Population Size Estimation of Men Who Have Sex with Men through the Network Scale-Up Method in Japan

Ezoe, Satoshi; Morooka, Takeo; Noda, Tatsuya; Sabin, Miriam Lewis; Koike, Soichi

doi:10.1371/journal.pone.0031184

Cited by 67 publications

(76 citation statements)

References 11 publications

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“…Research in Italy excluded some known populations using residual plot by comparing the association between the estimated population size and the statistics [12]. In Japan, data were adjusted to reflect the known distribution of gender and age groups in the general population; then the back estimation method was used to evaluate the reliability of the estimates of known populations [16]. Our study used a three-step approach to estimate c .…”

Section: Discussionmentioning

confidence: 99%

Estimating the Size of HIV Key Affected Populations in Chongqing, China, Using the Network Scale-Up Method

Guo¹,

Bao²,

Lin³

et al. 2013

PLoS ONE

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ObjectivesTo estimate the average social network size in the general population and the size of HIV key affected populations (KAPs) in Chongqing municipality using the network scale-up method (NSUM).MethodsA general population survey was conducted in 2011 through a multistage random sampling method. Participants aged between 18 and 60 years were recruited. The average social network size (c) was estimated and adjusted by known population method. The size of HIV KAP in Chongqing municipality was estimated using the adjusted c value with adjustment for the transmission effect using the scaled respect factor.Results3,026 inhabitants of Chongqing agreed to the survey, and 2,957 (97.7%) completed the questionnaire. The adjusted c value was 310. The estimated size of KAP was 28,418(95% Confidence Interval (CI):26,636∼30,201) for female sex workers (FSW), 163,199(95%CI:156,490∼169,908) for clients of FSW, 37,959(95%CI: 34,888∼41,030) for drug users (DU), 14,975(95%CI:13,047∼16,904) for injecting drug users (IDU) and 16,767(95%CI:14,602∼18,932) for men who have sex with men (MSM). The ratio of clients to FSW was 5.74∶1, and IDU accounted for 39.5% of the DU population. The estimates suggest that FSW account for 0.37% of the female population aged 15–49 years in Chongqing, and clients of FSW and MSM represent 2.09% and 0.21% of the male population aged 15–49 years in the city, respectively.ConclusionNSUM provides reasonable population size estimates for FSW, their clients, DU and IDU in Chongqing. However, it is likely to underestimate the population size of MSM even after adjusting for the transmission effect.

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Section: Discussionmentioning

confidence: 99%

Estimating the Size of HIV Key Affected Populations in Chongqing, China, Using the Network Scale-Up Method

Guo¹,

Bao²,

Lin³

et al. 2013

PLoS ONE

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“…In addition, network methodologies such as the network scale-up can be used to estimate the size of a hidden population. This approach was utilized by Ezoe et al [4•], who estimated that 2.9% of Japan’s male population are MSM.…”

Section: Introductionmentioning

confidence: 99%

Social Networks, Sexual Networks and HIV Risk in Men Who Have Sex with Men

2014

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Worldwide, men who have sex with men (MSM) remain one of the most HIV-vulnerable community populations. A global public health priority is developing new methods of reaching MSM, understanding HIV transmission patterns, and intervening to reduce their risk. Increased attention is being given to the role that MSM networks play in HIV epidemiology. This review of MSM network research studies demonstrates that: (1) Members of the same social network often share similar norms, attitudes, and HIV risk behavior levels; (2) Network interventions are feasible and powerful for reducing unprotected sex and potentially for increasing HIV testing uptake; (3) HIV vulnerability among African American MSM increases when an individual enters a high-risk sexual network characterized by high density and racial homogeneity; and (4) Networks are primary sources of social support for MSM, particularly for those living with HIV, with greater support predicting higher care uptake and adherence.

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“…These consist of estimating the probability that a respondent counts a contact that belongs to the group of interest as being a member of the group, and then dividing the NSUM size estimate by the estimated probability. 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.…”

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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Population Size Estimation of Men Who Have Sex with Men through the Network Scale-Up Method in Japan

Cited by 67 publications

References 11 publications

Estimating the Size of HIV Key Affected Populations in Chongqing, China, Using the Network Scale-Up Method

Estimating the Size of HIV Key Affected Populations in Chongqing, China, Using the Network Scale-Up Method

Social Networks, Sexual Networks and HIV Risk in Men Who Have Sex with Men

Estimating population size using the network scale up method

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