Biostats, Lec 1
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Introduction of Biostats & Epidemiology
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54 Terms
Policy
Biostatistics
Definition
“Biostatistics is the branch of statistics responsible for interpreting the scientific data generated in the biology, public health, and medicine fields.” - Daniel & Cross (2018)
Biostatics is a combination of
Analyzing Data
Medical Records
Techniques
Importance in Health Sciences
Informs public health _______ and decision-making
Evaluates effectiveness of new treatments and interventions
Identifies risk factors for diseases
Facilitates accurate diagnosis and prognosis
Branches of Biostatistics
Descriptive Biostatistics (Straightforward)
Summarizes data using numbers and graphs (mean, median, frequency)
What, when, who
No need for critical thinking
Example: Average hemoglobin level in a population
Inferential Biostatistics
Makes predictions or inferences from sample data
Uses probability, hypothesis, testing, confidence intervals
Example: Testing if a new diagnostic test is more accurate
Risk
Biostatistics
Definition
“Biostatistics is the branch of statistics responsible for interpreting the scientific data generated in the biology, public health, and medicine fields.” - Daniel & Cross (2018)
Biostatics is a combination of
Analyzing Data
Medical Records
Techniques
Importance in Health Sciences
Informs public health policy and decision-making
Evaluates effectiveness of new treatments and interventions
Identifies _________ factors for diseases
Facilitates accurate diagnosis and prognosis
Branches of Biostatistics
Descriptive Biostatistics (Straightforward)
Summarizes data using numbers and graphs (mean, median, frequency)
What, when, who
No need for critical thinking
Example: Average hemoglobin level in a population
Inferential Biostatistics
Makes predictions or inferences from sample data
Uses probability, hypothesis, testing, confidence intervals
Example: Testing if a new diagnostic test is more accurate
Straightforward
Biostatistics
Definition
“Biostatistics is the branch of statistics responsible for interpreting the scientific data generated in the biology, public health, and medicine fields.” - Daniel & Cross (2018)
Biostatics is a combination of
Analyzing Data
Medical Records
Techniques
Importance in Health Sciences
Informs public health policy and decision-making
Evaluates effectiveness of new treatments and interventions
Identifies risk factors for diseases
Facilitates accurate diagnosis and prognosis
Branches of Biostatistics
Descriptive Biostatistics (___________)
Summarizes data using numbers and graphs (mean, median, frequency)
What, when, who
No need for critical thinking
Example: Average hemoglobin level in a population
Inferential Biostatistics
Makes predictions or inferences from sample data
Uses probability, hypothesis, testing, confidence intervals
Example: Testing if a new diagnostic test is more accurate
Mean, median, frequency
Biostatistics
Definition
“Biostatistics is the branch of statistics responsible for interpreting the scientific data generated in the biology, public health, and medicine fields.” - Daniel & Cross (2018)
Biostatics is a combination of
Analyzing Data
Medical Records
Techniques
Importance in Health Sciences
Informs public health policy and decision-making
Evaluates effectiveness of new treatments and interventions
Identifies risk factors for diseases
Facilitates accurate diagnosis and prognosis
Branches of Biostatistics
Descriptive Biostatistics (Straightforward)
Summarizes data using numbers and graphs (______,______,______)
What, when, who
No need for critical thinking
Example: Average hemoglobin level in a population
Inferential Biostatistics
Makes predictions or inferences from sample data
Uses probability, hypothesis, testing, confidence intervals
Example: Testing if a new diagnostic test is more accurate
Descriptive Biostatistics
Biostatistics
Definition
“Biostatistics is the branch of statistics responsible for interpreting the scientific data generated in the biology, public health, and medicine fields.” - Daniel & Cross (2018)
Biostatics is a combination of
Analyzing Data
Medical Records
Techniques
Importance in Health Sciences
Informs public health policy and decision-making
Evaluates effectiveness of new treatments and interventions
Identifies risk factors for diseases
Facilitates accurate diagnosis and prognosis
Branches of Biostatistics
_________ __________ (Straightforward)
Summarizes data using numbers and graphs (mean, median, frequency)
What, when, who
No need for critical thinking
Example: Average hemoglobin level in a population
Inferential Biostatistics
Makes predictions or inferences from sample data
Uses probability, hypothesis, testing, confidence intervals
Example: Testing if a new diagnostic test is more accurate
Inferential Biostatistics
Biostatistics
Definition
“Biostatistics is the branch of statistics responsible for interpreting the scientific data generated in the biology, public health, and medicine fields.” - Daniel & Cross (2018)
Biostatics is a combination of
Analyzing Data
Medical Records
Techniques
Importance in Health Sciences
Informs public health policy and decision-making
Evaluates effectiveness of new treatments and interventions
Identifies risk factors for diseases
Facilitates accurate diagnosis and prognosis
Branches of Biostatistics
Descriptive Biostatistics (Straightforward)
Summarizes data using numbers and graphs (mean, median, frequency)
What, when, who
No need for critical thinking
Example: Average hemoglobin level in a population
___________ ________
Makes predictions or inferences from sample data
Uses probability, hypothesis, testing, confidence intervals
Example: Testing if a new diagnostic test is more accurate
Critical thinking
Biostatistics
Definition
“Biostatistics is the branch of statistics responsible for interpreting the scientific data generated in the biology, public health, and medicine fields.” - Daniel & Cross (2018)
Biostatics is a combination of
Analyzing Data
Medical Records
Techniques
Importance in Health Sciences
Informs public health policy and decision-making
Evaluates effectiveness of new treatments and interventions
Identifies risk factors for diseases
Facilitates accurate diagnosis and prognosis
Branches of Biostatistics
Descriptive Biostatistics (Straightforward)
Summarizes data using numbers and graphs (mean, median, frequency)
What, when, who
No need for ______ _______
Example: Average hemoglobin level in a population
Inferential Biostatistics
Makes predictions or inferences from sample data
Uses probability, hypothesis, testing, confidence intervals
Example: Testing if a new diagnostic test is more accurate
Predictions
Biostatistics
Definition
“Biostatistics is the branch of statistics responsible for interpreting the scientific data generated in the biology, public health, and medicine fields.” - Daniel & Cross (2018)
Biostatics is a combination of
Analyzing Data
Medical Records
Techniques
Importance in Health Sciences
Informs public health policy and decision-making
Evaluates effectiveness of new treatments and interventions
Identifies risk factors for diseases
Facilitates accurate diagnosis and prognosis
Branches of Biostatistics
Descriptive Biostatistics (Straightforward)
Summarizes data using numbers and graphs (mean, median, frequency)
What, when, who
No need for critical thinking
Example: Average hemoglobin level in a population
Inferential Biostatistics
Makes ___________ or inferences from sample data
Uses probability, hypothesis, testing, confidence intervals
Example: Testing if a new diagnostic test is more accurate
Accurate
Biostatistics
Definition
“Biostatistics is the branch of statistics responsible for interpreting the scientific data generated in the biology, public health, and medicine fields.” - Daniel & Cross (2018)
Biostatics is a combination of
Analyzing Data
Medical Records
Techniques
Importance in Health Sciences
Informs public health policy and decision-making
Evaluates effectiveness of new treatments and interventions
Identifies risk factors for diseases
Facilitates _________ diagnosis and prognosis
Branches of Biostatistics
Descriptive Biostatistics (Straightforward)
Summarizes data using numbers and graphs (mean, median, frequency)
What, when, who
No need for critical thinking
Example: Average hemoglobin level in a population
Inferential Biostatistics
Makes predictions or inferences from sample data
Uses probability, hypothesis, testing, confidence intervals
Example: Testing if a new diagnostic test is more accurate
Distributions
Epidemiology
Definition
“The study of the distribution and determinants (who,what,when) of health-related states or events in specified populations and the application of this study to the control of health problems” - Gordis (2014)
Importance in Health Sciences
Looks at patterns of diseases in groups, not individuals
Key for public health surveillance, outbreak control, and health policy
Core Epidemiologic Concepts (5)
___________
This refers to the frequency (how + often) and pattern (who, when, and where) of health events in a population
Who is affected?
Where is it happening?
When is it happening?
Example: More dengue cases occur during the rainy season in Metro Manila.
Determinants
These are the factors or causes the influence the occurrence of health problems. They answer the question:
Can be biological
Behavioral
Environmental
Social
Example: Poor water drainage and standing water can lead to more mosquito breeding and dengue cases.
Outcomes
Morbidity
Mortality
Recovery
Populations
Epidemiology always looks at groups of people, not just individuals
Example: Tracking how many people in Cebu tested positive for COVID-19 in July.
Application
This is the “so what?” of epidemiology. All the information gathered is used to:
Prevent new cases
Control existing health problems
Create health policies and programs
Evaluate the impact of interventions
Example: After finding a high number of TB cases in a region, the government starts a free screening program
Surveillance
Epidemiology
Definition
“The study of the distribution and determinants (who,what,when) of health-related states or events in specified populations and the application of this study to the control of health problems” - Gordis (2014)
Importance in Health Sciences
Looks at patterns of diseases in groups, not individuals
Key for public health ________, outbreak control, and health policy
Core Epidemiologic Concepts (5)
Distribution
This refers to the frequency (how + often) and pattern (who, when, and where) of health events in a population
Who is affected?
Where is it happening?
When is it happening?
Example: More dengue cases occur during the rainy season in Metro Manila.
Determinants
These are the factors or causes the influence the occurrence of health problems. They answer the question:
Can be biological
Behavioral
Environmental
Social
Example: Poor water drainage and standing water can lead to more mosquito breeding and dengue cases.
Outcomes
Morbidity
Mortality
Recovery
Populations
Epidemiology always looks at groups of people, not just individuals
Example: Tracking how many people in Cebu tested positive for COVID-19 in July.
Application
This is the “so what?” of epidemiology. All the information gathered is used to:
Prevent new cases
Control existing health problems
Create health policies and programs
Evaluate the impact of interventions
Example: After finding a high number of TB cases in a region, the government starts a free screening program
Epidemiology
___________
Definition
“The study of the distribution and determinants (who,what,when) of health-related states or events in specified populations and the application of this study to the control of health problems” - Gordis (2014)
Importance in Health Sciences
Looks at patterns of diseases in groups, not individuals
Key for public health surveillance, outbreak control, and health policy
Core Epidemiologic Concepts (5)
Distribution
This refers to the frequency (how + often) and pattern (who, when, and where) of health events in a population
Who is affected?
Where is it happening?
When is it happening?
Example: More dengue cases occur during the rainy season in Metro Manila.
Determinants
These are the factors or causes the influence the occurrence of health problems. They answer the question:
Can be biological
Behavioral
Environmental
Social
Example: Poor water drainage and standing water can lead to more mosquito breeding and dengue cases.
Outcomes
Morbidity
Mortality
Recovery
Populations
Epidemiology always looks at groups of people, not just individuals
Example: Tracking how many people in Cebu tested positive for COVID-19 in July.
Application
This is the “so what?” of epidemiology. All the information gathered is used to:
Prevent new cases
Control existing health problems
Create health policies and programs
Evaluate the impact of interventions
Example: After finding a high number of TB cases in a region, the government starts a free screening program
Frequency
Epidemiology
Definition
“The study of the distribution and determinants (who,what,when) of health-related states or events in specified populations and the application of this study to the control of health problems” - Gordis (2014)
Importance in Health Sciences
Looks at patterns of diseases in groups, not individuals
Key for public health surveillance, outbreak control, and health policy
Core Epidemiologic Concepts (5)
Distribution
This refers to the ________ (how + often) and pattern (who, when, and where) of health events in a population
Who is affected?
Where is it happening?
When is it happening?
Example: More dengue cases occur during the rainy season in Metro Manila.
Determinants
These are the factors or causes the influence the occurrence of health problems. They answer the question:
Can be biological
Behavioral
Environmental
Social
Example: Poor water drainage and standing water can lead to more mosquito breeding and dengue cases.
Outcomes
Morbidity
Mortality
Recovery
Populations
Epidemiology always looks at groups of people, not just individuals
Example: Tracking how many people in Cebu tested positive for COVID-19 in July.
Application
This is the “so what?” of epidemiology. All the information gathered is used to:
Prevent new cases
Control existing health problems
Create health policies and programs
Evaluate the impact of interventions
Example: After finding a high number of TB cases in a region, the government starts a free screening program
Pattern
Epidemiology
Definition
“The study of the distribution and determinants (who,what,when) of health-related states or events in specified populations and the application of this study to the control of health problems” - Gordis (2014)
Importance in Health Sciences
Looks at patterns of diseases in groups, not individuals
Key for public health surveillance, outbreak control, and health policy
Core Epidemiologic Concepts (5)
Distribution
This refers to the frequency (how + often) and ______ (who, when, and where) of health events in a population
Who is affected?
Where is it happening?
When is it happening?
Example: More dengue cases occur during the rainy season in Metro Manila.
Determinants
These are the factors or causes the influence the occurrence of health problems. They answer the question:
Can be biological
Behavioral
Environmental
Social
Example: Poor water drainage and standing water can lead to more mosquito breeding and dengue cases.
Outcomes
Morbidity
Mortality
Recovery
Populations
Epidemiology always looks at groups of people, not just individuals
Example: Tracking how many people in Cebu tested positive for COVID-19 in July.
Application
This is the “so what?” of epidemiology. All the information gathered is used to:
Prevent new cases
Control existing health problems
Create health policies and programs
Evaluate the impact of interventions
Example: After finding a high number of TB cases in a region, the government starts a free screening program
How + often
Epidemiology
Definition
“The study of the distribution and determinants (who,what,when) of health-related states or events in specified populations and the application of this study to the control of health problems” - Gordis (2014)
Importance in Health Sciences
Looks at patterns of diseases in groups, not individuals
Key for public health surveillance, outbreak control, and health policy
Core Epidemiologic Concepts (5)
Distribution
This refers to the frequency (_________) and pattern (who, when, and where) of health events in a population
Who is affected?
Where is it happening?
When is it happening?
Example: More dengue cases occur during the rainy season in Metro Manila.
Determinants
These are the factors or causes the influence the occurrence of health problems. They answer the question:
Can be biological
Behavioral
Environmental
Social
Example: Poor water drainage and standing water can lead to more mosquito breeding and dengue cases.
Outcomes
Morbidity
Mortality
Recovery
Populations
Epidemiology always looks at groups of people, not just individuals
Example: Tracking how many people in Cebu tested positive for COVID-19 in July.
Application
This is the “so what?” of epidemiology. All the information gathered is used to:
Prevent new cases
Control existing health problems
Create health policies and programs
Evaluate the impact of interventions
Example: After finding a high number of TB cases in a region, the government starts a free screening program
Who, where, when
Epidemiology
Definition
“The study of the distribution and determinants (who,what,when) of health-related states or events in specified populations and the application of this study to the control of health problems” - Gordis (2014)
Importance in Health Sciences
Looks at patterns of diseases in groups, not individuals
Key for public health surveillance, outbreak control, and health policy
Core Epidemiologic Concepts (5)
Distribution
This refers to the frequency (how + often) and pattern (who, whem, and where) of health events in a population
_______ is affected?
________ is it happening?
________ is it happening?
Example: More dengue cases occur during the rainy season in Metro Manila.
Determinants
These are the factors or causes the influence the occurrence of health problems. They answer the question:
Can be biological
Behavioral
Environmental
Social
Example: Poor water drainage and standing water can lead to more mosquito breeding and dengue cases.
Outcomes
Morbidity
Mortality
Recovery
Populations
Epidemiology always looks at groups of people, not just individuals
Example: Tracking how many people in Cebu tested positive for COVID-19 in July.
Application
This is the “so what?” of epidemiology. All the information gathered is used to:
Prevent new cases
Control existing health problems
Create health policies and programs
Evaluate the impact of interventions
Example: After finding a high number of TB cases in a region, the government starts a free screening program
Determinants
Epidemiology
Definition
“The study of the distribution and determinants (who,what,when) of health-related states or events in specified populations and the application of this study to the control of health problems” - Gordis (2014)
Importance in Health Sciences
Looks at patterns of diseases in groups, not individuals
Key for public health surveillance, outbreak control, and health policy
Core Epidemiologic Concepts (5)
Distribution
This refers to the frequency (how + often) and pattern (who, when, and where) of health events in a population
Who is affected?
Where is it happening?
When is it happening?
Example: More dengue cases occur during the rainy season in Metro Manila.
__________
These are the factors or causes the influence the occurrence of health problems. They answer the question:
Can be biological
Behavioral
Environmental
Social
Example: Poor water drainage and standing water can lead to more mosquito breeding and dengue cases.
Outcomes
Morbidity
Mortality
Recovery
Populations
Epidemiology always looks at groups of people, not just individuals
Example: Tracking how many people in Cebu tested positive for COVID-19 in July.
Application
This is the “so what?” of epidemiology. All the information gathered is used to:
Prevent new cases
Control existing health problems
Create health policies and programs
Evaluate the impact of interventions
Example: After finding a high number of TB cases in a region, the government starts a free screening program
Causes
Epidemiology
Definition
“The study of the distribution and determinants (who,what,when) of health-related states or events in specified populations and the application of this study to the control of health problems” - Gordis (2014)
Importance in Health Sciences
Looks at patterns of diseases in groups, not individuals
Key for public health surveillance, outbreak control, and health policy
Core Epidemiologic Concepts (5)
Distribution
This refers to the frequency (how + often) and pattern (who, when, and where) of health events in a population
Who is affected?
Where is it happening?
When is it happening?
Example: More dengue cases occur during the rainy season in Metro Manila.
Determinants
These are the factors or ________ the influence the occurrence of health problems. They answer the question:
Can be biological
Behavioral
Environmental
Social
Example: Poor water drainage and standing water can lead to more mosquito breeding and dengue cases.
Outcomes
Morbidity
Mortality
Recovery
Populations
Epidemiology always looks at groups of people, not just individuals
Example: Tracking how many people in Cebu tested positive for COVID-19 in July.
Application
This is the “so what?” of epidemiology. All the information gathered is used to:
Prevent new cases
Control existing health problems
Create health policies and programs
Evaluate the impact of interventions
Example: After finding a high number of TB cases in a region, the government starts a free screening program
Behavioral, environmenta
Epidemiology
Definition
“The study of the distribution and determinants (who,what,when) of health-related states or events in specified populations and the application of this study to the control of health problems” - Gordis (2014)
Importance in Health Sciences
Looks at patterns of diseases in groups, not individuals
Key for public health surveillance, outbreak control, and health policy
Core Epidemiologic Concepts (5)
Distribution
This refers to the frequency (how + often) and pattern (who, when, and where) of health events in a population
Who is affected?
Where is it happening?
When is it happening?
Example: More dengue cases occur during the rainy season in Metro Manila.
Determinants
These are the factors or causes the influence the occurrence of health problems. They answer the question:
Can be biological
________
________
Social
Example: Poor water drainage and standing water can lead to more mosquito breeding and dengue cases.
Outcomes
Morbidity
Mortality
Recovery
Populations
Epidemiology always looks at groups of people, not just individuals
Example: Tracking how many people in Cebu tested positive for COVID-19 in July.
Application
This is the “so what?” of epidemiology. All the information gathered is used to:
Prevent new cases
Control existing health problems
Create health policies and programs
Evaluate the impact of interventions
Example: After finding a high number of TB cases in a region, the government starts a free screening program
Morbidity, mortality, recovery
Epidemiology
Definition
“The study of the distribution and determinants (who,what,when) of health-related states or events in specified populations and the application of this study to the control of health problems” - Gordis (2014)
Importance in Health Sciences
Looks at patterns of diseases in groups, not individuals
Key for public health surveillance, outbreak control, and health policy
Core Epidemiologic Concepts (5)
Distribution
This refers to the frequency (how + often) and pattern (who, when, and where) of health events in a population
Who is affected?
Where is it happening?
When is it happening?
Example: More dengue cases occur during the rainy season in Metro Manila.
Determinants
These are the factors or causes the influence the occurrence of health problems. They answer the question:
Can be biological
Behavioral
Environmental
Social
Example: Poor water drainage and standing water can lead to more mosquito breeding and dengue cases.
Outcomes
_________
_________
_________
Populations
Epidemiology always looks at groups of people, not just individuals
Example: Tracking how many people in Cebu tested positive for COVID-19 in July.
Application
This is the “so what?” of epidemiology. All the information gathered is used to:
Prevent new cases
Control existing health problems
Create health policies and programs
Evaluate the impact of interventions
Example: After finding a high number of TB cases in a region, the government starts a free screening program
Outcomes
Epidemiology
Definition
“The study of the distribution and determinants (who,what,when) of health-related states or events in specified populations and the application of this study to the control of health problems” - Gordis (2014)
Importance in Health Sciences
Looks at patterns of diseases in groups, not individuals
Key for public health surveillance, outbreak control, and health policy
Core Epidemiologic Concepts (5)
Distribution
This refers to the frequency (how + often) and pattern (who, when, and where) of health events in a population
Who is affected?
Where is it happening?
When is it happening?
Example: More dengue cases occur during the rainy season in Metro Manila.
Determinants
These are the factors or causes the influence the occurrence of health problems. They answer the question:
Can be biological
Behavioral
Environmental
Social
Example: Poor water drainage and standing water can lead to more mosquito breeding and dengue cases.
________
Morbidity
Mortality
Recovery
Populations
Epidemiology always looks at groups of people, not just individuals
Example: Tracking how many people in Cebu tested positive for COVID-19 in July.
Application
This is the “so what?” of epidemiology. All the information gathered is used to:
Prevent new cases
Control existing health problems
Create health policies and programs
Evaluate the impact of interventions
Example: After finding a high number of TB cases in a region, the government starts a free screening program
Populations
Epidemiology
Definition
“The study of the distribution and determinants (who,what,when) of health-related states or events in specified populations and the application of this study to the control of health problems” - Gordis (2014)
Importance in Health Sciences
Looks at patterns of diseases in groups, not individuals
Key for public health surveillance, outbreak control, and health policy
Core Epidemiologic Concepts (5)
Distribution
This refers to the frequency (how + often) and pattern (who, when, and where) of health events in a population
Who is affected?
Where is it happening?
When is it happening?
Example: More dengue cases occur during the rainy season in Metro Manila.
Determinants
These are the factors or causes the influence the occurrence of health problems. They answer the question:
Can be biological
Behavioral
Environmental
Social
Example: Poor water drainage and standing water can lead to more mosquito breeding and dengue cases.
Outcomes
Morbidity
Mortality
Recovery
________
Epidemiology always looks at groups of people, not just individuals
Example: Tracking how many people in Cebu tested positive for COVID-19 in July.
Application
This is the “so what?” of epidemiology. All the information gathered is used to:
Prevent new cases
Control existing health problems
Create health policies and programs
Evaluate the impact of interventions
Example: After finding a high number of TB cases in a region, the government starts a free screening program
Groups of people
Epidemiology
Definition
“The study of the distribution and determinants (who,what,when) of health-related states or events in specified populations and the application of this study to the control of health problems” - Gordis (2014)
Importance in Health Sciences
Looks at patterns of diseases in groups, not individuals
Key for public health surveillance, outbreak control, and health policy
Core Epidemiologic Concepts (5)
Distribution
This refers to the frequency (how + often) and pattern (who, when, and where) of health events in a population
Who is affected?
Where is it happening?
When is it happening?
Example: More dengue cases occur during the rainy season in Metro Manila.
Determinants
These are the factors or causes the influence the occurrence of health problems. They answer the question:
Can be biological
Behavioral
Environmental
Social
Example: Poor water drainage and standing water can lead to more mosquito breeding and dengue cases.
Outcomes
Morbidity
Mortality
Recovery
Populations
Epidemiology always looks at ________ ___ ________, not just individuals
Example: Tracking how many people in Cebu tested positive for COVID-19 in July.
Application
This is the “so what?” of epidemiology. All the information gathered is used to:
Prevent new cases
Control existing health problems
Create health policies and programs
Evaluate the impact of interventions
Example: After finding a high number of TB cases in a region, the government starts a free screening program
Application
Epidemiology
Definition
“The study of the distribution and determinants (who,what,when) of health-related states or events in specified populations and the application of this study to the control of health problems” - Gordis (2014)
Importance in Health Sciences
Looks at patterns of diseases in groups, not individuals
Key for public health surveillance, outbreak control, and health policy
Core Epidemiologic Concepts (5)
Distribution
This refers to the frequency (how + often) and pattern (who, when, and where) of health events in a population
Who is affected?
Where is it happening?
When is it happening?
Example: More dengue cases occur during the rainy season in Metro Manila.
Determinants
These are the factors or causes the influence the occurrence of health problems. They answer the question:
Can be biological
Behavioral
Environmental
Social
Example: Poor water drainage and standing water can lead to more mosquito breeding and dengue cases.
Outcomes
Morbidity
Mortality
Recovery
Populations
Epidemiology always looks at groups of people, not just individuals
Example: Tracking how many people in Cebu tested positive for COVID-19 in July.
_________
This is the “so what?” of epidemiology. All the information gathered is used to:
Prevent new cases
Control existing health problems
Create health policies and programs
Evaluate the impact of interventions
Example: After finding a high number of TB cases in a region, the government starts a free screening program
So what
Epidemiology
Definition
“The study of the distribution and determinants (who,what,when) of health-related states or events in specified populations and the application of this study to the control of health problems” - Gordis (2014)
Importance in Health Sciences
Looks at patterns of diseases in groups, not individuals
Key for public health surveillance, outbreak control, and health policy
Core Epidemiologic Concepts (5)
Distribution
This refers to the frequency (how + often) and pattern (who, when, and where) of health events in a population
Who is affected?
Where is it happening?
When is it happening?
Example: More dengue cases occur during the rainy season in Metro Manila.
Determinants
These are the factors or causes the influence the occurrence of health problems. They answer the question:
Can be biological
Behavioral
Environmental
Social
Example: Poor water drainage and standing water can lead to more mosquito breeding and dengue cases.
Outcomes
Morbidity
Mortality
Recovery
Populations
Epidemiology always looks at groups of people, not just individuals
Example: Tracking how many people in Cebu tested positive for COVID-19 in July.
Application
This is the “____ _______?” of epidemiology. All the information gathered is used to:
Prevent new cases
Control existing health problems
Create health policies and programs
Evaluate the impact of interventions
Example: After finding a high number of TB cases in a region, the government starts a free screening program
Descriptive Epidemiology
Types of Epidemiology
________ ________
Describes disease occurence by person, place, time
Example: COVID-19 incidence by region in the Philippines
Analytic Epidemiology
Examines causes and associations
Uses case-control, cohort, and experimental studies
Example: Linking HPV infection to cervical cancer
Applied Epidemiology
Using epidemiologic data to implement and evaluate interventions
Example: DOH’s vaccine rollout guided by incidence maps
Relevance to Public Health
Epidemiology provides the framework for disease surveillance (e.g., TB, Dengue)
Guides resource allocation during epidemics and pandemics
Enables policy formulation
WHO (2020) and CDC (2012) highlight how epidemiologic data informs:
Outbreak response
Vaccination campaigns
Community health education
Types of Data (Pagano & Gauvreau, 2018)
Quantitative (Numerical) Data
Discrete - countable, finite values
Example: Number of red blood cells per mm^3
Continuous - measurable, infinite values within a range
Example: Patient’s weight or serum cholesterol level
Qualitative (Categorical) Data
Nominal - categories with no order
Example: Blood type (A, B, AB, O)
Ordinal - ordered categories
Example: Tumor stage (Stage I, II, III, IV)
Variables in Epidemiologic Studies
Independent Variable
Variable that is manipulated or categorized to see its effect
Example: Smoking status in a lung cancer study
Dependent Variable
Outcome being measured
Example: Lung cancer incidence
Confounding Variable
A factor associated with both the exposure and outcome
Example: Age could influence both smoking habits and lung cancer risk
Controlled Variable
Kept constant to prevent it from affecting the outcome
Example: Lab temperature in enzyme activity studies
Person, place, time
Types of Epidemiology
Descriptive Epidemiology
Describes disease occurence by ______,______,______
Example: COVID-19 incidence by region in the Philippines
Analytic Epidemiology
Examines causes and associations
Uses case-control, cohort, and experimental studies
Example: Linking HPV infection to cervical cancer
Applied Epidemiology
Using epidemiologic data to implement and evaluate interventions
Example: DOH’s vaccine rollout guided by incidence maps
Relevance to Public Health
Epidemiology provides the framework for disease surveillance (e.g., TB, Dengue)
Guides resource allocation during epidemics and pandemics
Enables policy formulation
WHO (2020) and CDC (2012) highlight how epidemiologic data informs:
Outbreak response
Vaccination campaigns
Community health education
Types of Data (Pagano & Gauvreau, 2018)
Quantitative (Numerical) Data
Discrete - countable, finite values
Example: Number of red blood cells per mm^3
Continuous - measurable, infinite values within a range
Example: Patient’s weight or serum cholesterol level
Qualitative (Categorical) Data
Nominal - categories with no order
Example: Blood type (A, B, AB, O)
Ordinal - ordered categories
Example: Tumor stage (Stage I, II, III, IV)
Variables in Epidemiologic Studies
Independent Variable
Variable that is manipulated or categorized to see its effect
Example: Smoking status in a lung cancer study
Dependent Variable
Outcome being measured
Example: Lung cancer incidence
Confounding Variable
A factor associated with both the exposure and outcome
Example: Age could influence both smoking habits and lung cancer risk
Controlled Variable
Kept constant to prevent it from affecting the outcome
Example: Lab temperature in enzyme activity studies
Analytic epidemiology
Types of Epidemiology
Descriptive Epidemiology
Describes disease occurence by person, place, time
Example: COVID-19 incidence by region in the Philippines
_________ _______
Examines causes and associations
Uses case-control, cohort, and experimental studies
Example: Linking HPV infection to cervical cancer
Applied Epidemiology
Using epidemiologic data to implement and evaluate interventions
Example: DOH’s vaccine rollout guided by incidence maps
Relevance to Public Health
Epidemiology provides the framework for disease surveillance (e.g., TB, Dengue)
Guides resource allocation during epidemics and pandemics
Enables policy formulation
WHO (2020) and CDC (2012) highlight how epidemiologic data informs:
Outbreak response
Vaccination campaigns
Community health education
Types of Data (Pagano & Gauvreau, 2018)
Quantitative (Numerical) Data
Discrete - countable, finite values
Example: Number of red blood cells per mm^3
Continuous - measurable, infinite values within a range
Example: Patient’s weight or serum cholesterol level
Qualitative (Categorical) Data
Nominal - categories with no order
Example: Blood type (A, B, AB, O)
Ordinal - ordered categories
Example: Tumor stage (Stage I, II, III, IV)
Variables in Epidemiologic Studies
Independent Variable
Variable that is manipulated or categorized to see its effect
Example: Smoking status in a lung cancer study
Dependent Variable
Outcome being measured
Example: Lung cancer incidence
Confounding Variable
A factor associated with both the exposure and outcome
Example: Age could influence both smoking habits and lung cancer risk
Controlled Variable
Kept constant to prevent it from affecting the outcome
Example: Lab temperature in enzyme activity studies
Causes and associations
Types of Epidemiology
Descriptive Epidemiology
Describes disease occurence by person, place, time
Example: COVID-19 incidence by region in the Philippines
Analytic Epidemiology
Examines _________ ___ _________
Uses case-control, cohort, and experimental studies
Example: Linking HPV infection to cervical cancer
Applied Epidemiology
Using epidemiologic data to implement and evaluate interventions
Example: DOH’s vaccine rollout guided by incidence maps
Relevance to Public Health
Epidemiology provides the framework for disease surveillance (e.g., TB, Dengue)
Guides resource allocation during epidemics and pandemics
Enables policy formulation
WHO (2020) and CDC (2012) highlight how epidemiologic data informs:
Outbreak response
Vaccination campaigns
Community health education
Types of Data (Pagano & Gauvreau, 2018)
Quantitative (Numerical) Data
Discrete - countable, finite values
Example: Number of red blood cells per mm^3
Continuous - measurable, infinite values within a range
Example: Patient’s weight or serum cholesterol level
Qualitative (Categorical) Data
Nominal - categories with no order
Example: Blood type (A, B, AB, O)
Ordinal - ordered categories
Example: Tumor stage (Stage I, II, III, IV)
Variables in Epidemiologic Studies
Independent Variable
Variable that is manipulated or categorized to see its effect
Example: Smoking status in a lung cancer study
Dependent Variable
Outcome being measured
Example: Lung cancer incidence
Confounding Variable
A factor associated with both the exposure and outcome
Example: Age could influence both smoking habits and lung cancer risk
Controlled Variable
Kept constant to prevent it from affecting the outcome
Example: Lab temperature in enzyme activity studies
Applied epidemiology
Types of Epidemiology
Descriptive Epidemiology
Describes disease occurence by person, place, time
Example: COVID-19 incidence by region in the Philippines
Analytic Epidemiology
Examines causes and associations
Uses case-control, cohort, and experimental studies
Example: Linking HPV infection to cervical cancer
_________ _________
Using epidemiologic data to implement and evaluate interventions
Example: DOH’s vaccine rollout guided by incidence maps
Relevance to Public Health
Epidemiology provides the framework for disease surveillance (e.g., TB, Dengue)
Guides resource allocation during epidemics and pandemics
Enables policy formulation
WHO (2020) and CDC (2012) highlight how epidemiologic data informs:
Outbreak response
Vaccination campaigns
Community health education
Types of Data (Pagano & Gauvreau, 2018)
Quantitative (Numerical) Data
Discrete - countable, finite values
Example: Number of red blood cells per mm^3
Continuous - measurable, infinite values within a range
Example: Patient’s weight or serum cholesterol level
Qualitative (Categorical) Data
Nominal - categories with no order
Example: Blood type (A, B, AB, O)
Ordinal - ordered categories
Example: Tumor stage (Stage I, II, III, IV)
Variables in Epidemiologic Studies
Independent Variable
Variable that is manipulated or categorized to see its effect
Example: Smoking status in a lung cancer study
Dependent Variable
Outcome being measured
Example: Lung cancer incidence
Confounding Variable
A factor associated with both the exposure and outcome
Example: Age could influence both smoking habits and lung cancer risk
Controlled Variable
Kept constant to prevent it from affecting the outcome
Example: Lab temperature in enzyme activity studies
WHO, CDC
Types of Epidemiology
Descriptive Epidemiology
Describes disease occurence by person, place, time
Example: COVID-19 incidence by region in the Philippines
Analytic Epidemiology
Examines causes and associations
Uses case-control, cohort, and experimental studies
Example: Linking HPV infection to cervical cancer
Applied Epidemiology
Using epidemiologic data to implement and evaluate interventions
Example: DOH’s vaccine rollout guided by incidence maps
Relevance to Public Health
Epidemiology provides the framework for disease surveillance (e.g., TB, Dengue)
Guides resource allocation during epidemics and pandemics
Enables policy formulation
________ (2020) and ________ (2012) highlight how epidemiologic data informs:
Outbreak response
Vaccination campaigns
Community health education
Types of Data (Pagano & Gauvreau, 2018)
Quantitative (Numerical) Data
Discrete - countable, finite values
Example: Number of red blood cells per mm^3
Continuous - measurable, infinite values within a range
Example: Patient’s weight or serum cholesterol level
Qualitative (Categorical) Data
Nominal - categories with no order
Example: Blood type (A, B, AB, O)
Ordinal - ordered categories
Example: Tumor stage (Stage I, II, III, IV)
Variables in Epidemiologic Studies
Independent Variable
Variable that is manipulated or categorized to see its effect
Example: Smoking status in a lung cancer study
Dependent Variable
Outcome being measured
Example: Lung cancer incidence
Confounding Variable
A factor associated with both the exposure and outcome
Example: Age could influence both smoking habits and lung cancer risk
Controlled Variable
Kept constant to prevent it from affecting the outcome
Example: Lab temperature in enzyme activity studies
Quantitative data (Numerical)
Types of Epidemiology
Descriptive Epidemiology
Describes disease occurence by person, place, time
Example: COVID-19 incidence by region in the Philippines
Analytic Epidemiology
Examines causes and associations
Uses case-control, cohort, and experimental studies
Example: Linking HPV infection to cervical cancer
Applied Epidemiology
Using epidemiologic data to implement and evaluate interventions
Example: DOH’s vaccine rollout guided by incidence maps
Relevance to Public Health
Epidemiology provides the framework for disease surveillance (e.g., TB, Dengue)
Guides resource allocation during epidemics and pandemics
Enables policy formulation
WHO (2020) and CDC (2012) highlight how epidemiologic data informs:
Outbreak response
Vaccination campaigns
Community health education
Types of Data (Pagano & Gauvreau, 2018)
_______ ________ ________
Discrete - countable, finite values
Example: Number of red blood cells per mm^3
Continuous - measurable, infinite values within a range
Example: Patient’s weight or serum cholesterol level
Qualitative (Categorical) Data
Nominal - categories with no order
Example: Blood type (A, B, AB, O)
Ordinal - ordered categories
Example: Tumor stage (Stage I, II, III, IV)
Variables in Epidemiologic Studies
Independent Variable
Variable that is manipulated or categorized to see its effect
Example: Smoking status in a lung cancer study
Dependent Variable
Outcome being measured
Example: Lung cancer incidence
Confounding Variable
A factor associated with both the exposure and outcome
Example: Age could influence both smoking habits and lung cancer risk
Controlled Variable
Kept constant to prevent it from affecting the outcome
Example: Lab temperature in enzyme activity studies
Discrete
Types of Epidemiology
Descriptive Epidemiology
Describes disease occurence by person, place, time
Example: COVID-19 incidence by region in the Philippines
Analytic Epidemiology
Examines causes and associations
Uses case-control, cohort, and experimental studies
Example: Linking HPV infection to cervical cancer
Applied Epidemiology
Using epidemiologic data to implement and evaluate interventions
Example: DOH’s vaccine rollout guided by incidence maps
Relevance to Public Health
Epidemiology provides the framework for disease surveillance (e.g., TB, Dengue)
Guides resource allocation during epidemics and pandemics
Enables policy formulation
WHO (2020) and CDC (2012) highlight how epidemiologic data informs:
Outbreak response
Vaccination campaigns
Community health education
Types of Data (Pagano & Gauvreau, 2018)
Quantitative (Numerical) Data
_________ - countable, finite values
Example: Number of red blood cells per mm^3
Continuous - measurable, infinite values within a range
Example: Patient’s weight or serum cholesterol level
Qualitative (Categorical) Data
Nominal - categories with no order
Example: Blood type (A, B, AB, O)
Ordinal - ordered categories
Example: Tumor stage (Stage I, II, III, IV)
Variables in Epidemiologic Studies
Independent Variable
Variable that is manipulated or categorized to see its effect
Example: Smoking status in a lung cancer study
Dependent Variable
Outcome being measured
Example: Lung cancer incidence
Confounding Variable
A factor associated with both the exposure and outcome
Example: Age could influence both smoking habits and lung cancer risk
Controlled Variable
Kept constant to prevent it from affecting the outcome
Example: Lab temperature in enzyme activity studies
Continuous
Types of Epidemiology
Descriptive Epidemiology
Describes disease occurence by person, place, time
Example: COVID-19 incidence by region in the Philippines
Analytic Epidemiology
Examines causes and associations
Uses case-control, cohort, and experimental studies
Example: Linking HPV infection to cervical cancer
Applied Epidemiology
Using epidemiologic data to implement and evaluate interventions
Example: DOH’s vaccine rollout guided by incidence maps
Relevance to Public Health
Epidemiology provides the framework for disease surveillance (e.g., TB, Dengue)
Guides resource allocation during epidemics and pandemics
Enables policy formulation
WHO (2020) and CDC (2012) highlight how epidemiologic data informs:
Outbreak response
Vaccination campaigns
Community health education
Types of Data (Pagano & Gauvreau, 2018)
Quantitative (Numerical) Data
Discrete - countable, finite values
Example: Number of red blood cells per mm^3
__________ - measurable, infinite values within a range
Example: Patient’s weight or serum cholesterol level
Qualitative (Categorical) Data
Nominal - categories with no order
Example: Blood type (A, B, AB, O)
Ordinal - ordered categories
Example: Tumor stage (Stage I, II, III, IV)
Variables in Epidemiologic Studies
Independent Variable
Variable that is manipulated or categorized to see its effect
Example: Smoking status in a lung cancer study
Dependent Variable
Outcome being measured
Example: Lung cancer incidence
Confounding Variable
A factor associated with both the exposure and outcome
Example: Age could influence both smoking habits and lung cancer risk
Controlled Variable
Kept constant to prevent it from affecting the outcome
Example: Lab temperature in enzyme activity studies
Countable
Types of Epidemiology
Descriptive Epidemiology
Describes disease occurence by person, place, time
Example: COVID-19 incidence by region in the Philippines
Analytic Epidemiology
Examines causes and associations
Uses case-control, cohort, and experimental studies
Example: Linking HPV infection to cervical cancer
Applied Epidemiology
Using epidemiologic data to implement and evaluate interventions
Example: DOH’s vaccine rollout guided by incidence maps
Relevance to Public Health
Epidemiology provides the framework for disease surveillance (e.g., TB, Dengue)
Guides resource allocation during epidemics and pandemics
Enables policy formulation
WHO (2020) and CDC (2012) highlight how epidemiologic data informs:
Outbreak response
Vaccination campaigns
Community health education
Types of Data (Pagano & Gauvreau, 2018)
Quantitative (Numerical) Data
Discrete - ________, finite values
Example: Number of red blood cells per mm^3
Continuous - measurable, infinite values within a range
Example: Patient’s weight or serum cholesterol level
Qualitative (Categorical) Data
Nominal - categories with no order
Example: Blood type (A, B, AB, O)
Ordinal - ordered categories
Example: Tumor stage (Stage I, II, III, IV)
Variables in Epidemiologic Studies
Independent Variable
Variable that is manipulated or categorized to see its effect
Example: Smoking status in a lung cancer study
Dependent Variable
Outcome being measured
Example: Lung cancer incidence
Confounding Variable
A factor associated with both the exposure and outcome
Example: Age could influence both smoking habits and lung cancer risk
Controlled Variable
Kept constant to prevent it from affecting the outcome
Example: Lab temperature in enzyme activity studies
Infinite
Types of Epidemiology
Descriptive Epidemiology
Describes disease occurence by person, place, time
Example: COVID-19 incidence by region in the Philippines
Analytic Epidemiology
Examines causes and associations
Uses case-control, cohort, and experimental studies
Example: Linking HPV infection to cervical cancer
Applied Epidemiology
Using epidemiologic data to implement and evaluate interventions
Example: DOH’s vaccine rollout guided by incidence maps
Relevance to Public Health
Epidemiology provides the framework for disease surveillance (e.g., TB, Dengue)
Guides resource allocation during epidemics and pandemics
Enables policy formulation
WHO (2020) and CDC (2012) highlight how epidemiologic data informs:
Outbreak response
Vaccination campaigns
Community health education
Types of Data (Pagano & Gauvreau, 2018)
Quantitative (Numerical) Data
Discrete - countable, finite values
Example: Number of red blood cells per mm^3
Continuous - measurable, _______ values within a range
Example: Patient’s weight or serum cholesterol level
Qualitative (Categorical) Data
Nominal - categories with no order
Example: Blood type (A, B, AB, O)
Ordinal - ordered categories
Example: Tumor stage (Stage I, II, III, IV)
Variables in Epidemiologic Studies
Independent Variable
Variable that is manipulated or categorized to see its effect
Example: Smoking status in a lung cancer study
Dependent Variable
Outcome being measured
Example: Lung cancer incidence
Confounding Variable
A factor associated with both the exposure and outcome
Example: Age could influence both smoking habits and lung cancer risk
Controlled Variable
Kept constant to prevent it from affecting the outcome
Example: Lab temperature in enzyme activity studies
Qualitative Data (Categorical)
Types of Epidemiology
Descriptive Epidemiology
Describes disease occurence by person, place, time
Example: COVID-19 incidence by region in the Philippines
Analytic Epidemiology
Examines causes and associations
Uses case-control, cohort, and experimental studies
Example: Linking HPV infection to cervical cancer
Applied Epidemiology
Using epidemiologic data to implement and evaluate interventions
Example: DOH’s vaccine rollout guided by incidence maps
Relevance to Public Health
Epidemiology provides the framework for disease surveillance (e.g., TB, Dengue)
Guides resource allocation during epidemics and pandemics
Enables policy formulation
WHO (2020) and CDC (2012) highlight how epidemiologic data informs:
Outbreak response
Vaccination campaigns
Community health education
Types of Data (Pagano & Gauvreau, 2018)
Quantitative (Numerical) Data
Discrete - countable, finite values
Example: Number of red blood cells per mm^3
Continuous - measurable, infinite values within a range
Example: Patient’s weight or serum cholesterol level
_______ _______ ______
Nominal - categories with no order
Example: Blood type (A, B, AB, O)
Ordinal - ordered categories
Example: Tumor stage (Stage I, II, III, IV)
Variables in Epidemiologic Studies
Independent Variable
Variable that is manipulated or categorized to see its effect
Example: Smoking status in a lung cancer study
Dependent Variable
Outcome being measured
Example: Lung cancer incidence
Confounding Variable
A factor associated with both the exposure and outcome
Example: Age could influence both smoking habits and lung cancer risk
Controlled Variable
Kept constant to prevent it from affecting the outcome
Example: Lab temperature in enzyme activity studies
Nominal
Types of Epidemiology
Descriptive Epidemiology
Describes disease occurence by person, place, time
Example: COVID-19 incidence by region in the Philippines
Analytic Epidemiology
Examines causes and associations
Uses case-control, cohort, and experimental studies
Example: Linking HPV infection to cervical cancer
Applied Epidemiology
Using epidemiologic data to implement and evaluate interventions
Example: DOH’s vaccine rollout guided by incidence maps
Relevance to Public Health
Epidemiology provides the framework for disease surveillance (e.g., TB, Dengue)
Guides resource allocation during epidemics and pandemics
Enables policy formulation
WHO (2020) and CDC (2012) highlight how epidemiologic data informs:
Outbreak response
Vaccination campaigns
Community health education
Types of Data (Pagano & Gauvreau, 2018)
Quantitative (Numerical) Data
Discrete - countable, finite values
Example: Number of red blood cells per mm^3
Continuous - measurable, infinite values within a range
Example: Patient’s weight or serum cholesterol level
Qualitative (Categorical) Data
_______ - categories with no order
Example: Blood type (A, B, AB, O)
Ordinal - ordered categories
Example: Tumor stage (Stage I, II, III, IV)
Variables in Epidemiologic Studies
Independent Variable
Variable that is manipulated or categorized to see its effect
Example: Smoking status in a lung cancer study
Dependent Variable
Outcome being measured
Example: Lung cancer incidence
Confounding Variable
A factor associated with both the exposure and outcome
Example: Age could influence both smoking habits and lung cancer risk
Controlled Variable
Kept constant to prevent it from affecting the outcome
Example: Lab temperature in enzyme activity studies
Ordinal
Types of Epidemiology
Descriptive Epidemiology
Describes disease occurence by person, place, time
Example: COVID-19 incidence by region in the Philippines
Analytic Epidemiology
Examines causes and associations
Uses case-control, cohort, and experimental studies
Example: Linking HPV infection to cervical cancer
Applied Epidemiology
Using epidemiologic data to implement and evaluate interventions
Example: DOH’s vaccine rollout guided by incidence maps
Relevance to Public Health
Epidemiology provides the framework for disease surveillance (e.g., TB, Dengue)
Guides resource allocation during epidemics and pandemics
Enables policy formulation
WHO (2020) and CDC (2012) highlight how epidemiologic data informs:
Outbreak response
Vaccination campaigns
Community health education
Types of Data (Pagano & Gauvreau, 2018)
Quantitative (Numerical) Data
Discrete - countable, finite values
Example: Number of red blood cells per mm^3
Continuous - measurable, infinite values within a range
Example: Patient’s weight or serum cholesterol level
Qualitative (Categorical) Data
Nominal - categories with no order
Example: Blood type (A, B, AB, O)
_______ - ordered categories
Example: Tumor stage (Stage I, II, III, IV)
Variables in Epidemiologic Studies
Independent Variable
Variable that is manipulated or categorized to see its effect
Example: Smoking status in a lung cancer study
Dependent Variable
Outcome being measured
Example: Lung cancer incidence
Confounding Variable
A factor associated with both the exposure and outcome
Example: Age could influence both smoking habits and lung cancer risk
Controlled Variable
Kept constant to prevent it from affecting the outcome
Example: Lab temperature in enzyme activity studies
No order
Types of Epidemiology
Descriptive Epidemiology
Describes disease occurence by person, place, time
Example: COVID-19 incidence by region in the Philippines
Analytic Epidemiology
Examines causes and associations
Uses case-control, cohort, and experimental studies
Example: Linking HPV infection to cervical cancer
Applied Epidemiology
Using epidemiologic data to implement and evaluate interventions
Example: DOH’s vaccine rollout guided by incidence maps
Relevance to Public Health
Epidemiology provides the framework for disease surveillance (e.g., TB, Dengue)
Guides resource allocation during epidemics and pandemics
Enables policy formulation
WHO (2020) and CDC (2012) highlight how epidemiologic data informs:
Outbreak response
Vaccination campaigns
Community health education
Types of Data (Pagano & Gauvreau, 2018)
Quantitative (Numerical) Data
Discrete - countable, finite values
Example: Number of red blood cells per mm^3
Continuous - measurable, infinite values within a range
Example: Patient’s weight or serum cholesterol level
Qualitative (Categorical) Data
Nominal - categories with ________ ______
Example: Blood type (A, B, AB, O)
Ordinal - ordered categories
Example: Tumor stage (Stage I, II, III, IV)
Variables in Epidemiologic Studies
Independent Variable
Variable that is manipulated or categorized to see its effect
Example: Smoking status in a lung cancer study
Dependent Variable
Outcome being measured
Example: Lung cancer incidence
Confounding Variable
A factor associated with both the exposure and outcome
Example: Age could influence both smoking habits and lung cancer risk
Controlled Variable
Kept constant to prevent it from affecting the outcome
Example: Lab temperature in enzyme activity studies
Ordered
Types of Epidemiology
Descriptive Epidemiology
Describes disease occurence by person, place, time
Example: COVID-19 incidence by region in the Philippines
Analytic Epidemiology
Examines causes and associations
Uses case-control, cohort, and experimental studies
Example: Linking HPV infection to cervical cancer
Applied Epidemiology
Using epidemiologic data to implement and evaluate interventions
Example: DOH’s vaccine rollout guided by incidence maps
Relevance to Public Health
Epidemiology provides the framework for disease surveillance (e.g., TB, Dengue)
Guides resource allocation during epidemics and pandemics
Enables policy formulation
WHO (2020) and CDC (2012) highlight how epidemiologic data informs:
Outbreak response
Vaccination campaigns
Community health education
Types of Data (Pagano & Gauvreau, 2018)
Quantitative (Numerical) Data
Discrete - countable, finite values
Example: Number of red blood cells per mm^3
Continuous - measurable, infinite values within a range
Example: Patient’s weight or serum cholesterol level
Qualitative (Categorical) Data
Nominal - categories with no order
Example: Blood type (A, B, AB, O)
Ordinal - _________ categories
Example: Tumor stage (Stage I, II, III, IV)
Variables in Epidemiologic Studies
Independent Variable
Variable that is manipulated or categorized to see its effect
Example: Smoking status in a lung cancer study
Dependent Variable
Outcome being measured
Example: Lung cancer incidence
Confounding Variable
A factor associated with both the exposure and outcome
Example: Age could influence both smoking habits and lung cancer risk
Controlled Variable
Kept constant to prevent it from affecting the outcome
Example: Lab temperature in enzyme activity studies
Independent variable
Types of Epidemiology
Descriptive Epidemiology
Describes disease occurence by person, place, time
Example: COVID-19 incidence by region in the Philippines
Analytic Epidemiology
Examines causes and associations
Uses case-control, cohort, and experimental studies
Example: Linking HPV infection to cervical cancer
Applied Epidemiology
Using epidemiologic data to implement and evaluate interventions
Example: DOH’s vaccine rollout guided by incidence maps
Relevance to Public Health
Epidemiology provides the framework for disease surveillance (e.g., TB, Dengue)
Guides resource allocation during epidemics and pandemics
Enables policy formulation
WHO (2020) and CDC (2012) highlight how epidemiologic data informs:
Outbreak response
Vaccination campaigns
Community health education
Types of Data (Pagano & Gauvreau, 2018)
Quantitative (Numerical) Data
Discrete - countable, finite values
Example: Number of red blood cells per mm^3
Continuous - measurable, infinite values within a range
Example: Patient’s weight or serum cholesterol level
Qualitative (Categorical) Data
Nominal - categories with no order
Example: Blood type (A, B, AB, O)
Ordinal - ordered categories
Example: Tumor stage (Stage I, II, III, IV)
Variables in Epidemiologic Studies
_________ _________
Variable that is manipulated or categorized to see its effect
Example: Smoking status in a lung cancer study
Dependent Variable
Outcome being measured
Example: Lung cancer incidence
Confounding Variable
A factor associated with both the exposure and outcome
Example: Age could influence both smoking habits and lung cancer risk
Controlled Variable
Kept constant to prevent it from affecting the outcome
Example: Lab temperature in enzyme activity studies
Manipulated
Types of Epidemiology
Descriptive Epidemiology
Describes disease occurence by person, place, time
Example: COVID-19 incidence by region in the Philippines
Analytic Epidemiology
Examines causes and associations
Uses case-control, cohort, and experimental studies
Example: Linking HPV infection to cervical cancer
Applied Epidemiology
Using epidemiologic data to implement and evaluate interventions
Example: DOH’s vaccine rollout guided by incidence maps
Relevance to Public Health
Epidemiology provides the framework for disease surveillance (e.g., TB, Dengue)
Guides resource allocation during epidemics and pandemics
Enables policy formulation
WHO (2020) and CDC (2012) highlight how epidemiologic data informs:
Outbreak response
Vaccination campaigns
Community health education
Types of Data (Pagano & Gauvreau, 2018)
Quantitative (Numerical) Data
Discrete - countable, finite values
Example: Number of red blood cells per mm^3
Continuous - measurable, infinite values within a range
Example: Patient’s weight or serum cholesterol level
Qualitative (Categorical) Data
Nominal - categories with no order
Example: Blood type (A, B, AB, O)
Ordinal - ordered categories
Example: Tumor stage (Stage I, II, III, IV)
Variables in Epidemiologic Studies
Independent Variable
Variable that is _________ or categorized to see its effect
Example: Smoking status in a lung cancer study
Dependent Variable
Outcome being measured
Example: Lung cancer incidence
Confounding Variable
A factor associated with both the exposure and outcome
Example: Age could influence both smoking habits and lung cancer risk
Controlled Variable
Kept constant to prevent it from affecting the outcome
Example: Lab temperature in enzyme activity studies
Dependent variable
Types of Epidemiology
Descriptive Epidemiology
Describes disease occurence by person, place, time
Example: COVID-19 incidence by region in the Philippines
Analytic Epidemiology
Examines causes and associations
Uses case-control, cohort, and experimental studies
Example: Linking HPV infection to cervical cancer
Applied Epidemiology
Using epidemiologic data to implement and evaluate interventions
Example: DOH’s vaccine rollout guided by incidence maps
Relevance to Public Health
Epidemiology provides the framework for disease surveillance (e.g., TB, Dengue)
Guides resource allocation during epidemics and pandemics
Enables policy formulation
WHO (2020) and CDC (2012) highlight how epidemiologic data informs:
Outbreak response
Vaccination campaigns
Community health education
Types of Data (Pagano & Gauvreau, 2018)
Quantitative (Numerical) Data
Discrete - countable, finite values
Example: Number of red blood cells per mm^3
Continuous - measurable, infinite values within a range
Example: Patient’s weight or serum cholesterol level
Qualitative (Categorical) Data
Nominal - categories with no order
Example: Blood type (A, B, AB, O)
Ordinal - ordered categories
Example: Tumor stage (Stage I, II, III, IV)
Variables in Epidemiologic Studies
Independent Variable
Variable that is manipulated or categorized to see its effect
Example: Smoking status in a lung cancer study
_________ ________
Outcome being measured
Example: Lung cancer incidence
Confounding Variable
A factor associated with both the exposure and outcome
Example: Age could influence both smoking habits and lung cancer risk
Controlled Variable
Kept constant to prevent it from affecting the outcome
Example: Lab temperature in enzyme activity studies
measured
Types of Epidemiology
Descriptive Epidemiology
Describes disease occurence by person, place, time
Example: COVID-19 incidence by region in the Philippines
Analytic Epidemiology
Examines causes and associations
Uses case-control, cohort, and experimental studies
Example: Linking HPV infection to cervical cancer
Applied Epidemiology
Using epidemiologic data to implement and evaluate interventions
Example: DOH’s vaccine rollout guided by incidence maps
Relevance to Public Health
Epidemiology provides the framework for disease surveillance (e.g., TB, Dengue)
Guides resource allocation during epidemics and pandemics
Enables policy formulation
WHO (2020) and CDC (2012) highlight how epidemiologic data informs:
Outbreak response
Vaccination campaigns
Community health education
Types of Data (Pagano & Gauvreau, 2018)
Quantitative (Numerical) Data
Discrete - countable, finite values
Example: Number of red blood cells per mm^3
Continuous - measurable, infinite values within a range
Example: Patient’s weight or serum cholesterol level
Qualitative (Categorical) Data
Nominal - categories with no order
Example: Blood type (A, B, AB, O)
Ordinal - ordered categories
Example: Tumor stage (Stage I, II, III, IV)
Variables in Epidemiologic Studies
Independent Variable
Variable that is manipulated or categorized to see its effect
Example: Smoking status in a lung cancer study
Dependent Variable
Outcome being __________
Example: Lung cancer incidence
Confounding Variable
A factor associated with both the exposure and outcome
Example: Age could influence both smoking habits and lung cancer risk
Controlled Variable
Kept constant to prevent it from affecting the outcome
Example: Lab temperature in enzyme activity studies
Confounding variable
Types of Epidemiology
Descriptive Epidemiology
Describes disease occurence by person, place, time
Example: COVID-19 incidence by region in the Philippines
Analytic Epidemiology
Examines causes and associations
Uses case-control, cohort, and experimental studies
Example: Linking HPV infection to cervical cancer
Applied Epidemiology
Using epidemiologic data to implement and evaluate interventions
Example: DOH’s vaccine rollout guided by incidence maps
Relevance to Public Health
Epidemiology provides the framework for disease surveillance (e.g., TB, Dengue)
Guides resource allocation during epidemics and pandemics
Enables policy formulation
WHO (2020) and CDC (2012) highlight how epidemiologic data informs:
Outbreak response
Vaccination campaigns
Community health education
Types of Data (Pagano & Gauvreau, 2018)
Quantitative (Numerical) Data
Discrete - countable, finite values
Example: Number of red blood cells per mm^3
Continuous - measurable, infinite values within a range
Example: Patient’s weight or serum cholesterol level
Qualitative (Categorical) Data
Nominal - categories with no order
Example: Blood type (A, B, AB, O)
Ordinal - ordered categories
Example: Tumor stage (Stage I, II, III, IV)
Variables in Epidemiologic Studies
Independent Variable
Variable that is manipulated or categorized to see its effect
Example: Smoking status in a lung cancer study
Dependent Variable
Outcome being measured
Example: Lung cancer incidence
________ _______
A factor associated with both the exposure and outcome
Example: Age could influence both smoking habits and lung cancer risk
Controlled Variable
Kept constant to prevent it from affecting the outcome
Example: Lab temperature in enzyme activity studies
Controlled variable
Types of Epidemiology
Descriptive Epidemiology
Describes disease occurence by person, place, time
Example: COVID-19 incidence by region in the Philippines
Analytic Epidemiology
Examines causes and associations
Uses case-control, cohort, and experimental studies
Example: Linking HPV infection to cervical cancer
Applied Epidemiology
Using epidemiologic data to implement and evaluate interventions
Example: DOH’s vaccine rollout guided by incidence maps
Relevance to Public Health
Epidemiology provides the framework for disease surveillance (e.g., TB, Dengue)
Guides resource allocation during epidemics and pandemics
Enables policy formulation
WHO (2020) and CDC (2012) highlight how epidemiologic data informs:
Outbreak response
Vaccination campaigns
Community health education
Types of Data (Pagano & Gauvreau, 2018)
Quantitative (Numerical) Data
Discrete - countable, finite values
Example: Number of red blood cells per mm^3
Continuous - measurable, infinite values within a range
Example: Patient’s weight or serum cholesterol level
Qualitative (Categorical) Data
Nominal - categories with no order
Example: Blood type (A, B, AB, O)
Ordinal - ordered categories
Example: Tumor stage (Stage I, II, III, IV)
Variables in Epidemiologic Studies
Independent Variable
Variable that is manipulated or categorized to see its effect
Example: Smoking status in a lung cancer study
Dependent Variable
Outcome being measured
Example: Lung cancer incidence
Confounding Variable
A factor associated with both the exposure and outcome
Example: Age could influence both smoking habits and lung cancer risk
_______ _________
Kept constant to prevent it from affecting the outcome
Example: Lab temperature in enzyme activity studies
Exposure and outcome
Types of Epidemiology
Descriptive Epidemiology
Describes disease occurence by person, place, time
Example: COVID-19 incidence by region in the Philippines
Analytic Epidemiology
Examines causes and associations
Uses case-control, cohort, and experimental studies
Example: Linking HPV infection to cervical cancer
Applied Epidemiology
Using epidemiologic data to implement and evaluate interventions
Example: DOH’s vaccine rollout guided by incidence maps
Relevance to Public Health
Epidemiology provides the framework for disease surveillance (e.g., TB, Dengue)
Guides resource allocation during epidemics and pandemics
Enables policy formulation
WHO (2020) and CDC (2012) highlight how epidemiologic data informs:
Outbreak response
Vaccination campaigns
Community health education
Types of Data (Pagano & Gauvreau, 2018)
Quantitative (Numerical) Data
Discrete - countable, finite values
Example: Number of red blood cells per mm^3
Continuous - measurable, infinite values within a range
Example: Patient’s weight or serum cholesterol level
Qualitative (Categorical) Data
Nominal - categories with no order
Example: Blood type (A, B, AB, O)
Ordinal - ordered categories
Example: Tumor stage (Stage I, II, III, IV)
Variables in Epidemiologic Studies
Independent Variable
Variable that is manipulated or categorized to see its effect
Example: Smoking status in a lung cancer study
Dependent Variable
Outcome being measured
Example: Lung cancer incidence
Confounding Variable
A factor associated with both the _______ ____ _______
Example: Age could influence both smoking habits and lung cancer risk
Controlled Variable
Kept constant to prevent it from affecting the outcome
Example: Lab temperature in enzyme activity studies
COVID-19
Applying Epidemiologic Tools - _______ _______
Incidence rate: New confirmed cases per week
Prevalence: Total active cases in a region
Case fatality rate (CFR): % of deaths among confirmed cases
Reproductive number (RO): Transmission potential of the virus
(Source: WHO COVID-19 Updates; CDC Principles (2012)
Summary
Biostatistics provides the math behind medical evidence
Epidemiology frames the understanding of disease in populations
Knowing data types and measurement levels is essential to good science
These tools help Medical Technologists become data-driven professionals
Incidence rate
Applying Epidemiologic Tools - COVID-19
________ ________: New confirmed cases per week
Prevalence: Total active cases in a region
Case fatality rate (CFR): % of deaths among confirmed cases
Reproductive number (RO): Transmission potential of the virus
(Source: WHO COVID-19 Updates; CDC Principles (2012)
Summary
Biostatistics provides the math behind medical evidence
Epidemiology frames the understanding of disease in populations
Knowing data types and measurement levels is essential to good science
These tools help Medical Technologists become data-driven professionals
Prevalence
Applying Epidemiologic Tools - COVID-19
Incidence rate: New confirmed cases per week
________: Total active cases in a region
Case fatality rate (CFR): % of deaths among confirmed cases
Reproductive number (RO): Transmission potential of the virus
(Source: WHO COVID-19 Updates; CDC Principles (2012)
Summary
Biostatistics provides the math behind medical evidence
Epidemiology frames the understanding of disease in populations
Knowing data types and measurement levels is essential to good science
These tools help Medical Technologists become data-driven professionals
Case fatality rate
Applying Epidemiologic Tools - COVID-19
Incidence rate: New confirmed cases per week
Prevalence: Total active cases in a region
______ ____ ______ (CFR): % of deaths among confirmed cases
Reproductive number (RO): Transmission potential of the virus
(Source: WHO COVID-19 Updates; CDC Principles (2012)
Summary
Biostatistics provides the math behind medical evidence
Epidemiology frames the understanding of disease in populations
Knowing data types and measurement levels is essential to good science
These tools help Medical Technologists become data-driven professionals
Reproductive number
Applying Epidemiologic Tools - COVID-19
Incidence rate: New confirmed cases per week
Prevalence: Total active cases in a region
Case fatality rate (CFR): % of deaths among confirmed cases
_______ ______ (RO): Transmission potential of the virus
(Source: WHO COVID-19 Updates; CDC Principles (2012)
Summary
Biostatistics provides the math behind medical evidence
Epidemiology frames the understanding of disease in populations
Knowing data types and measurement levels is essential to good science
These tools help Medical Technologists become data-driven professionals
Math
Applying Epidemiologic Tools - COVID-19
Incidence rate: New confirmed cases per week
Prevalence: Total active cases in a region
Case fatality rate (CFR): % of deaths among confirmed cases
Reproductive number (RO): Transmission potential of the virus
(Source: WHO COVID-19 Updates; CDC Principles (2012)
Summary
Biostatistics provides the _________ behind medical evidence
Epidemiology frames the understanding of disease in populations
Knowing data types and measurement levels is essential to good science
These tools help Medical Technologists become data-driven professionals