Racial and gender disparities among highly successful medical crowdfunding campaigns.

There has been growing recognition of the popularity of medical crowdfunding and research documenting how crowdfunding arises from, and contributes to, social and health inequities. While many researchers have surmised that racism could well play a role in medical crowdfunding campaign outcomes, research on these dynamics has been limited. No research to date has examined these dynamics among the most successful medical crowdfunding campaigns, focusing instead on average users' experiences or specific patient subpopulations. This paper analyzes key characteristics and demographics of the 827 most successful medical crowdfunding campaigns captured at a point in time in 2020 on the popular site GoFundMe, creating the first demographic archetype of "viral" or highly successful campaigns. We hypothesized that this sample would skew towards whiter, younger populations, more heavily represent men, and reflect critical illnesses and accidents affecting these populations, in addition to having visually appealing, well-crafted storytelling. Analysis supported these hypotheses, showing significant levels of racial and gender disparities among campaigners. While white men had the greatest representation, Black and Asian users, and black women in particular, were highly underrepresented. Like other studies, we find evidence that racial and gender disparities persist in terms of campaign outcomes as well. Alongside this quantitative analysis, a targeted discourse analysis revealed campaign narratives and comments reinforced racist and sexist tropes of selective deservingness. These findings add to growing calls for more health research into the ways that social media technologies shape health inequities for historically marginalized and disenfranchised populations. In particular, we underscore how successful crowdfunding campaigns, as a both a means of raising funds for health and a broader site of public engagement, may deepen and normalize gendered and racialized inequities. In this way, crowdfunding can be seen as a significant technological amplifier of the fundamental social causes of health disparities.

Medical Crowdfunding and Disparities in Health Care Access in the United States, 2016‒2020.

Objectives. To assess whether medical crowdfunding use and outcomes are aligned with health financing needs in the United States. Methods. We collected data on 437 596 US medical GoFundMe campaigns between 2016 and 2020. In addition to summarizing trends in campaign initiation and earnings, we used state- and county-level data to assess whether crowdfunding usage and earnings were higher in areas with greater medical debt, uninsured populations, and poverty. Results. Campaigns raised more than $2 billion from 21.7 million donations between 2016 and 2020. Returns were highly unequal, and success was low, especially in 2020: only 12% of campaigns met their goals, and 16% received no donations at all. Campaigns in 2020 raised substantially less money in areas with more medical debt, higher uninsurance rates, and lower incomes. Conclusions. Despite its popularity and portrayals as an ad-hoc safety net, medical crowdfunding is misaligned with key indicators of health financing needs in the United States. It is best positioned to help in populations that need it the least. (Am J Public Health. 2022;112(3):491-498. https://doi.org/10.2105/AJPH.2021.306617).

Patient Experiences at California Crisis Pregnancy Centers: A Mixed-Methods Analysis of Online Crowd-Sourced Reviews, 2010-2019.

OBJECTIVES: Crisis pregnancy centers (CPCs) are nonprofit antiabortion organizations that claim provision of pregnancy resources. With the Reproduction Freedom, Accountability, Comprehensive Care, and Transparency Act repealed, CPCs are no longer mandated to share information on state-funded family planning and abortion services. As patients increasingly seek healthcare guidance online, we evaluated crowd-sourced reviews of CPCs using the social networking site Yelp. METHODS: CPCs were identified with the CPC Map, a geo-based location resource. Of California's 145 CPCs, 84% had Yelp pages, and 619 reviews (2010-2019) were extracted. Thematic codes were individually applied to 220 excerpts and then analyzed in detail using thematic analysis to capture emergent themes related to motivations for and experiences of CPCs. To ensure thematic saturation, we applied a natural language-processing technique called the meaning extraction method to computationally derive themes of discussion from all of the extracted posts. RESULTS: Motivations to seek care from CPCs included pregnancy confirmation, gaps in healthcare coverage, parenting and emotional support, and abortion care. A review of experiences reveal that CPC faith-based practice garnered both positive- and negative-based experiences. Reviewers also articulated inaccurate medical information, lack of transparency, and reduced options at CPCs. CONCLUSIONS: This is the first study to analyze California CPCs using a social media platform. Pregnant patients turn to social media to share experiences about pregnancy resources, to find healthcare providers, and to increase transparency of services. This content provides valuable insight into the concerns of pregnant patients and offers an intimate view of California CPCs at a time when no federal regulations are in place.

Association Between Crowdsourced Health Care Facility Ratings and Mortality in US Counties.

Importance: Mortality across US counties varies considerably, from 252 to 1847 deaths per 100 000 people in 2018. Although patient satisfaction with health care is associated with patient- and facility-level health outcomes, the association between health care satisfaction and community-level health outcomes is not known. Objective: To examine the association between online ratings of health care facilities and mortality across US counties and to identify language specific to 1-star (lowest rating) and 5-star (highest rating) reviews in counties with high vs low mortality. Design, Setting, and Participants: This retrospective population-based cross-sectional study examined reviews and ratings of 95 120 essential health care facilities across 1301 US counties. Counties that had at least 1 essential health care facility with reviews available on Yelp, an online review platform, were included. Essential health care was defined according to the 10 essential health benefits covered by Affordable Care Act insurance plans. Main Outcomes and Measures: The mean rating of essential health care facilities was calculated by county from January 1, 2015, to December 31, 2019. Ratings were on a scale of 1 to 5 stars, with 1 being the worst rating and 5 the best. County-level composite measures of health behaviors, clinical care, social and economic factors, and physical environment were obtained from the University of Wisconsin School of Medicine and Public Health County Health Rankings database. The 2018 age-adjusted mortality by county was obtained from the Centers for Disease Control and Prevention Wide-ranging Online Data for Epidemiological Research database. Multiple linear regression analysis was used to estimate the association between mean facility rating and mortality, adjusting for county health ranking variables. Words with frequencies of use that were significantly different across 1-star and 5-star reviews in counties with high vs low mortality were identified. Results: The 95 120 facilities meeting inclusion criteria were distributed across 1301 of 3142 US counties (41.4%). At the county level, a 1-point increase in mean rating was associated with a mean (SE) age-adjusted decrease of 18.05 (3.68) deaths per 100 000 people (P < .001). Words specific to 1-star reviews in high-mortality counties included told, rude, and wait, and words specific to 5-star reviews in low-mortality counties included Dr, pain, and professional. Conclusions and Relevance: This study found that, at the county level, higher online ratings of essential health care facilities were associated with lower mortality. Equivalent online ratings did not necessarily reflect equivalent experiences of care across counties with different mortality levels, as evidenced by variations in the frequency of use of key words in reviews. These findings suggest that online ratings and reviews may provide insight into unequal experiences of essential health care.

The Use of Crowdfunding to Support Radiation Therapy Cancer Treatments.

BACKGROUND/AIM: The purpose of this study was to assess patients' use of a crowdfunding platform to raise funds for radiation treatment and to better understand the direct and indirect costs associated with treatments. MATERIALS AND METHODS: The GoFundMe crowdfunding database was queried for four unique categories related to radiation treatment campaigns. Covariates identified included clinical and demographic variables, and associations between amount raised and these predictors were analyzed using a generalized linear model. RESULTS: While 56% percent of campaigns cited direct costs associated with treatment, 73.4% of campaigns cited indirect costs related to treatment. Indirect expenses related to travel (31.7%) as well as living expenses (29.2%) were cited most often across all four treatment categories. CONCLUSION: This study enhances understanding regarding patients use of crowdfunding for radiation treatment. Increased focus should be placed on discussing the indirect costs of care with patients and their families.

The disproportion of crowd wisdom: The impact of status seeking on Yelp reviews.

This study shows that while status seeking motivates people to participate in crowdsourcing platforms, it also negatively impacts the bedrock of crowdsourcing-wisdom of crowds. Using Yelp restaurant reviews in 6 cities, we found that motivations of status seeking lead people to review a greater variety of restaurants, and achieving status further encourages this variety seeking as well as the targeting of more expensive restaurants for review. The impact of this individual-level tendency is confirmed by our aggregate-level analysis which shows that restaurants with higher price levels, higher uniqueness levels, and a larger percentage of elite reviews tend to obtain enough reviews to generate wisdom of crowds sooner than other restaurants. This leads to a different kind of distortion to crowd wisdom: an over-representation of status-conferring products and an under-representation of products that are not status-worthy. The findings suggest the importance of studying sources of distortion that are endemic to crowdsourcing itself.

The people vs science: can passively crowdsourced internet data shed light on host-parasite interactions?

Every internet search query made out of curiosity by anyone who observed something in nature, as well as every photo uploaded to the internet, constitutes a data point of potential use to scientists. Researchers have now begun to exploit the vast online data accumulated through passive crowdsourcing for studies in ecology and epidemiology. Here, we demonstrate the usefulness of iParasitology, i.e. the use of internet data for tests of parasitological hypotheses, using hairworms (phylum Nematomorpha) as examples. These large worms are easily noticeable by people in general, and thus likely to generate interest on the internet. First, we show that internet search queries (collated with Google Trends) and photos uploaded to the internet (specifically, to the iNaturalist platform) point to parts of North America with many sightings of hairworms by the public, but few to no records in the scientific literature. Second, we demonstrate that internet searches predict seasonal peaks in hairworm abundance that accurately match scientific data. Finally, photos uploaded to the internet by non-scientists can provide reliable data on the host taxa that hairworms most frequently parasitize, and also identify hosts that appear to have been neglected by scientific studies. Our findings suggest that for any parasite group likely to be noticeable by non-scientists, information accumulating through internet search activity, photo uploads, social media or any other format available online, represents a valuable source of data that can complement traditional scientific data sources in parasitology.

Learning from crowds in digital pathology using scalable variational Gaussian processes.

The volume of labeled data is often the primary determinant of success in developing machine learning algorithms. This has increased interest in methods for leveraging crowds to scale data labeling efforts, and methods to learn from noisy crowd-sourced labels. The need to scale labeling is acute but particularly challenging in medical applications like pathology, due to the expertise required to generate quality labels and the limited availability of qualified experts. In this paper we investigate the application of Scalable Variational Gaussian Processes for Crowdsourcing (SVGPCR) in digital pathology. We compare SVGPCR with other crowdsourcing methods using a large multi-rater dataset where pathologists, pathology residents, and medical students annotated tissue regions breast cancer. Our study shows that SVGPCR is competitive with equivalent methods trained using gold-standard pathologist generated labels, and that SVGPCR meets or exceeds the performance of other crowdsourcing methods based on deep learning. We also show how SVGPCR can effectively learn the class-conditional reliabilities of individual annotators and demonstrate that Gaussian-process classifiers have comparable performance to similar deep learning methods. These results suggest that SVGPCR can meaningfully engage non-experts in pathology labeling tasks, and that the class-conditional reliabilities estimated by SVGPCR may assist in matching annotators to tasks where they perform well.

Crowd-sourced hospital ratings are correlated with patient satisfaction but not surgical safety.

BACKGROUND: Hospital reviews posted online by patients are unsolicited and less structured than Hospital Consumer Assessment of Healthcare Providers and Systems surveys. The differences between online review platforms and their degrees of correlation with validated satisfaction and safety measures are unknown. METHODS: We identified 515 large acute care teaching hospitals in the United States. We collected patient satisfaction results and postsurgical patient safety indicators from Hospital Compare. We also collected hospital star ratings (1-5) from Facebook, Google, and Yelp. Mean ratings were compared with paired t tests. Concordance between ratings websites, Hospital Consumer Assessment of Healthcare Providers and Systems scores, and surgical safety indicators were assessed with Pearson's correlation coefficient. RESULTS: Mean Facebook ratings (3.81, interquartile range 3.5-4.3) were more favorable than Google (3.26, interquartile range 2.8-3.6) or Yelp (2.59, interquartile range 2.3-2.9). Facebook ratings were least strongly correlated with the Hospital Consumer Assessment of Healthcare Providers and Systems recommended hospital score (ρ = 0.356). Google was modestly correlated (ρ = 0.479), and Yelp was most strongly correlated (ρ = 0.500). The negative correlation between crowdsourced rating and composite safety indicator was too small to be meaningful on any platform. CONCLUSION: There is variation between platforms in consumer ratings of hospitals. Ratings on Facebook are more favorable than Google or Yelp. These are independently correlated with Hospital Consumer Assessment of Healthcare Providers and Systems scores. These findings suggest that unstructured consumer reviews generally reflect similar directionality as Hospital Consumer Assessment of Healthcare Providers and Systems satisfaction scores. Users should be aware of the significant difference between platforms. Consumer ratings platforms are not consistently correlated with postsurgical patient safety indicators, so online ratings may not reflect the safety of surgical care received.

Crowdsourced Tick Image-Informed Updates to U.S. County Records of Three Medically Important Tick Species.

Burgeoning cases of tick-borne disease present a significant public health problem in the United States. Passive tick surveillance gained traction as an effective way to collect epidemiologic data, and in particular, photograph-based tick surveillance can complement in-hand tick specimen identification to amass distribution data and related encounter demographics. We compared the Federal Information Processing Standards (FIPS) code of tick photos submitted to a free public identification service (TickSpotters) from 2014 to 2019 to published nationwide county reports for three tick species of medical concern: Ixodes scapularis Say (Ixodida: Ixodidae), Ixodes pacificus Cooley and Kohls (Ixodida: Ixodidae), and Amblyomma americanum Linneaus (Ixodida: Ixodidae). We tallied the number of TickSpotters submissions for each tick species according to "Reported" or "Established" criteria per county, and found that TickSpotters submissions represented more than half of the reported counties of documented occurrence, and potentially identified hundreds of new counties with the occurrence of these species. We detected the largest number of new county reports of I. scapularis presence in Michigan, North Carolina, and Texas. Tick image submissions revealed potentially nine new counties of occurrence for I. pacificus, and we documented the largest increase in new county reports of A. americanum in Kentucky, Illinois, Indiana, and Ohio. These findings demonstrate the utility of crowdsourced photograph-based tick surveillance as a complement to other tick surveillance strategies in documenting tick distributions on a nationwide scale, its potential for identifying new foci, and its ability to highlight at-risk localities that might benefit from tick-bite prevention education.

Crowdsourcing airway annotations in chest computed tomography images.

Measuring airways in chest computed tomography (CT) scans is important for characterizing diseases such as cystic fibrosis, yet very time-consuming to perform manually. Machine learning algorithms offer an alternative, but need large sets of annotated scans for good performance. We investigate whether crowdsourcing can be used to gather airway annotations. We generate image slices at known locations of airways in 24 subjects and request the crowd workers to outline the airway lumen and airway wall. After combining multiple crowd workers, we compare the measurements to those made by the experts in the original scans. Similar to our preliminary study, a large portion of the annotations were excluded, possibly due to workers misunderstanding the instructions. After excluding such annotations, moderate to strong correlations with the expert can be observed, although these correlations are slightly lower than inter-expert correlations. Furthermore, the results across subjects in this study are quite variable. Although the crowd has potential in annotating airways, further development is needed for it to be robust enough for gathering annotations in practice. For reproducibility, data and code are available online: http://github.com/adriapr/crowdairway.git.

Early Crowdfunding Response to the COVID-19 Pandemic: Cross-sectional Study.

BACKGROUND: As the number of COVID-19 cases increased precipitously in the United States, policy makers and health officials marshalled their pandemic responses. As the economic impacts multiplied, anecdotal reports noted the increased use of web-based crowdfunding to defray these costs. OBJECTIVE: We examined the web-based crowdfunding response in the early stage of the COVID-19 pandemic in the United States to understand the incidence of initiation of COVID-19-related campaigns and compare them to non-COVID-19-related campaigns. METHODS: On May 16, 2020, we extracted all available data available on US campaigns that contained narratives and were created between January 1 and May 10, 2020, on GoFundMe. We identified the subset of COVID-19-related campaigns using keywords relevant to the COVID-19 pandemic. We explored the incidence of COVID-19-related campaigns by geography, by category, and over time, and we compared the characteristics of the campaigns to those of non-COVID-19-related campaigns after March 11, when the pandemic was declared. We then used a natural language processing algorithm to cluster campaigns by narrative content using overlapping keywords. RESULTS: We found that there was a substantial increase in overall GoFundMe web-based crowdfunding campaigns in March, largely attributable to COVID-19-related campaigns. However, as the COVID-19 pandemic persisted and progressed, the number of campaigns per COVID-19 case declined more than tenfold across all states. The states with the earliest disease burden had the fewest campaigns per case, indicating a lack of a case-dependent response. COVID-19-related campaigns raised more money, had a longer narrative description, and were more likely to be shared on Facebook than other campaigns in the study period. CONCLUSIONS: Web-based crowdfunding appears to be a stopgap for only a minority of campaigners. The novelty of an emergency likely impacts both campaign initiation and crowdfunding success, as it reflects the affective response of a community. Crowdfunding activity likely serves as an early signal for emerging needs and societal sentiment for communities in acute distress that could be used by governments and aid organizations to guide disaster relief and policy.

Crowdsourcing to measure financial toxicity in gynecologic oncology.

OBJECTIVE: To utilize a novel crowdsourcing method to measure financial toxicity and its effects among a national cohort of gynecologic cancer patients. METHODS: Crowdsourcing methods were used to administer an online survey to women in the United States with gynecologic cancers. We used the Comprehensive Score for Financial Toxicity (COST) tool to measure financial toxicity and the EQ-5D-3L to measure quality of life (QOL). Based on prior work, we defined high financial toxicity as a COST score ≤ 23. We assessed correlation of COST scores with QOL. We used log-binomial regression to examine associations between high financial toxicity and cost-coping strategies. RESULTS: Among the final study sample of 334 respondents, 87% were white, median age at diagnosis was 55 (interquartile range 47-63), 52% had stage III or IV disease and 90% had private insurance or Medicare. Median COST score was 24 (interquartile range 15-32) and 49% of respondents reported high financial toxicity. Greater financial toxicity was correlated with worse QOL (p < 0.001). Participants reporting high financial toxicity were more likely to use cost-coping strategies, including spending less on basic goods (RR: 3.3; 95% CI: 2.1-5.1), borrowing money or applying for financial assistance (RR: 4.0; 95% CI: 2.4-6.9), and delaying or avoiding care (RR: 5.6; 95% CI: 2.6-12.1). CONCLUSIONS: Crowdsourcing is an effective tool to measure financial toxicity. Nearly half of respondents reported high financial toxicity, which was significantly associated with worse QOL, utilization of cost-coping strategies and delays or avoidance of care.

An evaluation of Cochrane Crowd found that crowdsourcing produced accurate results in identifying randomized trials.

BACKGROUND AND OBJECTIVES: Filtering the deluge of new research to facilitate evidence synthesis has proven to be unmanageable using current paradigms of search and retrieval. Crowdsourcing, a way of harnessing the collective effort of a "crowd" of people, has the potential to support evidence synthesis by addressing this information overload created by the exponential growth in primary research outputs. Cochrane Crowd, Cochrane's citizen science platform, offers a range of tasks aimed at identifying studies related to health care. Accompanying each task are brief, interactive training modules, and agreement algorithms that help ensure accurate collective decision-making.The aims of the study were to evaluate the performance of Cochrane Crowd in terms of its accuracy, capacity, and autonomy and to examine contributor engagement across three tasks aimed at identifying randomized trials. STUDY DESIGN AND SETTING: Crowd accuracy was evaluated by measuring the sensitivity and specificity of crowd screening decisions on a sample of titles and abstracts, compared with "quasi gold-standard" decisions about the same records using the conventional methods of dual screening. Crowd capacity, in the form of output volume, was evaluated by measuring the number of records processed by the crowd, compared with baseline. Crowd autonomy, the capability of the crowd to produce accurate collectively derived decisions without the need for expert resolution, was measured by the proportion of records that needed resolving by an expert. RESULTS: The Cochrane Crowd community currently has 18,897 contributors from 163 countries. Collectively, the Crowd has processed 1,021,227 records, helping to identify 178,437 reports of randomized controlled trials (RCTs) for Cochrane's Central Register of Controlled Trials. The sensitivity for each task was 99.1% for the RCT identification task (RCT ID), 99.7% for the RCT identification task of trials from ClinicalTrials.gov (CT ID), and 97.7% for the identification of RCTs from the International Clinical Trials Registry Platform (ICTRP ID). The specificity for each task was 99% for RCT ID, 98.6% for CT ID, and 99.1% for CT ICTRP ID. The capacity of the combined Crowd and machine learning workflow has increased fivefold in 6 years, compared with baseline. The proportion of records requiring expert resolution across the tasks ranged from 16.6% to 19.7%. CONCLUSION: Cochrane Crowd is sufficiently accurate and scalable to keep pace with the current rate of publication (and registration) of new primary studies. It has also proved to be a popular, efficient, and accurate way for a large number of people to play an important voluntary role in health evidence production. Cochrane Crowd is now an established part of Cochrane's effort to manage the deluge of primary research being produced.

How Do Older Adults Recruited Using MTurk Differ From Those in a National Probability Sample?

A growing number of studies within the field of gerontology have included samples recruited from Amazon's Mechanical Turk (MTurk), an online crowdsourcing portal. While some research has examined how younger adult participants recruited through other means may differ from those recruited using MTurk, little work has addressed this question with older adults specifically. In the present study, we examined how older adults recruited via MTurk might differ from those recruited via a national probability sample, the Health and Retirement Study (HRS), on a battery of outcomes related to health and cognition. Using a Latin-square design, we examined the relationship between recruitment time, remuneration amount, and measures of cognitive functioning. We found substantial differences between our MTurk sample and the participants within the HRS, most notably within measures of verbal fluency and analogical reasoning. Additionally, remuneration amount was related to differences in time to complete recruitment, particularly at the lowest remuneration level, where recruitment completion required between 138 and 485 additional hours. While the general consensus has been that MTurk samples are a reasonable proxy for the larger population, this work suggests that researchers should be wary of overgeneralizing research conducted with older adults recruited through this portal.

Cochrane Centralised Search Service showed high sensitivity identifying randomized controlled trials: A retrospective analysis.

BACKGROUND AND OBJECTIVES: The Cochrane Central Register of Controlled Trials (CENTRAL) is compiled from a number of sources, including PubMed and Embase. Since 2017, we have increased the number of sources feeding into CENTRAL and improved the efficiency of our processes through the use of application programming interfaces, machine learning, and crowdsourcing.Our objectives were twofold: (1) Assess the effectiveness of Cochrane's centralized search and screening processes to correctly identify references to published reports which are eligible for inclusion in Cochrane systematic reviews of randomized controlled trials (RCTs). (2) Identify opportunities to improve the performance of Cochrane's centralized search and screening processes to identify references to eligible trials. METHODS: We identified all references to RCTs (either published journal articles or trial registration records) with a publication or registration date between 1st January 2017 and 31st December 2018 that had been included in a Cochrane intervention review. We then viewed an audit trail for each included reference to determine if it had been identified by our centralized search process and subsequently added to CENTRAL. RESULTS: We identified 650 references to included studies with a publication year of 2017 or 2018. Of those, 634 (97.5%) had been captured by Cochrane's Centralised Search Service. Sixteen references had been missed by the Cochrane's Centralised Search Service: six had PubMed-not-MEDLINE status, four were missed by the centralized Embase search, three had been misclassified by Cochrane Crowd, one was from a journal not indexed in MEDLINE or Embase, one had only been added to Embase in 2019, and one reference had been rejected by the automated RCT machine learning classifier. Of the sixteen missed references, eight were the main or only publication to the trial in the review in which it had been included. CONCLUSION: This analysis has shown that Cochrane's centralized search and screening processes are highly sensitive. It has also helped us to understand better why some references to eligible RCTs have been missed. The CSS is playing a critical role in helping to populate CENTRAL and is moving us toward making CENTRAL a comprehensive repository of RCTs.

Crowdfunding Campaigns and Thyroid Surgery: Who, What, Where, and How Much?

BACKGROUND: Crowdfunding has become a unique response to the challenge of health care expenses, yet it has been rarely studied by the medical community. We looked to describe the scope of crowdfunding in thyroid surgery and analyze the factors that contribute toward a successful campaign. METHODS: In November 2018, active campaigns were retrieved from a popular crowdfunding Web site using search terms thyroidectomy and thyroid surgery and filtered to include only campaigns that originated in the United States. RESULTS: About 1052 thyroid surgery-related campaigns were analyzed. About 836 (79.5%) involved female patients and 43 (4.1%) pediatric patients. About 792 campaigns (75.3%) referred to thyroid cancer as a primary condition, 163 (15.5%) benign thyroid disease, and 97 (9.2%) other conditions. The average amount raised per campaign was $2514.54 (range, $0-$53,160). About 338 (32.1%) campaigns were self-posted, 317 (30.1%) posted by family, and 397 (37.7%) posted by friends. Median campaign duration was 20 mo, with a median number of 16 donors, 17 hearts, and 136 social media shares. Campaigns related to thyroid cancer raised more funds ($2729.97) than benign ($1669.84) or other ($2175.03) conditions (P < 0.001). Campaigns submitted by friends ($3524.78) received more funding than those by self ($1672.48) or family ($2147.19) (P < 0.001). Campaign duration, donor number, share number, and hearts were also significant predictors of amount raised. CONCLUSIONS: For thyroid surgery-related crowdfunding, campaigns referring to thyroid cancer had the highest amount of funds raised. Campaigns created by friends and other factors related to increased community engagement such as social media shares were also related to increased funds.