Association of Neighborhood Deprivation Index With Success in Cancer Care Crowdfunding | Health Disparities | JAMA Network Open | JAMA Network
[Skip to Navigation]
Sign In
Figure.  Results of Multivariable Regression Model for Amount Raised
Results of Multivariable Regression Model for Amount Raised

A, Bars represent mean amount of money raised in campaigns in each neighborhood deprivation index (NDI) level, with error bars denoting bootstrapped 95% CIs. Compared with the least deprived quartile (NDI 1), campaigns in NDI 2 areas raised 13.39% less, those in NDI 3 areas raised 18.14% less, and those in NDI 4 areas raised 26.07% less. B, Bars indicate mean amount of money raised when a particular text feature is present or absent, according to the fitted multivariable regression model described in Table 4. The differences in amounts raised between campaigns that did or did not mention the text feature were 5.23% for self-reliance, 15.40% for bravery, 11.87% for militaristic metaphors, 6.58% for type of treatment, 7.36% for out-of-pocket (OOP) costs, 1.39% for insurance, 9.58% for cancer type, 13.80% for warmth, and 0.09% for gratitude.

aP < .001.

bP < .05.

Table 1.  Descriptive Statistics
Descriptive Statistics
Table 2.  Amount Raised, Goal Amount, and Text Feature Usage by NDI Quartile
Amount Raised, Goal Amount, and Text Feature Usage by NDI Quartile
Table 3.  Amount Raised and Goal Amount by Campaign Text Characteristics
Amount Raised and Goal Amount by Campaign Text Characteristics
Table 4.  Results of Multivariable Regression Model for Amount Raised
Results of Multivariable Regression Model for Amount Raised
Limit 200 characters
Limit 25 characters
Conflicts of Interest Disclosure

Identify all potential conflicts of interest that might be relevant to your comment.

Conflicts of interest comprise financial interests, activities, and relationships within the past 3 years including but not limited to employment, affiliation, grants or funding, consultancies, honoraria or payment, speaker's bureaus, stock ownership or options, expert testimony, royalties, donation of medical equipment, or patents planned, pending, or issued.

Err on the side of full disclosure.

If you have no conflicts of interest, check "No potential conflicts of interest" in the box below. The information will be posted with your response.

Not all submitted comments are published. Please see our commenting policy for details.

Limit 140 characters
Limit 3600 characters or approximately 600 words
    Original Investigation
    Oncology
    December 3, 2020

    Association of Neighborhood Deprivation Index With Success in Cancer Care Crowdfunding

    Author Affiliations
    • 1Department of Medicine, Columbia University Irving Medical Center, New York, New York
    • 2Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, New York
    • 3Division of Digestive and Liver Diseases, Columbia University Irving Medical Center, New York, New York
    • 4Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, New York
    JAMA Netw Open. 2020;3(12):e2026946. doi:10.1001/jamanetworkopen.2020.26946
    Key Points

    Question  How is online crowdfunding for cancer care associated with existing socioeconomic health disparities in the US cancer care setting?

    Findings  In this cross-sectional study of 144 061 cancer crowdfunding campaigns, those located in US counties with high socioeconomic status raised significantly more than campaigns in lower–socioeconomic status counties. Crowdfunders who used campaign narratives to portray beneficiaries as worthy of donations raised significantly more than those without such portrayals, and the use of these portrayals was unequally distributed across socioeconomic strata.

    Meaning  These findings suggest that crowdfunding’s reliance on access to interpersonal wealth and proficiency in digital self-marketing may disproportionately benefit those with existing socioeconomic advantage.

    Abstract

    Importance  Financial toxicity resulting from cancer care poses a substantial public health concern, leading some patients to turn to online crowdfunding. However, the practice may exacerbate existing socioeconomic cancer disparities by privileging those with access to interpersonal wealth and digital media literacy.

    Objective  To test the hypotheses that higher county-level socioeconomic status and the presence (vs absence) of text indicators of beneficiary worth in campaign descriptions are associated with amount raised from cancer crowdfunding.

    Design, Setting, and Participants  This cross-sectional analysis examined US cancer crowdfunding campaigns conducted between 2010 and 2019 and data from the American Community Survey (2013-2017). Data analysis was performed from December 2019 to March 2020.

    Exposures  Neighborhood deprivation index of campaign location and campaign text features indicating the beneficiary’s worth.

    Main Outcomes and Measures  Amount of money raised.

    Results  This study analyzed 144 061 US cancer crowdfunding campaigns. Campaigns in counties with higher neighborhood deprivation raised less (–26.07%; 95% CI, –27.46% to –24.65%; P < .001) than those in counties with less neighborhood deprivation. Campaigns raised more funds when legitimizing details were provided, including clinical details about the cancer type (9.58%; 95% CI, 8.00% to 11.18%; P < .001) and treatment type (6.58%; 95% CI, 5.44% to 7.79%; P < .001) and financial details, such as insurance status (1.39%; 95% CI, 0.20% to 2.63%; P = .02) and out-of-pocket costs (7.36%; 95% CI, 6.18% to 8.55%; P < .001). Campaigns raised more money when beneficiaries were described as warm (13.80%; 95% CI, 12.30% to 15.26%; P < .001), brave (15.40%; 95% CI, 14.11% to 16.65%; P < .001), or self-reliant (5.23%; 95% CI, 3.77% to 6.72%; P < .001).

    Conclusions and Relevance  These findings suggest that cancer crowdfunding success ay disproportionately benefit those in high–socioeconomic status areas and those with the internet literacy necessary to portray beneficiaries as worthy. By rewarding those with existing socioeconomic advantage, cancer crowdfunding may perpetuate socioeconomic disparities in cancer care access. The findings also underscore the widespread nature of financial toxicity resulting from cancer care.

    Introduction

    US individuals experience high rates of disease-related financial burden, with 1 in 4 reporting trouble paying medical bills.1 Rates of financial distress due to medical expenses among patients with cancer in particular are estimated to range from 16% to 73%.2,3 Treatment-related financial hardship can lead patients to adopt unsustainable coping methods, including debt accumulation, medication nonadherence, foregoing medical appointments, and exhausting savings.4,5 Thus, it is not surprising that financial toxicity (FT) resulting from cancer care is associated with adverse mental and physical health outcomes.2,6,7 To alleviate FT, some patients with cancer have turned to online fundraising, or crowdfunding.

    Crowdfunding campaigns solicit donations from friends, family, and strangers, mediated by websites that host pages describing the beneficiary’s story and needs. Effective campaigns successfully engage more donors by relaying a sympathetic narrative of the recipient,8 thus demonstrating the beneficiary’s worth or deservingness for financial assistance. Not surprisingly, past work on medical crowdfunding has raised the concern that crowdfunding may exacerbate existing socioeconomic disparities by disproportionately benefiting those with access to interpersonal wealth and the internet, as well as those with the digital media literacy necessary to successfully frame an online campaign.9-13 This concern is especially poignant given the positive association between internet literacy and socioeconomic status (SES) among children,14 college students,15 and adults.16

    In the context of crowdfunding, internet and digital literacy may be understood as proficiency in presenting a beneficiary as worthy of donations by appealing to perceptions of deservingness. The stereotype content model is a framework for studying social judgments along 2 dimensions of perceived warmth and competence.17 Applied to SES, low-SES individuals are stereotyped as warmer but less competent than high-SES individuals.18 Consequently, emphasizing a beneficiary’s warmth, gratitude, and kindness can communicate their worth.19 Crowdfunders may also benefit by avoiding language that could lead donors to perceive the beneficiary as incompetent and responsible for their hardship or as misleading donors regarding the true extent of their needs.18,20-23 Specific to cancer, language describing an individual’s compliance with treatment by battling their cancer while remaining brave may also position a beneficiary as more deserving of donations.23

    Quantitative studies have yet to examine the associations among SES, text indicators of worth, and cancer crowdfunding on a large scale within the US cancer care setting. To address this gap in the literature, we leveraged open-source data mining tools to analyze all US cancer crowdfunding campaigns shared publicly on GoFundMe.com. Given past work highlighting the importance of digital literacy in perpetuating cancer care disparities through crowdfunding,8,9,11,12 we focused on crowdfunders’ ability to portray beneficiaries as deserving and sympathetic, in addition to the geographical socioeconomic context of the campaign. We tested the hypotheses that the amount raised by campaigns, but not goal amount, would be associated with higher county-level SES and text describing the beneficiary’s worth, including positive stereotypes of low-SES individuals, distancing from negative stereotypes of low-SES individuals, legitimizing clinical and financial details, and consistency with cancer narratives. We also posited that campaigns in higher-SES counties would more often use the text features describing the beneficiary’s worth.

    Methods

    This cross-sectional study used public crowdfunding data from GoFundMe.com and InternetArchive.org, socioeconomic data from the US Census Bureau, and geographic information from Mapbox24,25 and the Federal Communications Commission. Columbia University Medical Center’s institutional review board determined this research to be exempt and classified it as non–human subjects research; thus, informed consent was not needed, in accordance with 45 CFR §46. The code used for this analysis is available on Github.26 This report follows the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline for cross-sectional studies.

    Online Fundraising Data Collection and Reduction

    We implemented a web crawler in Python programming language version 3.7 (Python) to automatically retrieve information from individual campaign pages using GoFundMe.com’s public sitemap.27 To account for missing information and old campaigns taken down before our collection date, we deployed a second web crawler to scrape archived GoFundMe.com webpages available through InternetArchive.org’s Wayback Machine application programming interface (API).28 We conducted this search on September 10, 2019, and scraped 1 856 154 pages in total. For each campaign, we collected the campaign title, creation date, location, description, tag for campaign category, amount of money raised, goal amount, number of contributors, number of social media shares, and number of likes or followers.

    Next, we curated a cancer-specific subset of the resulting data set (Table 1), yielding 144 752 cancer crowdfunding campaigns. Campaigns were selected on the basis of a keyword search of titles and descriptions (eAppendix 1 in the Supplement). Because we intended to capture campaigns for individuals (rather than organizations or initiatives), we restricted campaigns to only those categorized as medical by users in GoFundMe.com’s mutually exclusive campaign categories.

    Using location names provided by the campaign posters, we geocoded these 144 752 campaigns using a geocoding API from Mapbox.24,25 We then used an API from the Federal Communications Commission to map the latitudes and longitudes to county federal information processing codes,29 allowing for linkage with county-level SES US Census data. Campaigns failed to geocode if the location was indecipherable (eg, a non–zip code number) or was not in the US. In total, 542 campaigns failed to geocode. We then excluded any campaigns with research in the title to reduce the risk of including campaigns not intended for individuals with cancer. This resulted in a final data set of 144 061 cancer crowdfunding campaigns between the years 2010 and 2019 (Table 1).

    After curating the final data set, we Winsorized the goal amount to handle extreme outliers by reassigning values below the 5th percentile and above the 95th percentile, as recommended by past work.30 Using this method, 6.32% of values (9106 campaigns) were reassigned, such that values above the 95th percentile (2113 campaigns [1.47%]) were reassigned to $100 000 (the 95th percentile value), and values below the 5th percentile (6993 campaigns [4.85%]) were reassigned to $2000 (the 5th percentile value).

    Exposures and Outcome
    Socioeconomic Data

    We extracted a number of socioeconomic variables31-33 from the American Community Survey using the US Census Bureau’s API (see eAppendix 2 and eFigure 1 in the Supplement for details).34 We used 5-year estimates (2013-2017) for all variables and performed a principal components analysis in 2 steps to compute a single neighborhood deprivation index (NDI), similar to past work.31-33

    After running the initial principal components analysis, we omitted variables with a factor loading less than 0.25.31,33 On the basis of this criterion, the final NDI included unemployment, poverty, percentage uninsured, high school completion, internet access, households headed by a single parent, and households with annual income less than $35 000. These factors explained 59.25% of the variability across counties, similar to past work,33,35 and we used them to calculate standardized NDIs (see eAppendix 2 in the Supplement for factor loadings and details). NDI scores were grouped into quartiles,31-33 and index scores were matched to campaigns according to the campaign’s county federal information processing code.

    Text Mining

    To extract features related to self-reliance, militaristic metaphors, bravery, clinical details, and financial details, we conducted a series of keyword searches using regular expressions, as detailed in eTable 1, eTable 2, eTable 3, and eAppendix 3 in the Supplement. Each search was intended to capture both a single key word (eg, brave) and words indicating similar constructs (eg, courage, strength, and hero). Campaigns were categorized as either containing or not containing the construct in question according to the results of the keyword search.

    Campaign Fundraising

    Our primary outcome was the amount raised by crowdfunding campaigns. We also examined campaign goal amount as a secondary campaign outcome.

    Statistical Analyses
    Univariable Analyses

    We generated descriptive statistics to characterize the monetary campaign information, NDI quartile distributions, data missingness, and presence of text features. For all analyses, we omitted cases with missing data for the variable of interest. To test the hypothesis that the amount raised by campaigns, but not goal amount, would be associated with higher county-level SES, we used 1-way analyses of variance comparing mean amount raised and goal amount across the 4 NDI quartiles. All tests for significance were 2-sided. We examined significant (P < .05) overall differences by NDI quartile using post hoc Tukey HSD tests. To test the hypothesis that the amount raised would be associated with text describing the beneficiary’s worth, we used independent samples t tests assessing differences in amount raised and goal amount by presence of the following text features: warmth, gratitude, self-reliance, cancer type, treatment type, insurance, out-of-pocket costs, bravery, and militaristic metaphors. We used χ2 tests of independence to compare the observed and expected counts of each text mention listed across NDI quartile. We probed significant overall associations using pairwise χ2 tests with a Bonferroni correction for the 6 pairwise comparisons between NDI quartiles, resulting in a significance threshold of P < .0083.36

    Multivariable Analysis

    We constructed a generalized linear regression model to estimate associations between amount raised with campaign textual characteristics and SES, while adjusting for variation attributable to social media shares, goal amount, year of creation, and number of campaign contributors. We selected these variables a priori on the basis of past work37 and author assumptions. We confirmed their inclusion in the model as potential confounders on the basis of significant univariable associations with amount raised (eTable 3 and eTable 4 in the Supplement). To account for nonnormality in the distribution of errors and considerable positive skew in amount raised (eTable 5 in the Supplement), we log-transformed the amount raised and entered it as the dependent variable in regression models. We calculated the expected percentage change in amount raised when a given feature was present.38 Data analysis was performed using Python version 3.7 and R statistical software version 3.6.3 (R Project for Statistical Computing) from December 2019 to March 2020.

    Results
    Univariable Analyses

    We curated the largest reported sample of US cancer crowdfunding campaigns to date, with 144 061 campaigns included in the analyses. Descriptive statistics are displayed in Table 1 and eTable 5 in the Supplement. An analysis of variance indicated significant differences in amount raised across NDI quartiles. The mean (SD) amounts raised were $7402.94 ($12 184.61) for NDI 1 (the least deprived quartile), $6009.28 ($10 740.33) for NDI 2, $5775.41 ($12 183.32) for NDI 3, and $4857.87 ($9797.49) for NDI 4 (the most deprived quartile) (F3,143 568 = 256.00; P < .001) (Table 2). Tukey honest significant different post hoc tests revealed a dose-dependent effect, such that the amount raised increased significantly with decreasing levels of deprivation. However, there was also a significant difference in the stated goal amount by NDI quartile. As with amount raised, those in less deprived counties sought a higher goal amount than those in more deprived counties, although the means for counties in the second and third deprivation quartiles did not significantly differ from one another (see eFigure 2 in the Supplement).

    As shown in Table 3, campaigns that mentioned a beneficiary’s warmth or gratitude raised significantly more money than those that did not (mean [SD], $8050.41 [$13 196.40] vs $5993.32 [$11 194.11]; t143 572 = 25.96; P < .001), but they also indicated a significantly higher goal amount. Campaigns that described the beneficiary as self-reliant raised significantly more than those that did not (mean [SD], $7515.91 [$13 398.57] vs $6167.24 [$11 258.40]; t143 572 = 15.67; P < .001) but had a significantly higher goal amount. Those who mentioned a specific cancer type, specific treatment, insurance type, or out-of-pocket costs raised significantly more money than campaigns that did not mention these features, although they also requested a higher goal amount. Finally, campaigns that invoked militaristic metaphors or described the beneficiary as brave raised significantly more money than campaigns that did not, but they also requested a higher goal amount than campaigns without these features (Table 3).

    We found that the use of text features indicating deservingness would be unequally distributed across NDI quartiles. In general, campaigns in the least deprived quartile used text features indicating deservingness more often than those in more deprived quartiles, with the exception of insurance, which was mentioned more often in campaigns in more deprived quartiles (see Table 2 for distributions and pairwise comparisons and eTable 6 in the Supplement for expected and observed counts).

    Multivariable Analysis

    The results of a generalized linear regression model with amount raised as the dependent variable are summarized in Table 4 and the Figure. After adjusting for potential effects of social media shares, campaign year, number of contributors, and goal amount on amount raised, we found results similar to those reported in univariable analyses (see eTable 7 in the Supplement for full regression model outputs). Campaigns in the most deprived NDI quartile group raised 26.07% (95% CI, –27.46% to –24.65%; P < .001) less than those in the least deprived NDI quartile group, those in the third NDI quartile raised 18.14% less than those in the least deprived quartile, and those in the second NDI quartile raised 13.39% less than those in the least deprived quartile. Campaigns that mentioned a beneficiary’s warmth raised significantly more than those that did not (13.80%; 95% CI, 12.30% to 15.26%; P < .001), but the association between amount raised and gratitude was not significant, as did campaigns that mentioned the beneficiary’s self-reliance (5.23%; 95% CI, 3.77% to 6.72%; P < .001) and bravery (15.40%; 95% CI, 14.11% to 16.65%; P < .001). We found that greater detail regarding the beneficiary’s need were associated with a greater amount raised. Campaigns that mentioned the beneficiary’s cancer type (9.58%; 95% CI, 8.00% to 11.18%; P < .001), treatment type (6.58%; 95% CI, 5.44% to 7.79%; P < .001), insurance (1.39%; 95% CI, 0.20% to 2.63%; P = .02), or out-of-pocket costs (7.36%; 95% CI, 6.18% to 8.55%; P < .001) raised significantly more than campaigns that did not mention these needs (Table 4). Finally, campaigns that mentioned the beneficiary’s bravery or used militaristic metaphors raised significantly more money than campaigns without these features.

    Discussion

    This cross-sectional analysis found that crowdfunding campaigns for cancer care expenses raised significantly more money when they were located in US counties with higher SES and when they described beneficiaries as worthy of donations (eg, as consistent with positive low-SES stereotypes, inconsistent with negative low-SES stereotypes, or aligned with cancer narratives of bravery and battle imagery).39,40 The use of these text features was unequally distributed across socioeconomic strata: campaigns in higher-SES counties tended to use these indicators more often than those in lower-SES counties. These findings suggest that online crowdfunding may exacerbate socioeconomic disparities in cancer care and also highlight the widespread nature of difficulty paying for cancer care. As cancer care costs continue to increase,41-43 FT resulting from cancer care poses a significant public health issue. Although lower-SES patients are at greater risk of FT, we found that these patients are the least likely to benefit from cancer crowdfunding as a way to mitigate it.

    Although qualitative and theoretical work on medical crowdfunding has suggested that the practice may perpetuate socioeconomic health disparities,9,11,12,44 quantitative work on cancer crowdfunding is limited. One Canadian study of 1788 campaigns linked geospatial campaign counts with corresponding socioeconomic data and found a positive association between crowdfunding use and SES.13 Our study confirms their findings in a much larger sample of 144 061 US cancer crowdfunding campaigns. In addition to corroborating previous associations between high SES and cancer crowdfunding, our results demonstrate the importance of campaign text characteristics, particularly characteristics that portray beneficiaries as worthy. As with other indicators of digital and internet literacy,15,16,45,46 the use of personal marketing strategies was unevenly distributed across socioeconomic strata, often to the benefit of those in higher-SES areas. This supports previously noted concerns that medical crowdfunding disproportionately benefits those with the requisite internet literacy.9,11,12,44

    Our findings are comparable to recent work by Cohen and colleagues47 focusing on explicit mentions of being insured and uninsured in US cancer crowdfunding campaigns. In their sample of 1035 campaigns, Cohen et al47 found no differences in amount raised by insurance status but did find higher goal amounts among uninsured and underinsured beneficiaries. We found similar results for goal amount, but we also found a small but significant association between mentioning insurance and amount raised. This discrepancy may be due to our broader definition of insurance mention but may also be related to our novel approach to web scraping and data reduction. Although previous studies have used GoFundMe.com’s internal search bar to find cancer crowdfunding campaigns,13,37,47 we leveraged the domain’s sitemap to collect all indexed campaigns. Because internal search bars commonly use search engine optimization to return results with greater engagement potential and more recent results, our method decreased the risk of this bias by collecting URLs directly from GoFundMe.com’s sitemap.

    Limitations

    Although, to our knowledge, our study offers the largest and most comprehensive quantitative analysis of cancer crowdfunding campaigns in the US to date, it is limited in some respects. First, using automated text mining rather than manual coding may include campaigns in counts of text features when the use context of certain words does not align with the connotation we assumed. Similarly, we were unable to extract information regarding cancer stage at campaign initiation. A related concern is that crowdfunding campaigns are often initiated by someone other than the beneficiaries themselves. Future work would do well to use natural language processing techniques to explore this issue. Our automated approach to geocoding the data set carries similar limitations related to trade-offs between precision and quantity. Because geocoding relied on user-provided locations with varying levels of granularity, the smallest unit of geographic analysis for US Census data was the county. Further insights may be gleaned by collaborating with crowdfunding platforms to obtain primary data. Because we did not have access to primary data such as the campaign initiator’s internet provider address, we were not able to compare the user-defined campaign location with the initiator’s location. Despite our novel approach to data collection, our sample is prone to biases in data availability. Earlier campaigns may be sparse because of users deleting campaigns in the time since initiation. Because campaigns can be listed as inactive for any reason, it is difficult to anticipate how these campaigns would influence results. We were able to recover data for some inactive campaigns by scraping archived GoFundMe.com webpages with the InternetArchive.org’s API. Future research regarding campaign closure, including time from initiation to closure and reasons for closure, may provide additional insights. Furthermore, we did not collect other data from campaigns that may be informative, including the number of campaign updates, campaign comments from other users, and image data.

    Conclusions

    To our knowledge, this cross-sectional study is the first to provide large-scale quantitative support for the notion that cancer crowdfunding may perpetuate SES disparities in US cancer care. In particular, we found that crowdfunders with the digital literacy necessary to market beneficiaries as worthy and those in higher-SES areas raised significantly more money through online cancer crowdfunding than those without these advantages. Our results suggest that rather than remedying socioeconomic disparities in cancer care, online crowdfunding provides additional privileges to those with existing socioeconomic advantage, potentially exacerbating SES disparities and further marginalizing those most at risk of FT.

    Back to top
    Article Information

    Accepted for Publication: September 25, 2020.

    Published: December 3, 2020. doi:10.1001/jamanetworkopen.2020.26946

    Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2020 Silver ER et al. JAMA Network Open.

    Corresponding Author: Nicholas P. Tatonetti, PhD, Department of Biomedical Informatics, Columbia University Irving Medical Center, 622 W 168th St, PH20, New York, NY 10032 (nick.tatonetti@columbia.edu).

    Author Contributions: Drs Hur and Tatonetti had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Ms Silver and Mr Truong contributed equally to this work.

    Concept and design: All authors.

    Acquisition, analysis, or interpretation of data: Silver, Truong, Ostvar, Hur.

    Drafting of the manuscript: All authors.

    Critical revision of the manuscript for important intellectual content: Silver, Truong, Hur, Tatonetti.

    Statistical analysis: Silver, Truong, Ostvar.

    Obtained funding: Tatonetti.

    Administrative, technical, or material support: Hur, Tatonetti.

    Supervision: Hur, Tatonetti.

    Conflict of Interest Disclosures: None reported.

    References
    1.
    Hamel  L, Norton  M, Pollitz  K,  et al. The burden of medical debt: results from the Kaiser Family Foundation/New York Times medical bills survey. Kaiser Family Foundation. Published January 5, 2016. Accessed November 3, 2020. https://www.kff.org/health-costs/report/the-burden-of-medical-debt-results-from-the-kaiser-family-foundationnew-york-times-medical-bills-survey/view/print/
    2.
    Gordon  LG, Merollini  KMD, Lowe  A, Chan  RJ.  A systematic review of financial toxicity among cancer survivors: we can’t pay the co-pay.   Patient. 2017;10(3):295-309. doi:10.1007/s40271-016-0204-xPubMedGoogle ScholarCrossref
    3.
    Smith  GL, Lopez-Olivo  MA, Advani  PG,  et al.  Financial burdens of cancer treatment: a systematic review of risk factors and outcomes.   J Natl Compr Canc Netw. 2019;17(10):1184-1192. doi:10.6004/jnccn.2019.7305PubMedGoogle ScholarCrossref
    4.
    Carrera  PM, Kantarjian  HM, Blinder  VS.  The financial burden and distress of patients with cancer: understanding and stepping-up action on the financial toxicity of cancer treatment.   CA Cancer J Clin. 2018;68(2):153-165. doi:10.3322/caac.21443PubMedGoogle ScholarCrossref
    5.
    Knight  TG, Deal  AM, Dusetzina  SB,  et al.  Financial toxicity in adults with cancer: adverse outcomes and noncompliance.   J Oncol Pract. 2018;14(11):e665-e673. doi:10.1200/JOP.18.00120PubMedGoogle ScholarCrossref
    6.
    Chan  RJ, Gordon  LG, Tan  CJ,  et al.  Relationships between financial toxicity and symptom burden in cancer survivors: a systematic review.   J Pain Symptom Manage. 2019;57(3):646-660.e1. doi:10.1016/j.jpainsymman.2018.12.003PubMedGoogle ScholarCrossref
    7.
    Kale  HP, Carroll  NV.  Self-reported financial burden of cancer care and its effect on physical and mental health-related quality of life among US cancer survivors.   Cancer. 2016;122(8):283-289. doi:10.1002/cncr.29808PubMedGoogle ScholarCrossref
    8.
    Snyder  J, Chow-White  P, Crooks  VA, Mathers  A.  Widening the gap: additional concerns with crowdfunding in health care.   Lancet Oncol. 2017;18(5):e240. doi:10.1016/S1470-2045(17)30259-0PubMedGoogle Scholar
    9.
    Berliner  LS, Kenworthy  NJ.  Producing a worthy illness: personal crowdfunding amidst financial crisis.   Soc Sci Med. 2017;187:233-242. doi:10.1016/j.socscimed.2017.02.008PubMedGoogle ScholarCrossref
    10.
    Gonzales  AL, Kwon  EY, Lynch  T, Fritz  N.  “Better everyone should know our business than we lose our house”: costs and benefits of medical crowdfunding for support, privacy, and identity.   New Media Soc. 2018;20(2):641-658. doi:10.1177/1461444816667723Google ScholarCrossref
    11.
    Paulus  TM, Roberts  KR.  Crowdfunding a “real-life superhero”: the construction of worthy bodies in medical campaign narratives.   Discourse Context Media. 2018;21:64-72. doi:10.1016/j.dcm.2017.09.008Google ScholarCrossref
    12.
    Snyder  J.  Crowdfunding for medical care: ethical issues in an emerging health care funding practice.   Hastings Cent Rep. 2016;46(6):36-42. doi:10.1002/hast.645PubMedGoogle ScholarCrossref
    13.
    van Duynhoven  A, Lee  A, Michel  R,  et al.  Spatially exploring the intersection of socioeconomic status and Canadian cancer-related medical crowdfunding campaigns.   BMJ Open. 2019;9(6):e026365. doi:10.1136/bmjopen-2018-026365PubMedGoogle Scholar
    14.
    Scherer  R, Siddiq  F.  The relation between students’ socioeconomic status and ICT literacy: findings from a meta-analysis.   Comput Educ. 2019;138:13-32. doi:10.1016/j.compedu.2019.04.011Google ScholarCrossref
    15.
    Hargittai  E.  Digital na(t)ives? variation in internet skills and uses among members of the “net generation”.   Sociol Inq. 2010;80(1):92-113. doi:10.1111/j.1475-682X.2009.00317.xGoogle ScholarCrossref
    16.
    Yates  S, Lockley  E.  Social media and social class.   Am Behav Sci. 2018;62(9):1291-1316. doi:10.1177/0002764218773821Google ScholarCrossref
    17.
    Fiske  ST, Cuddy  AJC, Glick  P, Xu  J.  A model of (often mixed) stereotype content: competence and warmth respectively follow from perceived status and competition.   J Pers Soc Psychol. 2002;82(6):878-902. doi:10.1037/0022-3514.82.6.878PubMedGoogle ScholarCrossref
    18.
    Durante  F, Tablante  CB, Fiske  ST.  Poor but warm, rich but cold (and competent): social classes in the stereotype content model.   J Soc Issues. 2017;73(1):138–157. doi:10.1111/josi.12208Google ScholarCrossref
    19.
    Meuleman  B, Roosma  F, Abts  K.  Welfare deservingness opinions from heuristic to measurable concept: the CARIN deservingness principles scale.   Soc Sci Res. 2020;85:102352. doi:10.1016/j.ssresearch.2019.102352PubMedGoogle Scholar
    20.
    Campbell  C, Gaddis  SM.  “I don’t agree with giving cash”: a survey experiment examining support for public assistance.   Soc Sci Q. 2017;98(5):1352–1373. doi:10.1111/ssqu.12338Google ScholarCrossref
    21.
    Fiske  ST, Durante  F. Mutual status stereotypes maintain inequality. In: Jetten  J, Peters  K, eds.  The Social Psychology of Inequality. Springer; 2019:335–348. doi:10.1007/978-3-030-28856-3_21
    22.
    Kim  JG, Kong  HK, Karahalios  K, Fu  W-T, Hong  H. The power of collective endorsements: credibility factors in medical crowdfunding campaigns. In:  Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems. ACM; 2016:4538–4549. https://dl.acm.org/doi/10.1145/2858036.2858289
    23.
    van der Aa  MJ, Paulus  ATG, Hiligsmann  MJC, Maarse  JAM, Evers  SMAA.  Varying opinions on who deserves collectively financed health care services: a discrete choice experiment on allocation preferences of the general public.   Inquiry. 2018;55:46958017751981. doi:10.1177/0046958017751981PubMedGoogle Scholar
    24.
    Mapbox. Mapbox geocoding API. Accessed November 21, 2019. https://docs.mapbox.com/api/search/#geocoding
    25.
    Mapbox. Geocoder 1.38.1 documentation. Accessed March 30, 2020. https://geocoder.readthedocs.io/providers/Mapbox.html
    26.
    Silver  ER, Truong  HQ, Ostvar  S, Hur  C, Tatonetti  NP. Association of neighborhood deprivation index with success in cancer care crowdfunding. Github repository. Published 2020. Accessed November 5, 2020. https://github.com/silverer/cancer-crowdfunding-explorer
    27.
    GoFundMe.com. GoFundMe sitemap. Published 2019. Accessed November 4, 2020. https://www.gofundme.com/sitemap.xml
    28.
    Internet Archive. Wayback machine APIs. Published 2013. Accessed January 22, 2020. https://archive.org/help/wayback_api.php
    29.
    Federal Communications Commission. FCC area API. Published 2019. Accessed November 4, 2020. https://geo.fcc.gov/api/census/#!/block/get_block_find
    30.
    Kwak  SK, Kim  JH.  Statistical data preparation: management of missing values and outliers.   Korean J Anesthesiol. 2017;70(4):407-411. doi:10.4097/kjae.2017.70.4.407PubMedGoogle ScholarCrossref
    31.
    Butler  DC, Petterson  S, Phillips  RL, Bazemore  AW.  Measures of social deprivation that predict health care access and need within a rational area of primary care service delivery.   Health Serv Res. 2013;48(2 pt 1):539-559. doi:10.1111/j.1475-6773.2012.01449.xPubMedGoogle ScholarCrossref
    32.
    Major  JM, Doubeni  CA, Freedman  ND,  et al.  Neighborhood socioeconomic deprivation and mortality: NIH-AARP diet and health study.   PLoS One. 2010;5(11):e15538. doi:10.1371/journal.pone.0015538PubMedGoogle Scholar
    33.
    Messer  LC, Laraia  BA, Kaufman  JS,  et al.  The development of a standardized neighborhood deprivation index.   J Urban Health. 2006;83(6):1041-1062. doi:10.1007/s11524-006-9094-xPubMedGoogle ScholarCrossref
    34.
    US Census Bureau. American Community Survey data. Published 2018. Accessed November 4, 2020. https://www.census.gov/programs-surveys/acs/data.html
    35.
    Kaufman-Shriqui  V, O’Campo  P, Misir  V,  et al; Community Child Health Network (CCHN) of Eunice Kennedy Shriver National Institute of Child Health and Human Development.  Neighbourhood-level deprivation indices and postpartum women’s health: results from the Community Child Health Network (CCHN) multi-site study.   Health Qual Life Outcomes. 2020;18(1):38. doi:10.1186/s12955-020-1275-xPubMedGoogle ScholarCrossref
    36.
    MacDonald  PL, Gardner  RC.  Type I error rate comparisons of post hoc procedures for I j chi-square tables.   Educ Psychol Measure. 2020;60(5):735-754. doi:10.1177/00131640021970871Google ScholarCrossref
    37.
    Loeb  S, Taneja  S, Walter  D, Zweifach  S, Byrne  N.  Crowdfunding for prostate cancer and breast cancer.   BJU Int. 2018;122(5):723-725. doi:10.1111/bju.14408PubMedGoogle ScholarCrossref
    38.
    Benoit  K. Linear regression models with logarithmic transformations. Published 2011. Accessed November 4, 2020. https://kenbenoit.net/assets/courses/ME104/logmodels2.pdf
    39.
    Oronsky  BT, Carter  CA, Oronsky  AL, Salacz  ME, Reid  T.  “No patient left behind”: an alternative to “the war on cancer” metaphor.   Med Oncol. 2016;33(6):55. doi:10.1007/s12032-016-0769-1PubMedGoogle ScholarCrossref
    40.
    Hauser  DJ, Schwarz  N.  The war on prevention II: battle metaphors undermine cancer treatment and prevention and do not increase vigilance.   Health Commun. 2020;35(13):1698-1704. doi:10.1080/10410236.2019.1663465Google ScholarCrossref
    41.
    Bach  PB.  Limits on Medicare’s ability to control rising spending on cancer drugs.   N Engl J Med. 2009;360(6):626-633. doi:10.1056/NEJMhpr0807774PubMedGoogle ScholarCrossref
    42.
    Mariotto  AB, Yabroff  KR, Shao  Y, Feuer  EJ, Brown  ML.  Projections of the cost of cancer care in the United States: 2010-2020.   J Natl Cancer Inst. 2011;103(2):117-128. doi:10.1093/jnci/djq495PubMedGoogle ScholarCrossref
    43.
    Yabroff  KR, Lund  J, Kepka  D, Mariotto  A.  Economic burden of cancer in the United States: estimates, projections, and future research.   Cancer Epidemiol Biomarkers Prev. 2011;20(10):2006-2014. doi:10.1158/1055-9965.EPI-11-0650PubMedGoogle ScholarCrossref
    44.
    Young  MJ, Scheinberg  E.  The rise of crowdfunding for medical care: promises and perils.   JAMA. 2017;317(16):1623-1624. doi:10.1001/jama.2017.3078PubMedGoogle ScholarCrossref
    45.
    Wei  L.  Number matters: the multimodality of internet use as an indicator of the digital inequalities.   J Comput-Mediated Comm. 2012;17(3):303-318. doi:10.1111/j.1083-6101.2012.01578.xGoogle ScholarCrossref
    46.
    Zillien  N, Hargittai  E.  Digital distinction: status-specific types of internet usage.   Soc Sci Q. 2009;90(2):274-291. doi:10.1111/j.1540-6237.2009.00617.xGoogle ScholarCrossref
    47.
    Cohen  AJ, Brody  H, Patino  G,  et al.  Use of an online crowdfunding platform for unmet financial obligations in cancer care.   JAMA Intern Med. 2019;179(12):1717-1720. doi:10.1001/jamainternmed.2019.3330PubMedGoogle ScholarCrossref
    ×