While family-centered rounds are considered the standard of care, coordinating the presence of all stakeholders without knowing when the medical team will arrive can make their implementation challenging. To address this barrier, we created novel software, Q-rounds, that is integrated into the electronic health record and creates a real-time rounding queue that updates nurses and family members on medical team rounding time and allows families to join in person or remotely via phone. A previous observational study showed that the implementation of Q-rounds in a neonatal intensive care unit (NICU) led to increased nurse and family presence. This study aimed to understand clinicians' perceptions of the use of Q-rounds on NICU rounding practices, such as the impact on rounding efficiency and clinician satisfaction, as well as facilitators and barriers in its implementation. Q-rounds was implemented in the NICU at M Health Fairview Masonic Children's Hospital in May 2023. In this survey study, surveys were distributed to physicians, advanced practice providers, and nurses 6 weeks before implementation and 6 weeks afterward to collect data on the impact of Q-rounds on family-centered rounds. Data were analyzed both quantitatively and qualitatively. There were 118 respondents in the preimplementation phase and 110 in the postimplementation phase. After implementation of Q-rounds, clinicians perceived an increase in nurse presence ("most" or "almost all of the time" n/N= 48/105, 46% to n/N=24/36, 75%; P<.001) and family presence (at least "sometimes" n/N=45/106, 46% to n/N=35/36, 97%; P<.001) on rounds. Respondents perceived rounds as more efficient (n/N=56/105, 53% to n/N=27/36, 75%; P=.006), and more respondents indicated being satisfied with rounds (n/N=59/105, 46% to n/N=62/84, 74%; P=.003). There was no perceived difference in rounding duration. These findings were supported by thematic analysis of open-ended responses. A novel virtual rounding queue software that notifies families and nurses of when to expect the rounding team was associated with increased clinician perceptions of efficiency, participation in rounds by nurses and families, and satisfaction with rounds.
Delays in access to ambulatory care are associated with adverse health outcomes, diminished patient experience, and increased system inefficiencies. U.S. health systems have adopted automated waitlists, which represents a technology-enabled tool that notifies patients of earlier appointment availability. Existing case reports suggest benefits. However, there is limited evidence on the efficacy of adopting, implementing, and sustaining an automated waitlist to improve access. The objective of the study was to evaluate automated waitlists to identify determinants that influence their adoption and ongoing use. Findings may inform health care organizations seeking to improve access to appointments in the ambulatory setting. A convergent, multi-site, mixed-methods study was conducted. Data were collected through a survey of 127 health systems with 90 reporting data about automated waitlist usage accompanied by a criterion-based purposive study of 10 participating health systems. Both qualitative and quantitative data were collected from the 10 participating systems. The Consolidated Framework for Implementation Research was used to report the determinants of the intervention's performance. Automated waitlists provide benefits to US health systems. High-performing health systems reported 38.8% (IQR 36.2%-45.7%) of appointments offered by the automated waitlist were filled. Participants reported a lower missed-appointment rate (3.1%, IQR 2.5%-4.8%) for appointments scheduled via the automated waitlist, as compared to all appointments (6.6%, IQR 4.1%-9.9%). Flexible configuration serves as a key facilitator of adoption and maintenance. External pressures, including peer benchmarking and high patient demand, accelerated implementation. Insurance and records requirements, seasonality, care-plan configuration, and digital inequities controlled which patients could benefit from the intervention. Additionally, specialty gatekeeping and clinician capacity constraints limited impact. Organizational context strongly shaped effectiveness, with cross-functional governance structures, leadership endorsement, and cultures of iteration enabling sustained use. Automated waitlists may offer a solution to health care organizations striving to positively impact access to care. The effectiveness of automated waitlists depends primarily on the implementation process and inner-setting organizational determinants. Rather than functioning as a stand-alone technical solution, automated waitlists are most impactful when integrated as a dynamic component of system-level scheduling infrastructure. Not applicable.
In this paper, the International Pharmaceutical Aerosol Consortium on Regulation and Science (IPAC-RS), discusses the current regulatory landscape associated with change management of orally inhaled and nasal drug products (OINDP) and other drug device combination products in the European Union. The paper also describes current challenges related to alignment of regulatory expectations, particularly for integral drug device combination products, and proposes topics for further discussion with regulatory agencies and stakeholders to help advance alignment. To further illustrate current challenges and industry approaches to meeting change management requirements, we also present results of an IPAC-RS benchmarking survey and case studies, and outcome from interactions with notified bodies and regulatory agencies. This document is intended to be used as a guideline for industry alignment.
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We aimed to describe the occurrence and characteristics of vibriosis in Aotearoa New Zealand and the knowledge gaps caused by current disease reporting structures. Data on infections involving Vibrio spp. (vibriosis) were collated from New Zealand databases recording notifiable diseases, clinical diagnostic results, hospitalisations and deaths (1998-2024). These data were examined for trends and the effects of data collection protocols. V. parahaemolyticus, V. alginolyticus and non-O1/non-O139 V. cholerae were the most common Vibrio spp. reported from gastroenteritis cases and soft tissue infections. Some infections progressed to severe health conditions, including those due to V. vulnificus. The results indicated demographic and clinical trends, but further insights were limited by the lack of comprehensive case data (including exposure information), changes to data generation and collection over time and a decentralised reporting structure. Surveillance that captures all infections involving Vibrio spp. and associated metadata would make it possible to establish a baseline disease profile, prioritise health interventions and predict future epidemiological trends.
Nurse leaders at our site identified a gap in evidence-based practice related to dementia-specific pain assessment. The purpose of this quality improvement project was to improve pain detection and management in patients with advanced dementia by implementing the observational Pain Assessment in Advanced Dementia (PAINAD) scale in the clinical electronic medical record. Our project team used a pre- poststudy design to evaluate practice changes in implementing the PAINAD scale. Nurses were notified of the change to documentation through hospital-wide newsletters, daily huddles, and unit managers. Data was retroactively collected through chart review of patients with advanced dementia for the four weeks prior to implementation, and at 4- and 6-weeks after the implementation. Data collected included documentation of an observational pain assessment, treatment (pharmacologic or nonpharmacologic), whether pain was present, use of psychotropic medications, and documented improvement in pain at reassessment. We also surveyed nurses to understand their perceptions of the PAINAD. Improvements were observed in rates of observational pain assessment, analgesic or nonpharmacologic treatment of pain, and avoidance of psychotropic medications at 6-weeks post-implementation. However, no improvement was observed in the proportion of patients with reduced pain at reassessment after treatment. No changes were statistically significant. Overall, nurses were comfortable using the PAINAD and perceived it as useful. Implementing the PAINAD scale appeared to improve observational pain assessments and pain treatment at our facility. Implementation of the PAINAD scale across all clinical care units and expanded staff education are necessary to improve pain outcomes for patients with advanced dementia.
The time of exposure to an index case of tuberculosis (TB) increases the risk of tuberculosis infection (TI) for contacts. Our aim was to determine the impact of exposure time on the TI risk for contacts of pulmonary TB index cases. We conducted a cross-sectional prevalence study of TI among pulmonary TB contacts in Catalonia notified 1 January 2019-30 June 2021. Contacts with TI or TB were identified by means of the tuberculin skin test and/or interferon gamma release assay. The association of exposure time with TI in contacts was analysed using adjusted OR (aOR) and the 95% confidence interval (CI). TI prevalence was 25.5% (1670/6537), and was higher in males than in females (28.5% versus 22.6%; p<0.001), all age groups with respect to children <5 years (12.4%; p<0.001), immigrants (37.4% versus 18.1%; p<0.001), and smokers (56.0% versus 29.7%; p<0.001), and increased with exposure time to the index case. Relative to <6h/week exposure, TI risk was greater for exposures of ≥6h/day (aOR=2.0; 95% CI: 1.5-2.6) and <6h/day, but ≥6h/week (aOR=1.8; 95% CI: 1.3-2.4). Since the risk of TI increases with time of exposure to the index case, it is necessary to record it in all contacts to advance to TB elimination.
Emergency department (ED) crowding is a global challenge with adverse effects on patient outcomes and staff well-being. Traditional crowding scores, such as the National ED Overcrowding Study (NEDOCS) score, have limited ability to predict imminent crowding. The aim of this study is to develop and validate a real-time, artificial intelligence (AI)-driven prediction model using a long short-term memory (LSTM) neural network to predict ED crowding, using ambulance diversion status as the reference standard and comparing performance with the (modified) NEDOCS score. In this single-centre, retrospective cohort study, we extracted parameters related to crowding from electronic health records and workforce data. Ambulance diversion status served as the reference standard for crowding. The LSTM model was trained and evaluated with walk-forward validation and performance was assessed using the area under the receiver operating characteristic curve (AUC). Model calibration was assessed using Brier score and expected calibration error (ECE). The optimal LSTM model used the previous hour of data to predict one hour ahead, yielding an AUC of 0.84 (95% CI 0.81-0.86). Key features contributing to the predictive quality of our model included the number of pre-notified arrivals, patients in the yellow and orange triage categories, and the number of ED supervisors. Compared with the modified NEDOCS score (AUC of 0.73 (95% CI 0.70-0.76)), the LSTM model showed higher discriminative performance in internal validation within this single-centre study. Our AI-driven prediction model demonstrated good predictive performance for ED crowding in internal validation. These findings highlight the potential of real-time prediction models to support ED clinical workflow.
Placing patients on contact precautions (CP) reduces the spread of infection. VA Bug Alert (VABA) was developed to notify VA infection control staff of new patients with MDRO colonization or infection. This study examined the link between VABA use and CP initiation. Patients who had an MDRO-positive culture were included. Medical chart reviews were conducted on 660 admissions for evidence of a CP order. Logistic regression determined the likelihood of patients being placed into CP within 24 hours of admission. Kaplan-Meier survival analysis assessed whether VABA utilization was associated with a shorter time to CP. The final analysis included 659 admissions. Half of the patients were placed into CP within 24 hours of admission. High VABA use had a 69% increased likelihood of being placed into contact precautions within 24 hours compared to low. Kaplan-Meier analysis showed that patients at facilities in the highest tertile of VABA use had faster time to CP (median 2.1 hours) than those in the lower tertiles (median 6.3 hours) (p = 0.036). VABA use is associated with earlier and more rapid placement into CP, demonstrating the alert system's effectiveness in rapidly identifying MDRO patients to reduce the spread of infection.
Tuberculosis control efforts have traditionally targeted symptomatic individuals; however, the role of asymptomatic cases in sustaining transmission is increasingly recognised. We aimed to quantify the contribution of asymptomatic tuberculosis to recent transmission using genomic and epidemiological data from a high-transmission setting. We conducted a retrospective genomic epidemiology study of Mycobacterium tuberculosis isolates collected in Mato Grosso do Sul, Brazil, between Aug 25, 2008, and March 19, 2024. Available isolates underwent whole-genome sequencing. Demographic, clinical, incarceration history, and laboratory metadata were obtained from surveillance records. From Jan 1, 2017, to March 19, 2024, active case finding was conducted in the state's three largest prisons (all male-only facilities), during which sputum samples were collected from individuals irrespective of symptoms and tested using GeneXpert and culture. Comparisons of transmission between individuals with and without symptoms were restricted to individuals who were incarcerated and were identified through active case finding and for whom high-quality, M tuberculosis lineage 4 genomes were available. Metrics of recent transmission included phylogenetic clustering, time-scaled haplotype density (THD), local branching index (LBI), and transmission probabilities inferred using Bayesian Reconstruction and Evolutionary Analysis of Transmission Histories. 4448 tuberculosis cases were notified in Mato Grosso do Sul in 2008-24. After excluding cases for which M tuberculosis isolates were not available or had low sequencing quality, who had contaminated cultures or mixed infection, or who were infected with non-lineage 4 M tuberculosis, we included 2362 lineage 4 M tuberculosis isolates with high-quality genome sequences. 1849 (78·3%) of 2362 isolates were part of a genomic cluster. Among 2362 individuals with tuberculosis, 1137 (48·1%) were incarcerated at diagnosis. Of these individuals, 505 were identified through active case finding in three male-only prisons. The median age was 30 years (IQR 25-37); 304 (60·2%) had mixed ethnicity, 90 (17·8%) were White, 56 (11·1%) were Black, 13 (2·6%) were Indigenous, and six (1·2%) were Asian. 277 (54·9%) had symptomatic disease and 228 (45·1%) had asymptomatic tuberculosis. There were no significant differences between symptomatic and asymptomatic individuals in phylogenetic clustering (213 [76·9%] of 277 vs 195 [85·5%] of 228; p=0·37), THD (median 0·39 [IQR 0·06-0·62] vs 0·50 [0·09-0·65]; p=0·12), or LBI (0·00863 [0·00810-0·00988] vs 0·00871 [0·00829-0·01020]; p=0·088). Bayesian transmission trees showed no significant difference in the number of secondary infections inferred from symptomatic compared with asymptomatic individuals (p=0·56). These findings were consistent across genomic clusters and robust to model assumptions. We identified no differences in transmission between individuals who were symptomatic and those who were asymptomatic using multiple genomic measures. In this high-transmission setting, where systematic screening is implemented, our findings indicate that asymptomatic tuberculosis substantially contributes to tuberculosis transmission at the population level. These results suggest that symptom-based case detection alone is likely to be insufficient to interrupt transmission and highlight the importance of expanded screening strategies in high-risk populations. US National Institutes of Health and the Brazilian National Research Council (CNPq).
Despite evidence regarding the severity of occupational accidents involving biological material (OAIBM) during dental instrument cleaning, these risks remain underestimated by many workers. Few studies, however, have focused on the epidemiology of OAIBM during dental instrument cleaning. Thus, this study aimed to identify, analyze, and estimate the incidence rate of OAIBM during dental instrument cleaning by dental care professionals in Brazil. Notifications registered between January 2015 and July 2020 were then extracted from the Notifiable Diseases Information System (SINAN), a Brazilian government agency responsible for reporting and investigating infectious diseases. Reports of OAIBM were analyzed by dental care professionals (dentists, oral health assistants [OHA], and oral health technicians [OHT]) from all 26 Brazilian states and the Federal District. The mean incidence rate of OAIBM among dental care professionals in Brazil during dental instrument cleaning was 314.5 cases per 100,000 professionals. Of these cases, 88.2% involved OHTs, and most affected individuals were female (94.4%). Serum, blood, or plasma accounted for most exposures (71.8%), and 85.1% of exposures were percutaneous. Gloves were the most commonly used personal protective equipment (PPE) at the time of the accidents. Therefore, this study highlights the role of the dentist as the leader and technical authority, responsible for providing resources, standardizing processes, training the team, and supervising performance to ensure occupational safety, quality processing, and contributions to patient safety.
Invasive group A streptococcal (iGAS) infection has resurged globally following the coronavirus disease 2019 pandemic; however, long-term nationwide data from Asia is limited. This study aimed to investigate the epidemiology, clinical burden and molecular characteristics of iGAS infection in the Republic of Korea (ROK) over a 10-year period. A nationwide multicentre study for iGAS was conducted across 23 university-affiliated hospitals from 2015 to 2024. iGAS was defined as culture-confirmed isolation of Streptococcus pyogenes from sterile sites. Clinical data were collected using standardised forms. Available isolates were emm-typed and whole genome sequencing was performed for emm1 isolates. A total of 454 iGAS cases were identified. The mean annual incidence was 4.72/100,000 admissions (95% CI 4.26-5.13), declining from 7.03 (95% CI 6.26-7.77) in 2015-2019 to 1.72 (95% CI 1.27-2.26) in 2020-2022. In 2023-2024, the incidence returned to pre-pandemic levels among children (10.45 per 100,000 admissions, 95% CI 5.95-14.39), whereas the adult incidence partially recovered (2.47 per 100,000 admissions, 95% CI 1.76-3.33). Skin and soft tissue infection (22.7%), streptococcal toxic shock syndrome (STSS, 19.6%), and bacteremia without focus (17.0%) were the most common iGAS infections. Intensive care unit admission was required in 28.9% of iGAS cases, and overall mortality was 15.5%. Mortality was independently associated with STSS (aOR 20.07, 95% CI 9.30-43.30; p < 0.0001), older age (aOR 20.07, 95% CI 9.30-43.30; p < 0.0001), chronic medical condition (aOR 2.96, 95% CI 1.41-6.22; p = 0.004), and immunocompromised condition (aOR 2.33, 95% CI 1.07-5.09; p = 0.034) among adults. Among 98 isolates, the predominant strains were emm1 (32/98, 32.7%), emm12 (13/98, 13.3%), emm28 (11/98, 11.2%), and emm89 (8/98, 8.2%). Of 32 emm1 isolates, two (6.3%) isolates belonged to the epidemic M1UK lineage were identified (2020, 2024). Following marked pandemic-associated suppression, iGAS incidence rapidly rebounded among children in the ROK. This study reports the first detection of the M1UK lineage in the ROK, highlighting the need for statutory notification of iGAS as a nationally notifiable disease to enable continuous national surveillance with molecular characterization for monitoring emerging epidemic-prone strains. This work was supported by the Korea Disease Control and Prevention Agency.
Illness trends are typically monitored by reportable disease and syndromic surveillance systems, but unanticipated health issues might not be captured. Using diagnosis codes, the New York City Health Department developed a novel data mining process to detect unusual increases in emergency department (ED) visits for any reason. We applied the tree-temporal scan statistic in TreeScan software to ICD-10-CM diagnosis codes for ED visits. We searched for unusual citywide increases in ED visits or hospital admissions, over any recent time period, and at any part of and level on the ICD-10-CM tree. We conducted proof-of-concept analyses for March 2020 when COVID-19 emerged, then investigated signals detected in daily, automated analyses during April-August 2025. If TreeScan analyses had been in place, then increasing hospital admissions for viral pneumonia (J12) would have triggered a signal on March 13, 2020, two days before widespread COVID-19 community transmission was announced. An extreme heat event in June 2025 triggered a signal for admissions for acute kidney failure (N17), prompting outreach to dialysis networks. A sustained signal for hand, foot, and mouth disease (B08.4) prompted outreach to child care programs. Other signals supported situational awareness, including a seasonal increase for swimmer's ear (H60.33) and burns (T30.0) related to consumer fireworks. TreeScan quickly detected credible increases in various diagnoses without pre-specification, from minor to severe, rare to common, acute to sustained, and foreseen to unforeseen. TreeScan can strengthen surveillance for health issues related to new pathogens, non-notifiable conditions, environmental exposures, and mass gatherings.
Uganda, in line with global efforts to eradicate polio by 2026, conducts surveillance for acute flaccid paralysis (AFP) among children <15 years to enable the timely detection of poliovirus. We analysed national AFP surveillance data to characterise case demographics, spatiotemporal patterns and clinical outcomes, and to evaluate surveillance performance and progress towards eradication. We conducted a retrospective descriptive analysis of routine AFP surveillance data from 2016 to 2023. Variables included timeliness indicators, non-polio AFP (NPAFP) rates, stool adequacy and demographic, clinical, vaccination and laboratory characteristics. Trends were assessed using the Mann-Kendall test, and spatial patterns were illustrated using choropleth maps. A total of 6409 AFP cases were reported, of which 5837 (91.1%) were confirmed as true AFP. The median age was 3 years (IQR: 1-7), with most cases occurring in children <5 years (59.3%) and males (57.8%). Timeliness indicators showed that 63% of cases were notified within 7 days, 93.4% were investigated within 48 hours and 76.5% of stool specimens were shipped within 3 days. Stool adequacy was 88.6% and 99.8% of samples arrived in good condition. The NPAFP rate averaged 3.4/100 000, meeting the ≥3.0 target in 5 of 8 years. Clinically, 90% of cases were community-identified; 91% had fever at onset, 86% showed rapid progression within 3 days and 80% had asymmetrical paralysis. Among cases with vaccination history, 77% had received ≥3 oral polio vaccine doses and 3% were unvaccinated. Of 6405 samples tested, 2% were classified as suspected poliovirus and 0.1% as mixed non-polio enterovirus and poliovirus. Among 823 followed-up cases, 30% had residual paralysis, 1% had died and 0.6% were lost to follow-up. District-level performance was variable and suboptimal, with averages of 63.4% districts achieving the NPAFP rate target, 74.8% stool adequacy and 49% both core indicators. Uganda's AFP surveillance system remains largely robust, though improvements in timeliness, regional equity and follow-up are needed to sustain progress towards polio eradication.
BACKGROUNDFrom 1 March 2020, the Centre for Sexual Health Amsterdam limits presumptive treatment for chlamydia or gonorrhoea to clients notified by steady partners. Treatment after notification by non-steady partners requires a positive test result.AIMWe aimed to evaluate this change in policy on unnecessary antibiotic treatment, time to treatment and lost to follow-up clients.METHODSWe included consultations (with testing) following partner notification for chlamydia or gonorrhoea between 1 March 2017 and 1 March 2023. Primary outcome was unnecessary antibiotic treatment (presumptive treatment with negative test result) before vs after the policy change. We also assessed return for treatment of confirmed and not presumptively treated individuals after the policy change.RESULTSFrom 4,579 consultations, unnecessary antibiotics were prescribed for 786/1,318 (59.6%) clients for chlamydia and 729/1,117 (65.3%) clients for gonorrhoea before 1 March 2020. After 1 March 2020, this decreased to 324/1,275 (25%) and 341/1,369 (25%), respectively, corresponding to a relative reduction of 58% for chlamydia (adjusted relative risk (aRR): 0.42; 95% confidence interval (CI): 0.37-0.47) and 63% (aRR: 0.37; 95% CI: 0.33-0.42) for gonorrhoea. After the policy change, clients not given presumptive treatment but later diagnosed returned after a median of 7 days (interquartile range (IQR): 5-8) for chlamydia and 6 days (IQR: 4-7) for gonorrhoea. Missed treatment occurred in 4/155 (2.6%) of chlamydia and 5/158 (3.2%) of gonorrhoea consultations.CONCLUSIONThe new partner management protocol substantially reduced unnecessary antibiotic treatment for chlamydia and gonorrhoea. The majority of confirmed infections still received appropriate treatment.
In October 2024, Parties to the United Nations Convention on Biological Diversity agreed to a new multilateral mechanism to fund biodiversity conservation through the sharing of benefits from open biodiversity data. Biological databases hosting genetic and other biological data, known as digital sequence information (DSI), are central to the implementation of the mechanism. This paper assesses the new international agreement and its implications for DSI databases. We walk through the database provisions in COP16 Decision 16/2, which include notifying users and submitters about the mechanism, improving metadata on geographical location of sample collection, and consistency with open access, as well as consideration of the FAIR, CARE, and TRUST principles. Drawing on surveys, interviews, and a workshop with biological database managers, we identify practical and scalable measures including updating terms of use, revising submission procedures, and strengthening user communication. We also propose approaches to capture and report non-monetary benefits such as capacity building, publications, interoperability, and training. These actions illustrate how DSI databases can remain open, sustainable, and globally connected while supporting benefit-sharing from the use of DSI on genetic resources.
Understanding temporal and epidemiological patterns of pediatric infectious diseases is essential for developing targeted prevention strategies. This study investigated long-term incidence trends and epidemiological characteristics of notifiable infectious diseases among children in Xuhui District, Shanghai, from 2015 to 2023. Surveillance data for children aged 0-17 years were obtained from the National Notifiable Disease Reporting System (NNDRS). Joinpoint regression was applied to identify temporal trends and significant inflection points. Age-specific distributions were analyzed and seasonal patterns were visualized using radar charts. From 2015 to 2023, a total of 27,940 pediatric cases involving 23 notifiable infectious diseases were reported, corresponding to an average annual incidence of 2,421.68 per 100,000 children. Joinpoint regression identified a significant inflection point in 2021. Overall incidence declined during 2015-2021 (APC = -11.71%, 95% CI: -30.10 to -1.50, P = 0.029) and increased sharply thereafter (APC = 141.05%, 95% CI: 34.40-244.50, P < 0.001). When COVID-19 cases were excluded, no significant long-term trend was observed (APC = -4.2%, 95% CI: -23.60 -20.80, P = 0.71), indicating that the apparent post-2021 increase was driven primarily by COVID-19 notifications rather than a generalized resurgence of other pediatric infections. Disease-specific trajectories varied: influenza showed a pronounced post-pandemic surge, despite the absence of a significant long-term monotonic trend, whereas varicella, mumps, and scarlet fever continued to decline; in contrast, other infectious diarrhea showed a sustained upward trend (APC = 8.00%, 95% CI: 2.60-13.70, P = 0.003). Over time, the age distribution shifted toward school-aged children, with a significantly increasing proportion of cases occurring among those aged ≥4 years (χ 2 trend = 1475.594, P < 0.01). Pediatric infectious disease epidemiology in Xuhui District underwent substantial changes across the pre-pandemic and post-pandemic periods. The sharp rise after 2021 was largely attributable to COVID-19, rather than a uniform rebound of all infectious diseases. Distinct temporal patterns across respiratory, enteric, and other infections underscore the importance of pathogen-specific transmission characteristics and age-related exposure in shaping long-term trends. These findings highlight the need for targeted, age-appropriate prevention strategies and sustained surveillance in the post-pandemic era.
The worldwide health emergency caused by SARS-CoV-2 has profoundly reshaped healthcare systems and social behaviors, leading many countries to implement digital contact tracing (DCT) technologies. This study assesses Peru's DCT strategy during COVID-19 by analyzing real-world data from 1.66 million users of the Perú en tus manos app, among whom 80,068 cases were confirmed. Although low adoption constrained individual-level tracing, the dataset allowed for an examination of macro-level mobility trends, showing how trip lengths and travel behaviors changed across different policy phases. It also facilitated the analysis of micro-level contact patterns using bipartite stream graphs, identifying that higher temporal connectivity and participation in smaller gatherings were associated with greater infection risk. The research further illustrates how socioeconomic disparities affected mobility and transmission dynamics, as lower-income populations displayed wider movement ranges and higher infection rates than more affluent groups. Beyond its original purpose of notifying individuals about potential exposures, the findings underscore the broader potential of DCT data to guide public health policies, improve resource distribution, and mitigate inequities in pandemic responses, even when user engagement is limited. To support ongoing research, we share a dataset that integrates reconstructed large-scale contact networks with infection statuses, seeking to advance the creation of more effective DCT solutions.
Tobacco litter is one of the most common forms of litter, comprising between 26 and 33% of all visible litter, leading to severe environmental damage. The current study attempts to assess the compliance with the Tobacco-Free Kumbh Mela (TFKM) mandate by analyzing the spatial distribution of tobacco product wastes (TPWs) in Haridwar, Uttarakhand, during mid-March and April 2021. We collected information about TPWs (smoking and smokeless tobacco) in the Kumbh Mela area during the busiest week. The litter count (mean ± S.D.) was calculated for total TPWs along with litter density (per sqm) in various public places. We also used Quantum Geographic Information System (QGIS) software to develop a graduated map of the density of TPWs across the Kumbh Mela area. Bathing areas at the TFKM, which had the largest population density, had the least litter count (4.33 ± 1.26) and density (0.9 per sqm). While in the remotest location, parking areas were found to have maximum litter count (30.0 ± 18.0) and density (6.5 per sqm). Overall, the surveyed sites were non-compliant with the Tobacco-free Kumbh Mela as notified by the state government. Mass awareness campaigns should be undertaken at large scale to educate tobacco users regarding the harmful effects of tobacco use and the TPWs while inculcating a pro-envirnoment attitude to ensure compliance with directives like TFKM.
Leprosy remains a neglected and transmissible disease and continues to challenge the World Health Organization elimination goals, mainly due to delays in diagnosis and treatment. This analytical cross-sectional study assessed indicators related to the performance of the Health Care Network (HCN) of Minas Gerais, Brazil, in leprosy care using secondary data from 2014 to 2023. The outcomes evaluated were early diagnosis, timely treatment initiation, and disability outcomes. Data were obtained from the Notifiable Diseases Information System (SINAN) and the National Register of Health Establishments (CNES) and analyzed using multivariate logistic regression with a 5% significance level. Among 9,630 cases analyzed for early diagnosis, 55.8% were diagnosed early and 44.2% late. Among 10,402 cases, 63.4% initiated treatment within two days, while 36.6% experienced delays. Regarding disability outcomes, among 6,048 cases evaluated, 8.9% worsened, 73.9% remained unchanged, and 17.3% improved. The results reveal persistent disparities related to gender, ethnicity, education, healthcare level, and geographic region, indicating gaps in equity, service organization, and continuity of care. Strengthening primary health care, decentralizing services, and improving professional training are essential strategies to enhance outcomes and support leprosy elimination efforts.