Identification and correction for collider bias in a genome-wide association study of diabetes-related heart failure.

Type 2 diabetes (T2D) is a major risk factor for heart failure (HF) and has elevated incidence among individuals with HF. Since genetics and HF can independently influence T2D, collider bias may occur when T2D (i.e., collider) is controlled for by design or analysis. Thus, we conducted a genome-wide association study (GWAS) of diabetes-related HF with correction for collider bias. We first performed a GWAS of HF to identify genetic instrumental variables (GIVs) for HF and to enable bidirectional Mendelian randomization (MR) analysis between T2D and HF. We identified 61 genomic loci, significantly associated with all-cause HF in 114,275 individuals with HF and over 1.5 million controls of European ancestry. Using a two-sample bidirectional MR approach with 59 and 82 GIVs for HF and T2D, respectively, we estimated that T2D increased HF risk (odds ratio [OR] 1.07, 95% confidence interval [CI] 1.04-1.10), while HF also increased T2D risk (OR 1.60, 95% CI 1.36-1.88). Then we performed a GWAS of diabetes-related HF corrected for collider bias due to the study design of index cases. After removing the spurious association of TCF7L2 locus due to collider bias, we identified two genome-wide significant loci close to PITX2 (chromosome 4) and CDKN2B-AS1 (chromosome 9) associated with diabetes-related HF in the Million Veteran Program and replicated the associations in the UK Biobank. Our MR findings provide strong evidence that HF increases T2D risk. As a result, collider bias leads to spurious genetic associations of diabetes-related HF, which can be effectively corrected to identify true positive loci.

D3EGFR: a webserver for deep learning-guided drug sensitivity prediction and drug response information retrieval for EGFR mutation-driven lung cancer.

As key oncogenic drivers in non-small-cell lung cancer (NSCLC), various mutations in the epidermal growth factor receptor (EGFR) with variable drug sensitivities have been a major obstacle for precision medicine. To achieve clinical-level drug recommendations, a platform for clinical patient case retrieval and reliable drug sensitivity prediction is highly expected. Therefore, we built a database, D3EGFRdb, with the clinicopathologic characteristics and drug responses of 1339 patients with EGFR mutations via literature mining. On the basis of D3EGFRdb, we developed a deep learning-based prediction model, D3EGFRAI, for drug sensitivity prediction of new EGFR mutation-driven NSCLC. Model validations of D3EGFRAI showed a prediction accuracy of 0.81 and 0.85 for patients from D3EGFRdb and our hospitals, respectively. Furthermore, mutation scanning of the crucial residues inside drug-binding pockets, which may occur in the future, was performed to explore their drug sensitivity changes. D3EGFR is the first platform to achieve clinical-level drug response prediction of all approved small molecule drugs for EGFR mutation-driven lung cancer and is freely accessible at https://www.d3pharma.com/D3EGFR/index.php.

Validity of forensic cartridge-case comparisons.

This article presents key findings from a research project that evaluated the validity and probative value of cartridge-case comparisons under field-based conditions. Decisions provided by 228 trained firearm examiners across the US showed that forensic cartridge-case comparison is characterized by low error rates. However, inconclusive decisions constituted over one-fifth of all decisions rendered, complicating evaluation of the technique's ability to yield unambiguously correct decisions. Specifically, restricting evaluation to only the conclusive decisions of identification and elimination yielded true-positive and true-negative rates exceeding 99%, but incorporating inconclusives caused these values to drop to 93.4% and 63.5%, respectively. The asymmetric effect on the two rates occurred because inconclusive decisions were rendered six times more frequently for different-source than same-source comparisons. Considering probative value, which is a decision's usefulness for determining a comparison's ground-truth state, conclusive decisions predicted their corresponding ground-truth states with near perfection. Likelihood ratios (LRs) further showed that conclusive decisions greatly increase the odds of a comparison's ground-truth state matching the ground-truth state asserted by the decision. Inconclusive decisions also possessed probative value, predicting different-source status and having a LR indicating that they increase the odds of different-source status. The study also manipulated comparison difficulty by using two firearm models that produce dissimilar cartridge-case markings. The model chosen for being more difficult received more inconclusive decisions for same-source comparisons, resulting in a lower true-positive rate compared to the less difficult model. Relatedly, inconclusive decisions for the less difficult model exhibited more probative value, being more strongly predictive of different-source status.

Testing for SARS-CoV-2: lessons learned and current use cases.

SUMMARYThe emergence and worldwide dissemination of SARS-CoV-2 required both urgent development of new diagnostic tests and expansion of diagnostic testing capacity on an unprecedented scale. The rapid evolution of technologies that allowed testing to move out of traditional laboratories and into point-of-care testing centers and the home transformed the diagnostic landscape. Four years later, with the end of the formal public health emergency but continued global circulation of the virus, it is important to take a fresh look at available SARS-CoV-2 testing technologies and consider how they should be used going forward. This review considers current use case scenarios for SARS-CoV-2 antigen, nucleic acid amplification, and immunologic tests, incorporating the latest evidence for analytical/clinical performance characteristics and advantages/limitations for each test type to inform current debates about how tests should or should not be used.

Patient and Community Health Global Burden in a World With More Celiac Disease.

Celiac disease is one of the most common life-long disorders worldwide, with a prevalence mostly ranging between 0.7% and 2.9% in the general population and a higher frequency in females and well-defined at-risk groups, such as relatives of affected individuals and patients with autoimmune comorbidities. Increasing clinical detection is facilitated by improving awareness, implementation of a case-finding approach, and serology availability for screening at-risk patients, among other factors. Nevertheless, due to huge clinical variability, many celiac disease cases still escape diagnosis in most countries, unless actively searched by proactive policies. The burden of celiac disease is increasing, as is the need for better longitudinal care. Pediatric screening of the general population could represent the road ahead for an efficient intervention of secondary prevention aimed to reduce the social and health burden of celiac disease. This review analyses the epidemiology of celiac disease continent by continent, discusses current strategies to improve the detection of celiac disease, and highlights challenges related to the burden of celiac disease globally.

Long-term Cost-effectiveness of Case Finding and Mass Screening for Celiac Disease in Children.

BACKGROUND & AIMS: Celiac disease (CD) is a common yet underdiagnosed autoimmune disease with substantial long-term consequences. High-accuracy point-of-care tests for CD antibodies conducted at youth primary health care centers may enable earlier identification of CD, but evidence about the cost-effectiveness of such strategies is lacking. We estimated the long-term cost-effectiveness of active case finding and mass screening compared with clinical detection in the Netherlands. METHODS: A decision tree and Markov model were used to simulate a cohort of 3-year-old children with CD according to each strategy, taking into account their impact on long-term costs (from a societal perspective) and quality-adjusted life-years (QALYs). Model parameters incorporated data from the GLUTENSCREEN project, the Dutch Celiac Society, the Dutch Pediatric Surveillance Unit, and published sources. The primary outcome was the incremental cost-effectiveness ratio (ICER) between strategies. RESULTS: Mass-screening produced 7.46 more QALYs and was €28,635 more costly compared with current care (ICER: €3841 per QALY), and case finding produced 4.33 more QALYs and was €15,585 more costly compared with current care (ICER: €3603 per QALY). At a willingness to pay of €20,000 per QALY, both strategies were highly cost-effective compared with current care. Scenario analyses indicated that mass screening is likely the optimal strategy, unless no benefit in detecting asymptomatic cases is assumed. CONCLUSIONS: An earlier identification of CD through screening or case finding in children using a point-of-care tests leads to improved health outcomes and is cost-effective in the long-term compared with current care. If the feasibility and acceptability of the proposed strategies are successful, implementation in Dutch regular care is needed.

Rare variant association on unrelated individuals in case-control studies using aggregation tests: existing methods and current limitations.

Over the past years, progress made in next-generation sequencing technologies and bioinformatics have sparked a surge in association studies. Especially, genome-wide association studies (GWASs) have demonstrated their effectiveness in identifying disease associations with common genetic variants. Yet, rare variants can contribute to additional disease risk or trait heterogeneity. Because GWASs are underpowered for detecting association with such variants, numerous statistical methods have been recently proposed. Aggregation tests collapse multiple rare variants within a genetic region (e.g. gene, gene set, genomic loci) to test for association. An increasing number of studies using such methods successfully identified trait-associated rare variants and led to a better understanding of the underlying disease mechanism. In this review, we compare existing aggregation tests, their statistical features and scope of application, splitting them into the five classical classes: burden, adaptive burden, variance-component, omnibus and other. Finally, we describe some limitations of current aggregation tests, highlighting potential direction for further investigations.

Multiple genetic variants at the SLC30A8 locus affect local super-enhancer activity and influence pancreatic ß-cell survival and function.

Variants at the SLC30A8 locus are associated with type 2 diabetes (T2D) risk. The lead variant, rs13266634, encodes an amino acid change, Arg325Trp (R325W), at the C-terminus of the secretory granule-enriched zinc transporter, ZnT8. Although this protein-coding variant was previously thought to be the sole driver of T2D risk at this locus, recent studies have provided evidence for lowered expression of SLC30A8 mRNA in protective allele carriers. In the present study, we examined multiple variants that influence SLC30A8 allele-specific expression. Epigenomic mapping has previously identified an islet-selective enhancer cluster at the SLC30A8 locus, hosting multiple T2D risk and cASE associations, which is spatially associated with the SLC30A8 promoter and additional neighboring genes. Here, we show that deletion of variant-bearing enhancer regions using CRISPR-Cas9 in human-derived EndoC-ßH3 cells lowers the expression of SLC30A8 and several neighboring genes and improves glucose-stimulated insulin secretion. While downregulation of SLC30A8 had no effect on beta cell survival, loss of UTP23, RAD21, or MED30 markedly reduced cell viability. Although eQTL or cASE analyses in human islets did not support the association between these additional genes and diabetes risk, the transcriptional regulator JQ1 lowered the expression of multiple genes at the SLC30A8 locus and enhanced stimulated insulin secretion.

Increased frequency and mortality in persons with neurological disorders during COVID-19.

Determining the frequency and outcomes of neurological disorders associated with coronavirus disease 2019 (COVID-19) is imperative for understanding risks and for recognition of emerging neurological disorders. We investigated the susceptibility and impact of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection among persons with premorbid neurological disorders, in addition to the post-infection incidence of neurological sequelae, in a case-control population-based cohort. Using health service data collected between 1 March 2020 and 30 June 2021, we constructed a cohort of SARS-CoV-2 RNA-positive (n = 177 892) and -negative (n = 177 800) adults who were age, sex and comorbidity matched and underwent RT-PCR testing at similar times. COVID-19-associated mortality rates were examined within the cohort. Neurological sequelae were analysed during the acute (<3 months) and the post-acute (3-9 months) phases post-infection. The risk of death was significantly greater in the SARS-CoV-2 RNA-positive (2140 per 100 000 person years) compared with RNA-negative (922 per 100 000 person years) over a follow-up of 9 months, particularly amongst those with premorbid neurological disorders: adjusted odds ratios (95% confidence interval) in persons with a prior history of parkinsonism, 1.65 (1.15-2.37); dementia, 1.30 (1.11-1.52); seizures, 1.91 (1.26-2.87); encephalopathy, 1.82 (1.02-3.23); and stroke, 1.74 (1.05-2.86). There was also a significantly increased risk for diagnosis of new neurological sequelae during the acute time phase after COVID-19, including encephalopathy, 2.0 (1.10-3.64); dementia, 1.36 (1.07-1.73); seizure, 1.77 (1.22-2.56); and brain fog, 1.96 (1.20-3.20). These risks persisted into the post-acute phase after COVID-19, during which inflammatory myopathy (2.57, 1.07-6.15) and coma (1.87, 1.22-2.87) also became significantly increased. Thus, persons with SARS-CoV-2 infection and premorbid neurological disorders are at greater risk of death, and SARS-CoV-2 infection was complicated by increased risk of new-onset neurological disorders in both the acute and post-acute phases of COVID-19.

Thromboembolism after coronavirus disease 2019 vaccination in atrial fibrillation/flutter: a self-controlled case series study.

BACKGROUND AND AIMS: Concerns about the safety of coronavirus disease 2019 (COVID-19) vaccines in patients with atrial fibrillation/flutter (AF/AFL) have arisen due to reports of thrombo-embolic events following COVID-19 vaccination in the general population. This study aimed to evaluate the risk of thrombo-embolic events after COVID-19 vaccination in patients with AF/AFL. METHODS: This was a modified self-controlled case-series study using a comprehensive nationwide-linked database provided by the National Health Insurance Service in South Korea to calculate incidence rate ratios (IRRs) of thrombo-embolic events. The study population included individuals aged ≥12 years who were either vaccinated (e.g. one or two doses) or unvaccinated during the period from February to December 2021. The primary outcome was a composite of thrombo-embolic events, including ischaemic stroke, transient ischaemic attack, and systemic thromboembolism. The risk period was defined as 0-21 days following COVID-19 vaccination. RESULTS: The final analysis included 124 127 individuals with AF/AFL. The IRR of thrombo-embolic events within 21 days after COVID-19 vaccination, compared with that during the unexposed control period, was 0.93 [95% confidence interval (CI) 0.77-1.12]. No significant risk variations were noted by sex, age, or vaccine type. However, patients without anticoagulant therapy had an IRR of 1.88 (95% CI 1.39-2.54) following vaccination. CONCLUSIONS: In patients with AF/AFL, COVID-19 vaccination was generally not associated with an increased risk of thrombo-embolic events. However, careful individual risk assessment is required when advising vaccination for those not on oral anticoagulant, as these patients exhibited an increased risk of thrombo-embolic events post-vaccination.

Meta-analysis of the human upper respiratory tract microbiome reveals robust taxonomic associations with health and disease.

BACKGROUND: The human upper respiratory tract (URT) microbiome, like the gut microbiome, varies across individuals and between health and disease states. However, study-to-study heterogeneity in reported case-control results has made the identification of consistent and generalizable URT-disease associations difficult. RESULTS: In order to address this issue, we assembled 26 independent 16S rRNA gene amplicon sequencing data sets from case-control URT studies, with approximately 2-3 studies per respiratory condition and ten distinct conditions covering common chronic and acute respiratory diseases. We leveraged the healthy control data across studies to investigate URT associations with age, sex, and geographic location, in order to isolate these associations from health and disease states. CONCLUSIONS: We found several robust genus-level associations, across multiple independent studies, with either health or disease status. We identified disease associations specific to a particular respiratory condition and associations general to all conditions. Ultimately, we reveal robust associations between the URT microbiome, health, and disease, which hold across multiple studies and can help guide follow-up work on potential URT microbiome diagnostics and therapeutics.

Relation between the Dantu blood group variant and bacteraemia in Kenyan children: a population-based case control study.

BACKGROUND: The Dantu blood group variant protects against P. falciparum infections but its wider consequences have not been previously explored. Here, we investigate the impact of Dantu on susceptibility to bacteraemia. METHODS: We conducted a case-control study in children presenting with community-acquired bacteraemia to Kilifi County Hospital in Kenya between 1998 and 2010. We used logistic regression to test for associations between the Dantu marker SNP rs186873296 A>G and both all-cause and pathogen-specific bacteraemia under an additive model. We used date of admission as a proxy measure of malaria transmission intensity, given known differences in malaria prevalence over the course of the study. RESULTS: Dantu was associated with protection from all-cause bacteraemia (OR=0.81, p=0.014), the association being greatest in homozygotes (OR=0.30, p=0.013). This protection was shared across the major bacterial pathogens but, notably, was only significant during the era of high malaria-transmission pre-2003 (OR=0.79, p=0.023). CONCLUSIONS: Consistent with previous studies showing the indirect impact on bacteraemia risk of other malaria-associated red cell variants, our study also shows that Dantu is protective against bacteraemia via its effect on malaria risk. Dantu does not appear to be under balancing selection through an increased risk of bacterial infections.

Cost-Effectiveness of Community-Based Active Case Finding Strategy for Tuberculosis: Evidence From Shenzhen, China.

BACKGROUND: Active case finding (ACF) is a potentially promising approach for the early identification and treatment of tuberculosis patients. However, evidence on its cost-effectiveness, particularly in low- and middle-income countries, remains limited. This study evaluates the cost-effectiveness of a community-based ACF practice in Shenzhen, China. METHODS: We employed a Markov model-based decision analytic method to assess the costs and effectiveness of 3 tuberculosis detection strategies: passive case finding (PCF), basic ACF, and advanced ACF. The analysis was conducted from a societal perspective on a dynamic cohort over a 20-year horizon, focusing on active tuberculosis (ATB) prevalence and the incremental cost-effectiveness ratio (ICER). RESULTS: Compared to the PCF strategy, the basic and advanced ACF strategies effectively reduced ATB cases by 6.8 and 10.2 per 100 000 population, respectively, by the final year of this 20-year period. The ICER for the basic and advanced ACF strategies were ¥14 757 and ¥8217 per quality-adjusted life-year, respectively. Both values fell below the cost-effectiveness threshold. CONCLUSIONS: Our findings indicate that the community-based ACF screening strategy, which targets individuals exhibiting tuberculosis symptoms, is cost-effective. This underscores the potential benefits of adopting similar community-based ACF strategies for symptomatic populations in tuberculosis-endemic areas.

Higher Levels of SARS-CoV-2 Genetic Variation in Immunocompromised Patients: A Retrospective Case-Control Study.

BACKGROUND: A severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection lasts longer in immunocompromised hosts than in immunocompetent patients. Prolonged infection is associated with a higher probability of selection for novel SARS-CoV-2 mutations, particularly in the spike protein, a critical target for vaccines and therapeutics. METHODS: From December 2020 to September 2022, respiratory samples from 444 immunocompromised patients and 234 health care workers positive for SARS-CoV-2, diagnosed at 2 hospitals in Paris, France, were analyzed using whole-genome sequencing using Nanopore technology. Custom scripts were developed to assess the SARS-CoV-2 genetic diversity between the 2 groups and within the host. RESULTS: Most infections were SARS-CoV-2 Delta or Omicron lineages. Viral genetic diversity was significantly higher in infections of immunocompromised patients than those of controls. Minor mutations were identified in viruses sequenced from immunocompromised individuals, which became signature mutations for newer SARS-CoV-2 variants as the epidemic progressed. Two patients were coinfected with Delta and Omicron variants. The follow-up of immunocompromised patients revealed that the SARS-CoV-2 genome evolution differed in the upper and lower respiratory tracts. CONCLUSIONS: This study found that SARS-CoV-2 infection in immunocompromised patients is associated with higher genetic diversity, which could lead to the emergence of new SARS-CoV-2 variants with possible immune evasion or different virulence characteristics.

Risk of cardiovascular events after influenza: A population-based Self Controlled Case Series study, Spain 2011-2018.

This study explores the relationship between influenza infection, both clinically diagnosed in primary-care and laboratory confirmed in hospital, and atherothrombotic events (acute myocardial infarction and ischemic stroke) in Spain. A population-based self-controlled case series design was used with individual-level data from electronic registries (n = 2,230,015). The risk of atherothrombotic events in subjects ≥50 years old increased more than 2-fold during the 14 days after the mildest influenza cases in patients with fewer risk factors and more than 4-fold after severe cases in the most vulnerable patients, remaining in them more than 2-fold for 2 months. The transient increase of the association, its gradient after influenza infection and the demonstration by 4 different sensitivity analyses provide further evidence supporting causality. This work reinforces the official recommendations for influenza prevention in at-risk groups and should also increase the awareness of even milder influenza infection and its possible complications in the general population.

penalizedclr: an R package for penalized conditional logistic regression for integration of multiple omics layers.

BACKGROUND: The matched case-control design, up until recently mostly pertinent to epidemiological studies, is becoming customary in biomedical applications as well. For instance, in omics studies, it is quite common to compare cancer and healthy tissue from the same patient. Furthermore, researchers today routinely collect data from various and variable sources that they wish to relate to the case-control status. This highlights the need to develop and implement statistical methods that can take these tendencies into account. RESULTS: We present an R package penalizedclr, that provides an implementation of the penalized conditional logistic regression model for analyzing matched case-control studies. It allows for different penalties for different blocks of covariates, and it is therefore particularly useful in the presence of multi-source omics data. Both L1 and L2 penalties are implemented. Additionally, the package implements stability selection for variable selection in the considered regression model. CONCLUSIONS: The proposed method fills a gap in the available software for fitting high-dimensional conditional logistic regression models accounting for the matched design and block structure of predictors/features. The output consists of a set of selected variables that are significantly associated with case-control status. These variables can then be investigated in terms of functional interpretation or validation in further, more targeted studies.

eSVD-DE: cohort-wide differential expression in single-cell RNA-seq data using exponential-family embeddings.

BACKGROUND: Single-cell RNA-sequencing (scRNA) datasets are becoming increasingly popular in clinical and cohort studies, but there is a lack of methods to investigate differentially expressed (DE) genes among such datasets with numerous individuals. While numerous methods exist to find DE genes for scRNA data from limited individuals, differential-expression testing for large cohorts of case and control individuals using scRNA data poses unique challenges due to substantial effects of human variation, i.e., individual-level confounding covariates that are difficult to account for in the presence of sparsely-observed genes. RESULTS: We develop the eSVD-DE, a matrix factorization that pools information across genes and removes confounding covariate effects, followed by a novel two-sample test in mean expression between case and control individuals. In general, differential testing after dimension reduction yields an inflation of Type-1 errors. However, we overcome this by testing for differences between the case and control individuals' posterior mean distributions via a hierarchical model. In previously published datasets of various biological systems, eSVD-DE has more accuracy and power compared to other DE methods typically repurposed for analyzing cohort-wide differential expression. CONCLUSIONS: eSVD-DE proposes a novel and powerful way to test for DE genes among cohorts after performing a dimension reduction. Accurate identification of differential expression on the individual level, instead of the cell level, is important for linking scRNA-seq studies to our understanding of the human population.

Random forests for the analysis of matched case-control studies.

BACKGROUND: Conditional logistic regression trees have been proposed as a flexible alternative to the standard method of conditional logistic regression for the analysis of matched case-control studies. While they allow to avoid the strict assumption of linearity and automatically incorporate interactions, conditional logistic regression trees may suffer from a relatively high variability. Further machine learning methods for the analysis of matched case-control studies are missing because conventional machine learning methods cannot handle the matched structure of the data. RESULTS: A random forest method for the analysis of matched case-control studies based on conditional logistic regression trees is proposed, which overcomes the issue of high variability. It provides an accurate estimation of exposure effects while being more flexible in the functional form of covariate effects. The efficacy of the method is illustrated in a simulation study and within an application to real-world data from a matched case-control study on the effect of regular participation in cervical cancer screening on the development of cervical cancer. CONCLUSIONS: The proposed random forest method is a promising add-on to the toolbox for the analysis of matched case-control studies and addresses the need for machine-learning methods in this field. It provides a more flexible approach compared to the standard method of conditional logistic regression, but also compared to conditional logistic regression trees. It allows for non-linearity and the automatic inclusion of interaction effects and is suitable both for exploratory and explanatory analyses.

Joint analysis of multiple phenotypes for extremely unbalanced case-control association studies.

In genome-wide association studies (GWAS) for thousands of phenotypes in biobanks, most binary phenotypes have substantially fewer cases than controls. Many widely used approaches for joint analysis of multiple phenotypes produce inflated type I error rates for such extremely unbalanced case-control phenotypes. In this research, we develop a method to jointly analyze multiple unbalanced case-control phenotypes to circumvent this issue. We first group multiple phenotypes into different clusters based on a hierarchical clustering method, then we merge phenotypes in each cluster into a single phenotype. In each cluster, we use the saddlepoint approximation to estimate the p value of an association test between the merged phenotype and a single nucleotide polymorphism (SNP) which eliminates the issue of inflated type I error rate of the test for extremely unbalanced case-control phenotypes. Finally, we use the Cauchy combination method to obtain an integrated p value for all clusters to test the association between multiple phenotypes and a SNP. We use extensive simulation studies to evaluate the performance of the proposed approach. The results show that the proposed approach can control type I error rate very well and is more powerful than other available methods. We also apply the proposed approach to phenotypes in category IX (diseases of the circulatory system) in the UK Biobank. We find that the proposed approach can identify more significant SNPs than the other viable methods we compared with.

Retrospective Analysis of Cholera/Acute Watery Diarrhea Outbreaks in Ethiopia From 2001 To 2023: Incidence, Case Fatality Rate, and Seasonal and Multiyear Epidemic Patterns.

BACKGROUND: The Ethiopian government has developed the multisectoral cholera elimination plan (NCP) with an aim of reducing cholera incidence and case fatality rate (CFR). To better understand and monitor the progress of this plan, a comprehensive review of national cholera epidemiology is needed. METHODS: Reported data on cholera/acute watery diarrhea (AWD) cases in the past 20 years were extracted from the Ethiopian Public Health Institute and World Health Organization databases. Descriptive statistics, Pearson χ2, and logistic regression analyses were conducted. RESULTS: From January 2001 to November 2023, a total of 215 205 cholera/AWD cases, 2355 deaths with a cumulative CFR of 1.10% (95% confidence interval [CI], 1.092-1.095), and a mean annual incidence rate of 8.9/100 000 (95% CI, 6.5-11.3) were reported. Two major upsurges of cholera epidemics were found in the last two decades with mean attack rate (AR) of 20.57/100 000 in 2006-2010 and 14.83/100 000 in 2016-2020. Another resurgence of outbreaks occured in 2021-2023 (mean AR, 8.63/100 000). In 2015-2023, 54.0% (53 990/99 945) of cases were aged 15-44 years. National cholera CFR (3.13% [95% CI: 2.1-4.5]) was the highest in 2022. The 2015-2023 cumulative cholera CFR was different across regions: Benishangul Gumuz (6.07%), Gambela (1.89%), Sidama (1.42%), Southern Nation, Nationalities, and Peoples' (1.34%), Oromia (1.10%), and Amhara (1.09%). Cholera/AWD patients in older adults (≥45 years), severe dehydration, peak rainy season (June-August), and outpatients were associated with higher risk of death. CONCLUSIONS: Cholera has been a public health problem in Ethiopia with case fatalities still above the global target. Case management needs to be improved particularly in outpatients and older populations. Outbreak preparedness should be rolled out well in advance of the typical rainy seasons. Significant investments are essential to advance the cholera surveillance system at healthcare setting and community level. Underlying factors of cholera deaths per areas should be further investigated to guide appropriate interventions to meet the NCP target by 2028.