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Night-light intensity and temporal factors associated with increased measles incidence in Ethiopia

Night-light intensity and temporal factors associated with increased measles incidence in Ethiopia
Photo by MICHEL ANDRADE / Unsplash
Key Takeaway
Note that night-light intensity and temporal factors associate with increased measles incidence in this Ethiopian cohort.

This retrospective spatio-temporal analysis utilized national measles surveillance data aggregated at the zonal level in Ethiopia from 2018 to 2024. The cohort included 71,635 measles cases to evaluate associations between various environmental and socioeconomic factors and measles incidence. Factors examined included night-light intensity, temperature, relative wealth index, underweight prevalence, and distance to health facilities, alongside temporal and spatial lag effects.

The analysis revealed that night-light intensity was strongly associated with increased measles incidence, with an incidence rate ratio (IRR) of 2.21 (p < 0.001). Temporal effects were also associated with increased incidence (IRR = 1.24; p = 0.028), and spatial lag effects showed a positive association (IRR = 1.73; p < 0.001). Conversely, higher temperature was inversely associated with incidence (IRR = 0.78; p = 0.005), and a higher relative wealth index was inversely associated (IRR = 0.40; p < 0.001). Underweight prevalence and distance to health facilities were not significant predictors of measles distribution in this model.

No safety data, adverse events, or discontinuations were reported, as this was an observational analysis of surveillance data rather than a clinical trial. Key limitations include the observational nature of the study, which precludes causal inference, and the reliance on aggregated zonal data which may mask local variations. The study did not report specific funding sources or conflicts of interest.

Incorporating spatio-temporal modeling into routine surveillance can enhance early detection and guide geographically targeted immunization, nutrition, and equity-focused interventions toward measles elimination. Clinicians should interpret these associations as indicators for resource allocation rather than direct causal mechanisms.

Study Details

Study typeCohort
EvidenceLevel 3
PublishedApr 2026
View Original Abstract ↓
BackgroundDespite the availability of an effective vaccine, measles remains a major public health concern in Ethiopia, with recurrent outbreaks and substantial spatial heterogeneity. Understanding its spatio-temporal patterns and determinants is critical for optimizing control strategies and achieving elimination goals.MethodsA retrospective spatio-temporal analysis was conducted using national measles surveillance data from 2018–2024, aggregated at the zonal level. Geographic clustering was assessed using Moran's I, Getis-Ord Gi*, and Local Indicators of Spatial Association (LISA) statistics. A negative binomial regression model incorporating spatial and temporal effects was fitted to identify determinants of measles distribution, integrating epidemiological, environmental, nutritional, and socioeconomic variables.ResultsBetween 2018 and 2024, 71,635 measles cases were reported, with the highest burdens observed in Oromia, Somali, Southern Ethiopia, and parts of Amhara. Significant spatial clustering was detected (Moran's I = 0.154, p = 0.003), with persistent hotspots in southern and southwestern zones. The model showed that higher night-light intensity (IRR = 2.21, p < 0.001) and temporal (IRR = 1.24, p = 0.028) and spatial lag effects (IRR = 1.73, p < 0.001) were strongly associated with increased measles incidence. Higher temperature (IRR = 0.78, p = 0.005) and relative wealth index (IRR = 0.40, p < 0.001) were inversely associated, while underweight prevalence and distance to health facilities were not significant predictors of measles distribution.ConclusionMeasles transmission in Ethiopia exhibits clear spatial clustering and temporal persistence, strongly influenced by socioeconomic inequities, human concentration, and climatic conditions. Incorporating spatio-temporal modeling into routine surveillance can enhance early detection and guide geographically targeted immunization, nutrition, and equity-focused interventions toward measles elimination.
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