Yiwen Cao1, Angelina S Carrillo1, Marta M Jankowska2, Yuyan Shi3. 1. Department of Family Medicine and Public Health, University of California San Diego, CA, USA. 2. Qualcomm Institute/Calit2, University of California San Diego, CA, USA. 3. Department of Family Medicine and Public Health, University of California San Diego, CA, USA. Electronic address: yus001@ucsd.edu.
Abstract
OBJECTIVES: To assess 1) the validity of online crowdsourcing platforms in enumerating licensed brick-and-mortar marijuana dispensaries and 2) the validity of state licensing directory and online crowdsourcing platforms in enumerating active brick-and-mortar marijuana dispensaries in California. METHODS: We obtained business lists from California Bureau of Cannabis Control (BCC) licensing directory and three online crowdsourcing platforms (Weedmaps, Leafly, and Yelp) in May 2019. Calls were made to verify street address, operation status, dispensary category (recreational-only, medical-only, recreational & medical), and presence of storefronts in May-July 2019. Validity measures, including sensitivity, specificity, positive predictive value, and negative predictive value, were calculated when applicable. RESULTS: In identifying licensed dispensaries in BCC, Leafly had the highest sensitivity (.66) and Yelp had the highest specificity (.87). The dispensary category posted on online crowdsourcing platforms in over 25 % licensed dispensaries and the dispensary category claimed in call verification in over 10 % licensed dispensaries disagreed with the approved category in BCC. There were 2121 businesses combined from BCC and online crowdsourcing platforms, among which 826 were verified to be active brick-and-mortar dispensaries. Weedmaps had the highest sensitivity (.80) and Yelp had the highest negative predictive value (.74) in identifying verified dispensaries. Weedmaps overall had the highest sensitivity in all three dispensary categories. Weedmaps had the highest sensitivity in more populated counties whereas BCC had the highest sensitivity in less populated counties. CONCLUSIONS: Each secondary data source has strengths and limitations. The findings inform surveillance and research regarding how to best strategize data use when resources are limited.
OBJECTIVES: To assess 1) the validity of online crowdsourcing platforms in enumerating licensed brick-and-mortar marijuana dispensaries and 2) the validity of state licensing directory and online crowdsourcing platforms in enumerating active brick-and-mortar marijuana dispensaries in California. METHODS: We obtained business lists from California Bureau of Cannabis Control (BCC) licensing directory and three online crowdsourcing platforms (Weedmaps, Leafly, and Yelp) in May 2019. Calls were made to verify street address, operation status, dispensary category (recreational-only, medical-only, recreational & medical), and presence of storefronts in May-July 2019. Validity measures, including sensitivity, specificity, positive predictive value, and negative predictive value, were calculated when applicable. RESULTS: In identifying licensed dispensaries in BCC, Leafly had the highest sensitivity (.66) and Yelp had the highest specificity (.87). The dispensary category posted on online crowdsourcing platforms in over 25 % licensed dispensaries and the dispensary category claimed in call verification in over 10 % licensed dispensaries disagreed with the approved category in BCC. There were 2121 businesses combined from BCC and online crowdsourcing platforms, among which 826 were verified to be active brick-and-mortar dispensaries. Weedmaps had the highest sensitivity (.80) and Yelp had the highest negative predictive value (.74) in identifying verified dispensaries. Weedmaps overall had the highest sensitivity in all three dispensary categories. Weedmaps had the highest sensitivity in more populated counties whereas BCC had the highest sensitivity in less populated counties. CONCLUSIONS: Each secondary data source has strengths and limitations. The findings inform surveillance and research regarding how to best strategize data use when resources are limited.
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