Public Health & Epidemiology

Key Facts

Study design selection depends on the research question, available resources, ethical considerations, and disease frequency. Each design occupies a position in the evidence hierarchy and has characteristic strengths, limitations, and measures of association.

Experimental Studies

• Randomised controlled trial (RCT): participants randomly allocated to intervention or control; gold standard for treatment evaluation; minimises selection bias and confounding
• Cluster RCT: randomisation at group level (e.g. GP practices, schools)
• Crossover trial: participants act as their own controls; receive both treatments sequentially
• Non-randomised controlled trial: intervention assigned without randomisation; more prone to confounding

Observational Analytical Studies

• Cohort study: follows exposed and unexposed groups over time; can be prospective or retrospective; measures incidence and relative risk
• Case-control study: identifies cases (with disease) and controls (without), then compares past exposure; retrospective; measures odds ratio
• Cross-sectional study: snapshot of exposure and outcome at one time point; measures prevalence; useful for needs assessment

Descriptive Studies

• Case report: single patient; useful for rare conditions or novel presentations
• Case series: group of similar cases; no comparison group
• Ecological study: population-level correlations; subject to ecological fallacy

Qualitative Research

• Explores experiences, meanings, and processes
• Methods: interviews, focus groups, ethnography
• Analysis: thematic analysis, grounded theory, phenomenology

Choosing the Right Study Design

• Treatment efficacy → RCT
• Aetiology/risk factors (common exposure) → Cohort study
• Aetiology/risk factors (rare disease) → Case-control study
• Disease burden/prevalence → Cross-sectional study
• Screening test accuracy → Cross-sectional (diagnostic accuracy study)
• Prognosis → Cohort study
• Harm/side effects → Cohort or case-control
• Systematic overview → Systematic review ± meta-analysis

Exam Tips

• Always identify the study design before interpreting results
• Know which measure of association belongs to which design (RR for cohort, OR for case-control)
• Understand the direction of enquiry (prospective vs retrospective)
• Be able to identify potential sources of bias for each design

Critical Appraisal Tools

• CASP checklists: structured tools for appraising different study designs
• Cochrane Risk of Bias tool: for assessing RCT quality
• Newcastle-Ottawa Scale: for cohort and case-control study quality
• GRADE framework: rates overall quality of evidence across studies

Key Questions for Appraisal

• Was the study design appropriate for the question?
• Was randomisation/allocation concealment adequate (RCTs)?
• Were participants/assessors blinded?
• Were groups comparable at baseline?
• Was follow-up adequate (>80%)?
• Were outcomes measured objectively?
• Were confounders identified and adjusted for?
• Are results clinically significant as well as statistically significant?

Application to Clinical Practice

• Use evidence hierarchy to guide clinical decisions
• Prefer systematic reviews and RCTs for treatment decisions
• Recognise that observational studies may be the best available evidence for some questions (rare diseases, long-term outcomes, harms)
• Apply GRADE framework: considers study design, risk of bias, inconsistency, indirectness, imprecision, publication bias
• Translate findings: calculate NNT/NNH for patient discussions

Reporting Standards

• CONSORT: Consolidated Standards of Reporting Trials (RCTs); includes flow diagram and checklist
• STROBE: Strengthening the Reporting of Observational Studies in Epidemiology
• PRISMA: Preferred Reporting Items for Systematic Reviews and Meta-Analyses
• STARD: Standards for Reporting of Diagnostic Accuracy Studies
• SPIRIT: Standard Protocol Items: Recommendations for Interventional Trials

• Study design directly determines the strength of evidence for clinical decisions
• RCTs provide the strongest evidence for treatment effects but may have limited external validity
• Real-world evidence from observational studies increasingly complements RCT data
• Meta-analyses pool results but are limited by heterogeneity and publication bias
• Poorly designed studies can lead to incorrect clinical conclusions and patient harm

Summary Table of Study Designs

Key Facts

The RCT is the most rigorous method for determining whether a cause-effect relationship exists between an intervention and outcome. Proper conduct requires attention to randomisation, blinding, sample size, and analysis.

Study Conduct

1. Define research question (PICO: Population, Intervention, Comparator, Outcome)
2. Calculate sample size (power calculation)
3. Recruit participants (inclusion/exclusion criteria)
4. Randomisation (simple, block, stratified, minimisation)
5. Allocation concealment
6. Intervention delivery
7. Blinding of participants, clinicians, outcome assessors
8. Follow-up and outcome measurement
9. Analysis (ITT primary; per-protocol secondary)
10. Reporting (CONSORT)

Types of RCT

• Parallel group: most common; two or more groups run simultaneously
• Crossover: each participant receives both treatments in sequence; washout period between
• Factorial: tests two or more interventions simultaneously (e.g. ISIS-2: aspirin × streptokinase)
• Cluster: randomises groups (e.g. GP practices) rather than individuals
• Non-inferiority/equivalence: tests whether new treatment is not worse than standard
• Adaptive: allows modification of trial design based on interim results

Randomisation Methods

• Simple: coin flip/random number generator
• Block: ensures balanced groups at regular intervals
• Stratified: stratifies by important prognostic factors before randomisation
• Minimisation: algorithm-based allocation balancing multiple factors

Interpreting RCT Results

• Primary outcome: pre-specified main outcome of interest
• Secondary outcomes: additional outcomes; hypothesis-generating
• Absolute risk reduction (ARR): difference in event rates between groups
• Relative risk reduction (RRR): proportional reduction in risk
• NNT: number needed to treat to prevent one event (1/ARR)
• Confidence interval: range within which true effect likely lies (95% CI); if CI for RR crosses 1.0, not statistically significant
• P-value: probability of observing result if null hypothesis true; p<0.05 conventionally significant

Threats to Validity

• Selection bias (inadequate randomisation/allocation concealment)
• Performance bias (lack of blinding)
• Attrition bias (differential loss to follow-up)
• Detection bias (unblinded outcome assessment)
• Reporting bias (selective outcome reporting)

Critical Appraisal of RCTs (CASP)

1. Was the trial focused on a clear question?
2. Was allocation randomised and concealed?
3. Were groups similar at baseline?
4. Were participants/clinicians/assessors blinded?
5. Were all participants accounted for (follow-up rate)?
6. Were outcomes measured in a standard, valid way?
7. Were results reported for all pre-specified outcomes?
8. Was the treatment effect size clinically important?
9. Were confidence intervals precise?
10. Can results be applied to your population?

Statistical Concepts

• Type I error (α): false positive (rejecting true null hypothesis); conventionally set at 0.05
• Type II error (β): false negative (failing to reject false null hypothesis); conventionally 0.1-0.2
• Power: 1 − β; probability of detecting a true difference
• Intention-to-treat: includes all randomised participants; conservative estimate
• Per-protocol: includes only compliant participants; may overestimate effect

Landmark UK Medical Trials

• MRC Streptomycin Trial (1948): first properly randomised clinical trial
• ISIS-2 (1988): aspirin + streptokinase in acute MI; factorial design
• HOPE (2000): ramipril in high-risk CVD patients
• UKPDS (1998): intensive glucose control in T2DM
• PARADIGM-HF (2014): sacubitril/valsartan in heart failure
• DAPA-HF (2019): dapagliflozin in heart failure
• RECOVERY (2020): dexamethasone in COVID-19

Ethical Considerations

• Equipoise: genuine uncertainty about which treatment is better
• Informed consent: participants fully informed of risks, benefits, alternatives
• Data Safety Monitoring Board (DSMB): independent review of interim safety data
• Stopping rules: pre-defined criteria for early termination (harm or overwhelming benefit)
• Research Ethics Committee: approval required before recruitment
• Clinical trial registration: mandatory prospective registration (e.g. ClinicalTrials.gov, ISRCTN)

• RCTs provide the most reliable evidence for treatment decisions when properly conducted
• Even well-designed RCTs may have limited external validity if populations are highly selected
• Publication bias means positive trials are more likely published; funnel plots and Egger test assess this
• Fragility index: number of patients whose outcome would need to change to make result non-significant
• Replication of findings across multiple RCTs increases confidence in treatment effects

CONSORT Flow Diagram Components

NNT Calculation Example

Key Facts

Cohort studies are powerful observational designs that follow groups of people over time to determine whether exposure is associated with outcome. They are the strongest observational design for establishing causality because they establish temporal sequence.

Design Types

• Prospective cohort: recruits participants, measures exposure, follows forward in time to outcome; highest quality observational evidence
• Retrospective cohort: identifies cohort from historical records; exposure and outcomes already occurred; faster but dependent on data quality
• Ambidirectional: combines prospective and retrospective elements

Key Measures

• Incidence rate: events per person-time at risk
• Relative risk (RR): incidence in exposed / incidence in unexposed
• Attributable risk (AR): incidence in exposed − incidence in unexposed
• Population attributable risk (PAR): proportion of disease in population attributable to exposure

Strengths and Limitations

• Can study multiple outcomes from single exposure
• Can measure incidence directly (unlike case-control)
• Can assess temporal relationship
• Suitable for common exposures
• Limitations: expensive, time-consuming (prospective), attrition bias, confounding (cannot randomise)

Bias in Cohort Studies

• Attrition bias: differential loss to follow-up between exposed and unexposed
• Information bias: differential accuracy of outcome ascertainment
• Healthy worker effect: occupational cohorts may be healthier than general population
• Confounding: addressed through matching, stratification, or multivariable regression

Landmark Cohort Studies

• Framingham Heart Study (1948-present): identified hypertension, hypercholesterolaemia, smoking, diabetes, obesity as CVD risk factors
• British Doctors Study (Doll & Hill, 1951-2001): established smoking-lung cancer causal link
• Whitehall Studies (1967-present): social gradient in health and mortality
• Nurses' Health Study (1976-present): women's health, HRT, diet, lifestyle
• UK Biobank (2006-present): 500,000 participants; genetics, lifestyle, health outcomes
• Millennium Cohort Study: child development and health in UK-born children

Clinical Application

• Identifying risk factors for disease (cardiovascular risk factors, cancer aetiology)
• Evaluating prognosis (outcomes after diagnosis)
• Assessing harm (drug side effects, occupational exposures)
• Informing screening programme design

Critical Appraisal (CASP Cohort Checklist)

1. Did the study address a clearly focused question?
2. Was the cohort recruited in an acceptable way?
3. Was exposure accurately measured?
4. Was outcome accurately measured?
5. Were important confounders identified and controlled for?
6. Was follow-up long enough and complete?
7. What are the results (RR, CI)?
8. How precise are the results (confidence intervals)?
9. Do you believe the results?
10. Can results be applied to your population?

Addressing Confounding in Cohort Studies

• At design stage: restriction (limit to subgroups), matching
• At analysis stage: stratification (Mantel-Haenszel method), multivariable regression (logistic, Cox)
• Propensity score matching: statistical method to create comparable groups from observational data
• Sensitivity analysis: assess how robust findings are to unmeasured confounding

Cohort Study vs Other Designs

• Choose cohort when: exposure is common, multiple outcomes of interest, need to measure incidence, need temporal sequence
• Choose case-control when: disease is rare, limited resources/time
• Choose RCT when: evaluating treatment, ethical to randomise, need strongest evidence

• Cohort studies are essential for establishing long-term prognosis and natural history of disease
• They identified the major modifiable risk factors for cardiovascular disease, cancer, and diabetes
• Loss to follow-up >20% significantly threatens internal validity
• Large cohort studies (UK Biobank) increasingly used for pharmacoepidemiology and genetic epidemiology
• Mendelian randomisation uses genetic variants as 'natural experiments' within cohort data to strengthen causal inference

Relative Risk Interpretation

Attributable Risk Calculations

Key Facts

Case-control studies start with the outcome and look backwards for exposure. They are retrospective, efficient, and particularly valuable for rare diseases. The key measure is the odds ratio.

Design

1. Define cases: individuals with the disease/outcome of interest
2. Select controls: individuals without the disease from the same source population
3. Measure past exposure in both groups (questionnaires, records, biological samples)
4. Compare exposure prevalence using odds ratio

Control Selection

• Controls should come from the same source population as cases
• Common sources: hospital controls, community controls, friend/neighbour controls
• Matching: individual matching (each case matched to control on key confounders) or frequency matching (groups matched on proportions)

Measure of Association

• Odds ratio (OR) = (a × d) / (b × c) from 2×2 table
• Where a = exposed cases, b = exposed controls, c = unexposed cases, d = unexposed controls
• OR >1 indicates positive association; OR <1 indicates protective association; OR = 1 indicates no association

Variants

• Nested case-control: cases arise from a defined cohort; controls sampled from same cohort at time of case occurrence
• Case-cohort: controls sampled from entire cohort at baseline regardless of disease status
• Case-crossover: each case serves as their own control (exposure at time of event vs earlier period)

Classic Case-Control Studies

• Doll & Hill (1950): smoking and lung cancer - established link using hospital-based case-control design
• Thalidomide and phocomelia: case-control study by McBride (1961) identified association
• Oral contraceptives and VTE: multiple case-control studies established risk
• SIDS and sleeping position: case-control studies led to 'Back to Sleep' campaign

When to Use Case-Control Design

• Disease is rare
• Long latency period between exposure and disease
• Limited resources/time
• Multiple exposures to investigate for single outcome
• Outbreak investigation (rapid assessment of risk factors)

Critical Appraisal (CASP Case-Control Checklist)

1. Did the study address a clearly focused question?
2. Was an appropriate case-control design used?
3. Were cases defined and selected appropriately?
4. Were controls selected appropriately from same source population?
5. Was exposure measured accurately (validated tools)?
6. Were confounders identified and controlled (matching, adjustment)?
7. What are the results (OR, CI)?
8. How precise are the results?
9. Do you believe the results?
10. Can results be applied locally?

Key Biases in Case-Control Studies

• Recall bias: cases may recall exposures differently from controls (particularly for traumatic exposures)
• Selection bias: controls not representative of source population
• Interviewer bias: knowledge of case/control status influences questioning
• Survival bias (Neyman bias): prevalent cases may differ from incident cases

Addressing Bias

• Recall bias: use objective exposure measures (records, biomarkers), blinding of exposure assessors
• Selection bias: use population-based controls, nested case-control design
• Confounding: match controls, stratify analysis, multivariable logistic regression

Odds Ratio Interpretation

• OR = 1: no association
• OR > 1: exposure associated with increased odds of disease
• OR < 1: exposure associated with decreased odds (protective)
• 95% CI not crossing 1.0: statistically significant
• OR approximates RR when disease is rare (<10% prevalence)

• Case-control studies have been instrumental in identifying risk factors for many diseases
• They are particularly valuable in outbreak investigation (rapid identification of exposure source)
• Well-conducted case-control studies can provide strong evidence of association
• Limitations in establishing causality (retrospective, recall bias, cannot measure incidence)
• Nested case-control designs within large cohorts combine efficiency with reduced bias

2×2 Table for Case-Control Studies

OR = (a × d) / (b × c)

OR Calculation Example

OR = (90 × 40) / (60 × 10) = 3600/600 = 6.0 Smokers have 6× the odds of lung cancer compared to non-smokers

Key Facts

Systematic reviews and meta-analyses provide the most reliable evidence for clinical decision-making when conducted rigorously. They synthesise evidence from multiple studies, increasing statistical power and precision.

Steps in Conducting a Systematic Review

1. Define research question (PICO format)
2. Develop protocol (register on PROSPERO)
3. Systematic literature search (multiple databases: MEDLINE, Embase, CENTRAL)
4. Study selection (screening by ≥2 reviewers, inclusion/exclusion criteria)
5. Data extraction (standardised forms)
6. Quality assessment (risk of bias tool)
7. Data synthesis (narrative and/or meta-analysis)
8. Assessment of certainty of evidence (GRADE)
9. Reporting (PRISMA)

Meta-Analysis Methods

• Fixed-effect model: assumes all studies estimate the same underlying effect; appropriate when heterogeneity is low
• Random-effects model: assumes true effects vary between studies; accounts for between-study heterogeneity; more conservative
• Mantel-Haenszel method: common fixed-effect method for dichotomous outcomes
• DerSimonian and Laird: common random-effects method

Assessing Quality

• Risk of bias: assessed for each included study (Cochrane RoB 2 for RCTs, ROBINS-I for non-randomised)
• Heterogeneity: clinical (population/intervention differences), methodological (design differences), statistical (I² and Chi² test)
• Publication bias: funnel plot asymmetry, Egger test, trim and fill method

Interpreting Forest Plots

• Each study represented by a square (size proportional to weight) and horizontal line (95% CI)
• Diamond at bottom represents pooled estimate (width = CI)
• Vertical line at RR/OR = 1 (line of no effect)
• If diamond does not cross line of no effect: statistically significant
• I² value and p-value for heterogeneity reported

Interpreting Funnel Plots

• Plots study effect size (x-axis) against study precision (y-axis, usually SE)
• Symmetric funnel shape expected in absence of publication bias
• Asymmetry (missing small negative studies) suggests publication bias

Subgroup and Sensitivity Analyses

• Subgroup analysis: explores whether effect varies by patient/study characteristics
• Sensitivity analysis: tests robustness (e.g. excluding high risk-of-bias studies)
• Meta-regression: explores sources of heterogeneity using study-level covariates

Critical Appraisal (CASP Systematic Review Checklist)

1. Did the review address a clearly focused question?
2. Did the authors look for the right type of papers?
3. Were all important relevant studies included?
4. Did the authors assess the quality of included studies?
5. If results were combined, was it reasonable to do so?
6. What is the overall result?
7. How precise are the results?
8. Can results be applied to the local population?
9. Were all important outcomes considered?
10. Are the benefits worth the harms and costs?

Key Statistical Concepts

• I²: percentage of variability due to heterogeneity rather than chance; >50% = substantial
• Chi² (Q) test: tests whether observed differences in results are compatible with chance alone
• Prediction interval: range of effects expected in a new study (wider than CI of pooled estimate)
• GRADE certainty: High, Moderate, Low, Very Low

Using Systematic Reviews in Practice

• Check Cochrane Library for existing reviews on clinical questions
• Assess GRADE certainty of evidence before applying to patients
• Consider applicability to your patient population
• NNT from meta-analysis may be the most reliable estimate available
• Living systematic reviews: continuously updated as new evidence emerges

Limitations

• Only as good as included studies ('garbage in, garbage out')
• Publication bias: negative studies less likely published
• Heterogeneity may limit pooling
• Ecological fallacy: pooled results may not apply to individual patients
• Time lag: may not include most recent evidence

• Systematic reviews and meta-analyses form the basis of clinical guidelines (NICE, WHO, Cochrane)
• They provide the most precise estimates of treatment effects through pooled analysis
• Updated/living reviews improve timeliness of evidence synthesis
• Network meta-analyses (comparing multiple treatments simultaneously) increasingly used for guideline development
• Individual patient data meta-analyses provide the most detailed evidence but require data sharing

I² Heterogeneity Interpretation

GRADE Certainty of Evidence

Key Facts

Screening aims to reduce morbidity and mortality by detecting disease at an early, treatable stage. However, screening programmes also cause harm and must be carefully evaluated. The balance of benefits and harms must favour the screened population.

Wilson-Jungner Criteria (1968)

1. Important health problem
2. Accepted treatment available
3. Facilities for diagnosis and treatment available
4. Recognisable latent or early symptomatic stage
5. Suitable test available
6. Test acceptable to population
7. Natural history of condition understood
8. Agreed policy on who to treat
9. Cost-effective
10. Screening should be a continuous process

Types of Screening

• Population screening: offered to all individuals in a defined population (e.g. NHS Breast Screening)
• Targeted screening: offered to high-risk groups (e.g. TB screening in new entrants from high-incidence countries)
• Opportunistic screening: offered during clinical encounters (e.g. blood pressure check at GP visit)
• Cascade screening: testing relatives of identified cases (e.g. familial hypercholesterolaemia)

Screening Test Properties

• Sensitivity: ability to detect true positives (high sensitivity = few false negatives)
• Specificity: ability to detect true negatives (high specificity = few false positives)
• PPV: proportion of positive results that are true positives (highly influenced by prevalence)
• NPV: proportion of negative results that are true negatives

UK NHS Screening Programmes

Antenatal and Newborn:

• Fetal anomaly scan (18-20+6 weeks)
• Combined screening for trisomies (11-13+6 weeks)
• Infectious disease screening (HIV, hepatitis B, syphilis, rubella immunity)
• Newborn blood spot (PKU, CHT, sickle cell, CF, MCADD + 5 others)
• Newborn hearing screening
• Newborn and infant physical examination (NIPE)

Cancer Screening:

• Breast: mammography every 3 years, women aged 50-71
• Cervical: primary HPV testing, 25-64 (3-yearly 25-49, 5-yearly 50-64)
• Bowel: FIT every 2 years, 50-74

Other:

• Abdominal aortic aneurysm: USS, men aged 65 (one-off)
• Diabetic eye screening: annual retinal photography for all diabetics aged ≥12
• NHS Health Check: CVD risk assessment, 40-74, every 5 years

Screening NOT Currently Recommended in UK

• Prostate cancer (PSA) - population screening; high false positive rate and overdiagnosis
• Lung cancer - NLST showed benefit but not yet implemented as national programme
• Ovarian cancer - UKCTOCS showed no mortality benefit

Evaluating Screening Programme Effectiveness

• RCT: gold standard for evaluating screening (e.g. HIP trial for breast screening, UKCTOCS for ovarian)
• Mortality reduction: primary outcome measure (disease-specific and all-cause)
• Lead-time bias: apparent survival improvement from earlier detection without actual prolongation of life
• Length-time bias: screening preferentially detects slower-growing disease
• Overdiagnosis bias: detecting disease that would never have caused symptoms or death
• Volunteer bias: those who attend screening may be healthier than non-attenders

Test Performance Metrics

• Sensitivity and specificity (intrinsic to test)
• PPV and NPV (influenced by prevalence)
• ROC curve: plots sensitivity vs (1-specificity) at different cut-off points
• Area under ROC curve (AUC): overall test performance (1.0 = perfect, 0.5 = useless)

Maximising Screening Programme Effectiveness

• High uptake/coverage (target >80%)
• Quality assurance: standardised protocols, training, audit
• Minimise interval cancers (cancers arising between screening rounds)
• Prompt follow-up of abnormal results
• Informed consent: explain benefits AND harms

Communicating Screening Results

• Use absolute numbers rather than percentages (e.g. '3 in 1,000' rather than '0.3%')
• Explain that screening is not diagnostic; positive results require further investigation
• Address false positive and false negative possibilities
• Provide written information before screening (informed choice)

Harms of Screening

• False positives: anxiety, unnecessary investigations, potential complications of investigation
• False negatives: false reassurance, delayed presentation
• Overdiagnosis: detecting conditions that would never cause harm (e.g. screen-detected DCIS, low-risk prostate cancer)
• Overtreatment: treating overdiagnosed conditions with associated morbidity
• Psychological harm: anxiety from screening invitation, results, further investigation
• Opportunity cost: resources used for screening could be used elsewhere

• Breast screening: estimated to prevent 1,300 deaths/year in UK; but causes overdiagnosis in approximately 4,000 women/year
• Cervical screening: prevents approximately 5,000 deaths/year in UK; HPV vaccination will further reduce need over time
• Bowel screening: reduces CRC mortality by 16-25% in screened populations
• AAA screening: reduces AAA-related mortality in men by approximately 42%
• Newborn blood spot: early detection of PKU prevents severe intellectual disability; early treatment of CHT prevents cretinism
• Effective screening programmes represent excellent value for money when Wilson-Jungner criteria are met

Lead-Time Bias Illustration

Overdiagnosis Examples

Key Facts

Sensitivity and specificity are fundamental to understanding diagnostic test performance. They are intrinsic properties of a test (fixed regardless of prevalence), while predictive values vary with disease prevalence in the tested population.

2×2 Table

Formulae

• Sensitivity = a / (a + c)
• Specificity = d / (b + d)
• PPV = a / (a + b)
• NPV = d / (c + d)
• Prevalence = (a + c) / (a + b + c + d)
• Accuracy = (a + d) / (a + b + c + d)

Clinical Mnemonics

• SnNOUT: if a test has high Sensitivity, a Negative result rules OUT disease
• SpPIN: if a test has high Specificity, a Positive result rules IN disease

Trade-off Between Sensitivity and Specificity

• Adjusting the test cut-off point affects both: increasing sensitivity decreases specificity and vice versa
• The optimal cut-off depends on the clinical context (consequences of false positives vs false negatives)
• Screening tests prioritise high sensitivity (don't miss disease)
• Confirmatory tests prioritise high specificity (don't falsely diagnose)

Clinical Examples

• D-dimer for PE: highly sensitive (~95%) but poorly specific (~40%); negative D-dimer rules out PE (SnNOUT)
• Troponin for MI: highly sensitive and specific with hs-troponin assays
• Anti-CCP for RA: highly specific (~96%) but less sensitive (~67%); positive result strongly supports RA (SpPIN)
• RADT for GAS pharyngitis: high specificity (~95%) but moderate sensitivity (~70-90%)

Effect of Prevalence on Predictive Values

• In high-prevalence settings: PPV increases, NPV may decrease
• In low-prevalence settings: PPV decreases (more false positives), NPV increases
• This is why screening in low-prevalence populations produces many false positives
• Pre-test probability (clinical suspicion + prevalence) is essential for interpreting test results

ROC Curve Analysis

• Receiver Operating Characteristic curve
• Plots sensitivity (true positive rate) on y-axis against 1−specificity (false positive rate) on x-axis
• Each point on the curve represents a different cut-off value
• AUC (area under the curve) summarises overall test performance
• AUC interpretation: 1.0 = perfect, 0.5 = no discriminatory value (diagonal line)
• Optimal cut-off: point nearest top-left corner (maximises both sensitivity and specificity)

Likelihood Ratios

• Convert pre-test probability to post-test probability
• LR+ >10: large and often conclusive increase in probability
• LR+ 5-10: moderate increase
• LR+ 2-5: small increase
• LR− <0.1: large and often conclusive decrease in probability
• LR− 0.1-0.2: moderate decrease
• LR− 0.2-0.5: small decrease
• Can be used with Fagan nomogram for bedside estimation

Choosing the Right Test

• Screening: choose high sensitivity (minimise false negatives; don't miss disease)
• Confirmation: choose high specificity (minimise false positives; don't falsely diagnose)
• Sequential testing: screen with sensitive test → confirm with specific test

Clinical Application

• Always consider pre-test probability before ordering tests
• A test is most useful when pre-test probability is intermediate (50%)
• Tests add little information when pre-test probability is very high or very low

• Bayesian reasoning: update probability with each piece of information

• Understanding sensitivity and specificity is fundamental to evidence-based test interpretation
• Predictive values change dramatically with disease prevalence
• In rare diseases, even highly specific tests produce many false positives in population screening
• Point-of-care testing with known sensitivity/specificity allows bedside probability estimation
• AI-assisted diagnostics increasingly assessed using ROC analysis and AUC metrics

Effect of Prevalence on PPV (Example)

LR Interpretation Guide

Key Facts

NNT translates the results of clinical trials into a clinically meaningful metric. It answers the question: 'How many patients do I need to treat to prevent one adverse outcome?' It is more intuitive than relative measures and essential for shared decision-making.

Calculation

• ARR = CER − EER (absolute difference in event rates)
• NNT = 1 / ARR
• NNH = 1 / ARI (where ARI = absolute risk increase for harm)

Example

• Control event rate = 20%, Treatment event rate = 15%
• ARR = 0.20 − 0.15 = 0.05 (5%)
• NNT = 1/0.05 = 20
• Interpretation: 20 patients need to be treated for 1 to benefit

RRR vs ARR

• RRR = ARR / CER = 0.05 / 0.20 = 25%
• RRR sounds more impressive but does not convey absolute benefit

• Always report both relative and absolute measures

NNT for Common Interventions

• Statins for primary CVD prevention (5 years): NNT approximately 100-200
• Statins for secondary CVD prevention (5 years): NNT approximately 20-30
• Aspirin for secondary prevention: NNT approximately 67 (2 years)
• Thrombolysis for acute STEMI: NNT approximately 30-50
• Antibiotics for sore throat (symptom relief at day 3): NNT approximately 6

Using NNT in Clinical Practice

• Compare NNT with NNH to assess treatment benefit vs harm
• Net clinical benefit: if NNT < NNH for a serious outcome, treatment is likely beneficial
• Communicate to patients: 'If I treat 20 people like you, 1 will benefit; the other 19 would not have had the event anyway'
• NNT should be considered alongside patient preferences, comorbidities, and treatment burden

Limitations of NNT

• Time-dependent: NNT changes with duration of treatment
• Baseline risk-dependent: NNT differs for high-risk vs low-risk patients
• Assumes constant treatment effect across populations
• Cannot be directly added or subtracted between studies
• Confidence intervals for NNT can be counterintuitive (can pass through infinity)

Calculating NNT from Trial Data

1. Extract event rates from published data (2×2 table or Kaplan-Meier curves)
2. Calculate ARR = CER − EER
3. NNT = 1 / ARR (round up to next whole number)
4. Calculate 95% CI for NNT from CI for ARR

NNT from Odds Ratios

• When only OR is available (e.g. from meta-analysis), NNT can be estimated using: NNT = 1 / [CER × (1 − OR) / (1 − CER × (1 − OR))]
• Approximation when disease is rare: NNT ≈ 1 / [CER × (1 − OR)]

Confidence Intervals

• 95% CI for NNT calculated from 95% CI for ARR
• If CI for ARR includes zero (non-significant result), CI for NNT passes through infinity
• Expressed as: NNT = X (95% CI: Y to infinity to −Z)

Clinical Decision-Making with NNT

• Consider NNT in context of disease severity and patient baseline risk
• Higher baseline risk → lower NNT (treatment more beneficial)
• Balance NNT against NNH for specific side effects
• Use NNT for shared decision-making conversations with patients
• Integrate NNT with QRISK, FRAX, or other risk calculators for personalised treatment decisions

NNT in Guidelines

• NICE guidelines increasingly report NNT/NNH alongside recommendations
• Statin guidelines: QRISK ≥10% → offer statin (NNT varies with baseline risk)
• Anticoagulation in AF: CHA₂DS₂-VASc guided; NNT for stroke prevention varies with score
• Breast cancer screening: NNT to detect one cancer ≈ 500 per screening round; NNT to prevent one death ≈ 1,000-2,000 over 10 years

• NNT provides the most clinically useful measure of treatment effect for patient communication
• Treatments with low NNT for serious outcomes are strong candidates for widespread implementation
• NNT varies significantly with baseline risk - personalised medicine considers individual risk profiles
• Population-level NNT informs public health policy (e.g. vaccination programme NNT)
• The concept of NNT has improved transparency in clinical trial reporting since its introduction in 1988 (Laupacis et al.)

NNT Examples in Clinical Practice

RRR vs ARR Illustration

Same RRR (50%) but very different NNT (5 vs 100) due to different baseline risk

Key Facts

RR and OR quantify the strength of association between an exposure and outcome. Correct interpretation requires understanding which study design produces which measure and the limitations of each.

Relative Risk

• RR = [a/(a+b)] / [c/(c+d)] from 2×2 table
• Measures how many times more (or less) likely exposed individuals are to develop the outcome compared to unexposed
• Only valid when incidence can be calculated (cohort studies, RCTs)
• RR = 2.0 means exposed group has twice the risk
• RR = 0.5 means exposed group has half the risk (50% reduction)

Odds Ratio

• OR = (a × d) / (b × c) from 2×2 table
• Measures the ratio of odds of exposure in cases vs controls
• Can be calculated from case-control studies, cross-sectional studies, and logistic regression
• OR approximates RR when disease is rare (<10% prevalence)
• OR always overestimates RR when RR >1 and underestimates when RR <1

Hazard Ratio

• From Cox proportional hazards regression (survival analysis)
• Accounts for time-to-event and censored data
• Interpreted similarly to RR
• Used in most RCTs with time-to-event outcomes
• HR = 0.7 means 30% reduction in hazard (instantaneous rate) at any time point

Confidence Intervals and Statistical Significance

• 95% CI: range within which the true value lies with 95% probability
• If 95% CI for RR, OR, or HR crosses 1.0: NOT statistically significant
• Wider CI = less precision (small sample size)
• Narrow CI = more precision (large sample size)

Clinical Interpretation Examples

• Smoking and lung cancer RR = 15: smokers have 15× the risk of lung cancer
• Statins in 4S trial: RR for major coronary events = 0.66 (34% relative risk reduction)
• COC and VTE: OR approximately 3-4 (3-4× increased odds of VTE)
• ARISTOTLE trial: apixaban vs warfarin HR for stroke = 0.79 (21% reduction in stroke/SE)

Common Mistakes

• Using OR when RR is appropriate (and vice versa)
• Interpreting RR of 2.0 as '100% will get disease' (it's relative, not absolute)
• Confusing statistical significance (p-value/CI) with clinical significance (effect size/NNT)
• Failing to consider baseline risk (large RR with tiny baseline risk = tiny absolute difference)
• Interpreting association as causation without considering confounding

When to Use Which Measure

• RR: cohort studies, RCTs (where incidence can be calculated)
• OR: case-control studies, logistic regression, meta-analyses (can pool ORs)
• HR: time-to-event analyses (Kaplan-Meier curves, Cox regression)
• Rate ratio: when person-time data available (accounting for variable follow-up)

Reporting Standards

• Always report 95% CI alongside point estimate
• Present both relative (RR/OR/HR) and absolute measures (ARR, NNT)
• Specify the reference group clearly
• State whether adjusted or unadjusted (and for which confounders)

From Measures of Association to Clinical Action

• Large RR/OR with narrow CI and biological plausibility → strong evidence of association
• Translate to absolute measures for patient communication (NNT, NNH)
• Consider dose-response relationship (biological gradient)
• Apply Bradford Hill criteria for causality assessment
• Use risk calculators (QRISK, FRAX) that incorporate multiple RRs for clinical decisions

• RR and OR are essential for understanding the magnitude of treatment effects and risk factor associations
• Relative measures alone can be misleading; absolute measures provide clinical context
• Meta-analyses often pool ORs across studies to generate summary estimates
• Understanding these measures is fundamental to interpreting clinical guidelines and making evidence-based decisions
• Personalised medicine increasingly uses individual risk profiles incorporating multiple RRs

OR vs RR Divergence with Prevalence

OR always overestimates RR when RR >1; more so with higher prevalence

RR Interpretation Guide

Key Facts

Health promotion goes beyond individual behaviour change to address the wider determinants of health. It combines education, environmental modification, and policy change to improve population health and reduce health inequalities.

Approaches to Health Promotion

• Medical/preventive: immunisation, screening, risk factor identification
• Behaviour change: motivational interviewing, brief interventions, health education
• Empowerment: community development, peer support, self-management
• Social change: policy, legislation, advocacy (tobacco legislation, sugar tax, minimum unit pricing)

Levels of Prevention

• Primary prevention: preventing disease before it occurs (vaccination, health education, smoking cessation)
• Secondary prevention: early detection and treatment of disease (screening, opportunistic case-finding)
• Tertiary prevention: minimising impact of established disease (rehabilitation, self-management support)
• Primordial prevention: preventing risk factors from developing in the first place (addressing upstream determinants)

Behaviour Change Models

• Health Belief Model: perceived susceptibility, severity, benefits, barriers, cues to action, self-efficacy

• COM-B Model (Michie): Capability, Opportunity, Motivation → Behaviour; underpins NICE behaviour change guidance
• Nudge theory: choice architecture to make healthy option the easy option

Health Promotion in Primary Care

• Making Every Contact Count (MECC): brief intervention at every consultation opportunity
• Key topics: smoking, alcohol, diet, physical activity, mental health, sexual health
• Very Brief Advice (VBA): Ask, Advise, Act - applicable to smoking, alcohol, weight
• Motivational interviewing skills: express empathy, develop discrepancy, roll with resistance, support self-efficacy
• Social prescribing: linking patients with community resources (exercise, arts, volunteering)

Population-Level Interventions

• Tobacco legislation (smoking ban 2007, plain packaging 2016)
• Sugar tax (Soft Drinks Industry Levy 2018)
• Alcohol minimum unit pricing (Scotland 2018)
• 5-a-day campaign
• Change4Life and Better Health campaigns
• Active travel and urban planning initiatives

Evidence for Health Promotion

• NICE public health guidance provides evidence base for interventions
• Community-based trials and natural experiments increasingly used
• Cost-effectiveness analysis essential for resource allocation
• Health impact assessment: prospective evaluation of policy effects on health

Measuring Health Promotion Effectiveness

• Process measures: reach, engagement, participation rates
• Intermediate outcomes: knowledge, attitudes, behaviour change
• Health outcomes: disease incidence, mortality, disability
• Equity outcomes: reduction in health inequalities
• Patient-reported outcome measures (PROMs) and experience measures (PREMs)

Key Policy Frameworks

• Marmot Review (2010): Fair Society, Healthy Lives - recommended proportionate universalism, early years investment, fair employment
• NHS Long Term Plan: focus on prevention, personalised care, population health management
• Core20PLUS5: NHS approach to reducing health inequalities; focuses on most deprived 20%, plus inclusion health groups
• Integrated Care Systems (ICS): collaborative approach to population health

GP Role in Health Promotion

• Opportunistic health promotion at every consultation (MECC)
• Structured chronic disease management with lifestyle advice
• Social prescribing referral
• Participation in public health campaigns (flu vaccination, cancer screening)
• Advocacy for patients and communities
• Contributing to local health needs assessments

• Population-level interventions (legislation, taxation) have the greatest impact on health outcomes
• Individual behaviour change interventions have modest but measurable effects
• Health promotion interventions are generally highly cost-effective
• Tobacco control is the most successful public health intervention: UK smoking rates halved from 46% (1974) to 13% (2023)
• Addressing social determinants has the greatest potential to reduce health inequalities
• Health promotion requires sustained, multi-level action across sectors

Dahlgren-Whitehead Model (Layers of Influence)

Transtheoretical Model (Stages of Change)

Key Facts

Health inequalities are differences in health outcomes that are systematic, socially produced, and avoidable. They reflect broader societal inequalities and represent a major challenge for the NHS and public health system.

Key Frameworks

• Dahlgren-Whitehead model: layers of influence from individual to societal
• Marmot Review (2010): six policy objectives to reduce health inequalities
• Acheson Report (1998): first comprehensive UK government review of health inequalities
• Black Report (1980): identified social class gradient in health; controversial suppression by government

Dimensions of Health Inequality

• Socioeconomic: income, education, employment, housing
• Geographic: north-south divide, urban-rural differences, coastal towns
• Ethnic: higher rates of diabetes, CVD in South Asian populations; higher maternal mortality in Black women
• Gender: men have shorter life expectancy; women have more years of disability
• Age: older adults face multiple disadvantages
• Disability: people with learning disabilities die 15-20 years earlier
• Inclusion health: homeless, Travellers, prisoners face extreme health disadvantage

Statistics

• Life expectancy gap (most vs least deprived): 9.4 years (men), 7.4 years (women)
• Healthy life expectancy: men in most deprived areas reach disability at 52 years vs 71 years in least deprived
• Infant mortality rate: 70% higher in most deprived vs least deprived
• Smoking prevalence: 25% in most deprived quintile vs 8% in least deprived

Impact on Clinical Presentation

• Later presentation of disease in deprived communities
• Higher prevalence of multimorbidity (2+ chronic conditions) at younger ages
• Multiple risk factor clustering (smoking + obesity + alcohol + poor diet)
• Higher rates of mental health conditions coexisting with physical disease
• Poorer access to and experience of healthcare services

Examples of Health Inequalities

• Cardiovascular disease: mortality rate 60% higher in most deprived quintile
• Cancer: lung cancer incidence 3× higher in most deprived; later stage at diagnosis
• Diabetes: T2DM prevalence 2× higher in most deprived areas
• Mental health: depression and anxiety rates 2× higher in lowest income group
• COVID-19: mortality rate 2× higher in most deprived areas; 2-4× higher in some ethnic minority groups
• Maternal health: maternal mortality 5× higher in Black women vs White women in UK

Measuring Health Inequalities

• Index of Multiple Deprivation (IMD): composite measure of area deprivation (income, employment, education, health, crime, barriers to housing, living environment)
• Slope index of inequality: measures absolute gap in health between most and least deprived
• Relative index of inequality: measures relative gap
• PHE Fingertips tool: interactive data on health outcomes by area and deprivation
• JSNA (Joint Strategic Needs Assessment): local population health assessment

Equity Audit

• Examines whether services are distributed proportionate to need
• Steps: identify population, define equity, collect data, analyse, develop recommendations
• Examples: screening uptake by deprivation quintile, vaccination coverage in minority groups

Marmot Review Policy Objectives (2010)

1. Give every child the best start in life
2. Enable all children, young people, and adults to maximise their capabilities
3. Create fair employment and good work for all
4. Ensure a healthy standard of living for all
5. Create and develop healthy and sustainable places and communities
6. Strengthen the role and impact of ill health prevention

NHS Approaches

• Core20PLUS5: target most deprived 20% + inclusion health groups for: maternity, severe mental illness, chronic respiratory disease, early cancer diagnosis, hypertension case-finding
• Anchor institutions: NHS as a major employer can address local economic and social determinants
• Population health management: using data to identify and target at-risk populations
• Social prescribing: linking patients with community resources
• Personalised care: shared decision-making, health coaching, supported self-management

GP Practice Actions

• Practice-level equity audit of chronic disease management
• Targeted outreach for screening and vaccination
• Enhanced access for deprived populations
• Training in health literacy and culturally sensitive care
• Advocacy for patients and communities
• Participation in local health inequality programmes

• Without action, health inequalities are widening in the UK (Marmot Review 10 Years On, 2020)
• Life expectancy improvements have stalled since 2010, with declines in most deprived areas
• COVID-19 exacerbated existing health inequalities
• Integrated care systems (ICS) offer opportunity for place-based population health approaches
• Evidence shows early years investment is the most effective long-term strategy
• Cost of health inequalities estimated at £31-33 billion/year to the NHS and £60 billion to the wider economy

Core20PLUS5 Clinical Areas

Index of Multiple Deprivation Domains

Key Facts

Outbreak investigation is a core public health competency. Rapid identification, investigation, and control of outbreaks prevents further cases and protects public health. The principles apply to all infectious disease outbreaks.

Steps of Outbreak Investigation

1. Confirm the outbreak: verify diagnosis, confirm excess above expected baseline
2. Establish case definition: clinical and epidemiological criteria (confirmed, probable, possible)
3. Identify and count cases: active case finding, line listing
4. Describe: person (age, sex, risk factors), place (mapping), time (epidemic curve)
5. Generate hypotheses: from descriptive epidemiology, interviews, environmental assessment
6. Test hypotheses: analytical study (cohort or case-control), microbiological investigation
7. Implement control measures: source control, transmission prevention, population protection
8. Communicate: risk communication to public, media, healthcare professionals
9. Evaluate and report: outbreak report, lessons learned

Outbreak Team Members

• Consultant in Communicable Disease Control (CCDC)
• Microbiologist
• Environmental Health Officer
• Infection prevention and control team
• Communications officer
• Other specialists as needed (food safety, water, veterinary)

Legal Framework

• Health Protection (Notification) Regulations 2010: list of notifiable diseases
• Public Health (Control of Disease) Act 1984 (amended 2008): powers to investigate and control
• UK Health Security Agency (UKHSA) provides national coordination

Epidemic Curve Patterns

• Point source: all cases exposed at same time; bell-shaped curve; incubation period determines timing
• Continuous source: ongoing exposure; plateau pattern; cases continue until source removed
• Propagated (person-to-person): successive waves with peaks separated by incubation period
• Intermittent source: irregular pattern; intermittent exposure

Common Outbreak Scenarios

• Norovirus in care home: rapid onset vomiting and diarrhoea; highly infectious; environmental contamination
• Food-borne Salmonella: cohort exposed at common event; identify food vehicle using attack rates
• Measles in unvaccinated community: propagated spread; contact tracing and MMR catch-up
• TB cluster: linked cases identified through contact tracing; molecular typing confirms transmission
• Legionella: environmental source (cooling towers, water systems); no person-to-person spread

Red Flags for Outbreak Declaration

• Unusual clustering of cases (time, place, person)
• Disease more severe than expected
• Unusual organism or resistance pattern
• Cases in healthcare workers
• Bioterrorism potential (anthrax, smallpox, plague)

Descriptive Epidemiology

• Line listing: spreadsheet of all cases with demographics, symptoms, exposure history
• Attack rate calculation: cases from exposed group / total exposed × 100
• Epidemic curve: plot cases by date of onset
• Spot map: geographic distribution of cases

Analytical Studies

• Cohort study (for defined populations, e.g. wedding guests): compare attack rates in exposed vs unexposed
• Case-control study (for open populations): compare exposures in cases vs matched controls
• Calculate RR (cohort) or OR (case-control) for suspected exposures

Microbiological Investigation

• Clinical samples: stool, blood, respiratory samples for culture, PCR, serology
• Environmental samples: food, water, surfaces
• Molecular typing: whole genome sequencing (WGS) to confirm linked cases
• Reference laboratory: UKHSA provides specialist typing services

Environmental Investigation

• Food safety inspection of premises
• Water sampling and testing
• Review of food handling, storage, cooking processes
• Inspection of cooling towers (Legionella)

Control Measures

Source control:

• Remove contaminated food from sale
• Close implicated food premises
• Treat contaminated water supply
• Decontaminate cooling towers
• Isolate infectious cases

Transmission prevention:

• Infection control measures (hand hygiene, PPE, environmental cleaning)
• Exclusion of cases from work/school (48-hour rule for GI infections)
• Cohorting of cases in healthcare settings
• Enhanced cleaning protocols

Population protection:

• Post-exposure prophylaxis (e.g. rifampicin for meningococcal contacts, MMR for measles contacts)
• Vaccination campaigns
• Public health messaging
• Contact tracing

Communication

• Clear, timely communication to public, media, and healthcare professionals
• Risk communication principles: acknowledge uncertainty, provide actionable advice, maintain trust
• UKHSA and local authority issue joint communications

Notification

• All suspected notifiable diseases must be reported to local authority proper officer
• Immediate notification by phone for urgent cases
• Written notification within 3 days
• UKHSA Second Generation Surveillance System (SGSS) for laboratory reports

• Rapid outbreak investigation and control reduces secondary cases and saves lives
• UK has robust surveillance and response systems through UKHSA
• Most food-borne outbreaks are self-limiting; control measures prevent further cases
• Healthcare-associated outbreaks (norovirus, C. difficile) cause significant morbidity and mortality in vulnerable patients
• Vaccine-preventable outbreaks (measles) are entirely avoidable with adequate coverage
• Lessons from outbreaks improve future preparedness and policy

Selected UK Notifiable Diseases

Attack Rate Calculation Example

Chicken salad RR = 75%/8% = 9.4 (strong association)

Key Facts

Notification of infectious diseases is a cornerstone of public health surveillance. It enables early detection of outbreaks, implementation of control measures, and monitoring of disease trends. The duty to notify rests with the clinician who suspects the diagnosis.

Legal Basis

• Health Protection (Notification) Regulations 2010
• Public Health (Control of Disease) Act 1984 (amended 2008)
• Applies to England; similar regulations in devolved nations

Notification Process

1. Clinician suspects notifiable disease
2. Notify local authority proper officer (usually CCDC via local health protection team)
3. Urgent diseases: notify immediately by phone
4. Routine diseases: written notification within 3 days
5. Include: patient demographics, diagnosis, date of onset, relevant risk factors
6. Laboratory confirmation is NOT required for notification

Purpose of Notification

• Surveillance: monitoring disease trends, detecting changes in incidence
• Outbreak detection: identifying clusters requiring investigation
• Contact tracing: identifying and protecting exposed contacts
• Public health action: prophylaxis, vaccination, exclusion, environmental measures
• Policy: informing vaccination programme decisions, travel advice
• International: reporting to WHO under International Health Regulations

Key Notifiable Diseases in UK Practice

Gastrointestinal:

• Food poisoning (most commonly notified)
• Cholera
• Typhoid/paratyphoid
• Hepatitis A, E

Respiratory:

• Tuberculosis
• Legionnaires' disease
• Whooping cough (pertussis)
• SARS, MERS

Vaccine-preventable:

• Measles, mumps, rubella
• Diphtheria
• Tetanus
• Poliomyelitis

Blood-borne/STI:

• Hepatitis B, C (acute)
• HIV (new diagnosis - some local arrangements)

Other:

• Meningococcal disease
• Malaria
• Rabies
• Plague
• Viral haemorrhagic fever
• Anthrax, botulism

When to Notify

• On clinical suspicion - do not wait for laboratory confirmation
• Even if unsure - err on the side of notification
• Even if the patient refuses testing or treatment

Notification Requirements

• Patient name, date of birth, address, sex
• Suspected disease
• Date of onset of symptoms
• Date of notification
• Name and address of notifying doctor
• Relevant travel history, occupation, risk factors

Laboratory Surveillance

• Parallel system to clinical notification
• Laboratories report confirmed diagnoses to UKHSA via SGSS (Second Generation Surveillance System)
• Molecular typing (WGS) enables cluster detection
• Reference laboratories provide specialist identification and typing

Surveillance Outputs

• Weekly communicable disease reports
• Annual epidemiological reports
• Real-time surveillance dashboards
• International reporting to WHO/ECDC

Public Health Response to Notification

• Risk assessment: CCDC assesses significance and required action
• Contact tracing: identify and advise contacts; offer prophylaxis/vaccination where appropriate
• Exclusion: advise on exclusion from work/school (e.g. 48-hour rule for GI infections)
• Post-exposure prophylaxis: e.g. rifampicin for meningococcal contacts, immunoglobulin for hepatitis A contacts
• Outbreak investigation: if cluster identified (activate OCT)
• Environmental investigation: food/water sampling, premises inspection
• Vaccination: catch-up campaigns for vaccine-preventable diseases

GP Responsibilities

• Be aware of current list of notifiable diseases
• Notify on clinical suspicion
• Provide relevant clinical and epidemiological information
• Cooperate with public health investigation
• Advise patients on exclusion, treatment, and follow-up
• Ensure practice staff aware of notification procedures

• Effective notification and surveillance have contributed to control of many infectious diseases in the UK
• Delays in notification can result in missed opportunities for outbreak control
• Under-notification is a recognised problem (estimated 50-80% of cases not notified for some diseases)
• Electronic notification systems and laboratory automation are improving timeliness
• Whole genome sequencing has revolutionised outbreak detection and cluster investigation
• International Health Regulations require notification of diseases of international concern to WHO

Full List of Notifiable Diseases in England (2010 Regulations)

Key Facts

Environmental health is a broad discipline addressing the interaction between human health and the physical, chemical, and biological environment. Environmental factors are estimated to contribute to 23% of global disease burden.

Key Environmental Health Domains

• Air quality: outdoor (particulate matter, NO₂, ozone) and indoor (radon, CO, mould spores)
• Water and sanitation: drinking water quality, sewage treatment, waterborne disease
• Food safety: food hygiene, food-borne illness prevention, food standards
• Housing: damp, cold, overcrowding, housing fitness standards
• Occupational health: workplace hazards, occupational diseases
• Radiation: ionising (radon, medical) and non-ionising (UV, EMF)
• Noise: environmental and occupational noise exposure
• Climate change: extreme weather, vector-borne disease, food security, mental health
• Waste management: hazardous waste, landfill, pollution

Regulatory Framework

• Environmental Protection Act 1990
• Clean Air Acts (1956, 1968, 1993)
• Food Safety Act 1990 and EU-derived food hygiene regulations
• Housing Act 2004 (Housing Health and Safety Rating System)
• Control of Substances Hazardous to Health (COSHH) Regulations 2002
• Environment Act 2021 (air quality targets)

Health Effects of Environmental Exposures

Air pollution:

• Exacerbation of asthma and COPD
• Increased cardiovascular events (MI, stroke)
• Lung cancer (PM2.5 exposure)
• Low birth weight and preterm birth
• Cognitive decline in elderly

Water contamination:

• Gastroenteritis (Cryptosporidium, E. coli, norovirus)
• Chemical contamination (lead, fluoride, nitrates)

Housing:

• Damp and mould: respiratory disease, asthma exacerbation (especially children)
• Cold homes: excess winter deaths (approximately 25,000/year in England)
• Overcrowding: infectious disease transmission (TB, COVID-19)

Climate change:

• Heat-related illness and mortality (heatwaves)
• Flooding: physical injury, mental health impact, waterborne disease
• Changing patterns of vector-borne disease (tick-borne encephalitis, Lyme disease)
• Food insecurity
• Mental health: eco-anxiety, displacement

Red Flags for Environmental Health Concerns

• Cluster of unexplained illness in geographic area
• Respiratory symptoms in multiple occupants of same building (sick building syndrome)
• Recurrent childhood asthma exacerbations with visible mould in home
• Carbon monoxide poisoning (headache, nausea, confusion - from faulty gas appliances)

Environmental Assessment

• Environmental health officer inspection (housing, food premises, workplaces)
• Air quality monitoring (DEFRA network)
• Water quality testing (local water company, Environment Agency)
• Radon testing kits (PHE/UKHSA)
• Noise monitoring
• Occupational hygiene assessment

Clinical Investigation

• Carboxyhaemoglobin: CO poisoning
• Blood lead level: lead exposure
• Urinary arsenic, mercury: heavy metal exposure
• Spirometry: occupational lung disease
• CXR/CT: asbestosis, mesothelioma
• Patch testing: occupational contact dermatitis

Interventions

Air quality:

• Clean air zones in cities (ULEZ in London)
• Promotion of active travel (cycling, walking)
• Advice to vulnerable patients on high pollution days (stay indoors, avoid exertion)
• Environment Act 2021 targets for PM2.5

Housing:

• HHSRS (Housing Health and Safety Rating System) assessment by local authority
• Landlord obligations for damp/mould remediation
• Warm Home Discount and Winter Fuel Payment schemes
• Decent Homes Standard

Food safety:

• Food hygiene ratings (Scores on the Doors)
• HACCP (Hazard Analysis and Critical Control Points) in food businesses
• EHO inspections and enforcement

Climate adaptation:

• Heatwave plans (NHS)
• Flood resilience planning
• Green infrastructure (urban green spaces, trees)

GP Role

• Recognise environmental contributions to patient illness
• Ask about housing conditions, workplace exposures
• Refer to environmental health for housing concerns
• Report suspected occupational diseases (RIDDOR)
• Advocate for healthy environments
• Advise vulnerable patients during extreme weather events

• Air pollution reduction has shown measurable health benefits (London ULEZ: 13% reduction in NO₂)
• Radon remediation reduces lung cancer risk
• Housing improvements reduce respiratory disease and improve mental health
• Climate change will increasingly impact health in the UK (heatwaves, flooding, infectious disease patterns)
• Environmental health interventions are generally highly cost-effective
• The Built Environment contributes significantly to physical activity levels and obesity

Air Quality Index (DAQI)

Key Environmental Health Legislation

Key Facts

Global health is concerned with improving health and achieving health equity for all people worldwide. It addresses issues that transcend national borders and require international cooperation.

Global Disease Burden

• DALYs (Disability-Adjusted Life Years): composite measure of years of life lost + years lived with disability
• Top 5 DALY causes globally: neonatal conditions, ischaemic heart disease, stroke, lower respiratory infections, diarrhoeal diseases
• Epidemiological transition: shift from infectious disease to NCD dominance as countries develop
• Double burden of disease: many LMIC face both infectious diseases and rising NCDs simultaneously

Sustainable Development Goals (SDGs) Health Targets (SDG 3)

• Reduce maternal mortality to <70 per 100,000 live births by 2030
• End preventable deaths of newborns and children under 5
• End epidemics of AIDS, TB, malaria, and neglected tropical diseases
• Reduce premature NCD mortality by one-third
• Achieve universal health coverage
• Ensure access to sexual and reproductive healthcare
• Strengthen capacity for health risk warning and management

Key Global Health Organisations

• WHO: coordination, standard-setting, technical support, IHR
• World Bank: health financing, development loans
• GAVI: vaccine alliance; immunisation in LMICs
• Global Fund: financing for HIV, TB, malaria
• MSF (Doctors Without Borders): medical humanitarian organisation
• DFID/FCDO: UK international development

Major Global Health Challenges

Infectious diseases:

• HIV/AIDS: 38 million living with HIV; 650,000 deaths/year; UNAIDS 95-95-95 targets
• Tuberculosis: 10 million new cases/year; 1.5 million deaths; MDR-TB growing threat
• Malaria: 240 million cases/year; 620,000 deaths; mostly children <5 in sub-Saharan Africa
• Neglected tropical diseases (NTDs): affect >1 billion people

Non-communicable diseases:

• CVD, cancer, diabetes, chronic respiratory disease - rising rapidly in LMICs
• Tobacco: 8 million deaths/year globally
• Alcohol: 3 million deaths/year globally
• Obesity: tripled since 1975; 1.9 billion overweight adults

Maternal and child health:

• Maternal mortality ratio: 223 per 100,000 globally (vs 10 per 100,000 in UK)
• Neonatal mortality: 2.3 million deaths/year in first 28 days
• Child malnutrition: underlying cause in 45% of under-5 deaths

Emerging threats:

• Pandemic preparedness (COVID-19 highlighted weaknesses)
• Antimicrobial resistance (predicted 10 million deaths/year by 2050 without action)
• Climate change (heat stress, vector-borne disease, food insecurity, displacement)

Measuring Global Health

• DALYs: years of healthy life lost (YLL + YLD)
• QALY (Quality-Adjusted Life Year): measure of health gain for economic evaluation
• Maternal mortality ratio: maternal deaths per 100,000 live births
• Under-5 mortality rate: deaths per 1,000 live births
• Healthy life expectancy (HALE): years lived in full health
• Global Burden of Disease Study (IHME): comprehensive data on disease burden by country

Key Data Sources

• WHO Global Health Observatory
• IHME Global Burden of Disease
• UNICEF State of the World's Children
• World Bank development indicators
• Lancet commissions and series

Global Health Strategies

Infectious disease control:

• GAVI vaccination programmes (>1 billion children vaccinated since 2000)
• Global Fund programmes for HIV (antiretroviral scale-up), TB (DOTS strategy), malaria (bed nets, ACTs)
• WHO elimination targets for NTDs

NCD prevention:

• WHO Framework Convention on Tobacco Control (FCTC)
• WHO 'Best Buys': cost-effective NCD interventions (tobacco taxation, salt reduction, alcohol pricing, HPV vaccination)
• HEARTS package for CVD management in primary care

Health systems strengthening:

• Universal Health Coverage (UHC) as overarching goal
• Primary healthcare as foundation (Astana Declaration 2018)
• Health workforce development
• Essential medicines list (WHO EML)
• Health financing reform

UK contributions:

• FCDO global health programmes
• UK participation in WHO, GAVI, Global Fund
• NHS Global Health Exchange Programme
• Research: major UK contribution to global health research (LSHTM, Oxford, Edinburgh)

GP Relevance

• Understanding migrant and refugee health needs
• Recognising tropical diseases in returning travellers
• Cultural competence in diverse UK populations
• Awareness of global health determinants affecting local communities
• Ethical considerations in global health partnerships

• Global under-5 mortality has decreased by 60% since 1990 (12.7 million to 5 million deaths/year)
• Maternal mortality has decreased by 34% since 2000
• HIV-related deaths have decreased by 68% since peak in 2004
• Malaria mortality has decreased by 60% since 2000
• However, progress is uneven; COVID-19 reversed gains in many areas
• AMR threatens to undo progress in infectious disease control
• Climate change will increasingly impact health systems globally
• SDG health targets are unlikely to be fully met by 2030 at current trajectory

Millennium Development Goals vs Sustainable Development Goals

WHO Essential Medicines List