Background: Vulnerable populations in Canada shoulder a disproportionately high burden of disease. Transmission of sexually transmitted infections is behaviourally mediated. Previous research indicates an association between depression and sexual risk-taking. Evidence also suggests that social support is an effect modifier. Methods: Data were collected from a population of injection drug users, between 2003 and 2004 in Winnipeg, using respondent driven sampling. Demographic and social behaviors were analyzed to characterize the population, as well as social networks and ego networks. Logistic regression was used to examine the association between depressive symptoms and sexual risk interactions. Social support was examined as an effect modifier. Results: The majority of the study participants and network members were aged between 35 and 44, and a high percentage identified as Native Canadians. The highest percentage of people reported welfare as their primary source of income, and injecting stimulants, as their most frequently injected drug. Logistic regression models indicated an increase in the odds of individuals engaging in high-risk sexual interactions, if they had also self-reported elevated depressive symptoms. It was not possible to conclude that social support was an effect modifier. Conclusion: This research supports a positive association between elevated depressive symptoms, and higher levels of sexual risk interactions. Further research is needed to understand the role of social support.

Data that can be represented as a network, where there are measurements both on units and on pairs of units, are becoming increasingly prevalent in the social sciences and public health. Homophily in network data, or the tendency of units to connect based on similar nodal attribute values (i.e. income, HIV status) more often than expected by chance is receiving strong attention from researchers in statistics, medicine, sociology, public health and others. Respondent-Driven Sampling (RDS) is a link-tracing network sampling strategy heavily used in public health worldwide that is cost efficient and allows us to survey populations inaccessible by conventional techniques. Via extensive simulation we study the performance of existing methods of estimating population averages, and show that they have poor performance if there is homophily on the quantity surveyed. We propose the first model-based approach for this setting and show its superiority as a point estimator and in terms of uncertainty intervals coverage rates, and demonstrate its application to a real life RDS-based survey. We study how the strength of homophily effects can be estimated and compared across networks and different binary attributes under several network sampling schemes. We give a proof that homophily can be effectively estimated under RDS and propose a new homophily index. This work moves towards a deeper understanding of network structure as a function of nodal attributes and network sampling under homophily. / Statistics

Master of Science / Department of Statistics / Perla E. Reyes Cuellar / The term network has become part of our everyday vocabulary. The more popular are perhaps the social ones, but the concept also includes business partnerships, literature citations, biological networks, among others. Formally, networks are defined as sets of items and their connections. Often modeled as the mathematic object known as a graph, networks have been studied extensively for several years, and research is widely available. In statistics, a variety of modeling techniques and statistical terms have been developed to analyze them and predict individual behaviors. Specifically, certain statistics like degree distribution, clustering coefficient, and so on are considered important indicators in traditional social network studies. However, while conventional network models assume that the whole network population is known, complete information is not always available. Thus, different sampling methods are often required when the population data is inaccessible. Less time has been dedicated to studying the accuracy of these sampling methods to produce a representative sample. As such, the aim of this report is to identify the capacity of sampling techniques to reflect the features of the original network. In particular, we study Anti-cluster Respondent Driven Sampling (AC-RDS). We also explore whether standard modeling techniques paired with sample data could estimate statistics often used in the study of social networks. Respondent Driven Sampling (RDS) is a chain referral approach to study rare and/or hidden populations. Originating from the link-tracing design, RDS has been further developed into a series of methods utilized in social network studies, such as locating target populations or estimating the number and proportion of needle-sharing among drug addicts. However, RDS does not always perform as well as expected. When the social network contains tight communities (or clusters) with few connections between them, traditional RDS tends to oversample one community, introducing bias. AC-RDS is a special Markov chain process that collects samples across communities, capturing the whole network. With special referral requests, the initial seeds are more likely to refer to the individuals that are outside their communities. In this report, we fitted the Exponential Random Graph Model (ERGM) and a Stochastic Block Model (SBM) to an empirical study of the Facebook friendship network of 1034 participants. Then, given our goal of identifying techniques that will produce a representative sample, we decided to compare two version of AC-RDSs, in addition to traditional RDS, with Simple Random Sampling (SRS). We compared the methods by drawing 100 network samples using each sampling technique, then fitting an SBM to each sample network we used the results to project the network into one of population size. We calculated essential network statistics, such as degree distribution, of each sampling method and then compared the result to the original network observed statistics.

Respondent-driven sampling (RDS) is very useful in collecting data from individuals in hidden populations, where a sampling frame does not exist. It starts with researchers choosing initial respondents from a group which may be involved in taboo or illegal activities, after which they recruit other peers who belong to the same group. Analysis results in unbiased estimates of population proportions though with strong assumptions about the underlying social network and RDS recruitment process. These assumptions bear little resemblance to reality, and thus compromise the estimation of any means, population proportions or variances inferred from studies. The topology of the contact network, denoted by the number of links each person has, provides insight into the processes of infectious disease spread. The overall objective of the thesis is to identify the topology of an injection drug use network, and critically review the methods developed to produce estimates. The topology of people who inject drugs (PWID) collected by RDS in Ottawa, 2006 was compared with a Poisson distribution, an exponential distribution, a power-law distribution, and a lognormal distribution. The contact distribution was then evaluated against a small-world network characterized by high clustering and low average distances between individuals. Last a systematic review of the methods used to produce RDS mean and variance estimates was conducted. The Poisson distribution, a type of random distribution, was not an appropriate fit for PWID network. However, the PWID network can be classified as a small world network organised with many connections and short distances between people. Prevention of transmission in such networks should be focussed on the most active people (clustered individuals and hubs) as intervention with any others is less effective. The systematic review contained 32 articles which included the development and evaluation of 12 RDS mean and 6 variance estimators. Overall, the majority of estimators perform roughly the same, with the exception of RDSIEGO which outperformed the 6 other RDS mean estimators. The Tree bootstrap variance estimate does not rely on modelling RDS as a first order Markov (FOM) process, which seems to be the main limitation of the other existing estimators. The lack of FOM as an assumption and the flexible application of this variance estimator to any RDS point estimate make the Tree bootstrapping estimator a more efficient choice.

Philosophiae Doctor - PhD / This thesis presents research on the use of modified respondent driven sampling (mRDS) methodology to enhance identification and recruitment of key populations (KP) into an HIV prevention trial in Kisumu, western Kenya through a three phase mixed method study. The study was carried out in Kisumu, western Kenya within the Kenya Medical Research Institute (KEMRI) and the US Centers for Disease Control and Prevention (CDC) Research and Public Health Collaboration platform. The three phases included: 1. PHASE I: Identification and determination of categories of KPs and techniques of locating and motivating them to participate in HIV prevention trials.2. PHASE II: Design and Implementation of a mRDS methodology in recruiting Ks into HIV prevention trials.3. PHASE III: Evaluation of the mRDS in recruitment of KPs into an HIV incidence cohort study. Methods Phase I of the study included the conduct of in depth interviews which were used to identify different categories of persons considered to be KPs within Kisumu, identify strategies of locating the KPs and determine motivators and inhibitors of KPs participation in HIV prevention trials. Phase II on the other hand included the administration of a survey that had been refined in Phase I. The survey was used to design a mRDS methodology which was then implemented to recruit KPs into the survey. Phase III evaluated the success of the mRDS in recruiting KPs into an HIV prevention study by assessing the risk profiles for participants screened and enrolled into the Phase III study. Ethical approval for the study was sought from the ethics committee of the Kenya Medical Research Institute, the US CDC and the University of the Western Cape.The study recruited 53 individuals into phase I and was able to 8 identify different categories of people considered to be KPs and the 4 salient strategies that could be used to recruit them into HIV prevention studies. The phase also identified 8 potential motivators and 9 potential inhibitors to participation in HIV prevention research. These categories and salient strategies were used in phase II to develop and pilot a mRDS methodology in recruiting 203 individuals into a survey. The survey was used as a validation tool for the risk levels of persons recruited by the mRDS using the variables of inconsistent condom use and having multiple partners. The validated mRDS was then applied in the recruitment of 1,292 participants in phase III of thestudy. These study participants had characteristics similar to those seen in similar studies and elucidated from phases I and II of the study. HIV seropositivity was used as the variable for validating risk levels of participants in this study and this was found to be higher that that seen in general population and comparable to that seen in other KPs groups in the region. Results: Overall the study was able to identify different categories of people considered to be at high riskof HIV acquisition. The groups identified included people who frequent bars (e.g. bar workers, drunkards, sex workers, businessmen), people who work in transportation (e.g. truck drivers, matatu drivers, motorcycle drivers, taxi drivers, bicycle taxi drivers), fishermen/fishmongers, MSM and hair salon workers. The study also identified using personal contact, link persons, peer mobilisers and leaders as strategies of identifying and locating KPs. The study used the mRDS successfully in recruiting participants with evaluation of inconsistent condom use and multiple sexual partnerships showing the participants to be of high risk behaviour. Of all the females in the study, only 3.3% were pregnant. The prevalence of Chlamydia was 2.9%, gonorrhoea was 5.0%, syphilis was 0.4% and HSV-2 was 46.0%. Those who tested positive for HIV were 26.2% with 42.3% of the HIV positive participants having CD4 counts of between 250 – 500 cells/ml. Recommendations and Conclusion:The mRDS was successful in recruiting KPs in an HIV prevention trial. Majority of the participants reported inconsistent condom use and having multiple sex partners. In addition to MSM, SW and transport industry workers, fisherfolk, discordant couples, widowers, street youth, car washers and police also form part of KPs groups. The HIV prevalence was higher amongst these groups compared to general population with discordant couples having the highest HIV prevalence. The study recommends that mRDS should be used to identify and recruit KPs as it not only allows for faster recruitment of KPs, it also reduces the expense and complexity associated with coupon management in the standard RDS.

<b>Background:</b> Women who inject drugs face vast disparities in health outcomes relative to their counterparts in the general population, most notably in HIV. Intimate partner violence (IPV) victimisation has a detrimental individual effect on women's HIV risk behaviour. Furthermore, IPV often co-occurs with substance use and poor mental health among women in high-income countries, but little is known about the cumulative and interactive effects of these conditions on women's HIV risk behaviour in low- and middle-income countries. This thesis applied an ecological approach guided primarily by syndemics theory to understand influences on women's HIV behavioural outcomes. It examined associations and mechanisms linking IPV, substance use, and depression, with HIV sexual and injecting risk outcomes in the first quantitative study of Indonesian women who inject drugs, the Women Speak Out study. <b>Methods:</b> This study combined community-based participatory approaches and extensive formative research with quantitative survey methods. 731 women, ≥18 years of age, and injecting illicit drugs in the preceding year were recruited using respondent-driven sampling (RDS) from urban settings in Greater Jakarta and Bandung, Indonesia. Network characteristics of the sample were assessed using the RDS software package for Stata 14. Data were analysed using multivariate logistic regressions, marginal effects models, and interaction analyses on the additive and multiplicative scales. The study was conducted in collaboration with the Indonesian Drug User Network. <b>Results:</b> Paper 1: Past-year IPV victimisation doubled the odds of engaging in one or more sexual HIV risk behaviours. Several covariates were associated with higher odds of sexual risk behaviour: HIV-positive status, non-injection crystal methamphetamine (crystal meth) use, low educational attainment, younger age, and being single. Co-occurrence of psychological, physical and/or injurious, and sexual forms of IPV had cumulative effects: sexual risk behaviour was reported by 62% of women who did not experience any form of IPV, but increased to 89% among those exposed to all three forms. Paper 2: Past-year IPV elevated women's odds of receptive syringe sharing. These effects remained after controlling for socio-demographic confounders. Two covariates, injecting illicit pharmaceuticals (vs heroin only) and housing instability and/or homelessness, remained associated with receptive syringe sharing in multivariate analyses. Paper 3: More than 1 in 4 women experienced concurrent IPV, depressive symptoms, and crystal meth use. All three exposures had independent negative effects on HIV sexual risk outcomes. The co-occurrence of all three factors produced a 4-fold increase in rates of survival sex work, 5-fold increase in STI symptomatology, and a 7-fold increase in inconsistent condom use. The joint effect of depressive symptoms and crystal meth use together was greater than the product of the estimated effects of each exposure alone on STI symptomatology, indicating an interaction on the multiplicative scale. Statistically-significant positive additive interaction was detected between IPV victimisation and crystal meth on inconsistent condom use; depression and crystal meth on STI symptomatology and on survival sex work; and IPV and depression on STI symptomatology and survival sex work. <b>Conclusion:</b> This thesis provides new evidence of the individual and cumulative effects of IPV, methamphetamine use, and depression on HIV risk outcomes among Indonesian women who inject drugs. The interaction analyses are the first to empirically test the assertion that these co-occurring conditions interact synergistically to increase drug-using women's HIV risk. This thesis furthers our understanding of how syndemics function within women who inject drugs to produce health disparities, and contributes to the problem theory for HIV risk behaviour in this population. The findings of this study have great public health significance and important implications for future longitudinal research, interventions, and policy.

Il campionamento a valanga è considerato un tipo di campionamento non probabilistico, la cui rappresentatività può essere valutata solo sulla base di considerazioni soggettive. D’altro canto esso risulta spesso il solo praticamente utilizzabile nel caso di popolazioni senza lista di campionamento. La tesi si divide in due parti. La prima, teorica, descrive alcuni tentativi proposti in letteratura di ricondurre le forme di campionamento a valanga nell’alveo dei campionamenti probabilistici; tra questi è degno di nota il Respondent Driven Sampling, un disegno campionario che dovrebbe combinare il campionamento a valanga con un modello matematico che pesa le unità estratte in modo da compensare la non casualità dell’estrazione e permettere così l’inferenza statistica. La seconda, empirica, indaga le prestazioni del RDS sia attraverso simulazioni sia con una web-survey su una comunità virtuale in Internet, di cui si conoscono la struttura delle relazioni e alcune caratteristiche demografiche per ogni individuo. Le stime RDS, calcolate a partire dai dati delle simulazioni e della web-survey, sono confrontate con i valori veri della popolazione e le potenziali fonti di distorsione (in particolare quelle relative all’assunzione di reclutamento casuale) sono analizzate. / Populations without sampling frame are inherently hard to sample by conventional sampling designs. Often the only practical methods of obtaining the sample involve following social links from some initially identified respondents to add more research participants to the sample. These kinds of link-tracing designs make the sample liable to various forms of bias and make extremely difficult to generalize the results to the population studied. This thesis is divided into two parts. The first part of the thesis describes some attempts to build a statistical theory of link-tracing designs and illustrates, deeply, the Respondent-Driven Sampling, a link-tracing sampling design that should allow researchers to make, in populations without sampling frame, asymptotically unbiased estimates under certain conditions. The second part of the thesis investigates the performance of RDS by simulating sampling from a virtual community on the Internet, which are available in both the network structure of the population and demographic traits for each individual. In addition to simulations, this thesis tests the RDS by making a web-survey of the same population. RDS estimates from simulations and web-survey are compared to true population values and potential sources of bias (in particular those related to the random recruitment assumption) are discussed.

Made available in DSpace on 2016-04-20T12:32:03Z (GMT). No. of bitstreams: 2 183.pdf: 6340906 bytes, checksum: 2c02c828416d1300652253066f8898e4 (MD5) license.txt: 1748 bytes, checksum: 8a4605be74aa9ea9d79846c1fba20a33 (MD5) Previous issue date: 2013 / Fundação Oswaldo Cruz. Centro de Pesquisas Aggeu Magalhães. Recife, PE, Brasil / Introdução: Os usuários de drogas têm sido desproporcionalmente afetados pelo HIV, desempenhando papel relevante na epidemia de aids no Brasil. Objetivos: Analisar o perfil e indicadores de vulnerabilidade, prevalência e fatores associados à infecção pelo HIV em usuários de drogas do Recife recrutados pelo método respondent-driven sampling (RDS). Materiais e métodos: Estudo sociocomportamental e de soroprevalência, com base no critério da Organização Pan-americana de Saúde para definição de usuários de drogas de alto risco de adquirir/transmitir HIV. Foram feitas entrevista com auxílio de computador com áudio, e realizados testes rápidos para HIV e sífilis, além de VDRL. Bairros de moradia e de uso de drogas foram georreferenciados, os dados ponderados pelo estimador RDS II e os fatores associados avaliados pela odds ratio. Resultados: Foram recrutados 400 usuários de drogas, residentes em 48 por cento dos bairros do Recife, distribuídos nas suas seis regiões político administrativas, e que faziam uso de drogas em 71 por cento dos bairros. A maioria homens (71 por cento), jovens, solteiros, com filhos e baixa condição socioeconômica, 22 por cento em situação de rua. População estigmatizada e criminalizada, com frequente vivência de violência física e sexual. Usavam crack 96 por cento dos usuários, pelo menos quatro dias por semana e consumiam múltiplas drogas. Metade iniciou há mais de seis anos o uso de drogas ilícitas, exceto maconha, antes dos 18 anos de idade, e não faz uso em locais públicos. Referiram trocar sexo por dinheiro ou drogas 27,5 por cento dos homens e 58,6 por cento das mulheres. Metade dos que foram a serviços de saúde, em decorrência de sintomas de DST, não receberam orientação quanto a medidas de prevenção. A cobertura de testagem para HIV foi de 39 por cento, abaixo da verificada para os homens que fazem sexo com outros homens e mulheres profissionais do sexo / A prevalência de sífilis foi de 22,4 por cento (IC95 por cento: 16,2 30,3) e a de HIV de 5,4 por cento (IC95 por cento: 2,9 9,7), associada na análise univariada ao sexo feminino, estar casado, ter parceiro fixo, primeira relação sexual antes dos 15 anos, deixar de usar preservativo por conta do uso de álcool/outra droga, ter pelo menos um sintoma de DST, não ter curado o último episódio de DST e ser sífilis positivo. Considerações Finais: O método RDS mostrou-se apropriado para recrutamento de usuários de drogas no Recife. Foram verificadas elevadas taxas de sífilis e HIV, num contexto socialmente marginalizado e com desigualdades de gênero, desafios sociais e programáticos para a política de atenção aos usuários de drogas e de controle da epidemia de aids

Made available in DSpace on 2011-05-04T12:36:24Z (GMT). No. of bitstreams: 0 Previous issue date: 2009 / Diversos fatores podem dificultar a caracterização acurada do perfil de umapopulação por amostragem. Se a característica que define a população é de difícil observação seja porque exige testes caros para detecção ou porque é uma característica de comportamento ilegal ou estigmatizado que dificulta a identificação, torna-se praticamente impossível aplicar os métodos clássicos de amostragem, pois não se pode definir uma base de amostragem (sampling frame). Populações desse tipo são conhecidas como populações ocultas, ou escondidas, e alguns exemplos comumente estudados são homens que fazem sexo com homens, trabalhadores do sexo e usuários de drogas. Essa dissertação discute a técnica de amostragem conhecida como Respondent-Driven Sampling (RDS), originalmente proposta por Heckathorn (1997), e que vem sendo amplamente utilizada na estimação de prevalências de doenças transmissíveis em populações ocultas. Esse método pertence à família de amostragens por bola-de-neve, na qual os elementos seguintes da amostra são recrutados a partir da rede de conhecidos dos elementos já presentes na amostra, formando as cadeias de referência. Com este método, além das informações individuais, é possível estudar também as relações entre os indivíduos. O recrutamento por bola de neve não gera uma amostra aleatória, e está sujeito às propriedades das redes sociais das populações em estudo, que deve mudar de lugar para lugar e potencialmente influenciar as medidas de prevalência geradas. As redes sociais são estruturas complexas, e compreender como que a amostragem RDS é influenciada por estas estruturas é um dos objetivos dessa dissertação. Além disso, se o interesse de um estudo epidemiológico é estimar a prevalência de uma doença transmissível, há de se considerar que muitas vezes a própria rede social pode estar correlacionada com as redes de transmissão, gerando potenciais dependências entre o processo de amostragem e a distribuição da variável desfecho. Essa dissertação teve por objetivo avaliar estimativas de prevalência geradas a partir de amostras obtidas com a utilização da metodologia RDS, considerando estruturas populacionais complexas, ou seja, populações com estruturas distintas de ligação entre os indivíduos e de disseminação de doenças. Para isso, foram realizados experimentos de simulação combinando quatro modelos geradores de redes sociais e quatro modelos de distribuição de casos infectados na população. Para cada uma, foram obtidas amostras utilizando RDS e as respectivas prevalências foram estimadas.Com os resultados encontrados, foi possível realizar uma avaliação tanto do RDS como forma de recrutamento, como o modelo proposto por Heckathorn (2002) para a ponderação e estimação de prevalências. Basicamente, três aspectos foram considerados nessa avaliação: 1. o tempo necessário para concluir a amostragem, 2. a precisão das estimativas obtidas, independente da ponderação, e 3. o método deponderação. De forma geral, o método apresentou bons resultados sob esses três aspectos, refletindo a possibilidade de sua utilização, ainda que exigindo cautela. Os achados apresentam-se limitados, pois são escassos os trabalhos que abordem essa metodologia e que permitam estabelecer comparações. Espera-se, no entanto,despertar o interesse para que outros trabalhos nessa linha sejam desenvolvidos. / Several factors may hamper the accurate characterization of a population. If the defining feature of the population is difficult to apply - either because it requires expensive tests for detection or because it is a stigmatized or illegal behavior that hinders the identification, it is virtually impossible to apply traditional methods for sampling, because sampling frame cannot be define. The latter are called “hidden populations”, and some examples are men who have sex with men, sexual workers and drug users. This dissertation focus on Respondent-Driven Sampling (RDS), a sampling method originally proposed by Heckathorn (1997), which has been widely used to estimate the prevalence of infectious diseases in hidden populations. RDS is a snowball sampling method, in which new elements for the sample are recruited from the network of the elements already present in the sample, forming reference chains. With this method, besides individual informations, it is also possible to study the relationships between individuals. Snowball sampling does not generate random samples, and its properties are likely to depend on the properties of the social networks underlying the recruitment process, which may change from place to place and potentially influence the measures of prevalence generated. Social networks are complex structures, and understanding how the different implementations of RDS sampling is influenced by these structures is one of the objectives of this dissertation. Moreover, if the interest of an epidemiological study is to estimate the prevalence of a disease, it is should be considered that very often, social network may be correlated with the transmission networks, generating potential dependencies between the process of sampling and distribution of outcome variable. The aim of this dissertation was to assess the behavior of prevalence estimators using RDS data in scenarios of populations organized in complex structures, i.e. Combinations of social networks structures and spreading patterns. To achieve that, theoretical experiments were performed using simulation models combining four generators of social networks and four models of distribution of infected cases in the population. For each one, samples were obtained using RDS and prevalence, estimated. Findings were used to evaluate RDS as a recruiting process itself, as well as Heckathorn’s (2002) model to estimate prevalences. Three aspects were considered in such analyses: 1. the time elapsed before obtaining the sample; 2. the accuracy of the estimates without taking in consideration the weighting strategies; and 3. the weighting strategy. Overall, RDS performed well in these three areas, showing it is a valid method to assess hidden populations, despite the fact its use should be made with the necessary caution. The interpretation of our findings was constrained by the scarcity of studies using the same methodology, what compromised the comparability of our findings. We hope, however, that our findings may foster the development of additional studies in this field.

Yes / With the aim of targeting high-risk hidden heterosexual young people for Chlamydia trachomatis (CT) testing, an innovative web-based screening strategy using Respondent Driven Sampling (RDS) and home-based CT testing, was developed, piloted and evaluated. Two STI clinic nurses encouraged 37 CT positive heterosexual young people (aged 16-25 years), called index clients, to recruit peers from their social and sexual networks using the web-based screening strategy. Eligible peers (young, living in the study area) could request a home-based CT test and recruit other peers. Twelve (40%) index clients recruited 35 peers. Two of these peers recruited other peers (n = 7). In total, 35 recruited peers were eligible for participation; ten of them (29%) requested a test and eight tested. Seven tested for the first time and one (13%) was positive. Most peers were female friends (80%). Nurses were positive about using the strategy. The screening strategy is feasible for targeting the hidden social network. However, uptake among men and recruitment of sex-partners is low and RDS stopped early. Future studies are needed to explore the sustainability, cost-effectiveness, and impact of strategies that target people at risk who are not effectively reached by regular health care.