Health Impact Analysis
Project Overview
"Conduct a comprehensive health impact assessment for an air quality intervention in your school or community, quantifying the expected health benefits using concentration-response functions."
Health Impact Assessment Framework
- Characterize baseline exposure: Current pollutant concentrations and population exposed
- Define intervention scenario: Expected concentration changes from intervention
- Select health endpoints: Mortality, hospitalizations, asthma exacerbations, etc.
- Apply concentration-response functions: From epidemiological literature
- Calculate attributable cases: Health events prevented or caused
- Monetize health benefits: Economic valuation (optional)
The Health Impact Function
Delta_Y = Y0 x (1 - exp(-beta x Delta_C)) x Pop
- Delta_Y: Change in health outcomes (cases prevented)
- Y0: Baseline incidence rate (cases per person per year)
- beta: Concentration-response coefficient (from epidemiology)
- Delta_C: Change in concentration (ug/m3)
- Pop: Exposed population
Linear approximation (for small changes): Delta_Y = Y0 x beta x Delta_C x Pop
Concentration-Response Coefficients
| Health Endpoint | Beta (per 10 ug/m3 PM2.5) | Source |
|---|---|---|
| All-cause mortality | 0.06 (6% increase) | Krewski et al. 2009 |
| Cardiovascular mortality | 0.09-0.12 | Pope et al. 2004 |
| Hospital admissions (respiratory) | 0.02 | Zanobetti et al. 2009 |
| Asthma ED visits | 0.05-0.08 | Various meta-analyses |
| School absences | 0.03-0.04 | Gilliland et al. 2001 |
Project Requirements
Part 1: Exposure Assessment (30%)
- Characterize current air quality in your setting
- Identify exposed population (size, demographics)
- Estimate exposure duration and concentration
- Document data sources and assumptions
Part 2: Intervention Analysis (30%)
- Define a realistic intervention
- Estimate concentration reduction
- Apply health impact functions
- Calculate cases prevented for multiple endpoints
Part 3: Uncertainty Analysis (20%)
- Identify sources of uncertainty
- Calculate range using high/low estimates
- Discuss confidence in results
Part 4: Policy Brief (20%)
- Summarize findings for decision-makers
- Compare costs to health benefits
- Make evidence-based recommendations
Example Calculation
Scenario: Installing HEPA air cleaners in a school
- Population: 500 students
- Baseline PM2.5: 15 ug/m3
- Post-intervention PM2.5: 8 ug/m3
- Delta_C = 7 ug/m3 reduction
- Baseline asthma-related absences: 50 per year (100 per 1000 students)
Calculation:
Cases prevented = Y0 x beta x Delta_C x Pop
= 0.1 x 0.004 x 7 x 500 = 1.4 asthma absences prevented per year
Note: This is one endpoint. A complete HIA would include multiple endpoints (respiratory infections, academic performance, etc.).
Assessment Rubric
| Criterion | Excellent (4) | Proficient (3) | Developing (2) | Beginning (1) |
|---|---|---|---|---|
| Data Quality | Rigorous data with documented sources | Good data with most sources | Limited data documentation | Poor data quality |
| Methodology | Correct HIA framework, appropriate CRFs | Minor methodological issues | Significant gaps in approach | Framework not applied |
| Uncertainty | Comprehensive uncertainty analysis | Adequate uncertainty discussion | Limited uncertainty consideration | No uncertainty analysis |
| Communication | Clear, professional, compelling policy brief | Clear presentation | Unclear in places | Difficult to follow |
Unit Summary
This unit has explored how air pollution causes disease across multiple organ systems - from the acute inflammation of asthma to the chronic destruction of COPD, from cardiovascular events to cancer. Understanding these mechanisms and applying quantitative risk assessment tools empowers us to make evidence-based decisions about air quality interventions. The health impact assessment framework provides a systematic approach for translating scientific knowledge into policy action.