Public Health Decision Research Articles

DEVELOPMENT OF A RAPID SNP PCR ASSAY TO DIFFERENTIATE BETWEEN EUROPEAN AND NORTH AMERICAN HAPLOTYPES OF ECHINOCOCCUS MULTILOCULARIS.

Herein we describe a single nucleotide polymorphism-specific polymerase chain reaction (PCR) assay to rapidly detect and differentiate variants belonging to the European and North American lineages of Echinococcus multilocularis in clinical samples. This is an extremely relevant and applicable test in North America because the range of E. multilocularis continues to expand across the continent and because of a rise in prevalence in wildlife, domestic animals, and humans. The endemic North American (NA) and introduced European (EU) variants are believed to have different pathogenic potentials, with the EU variants being more infective and pathogenic than the NA variants. The rise of the EU variants of E. multilocularis increases the risk of spillover from wildlife to humans because of its increased potential for infectivity. Current PCR-based diagnostics can detect E. multilocularis deoxyribonucleic acid (DNA), but DNA sequencing is required to identify the specific variant. Our assay provides a straightforward conventional PCR method to differentiate the NA and EU variants, and we suggest this same approach could be used for the diagnosis of other parasites or variants that are genetically very similar. As surveillance continues for E. multilocularis across North America, identifying the different genetic variants from different geographic regions will become essential to understanding the current epidemiological shift that the parasite is experiencing, as well as informing public health decisions in affected areas.

Adaptation of WHO COVID-19 guidelines by Caribbean countries and territories.

The normative role of the World Health Organization (WHO) involves creating evidence-based, principled guidelines to guide its Member States in making well-informed public health decisions. While these guidelines often need to be adapted to ensure contextual relevance, foster better implementation and adherence, adapting existing guidelines is more efficient than creating new ones. Here we describe the adaptation of the WHO coronavirus disease 2019 (COVID-19) living guideline on pharmacological interventions for the Caribbean using the grading of recommendations, assessment, development and evaluation (GRADE)-ADOLOPMENT method. The Caribbean Public Health Agency and the Pan American Health Organization led the effort, assembling a diverse panel of 16 experts from seven Caribbean countries and territories. The adaptation process, involving 15 steps, was guided by an experienced methodologist and included selecting relevant clinical questions and prioritizing them based on regional needs. The panel evaluated the latest WHO guidelines and integrated additional local data. They adjusted the direction and strength of several recommendations to better fit the Caribbean context, considering local values and preferences, resources, accessibility, feasibility and impact on health equity. Ultimately, we changed the direction of two recommendations and the strength of five, tailoring them to regional realities. This effort highlights the importance of adapting global guidelines to local settings, improving their applicability and effectiveness. The adaptation process also serves as a valuable opportunity for skill transfer and capacity-building in guideline development. Continued research is needed to assess the impact of these adaptations on health-care outcomes in the Caribbean.

Data-driven decision-making in public health: The role of advanced statistical models in epidemiology

This paper critically examines the transformative role of data-driven decision-making in public health, focusing on the integration of advanced statistical models in epidemiology. As the volume and complexity of health data increase, leveraging predictive analytics, machine learning, and real-time data integration has become essential for improving public health outcomes. The study explores how these technologies have shifted public health strategies from reactive to proactive approaches, particularly in areas such as disease surveillance, chronic disease management, and health equity. Through comprehensive analysis, the paper identifies key advancements, such as hybrid models that combine traditional epidemiological frameworks with AI, and the integration of multi-modal data sources that enhance predictive accuracy. The findings emphasize the potential of these models to optimize resource allocation, address health disparities, and provide timely interventions. However, challenges such as data quality, algorithmic bias, and the ethical implications of model transparency are highlighted as critical issues requiring ongoing research. The study concludes that for these models to be effectively adopted, there must be a balance between technological innovation and ethical considerations. Recommendations include the need for interdisciplinary collaboration, improved data governance frameworks, and the development of more inclusive models that are generalizable across diverse populations. This research underscores the necessity of combining robust analytical tools with ethical frameworks to enhance the reliability and equity of public health interventions.

Towards geospatially-resolved public-health surveillance via wastewater sequencing

Wastewater is a geospatially- and temporally-linked microbial fingerprint of a given population, making it a potentially valuable tool for tracking public health across locales and time. Here, we integrate targeted and bulk RNA sequencing (N = 2238 samples) to track the viral, bacterial, and functional content over geospatially distinct areas within Miami Dade County, USA, from 2020-2022. We used targeted amplicon sequencing to track diverse SARS-CoV-2 variants across space and time, and we found a tight correspondence with positive PCR tests from University students and Miami-Dade hospital patients. Additionally, in bulk metatranscriptomic data, we demonstrate that the bacterial content of different wastewater sampling locations serving small population sizes can be used to detect putative, host-derived microorganisms that themselves have known associations with human health and diet. We also detect multiple enteric pathogens (e.g., Norovirus) and characterize viral diversity across sites. Moreover, we observed an enrichment of antimicrobial resistance genes (ARGs) in hospital wastewater; antibiotic-specific ARGs correlated to total prescriptions of those same antibiotics (e.g Ampicillin, Gentamicin). Overall, this effort lays the groundwork for systematic characterization of wastewater that can potentially influence public health decision-making.

Public Health Decision-Making Using Uncertainty Displays

Public Health Decision-Making Using Uncertainty Displays

Risk-benefit assessment of foods and its role to inform policy decisions: outcome of an international workshop.

Policy decisions in public health require consideration and evaluation of trade-offs for which transparency and science-based evidence is needed. Improvement of decision-support tools is essential to help guide food policy decisions that promote healthy diets and meet the challenges of food systems without compromising food security, food safety, and sovereignty. Risk-benefit assessment of foods (RBA) is an established methodological approach designed to inform policy decisions within the area of nutrition and food safety. Despite methodological developments, translation of RBA findings into policies is still limited. In this context, a stakeholder workshop held in May 2023 gathered RBA experts and food regulators from Europe to identify the challenges, obstacles and opportunities in using evidence generated through RBAs to inform food policy decisions. A structured process was implemented to collect their views through online surveys, breakout groups, and plenary discussions. As a secondary objective, food regulators' views on other approaches for holistic risk assessment fit for food systems analysis were also explored. This paper summarizes the main findings of the workshop and discusses policy implications and future perspectives to improve the area of RBA and its role in food policymaking.

Bridging basic science and applied diagnostics: Comprehensive viral diagnostics enabled by graphene-based electronic biosensor technology advancements

This study presents a graphene field-effect transistor (gFET) biosensor with dual detection capabilities for SARS-CoV-2: one RNA detection assay to confirm viral positivity and the other for nucleocapsid (N-)protein detection as a proxy for infectiousness of the patient. This technology can be rapidly adapted to emerging infectious diseases, making an essential tool to contain future pandemics. To detect viral RNA, the highly conserved E-gene of the virus was targeted, allowing for the determination of SARS-CoV-2 presence or absence using nasopharyngeal swab samples. For N-protein detection, specific antibodies were used. Tested on 213 clinical nasopharyngeal samples, the gFET biosensor showed good correlation with RT-PCR cycle threshold values, proving its high sensitivity in detecting SARS-CoV-2 RNA. Specificity was confirmed using 21 pre-pandemic samples positive for other respiratory viruses. The gFET biosensor had a limit of detection (LOD) for N-protein of 0.9 pM, establishing a foundation for the development of a sensitive tool for monitoring active viral infection. Results of gFET based N-protein detection corresponded to the results of virus culture in all 16 available clinical samples and thus it also proved its capability to serve as a proxy for infectivity. Overall, these findings support the potential of the gFET biosensor as a point-of-care device for rapid diagnosis of SARS-CoV-2 infection and indirect assessment of infectiousness in patients, providing additional information for clinical and public health decision-making.

Training of Field-Workers for Rapid Assessment of Scabies Prevalence: A Diagnostic Accuracy Study in Mozambique.

Scabies is endemic in many resource-poor tropical areas, causing significant morbidity. However, our understanding of the true burden of scabies in Africa is limited, partly owing to limited capacity and challenges accessing the currently recommended diagnostic tools. The primary objective of this study was to assess the diagnostic accuracy of scabies assessments made by minimally trained field-workers. We trained field-workers with a minimum of secondary school education in the diagnosis of scabies. After the training, we assessed the diagnostic accuracy of assessments made by nine field-workers compared with the reference standard. In all, 193 individuals were assessed for scabies. The sensitivity, specificity, positive predictive value, and negative predictive value were calculated, as well as agreement (κ coefficients) between medical doctors and between field-workers. Of the 193 participants, 26% had scabies according to the reference standard. The sensitivity of field-worker diagnosis compared with the reference standard was 94% (95% CI: 90-99%), and the specificity was 96% (95% CI: 90-97%). The determination of severity by field-workers was less accurate; the sensitivity for severe scabies was 61% (95% CI: 48-74%), and the mean specificity was 97% (95% CI: 93-100%). This study demonstrated that field-workers without medical qualifications were capable of diagnosing scabies to a similar level of accuracy as experienced medical doctors after a short period of focal training. This may facilitate rapid assessments of scabies prevalence for public health purposes and decisions about mass drug administration implementation in similar settings.

Systematic literature review and meta-analysis on the prevalence of rotavirus genotypes in Europe and the Middle East in the post-licensure period

ABSTRACT Previous systematic literature reviews of rotavirus genotype circulation in Europe and the Middle East are limited because they do not include country-specific prevalence data. This study documents country-specific evidence on the prevalence of rotavirus genotypes in Europe and the Middle East to enable more precise epidemiological modeling and contribute to the evidence-base about circulating rotavirus genotypes in the post-vaccination era. This study systematically searched PubMed, Embase and Scopus for all empirical epidemiological studies that presented genotype-specific surveillance data for countries in Europe and the Middle East published between 2006 and 2021. The STROBE checklist was used to assess the quality of included studies. Proportional meta-analysis was conducted using the generic inverse variance method with arcsine transformation and generalized linear-mixed models to summarize genotype prevalence. Our analysis estimated the genotype prevalence by country across three date categories corresponding with rotavirus seasons: 2006–2010, 2011–2015, 2016–2021. A total of 7601 deduplicated papers were identified of which 88 studies were included in the final review. Rotavirus genotypes exhibited significant variability across regions and time periods, with G1P[8], G2P[4], G3P[8], G4P[8], G9P[8], and, to a lesser extent G12P[8], being the most prevalent genotypes through different regions and time-periods. Uncommon genotypes included G3P[9] in Poland, G2P[6] in Iraq, G4P[4] in Qatar, and G9P[4] as reported by the European Rotavirus Network. There was high genotype diversity with routinely identified genotypes being G1P[8], G2P[4], G3P[8], G4P[8], and G9P[8]; there was high variability across time periods and regions. Continued surveillance at the national and regional levels is relevant to support further research and inform public health decision-making.

Linkage of Hospital and Community level records for Epidemiological Research: Experiences from Kagando Health and Demographic Surveillance System in Western Uganda

BackgroundLinkage of census data and hospital records is crucial in conducting epidemiological research to inform public health decisions. In this study, we share our experiences in linking community-level data collected through census with hospital records and highlight the challenges and opportunities from our engagements with stakeholders. MethodologyWe undertook engagements with Uganda Christian University Ethics Research Committee, Hospital Management, and Community Health Volunteers to set up a platform to integrate hospital and census records using key identifiers. Trained data clerks prospectively linked data during patients’ visits following the data protection guidelines. ResultsFrom a population of approximately 30,000 people, we managed to link 1000 records within three months. Lessons learnt and opportunities: It is possible to link data with a set of identifiers, not only relying on common identifiers, especially for communities where ownership of a national identity card or birth certificate is not universal. Building stakeholder partnerships is essential, and frequent meetings with the hospital staff, epidemiologists and data programmers helped to identify problems and solutions. ChallengesLack of local capacity at the health facilities to develop linkage platforms. However, after development they can efficiently run the program. Frequent changes in the versions of the electronic system were later resolved. RecommendationsThere is a need to conduct regular capacity building for stakeholders to help them understand the importance of data linkage. ConclusionPublic engagement is an ethical imperative that enhances the quality, credibility, and long-term impact of research by incorporating the perspectives of the communities being studied.

AI Powered Bioinformatics - Expediting Diagnostic Testing: A Survey

Research has demonstrated the positive impact of artificial intelligence and Bioinformatics in the field of clinical diagnosis. The integration of AI methodologies into bioinformatics has opened new avenues for breakthroughs in genomics, proteomics, and personalized medicine. The document emphasizes the role of AI in early disease detection, improving patient outcomes, and enhancing healthcare systems by avoiding the need for expensive and time-consuming operations as illnesses worsen. The methodology section provides insights into the approach utilized, including the review of 30 articles from highly regarded journals about AI and bioinformatics that expedite diagnostic testing in the medical field. using survey to gather information and divide it into sub-sections focusing on diagnostic cancer diseases, COVID-19, and genetic and chronic diseases. The survey gathered 52 responses, and the results revealed significant agreements with the findings in the papers, particularly in the importance of developing novel biosensors and diagnostic tools for rapid and accessible detection of SARS-CoV-2, and the potential of AI in laboratory settings, pharmaceutical industry, and disease diagnosis. Overall, the document provides a comprehensive overview of the transformative role of AI in bioinformatics, emphasizing its potential to revolutionize disease diagnosis, treatment, and public health decision-making, while also addressing the challenges and opportunities associated with the integration of AI technologies in the healthcare industry. The rigorous methodology and alignment of survey results with the research findings validate the significance of AI-powered bioinformatics in expediting diagnostic testing and improving patient safety in healthcare.

Deep learning infused SIRVD model for COVID-19 prediction: XGBoost-SIRVD-LSTM approach.

The global impact of the ongoing COVID-19 pandemic, while somewhat contained, remains a critical challenge that has tested the resilience of humanity. Accurate and timely prediction of COVID-19 transmission dynamics and future trends is essential for informed decision-making in public health. Deep learning and mathematical models have emerged as promising tools, yet concerns regarding accuracy persist. This research suggests a novel model for forecasting the COVID-19's future trajectory. The model combines the benefits of machine learning models and mathematical models. The SIRVD model, a mathematical based model that depicts the reach of the infection via population, serves as basis for the proposed model. A deep prediction model for COVID-19 using XGBoost-SIRVD-LSTM is presented. The suggested approach combines Susceptible-Infected-Recovered-Vaccinated-Deceased (SIRVD), and a deep learning model, which includes Long Short-Term Memory (LSTM) and other prediction models, including feature selection using XGBoost method. The model keeps track of changes in each group's membership over time. To increase the SIRVD model's accuracy, machine learning is applied. The key properties for forecasting the spread of the infection are found using a method called feature selection. Then, in order to learn from these features and create predictions, a model involving deep learning is applied. The performance of the model proposed was assessed with prediction metrics such as R 2, root mean square error (RMSE), mean absolute percentage error (MAPE), and normalized root mean square error (NRMSE). The results are also validated to those of other prediction models. The empirical results show that the suggested model outperforms similar models. Findings suggest its potential as a valuable tool for pandemic management and public health decision-making.

Zero-covid advocacy during the COVID-19 pandemic: a case study of views on Twitter/X.

During the COVID-19 pandemic, many advocacy groups and individuals criticized governments on social media for doing either too much or too little to mitigate the pandemic. In this article, we review advocacy for COVID-19 elimination or "zero-covid" on the social media platform X (Twitter). We present a thematic analysis of tweets by 20 influential co-signatories of the World Health Network letter on ten themes, covering six topics of science and mitigation (zero-covid, epidemiological data on variants, long-term post-acute sequelae (Long COVID), vaccines, schools and children, views on monkeypox/Mpox) and four advocacy methods (personal advice and promoting remedies, use of anecdotes, criticism of other scientists, and ofauthorities). The advocacy, although timely and informative, often appealed to emotions and values using anecdotes and strong criticism of authorities and other scientists. Many tweets received hundreds or thousands of likes. Risks were emphasized about children's vulnerability, Long COVID, variant severity, and Mpox, and via comparisons with human immunodeficiency viruses (HIV). Far-reaching policies and promotion of remedies were advocated without systematic evidence review, or sometimes, core field expertise. We identified potential conflicts of interest connected to private companies. Our study documents a need for public health debates to be less polarizing and judgmental, and more factual. In order to protect public trust in science during a crisis, we suggest the development of mechanisms to ensure ethical guidelines for engagement in "science-based" advocacy, and consideration of cost-benefit analysis of recommendations for public health decision-making.

Development of public health decision-making based on semantic evaluation of EHR data

The study examines the way semantic analysis of data from electronic health records (EHRs) is performed. Implementing machine learning (ML) is challenging, EHRs are essential for acquiring medical data because they include textual information from physicians about patients' ailments and treatment plans, supporting well-informed public health decision-making. The goal of the study was to allow computers to semantically comprehend assessments, physical parts, indicators, and therapies in addition to implicitly identifying medical terms in EHRs. To efficiently identify medical phrases in electronic health records, a novel cubic support vector machine with a dipper-throated optimization (CSVM-DTO) strategy is suggested. This study uses tokenization as a pre-processing step for the data gathered from public sources. The semantic data of medical terminology is then extracted using the word-2-vector technique. Next, the named entities are found using the CSVM, and their efficiency is improved by implementing the DTO technique. Based on the experimental results, we can conclude that our suggested approach outperformed other current approaches in locating the identified entities inside the EHRs.

The resurgence of influenza A/H3N2 virus in Australia after the relaxation of COVID-19 restrictions during the 2022 season.

This study retrospectively analyzed the genetic characteristics of influenza A H3N2 (A/H3N2) viruses circulating in New South Wales (NSW), the Australian state with the highest number of influenza cases in 2022, and explored the phylodynamics of A/H3N2 transmission within Australia during this period. Sequencing was performed on 217 archived specimens, and A/H3N2 evolution and spread within Australia were analyzed using phylogenetic and phylodynamic methods. Hemagglutinin genes of all analyzed NSW viruses belonged to subclade 3C.2a1b.2a.2 and clustered together with the 2022 vaccine strain. Complete genome analysis of NSW viruses revealed highly frequent interclade reassortments between subclades 3C.2a1b.2a.2 and 3C.2a1b.1a. The estimated earliest introduction time of the dominant subgroup 3C.2a1b.2a.2a.1 in Australia was February 22, 2022 (95% highest posterior density: December 19, 2021-March 13, 2022), following the easing of Australian travel restrictions, suggesting a possible international source. Phylogeographic analysis revealed that Victoria drove the transmission of A/H3N2 viruses across the country during this season, while NSW did not have a dominant role in viral dissemination to other regions. This study highlights the importance of continuous surveillance and genomic characterization of influenza viruses in the postpandemic era, which can inform public health decision-making and enable early detection of novel strains with pandemic potential.

Clinical decision-making process and distributive justice: The mediating role of economic analysis. Empirical evidence from Italy.

The COVID-19 pandemic has not only tested the resilience of public health systems but also underscored the criticality of allocative choices on health resources. These choices, however, are not confined to health emergencies but are integral to public health decisions, which inherently grapple with limited resources. In this context, physicians play a pivotal role as the architects of clinical actions in various scenarios. Therefore, doctors are called upon to make their decisions by considering not only the criteria of clinical appropriateness but also the ethical aspects linked, in particular, to the principle of justice. Indeed, the assessment of the effectiveness of a treatment for a particular patient must be balanced against criteria of equity and justice for the whole. To be fully applied, the principle of justice presupposes the use of economic evaluation techniques designed to drive the organisation decisions by effectiveness and efficiency. The present paper aims to empirically analyse whether and to what extent economic evaluation is known and used by doctors in healthcare decision-making and, therefore, what the most widespread approaches are used in such processes. In particular, this paper intends to present the results of an empirical study on a sample of doctors registered with the Order of Physicians in Lombardy (Italy), one of the areas most affected by the COVID-19 pandemic. The research reveals a particular awareness of the criticality of allocation issues accompanied by a lack of knowledge of the economic evaluation techniques or, more broadly, by an almost total disuse of financial criteria. The main reasons are doctors' need for more knowledge of these tools and insufficient availability of economic information at the country system level. In the conclusion, we propose some suggestions to facilitate the transition to more current decision-making models consistent with the characteristics of more advanced national healthcare contexts.

The power of interactive maps for communicating spatio-temporal data to health professionals.

While more and more health-related data is being produced and published every day, few of it is being prepared in a way that would be beneficial for daily use outside the scientific realm. Interactive visualizations that can slice and condense enormous amounts of multi-dimensional data into easy-to-digest portions are a promising tool that has been under-utilized for health-related topics. Here we present two case studies for how interactive maps can be utilized to make raw health data accessible to different target audiences: i) the European Notifiable Diseases Interactive Geovisualization (ENDIG) which aims to communicate the implementation status of disease surveillance systems across the European Union to public health experts and decision makers, and ii) the Zoonotic Infection Risk in Twente-Achterhoek Map (ZIRTA map), which aims to communicate information about zoonotic diseases and their regional occurrence to general practitioners and other healthcare providers tasked with diagnosing infectious diseases on a daily basis. With these two examples, we demonstrate that relatively straight-forward interactive visualization approaches that are already widely used elsewhere can be of benefit for the realm of public health.

RT-qPCR Testing and Performance Metrics in the COVID-19 Era.

The COVID-19 pandemic highlighted the crucial role of diagnostic testing in managing infectious diseases, particularly through the use of reverse transcription-quantitative polymerase chain reaction (RT-qPCR) tests. RT-qPCR has been pivotal in detecting and quantifying viral RNA, enabling the identification and management of SARS-CoV-2 infections. However, despite its widespread use, there remains a notable gap in understanding fundamental diagnostic metrics such as sensitivity and specificity among many scientists and healthcare practitioners. This gap is not merely academic; it has profound implications for interpreting test results, making public health decisions, and affecting patient outcomes. This review aims to clarify the distinctions between laboratory- and field-based metrics in the context of RT-qPCR testing for SARS-CoV-2 and summarise the global efforts that led to the development and optimisation of these tests during the pandemic. It is intended to enhance the understanding of these fundamental concepts among scientists and healthcare professionals who may not be familiar with the nuances of diagnostic test evaluation. Such knowledge is crucial for accurately interpreting test results, making informed public health decisions, and ultimately managing infectious disease outbreaks more effectively.

School absence legislation governing in Norway, Sweden and Denmark for children with chronic illness in compulsory education-A comparative study.

Health and education are interrelated and influence social, economic and lifestyle perspectives. Children with chronic illnesses experience barriers in the educational system regarding school attendance and social isolation. Gaining knowledge of compulsory education and how children with chronic illnesses are supported is crucial for the implications of future education policy and legislation in Scandinavia. This study compares Scandinavian legislation frameworks on compulsory education, chronic illness and school absence to form the basis of future research on education for children with chronic illness. The study uses a comparative approach to explore the support of children with chronic illnesses in compulsory education across Norway, Sweden and Denmark. The documents included are 3 education acts and 15 secondary documents, which are notes and guidelines for the education acts. The data were analysed using a manifest content analysis. We found four categories and six subcategories: (1) school obligation and rights; (2) chronic illness; (3) school absence: (a) categorisation of absence; (b) registration of absence; and (c) sanction; and (4) education support: (a) Hospital school support; (b) Home instruction support; and (c) technological support. This study's findings demonstrate the similarities and differences in the Scandinavian compulsory education legislation and guidelines regarding chronic illness and school absence. We found similarities across the countries regarding chronic illness and school absence. Still, the findings showed differences in the systematic registration of school absence and requirements for attendance with compulsory education in Norway and Denmark compared with compulsory schooling in Sweden. This knowledge will inform and enlighten future discussions and decisions in education and public health. The results can contribute to awareness of the opportunities for educational support and perspectives about education for children with chronic illnesses. Future research focusing on the experience of children with chronic illness and educational support is needed.

Incorporating connectivity among Internet search data for enhanced influenza-like illness tracking.

Big data collected from the Internet possess great potential to reveal the ever-changing trends in society. In particular, accurate infectious disease tracking with Internet data has grown in popularity, providing invaluable information for public health decision makers and the general public. However, much of the complex connectivity among the Internet search data is not effectively addressed among existing disease tracking frameworks. To this end, we propose ARGO-C (Augmented Regression with Clustered GOogle data), an integrative, statistically principled approach that incorporates the clustering structure of Internet search data to enhance the accuracy and interpretability of disease tracking. Focusing on multi-resolution %ILI (influenza-like illness) tracking, we demonstrate the improved performance and robustness of ARGO-C over benchmark methods at various geographical resolutions. We also highlight the adaptability of ARGO-C to track various diseases in addition to influenza, and to track other social or economic trends.