L1 (P): Introduction to Biostatistics and Epidemiology

Biostatistics

the branch of statistics responsible for interpreting the scientific data generated in the biology, public health, and medical fields." - Daniel and Cross (2018)

Descriptive Biostatistics

Summarizes data using numbers and graphs (mean, median, frequency)

Ex. Average hemoglobin level in a population.

Inferential Biostatistics

Makes predictions or inferences from sample data.

Uses probability, hypothesis testing, confidence intervals.

Ex. Testing a new diagnostic test is more accurate.

Epidemiology

"the study of the distribution and determinants of health-related states or events in specified populations and the application of this study to control of health problems." - Gordis (2014)

Importance of Biostatistics 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

Importance of Epidemiology in Health Sciences

Looks at patterns of diseases in groups, not individuals.

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

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?

Ex. More dengue cases occur during the rainy season in Metro Man

ila.

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

Ex. 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.

Ex. 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

Ex. After finding a high number of TB cases in a region, the government starts a free screening program.

Descriptive Epidemiology

describes disease occurrence by person, place, time

Ex. COVID-19 incidence by region in the Philippines

Analytic Epidemiology

examines causes and associations

uses case-control, cohort, and experimental studies

Ex. Linking HPV infection to cervical cancer

Applied Epidemiology

using epidemiologic data to implement and evaluate interventions

Ex. DOH's vaccine rollout guided by incidence maps

Relevance to Public Health

Epidemiology provides the framework for disease surveillance (eg. TB, dengue)

Guides resource allocation during epidemics and pandemics

Enables policy formulation

Outbreak response

Vaccination campaigns

Community health education

WHO (2020) and CDC (2012) highlight how epidemiologic data informs:

A. Quantitative (Numerical Data) - DISCRETE

countable, finite values

Ex. Number of red blood cells per mm³

A. Quantitative (Numerical Data) - CONTINUOUS

measurable, infinite values within a range

Ex. Patient's weight or serum cholesterol level

B. Qualitative (Categorical) Data - NOMINAL

categories with no order

Ex. Blood type (A, B, AB, O

B. Qualitative (Categorical) Data - ORDINAL

ordered categories

Ex. Tumor stage (Stage I, II, III, IV)

Independent Variable

manipulated or categorized to see its effect.

Ex. Smoking status in a lung cancer study

Dependent Variable

outcome being measured.

Ex. Lung cancer incidence

Confounding Variable

a factor associated with both the exposure and outcome.

Ex. Age could influence both smoking habits and lung cancer risks

Controlled Variable

kept constant to prevent it from affecting the outcome.

Ex. Lab temperature in enzyme activity studies