references.bib

@article{amirkhanianRandomizedSocialNetwork2005,
  title = {A Randomized Social Network {{HIV}} Prevention Trial with Young Men Who Have Sex with Men in {{Russia}} and {{Bulgaria}}},
  author = {Amirkhanian, Yuri A. and Kelly, Jeffrey A. and Kabakchieva, Elena and Kirsanova, Anna V. and Vassileva, Sylvia and Takacs, Judit and DiFranceisco, Wayne J. and McAuliffe, Timothy L. and Khoursine, Roman A. and Mocsonaki, Laszlo},
  year = {2005},
  month = nov,
  journal = {AIDS},
  volume = {19},
  number = {16},
  pages = {1897--1905},
  issn = {0269-9370},
  doi = {10.1097/01.aids.0000189867.74806.fb},
  urldate = {2022-06-22},
  abstract = {Objective:~         To evaluate the effects of an HIV prevention intervention with social networks of young men who have sex with men (YMSM) in St. Petersburg, Russia and Sofia, Bulgaria.         Design:~         A two-arm randomized trial with a longitudinally-followed community cohort.         Methods:~         Fifty-two MSM social networks were recruited through access points in high-risk community venues. Network members (n = 276) were assessed to determine risk characteristics, administered sociometric measures to empirically identify the social leader of each network, and counseled in risk reduction. The leaders of 25 experimental condition networks attended a nine-session program that provided training and guidance in delivering ongoing theory-based HIV prevention advice to other network members. Leaders successively targeted network members' AIDS risk-related knowledge and risk reduction norms, attitudes, intentions, and self-efficacy. Participants were re-administered risk assessment measures at 3- and 12-month follow-ups.         Results:~         Among changes produced, the percentage of experimental network members reporting unprotected intercourse (UI) declined from 71.8 to 48.4\% at 3-month follow up (P = 0.0001). The percentage who engaged in UI with multiple partners reduced from 31.5 to 12.9\% (P = 0.02). After 12 months, the effects became attenuated but remained among participants who had multiple recent sexual partners, the most vulnerable group. Little change was found in control group networks.         Conclusions:~         Interventions that engage the identified influence leaders of at-risk YMSM social networks to communicate theory-based counseling and advice can produce significant sexual risk behavior change. This model is culturally pertinent for HIV prevention efforts in former socialist countries, as well as elsewhere for other hard-to-reach vulnerable community populations.},
  langid = {american}
}

@article{bagrowNetworkCardsConcise2022,
  title = {Network Cards: Concise, Readable Summaries of Network Data},
  shorttitle = {Network Cards},
  author = {Bagrow, James and Ahn, Yong-Yeol},
  year = {2022},
  month = dec,
  journal = {Applied Network Science},
  volume = {7},
  number = {1},
  pages = {1--17},
  publisher = {{SpringerOpen}},
  issn = {2364-8228},
  doi = {10.1007/s41109-022-00514-7},
  urldate = {2023-01-03},
  abstract = {The deluge of network datasets demands a standard way to effectively and succinctly summarize network datasets. Building on similar efforts to standardize the documentation of models and datasets in machine learning, here we propose network cards, short summaries of network datasets that can capture not only the basic statistics of the network but also information about the data construction process, provenance, ethical considerations, and other metadata. In this paper, we lay out (1) the rationales and objectives for network cards, (2) key elements that should be included in network cards, and (3) example network cards to underscore their benefits across a variety of research domains. We also provide a schema, templates, and a software package for generating network cards.},
  copyright = {2022 The Author(s)},
  langid = {english}
}

@book{barabasiNetworkScience2016,
  title = {Network Science},
  author = {Barab{\'a}si, Albert-L{\'a}szl{\'o} and P{\'o}sfai, M{\'a}rton},
  year = {2016},
  publisher = {{Cambridge University Press}},
  address = {{Cambridge, United Kingdom}},
  abstract = {"Networks are everywhere, from the Internet, to social networks, and the genetic networks that determine our biological existence. Illustrated throughout in full colour, this pioneering textbook, spanning a wide range of topics from physics to computer science, engineering, economics and the social sciences, introduces network science to an interdisciplinary audience. From the origins of the six degrees of separation to explaining why networks are robust to random failures, the author explores how viruses like Ebola and H1N1 spread, and why it is that our friends have more friends than we do. Using numerous real-world examples, this innovatively designed text includes clear delineation between undergraduate and graduate level material"--Page [4] of cover},
  isbn = {978-1-107-07626-6},
  lccn = {TK5105.5 .B37 2016},
  keywords = {Computer networks,Information networks},
  annotation = {OCLC: ocn910772793}
}

@article{barratPropertiesSmallworldNetwork2000,
  title = {On the Properties of Small-World Network Models},
  author = {Barrat, A. and Weigt, M.},
  year = {2000},
  month = feb,
  journal = {The European Physical Journal B - Condensed Matter and Complex Systems},
  volume = {13},
  number = {3},
  pages = {547--560},
  issn = {1434-6036},
  doi = {10.1007/s100510050067},
  urldate = {2023-11-21},
  abstract = {We study the small-world networks recently introduced by Watts and Strogatz [Nature 393, 440 (1998)], using analytical as well as numerical tools. We characterize the geometrical properties resulting from the coexistence of a local structure and random long-range connections, and we examine their evolution with size and disorder strength. We show that any finite value of the disorder is able to trigger a ``small-world'' behaviour as soon as the initial lattice is big enough, and study the crossover between a regular lattice and a ``small-world'' one. These results are corroborated by the investigation of an Ising model defined on the network, showing for every finite disorder fraction a crossover from a high-temperature region dominated by the underlying one-dimensional structure to a mean-field like low-temperature region. In particular there exists a finite-temperature ferromagnetic phase transition as soon as the disorder strength is finite. [0.5cm]},
  langid = {english},
  keywords = {{PACS. 05.50.+q Lattice theory and statistics (Ising, Potts, etc.) - 64.60.Cn Order-disorder transformations; statistical mechanics of model systems - 05.70.Fh Phase transitions: general studies}}
}

@article{basseAnalyzingTwoStageExperiments2018,
  title = {Analyzing {{Two-Stage Experiments}} in the {{Presence}} of {{Interference}}},
  author = {Basse, Guillaume and Feller, Avi},
  year = {2018},
  month = jan,
  journal = {Journal of the American Statistical Association},
  volume = {113},
  number = {521},
  pages = {41--55},
  publisher = {{Taylor \& Francis}},
  issn = {0162-1459},
  doi = {10.1080/01621459.2017.1323641},
  urldate = {2022-06-22},
  abstract = {Two-stage randomization is a powerful design for estimating treatment effects in the presence of interference; that is, when one individual's treatment assignment affects another individual's outcomes. Our motivating example is a two-stage randomized trial evaluating an intervention to reduce student absenteeism in the School District of Philadelphia. In that experiment, households with multiple students were first assigned to treatment or control; then, in treated households, one student was randomly assigned to treatment. Using this example, we highlight key considerations for analyzing two-stage experiments in practice. Our first contribution is to address additional complexities that arise when household sizes vary; in this case, researchers must decide between assigning equal weight to households or equal weight to individuals. We propose unbiased estimators for a broad class of individual- and household-weighted estimands, with corresponding theoretical and estimated variances. Our second contribution is to connect two common approaches for analyzing two-stage designs: linear regression and randomization inference. We show that, with suitably chosen standard errors, these two approaches yield identical point and variance estimates, which is somewhat surprising given the complex randomization scheme. Finally, we explore options for incorporating covariates to improve precision. We confirm our analytic results via simulation studies and apply these methods to the attendance study, finding substantively meaningful spillover effects.},
  keywords = {Causal inference under interference,Randomization inference,Student attendance,Two-stage randomization}
}

@article{buchananAssessingIndividualDisseminated2018,
  title = {Assessing {{Individual}} and {{Disseminated Effects}} in {{Network-Randomized Studies}}},
  author = {Buchanan, Ashley L. and Vermund, Sten H. and Friedman, Samuel R. and Spiegelman, Donna},
  year = {2018},
  month = nov,
  journal = {American Journal of Epidemiology},
  volume = {187},
  number = {11},
  pages = {2449--2459},
  issn = {1476-6256},
  doi = {10.1093/aje/kwy149},
  abstract = {Implementation trials often involve clustering via risk networks, where only some participants directly receive the intervention. The individual effect is that among directly treated persons beyond being in an intervention network; the disseminated effect is that among persons engaged with those directly treated. In this article, we employ a causal inference framework and discuss assumptions and estimators for individual and disseminated effects and apply them to the HIV Prevention Trials Network 037 Study. HIV Prevention Trials Network 037 was a phase III, network-level, randomized controlled human immunodeficiency virus (HIV) prevention trial conducted in the United States and Thailand from 2002 to 2006 that recruited injection drug users, who were assigned to either an intervention group or a control group, and their risk network members, who received no direct intervention. Combining individual and disseminated effects, we observed a 35\% composite rate reduction in the adjusted model (risk ratio = 0.65, 95\% confidence interval: 0.47, 0.90). Methodology is now available for estimating the full set of these effects, enhancing knowledge gained from network-randomized trials. Although the overall effect gains validity from network randomization, we show that it will generally be less than the composite effect. Additionally, if only index participants benefit from the intervention, as the network size increases, the overall effect tends toward the null-an unfortunate and misleading conclusion.},
  langid = {english},
  pmcid = {PMC6211234},
  pmid = {30052722},
  keywords = {Causality,{Clinical Trials, Phase III as Topic},Epidemiologic Research Design,Health Education,HIV Infections,Humans,Randomized Controlled Trials as Topic}
}

@article{buchananDisseminatedEffectsAgentBased2021,
  title = {Disseminated {{Effects}} in {{Agent-Based Models}}: {{A Potential Outcomes Framework}} and {{Application}} to {{Inform Preexposure Prophylaxis Coverage Levels}} for {{HIV Prevention}}},
  shorttitle = {Disseminated {{Effects}} in {{Agent-Based Models}}},
  author = {Buchanan, Ashley L and Bessey, S and Goedel, William C and King, Maximilian and Murray, Eleanor J and Friedman, Samuel R and Halloran, M Elizabeth and Marshall, Brandon D L},
  year = {2021},
  month = may,
  journal = {American Journal of Epidemiology},
  volume = {190},
  number = {5},
  pages = {939--948},
  issn = {0002-9262},
  doi = {10.1093/aje/kwaa239},
  urldate = {2021-12-07},
  abstract = {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.},
  copyright = {All rights reserved}
}

@article{buchananSpilloverBenefitPreexposure2022,
  title = {Spillover Benefit of Pre-Exposure Prophylaxis for {{HIV}} Prevention: Evaluating the Importance of Effect Modification Using an Agent-Based Model},
  shorttitle = {Spillover Benefit of Pre-Exposure Prophylaxis for {{HIV}} Prevention},
  author = {Buchanan, Ashley L. and Park, Carolyn J. and Bessey, Sam and Goedel, William C. and Murray, Eleanor J. and Friedman, Samuel R. and Halloran, M. Elizabeth and Katenka, Natallia V. and Marshall, Brandon D. L.},
  year = {2022/ed},
  journal = {Epidemiology \& Infection},
  volume = {150},
  pages = {e192},
  publisher = {{Cambridge University Press}},
  issn = {0950-2688, 1469-4409},
  doi = {10.1017/S0950268822001650},
  urldate = {2023-03-08},
  abstract = {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 ({$\geq$}3 agents with {$\geq$}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 {$\geq$}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.},
  langid = {english},
  keywords = {Agent based models,causal inference,effect modification,HIV prevention,interference,network,pre-exposure prophylaxis,spillover}
}

@misc{caiCausalIdentificationInfectious2021,
  title = {Causal Identification of Infectious Disease Intervention Effects in a Clustered Population},
  author = {Cai, Xiaoxuan and Kenah, Eben and Crawford, Forrest W.},
  year = {2021},
  month = may,
  number = {arXiv:2105.03493},
  eprint = {2105.03493},
  primaryclass = {stat},
  publisher = {{arXiv}},
  doi = {10.48550/arXiv.2105.03493},
  urldate = {2023-03-13},
  abstract = {Causal identification of treatment effects for infectious disease outcomes in interconnected populations is challenging because infection outcomes may be transmissible to others, and treatment given to one individual may affect others' outcomes. Contagion, or transmissibility of outcomes, complicates standard conceptions of treatment interference in which an intervention delivered to one individual can affect outcomes of others. Several statistical frameworks have been proposed to measure causal treatment effects in this setting, including structural transmission models, mediation-based partnership models, and randomized trial designs. However, existing estimands for infectious disease intervention effects are of limited conceptual usefulness: Some are parameters in a structural model whose causal interpretation is unclear, others are causal effects defined only in a restricted two-person setting, and still others are nonparametric estimands that arise naturally in the context of a randomized trial but may not measure any biologically meaningful effect. In this paper, we describe a unifying formalism for defining nonparametric structural causal estimands and an identification strategy for learning about infectious disease intervention effects in clusters of interacting individuals when infection times are observed. The estimands generalize existing quantities and provide a framework for causal identification in randomized and observational studies, including situations where only binary infection outcomes are observed. A semiparametric class of pairwise Cox-type transmission hazard models is used to facilitate statistical inference in finite samples. A comprehensive simulation study compares existing and proposed estimands under a variety of randomized and observational vaccine trial designs.},
  archiveprefix = {arxiv},
  keywords = {Statistics - Applications,Statistics - Methodology}
}

@article{caiIdentificationCausalIntervention2021,
  title = {Identification of Causal Intervention Effects under Contagion},
  author = {Cai, Xiaoxuan and Loh, Wen Wei and Crawford, Forrest W.},
  year = {2021},
  month = jan,
  journal = {Journal of Causal Inference},
  volume = {9},
  number = {1},
  pages = {9--38},
  publisher = {{De Gruyter}},
  issn = {2193-3685},
  doi = {10.1515/jci-2019-0033},
  urldate = {2022-09-09},
  abstract = {Defining and identifying causal intervention effects for transmissible infectious disease outcomes is challenging because a treatment {\textendash} such as a vaccine {\textendash} given to one individual may affect the infection outcomes of others. Epidemiologists have proposed causal estimands to quantify effects of interventions under contagion using a two-person partnership model. These simple conceptual models have helped researchers develop causal estimands relevant to clinical evaluation of vaccine effects. However, many of these partnership models are formulated under structural assumptions that preclude realistic infectious disease transmission dynamics, limiting their conceptual usefulness in defining and identifying causal treatment effects in empirical intervention trials. In this paper, we propose causal intervention effects in two-person partnerships under arbitrary infectious disease transmission dynamics, and give nonparametric identification results showing how effects can be estimated in empirical trials using time-to-infection or binary outcome data. The key insight is that contagion is a causal phenomenon that induces conditional independencies on infection outcomes that can be exploited for the identification of clinically meaningful causal estimands. These new estimands are compared to existing quantities, and results are illustrated using a realistic simulation of an HIV vaccine trial.},
  langid = {english},
  keywords = {infectiousness,interference,mediation,susceptibility,transmission,vaccine}
}

@article{coleConsistencyStatementCausal2009,
  title = {The {{Consistency Statement}} in {{Causal Inference}}: {{A Definition}} or an {{Assumption}}?},
  shorttitle = {The {{Consistency Statement}} in {{Causal Inference}}},
  author = {Cole, Stephen R. and Frangakis, Constantine E.},
  year = {2009},
  month = jan,
  journal = {Epidemiology},
  volume = {20},
  number = {1},
  pages = {3--5},
  issn = {1044-3983},
  doi = {10.1097/EDE.0b013e31818ef366},
  urldate = {2022-06-22},
  langid = {english}
}

@article{csardiIgraphSoftwarePackage2005,
  title = {The {{Igraph Software Package}} for {{Complex Network Research}}},
  author = {Csardi, Gabor and Nepusz, Tamas},
  year = {2005},
  month = nov,
  journal = {InterJournal},
  volume = {Complex Systems},
  pages = {1695}
}

@article{drescherQueerDiagnosesRevisited2015,
  title = {Queer Diagnoses Revisited: {{The}} Past and Future of Homosexuality and Gender Diagnoses in {{DSM}} and {{ICD}}},
  shorttitle = {Queer Diagnoses Revisited},
  author = {Drescher, Jack},
  year = {2015},
  month = sep,
  journal = {International Review of Psychiatry},
  volume = {27},
  number = {5},
  pages = {386--395},
  publisher = {{Taylor \& Francis}},
  issn = {0954-0261},
  doi = {10.3109/09540261.2015.1053847},
  urldate = {2023-01-12},
  abstract = {The American Psychiatric Association (APA) recently completed a several year process of revising the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5). During that time, there were objections raised to retaining DSM's gender identity disorder diagnoses and calls to remove them, just as homosexuality had been removed from DSM-II in 1973. At the conclusion of the DSM-5 revision process, the gender diagnoses were retained, albeit in altered form and bearing the new name of `gender dysphoria'. The author of this paper was a member of the DSM-5 Workgroup on Sexual and Gender Identity Disorders and presently serves on the WHO Working Group on Sexual Disorders and Sexual Health. Both groups faced similar tasks: reconciling patients' needs for access to care with the stigma of being given a psychiatric diagnosis. The differing nature of the two diagnostic manuals led to two different outcomes. As background, this paper updates the history of homosexuality and the gender diagnoses in the DSM and in the International Statistical Classification of Diseases and Related Health Problems (ICD) as well as what is expected to happen to the homosexuality and gender diagnoses following the current ICD-11 revision process.},
  pmid = {26242413}
}

@article{etheridgeImplementationDeviceBriefing2023,
  title = {Implementation of a {{Device Briefing Tool}} Reduces Interruptions in Surgery: {{A}} Nonrandomized Controlled Pilot Trial},
  shorttitle = {Implementation of a {{Device Briefing Tool}} Reduces Interruptions in Surgery},
  author = {Etheridge, James C. and {Moyal-Smith}, Rachel and Lim, Shu Rong and Yong, Tze Tein and Tan, Hiang Khoon and Lim, Christine and Rothbard, Sarah and Murray, Eleanor J. and Sonnay, Yves and Brindle, Mary E. and Havens, Joaquim M.},
  year = {2023},
  month = jan,
  journal = {Surgery},
  volume = {0},
  number = {0},
  publisher = {{Elsevier}},
  issn = {0039-6060, 1532-7361},
  doi = {10.1016/j.surg.2022.12.009},
  urldate = {2023-01-11},
  langid = {english}
}

@article{goedelImplementationSyringeServices2020,
  title = {Implementation of {{Syringe Services Programs}} to {{Prevent Rapid Human Immunodeficiency Virus Transmission}} in {{Rural Counties}} in the {{United States}}: {{A Modeling Study}}},
  shorttitle = {Implementation of {{Syringe Services Programs}} to {{Prevent Rapid Human Immunodeficiency Virus Transmission}} in {{Rural Counties}} in the {{United States}}},
  author = {Goedel, William C and King, Maximilian R F and Lurie, Mark N and Galea, Sandro and Townsend, Jeffrey P and Galvani, Alison P and Friedman, Samuel R and Marshall, Brandon D L},
  year = {2020},
  month = mar,
  journal = {Clinical Infectious Diseases},
  volume = {70},
  number = {6},
  pages = {1096--1102},
  issn = {1058-4838},
  doi = {10.1093/cid/ciz321},
  urldate = {2023-03-13},
  abstract = {Syringe services programs (SSPs) are effective venues for delivering harm-reduction services to people who inject drugs (PWID). However, SSPs often face significant barriers to implementation, particularly in the absence of known human immunodeficiency virus (HIV) outbreaks.Using an agent-based model, we simulated HIV transmission in Scott County, Indiana, a rural county with a 1.7\% prevalence of injection drug use. We compared outcomes arising in the absence of an SSP, in the presence of a pre-existing SSP, and with implementation of an SSP after the detection of an HIV outbreak among PWID over 5 years following the introduction of a single infection into the network.In the absence of an SSP, the model predicted an average of 176 infections among PWID over 5 years or an incidence rate of 12.1/100 person-years. Proactive implementation averted 154 infections and decreased incidence by 90.3\%. With reactive implementation beginning operations 10 months after the first infection, an SSP would prevent 107 infections and decrease incidence by 60.8\%. Reductions in incidence were also observed among people who did not inject drugs.Based on model predictions, proactive implementation of an SSP in Scott County had the potential to avert more HIV infections than reactive implementation after the detection of an outbreak. The predicted impact of reactive SSP implementation was highly dependent on timely implementation after detecting the earliest infections. Consequently, there is a need for expanded proactive SSP implementation in the context of enhanced monitoring of outbreak vulnerability in Scott County and similar rural contexts.}
}

@article{grovDeterminingRolesThat2019,
  title = {Determining the {{Roles}} That {{Club Drugs}}, {{Marijuana}}, and {{Heavy Drinking Play}} in {{PrEP Medication Adherence Among Gay}} and {{Bisexual Men}}: {{Implications}} for {{Treatment}} and {{Research}}},
  shorttitle = {Determining the {{Roles}} That {{Club Drugs}}, {{Marijuana}}, and {{Heavy Drinking Play}} in {{PrEP Medication Adherence Among Gay}} and {{Bisexual Men}}},
  author = {Grov, Christian and Rendina, H. Jonathon and John, Steven A. and Parsons, Jeffrey T.},
  year = {2019},
  month = may,
  journal = {AIDS and Behavior},
  volume = {23},
  number = {5},
  pages = {1277--1286},
  issn = {1090-7165, 1573-3254},
  doi = {10.1007/s10461-018-2309-9},
  urldate = {2022-06-22},
  abstract = {Researchers have established that substance use interferes with anti-retroviral medication adherence among gay and bisexual men (GBM) living with HIV. There is limited parallel examination of pre-exposure prophylaxis (PrEP) adherence among HIV-negative GBM. We conducted retrospective 30-day timeline follow-back interviews and prospective semi-weekly diary data for 10 weeks with 104 PrEP-using GBM, half of whom engaged in club drug use (ketamine, ecstasy, GHB, cocaine, or methamphetamine){\textemdash}generating 9532 days of data. Participants reported their day-by-day PrEP, club drug, marijuana, and heavy alcohol use (5\,+ drinks in one sitting). On average, club drug users were no more likely to miss a dose of PrEP than non-club drug users (M\,=\,1.6 doses, SD\,=\,3.0, past 30 days). However, we found that club drug use (at the event level) increased the odds of missing a dose on the same day by 55\% and the next day (e.g., a ``carryover effect'') by 60\%. Further, missing a dose on one day increased the odds of missing a dose the following day by eightfold. We did not identify an eventlevel effect of marijuana use or heavy drinking on PrEP adherence. Our data suggest club drug users could have greater protective effects from daily oral or long-acting injectable PrEP compared to a time-driven PrEP regimen because of the concurrence of club drug use and PrEP non-adherence.},
  langid = {english}
}

@article{halloranDependentHappeningsRecent2016,
  title = {Dependent {{Happenings}}: {{A Recent Methodological Review}}},
  shorttitle = {Dependent {{Happenings}}},
  author = {Halloran, M. Elizabeth and Hudgens, Michael G.},
  year = {2016},
  month = dec,
  journal = {Current epidemiology reports},
  volume = {3},
  number = {4},
  pages = {297--305},
  issn = {2196-2995},
  doi = {10.1007/s40471-016-0086-4},
  urldate = {2023-04-06},
  abstract = {One hundred years ago Sir Ronald Ross published his treatise on a general Theory of Happenings. Dependent happenings are those in which the frequency depends on the number already affected. When there is dependency of events, interventions can have different types of effects. Interventions such as vaccination can have direct protective effects for the person receiving the treatment, as well as indirect/spillover effects for others in the population. Causal inference is a framework for carefully defining the causal effect of a treatment, exposure, or policy, and then determining conditions under which such effects can be estimated from the observed data. We consider here scenarios in which the potential outcomes of an individual can depend on the treatment of other individuals in the population, known as causal inference with interference. Much of the research so far has assumed the population is divided into groups or clusters, and individuals can interfere with others within their clusters but not across clusters. Recent developments have assumed more general forms of interference. We review some of the different types of effects that have been defined for dependent happenings, particularly using the methods of causal inference with interference. Many of the methods are applicable across disciplines, such as infectious diseases, social sciences, and economics.},
  pmcid = {PMC5267358},
  pmid = {28133589}
}

@article{halloranStudyDesignsDependent1991,
  title = {Study {{Designs}} for {{Dependent Happenings}}},
  author = {Halloran, M. Elizabeth and Struchiner, Claudio J.},
  year = {1991},
  journal = {Epidemiology},
  volume = {2},
  number = {5},
  eprint = {20065696},
  eprinttype = {jstor},
  pages = {331--338},
  publisher = {{Lippincott Williams \& Wilkins}},
  issn = {1044-3983},
  urldate = {2023-01-25},
  abstract = {In 1916, Sir Ronald Ross defined "dependent happenings" as events where the number affected in a unit of time depends on the number already affected. That is, the incidence depends on the prevalence, a characteristic of many infectious diseases. Because of this dependence, interventions against infectious diseases can have not only direct protective effects for the person receiving an intervention, but also indirect effects resulting from changes in the intensity of transmission in the population. This paper develops the conceptual framework for four types of study designs that differentiate and account for direct and indirect effects of intervention programs in dependent happenings.}
}

@article{hoffmanPeereducatorNetworkHIV2013,
  title = {A Peer-Educator Network {{HIV}} Prevention Intervention among Injection Drug Users: Results of a Randomized Controlled Trial in {{St}}. {{Petersburg}}, {{Russia}}},
  shorttitle = {A Peer-Educator Network {{HIV}} Prevention Intervention among Injection Drug Users},
  author = {Hoffman, Irving F. and Latkin, Carl A. and Kukhareva, Polina V. and Malov, Sergey V. and Batluk, Julia V. and Shaboltas, Alla V. and Skochilov, Roman V. and Sokolov, Nicolay V. and Verevochkin, Sergei V. and Hudgens, Michael G. and Kozlov, Andrei P.},
  year = {2013},
  month = sep,
  journal = {AIDS and behavior},
  volume = {17},
  number = {7},
  pages = {2510--2520},
  issn = {1573-3254},
  doi = {10.1007/s10461-013-0563-4},
  abstract = {We evaluated the efficacy of a peer-educator network intervention as a strategy to reduce HIV acquisition among injection drug users (IDUs) and their drug and/or sexual networks. A randomized controlled trial was conducted in St. Petersburg, Russia among IDU index participants and their risk network participants. Network units were randomized to the control or experimental intervention. Only the experimental index participants received training sessions to communicate risk reduction techniques to their network members. Analysis includes 76 index and 84 network participants who were HIV uninfected. The main outcome measure was HIV sero-conversion. The incidence rates in the control and experimental groups were 19.57 (95~\% CI 10.74-35.65) and 7.76 (95~\% CI 3.51-17.19) cases per 100~p/y, respectively. The IRR was 0.41 (95~\% CI 0.15-1.08) without a statistically significant difference between the two groups (log rank test statistic X(2)~=~2.73, permutation p value~=~0.16). Retention rate was 67~\% with a third of the loss due to incarceration or death. The results show a promising trend that this strategy would be successful in reducing the acquisition of HIV among IDUs.},
  langid = {english},
  pmcid = {PMC3950300},
  pmid = {23881187},
  keywords = {Adult,AIDS Serodiagnosis,{Blotting, Western},Communication,Cross-Sectional Studies,Enzyme-Linked Immunosorbent Assay,Female,Follow-Up Studies,Health Education,HIV Infections,HIV Seronegativity,Humans,Male,Peer Group,Risk Reduction Behavior,Russia,Social Support,{Substance Abuse, Intravenous},Unsafe Sex}
}

@article{hongEvaluatingKindergartenRetention2006,
  title = {Evaluating {{Kindergarten Retention Policy}}},
  author = {Hong, Guanglei and Raudenbush, Stephen W},
  year = {2006},
  month = sep,
  journal = {Journal of the American Statistical Association},
  volume = {101},
  number = {475},
  pages = {901--910},
  publisher = {{Taylor \& Francis}},
  issn = {0162-1459},
  doi = {10.1198/016214506000000447},
  urldate = {2022-06-22},
  abstract = {This article considers the policy of retaining low-achieving children in kindergarten rather than promoting them to first grade. Under the stable unit treatment value assumption (SUTVA) as articulated by Rubin, each child at risk of retention has two potential outcomes: Y(1) if retained and Y(0) if promoted. But SUTVA is questionable, because a child's potential outcomes will plausibly depend on which school that child attends and also on treatment assignments of other children. We develop a causal model that allows school assignment and peer treatments to affect potential outcomes. We impose an identifying assumption that peer effects can be summarized through a scalar function of the vector of treatment assignments in a school. Using a large, nationally representative sample, we then estimate (1) the effect of being retained in kindergarten rather than being promoted to the first grade in schools having a low retention rate, (2) the retention effect in schools having a high retention rate, and (3) the effect of being promoted in a low-retention school as compared to being promoted in a high-retention school. This third effect is not definable under SUTVA. We use multilevel propensity score stratification to approximate a two-stage experiment. At the first stage, intact schools are blocked on covariates and then, within blocks, randomly assigned to a policy of retaining comparatively more or fewer children in kindergarten. At the second stage, ``at-risk'' students within schools are blocked on covariates and then assigned at random to be retained. We find evidence that retainees learned less on average than did similar children who were promoted, a result found in both high-retention and low-retention schools. We do not detect a peer treatment effect on low-risk students.},
  keywords = {Grade retention,Multilevel design,Potential outcomes,Propensity score,Stable unit treatment value assumption}
}

@article{hudgensCausalInferenceInterference2008,
  title = {Toward {{Causal Inference With Interference}}},
  author = {Hudgens, Michael G. and Halloran, M. Elizabeth},
  year = {2008},
  month = jun,
  journal = {Journal of the American Statistical Association},
  volume = {103},
  number = {482},
  pages = {832--842},
  issn = {0162-1459},
  doi = {10.1198/016214508000000292},
  abstract = {A fundamental assumption usually made in causal inference is that of no interference between individuals (or units); that is, the potential outcomes of one individual are assumed to be unaffected by the treatment assignment of other individuals. However, in many settings, this assumption obviously does not hold. For example, in the dependent happenings of infectious diseases, whether one person becomes infected depends on who else in the population is vaccinated. In this article, we consider a population of groups of individuals where interference is possible between individuals within the same group. We propose estimands for direct, indirect, total, and overall causal effects of treatment strategies in this setting. Relations among the estimands are established; for example, the total causal effect is shown to equal the sum of direct and indirect causal effects. Using an experimental design with a two-stage randomization procedure (first at the group level, then at the individual level within groups), unbiased estimators of the proposed estimands are presented. Variances of the estimators are also developed. The methodology is illustrated in two different settings where interference is likely: assessing causal effects of housing vouchers and of vaccines.},
  langid = {english},
  pmcid = {PMC2600548},
  pmid = {19081744}
}

@misc{ItTimeReconsider,
  title = {It's Time to Reconsider How We Define Health: {{Perspective}} from Disability and Chronic Condition - {{ScienceDirect}}},
  urldate = {2023-01-18},
  howpublished = {https://www.sciencedirect.com/science/article/pii/S1936657421000753}
}

@article{marshallPotentialEffectivenessLongacting2018,
  title = {Potential Effectiveness of Long-Acting Injectable Pre-Exposure Prophylaxis for {{HIV}} Prevention in Men Who Have Sex with Men: A Modelling Study},
  shorttitle = {Potential Effectiveness of Long-Acting Injectable Pre-Exposure Prophylaxis for {{HIV}} Prevention in Men Who Have Sex with Men},
  author = {Marshall, Brandon D. L. and Goedel, William C. and King, Maximilian R. F. and Singleton, Alyson and Durham, David P. and Chan, Philip A. and Townsend, Jeffrey P. and Galvani, Alison P.},
  year = {2018},
  month = sep,
  journal = {The lancet. HIV},
  volume = {5},
  number = {9},
  pages = {e498-e505},
  issn = {2352-3018},
  doi = {10.1016/S2352-3018(18)30097-3},
  abstract = {BACKGROUND: Oral pre-exposure prophylaxis (PrEP) prevents HIV infection in men who have sex with men (MSM); however, adherence is an ongoing concern. Long-acting injectable PrEP is being tested in phase 3 trials and could address challenges associated with adherence. We examined the potential effectiveness of long-acting injectable PrEP compared with oral PrEP in MSM. METHODS: We used an agent-based model to simulate HIV transmission in a dynamic network of 11\hphantom{,}245 MSM in Atlanta, GA, USA. We used raw data from studies in macaque models and pharmacokinetic data from safety trials to estimate the time-varying efficacy of long-acting injectable PrEP. The effect of long-acting injectable PrEP on the cumulative number of new HIV infections over 10 years (2015-24) was compared with no PrEP and daily oral PrEP across a range of coverage levels. Sensitivity analyses were done with varying maximum efficacy and drug half-life values. FINDINGS: In the absence of PrEP, the model predicted 2374 new HIV infections (95\% simulation interval [SI] 2345-2412) between 2015 and 2024. The cumulative number of new HIV infections was reduced in all scenarios in which MSM received long-acting injectable PrEP compared with oral PrEP. At a coverage level of 35\%, compared with no PrEP, long-acting injectable PrEP led to a 44\% reduction in new HIV infections (1044 new infections averted [95\% SI 1018-1077]) versus 33\% (792 infections averted [763-821]) for oral PrEP. The relative benefit of long-acting injectable PrEP was sensitive to the assumed efficacy of injections received every 8 weeks, discontinuation rates, and terminal drug half-life. INTERPRETATION: Long-acting injectable PrEP has the potential to produce larger reductions in HIV transmission in MSM than oral PrEP. However, the real-world, population-level impact of this approach will depend on uptake of this prevention method and its effectiveness, as well as retention of patients in clinical care. FUNDING: National Institute on Drug Abuse and National Institute of Mental Health.},
  langid = {english},
  pmcid = {PMC6138558},
  pmid = {29908917},
  keywords = {Adolescent,Adult,Animals,Anti-HIV Agents,Chemoprevention,Delayed-Action Preparations,{Disease Models, Animal},{Disease Transmission, Infectious},HIV Infections,{Homosexuality, Male},Humans,Injections,Macaca,Male,Middle Aged,Pre-Exposure Prophylaxis,United States,Young Adult}
}

@article{martinezConceptualizationOperationalizationUtilization2022,
  title = {Conceptualization, {{Operationalization}}, and {{Utilization}} of {{Race}} and {{Ethnicity}} in {{Major Epidemiology Journals}}, 1995{\textendash}2018: {{A Systematic Review}}},
  shorttitle = {Conceptualization, {{Operationalization}}, and {{Utilization}} of {{Race}} and {{Ethnicity}} in {{Major Epidemiology Journals}}, 1995{\textendash}2018},
  author = {Martinez, Rae Anne M and Andrabi, Nafeesa and Goodwin, Andrea N and Wilbur, Rachel E and Smith, Natalie R and Zivich, Paul N},
  year = {2022},
  month = aug,
  journal = {American Journal of Epidemiology},
  pages = {kwac146},
  issn = {0002-9262},
  doi = {10.1093/aje/kwac146},
  urldate = {2023-01-18},
  abstract = {Despite repeated calls by scholars to critically engage with the concepts of race and ethnicity in US epidemiologic research, the incorporation of these social constructs in scholarship may be suboptimal. This study characterizes the conceptualization, operationalization, and utilization of race and ethnicity in US research published in leading journals whose publications shape discourse and norms around race, ethnicity, and health within the field of epidemiology. We systematically reviewed randomly selected articles from prominent epidemiology journals across 5 periods: 1995{\textendash}1999, 2000{\textendash}2004, 2005{\textendash}2009, 2010{\textendash}2014, and 2015{\textendash}2018. All original human-subjects research conducted in the United States was eligible for review. Information on definitions, measurement, coding, and use in analysis was extracted. We reviewed 1,050 articles, including 414 (39\%) in our analyses. Four studies explicitly defined race and/or ethnicity. Authors rarely made clear delineations between race and ethnicity, often adopting an ethnoracial construct. In the majority of studies across time periods, authors did not state how race and/or ethnicity was measured. Top coding schemes included ``Black, White'' (race), ``Hispanic, non-Hispanic'' (ethnicity), and ``Black, White, Hispanic'' (ethnoracial). Most often, race and ethnicity were deemed ``not of interest'' in analyses (e.g., control variables). Broadly, disciplinary practices have remained largely the same between 1995 and 2018 and are in need of improvement.}
}

@article{murrayEmulatingTargetTrials2021,
  title = {Emulating {{Target Trials}} to {{Improve Causal Inference From Agent-Based Models}}},
  author = {Murray, Eleanor J and Marshall, Brandon D L and Buchanan, Ashley L},
  year = {2021},
  month = aug,
  journal = {American Journal of Epidemiology},
  volume = {190},
  number = {8},
  pages = {1652--1658},
  issn = {0002-9262},
  doi = {10.1093/aje/kwab040},
  urldate = {2021-12-07},
  abstract = {Agent-based models are a key tool for investigating the emergent properties of population health settings, such as infectious disease transmission, where the exposure often violates the key ``no interference'' assumption of traditional causal inference under the potential outcomes framework. Agent-based models and other simulation-based modeling approaches have generally been viewed as a separate knowledge-generating paradigm from the potential outcomes framework, but this can lead to confusion about how to interpret the results of these models in real-world settings. By explicitly incorporating the target trial framework into the development of an agent-based or other simulation model, we can clarify the causal parameters of interest, as well as make explicit the assumptions required for valid causal effect estimation within or between populations. In this paper, we describe the use of the target trial framework for designing agent-based models when the goal is estimation of causal effects in the presence of interference, or spillover.},
  copyright = {All rights reserved}
}

@article{ogburnCausalDiagramsInterference2014,
  title = {Causal {{Diagrams}} for {{Interference}}},
  author = {Ogburn, Elizabeth L. and VanderWeele, Tyler J.},
  year = {2014},
  month = nov,
  journal = {Statistical Science},
  volume = {29},
  number = {4},
  issn = {0883-4237},
  doi = {10.1214/14-STS501},
  urldate = {2022-06-22},
  abstract = {The term ``interference'' has been used to describe any setting in which one subject's exposure may affect another subject's outcome. We use causal diagrams to distinguish among three causal mechanisms that give rise to interference. The first causal mechanism by which interference can operate is a direct causal effect of one individual's treatment on another individual's outcome; we call this direct interference. Interference by contagion is present when one individual's outcome may affect the outcomes of other individuals with whom he comes into contact. Then giving treatment to the first individual could have an indirect effect on others through the treated individual's outcome. The third pathway by which interference may operate is allocational interference. Treatment in this case allocates individuals to groups; through interactions within a group, individuals may affect one another's outcomes in any number of ways. In many settings, more than one type of interference will be present simultaneously. The causal effects of interest differ according to which types of interference are present, as do the conditions under which causal effects are identifiable. Using causal diagrams for interference, we describe these differences, give criteria for the identification of important causal effects, and discuss applications to infectious diseases.},
  langid = {english}
}

@article{rendinaAggregateEventlevelAssociations2015,
  title = {Aggregate and Event-Level Associations between Substance Use and Sexual Behavior among Gay and Bisexual Men: {{Comparing}} Retrospective and Prospective Data},
  shorttitle = {Aggregate and Event-Level Associations between Substance Use and Sexual Behavior among Gay and Bisexual Men},
  author = {Rendina, H. Jonathon and Moody, Raymond L. and Ventuneac, Ana and Grov, Christian and Parsons, Jeffrey T.},
  year = {2015},
  month = sep,
  journal = {Drug and Alcohol Dependence},
  volume = {154},
  pages = {199--207},
  issn = {03768716},
  doi = {10.1016/j.drugalcdep.2015.06.045},
  urldate = {2022-06-22},
  abstract = {Background: Despite limited research, some evidence suggests that examining substance use at multiple levels may be of greater utility in predicting sexual behavior than utilizing one level of measurement, particularly when investigating different substances simultaneously. We aimed to examine aggregate and event-level associations between three forms of substance use {\textendash} alcohol, marijuana, and club drugs {\textendash} and two sexual behavior outcomes {\textendash} sexual engagement and condomless anal sex (CAS). Method: Analyses focused on both 6-week timeline follow-back (TLFB; retrospective) and 30-day daily diary (prospective) data among a demographically diverse sample of 371 highly sexually active HIVpositive and HIV-negative gay and bisexual men. Results: Models from both TLFB and diary showed that event-level use of alcohol, marijuana, and club drugs was associated with increased sexual engagement, while higher aggregated frequency marijuana and any frequency club drug use were associated with decreased sexual engagement. Event-level use of club drugs was consistently associated with increased odds of CAS across both TLFB and diary models while higher frequency marijuana use was most consistently associated with a lower odds of CAS. Conclusions: Findings indicated that results are largely consistent between retrospective and prospective data, but that retrospective results for substance use and sexual engagement were generally greater in magnitude. These results suggest that substance use primarily acts to increase sexual risk at the eventlevel and less so through individual-level frequency of use; moreover, it primarily does so by increasing the likelihood of sex on a given day with fewer significant associations with the odds of CAS on sex days. {\textcopyright} 2015 Elsevier Ireland Ltd. All rights reserved.},
  langid = {english}
}

@misc{risebergDevelopmentApplicationEvidencebased2022,
  title = {Development and Application of an Evidence-Based Directed Acyclic Graph to Evaluate the Associations between Metal Mixtures and Cardiometabolic Outcomes},
  author = {Riseberg, Emily and Melamed, Rachel D. and James, Katherine A. and Alderete, Tanya L. and Corlin, Laura},
  year = {2022},
  month = aug,
  pages = {2021.03.05.21252993},
  publisher = {{medRxiv}},
  doi = {10.1101/2021.03.05.21252993},
  urldate = {2023-04-17},
  abstract = {Objectives Specifying analytic models to assess relationships among metal mixtures and cardiometabolic outcomes requires evidence-based models of the causal structures; however, such models have not been previously published. The objective of this study was to develop and evaluate a directed acyclic graph diagraming metal mixture exposure and cardiometabolic outcomes. Methods We conducted a systematic literature search to develop the directed acyclic graph (DAG) of metal mixtures and cardiometabolic outcomes. To evaluate consistency of the DAG, we tested the suggested conditional independence statements using linear and logistic regression analyses with data from the San Luis Valley Diabetes Study (SLVDS; n=1795). We compared the proportion of statements supported by the data to the proportion of conditional independence statements supported by 100 DAGs with the same structure but randomly permuted nodes. Next, we used our DAG to identify minimally sufficient adjustment sets needed to estimate the association between metal mixtures and cardiometabolic outcomes in the SLVDS and applied them using Bayesian kernel machine regression models. Results From the 42 articles included in the review, we developed an evidence-based DAG with 163 testable conditional independence statements (64\% supported by SLVDS data). Only 5\% of DAGs with randomly permuted nodes indicated more agreement with the data than our evidence-based DAG. We did not observe evidence for an association between metal mixtures and cardiometabolic outcomes in the pilot analysis. Conclusions We developed, tested, and applied an evidence-based approach to analyze associations between metal mixtures and cardiometabolic health.},
  archiveprefix = {medRxiv},
  copyright = {{\textcopyright} 2022, Posted by Cold Spring Harbor Laboratory. This pre-print is available under a Creative Commons License (Attribution-NoDerivs 4.0 International), CC BY-ND 4.0, as described at http://creativecommons.org/licenses/by-nd/4.0/},
  langid = {english}
}

@article{rubinBayesianInferenceCausal1978,
  title = {Bayesian {{Inference}} for {{Causal Effects}}: {{The Role}} of {{Randomization}}},
  shorttitle = {Bayesian {{Inference}} for {{Causal Effects}}},
  author = {Rubin, Donald B.},
  year = {1978},
  month = jan,
  journal = {The Annals of Statistics},
  volume = {6},
  number = {1},
  pages = {34--58},
  publisher = {{Institute of Mathematical Statistics}},
  issn = {0090-5364, 2168-8966},
  doi = {10.1214/aos/1176344064},
  urldate = {2022-06-22},
  abstract = {Causal effects are comparisons among values that would have been observed under all possible assignments of treatments to experimental units. In an experiment, one assignment of treatments is chosen and only the values under that assignment can be observed. Bayesian inference for causal effects follows from finding the predictive distribution of the values under the other assignments of treatments. This perspective makes clear the role of mechanisms that sample experimental units, assign treatments and record data. Unless these mechanisms are ignorable (known probabilistic functions of recorded values), the Bayesian must model them in the data analysis and, consequently, confront inferences for causal effects that are sensitive to the specification of the prior distribution of the data. Moreover, not all ignorable mechanisms can yield data from which inferences for causal effects are insensitive to prior specifications. Classical randomized designs stand out as especially appealing assignment mechanisms designed to make inference for causal effects straightforward by limiting the sensitivity of a valid Bayesian analysis.},
  keywords = {62A15,62B15,62C10,62F15,62K99,Bayesian,causality,experimentation,inference,missing data,Randomization}
}

@article{schaferOlderAdultsHIV2021,
  title = {Do {{Older Adults}} with {{HIV Have Distinctive Personal Networks}}? {{Stigma}}, {{Network Activation}}, and the {{Role}} of {{Disclosure}} in {{South Africa}}},
  shorttitle = {Do {{Older Adults}} with {{HIV Have Distinctive Personal Networks}}?},
  author = {Schafer, Markus H. and Upenieks, Laura and DeMaria, Julia},
  year = {2021},
  journal = {AIDS and Behavior},
  volume = {25},
  number = {5},
  pages = {1560--1572},
  issn = {1090-7165},
  doi = {10.1007/s10461-020-02996-x},
  urldate = {2022-06-22},
  abstract = {This study considers whether the personal networks of older South African people living with HIV (PLHIV) differ from those without HIV. Using recent survey data (N\,=\,5059), results suggest that PLHIV reported more core network members than their peers without HIV (IRR 1.08; 95\% CI 1.03, 1.13), but were equally likely to receive emotional support from network members (1.21; 95\% CI 0.93, 1.58). PLHIV who had yet to disclose their serostatus were more likely than others to have friends and other non-kin in their core network (B 0.08; 95\% CI 0.02, 0.13) and to maintain networks of non-overlapping members (OR 2.11; 95\% CI 1.33, 3.34). Even as HIV remains highly stigmatized in South Africa, PLHIV tend to maintain relatively large and supportive networks. Still, a sizeable proportion of PLHIV do not disclose their illness{\textemdash}these individuals disproportionately inhabit networks marked by non-kin and by high bridging potential.},
  pmcid = {PMC7415327},
  pmid = {32776180}
}

@article{singletonNetworkCharacteristicsAssociated2019,
  title = {Network Characteristics Associated with Rapid {{HIV}} Transmission among People Who Inject Drugs in a Rural County in the {{United States}}: A Modelling Study},
  shorttitle = {Network Characteristics Associated with Rapid {{HIV}} Transmission among People Who Inject Drugs in a Rural County in the {{United States}}},
  author = {Singleton, Alyson},
  year = {2019},
  publisher = {{Brown University}},
  doi = {10.26300/XZ96-1P36},
  urldate = {2022-09-28}
}

@article{singletonNetworkStructureRapid2021,
  title = {Network Structure and Rapid {{HIV}} Transmission among People Who Inject Drugs: {{A}} Simulation-Based Analysis},
  shorttitle = {Network Structure and Rapid {{HIV}} Transmission among People Who Inject Drugs},
  author = {Singleton, Alyson L. and Marshall, Brandon D. L. and Bessey, S. and Harrison, Matthew T. and Galvani, Alison P. and Yedinak, Jesse L. and Jacka, Brendan P. and Goodreau, Steven M. and Goedel, William C.},
  year = {2021},
  month = mar,
  journal = {Epidemics},
  volume = {34},
  pages = {100426},
  issn = {1755-4365},
  doi = {10.1016/j.epidem.2020.100426},
  urldate = {2022-09-28},
  abstract = {As HIV incidence among people who inject drugs grows in the context of an escalating drug overdose epidemic in North America, investigating how network structure may affect vulnerability to rapid HIV transmission is necessary for preventing outbreaks. We compared the characteristics of the observed contact tracing network from the 2015 outbreak in rural Indiana with 1000 networks generated by an agent-based network model with approximately the same number of individuals (n\,=\,420) and ties between them (n\,=\,913). We introduced an initial HIV infection into the simulated networks and compared the subsequent epidemic behavior (e.g., cumulative HIV infections over 5 years). The model was able to produce networks with largely comparable characteristics and total numbers of incident HIV infections. Although the model was unable to produce networks with comparable cohesiveness (where the observed network had a transitivity value 35.7 standard deviations from the mean of the simulated networks), the structural variability of the simulated networks allowed for investigation into their potential facilitation of HIV transmission. These findings emphasize the need for continued development of injection network simulation studies in tandem with empirical data collection to further investigate how network characteristics played a role in this and future outbreaks.},
  langid = {english},
  keywords = {HIV,Injection drug use,Outbreak,Rural health,Social network analysis}
}

@article{stadtfeldNetSimSocialNetworks,
  title = {{{NetSim}}: {{A Social Networks Simulation Tool}} in {{R}}},
  author = {Stadtfeld, Christoph},
  pages = {26},
  abstract = {NetSim is an R package that allows to simulate the co-evolution of social networks and individual attributes. It can be used to study the impact of micro models that describe the behavior of individuals on the macro outcome of social networks. NetSim is based on a flexible Markov framework that enables the combination of a variety of different models.},
  langid = {english}
}

@article{sullivanExplainingRacialDisparities2015,
  title = {Explaining Racial Disparities in {{HIV}} Incidence in Black and White Men Who Have Sex with Men in {{Atlanta}}, {{GA}}: A Prospective Observational Cohort Study},
  shorttitle = {Explaining Racial Disparities in {{HIV}} Incidence in Black and White Men Who Have Sex with Men in {{Atlanta}}, {{GA}}},
  author = {Sullivan, Patrick S. and Rosenberg, Eli S. and Sanchez, Travis H. and Kelley, Colleen F. and Luisi, Nicole and Cooper, Hannah L. and Diclemente, Ralph J. and Wingood, Gina M. and Frew, Paula M. and Salazar, Laura F. and Del Rio, Carlos and Mulligan, Mark J. and Peterson, John L.},
  year = {2015},
  month = jun,
  journal = {Annals of Epidemiology},
  volume = {25},
  number = {6},
  pages = {445--454},
  issn = {1873-2585},
  doi = {10.1016/j.annepidem.2015.03.006},
  abstract = {PURPOSE: To describe factors associated with racial disparities in HIV (human immunodeficiency virus) incidence among men who have sex with men (MSM) in the United States. METHODS: In a longitudinal cohort of black and white HIV-negative MSM in Atlanta, HIV incidence rates were compared by race. Incidence hazard ratios (HRs) between black and white MSM were estimated with an age-scaled Cox proportional hazards model. A change-in-estimate approach was used to understand mediating time-independent and -dependent factors that accounted for the elevated HR. RESULTS: Thirty-two incident HIV infections occurred among 260 black and 302 white MSM during 843 person-years (PY) of follow-up. HIV incidence was higher among black MSM (6.5/100 PY; 95\% confidence interval [CI]: 4.2-9.7) than white MSM (1.7/100 PY; CI: 0.7-3.3) and highest among young (18-24~years) black MSM (10.9/100 PY; CI: 6.2-17.6). The unadjusted hazard of HIV infection for black MSM was 2.9 (CI: 1.3-6.4) times that of white MSM; adjustment for health insurance status and partner race explained effectively all of the racial disparity. CONCLUSIONS: Relative to white MSM in Atlanta, black MSM, particularly young black MSM, experienced higher HIV incidence that was not attributable to individual risk behaviors. In a setting where partner pool risk is a driver of disparities, it is also important to maximize care and treatment for HIV-positive MSM.},
  langid = {english},
  pmcid = {PMC4433604},
  pmid = {25911980},
  keywords = {Adolescent,Adult,African Americans,Cohort studies,Georgia,Health Status Disparities,HIV incidence,HIV Infections,{Homosexuality, Male},Humans,Incidence,Kaplan-Meier Estimate,Longitudinal Studies,Male,Men who have sex with men,Proportional Hazards Models,Prospective Studies,Racial disparities,Risk-Taking,Socioeconomic Factors,Whites,Young Adult}
}

@article{tchetgentchetgenCausalInferencePresence2012,
  title = {On Causal Inference in the Presence of Interference},
  author = {Tchetgen Tchetgen, Eric J. and VanderWeele, Tyler J.},
  year = {2012},
  month = feb,
  journal = {Statistical Methods in Medical Research},
  volume = {21},
  number = {1},
  pages = {55--75},
  issn = {1477-0334},
  doi = {10.1177/0962280210386779},
  abstract = {Interference is said to be present when the exposure or treatment received by one individual may affect the outcomes of other individuals. Such interference can arise in settings in which the outcomes of the various individuals come about through social interactions. When interference is present, causal inference is rendered considerably more complex, and the literature on causal inference in the presence of interference has just recently begun to develop. In this article we summarise some of the concepts and results from the existing literature and extend that literature in considering new results for finite sample inference, new inverse probability weighting estimators in the presence of interference and new causal estimands of interest.},
  langid = {english},
  pmcid = {PMC4216807},
  pmid = {21068053},
  keywords = {Biomedical Research,Causality,Communicable Diseases,{Data Interpretation, Statistical},Humans,{Models, Statistical},Randomized Controlled Trials as Topic,Vaccination}
}

@article{vanderweeleConcerningConsistencyAssumption2009,
  title = {Concerning the {{Consistency Assumption}} in {{Causal Inference}}},
  author = {VanderWeele, Tyler J.},
  year = {2009},
  month = nov,
  journal = {Epidemiology},
  volume = {20},
  number = {6},
  pages = {880--883},
  issn = {1044-3983},
  doi = {10.1097/EDE.0b013e3181bd5638},
  urldate = {2022-06-22},
  abstract = {Cole and Frangakis (Epidemiology. 2009;20:3{\textendash}5) introduced notation for the consistency assumption in causal inference. I extend this notation and propose a refinement of the consistency assumption that makes clear that the consistency statement, as ordinarily given, is in fact an assumption and not an axiom or definition. The refinement is also useful in showing that additional assumptions (referred to here as treatment-variation irrelevance assumptions), stronger than those given by Cole and Frangakis, are in fact necessary in articulating the ordinary assumptions of ignorability or exchangeability. The refinement furthermore sheds light on the distinction between intervention and choice in reasoning about causality. A distinction between the range of treatment variations for which potential outcomes can be defined and the range for which treatment comparisons are made is discussed in relation to issues of nonadherence. The use of stochastic counterfactuals can help relax what is effectively being presupposed by the treatment-variation irrelevance assumption and the consistency assumption.},
  langid = {american}
}

@article{vanderweeleNewCriterionConfounder2011,
  title = {A New Criterion for Confounder Selection},
  author = {VanderWeele, Tyler J. and Shpitser, Ilya},
  year = {2011},
  month = dec,
  journal = {Biometrics},
  volume = {67},
  number = {4},
  pages = {1406--1413},
  issn = {1541-0420},
  doi = {10.1111/j.1541-0420.2011.01619.x},
  abstract = {We propose a new criterion for confounder selection when the underlying causal structure is unknown and only limited knowledge is available. We assume all covariates being considered are pretreatment variables and that for each covariate it is known (i) whether the covariate is a cause of treatment, and (ii) whether the covariate is a cause of the outcome. The causal relationships the covariates have with one another is assumed unknown. We propose that control be made for any covariate that is either a cause of treatment or of the outcome or both. We show that irrespective of the actual underlying causal structure, if any subset of the observed covariates suffices to control for confounding then the set of covariates chosen by our criterion will also suffice. We show that other, commonly used, criteria for confounding control do not have this property. We use formal theory concerning causal diagrams to prove our result but the application of the result does not rely on familiarity with causal diagrams. An investigator simply need ask, "Is the covariate a cause of the treatment?" and "Is the covariate a cause of the outcome?" If the answer to either question is "yes" then the covariate is included for confounder control. We discuss some additional covariate selection results that preserve unconfoundedness and that may be of interest when used with our criterion.},
  langid = {english},
  pmcid = {PMC3166439},
  pmid = {21627630},
  keywords = {Algorithms,Biometry,Computer Simulation,Confidence Intervals,{Confounding Factors, Epidemiologic},{Data Interpretation, Statistical},Epidemiologic Methods,Humans,{Models, Statistical},{Numerical Analysis, Computer-Assisted},{Outcome Assessment, Health Care},Proportional Hazards Models,Risk Assessment,Risk Factors,Statistical Distributions}
}

@misc{vaughanFurrrApplyMapping2022,
  title = {Furrr: {{Apply Mapping Functions}} in {{Parallel}} Using {{Futures}}},
  shorttitle = {Furrr},
  author = {Vaughan, Davis and Dancho, Matt and RStudio},
  year = {2022},
  month = aug,
  urldate = {2023-03-06},
  abstract = {Implementations of the family of map() functions from 'purrr' that can be resolved using any 'future'-supported backend, e.g. parallel on the local machine or distributed on a compute cluster.},
  copyright = {MIT + file LICENSE},
  keywords = {HighPerformanceComputing}
}

@article{wattsCollectiveDynamicsSmallworld1998,
  title = {Collective Dynamics of `Small-World' Networks},
  author = {Watts, Duncan J. and Strogatz, Steven H.},
  year = {1998},
  month = jun,
  journal = {Nature},
  volume = {393},
  number = {6684},
  pages = {440--442},
  publisher = {{Nature Publishing Group}},
  issn = {1476-4687},
  doi = {10.1038/30918},
  urldate = {2023-11-21},
  abstract = {Networks of coupled dynamical systems have been used to model biological oscillators1,2,3,4, Josephson junction arrays5,6, excitable media7, neural networks8,9,10, spatial games11, genetic control networks12 and many other self-organizing systems. Ordinarily, the connection topology is assumed to be either completely regular or completely random. But many biological, technological and social networks lie somewhere between these two extremes. Here we explore simple models of networks that can be tuned through this middle ground: regular networks `rewired' to introduce increasing amounts of disorder. We find that these systems can be highly clustered, like regular lattices, yet have small characteristic path lengths, like random graphs. We call them `small-world' networks, by analogy with the small-world phenomenon13,14 (popularly known as six degrees of separation15). The neural network of the worm Caenorhabditis elegans, the power grid of the western United States, and the collaboration graph of film actors are shown to be small-world networks. Models of dynamical systems with small-world coupling display enhanced signal-propagation speed, computational power, and synchronizability. In particular, infectious diseases spread more easily in small-world networks than in regular lattices.},
  copyright = {1998 Macmillan Magazines Ltd.},
  langid = {english},
  keywords = {Humanities and Social Sciences,multidisciplinary,Science}
}

@article{westreichInvitedCommentaryPositivity2010,
  title = {Invited Commentary: Positivity in Practice},
  shorttitle = {Invited Commentary},
  author = {Westreich, Daniel and Cole, Stephen R.},
  year = {2010},
  month = mar,
  journal = {American Journal of Epidemiology},
  volume = {171},
  number = {6},
  pages = {674-677; discussion 678-681},
  issn = {1476-6256},
  doi = {10.1093/aje/kwp436},
  abstract = {Positivity, or the experimental treatment assignment assumption, requires that there be both exposed and unexposed participants at every combination of the values of the observed confounders in the population under study. Positivity is essential for inference but is often overlooked in practice by epidemiologists. This issue of the Journal includes 2 articles featuring discussions related to positivity. Here the authors define positivity, distinguish between deterministic and random positivity, and discuss the 2 relevant papers in this issue. In addition, the commentators illustrate positivity in simple 2 x 2 tables, as well as detail some ways in which epidemiologists may examine their data for nonpositivity and deal with violations of positivity in practice.},
  langid = {english},
  pmcid = {PMC2877454},
  pmid = {20139125},
  keywords = {Bias,Biometry,Causality,{Confounding Factors, Epidemiologic},{Data Interpretation, Statistical},Epidemiologic Methods,Humans,Propensity Score}
}