Counting hard-to-count populations: the network scale-up method for public health

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.

Application of a Network Scale-up Method to Estimate the Size of Population of Breast, Ovarian/Cervical, Prostate and Bladder Cancers

“…On the other hand, in the real world all people do not have perfect knowledge about their social network, so transmission effect occurred. The nature of human's mind is suffered from not reminding all data in short time (less than 30 seconds), as a result estimation effect (recall bias) is always happened when the methodology of research is based on reminding events (McCarty et al, 2001;Kadushin et al, 2006;Bernard et al, 2010). To overcome the estimation effect, we suggest that future studies exactly asked about the people who go to doctor because of her/his cancer.…”

“…On the other hand, meeting assumptions behind indirect methods like capture-recapture and multiplier is sometimes very difficult. An almost novel indirect method is network scale up (NSU) which has been almost used in hard to reach populations but the point about NSU like more applicability, easiness, and cost-effectiveness to estimate the size of many sub-populations than other indirect methods differentiate it from the other methods (Jackson et al, 2005;Bernard et al, 2010). Because of the basic assumption of the method, active social network, every individual transfer information behalf of their network, therefore less samples are needed to evaluate (Jackson et al, 2005).…”

“…Because of the basic assumption of the method, active social network, every individual transfer information behalf of their network, therefore less samples are needed to evaluate (Jackson et al, 2005). Consequently, reported number of people known (m) in a subpopulation of the general population (T) could calculated proportion of subpopulation (e) [m/ C=e/T]; when: 1) individuals have an equal chance to the subpopulation of interest, 2) the number of people in social network (C) is constant, and 3) individuals have perfect knowledge about the members of theirs active social network (Bernard et al, 2010) .…”

Total and Partial Prevalence of Cancer Across Kerman Province, Iran, in 2014, Using an Adapted Generalized Network Scale-Up Method

“…Generalized Network Scale-Up (NSU) Method (Bernard et al, 2010) In the basic NSU method, a representative sample of the population is interviewed. The respondents answer the following questions: 1) How many people do you know?…”

Size Estimation of Most-at-Risk Groups of HIV/AIDS Using Network Scale-up in Tabriz, Iran

“…To begin to manage a serious social problem such as homelessness, rape, AIDS, HIV infection, homicide and suicide it is vital that we know its true extent, namely, the size of the subpopulation related to the problem [17], because by size estimation of risk groups, a country can design its strategic plans, allocate resources appropriately and improve its epidemic model [15][16][18][19][20].…”