BIOSTATS: L1 (P) (Introduction)

Biostatistics

Is the branch of statistics responsible for interpreting the scientific data generated in the biology, public health, and medicine fields

Importance in Health Sciences of Biostatistics

• 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

1. Descriptive Biostatistics

2. Inferential Biostatistics

Descriptive

Branches of Biostatistics:

1. _____ Biostatistics

2. Inferential Biostatistics

Inferential

Branches of Biostatistics:

1. Descriptive Biostatistics

2. _____ Biostatistics

Epidemiology

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

Importance in Health Sciences of Epidemiology

• Looks at patterns of diseases in groups, not individuals.

• Key for public health surveillance, outbreak control, and health policy.

Core Epidemiologic Concepts

1. Distribution - Frequency and pattern of health events

2. Determinants - Causes and risk factors that influence the occurrence of health problems

3. Outcomes - Morbidity, mortality, recovery

4. Population-based - Unit of analysis is population, not individual

5. Application - Prevention and intervention strategies

Distribution

Frequency and pattern of health events

Determinants

Causes and risk factors that influence the occurrence of health problems

Outcomes

Morbidity, mortality, recovery

Population-based

Unit of analysis is population, not individua

Application

Prevention and intervention strategies

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 that influence the occurrence of health problems. They answer the question:

• Why did it happen?

• Can be biological Behavioral Environmental or 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 COVID19 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.

Types of Epidemiology

• Descriptive Epidemiology

• Analytic Epidemiology

• Applied Epidemiology

Descriptive

Types of Epidemiology

• _____ Epidemiology

• Analytic Epidemiology

• Applied Epidemiology

Analytic

Types of Epidemiology

• Descriptive Epidemiology

• _____ Epidemiology

• Applied Epidemiology

Applied

Types of Epidemiology

• Descriptive Epidemiology

• Analytic Epidemiology

• _____ Epidemiology

Descriptive Epidemiology

• Describes disease occurrence 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

A. Quantitative (Numerical) Data

• Discrete – countable, finite values

  • Example: Number of red blood cells per mm³

• Continuous – measurable, infinite values within a range

  • Example: Patient’s weight or serum cholesterol level

B. 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)

Quantitative (Numerical)

Types of Data

A. _____ (_____) Data

• Discrete – countable, finite values

  • Example: Number of red blood cells per mm³

• Continuous – measurable, infinite values within a range

  • Example: Patient’s weight or serum cholesterol level

B. 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)

Qualitative (Categorical)

Types of Data

A. Quantitative (Numerical) Data

• Discrete – countable, finite values

  • Example: Number of red blood cells per mm³

• Continuous – measurable, infinite values within a range

  • Example: Patient’s weight or serum cholesterol level

B. _____ (_____) Data

• Nominal – categories with no order

  • Example: Blood type (A, B, AB, O)

• Ordinal – ordered categories

  • Example: Tumor stage (Stage I, II, III, IV)

Discrete

• Countable, finite values

  • Example: Number of red blood cells per mm³

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

1. Independent Variable

   • Variable that is manipulated or categorized to see its effect

     Example: Smoking status in a lung cancer study

2. Dependent Variable

   • Outcome being measured

     Example: Lung cancer incidence

3. Confounding Variable

   • A factor associated with both the exposure and outcome

     Example: Age could influence both smoking habits and lung cancer risk

4. Controlled Variable

   • Kept constant to prevent it from affecting the outcome

     Example: Lab temperature in enzyme activity studies

Independent

Variables In Epidemiologic Studies

1. _____ Variable

   • Variable that is manipulated or categorized to see its effect

     Example: Smoking status in a lung cancer study

2. Dependent Variable

   • Outcome being measured

     Example: Lung cancer incidence

3. Confounding Variable

   • A factor associated with both the exposure and outcome

     Example: Age could influence both smoking habits and lung cancer risk

4. Controlled Variable

   • Kept constant to prevent it from affecting the outcome

     Example: Lab temperature in enzyme activity studies

Dependent

Variables In Epidemiologic Studies

1. Independent Variable

   • Variable that is manipulated or categorized to see its effect

     Example: Smoking status in a lung cancer study

2. _____ Variable

   • Outcome being measured

     Example: Lung cancer incidence

3. Confounding Variable

   • A factor associated with both the exposure and outcome

     Example: Age could influence both smoking habits and lung cancer risk

4. Controlled Variable

   • Kept constant to prevent it from affecting the outcome

     Example: Lab temperature in enzyme activity studies

Confounding

Variables In Epidemiologic Studies

1. Independent Variable

   • Variable that is manipulated or categorized to see its effect

     Example: Smoking status in a lung cancer study

2. Dependent Variable

   • Outcome being measured

     Example: Lung cancer incidence

3. _____ Variable

   • A factor associated with both the exposure and outcome

     Example: Age could influence both smoking habits and lung cancer risk

4. Controlled Variable

   • Kept constant to prevent it from affecting the outcome

     Example: Lab temperature in enzyme activity studies

Controlled

Variables In Epidemiologic Studies

1. Independent Variable

   • Variable that is manipulated or categorized to see its effect

     Example: Smoking status in a lung cancer study

2. Dependent Variable

   • Outcome being measured

     Example: Lung cancer incidence

3. Confounding Variable

   • A factor associated with both the exposure and outcome

     Example: Age could influence both smoking habits and lung cancer risk

4. _____ Variable

   • Kept constant to prevent it from affecting the outcome

     Example: Lab temperature in enzyme activity studies

Four Levels of Measurement

1. Nominal

2. Ordinal

3. Interval

4. Ratio

Nominal

Definition: Categories, no order

Example: Gender, Blood type

Analysis Techniques: Frequency, Mode

Ordinal

Definition: Ordered, unequal intervals
Example: Cancer stages, Likert scale
Analysis Techniques: Median, Nonparametric tests

Interval

Definition: Ordered, equal intervals, no true zero
Example: Temperature in °C
Analysis Techniques: Mean, SD, Correlation

Ratio

Definition: Like interval + absolute zero
Example: Weight, Age, Blood pressure
Analysis Techniques: All statistical operations

Applying Epidemiologic Tools – COVID-19

1. Incidence rate: New confirmed cases per week

2. Prevalence: Total active cases in a region

3. Case fatality rate (CFR): % of deaths among confirmed cases

4. Reproductive number (R0): Transmission potential of the virus

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 (R0)

Transmission potential of the virus