the
degree to which a
measurement
measures what it
purports to measure
Sensitivity
The measurement ability of the
test to identify correctly those
who have the disease
Specificity
The measurement ability of the
test to identify correctly those
who do not have the disease
Determining the Sensitivity and Specificity
of a New Measurement
“Gold standard” or reference standard measurement
100% accurate, or best measurement available
Often invasive or expensive

New measurement
-Less expensive and/or easier to administer

Compare the performance of the new measurement
to the reference standard measurement
Sensitivity formula
= # positive among those with disease/ total # with disease

= Pr ( + | Disease)
Specificity formula
# negative among those with no disease/ total # with no disease

= Pr ( - | No Disease)
Type 2 Diabetes Mellitus
High prevalence in the U.S. and increasing,
especially obese populations
Reference standard test for diagnosis is the oral
glucose tolerance test; requires drinking a high
glucose drink and multiple blood draws over
several hours
Screening test is a fasting plasma glucose level
Where to Draw the Cutpoint?
(Criterion of Positivity)
Minimize the number of false positives (FP) if the
gold standard test is expensive or invasive →
Increases specificity

Minimize the number of false negatives (FN) if the
penalty for missing cases is high (e.g., disease is
serious but treatment exists, disease easily spreads,
screening and diagnostic tests are cheap and have
low risk) → Increases sensitivity

Balance severity of FP and FN
Use of Multiple Tests
Two-Stage Screening (Sequential)

Use a less expensive, less invasive, or
less uncomfortable test first

Rescreen those who screened positive
with a more expensive, more invasive,
or more uncomfortable test (or the
same one)
Two-Stage Sequential Screening
People are identified/defined as having the
disease only if they test positive twice

Purpose of re-testing those who screened
positive is to have a second chance to rule out
the false positives

Ruling out more false positives increases
specificity

Undesirable consequence is that true positives may become false negatives; this decreases sensitivity

No need to re-test those who initially screened negative, as they won’t meet definition of disease
Simultaneous Screening
Purpose of re-testing everyone is to have a
second chance to rule out the false negatives
(i.e., identify more positives)

Ruling out more false negatives increases
sensitivity

Undesirable consequence is that true negatives
may be mistakenly labeled as false positives;
this decreases specificity

Those who tested positive the first time really do
not need to be re-tested as they already meet
the definition of disease, but this is done for
convenience with little added cost
Simultaneous Screening – Net Sensitivity
Net sensitivity = test positive on both or either test /
total # with disease

Test positive on both tests = test A sensitivity x test B sensitivity x total # with disease

Test positive on just one test = correctly tested positive for that test minus overlap
Simultaneous Screening
Net Sensitivity Formula
= sensitivity test A + sensitivity test B – (sensitivity
test A x sensitivity test B)
Simultaneous Screening – Net Specificity
Net specificity = test negative both tests / total # with no disease

Test negative on both tests = test A specificity x test B specificity x total # with no disease
Simultaneous Screening
Net Specificity Formula
= test A specificity x test B specificity
Comparison of Simultaneous
and Sequential Testing
Versus Using Either Test Alone
Sequential testing
– “Positive” = positive result on both tests
– Net loss in sensitivity
– Net gain in specificity

Simultaneous testing
– “Positive” = positive result on either test
– Net gain in sensitivity
– Net loss in specificity
Positive Predictive Value
Of those who test positive, the proportion who actually
have the disease
Pr ( Disease | +)

= # tested positive among those with disease /
total # tested positive

synonyms: predictive value positive, PVP,
PPV, PV+

PPV is not a fixed characteristic of the test
Negative Predictive Value
Of those who test negative, the proportion of
patients who are actually free of the disease
Pr ( No Disease | -)

= # tested negative among those with no disease / total # tested negative

synonyms: predictive value negative,
PVN, NPV, PV

NPV is not a fixed characteristic of the test
Predictive Value of a Test
Depends on
• The prevalence of the disease in the
population tested, and
• The specificity of that test (if the disease
prevalence is low)

Recall that the PV is not a fixed
characteristic of the test.
Evaluating Measurements
Reliability
Can the result of the test be replicated
if the test is repeated?

i.e., consistency of results under repeated
examination (by the same people, or similarly
trained people, under the same/similar
conditions)

Synonyms: repeatability, precision
Percent agreement
Overall percent agreement (concordance)
for test with dichotomous outcomes:
Percent agreement = (a + d)/total x 100
Correcting Overall Agreement
for Chance Agreement
Two observers - some portion of agreement may
be due to chance alone, especially when a large
proportion of test results fall into one category
(e.g., most tests are negative)

Example:
Pick 2 people in the class who have no experience
reading x-rays
Each ‘reads’ some as positive but most as negative
By chance alone, they agree on many
Kappa Statistic
((Observed Agreement(%))-(Agreement Expected By Chance Alone (%))) / (100% - (Agreement Expected By Chance Alone (%)))
Kappa Statistic Numerator:
How much better is the observers’ agreement than by chance alone?
Kappa Statistic Denominator:
What is the maximum
the observers could possibly
improve agreement over
chance alone?
Interpreting Values of Kappa
Value of Kappa vs. Strength of Agreement
0.0 -No agreement better than chance alone
< 0.20 -Poor
0.41 - 0.60 -Fair
0.61 - 0.80 -Moderate
0.81 - 1.00 -Good
0.21 - 0.40 -Very Good
Predictive Value of a Test
Depends on Prevalence
• The prevalence of the disease in the
population affects the PV of a given test.
• Recall that the PV is not a fixed
characteristic of the test.
Descriptive Epidemiology
Epidemiology =
The study of the occurrence and distribution of
health-related states or events in specified
populations, including the study of determinants
influencing such states, and the application of
this knowledge to control the health problems.
Descriptive Epidemiology: Why?
Public health planning: How many people are
affected?
–How important is a health problem is to individuals
and to society
• Does the problem merit resources and risks and
benefits of intervention?

How do the patterns of occurrence of the public
health problem vary by different characteristics?
–Helps to generate hypotheses about the causes of
the problem (descriptive >> analytical)
Analytical Epidemiology: Why?
• Determining risk factors and causes of disease
- Is childhood obesity associated with increased
incidence of Type 2 diabetes?

• Evaluating preventive and therapeutic interventions
that alter the course of disease
- Does beginning HIV treatment earlier (i.e., at
higher CD4 levels) lead to better health
outcomes (e.g., non-detectable viral load)?
Experimental Trial
• Studies prevention and treatment of disease
• Investigator actively manipulates which groups receive the exposure/intervention
Observational
• Studies causes, prevention and treatment for diseases
• Investigator passively watches as nature takes its course
Cohort
• Examines multiple health effects of an exposure
• Subjects defined by exposure levels and follow for
disease occurrence
Case-Control
• Typically examines multiple exposures in relation to a
disease
• Subjects are defined as cases and controls and exposure
histories compared
Cross-sectional
• Examines relationship between exposure and disease
prevalence in a defined population at one point in time
Ecologic
• Examines relationship between exposure and disease
with population-level data rather than individual-level data
Goals of analytic studies
• Determine risk factors and causes of disease
• Evaluate preventive and therapeutic
interventions that alter the course of disease
Populations in the context of study designs:
Target – The population about which inferences
are desired
Source – The source of subjects for a particular
study, subset of target population, can be
enumerated
Study – Subset of source population, the
subjects who actually participate in study
Key Parameters of Study Design
Unit of Observation
Allocation of Exposure Under Study
Timing of Observations
Unit of Observation
1. Group (of individuals) as unit
– Examples: Lecture hall of students, city,
country
– Ecologic study
2. Individual as unit
– Examples: Person or event (e.g.,
pregnancy)
Allocation of Exposure Under Study
1. Experimental
– Performed by researcher
– May or may not be random
– Example: Clinical trial
2. Non-experimental (i.e., observational)
– By nature, self-selection, imposed by
others, etc
– Researcher does not want to interfere
with exposure allocation
Timing of Observations
1. Longitudinal recording of exposures and
outcomes over time (i.e., data collection at
least two time points)
2. Cross-sectional
- Information on exposures, outcomes,
other factors collected at the same time
(i.e., data collection at only one time point)
- Prevalence data
- No attempt to reconstruct exposure
history
Relevant Etiologic Time Window
Appropriate time window must be defined
based upon:
• Available knowledge of disease
• Hypothesized effect of exposure
Sampling of Source Population
1. By exposure status
– Exposed versus unexposed
– Cohort studies
2. By outcome status
– Disease (i.e., case) versus
no disease (i.e., control)
– Case control studies
Type of Data
1. Primary data – Collected for the purpose of
a study
– Example: Questionnaires
– Large studies may have various exposures
and outcomes
2. Secondary data – Collected primarily for
other purposes
– Example: Medical records
– Use with caution!
Ecologic Study
Association based on sample of
groups, rather than sample of
individuals

Distinguished by the fact that
groups are the “unit of
observation”
Ecologic Studies
Examine rates of disease in relation to a
population-level exposure factor
-Summaries of individual population
members, environmental measures, global
measures (e.g., health care system)

Populations/groups may be defined by
time or place or both

Measure of association is often the
correlation coefficient (r) - not an
estimate of risk
Advantages in Ecologic Studies
Can usually be completed relatively quickly if
based on available data

Inexpensive if data are available

Good for hypothesis generation

Can examine wide range of exposure levels
Limitations in Ecologic Studies
Individuals who are exposed may not be
the same as those with relevant outcome

Cannot adjust for other extraneous factors
(i.e., lack of information on important
variables)

Can be difficult to interpret, as data are
crude by nature

Cannot establish temporality or causation
Key Limitation: Ecologic Fallacy
The bias that may occur because an
association observed between variables
on the aggregate/group level does not
necessarily represent the association that
exists at the individual level.

Therefore, we cannot ascribe group
characteristics to individual members of
the group.
Cross-sectional Study
Observational / non-experimental study

Individual is unit of observation

Data on exposures, outcomes, other
factors collected from defined population
at the same time, and at a single point in
time

Participants are often selected without
regard to exposure or outcome status

Advantages:
- Generate inferences and hypotheses
- Quick
- Low cost
- Highly generalizable, when based on sample of
general population

Often used as basis for public health policy
and programming decisions
Example: NHANES (National Health and
Nutrition Examination Survey)
Limitations of Cross-sectional studies
Cannot establish temporality

Cannot determine causation

May miss prevalent cases of short duration

Healthy worker effect