Public Decision Research Articles

Development of a unit conversion tool for five quantitative anti-spike assays and agreement analysis of three qualitative anti-nucleocapsid assays for SARS-CoV-2.

Commercially available assays for measuring severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) anti-spike (S) or anti-nucleocapsid (N) antibodies differ in units, making results comparisons challenging. This study aimed to develop conversion equations between five quantitative anti-S antibody tests and to assess the agreement over time between three qualitative anti-N antibody tests. Blood samples from 24 216 vaccinated healthcare workers in Hiroshima Prefecture, Japan, were analyzed for anti-S antibodies using five quantitative tests (Abbott, Fujirebio, Ortho, Sysmex, Roche) and for anti-N antibodies using three qualitative tests (Abbott, Sysmex, Roche). Geometric mean regression was performed to establish equations for converting measured values between the five quantitative tests. Fleiss κ statistic was used to assess the agreement between the three qualitative tests. A strong correlation (Pearson's coefficient r > 0.9) was found for each pair of the five quantitative tests measuring anti-S antibodies, enabling the development of equations to convert values between each pair. Using these equations, which are based on the original output unit of each test, values obtained from one test can be transformed to be equivalent to the corresponding values in another test. For the three tests for anti-N antibodies, the agreement was substantial in the total sample (Fleiss' κ, 0.74) and moderate among those with self-reported past coronavirus disease 2019 (COVID-19) infection (Fleiss' κ, 0.39). The agreement decreased with time after infection. Reduced agreement between anti-N antibodies tests over time suggests caution in comparing seroepidemiological studies of COVID-19 exposure based on anti-N antibodies measurement. The findings could help improve antibody measurement systems and inform public health decision-makers.

“It’s changing our lives, not for the better. It’s important that we have a say”. The role of young people in informing public health and policy decisions about gambling marketing

BackgroundMarketing has a significant impact on the normalisation of gambling for youth across the globe. This has included shaping positive attitudes towards gambling, as well as increasing the social and cultural acceptance of gambling – particularly aligned with valued activities such as sport. Because of this, public health experts argue that gambling marketing poses a significant risk to the health and wellbeing of youth. While young people are increasingly exposed to, and impacted by marketing for gambling products, they are rarely consulted about policy issues and options. This study aimed to explore young Australians’ perceptions of current policy responses to gambling advertising, whether they thought young people should be involved in discussions and decisions about gambling marketing regulations, and their perceptions of the duty of governments to protect young people from gambling industry marketing strategies.MethodsQualitative focus groups (n = 22) were held with n = 64, 12–17 year olds in the Australian states of Victoria and New South Wales. Participants were asked to reflect on current gambling policies, particularly relating to marketing, what they thought should be done about gambling marketing, and if and how young people should be included in public health responses to gambling. An interpretivist ‘Big Q’ approach to reflexive thematic analysis was used.ResultsYoung people highlighted the need for more effective regulations around the content and frequency of gambling marketing. They also wanted to see more realistic representations of the negative impacts of gambling to counter persistent positive commercial marketing messages. Most thought that young people should be given an opportunity to have a say about responses to gambling due to their unique experiences. Participants identified mechanisms to increase young people’s engagement in decision making, such as direct lines of communication to different levels of government, involvement in research, and diversifying ways of engagement. Specific recommendations included more regulatory action such as bans on gambling advertising.ConclusionsCreating formal structures that facilitate the inclusion of young people’s perspectives in decisions made about gambling can result in more innovative and effective strategies to prevent the harms from gambling industry products, promotions, and practices.

Second-level agenda-setting effects of news media and public policy on social media discourse across platforms: immigration during the COVID-19 pandemic in the U.S.

ABSTRACT Integrating intermedia, second-level agenda-setting, and cross-platform research, this study examines the role of news media attention and public policy decisions in driving the salience of issue attributes on multiple social media platforms. We collected three immigration-related datasets (i.e., social media posts, news media headlines, and policy documents covering the first year of the COVID-19 pandemic that also marked the last year of the Trump presidency) and applied manual coding, automated text analysis, and time series modeling to analyze the relationships. The results show that news media set the public agenda across Facebook, Twitter, and YouTube at the second level, with conservative media elevating travel-related discussion and liberal media driving border- and court-rulings-related discussion on those platforms. Compared to news media, the second-level agenda-setting effect of policy announcements was small and platform-specific. Among the three platforms, public discourse on YouTube was the most responsive to media and policy agendas. These findings advance second-level agenda-setting research, demonstrate partisan differences in news media’s impact, reveal the limited role of public policy, and provide a nuanced portrayal of cross-platform public discourse dynamics.

Integrating Genomic, Climatic, and Immunological Factors to Analyze Seasonal Patterns of Influenza Variants

Influenza, often referred to as the flu, is an extremely contagious respiratory illness caused by influenza viruses, impacting populations globally with significant health consequences annually. A hallmark of influenza is its seasonal patterns, influenced by a mix of geographic, evolutionary, immunological, and environmental factors. Understanding these seasonal trends is crucial for informing public health decisions, including the planning of vaccination campaigns and their formulation. In our study, we introduce a genotype-structured infectious disease model for influenza transmission, immunity, and evolution. In this model, the population of infected individuals is structured according to the virus they harbor. It considers a symmetrical fitness landscape where the influenza A and B variants are considered. The model incorporates the effects of population immunity, climate, and epidemic heterogeneity, which makes it suitable for investigating influenza seasonal dynamics. We parameterize the model to the genomic surveillance data of flu in the US and use numerical simulations to elucidate the scenarios that result in the alternating or consecutive prevalence of flu variants. We show that the speed of virus evolution determines the alternation and co-circulation patterns of seasonal influenza. Our simulations indicate that slow immune waning reduces how often variants change, while cross-immunity regulates the co-circulation of variants. The framework can be used to predict the composition of future influenza outbreaks and guide the development of cocktail vaccines and antivirals that mitigate influenza in both the short and long term.

NIH funding and journal selection: trends in dermatology publication models across tiers from 2021 to 2023.

This study examines the influence of National Institutes of Health (NIH) funding on the publication choices of dermatologists, particularly in terms of journal tiers and pay-to-publish (P2P) versus free-to-publish (F2P) models. Utilizing k-means clustering for journal ranking based on SCImago Journal Rank, h-index, and Impact Factor, journals were categorized into three tiers and 54,530 dermatology publications from 2021 to 2023 were analyzed. Authors were classified as Top NIH Funded or Non-Top NIH Funded according to Blue Ridge Institute for Medical Research rankings. The study finds significant differences in publication patterns, with Top NIH Funded researchers in Tier I journals demonstrating a balanced use of P2P and F2P models, while they preferred F2P models in Tier II and III journals. Non-Top NIH Funded authors, however, opted for P2P models more frequently across all tiers. These data suggest NIH funding allows researchers greater flexibility to publish in higher-tier journals despite publication fees, while prioritizing F2P models in lower-tier journals. Such a pattern indicates that funding status plays a critical role in strategic publication decisions, potentially impacting research visibility and subsequent funding. The study's dermatology focus limits broader applicability, warranting further research to explore additional factors like geographic location, author gender, and research design.

City Limits: Urban Nature Rules and the Role of Civic Engagement in Minneapolis—St. Paul Metro Area, USA

Formal rules define urban governance, yet in democratic systems like the United States, institutions governing public input shape decision-making. Governance literature needs more breadth on how civic engagement varies across types of nature and its implications for urban social-ecological systems. We analyzed five cities’ ordinances within the Minneapolis-St. Paul metropolitan region, identifying nature-based rules and civic engagement mechanisms. We found ten engagement mechanisms across six nature types, primarily discussed within zoning ordinances—farm animals being the exception. Public hearings were the most frequently mentioned engagement mechanism, yet they have documented biases toward elite perspectives. Despite codification of engagement opportunities, it remains unclear whether utilized mechanisms support substantive input and influence into urban nature governance. Our study not only lays groundwork for research on how to address the inequitable distribution of nature-related benefits and burdens felt by some urban communities, but also inspires further exploration into this crucial area of study.

Cost-effectiveness of robot-assisted total hysterectomy for benign pathologies compared to laparoscopic surgery: A retrospective study with propensity score

BackgroundHysterectomy for benign pathologies is one of the most common gynecological surgeries. In recent years, robotic surgery has become an alternative to traditional surgery, but at a higher cost. ObjectiveEstimate the cost of benign robot-assisted hysterectomy for the purpose of supporting public decision-making, as well as the additional cost per major postoperative complication (ClavienDindo score ≥ 3) avoided one month after surgery robotic versus traditional laparoscopic. MethodsSingle-center retrospective study including patients operated on for benign hysterectomy at La Pitié Salpêtrière hospital between January 2016 and December 2019: 99 by robotic approach, and 86 by laparoscopic approach. Comparison of robotic surgery to laparoscopy. Calculation of a cost-effectiveness ratio (ICER). Use of the propensity score inverse weighting method to ensure comparability of groups. ResultsRobotic surgery has a total cost of € 6,615 at 1 month per patient compared to € 3,859 for laparoscopic surgery with an additional cost of € 377,534 per major postoperative complication avoided, longer operating time and an absence of significant difference in terms of complications and length of hospitalization. ConclusionIn terms of cost-effectiveness, according to this study, the robot does not appear to be better than laparoscopy. In the years to come, we can expect a development of robotic surgery with rationalization of the practice, with appropriate selection of patients for robotic surgery, development of outpatient surgery and a reduction in the cost of the equipment.

Paradoxical founders’ identity and its impact on social venture performance

Purpose This study explores the structure of founders’ hybrid personal identities and their relationship to social venture performance. The authors hypothesize that founders experience the tension between the social and commercial goals of their venture as a paradox rooted in their personal values. Design/methodology/approach The authors surveyed 112 social enterprise founders in Chile and used structural equation modeling to test hypotheses about the relationship between self-transcendent values (i.e. promotion of others’ welfare and care for nature) and self-enhancement values (i.e. pursuing own interests and power over others) on a multidimensional scale of social venture performance. Findings Self-transcendence and self-enhancement are distinct yet interrelated values that coexist within social venture founders (i.e. they constitute a paradox). Self-transcendence values negatively moderate the positive relationship between self-enhancement values and social venture performance. Practical implications Mere benevolence is insufficient for effective social venturing; success depends on the founder’s self-enhancement values or their “drive” to succeed. Founder values can thus inform organizational design choices (e.g. tasks, team composition, structures and processes) and guide public and private investment decisions. Originality/value This research empirically assesses the structure of hybrid personal identities, uncovering how the concurrent action of self-transcendence and self-enhancement values influences social venture performance. The authors challenge the belief that balancing social and commercial goals benefits social venture performance. Best-performing ventures are those whose founder’s exhibit high self-enhancement and low self-transcendence values.

Antimicrobial Resistance in the WHO African Region: A Systematic Literature Review 2016-2020.

Antimicrobial resistance (AMR) is a significant global public health threat. This review presents the most recent in-depth review of the situation of the main AMR types in relation to the most commonly prescribed antibiotics in the World Health Organization (WHO) African Region. Underlying genes of resistance have been analyzed where possible. A search to capture published research data on AMR from articles published between 2016 and 2020 was done using PubMed and Google Scholar, with rigorous inclusion/exclusion criteria. Out of 48003 articles, only 167 were included. Among the tested gram-negative bacteria species, Klebsiella spp. remain the most tested, and generally the most resistant. The highest overall phenotypic resistance for imipenem was reported in E. coli, whereas for meropenem, E. coli and Haemophilus spp. showed an equal resistance proportion at 2.5%. For gram-positive bacteria, Streptococcus pneumoniae displayed high resistance percentages to trimethoprim/sulfamethoxazole (64.3%), oxacillin (32.2%), penicillin (23.2%), and tetracycline (28.3%), whereas Staphylococcus aureus contributed to 22.8% and 10% resistance to penicillin and oxacillin, respectively. This review shows that AMR remains a major public health threat. The present findings will help public health decision-makers in developing efficient preventive strategies and adequate policies for antibiotic stewardship and surveillance in line with the global action plan for AMR.

Opportunities to strengthen respiratory virus surveillance systems in Australia: lessons learned from the COVID-19 response.

Disease surveillance data was critical in supporting public health decisions throughout the coronavirus disease 2019 (COVID-19) pandemic. At the same time, the unprecedented circumstances of the pandemic revealed many shortcomings of surveillance systems for viral respiratory pathogens. Strengthening of surveillance systems was identified as a priority for the recently established Australian Centre for Disease Control, which represents a critical opportunity to review pre-pandemic and pandemic surveillance practices, and to decide on future priorities, during both pandemic and inter-pandemic periods. On 20 October 2022, we ran a workshop with experts from the academic and government sectors who had contributed to the COVID-19 response in Australia on 'The role of surveillance in epidemic response', at the University of New South Wales, Sydney, Australia. Following the workshop, we developed five recommendations to strengthen respiratory virus surveillance systems in Australia, which we present here. Our recommendations are not intended to be exhaustive. We instead chose to focus on data types that are highly valuable yet typically overlooked by surveillance planners. Three of the recommendations focus on data collection activities that support the monitoring and prediction of disease impact and the effectiveness of interventions (what to measure) and two focus on surveillance methods and capabilities (how to measure). Implementation of our recommendations would enable more robust, timely, and impactful epidemic analysis.

MGLEP: Multimodal Graph Learning for Modeling Emerging Pandemics with Big Data

Accurate forecasting and analysis of emerging pandemics play a crucial role in effective public health management and decision-making. Traditional approaches primarily rely on epidemiological data, overlooking other valuable sources of information that could act as sensors or indicators of pandemic patterns. In this paper, we propose a novel framework, MGLEP, that integrates temporal graph neural networks and multi-modal data for learning and forecasting. We incorporate big data sources, including social media content, by utilizing specific pre-trained language models and discovering the underlying graph structure among users. This integration provides rich indicators of pandemic dynamics through learning with temporal graph neural networks. Extensive experiments demonstrate the effectiveness of our framework in pandemic forecasting and analysis, outperforming baseline methods across different areas, pandemic situations, and prediction horizons. The fusion of temporal graph learning and multi-modal data enables a comprehensive understanding of the pandemic landscape with less time lag, cheap cost, and more potential information indicators.

Factors Associated With Increased Health Care Utilization for Patients With Dementia With Lewy Bodies: A Narrative Review.

Numerous studies have demonstrated that dementia is associated with increased utilization of health care services, which in turn results in increased costs of care. Dementia with Lewy bodies (DLB) is associated with greater costs of care relative to other forms of dementia due to higher rates of hospitalization and nursing home placement directly related to neuropsychiatric symptoms, parkinsonism, increased susceptibility to delirium, and elevated rates of caregiver burden. There is a critical need for researchers to identify potentially modifiable factors contributing to increased costs of care and poor clinical outcomes for patients with DLB, which may include comorbidities, polypharmacy/contraindicated medications, and access to specialty care. Previous research has utilized Medicare claims data, limiting the ability to study patients with early-onset (ie, prior to age 65) DLB. Integrated health systems offer the ability to combine electronic medical record data with Medicare, Medicaid, and commercial claims data and may therefore be ideal for utilization research in this population. The goals of this narrative review are to 1) synthesize and describe the current literature on health care utilization studies for patients with DLB, 2) highlight the current gaps in the literature, and 3) provide recommendations for stakeholders, including researchers, health systems, and policymakers. It is important to improve current understanding of potentially modifiable factors associated with increased costs of care among patients with DLB to inform public health policies and clinical decision-making, as this will ultimately improve the quality of patient care.

Discretionary Decisions in Public Administration Based on Machine Analytics: Challenges and Risks

The article is devoted to the problems and risks of machine analytics and other digital technological resources in making managerial decisions in public administration. The author raises the difficult question of the fact that against the background of loud victory speeches about mass measures of digital transformation in public administration, some timid and sensible voices have been lost, asking: can everything be perfect and without flaws? No technological nodes and solutions today work with 100 per cent reliability. The same is true of human organisations. No one and nothing is perfect. Therefore, defects, dysfunctions, imbalances, and errors in public administration have been in the past, are now, and will be in the future. The problem is that digital technologies, while helping to combat some of these defects, dysfunctions, imbalances, and errors in public administration, drive other defects, imbalances, and errors in public administration very deeply into the ground and create third ones. And we have not yet learnt how to diagnose all this. The article gives some eloquent examples of great harm caused by failures of machine analytics to support public administration decision-making. The author shows typical problems of digitalisation in public administration, but concludes that these problems are solvable.

Informing pandemic management in Germany with trustworthy living evidence syntheses and guideline development: lessons learned from the COVID-19 evidence ecosystem

ObjectivesWe present the ‘COVID-19 evidence ecosystem’ (CEOsys) as a German network to inform pandemic management and to support clinical and public health decision-making. We discuss challenges faced when organizing the ecosystem and derive lessons learned for similar networks acting during pandemics or health-related crises. Study Design and SettingBringing together 18 university hospitals and additional institutions, CEOsys key activities included research prioritization, conducting living systematic reviews (LSRs), supporting evidence-based (living) guidelines, knowledge translation (KT), detecting research gaps, and deriving recommendations, backed by technical infrastructure and capacity building. ResultsCEOsys rapidly produced 31 high-quality evidence syntheses and supported three living guidelines on COVID-19-related topics, while also developing methodological procedures. Challenges included CEOsys’ late initiation in relation to the pandemic outbreak, the delayed prioritization of research questions, the continuously evolving COVID-19-related evidence, and establishing a technical infrastructure. Methodological-clinical tandems, the cooperation with national guideline groups and international collaborations were key for efficiency. ConclusionCEOsys provided a proof-of-concept for a functioning evidence ecosystem at the national level. Lessons learned include that similar networks should, among others, involve methodological and clinical key stakeholders early on, aim for (inter)national collaborations, and systematically evaluate their value. We particularly call for a sustainable network.

Use of Cost-Effectiveness Thresholds in Healthcare Public Policy: Progress and Challenges.

The article offers a comparative analysis of the influence of cost-effectiveness thresholds in the decision-making processes in financing policies, coverage, and price regulation of health technologies in nine countries. We investigated whether countries used cost-effectiveness thresholds for public health policy decision making and found that few countries have adopted the cost-effectiveness threshold as an official criterion for financing, reimbursement, or pricing. However, in countries where it is applied, such as Thailand, the results have been very favorable in terms of minimizing health technology prices and ensuring the financial sustainability of the health system. Although the cost-effectiveness threshold has opportunities for improvement, particularly in certain institutional contexts and with adequate participation of the different strategic actors in the formulation of public policy, its potential use and added value are significant in various aspects.

Emerging Microorganisms and Infectious Diseases: One Health Approach for Health Shared Vision.

Emerging infectious diseases (EIDs) are newly emerging and reemerging infectious diseases. The National Institute of Allergy and Infectious Diseases identifies the following as emerging infectious diseases: SARS, MERS, COVID-19, influenza, fungal diseases, plague, schistosomiasis, smallpox, tick-borne diseases, and West Nile fever. The factors that should be taken into consideration are the genetic adaptation of microbial agents and the characteristics of the human host or environment. The new approach to identifying new possible pathogens will have to go through the One Health approach and omics integration data, which are capable of identifying high-priority microorganisms in a short period of time. New bioinformatics technologies enable global integration and sharing of surveillance data for rapid public health decision-making to detect and prevent epidemics and pandemics, ensuring timely response and effective prevention measures. Machine learning tools are being more frequently utilized in the realm of infectious diseases to predict sepsis in patients, diagnose infectious diseases early, and forecast the effectiveness of treatment or the appropriate choice of antibiotic regimen based on clinical data. We will discuss emerging microorganisms, omics techniques applied to infectious diseases, new computational solutions to evaluate biomarkers, and innovative tools that are useful for integrating omics data and electronic medical records data for the clinical management of emerging infectious diseases.

Canada's approach to SARS-CoV-2 sero-surveillance: Lessons learned for routine surveillance and future pandemics.

In Canada's federated healthcare system, 13 provincial and territorial jurisdictions have independent responsibility to collect data to inform health policies. During the COVID-19 pandemic (2020-2023), national and regional sero-surveys mostly drew upon existing infrastructure to quickly test specimens and collect data but required cross-jurisdiction coordination and communication. There were 4 national and 7 regional general population SARS-CoV-2 sero-surveys. Survey methodologies varied by participant selection approaches, assay choices, and reporting structures. We analyzed Canadian pandemic sero-surveillance initiatives to identify key learnings to inform future pandemic planning. Over a million samples were tested for SARS-CoV-2 antibodies from 2020 to 2023 but siloed in 11 distinct datasets. Most national sero-surveys had insufficient sample size to estimate regional prevalence; differences in methodology hampered cross-regional comparisons of regional sero-surveys. Only four sero-surveys included questionnaires. Sero-surveys were not directly comparable due to different assays, sampling methodologies, and time-frames. Linkage to health records occurred in three provinces only. Dried blood spots permitted sample collection in remote populations and during stay-at-home orders. To provide timely, high-quality information for public health decision-making, routine sero-surveillance systems must be adaptable, flexible, and scalable. National capability planning should include consortiums for assay design and validation, defined mechanisms to improve test capacity, base documents for data linkage and material transfer across jurisdictions, and mechanisms for real-time communication of data. Lessons learned will inform incorporation of a robust sero-survey program into routine surveillance with strategic sampling and capacity to adapt and scale rapidly as a part of a comprehensive national pandemic response plan.

Singapore's COVID-19 crisis decision-making through centralization, legitimacy, and agility: an empirical analysis

Decision-making during health crises differs from routine decision-making and is constrained by ambiguity about evolving epidemiological situations, urgency of response, lack of evidence, and fear. Recent analyses of governance and decision-making during COVID-19, focusing on leadership qualities, involvement of specific stakeholders, and effective resource management, do not adequately address a persisting gap in understanding the determinants of decision-making during health crises at the national level. We undertook a study to understand the processes and characteristics of decision-making during the COVID-19 pandemic in Singapore. We used a case study approach and collected empirical evidence about public health decision-making, using a combination of key informant interviews and focus group discussions with stakeholders from government, academia and civil society organizations. We argue that administrative centralization and political legitimacy played important roles in agile governance and decision-making during the pandemic in Singapore. We demonstrate the role of the Singapore government's centralization in creating a unified and coherent governance model for emergency response and the People's Action Party's (PAP) legitimacy in facilitating people's trust in the government. Health system resilience and financial reserves further facilitated an agile response, yet community participation and prioritization of vulnerable migrant populations were insufficient in the governance processes. Our analysis contributes to the theory and practice of crisis decision-making by highlighting the role of political and administrative determinants in agile crisis decision-making. This study is funded by the U.S. Centers for Disease Control and Prevention through a Cooperative Research Agreement (NU2HGH2020000037).

GREEN GOVERNANCE: INTEGRATING ENVIRONMENTAL POLICY INTO PUBLIC DECISION-MAKING

<em>This study delves into the multifaceted domain of Green Governance, aimed at integrating environmental policies into public decision-making processes. Grounded in qualitative research methods utilizing secondary data, the investigation unfolds key insights derived from various thematic domains. The exploration begins by uncovering the foundational elements crucial to Green Governance, unveiling key components and imperatives for effective environmental policy implementation. Stakeholder perspectives emerge as pivotal, shaping policy realities and influencing the trajectory of environmental initiatives. The study navigates through theoretical perspectives, shedding light on the intricacies of policy integration into public decision-making, drawing from diverse lenses such as the Advocacy Coalition Framework and Institutional Theory. Additionally, the research meticulously explores various governance models, dissecting collaborative and network-based approaches and elucidating their distinguishing features and comparative advantages in integrating environmental policies. Findings underscore the significance of collaboration, adaptive structures, stakeholder engagement, and inclusive decision-making in crafting effective green governance frameworks. Ultimately, this research culminates in a comprehensive understanding of Green Governance, providing insights vital for policymakers, practitioners, and stakeholders.</em>

Examining Educational Research's Contribution to Civic Engagement in Public Decision Making: A Bibliometric Overview

Examining Educational Research's Contribution to Civic Engagement in Public Decision Making: A Bibliometric Overview