Incorporating respondent-driven sampling into web-based discrete choice experiments: preferences for COVID-19 mitigation measures
Por:
Johnson C.A., Tran D.N., Mwangi A., Sosa-Rubí S.G., Chivardi C., Romero-Martínez M., Pastakia S., Robinson E., Jennings Mayo-Wilson L., Galárraga O.
Publicada:
1 ene 2022
Resumen:
To slow the spread of COVID-19, most countries implemented stay-at-home orders, social distancing, and other nonpharmaceutical mitigation strategies. To understand individual preferences for mitigation strategies, we piloted a web-based Respondent Driven Sampling (RDS) approach to recruit participants from four universities in three countries to complete a computer-based Discrete Choice Experiment (DCE). Use of these methods, in combination, can serve to increase the external validity of a study by enabling recruitment of populations underrepresented in sampling frames, thus allowing preference results to be more generalizable to targeted subpopulations. A total of 99 students or staff members were invited to complete the survey, of which 72% started the survey (n = 71). Sixty-three participants (89% of starters) completed all tasks in the DCE. A rank-ordered mixed logit model was used to estimate preferences for COVID-19 nonpharmaceutical mitigation strategies. The model estimates indicated that participants preferred mitigation strategies that resulted in lower COVID-19 risk (i.e. sheltering-in-place more days a week), financial compensation from the government, fewer health (mental and physical) problems, and fewer financial problems. The high response rate and survey engagement provide proof of concept that RDS and DCE can be implemented as web-based applications, with the potential for scale up to produce nationally-representative preference estimates. © 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
Filiaciones:
Johnson C.A.:
Department of Health Services, Policy, and Practice, Brown University School of Public Health, 121 South Main Street, Box G-S121-2, Providence, RI 02912, United States
Tran D.N.:
Department of Pharmacy Practice, Temple University School of Pharmacy, Philadelphia, PA, United States
Mwangi A.:
Department of Behavioural Science, School of Medicine, Moi University, Eldoret, Kenya
Sosa-Rubí S.G.:
National Institute of Public Health (INSP), Morelos, Cuernavaca, Mexico
Chivardi C.:
National Institute of Public Health (INSP), Morelos, Cuernavaca, Mexico
Romero-Martínez M.:
National Institute of Public Health (INSP), Morelos, Cuernavaca, Mexico
Pastakia S.:
Center for Health Equity and Innovation, Purdue University College of Pharmacy, Indianapolis, IN, United States
Purdue University College of Pharmacy, Indianapolis, IN, United States
Robinson E.:
Purdue University College of Pharmacy, Indianapolis, IN, United States
Jennings Mayo-Wilson L.:
Department of Applied Health Science, Indiana University School of Public Health, Bloomington, IN, United States
Galárraga O.:
Department of Health Services, Policy, and Practice, Brown University School of Public Health, 121 South Main Street, Box G-S121-2, Providence, RI 02912, United States
All Open Access, Bronze
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