[2.1] Principles of Biostatistics in Public Health Practice Flashcards
Is an approach that relies on empirical data, data analysis, and insights to inform
strategies that are aimed at improving outcomes and increasing efficiency across various domains
Data-driven decision-making
Is essential at every step in approaching public health problems. While some public health decisions may be based on expert knowledge, data provides a solid foundation for evidence-based decision-making and significantly enhances the effectiveness and efficiency of public health strategies.
Data
Primary use of data-driven public health decision-making
Surveillance
The continuous, systematic collection, analysis and interpretation of health-related data is needed to plan, implement, and evaluate public health initiatives
Surveillance
Data analytics enables public health professionals to identify and understand the risk factors associated with various diseases and health conditions
Risk Factor Identification
Data provides concrete evidence of what works and what may not, allowing policymakers to make informed adjustments to improve outcomes and allocate resources more efficiently
Intervention Evaluation
Data analysis allows decision-makers to identify geographical locations or demographics that require more
attention and support.
Implementation
Who said “Statistics is the science which deals with collection, classification, and tabulation of numerical facts as the basis for explanation, description, and comparison of a phenomenon”
Lovitt
What does statistics cover
Planning
Design
Execution (Data collection)
Data Processing
Data analysis
Presentation
Interpretation
Publication
Set of values of one or more variables recorded
on one or more observational units
Data
What are the 4 sources of data
- Routinely kept records
- Surveys (census)
- Experiments
- External source
Category of data: Observation, questionnaire, record form, interviews, survey
Primary data
Category of data: Census, medical record, registry
Secondary data
Quantitative information about a population’s “vital events” such as the number of births (natality), deaths (mortality), marriages (nuptiality), and divorces
Vital Statistics
3 Types of Data
QUALITATIVE DATA
DISCRETE QUANTITATIVE
CONTINOUS QUANTITATIVE
Example:
Sex (F or M),
Nominal Qualitative
Example:
Blood group (A, B, O or AB)
Nominal Qualitative
Example: Severity of disease (mild, moderare, severe)
Ordinal Qualitative
Example: Exam Result (P or F)
Nominal Qualitative
Example: Response to treatment (poor, fair, good)
Ordinal Qualitative
Example: No of family members
Discrete Quantitative
Example: No of heart beats
Discrete Quantitative
Example: No of admission in a day
Discrete Quantitative
Example: Height or Weight
Continuous Quantitative
