Accepted for/Published in: JMIR Dermatology
Date Submitted: Oct 6, 2021
Date Accepted: Feb 24, 2022
Date Submitted to PubMed: Aug 26, 2023
Crowdsourcing medical costs in dermatology: A cross-sectional study analyzing dermatologic GoFundMe campaigns
ABSTRACT
Background:
Crowdfunding medical costs is increasingly popular. Few studies have previously described fundraising characteristics and qualities associated with success.
Objective:
To characterize and investigate qualities associated with successful dermatologic fundraisers.
Methods:
This cross-sectional study of dermatologic GoFundMe campaigns collected data including demographic variables, thematic variables using an inductive qualitative method, and quantitative information. Linear regression examined qualities associated with success, defined based on funds raised when controlling for campaign goal. Logistic regression was used to examine qualities associated with extremely successful campaigns, defined as those raising greater than 1.5 times the interquartile range.
Results:
A total of 2,008 publicly available campaigns at the time of data collection were evaluated. Non-modifiable factors associated with greater success included male gender, age 20-40, and white race. Modifiable factors associated with success included more updates posted to the campaign page, non-self identity of the campaign creator, mention of a chronic condition, and smiling in campaign profile photographs. Significance was set at P<.05.
Conclusions:
Understanding modifiable factors of medical crowdfunding may inform future campaigns, and non-modifiable factors may have policy implications for improving healthcare equity and financing. Crowdfunding for medical disease treatment may have potential implications for medical privacy and exacerbation of existing healthcare disparities. This study was limited to publicly available GoFundMe campaigns. Intercoder variability, misclassification bias due to the data abstraction process, and prioritization of campaigns based on proprietary GoFundMe algorithm are potential limitations.
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Copyright
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