🟠 Moderate Evidence
Armed conflict is significantly associated with increased measles cases across 193 countries between 2000 and 2023, according to a structural equation modeling analysis published in PLOS Medicine. The study, led by Tyler Y. Headley and Yesim Tozan, analyzed 4,632 country-year observations to quantify how battle-related deaths, population displacement, and socioeconomic development collectively shape measles burden.
Key takeaways
- Each standard deviation increase in battle-related deaths was associated with approximately 0.20 standard deviation increase in measles cases—equivalent to 2,500 additional reported cases for every 3,700 battle deaths
- The effect of prior-year conflict on measles cases persisted even after accounting for population displacement and economic factors
- All four structural models demonstrated excellent statistical fit (CFI 0.991–0.996), suggesting robust causal pathways between conflict and disease burden
Study at a Glance
| Source | PLOS Medicine |
| Study type | Longitudinal fixed-effects panel regression with structural equation modeling |
| Sample size | 4,632 country-year observations |
| Population | 193 countries, 2000–2023 |
| Country | Global (multi-country analysis) |
Pathways from Armed Conflict to Measles Burden
Effect sizes (standardized beta coefficients) for direct and indirect pathways in structural models, 2000–2023
Source: Headley & Tozan, PLOS Medicine, 2025 | Georgian Medical Journal News
Conflict Creates Cascading Health System Collapse
Higher contemporaneous battle-related deaths were significantly associated with higher measles cases (β = 0.17; 95% CI [0.14, 0.20]; p < 0.001), according to the PLOS Medicine analysis. This direct effect persisted even after adjusting for population displacement and economic development, suggesting that conflict itself—through damage to health infrastructure, healthcare workforce depletion, and disruption of routine immunization services—independently elevates measles transmission risk.
The lagged analysis revealed that prior-year battle-related deaths remained significantly associated with measles cases in the subsequent year (β = 0.14; 95% CI [0.08, 0.20]; p < 0.001), though this effect was mediated partly by displacement. This suggests conflict’s public health consequences extend beyond the immediate conflict period, possibly through sustained immunization service disruption and healthcare system fragility.
Displacement Amplifies Disease Risk in Fragile Settings
Forcibly displaced populations demonstrated a consistent positive association with measles cases across all models, indicating that population movement—whether triggered by conflict or occurring concurrently—exacerbates measles burden. The PLOS Medicine study found this effect held even after controlling for battle-related deaths and socioeconomic development, pointing to the unique vulnerability of displaced groups: limited access to vaccination programmes, overcrowded living conditions, and reduced healthcare literacy.
The structural models incorporated socioeconomic development as a latent variable constructed from gross domestic product per capita, life expectancy, and mean years of schooling. Across all models, higher development was protective against measles burden, but this benefit was partially offset by conflict’s direct damage to health systems independent of economic growth. This finding underscores that GDP alone does not predict disease control in conflict-affected regions.
Model Robustness Confirms Causal Pathways
All four constructed models demonstrated excellent statistical fit, with Comparative Fit Index (CFI) values ranging from 0.991 to 0.996, Tucker–Lewis Index (TLI) values from 0.976 to 0.989, and Root Mean Square Error of Approximation (RMSEA) from 0.046 to 0.062, according to Headley and Tozan’s analysis. These fit statistics indicate that the theoretical causal architecture—linking conflict through displacement and socioeconomic pathways to measles outcomes—is robust and replicable across model specifications.
The study’s use of longitudinal fixed-effects regression alongside structural equation modeling allowed researchers to isolate conflict’s effect from time-invariant country-level characteristics (e.g., geography, governance quality) while simultaneously testing complex causal chains. This methodological approach strengthens confidence in the findings’ causal interpretation, though observational data cannot entirely eliminate confounding bias.
Each standard deviation increase in a country’s battle-related deaths was associated with approximately 0.20 standard deviation increase in measles cases—equivalent to 2,500 additional reported cases for every 3,700 battle-related deaths, even after accounting for population displacement and economic development.
— Tyler Y. Headley & Yesim Tozan, School of Public Health, Yale University (PLOS Medicine, 2025)
What this means
Frequently asked questions
Why does conflict increase measles cases beyond simple vaccination disruption?
The study’s structural models found that battle-related deaths directly predicted measles cases independent of displacement and economic factors. This suggests conflict damages health infrastructure (clinics, vaccine cold chains), depletes healthcare workers through casualties and migration, and may reduce vaccine confidence through health system collapse—all independent mechanisms beyond immunization programme interruption alone.
Does measles risk return to normal once conflict ends?
No. Prior-year battle-related deaths remained significantly associated with measles cases in the following year, even after accounting for displacement. This suggests health system recovery lags conflict resolution, and susceptible populations accumulated during conflict years fuel outbreaks well after fighting stops. Sustained vaccination campaigns are needed post-conflict.
Which countries are at highest risk based on this analysis?
The 193-country analysis does not identify specific high-risk countries, but the statistical associations suggest nations experiencing ongoing armed conflict (e.g., Syria, Yemen, Democratic Republic of Congo, Somalia) combined with low baseline socioeconomic development and large displaced populations face compounded measles risk. Countries emerging from recent conflict with incomplete vaccination coverage recovery are also vulnerable.
This structural equation modeling analysis provides quantitative evidence for health policymakers and humanitarian agencies that measles control in conflict-affected regions requires proactive integration of immunization services into conflict response frameworks, rather than treating vaccination as a post-conflict reconstruction priority. The persistence of measles burden in the year following conflict underscores the need for sustained funding and technical support for health system rebuilding, particularly vaccination programme restoration.
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