Historical and theoretical roots of the big events framework.

This article traces the evolution of the Big Events framework since it began as an attempt to understand why sociopolitical transitions in the Former Soviet Union, South Africa, and Indonesia were followed by HIV outbreaks. Big Events frameworks have evolved over time, but all versions try to concretize how macrosocial changes lead to social, personal and environmental changes that shape risk environments and drug use or other behavioral patterns in ways that may lead to epidemics. Important stages in the evolution of the Big Events framework included understanding that the sequelae of Big Events were contingent rather than deterministic, and the development of new survey measures to understand pathways through which Big Events affect social and epidemiologic outcomes. On a broader level, the Big Events framework is a useful crystallization and application of more abstract sociological, social epidemiologic and Marxist frameworks about upstream/downstream relationships and how major social changes are related to epidemics. As such, they raise issues of how to conduct research on dialectical interaction processes. On another level, this article traces the Big Events "style of thought" as Mannheim (Mannheim, 1971) termed it, within the historical context of changes in public health and social science theory, particularly during and after the 1960s.

Association of Cardiopulmonary Hemodynamics and Outcomes in Pulmonary Hypertension Following Kidney Transplant: A Multicenter Retrospective Cohort Study.

BACKGROUND: Pulmonary Hypertension (PH) frequently complicates the evaluation of kidney transplant (KT) candidates, and is associated with increased adverse outcomes (mortality, delayed graft function (DGF), and major adverse cardiovascular events (MACE)) following KT. RESEARCH QUESTION: What is the relationship between cardiopulmonary hemodynamics and post-KT outcomes? STUDY DESIGN AND METHODS: We conducted a multicenter retrospective cohort study of adults undergoing KT between 10/1/11 and 10/1/21, who underwent right heart catheterization (RHC) to assess cardiopulmonary hemodynamics within a year of transplantation. Frailty models and logistic regression models were used to evaluate the association between cardiopulmonary hemodynamics and outcomes (mortality, DGF, MACE) following KT. RESULTS: A total of 117 patients were included in the final analysis, predominantly male (72%), with a median age of 57 years. PH, defined as mean pulmonary artery pressure (mPAP) > 20mmHg, was present in the majority of the cohort (N=93, 79%). The cohort was followed for a median of 29.9 months post-KT, during which about one-fourth experienced mortality (23%) or DGF (25%) events, and approximately one-third (34%) experienced MACE. Though echocardiographic measures of pulmonary artery pressure failed to identify post-KT outcomes, a mPAP of ≥ 30mmHg on RHC was associated with post-KT MACE (Hazard Ratio 2.60, 95% Confidence Interval [1.10, 6.10]) and more prevalent in those experiencing post-KT mortality (63% vs 32%, p=0.001). Pre-capillary pulmonary hypertension was also associated with post-KT mortality (Hazard Ratio 3.71, 95% Confidence Interval [1.07, 12.90]). INTERPRETATION: Pre-capillary pulmonary hypertension and a mPAP of ≥ 30mmHg on RHC, but not echocardiographic evidence of PH, was associated with mortality and MACE following KT. These data suggest that RHC hemodynamics are superior to echocardiographic measures of PH in associating with outcomes following KT, and RHC-derived mPAP in particular may have value in predicting MACE and mortality post-KT.

Integrating Clinical, Genetic, and Electrocardiogram-Based Artificial Intelligence to Estimate Risk of Incident Atrial Fibrillation.

Background: AF risk estimation is feasible using clinical factors, inherited predisposition, and artificial intelligence (AI)-enabled electrocardiogram (ECG) analysis. Objective: To test whether integrating these distinct risk signals improves AF risk estimation. Methods: In the UK Biobank prospective cohort study, we estimated AF risk using three models derived from external populations: the well-validated Cohorts for Aging in Heart and Aging Research in Genomic Epidemiology AF (CHARGE-AF) clinical score, a 1,113,667-variant AF polygenic risk score (PRS), and a published AI-enabled ECG-based AF risk model (ECG-AI). We estimated discrimination of 5-year incident AF using time-dependent area under the receiver operating characteristic (AUROC) and average precision (AP). Results: Among 49,293 individuals (mean age 65±8 years, 52% women), 825 (2.4%) developed AF within 5 years. Using single models, discrimination of 5-year incident AF was higher using ECG-AI (AUROC 0.705 [95%CI 0.686-0.724]; AP 0.085 [0.071-0.11]) and CHARGE-AF (AUROC 0.785 [0.769-0.801]; AP 0.053 [0.048-0.061]) versus the PRS (AUROC 0.618, [0.598-0.639]; AP 0.038 [0.028-0.045]). The inclusion of all components ("Predict-AF3") was the best performing model (AUROC 0.817 [0.802-0.832]; AP 0.11 [0.091-0.15], p<0.01 vs CHARGE-AF+ECG-AI), followed by the two component model of CHARGE-AF+ECG-AI (AUROC 0.802 [0.786-0.818]; AP 0.098 [0.081-0.13]). Using Predict-AF3, individuals at high AF risk (i.e., 5-year predicted AF risk >2.5%) had a 5-year cumulative incidence of AF of 5.83% (5.33-6.32). At the same threshold, the 5-year cumulative incidence of AF was progressively higher according to the number of models predicting high risk (zero: 0.67% [0.51-0.84], one: 1.48% [1.28-1.69], two: 4.48% [3.99-4.98]; three: 11.06% [9.48-12.61]), and Predict-AF3 achieved favorable net reclassification improvement compared to both CHARGE-AF+ECG-AI (0.039 [0.015-0.066]) and CHARGE-AF+PRS (0.033 [0.0082-0.059]). Conclusions: Integration of clinical, genetic, and AI-derived risk signals improves discrimination of 5-year AF risk over individual components. Models such as Predict-AF3 have substantial potential to improve prioritization of individuals for AF screening and preventive interventions.

Comparison of a novel expanded social network recruitment intervention with risk network recruitment to HIV testing: locating undiagnosed cases in South Africa.

OBJECTIVE: To ascertain whether a novel expanded social network recruitment to HIV testing (E-SNRHT) intervention recruits men and individuals with previously undiagnosed HIV at higher rates than risk network recruitment. DESIGN: Initial "seed" participants were prospectively randomly assigned to the E-SNRHT intervention or to risk network recruitment. Their network members were included in the study arm of their recruiter. SETTING: Three Department of Health clinics and two drug treatment centers (DTCs) in the Msunduzi municipality of KwaZulu-Natal, South Africa. PARTICIPANTS: Clinics and DTCs referred 110 newly HIV-diagnosed adult "seeds" to the study from June 2022 to February 2023. E-SNRHT seeds were asked to recruit network members as described below; risk network recruitment arm seeds were asked to recruit recent sex and/or injection partners. Presenting a recruitment coupon (from clinic/DTC staff or another participant) was required for eligibility. INTERVENTION: E-SNRHT seeds were shown educational material about HIV transmission risks and then asked to recruit anyone they know (e.g., friends, family) whom they thought could benefit from HIV testing. MAIN OUTCOME MEASURES: Rates of recruiting men to HIV testing and locating individuals with previously undiagnosed HIV. RESULTS: E-SNRHT recruited significantly higher proportions of men to HIV testing (70.3 vs. 40.4%; χ2  = 16.33; P  < 0.0005) and located significantly more previously undiagnosed cases of HIV per seed than risk network recruitment (rate ratio = 9.40; P  < 0.0001). E-SNRHT also recruited significantly higher proportions of women with previously undiagnosed HIV (29.0 vs. 10.7%; χ2  = 3.87; P  = 0.049). CONCLUSION: E-SNRHT is an important strategy to expand the reach of HIV testing among men and undiagnosed cases of HIV in KwaZulu-Natal.

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.

Deep learning of left atrial structure and function provides link to atrial fibrillation risk.

Increased left atrial volume and decreased left atrial function have long been associated with atrial fibrillation. The availability of large-scale cardiac magnetic resonance imaging data paired with genetic data provides a unique opportunity to assess the genetic contributions to left atrial structure and function, and understand their relationship with risk for atrial fibrillation. Here, we use deep learning and surface reconstruction models to measure left atrial minimum volume, maximum volume, stroke volume, and emptying fraction in 40,558 UK Biobank participants. In a genome-wide association study of 35,049 participants without pre-existing cardiovascular disease, we identify 20 common genetic loci associated with left atrial structure and function. We find that polygenic contributions to increased left atrial volume are associated with atrial fibrillation and its downstream consequences, including stroke. Through Mendelian randomization, we find evidence supporting a causal role for left atrial enlargement and dysfunction on atrial fibrillation risk.

Too Many Deaths, Too Many Left Behind: A People's External Review of the U.S. Centers for Disease Control and Prevention's COVID-19 Pandemic Response.

The U.S. population has suffered worse health consequences owing to COVID-19 than comparable wealthy nations. COVID-19 had caused more than 1.1 million deaths in the U.S. as of May 2023 and contributed to a 3-year decline in life expectancy. A coalition of public health workers and community activists launched an external review of the Centers for Disease Control and Prevention's pandemic management from January 2021 to May 2023. The authors used a modified Delphi process to identify core pandemic management areas, which formed the basis for a survey and literature review. Their analysis yields 3 overarching shortcomings of the Centers for Disease Control and Prevention's pandemic management: (1) Centers for Disease Control and Prevention leadership downplays the serious impacts and aerosol transmission risks of COVID-19, (2) Centers for Disease Control and Prevention leadership has aligned public guidance with commercial and political interests over scientific evidence, and (3) Centers for Disease Control and Prevention guidance focuses on individual choice rather than emphasizing prevention and equity. Instead, the agency must partner with communities most impacted by the pandemic and encourage people to protect one another using layered protections to decrease COVID-19 transmission. Because emerging variants can already evade existing vaccines and treatments and Long COVID can be disabling and lacks definitive treatment, multifaceted, sustainable approaches to the COVID-19 pandemic are essential to protect people, the economy, and future generations.

What would it really take to solve the overdose epidemic in the United States?

The high overdose mortality rates in the United States poses several questions: Why have they been increasing exponentially since 1979? Why are they so high? And how can they be greatly reduced? Building on past research, the causes of the increase seem to be deeply rooted in US social and economic structures and processes, rather than due only to opioid prescription patterns or the advent of synthetic opioids. Given this, we consider what changes might be needed to reverse the exponentially-increasing overdose mortality. We use a path dependency argument to argue that the United States political, economic, and public health systems have helped create this crisis and, unfortunately, continue to heighten it. These same systems suggest that proposals to expand harm reduction and drug treatment capacity, to decriminalize or legalize drugs, or to re-industrialize the country sufficiently to reduce "communities of despair" will not be enacted at a scale sufficient to end the overdose crisis. We thus suggest that in the United States serious improvements in overdose rates and related policies and structures require massive social movements with a broad social change agenda.

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.

Molecular epidemiology of HIV among people who inject drugs after the HIV-outbreak in Athens, Greece: Evidence for a 'slow burn' outbreak.

BACKGROUND: New diagnoses of HIV-1 infection among people who inject drugs (PWID) in Athens, Greece, saw a significant increase in 2011 and a subsequent decline after 2013. Despite this, ongoing HIV-1 transmission persisted from 2014 to 2020 within this population. Our objective was to estimate the time of infection for PWID in Athens following the HIV-1 outbreak, explore the patterns of HIV-1 dispersal over time, and determine the duration from infection to diagnosis. METHODS: Time from HIV-1 infection to diagnosis was estimated for 844 individuals infected within 4 PWID-specific clusters and for 8 PWID infected with sub-subtype A6 diagnosed during 2010-2019. Phylogeny reconstruction was performed using the maximum-likelihood method. HIV-1 infection dates were based on molecular clock calculations. RESULTS: In total 86 of 92 (93.5%) sequences from PWID diagnosed during 2016-2019 were either related to the previously identified PWID-specific clusters (n = 81) or belonged to a new A6 cluster (n = 5). The median time between infection and diagnosis was 0.42 years during the outbreak period and 0.70 years during 2016-2019 (p < 0.001). The proportion of clustered sequences from PWID was very low at 5.3% during the pre-outbreak period (1998-2009), saw an increase to 41.7% one year before the outbreak in 2010, and consistently remained high during the whole period after 2011, spanning the post-outbreak period (2016-2019) with a range from 92.9% to 100%. CONCLUSIONS: The substantial proportion of clustered infections (93.5%) during 2016-2019 implies a persistent 'slow burn' HIV outbreak among PWID in Athens, suggesting that the outbreak was not successfully eliminated. The consistently high proportion of clustered sequences since the onset of the outbreak suggests the persistence of ongoing HIV-1 transmission attributed to injection practices. Our findings underscore the importance of targeted interventions among PWID, considering the ongoing transmission rate and prolonged time from infection to diagnosis.

Cardiovascular Significance and Genetics of Epicardial and Pericardial Adiposity.

Importance: Epicardial and pericardial adipose tissue (EPAT) has been associated with cardiovascular diseases such as atrial fibrillation or flutter (AF) and coronary artery disease (CAD), but studies have been limited in sample size or drawn from selected populations. It has been suggested that the association between EPAT and cardiovascular disease could be mediated by local or paracrine effects. Objective: To evaluate the association of EPAT with prevalent and incident cardiovascular disease and to elucidate the genetic basis of EPAT in a large population cohort. Design, Setting, and Participants: A deep learning model was trained to quantify EPAT area from 4-chamber magnetic resonance images using semantic segmentation. Cross-sectional and prospective cardiovascular disease associations were evaluated, controlling for sex and age. Prospective associations were additionally controlled for abdominal visceral adipose tissue (VAT) volumes. A genome-wide association study was performed, and a polygenic score (PGS) for EPAT was examined in independent FinnGen cohort study participants. Data analyses were conducted from March 2022 to December 2023. Exposures: The primary exposures were magnetic resonance imaging-derived continuous measurements of epicardial and pericardial adipose tissue area and visceral adipose tissue volume. Main Outcomes and Measures: Prevalent and incident CAD, AF, heart failure (HF), stroke, and type 2 diabetes (T2D). Results: After exclusions, this study included 44 475 participants (mean [SD] age, 64.1 [7.7] years; 22 972 female [51.7%]) from the UK Biobank. Cross-sectional and prospective cardiovascular disease associations were evaluated for a mean (SD) of 3.2 (1.5) years of follow-up. Prospective associations were additionally controlled for abdominal VAT volumes for 38 527 participants. A PGS for EPAT was examined in 453 733 independent FinnGen cohort study participants. EPAT was positively associated with male sex (ß = +0.78 SD in EPAT; P < 3 × 10-324), age (Pearson r = 0.15; P = 9.3 × 10-229), body mass index (Pearson r = 0.47; P < 3 × 10-324), and VAT (Pearson r = 0.72; P < 3 × 10-324). EPAT was more elevated in prevalent HF (ß = +0.46 SD units) and T2D (ß = +0.56) than in CAD (ß = +0.23) or AF (ß = +0.18). EPAT was associated with incident HF (hazard ratio [HR], 1.29 per +1 SD in EPAT; 95% CI, 1.17-1.43), T2D (HR, 1.63; 95% CI, 1.51-1.76), and CAD (HR, 1.19; 95% CI, 1.11-1.28). However, the associations were no longer significant when controlling for VAT. Seven genetic loci were identified for EPAT, implicating transcriptional regulators of adipocyte morphology and brown adipogenesis (EBF1, EBF2, and CEBPA) and regulators of visceral adiposity (WARS2 and TRIB2). The EPAT PGS was associated with T2D (odds ratio [OR], 1.06; 95% CI, 1.05-1.07; P =3.6 × 10-44), HF (OR, 1.05; 95% CI, 1.04-1.06; P =4.8 × 10-15), CAD (OR, 1.04; 95% CI, 1.03-1.05; P =1.4 × 10-17), AF (OR, 1.04; 95% CI, 1.03-1.06; P =7.6 × 10-12), and stroke in FinnGen (OR, 1.02; 95% CI, 1.01-1.03; P =3.5 × 10-3) per 1 SD in PGS. Conclusions and Relevance: Results of this cohort study suggest that epicardial and pericardial adiposity was associated with incident cardiovascular diseases, but this may largely reflect a metabolically unhealthy adiposity phenotype similar to abdominal visceral adiposity.

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.

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

Intervention packages may result in a greater public health impact than single interventions. Understanding the separate impact of each component in the overall package effectiveness can improve intervention delivery. 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 themselves under intervention in the network versus no intervention in a control network. We estimated 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. 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). These methods will be useful to evaluate intervention packages in studies with network features.

Occult right ventricular dysfunction and right ventricular-vascular uncoupling in left ventricular assist device recipients.

BACKGROUND: Detecting right heart failure post left ventricular assist device (LVAD) is challenging. Sensitive pressure-volume loop assessments of right ventricle (RV) contractility may improve our appreciation of post-LVAD RV dysfunction. METHODS: Thirteen LVAD patients and 20 reference (non-LVAD) subjects underwent comparison of echocardiographic, right heart cath hemodynamic, and pressure-volume loop-derived assessments of RV contractility using end-systolic elastance (Ees), RV afterload by effective arterial elastance (Ea), and RV-pulmonary arterial coupling (ratio of Ees/Ea). RESULTS: LVAD patients had lower RV Ees (0.20 ± 0.08 vs 0.30 ± 0.15 mm Hg/ml, p = 0.01) and lower RV Ees/Ea (0.37 ± 0.14 vs 1.20 ± 0.54, p < 0.001) versus reference subjects. Low RV Ees correlated with reduced RV septal strain, an indicator of septal contractility, in both the entire cohort (r = 0.68, p = 0.004) as well as the LVAD cohort itself (r = 0.78, p = 0.02). LVAD recipients with low RV Ees/Ea (below the median value) demonstrated more clinical heart failure (71% vs 17%, p = 0.048), driven by an inability to augment RV Ees (0.22 ± 0.11 vs 0.19 ± 0.02 mm Hg/ml, p = 0.95) to accommodate higher RV Ea (0.82 ± 0.38 vs 0.39 ± 0.08 mm Hg/ml, p = 0.002). Pulmonary artery pulsatility index (PAPi) best identified low baseline RV Ees/Ea (≤0.35) in LVAD patients ((area under the curve) AUC = 0.80); during the ramp study, change in PAPi also correlated with change in RV Ees/Ea (r = 0.58, p = 0.04). CONCLUSIONS: LVAD patients demonstrate occult intrinsic RV dysfunction. In the setting of excess RV afterload, LVAD patients lack the RV contractile reserve to maintain ventriculo-vascular coupling. Depression in RV contractility may be related to LVAD left ventricular unloading, which reduces septal contractility.

Deep learned representations of the resting 12-lead electrocardiogram to predict at peak exercise.

AIMS: To leverage deep learning on the resting 12-lead electrocardiogram (ECG) to estimate peak oxygen consumption (V˙O2peak) without cardiopulmonary exercise testing (CPET). METHODS AND RESULTS: V ˙ O 2 peak estimation models were developed in 1891 individuals undergoing CPET at Massachusetts General Hospital (age 45 ± 19 years, 38% female) and validated in a separate test set (MGH Test, n = 448) and external sample (BWH Test, n = 1076). Three penalized linear models were compared: (i) age, sex, and body mass index ('Basic'), (ii) Basic plus standard ECG measurements ('Basic + ECG Parameters'), and (iii) basic plus 320 deep learning-derived ECG variables instead of ECG measurements ('Deep ECG-V˙O2'). Associations between estimated V˙O2peak and incident disease were assessed using proportional hazards models within 84 718 primary care patients without CPET. Inference ECGs preceded CPET by 7 days (median, interquartile range 27-0 days). Among models, Deep ECG-V˙O2 was most accurate in MGH Test [r = 0.845, 95% confidence interval (CI) 0.817-0.870; mean absolute error (MAE) 5.84, 95% CI 5.39-6.29] and BWH Test (r = 0.552, 95% CI 0.509-0.592, MAE 6.49, 95% CI 6.21-6.67). Deep ECG-V˙O2 also outperformed the Wasserman, Jones, and FRIEND reference equations (P < 0.01 for comparisons of correlation). Performance was higher in BWH Test when individuals with heart failure (HF) were excluded (r = 0.628, 95% CI 0.567-0.682; MAE 5.97, 95% CI 5.57-6.37). Deep ECG-V˙O2 estimated V˙O2peak <14 mL/kg/min was associated with increased risks of incident atrial fibrillation [hazard ratio 1.36 (95% CI 1.21-1.54)], myocardial infarction [1.21 (1.02-1.45)], HF [1.67 (1.49-1.88)], and death [1.84 (1.68-2.03)]. CONCLUSION: Deep learning-enabled analysis of the resting 12-lead ECG can estimate exercise capacity (V˙O2peak) at scale to enable efficient cardiovascular risk stratification.

Researchers here present data describing a method of estimating exercise capacity from the resting electrocardiogram. Electrocardiogram estimation of exercise capacity was accurate and was found to predict the onset of the wide range of cardiovascular diseases including heart attacks, heart failure, arrhythmia, and death.This approach offers the ability to estimate exercise capacity without dedicated exercise testing and may enable efficient risk stratification of cardiac patients at scale.

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).

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.

Sociopolitical Diagnostic Tools to Understand National and Local Response Capabilities and Vulnerabilities to Epidemics and Guide Research into How to Improve the Global Response to Pathogens.

The AIDS and COVID-19 pandemics demonstrated that nations at similar economic development levels varied widely in their capacity to protect the health of their residents. For AIDS, Britain and Australia brought gay representatives into official counsels and adopted harm reduction far more rapidly than the United States or Spain, and East African countries responded more effectively than South Africa or the Democratic Republic of the Congo. National responses to COVID-19 varied widely, with New Zealand, China, and Vietnam more effective than Italy, Brazil, or the United States. Further, as phylogenetic research has demonstrated, these pandemics spread from one country to another, with those that responded poorly acting as sources for mutations and potentially sources of transmission to countries with more effective responses. Many observers expressed surprise at the poor responses of the United States to COVID-19, but in retrospect the cutbacks in public health funding at state and national levels made it clear that this was a predictable weakness even in addition to the political vacillations that crippled the US and Brazilian responses. In a time of global sociopolitical and climate instability, it is important to measure and conduct research into spatial and time variations in 1. public health and medical funding, 2. social influence networks, social cohesion and trust, and stigmatization, 3. income inequality, 4. social conflict, and 5. other factors that affect responsiveness to pandemics.