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In pandemic mitigation, strategies such as social distancing and mask-wearing are vital to prevent disease resurgence. Yet, monitoring adherence is challenging, as individuals might be reluctant to share behavioral data with public health authorities. To address this challenge and demonstrate a framework for conducting observational research with sensitive data in a privacy-conscious manner, we employ a privacy-centric epidemiological study design: the federated cohort. This approach leverages recent computational advances to allow for distributed participants to contribute to a prospective, observational research study while maintaining full control of their data. We apply this strategy here to explore pandemic intervention adherence patterns. Participants (n = 3808) were enrolled in our federated cohort via the "Google Health Studies" mobile application. Participants completed weekly surveys and contributed empirically measured mobility data from their Android devices between November 2020 to August 2021. Using federated analytics, differential privacy, and secure aggregation, we analyzed data in five 6-week periods, encompassing the pre- and post-vaccination phases. Our results showed that participants largely utilized non-pharmaceutical intervention strategies until they were fully vaccinated against COVID-19, except for individuals without plans to become vaccinated. Furthermore, this project offers a blueprint for conducting a federated cohort study and engaging in privacy-preserving research during a public health emergency., (© 2024. The Author(s).)

Background and Objectives: Geographic accessibility predicts pediatric preventive care utilization, including vaccine uptake. However, spatial inequities in the pediatric coronavirus disease 2019 (COVID-19) vaccination rollout remain underexplored. We assessed the spatial accessibility of vaccination sites and analyzed predictors of vaccine uptake., Methods: In this cross-sectional study of pediatric COVID-19 vaccinations from the US Vaccine Tracking System as of July 29, 2022, we described spatial accessibility by geocoding vaccination sites, measuring travel times from each Census tract population center to the nearest site, and weighting tracts by their population demographics to obtain nationally representative estimates. We used quasi-Poisson regressions to calculate incidence rate ratios, comparing vaccine uptake between counties with highest and lowest quartile Social Vulnerability Index scores: socioeconomic status (SES), household composition and disability (HCD), minority status and language (MSL), and housing type and transportation., Results: We analyzed 15 233 956 doses administered across 27 526 sites. Rural, uninsured, white, and Native American populations experienced longer travel times to the nearest site than urban, insured, Hispanic, Black, and Asian American populations. Overall Social Vulnerability Index, SES, and HCD were associated with decreased vaccine uptake among children aged 6 months to 4 years (overall: incidence rate ratio 0.70 [95% confidence interval 0.60-0.81]; SES: 0.66 [0.58-0.75]; HCD: 0.38 [0.33-0.44]) and 5 years to 11 years (overall: 0.85 [0.77-0.95]; SES: 0.71 [0.65-0.78]; HCD: 0.67 [0.61-0.74]), whereas social vulnerability by MSL was associated with increased uptake (6 months-4 years: 5.16 [3.59-7.42]; 5 years-11 years: 1.73 [1.44-2.08])., Conclusions: Pediatric COVID-19 vaccine uptake and accessibility differed by race, rurality, and social vulnerability. National supply data, spatial accessibility measurement, and place-based vulnerability indices can be applied throughout public health resource allocation, surveillance, and research., (Copyright © 2024 by the American Academy of Pediatrics.)

Background: Doctor review websites have become increasingly popular as a source of information for patients looking to select a primary care provider. Zocdoc is one such platform that allows patients to not only rate and review their experiences with doctors but also directly schedule appointments. This study examines how several physician characteristics including gender, age, race, languages spoken in a physician's office, education, and facial attractiveness impact the average numerical rating of primary care doctors on Zocdoc., Objective: The aim of this study was to investigate the association between physician characteristics and patient satisfaction ratings on Zocdoc., Methods: A data set of 1455 primary care doctor profiles across 30 cities was scraped from Zocdoc. The profiles contained information on the physician's gender, education, and languages spoken in their office. Age, facial attractiveness, and race were imputed from profile pictures using commercial facial analysis software. Each doctor profile listed an average overall satisfaction rating, bedside manner rating, and wait time rating from verified patients. Descriptive statistics, the Wilcoxon rank sum test, and multivariate logistic regression were used to analyze the data., Results: The average overall rating on Zocdoc was highly positive, with older age, lower facial attractiveness, foreign degrees, allopathic degrees, and speaking more languages negatively associated with the average rating. However, the effect sizes of these factors were relatively small. For example, graduates of Latin American medical schools had a mean overall rating of 4.63 compared to a 4.77 rating for US graduates (P<.001), a difference roughly equivalent to a 2.8% decrease in appointments. On multivariate analysis, being Asian and having a doctor of osteopathic medicine degree were positively associated with higher overall ratings, while attending a South Asian medical school and speaking more European and Middle Eastern languages in the office were negatively associated with higher overall ratings., Conclusions: Overall, the findings suggest that age, facial attractiveness, education, and multilingualism do have some impact on web-based doctor reviews, but the numerical effect is small. Notably, bias may play out in many forms. For example, a physician's appearance or accent may impact a patient's trust, confidence, or satisfaction with their physician, which could in turn influence their take-up of preventative services and lead to either better or worse health outcomes. The study highlights the need for further research in how physician characteristics influence patient ratings of care., (©Neil K R Sehgal, Benjamin Rader, John S Brownstein. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 29.07.2024.)

The COVID-19 pandemic has highlighted the need to upgrade systems for infectious disease surveillance and forecasting and modeling of the spread of infection, both of which inform evidence-based public health guidance and policies. Here, we discuss requirements for an effective surveillance system to support decision making during a pandemic, drawing on the lessons of COVID-19 in the U.S., while looking to jurisdictions in the U.S. and beyond to learn lessons about the value of specific data types. In this report, we define the range of decisions for which surveillance data are required, the data elements needed to inform these decisions and to calibrate inputs and outputs of transmission-dynamic models, and the types of data needed to inform decisions by state, territorial, local, and tribal health authorities. We define actions needed to ensure that such data will be available and consider the contribution of such efforts to improving health equity., Competing Interests: RS was employed by company Siemens Healthcare Diagnostics. JV was employed by company SIGA Technologies. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Lipsitch, Bassett, Brownstein, Elliott, Eyre, Grabowski, Hay, Johansson, Kissler, Larremore, Layden, Lessler, Lynfield, MacCannell, Madoff, Metcalf, Meyers, Ofori, Quinn, Bento, Reich, Riley, Rosenfeld, Samore, Sampath, Slayton, Swerdlow, Truelove, Varma and Grad.)

Background: Participatory surveillance of self-reported symptoms and vaccination status can be used to supplement traditional public health surveillance and provide insights into vaccine effectiveness and changes in the symptoms produced by an infectious disease. The University of Maryland COVID Trends and Impact Survey provides an example of participatory surveillance that leveraged Facebook's active user base to provide self-reported symptom and vaccination data in near real-time., Methods: Here, we develop a methodology for identifying changes in vaccine effectiveness and COVID-19 symptomatology using the University of Maryland COVID Trends and Impact Survey data from three middle-income countries (Guatemala, Mexico, and South Africa). We implement conditional logistic regression to develop estimates of vaccine effectiveness conditioned on the prevalence of various definitions of self-reported COVID-like illness in lieu of confirmed diagnostic test results., Results: We highlight a reduction in vaccine effectiveness during Omicron-dominated waves of infections when compared to periods dominated by the Delta variant (median change across COVID-like illness definitions: -0.40, IQR[-0.45, -0.35]. Further, we identify a shift in COVID-19 symptomatology towards upper respiratory type symptoms (i.e., cough and sore throat) during Omicron periods of infections. Stratifying COVID-like illness by the National Institutes of Health's (NIH) description of mild and severe COVID-19 symptoms reveals a similar level of vaccine protection across different levels of COVID-19 severity during the Omicron period., Conclusions: Participatory surveillance data alongside methodologies described in this study are particularly useful for resource-constrained settings where diagnostic testing results may be delayed or limited., (© 2024. The Author(s).)

Tools to advance antimicrobial stewardship in the primary health care setting, where most antimicrobials are prescribed, are urgently needed. The aim of this study was to evaluate OPEN Stewarship (Online Platform for Expanding aNtibiotic Stewardship), an automated feedback intervention, among a cohort of primary care physicians. We performed a controlled, interrupted time-series study of 32 intervention and 725 control participants, consisting of primary care physicians from Ontario, Canada and Southern Israel, from October 2020 to December 2021. Intervention participants received three personalized feedback reports targeting several aspects of antibiotic prescribing. Study outcomes (overall prescribing rate, prescribing rate for viral respiratory conditions, prescribing rate for acute sinusitis, and mean duration of therapy) were evaluated using multilevel regression models. We observed a decrease in the mean duration of antibiotic therapy (IRR = 0.94; 95% CI: 0.90, 0.99) in intervention participants during the intervention period. We did not observe a significant decline in overall antibiotic prescribing (OR = 1.01; 95% CI: 0.94, 1.07), prescribing for viral respiratory conditions (OR = 0.87; 95% CI: 0.73, 1.03), or prescribing for acute sinusitis (OR = 0.85; 95% CI: 0.67, 1.07). In this antimicrobial stewardship intervention among primary care physicians, we observed shorter durations of therapy per antibiotic prescription during the intervention period. The COVID-19 pandemic may have hampered recruitment; a dramatic reduction in antibiotic prescribing rates in the months before our intervention may have made physicians less amenable to further reductions in prescribing, limiting the generalizability of the estimates obtained.IMPORTANCEAntibiotic overprescribing contributes to antibiotic resistance, a major threat to our ability to treat infections. We developed the OPEN Stewardship (Online Platform for Expanding aNtibiotic Stewardship) platform to provide automated feedback on antibiotic prescribing in primary care, where most antibiotics for human use are prescribed but where the resources to improve antibiotic prescribing are limited. We evaluated the platform among a cohort of primary care physicians from Ontario, Canada and Southern Israel from October 2020 to December 2021. The results showed that physicians who received personalized feedback reports prescribed shorter courses of antibiotics compared to controls, although they did not write fewer antibiotic prescriptions. While the COVID-19 pandemic presented logistical and analytical challenges, our study suggests that our intervention meaningfully improved an important aspect of antibiotic prescribing. The OPEN Stewardship platform stands as an automated, scalable intervention for improving antibiotic prescribing in primary care, where needs are diverse and technical capacity is limited., Competing Interests: The authors declare no conflict of interest.

Objectives: State-level abortion bans in the United States have created a complex legal landscape that forces many prospective patients to travel long distances to access abortion care. The financial strain and logistical difficulties associated with travelling out of state for abortion care may present an insurmountable barrier to some individuals, especially to those with limited resources. Tracking the impact of these abortion bans on travel and housing is crucial for understanding abortion access and economic changes following the Dobbs U.S. Supreme Court decision., Study Design: This study used occupancy data from an average of 2,349,635 (standard deviation = 111,578) U.S. Airbnb listings each month from October 1st, 2020, through April 30th, 2023, to measure the impact of abortion bans on travel for abortion care and the resulting economic effects on regional economies., Methods: The study used a synthetic difference-in-differences design to compare monthly-level occupancy rate data from 1-bedroom entire-place Airbnb rentals within a 30-min driving distance of abortion clinics in states with and without abortion bans., Results: The study found a 1.4 percentage point decrease in occupancy rates of Airbnbs around abortion clinics in states where abortion bans were in effect, demonstrating reductions in Airbnb use in states with bans. In the 6-month period post Dobbs, this decrease translates to 16,548 fewer renters and a $1.87 million loss in revenue for 1-bedroom entire-place Airbnbs within a 30-min catchment area of abortion facilities in states with abortion restrictions., Conclusion: This novel use of Airbnb data provides a unique perspective on measuring demand for abortion and healthcare services and demonstrates the value of this data stream as a tool for understanding economic impacts of health policies., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

An interrupted time-series study design was implemented to evaluate the impact of antibiotic stewardship interventions on antibiotic prescribing among veterinarians. A total of 41 veterinarians were enrolled in Canada and Israel and their prescribing data between 2019 and 2021 were obtained. As an intervention, veterinarians periodically received three feedback reports comprising feedback on the participants' antibiotic prescribing and prescribing guidelines. A change in the level and trend of antibiotic prescribing after the administration of the intervention was compared using a multi-level generalized linear mixed-effect negative-binomial model. After the receipt of the first (incidence rate ratios [IRR] = 0.88; 95% confidence interval (CI): 0.79, 0.98), and second (IRR = 0.85; 95% CI: 0.75, 0.97) feedback reports, there was a reduced prescribing rate of total antibiotic when other parameters were held constant. This decline was more pronounced among Israeli veterinarians compared to Canadian veterinarians. When other parameters were held constant, the prescribing of critical antibiotics by Canadian veterinarians decreased by a factor of 0.39 compared to that of Israeli veterinarians. Evidently, antibiotic stewardship interventions can improve antibiotic prescribing in a veterinary setting. The strategy to sustain the effect of feedback reports and the determinants of differences between the two cohorts should be further explored.

The COVID-19 pandemic offers an unprecedented natural experiment providing insights into the emergence of collective behavioral changes of both exogenous (government mandated) and endogenous (spontaneous reaction to infection risks) origin. Here, we characterize collective physical distancing-mobility reductions, minimization of contacts, shortening of contact duration-in response to the COVID-19 pandemic in the pre-vaccine era by analyzing de-identified, privacy-preserving location data for a panel of over 5.5 million anonymized, opted-in U.S. devices. We define five indicators of users' mobility and proximity to investigate how the emerging collective behavior deviates from typical pre-pandemic patterns during the first nine months of the COVID-19 pandemic. We analyze both the dramatic changes due to the government mandated mitigation policies and the more spontaneous societal adaptation into a new (physically distanced) normal in the fall 2020. Using the indicators here defined we show that: a) during the COVID-19 pandemic, collective physical distancing displayed different phases and was heterogeneous across geographies, b) metropolitan areas displayed stronger reductions in mobility and contacts than rural areas; c) stronger reductions in commuting patterns are observed in geographical areas with a higher share of teleworkable jobs; d) commuting volumes during and after the lockdown period negatively correlate with unemployment rates; and e) increases in contact indicators correlate with future values of new deaths at a lag consistent with epidemiological parameters and surveillance reporting delays. In conclusion, this study demonstrates that the framework and indicators here presented can be used to analyze large-scale social distancing phenomena, paving the way for their use in future pandemics to analyze and monitor the effects of pandemic mitigation plans at the national and international levels., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Klein et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

The last decade has seen major advances and growth in internet-based surveillance for infectious diseases through advanced computational capacity, growing adoption of smart devices, increased availability of Artificial Intelligence (AI), alongside environmental pressures including climate and land use change contributing to increased threat and spread of pandemics and emerging infectious diseases. With the increasing burden of infectious diseases and the COVID-19 pandemic, the need for developing novel technologies and integrating internet-based data approaches to improving infectious disease surveillance is greater than ever. In this systematic review, we searched the scientific literature for research on internet-based or digital surveillance for influenza, dengue fever and COVID-19 from 2013 to 2023. We have provided an overview of recent internet-based surveillance research for emerging infectious diseases (EID), describing changes in the digital landscape, with recommendations for future research directed at public health policymakers, healthcare providers, and government health departments to enhance traditional surveillance for detecting, monitoring, reporting, and responding to influenza, dengue, and COVID-19., (© 2024. The Author(s).)

In recent years, there has been a rapid increase in the application of machine learning methods about predicting the incidence of dengue fever. However, the predictive factors and models employed in different studies vary greatly. Hence, we conducted a systematic review to summarize machine learning methods and predictors in previous studies. We searched PubMed, ScienceDirect, and Web of Science databases for articles published up to July 2023. The selected papers included not only the forecast of dengue incidence but also machine learning methods. A total of 23 papers were included in this study. Predictive factors included meteorological factors (22, 95.7%), historical dengue data (14, 60.9%), environmental factors (4, 17.4%), socioeconomic factors (4, 17.4%), vector surveillance data (2, 8.7%), and internet search data (3, 13.0%). Among meteorological factors, temperature (20, 87.0%), rainfall (20, 87.0%), and relative humidity (14, 60.9%) were the most commonly used. We found that Support Vector Machine (SVM) (6, 26.1%), Long Short-Term Memory (LSTM) (5, 21.7%), Random Forest (RF) (4, 17.4%), Least Absolute Shrinkage and Selection Operator (LASSO) (2, 8.7%), ensemble model (2, 8.7%), and other models (4, 17.4%) were identified as the best models based on evaluation metrics used in each article. These results indicate that meteorological factors are important predictors that cannot be ignored and SVM and LSTM algorithms are the most commonly used models in dengue fever prediction with good predictive performance. This review will contribute to the development of more robust early dengue warning systems and promote the application of machine learning methods in predicting climate-related infectious diseases., (© 2023. The Author(s) under exclusive licence to International Society of Biometeorology.)

The role of informatics in public health has increased over the past few decades, and the coronavirus disease 2019 (COVID-19) pandemic has underscored the critical importance of aggregated, multicenter, high-quality, near-real-time data to inform decision-making by physicians, hospital systems, and governments. Given the impact of the pandemic on perioperative and critical care services (eg, elective procedure delays; information sharing related to interventions in critically ill patients; regional bed-management under crisis conditions), anesthesiologists must recognize and advocate for improved informatic frameworks in their local environments. Most anesthesiologists receive little formal training in public health informatics (PHI) during clinical residency or through continuing medical education. The COVID-19 pandemic demonstrated that this knowledge gap represents a missed opportunity for our specialty to participate in informatics-related, public health-oriented clinical care and policy decision-making. This article briefly outlines the background of PHI, its relevance to perioperative care, and conceives intersections with PHI that could evolve over the next quarter century., Competing Interests: The authors declare no conflicts of interest., (Copyright © 2023 International Anesthesia Research Society.)

It is widely acknowledged that infectious diseases have wrought immense havoc on human society, being regarded as adversaries from which humanity cannot elude. In recent years, the advancement of Artificial Intelligence (AI) technology has ushered in a revolutionary era in the realm of infectious disease prevention and control. This evolution encompasses early warning of outbreaks, contact tracing, infection diagnosis, drug discovery, and the facilitation of drug design, alongside other facets of epidemic management. This article presents an overview of the utilization of AI systems in the field of infectious diseases, with a specific focus on their role during the COVID-19 pandemic. The article also highlights the contemporary challenges that AI confronts within this domain and posits strategies for their mitigation. There exists an imperative to further harness the potential applications of AI across multiple domains to augment its capacity in effectively addressing future disease outbreaks., (© 2024 Wiley Periodicals LLC.)

Background: Research has demonstrated the negative impact of racism on health, yet the measurement of racial sentiment remains challenging. This article provides practical guidance on using social media data for measuring public sentiment., Methods: We describe the main steps of such research, including data collection, data cleaning, binary sentiment analysis, and visualization of findings. We randomly sampled 55,844,310 publicly available tweets from 1 January 2011 to 31 December 2021 using Twitter's Application Programming Interface. We restricted analyses to US tweets in English using one or more 90 race-related keywords. We used a Support Vector Machine, a supervised machine learning model, for sentiment analysis., Results: The proportion of tweets referencing racially minoritized groups that were negative increased at the county, state, and national levels, with a 16.5% increase at the national level from 2011 to 2021. Tweets referencing Black and Middle Eastern people consistently had the highest proportion of negative sentiment compared with all other groups. Stratifying temporal trends by racial and ethnic groups revealed unique patterns reflecting historical events specific to each group, such as the killing of George Floyd regarding sentiment of posts referencing Black people, discussions of the border crisis near the 2018 midterm elections and anti-Latinx sentiment, and the emergence of COVID-19 and anti-Asian sentiment., Conclusions: This study demonstrates the utility of social media data as a quantitative means to measure racial sentiment over time and place. This approach can be extended to a range of public health topics to investigate how changes in social and cultural norms impact behaviors and policy.A supplemental digital video is available at http://links.lww.com/EDE/C91., Competing Interests: The authors report no conflicts of interest., (Copyright © 2023 The Author(s). Published by Wolters Kluwer Health, Inc.)

Mosquito vectors of pathogens (e.g., Aedes, Anopheles, and Culex spp. which transmit dengue, Zika, chikungunya, West Nile, malaria, and others) are of increasing concern for global public health. These vectors are geographically shifting under climate and other anthropogenic changes. As small-bodied ectotherms, mosquitoes are strongly affected by temperature, which causes unimodal responses in mosquito life history traits (e.g., biting rate, adult mortality rate, mosquito development rate, and probability of egg-to-adult survival) that exhibit upper and lower thermal limits and intermediate thermal optima in laboratory studies. However, it remains unknown how mosquito thermal responses measured in laboratory experiments relate to the realized thermal responses of mosquitoes in the field. To address this gap, we leverage thousands of global mosquito occurrences and geospatial satellite data at high spatial resolution to construct machine-learning based species distribution models, from which vector thermal responses are estimated. We apply methods to restrict models to the relevant mosquito activity season and to conduct ecologically plausible spatial background sampling centered around ecoregions for comparison to mosquito occurrence records. We found that thermal minima estimated from laboratory studies were highly correlated with those from the species distributions (r = 0.87). The thermal optima were less strongly correlated (r = 0.69). For most species, we did not detect thermal maxima from their observed distributions so were unable to compare to laboratory-based estimates. The results suggest that laboratory studies have the potential to be highly transportable to predicting lower thermal limits and thermal optima of mosquitoes in the field. At the same time, lab-based models likely capture physiological limits on mosquito persistence at high temperatures that are not apparent from field-based observational studies but may critically determine mosquito responses to climate warming. Our results indicate that lab-based and field-based studies are highly complementary; performing the analyses in concert can help to more comprehensively understand vector response to climate change. Author summary: Mosquito vectors are strongly affected by temperature, and their distributions are likely to shift under climate change. Lab studies show that mosquito abundance has a unimodal response to temperature with thermal optima, upper and lower thermal limits. However, it remains unknown how mosquito laboratory-derived thermal responses relate to the thermal responses of mosquitoes in nature. We used a global database of field-collected mosquito occurrences, geospatial environmental covariates, and species distribution models to estimate the relationship between temperature and probability of mosquito occurrence. We found that thermal minima (r = 0.87) and, to a lesser degree, thermal optima (r = 0.69) estimated from laboratory studies were correlated with those from the species distribution models. For most species, we did not detect thermal maxima. These results suggest that laboratory studies and field-based machine learning studies are complementary. Together, they can help to better understand vector response to climate change. [ABSTRACT FROM AUTHOR]

Vaccine hesitancy was a major challenge during the COVID-19 pandemic. A common but sometimes ineffective intervention to reduce vaccine hesitancy involves providing information on vaccine effectiveness, side effects, and related probabilities. Could biased processing of this information contribute to vaccine refusal? We examined the information inspection of 1200 U.S. participants with anti-vaccination, neutral, or pro-vaccination attitudes before they stated their willingness to accept eight different COVID-19 vaccines. All participants—particularly those who were anti-vaccination—frequently ignored some of the information. This deliberate ignorance, especially toward probabilities of extreme side effects, was a stronger predictor of vaccine refusal than typically investigated demographic variables. Computational modeling suggested that vaccine refusals among anti-vaccination participants were driven by ignoring even inspected information. In the neutral and pro-vaccination groups, vaccine refusal was driven by distorted processing of side effects and their probabilities. Our findings highlight the necessity for interventions tailored to individual information-processing tendencies. [ABSTRACT FROM AUTHOR]

Background: Seasonal fluctuations in weather are recognized as factors that affect both Aedes (Ae.) aegypti mosquitoes and the diseases they carry, such as dengue fever. The El Niño-Southern Oscillation (ENSO) is widely regarded as one of the most impactful atmospheric phenomena on Earth, characterized by the interplay of shifting ocean temperatures, trade wind intensity, and atmospheric pressure, resulting in extensive alterations in climate conditions. In this study, we investigate the influence of ENSO and local weather conditions on the spatio-temporal variability of Ae. aegypti infestation index. Methods: We collected seasonal entomological survey data of immature forms of Ae. aegypti mosquitoes (Breteau index), as well as data on temperature, rainfall and the Oceanic Niño Index (ONI) for the period 2008–2018 over the 645 municipalities of the subtropical State of São Paulo (Brazil). We grounded our analytical approach on a Bayesian framework and we used a hierarchical spatio-temporal model to study the relationship between ENSO tracked by ONI, seasonal weather fluctuations and the larval index, while adjusting for population density and wealth inequalities. Results: Our results showed a relevant positive effect for El Niño on the Ae. aegypti larval index. In particular, we found that the number of positive containers would be expected to increase by 1.30-unit (95% Credible Intervals (CI): 1.23 to 1.37) with El Niño events (i.e., ≥ 1°C, moderate to strong) respect to neutral (and weak) events. We also found that seasonal rainfall exceeding 153.12 mm appears to have a notable impact on vector index, leading potentially to the accumulation of ample water in outdoor discarded receptacles, supporting the aquatic phase of mosquito development. Additionally, seasonal temperature above 23.30°C was found positively associated to the larval index. Although the State of São Paulo as a whole has characteristics favourable to proliferation of the vector, there were specific areas with a greater tendency for mosquito infestation, since the most vulnerable areas are predominantly situated in the central and northern regions of the state, with hot spots of abundance in the south, especially during El Niño events. Our findings also indicate that social disparities present in the municipalities contributes to Ae. aegypti proliferation. Conclusions: Considering the anticipated rise in both the frequency and intensity of El Niño events in the forthcoming decades as a consequence of climate change, the urgency to enhance our ability to track and diminish arbovirus outbreaks is crucial. Author summary: Dengue fever is a dramatically expanding disease transmitted by mosquitoes. Earlier research has demonstrated the susceptibility of dengue-carrying mosquitoes to weather conditions, with rainfall and temperature significantly impacting dengue transmission. While there is evidence of a connection between weather factors, the El Niño-Southern Oscillation (ENSO) and the risk of dengue, their impact on the behaviour of Aedes (Ae.) aegypti mosquitoes, which is the main disease vectors, remains poorly comprehended. In this study we analyse municipality-specific time series of a larval index of Ae. aegypti population in the State of São Paulo (southern Brazil) from 2008 to 2018. The main goal was to explore how intra- and inter-annual changes in weather conditions and ENSO periodic fluctuations affected mosquito abundance. We found a strong and significant coherence between the temporal pattern of mosquito abundance and El Niño phases, rainfall and temperature, showing the role that climate plays in driving infestation periodicity. Specifically, we identified a relationship between Ae. aegypti infestation and the strength of the ENSO phenomenon: periods characterized by El Niño phase (moderate to strong) were associated with a higher abundance of Ae. aegypti mosquito larvae. Additionally, we found that wealth inequalities increase opportunities for Ae. aegypti proliferation. Our discoveries carry significant implications for vector control strategies, as they offer valuable insights for categorizing areas based on spatial risk, enabling the prioritization of intensified vector control measures. Given the anticipated rise in the frequency of El Niño events due to climate change, these findings suggest that arbovirus outbreaks could also see an increase, and that El Niño occurrences might serve as a predictive tool for such outbreaks. [ABSTRACT FROM AUTHOR]

Background: While Human Factors (HF) methods have been applied to the design of decision support systems (DSS) to aid clinical decision-making, the role of HF to improve decision-support for population health outcomes is less understood. We sought to comprehensively understand how HF methods have been used in designing digital population health DSS. Materials and methods: We searched English documents published in health sciences and engineering databases (Medline, Embase, PsychINFO, Scopus, Comendex, Inspec, IEEE Xplore) between January 1990 and September 2023 describing the development, validation or application of HF principles to decision support tools in population health. Results: We identified 21,581 unique records and included 153 studies for data extraction and synthesis. We included research articles that had a target end-user in population health and that used HF. HF methods were applied throughout the design lifecycle. Users were engaged early in the design lifecycle in the needs assessment and requirements gathering phase and design and prototyping phase with qualitative methods such as interviews. In later stages in the lifecycle, during user testing and evaluation, and post deployment evaluation, quantitative methods were more frequently used. However, only three studies used an experimental framework or conducted A/B testing. Conclusions: While HF have been applied in a variety of contexts in the design of data-driven DSSs for population health, few have used Human Factors to its full potential. We offer recommendations for how HF can be leveraged throughout the design lifecycle. Most crucially, system designers should engage with users early on and throughout the design process. Our findings can support stakeholders to further empower public health systems. [ABSTRACT FROM AUTHOR]

Background: This study aimed to investigate the relationship between parent and child COVID-19 immunization series completion using vaccine records. Methods: A cross-sectional chart review was performed on parent and child patients at the Albany Medical Center Internal Medicine and Pediatrics practice. Patient and parent demographic data, COVID-19 immunization status, and health care worker status was collected. Patient data was further separated into 2 cohorts to account for vaccine eligibility among child age groups, with 5–11 years and 6 months-4 years cohorts. Chi square or Fisher's exact test was used where appropriate. Results: Across both cohorts, 371 child patients were identified and cross-matched with corresponding parents. Neither cohort offered evidence linking child immunization series completion with the child's race, ethnicity, or county of residence. However, rates of series completion were higher for children with private insurance versus public options in both the 5–11 years and 6 months-4 years cohorts (both p < 0.001). Children were more likely to be immunized against COVID-19 if their parents were fully immunized against COVID-19 (both p < 0.05). Children aged 6 months-4 years were more likely to be immunized against COVID-19 if their parent was a health care worker (p = 0.038). Conclusions: This study demonstrates a significance between child insurance status, as well as between parental vaccination status and child vaccination status. Only children under 5 years were more likely to be vaccinated against COVID-19 if their parent was a health care worker. [ABSTRACT FROM AUTHOR]

In part two of this mini-series, we evaluate the range of machine-learning tools now available for application to veterinary clinical text-mining. These tools will be vital to automate extraction of information from large datasets of veterinary clinical narratives curated by projects such as the Small Animal Veterinary Surveillance Network (SAVSNET) and VetCompass, where volumes of millions of records preclude reading records and the complexities of clinical notes limit usefulness of more "traditional" text-mining approaches. We discuss the application of various machine learning techniques ranging from simple models for identifying words and phrases with similar meanings to expand lexicons for keyword searching, to the use of more complex language models. Specifically, we describe the use of language models for record annotation, unsupervised approaches for identifying topics within large datasets, and discuss more recent developments in the area of generative models (such as ChatGPT). As these models become increasingly complex it is pertinent that researchers and clinicians work together to ensure that the outputs of these models are explainable in order to instill confidence in any conclusions drawn from them. [ABSTRACT FROM AUTHOR]

Bats (Chiroptera), the second largest group of mammals, are known for their unique immune system and their ability to act as vectors for various zoonoses. Bats also act as important carriers of fungi, which include plant, animal, and human pathogens. Their roosting areas, foraging behaviors, and even migration routes make bats ideal vectors for fungi. We isolated 75 culturable fungal species from bats in Yunnan Province, China, with 36 species representing known pathogens of plants, animals, and humans, while 39 species are non-pathogenic fungi. Among these species, 77% (58 species) belonged to Ascomycota, 9% (seven species) belonged to Basidiomycota, and 13% (10 species) belonged to Mucoromycota. Even though several taxonomic studies on fungi associated with bats have been published, studies exploring the role of bats as fungal vectors are lacking. This study discusses the fungi host-specific traits and pathogenicity and the impact and ecological significance of bats as fungal vectors. [ABSTRACT FROM AUTHOR]

Background: While dengue NS1 antigen has been shown to be associated with disease pathogenesis in some studies, it has not been linked in other studies, with the reasons remaining unclear. NS1 antigen levels in acute dengue are often associated with increased disease severity, but there has been a wide variation in results based on past dengue infection and infecting dengue virus (DENV) serotype. As NS1 engages with many host lipids, we hypothesize that the type of NS1-lipid interactions alters its pathogenicity. Methods: Primary human monocyte derived macrophages (MDMs) were co-cultured with NS1 alone or with HDL, LDL, LPS and/or platelet activating factor (PAF) from individuals with a history of past dengue fever (DF = 8) or dengue haemorrhagic fever (DHF = 8). IL-1β levels were measured in culture supernatants, and gene expression analysis carried out in MDMs. Monocyte subpopulations were assessed by flow cytometry. Hierarchical cluster analysis with Euclidean distance calculations were used to differentiate clusters. Differentially expressed variables were extracted and a classifier model was developed to differentiate between past DF and DHF. Results: Significantly higher levels of IL-1β were seen in culture supernatants when NS1 was co-cultured with LDL (p = 0.01, median = 45.69 pg/ml), but lower levels when NS1 was co-cultured with HDL (p = 0.05, median = 4.617 pg/ml). MDMs of those with past DHF produced higher levels of IL-1β when NS1 was co-cultured with PAF (p = 0.02). MDMs of individuals with past DHF, were significantly more likely to down-regulate RPLP2 gene expression when macrophages were co-cultured with either PAF alone, or NS1 combined with PAF, or NS1 combined with LDL. When NS1 was co-cultured with PAF, HDL or LDL two clusters were detected based on IL10 expression, but these did not differentiate those with past DF or DHF. Conclusions: As RPLP2 is important in DENV replication, regulating cellular stress responses and immune responses and IL-10 is associated with severe disease, it would be important to further explore how differential expression of RPLP2 and IL-10 could lead to disease pathogenesis based on NS1 and lipid interactions. [ABSTRACT FROM AUTHOR]

The Zika virus (ZIKV) is a serious global public health crisis. A major control challenge is its multiple transmission modes. This paper aims to simulate the transmission patterns of ZIKV using a dynamic process-based epidemiological model written in ordinary differential equations, which incorporates the human-to-mosquito infection by bites and sewage, mosquito-to-human infection by bites, and human-to-human infection by sex. Mathematical analyses are carried out to calculate the basic reproduction number and backward bifurcation, and prove the existence and stability of the equilibria. The model is validated with infection data by applying it to the 2015–2016 ZIKV epidemic in Brazil. The results indicate that the reproduction number is estimated to be 2.13, in which the contributions by mosquito bite, sex and sewage account for 85.7%, 3.5% and 10.8%, respectively. This number and the morbidity rate are most sensitive to parameters related to mosquito ecology, rather than asymptomatic or human-to-human transmission. Multiple transmission routes and suitable temperature exacerbate ZIKV infection in Brazil, and the vast majority of human infection cases were prevented by the intervention implemented. These findings may provide new insights to improve the risk assessment of ZIKV infection. [ABSTRACT FROM AUTHOR]

Partisans' stated beliefs about the economy vary dramatically depending on the party that holds the presidency. Do these responses represent genuine differences in beliefs about the economy, or do they reflect partisans' expressive reporting on surveys? To answer this question, we rely on a novel dataset of Bing searches related to housing, automobiles, and stock market purchases by partisans from February 2016 to July 2017. We find that in the aftermath of the 2016 election, Democrats, as members of the losing party, were modestly less likely to search for both house and car purchase terms. Republicans showed no change. This shift in investment behavior among Democrats suggests that partisans' survey responses are at least partially due to different beliefs about the economy, rather than just expressive reporting. [ABSTRACT FROM AUTHOR]

Material scientists are becoming less and less optimistic about the use of renewable resources in novel applications. In turn, there is significant promise for carbon dots (CDs), carbon dot-silver nanocomposite (CD-AgNC), and other zero-dimensional carbon-based nanomaterials to be used as anti-bacterial, anti-cancer, and mosquitocidal agents in the fight against new infectious illnesses. Further, it possesses acceptable properties such as a high level of stability, straightforward synthesis protocol (green technology), and non-toxicity to the environment. In our present study, we aimed to investigate the potential of CD-AgNC on mosquitocidal, anti-bacterial, and anti-cancer using the marine algae–mediated synthesis of CD-AgNC as a novel approach. The c-dot precursor in this experiment was the marine red algae Chondrococcus hornemanni. The CD-AgNC was produced without the use of a reducing agent using a straightforward thermal processing approach. UV, FTIR, XRD, TEM, EDX, and Zeta potential were used to characterize the synthesized CD-AgNC. For mosquitocidal activity, we adopted the WHO method to determine the anti-larvicidal. The antibacterial study was performed using the disk diffusion method and anticancer studies were demonstrated with breast cancer cell line (MDA-MB-231) and the fluorescence staining analysis (AO/EtBr, DCFH-DA, Rho-123 (ΔΨm), and Hoechst-33342) was used to prove the cell apoptosis. The CD-Ag showed high larvicidal activity against Ae. aegypti, in particular, the LC50 of CD-AgNC against 3rd instar larvae at 27.59 µg/mL and 4th instar larvae at 39.18 µg/mL. The CD-AgNC decreased the longevity and fecundity of Ae. aegypti. Moreover, CD-AgNC showed significant anti-bacterial effects against several pathogenic. Mainly, CD-AgNC induces the IC50 value at 12 µg/mL with produced dead cells in MDA-MB-231. More on, it creates hyper ROS with altered MMP (ΔΨm) in the cell cytoplasm. The overall findings illustrated that the C. hornemanni–mediated synthesized CD-AgNC actively works against the dengue vector and shows a good anti-bacterial effect along with anti-cancer properties characterized by cytotoxicity effect. [ABSTRACT FROM AUTHOR]

This paper compares formal accounts that laboratory members use to describe a digital psychiatry intervention with lab practices as they were observed ethnographically. The intervention uses correlations between wearable data and self-reports of mood to produce behavior change recommendations, which participants then implement with the help of a guide. In formal accounts, the trial is described as the accomplishment of the individual participant who is empowered by data-driven insights. Formal accounts from the lab present this participant as multi-layered. In observation, however, participants do not seem multiple. Instead, the contingencies of living and working environments come to the fore. Observations of open-ended interactions in guidance sessions, which are minimized in formal accounts, are particularly powerful in capturing these contingencies. The critique of representation from philosophical pragmatism shows how the multi-layered participant is similar to the multiple representational meanings entities take on according to theories common in twentieth-century psychiatry, and supports the ethnographic approach as a way out of these paradoxical formulations. The paper, thus, explains the role of otherwise underspecified human labor involved in the trial, while demonstrating ethnography's ability to account for interactions with participants, through which we gain insights into the contingent, emplaced process of implementing the trial. [ABSTRACT FROM AUTHOR]

Realistic and modifiable infection models are required to study the pathogenesis of amphibian chytridiomycosis. Understanding the mechanism by which Batrachochytrium dendrobatidis (Bd) can infect and kill diverse amphibians is key to mitigating this pathogen and preventing further loss of biodiversity. In vitro studies of Bd typically rely on a tryptone based growth media, whereas the recent development of a kidney cell-line infection model has provided a more realistic alternative, without the need for live animals. Here we use expression of a fluorescent reporter to enhance the in vitro cell-line based growth assay, and show that transformed Bd cells are able to invade and grow in an amphibian kidney epithelial cell line (A6) as well as in a new system using a lung fibroblast cell line (DWJ). Both Bd and host cells were modified to express reporter fluorescent proteins, enabling immediate and continuous observation of the infection process without the need for destructive sampling for fixation and staining. Plasmid DNA conferring hygromycin resistance and TdTomato (RFP) expression was delivered to Bd zoospores via electroporation, and continuous antibiotic selection after recovery produced stable fluorescent Bd transformants. Host cells (A6 and DWJ) were transfected before each assay using lipofection to deliver plasmid DNA conferring green fluorescent protein (GFP) and containing an empty shRNA expression cassette. Bd RFP expression allowed easy localisation of fungal cells and identification of endobiotic growth was assisted by host GFP expression, by allowing visualization of the space in the host cell occupied by the invading fungal body. In addition to enabling enhanced live imaging, these methods will facilitate future genetic modification and characterisation of specific genes and their effect on Bd virulence. [ABSTRACT FROM AUTHOR]

Introduction: The COVID-19 pandemic has raised concerns about the global capacity for timely outbreak reporting. However, gaps remain in our understanding of barriers and enablers to outbreak reporting, particularly at the local level. Field epidemiology training program (FETP) fellows often participate in the outbreak reporting process as part of both their training and the public health roles they assume after graduating; they therefore represent a potentially valuable source of information for better understanding these barriers and enablers. This study will investigate the barriers and enablers to outbreak reporting through a mixed methods approach that will encompass a review of the existing literature as well as surveying and interviewing FETP trainees and graduates from the Asia-Pacific region. Methods: This study will begin with a scoping review of the literature to identify existing evidence of barriers and enablers to outbreak reporting. Based on our findings from the scoping review, we will administer a survey to FETP trainees and graduates from the World Health Organization Western Pacific and South-East Asian Regions and conduct interviews with a subset of survey respondents to investigate the survey findings in more detail. We will summarise and compare the survey results according to various country-level economic and political indicators, and we will employ thematic analysis to evaluate the interview responses. Based on the findings from the scoping review, survey, and interviews, we will construct a model to comprehensively describe the various barriers and enablers to outbreak reporting. Conclusion: This study will contribute to our understanding of the determinants of outbreak reporting across several geographic, political, and economic contexts by eliciting the viewpoints and experiences of persons involved with outbreak reporting, particularly at the local level. This information will help improve the outbreak reporting process, allowing for more timely reporting and helping prevent future outbreaks from becoming pandemics. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background: Although clinical substance use disorder (SUD) care is multidisciplinary there are few opportunities to collaborate for quality improvement or systems change. In Oregon, the Project ECHO (Extension for Community Healthcare Outcomes) model was adapted to create a novel multidisciplinary SUD Leadership ECHO. The objective of this study was to understand the unique effects of the adapted ECHO model, determine if the SUD Leadership ECHO could promote systems change, and identify elements that enabled participant-leaders to make changes. Methods: Four focus groups were conducted between August and September of 2022 with a purposive sample of participants from the second cohort of the Oregon ECHO Network's SUD Leadership ECHO that ran January to June 2022. Focus group domains addressed the benefits of the adapted ECHO model, whether and why participants were able to make systems change following participation in the ECHO, and recommendations for improvement. Thematic analysis developed emergent themes. Results: 16 of the 53 ECHO participants participated in the focus groups. We found that the SUD Leadership ECHO built a multi-disciplinary community of practice among leaders and reduced isolation and burnout. Three participants reported making organizational changes following participation in the ECHO. Those who successfully made changes heard best practices and how other organizations approached problems. Barriers to initiating practice and policy changes included lack of formal leadership authority, time constraints, and higher-level systemic issues. Participants desired for future iterations of the ECHO more focused presentations on a singular topic, and asked for a greater focus on solutions, advocacy, and next steps. Conclusions: The adapted ECHO model was well received by focus group participants, with mixed reports on whether participation equipped them to initiate organizational or policy changes. Our findings suggest that the SUD Leadership ECHO model, with fine-tuning, is a promising avenue to support SUD leaders in promoting systems change and reducing isolation among SUD leaders. [ABSTRACT FROM AUTHOR]

Background: Sclerotinia sclerotiorum is a highly destructive phytopathogenic fungus that poses a significant threat to a wide array of crops. The current constraints in genetic manipulation techniques impede a thorough comprehension of its pathogenic mechanisms and the development of effective control strategies. Results: Herein, we present a highly efficient genetic transformation system for S. sclerotiorum, leveraging the use of fusiform nanoparticles, which are synthesized with FeCl3 and 2,6-diaminopyrimidine (DAP). These nanoparticles, with an average longitude length of 59.00 nm and a positively charged surface, facilitate the direct delivery of exogenous DNA into the mycelial cells of S. sclerotiorum, as well as successful integration with stable expression. Notably, this system circumvents fungal protoplast preparation and tedious recovery processes, streamlining the transformation process considerably. Furthermore, we successfully employed this system to generate S. sclerotiorum strains with silenced oxaloacetate acetylhydrolase-encoding gene Ss-oah1. Conclusions: Our findings demonstrate the feasibility of using nanoparticle-mediated delivery as a rapid and reliable tool for genetic modification in S. sclerotiorum. Given its simplicity and high efficiency, it has the potential to significantly propel genetic research in filamentous fungi, offering new avenues for elucidating the intricacies of pathogenicity and developing innovative disease management strategies. [ABSTRACT FROM AUTHOR]

Objective: The pandemic contributed to increased mental and cognitive health concerns as well as reduced utilization of preventive and treatment focused care. Deferred care can contribute to negative clinical outcomes, including increased acuity of mental and cognitive health concerns that benefit from early intervention. A new visit type was launched with the aim of reaching patients who may need care and supporting early identification of cognitive and mental health issues. Methods: We developed the Healthy Mind visit, a preventative visit administered by Primary Care Providers (PCPs) within outpatient clinics. The Healthy Mind visit included use of a pre-visit mental health screener as well as a brief computerized cognitive assessment. The clinical interaction focused on identifying mental and cognitive health concerns and developing a plan for wellness. Outcomes data collection occurred over nearly 2.5 years and focused on analyzing booking patterns, depression screening and follow-up, and clinical outcomes such as health confidence and post-visit motivation. Results: The visit was effective in supporting depression screening and follow-up with 29.6% of those participating in a Healthy Mind visit receiving a PHQ-9, and 82.5% of patients with elevated PHQ-9 scores attending a subsequent visit with their PCP. Improvements in health confidence and high patient reported motivation post-visit represent other notable clinical outcomes. The visit also promoted care utilization, with 73.5% of patients who attended a Healthy Mind visit being new to the practice or those who had not attended an appointment in the past 6 months. Conclusions: The Healthy Mind visit, with a focus on mental and cognitive health, was successful in reaching patients who may not have otherwise sought care and supported positive clinical outcomes including early identification and treatment of depression and increased health confidence. Recommendations: These findings reflect the importance of developing innovative programs to connect patients with care, especially those who may have deferred care due to a variety of factors. Focusing on mental and cognitive health with the use of innovative tools such as a computerized assessment, can drive patient interest in care offerings and support positive clinical outcomes. [ABSTRACT FROM AUTHOR]

Background: Describing the transmission dynamics of infectious diseases across different regions is crucial for effective disease surveillance. The multivariate time series (MTS) model has been widely adopted for constructing cross-regional infectious disease transmission networks due to its strengths in interpretability and predictive performance. Nevertheless, the assumption of constant parameters frequently disregards the dynamic shifts in disease transmission rates, thereby compromising the accuracy of early warnings. This study investigated the applicability of time-varying MTS models in multi-regional infectious disease monitoring and explored strategies for model selection. Methods: This study focused on two prominent time-varying MTS models: the time-varying parameter-stochastic volatility-vector autoregression (TVP-SV-VAR) model and the time-varying VAR model using the generalized additive framework (tvvarGAM), and intended to explore and verify their applicable conditions for the surveillance of infectious diseases. For the first time, this study proposed the time delay coefficient and spatial sparsity indicators for model selection. These indicators quantify the temporal lags and spatial distribution of infectious disease data, respectively. Simulation study adopted from real-world infectious disease surveillance was carried out to compare model performances under various scenarios of spatio-temporal variation as well as random volatility. Meanwhile, we illustrated how the modelling process could help the surveillance of infectious diseases with an application to the influenza-like case in Sichuan Province, China. Results: When the spatio-temporal variation was small (time delay coefficient: 0.1–0.2, spatial sparsity:0.1–0.3), the TVP-SV-VAR model was superior with smaller fitting residuals and standard errors of parameter estimation than those of the tvvarGAM model. In contrast, the tvvarGAM model was preferable when the spatio-temporal variation increased (time delay coefficient: 0.2–0.3, spatial sparsity: 0.6–0.9). Conclusion: This study emphasized the importance of considering spatio-temporal variations when selecting appropriate models for infectious disease surveillance. By incorporating our novel indicators—the time delay coefficient and spatial sparsity—into the model selection process, the study could enhance the accuracy and effectiveness of infectious disease monitoring efforts. This approach was not only valuable in the context of this study, but also has broader implications for improving time-varying MTS analyses in various applications. [ABSTRACT FROM AUTHOR]

Long Covid is a chronic disease that affects more than 65 million people worldwide, characterized by a wide range of persistent symptoms following a Covid-19 infection. Previous studies have investigated potential risk factors contributing to elevated vulnerability to Long Covid. However, research on the social traits associated with affected patients is scarce. This study introduces an innovative methodological approach that allows us to extract valuable insights directly from patients' voices. By analyzing written texts shared on social media platforms, we aim to collect information on the psychological aspects of people who report experiencing Long Covid. In particular, we collect texts of patients they wrote BEFORE they were afflicted with Long Covid. We examined the differences in communication style, sentiment, language complexity, and psychological factors of natural language use among the profiles of 6.107 Reddit users, distinguishing between those who claim they have never contracted Covid -19, those who claim to have had it, and those who claim to have experienced Long Covid symptoms. Our findings reveal that people in the Long Covid group frequently discussed health-related topics before the pandemic, indicating a greater focus on health-related concerns. Furthermore, they exhibited a more limited network of connections, lower linguistic complexity, and a greater propensity to employ emotionally charged expressions than the other groups. Using social media data, we can provide a unique opportunity to explore potential risk factors associated with Long Covid, starting from the patient's perspective. [ABSTRACT FROM AUTHOR]

After transplantation self-management is essential for graft survival and optimal quality of life. To address the need for home-based support in self-management, we implemented the "SelfCare after Renal Transplantation" (SeCReT) box, containing home-monitoring equipment combined with a smartphone application that was linked to the electronic patient records. This study investigated the uptake and continuation, protocol adherence, and subjective evaluation of this home-monitoring program. All "de novo" kidney recipients who received the SeCReT-box in the study period (Aug 2021-Dec 2022) were eligible for inclusion. Protocol adherence was defined as ≥75%. Subjective evaluation was assessed with a 5-item questionnaire. Of the 297 recipients transplanted, 178 participants (60%) were included in the analysis. Protocol adherence was 83%, 73%, 66%, and 57% respectively at 5, 10, 20, and 40 weeks of the protocol. With regard to continuation, 135 participants were still in the program at the end of the study period (75% retention rate). Regarding subjective evaluations, 82% evaluated the program positively, and 52% reported lower care needs due to home-monitoring. Results are positive among those who entered and continued the program. Qualitative research is needed on barriers to entering the program and facilitators of use in order to promote optimal implementation. [ABSTRACT FROM AUTHOR]

Background: China has the third largest number of TB cases in the world, and the average annual floating population in China is more than 200 million, the increasing floating population across regions has a tremendous potential for spreading infectious diseases, however, the role of increasing massive floating population in tuberculosis transmission is yet unclear in China. Methods: 29,667 tuberculosis flow data were derived from the new smear-positive pulmonary tuberculosis cases in China. Spatial variation of TB transmission was measured by geodetector q-statistic and spatial interaction model was used to model the tuberculosis flow and the regional socioeconomic factors. Results: Tuberculosis transmission flow presented spatial heterogeneity. The Pearl River Delta in southern China and the Yangtze River Delta along China's east coast presented as the largest destination and concentration areas of tuberculosis inflows. Socioeconomic factors were determinants of tuberculosis flow. Some impact factors showed different spatial associations with tuberculosis transmission flow. A 10% increase in per capita GDP was associated with 10.2% in 2010 or 2.1% in 2012 decrease in tuberculosis outflows from the provinces of origin, and 1.2% in 2010 or 0.5% increase in tuberculosis inflows to the destinations and 18.9% increase in intraprovincial flow in 2012. Per capita net income of rural households and per capita disposable income of urban households were positively associated with tuberculosis flows. A 10% increase in per capita net income corresponded to 14.0% in 2010 or 3.6% in 2012 increase in outflows from the origin, 44.2% in 2010 or 12.8% increase in inflows to the destinations and 47.9% increase in intraprovincial flows in 2012. Tuberculosis incidence had positive impacts on tuberculosis flows. A 10% increase in the number of tuberculosis cases corresponded to 2.2% in 2010 or 1.1% in 2012 increase in tuberculosis inflows to the destinations, 5.2% in 2010 or 2.0% in 2012 increase in outflows from the origins, 11.5% in 2010 or 2.2% in 2012 increase in intraprovincial flows. Conclusions: Tuberculosis flows had clear spatial stratified heterogeneity and spatial autocorrelation, regional socio-economic characteristics had diverse and statistically significant effects on tuberculosis flows in the origin and destination, and income factor played an important role among the determinants. [ABSTRACT FROM AUTHOR]

The hepatitis A virus (HAV) endemicity in the Philippines ranges from moderate to high. Despite this, health risks related to local seafood consumption are associated with red tide or mass fish kill. However, government agencies should consider the safety of local shellfish and culture sites to HAV. This article examines the current trends relevant to the presence of HAV along the local shellfish supply chain-including production levels, microbial concentration, consumer behavior, and food safety regulations. Despite the susceptibility of local shellfish to HAV, the study identified crucial data gaps: [1] reliance on bacterial indicators, [2] absence of monitoring records for HAV concentration in local shellfish products and culturing sites, and [3] limited studies regarding the effect of local cooking practices on HAV inactivation. The synthesis of this analysis will guide the national food safety risk managers in addressing the identified data gaps to facilitate a full-blown food risk analysis of HAV in Philippine shellfish. [ABSTRACT FROM AUTHOR]