The public health impact of making chloramphenicol an over-the-counter antibiotic: a systematic literature review

PURPOSE: Chloramphenicol is an over-the-counter ocular antibiotic in some countries (for example Australia since 2010 and the United Kingdom since 2005), but concerns regarding resistance and adverse effects have prevented other countries from doing the same. Therefore, this systematic review was conducted to answer the question of whether chloramphenicol is a safe and effective topical antibiotic for treating ocular infections, and whether it should be reclassified as an over-the-counter medication. METHODS: A search of the Medline and Embase databases was conducted to identify articles concerning the effectiveness of chloramphenicol in treating ocular infections, bacterial susceptibility towards chloramphenicol, the risk of adverse effects, and the results of reclassifying chloramphenicol as an over-the-counter antibiotic in the past. A total of 131 articles were evaluated for this systematic review. RESULTS: The literature did not support any concerns regarding side effects such as aplastic anaemia. Chloramphenicol was comparable in efficacy to other antibiotics in treating ocular infections, and resistance has remained low over the past decade, save for bacterial species with known intrinsic resistance such as Pseudomonas aeruginosa. Chloramphenicol use increased in the first few years following reclassification as an over-the-counter medication, but eventually plateaued. CONCLUSION: Chloramphenicol is a safe and effective topical antibiotic for treating ocular infections, and there is minimal concern of adverse effects such as aplastic anaemia resulting from topical use. Nonetheless, multiple clinical and socioeconomic factors should be factored in when considering reclassification, and contextual factors such as population density, eye care accessibility and health-seeking behaviours may influence the decision

Meeting Report on an Integrated Research Agenda for Mosquito-Borne Arboviruses.

The emergence and re-emergence of mosquito-borne arbovirus (MBV) diseases pose a rapidly expanding global health threat fueled by the convergence of multiple ecologic, economic, and social factors, including climate change, land use, poverty, deficiencies of water storage and sanitation, and limitations of vector control programs. On December 6, 2023, the Wellcome Trust and the University of Minnesota's Center for Infectious Disease Research and Policy held a meeting titled "An integrated approach to mosquito-borne arboviruses: a priority research agenda." The meeting comprised presentations, panels, and facilitated discussions aimed at describing the state of the field, highlighting recent accomplishments, identifying novel strategies, and defining priority research goals and approaches for addressing MBV disease preparedness and response. This report summarizes meeting discussions in 3 key areas: the changing epidemiology of MBV disease, current and potential transmission- and disease-monitoring strategies, and evolutionary impacts on disease burden and transmission. It concludes with a list of priority strategies for research and investment in MBV disease prevention, preparedness, and control. To prepare for future epidemics of MBV diseases, research and policy will benefit from a multipathogen approach to MBVs. Building on existing knowledge and systems, these efforts must address social and ecological factors and connect with other global health agendas

Harnessing artificial intelligence to address diseases attributable to unsafe drinking water: challenges, potentials, and recommendations

Unsafe drinking water is a global concern that poses serious risks to public health, especially in developing nations, where tainted water can spread diseases such as cholera, polio, and diarrhea, which can result in several health issues and deaths. Children and immunocompromised people are the most vulnerable groups that suffer disproportionately from waterborne illnesses. Promising approaches to reduce the burden of waterborne diseases and revolutionize drinking water management are provided by artificial intelligence (AI). Public health authorities and water industries can improve safe drinking water distribution, treatment, and monitoring using AI-powered models and approaches. AI enables predictive modeling to support sustainable water management techniques, maximize resource usage, and identify problems with infrastructure and water quality earlier. AI, coupled with Geographic Information Systems (GIS) and machine learning (ML) models such as random forest classifiers, aids in cholera risk prediction and enhances waterborne disease detection. Advanced AI models facilitate drought forecasting, reservoir optimization, and real-time water monitoring, improving water management and resource conservation. AI-driven systems, including predictive analytics and intelligent water distribution models, show potential for enhancing water safety, mitigating risks, and promoting sustainable water practices. However, several challenges must be overcome when incorporating AI into water management, such as concerns about data quality, infrastructure constraints, and ethical difficulties. Genetic sequencing and metagenomic analyses, which provide insights into microbial dynamics and water quality maintenance, are potential future areas in AI applications for water management. A balanced approach prioritizing equitable deployment, infrastructure readiness, workforce development, robust governance, collaborative efforts, ethical standards, and transparent regulatory frameworks, ensuring social equity and economic efficiency with current norms and policies, is required for AI integration to address diseases attributable to unsafe drinking water. These AI models are expedient to fully optimize WASH disease management to increase access to clean water, reduce the incidence of waterborne diseases, and advance global health

A Machine Learning Model Integrating Remote Sensing, Ground Station, and Geospatial Data to Predict Fine-Resolution Daily Air Temperature for Tuscany, Italy.

Heat-related morbidity and mortality are increasing due to climate change, emphasizing the need to identify vulnerable areas and people exposed to extreme temperatures. To improve heat stress impact assessment, we developed a replicable machine learning model that integrates remote sensing, ground station, and geospatial data to estimate daily air temperature at a spatial resolution of 100 m × 100 m across the region of Tuscany, Italy. Using a two-stage approach, we first imputed missing land surface temperature data from MODIS using gradient-boosted trees and spatio-temporal predictors. Then, we modeled daily maximum and minimum air temperatures by incorporating monitoring station observations, satellite-derived data (MODIS, Landsat 8), topography, land cover, meteorological variables (ERA5-land), and vegetation indices (NDVI). The model achieved high predictive accuracy, with R2 values of 0.95 for Tmax and 0.92 for Tmin, and root mean square errors (RMSE) of 1.95 °C and 1.96 °C, respectively. It effectively captured both temporal (R2: 0.95; 0.94) and spatial (R2: 0.92; 0.72) temperature variations, allowing for the creation of high-resolution maps. These results highlight the potential of integrating Earth Observation and machine learning to generate high-resolution temperature maps, offering valuable insights for urban planning, climate adaptation, and epidemiological studies on heat-related health effects

Impact of Viral Co-Detection on the Within-Host Viral Diversity of Influenza Patients.

Numerous studies have documented the evidence of virus-virus interactions at the population, host, and cellular levels. However, the impact of these interactions on the within-host diversity of influenza viral populations remains unexplored. Our study identified 13 respiratory viral pathogens from the nasopharyngeal swab samples (NPSs) of influenza-like-illness (ILI) patients during the 2012/13 influenza season using multiplex RT-PCR. Subsequent next-generation sequencing (NGS) of RT-PCR-confirmed influenza A infections revealed all samples as subtype A/H3N2. Out of the 2305 samples tested, 538 (23.3%) were positive for the influenza A virus (IAV), while rhinovirus (RV) and adenoviruses (Adv) were detected in 264 (11.5%) and 44 (1.9%) samples, respectively. Among these, the co-detection of more than one virus was observed in ninety-six samples, and five samples showed co-detections involving more than two viruses. The most frequent viral co-detection was IAV-RV, identified in 48 out of the 96 co-detection cases. Of the total samples, 150 were processed for whole-genome sequencing (WGS), and 132 met the criteria for intra-host single-nucleotide variant (iSNV) calling. Across the genome, 397 unique iSNVs were identified, with most samples containing fewer than five iSNVs at frequencies below 10%. Seven samples had no detectable iSNVs. Notably, the majority of iSNVs (86%) were unique and rarely shared across samples. We conducted a negative binomial regression analysis to examine factors associated with the number of iSNVs detected within hosts. Two age groups-elderly individuals (>64 years old) and school-aged children (6-18 years old)-were significantly associated with higher iSNV counts, with incidence rate ratios (IRR) of 1.80 (95% confidence interval [CI]: 1.09-3.06) and 1.38 (95% CI: 1.01-1.90), respectively. Our findings suggest a minor or negligible contribution of these viral co-detections to the evolution of influenza viruses. However, the data available in this study may not be exhaustive, warranting further, more in-depth investigations to conclusively determine the impact of virus-virus interactions on influenza virus genetic diversity

Primary prevention in hospitals in 20 high-income countries in Europe - A case of not "Making Every Contact Count"?

This article provides a snapshot of primary prevention activities in hospitals in 20 European high-income countries, based on inputs from experts of the Observatory's Health Systems and Policies Monitor (HSPM) network using a structured questionnaire. We found that in the vast majority of countries (15), there are no systematic national policies on primary prevention in hospitals. Five countries (Cyprus, Finland, Ireland, Romania and the United Kingdom) reported systematic primary prevention activities in hospitals, although in one of them (Cyprus) this was due to the fact that small hospitals in rural areas or less populated districts host providers of primary care. In two of the five countries with systematic national policies on primary prevention, there are no incentives (financial or otherwise) to provide these interventions. The remaining three countries (Finland, Romania and the United Kingdom) report the existence of incentives, but only two of them (Romania and the United Kingdom) provide financial incentives in the form of additional funding. Only two of the 20 countries (Ireland and the United Kingdom) make explicit use of the Making Every Contact Count (MECC) approach. Overall, it can be concluded that there is little focus on primary prevention in hospitals in Europe, which may be seen as a missed opportunity

Inclusive policy development from the ground up: Insights from the household water-energy-food nexus

Despite substantial contemporary research and a growing trend in exploring the water-energy-food (WEF) nexus, most research efforts have been invested in macro-level supply-side infrastructure and policies. However, prioritizing demand-side management policies can provide new opportunities and untapped potential for addressing interconnected resource challenges. Demand management inherently encompasses users’ consumption patterns, behaviors, socio-economic conditions, and choices, thereby necessitating active engagement and participation. Understanding household-level demands is fundamental to assess the demand for and consumption of water, energy, and food, as well as to inform policy decisions. In this context, our study investigated household consumption patterns within the interconnected WEF nexus, including daily practices such as cooking and washing, conservation measures, household governance, and their cross-cutting relationships with climate change. As a case study, we conducted our research in the Jabal Al Natheef neighborhood of Amman City, Jordan. Our findings reveal that households can propose and enact climate-friendly decisions. Significant gender-related differences were also observed in decisions made across WEF household practices. Additionally, households’ perspectives highlighted governance issues and revealed gaps in policy implementation along with the need for more inclusive decision-making processes. Our results underscore the importance of understanding household-level WEF nexus dynamics and daily practices in informing environmental policies, particularly those related to climate action. Such policies are best developed from the bottom-up by incorporating household insights, rather than relying solely on top-down, one-size-fits-all solutions

A new method for detecting mixed Mycobacterium tuberculosis infection and reconstructing constituent strains provides insights into transmission.

BACKGROUND: Mixed infection with multiple strains of the same pathogen in a single host can present clinical and analytical challenges. Whole genome sequence (WGS) data can identify signals of multiple strains in samples, though the precision of previous methods can be improved. Here, we present MixInfect2, a new tool to accurately detect mixed samples from Mycobacterium tuberculosis short-read WGS data. We then evaluate three approaches for reconstructing the underlying mixed constituent strain sequences. This allows these samples to be included in downstream analysis to gain insights into the epidemiology and transmission of mixed infections. METHODS: We employed a Gaussian mixture model to cluster allele frequencies at mixed sites (hSNPs) in each sample to identify signals of multiple strains. Building upon our previous tool, MixInfect, we increased the accuracy of classifying in vitro mixed samples through multiple improvements to the bioinformatic pipeline. Major and minor proportion constituent strains were reconstructed using three approaches and assessed by comparing the estimated sequence to the known constituent strain sequence. Lastly, mixed infections in a real-world Mycobacterium tuberculosis population from Moldova were detected with MixInfect2 and clusters of recent transmission that included major and minor constituent strains were built. RESULTS: All 36/36 in vitro mixed and 12/12 non-mixed samples were correctly classified with MixInfect2, and major strain proportions were estimated with high accuracy (within 3% of the true strain proportion), outperforming previous tools. Reconstructed major strain sequences closely matched the true constituent sequence by taking the allele at the highest frequency at hSNPs, while the best-performing approach to reconstruct the minor proportion strain sequence was identifying the closest non-mixed isolate in the same population, though no approach was effective when the minor strain proportion was at 5%. Finally, fewer mixed infections were identified in Moldova than previous estimates (6.6% vs 17.4%) and we found multiple instances where the constituent strains of mixed samples were present in transmission clusters. CONCLUSIONS: MixInfect2 accurately detects samples with evidence of mixed infection from short-read WGS data and provides an excellent estimate of the mixture proportions. While there are limitations in reconstructing the constituent strain sequences of mixed samples, we present recommendations for the best approach to include these isolates in further analyses

Disability-inclusive graduation programme intervention on social participation among ultra-poor people with disability in North Uganda: a cluster randomized trial.

BACKGROUND: People with disabilities encounter significant barriers to social participation due to inaccessible environments and negative attitudes. This study evaluated the effectiveness of a comprehensive disability-inclusive graduation (DIG) programme in enhancing social participation among ultra-poor people with disabilities in rural Uganda. METHODS: A two-arm, cluster-randomized controlled trial was conducted in four Northern Ugandan districts, involving 96 intervention and 89 control clusters. The DIG intervention encompassed four pillars: Livelihoods, Social Protection, Financial Inclusion, and Social Empowerment. The study identified households with disabilities using the Washington Group Short Set questions, verified by BRAC programme managers, comprising 370 working-age people with disabilities in the intervention group and 321 in the control group at baseline. Treatment clusters received an 18-month DIG intervention from December 2020 to June 2022. Social participation was measured using the SINTEF Participation Question Set at baseline, immediately post-intervention, and 16 months post-intervention, covering household and societal participation domains. Intervention effects were analyzed through linear mixed-effects regression models, reporting minimally adjusted and fully adjusted mean differences (MAMD and FAMD) with 95% confidence intervals. RESULTS: Immediately after the intervention, the DIG programme showed a positive trend in overall social participation (3.04 point increase in intervention group vs. - 0.29 in control), though not statistically significant in fully adjusted analysis (FAMD = 3.14, 95% CI = (- 1.26, 7.54); p = 0.17), possibly due to sample size limitations and variability in individual responses. A larger improvement in societal participation was observed favouring the intervention group (5.92 point increase versus 0.21 in control), with the fully adjusted analysis approaching statistical significance (FAMD = 5.84, 95% CI = (- 0.01, 11.69); p = 0.05). No significant differences were found in the domain of household participation. At 16 months post-intervention, no significant differences were observed between the intervention and control groups in overall scores or any subdomain, suggesting challenges in maintaining initial improvements over time. CONCLUSIONS: The DIG programme showed short-term positive effects on social participation among ultra-poor people with disabilities, especially in societal engagement. The absence of long-term sustained improvements underscores the complexity of disability inclusion in resource-constrained settings. Future interventions should develop strategies like extended support or booster sessions to maintain initial gains. TRIAL REGISTRATION: Registry for International Development Impact Evaluations (RIDIE-STUDY-ID-626008898983a) and ISRCTN (ISRCTN-78592382)

Stakeholder Interviews to Inform Best Practice for Public Facing COVID-19 Wastewater Dashboards.

BACKGROUND: Wastewater (WW) -based epidemiology is the detection of pathogens and chemicals from wastewater, typically sewage systems. Its use gained popularity during the COVID-19 pandemic as a rapid and non-invasive way to assess infection prevalence in a population. Public facing dashboards for SARS-CoV-2 were developed in response to the discovery that RNA biomarkers were being shed in faeces before symptoms. However, there is not a standard template or guidance for countries to follow. The aim of this research is to reflect on how currently available dashboards evolved during the pandemic and identify suitable content and rationale from these experiences. METHODS: Interviews were carried out with implementers and users of dashboards for SARS-CoV-2 WW data across Europe and North America. The interviews addressed commonalities and inconsistencies in displaying epidemiological data of SARS-CoV-2, clinical parameters of COVID-19, data on variants, and data transparency. RESULTS: The thematic analysis identified WW dashboard elements that can facilitate standardization, or at least interoperability. These elements emphasise communication among developers under the same organization, open access for identified stakeholders, and data summarized with a time-intensive graphic analysis through normalizing at least by population. Simultaneous communication of clinical surveillance is recommended. More research is needed on flow and faecal indicators for normalization of WW data, and on the analysis and representation of variants. DISCUSSION: WW dashboard development between 2020-2023 provided a 'real-time' iterative process of data representation, and several recommendations have been identified. Communication of data through dashboards has the potential to support early warning systems for infectious diseases