JAC-Antimicrobial Resistance

Background: Metronidazole (MTZ) is a first-line antibiotic for several enteric infections. Its use is common in low-income countries, where most primary-care consultations are conducted by nurses. However, increasing resistance among some enteric pathogens is a growing concern. Using WHO guidelines, we conducted a register-based cross-sectional study to assess MTZ prescribing practices and their determinants in public and private primary healthcare facilities in South Benin. Methods: We performed a register-based cross-sectional study covering the year 2020 in 11 primary healthcare facilities (5 public and 6 private) in Abomey-Calavi, South Benin, following WHO recommendations. In total, 200 visits per facility were selected using systematic random sampling. The primary outcome was the prevalence of MTZ prescription. Determinants of MTZ prescription were identified using multivariable logistic regression analysis. Results: In total, 2,200 medical visits were analyzed. The median age of patients was 19 years, and 57% were female. Antimalarials were prescribed in 52% of visits. Antibacterial agents were prescribed in the majority of visits, with MTZ being the second most frequently prescribed antibiotic (18%), after aminopenicillins (27%). In multivariable analysis, digestive symptoms (adjusted odds ratio [aOR], 8.65; 95% confidence interval [CI], 6.49-11.6), genitourinary symptoms (aOR, 6.84; 95% CI, 3.18-15.0), and skin lesions (aOR, 2.39; 95% CI, 1.58-3.60) were independently associated with increased odds of MTZ prescription. In contrast, fever (aOR, 0.66; 95% CI, 0.49-0.87), respiratory symptoms (aOR, 0.44; 95% CI, 0.26-0.71), and malaria (aOR, 0.21; 95% CI, 0.15-0.28) were associated with decreased odds. Visits in the private sector were also associated with higher odds of MTZ prescription compared with the public sector (aOR, 2.31; 95% CI, 1.78-3.02). Conclusion: MTZ is the second most commonly prescribed antibiotic in primary care in the study area, with its use largely driven by digestive symptoms. Further studies are needed to assess the appropriateness of this prescription. Additionally, research is warranted to understand better the determinants of higher antimicrobial prescribing in the private healthcare sector.

PurposeAntibiotic use (ABU) is a major driver of antimicrobial resistance (AMR), but ABU patterns are poorly understood in low-income countries where the burden of AMR is great and ABU is insufficiently regulated. Here, we report ABU from ten sites ranging from rural villages to small cities in Madagascar, a country with high AMR levels, and present results from modeling to identify factors that may be associated with ABU in this setting. MethodsWe conducted surveys of 290 individuals from ten sites in the SAVA Region of northeast Madagascar to gather data on sociodemographic characteristics, agricultural and animal husbandry practices, recent antibiotic use, the antibiotics that participants recalled using in their lifetimes, and the sources of their antibiotics. Using these data, we conducted statistical analyses with a mixed-effects logistic model to determine which characteristics were associated with recent antibiotic use. ResultsNearly all respondents (N=283, 97.6%) reported ABU in their lifetimes, with amoxicillin being the most widely reported antibiotic (N=255, 90.1% of those reporting ABU). All recalled antibiotics were classified as frontline drugs except for ciprofloxacin. Most respondents who reported antibiotic use also reported obtaining antibiotics without prescriptions from local stores (N=273, 96.5%), while only 52.3% (N=148) reported obtaining antibiotics through a prescriptive route, such as from a health clinic or private doctor. Of the 127 individuals (44.9%) who reported recent ABU, men were found to be significantly less likely to have recently taken antibiotics than women. ConclusionsOur findings provide new insights into ABU in agricultural settings in low-income countries, which have historically been understudied in AMR and pharmacoepidemiologic research. Knowledge of ABU patterns supports understanding of AMR dynamics and AMR control efforts in these contexts, such as interventions on inappropriate antibiotic dispensing. Key pointsO_LIAntibiotic use (ABU) in Madagascar is largely unstudied despite its role in antimicrobial resistance (AMR), which Madagascar faces a high burden of. C_LIO_LIABU was widespread among livestock owners in northeast Madagascar, with the majority of study participants reporting ABU in their lifetimes and most people reporting ABU also having taken antibiotics in the previous three months. C_LIO_LIMost respondents reported obtaining their antibiotics from non-pharmaceutical stores, indicating high levels of unregulated ABU, though more than half also reported sourcing their antibiotics through prescriptive means (like doctors and health clinics). C_LIO_LIMen were less likely than women to have taken antibiotics in the previous three months. C_LIO_LIThese findings support the development of interventions to mitigate the burden of AMR in Madagascar and similar contexts while underscoring the need for more comprehensive research on the drivers and patterns of ABU. C_LI Plain language summaryIn this study, we provide basic information on antibiotic use (ABU) patterns in Madagascar, a country that experiences high levels of resistance but has been particularly understudied in AMR and pharmacological research. We surveyed 290 farmers with livestock from ten sites across northeast Madagascar about their ABU and found that nearly all study participants (N=283, 97.6%) have used antibiotics in their lifetimes, while a little under half of those who reported ABU also reported using antibiotics in the previous three months (N=127, 44.9%). The most used antibiotic was amoxicillin (N=255, 90.1%). Most people obtained their antibiotics from sources that do not require prescriptions, like general stores, indicating that most ABU is unregulated. Through modeling, we also found that men were less likely than women to have taken antibiotics in the previous three months (OR=0.50, CI 0.30-0.82). These findings help us better understand the dynamics of ABU in low-income countries, which have historically been understudied in AMR and pharmacological research. They also support efforts to mitigate the burden of AMR by revealing ABU dynamics that may contribute to the emergence and spread of AMR, as well as identifying targets for intervention to curb inappropriate ABU.

Antimicrobial resistance (AMR) is a growing global public health threat that complicates the treatment and control of bacterial infections. Shigella spp., a leading cause of bacterial diarrhea worldwide, has increasingly exhibited resistance to multiple antimicrobial agents that are commonly recommended therapy for severe shigellosis. Although conventional antimicrobial susceptibility testing (AST) remains the reference standard, it is time-consuming and provides limited insight into the genetic mechanisms underlying resistance. Whole-genome sequencing (WGS) has emerged as a complementary approach for AMR detection by enabling direct identification of resistance genetic determinants encoded in bacterial genomes. Machine learning (ML) methods applied to genomic features such as k-mers have shown promise for predicting resistance phenotypes from WGS data; however, applications to Shigella remain limited. In this study, we developed and evaluated an interpretable ML framework for predicting ciprofloxacin resistance using k-mer features derived from WGS data of 1,424 Shigella isolates collected in Ontario, Canada, between 2018 and 2025. K-mers were extracted from known gene targets associated with ciprofloxacin resistance, including chromosomal quinoline resistance-determining regions (QRDRs: gyrA and parC) and plasmid-mediated determinants (qnr). Supervised ML approaches were trained and compared. We evaluated the influence of k-mer lengths (k=11, 15, 21 and 31) on predictive performance and model interpretability; and compared models based on chromosomal determinants alone and models incorporating both chromosomal and plasmid-mediated determinants. Randon Forest classifier achieved the most consistent performance across models. Inclusion of plasmid-mediated determinants improved predictive accuracy relative to chromosomal-only models. Although differences across k-mer lengths were modest, k = 11 produced the highest area under the receiver operating characteristic curve (AUC) and the lowest Brier score. SHAP analyses localized high-impact features within QRDRs of gyrA and parC, supporting biological interpretability. These findings demonstrate that biologically-informed k-mer-based ML models can accurately and transparently predict ciprofloxacin resistance in Shigella, supporting their potential integration into genomic AMR surveillance and digital public health frameworks. Author summaryIn this study, we used genome sequencing data to develop machine learning models that predict ciprofloxacin resistance for Shigella directly from bacterial DNA. We focused on small DNA fragments (k-mers) derived from known resistance genes and mutations. Among the approaches tested, a Random Forest model showed the most consistent performance. Combining chromosomal mutations with plasmid-mediated resistance genes improved prediction accuracy and helped identify key genetic regions associated with resistance. These findings demonstrate that machine learning applied to genomic data can accurately and interpretable predict antibiotic resistance, supporting its potential use in genomic surveillance and public health monitoring.

Background Five-biomarker-defined hypervirulent Klebsiella pneumoniae (hvKp) causes invasive infections, but its burden in bloodstream infections versus classical K. pneumoniae (cKp) is unclear. Methods This retrospective cohort study at a tertiary hospital in Japan included K. pneumoniae bloodstream infection episodes from January 2022-December 2024. hvKp was defined by the presence of all 5 genotypic biomarkers (rmpA, rmpA2, iucA, iroB, and peg-344). The primary outcome was abscess complications, and secondary outcomes were length of stay and antibiotic duration. Whole-genome sequencing was performed for 164 isolates. Results Among the 207 episodes, 28 (14%) were of hvKp. Abscess complication occurred in 17 (61%) hvKp versus 23 (13%) cKp episodes (adjusted odds ratio 10.7; 95% CI, 4.36-26.2). Median length of stay in hvKp versus cKp was 28 versus 14 days (adjusted ratio 1.60; 95% CI, 1.18-2.16) and median antibiotic duration was 43 versus 14 days (adjusted ratio 2.13; 95% CI, 1.64-2.77). These associations were attenuated after adjusting for abscess-related complications. No significant difference in 30-day mortality was observed, although the study was underpowered. Multidrug resistance was less frequent in hvKp strains than in cKp strains (11% vs. 30%; P = .040). Among the sequenced hvKp episodes, abscess rates varied across lineages, from 9 of 10 in ST23 to 1 of 4 in ST412. Conclusions Five biomarker-defined hvKp strains delineated a bloodstream infection subgroup with frequent abscess complications and prolonged care. hvKp and cKp present distinct clinical challenges; diagnostic tools distinguishing these subgroups may aid abscess evaluation and source control.

BackgroundLaboratory services are essential to the provision of health service delivery across the world. In resource-constrained settings such as in low- and middle-income countries like Ghana, maintenance of a strong capacity could be more challenging. This study assessed the capacity and gaps in laboratory service delivery in three districts of the Savannah Region of Ghana. MethodsThe WHO laboratory assessment tool (LAT) was adapted to collect data in 10 health facilities based on 11 operational system modules. Data were collected through interviews. Capacity was defined based on a 100-point score scale and interpreted as weak (<50%), moderate (50-80%) and strong (>80%). Differences in median scores were determined using Friedman and Kruska-Wallis tests. Statistical significance was set at p < 0.05. A scale (0-5) was used to identify the needs of the laboratory. ResultsOverall, capacity score was moderate, mean 50% {+/-} 25.7 with a median score of 52.5%, IQR: 30.0-68.5%. Testing module received the highest score, 71.5%, while document module scored the lowest, 14.5%. Scores did not differ significantly between system components after multiple comparisons, p>adjusted alpha. Hospital-level laboratories performed significantly higher than polyclinics (adjusted p = 0.044) and health centers (adjusted p<0.001). The biggest needs were biosafety, equipment maintenance, human and financial resources (median gap score: 3-4). ConclusionThe laboratory capacity in the health system of the Savannah Region was moderate, requiring improvements in all operational areas. The biggest needs include safety, equipment, human and financial support systems. Addressing these critical gaps would have direct impact on public health and patient outcomes.

Acute myocardial infarction (AMI) is an increasing cause of morbidity and mortality in Sub-Saharan Africa (SSA) but is often underdiagnosed and undertreated. To address this gap, the Multicomponent Intervention to Improve Myocardial Infarction Care (MIMIC) was developed and implemented in the emergency department (ED) of a regional referral center in northern Tanzania. We conducted in-depth interviews with 20 key stakeholders (physicians, nurses, administrators, and patients) who participated in MIMIC during the first year of implementation. Purposive sampling was used to recruit a broad range of participants. Interviews were guided by a semi-structured interview guide informed by the Theoretical Framework of Acceptability (TFA). Interview transcripts were thematically analyzed by a team of coders using an inductive, grounded theory approach guided by the seven TFA domains. Nineteen major themes emerged across all TFA domains. Overall, participants described MIMIC as highly acceptable, minimally burdensome, and well-aligned with professional and ethical values. Perceived effectiveness was most emphasized, with staff citing improvements in AMI recognition, ECG and troponin testing, and use of evidence-based therapies. All components were highlighted as effective and easily integrated into existing workflows. Patients valued the educational pamphlet for improving knowledge and self-efficacy, though staff expressed concerns about distributing it during acute care, contributing to inconsistent delivery. Champions were viewed as key in promoting adherence and sustaining implementation of the intervention. MIMIC was widely acceptable in all seven TFA domains among ED providers and patients, with perceived effectiveness driving positive attitudes across stakeholder groups. Use of a co-design approach in MIMIC development likely contributed to high intervention acceptability. Patient education strategies may require adaptation to improve fidelity. These findings suggest that continued implementation and future adaptation of MIMIC may be feasible.

BackgroundTo date, accessible diagnostic tools to identify whether a patients pneumonia is a bacterial, or viral infection, are not accurate or timely enough to prevent preemptive antibiotic administration. Relying on single biomarkers or clinical presentations has been insufficient. We aimed to incorporate a wide range of novel biomarkers and clinical presentations in a multivariable model and validate its capacity to differentiate cases of bacterial and viral pneumonia. MethodsData from 457 children aged 2-59 months, admitted to Kilifi County Referral Hospital, Kenya, with bacterial (n = 229) and viral (n = 228) infections, were used to develop and validate a predictive multivariable Poisson regression model to differentiate pneumonia etiology. The Receiver Operating Characteristic curve was used to assess biomarker performance and validate the model internally. ResultsSixty-three percent (63%) of the children presented with severe pneumonia. 72% with viral pneumonia had severe pneumonia, compared to 54% with bacterial pneumonia who had severe pneumonia. In crude analyses, chest-wall indrawing, cough, convulsions, crackles, angiotensinogen, and Serpin Family A Member 1 were significantly associated with pneumonia etiology, controlling for age. However, only chest-wall indrawing remained significant in multivariable analyses after controlling for age. The model demonstrated fair, but inadequate, discrimination, with an Area Under the Curve of 0.61. ConclusionAmong the children admitted to hospital with WHO defined pneumonia, a wide range of biomarkers and clinical presentations still failed to distinguish bacterial from viral pneumonia.

Streptococcus pneumoniae is the leading cause of empyema and pneumonia in children, and monitoring of effectiveness of polyvalent pneumococcal vaccines has been essential for controlling invasive pneumococcal disease (IPD) in children and elderly adults. Conventional serotyping of pneumococci has relied on Quellung reaction following laboratory culture, however more recently whole genome sequencing (WGS) has been implemented in many reference laboratories to enhance traditional typing. Pleural fluid samples from cases with empyema are often culture negative, limiting the utility of WGS and requiring polymerase chain reaction (PCR) or 16S rRNA sequencing to detect S. pneumoniae. These molecular methods have limited sensitivity and capacity to characterise pneumococcus in clinical samples, especially in specimens with a low pathogen abundance. This study applied capture-based enrichment (tNGS) to identify and characterise S. pneumoniae directly from pleural fluid samples. A total of 51 pleural fluid samples were subjected to tNGS with a custom probe panel, for 39 known positive fluids collected from IPD cases between 2018-2025 in New South Wales, Australia. tNGS results were benchmarked against molecular-based serotyping. Our tNGS achieved 100% sensitivity and specificity in detecting S. pneumoniae. Serotyping results were concordant with PCR and 95% (37/39) of S. pneumoniae PCR positive pleural fluid cases could be serotyped using tNGS. Standard molecular methods however could only determine serotype in 56% (22/39) of samples. This tNGS enabled 39% improvement in ability to directly identify and serotype IPD-associated serotypes of S. pneumoniae in difficult-to-culture pleural fluids can significantly enhance laboratory surveillance of IPD as well as our understanding of vaccine effectiveness.

In the context of global health, the ability of frontline primary health providers to identify potential Drug-Drug Interactions (DDIs) is a critical component of patient safety. This is particularly true in settings like Tanzania, where drug dispensers often serve as the primary point of contact for healthcare. In this study, we establish a baseline for drug decision-making capabilities across multiple cadres of healthcare providers in Kibaha, Tanzania. We specifically distinguish between the ability to recognize safe drug combinations versus harmful ones. The findings reveal a critical asymmetry in provider performance: while professional training improves the recognition of safe combinations, it provides no advantage over lay intuition (and in some cases, a significant disadvantage) in detecting potentially harmful interactions.

ObjectiveBiologically defined sepsis subtypes have been identified in low- and middle-income countries (LMICs), but limited access to molecular diagnostics challenges broader evaluation and implementation in resource-limited settings. We assessed whether models including bedside clinical and rapid microbiologic data could accurately stratify Ugandan adults with sepsis by molecular subtype. DesignSecondary analysis of two prospective observational sepsis cohorts, testing bedside-adaptable classifier models against transcriptomic and proteomic subtype assignments. SettingEntebbe Regional Referral Hospital (urban) and Tororo General Hospital (rural), Uganda. PatientsAdults ([&ge;]18 years) hospitalized with sepsis, with available transcriptomic (N=355) and/or proteomic (N=495) profiling enabling subtype assignment. InterventionsNone. Measurements and Main ResultsUsing data from two prospective cohorts (RESERVE-U-2-TOR and RESERVE-U-1-EBB), we evaluated bedside-adaptable models against Uganda-derived molecular sepsis subtypes, and, secondarily, against molecular subtypes and axes derived in high-income countries. In RESERVE-U-2-TOR, clinical models including demographics and bedside physiological variables demonstrated moderate discrimination for transcriptomic and proteomic subtype assignment (AUROC 0.75 [95% CI, 0.69-0.81] and 0.73 [0.66-0.80], respectively) with strong calibration (Integrated Calibration Index [Eavg] [&le;]0.015 for both models). Adding rapid diagnostic results for HIV, malaria, and tuberculosis produced similar performance (AUROC 0.76 and 0.74; Eavg [&le;]0.016). In RESERVE-U-1-EBB, discrimination for clinical and clinico-microbiological models was more variable (AUROC range 0.63-0.75) while calibration remained acceptable (Eavg [&le;]0.053). Performance was similar when models were evaluated against molecular sepsis frameworks derived in high-income countries, with acceptable calibration and moderate discrimination. ConclusionsBedside-adaptable clinical models, with or without rapid microbiologic testing, demonstrated acceptable calibration but only modest discrimination for molecular sepsis subtype assignment in Uganda. Expanding laboratory capacity and access to scalable, low-cost molecular biomarker assays will be necessary to advance precision sepsis care in LMIC settings. Key PointsO_ST_ABSQuestionC_ST_ABSAmong adults hospitalized with sepsis in a resource-limited setting, can bedside clinical variables, alone or combined with rapid pathogen diagnostics, accurately stratify molecular sepsis subtype assignments? FindingsIn two prospective Ugandan sepsis cohorts, bedside clinical and clinico-microbiologic models showed robust calibration but only modest discrimination for classifying Uganda-derived transcriptomic and proteomic subtypes. Models also achieved moderate performance for stratifying high-income-country-derived transcriptomic subtypes and immune dysfunction axes, suggesting bedside variables reflect illness severity but incompletely capture underlying molecular signatures. MeaningBedside-adaptable models can support reasonably calibrated risk estimation for molecular sepsis stratification in resource-limited settings but lack sufficient discriminatory power to serve as stand-alone tools. These findings support efforts to improve acute-care laboratory capacity and access to scalable molecular biomarker panels, with the goal of enabling precision sepsis care in low- and middle-income countries.

BackgroundIn the absence of structured donor registries, social media platforms have become a dominant mechanism for blood donor recruitment in many low-resource settings. However, the implications of this shift for transfusion timeliness and system reliability remain unclear. ObjectiveTo evaluate the impact of social media-sourced donors on transfusion delay, donor reliability, and hemovigilance-related outcomes compared with conventional donor pathways. MethodsThis prospective analytical study included 400 transfusion episodes across tertiary hospitals in Bangladesh. Donor sources were categorized as social media (SM) or conventional (CON). The primary outcome was delay-to-transfusion. Secondary outcomes included donor-related irregularities, documentation completeness, near-miss events, and acute transfusion reactions. Multivariable logistic regression identified predictors of delay [&ge;]4 hours. ResultsSocial media-sourced donors were associated with significantly longer transfusion delays (5.98 vs 2.97 hours; p<0.001). Delay [&ge;]4 hours occurred in 83.6% of SM cases versus 17.6% of CON cases (OR 23.78). Donor-related irregularities were observed in 85% of SM episodes and absent in CON donors. Safety outcomes did not differ significantly between groups. Social media donor sourcing remained the strongest independent predictor of delay (adjusted OR 18.09). ConclusionUnregulated social media-based donor recruitment introduces substantial delays and undermines system reliability without improving access. Integration of digital tools into regulated donor systems is essential to strengthen transfusion timeliness and hemovigilance in resource-limited settings.

Background: HIV/AIDS remains a major public health challenge in Tanzania, where viral load suppression among adults on ART stands at 78% and HVL testing uptake among eligible patients is approximately 22%. Since the introduction of the National HVL Testing Guideline in 2015, little has been done to systematically evaluate its implementation. Objective: To evaluate adherence to the National HVL Testing Guideline across CTC clinics in Dar es Salaam Region, covering ART monitoring, documentation, turnaround time, and factors affecting implementation. Methods: A cross-sectional study was conducted in 2021 across 15 public health facilities with CTC clinics in all five Dar es Salaam districts. A total of 330 PLHIV on ART for more than six months were selected through systematic random sampling with proportional to size allocation, and 45 healthcare providers through convenient sampling. Data were collected via abstraction forms and self-administered questionnaires, and analysed using SPSS Version 23 with descriptive statistics, bivariate analysis, and binary logistic regression. Results: Only 25.1% of patients had their first HVL sample taken at six months as per guideline, with 68.8% delayed beyond six months. Second and third samples were similarly delayed. MoHCDGEC sample tracking forms were absent in 96.7% of facilities and incomplete in 99.1%, and no facility captured specimen acceptance or rejection as site feedback. Turnaround time exceeded the 14-day guideline threshold in 64.5%, 66.7%, and 69.4% of first, second, and third results respectively. Patient negligence (AOR=9.84; 95% CI: 1.83-52.77) and storage (AOR=5.72; 95% CI: 0.94-35.0) were independently associated with guideline adherence. Conclusion: Adherence to the National HVL Testing Guideline in Dar es Salaam is suboptimal across testing timelines, documentation, and turnaround time, with patient negligence and storage capacity as significant determinants. Targeted interventions are needed to strengthen patient education, improve storage infrastructure, enhance documentation systems, and support providers in adhering to guideline-specified timelines.

BackgroundDelayed tuberculosis (TB) treatment remains a major challenge to TB control and is associated with increased mortality, drug resistance, and onward transmission. Food insecurity may contribute to delayed TB treatment through economic, physical, and psychosocial pathways. Depression and anxiety are also associated with delayed TB treatment and may mediate the relationship between food insecurity and delayed TB treatment. This study examined the association between food insecurity and delayed TB treatment initiation and assessed the mediation roles of depression and anxiety for this relationship among people newly diagnosed with TB. MethodsWe recruited 180 participants newly diagnosed with TB in Gaborone, Botswana. Food insecurity, depression, and anxiety were measured using the Household Food Insecurity Access Scale, PHQ-9, and Zung Self-Rating Anxiety Scale, respectively. Delayed TB treatment was defined as > 2 months since first TB symptoms. Logistic regression was used to examine the association between food insecurity and delayed TB treatment. Causal mediation analysis was conducted to assess the mediating roles of depression and anxiety. ResultsAmong the 180 participants, 45 (25%) experienced delayed TB treatment initiation. Participants with delayed TB treatment had slightly higher median scores for food insecurity (2 vs. 1, p = 0.11), depression (9 vs. 6, p = 0.001), and anxiety (37 vs. 34, p = 0.05). There was insufficient evidence of an overall association between food insecurity and delayed TB treatment initiation (OR = 1.04, 95% CI 0.98-1.11, p = 0.20). Mediation analysis found insufficient evidence of total and direct effects through depression and anxiety. However, there was evidence of significant indirect effect through depression (OR = 1.04, 95% CI 1.01-1.08, p < 0.001) and a borderline indirect effect through anxiety (OR = 1.02, 95% CI 1.00-1.04, p = 0.05). ConclusionMediation analysis revealed associations between food insecurity and delayed TB treatment initiation mediated by depression and anxiety which were not evident in total effects analysis. These findings highlight the importance of considering both socioeconomic and psychological factors in addressing delayed TB treatment. Further studies are needed to confirm these pathways.

BackgroundDigital health plays an indispensable role in facilitating data analysis and use for enhancing healthcare delivery across health settings. However, there is scant information on the extent to which digital health influences the improvement of primary health services delivery through data use. This study examined the determinants that influence the use of digital health to improve health service delivery in council hospitals in Tanzania. MethodsA cross-sectional design was employed in six regions, involving 12 council hospitals. We used a self-administered questionnaire to collect data from 203 members of hospital quality improvement teams. Descriptive analysis was used to determine the frequency, proportion, and mean of responses, while bootstrapping analysis was conducted to test the statistically significant influence of digital health factors on data use for improving health service delivery. ResultsResults show moderate agreement on data compatibility for planning and decision-making, with 40.4% of respondents agreeing it supports ordering commodities, 43.8% for staff allocation, and 38.4% for planning. However, dissatisfaction was higher for user-friendliness (47.8%), reliability (up to 65.5%), and usefulness (up to 63.5%). Overall, 50.2% (M=2.74{+/-}0.87) disagreed that digital systems effectively support data use. Structural model analysis confirmed significant positive influence of usefulness ({beta}=0.199, p<0.001) and access to quality data ({beta}=0.729, p<0.001) on data use, which strongly impacted service delivery ({beta}=0.593, p<0.001), despite some factors showing no direct influence. ConclusionThe study finds that current digital health initiatives only modestly improve the user-friendliness, reliability, and usefulness of data systems, partly due to fragmented, non-interoperable platforms that burden data management. However, compatibility, usability, reliability, and usefulness of digital tools significantly enhance access to quality data and data-driven decisions. The study recommends strengthening and integrating existing systems and providing continuous digital health training to institutionalize data-informed decision-making.

BackgroundThe World Health Organization has developed several global template protocols for epidemiological investigations, including for household transmission investigations (HHTIs). These investigations facilitate rapid characterisation of novel or re-emerging respiratory pathogens and support evidence-based public health actions. Beyond technical readiness, community buy-in is central to the feasibility and acceptability of HHTIs. Research is needed to determine the perceived legitimacy among the community to inform local protocol adaptation and development of implementation plans that consider community attitudes and needs. MethodsIn 2025, we conducted a convenience survey of community members living in Victoria, Australia to explore: their understanding of emerging respiratory diseases; their willingness to take part in public health surveillance activities such as HHTIs; the acceptability of clinical and epidemiological data collection and respiratory/blood sample collection as main components of HHTIs, and; participant comfort towards including their companion animals in HHTIs. ResultsWe received 282 survey responses, of which 235 were included in the analysis dataset. Compared to the general Victorian population, our participants included a higher proportion of participants who reported being female, tertiary-educated, of Aboriginal and/or Torres Strait Islander heritage, born in Australia and speaking only English at home. Participants indicated overall high levels of comfort and acceptability towards participation in HHTIs, particularly in relation to clinical and epidemiological data collection, with lesser but still high levels of comfort with providing multiple respiratory specimens in a 14-day period. Participants were least comfortable with other specimens such as urine and blood. Involving companion animals in HHTIs was similarly acceptable as human-focused components. ConclusionsDespite our survey population being non-representative of the general Victorian population, our findings provide valuable descriptive insights into the acceptability of HHTIs in Victoria, Australia from which to benchmark future local and international surveys and community engagement activities.

Misinformation circulating through digital platforms and community networks increasingly challenges public health communication, particularly in low- and middle-income countries. Frontline health educators play a critical role in addressing misinformation and promoting accurate health information within primary health care systems; however, empirical evidence on their preparedness to manage infodemics remains limited. This study assessed the training needs and response capacity of primary health care health educators in Lagos State, Nigeria. A convergent mixed-methods design was employed across three districts. Quantitative data were collected from 95 health educators using the 30-item Health Educators Infodemic Management Training Needs Assessment Questionnaire (HEIM-TNAQ). Qualitative data were obtained through six focus group discussions involving 56 educators and 25 key informant interviews with supervisors and programme managers. Quantitative data were analysed using descriptive statistics and t-tests, while qualitative data were analysed thematically. Participants demonstrated relatively strong knowledge of health misinformation (mean = 71.5), but only moderate decision-response skills (48.6) and low confidence in addressing misinformation (42.5). Integration of misinformation response into routine practice was also limited (46.3), and no significant differences were observed between respondents with or without prior training. Qualitative findings revealed frequent exposure to vaccine rumours, spiritual explanations for illness, and misinformation circulating through social media and community networks. Strengthening infodemic management within primary health care requires practical training, behavioural communication skills, and institutional mechanisms for systematic rumour monitoring and response.

Background: Continuous adhesive patch electrocardiographic (ECG) wearables are increasingly prescribed. Patient experience with these devices can influence adherence, but research in this area is limited. This study aimed to explore the perceptions and experiences of patients receiving wearable cardiac monitoring technology as part of their routine care through the lens of treatment burden. Methods: This was a qualitative study with semi-structured phone interviews conducted between February and May 2024. We recruited participants from primary care and outpatient clinics using maximum variation sampling to ensure diversity in sex, ethnicity, and education levels. Interviews were audio-recorded, transcribed, and analysed using reflexive thematic analysis. Results: Sixteen participants (mean age 51 years, 63% female) were interviewed (average duration: 33 minutes). Three themes were developed: 1) ?Experience using the device: Burden vs Ease of Use?, which captured participants? perceptions of how easily they could integrate the device in their daily lives; 2) ?Individual variability in responses to ECG self-monitoring? covered participants? emotional and cognitive response to knowing their heart rhythm was monitored; and 3) ?The care process shapes patient experiences? reflected support preferences during the set-up and monitoring period and the uncertainty regarding timely clinical and device feedback. Conclusions: Patients valued cardiac wearables for facilitating diagnosis and felt reassured knowing they were clinically monitored. However, gaps in information provided to patients seemed to cause anxiety for some participants. These concerns could be mitigated through clearer clinician communication and patient education at the time of prescription.

Automating Hierarchical Condition Category (HCC) assignment directly from unstructured electronic health record (EHR) notes remains an important but understudied problem in clinical informatics. We present HCC-Coder, an end to end NLP system that maps narrative documentation to 115 Centers for Medicare & Medicaid Services(CMS) HCC codes in a multi-label setting. On the test dataset, HCC-Coder achieves a macro-F1 of 0.779 and a micro-F1 of 0.756, with a macro-sensitivity of 0.819 and macro-specificity of 0.998. By contrast, Generative Pre-trained Transformer (GPT)-4o achieves highest score of a macro-F1 of 0.735 and a micro-F1 of 0.708 under five-shot prompting. The fine-tuned model demonstrates consistent absolute improvements of 4%-5% in F1-scores over GPT-4o. To address severe label imbalance, we incorporate inverse-frequency weighting and per-label threshold calibration. These findings suggest that domain-adapted transformers provide more balanced and reliable performance than prompt-based large language models for hierarchical clinical coding and risk adjustment.

Standard in vitro antimicrobial susceptibility testing (AST) using Mueller-Hinton broth (MHB) does not reflect infection-site conditions, and its results often do not correlate with therapeutic outcomes. Here, we compared the antibiotic susceptibility of methicillin-resistant Staphylococcus aureus (MRSA), a common chronic wound pathogen, in simulated wound fluid (SWF) resembling wound exudate versus MHB, revealing discordant AST results across six of nine tested antibiotic classes. The most significant were 128-fold increased resistance to tetracyclines and 256-fold sensitization to {beta}-lactams in SWF. Tetracycline resistance was mediated by MntC, an extracellular manganese-binding protein, whereas {beta}-lactam sensitization was driven by cell envelope remodelling in SWF. Galleria mellonella wound infection results matched the SWF susceptibility phenotypes, suggesting SWF better predicts in vivo wound infection therapeutic outcomes. These comprehensive phenotypic and mechanistic insights into MRSA antibiotic responses under wound-infection-mimetic conditions with direct in vivo validation identify a potential new antibiotic adjuvant target and may guide improved antibiotic therapy for MRSA wound infections.

Safety net healthcare delivery organizations (SNOs) serve vulnerable populations but face persistent challenges in adopting new technologies, including AI. While systematic barriers to technology adoption in SNOs are well documented, little is known about how AI is implemented in these settings. This study explored real-world AI adoption in SNOs, focusing on identifying barriers encountered across the AI lifecycle and strategies used to overcome them. Five SNOs in the U.S. participated in a 12-month technical assistance program, the Practice Network, to implement AI tools of their choosing. Observed barriers and mitigation strategies were documented throughout program activities and, at the conclusion of the program, reviewed and refined with participants using a participatory research approach to ensure findings reflected lived experiences and organizational contexts. Key barriers emerged during the Integration and Lifecycle Management phases and included gaps in AI performance evaluation and impact assessments, communication with patients about AI use, foundational AI education, financial resources for purchasing and maintaining AI tools, and AI governance structures. Effective strategies for addressing these barriers were primarily supported through centralized expertise, structured guidance, and peer learning. These findings provide granular, actionable insights for SNO leaders, offering guidance for anticipating barriers and proactively planning mitigation strategies. By including SNO perspectives, the study also contributes to the broader health AI ecosystem and underscores the importance of participatory, collaborative approaches to support safe, effective, and ethical AI adoption in resource-constrained settings. Author SummarySafety net organizations (SNOs) are healthcare systems that primarily serve low-income and underinsured patients. While interest in artificial intelligence (AI) in healthcare has grown rapidly, little is known about how these organizations experience AI adoption in practice. In this study, we partnered with five SNOs over a 12-month program to document the challenges they encountered when implementing AI tools and the strategies they used to address them. We worked closely with SNO staff throughout the process to ensure our findings reflected their lived experiences with AI implementation. We found that the most common challenges arose when organizations tried to integrate AI into daily operations and monitor and maintain those tools over time. Specific barriers included difficulty evaluating whether AI was performing as expected, limited guidance on communicating with patients about AI use, a lack of resources for staff training, limited financial resources, and the absence of formal governance structures. Successful strategies for overcoming these challenges drew on shared knowledge and structured support provided by the program, as well as learning from peer organizations. These findings offer practical guidance for SNO leaders planning or managing AI adoption, and contribute to a broader conversation about what is required to implement AI safely and effectively in healthcare settings that serve the most medically and socially vulnerable patients.