Evaluating the Mediating Role of Recall of Intervention Knowledge in the Relationship Between a Peer-Driven Intervention and HIV Risk Behaviors Among People Who Inject Drugs.

Peer-driven interventions can be effective in reducing HIV injection risk behaviors among people who inject drugs (PWID). We employed a causal mediation framework to examine the mediating role of recall of intervention knowledge in the relationship between a peer-driven intervention and subsequent self-reported HIV injection-related risk behavior among PWID in the HIV Prevention Trials Network (HPTN) 037 study. For each intervention network, the index participant received training at baseline to become a peer educator, while non-index participants and all participants in the control networks received only HIV testing and counseling; recall of intervention knowledge was measured at the 6-month visit for each participant, and each participant was followed to ascertain HIV injection-related risk behaviors at the 12-month visit. We used inverse probability weighting to fit marginal structural models to estimate the total effect (TE) and controlled direct effect (CDE) of the intervention on the outcome. The proportion eliminated (PE) by intervening to remove mediation by the recall of intervention knowledge was computed. There were 385 participants (47% in intervention networks) included in the analysis. The TE and CDE risk ratios for the intervention were 0.47 [95% confidence interval (CI): 0.28, 0.78] and 0.73 (95% CI: 0.26, 2.06) and the PE was 49%. Compared to participants in the control networks, the peer-driven intervention reduced the risk of HIV injection-related risk behavior by 53%. The mediating role of recall of intervention knowledge accounted for less than 50% of the total effect of the intervention, suggesting that other potential causal pathways between the intervention and the outcome, such as motivation and skill, self-efficacy, social norms and behavior modeling, should be considered in future studies.

Randomized controlled trials of biomarker targets.

INTRODUCTION: Randomized controlled trials are used to estimate the causal effect of a treatment on a health outcome of interest in a patient population. Often the specified treatment in a randomized controlled trial is a medical intervention-such as a drug or procedure-experienced directly by the patient. Sometimes the "treatment" in a randomized controlled trial is a target-such as a goal biomarker measurement-that the patient's physician attempts to reach using available medications or procedures. Large randomized controlled trials of biomarker targets are common in clinical research, and trials have been conducted to compare targets in the management of hypertension, diabetes, anemia, and acute respiratory distress syndrome. However, different randomized controlled trials intended to evaluate the same biomarker targets have produced conflicting recommendations, and meta-analyses that aggregate results of trials of biomarker targets have been inconclusive. METHODS: We use causal reasoning to explain why randomized controlled trials of biomarker targets can arrive at conflicting or misleading conclusions. We describe four key threats to the validity of trials of targets: (1) intention-to-treat analysis can be misleading when a direct effect of target assignment on the outcome exists due to lack of blinding; (2) incomparability in results across trials of targets; (3) time-varying adaptive treatment strategies; and (4) Goodhart's law, "when a measure becomes a target, it ceases to be a good measure." RESULTS: We illustrate these findings using evidence from 15 randomized controlled trials of blood pressure targets for management of hypertension. Randomized trials of blood pressure targets exhibit substantial variation in the trial patient populations and antihypertensives used to achieve the blood pressure targets assigned in the trials. The trials did not compare or account for time-varying treatment strategies used to reach the randomized targets. Possible "off-target" effects of antihypertensive medications needed to reach lower blood pressure targets may explain the absence of a clear benefit from intensive blood pressure control. DISCUSSION: Researchers should critically assess meta-analyses of trials of targets for variation in the types, distributions, and off-target effects of therapies studied. Trial investigators should release detailed information about the biomarker targets compared in new randomized trials, as well as confounders, treatments delivered, and outcomes. New randomized controlled trials should experimentally compare treatment algorithms incorporating biomarkers, rather than targets alone. Causal inference methodology that adjusts for time-varying confounding should be used to compare time-varying treatment strategies in observational settings.

Sociodemographic characteristics and HIV risk behaviors of native-born and displaced Syrian men and transgender women who have sex with men in Lebanon.

HIV rates among men and transgender women who have sex with men (MTWSM) in Lebanon are consistent with a concentrated epidemic. Geopolitical and social circumstances leave these communities vulnerable to HIV spread. To document this risk encountered by Lebanese native and displaced Syrian MTWSM, participants, recruited by respondent driven sampling beginning with Syrian seeds, completed a survey with questions covering sociodemographic, behavioral, medical, and stigma, followed by opt-out HIV testing. Analyses included descriptive statistics and linear regression to differentiate between native Lebanese and Syrians who migrated after the onset of the civil war to identify correlations among sociodemographic factors, stigma, and risk behavior as a function of country of birth. Experienced and internalized stigmas were higher in the Syrian born MTWSM and correlated with elements of HIV risk. Combatting the intersectional stigmas of Syrian MTWSM in Lebanon would be most beneficial in mitigating HIV risk for these individuals.

RESUMEN: Las tasas de VIH entre hombres y mujeres transgénero que tienen sexo con hombres (HMTSH) en el Líbano son consistentes con una epidemia concentrada. Las circunstancias geopolíticas y sociales dejan a estas comunidades vulnerables a la propagación del VIH. Para documentar este riesgo al que se enfrentan los HMTSH nativos libaneses y HMTSH sirios desplazados, los participantes, reclutados mediante un muestreo impulsado por los encuestados que comenzó con semillas sirias, completaron una encuesta con preguntas que cubrían aspectos sociodemográficos, conductuales, médicos y de estigma, seguidas de una prueba de VIH de exclusión voluntaria. Los análisis incluyeron estadísticas descriptivas y regresión lineal para diferenciar entre libaneses nativos y sirios que emigraron después del inicio de la guerra civil para identificar correlaciones entre factores sociodemográficos, estigma y comportamiento de riesgo como función del país de nacimiento. Los estigmas experimentados e internalizados fueron más altos en los HMTSH nacidos en Siria y se correlacionaron con elementos de riesgo de VIH. Combatir los estigmas interseccionales de los HMTSH sirios en el Líbano sería lo más beneficioso para mitigar el riesgo de VIH para estos individuos.

Randomization for the susceptibility effect of an infectious disease intervention.

Randomized trials of infectious disease interventions, such as vaccines, often focus on groups of connected or potentially interacting individuals. When the pathogen of interest is transmissible between study subjects, interference may occur: individual infection outcomes may depend on treatments received by others. Epidemiologists have defined the primary parameter of interest-called the "susceptibility effect"-as a contrast in infection risk under treatment versus no treatment, while holding exposure to infectiousness constant. A related quantity-the "direct effect"-is defined as an unconditional contrast between the infection risk under treatment versus no treatment. The purpose of this paper is to show that under a widely recommended randomization design, the direct effect may fail to recover the sign of the true susceptibility effect of the intervention in a randomized trial when outcomes are contagious. The analytical approach uses structural features of infectious disease transmission to define the susceptibility effect. A new probabilistic coupling argument reveals stochastic dominance relations between potential infection outcomes under different treatment allocations. The results suggest that estimating the direct effect under randomization may provide misleading conclusions about the effect of an intervention-such as a vaccine-when outcomes are contagious. Investigators who estimate the direct effect may wrongly conclude an intervention that protects treated individuals from infection is harmful, or that a harmful treatment is beneficial.

Repeat SARS-CoV-2 testing models for residential college populations.

Residential colleges are considering re-opening under uncertain futures regarding the COVID-19 pandemic. We consider repeat SARS-CoV-2 testing models for the purpose of containing outbreaks in the residential campus community. The goal of repeat testing is to detect and isolate new infections rapidly to block transmission that would otherwise occur both on and off campus. The models allow for specification of aspects including scheduled on-campus resident screening at a given frequency, test sensitivity that can depend on the time since infection, imported infections from off campus throughout the school term, and a lag from testing until student isolation due to laboratory turnaround and student relocation delay. For early- (late-) transmission of SARS-CoV-2 by age of infection, we find that weekly screening cannot reliably contain outbreaks with reproductive numbers above 1.4 (1.6) if more than one imported exposure per 10,000 students occurs daily. Screening every three days can contain outbreaks providing the reproductive number remains below 1.75 (2.3) if transmission happens earlier (later) with time from infection, but at the cost of increased false positive rates requiring more isolation quarters for students testing positive. Testing frequently while minimizing the delay from testing until isolation for those found positive are the most controllable levers for preventing large residential college outbreaks. A web app that implements model calculations is available to facilitate exploration and consideration of a variety of scenarios.

Transmission Modeling with Regression Adjustment for Analyzing Household-based Studies of Infectious Disease: Application to Tuberculosis.

BACKGROUND: Household contacts of people infected with a transmissible disease may be at risk due to this proximate exposure, or from other unobserved sources. Understanding variation in infection risk is essential for targeting interventions. METHODS: We develop an analytical approach to estimate household and exogenous forces of infection, while accounting for individual-level characteristics that affect susceptibility to disease and transmissibility. We apply this approach to a cohort study conducted in Lima, Peru, of 18,544 subjects in 4,500 households with at least one active tuberculosis (TB) case and compare the results to those obtained by Poisson and logistic regression. RESULTS: HIV-coinfected (susceptibility hazard ratio [SHR] = 3.80, 1.56-9.29), child (SHR = 1.72, 1.32-2.23), and teenage (SHR = 2.00, 1.49-2.68) household contacts of TB cases experience a higher hazard of TB than do adult contacts. Isoniazid preventive therapy (SHR = 0.30, 0.21-0.42) and Bacillus Calmette-Guérin (BCG) vaccination (SHR = 0.66, 0.51-0.86) reduce the risk of disease among household contacts. TB cases without microbiological confirmation exert a smaller hazard of TB among their close contacts compared with smear- or culture-positive cases (excess hazard ratio = 0.88, 0.82-0.93 for HIV- cases and 0.82, 0.57-0.94 for HIV+ cases). The extra household force of infection results in 0.01 (95% confidence interval [CI] = 0.004, 0.028) TB cases per susceptible household contact per year and the rate of transmission between a microbiologically confirmed TB case and susceptible household contact at 0.08 (95% CI = 0.045, 0.129) TB cases per pair per year. CONCLUSIONS: Accounting for exposure to infected household contacts permits estimation of risk factors for disease susceptibility and transmissibility and comparison of within-household and exogenous forces of infection.

Suspected heroin-related overdoses incidents in Cincinnati, Ohio: A spatiotemporal analysis.

BACKGROUND: Opioid misuse and deaths are increasing in the United States. In 2017, Ohio had the second highest overdose rates in the US, with the city of Cincinnati experiencing a 50% rise in opioid overdoses since 2015. Understanding the temporal and geographic variation in overdose emergencies may help guide public policy responses to the opioid epidemic. METHODS AND FINDINGS: We used a publicly available data set of suspected heroin-related emergency calls (n = 6,246) to map overdose incidents to 280 census block groups in Cincinnati between August 1, 2015, and January 30, 2019. We used a Bayesian space-time Poisson regression model to examine the relationship between demographic and environmental characteristics and the number of calls within block groups. Higher numbers of heroin-related incidents were found to be associated with features of the built environment, including the proportion of parks (relative risk [RR] = 2.233; 95% credible interval [CI]: [1.075-4.643]), commercial (RR = 13.200; 95% CI: [4.584-38.169]), manufacturing (RR = 4.775; 95% CI: [1.958-11.683]), and downtown development zones (RR = 11.362; 95% CI: [3.796-34.015]). The number of suspected heroin-related emergency calls was also positively associated with the proportion of male population, the population aged 35-49 years, and distance to pharmacies and was negatively associated with the proportion aged 18-24 years, the proportion of the population with a bachelor's degree or higher, median household income, the number of fast food restaurants, distance to hospitals, and distance to opioid treatment programs. Significant spatial and temporal heterogeneity in the risks of incidents remained after adjusting for covariates. Limitations of this study include lack of information about the nature of incidents after dispatch, which may differ from the initial classification of being related to heroin, and lack of information on local policy changes and interventions. CONCLUSIONS: We identified areas with high numbers of reported heroin-related incidents and features of the built environment and demographic characteristics that are associated with these events in the city of Cincinnati. Publicly available information about opiate overdoses, combined with data on spatiotemporal risk factors, may help municipalities plan, implement, and target harm-reduction measures. In the US, more work is necessary to improve data availability in other cities and states and the compatibility of data from different sources in order to adequately measure and monitor the risk of overdose and inform health policies.

Interpretation of the Individual Effect Under Treatment Spillover.

Some interventions are intended to benefit both individuals and the groups to which they belong. When a treatment given to one person exerts a causal effect on others, the treatment is said to exhibit spillover, dissemination, or interference. However, defining meaningful causal effects under spillover can be challenging. In this commentary, we discuss the meaning of the "individual effect," a quantity proposed to summarize the effect of treatment on the person who receives it, when spillover may be present.

Empirical evidence of recruitment bias in a network study of people who inject drugs.

Background: Epidemiologic surveys of people who inject drugs (PWID) can be difficult to conduct because potential participants may fear exposure or legal repercussions. Respondent-driven sampling (RDS) is a procedure in which subjects recruit their eligible social contacts. The statistical validity of RDS surveys of PWID and other risk groups depends on subjects recruiting at random from among their network contacts. Objectives: We sought to develop and apply a rigorous definition and statistical tests for uniform network recruitment in an RDS survey. Methods: We undertook a detailed study of recruitment bias in a unique RDS study of PWID in Hartford, CT, the USA in which the network, individual-level covariates, and social link attributes were recorded. A total of n=527 participants (402 male, 123 female, and two individuals who did not specify their gender) within a network of 2626 PWID were recruited. Results: We found strong evidence of recruitment bias with respect to age, homelessness, and social relationship characteristics. In the discrete model, the estimated hazard ratios regarding the significant features of recruitment time and choice of recruitee were: alter's age 1.03 [1.02, 1.05], alter's crack-using status 0.70 [0.50, 1.00], homelessness difference 0.61 [0.43, 0.87], and sharing activities in drug preparation 2.82 [1.39, 5.72]. Under both the discrete and continuous-time recruitment regression models, we reject the null hypothesis of uniform recruitment. Conclusions: The results provide the evidence that for this study population of PWID, recruitment bias may significantly alter the sample composition, making results of RDS surveys less reliable. More broadly, RDS studies that fail to collect comprehensive network data may not be able to detect biased recruitment when it occurs.

Identification of Homophily and Preferential Recruitment in Respondent-Driven Sampling.

Respondent-driven sampling (RDS) is a link-tracing procedure used in epidemiologic research on hidden or hard-to-reach populations in which subjects recruit others via their social networks. Estimates from RDS studies may have poor statistical properties due to statistical dependence in sampled subjects' traits. Two distinct mechanisms account for dependence in an RDS study: homophily, the tendency for individuals to share social ties with others exhibiting similar characteristics, and preferential recruitment, in which recruiters do not recruit uniformly at random from their network alters. The different effects of network homophily and preferential recruitment in RDS studies have been a source of confusion and controversy in methodological and empirical research in epidemiology. In this work, we gave formal definitions of homophily and preferential recruitment and showed that neither is identified in typical RDS studies. We derived nonparametric identification regions for homophily and preferential recruitment and showed that these parameters were not identified unless the network took a degenerate form. The results indicated that claims of homophily or recruitment bias measured from empirical RDS studies may not be credible. We applied our identification results to a study involving both a network census and RDS on a population of injection drug users in Hartford, Connecticut (2012-2013).

Exposure, hazard, and survival analysis of diffusion on social networks.

Sociologists, economists, epidemiologists, and others recognize the importance of social networks in the diffusion of ideas and behaviors through human societies. To measure the flow of information on real-world networks, researchers often conduct comprehensive sociometric mapping of social links between individuals and then follow the spread of an "innovation" from reports of adoption or change in behavior over time. The innovation is introduced to a small number of individuals who may also be encouraged to spread it to their network contacts. In conjunction with the known social network, the pattern of adoptions gives researchers insight into the spread of the innovation in the population and factors associated with successful diffusion. Researchers have used widely varying statistical tools to estimate these quantities, and there is disagreement about how to analyze diffusion on fully observed networks. Here, we describe a framework for measuring features of diffusion processes on social networks using the epidemiological concepts of exposure and competing risks. Given a realization of a diffusion process on a fully observed network, we show that classical survival regression models can be adapted to estimate the rate of diffusion, and actor/edge attributes associated with successful transmission or adoption, while accounting for the topology of the social network. We illustrate these tools by applying them to a randomized network intervention trial conducted in Honduras to estimate the rate of adoption of 2 health-related interventions-multivitamins and chlorine bleach for water purification-and determine factors associated with successful social transmission.

HIV Testing and awareness of HIV status among people who inject drugs in greater Kuala Lumpur, Malaysia.

HIV testing services are the gateway into HIV treatment and are critical for monitoring the epidemic. HIV testing is recommended at least annually in high-risk populations, including people who inject drugs (PWID). In Malaysia, the HIV epidemic is concentrated among PWID, but their adherence to testing recommendations and the proportion of HIV-positive PWID who are aware of their status remain unknown. We recruited 460 PWID in Greater Kuala Lumpur using respondent-driven sampling and conducted HIV testing. We examined past testing behaviors, estimating testing frequency, correlates of testing in the past 12 months, and the proportion of those living with HIV who were aware of their status. Results showed that most PWID living with HIV (90.4%, 95% CI: 83.6%-95.9%) were aware of their status. Among those never previously diagnosed with HIV, few had accessed HIV testing in the past 12 months (14.3%, 95% CI: 11.1%-18.0%). Prison (57.0%) and compulsory drug detention centers (36.1%) were the primary locations where PWID reported ever being HIV tested, and the main correlate of recent testing in regression was recent criminal justice involvement. Although awareness of HIV status may be high among PWID living with HIV in Kuala Lumpur, testing occurs primarily in prisons and compulsory drug detention centers, where it is involuntary and linkage to care is limited. A shift in HIV testing policy is needed to align health and human rights objectives, replacing mandatory testing with voluntary testing in settings where individuals can be rapidly linked to HIV care.

Birth/birth-death processes and their computable transition probabilities with biological applications.

Birth-death processes track the size of a univariate population, but many biological systems involve interaction between populations, necessitating models for two or more populations simultaneously. A lack of efficient methods for evaluating finite-time transition probabilities of bivariate processes, however, has restricted statistical inference in these models. Researchers rely on computationally expensive methods such as matrix exponentiation or Monte Carlo approximation, restricting likelihood-based inference to small systems, or indirect methods such as approximate Bayesian computation. In this paper, we introduce the birth/birth-death process, a tractable bivariate extension of the birth-death process, where rates are allowed to be nonlinear. We develop an efficient algorithm to calculate its transition probabilities using a continued fraction representation of their Laplace transforms. Next, we identify several exemplary models arising in molecular epidemiology, macro-parasite evolution, and infectious disease modeling that fall within this class, and demonstrate advantages of our proposed method over existing approaches to inference in these models. Notably, the ubiquitous stochastic susceptible-infectious-removed (SIR) model falls within this class, and we emphasize that computable transition probabilities newly enable direct inference of parameters in the SIR model. We also propose a very fast method for approximating the transition probabilities under the SIR model via a novel branching process simplification, and compare it to the continued fraction representation method with application to the 17th century plague in Eyam. Although the two methods produce similar maximum a posteriori estimates, the branching process approximation fails to capture the correlation structure in the joint posterior distribution.

Markov counting models for correlated binary responses.

We propose a class of continuous-time Markov counting processes for analyzing correlated binary data and establish a correspondence between these models and sums of exchangeable Bernoulli random variables. Our approach generalizes many previous models for correlated outcomes, admits easily interpretable parameterizations, allows different cluster sizes, and incorporates ascertainment bias in a natural way. We demonstrate several new models for dependent outcomes and provide algorithms for computing maximum likelihood estimates. We show how to incorporate cluster-specific covariates in a regression setting and demonstrate improved fits to well-known datasets from familial disease epidemiology and developmental toxicology.

Historical Biogeography Using Species Geographical Ranges.

Spatial variation in biodiversity is the result of complex interactions between evolutionary history and ecological factors. Methods in historical biogeography combine phylogenetic information with current species locations to infer the evolutionary history of a clade through space and time. A major limitation of most methods for historical biogeographic inference is the requirement of single locations for terminal lineages, reducing contemporary species geographical ranges to a point in two-dimensional space. In reality, geographic ranges usually show complex geographic patterns, irregular shapes, or discontinuities. In this article, we describe a method for phylogeographic analysis using polygonal species geographic ranges of arbitrary complexity. By integrating the geographic diversification process across species ranges, we provide a method to infer the geographic location of ancestors in a Bayesian framework. By modeling migration conditioned on a phylogenetic tree, this approach permits reconstructing the geographic location of ancestors through time. We apply this new method to the diversification of two neotropical bird genera, Trumpeters (Psophia) and Cinclodes ovenbirds. We demonstrate the usefulness of our method (called rase) in phylogeographic reconstruction of species ancestral locations and contrast our results with previous methods that compel researchers to reduce the distribution of species to one point in space. We discuss model extensions to enable a more general, spatially explicit framework for historical biogeographic analysis.

Coupling bounds for approximating birth-death processes by truncation.

Birth-death processes are continuous-time Markov counting processes. Approximate moments can be computed by truncating the transition rate matrix. Using a coupling argument, we derive bounds for the total variation distance between the process and its finite approximation.

On the distribution of interspecies correlation for Markov models of character evolution on Yule trees.

Efforts to reconstruct phylogenetic trees and understand evolutionary processes depend fundamentally on stochastic models of speciation and mutation. The simplest continuous-time model for speciation in phylogenetic trees is the Yule process, in which new species are "born" from existing lineages at a constant rate. Recent work has illuminated some of the structural properties of Yule trees, but it remains mostly unknown how these properties affect sequence and trait patterns observed at the tips of the phylogenetic tree. Understanding the interplay between speciation and mutation under simple models of evolution is essential for deriving valid phylogenetic inference methods and gives insight into the optimal design of phylogenetic studies. In this work, we derive the probability distribution of interspecies covariance under Brownian motion and Ornstein-Uhlenbeck models of phenotypic change on a Yule tree. We compute the probability distribution of the number of mutations shared between two randomly chosen taxa in a Yule tree under discrete Markov mutation models. Our results suggest summary measures of phylogenetic information content, illuminate the correlation between site patterns in sequences or traits of related organisms, and provide heuristics for experimental design and reconstruction of phylogenetic trees.

Obesity not associated with severity among hospitalized adults with seasonal influenza virus infection.

We examined seasonal influenza severity [artificial ventilation, intensive care unit (ICU) admission, and radiographic-confirmed pneumonia] by weight category among adults hospitalized with laboratory-confirmed influenza. Using multivariate logistic regression models, we found no association between obesity or severe obesity and artificial ventilation or ICU admission; however, overweight and obese patients had decreased risk of pneumonia. Underweight was associated with pneumonia (adjusted odds ratio 1.31; 95 % confidence interval 1.04, 1.64).