01 Epidemiology of Non-communicable Diseases

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Epidemiology of Non-communicable Diseases
Raymark D. Salonga, RN MPH
Course Outcomes
• Identify the major known determinants of chronic disease and their
distribution by country, community, and key demographic
characteristics both in the Philippines and globally.
• Assess how the social, political and economic environment shape
policy and other responses to major non-communicable disease
whilst examining the actions necessary to control key noncommunicable diseases
Course Outcomes
• Assess the organization, delivery, and financing of health services in
the Philippines and globally influence the provision of health care
aimed at the prevention, management, and treatment of chronic
disease.
• Analyze the factors that influence public health’s approach to
addressing chronic disease, including health impact; availability of
health data; political, economic, and cultural influences; and
feasibility.
Course Outcomes
• Apply systematic approaches to develop, implement, and evaluate
programs to prevent the occurrence of chronic disease and to
improve quality of life for persons with these conditions.
Learning Outcomes
• Explore and explain the trends and patterns in the burden of noncommunicable disease, both globally and in the Philippines.
• Critically discuss the epidemiology and risk factors of major non
communicable diseases, globally and in the Philippines
• Examine how disease burden is measured and critique the strengths
and weaknesses of the various methods.
Learning Outcomes
• Critical understanding of health inequalities relating to key noncommunicable diseases.
• Critically assess the importance of the social, political and economic
environment in shaping policy and other responses to major noncommunicable disease.
• Critically examine the actions necessary to control key noncommunicable diseases.
• Propose possible strategies for disease prevention as applicable and
discuss appropriate social determinants of diseases.
Basic Epidemiologic Principles
1.
2.
3.
4.
Principles of Disease Causation
Study Designs for Epidemiology
Measures of Disease Frequency/Occurrence
Estimates of Association
Principles of Disease Causation
• Epidemiology is 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
• Epidemiology is often described as the basic science of public health
Principles of Disease Causation
• Measure disease frequency
• Quantify disease
• Assess distribution of disease
• Who is getting disease?
• Where is disease occurring?
• When is disease occurring?
Formulation of hypotheses concerning causal and preventive factors
• Identify determinants of disease
• Hypotheses are tested using epidemiologic studies
Principles of Disease Causation
Epidemiology is often described as the basic science of public health…
• epidemiology is a quantitative discipline that relies on a working
knowledge of probability, statistics, and sound research methods
• it is a method of causal reasoning based on developing and testing
hypotheses grounded in other fields of science
• It provides the foundation for directing practical and appropriate
public health action based on this science and causal reasoning
Principles of Disease Causation
A critical premise of epidemiology is that disease and other health
events do not occur randomly in a population, but are more likely to
occur in some members of the population than others because of risk
factors that may not be distributed randomly in the population.
Principles of Disease Causation
Epidemiologic Triad
Principles of Disease Causation
Epidemiologic Triad
• The triad consists of an external agent, a susceptible host, and
an environment that brings the host and agent together.
• Development of appropriate, practical, and effective public health
measures to control or prevent disease usually requires assessment of
all three components and their interactions.
Principles of Disease Causation
Epidemiologic Triad
• Agent originally referred to an infectious microorganism or pathogen:
a virus, bacterium, parasite, or other microbe. Generally, the agent
must be present for disease to occur
• Over time, the concept of agent has been broadened to include
chemical and physical causes of disease or injury.
Principles of Disease Causation
Epidemiologic Triad
• Host refers to the human who can get the disease. A variety of factors
intrinsic to the host, sometimes called risk factors, can influence an
individual's exposure, susceptibility, or response to a causative agent
• Opportunities for exposure are often influenced by behaviors such as
sexual practices, hygiene, and other personal choices as well as by
age and sex.
Principles of Disease Causation
Epidemiologic Triad
• Environment refers to extrinsic factors that affect the agent and the
opportunity for exposure.
• Environmental factors include physical factors such as geology and
climate, biologic factors such as insects that transmit the agent, and
socioeconomic factors such as crowding, sanitation, and the
availability of health services.
Principles of Disease Causation
Epidemiologic Triad
• What are the limitations of Epidemiologic Triad as a model for disease
causation for non-communicable diseases?
Principles of Disease Causation
Principles of Disease Causation
Component causes and causal pies (Rothman, 1976)
Principles of Disease Causation
Component causes and causal pies (Rothman, 1976)
• The individual factors are called component causes.
• The complete pie, which might be considered a causal pathway, is
called a sufficient cause. A disease may have more than one sufficient
cause, with each sufficient cause being composed of several
component causes that may or may not overlap.
• A component that appears in every pie or pathway is called
a necessary cause, because without it, disease does not occur.
Principles of Disease Causation
Component causes and causal pies: Types of Causal Relationships
• Necessary and sufficient – without the factor, disease never develops
• Necessary but not sufficient – the factor in and of itself is not enough
to cause disease
• Sufficient but not necessary – the factor alone can cause disease, but
so can other factors in its absence
• Neither sufficient nor necessary – the factor cannot cause disease on
its own, nor is it the only factor that can cause that disease
Principles of Disease Causation
• For each group, draw two complete/sufficient causal pies for lung
cancer.
• Identify the necessary causes for lung cancer
Principles of Disease Causation
For each of the following risk factors and health outcomes, identify
whether they are necessary causes, sufficient causes, or component
causes.
_____ Hypertension / Stroke
_____ Treponema pallidum / Syphilis
_____ Type A personality / Heart disease
_____ Skin contact with a strong acid /Burn
Measures of Disease
Frequency/Occurrence
• Incidence is the number of new cases of a disease occurring in an atrisk population during a defined time interval
Example: The incidence of kidney disease in a community is 30 new
cases for this year
Measures of Disease
Frequency/Occurrence
• Incidence proportion - the number of new cases of a disease
occurring divided by the total number of people at risk
Example: The incidence proportion of kidney disease in a community is
3 new cases per 10 adults
Measures of Disease
Frequency/Occurrence
• Incidence rate/density - The numerator is the same as the numerator
of incidence proportion but the denominator accumulates time at risk
of the event. The denominator is not just the number of people at
risk.
Example: In a study of breast cancer, an individual who was followed
for 5 years will contribute 5 person-years of follow-up to the
denominator, while another individual with 3 years of follow-up will
contribute 3 years to the denominator
Incidence rate of breast cancer = 25 per 10,000 person-years
Measures of Disease
Frequency/Occurrence
• Prevalence - the proportion of population with the disease
Example: global prevalence of lung cancer was 32% (1.86 billion
people).
Measures of Disease
Frequency/Occurrence
• Prevalence - the proportion of population with the disease
- It measures the extent (amount) of the event (disease) in
the population in a specified time
- The numerator includes both new and existing cases of
disease
Measures of Disease
Frequency/Occurrence
• A chronic, incurable disease, such as diabetes, can have a ????
incidence but ???? prevalence.
• A short-duration, curable disease, such as the common cold, can have
a ???? incidence but ???? prevalence
Study Designs for Epidemiology
• Disease is not randomly distributed throughout a population
• Epidemiology uses systematic approach to study the
differences in disease distribution in subgroups
• Allows for study of causal and preventive factors
Study Designs for Epidemiology
• Measure disease frequency
• Quantify disease
• Assess distribution of disease
• Who is getting disease?
• Where is disease occurring?
• When is disease occurring?
Formulation of hypotheses concerning causal and
preventive factors
• Identify determinants of disease
• Hypotheses are tested using epidemiologic studies
Study Designs for Epidemiology
• Descriptive studies (to generate hypotheses)
• Case-reports
• Case-series
Study Designs for Epidemiology
• Analytic studies (to test hypotheses)
• Experimental studies
• Clinical trials
• Field trials
• Intervention studies
• Observational studies
• Cross sectional (can also be descriptive)
• Case-control studies
• Cohort studies
• Ecologic studies
Study Designs for Epidemiology
•Types by timeframe
• Prospective Study - looks forward, looks to the
future, examines future events, follows a condition,
concern or disease into the future
• Retrospective Study - “to look back”, looks back in
time to study events that have already occurred
Descriptive Study: Case Reports
• Detailed presentation of a single case or handful of cases
• Generally report a new or unique finding
• e.g. previous undescribed disease
• e.g. unexpected link between diseases
• e.g. unexpected new therapeutic effect
• e.g. adverse events
Descriptive Study: Case Series
• Experience of a group of patients with a similar diagnosis
(more than 2)
• Assesses prevalent disease
• Cases may be identified from a single or multiple sources
• Generally report on new/unique condition
• May be only realistic design for rare disorders
Descriptive Studies
• Advantages
• Useful for hypothesis generation
• Informative for very rare disease with few
established risk factors
• Characterizes averages for disorder
• Disadvantages
• Cannot study cause and effect relationships
• Cannot assess disease frequency
Analytic Study: Experimental
• treatment and exposures occur in a
“controlled” environment
• planned research designs
• clinical trials are the most well known
experimental design. Clinical trials use
randomly assigned data.
• Community trials use nonrandom data
Analytic Study: Experimental
• investigator can “control” the exposure
• akin to laboratory experiments except living
populations are the subjects
• generally involves random assignment to groups
• clinical trials are the most well known
experimental design
Analytic Study: Experimental
• In an experiment, we are interested in the
consequences of some treatment on some
outcome.
• The subjects in the study who actually receive
the treatment of interest are called the
treatment group.
• The subjects in the study who receive no
treatment or a different treatment are called
the comparison group.
Analytic Study: Experimental
• Randomized Controlled Trials (RCTs)
• a design with subjects randomly assigned to
“treatment” and “comparison” groups
• provides most convincing evidence of
relationship between exposure and effect
• not possible to use RCTs to test effects of
exposures that are expected to be harmful, for
ethical reasons
outcome
RANDOMIZATION
Intervention
no outcome
Study
population
outcome
Control
no outcome
baseline
future
time
Study begins here (baseline point)
Analytic Study: Experimental
• Randomized Controlled Trials (RCTs)
• the “gold standard” of research designs
• provides most convincing evidence of
relationship between exposure and effect
Analytic Study: Intervention Studies
1. Pretest-posttest control-group design
-samples are randomized into two groups: one intervention and one
control group; both were tested before and after intervention
2. Posttest-only control-group design
-samples are randomized into two groups: one intervention and one
control group; both were tested after intervention
3. Non-randomized comparison group pretest-posttest
-samples are allocated into intervention and control groups but not
randomly; both were tested before and after intervention
4. Time-series quasi-experimental design / interrupted time-series
-one or two groups are tested in multiple time points
Analytic Study: Cross-sectional
• An “observational” design that surveys
exposures and disease status at a single point in
time (a cross-section of the population)
Analytic Study: Cross-sectional
factor present
No Disease
factor absent
Study
population
factor present
Disease
factor absent
time
Study only exists at this point in time
Analytic Study: Cross-sectional
• Often used to study conditions that are relatively frequent with
long duration of expression (nonfatal, chronic conditions, e.g.
non-communicable diseases)
• It measures prevalence, not incidence of disease
• Example: community surveys
• Not suitable for studying rare or highly fatal diseases or a
disease with short duration of expression
Analytic Study: Cross-sectional
• Disadvantages
• Weakest observational design,
(it measures prevalence, not incidence of disease).
• The temporal sequence of exposure and effect may be
difficult or impossible to determine
• Usually don’t know when disease occurred
• Rare events a problem. Quickly emerging diseases a
problem
Analytic Study: Case-Control
• an “observational” design comparing
exposures in disease cases vs. healthy controls
from same population
• exposure data collected retrospectively
• most feasible design where disease outcomes
are rare
factor present
Cases
(disease)
factor absent
Study
population
factor present
Controls
(no disease)
factor absent
present
past
time
Study begins here
Analytic Study: Case-Control
• Strengths
• Less expensive and time consuming
• Efficient for studying rare diseases
• Limitations
• Inappropriate when disease outcome for a specific
exposure is not known at start of study
• Exposure measurements taken after disease occurrence
(We do not know the time period between exposure and
disease)
Analytic Study: Cohort Study
• an “observational” design comparing individuals with a
known risk factor or exposure with others without the risk
factor or exposure
• looking for a difference in the risk (incidence) of a disease
over time
• best observational design
• data usually collected prospectively (some retrospective)
disease
Factor
present
Study
population
free of
disease
Factor
absent
no disease
disease
no disease
present
future
time
Study begins here
Prospective Cohort study
Exposed
Outcome
Non-exposed
Outcome
Measure exposure
and confounder
variables
Baseline
time
Study begins here
Retrospective Cohort study
Exposed
Outcome
Non-exposed
Outcome
Measure exposure
and confounder
variables
Baseline
time
Study begins here
Analytic Study: Cohort Study
• Strengths
• Exposure status determined before disease detection
• Subjects selected before disease detection
• Can study several outcomes for each exposure
• Limitations
• Expensive and time-consuming
• Inefficient for rare diseases or diseases with long latency
• Loss to follow-up
Study Designs for Epidemiology
??????
??????
??????
??????
Study Designs for Epidemiology
____ 1. Representative sample of residents were telephoned and asked how much they
exercise each week and whether they currently have (have ever been diagnosed with) heart
disease.
____ 2. Occurrence of cancer was identified between April 1991 and July 2002 for 50,000
troops who served in the first Gulf War (ended April 1991) and 50,000 troops who served
elsewhere during the same period.
____ 3. Persons diagnosed with new-onset Lyme disease were asked how often they walk
through woods, use insect repellant, wear short sleeves and pants, etc. Twice as many
patients without Lyme disease from the same physician's practice were asked the same
questions, and the responses in the two groups were compared.
____ 4. Subjects were children enrolled in a health maintenance organization. At 2 months,
each child was randomly given one of two types of a new vaccine against rotavirus
infection. Parents were called by a nurse two weeks later and asked whether the children
had experienced any of a list of side-effects
Measures of Association
The findings for most epidemiologic studies
can be presented in the 2x2 table
Disease
Yes
No
Total
Yes
a
b
a+b
No
c
d
c+d
a+c
b+d
a+b+c+d
Exposure
Total
Measures of Association
Cohort Study: the outcome measure is the relative
risk (or risk ratio or rate ratio)
• In cohort studies you begin with the exposure of
interest and then determine the rate of developing
disease
• RR measures the likelihood of getting the disease if
you are exposed relative to those who are unexposed
• RR = incidence in the exposed/incidence in the unexposed
Measures of Association
Case-control study: the outcome measure is an
estimate of the relative risk or the odds ratio
(relative odds)
• In a case-control study, you begin with disease status and then
estimate exposure
• RR is estimated because patients are selected on disease status
and we cannot calculate incidence based on exposure
• The estimate is the odds ratio (OR) or the likelihood of having the
exposure if you have the disease relative to those who do not
have the disease
Measures of Association
• If the risk/odds ratio is 1, it suggests no difference in risk/odds (incidence
in each group is the same).
• A risk/odds ratio > 1 suggests an increased risk/odds of that outcome in
the exposed group. The outcome is associated to the exposure and it is a
risk factor.
• A risk/odds ratio < 1 suggests a reduced risk/odds in the exposed group.
The outcome is associated to the exposure and it is a protective factor.
• The confidence interval tells us if the association is statistically significant
Measures of Association
• If the risk/odds ratio is 1, it suggests no difference in risk/odds (incidence
in each group is the same).
• A risk/odds ratio > 1 suggests an increased risk/odds of that outcome in
the exposed group. The outcome is associated to the exposure and it is a
risk factor.
• A risk/odds ratio < 1 suggests a reduced risk/odds in the exposed group.
The outcome is associated to the exposure and it is a protective factor.
• The confidence interval and p-value tell us if the association is statistically
significant
Measures of Association
Table1. Relationship between Variables and Obesity in Preschool Children (2–5 Years)
Variable
Odds Ratio
95% Confidence
Interval
P-value
# of hours of sleep at
night
Parent’s BMI
0.89
0.75-0.92
0.001
1.89
1.56-2.02
0.01
High Fat diet
1.10
0.89-1.15
0.10
High caloric intake
2.21
1.50-2.40
0.002
# of hours of physical
activity per week
0.72
0.70-1.01
0.06
Measures of Association
• A cohort study examined the association between smoking and lung
cancer after following 400 smokers and 600 non-smokers for 15 years. At
the conclusion of the study the investigators found a risk ratio = 17 (95%
CI = 15.0-18.0) for smoking. What is the interpretation of this risk ratio?
• A study is done to examine whether there is an association between the
daily use of vitamins C & E and risk of coronary artery disease (heart
attacks) over a 10 year period. When subjects who took both vitamins
were compared to those who took not vitamins at all, the risk ratio was
found to be 0.70 (95% CI = 0.50 – 0.90). What is the correct interpretation
of this finding?
Thank you!
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