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Counting hard-to-count populations: the network scale-up method for public health
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 appro...
Autores principales: | , , , , , , , , , , , , , |
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Formato: | Texto |
Lenguaje: | English |
Publicado: |
BMJ Group
2010
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3010902/ https://www.ncbi.nlm.nih.gov/pubmed/21106509 http://dx.doi.org/10.1136/sti.2010.044446 |
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author | Bernard, H Russell Hallett, Tim Iovita, Alexandrina Johnsen, Eugene C Lyerla, Rob McCarty, Christopher Mahy, Mary Salganik, Matthew J Saliuk, Tetiana Scutelniciuc, Otilia Shelley, Gene A Sirinirund, Petchsri Weir, Sharon Stroup, Donna F |
author_facet | Bernard, H Russell Hallett, Tim Iovita, Alexandrina Johnsen, Eugene C Lyerla, Rob McCarty, Christopher Mahy, Mary Salganik, Matthew J Saliuk, Tetiana Scutelniciuc, Otilia Shelley, Gene A Sirinirund, Petchsri Weir, Sharon Stroup, Donna F |
author_sort | Bernard, H Russell |
collection | PubMed |
description | 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. |
format | Text |
id | pubmed-3010902 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2010 |
publisher | BMJ Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-30109022011-09-04 Counting hard-to-count populations: the network scale-up method for public health Bernard, H Russell Hallett, Tim Iovita, Alexandrina Johnsen, Eugene C Lyerla, Rob McCarty, Christopher Mahy, Mary Salganik, Matthew J Saliuk, Tetiana Scutelniciuc, Otilia Shelley, Gene A Sirinirund, Petchsri Weir, Sharon Stroup, Donna F Sex Transm Infect Supplement 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. BMJ Group 2010-11-23 2010-12 /pmc/articles/PMC3010902/ /pubmed/21106509 http://dx.doi.org/10.1136/sti.2010.044446 Text en © 2010, Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions. 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. |
spellingShingle | Supplement Bernard, H Russell Hallett, Tim Iovita, Alexandrina Johnsen, Eugene C Lyerla, Rob McCarty, Christopher Mahy, Mary Salganik, Matthew J Saliuk, Tetiana Scutelniciuc, Otilia Shelley, Gene A Sirinirund, Petchsri Weir, Sharon Stroup, Donna F Counting hard-to-count populations: the network scale-up method for public health |
title | Counting hard-to-count populations: the network scale-up method for public health |
title_full | Counting hard-to-count populations: the network scale-up method for public health |
title_fullStr | Counting hard-to-count populations: the network scale-up method for public health |
title_full_unstemmed | Counting hard-to-count populations: the network scale-up method for public health |
title_short | Counting hard-to-count populations: the network scale-up method for public health |
title_sort | counting hard-to-count populations: the network scale-up method for public health |
topic | Supplement |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3010902/ https://www.ncbi.nlm.nih.gov/pubmed/21106509 http://dx.doi.org/10.1136/sti.2010.044446 |
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