Assessing Direct and Spillover Effects of Intervention Packages in Network-randomized Studies.

BACKGROUND: Intervention packages may result in a greater public health impact than single interventions. Understanding the separate impact of each component on the overall package effectiveness can improve intervention delivery. METHODS: We adapted an approach to evaluate the effects of a time-varying intervention package in a network-randomized study. In some network-randomized studies, only a subset of participants in exposed networks receive the intervention themselves. The spillover effect contrasts average potential outcomes if a person was not exposed to themselves under intervention in the network versus no intervention in a control network. We estimated the effects of components of the intervention package in HIV Prevention Trials Network 037, a Phase III network-randomized HIV prevention trial among people who inject drugs and their risk networks using marginal structural models to adjust for time-varying confounding. The index participant in an intervention network received a peer education intervention initially at baseline, then boosters at 6 and 12 months. All participants were followed to ascertain HIV risk behaviors. RESULTS: There were 560 participants with at least one follow-up visit, 48% of whom were randomized to the intervention, and 1,598 participant visits were observed. The spillover effect of the boosters in the presence of initial peer education training was a 39% rate reduction (rate ratio = 0.61; 95% confidence interval = 0.43, 0.87). CONCLUSIONS: These methods will be useful for evaluating intervention packages in studies with network features.

Changes in Stigma and Social Support among Participants in a Randomized Trial of a Novel Expanded Social Network-based HIV Testing Intervention in KwaZulu-Natal, South Africa.

HIV-related stigma is a well-documented barrier to HIV testing in South Africa, and may be particularly likely to create reluctance to test among South African men, who have reported feeling blamed for HIV by their partners and communities. The present study presents a novel expanded social network recruitment to HIV testing (E-SNRHT) intervention explicitly designed to reduce stigma as a barrier to testing by asking people to recruit anyone they know to testing, thus allowing them to avoid the potential for increased stigma and/or blame associated with direct risk partner recruitment, and helping to normalize openly discussing HIV among social networks. We examined baseline and 6-10-week follow-up data from a 2022-2023 randomized trial in KwaZulu-Natal, South Africa that recruited 110 individuals who had been newly diagnosed with HIV and randomly assigned them to recruit people to HIV testing either via the E-SNRHT intervention or via risk network recruitment. Participants in the E-SNRHT intervention reported significant decreases in anticipated and enacted HIV-related stigma between baseline and follow-up; and the E-SNRHT intervention was more effective at decreasing enacted HIV-related stigma than was risk network recruitment. Individuals newly diagnosed with HIV by the E-SNRHT intervention reported significant increases in social support between intervention enrollment and follow-up, and all of these individuals reported participating in positive conversations about HIV services with peers in the 6-10 weeks after intervention enrollment. These findings suggest that E-SNRHT is a potentially important strategy to reduce HIV-related stigma as a barrier to HIV testing among peer networks in KwaZulu-Natal.

Overall, Direct, Spillover, and Composite Effects of Components of a Peer-Driven Intervention Package on Injection Risk Behavior Among People Who Inject Drugs in the HPTN 037 Study.

We sought to disentangle effects of the components of a peer-education intervention on self-reported injection risk behaviors among people who inject drugs (n = 560) in Philadelphia, US. We examined 226 egocentric groups/networks randomized to receive (or not) the intervention. Peer-education training consisted of two components delivered to the intervention network index individual only: (1) an initial training and (2) "booster" training sessions during 6- and 12-month follow up visits. In this secondary data analysis, using inverse-probability-weighted log-binomial mixed effects models, we estimated the effects of the components of the network-level peer-education intervention upon subsequent risk behaviors. This included contrasting outcome rates if a participant is a network member [non-index] under the network exposure versus under the network control condition (i.e., spillover effects). We found that compared to control networks, among intervention networks, the overall rates of injection risk behaviors were lower in both those recently exposed (i.e., at the prior visit) to a booster (rate ratio [95% confidence interval]: 0.61 [0.46-0.82]) and those not recently exposed to it (0.81 [0.67-0.98]). Only the boosters had statistically significant spillover effects (e.g., 0.59 [0.41-0.86] for recent exposure). Thus, both intervention components reduced injection risk behaviors with evidence of spillover effects for the boosters. Spillover should be assessed for an intervention that has an observable behavioral measure. Efforts to fully understand the impact of peer education should include routine evaluation of spillover effects. To maximize impact, boosters can be provided along with strategies to recruit especially committed peer educators and to increase attendance at trainings. Clinical Trials Registration Clinicaltrials.gov NCT00038688 June 5, 2002.

RESUMEN: Intentamos desenmarañar los efectos de los componentes de una intervención de educación entre pares sobre los comportamientos de inyección de riesgo autorreportados entre personas que se inyectan drogas (n = 560; 226 grupos/redes egocéntricos(as)) aleatorizados(as) a recibir (o no) la intervención en Filadelfia, EUA. Dos componentes fueron administrados a índices de redes de intervención: una capacitación inicial y sesiones de "refuerzo" durante visitas de seguimiento. Usando modelos log-binomial de efectos mixtos ponderados por probabilidad inversa, estimamos los efectos de dichos componentes sobre los comportamientos de riesgo posteriores. Encontramos que en comparación con las redes control, en las redes de intervención, las tasas generales de comportamientos de inyección de riesgo fueron más bajas en ambas aquellas expuestas recientemente a un refuerzo (razón de tasas [intervalo de confianza del 95%]: 0.61 [0.46­0.82]) y aquellas no expuestas recientemente (0.81 [0.67­0.98]). Solamente los refuerzos tuvieron efectos derrame (i.e., contraste de las tasas de resultados si es miembro [no índice] de una red en una red con exposición reciente versus bajo la condición control) significativos (p. ej., 0.59 [0.41­0.86] para la exposición reciente).

Structural Discrimination against and Structural Support for Lesbian, Gay, and Bisexual People as a Predictor of Late HIV Diagnoses among Black Men who Have Sex with Men.

Black men who have sex with men (MSM) have been consistently reported to have the highest estimated HIV incidence and prevalence among MSM. Despite broad theoretical understanding that discrimination is a major social and structural determinant that contributes to disparate HIV outcomes among Black MSM, relatively little extant research has empirically examined structural discrimination against sexual minorities as a predictor of HIV outcomes among this population. The present study therefore examines whether variation in policies that explicitly discriminate against lesbian, gay, and bisexual (LGB) people and variation in policies that explicitly protect LGB people differentially predict metropolitan statistical-area-level variation in late HIV diagnoses among Black MSM over time, from 2008 to 2014. HIV surveillance data on late HIV diagnoses among Black MSM in each of the 95 largest metropolitan statistical areas in the United States, from 2008 to 2014, were used along with data on time-varying state-level policies pertaining to the rights of LGB people. Results from multilevel models found a negative relationship between protective/supportive laws and late HIV diagnoses among Black MSM, and a positive relationship between discriminative laws and late HIV diagnoses among Black MSM. These findings illuminate the potential epidemiological importance of policies pertaining to LGB populations as structural determinants of HIV outcomes among Black MSM. They suggest a need for scrutiny and elimination of discriminatory policies, where such policies are currently in place, and for advocacy for policies that explicitly protect the rights of LGB people where they do not currently exist.

ECG-Based Deep Learning and Clinical Risk Factors to Predict Atrial Fibrillation.

BACKGROUND: Artificial intelligence (AI)-enabled analysis of 12-lead ECGs may facilitate efficient estimation of incident atrial fibrillation (AF) risk. However, it remains unclear whether AI provides meaningful and generalizable improvement in predictive accuracy beyond clinical risk factors for AF. METHODS: We trained a convolutional neural network (ECG-AI) to infer 5-year incident AF risk using 12-lead ECGs in patients receiving longitudinal primary care at Massachusetts General Hospital (MGH). We then fit 3 Cox proportional hazards models, composed of ECG-AI 5-year AF probability, CHARGE-AF clinical risk score (Cohorts for Heart and Aging in Genomic Epidemiology-Atrial Fibrillation), and terms for both ECG-AI and CHARGE-AF (CH-AI), respectively. We assessed model performance by calculating discrimination (area under the receiver operating characteristic curve) and calibration in an internal test set and 2 external test sets (Brigham and Women's Hospital [BWH] and UK Biobank). Models were recalibrated to estimate 2-year AF risk in the UK Biobank given limited available follow-up. We used saliency mapping to identify ECG features most influential on ECG-AI risk predictions and assessed correlation between ECG-AI and CHARGE-AF linear predictors. RESULTS: The training set comprised 45 770 individuals (age 55±17 years, 53% women, 2171 AF events) and the test sets comprised 83 162 individuals (age 59±13 years, 56% women, 2424 AF events). Area under the receiver operating characteristic curve was comparable using CHARGE-AF (MGH, 0.802 [95% CI, 0.767-0.836]; BWH, 0.752 [95% CI, 0.741-0.763]; UK Biobank, 0.732 [95% CI, 0.704-0.759]) and ECG-AI (MGH, 0.823 [95% CI, 0.790-0.856]; BWH, 0.747 [95% CI, 0.736-0.759]; UK Biobank, 0.705 [95% CI, 0.673-0.737]). Area under the receiver operating characteristic curve was highest using CH-AI (MGH, 0.838 [95% CI, 0.807 to 0.869]; BWH, 0.777 [95% CI, 0.766 to 0.788]; UK Biobank, 0.746 [95% CI, 0.716 to 0.776]). Calibration error was low using ECG-AI (MGH, 0.0212; BWH, 0.0129; UK Biobank, 0.0035) and CH-AI (MGH, 0.012; BWH, 0.0108; UK Biobank, 0.0001). In saliency analyses, the ECG P-wave had the greatest influence on AI model predictions. ECG-AI and CHARGE-AF linear predictors were correlated (Pearson r: MGH, 0.61; BWH, 0.66; UK Biobank, 0.41). CONCLUSIONS: AI-based analysis of 12-lead ECGs has similar predictive usefulness to a clinical risk factor model for incident AF and the approaches are complementary. ECG-AI may enable efficient quantification of future AF risk.

Finding influential subjects in a network using a causal framework.

Researchers across a wide array of disciplines are interested in finding the most influential subjects in a network. In a network setting, intervention effects and health outcomes can spill over from one node to another through network ties, and influential subjects are expected to have a greater impact than others. For this reason, network research in public health has attempted to maximize health and behavioral changes by intervening on a subset of influential subjects. Although influence is often defined only implicitly in most of the literature, the operative notion of influence is inherently causal in many cases: influential subjects are those we should intervene on to achieve the greatest overall effect across the entire network. In this work, we define a causal notion of influence using potential outcomes. We review existing influence measures, such as node centrality, that largely rely on the particular features of the network structure and/or on certain diffusion models that predict the pattern of information or diseases spreads through network ties. We provide simulation studies to demonstrate when popular centrality measures can agree with our causal measure of influence. As an illustrative example, we apply several popular centrality measures to the HIV risk network in the Transmission Reduction Intervention Project and demonstrate the assumptions under which each centrality can represent the causal influence of each participant in the study.

Hepatitis C Virus in people with experience of injection drug use following their displacement to Southern Ukraine before 2020.

BACKGROUND: Due to practical challenges associated with genetic sequencing in low-resource environments, the burden of hepatitis C virus (HCV) in forcibly displaced people is understudied. We examined the use of field applicable HCV sequencing methods and phylogenetic analysis to determine HCV transmission dynamics in internally displaced people who inject drugs (IDPWID) in Ukraine. METHODS: In this cross-sectional study, we used modified respondent-driven sampling to recruit IDPWID who were displaced to Odesa, Ukraine, before 2020. We generated partial and near full length genome (NFLG) HCV sequences using Oxford Nanopore Technology (ONT) MinION in a simulated field environment. Maximum likelihood and Bayesian methods were used to establish phylodynamic relationships. RESULTS: Between June and September 2020, we collected epidemiological data and whole blood samples from 164 IDPWID (PNAS Nexus.2023;2(3):pgad008). Rapid testing (Wondfo® One Step HCV; Wondfo® One Step HIV1/2) identified an anti-HCV seroprevalence of 67.7%, and 31.1% of participants tested positive for both anti-HCV and HIV. We generated 57 partial or NFLG HCV sequences and identified eight transmission clusters, of which at least two originated within a year and a half post-displacement. CONCLUSIONS: Locally generated genomic data and phylogenetic analysis in rapidly changing low-resource environments, such as those faced by forcibly displaced people, can help inform effective public health strategies. For example, evidence of HCV transmission clusters originating soon after displacement highlights the importance of implementing urgent preventive interventions in ongoing situations of forced displacement.

The Use of Artificial Intelligence to Predict the Development of Atrial Fibrillation.

PURPOSE OF REVIEW: Atrial fibrillation (AF) is a major public health problem associated with preventable morbidity. Artificial intelligence (AI) is emerging as potential tool to prioritize individuals at increased risk for AF for preventive interventions. This review summarizes recent advances in the use of AI models to estimate AF risk. RECENT FINDINGS: Several AI-enabled models have been recently developed which can discriminate AF risk with reasonable accuracy. AI models utilizing the electrocardiogram waveform appear to extract predictive information which is additive beyond traditional clinical risk factors. By identifying individuals at higher risk for AF, AI-based models may improve the efficiency of preventive efforts (e.g., screening, risk factor modification) intended to reduce risk of AF and associated morbidity.

Will the Russian war in Ukraine unleash larger epidemics of HIV, TB and associated conditions and diseases in Ukraine?

The Russian war in Ukraine poses many risks for the spread of HIV, TB and associated conditions, including possible increases in the numbers of people who inject drugs or engage in sex work in the years ahead. Ukrainian civil society and volunteer efforts have been able to maintain and at times expand services for HIV Key Populations. The extent of mutual-aid and volunteer efforts as well as the continued strength and vitality of harm reduction organizations such as the Alliance for Public Health and the rest of civil society will be crucial resources for postwar efforts to assist Key Populations and prevent the spread of HIV, TB and other diseases. The postwar period will pose great economic and political difficulties for Ukrainians, including large populations of people physically and/or psychically damaged and in pain who might become people who inject drugs. Local and international support for public health and for harm reduction will be needed to prevent potentially large-scale increases in infectious disease and related mortality.

PrEP Care Continuum Engagement Among Persons Who Inject Drugs: Rural and Urban Differences in Stigma and Social Infrastructure.

Pre-exposure prophylaxis (PrEP) is a medication that prevents HIV acquisition, yet PrEP uptake has been low among people who inject drugs. Stigma has been identified as a fundamental driver of population health and may be a significant barrier to PrEP care engagement among PWID. However, there has been limited research on how stigma operates in rural and urban settings in relation to PrEP. Using in-depth semi-structured qualitative interviews (n = 57) we explore PrEP continuum engagement among people actively injecting drugs in rural and urban settings. Urban participants had more awareness and knowledge. Willingness to use PrEP was similar in both settings. However, no participant was currently using PrEP. Stigmas against drug use, HIV, and sexualities were identified as barriers to PrEP uptake, particularly in the rural setting. Syringe service programs in the urban setting were highlighted as a welcoming space where PWID could socialize and therefore mitigate stigma and foster information sharing.

Spillover benefit of pre-exposure prophylaxis for HIV prevention: evaluating the importance of effect modification using an agent-based model.

We developed an agent-based model using a trial emulation approach to quantify effect measure modification of spillover effects of pre-exposure prophylaxis (PrEP) for HIV among men who have sex with men (MSM) in the Atlanta-Sandy Springs-Roswell metropolitan area, Georgia. PrEP may impact not only the individual prescribed, but also their partners and beyond, known as spillover. We simulated a two-stage randomised trial with eligible components (≥3 agents with ≥1 HIV+ agent) first randomised to intervention or control (no PrEP). Within intervention components, agents were randomised to PrEP with coverage of 70%, providing insight into a high PrEP coverage strategy. We evaluated effect modification by component-level characteristics and estimated spillover effects on HIV incidence using an extension of randomisation-based estimators. We observed an attenuation of the spillover effect when agents were in components with a higher prevalence of either drug use or bridging potential (if an agent acts as a mediator between ≥2 connected groups of agents). The estimated spillover effects were larger in magnitude among components with either higher HIV prevalence or greater density (number of existing partnerships compared to all possible partnerships). Consideration of effect modification is important when evaluating the spillover of PrEP among MSM.

The stigma system: How sociopolitical domination, scapegoating, and stigma shape public health.

Stigma is a fundamental driver of adverse health outcomes. Although stigma is often studied at the individual level to focus on how stigma influences the mental and physical health of the stigmatized, considerable research has shown that stigma is multilevel and structural. This paper proposes a theoretical approach that synthesizes the literature on stigma with the literature on scapegoating and divide-and-rule as strategies that the wealthy and powerful use to maintain their power and wealth; the literatures on racial, gender, and other subordination; the literature on ideology and organization in sociopolitical systems; and the literature on resistance and rebellion against stigma, oppression and other forms of subordination. we develop a model of the "stigma system" as a dialectic of interacting and conflicting structures and processes. Understanding this system can help public health reorient stigma interventions to address the sources of stigma as well as the individual problems that stigma creates. On a broader level, this model can help those opposing stigma and its effects to develop alliances and strategies with which to oppose stigma and the processes that create it.

Disseminated Effects in Agent-Based Models: A Potential Outcomes Framework and Application to Inform Preexposure Prophylaxis Coverage Levels for HIV Prevention.

Preexposure prophylaxis (PrEP) for prevention of human immunodeficiency virus (HIV) infection may benefit not only the person who uses it but also their uninfected sexual risk contacts. We developed an agent-based model using a novel trial emulation approach to quantify disseminated effects of PrEP use among men who have sex with men in Atlanta, Georgia, from 2015 to 2017. Model components (subsets of agents connected through partnerships in a sexual network but not sharing partnerships with any other agents) were first randomized to an intervention coverage level or the control group; then, within intervention components, eligible agents were randomized to receive or not receive PrEP. We calculated direct and disseminated (indirect) effects using randomization-based estimators and report corresponding 95% simulation intervals across scenarios ranging from 10% coverage in the intervention components to 90% coverage. A population of 11,245 agents was simulated, with an average of 1,551 components identified. When comparing agents randomized to no PrEP in 70% coverage components with control agents, there was a 15% disseminated risk reduction in HIV incidence (risk ratio = 0.85, 95% simulation interval: 0.65, 1.05). Persons not on PrEP may receive a protective benefit by being in a sexual network with higher PrEP coverage. Agent-based models are useful for evaluating possible direct and disseminated effects of HIV prevention modalities in sexual networks.

A Critical Review of the Social and Behavioral Contributions to the Overdose Epidemic.

More than 750,000 people in the United States died from an overdose between 1999 and 2018; two-thirds of those deaths involved an opioid. In this review, we present trends in opioid overdose rates during this period and discuss how the proliferation of opioid prescribing to treat chronic pain, changes in the heroin and illegally manufactured opioid synthetics markets, and social factors, including deindustrialization and concentrated poverty, contributed to the rise of the overdose epidemic. We also examine how current policies implemented to address the overdose epidemic may have contributed to reducing prescription opioid overdoses but increased overdoses involving illegal opioids. Finally, we identify new directions for research to understand the causes and solutions to this critical public health problem, including research on heterogeneous policy effects across social groups, effective approaches to reduce overdoses of illegal opioids, and the role of social contexts in shaping policy implementation and impact.

Harmonizing semantic annotations for computational models in biology.

Life science researchers use computational models to articulate and test hypotheses about the behavior of biological systems. Semantic annotation is a critical component for enhancing the interoperability and reusability of such models as well as for the integration of the data needed for model parameterization and validation. Encoded as machine-readable links to knowledge resource terms, semantic annotations describe the computational or biological meaning of what models and data represent. These annotations help researchers find and repurpose models, accelerate model composition and enable knowledge integration across model repositories and experimental data stores. However, realizing the potential benefits of semantic annotation requires the development of model annotation standards that adhere to a community-based annotation protocol. Without such standards, tool developers must account for a variety of annotation formats and approaches, a situation that can become prohibitively cumbersome and which can defeat the purpose of linking model elements to controlled knowledge resource terms. Currently, no consensus protocol for semantic annotation exists among the larger biological modeling community. Here, we report on the landscape of current annotation practices among the COmputational Modeling in BIology NEtwork community and provide a set of recommendations for building a consensus approach to semantic annotation.

A novel in-home digital treatment to improve processing speed in people with multiple sclerosis: A pilot study.

OBJECTIVE: To assess whether a videogame-like digital treatment is superior to a control in improving processing speed in adults with multiple sclerosis (MS). METHODS: Adults with MS and baseline Symbol Digit Modalities Test (SDMT) z-scores between -2 and 0 were enrolled in a double-blind randomized controlled clinical trial. After completing a baseline in-clinic evaluation (Visit 1), they were randomized to complete an in-home, tablet-based videogame-like digital treatment (AKL-T03) or control word game (AKL-T09) for up to 25 minutes/day, 5 days/week, for 6 weeks. A repeat in-clinic evaluation occurred at 6 weeks (Visit 2), and again 8 weeks later to determine persistence of effects (Visit 3). The pre-specified primary outcome was change in SDMT score between Visits 1 and 2. RESULTS: SDMT increased at Visit 2 for participants randomized to both AKL-T03 (p < 0.001) and AKL-T09 (p = 0.024). These respective mean improvements were +6.10 and +3.55 (comparison p = 0.21). At Visit 3, 70% of participants randomized to AKL-T03 maintained a clinically meaningful 4+-point increase in SDMT above their baseline, compared with 37% for AKL-T09 (p = 0.038). CONCLUSION: This in-home digital intervention resulted in substantial and durable improvements in processing speed. A larger randomized controlled clinical trial is planned. TRIAL REGISTRATION: This trial is registered on ClinicalTrials.gov under "NCT03569618," https://clinicaltrials.gov/ct2/show/NCT03569618.

Sexual Mixing and HIV Transmission Potential Among Greek Men Who have Sex with Men: Results from SOPHOCLES.

HIV incidence among men who have sex with men (MSM) in Greece remains unchanged despite effective response to a recent outbreak among people who inject drugs (PWID). Network factors are increasingly understood to drive transmission in epidemics. The primary objective of the study was to characterize MSM in Greece, their sexual behaviors, and sexual network mixing patterns. We investigated the relationship between serostatus, sexual behaviors, and self-reported sex networks in a sample of MSM in Athens, Greece, generated using respondent driven sampling. We estimated mixing coefficients (r) based on survey-generated egonets. Additionally, multiple logistic regression was used to estimate adjusted odds ratios (AOR) and to assess relationships between serostatus, sexual behaviors, and sociodemographic indicators. A sample of 1,520 MSM participants included study respondents (n = 308) and their network members (n = 1,212). Mixing based on serostatus (r = 0.12, σr = 0.09-0.15) and condomless sex (r = 0.11, σr = 0.07-0.14) was random. However, mixing based on sex-drug use was highly assortative (r = 0.37, σr = 0.32-0.42). This study represents the first analysis of Greek MSM sexual networks. Our findings highlight protective behavior in two distinct network typologies. The first typology mixed assortatively based on serostatus and sex-drug use and was less likely to engage in condomless sex. The second typology mixed randomly based on condomless sex but was less likely to engage in sex-drug use. These findings support the potential benefit of HIV prevention program scale-up for this population including but not limited to PrEP.

Molecular epidemiology reveals the role of war in the spread of HIV in Ukraine.

Ukraine has one of the largest HIV epidemics in Europe, historically driven by people who inject drugs (PWID). The epidemic showed signs of stabilization in 2012, but the recent war in eastern Ukraine may be reigniting virus spread. We investigated the movement of HIV-infected people within Ukraine before and during the conflict. We analyzed HIV-1 subtype-A pol nucleotide sequences sampled during 2012-2015 from 427 patients of 24 regional AIDS centers and used phylogeographic analysis to reconstruct virus movement among different locations in Ukraine. We then tested for correlations between reported PWID behaviors and reconstructed patterns of virus spread. Our analyses suggest that Donetsk and Lugansk, two cities not controlled by the Ukrainian government in eastern Ukraine, were significant exporters of the virus to the rest of the country. Additional analyses showed that viral dissemination within the country changed after 2013. Spearman correlation analysis showed that incoming virus flow was correlated with the number of HIV-infected internally displaced people. Additionally, there was a correlation between more intensive virus movement and locations with a higher proportion of PWID practicing risky sexual behaviors. Our findings suggest that effective prevention responses should involve internally displaced people and people who frequently travel to war-affected regions. Scale-up of harm reduction services for PWID will be an important factor in preventing new local HIV outbreaks in Ukraine.

Assessing Cognitive Function in Multiple Sclerosis With Digital Tools: Observational Study.

BACKGROUND: Cognitive impairment (CI) is one of the most prevalent symptoms of multiple sclerosis (MS). However, it is difficult to include cognitive assessment as part of MS standard care since the comprehensive neuropsychological examinations are usually time-consuming and extensive. OBJECTIVE: To improve access to CI assessment, we evaluated the feasibility and potential assessment sensitivity of a tablet-based cognitive battery in patients with MS. METHODS: In total, 53 participants with MS (24 [45%] with CI and 29 [55%] without CI) and 24 non-MS participants were assessed with a tablet-based cognitive battery (Adaptive Cognitive Evaluation [ACE]) and standard cognitive measures, including the Symbol Digit Modalities Test (SDMT) and the Paced Auditory Serial Addition Test (PASAT). Associations between performance in ACE and the SDMT/PASAT were explored, with group comparisons to evaluate whether ACE modules can capture group-level differences. RESULTS: Correlations between performance in ACE and the SDMT (R=-0.57, P<.001), as well as PASAT (R=-0.39, P=.01), were observed. Compared to non-MS and non-CI MS groups, the CI MS group showed a slower reaction time (CI MS vs non-MS: P<.001; CI MS vs non-CI MS: P=.004) and a higher attention cost (CI MS vs non-MS: P=.02; CI MS vs non-CI MS: P<.001). CONCLUSIONS: These results provide preliminary evidence that ACE, a tablet-based cognitive assessment battery, provides modules that could potentially serve as a digital cognitive assessment for people with MS. TRIAL REGISTRATION: ClinicalTrials.gov NCT03569618; https://clinicaltrials.gov/ct2/show/NCT03569618.

Sociometric Risk Network Structure, HIV Prevalence, and Drug Injection-Related Norms among People Who Inject Drugs (PWID) in Athens, Greece.

Background: Structural properties of sociometric networks have been associated with behaviors related to HIV transmission. Very few studies, however, have explored the correlation between sociometric network factors and drug injection-related norms. Methods: This exploratory work: (i) describes basic structural qualities of a sociometric risk network of participants in the Transmission Reduction Intervention Project (TRIP) in Athens, Greece, in the context of a large HIV outbreak among people who inject drugs (PWID); (ii) measures HIV prevalence within specific structures within the sociometric risk network of PWID in TRIP; and (iii) explores the association of structural properties of the sociometric risk network in TRIP with drug injection-related norms. Results: The sociometric risk network in TRIP consisted of a large component (n = 241, 67.8%), a few small components (n = 36, 10.1%) with 2-10 individuals each, and some isolates (n = 79, 22.2%). HIV prevalence was significantly higher in the large component (55.6%), the 2-core (59.1%) and 3-core (66.3%) of the large component, and the 3-cliques of the cores. Drug injection-related norms were significantly associated with structural characteristics of the sociometric risk network. A safe behavioral pattern (use of unclean cooker/filter/rinse water was never encouraged) was significantly (p = 0.03) less normative among people who TRIP participants of the 2-core injected with (40.5%) than among network contacts of TRIP participants outside the 2-core (55.6%). On the contrary, at drug-using venues, 2-core members reported that safer behaviors were normative compared to what was reported by those without 2-core membership. Conclusions: Sociometric network data can give useful insights into HIV transmission dynamics and inform prevention strategies.Supplemental data for this article is available online at https://doi.org/10.1080/10826084.2021.1914103 .