Network Scale-up Method Research Articles

Prevalence of alcoholic beverage consumption among a population with a history of methanol poisoning in the southeast of Iran

ABSTRACT Background The use of alcoholic substances in adults is often associated with a wide variety of negative consequences. The aim of this study was to assess the prevalence of alcoholic beverage consumption in a population with a history of methanol poisoning in the southeast of Iran. Methods A total of 876 individuals were interviewed using a cross-sectional survey between 2020 and 2021. We used two methods of asking direct questions (ADQ) and network scale-up (NSU) for estimating the prevalence of alcoholic beverage consumption. Results The mean age of the participants was 41.07 ± 14.52; 46% were female. Based on the method of ADQ, the prevalence of alcoholic beverage consumption was 22.2% (95% confidence interval (CI): 18.45–25.95%) and 4.4% (95% CI: 2.24–6.16%) among men and women, respectively. While these values for the NSU method were 26.53% (95% CI: 19.93–35.88%) and 8.31% (95% CI: 6.32–10.58%) among men and women, respectively. Conclusions Our results show that asking direct questions about illegal behavior can provide a lower estimate than indirect questioning approaches. Due to the high prevalence of alcoholic beverage consumption in men, this issue may increase the risk of unintentional poisoning by methanol in this area.

Automated Name Selection for the Network Scale-up Method

To estimate the distribution of the number of acquaintances of the members of a society, the network scale-up method asks survey respondents about the number of people they know with features for which national statistics are available. While many features have been used for this purpose, first names have been suggested to produce particularly low levels of transmission error and recall bias. For this method to be precise, a set of names needs to be selected for the survey that jointly represents the population in relevant variables such as gender or age. This article provides a solution approach to finding the optimal set of names. This can be applied to any population for which a joint distribution of first names and relevant variables is available. We show that our approach successfully provides sets of names closely mirroring the population distributions for six countries with different name statistics.

Comparing the Robustness of Simple Network Scale-Up Method Estimators.

The network scale-up method (NSUM) is a cost-effective approach to estimating the size or prevalence of a group of people that is hard to reach through a standard survey. The basic NSUM involves two steps: estimating respondents’ degrees and estimating the prevalence of the hard-to-reach population of interest using respondents’ estimated degrees and the number of people they report knowing in the hard-to-reach group. Each of these two steps involves taking either an average of ratios or a ratio of averages. Using the ratio of averages for each step has so far been the most common approach. However, the authors present theoretical arguments that using the average of ratios at the second, prevalence-estimation step often has lower mean squared error when the random mixing assumption is violated, which seems likely in practice; this estimator was proposed early in NSUM development but has largely been unexplored and unused. Simulation results using an example network data set also support these findings. On the basis of this theoretical and empirical evidence, the authors suggest that future surveys that use a simple estimator may want to use this mixed estimator, and estimation methods based on this estimator may produce new improvements.

Size estimation of key populations and ‘bridge populations’ based on the network scale-up method in Ukraine

IntroductionCorrect estimation of the size of key and bridge populations is crucial for an efficient HIV/AIDS response in resource-limited settings, enabling efficient program planning and resource allocation. The hidden nature of these groups poses challenges to traditional methods, leading to the adoption of innovative approaches like the network scale-up method (NSUM). In this article we present the results of a NSUM study conducted in 2020 in Ukraine, focusing on four key populations and three bridge populations, highlighting challenges and contributions to development of the method.MethodsFrom July to September 2020, we conducted a nationally representative survey in Ukraine via computer-assisted telephone interviews, and applied the known population method and summation method to estimate social networks sizes. Results were weighted based on individual sampling probability and adjusted for social respect and visibility factors to address potential limitations.ResultsOur study achieved a 20% response rate with 10,000 completed interviews. The social network size, using the known population method, was 213 people, and 125 using the summation method. Adjusting for the social respect and visibility, estimated key populations sizes were 295,857 [248,714–343,001] people who inject drugs, 152,267 [109,960–194,573] men who have sex with men, 78,385 [57,146–99,619] sex workers, and 9,963 [7,352–12,571] transgender people, detailed by age and gender. Bridge populations were estimated at 62,162 [50,445–73,879] sexual partners of people who inject drugs, 284,348 [233,113–335,583] clients of sex workers, and 13,697 [7,370–20,026] female partners of men who have sex with men.ConclusionsNSUM proves reliable for estimating key populations size with appropriate corrections. It shows promise for further use in Ukraine, considering limited geographic coverage of the integrated bio-behavioral studies to use multiplier-based methods. However, the validity concerns persist for estimating bridge populations size, emphasizing the need for further method refinement and addressing implementation issues, particularly those related to data collection.

Nowcasting Temporal Trends Using Indirect Surveys

Indirect surveys, in which respondents provide information about other people they know, have been proposed for estimating (nowcasting) the size of a hidden population where privacy is important or the hidden population is hard to reach. Examples include estimating casualties in an earthquake, conditions among female sex workers, and the prevalence of drug use and infectious diseases. The Network Scale-up Method (NSUM) is the classical approach to developing estimates from indirect surveys, but it was designed for one-shot surveys. Further, it requires certain assumptions and asking for or estimating the number of individuals in each respondent's network. In recent years, surveys have been increasingly deployed online and can collect data continuously (e.g., COVID-19 surveys on Facebook during much of the pandemic). Conventional NSUM can be applied to these scenarios by analyzing the data independently at each point in time, but this misses the opportunity of leveraging the temporal dimension. We propose to use the responses from indirect surveys collected over time and develop analytical tools (i) to prove that indirect surveys can provide better estimates for the trends of the hidden population over time, as compared to direct surveys and (ii) to identify appropriate temporal aggregations to improve the estimates. We demonstrate through extensive simulations that our approach outperforms traditional NSUM and direct surveying methods. We also empirically demonstrate the superiority of our approach on a real indirect survey dataset of COVID-19 cases.

Using the network scale-up method to characterise kidney trafficking in Kalai Upazila, Bangladesh

This study aimed to estimate the prevalence of illegal kidney sales in Kalai Upazila, Bangladesh, using the Network Scale-Up Method (NSUM), an ego-centric network survey-based technique used to estimate the...

Self-reported abortion experiences in Ethiopia and Uganda, new evidence from cross-sectional community-based surveys.

Unsafe abortion is a major contributor to maternal morbidity and mortality. To gain insight into the ways in which abortion restrictions and stigma may shape reproductive health outcomes, we present self-reported data on abortions in Ethiopia and Uganda and compare these findings across the two varying legal contexts. W investigate differences in sociodemographic characteristics by whether or not a woman self-reported an abortion, and we describe the characteristics of women's most recent self-reported abortion. In Ethiopia only, we classified abortions as being either safe, less safe, or least safe. Finally, we estimate minimum one-year induced abortion incidence rates using the Network Scale-Up Method (NSUM). We find that women who self-reported abortions were more commonly older, formerly married, or had any children compared to women who did not report an abortion. While three-quarters of women in both settings accessed their abortion in a health facility, women in Ethiopia more commonly used public facilities as compared to in Uganda (23.0% vs 12.6%). In Ethiopia, 62.4% of self-reported abortions were classified as safe, and treated complications were more commonly reported among least and less safe abortions compared to safe abortions (21.4% and 23.1% vs. 12.4%, respectively). Self-reported postabortion complications were more common in Uganda (37.2% vs 16.0%). The NSUM estimate for the minimum one-year abortion incidence rate was 4.7 per 1000 in Ethiopia (95% CI 3.9-5.6) and 19.4 per 1000 in Uganda (95% C 16.2-22.8). The frequency of abortions and low levels of contraception use at the time women became pregnant suggest a need for increased investments in family planning services in both settings. Further, it is likely that the broadly accessible nature of abortion in Ethiopia has made abortions safer and less likely to result in complications in Ethiopia as compared to Uganda.

An Empirical Comparison of a Traditional Strategy and Network Scale-Up Method for Prevalence Estimation of Child Trafficking in Sierra Leone

The goal of this paper is to compare a traditional survey method with the network scale-up method (NSUM) for the prevalence estimation of child trafficking in Sierra Leone in 2020. The traditional survey method involved a probability-based, stratified, and clustered multistage sampling design in which adult respondents in 3,070 households were interviewed about trafficking of children who reside in their household in three selected districts. This paper details the first attempt to estimate the prevalence of child trafficking using NSUM, which entailed questioning the same adult respondents about the trafficking-related activities of children in their personal networks. Findings and interpretation of these results are presented, along with implications and recommendations for future studies.

Further Exploring the Public Health Implications of the Network Scale-Up Method: Cross-Sectional Survey Study.

The decline in the number of new HIV infections among adults has slowed down, gradually becoming the biggest obstacle to achieving the 2030 target of ending the HIV/AIDS epidemic. Thus, a political declaration to ensure that 90% of people at high risk of HIV infection can access comprehensive prevention services was proposed by the United Nations General Assembly. Therefore, obtaining an accurate estimated size of high-risk populations is required as a prior condition to plan and implement HIV prevention services. The network scale-up method (NSUM) was recommended by the United Nations Programme on HIV/AIDS and the World Health Organization to estimate the sizes of populations at high risk of HIV infection; however, we found that the NSUM also revealed underlying population characteristics of female sex workers in addition to being used to estimate the population size. Such information on underlying population characteristics is very useful in improving the planning and implementation of HIV prevention services. This is especially relevant for people who inject drugs, where in addition to stigma and discrimination, criminalization further hinders access to HIV prevention services. We aimed to conduct a further exploration of the public health implications of the NSUM by using it to estimate the population size, popularity ratio, and information transmission rate among people who inject drugs. A stratified 2-stage cluster survey of the general population and a respondent-driven sampling survey of people who inject drugs were conducted in the urban district of Taiyuan, China, in 2021. The estimated size of the population of people who inject drugs in Taiyuan was 1241.9 (95% CI 1009.2-1474.9), corresponding to 4.4×10-2% (95% CI 3.6×10-2% to 5.2×10-2%) of the adult population aged 15-64 years. The estimated popularity ratio of people who inject drugs was 53.6% (95% CI 47.2%-60.1%), and the estimated information transmission rate was 87.9% (95% CI 86.5%-89.3%). In addition to being used to estimate the size of the population of people who inject drugs, the NSUM revealed that they have smaller-sized personal social networks while concealing their drug use, and these underlying population characteristics are extremely useful for planning appropriate service delivery approaches with the fewest barriers for people who inject drugs to access HIV prevention services. Therefore, more cost-effectiveness brings new public health implications for the NSUM, which makes it even more promising for its application.

Application of the network scale-up method to estimate the sizes of key populations for HIV in Singapore using online surveys.

Singapore lacks robust data on the sizes of the key populations that are most at risk for HIV. Using the network scale-up method for hidden or hard-to-reach populations, we estimate the sizes of five key populations-male clients of female sex workers (MCFSW), men who have sex with men (MSM), female sex workers (FSW), people who inject drugs (PWID) and transgender people-and profile the ages and ethnicities of respondents with the high-risk contacts they report knowing. We conducted a cross-sectional online survey between March and May 2019 (n = 2802) using a network scale-up instrument previously developed for Singapore. Participants were recruited using an existing panel and online advertising, and the sample reweighted by age, sex, ethnicity and education attained to represent the general adult population. We built a Bayesian hierarchical model to estimate the sizes of the five key populations for HIV in Singapore. After adjustment, the sizes of the at-risk populations are estimated to be: 76,800 (95% credible interval [CI]: 64,200-91,800) MCFSW; 139,000 (95% CI: 120,000-160,000) MSM; 8030 (95% CI: 3980-16,200) FSW; 3470 (95% CI: 1540-7830) PWID and 18,000 (95% CI: 14,000-23,200) transgender people. Generally, men reported knowing more people in all the high-risk groups; older people reported knowing more MCFSW, FSW and transgender people; and younger people reported knowing more MSM. There was a bimodal effect of age on those who reported knowing more PWIDs: people in their 20s and 60s reported more contacts. This study demonstrates that a size estimation study of hidden populations is quickly and efficiently scalable through using online surveys in a socially conservative society, like Singapore, where key populations are stigmatized or criminalized. The approach may be suitable in other countries where stigma is prevalent and where barriers to surveillance and data collection are numerous.

Population size estimation of female sex workers using network scale-up method in Kermanshah city

BackgroundAppropriate estimate of size of the female sex workers as well as the other hard-to-reach-populations plays a crucial role in reaching them with effective preventive measures. This study aimed to estimate the population size of female sex workers in Kermanshah City using the network scale-up method.MethodIn this cross-sectional study, of the 1000 pedestrians aged between 18 and 65 years, selected from the population of Kermanshah City via a multistage sampling, were recruited in the study. Face-to-face interview using a questionnaire including the number of FSWs in the social network of the respondent was used to collect the data.ResultsThe overall estimated number of female sex workers in the general population was 4848(UI 95%: 4597, 5074). Accordingly, the prevalence of FSWs among women 18 years and older in Kermanshah in 2018 was about 11.92 (UI 95%: 11.30, 12.47) in one thousand. More than half of the female sex workers in the respondent’s social network were unmarried and were in the age range of 18 to 29, and had a high school or university degree. Among high-risk behaviors, a history of alcohol consumption accounted for the highest percentage.ConclusionThe number of female sex workers estimated in this study is considerable, which highlights the importance of planning harm reduction interventions in order to alleviate the burden of HIV infection in the population.

Describing the safety of abortion at the population level using network-based survey approaches

BackgroundDespite the negative impact of unsafe abortions on women's health and rights, the degree of abortion safety remains strikingly undocumented for a large share of abortions globally. Data on how women induce abortions (method, setting, provider) are central to the measurement of abortion safety. However, health-facility statistics and direct questioning in population surveys do not yield representative data on abortion care seeking pathways in settings where access to abortion services is highly restricted. Recent developments in survey methodologies to study stigmatized / illegal behaviour and hidden populations rely on the fact that such information circulates within social networks; however, such efforts have yet to give convincing results for unsafe abortions.ObjectiveThis article presents the protocol of a study whose purpose is to apply and develop further two network-based methods to contribute to the generation of reliable population-level information on the safety of abortions in contexts where access to legal abortion services is highly restricted.MethodsThis study plans to obtain population-level data on abortion care seeking in two Health and Demographic Surveillance Systems in urban Kenya and rural Burkina Faso by applying two methods: Anonymous Third-Party Reporting (ATPR) (also known as confidantes’ method) and Respondent Driven Sampling (RDS). We will conduct a mixed methods formative study to determine whether these network-based approaches are pertinent in the study contexts. The ATPR will be refined notably by incorporating elements of the Network Scale-Up Method (NSUM) to correct or account for certain of its biases (transmission, barrier, social desirability, selection). The RDS will provide reliable alternative estimates of abortion safety if large samples and equilibrium can be reached; an RDS multiplex variant (also including social referents) will be tested.DiscussionThis study aims at documenting abortion safety in two local sites using ATPR and RDS. If successful, it will provide data on the safety profiles of abortion seekers across sociodemographic categories in two contrasted settings in sub-Saharan Africa. It will advance the formative research needed to determine whether ATPR and RDS are applicable or not in a given context. It will improve the questionnaire and correcting factors for the ATPR, improve the capacity of RDS to produce quasi-representative data on abortion safety, and advance the validation of both methods.

A Sample Size Formula for Network Scale-up Studies

The network scale-up method (NSUM) is a survey-based method for estimating the number of individuals in a hidden or hard-to-reach subgroup of a general population. In NSUM surveys, sampled individuals report how many others they know in the subpopulation of interest (e.g. “How many sex workers do you know?”) and how many others they know in subpopulations of the general population (e.g. “How many bus drivers do you know?”). NSUM is widely used to estimate the size of important sociological and epidemiological risk groups, including men who have sex with men, sex workers, HIV+ individuals, and drug users. Unlike several other methods for population size estimation, NSUM requires only a single random sample and the estimator has a conveniently simple form. Despite its popularity, there are no published guidelines for the minimum sample size calculation to achieve a desired statistical precision. Here, we provide a sample size formula that can be employed in any NSUM survey. We show analytically and by simulation that the sample size controls error at the nominal rate and is robust to some forms of network model mis-specification. We apply this methodology to study the minimum sample size and relative error properties of several published NSUM surveys.

Progress in research of population size estimation by network scale-up method

Network scale-up method is an indirect population size estimation method based on social network theory. It is widely used in estimating the hidden population size because it does not need to contact the target population. In order to provide reference for the further application of this method, this paper summarizes the traditional network scale-up method, the progress of this method and its combination with new survey techniques.

Population Size Estimation of People Who Use Illicit Drugs and Alcohol in Iran (2015-2016).

Estimating the number of people using illicit drugs and alcohol is necessary for informing health policy and programming. However, it is often challenging to reliably estimate the size of these marginalized populations through direct methods. In this study, we estimated the population size of these groups using the indirect Network Scale-Up (NSU) method in Iran from 2015 to 2016. Using a self-administered questionnaire, we asked 15 124 individuals (54% men) about the number of people they know who used different types of drugs at least once in the past 12 months. Prevalence estimates were reported per 100 000 population. The uncertainty level (UL) was calculated using the bootstrap method. The average age of the respondents was 33 years old, and 35.1% of them were unmarried. The most common drugs and their prevalence were as follows: opium (2534 [95% UL: 2467-2598]), hashish (849 [95% UL: 811-886]), stimulants (methamphetamine, ecstasy pills, cocaine, and Ritalin) (842 [95% UL: 802-879]), heroin/crack (578 [95% UL: 550-607]), and drug injection (459 [95% UL: 438-484]). Additionally, we estimated the prevalence of alcohol use as 2797 (95% UL: 2731-2861). On average, substance use was 5.23 times more prevalent among men than women. Opium use was more prevalent among individuals aged >50 years old. Moreover, alcohol use was more prevalent among participants between 18 and 30 years old (5164 per 100 000 population). Although opium continues to be the most prevalent illicit drug in Iran, the patterns of illicit drug use are heterogeneous among different age groups, genders, and provinces. Age-gender specific and culturally appropriate interventions are warranted to meet the needs of people in different subgroups.

Survey Methods for Estimating the Size of Weak-Tie Personal Networks

Researchers increasingly use aggregate relational data to learn about the size and distribution of survey respondents’ weak-tie personal networks. Aggregate relational data are collected by asking questions about respondents’ connectedness to many different groups (e.g., “How many teachers do you know?”). This approach can be powerful, but to use aggregate relational data, researchers must locate external information about the size of each group from a census or administrative records (e.g., the number of teachers in the population). This need for external information makes aggregate relational data difficult or impossible to collect in many settings. Here, the authors show that relatively simple modifications can overcome this need for external data, significantly increasing the flexibility of the method and weakening key assumptions required by the associated estimators. The key idea is to estimate the size of these groups from the sample of survey respondents, rather than relying on external sources of information. These methods are appropriate for using a sample survey to study the size and distribution of weak-tie network connections. They can also be used as part of the network scale-up method to estimate the size of hidden populations. The authors illustrate this approach with two empirical studies: a large simulation study and original household survey data collected in Hanoi, Vietnam.

A Review of Prevalence Estimation Methods for Human Trafficking Populations.

Human trafficking has long-lasting implications for the well-being of trafficked people, families, and affected communities. Prevention and intervention efforts, however, have been stymied by a lack of information on the scale and scope of the problem. Because trafficked people are mostly hidden from view, traditional methods of establishing prevalence can be prohibitively expensive in the recruitment, participation, and retention of survey participants. Also, trafficked people are not randomly distributed in the general population. Researchers have therefore begun to apply methods previously used in public health research and other fields on hard-to-reach populations to measure the prevalence of human trafficking. In this topical review, we examine how these prevalence methods used for hard-to-reach populations can be used to measure the prevalence of human trafficking. These methods include network-based approaches, such as respondent-driven sampling and the network scale-up method, and venue-based methods. Respondent-driven sampling is useful, for example, when little information about the trafficked population has been produced and when an adequate sampling frame does not exist. The network scale-up method is unique in that it does not target the hidden population directly. The implications of our work internationally include the need for documenting and validating the various prevalence estimation methods in the United States in a more robust way than was done in existing efforts. In providing this roadmap for estimating the prevalence of human trafficking, our overarching goal is to promote the equitable treatment and overall well-being of the socially disadvantaged populations who disproportionately experience human trafficking.

Estimation of Injuries in a Population-based Study by Stratified Cluster Sampling and Network Scale-up Methods

Introduction: Today, one of the most important causes of morbidity and mortality among people all across the world is Injury. Injuries pose an important challenge for the health system and account for 28% of the deaths per day in Iran. Iran is in the 5th place for road traffic death rates globally.
 Objective: This was a cross sectional study aimed to estimate annual incidence rates of injury in individuals above 15 in Kashan, Iran.
 Methods: In this population-based cross-sectional study, people above 15 years in households residing in Kashan during 2018-2019 were studied The sample had a twofold design, Stratified Cluster Sampling method and network Scale-up method. Data analysis was performed using SPSS 22 (Chi-square and T-test and Logistic regression analysis) and Stata 14 software.
 Results: This study describes the study setting, sampling, data collection and management, quality control and assurance, and the statistical analysis. In this study, the annual incidence rate from all kinds of trauma was estimated at 70.61(62.60-78.70) per 1000 person-years, and the death caused by injury was 4.63(34.32-56.12) per 1000 person-years. In addition, in network scale-up method incidence rate of all kinds of trauma was 57.1 and the death caused by trauma was 3.47 per 1000 person-years.
 Conclusion: population-based studies were used to evaluate injuries and outcomes of trauma, so that managers realize where attention is needed regarding preventive programs in this city. Using indirect sampling techniques can be cost-effective and time-consuming, and can provide us with the information urgently needed.

Measure of Personal Network Size Using the Known Population Method: A Methodological Guide.

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. (Am J Public Health. 2022;112(5):747-753. https://doi.org/10.2105/AJPH.2022.306731).

Estimating the prevalence of high-risk behaviors using network scale-up method in medical university students.

There is not much information about high-risk behaviors in young groups, especially students. This study was conducted to estimate the prevalence of high-risk behaviors in students of universities of medical sciences in West Azerbaijan Province, Iran, by network scale-up (NSU) method. This cross-sectional study was performed on 450 students from the universities of medical sciences. A researcher-developed checklist was used to collect the data. We considered number 16 for the social network size of students according to a previous study. Based on the response of individuals to each of the high-risk behaviors (including cigarette smoking, hookah use, opium consumption, alcohol drinking, tramadol/ecstasy taking, and extramarital sex) in their social network, the prevalence of these behaviors was estimated. The required calculations were performed using the NSU method. Furthermore, 95% uncertainty interval (UI) was calculated using the bootstrap method. Totally, 196 (44%) participants were male. The mean age (standard deviation) of the participants was 22 ± 2 years. Results showed that hookah use (20% 95% UI [18.9-21.1]) and opium consumption (0.4% 95% UI [0.24-0.6]) had the highest and lowest frequencies, respectively. Cigarette smoking (17% 95% UI [15.8-18]), alcohol use (8.3% 95% UI [7.5-9.1]), extramarital sex (8.2% 95% UI [7.4-9]), and tramadol/ecstasy taking (4% 95% UI [6.4-4.6]) were the next most common high-risk behaviors, respectively. Given that hookah use and cigarette smoking are the most common high-risk behaviors in students, especially males, appropriate cultural activities and educational programs should be employed by relevant authorities to reduce these behaviors.