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Counting hard-to-count populations: the network scale-up method for public health
  1. H Russell Bernard1,
  2. Tim Hallett2,
  3. Alexandrina Iovita3,
  4. Eugene C Johnsen4,
  5. Rob Lyerla5,
  6. Christopher McCarty6,
  7. Mary Mahy7,
  8. Matthew J Salganik8,
  9. Tetiana Saliuk9,
  10. Otilia Scutelniciuc10,
  11. Gene A Shelley11,
  12. Petchsri Sirinirund12,
  13. Sharon Weir13,
  14. Donna F Stroup14
  1. 1Department of Anthropology, University of Florida, Gainesville, Florida, USA
  2. 2Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, UK
  3. 3UNAIDS, Chişinău, Republic of Moldova
  4. 4Department of Mathematics, University of California, Santa Barbara, Santa Barbara, California, USA
  5. 5Fogarty International Center, National Institutes of Health, Bethesda, Maryland, USA
  6. 6Bureau of Economics and Business Research, University of Florida, Gainesville, Florida, USA
  7. 7Division of Monitoring and Evaluation, UNAIDS, Geneva, Switzerland
  8. 8Department of Sociology and Office of Population Research, Princeton University, Princeton, New Jersey, USA
  9. 9International HIV/AIDS Alliance and School of Public Health, National University of Kiev-Mohyla Academy, Kiev, Ukraine
  10. 10National Centre of Health Management, Chişinău, Republic of Moldova
  11. 11School of Public Health, Georgia State University, Atlanta, Georgia, USA
  12. 12Ministry of Public Health, Bangkok, Thailand
  13. 13Department of Epidemiology and Carolina Population Center, University of North Carolina, Chapel Hill, North Carolina, USA
  14. 14Data for Solutions, Inc., Decatur, Georgia, USA
  1. Correspondence to Donna F Stroup, Data for Solutions, Inc., PO Box 894, Decatur, GA 30031-0894, USA; donnafstroup{at}dataforsolutions.com

Abstract

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.

  • Population size
  • personal network size
  • summation method
  • known population method
  • AIDS
  • HIV
  • NSU
  • surveillance

This is an open-access article distributed under the terms of the Creative Commons Attribution Non-commercial License, which permits use, distribution, and reproduction in any medium, provided the original work is properly cited, the use is non commercial and is otherwise in compliance with the license. See: http://creativecommons.org/licenses/by-nc/2.0/ and http://creativecommons.org/licenses/by-nc/2.0/legalcode.

http://dx.doi.org/10.1136/sti.2010.044446

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    Footnotes

    • Gene A Shelley is now with the Centers for Disease Control and Prevention, Atlanta, Georgia, USA.

    • In Memoriam: Peter D Killworth, oceanographer and social scientist.

    • The foundations of this work were presented at an Expert Symposium on Network Scale-Up Methods convened by UNAIDS, New York City, New York, September 2008.

    • Funding The preparation of this article was partially supported by UNAIDS. Work to develop the NSUM method was supported by the National Science Foundation. MJS acknowledges funding from the National Institutes of Health (NICHD), USA.

    • Competing interests None.

    • Provenance and peer review Not commissioned; externally peer reviewed.