Schistosoma mansoni infection in residents of a riverside community in Eastern Amazon.

Schistosoma mansoni worms cause a waterborne parasitic disease called schistosomiasis. It commonly affects individuals in lack of sanitation structure. In Brazil, Pará state has Belém as one of the worst sanitation-ranking places in 2023, where schistosomiasis transmission was already documented. This study reports the occurrence of schistosomiasis in residents of Ilha das Onças, an island next to Belém. Stool samples were obtained from participants over 2 years old, all residents from Furo do Rio Grande, one of the rivers on the island. The Kato-Katz technique was performed for parasite investigation in the stool samples. Each participant responded to a sociodemographic and clinical questionnaire. The residences were georeferenced for map designing. Three out of 263 participants were S. mansoni positive, all men, ages ranging from 19 to 41 years old, with low parasitic load. Malacological surveys were carried out, but no Biomphalaria snails were found. Risk factors for schistosomiasis establishment are present on the island, and the lack of sanitation makes it a potential risk area. Malacological surveys are highly encouraged as preventive measures, as well as health surveillance for riverside populations, generating data that will help health authorities in the management and planning of preventive control actions.

Syndromic case definitions for lower respiratory tract infection (LRTI) are less sensitive in older age: an analysis of symptoms among hospitalised adults.

BACKGROUND: Lower Respiratory Tract Infections (LRTI) pose a serious threat to older adults but may be underdiagnosed due to atypical presentations. Here we assess LRTI symptom profiles and syndromic (symptom-based) case ascertainment in older (≥ 65y) as compared to younger adults (< 65y). METHODS: We included adults (≥ 18y) with confirmed LRTI admitted to two acute care Trusts in Bristol, UK from 1st August 2020- 31st July 2022. Logistic regression was used to assess whether age ≥ 65y reduced the probability of meeting syndromic LRTI case definitions, using patients' symptoms at admission. We also calculated relative symptom frequencies (log-odds ratios) and evaluated how symptoms were clustered across different age groups. RESULTS: Of 17,620 clinically confirmed LRTI cases, 8,487 (48.1%) had symptoms meeting the case definition. Compared to those not meeting the definition these cases were younger, had less severe illness and were less likely to have received a SARS-CoV-2 vaccination or to have active SARS-CoV-2 infection. Prevalence of dementia/cognitive impairment and levels of comorbidity were lower in this group. After controlling for sex, dementia and comorbidities, age ≥ 65y significantly reduced the probability of meeting the case definition (aOR = 0.67, 95% CI:0.63-0.71). Cases aged ≥ 65y were less likely to present with fever and LRTI-specific symptoms (e.g., pleurisy, sputum) than younger cases, and those aged ≥ 85y were characterised by lack of cough but frequent confusion and falls. CONCLUSIONS: LRTI symptom profiles changed considerably with age in this hospitalised cohort. Standard screening protocols may fail to detect older and frailer cases of LRTI based on their symptoms.

Long term follow-up of heart rate variability in healthcare workers with mild COVID-19.

Introduction: Prior investigations into post-COVID dysautonomia often lacked control groups or compared affected individuals solely to healthy volunteers. In addition, no data on the follow-up of patients with SARS-CoV-2-related autonomic imbalance are available. Methods: In this study, we conducted a comprehensive clinical and functional follow-up on healthcare workers (HCWs) with former mild COVID-19 (group 1, n = 67), to delineate the trajectory of post-acute autonomic imbalance, we previously detected in a case-control study. Additionally, we assessed HCWs for which a test before SARS-CoV-2 infection was available (group 2, n = 29), who later contracted SARS-CoV-2, aiming to validate findings from our prior case-control investigation. We evaluated autonomic nervous system heart modulation by means of time and frequency domain heart rate variability analysis (HRV) in HCWs during health surveillance visits. Short-term electrocardiogram (ECG) recordings, were obtained at about 6, 13 months and both at 6 and 13 months from the negative SARS-CoV-2 naso-pharyngeal swab (NPS) for group 1 and at about 1-month from the negative NPS for group 2. HCWs who used drugs, had comorbidities that affected HRV, or were hospitalized with severe COVID-19 were excluded. Results: Group 1 was split into three subgroups clinically and functionally followed at, about 6 months (subgroup-A, n = 17), 13 months (subgroup-B, n = 37) and both at 6 and 13 months (subgroup-C, n = 13) from the negative SARS-CoV-2 NPS. In subgroup-A, at 6-month follow-up compared with baseline, the spectral components in the frequency domain HRV parameters, showed an increase in normalized high frequency power (nHF) (t = 2.99, p = 0.009), a decrease in the normalized low frequency power (nLF) (t = 2.98, p = 0.009) and in the LF/HF ratio (t = 3.13, p = 0.006). In subgroup B, the comparison of the spectral components in the frequency domain HRV parameters, at 13-month follow-up compared with baseline, showed an increase in nHF (t = 2.54, p = 0.02); a decrease in nLF (t = 2.62, p = 0.01) and in the LF/HF ratio (t = 4.00, p = 0.0003). In subgroup-C, at both 6 and 13-month follow-ups, the spectral components in the frequency domain HRV parameters were higher than baseline in nHF (t = 2.64, p = 0.02 and (t = 2.13, p = 0.05, respectively); lower in nLF (t = 2.64, p = 0.02 and (t = 2.13, p = 0.05, respectively), and in LF/HF (t = 1.92, p = 0.08 and (t = 2.43, p = 0.03, respectively). A significant proportion of HCWs reported persistent COVID-19 symptoms at both the 6 and 13-month follow-ups, seemingly unrelated to cardiac autonomic balance. In group 2 HCWs, at 1-month follow-up compared with baseline, the spectral components in the frequency domain HRV parameters, showed a decrease in nHF (t = 2.19, p = 0.04); an increase in nLF (t = 2.15, p = 0.04) and in LF/HF (t = 3.49, p = 0.002). Conclusion: These results are consistent with epidemiological data suggesting a higher risk of acute cardiovascular complications during the first 30 days after COVID-19. The SARS-CoV-2 associated autonomic imbalance in the post-acute phase after recovery of mild COVID-19 resolved 6 months after the first negative SARS-CoV-2 NPS. However, a significant proportion of HCWs reported long-term COVID-19 symptoms, which dot not seems to be related to cardiac autonomic balance. Future research should certainly further test whether autonomic imbalance has a role in the mechanisms of long-COVID syndrome.

Corrigendum: Implementing a provisional overarching intervention for COVID-19 monitoring and control in the Brazil-Colombia-Peru frontier.

[This corrects the article DOI: 10.3389/fpubh.2023.1330347.].

Assessment of the pathogen genomics landscape highlights disparities and challenges for effective AMR Surveillance and outbreak response in the East African community.

The East African Community (EAC) grapples with many challenges in tackling infectious disease threats and antimicrobial resistance (AMR), underscoring the importance of regional and robust pathogen genomics capacities. However, a significant disparity exists among EAC Partner States in harnessing bacterial pathogen sequencing and data analysis capabilities for effective AMR surveillance and outbreak response. This study assesses the current landscape and challenges associated with pathogen next-generation sequencing (NGS) within EAC, explicitly focusing on World Health Organization (WHO) AMR-priority pathogens. The assessment adopts a comprehensive approach, integrating a questionnaire-based survey amongst National Public Health Laboratories (NPHLs) with an analysis of publicly available metadata on bacterial pathogens isolated in the EAC countries. In addition to the heavy reliance on third-party organizations for bacterial NGS, the findings reveal a significant disparity among EAC member States in leveraging bacterial pathogen sequencing and data analysis. Approximately 97% (n = 4,462) of publicly available high-quality bacterial genome assemblies of samples collected in the EAC were processed and analyzed by external organizations, mainly in Europe and North America. Tanzania led in-country sequencing efforts, followed by Kenya and Uganda. The other EAC countries had no publicly available samples or had all their samples sequenced and analyzed outside the region. Insufficient local NGS sequencing facilities, limited bioinformatics expertise, lack of adequate computing resources, and inadequate data-sharing mechanisms are among the most pressing challenges that hinder the EAC's NPHLs from effectively leveraging pathogen genomics data. These insights emphasized the need to strengthen microbial pathogen sequencing and data analysis capabilities within the EAC to empower these laboratories to conduct pathogen sequencing and data analysis independently. Substantial investments in equipment, technology, and capacity-building initiatives are crucial for supporting regional preparedness against infectious disease outbreaks and mitigating the impact of AMR burden. In addition, collaborative efforts should be developed to narrow the gap, remedy regional imbalances, and harmonize NGS data standards. Supporting regional collaboration, strengthening in-country genomics capabilities, and investing in long-term training programs will ultimately improve pathogen data generation and foster a robust NGS-driven AMR surveillance and outbreak response in the EAC, thereby supporting global health initiatives.

Dentistry and Bioterrorism: A Legitimate Threat.

Bioterrorism involves the deliberate release of harmful biological agents, such as bacteria and viruses, aimed at causing mass casualties within a population. Often referred to as "poor man's nuclear weapons," chemical and biological weapons pose a significant threat due to their potential for mass destruction. Detecting and preventing bioterrorist attacks is challenging, making them one of the most feared scenarios. Dentistry plays a crucial role in responding to bioterrorism and other catastrophic events, leveraging its personnel and facilities effectively. This paper explores the signs and symptoms of biological agents used in mass destruction, as well as the oral and dental manifestations of both naturally occurring and bioengineered infectious agents. Furthermore, the article stresses the importance of countermeasures against bioterrorism, including deterrence, prevention, surveillance, medical management, and training. Emphasis is placed on implementing surveillance systems, bolstering medical readiness, and conducting training programs to effectively detect, assess, and respond to bioterrorism threats. Ultimately, the article underscores the critical role of dentists and healthcare professionals in collaborative efforts to mitigate the impacts of bioterrorism through proactive measures.

Epidemiological surveillance and infectious disease outbreaks during mass international summertime sports gatherings: A narrative review.

The XXXIIIrd Paris Summer Olympics followed by the XVIIth Paralympics Games will take place in France, predominantly in and around Paris, from July 26 to September 8, 2024. Public health stakeholders and decision-makers are called upon to set up or strengthen surveillance systems in areas hosting Olympic or Paralympic Games (OPGs) or large-scale international competitions, the objective being to detect and manage outbreaks should they occur during that period. We undertook a narrative review of the literature so as to identify major reported infectious disease outbreaks linked with or during OPGs / international sporting events during warm seasons. Our review found that since 1992, Summer Olympic and Paralympic games and international football competitions have been associated with sporadic cases of infectious diseases, principally respiratory, gastrointestinal/foodborne, but not with any major communicable or other infectious disease outbreak. Communicable disease risks should be assessed for the population taken as a whole, an integrated ecosystem with several population compartments potentially exchanging pathogens among one another. Although the Games afford an opportunity to federate or invent new surveillance systems to fill a gap, surveillance should be based on existing medical and laboratory systems, proven tools reinforced with the necessary human and financial resources. The performance of the public health surveillance system is ultimately predicated on trust on the part of participating clinicians, policymakers and international partners.

Molecular and spatial evaluation of small rodents and Didelphimorphis infected with Mycobacterium leprae in the southern Amazon, Brazil.

BACKGROUND: The high levels of recent transmission of leprosy worldwide demonstrate the necessity of epidemiologic surveillance to understand and control its dissemination. Brazil remains the second in number of cases around the world, indicating active transmission of Mycobacterium leprae (M. leprae) in the population. At this moment, there is a consensus that the bacillus is transmitted by inter-human contact, however, different serologic, molecular, and histopathological approaches indicate the existence of non-human transmission sources. METHODS AND RESULTS: The qPCR assay was used to amplify the molecular targets 16S RNAr and RLEP, in samples of liver, spleen, and ear of wild animals belonging to Didelphimorphia and Rodentia orders, in highly endemic areas of Mato Grosso, Brazil. The RLEP repetitive sequence was positive in 202 (89.0%) samples, with 96 (42.3%) of these also being positive for the 16S gene. Regarding the collection sites, it was observed that the animals were found in areas profoundly deforested, close to urban areas. CONCLUSIONS: Our results suggest that wild animals can play an important role in the maintenance of M. leprae in endemic regions with major anthropic action in Brazil. Therefore, integrating human, animal, and environmental health care with the One Health initiative is highly efficient for the development of effective strategies to contain and control leprosy in Brazil.

Epidemiological anomaly detection in Philippine public health surveillance data through Newcomb-Benford analysis.

BACKGROUND: Public health surveillance is vital for monitoring and controlling disease spread. In the Philippines, an effective surveillance system is crucial for managing diverse infectious diseases. The Newcomb-Benford Law (NBL) is a statistical tool known for anomaly detection in various datasets, including those in public health. METHODS: Using Philippine epidemiological data from 2019 to 2023, this study applied NBL analysis. Diseases included acute flaccid paralysis, diphtheria, measles, rubella, neonatal tetanus, pertussis, chikungunya, dengue, leptospirosis and others. The analysis involved Chi-square tests, Mantissa Arc tests, Mean Absolute Deviation (MAD) and Distortion Factor calculations. RESULTS: Most diseases exhibited nonconformity to NBL, except for measles. MAD consistently indicated nonconformity, highlighting potential anomalies. Rabies consistently showed substantial deviations, while leptospirosis exhibited closer alignment, especially in 2021. Annual variations in disease deviations were notable, with acute meningitis encephalitis syndrome in 2019 and influenza-like illness in 2023 having the highest deviations. CONCLUSIONS: The study provides practical insights for improving Philippine public health surveillance. Despite some diseases showing conformity, deviations suggest data quality issues. Enhancing the PIDSR, especially in diseases with consistent nonconformity, is crucial for accurate monitoring and response. The NBL's versatility across diverse domains emphasizes its utility for ensuring data integrity and quality assurance.

Enhancing Public Health Surveillance: Outbreak Detection Algorithms Deployed for Syndromic Surveillance During Arbaeenia Mass Gatherings in Iraq.

BACKGROUND: Large gatherings often involve extended and intimate contact among individuals, creating environments conducive to the spread of infectious diseases. Despite this, there is limited research utilizing outbreak detection algorithms to analyze real syndrome data from such events. This study sought to address this gap by examining the implementation and efficacy of outbreak detection algorithms for syndromic surveillance during mass gatherings in Iraq. METHODS: For the study, 10 data collectors conducted field data collection over 10 days from August 25, 2023, to September 3, 2023. Data were gathered from 10 healthcare clinics situated along Ya Hussein Road, a major route from Najaf to Karbala in Iraq. Various outbreak detection algorithms, such as moving average, cumulative sum, and exponentially weighted moving average, were applied to analyze the reported syndromes. RESULTS: During the 10 days from August 25, 2023, to September 3, 2023, 12202 pilgrims visited 10 health clinics along a route in Iraq. Most pilgrims were between 20 and 59 years old (77.4%, n=9444), with more than half being foreigners (58.1%, n=7092). Among the pilgrims, 40.5% (n=4938) exhibited syndromes, with influenza-like illness (ILI) being the most common (48.8%, n=2411). Other prevalent syndromes included food poisoning (21.2%, n=1048), heatstroke (17.7%, n=875), febrile rash (9.0%, n=446), and gastroenteritis (3.2%, n=158). The cumulative sum (CUSUM) algorithm was more effective than exponentially weighted moving average (EWMA) and moving average (MA) algorithms for detecting small shifts. CONCLUSION: Effective public health surveillance systems are crucial during mass gatherings to swiftly identify and address emerging health risks. Utilizing advanced algorithms and real-time data analysis can empower authorities to improve their readiness and response capacity, thereby ensuring the protection of public health during these gatherings.

Unifying Public Health Surveillance: A Scoping Review of Global Use of the Youth Risk Behavior Survey.

PURPOSE: The Youth Risk Behavior Survey (YRBS) is a well-established surveillance tool designed to document the health risk behaviors of youth. However, there is limited insight into the use of the survey outside of the United States. The aim of this scoping review was to assess the global presence and utilization of the YRBS. METHODS: A structured electronic search of all publication years (through February 2020) was conducted to identify articles in PubMed and EBSCOhost. The search identified 128 articles that used the YRBS beyond the United States. RESULTS: More than one-third of all countries, territories, and dependencies were represented in the articles, with the greatest use among upper-middle and high-income economies and those in the East Asia and Pacific geographic region. Priority health-risk behaviors identified were alcohol and other drug use (51%), tobacco use (48%), and unintentional and intentional injuries (44%). The articles predominantly suggested that the survey data be used to influence programs, policies, and practices (57%). DISCUSSION: The development and proliferation of surveillance systems has allowed for important contributions to public health. Extensive use of the YRBS is notable; however, greater efforts are needed to support more systematic and collaborative approaches for evaluating youth behaviors around the world.

Data Equity as a Building Block for Health Equity: Improving Surveillance Data for People With Disabilities, With Substance Use Disorder, or Experiencing Homelessness, United States.

OBJECTIVES: People with disabilities, people experiencing homelessness, and people who have substance use disorders face unique health challenges. Gaps in public health surveillance data limit the identification of public health needs of these groups and data-driven action. This study aimed to identify current practices, challenges, and opportunities for collecting and reporting COVID-19 surveillance data for these populations. METHODS: We used a rapid qualitative assessment to explore COVID-19 surveillance capacities. From July through October 2021, we virtually interviewed key informants from the Centers for Disease Control and Prevention, state and local health departments, and health care providers across the United States. We thematically analyzed and contextualized interview notes, peer-reviewed articles, and participant documents using a literature review. RESULTS: We identified themes centered on foundational structural and systems issues that hinder actionable surveillance data for these and other populations that are disproportionately affected by multiple health conditions. Qualitative data analysis of 61 interviews elucidated 4 primary challenges: definitions and policies, resources, data systems, and articulation of the purpose of data collection to these groups. Participants noted the use of multisector partnerships, automated data collection and integration, and data scorecards to circumvent challenges. CONCLUSIONS: This study highlights the need for multisector, systematic improvements in surveillance data collection and reporting to advance health equity. Improvements must be buttressed with adequate investment in data infrastructure and promoted through clear communication of how data are used to protect health.

Innovations in public health surveillance: An overview of novel use of data and analytic methods.

Innovative data sources and methods for public health surveillance (PHS) have evolved rapidly over the past 10 years, suggesting the need for a closer look at the scientific maturity, feasibility, and utility of use in real-world situations. This article provides an overview of recent innovations in PHS, including data from social media, internet search engines, the Internet of Things (IoT), wastewater surveillance, participatory surveillance, artificial intelligence (AI), and nowcasting. Examples identified suggest that novel data sources and analytic methods have the potential to strengthen PHS by improving disease estimates, promoting early warning for disease outbreaks, and generating additional and/or more timely information for public health action. For example, wastewater surveillance has re-emerged as a practical tool for early detection of the coronavirus disease 2019 (COVID-19) and other pathogens, and AI is increasingly used to process large amounts of digital data. Challenges to implementing novel methods include lack of scientific maturity, limited examples of implementation in real-world public health settings, privacy and security risks, and health equity implications. Improving data governance, developing clear policies for the use of AI technologies, and public health workforce development are important next steps towards advancing the use of innovation in PHS.

Geographic range and minimum infection rate of Borrelia burgdorferi, Anaplasma phagocytophilum, Babesia microti, and Borrelia miyamotoi in Ixodes scapularis (Acari: Ixodidae) ticks in Manitoba, Canada from 1995 to 2017.

INTRODUCTION: The expanding geographical range of blacklegged ticks (BLTs), Ixodes scapularis, and its ability to transmit Borrelia burgdorferi, Anaplasma phagocytophilum, Babesia microti, and Borrelia miyamotoi poses an emerging public health risk. Our study determined the geographic distribution and the minimum infection rate (MIR) of B. burgdorferi-, A. phagocytophilum-, Ba. microti-, and B. miyamotoi-infected BLTs in Manitoba submitted to the Public Health Agency of Canada's passive tick surveillance programme from 1995 to 2017. METHODS: Regression models were used to test the association of the MIR by year for each pathogen. Ticks were tested using PCR for B. burgdorferi since 1995, A. phagocytophilum since 2006, and Ba. microti and B. miyamotoi since 2013. The global positioning system coordinates of infected and uninfected ticks submitted during the surveillance period were plotted on a map of Manitoba using ArcGIS Pro version 3.1.2 to detect changes in the geographic distribution of ticks over time. RESULTS: The overall MIR for B. burgdorferi was 139.7 (95% confidence interval [CI]: 129.0-150.5) per 1000 BLTs; however, it varied over time. After remaining stable from 1995 to 2005, the MIR increased by 12.1 per 1000 BLTs per year from 2005 to 2017 (95% CI: 7.0%-17.2%, p-value <0.01). The geographic distribution of B. burgdorferi-infected BLTs was centred around Winnipeg, Manitoba, and spread outward from this locality. The MIRs of A. phagocytophilum, Ba. microti, and B. miyamotoi were 44.8 per 1000 BLTs (95% CI: 38.1-51.6), 10.8 (95% CI: 6.6-15.0), and 5.2 (95% CI: 2.3-8.1) per 1000 BLTs, respectively, and showed no significant change over time. CONCLUSION: Passive surveillance revealed the presence of A. phagocytophilum-, Ba. microti-, and B. miyamotoi-infected BLTs in southern Manitoba and revealed an increased risk of exposure to B. burgdorferi-infected BLTs due to the increasing geographic range and MIR.

Effectiveness of mass dengue vaccination with CYD-TDV (Dengvaxia®) in the state of Paraná, Brazil: integrating case-cohort and case-control designs.

Background: CYD-TDV (Dengvaxia®) was the first dengue vaccine approved, launched in Brazil in 2015 for individuals aged 9-44 years. We aimed to estimate the effectiveness of CYD-TDV in preventing symptomatic dengue cases during a campaign targeting individuals aged 15-27 years in selected municipalities in Paraná, Brazil. Additionally, we examined whether a history of dengue, as recorded by the surveillance system, modified the vaccine's effectiveness. Methods: We conducted a case-cohort analysis comparing the frequency of vaccination, with at least one dose of CYD-TDV, in individuals with dengue confirmed by RT-PCR, identified by the surveillance system during 2019 and 2020, with the vaccination coverage in the target population. Moreover, in a case-control design using weighted controls, we assessed the documented history of dengue as a modifier of the vaccine's effectiveness. We used a logistic random-effects regression model, with data clustered in municipalities and incorporating covariates such as the incidence of dengue before the campaign, age, and sex. We calculated vaccine effectiveness (VE) as (1-relative risk) x 100%. Findings: 1869 dengue cases were identified, which had a vaccination frequency significantly lower than the overall vaccination coverage in the target population (50.3% vs. 57.2%, respectively; overall VE: 21.3%; 95% confidence interval [CI]: 13.4%-28.4%). In individuals with a documented history of dengue, vaccination had a VE of 71% (95% CI: 58%-80%) in reducing the incidence of dengue. However, vaccination was not associated with a significant reduction in the overall dengue case risk in individuals without a documented history of dengue (VE: 12%; 95% CI: -21% to 36%). In this last stratum, vaccination was associated with reduced cases due to DENV-1 and DENV-4, but an excess of DENV-2 cases. Interpretation: Vaccination led to a significant reduction in reported dengue cases within the target population. The case-control design suggested that this reduction was primarily driven by the benefits observed in individuals with a documented history of dengue. In endemic regions with limited serological testing facilities, a previous history of dengue diagnosis recorded by epidemiological surveillance could be used to triage candidates for CYD-TDV vaccination. Funding: Research supported by Sanofi.

Prioritization of ethical concerns regarding HIV molecular epidemiology by public health practitioners and researchers.

BACKGROUND: HIV molecular epidemiology (HIV ME) can support the early detection of emerging clusters of new HIV infections by combining HIV sequence data routinely obtained during the clinical treatment of people living with HIV with behavioral, geographic, and sociodemographic information. While information about emerging clusters promises to facilitate HIV prevention and treatment efforts, the use of this data also raises several ethical concerns. We sought to assess how those working on the frontlines of HIV ME, specifically public health practitioners (PHPs) and researchers, prioritized these issues. METHODS: Ethical issues were identified through literature review, qualitative in-depth interviews, and stakeholder engagement. PHPs and researchers using HIV ME prioritized the issues using best-worst scaling (BWS). A balanced incomplete block design was used to generate 11 choice tasks each consisting of a sub-set of 5 ethical concerns. In each task, respondents were asked to assess the most and least concerning issue. Data were analyzed using conditional logit, with a Swait-Louviere test of poolability. Latent class analysis was then used to explore preference heterogeneity. RESULTS: In total, 57 respondents completed the BWS experiment May-June 2023 with the Swait-Louviere test indicating that researchers and PHPs could be pooled (p = 0.512). Latent class analysis identified two classes, those highlighting "Harms" (n = 29) (prioritizing concerns about potential risk of legal prosecution, individual harm, and group stigma) and those highlighting "Utility" (n = 28) (prioritizing concerns about limited evidence, resource allocation, non-disclosure of data use for HIV ME, and the potential to infer the directionality of HIV transmission). There were no differences in the characteristics of members across classes. CONCLUSIONS: The ethical issues of HIV ME vary in importance among stakeholders, reflecting different perspectives on the potential impact and usefulness of the data. Knowing these differences exist can directly inform the focus of future deliberations about the policies and practices of HIV ME in the United States.

Rethinking students' mental health assessment through GHQ-12: evidence from the IRT approach.

BACKGROUND: The General Health Questionnaire-12 (GHQ-12) is a widely used screening tool for mental health assessment however its traditional scoring methods and cutoffs may not adequately capture the mental health complexities of younger populations. METHODS: This study explores GHQ-12 responses from a sample of university students. Possible differences in means scores considering gender, age, academic field and degree course were assessed through t-test or one-way ANOVA as appropriate. To deeper understanding different levels of severity and individual item impact on general distress measurement, we applied Item-Response-Theory (IRT) techniques (two-parameters logistic model). We compared students' population with a population of workers who underwent a similar psychological evaluation. RESULTS: A total of 3834 university students participated in the study. Results showed that a significant proportion (79%) of students reported psychological distress. Females and younger students obtained significantly higher average scores compared to others. IRT analysis found item-specific variations in mental distress levels, with more indicative items for short-term fluctuations and potential severe mental health concerns. Latent class analysis identified three distinct subgroups among students (including 20%, 37%, 43% of the participants respectively) with different levels of psychological distress severity. Comparison with a population of adults showed that students reported significantly higher scores with differences in the scale behavior. CONCLUSION: Our results highlighted the unique mental health challenges faced by students, suggesting a reevaluation of GHQ-12 applicability and cutoff scores for younger populations, emphasizing the need for accurate instruments in mental health evaluation.

MENDS-on-FHIR: leveraging the OMOP common data model and FHIR standards for national chronic disease surveillance.

Objectives: The Multi-State EHR-Based Network for Disease Surveillance (MENDS) is a population-based chronic disease surveillance distributed data network that uses institution-specific extraction-transformation-load (ETL) routines. MENDS-on-FHIR examined using Health Language Seven's Fast Healthcare Interoperability Resources (HL7® FHIR®) and US Core Implementation Guide (US Core IG) compliant resources derived from the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) to create a standards-based ETL pipeline. Materials and Methods: The input data source was a research data warehouse containing clinical and administrative data in OMOP CDM Version 5.3 format. OMOP-to-FHIR transformations, using a unique JavaScript Object Notation (JSON)-to-JSON transformation language called Whistle, created FHIR R4 V4.0.1/US Core IG V4.0.0 conformant resources that were stored in a local FHIR server. A REST-based Bulk FHIR $export request extracted FHIR resources to populate a local MENDS database. Results: Eleven OMOP tables were used to create 10 FHIR/US Core compliant resource types. A total of 1.13 trillion resources were extracted and inserted into the MENDS repository. A very low rate of non-compliant resources was observed. Discussion: OMOP-to-FHIR transformation results passed validation with less than a 1% non-compliance rate. These standards-compliant FHIR resources provided standardized data elements required by the MENDS surveillance use case. The Bulk FHIR application programming interface (API) enabled population-level data exchange using interoperable FHIR resources. The OMOP-to-FHIR transformation pipeline creates a FHIR interface for accessing OMOP data. Conclusion: MENDS-on-FHIR successfully replaced custom ETL with standards-based interoperable FHIR resources using Bulk FHIR. The OMOP-to-FHIR transformations provide an alternative mechanism for sharing OMOP data.

Sexual violence against children and adolescents in Paraná State: geospatial analysis and main socioeconomic indicators.

OBJECTIVE: Child sexual violence is a multidimensional problem of many contemporary societies, affecting people of all sexes, social stratum and age groups. Offenses involving children and adolescents are more serious, given their total or partial dependence on parents and caregivers. Information on child sexual violence in Brazil is found in raw form and without detail. The objective was to compare the information with social and economic data in the state of Paraná. METHODS: The authors conducted a retrospective study of secondary data from the Notifiable Diseases Information System (SINAN) on cases of sexual violence involving victims aged 0 to 19 years. Results are presented according to notification characteristics. The authors applied exploratory spatial data analysis to assess spatial autocorrelations and investigated relationships by the ordinary least squares regression model. RESULTS: Between 2017 and 2021, there were 13,403 reports of child sexual violence in Paraná State, Brazil. Most victims (82.8%) were female and aged between 10 and 14 years. The majority of sexual violence cases (67.8%) occurred in the home environment. The highest rates on a population basis were observed in the North Central and Greater Curitiba regions, mainly in cities with higher population density and with higher rates of other types of violence. CONCLUSION: The results provide data that can promote a broader understanding of the distribution of sexual violence and the state and associated variations. It is expected to improve the provision of care for victims of child sexual violence and assist in strategic planning to prevent future offenses.

Seasonality of respiratory, enteric, and urinary viruses revealed by wastewater genomic surveillance.

Wastewater surveillance can reveal population-level infectious disease burden and emergent public health threats can be reliably assessed through wastewater surveillance. While molecular methods for wastewater monitoring of microorganisms have traditionally relied on PCR-based approaches, next-generation sequencing (NGS) can provide deeper insights via genomic analyses of multiple diverse pathogens. We conducted a year-long sequencing surveillance of 1,408 composite wastewater samples collected from 12 neighborhood-level access points in the greater Tempe area, Arizona, USA, and show that variation in wastewater viruses is driven by seasonal time and location. The temporal dynamics of viruses in wastewater were influenced cyclically, with the most dissimilarity between samples 23 weeks apart (i.e., winter vs summer, spring vs fall). We identified diverse urinary and enteric viruses including polyomaviruses, astroviruses, and noroviruses, and showed that their genotypes/subtypes shifted across seasons. We show that while wastewater data of certain respiratory viruses like severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) strongly correlate with clinical case rates, laboratory-reported case incidences were discordant with surges of high viral load in wastewater for other viruses like human coronavirus 229E. These results demonstrate the utility of wastewater sequencing for informing decision-making in public health.IMPORTANCEWastewater surveillance can provide insights into the spread of pathogens in communities. Advances in next-generation sequencing (NGS) methodologies allow for more precise detection of viruses in wastewater. Long-term wastewater surveillance of viruses is an important tool for public health preparedness. This system can act as a public health observatory that gives real-time early warning for infectious disease outbreaks and improved response times.