Jody Clay-Warner and Tenshi Kawashima are with the Department of Sociology, University of Georgia, Athens. Timothy G. Edgemon is with the Department of Sociology, Anthropology, and Social Work, Auburn University, Auburn, AL.
Am J Public Health. 2022 May;112(5):747-753. doi: 10.2105/AJPH.2022.306731. Epub 2022 Mar 17.
The network scale-up method (NSUM) has shown promise in measuring the prevalence of hidden public health problems and at-risk populations. The technique involves asking survey respondents how many people they know with the health problem or characteristic of interest and extrapolating this information to the population level. An important component of the NSUM estimate is the size of each respondent's network, which is determined by asking respondents about the number of people they know who belong to populations of known size. There is little systematic discussion, however, to guide selection of these questions. Furthermore, many of the most commonly used known population questions are appropriate only in countries with a robust data infrastructure. Here, we draw from the NSUM literature to present a set of best practices in the selection of NSUM known population questions. Throughout, we address the unique situations that many researchers face in collecting prevalence data in the developing world, where innovative prevalence estimation techniques, such as NSUM, are most needed. (. 2022;112(5):747-753. https://doi.org/10.2105/AJPH.2022.306731).
网络扩展方法（NSUM）已显示出在衡量隐藏的公共卫生问题和高危人群的流行程度方面的潜力。该技术涉及询问调查对象他们认识多少患有感兴趣的健康问题或特征的人，并将这些信息推断到人群水平。NSUM 估计的一个重要组成部分是每个受访者网络的规模，这是通过询问受访者他们认识的属于已知规模人群的人数来确定的。然而，几乎没有系统的讨论来指导这些问题的选择。此外，许多最常用的已知人群问题仅适用于具有强大数据基础设施的国家。在这里，我们从 NSUM 文献中提取了一套最佳实践，用于选择 NSUM 已知人群问题。在整个过程中，我们都在解决许多研究人员在发展中国家收集流行数据时所面临的独特情况，在这些国家，需要创新的流行估计技术，如 NSUM。（2022 年；112（5）：747-753。https://doi.org/10.2105/AJPH.2022.306731）。
