H2R Newsletter


June 2024

New Approaches in H2R Research Methods

'Social Respect' Measures for Population Size Estimation

Accurate knowledge of key population (KP) sizes is crucial for an effective public health response, particularly in resource-limited settings. Population size estimation (PSE) plays a crucial role in filling this knowledge gap. The challenge is what if KPs are hidden and hard to reach. They can’t be reached through traditional methods, including location/venue-based, or peer-to-peer approaches. Innovative approaches like the network scale-up (NSU) method can be used to estimate the population size of KPs. 


Kovtun et al published their NSU methods and results in BMC Public Health in April 2024. They were able to apply NSU in Ukraine in 2020 to estimate the population size of four key populations (PWID, MSM, SW, TG) and three ‘bridge’ populations (sexual partners of PWID, clients of SW, female partners of MSM). Interestingly, they used ‘social respect’ measures to adjust their estimates for potential biases. We recommend you read this interesting paper.

Kovtun O, Paniotto V, Sakhno Y, Dumchev K. Size estimation of key populations and 'bridge populations' based on the network scale-up method in Ukraine. BMC Public Health. 2024 Apr 8;24(1):979. doi: 10.1186/s12889-024-18501-1.

Read more

Methods & Quality Checklist

Network Scale-up (1,2,3)

Members of the general population (not necessarily KP themselves) are asked about the overall number of members in their network and the number of KP they know in their networks. If we ask this question of a large representative number of people in a community, the collective information can provide a robust estimate of the KP population size as a whole.

Advantages

  • It does not require a study of KP itself
  • It can estimate more than one key population in one round
  • It can be integrated into national surveys among general populations and so produce national counts

Weaknesses

  • Hard to define what you mean by “know” a friend
  • Requires access to a representative general population survey
  • May need to add many questions to the survey
  • To measure the correction factors for adjustment for possible biases (see below), a study of KP themselves may be required

Possible Biases

  • Transmission bias: People do not tell others they are KP
  • Barrier effect: People may live away from locations or networks where KP is
  • Recall bias: People don’t always remember they know a KP
  • Stigma bias: People may not like to admit they know a KP

Network Scale-up Method Quality Checklist

  • The overall number of members in the social networks (network size) of the general population is to be known or estimated accurately. This is required to estimate the population size using the Network Scale-Up.
  • The survey of the general population includes a large representative (i.e., selected by random) number of people.
  • The analysis of the general population survey is weighted (if survey respondents have different probabilities of selection which makes the sample not represent the desired population)
  • There is no “Transmission bias” or a correction factor (called Visibility factor3) applied to adjust the estimates.
  • There is no “Barrier effect” or a correction factor (called Popularity factor1) applied to adjust the estimates.
  • There is no “Recall bias” or a correction factor applied to adjust the estimates.
  • There is no “Stigma bias” or a correction factor applied to adjust the estimates.

[1] Sulaberidze L, Mirzazadeh A, Chikovani I, Shengelia N, Tsereteli N, & Gotsadze G (2016). "Population Size Estimation of Men Who Have Sex with Men in Tbilisi, Georgia; Multiple Methods and Triangulation of Findings." PLoS ONE, 11(2).

[2] Baneshi, M.R., Zolala, F., Haji-Maghsoudi, S., Zamanian, M., Haghdoost, A.A., Mirzazadeh, A. (2021). Estimating the Size of Hidden Groups. In: Rutherford, G. (eds) Methods in Epidemiology. Advances in Experimental Medicine and Biology, vol 1333. Springer, Cham. https://doi.org/10.1007/978-3-030-75464-8_3.

[3] Haghdoost A, Ahmadi Gohari M, Mirzazadeh A, Zolala F, Baneshi MR. A review of methods to estimate the visibility factor for bias correction in network scale-up studies. Epidemiol Health. 2018;40:e2018041.

Data Corner & Consultation

Data Request

If you are a student, post-doc, researcher, faculty, physician, or health officer, it is never too late to start your H2R research or conduct secondary data analysis and write a paper for a health topic on H2R populations.


Submit your data request online to be involved in one of our past or current projects on an H2R population.

Submit Data Request

Office Hours

We have office hours for those who want to meet virtually or in person and consult with one of our team members about an H2R population study or method.



  • Tuesdays (17:00 to 18:00 PT)
  • Wednesdays (16:00 to 17:00 PT)
Submit Office Hour Request

Team Achievements

Publications

  • Wesson P, Das M, Chen M, Hsu L, McFarland W, Kennedy E, Jewell NP. Evaluating a Targeted Minimum Loss-Based Estimator for Capture-Recapture Analysis: An Application to HIV Surveillance in San Francisco, California. Am J Epidemiol. 2024 Apr 8;193(4):673-683. doi: 10.1093/aje/kwad231.
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  • Tavakoli F, Dehghan M, Haghdoost AA, Mirzazadeh A, Gouya MM, Sharifi H. A qualitative study exploring approaches, barriers, and facilitators of the HIV partner notification program in Kerman, Iran. BMC Health Serv Res. 2024 May 2;24(1):570. doi: 10.1186/s12913-024-11049-1.
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  • Laban M, Nanyonjo G, Wambuzi M, Ssetaala A, Basalirwa G, Muramuzi D, Lugemwa JK, Okech B, Mirzazadeh A. Uptake of Human Papilloma Virus vaccine among young women living in fishing communities in Wakiso and Mukono districts, Uganda. PLOS Glob Public Health. 2024 Apr 18;4(4):e0003106. doi: 10.1371/journal.pgph.0003106.
Read more

H2R Studies

Mapping, Population Size Estimation, and Bio-Behavioral Surveys of PWID, MSM, TG, and FSW in 9 Counties of Kenya in 2023-2024

Technical Consultant: Alexander Marr


  • UCSF, in conjunction with NASCOP (National AIDS and STI's Control Programme) and the Centers for Disease Control and Prevention (CDC), is conducting a bio-behavioral survey in Kenya.
  • This is a large survey covering people who inject drugs, men who have sex with men, transgender persons, and female sex workers.
  • The study uses respondent-driven sampling to reach 12,000 individuals. Depending on risk, participants can be tested for HIV, syphilis, and hepatitis.
  • In addition to estimating prevalence of HIV, we are also estimating population size (PSE) using service multiplier and SS-PSE methods.
  • UCSF has been instrumental in providing technical assistance to both the ministry and CDC partners.
  • Data collection should wrap up in the next couple of weeks, culminating in a data analysis and PSE workshop in early June.
  • This survey has many firsts:
  • (1) Simultaneous recruitment of 4 populations, one of the fastest recruitments of an RDS study (<70 days);
  • (2) A real-time monitoring dashboard for partners to review convergence, bottlenecks, and other indicators by study site. 

Call for content

We would love to highlight any work from you, our community.


Please send us any content or suggestions you may have for future issues at H2R@ucsf.edu.

Issue No. 3


This newsletter is a collaboration between staff and faculty at the University of California, San Francisco, who are focused on supporting research for hard-to-reach populations. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH, NIMH, or other funding institutes.


Issue Author: Ali Mirzazadeh, Alexander Marr.


Issue Editors: Ali Mirzazadeh, Maeve Forster, Alexander Marr.


Sponsor: NIH - Office of the Director

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