The game of contacts: Estimating the social visibility of groups

Salganik, Matthew J.; Mello, Maeve B.; Abdo, Alexandre Hannud; Bertoni, Neilane; Fazito, Dimitri; Bastos, Francisco I.

doi:10.1016/j.socnet.2010.10.006

Cited by 63 publications

(81 citation statements)

References 51 publications

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“…Ezoe et al (2012) surveyed men who have sex with men, the population of interest, to find out how many people in the MSMs’ networks knew about their group status. Salganik et al (2011b)’s implementation of NSUM estimates in Curitiba, Brazil included a game of contacts method where the researchers surveyed heavy drug users to estimate the proportion of their network that are aware of their drug use status. The game of contacts method involves asking heavy drug users about the number of people they know with certain names and then asking if those contacts are aware of the respondent’s drug use status as well as the contacts’ own drug use status.…”

Section: Introductionmentioning

confidence: 99%

Estimating population size using the network scale up method

Maltiel¹,

Raftery²,

McCormick³

et al. 2015

Ann. Appl. Stat.

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We develop methods for estimating the size of hard-to-reach populations from data collected using network-based questions on standard surveys. Such data arise by asking respondents how many people they know in a specific group (e.g. people named Michael, intravenous drug users). The Network Scale up Method (NSUM) is a tool for producing population size estimates using these indirect measures of respondents’ networks. Killworth et al. (1998a,b) proposed maximum likelihood estimators of population size for a fixed effects model in which respondents’ degrees or personal network sizes are treated as fixed. We extend this by treating personal network sizes as random effects, yielding principled statements of uncertainty. This allows us to generalize the model to account for variation in people’s propensity to know people in particular subgroups (barrier effects), such as their tendency to know people like themselves, as well as their lack of awareness of or reluctance to acknowledge their contacts’ group memberships (transmission bias). NSUM estimates also suffer from recall bias, in which respondents tend to underestimate the number of members of larger groups that they know, and conversely for smaller groups. We propose a data-driven adjustment method to deal with this. Our methods perform well in simulation studies, generating improved estimates and calibrated uncertainty intervals, as well as in back estimates of real sample data. We apply them to data from a study of HIV/AIDS prevalence in Curitiba, Brazil. Our results show that when transmission bias is present, external information about its likely extent can greatly improve the estimates. The methods are implemented in the NSUM R package.

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Section: Introductionmentioning

confidence: 99%

Estimating population size using the network scale up method

Maltiel¹,

Raftery²,

McCormick³

et al. 2015

Ann. Appl. Stat.

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show abstract

“…Building on earlier work,19 they asked sampled members of the high risk group about people socially connected to the respondent. The respondent then provided information about each of these social contacts, including whether they were aware that the respondent was in the high risk group 28. In another example, one author (SW) included a similar question in a survey of women working in venues where people meet new sexual partners; women were asked if they exchanged sex for money, self-identified as a sex worker and whether their friends identified the respondent as a sex worker or not.…”

Section: Discussionmentioning

confidence: 99%

Counting hard-to-count populations: the network scale-up method for public health

Bernard

Hallett

Iovita³

et al. 2010

Sexually Transmitted Infections

125

175

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Estimating sizes of hidden or hard-to-reach populations is an important problem in public health. For example, estimates of the sizes of populations at highest risk for HIV and AIDS are needed for designing, evaluating and allocating funding for treatment and prevention programmes. A promising approach to size estimation, relatively new to public health, is the network scale-up method (NSUM), involving two steps: estimating the personal network size of the members of a random sample of a total population and, with this information, estimating the number of members of a hidden subpopulation of the total population. We describe the method, including two approaches to estimating personal network sizes (summation and known population). We discuss the strengths and weaknesses of each approach and provide examples of international applications of the NSUM in public health. We conclude with recommendations for future research and evaluation.

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“…Also, a side study estimated TE sizes. To this end, 415 patients were interviewed according to the proposed method of Salganik et al (Salganik et al, 2011b), using standard interview forms (the details of which will be published separately). In short, patients were randomly selected from oncology centers across Kerman.…”

Section: Methodsmentioning

confidence: 99%

Total and Partial Prevalence of Cancer Across Kerman Province, Iran, in 2014, Using an Adapted Generalized Network Scale-Up Method

Vardanjani¹,

Baneshi²,

Haghdoost³

2015

Asian Pacific Journal of Cancer Prevention

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Due to the lack of nationwide population-based cancer registration, the total cancer prevalence in Iran is unknown. Our previous work in which we used a basic network scale-up (NSU) method, failed to provide plausible estimates of total cancer prevalence in Kerman. The aim of the present study was to estimate total and partial prevalence of cancer in southeastern Iran using an adapted version of the generalized network scale-up method. A survey was conducted in 2014 using multi-stage cluster sampling. A total of 1995 face-to-face gender-matched interviews were performed based on an adapted version of the NSU questionnaire. Interviewees were asked about their family cancer history. Total and partial prevalence were estimated using a generalized NSU estimator. The Monte Carlo method was adopted for the estimation of upper/lower bounds of the uncertainty range of point estimates. One-yr, 2-3 yr, and 4-5 yr prevalence (per 100,000 people) was respectively estimated at 78 (95%CI, 66, 90), 128 (95%CI, 118, 147), and 59 (95%CI, 49, 70) for women, and 48 (95%CI, 38, 58), 78 (95%CI, 66, 91), and 42 (95%CI, 32, 52) for men. The 5-yr prevalence of all cancers was estimated at 0.18 percent for men, and 0.27 percent for women. This study showed that the generalized familial network scale-up method is capable of estimating cancer prevalence, with acceptable precision.

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The game of contacts: Estimating the social visibility of groups

Cited by 63 publications

References 51 publications

Estimating population size using the network scale up method

Estimating population size using the network scale up method

Counting hard-to-count populations: the network scale-up method for public health

Total and Partial Prevalence of Cancer Across Kerman Province, Iran, in 2014, Using an Adapted Generalized Network Scale-Up Method

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