Alan E. Hubbard

Background: Due to high service demand, there is a critical need to rapidly identify injured patients in the greatest need of emergency trauma care resources in the Cameroon health system. We evaluated context‐specific indicators of mortality to develop a triage tool for trauma team activation (TTA) in Cameroon. Methods: Data were extracted from the 10‐site Cameroon Trauma Registry between July 2022 and December 2023. Injury and care patterns were evaluated as potential predictors of hospital mortality using logistic regression. Variables associated with mortality were included in the novel Cameroon triage criteria (CTC). Receiver operating characteristics (ROC) curves compared discrimination of mortality for CTC, estimated Injury Severity Score (eISS), and a United States level 1 trauma center team activation criteria. Results: Of 6912 patients, there were 313 (5%) in‐hospital deaths. Factors independently associated with in‐hospital death included burn injury (adjusted odds ratio [aOR] = 2.62), falls in patients greater than age 60 (aOR = 2.76), moderate‐to‐severe head injury (aOR = 12.5), and primary survey abnormalities (aOR = 11.8). The CTC (ROC AUC = 0.76, 95% CI = 0.73–0.79) was found to have superior discrimination to the US criteria (ROC AUC = 0.70, 95% CI = 0.67–0.72) and similar discrimination to eISS (ROC AUC = 0.78, 95% CI = 0.75–0.80). Conclusion: The CTC demonstrates mortality prediction using injury mechanism and primary survey data that can be obtained within minutes of arrival to identify highest risk patients in need of resources. We can prospectively validate these criteria in our 10‐hospital registry network to evaluate the impact of CTC activation on trauma survival.

Background: Colorectal cancer remains a major global health burden, and although population‐based screening is beneficial, current programs have notable limitations. Risk‐stratified screening has been proposed, yet existing prediction models vary in performance and in the inclusion of predictors. Data‐adaptive modeling (machine learning) may strengthen risk prediction but depends on high‐quality input data. Expert consensus methods offer a structured way to identify clinically meaningful variables for future tools. Objective: To establish expert consensus on the selection of relevant variables and refinement of self‐reported questionnaire items for next‐generation colorectal cancer risk‐prediction tools. Methods: A modified Delphi study was conducted with seventeen internationally recognized experts. The process consisted of anonymized, email‐based rounds aimed at refining a questionnaire for colorectal cancer risk prediction. Across three iterative rounds, experts rated item relevance, proposed revisions, and identified missing domains while reviewing group‐level summaries of ratings and comments. Items not meeting predefined thresholds were removed, and validated lifestyle‐related items were incorporated. Consensus was defined as 15 of 17 experts rating an item within a similar three‐point range. Results: All seventeen experts completed all rounds. The final I‐CRC‐RISK Questionnaire comprises 23 items across seven categories: Demography , Health Status, Family Health History, Dietary Patterns, Physical Activity, Smoking , and Alcohol Consumption . Expert consensus was reached for 19 items. Conclusion: The I ‐ CRC ‐ RISK Questionnaire provides an expert‐informed set of self‐reported variables to support future risk prediction efforts. It offers a structured basis for further validation and psychometric testing before prospective data collection may be undertaken. This foundational work could enhance the predictive performance of both traditional and machine‐learning approaches by improving the consistency and relevance of input data.

The mammary gland produces nutrient-rich human milk (HM), yet how mammary functional state shapes HM composition and infant outcomes remains poorly understood. We used HM multi-omics - metabolomics, proteomics, micronutrients, macronutrients, and HM oligosaccharides - across 1,543 samples from three cohorts, including two randomized trials, as a noninvasive readout of mammary functional state. Trajectory modeling and multi-omic integration showed that maternal supplementation improved recovery from early growth faltering and revealed a shared HM compositional axis linking maternal nutrition to infant growth. Machine learning, with Human Protein Atlas–informed proteomics, identified seven HM biomarkers reflecting variation across key mammary domains, indexing milk synthesis, barrier integrity, and immune/repair activity. Elevated valerylcarnitine and low pantothenic acid emerged as metabolic signals linking mammary functional state to infant growth.

Background: Long COVID is a syndrome characterized by symptoms and conditions across all biological systems. This breadth of Long COVID phenotypes impedes efforts to identify the mechanistic pathways of Long COVID. Low serotonin may play a role in long-term sequelae of COVID-19, and selective serotonin reuptake inhibitors (SSRIs) may prevent these sequelae. Evaluation of the relationship between SSRIs and distinct categories of symptoms and conditions associated with Long COVID can highlight the mechanistic pathways that drive these relationships. Methods: We evaluated electronic health record data from a retrospective cohort of patients in the National Clinical Cohort Collaborative with comorbid depression and COVID-19 between October 2021 and February 2024. We estimated the relationship between SSRI prescription (versus no SSRI prescription) during acute COVID-19 and the one-year cumulative incidence of Long COVID-related conditions and symptoms across 14 human phenotype ontology categories. We applied Super Learner and targeted maximum likelihood estimation to estimate risk ratios while adjusting for confounders of interest and correcting for false discoveries from repeated testing. Results: We evaluated EHR data from 542,938 patients. We found that patients who were prescribed SSRIs during COVID-19 had a significantly lower risk of symptoms and conditions related to gastrointestinal factors (adjusted risk ratio (aRR) 0.95, 95% CI 0.92, 0.97), general health (aRR 0.91, 95% CI 0.88, 0.95), headaches (aRR 0.96, 95% CI 0.92, 0.99) and skin (aRR 0.92, 95% CI 0.87, 0.98). Discussion: We found that the prescription of SSRIs during acute COVID-19 was associated with a significantly lower risk of post-COVID sequelae related to gastrointestinal, headache-related, skin-related, and general symptoms and conditions, compared with no SSRI prescription. These findings highlight the role of serotonin in Long COVID and specific sequelae that may be reduced by SSRIs.

Background: Metformin is one of the most commonly prescribed medications for individuals with diabetes and may provide protection against long-term sequelae of COVID-19. Methods: We evaluated a retrospective cohort of individuals in the National Clinical Cohort Collaborative with type 2 diabetes mellitus and COVID-19 who were prescribed metformin or a dipeptidyl peptidase-4 inhibitor (DPP4i) at least 30 days before the onset of acute COVID-19 between October 1, 2021, and November 15, 2023. We compared the 12-month cumulative incidence of Long COVID diagnosis (ICD-10 U09.9: Post COVID-19 condition, unspecified), probable Long COVID (based on a model-derived phenotype), and mortality between individuals prescribed metformin vs. DPP4i. We applied Super Learner and targeted maximum likelihood estimation to obtain risk ratios while adjusting for covariates of interest. Results: In our sample of 53,332 individuals with type 2 diabetes and COVID-19, we found that metformin prescription was associated with a lower risk of all-cause mortality after COVID-19 (adjusted risk ratio [aRR] 0.61, 95% CI 0.51, 0.73). We also observed that metformin users, compared to DPP4i users, had a slightly lower risk of probable Long COVID (aRR 0.87, 95% CI 0.81, 0.94) but did not detect a significant relationship with Long COVID diagnosis (aRR 0.90, 95% CI 0.68, 1.20), although we observed similar point estimates across Long COVID outcomes. Conclusions: These findings support the hypothesis that metformin prescription during acute COVID-19 may be associated with lower mortality among adults with diabetes. These analyses also provide modest evidence of a protective association against Long COVID in adults with diabetes, although estimates were imprecise.

Background Commercial motorcycle riders are among the most vulnerable road users in low- and middle-income countries and contribute substantially to the burden of road traffic injuries. The use of personal protective equipment (PPE), including helmets and protective clothing, reduces injury severity; however, uptake remains suboptimal. This study evaluated the effectiveness of a theory-driven health education intervention in improving knowledge, attitudes, and use of PPE among commercial motorcycle riders in Cameroon. Methods A quasi-experimental, non-randomized controlled before-and-after study was conducted in Limbe (intervention) and Tiko (control) Health Districts between August 4, 2024, and April 6, 2025. Participants were recruited from a cohort of commercial motorcycle riders and followed over an eight-month intervention period. The intervention, guided by the Health Belief Model and developed using the Intervention Mapping framework, combined face-to-face sensitization sessions with mobile phone–based educational messaging adapted to participants’ literacy levels and communication preferences. Data were collected at baseline and endline using structured questionnaires and direct observation checklists. Intervention effects were estimated using difference-in-differences analysis with generalized estimating equations, adjusting for socio-demographic factors. Results A total of 313 riders were enrolled at baseline (183 intervention, 130 control), with 249 retained at endline (149 intervention, 100 control). The intervention was associated with significant improvements in PPE knowledge (β = 2.91; 95% CI: 2.14–3.68; p < 0.001) and attitudes (β = 5.76; 95% CI: 4.32–7.21; p < 0.001) compared with the control group. No statistically significant effect was observed for PPE practice scores (β = 0.21; 95% CI: −0.09–0.52; p = 0.171). Among individual PPE items, helmet use increased significantly in the intervention group relative to the control group (AOR = 2.38; 95% CI: 1.19–9.45; p = 0.036), while no significant effects were observed for gloves, trousers, eyeglasses, or closed-toe shoes. Conclusion The theory-driven health education intervention significantly improved knowledge and attitudes toward PPE and increased helmet use among commercial motorcycle riders but did not lead to broader improvements in the uptake of other protective equipment. These findings highlight the need for complementary structural and policy interventions to address persistent barriers to PPE use in similar low-resource settings.

Epigenetic clocks estimate biological age from DNA methylation patterns at CpG sites, providing robust predictions of mortality and morbidity risk. “Blue zones”—regions of exceptional longevity—offer a unique opportunity to investigate how biological aging diverges from chronological age. However, standard clocks are typically trained on large, heterogeneous datasets, reflecting average population trends rather than region-specific dynamics. Using data from the Costa Rican Longevity and Healthy Aging Study (CRELES), we profiled DNA methylation from residents of the Nicoya blue zone ( n = 206) and a comparison population in other parts of Costa Rica ( n = 875). We propose training a SuperLearner , an ensemble machine learning approach, on the non-Nicoyan Costa Ricans to optimize predictive performance across existing clocks and flexible machine learners. Theoretically justified by its Oracle property, SuperLearner performs asymptotically as well as the best candidate predictor in the ensemble, resulting in a weighted combination of algorithms used to predict age. We then used this trained model to construct a calibrated hypothesis test comparing residual age distributions between the blue zone region and the comparison population. Comparing our approach to the five top-performing epigenetic clocks (ranked by MSE) in the Costa Rican cohort, only SuperLearner suggested age deceleration (an average of ∼ 1 year) in the non-Nicoyan reference group. Before calibration, SuperLearner showed the strongest evidence for slowed biological aging among blue zone Nicoyans, estimating a three-year reduction ( R̄ ₁ = −3.05, 95% CI [−3.64, −2.46]) in epigenetic age. Calibrating with non-Nicoyan Costa Ricans improved consistency between estimates in all clocks, decreasing the estimated aging advantage in Nicoyans to about two years (Δ̂ = −1.96, 95% CI [−2.56, −1.37]). This approach provides a robust framework for estimating longevity in distinct regions when a relevant comparison population is available.

Understanding the complexity of trauma-induced thromboinflammation necessitates data-driven approaches. We hypothesized that longitudinal plasma profiling could reveal underlying differences in patients with injury who present with similar clinical characteristics but ultimately have different outcomes. Here, we performed multiomic analyses of longitudinal plasma samples from a clinical trial of patients with traumatic injury to identify molecular endotypes and trajectories that were associated with patient outcomes. The pathophysiologic states of patients with trauma were defined by the longitudinal proteomic and metabolomic plasma profiles from a diverse cohort. Then, patients were endotyped according to their longitudinal trajectories through trauma omic states, and injury patterns and outcomes were compared. We identified endotypes associated with divergent clinical outcomes despite similar injury patterns at presentation. Organ failure and time spent in the intensive care unit (ICU) were predicted with high accuracy using omic markers. Patients who presented with evidence of elevated proteasome activation, catabolism, and superoxide formation were vulnerable to heart failure, lung failure, and acute lung injury, respectively. In addition, omic markers of increased hypoxia, RBC lysis, and hydrolase activity better fit mortality and ICU time compared with injury covariates, while providing biological insight. Injury and outcome patterns persisted in a validation trauma cohort after endotype assignment at a single, early time point. These data align with the understanding that patients with trauma may experience markedly different biological responses and outcomes despite similar clinical presentations. We suggest that mapping patient trajectories through biological injury states could provide a framework for personalized patient treatment after trauma.