Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress
Clinical Collections
Advances in Alzheimer’s

Keynote webinar | Spotlight on medication adherence

LIVE: Thursday 27th June 2024, 18:00-19:30 (CEST)
Join our expert panel to discover why you need to understand the drivers of non-adherence in your patients, and how you can optimize medication adherence in your clinics to drastically improve patient outcomes.
ADVANCED SEARCH
Log in
Top
BMC Medical Informatics and Decision Making
Published in: BMC Medical Informatics and Decision Making 1/2023

Open Access 01-12-2023 | Software

Design and implementation of a web-based, respondent-driven sampling solution

Authors: Catherine R. McGowan, Promise Ekoriko, Mervat Alhaffar, Sarah Cassidy-Seyoum, Steven Whitbread, Phil Rogers, Lucy Bell, Francesco Checchi

Published in: BMC Medical Informatics and Decision Making | Issue 1/2023

Login to get access

Abstract

Background

Respondent-driven sampling (RDS) refers both to a chain-referral sampling method and an analytical model for analysing sampled data. Web-based respondent-driven sampling (webRDS) uses internet-based recruitment coupled with an electronic survey to carry out RDS studies; there is currently no commercially available webRDS solution. We designed and developed a webRDS solution to support a research study aimed at estimating conflict-attributable mortality in Yemen. Our webRDS solution is composed of an existing survey platform (i.e. ODK) and a bespoke RDS system. The RDS system is designed to administer and manage an RDS survey cascade and includes: (1) an application programming interface, (2) a study participant client, and (3) an administrator interface. We report here on the design of the webRDS solution and its implementation.

Results

We consulted members of the Yemeni diaspora throughout the development of the solution. Technical obstacles were largely the result of: WhatsApp’s policies on bulk messaging and automated messaging behaviour, the inherent constraints of SMS messaging, and SMS filtering behaviour. Language support was straight-forward yet time consuming. Survey uptake was lower than expected. Factors which may have impacted uptake include: our use of consumable survey links, low interest amongst the diaspora population, lack of material incentives, and the length and subject matter of the survey itself. The SMS/WhatsApp messaging integration was relatively complex and limited the information we could send potential participants.

Conclusion

Despite lower-than expected survey uptake we believe our webRDS solution provides efficient and flexible means to survey a globally diverse population.
Appendix
Available only for authorised users
Literature
1.
Heckathorn DD. Respondent-driven sampling: a New Approach to the study of hidden populations. Soc Probl. 1997;44(2):174–99.CrossRef
2.
Abdesselam K, Verdery A, Pelude L, Dhami P, Momoli F, Jolly AM. The development of respondent-driven sampling (RDS) inference: a systematic review of the population mean and variance estimates. Drug Alcohol Depend. 2020;206:107702.CrossRefPubMed
3.
4.
Johnston LG, Hakim AJ, Dittrich S, Burnett J, Kim E, White RG. AIDS Behav. 2016;20(8):1754–76. A Systematic Review of Published Respondent-Driven Sampling Surveys Collecting Behavioral and Biologic Data.
5.
Helms YB, Hamdiui N, Kretzschmar MEE, Rocha LEC, van Steenbergen JE, Bengtsson L, et al. Applications and recruitment performance of web-based respondent-driven sampling: scoping review. J Med Internet Res. 2021;23(1):e17564.CrossRefPubMedPubMedCentral
6.
Weinmann T, AlZahmi A, Schneck A, Mancera Charry JF, Fröschl G, Radon K. Population-based assessment of health, healthcare utilisation, and specific needs of syrian migrants in Germany: what is the best sampling method? BMC Med Res Methodol. 2019;19(1):5.CrossRefPubMedPubMedCentral
7.
Bengtsson L, Lu X, Nguyen QC, Camitz M, Hoang NL, Nguyen TA, et al. Implementation of web-based respondent-driven sampling among men who have sex with men in Vietnam. PLoS ONE. 2012;7(11):e49417.CrossRefPubMedPubMedCentral
8.
Strömdahl S, Lu X, Bengtsson L, Liljeros F, Thorson A. Implementation of web-based Respondent Driven Sampling among Men who have sex with men in Sweden. PLoS ONE. 2015;10(10):e0138599.CrossRefPubMedPubMedCentral
9.
Bauermeister JA, Zimmerman MA, Johns MM, Glowacki P, Stoddard S, Volz E. Innovative recruitment using online networks: lessons learned from an online study of alcohol and other drug use utilizing a web-based, respondent-driven sampling (webRDS) strategy. J Stud Alcohol Drugs. 2012;73(5):834–8.CrossRefPubMedPubMedCentral
10.
Stoddard SA, Bauermeister JA, Gordon-Messer D, Johns M, Zimmerman MA. Permissive norms and young adults’ alcohol and marijuana use: the role of online communities. J Stud Alcohol Drugs. 2012;73(6):968–75.CrossRefPubMedPubMedCentral
11.
Jonsson J, Stein M, Johansson G, Bodin T, Strömdahl S. A performance assessment of web-based respondent driven sampling among workers with precarious employment in Sweden. PLoS ONE. 2019;14(1):e0210183.CrossRefPubMedPubMedCentral
12.
13.
Hildebrand J, Burns S, Zhao Y, Lobo R, Howat P, Allsop S, et al. Potential and Challenges in collecting social and behavioral data on adolescent alcohol norms: comparing Respondent-Driven Sampling and web-based respondent-driven sampling. J Med Internet Res. 2015;17(12):e285.CrossRefPubMedPubMedCentral
14.
Stein ML, van Steenbergen JE, Chanyasanha C, Tipayamongkholgul M, Buskens V, van der Heijden PG, et al. Online respondent-driven sampling for studying contact patterns relevant for the spread of close-contact pathogens: a pilot study in Thailand. PLoS ONE. 2014;9(1):e85256.CrossRefPubMedPubMedCentral
15.
Wejnert C, Heckathorn DD. Web-based Network Sampling:efficiency and efficacy of Respondent-Driven Sampling for Online Research. Sociol Methods Res. 2008;37(1):105–34.CrossRef
16.
UN Office for the Coordination of Humanitarian Affairs. Yemen Humanitarian Response Plan 2022 (April 2022). New York, NY: UNOCHA; 2022.
17.
McGowan CR, Alhaffar M, Ekoriko P, Al-Refai S, Bell L, Checchi F. Adult mortality patterns in Yemen before and during armed conflict: Evidence from a web survey of the global diaspora. Conflict & Health. 2023;[in press].
18.
19.
20.
McGowan CR, Houlihan CF, Kingori P, Glynn JR. The acceptability of online consent in a self-test serosurvey of responders to the 2014–2016 west african Ebola Outbreak. Public Health Ethics. 2017;11(2):201–12.CrossRefPubMedPubMedCentral
21.
22.
23.
24.
Taslakian EN, Garber K, Shekherdimian S. Diaspora engagement: a scoping review of diaspora involvement with strengthening health systems of their origin country. Glob Health Action. 2022;15(1):2009165.CrossRefPubMed
Metadata
Title
Design and implementation of a web-based, respondent-driven sampling solution
Authors
Catherine R. McGowan
Promise Ekoriko
Mervat Alhaffar
Sarah Cassidy-Seyoum
Steven Whitbread
Phil Rogers
Lucy Bell
Francesco Checchi
Publication date
01-12-2023
Publisher
BioMed Central
Published in
BMC Medical Informatics and Decision Making / Issue 1/2023
Electronic ISSN: 1472-6947
DOI
https://doi.org/10.1186/s12911-023-02217-0

Other articles of this Issue 1/2023

BMC Medical Informatics and Decision Making 1/2023 Go to the issue

Research

Machine learning for early prediction of acute myocardial infarction or death in acute chest pain patients using electrocardiogram and blood tests at presentation

Research

In-hospital fall prediction using machine learning algorithms and the Morse fall scale in patients with acute stroke: a nested case-control study

Research

Data-driven meal events detection using blood glucose response patterns

Research

Smartwatches in healthcare medicine: assistance and monitoring; a scoping review

Research article

Examining clinician choice to follow-up (or not) on automated notifications of medication non-adherence by clinical decision support systems

Research

Machine learning algorithms to predict intraoperative hemorrhage in surgical patients: a modeling study of real-world data in Shanghai, China