Unmet Financial Needs and Crowdfunding Patterns Among Patients With Endometriosis
Bibliographic record
Abstract
INTRODUCTION: Endometriosis, affecting 10–15% of reproductive-aged females, significantly affects multiple aspects of life, including physical, social, and financial. The average cost of endometriosis to a patient ranges $1,459–20,239 per year. The concept of financial toxicity, the adverse effects that result from cancer’s cost, is well described in the oncology literature. Considering the decreased work productivity, financial toxicity may also play a role for endometriosis patients, who may turn to crowdfunding, the act of raising funds using internet-based means. OBJECTIVE: To describe crowdfunding patterns among patients with endometriosis, evaluate the impact of state of residence Medicaid expansion plan (MEP) status on campaign characteristics, and evaluate factors that are associated with money-raising success. METHODS: We queried the crowdfunding forum GoFundMe for the term “endometriosis.” We excluded campaigns that were not related to endometriosis or that did not originate in the United States. We then extracted and analyzed campaign characteristics and requests. To evaluate the impact of state Medicaid expansion status, we compared campaigns originating in states with MEP to those without and evaluated the proportion of campaigns from the latter group compared to the expected proportion based on the population in those states. To evaluate factors associated with money-raising success, we compared campaigns that raised below and above the median sum. RESULTS: Of 460 campaigns collected initially, 196 (63.2%) were published in the United States and were included and analyzed. The median monetary amount requested was $7,891.5, while the median total amount raised was $300.0. Only four (2.0%) campaigns met their goal. The most common reason for fundraising was to support routine living costs (n=126, 64.3%). When comparing campaigns from states with and without MEPs, the amounts requested in both groups were comparable (Figure 1A). However, campaigns from states with MEPs raised a significantly higher percentage of the goal amount (p=.039, Figures 1B and 2A). In addition, there was a significantly higher proportion of campaigns from states without MEPs compared to the expected population proportion (38.8% vs. 26.9%, p<.001, Figure 2B). When comparing campaigns that raised below and above the median sum, campaigns that raised above the median requested more funds (p=.018) and were more often from states with MEPs (p=.029). In multivariable logistic regression analysis, campaigns from states without MEPs were independently associated with raising below the median amount (aOR 0.46, 95% CI 0.24–0.86). CONCLUSIONS: Endometriosis crowdfunding campaigns rarely met their requested goal, the proportion of campaigns from states without MEPs was higher than expected, and these campaigns were associated with raising less money. Endometriosis can have a detrimental effect on patients’ work productivity and income. Crowdfunding campaigns have been shown to mitigate financial toxicity for cancer patients and can potentially play a similar role for endometriosis patients. Our data outlines the crowdfunding patterns for endometriosis to better understand the unmet needs of individuals affected by this condition. To the best of our knowledge, crowdfunding has not been described among patients with endometriosis. This study is limited by campaigns’ missing information, for example, insurance status.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".