Curriculum standards

Knowledge guides

LG14: Scientific foundations of public health medicine

Epidemiology and research methods

Advanced Trainees will have in-depth knowledge of the epidemiological concepts and statistical methods listed. Trainees should be able to describe the underlying rationale, the indications for using one study design or statistical method over another, the strengths and weaknesses of different methods / designs, and the correct interpretation of the results.

For issues in study conduct, trainees should be able to describe the threats to study precision, validity, and accuracy, and relevant measures to manage these threats.

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Important specific issues

Advanced Trainees will identify important specific situations and the related epidemiological and statistical tools.

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Epidemiology

Causation and causal inference:
- causal ‘criteria’
- sufficient and component causes
- other methods contributing to causal inference, such as Bayesian methods, structural equation modelling, and mediation analysis
Measures of disease burden:
- disability adjusted life years (DALYs)
- health adjusted life years (HALYs)
- morbidity
- mortality
- quality adjusted life years (QALYs)
Measures of disease frequency:
- cumulative incidence
- incidence
- incidence rate
- prevalence
Study designs:
- systematic review
- qualitative methods:
  - focus groups
  - in-depth interviews
  - participant observation
- quantitative methods:
  - ecological
  - cross-sectional
  - case-control
  - cohort
  - randomised trials
  - data linkage / big data
  - modelling
  - spatial
  - meta-analysis
Study conduct:
- sampling strategies and study power
- ethical issues and ethics approval
- measurement of exposure(s), outcome, and confounders
- precision, validity, and accuracy:
  - chance
  - measurement bias
  - selection bias
  - confounding (including matching)
  - effect modification
Quantitative data analysis:
- data types:
  - categorical
  - continuous
  - discrete
  - ordinal
- descriptive statistics:
  - frequency
  - mean and standard deviation
  - median, interquartile range, and percentiles
  - standardisation
- analytic statistics:
  - confidence intervals (and p-values)
  - multivariable analyses, including linear and logistic regression, Cox regression, and Poisson regression
  - survival analysis
  - sensitivity analyses
  - meta-analysis
Measures of effect and association:
- absolute risk
- attributable risk, attributable difference, attributable proportion, and attributable fraction
- hazard ratio
- number needed to:
  - treat
  - harm
  - screen
- odds ratio
- relative risk
Qualitative data analysis:
- content analysis
- discourse analysis
- grounded theory analysis
- narrative analysis
- thematic analysis
Standard reporting templates:
- CONSORT (Consolidated Standards of Reporting Trials)
- PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses)
- STROBE (Strengthening the Reporting of Observational Studies in Epidemiology)

Research methods

Framing a testable research question
Identifying the relevant study population and study sample
Awareness of Indigenous research methods
Data collection instruments:
- objective
- subjective
- piloting
- pre-testing
- test characteristics, such as internal consistency
Database design
Analytical software:
- spreadsheet
- statistical software or programming packages
Documentation:
- issues in study conduct
- methods, including changes during study
- rationale
Journal publication:
- co-authorship
- peer review processes
- responses to reviewer comments
Using reference management software
Writing grant proposals

Environmental epidemiology

Exposure assessment
Exposure versus dose
Group-level versus individual level
Objective
Subjective
Modelling
Biomarkers
Study designs and analytic tools
Ecologic studies
Cross-sectional, case-control, cohort, and case-crossover designs
Poisson regression (for modelling counts or rates of events)
Spatial mapping, such as geographic information systems (GIS)
Special considerations for confounding and clustering
Time-series analyses
Distributed lag modelling (linear / non-linear)

Health systems

Appropriate investigation and management of serious adverse events and complaints about health services, programs, and practitioners
Geographic, demographic, economic, and environmental challenges of providing health services in rural and remote areas

Health technology assessment

Key decision making and planning mechanisms
Major components of the health and disability sectors
Models of optimal development and operation of:
- secondary and tertiary health services
- primary care sector
Quality improvement frameworks to develop and change health services
Regulation of:
- medicines
- vaccines
- medical devices
Workforce planning for health services, including in rural and remote areas

Infectious disease epidemiology

Timelines of infection
Infectious period
Latent period
Timeline of disease
Constructing an epidemic curve
Incubation period
Symptomatic period
Pathogenicity
Transmission probability
Secondary attack rate
Transmission probability ratio
Basic and effective reproductive numbers
Use of whole genome sequencing
Case fatality ratio

Screening

Measures of test performance
Area under the curve
Likelihood ratios
Negative predictive value
Positive predictive value
Receiver operating characteristic curve
Reliability
Sensitivity
Specificity

Social epidemiology

Disparities in health related to:
- gender
- income inequality
- intersectionalities
- labour markets and employment policies
- neighbourhood and urban characteristics
- racism and other forms of discrimination, such as sexism, misogyny, transphobia, ableism, and homophobia
- socioeconomic status
- working conditions
- ameliorating factors
- experiences, culture, and connection to country of Aboriginal and Torres Strait Islander peoples and Māori (tangata whenua)
- social networks
- social capital, social cohesion, and health
- biological pathways underpinning social disparities in health

Surveillance

Descriptive epidemiology of health problems
Uses:
- detection of infectious disease outbreaks
- detection of patterns of chronic disease, such as geographic
- future projections
- health advocacy
- links to services, such as notifiable diseases
- monitoring and evaluation of interventions / public health programs
- research, such as generating research questions
- detection and management of adverse events relating to:
  - medicines
  - vaccines
  - medical devices
Elements of a surveillance system:
- case definition
- confidentiality
- cycle of surveillance
- incentives to participation, such as for clinicians
- population under surveillance
Approaches to surveillance:
- active versus passive
- community-based surveillance
- information systems
- laboratory-based surveillance
- notifiable disease reporting
- record linkage
- registries
- sentinel events
- surveys
- syndromic
Analysis of surveillance data:
- descriptive statistics
- advanced techniques to adjust for sampling designs
Attributes of surveillance systems:
- fit-for-purpose
- simplicity
- sensitivity
- flexibility
- data quality
- acceptability
- accuracy and completeness of descriptive information
- predictive value
- representativeness
- timeliness
- stability

Health needs assessment

Definitions, types, scope, purpose, and feasibility
Resources – team, time, finances, data acquisition
Governance and stakeholders
Background and context:
- sociopolitical context and determinants of health
- previous reports, literature, and current best practice
Issue identification and conceptualisation of health needs:
- populations, settings, risk factors, burden of disease, and existing service provision
- different types of ‘need’:
  - felt needs
  - expressed needs
  - comparative needs
  - normative needs
  - perceived needs
- health versus healthcare
Data collection and analysis:
- types of data – primary / secondary; quantitative / qualitative / mixed methods
- ethics, including need for HREC approval
- biases, incomplete data, and limitations of public health information, including disease registries and national statistics
Priorities and recommendations
Evidence-based strategies prioritised by:
- effectiveness
- easy wins
- acceptability
- equity
- feasibility
- sustainability
- cost
- budget allocations
Consultation, including community interpretation of data analysis and priorities
Implementation, monitoring and evaluation:
- integration for impact, such as health services and strategic planning, budget allocations, and advocacy
- indicators to monitor responses to needs assessment, including future surveillance, disease trajectories, and quality improvement
Communication of outcomes clearly articulated to relevant audiences using a variety of methods