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PubMed2026

The impact of alcohol sale restrictions on unnatural deaths during the COVID-19 pandemic in Johannesburg, South Africa.

The COVID-19-related restrictions on the sale of alcohol in South Africa presented a unique opportunity to examine the association between alcohol availability and the prevalence of unnatural deaths. The study sample included all unnatural deaths investigated by the Johannesburg Forensic Pathology Services Medico-Legal Laboratory during the four COVID-19 alcohol restriction periods compared to the same time periods in the previous year when there were no restrictions. When alcohol sales were initially prohibited, there was a 54.2% decrease in cases of unnatural deaths (p&#x2009;<&#x2009;0.05), suggesting a link between alcohol use and the occurrence of such deaths. Over all four periods of alcohol sale bans, there was a total reduction of unnatural deaths by 26.4% with declining frequencies in all demographics. There were significant decreases (p&#x2009;<&#x2009;0.05) in the frequency of deaths in males, Black and Coloured individuals, and the 21-40 years age group. Deaths whose circumstances were a result of motor vehicle accidents, pedestrian vehicle accidents, and firearm discharges decreased significantly (p&#x2009;<&#x2009;0.05). The cause of death due to blunt forces trauma and gunshot wounds also significantly decreased (p&#x2009;<&#x2009;0.05). This study highlights the significant impact alcohol consumption has on mortality in South Africa.

Humans↗
PubMed2026

The efficacy of modified psychodynamic psychotherapy for patients with schizophrenia-spectrum disorders in Germany: a prospective, single-centre, assessor-blinded, parallel-group, randomised controlled trial.

BACKGROUND: People with schizophrenia-spectrum disorders have difficulties in interpersonal functioning that remain insufficiently addressed by standard care. Despite long-standing clinical use, psychodynamic psychotherapy has little empirical support compared with other psychosocial treatments for people with schizophrenia-spectrum disorders. We evaluated the efficacy of Modified Psychodynamic Psychotherapy for Schizophrenia (MPP-S), a manualised treatment tailored to the interpersonal vulnerability characteristic of this population, plus treatment as usual (TAU), compared with TAU alone. METHODS: This prospective, single-centre, assessor-blinded, parallel-group, randomised controlled trial was conducted at the Psychiatric University Hospital of the Charit&#xe9; at St Hedwig Hospital in Berlin, Germany. Participants were outpatients aged 18-64 years who were diagnosed with schizophrenia or schizoaffective disorder and exclusion criteria included organic brain disorder, somatic illness affecting cerebral function, and current or past alcohol or illicit drug misuse requiring addiction-specific treatment. Participants were randomly assigned 1:1 in blocks of ten to MPP-S (minimum 30 sessions) plus TAU or TAU alone. Outcome assessors were masked, but participants and therapists were not. The primary outcome was psychosocial functioning, measured using the Mini International Classification of Functioning, Disability and Health Rating for Limitations of Activities and Participation in Psychological Disorders (Mini-ICF-APP) and evaluated at baseline and prespecified post-treatment (24 months) and follow-up (36 months) assessments. Analyses followed the intention-to-treat principle. Linear mixed models were used to analyse incomplete longitudinal data under a missing-at-random assumption. People with lived experience were not formally involved in the design, conduct, or reporting of this study. This trial was preregistered at ClinicalTrials.gov (NCT02576613) and is complete. FINDINGS: From Oct 12, 2015, to Dec 7, 2021, 130 participants (57 [44%] female and 73 [56%] male) were randomly assigned to either MPP-S plus TAU (n=65) or TAU alone (n=64). One participant withdrew consent to data analysis. Regarding the primary outcome of psychosocial functioning, linear mixed models showed significant group-by-time interactions favouring the intervention: estimated marginal means indicated adjusted between-group differences in Mini-ICF-APP scores of -3&#xb7;45 (95% CI -5&#xb7;51 to -1&#xb7;40; p=0&#xb7;0011) at 24 months and -4&#xb7;07 (-6&#xb7;19 to -1&#xb7;94; p=0&#xb7;0002) at 36 months. The frequency of adverse events was similar between groups. There were three serious adverse events: two participants died by suicide (one in the MPP-S plus TAU group who did not start psychotherapy and one in the TAU alone group) and one participant in the MPP-S plus TAU group was admitted to a forensic hospital. INTERPRETATION: MPP-S added to TAU could improve psychosocial functioning compared with TAU alone. Our findings suggest efficacy and possible long-term benefits of psychodynamic psychotherapy for schizophrenia-spectrum disorders and indicate its potential role alongside other psychotherapeutic and psychosocial treatments. FUNDING: Berlin Institute of Health, Deutsche Gesellschaft f&#xfc;r Psychoanalyse, Psychotherapie, Psychosomatik und Tiefenpsychologie, International Psychoanalytic University Berlin, and K&#xf6;hler-Stiftung.

Humans↗
PubMed2026

Feasibility and acceptability of skills training in affective recovery (STAR) video and TextSTAR text message program for recent sexual assault survivors.

Background: Sexual assault (SA) affects more than 50% of women in the United States and is associated with elevated risk for impairing posttraumatic stress symptoms and substance, particularly opioid, misuse. Intervening following SA exposure could attenuate these risks.Objective: This study examined the acceptability and feasibility of a 17-minute video, Skills Training in Affective Recovery (STAR) and a 21-day text message program (TextSTAR), that were delivered in the acute post-SA period to prevent the onset or escalation of posttraumatic stress symptoms and substance misuse. The interventions provide psychoeducation about trauma, strategies to reduce fear and avoidance, suggestions to increase social support, and coping strategies for substance use.Method: Participants were 50 women age 18 or older who presented for a Sexual Assault Medical Forensic Exam (SAMFE) within seven days of a SA. Using a Sequential Multiple Assignment Randomized Controlled Trial (SMART) design, participants were randomized to receive the STAR video (n&#x2009;=&#x2009;25) or no video (n&#x2009;=&#x2009;25) at the SAMFE. Those who received the video answered questions about video acceptability immediately after viewing the video. At 1-week post-SAMFE, participants completed online questionnaires about substance use and posttraumatic stress; those above threshold on acute stress or opioid use (n&#x2009;=&#x2009;36) were randomized to TextSTAR (n&#x2009;=&#x2009;18) or no-text program (n&#x2009;=&#x2009;18). Participants completed weekly online surveys 2-4 weeks post-SAMFE about symptoms and text program acceptability.Results: Participants found STAR and TextSTAR at least moderately acceptable; 80% who received STAR found it extremely acceptable while 60-90% (depending on timepoint) who received TextSTAR found it at least moderately acceptable. Our ability to enroll 50 participants and retain 94% at 1 week and 90% at 4 weeks suggests the intervention trial is feasible.Conclusions: STAR and TextSTAR are acceptable and feasible brief interventions for recent sexual assault survivors.Trial registration: ClinicalTrials.gov identifier: NCT06456190.

Humans↗
PubMed2026

An introductory practical guide to secondary data analysis in pediatric urology.

INTRODUCTION: Secondary data analysis (SDA) has become an increasingly important approach in pediatric urology, enabling the study of long-term outcomes, care variation, and disparities in populations with chronic or congenital urologic conditions. With the growing availability of large datasets, a structured approach to designing and conducting SDA studies is increasingly relevant. OBJECTIVES: To provide an introductory, practical guide to SDA in pediatric urology by (1) summarizing commonly used data sources with representative studies, (2) outlining a stepwise approach to designing and executing SDA studies, and (3) highlighting key methodological considerations, limitations, and opportunities for future work. STUDY DESIGN: Narrative review of existing literature and commonly used datasets relevant to pediatric urology, including administrative claims, hospital encounter databases, clinical registries, electronic health record networks, and population-based surveys. RESULTS: Data sources differ in scope, clinical granularity, longitudinal follow-up, and representativeness, and each is suited to specific research questions. We present a practical workflow for SDA, including dataset selection, cohort definition, and analytic planning. Linkage across datasets can provide a more comprehensive view of care patterns and outcomes, although feasibility is influenced by legal, technical, and data-quality constraints. DISCUSSION: SDA enables population-level analyses and the study of rare conditions that are challenging to evaluate through single-center or prospective designs. However, careful cohort definition, feasibility assessment, and awareness of data limitations are essential to ensure validity and interpretability. CONCLUSION: SDA provides a scalable, cost-efficient framework for generating meaningful evidence in pediatric urology. Continued efforts to harmonize data elements, improve linkage infrastructure, and support cross-institution collaboration will enhance the quality and impact of future research. This article provides a practical framework and examples to support the design and execution of SDA studies.

Humans↗
PubMed2026

Computational metabolomics at scale: from open data to insight.

Metabolomics data are currently generated at scale thanks to the evolution of technologies that have led to marked improvements in the number of metabolites detected, spanning all chemical classes. These data are increasingly submitted to public repositories for data reuse, integration, and interpretation. Despite the availability of public resources and associated computational tools, the field still lacks a widely adopted, consistent data and analytics infrastructure capable of transforming this wealth of information into scientific insight. Indeed, the metabolomics field is just now scratching the surface of being able to harness the power of new computational technologies. In this review, we summarize discussions from the "Dagstuhl-Seminar 24181 Computational Metabolomics: Towards Molecules, Models, and their Meaning" with a focus on public data availability, open data standards, data and knowledge integration, and education. Our goal is to raise awareness and adoption of the latest open science resources while highlighting key areas needing further development.

Metabolomics↗
PubMed2026

Assessing the public health impact of routinely collected electronic healthcare record data in NICE guidelines: A systematic review of CPRD research.

OBJECTIVES: Evidence used in NICE guidance has traditionally prioritised randomised controlled trials, but increasing availability of electronic health record (EHR) data has expanded opportunities for real-world evidence. The Clinical Practice Research Datalink (CPRD) is a commonly used UK primary care EHR resource, yet the extent to which CPRD studies have informed NICE guidelines in the past decade is unclear. STUDY DESIGN: The systematic review was conducted in accordance with PRISMA guidelines. METHODS: We conducted a systematic review of CPRD studies in PubMed, MEDLINE, and Embase published between 04/16-09/25. For each eligible CPRD study, targeted searches of NICE guidelines were performed to identify explicit citations in NICE guidelines. Two reviewers screened and extracted data independently, resolving disagreements by consensus or third reviewer. Guideline information, number of guidelines over time, type of guidelines, and disease area guidelines (using British National Formulary (BNF) chapters) were described. RESULTS: 7181 records were identified. After de-duplication, 2704 unique CPRD studies were screened against NICE guidelines. Of these, 92 CPRD-based studies met inclusion criteria and were cited across 67 NICE documents. The annual number of NICE guidelines citing CPRD studies increased between 2016 and 2025; 1.5% of identified guidelines published in 2016 and 27.7% in 2025. The guideline citing the most CPRD studies was cancer related. The most common types of guidelines included clinical guidelines (49.3%) and technology appraisals (32.8%). Guidelines made up 12 different BNF categories, most frequently central nervous system related (23.9%; n&#x202f;=&#x202f;16). CONCLUSION: Observational CPRD studies are increasingly referenced in NICE guidelines across multiple disease areas, supporting the growing role of EHR data in national guideline development.

Clinical studies↗
PubMed2026

Data-centric, robust, and explainable multimodal deep learning for clinical decision support: A systematic review.

PURPOSE: Multimodal deep learning is increasingly proposed for clinical decision support (CDS) under a "data-centric" framing that prioritizes label quality, missing-modality robustness, distribution shift, calibration, and explainability. Prior reviews have examined multimodal medical AI, CDS, and data-centric methods separately, but none address their intersection. We mapped the modalities, fusion strategies, and data-centric and explainability techniques used in this recent literature, quantified how often each is implemented rather than merely mentioned, assessed deployment-relevant evidence (external validation, clinical-outcome measurement, equity), and formally appraised study-level risk of bias. METHODS: Following the PRISMA 2020 statement (PROSPERO CRD420261427815; registered retrospectively), we screened 150 records and included primary, clinical, multimodal studies that applied machine or deep learning to a decision-support task and reported at least one quantitative result. Two reviewers screened and extracted data with consensus adjudication. Each study was coded against pre-specified operational definitions, separating implemented or empirically evaluated techniques from those only mentioned. Study-level risk of bias was assessed with PROBAST + AI. Synthesis was narrative. RESULTS: Thirty-one studies met inclusion; 30 (97%) were published between 2024 and 2026, with a median of three modalities (range 2-6), most commonly structured EHR (71%) and imaging (39%). Data-centric techniques were frequently reported (74-84% across label-noise, distribution-shift, calibration, missing-modality and class-imbalance handling; equity 61%). However, external validation was reported in only 4/31 studies (13%), a clinical or provider outcome in 3/31 (10%), and no study reported routine deployment. Overall risk of bias was high in 27/31 studies (87%), driven by the analysis domain. CONCLUSION: Within this recent, self-selected slice of the field, technical robustness and explainability techniques are widely reported but rarely validated out-of-distribution or against clinical outcomes, and the underlying evidence is at high risk of bias. Progress requires external multi-site validation, clinical-outcome measurement, formal bias appraisal, and adherence to AI reporting standards (e.g., TRIPOD + AI) before deployment can be justified.

Deep Learning↗
PubMed2026

Recovering membrane interaction kinetics of single molecules from 3D tracking data.

Interactions between cytosolic biomolecules and the bacterial inner membrane are fundamental to many cellular processes, yet directly measuring their binding kinetics in living cells remains challenging. Conventional 2D single-molecule tracking analyses can be insufficient, particularly when membrane association does not markedly alter the diffusion rate. Here, we present a method to recover membrane interaction kinetics from 3D single-molecule trajectories in rod-shaped bacteria. Using simulated 3D tracking data, we identify membrane-associated motion by quantifying how well short trajectory segments follow the circular curvature of the cell membrane. The resulting measure is further analyzed using a hidden Markov modeling framework, enabling robust discrimination between cytosolic and membrane-bound states and capturing the dynamics of state transitions without requiring diffusion-rate changes or direct colocalization with membrane markers. This work establishes a general framework for extracting membrane interaction kinetics from 3D single-molecule tracking data in live bacteria and highlights the value of realistic microscopy simulations for quantitative interpretation and systematic bias assessment.

Kinetics↗
PubMed2026

Privacy, security, and reliability risks of artificial intelligence in healthcare: a systematic review of empirical evidence.

BACKGROUND: Artificial intelligence (AI) is increasingly integrated into healthcare information systems, supporting clinical decision-making, imaging analysis, and predictive modeling. While these applications offer operational and clinical benefits, they also introduce emerging risks to patient privacy, data security, and system reliability. OBJECTIVE: To systematically review empirical evidence on privacy breaches, security vulnerabilities, and misuse associated with AI applications in healthcare settings. METHODS: PubMed, Embase, Web of Science, Scopus, IEEE Xplore, and ACM Digital Library were searched for empirical studies published between January 2015 and November 2025 that evaluated AI use or misuse in clinical diagnosis, treatment, or decision-making. Two reviewers independently screened studies and extracted data using a standardized form. Findings were synthesized narratively due to heterogeneity in study designs, AI methods, and reported outcomes. RESULTS: Of 7,285 records identified through database searches and 205 through citation screening, 22 empirical studies met the inclusion criteria, spanning multiple clinical domains and data modalities, predominantly medical imaging applications. Five recurring threat categories were identified: patient re-identification, membership inference, unauthorized access and adversarial exploitation, input manipulation, and misuse or overinterpretation of AI outputs. Across studies, AI models were shown to encode latent biometric signals across diverse data types, limiting the effectiveness of traditional anonymization and synthetic data approaches. Adversarial attacks and input manipulation were also shown to compromise diagnostic performance and system integrity. CONCLUSION: This systematic review provides empirical evidence suggesting that contemporary AI systems in healthcare introduce privacy and security risks that may challenge traditional assumptions about data protection. These findings underscore the need for privacy- and security-by-design approaches and governance frameworks that address risks across the AI lifecycle.

Humans↗
PubMed2026

Effects of H-coil TMS on suicidality in major depression: A secondary analysis of data from a multisite randomized trial comparing accelerated to once-a-day stimulation.

Suicide is the 10th leading cause of death in US adults. Standard once-daily repetitive transcranial magnetic stimulation (rTMS) can reduce suicidal ideation. Yet, antidepressant and anti-suicidal effects often take several weeks to emerge, while rapid improvement is often required. Accelerated TMS has been proposed as a strategy to hasten therapeutic response. A recent FDA-regulated multicenter trial evaluated accelerated intermittent theta burst Deep TMS with the H1-coil versus standard high-frequency Deep TMS in MDD. Both groups demonstrated high remission and response rates for depression, with the accelerated protocol showing non-inferiority and a shorter time to remission. The goal of this exploratory secondary analysis was to evaluate the impact of these two H-coil TMS dosing paradigms on suicidal ideation. The Scale for Suicide Ideation (SSI), as well as suicidality items of HDRS, MADRS and CUDOS were collected and analyzed. On all scales, both accelerated and standard Deep TMS protocols were associated with meaningful reductions in suicidality. The accelerated protocol achieved a faster onset of improvement. Comparison between the timeline of improvement in suicidality and in overall depressive symptoms found a trend for faster improvement in suicidality, especially with the accelerated protocol. These findings highlight the importance of treatment frequency in determining time to clinical benefit and support the use of scalable accelerated protocols for patients requiring more rapid symptom relief.

Humans↗
PubMed2026

Building phenotypic character matrices for phylogenetic inference: exploration of 35&#x2009;years of practice.

Recent methodological development in phylogenetic inference has focused predominantly on molecular data. However, renewed interest in other data types, particularly morphological data, has followed from the increased recognition of the power of total evidence and tip-dating approaches, including fossil data, for inference of time-scaled trees and rates of evolution. However, attention has largely focused on the improvement of models of morphological evolution and other analytical tools with much less discussion about data acquisition itself. Here we review past and current practice for describing and collecting morphological data for phylogenetic inference. We present a systematic review of 164 phylogenetic analyses conducted over the last 35&#x2009;years and focused on a diverse group of extinct arthropods: trilobites. Trends in increasing matrix size, data type, and coding strategy are evident. Where present, polymorphic characters have been predominantly derived from discretized continuous characters, although increasingly practitioners are utilizing alternative approaches for the treatment of quantitative characters. Not surprisingly, traditional indices that describe character consistency are highly correlated with matrix size but show surprising variation at different taxonomic scales. More recent attempts to describe data quality using information theory imply that characters can have high information content even if data are missing for many tips, providing support against the exclusion of characters because of missing data. In consideration of this, as well as advances in the study of developmental biology and variational complexity, we identify several avenues for increasing the quality and quantity of morphological data going forward.

Phylogeny↗
PubMed2026

Relationship Between Number of Acute Pancreatitis Episodes and Risk of New-onset Diabetes in the U.S.: A Real-world Data Analysis.

INTRODUCTION: Acute pancreatitis (AP) is a common inflammatory disorder that is associated with increased risk for diabetes mellitus (DM). It remains unclear whether recurrent acute pancreatitis (RAP) is associated with further increased risk of incident DM. This study aims to investigate the association between RAP and incident DM using real-world data. METHODS: We conducted a retrospective cohort study using the MerativeTM MarketScan&#xae; claims database (2016-2023), identifying patients with AP and no prior history of DM at baseline. The primary exposure of interest, RAP, was defined as one or more episodes of AP occurring &#x2265;90 days after the index AP diagnosis, whereas one episode of AP referred to a single episode of AP (SAP) with no subsequent recurrence within 90 days following the index event. A multivariable stratified Cox proportional hazards regression models were used to determine the association between RAP and incident DM, identified using ICD-10 codes. RESULTS: In total, 16,184 individuals with AP (mean [SD] age: 45.8 [12.3]) contributed 40,712 person-years of follow-up, during which 1,477 incident cases of DM were documented. Individuals with RAP had an increased risk of incident DM compared with those with a SAP(adjusted HR, 1.92; 95% CI, 1.61-2.29). The risk increased significantly with the frequency of RAP. In comparing the modifying effect of patient demographics and comorbidities, a stronger association between RAP and incident DM was observed in females (adjusted HR, 2.44; 95% CI, 1.87-3.19) than in males (adjusted HR, 1.64; 95% CI, 1.30-2.07; Pinteraction=0.03). Also, stronger associations were observed among younger patients (18-46&#xa0;y) (adjusted HR=2.56; 95% CI, 1.97-3.31) and among non-tobacco abuse (adjusted HR=2.19; 95% CI, 1.81-2.65), with significant interactions for all comparisons (Pinteraction<0.05. CONCLUSIONS: In this real-world study, RAP was associated with an increased risk of incident DM. Our findings highlight an opportunity for glycemic monitoring and proactive management of patients with RAP to mitigate their risk of developing DM.

AP↗
PubMed2026

Identification Matters: How Data Sharing Affects Pupil Honesty and Engagement in Universal School Well-Being Assessments.

PURPOSE: Universal well-being assessments in schools may support early identification of pupils needing mental health support. However, little is known about how privacy and confidentiality concerns influence pupils' acceptability of assessments and willingness to engage authentically. This study examined how hypothetical identification, where responses are linked to pupils and shared with key stakeholders, affects pupils' anticipated honesty and engagement, and whether known help-seeking barriers predict negative responses. METHODS: Cross-sectional data were collected from 12,377 primary (ages 8-10) and secondary pupils (ages 11-17) across 55 schools in England. Pupils reported whether their responses would change if identifiable and shared with school staff, parents/guardians, or external professionals. Responses indicating reduced honesty or likelihood of disengagement were coded as negative. Predictors were examined using mixed-effects logistic regression models, including demographics, school connectedness, and mental well-being. RESULTS: Identification and data sharing influenced pupils' anticipated engagement, particularly in secondary schools. Identification by school staff elicited the highest proportion of negative responses in both phases, whereas external professionals elicited the fewest. Most primary pupils reported they would respond authentically, while a larger proportion of secondary pupils indicated they would respond less honestly or disengage when responses were identifiable and shared. Across primary and secondary samples, low well-being, low school connectedness, and being female were associated with greater likelihood of negative response. DISCUSSION: Pupils' anticipated engagement with well-being assessments is shaped by who accesses their data, with marked developmental differences. Strengthening trust, privacy, and connectedness, and supporting pupils' autonomy, may improve the acceptability and response accuracy.

Humans↗
PubMed2026

Reliability-aware hierarchical learning for Chagas disease screening from 12-lead ECGs: tackling label uncertainty and class imbalance.

Objective.Chagas disease, a neglected tropical disease (NTD) with significant cardiovascular impact, remains underdiagnosed in resource-limited regions. Electrocardiogram (ECG) screening offers a low-cost tool for detecting cardiac involvement, yet algorithm development is challenged by label noise, data scarcity, and the latent nature of infection. This study proposes a robust ECG-based screening framework that explicitly addresses these constraints.Approach.We introduce aReliability-Aware Hierarchical Learningstrategy that calibrates supervision according to data provenance, prioritizing serology-confirmed labels over noisy self-reports. To mitigate data scarcity, we compare a specialized convolutional neural network (CNN) trained from scratch with a transfer learning approach based on a Spatio-Temporal ECG foundation Model (FM). Performance is evaluated across varying data scales, and the representation structure is analyzed to interpret model behavior.Main results.On the official hidden test set of the George B. Moody PhysioNet/Computing in Cardiology Challenge 2025, our approach achieved a Challenge Score of 0.163. We observe that while the specialized CNN performs competitively in data-rich regimes, the FM exhibits superior robustness in extreme low-resource settings. Furthermore, performance reaches a plateau imposed by underlying disease physiology. Bimodal score distributions suggest that models distinguish established cardiomyopathy from indeterminate infection, which remains electrophysiologically indistinguishable from healthy controls.Significance.These findings clarify both the potential and intrinsic limits of ECG-based AI screening for NTD-associated cardiac involvement. Reliability-aware supervision and data-efficient transfer learning provide a practical framework toward scalable and clinically meaningful ECG screening systems in resource-constrained environments.

Humans↗
PubMed2026

List randomization for prevalence estimation of sensitive behavioral data among women with HIV of reproductive age in Lilongwe, Malawi.

Self-reported data are subject to reporting biases, including social desirability bias. List randomization is one method that can help mitigate the impact of such biases. Here, we examined the utility of list randomization among women of reproductive age living with HIV in sub-Saharan Africa. In the Family Planning and Antiretroviral Therapy study, participants were randomized to answer 5 blocks of true/false statements via either direct or list response. Each block contained 3 nonsensitive statements and 1 sensitive statement related to either condom use or HIV disclosure. For each sensitive statement, we calculated the prevalence difference (PD) comparing list response to direct response overall and stratified by socioeconomic status. The PD for 4 of the sensitive statements was negligible. However, we found that self-report of always using a condom was reported by 53.1% at list response visits vs 34.7% at direct response visits (PD, 18.5%; 95% CI, 6.2%-30.7%), a difference that was attenuated among those with higher socioeconomic status. In this setting, list randomization did not meaningfully change the estimated prevalence for most questions, except for one question, which unexpectedly produced a higher estimate for a positive behavior. Examining this method in other settings and populations is warranted.

Humans↗