Global Health for Engineers

Course Structure

An introduction to the professional field of Global Health, particularly focused on the areas of global health that engineers often contribute to – i.e. community environmental health. In a short course, we won’t have time to touch on things like health systems organization but there are many resources for additional learning.

We will be reading published peer reviewed studies of global health interventions – learning how to search for, read and analyze these kinds of studies is fundamental to being conversant in the field of global health. Many of these papers have engineers as co-authors.

This class is also an opportunity for group and 1:1 discussion. I am happy to meet with each of you individually to discuss career objectives and networking

Class will be heavy on case studies, with student-led facilitation

All readings posted in Canvas. Textbook is self-paced with online Canvas quizzes

See Canvas Syllabus for link to class schedule

What is Global Health?

"An area for study, research, and practice that places a priority on improving health and achieving equity in health for all people worldwide. Global health emphasizes transnational health issues, determinants, and solutions; involves many disciplines within and beyond the health sciences and promotes interdisciplinary collaboration; and is a synthesis of population-based prevention with individual-level clinical care."

— Consortium of Universities for Global Health

Overview Objectives

Define the determinants, including social and economic, that impact health.

Highlight the differences in disease and life expectancy between high- and low-income countries.

Identify some of the dynamics in developing countries that impact health trends.

Key Concepts

Disciplines: Public Health · Public Policy · Medicine · Social Sciences · Behavioural Sciences · Law · Economics · History · Engineering · Biomedical Sciences · Environmental Sciences · Anthropology

Disease & Determinants of Health

Examples of disease that disproportionately impact developing countries

Malaria

Diarrhea

Pneumonia

HIV / Aids

99% of the children under the age of 5 who die every year lived in developing countries.

Determinants of Health

Genetics

Age

Gender

Lifestyle choices

Community influences

Income status

Geographical location

Urbanization

Climate Change

Governance

Culture

Environmental factors

Work conditions

Education

Access to health services

Source: Dahlgren G. and Whitehead M. 1991

Multi-sectoral Dimension of the Determinants of Health

Malnutrition –

• more susceptible to disease and less likely to recover

Cooking with wood and coal –

• lung diseases

Poor sanitation –

• more intestinal infections

Poor life circumstances –

• commercial sex work and STIs, HIV/AIDS

Advertising tobacco and alcohol –

• addiction and related diseases

Rapid growth in vehicular traffic often with untrained drivers on unsafe roads-

• road traffic accidents

Governance & Armed Conflict

Governance

Governance has a direct impact on socioeconomic status, health inequalities, and development

Allocation of Resources

Control of policy

Trade agreements

Regional politics

Abuse of power

Education

Image: Lvakurwa, 2009

Armed Conflict

The Results:

Disparities over resources and power

Broken relationship with neighboring countries

Lack of development

Inequality along race/gender lines

Resources diverted from health care to support conflict

Displacement

Health Implications:

Malnutrition

Diarrhea

Respiratory infections

AIDS

Extreme poverty

Negative long-term effects on health

Communicable Diseases

Defined as

• “any condition which is transmitted directly or indirectly to a person from an infected person or animal through the agency of an intermediate animal, host, or vector, or through the inanimate environment”.

Transmission is facilitated by the following (IOM)

• more frequent human contact due to
• Increase in the volume and means of transportation (affordable international air travel),
• globalization (increased trade and contact)
• Microbial adaptation and change
• Breakdown of public health capacity at various levels
• Change in human demographics and behavior
• Economic development and land use patterns

CD- Modes of transmission

Direct

• Blood-borne or sexual – HIV, Hepatitis B,C
• Inhalation – Tuberculosis, influenza, anthrax
• Food-borne – E.coli, Salmonella,
• Contaminated water- Cholera, rotavirus, Hepatitis A

Indirect

• Vector-borne- malaria, onchocerciasis, trypanosomiasis
• Formites

Zoonotic diseases – animal handling and feeding practices (Mad cow disease, Avian Influenza)

Impact of Communicable Diseases

Disease Burden

CDs account for about 30% of the global BoD and 60% of the BoD in Africa.

CDs typically affect LIC and MICs disproportionately.

• Account for 40% of the disease burden in low and middle income countries

Most communicable diseases are preventable or treatable.

Social Impact

Disruption of family and social networks

• Child-headed households, social exclusion

Widespread stigma and discrimination

• TB, HIV/AIDS, Leprosy
• Discrimination in employment, schools, migration policies

Orphans and vulnerable children

• Loss of primary care givers
• Susceptibility to exploitation and trafficking

Interventions such as quarantine measures may aggravate the social disruption

Economic Impact

At the macro level

• Reduction in revenue for the country (e.g. tourism)
• Drop in international travel to affected countries by 50-70%
• Malaria causes an average loss of 1.3% annual GDP in countries with intense transmission

At the household level

• Poorer households are disproportionately affected
• Substantial loss in productivity and income for the infirmed and caregiver
• Catastrophic costs of treating illness

Malaria Control

Malaria control

• Early diagnosis and prompt treatment to cure patients and reduce parasite reservoir
• Vector control:
• Indoor residual spraying
• Long lasting insecticide treated bed nets
• Intermittent preventive treatment of pregnant women

Challenges in malaria control

• Widespread resistance to conventional anti-malaria drugs
• Malaria and HIV
• Health Systems Constraints
• Access to services
• Coverage of prevention interventions

HIV/AIDS

In 2023, 39.9 million people worldwide were living with HIV, of which 67% live in SSA

• 4.1 million people worldwide became newly infected
• 2.8 million people lost their lives to AIDS

New infections occur predominantly among the 15-24 age group.

First identified in the early 1980s. Has affected over 85 million people since the start of the epidemic.

Source: UNAIDS Global AIDS Update 2023

HIV Co-infections

Impact of TB on HIV

• TB considerably shortens the survival of people with HIV/AIDS.
• TB kills up to half of all AIDS patients worldwide.
• TB bacteria accelerate the progress of AIDS infection in the patient

HIV and Malaria

• Diseases of poverty
• HIV infected adults are at risk of developing severe malaria
• Acute malaria episodes temporarily increase HIV viral load
• Adults with low CD4 count more susceptible to treatment failure

Epidemiology Key Terms

epidemic or outbreak: disease occurrence among a population that is in excess of what is expected in a given time and place.

cluster: group of cases in a specific time and place that might be more than expected.

endemic: disease or condition present among a population at all times.

pandemic: a disease or condition that spreads across regions.

rate: number of cases occurring during a specific period; always dependent on the size of the population during that period.

Measurement of Health Status

Cause of death

• Obtained from death certification but limited because of incomplete coverage

Life expectancy at birth

• The average number of years a new-borns baby could expect to live if current trends in mortality were to continue for the rest of the new-born's life

Maternal mortality rate

• The number of women who die as a result of childbirth and pregnancy related complications per 100,000 live births in a given year

Infant mortality rate

• The number of deaths in infants under 1 year per 1,000 live births for a given year

Neonatal mortality rate

• The number of deaths among infants under 28 days in a given year per 1,000 live births in that year

Child mortality rate

• The probability that a new-born will die before reaching the age of five years, expressed as a number per 1,000 live births

Scenario: Unexplained Pneumonia

July 21–24

July 26–Aug 1

August 2

(Morning)

August 2

(Evening)

American Legion Convention,

Philadelphia, Pennsylvania

18 deaths reported among conventioneers

Health care provider at a veterans’ hospital in Philadelphia calls CDC to report casesof severe respiratory illness among attendees of the American Legion Convention

71 additional cases reported

Fraser DW, Tsai, T, Orenstein W, et al. Legionnaires’ disease: description of an epidemic of pneumonia. New Engl J Med 1977;297:1189–97.

Epidemiology Study Types

Epidemiology study

types

Experimental

Observational

Descriptive

Analytic

Confounding

Occurs when an extrinsic factor is associated with a disease outcome and, independent of that association, is also associated with the exposure

Exposure Outcome

Confounder

Measuring the association between exposure and outcome variables

The appropriate measure of association to use depends on the nature of the data

When exposure and outcome variables are dichotomous (two-level nominal data)

• Odds ratio – use with case-control study (observational)
• Risk ratio – use with cohort study (controlled)
• Rate ratio – use with cohort study (controlled)

“Risk” refers to the probability of occurrence of an event or outcome.

“Odds” refers to the probability of occurrence of an event/probability of the event not occurring.

Expressing RRs as Percentages

We can also express these RRs as percent change

• RR > 1 % Increase Change = (RR – 1) × 100
• RR < 1 % Decrease Change = (1 – RR) × 100

When does the odds ratio approximate the risk ratio?

For health-related states or events that are rare (i.e., affecting less than 10% of the population),a + b can be approximated by b, and c + d can be approximated by d

Approaches to Interventions

Personal responsibility and action

Utilitarian Approaches – "Greatest good for the greatest number"

• Including non Health Systems Interventions.

Regulations and Laws

Partnerships and Collaboration

Enlightened Self Interest

Education

Develop favorable attitudes towards the behavior

Training (i.e. Community health workers)

Participatory engagement

Provide sustainable access

Utilizing underlying skills of the local people

Supportive Environment

• Utilizing families, local organizations, community leaders, policy makers

Behavior Change

Why Do People NOT Change Behavior?

Do they understand the message?

Do they see themselves as vulnerable?

Do they trust the ones who present the message?

Do they think the benefits of change are worth the long-term benefits?

Is it too costly?

Does change contradict with their religious beliefs?

Health Belief Model

Perceived susceptibility – risk of acquiring the disease

Perceived severity – perception on the risk of acquiring the disease

Perceived benefits – is it worth the change?

Perceived barriers – obstacles to achieving health change

Cue to action – what will trigger this change?

Self-efficacy – how confident is the person to successfully perform a behavioral change?

Programme Overview

The Tubeho Neza ("Live Well") programme was one of the world's first carbon-credit-financed health interventions, distributing water filters and improved cookstoves across Western Province, Rwanda.

• First-ever UN CDM and Gold Standard programmes earning carbon credits for water treatment
• Carbon credit revenue funded distribution, training, and ongoing monitoring
• Implemented through Rwanda's existing Community-Based Environmental Health Promotion Programme (CBEHPP)
• Community health workers (CHWs) served as the primary delivery and behavior change channel
• Cluster-randomized controlled trial design to rigorously evaluate health impacts

Evan A. Thomas, PhD, PE, MPH — Director, Professor, University of Colorado Boulder

Field & Regional Context

Programme Implementation & Monitoring

Key Results

• 73% reduction in indoor air pollution among outdoor cooks
• 38% reduction in cryptosporidium exposure seroconversion
• 97.5% reduction in fecal contamination of drinking water (RCT finding)
• 48% reduction in cooking area air pollution
• 25% reduction in acute respiratory infections in children under 5
• Over 90% adoption rate maintained through CHW-delivered behavior change

Sources: Thomas et al., Lancet Planetary Health (2018); Kirby et al., PLOS ONE (2014)

Technology & Digital Monitoring

Water Filters

• LifeStraw Family 2.0 household water filters
• Significant microbiological effectiveness reducing E. coli contamination
• Free distribution with carbon waiver for credit generation

Remote Sensing Innovation

• Electronic sensors remotely transmitting usage data
• Sensor-reported use was substantially lower than self-reported use
• Demonstrated critical value of objective digital monitoring
• Published in ACS Environmental Science & Technology

Carbon Credit Model

• Pay-for-performance model funded by voluntary carbon credits
• Health, livelihood, and environmental benefits substantially outweighed costs
• Fuel savings and averted healthcare costs = largest economic gains

Scale & Lessons Learned

Published Research (13 Papers)

The Tubeho Neza Program

Central Case Study: Tubeho Neza ("Live Well") Trial — Large-scale distribution of water filters and cookstoves to 101,000+ households in Western Province, Rwanda

The Intervention

• Free distribution + promotion of LifeStraw Family 2.0 water filter and EcoZoom Dura rocket cookstove
• To 101,000+ households (poorest 25%) in Western Province, Rwanda
• Delivered by community health workers

The Evaluation Questions

• Did this program actually reduce diarrhea in children under 5? Acute respiratory infections (ARI)?
• Did it improve household water quality? Personal air quality (PM_2.5)?

Published: Kirby, Nagel et al. (2019) PLoS Medicine  |  Design: Nagel, Kirby et al. (2016) CCTC

Biased Approaches to Evaluation

Biased Approach #1: Before vs. After

Diarrhea prevalence drops from 15% before distribution to 9% after. Tempting conclusion: a 40% reduction.

• Seasonality: The rainy season ended between measurements — diarrhea is seasonal
• Concurrent changes: Rotavirus vaccine campaigns, economic growth, other WASH programs
• Regression to mean: Starting during a disease spike means prevalence naturally returns to average

Before/after comparisons confound the intervention effect with all temporal changes.

Biased Approach #2: Users vs. Non-Users

Filter users have 40% less diarrhea — but users are more educated, practice handwashing, have better healthcare access.

This is selection bias: the comparison group is not exchangeable with the treatment group.

Rwanda Trial: Baseline diarrhea was 15.3% (intervention) vs 13.7% (control) — already different before intervention.

The Three Systematic Threats to Validity

Every epidemiological study faces two kinds of error. Random error shrinks with larger samples. Systematic error — bias — does not.

Confounding, Selection Bias & Information Bias

Confounding: When a Third Variable Creates a False Association

Three criteria for a confounder: (1) associated with the exposure, (2) independently associated with the outcome, (3) not on the causal pathway.

Control methods: Design: randomization, restriction, matching. Analysis: stratification, regression, propensity scores.

Selection Bias: When Who Gets Studied Distorts Findings

• Loss to follow-up: Differential attrition biases the comparison
• Collider bias: Conditioning on a common effect creates spurious associations
• Volunteer/self-selection: Study volunteers differ systematically from non-volunteers

Information Bias: When Measurement Is Systematically Wrong

• Differential misclassification: Error differs between groups (recall bias, courtesy bias)
• Non-differential misclassification: Equal error across groups — biases toward the null

Recognizing Bias & Direction of Effects

Direction of Bias

• Non-differential misclassification: Toward null — dilutes the true effect
• Uncontrolled confounding: Either direction — depends on the confounder
• Differential misclassification: Either direction — unpredictable (recall bias, courtesy bias)

Study Design Hierarchy for Causal Inference

Each step down the hierarchy requires stronger assumptions to claim causation.

Cross-sectional study: Snapshot — exposure and outcome measured simultaneously. Weakest for causation.

The Counterfactual & Potential Outcomes

Six months after distribution, 7-day diarrhea prevalence among children under 5 is 8.7%. Is that good or bad?

You cannot answer without knowing what the prevalence would have been without the intervention. This unobservable quantity is the counterfactual.

The Potential Outcomes Framework (Neyman-Rubin)

For each child i: Y_i(1) = outcome if treated; Y_i(0) = outcome if not treated.

Causal effect = Y_i(1) − Y_i(0). The fundamental problem: we can only observe ONE potential outcome per child.

Average Treatment Effect (ATE) = E[Y_i(1)] − E[Y_i(0)]. Randomization makes the control group an unbiased estimate of the treated group's counterfactual.

Why Randomization Solves Confounding

Randomization ensures that, on average, treatment and control groups are identical in every way except the intervention — including unmeasured confounders.

Intention to Treat (ITT)

The ATE of being assigned to treatment, regardless of compliance. In Rwanda: the effect of living in a sector that received filters — not the effect of actually using the filter. ITT is the primary estimand for RCTs.

Rwanda Trial: Randomization at the sector level balanced observed covariates (age, sex, water source, sanitation type).

Individual vs. Cluster Randomization

Rwanda Trial: 96 sectors cluster-randomized 3:1. Sectors contain ~40 villages each. Stratified by district (7 districts). 3:1 ratio driven by programmatic goal of reaching 75% of the province in year 1.

Rwanda Trial: Two Studies in One

Design lesson: When feasible, nest an intensive sub-study within a larger pragmatic trial.

Randomization vs. Random Sampling

Two distinct uses of randomness serving fundamentally different purposes.

Key insight: Most trials have randomization without random sampling — establishing causation within a specific context.

Stratification & Quasi-Experimental Designs

Stratified Randomization

Ensures treatment/control balance on key variables. With only 96 clusters, pure randomization could produce imbalanced groups. Stratifying by district (7 districts) ensures proportional representation. Restricted randomization: generate many allocations, reject those with poor covariate balance.

Difference-in-Differences (DiD)

Compare change over time in treated vs. untreated groups. ΔTreated − ΔControl = causal effect. Key assumption: parallel trends — both groups would have followed the same trajectory without intervention.

Other Quasi-Experimental Designs

• Regression Discontinuity (RDD): Exploit an eligibility cutoff. Households just above/below the Ubudehe threshold are similar but one gets the intervention. Estimates local effect at the cutoff.
• Interrupted Time Series (ITS): Track outcomes over time; look for level/slope change at intervention point. Requires long pre-intervention series (8+ data points).

Reliability & Validity of Measurement

Any measurement instrument must demonstrate both before its data can be trusted.

Key Metrics

Disease Frequency: Incidence & Prevalence

Rwanda Trial: Primary outcome was 7-day period prevalence of caregiver-reported diarrhea and ARI. Repeated cross-sectional visits at ~4-month intervals.

Measures of Association: Relative & Absolute

Relative Measures

Rwanda Trial primary estimand: Prevalence Ratio from log-binomial GEE.

Values < 1 indicate a protective effect; > 1 indicates increased risk.

Absolute Measures

• Risk/Prevalence Difference: RD = Risk(exposed) − Risk(unexposed). Rwanda diarrhea: 8.7% − 12.9% = −4.2 percentage points
• Number Needed to Treat: NNT = 1/|RD|. Rwanda: 1/0.042 ≈ 24 households per case prevented in a 7-day window

Relative measures tell you how strong the effect is. Absolute measures tell you how much disease you actually prevent. Always report both.

Self-Report vs. Sensor & Evaluation Design

Filter use declined: 75.5% → 67.6% → 64.8%. Traditional stove use increased: 24.1% → 49.4% ("stove stacking").

Implications for Evaluation Design

• Triangulate: Self-report + observation + sensor + biomarker. Avoid reliance on a single method.
• Negative controls: Include outcomes that shouldn't be affected (Rwanda: toothache). If treatment reduces toothache, you have a bias problem.
• Blinding matters: Unblinded trials with subjective outcomes overestimate effects. Pooled masked HWT trials found no significant effect on diarrhea.
• Plan measurement validation: Budget for sensor sub-studies and spot-checks — these are essential, not extras.

The best study design in the world is undermined by poor measurement.

The Core Problem of Statistical Inference

We want to learn about a population, but we can only observe a sample.

Key Concepts

• Sampling distribution: Distribution of a statistic across all possible samples of the same size
• Central Limit Theorem: Sampling distribution of the mean approaches normal as n increases
• Standard error: SD of the sampling distribution — measures estimate precision. Larger samples → smaller SEs

Key intuition: Probability distributions let us quantify uncertainty — which results would be common vs. surprising given a particular assumption.

The Logic of Hypothesis Testing

Proof by contradiction: assume no effect exists, show the data would be very unlikely under that assumption, conclude the effect is probably real.

Two Ways to Be Wrong

Choosing the Right Statistical Test

Before choosing a test, answer three questions: (1) What is your outcome variable? (2) How many groups? (3) Are assumptions met?

Go non-parametric when: n < 30 and non-normal • Skewed data (income, costs) • Ordinal scales (Likert, staging)

Correlation, Regression & Survival Analysis

Correlation

• Pearson’s r: Both continuous and normal. Linear relationship. e.g., income vs. water quality
• Spearman’s rho: Ordinal or non-normal. Monotonic relationship. e.g., education level vs. health knowledge rank

Regression

• Linear regression: Continuous outcome. Y = b₀ + b₁X + e. e.g., predict child BMI from diet and activity
• Logistic regression: Binary outcome. Log-odds as linear function. e.g., predict diarrhea Y/N from WASH variables
• Poisson / Negative binomial: Count outcome. Rate of events per unit time. e.g., clinic visits per month

Survival Analysis

• Kaplan-Meier: Estimate survival curves. Handles censoring
• Log-Rank test: Compare survival between 2+ groups
• Cox regression: Model hazard with covariates. Produces hazard ratios

Statistical Test Decision Tree

What is your outcome variable?

Quick Rules of Thumb

• Continuous outcome + 2 groups → start with a t-test
• Binary outcome → chi-square for comparison, logistic regression for modeling
• Censored follow-up time → survival analysis (Kaplan-Meier, log-rank, Cox)
• Clustered data → mixed models or GEE, not standard tests

Rwanda Trial: Primary Analysis in Detail

The Analytic Pipeline

• Outcome: Binary (diarrhea yes/no in past 7 days)
• Model: Log-binomial regression → directly estimates prevalence ratio
• Clustering: GEE with exchangeable working correlation, robust (sandwich) SEs at village level
• Covariates: Child age (months) and sex only — minimal adjustment since randomization handles confounding
• Weights: Sampling weights to account for PPES design in village-level sub-study

Water quality improved significantly. Air quality did not — consistent with stove stacking behavior detected by sensors.

The Issue of Clustering

Children within the same village share water sources, sanitation, disease ecology, climate, economic conditions, CHW quality, and health facility access. Their health outcomes are correlated — not independent.

Intraclass Correlation & Design Effect

ICC = σ²(between) / [σ²(between) + σ²(within)] — proportion of variance due to between-cluster differences.

Design Effect (DEFF) = 1 + (m − 1) × ICC, where m = average cluster size. Effective sample size = n / DEFF.

Ignoring Clustering & Sample Size

What Happens When You Ignore Clustering

If you analyze 5,000 children from 10 clusters as independent observations, your nominal 5% significance level may actually be 20–30%. This is one of the most common methodological errors in global health research.

Sample Size Under Clustering

The number of clusters matters more than individuals per cluster.

Clusters needed per arm: k = (Z_α + Z_β)² × DEFF × 2σ² / δ². Adding more people per cluster gives diminishing returns once m × ICC > 1.

Better to have 20 clusters of 50 than 10 clusters of 100. Always calculate cluster-adjusted sample sizes.

Analytic Solutions for Clustering

For policy questions ("what’s the average effect?"), GEE is preferred. For prediction ("what will happen in this village?"), mixed models are preferred.

Rwanda Trial: GEE with exchangeable working correlation and robust SEs, clustering at village level.

Missing Data Mechanisms

Rubin’s classification defines why data are missing — and determines which analytic methods are valid.

Analytic Solutions for Missing Data

External Validity: Not All Water Treatment Works

Randomized trials of chlorination in Bangladesh, Kenya, and Zimbabwe found NO significant effect on childhood diarrhea.

The generalizability question isn’t "does water treatment work?" It’s "which treatment, against which pathogens, in which context, with what delivery model?"

Internal vs. External Validity

The Rwanda trial has strong internal validity (cluster-RCT, pre-registered, large sample). But results are specific to: Western Province, Rwanda | Poorest quartile | Rural | LifeStraw + EcoZoom | CHW-delivered with carbon credit financing.

Effect Modification: Why Effects Vary

Treatment effects are not universal constants. They vary across subgroups and contexts.

Always report subgroup analyses and discuss which modifiers might limit transportability. Rwanda’s strong CHW system is unusual — replication elsewhere may yield smaller effects.