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Ethiopia has experienced a marked malaria resurgence in recent years, with the Amhara Region disproportionately affected. Although Ethiopia's national strategy emphasizes test-before-treat and a public-private mix, implementation fidelity of the malaria test-and-treat guideline during resurgence has not been well characterized. This study assessed fidelity to malaria diagnosis and treatment guidelines in public and private health facilities in the Amhara Region within this resurgence context. We conducted a convergent parallel mixed-methods study from February to March 2025 in 53 health facilities (38 public, 15 private) in Amhara Region. The facility was the unit of analysis; one provider primarily responsible for malaria case management was interviewed per facility (n = 53). Implementation fidelity was operationalized using Carroll's framework across three domains: content (adherence to key diagnostic/treatment steps), coverage (proportion of suspected cases tested before treatment, extracted from facility registers), and frequency (self-reported consistency of testing for febrile patients in the preceding month). Domain scores were standardized to 0-100 and averaged with equal weights to form a composite fidelity score; ≥ 75% indicated high fidelity. To explain quantitative patterns, we conducted 32 in-depth interviews and analyzed data using inductive thematic analysis with CFIR-informed interpretation. Quantitative analysis used nonparametric tests and parsimonious multivariable linear regression, with prespecified sensitivity analyses excluding the self-reported frequency domain. Overall mean implementation fidelity was 64.3% (SD 12.1); 40% of facilities had high fidelity (≥75%), and 13% scored <50%. Public facilities had higher fidelity than private facilities (median 67% [IQR 60-77] vs 63% [IQR 56-70]; Wilcoxon rank-sum p = 0.041). In multivariable analysis, higher fidelity was associated with higher participant responsiveness (β = 3.4, p < 0.001), stronger facilitation strategies (β = 2.8, p < 0.001), and lower perceived intervention complexity (reverse-coded; β = 2.1, p < 0.001). Interviews indicated that fidelity gaps were driven by diagnostic and treatment deviations (including non-species-specific prescribing), inconsistent counseling and follow-up mechanisms, supply constraints, and patient pressure, with challenges more frequently emphasized in private facilities. Implementation fidelity to Ethiopia's malaria test-and-treat guideline in Amhara during resurgence was moderate, with lower fidelity in private facilities. Provider responsiveness, facilitation strategies, and lower intervention complexity were identified as factors associated with implementation fidelity. Strengthening supportive supervision and mentorship with explicit inclusion of private facilities, improving supply reliability, and simplifying decision supports may improve adherence during resurgence.

Background Ethiopia has experienced a marked resurgence of malaria, with confirmed cases rising from 1.0 million in 2018 to more than 7.3 million by October 2024. This resurgence is particularly pronounced in the Amhara Region and threatens national elimination goals. This study explored perceived drivers and contextual determinants of malaria resurgence from the perspectives of frontline health workers and community members in the Amhara Region. Methods Between 1 and 28 February 2025, we conducted 28 semi-structured key informant interviews, two focus group discussions ( n  = 20), and 12 in-depth interviews. Participants were purposively selected based on professional roles or community leadership. Data were collected in Amharic, audio recorded, transcribed verbatim, and translated into English, then analyzed inductively using reflexive thematic analysis. The Health Belief Model, which focuses on risk perception and care-seeking behavior, was used as a sensitizing framework to interpret behavioral findings. Results Participants perceived malaria resurgence as increasingly widespread, with near year-round transmission, expansion into previously low-risk highland areas, and a rising burden among children under five years and pregnant women. Perceived ecological drivers included irregular rainfall, land use change, irrigation schemes, and construction-related water storage. Reported health system challenges included recurrent stockouts of antimalarials and diagnostics, delayed or incomplete vector control, and reliance on passive surveillance. Behavioral factors such as low perceived susceptibility and severity, reliance on traditional and religious healers, misuse of insecticide-treated nets, and weak regulation of private providers were seen to delay care-seeking and undermine prevention, particularly in conflict-affected and remote areas. Conclusions Stakeholders and community members perceived malaria resurgence in the Amhara Region as arising from interacting ecological change, health system fragility, and behavioral dynamics, exacerbated by conflict and recent public health emergencies. These hypothesis-generating findings suggest that locally tailored, equity-focused interventions, integrating behavioral insights with strengthened supply chains, surveillance, and community-centered vector control, could inform efforts to address resurgence and support Ethiopia’s elimination goals.

Background Despite substantial control gains over the past 2 decades, malaria remains a major public health threat in Ethiopia. The Amhara Region has recently experienced a significant resurgence, threatening to reverse previous progress. However, comprehensive analyses of this resurgence integrating long-term trends, parasite species dynamics, and spatiotemporal patterns are limited. Therefore, this study examines trends, spatial expansion, and control challenges associated with malaria resurgence in the Amhara Region. Methods A retrospective analysis of 11 years (January 2014–December 2024) of weekly malaria surveillance data from 166 districts in the Amhara Region, Ethiopia. Joinpoint regression was employed to detect significant temporal trend changes. A resurgence threshold was defined a priori as a ≥ 20% increase in cases compared to the 3-year average baseline for the same period (Sep–Dec 2021–2023), based on an expert consensus survey. Auto-regressive integrated moving average modelling was used to characterize and forecast the test positivity rate (TPR). The Getis-Ord Gi* statistic was used to identify spatial clustering and detect transmission hotspots. Results During the study period, 7,710,733 malaria cases and 162 deaths were reported. Adults (≥ 15 years) contributed 62% of cases. A single trend inflection point occurred in late 2018, marking a shift from a significant decline (Annual Percent Change [APC]: −13.2%) to a sharp resurgence (APC: + 12.6%, 2018–2024). The Annual Parasite Incidence (API) rose from 10.9 (95% CI 10.8–11.0) per 1000 in 2018 to 74.8 (95% CI 74.72–74.93) per 1000 in 2024. Plasmodium vivax became increasingly prominent, with its contribution to the total case burden rising from 25 to 43% (χ 2  = 190,789.55, p < 0.001). Its incidence surged 11-fold between 2018 and 2024 (from 2.7 to 32.4 per 1000). TPR increased from 10% (2018) to > 50% during peak months by 2024. In 2024, a resurgence occurred in 83% of districts; hotspots expanded into urban centres and previously low-transmission highland areas. Conclusion The Amhara Region is experiencing a malaria resurgence characterized by intensified transmission, geographic expansion into urban and highland areas, and a significant shift toward Plasmodium vivax dominance. These findings exemplify an \"elimination-resurgence paradox,\" where prior success increases vulnerability to threats such as invasive vectors, conflict, and climate shifts.

Background After substantial progress in malaria control, Ethiopia's Amhara Region experienced a marked resurgence since 2018. The relative contributions of climate variability, environmental context, intervention coverage, and unmeasured factors to this resurgence remain inadequately quantified. This study used a Bayesian spatiotemporal framework to estimate factor associations with malaria incidence, decompose spatial versus temporal climate effects, and identify persistent hotspots. Methods We conducted an ecological district-level panel analysis of 13,944 district-month observations from 166 districts (January 2018–December 2024). Monthly confirmed malaria counts (total,  Plasmodium falciparum ,  P. vivax ) were modelled using Bayesian hierarchical negative binomial regression with BYM2 spatial and AR(1) temporal random effects, fitted with integrated nested Laplace approximation. Covariates included lagged rainfall, temperature, NDVI, elevation, and programmatic indicators (ITN ownership, IRS protection, and larval source management [LSM] intensity). Climate covariates were decomposed into between-district (spatial) means and within-district (temporal) deviations. Sensitivity analyses included alternative IRS protection windows and district fixed-effects models. Results A total of 5,746,571 confirmed cases were reported (64.3%  P. falciparum , 35.7%  P. vivax ). Mean monthly incidence increased 5.5-fold from 1.19 per 1,000 (2018) to 6.53 per 1,000 (2024), while regional mean maximum temperature showed a small declining trend over the period. In fully adjusted models, higher lagged maximum temperature and rainfall were associated with higher incidence, and elevation was protective. IRS protection, higher ITN ownership, and higher LSM intensity were each associated with lower incidence; effect directions were consistent in within-district sensitivity analyses, although residual confounding and measurement error cannot be excluded. Climate–incidence associations were predominantly spatial (between-district) rather than temporal (within-district), suggesting that geographic ecological suitability explains much of the spatial patterning, rather than temporal warming trends explaining the resurgence. Districts with persistently elevated residual spatial risk (exceedance probability of residual RR > 1.25) clustered in low-elevation western border areas. Conclusions Malaria resurgence in Amhara (2018–2024) occurred alongside strong spatial climatic and elevational gradients and was not consistent with a temporal warming-driven explanation at the regional scale. Remaining unexplained spatiotemporal variation highlights the likely importance of unmeasured drivers (e.g., conflict-related service disruption, vector/insecticide resistance dynamics, and population mobility). Climate-informed, spatially targeted intervention packages prioritizing districts with persistently high residual risk are warranted.

Objective To evaluate the importance of external quality assessment program on malaria microscopic diagnosis. Results A total of 3148 slides were collected in 4 consecutive external quality assessment rounds and blindly rechecked at Amhara Public Health Institute. The average agreement between health facility and APHI slide readers was 96.6%. The percent agreement for parasite detection and species identification for P. falciparum became improved in four consecutive EQA rounds from 93.88 to 99.24% and 92.67 to 97.35% respectively. The rates of false positive and false negative were also dramatically decreased in each round from 10.5 to 0.79% and 2.14 to 0.74% respectively. Therefore, we recommend that malaria EQA program should maintain and expand in all malaria diagnostic health facilities in the region to provide accurate and reliable malaria microscopic service.

Background Despite global intervention efforts, malaria remains a major public health concern in many parts of the world. Understanding geographic variation in malaria patterns and their environmental determinants can support targeting of malaria control and development of elimination strategies. Methods We used remotely sensed environmental data to analyze the influences of environmental risk factors on malaria cases caused by Plasmodium falciparum and Plasmodium vivax from 2014 to 2017 in two geographic settings in Ethiopia. Geospatial datasets were derived from multiple sources and characterized climate, vegetation, land use, topography, and surface water. All data were summarized annually at the sub-district ( kebele ) level for each of the two study areas. We analyzed the associations between environmental data and malaria cases with Boosted Regression Tree (BRT) models. Results We found considerable spatial variation in malaria occurrence. Spectral indices related to land cover greenness (NDVI) and moisture (NDWI) showed negative associations with malaria, as the highest malaria rates were found in landscapes with low vegetation cover and moisture during the months that follow the rainy season. Climatic factors, including precipitation and land surface temperature, had positive associations with malaria. Settlement structure also played an important role, with different effects in the two study areas. Variables related to surface water, such as irrigated agriculture, wetlands, seasonally flooded waterbodies, and height above nearest drainage did not have strong influences on malaria. Conclusion We found different relationships between malaria and environmental conditions in two geographically distinctive areas. These results emphasize that studies of malaria-environmental relationships and predictive models of malaria occurrence should be context specific to account for such differences.

Background Despite remarkable progress in the reduction of malaria incidence, this disease remains a public health threat to a significant portion of the world’s population. Surveillance, combined with early detection algorithms, can be an effective intervention strategy to inform timely public health responses to potential outbreaks. Our main objective was to compare the potential for detecting malaria outbreaks by selected event detection methods. Methods We used historical surveillance data with weekly counts of confirmed Plasmodium falciparum (including mixed) cases from the Amhara region of Ethiopia, where there was a resurgence of malaria in 2019 following several years of declining cases. We evaluated three methods for early detection of the 2019 malaria events: 1) the Centers for Disease Prevention and Control (CDC) Early Aberration Reporting System (EARS), 2) methods based on weekly statistical thresholds, including the WHO and Cullen methods, and 3) the Farrington methods. Results All of the methods evaluated performed better than a naïve random alarm generator. We also found distinct trade-offs between the percent of events detected and the percent of true positive alarms. CDC EARS and weekly statistical threshold methods had high event sensitivities (80–100% CDC; 57–100% weekly statistical) and low to moderate alarm specificities (25–40% CDC; 16–61% weekly statistical). Farrington variants had a wide range of scores (20–100% sensitivities; 16–100% specificities) and could achieve various balances between sensitivity and specificity. Conclusions Of the methods tested, we found that the Farrington improved method was most effective at maximizing both the percent of events detected and true positive alarms for our dataset (> 70% sensitivity and > 70% specificity). This method uses statistical models to establish thresholds while controlling for seasonality and multi-year trends, and we suggest that it and other model-based approaches should be considered more broadly for malaria early detection.

Background Early indication of an emerging malaria epidemic can provide an opportunity for proactive interventions. Challenges to the identification of nascent malaria epidemics include obtaining recent epidemiological surveillance data, spatially and temporally harmonizing this information with timely data on environmental precursors, applying models for early detection and early warning, and communicating results to public health officials. Automated web-based informatics systems can provide a solution to these problems, but their implementation in real-world settings has been limited. Methods The Epidemic Prognosis Incorporating Disease and Environmental Monitoring for Integrated Assessment (EPIDEMIA) computer system was designed and implemented to integrate disease surveillance with environmental monitoring in support of operational malaria forecasting in the Amhara region of Ethiopia. A co-design workshop was held with computer scientists, epidemiological modelers, and public health partners to develop an initial list of system requirements. Subsequent updates to the system were based on feedback obtained from system evaluation workshops and assessments conducted by a steering committee of users in the public health sector. Results The system integrated epidemiological data uploaded weekly by the Amhara Regional Health Bureau with remotely-sensed environmental data freely available from online archives. Environmental data were acquired and processed automatically by the EASTWeb software program. Additional software was developed to implement a public health interface for data upload and download, harmonize the epidemiological and environmental data into a unified database, automatically update time series forecasting models, and generate formatted reports. Reporting features included district-level control charts and maps summarizing epidemiological indicators of emerging malaria outbreaks, environmental risk factors, and forecasts of future malaria risk. Conclusions Successful implementation and use of EPIDEMIA is an important step forward in the use of epidemiological and environmental informatics systems for malaria surveillance. Developing software to automate the workflow steps while remaining robust to continual changes in the input data streams was a key technical challenge. Continual stakeholder involvement throughout design, implementation, and operation has created a strong enabling environment that will facilitate the ongoing development, application, and testing of the system.