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Evaluation of Cancer Awareness Months and Cancer Screening Tests Through Internet Search Big Data

By Alex Vanderwiel, Diamone Gathers, MD, V. Shane Pankratz, PhD, Mikaela Kosich, MPH, Bernard Tawfik, MD

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Citation

Goodman M. Potential economic burden contributing to treatment nonadherence in end-stage renal disease . HPHR. 2022;54.  

Potential Economic Burden Contributing to Treatment Non-Adherence in End Stage Renal Disease

Abstract

Background

Cancer Awareness Months (CAMs) aim to increase public recognition of cancers through education and screening promotion, but their effectiveness is uncertain. This study was designed to characterize the relationship between internet search interest (ISI) of breast, colon, lung, and prostate cancers and screening terms with their respective CAMs to determine CAM effectiveness.

Methods

ISI was collected from sampled Google Trends between 2004 and 2021 in June of 2021. The ISI for cancer and cancer screening terms for each respective CAM was compared to ISI for nonadjacent months and across regionality.

Results

Breast CAM (BCAM) was associated with increased ISI for cancer (p>0.001) and cancer screening (p>0.001) terms. Colon CAM (CCAM) was associated with higher cancer term ISI (p=0.004) but had mixed effects on screening ISI. Lung CAM (LCAM) was not associated with change in cancer term ISI (p=0.573) and had mixed effects across screening terms. Prostate CAM (PCAM) was not linked to ISI changes for any associated terms. Regional variation in ISI was found in all cases.

Conclusion

Given its high accessibility and scalability, Google Trends can be used to practically evaluate and guide the strategies of CAM campaigns. Future directions include broadening the geographic scope and comparatively assessing specific CAM campaign strategies.

Introduction

Cancer Burden

 Cancer represents the second leading cause of death in the United States and globally.1 In the United States in 2021, 1.9 million persons were diagnosed and 600,000 died from cancer.2 Direct cancer-related medical costs in the U.S. were estimated to total $183 billion, an amount expected to increase to $246 billion by 2030.2 Recommended screening practices may reduce breast, colon, lung, and prostate cancer mortality through early detection.3–5 Screening can generate $26 billion of cost-savings annually in the United States.6

Cancer Awareness Months

Since the federal recognition of October as National Breast Cancer Awareness Month (BCAM) in 1990,7 other months have been recognized as cancer awareness months (CAMs); National Colorectal CAM (CCAM) in March,8 National Prostate CAM (PCAM) in September,9 and National Lung CAM (LCAM) in November.10 These months are designated to increase screening for their cancers through events and public campaigns.11 BCAM has been found to increase screening and diagnosis rates.11,12 Previous research has found that BCAM is effective at generating internet searches for both “breast cancer” and “mammography” during October.12–14 However, effectiveness differed over geographic area.12 Limited studies available on PCAM in the United States found no increase in prostate cancer or screening ISI compared with other months.14–18 The literature is conflicted over the effectiveness of CCAM at generating internet search activity.14,15,17,18 LCAM was not associated with increased ISI, though this was not a consistent finding.15

The Health Belief Model and Cancer Awareness

The Health Belief Model posits that knowledge factors into one’s perceptions about the threat posed by disease and the benefits and barriers to prevention and/or treatment.20 These individual beliefs then interact with cues to action that result in health behavior. Within the context of this model, individual beliefs about the threat posed by cancer are represented by the level of cancer awareness. Cancer screening awareness can be representative of individual perception of the benefits and barriers to screening. CAMs can serve as the cue to action that facilitates the integration of individual beliefs into the decision whether to adopt a health behavior such as cancer screening. Consequently, both cancer risk awareness and cancer screening awareness are needed to result in screening uptake and early detection.

Cancer Applications of Internet Search Interest

Although BCAM has been linked to increased ISI and screening rates, the effectiveness of other CAMs is less known.11,12 Almost fifty percent of American adults report searching for cancer information, and of those, more than half reported using the internet first.21 ISI in cancer screening has been shown to correlate with cancer screening use, with the strength of the correlation dependent on the type of screening examined.22 ISI has been shown to positively correlate with diagnosis and mortality rates of certain cancers, such as lung and colon cancer.23 Analysis of ISI has proven useful in cancer surveillance for being able to capture data nimbly and at a scale not possible in traditional cancer surveillance programs.22 This study was designed to characterize the relationship between ISI of cancers and related screening test terms with their CAMs with the goal of better understanding CAM effectiveness and geographic variation. 

Methods

Term Determination

Cancer terms were selected based on lay terminology: “breast cancer,”  “prostate cancer,” “lung cancer,” and “colon cancer.” This grouping of queries will be referred to as “cancer terms,” serving as a gauge for cancer awareness. For cancer screening, “mammography” was chosen for breast cancer, “PSA” and “prostate specific antigen” for prostate cancer, “lung cancer screening” and “CAT scan” for lung cancer, and “colonoscopy,” “FIT,” and “FOT” for colon cancer. “FIT” is an acronym for “fecal immunochemical test” and “FOT” refers to fecal occult blood test. The term “cologuard” was not used due to having fewer searches. This latter grouping of queries will be referred to as “cancer screening terms,” which were used to evaluate cancer screening awareness. Terms were selected based on researcher conceptions of popular usage as well as Google Trends data sufficiency. Some terms had insufficient data, such as “low dose CT scans”, for evaluation. 

Data Collection

The two types of RRT are peritoneal dialysis (PD) and hemodialysis (HD). HD is a blood and dialysis solution circuit apparatus that filters the blood of patients, typically through the arm, for a few hours about thrice weekly. PD infuses a solution into the peritoneal cavity and collects waste from the blood by using the peritoneal membrane as an exchanger on an ongoing but daily regimen. Each form of RRT has varying costs and burdens to the patient. For example, PD is 22.7% cheaper than HD (or $75,140 compared to $88,750), and lifetime healthcare costs $204,442 for PD compared to $237,795 for HD (Gansevoort, et al., 2013; United States Renal Data System, 2021). Between 2015 and 2016, HD care increased by $2 billion to $26.8 billion, and HD spending per person per year increased by $2,000 to $88,750 (United States Renal Data System, 2018). However, while HD total spending increased by 5.7%, PD spending per person per year increased by 1.4% during the same period of time. This is because HD has more outpatient care, which makes up 66% of its total costs, whereas 51% of PD costs are from dialysate fluids (Eriksson, Neovius, Jacobson, Elinder, & Hylander, 2016). Additionally, HD has high fixed costs from the HD machines and staff, with HD machines costing about $24,000 on average with about a decade life cycle, as well as variable factors including the dialyzers, maintenance, and transportation. PD costs are largely due to variable costs such as the dialysate (Just, et al., 2008). However, PD is generally reserved for those patients who feel comfortable or are independent enough to manage their dialysis care at home and could be trained, as there is a risk of contamination and severe infection. The cost of training could be over $7,000, and training is limited by region (Eggers, 2011). If patients with ESRD were to receive home HD, it would be about 55% cheaper than in-center hemodialysis, though home HD has the same complications as PD, with the additional home dialysis equipment and storage costs and training of over $16,000.

 

These costs can be tremendous for the ESRD population, for who only 23% to 24% are employed in the United States, and only about 40% are informed of therapeutic alternatives to their current treatment (Van Biesen, van der Veer, Murphey, Loblova, & Davies, 2014; Hallab & Wish, 2018). It may mean many of ESRD costs are from poor patient-informed consent. The lack of true informed consent that patients with ESRD receive may be due to the way dialysis centers discuss PD, as few nephrologists are trained in PD and PD management (Kaplan, 2017). It appears that when patients with ESRD are informed of their choices, more than 50% choose home-based treatments; currently, only about 10% of patients are dialyzed at home (Oliver, et al., 2007; United States Renal Data System, 2021). Of note is that a Taiwanese cross-sectional survey found HD and PD to have similar quality-adjusted life expectancy (Chang, et al., 2016). Another study stated that in-center dialysis provides the highest societal cost and lowest quality of life, whereas kidney transplantation provides the lowest societal cost and highest quality of life (Vanholder, et al., 2017). Additionally, the ESRD Treatment Choices (ETC) Model, which encourages home dialysis and kidney transplantation, was found to have an incremental cost effectiveness ratio of $67,528 per quality-adjusted life year to the United States healthcare system when compared to in-center dialysis (Cha, Zimmerman, & Hansen, 2020). Because of the short-term costs, stress, side effects, and potential lack of informed consent, rates of nonadherence to dialysis have been reported as high as 98% in one study (Childers, Dwosky, Kominski, & Maggard-Gibbons, 2019; Griva, et al., 2014). This rate is both alarming and very concerning as dialysis nonadherence can be deadly. Future studies and immediate action should take place to understand and mitigate the aforementioned factors that could encourage dialysis nonadherence.

Statistical Analysis

The means and standard error of the means for each term were estimated according to the relevant CAM, the months preceding and proceeding that CAM (pre-CAM and post-CAM respectively), and the 9 other months of the year. Following this, an analysis of variance (ANOVA) was performed to test for the significance of the interaction of the national ISI data for each cancer type by year, CAM, search term, and the combination of CAM and search term. In the case of lung and prostate cancers in which major changes in USPSTF recommendations were made between 2004 and 2021, this analysis included examining the significance of the temporal interaction of the period before and after these changes on ISI. The significance of the combined results of this global ANOVA test was a precondition to further pairwise comparisons, thereby serving to prevent the occurrence of type 1 errors within the analysis.27 If the combination of CAM and search term data from the initial ANOVA was found to significantly interact with ISI, a pairwise difference t-test was performed for each term to determine whether CAMs, pre-CAMs, post-CAMs, and other months had significant interaction on ISI relative to each other. 

 

Next, the interaction of individual events on ISI was analyzed. The national cancer term data was sorted chronologically and peaks were identified through the use of Bollinger Bands, a stock market calculation used to compare current and past temporal trends in share price that has been used for healthcare monitoring.28 Months in which ISI was higher than the sum of the 12-month moving average and two 12 months standard deviations were identified as peaks. Geographic ISI data for cancer and cancer screening terms was then examined at a state and metro level. Global tests were used to test for significant differences across each level. Given the significance of these differences, states and metro areas were sorted according to ISI and pairwise difference t-tests were performed to sort these areas into 5 clustered groups with statistically different average values. Terms that were missing more than 25% of state or metro ISI were excluded from analysis at that scale. 

 

Two-sided tests at p=0.05 were used to determine significance when performing analyses of variance and pairwise differences tests.

 

Results

Figure 1. ISI for breast, colon, lung, and prostate cancer and screening terms by month from 2004-2021

Respective CAMs are shown in shaded lines and data peaks are marked by black dots, which correspond to the correlating events in Table 3. Significant CAM-associated cancer ISI increases were noted for breast and colon cancer, but not lung and prostate cancer. Significant CAM-associated screening ISI increases were noted for “mammography,” “colonoscopy,” and “lung cancer screening.”

Figure 2. Differences in Average Cancer and Cancer Screening Term ISI Respective To CAM

Bars with a positive value indicate that CAM ISI values were higher than those of non-CAMs. Dark bars indicate statistically significant differences. The significance of each difference is labeled with a corresponding P-value. Pairwise difference P-values were not calculated for prostate cancer terms.

Figure 3. American clustered distribution of cancer term ISI by state and metro region

Figure 4. Average Cancer and Cancer Screening Grouping by State

Trendline: y = 1.18214 × x + -0.045425, R2 = 0.0503392, p > 0.0001

Table 1. Effect of Year, Search Term, and Cancer Awareness Month on Cancer ISI.

 

Cancer and Cancer Screening Terms

Year

Search Term

CAM

Search Term*CAM

Breast

0.0056

0.0078

0.0000

0.0000

Colon

0.0000

0.0000

0.7111

0.0002

Lung

0.0594

0.0000

0.0031

0.0016

Prostate

0.0000

0.0000

0.2534

0.9475

Significant P values are shown in bold black, and nonsignificant P values are shown in gray.

Table 2. Cancer Term ISI Peaks and Event Correlations.

Breast Cancer

Colon Cancer

Lung Cancer

Prostate Cancer

Peak Date

Event(s)

Peak Date

Event(s)

Peak Date

Event(s)

Peak Date

Event(s)

09/2006

No known event

03/2010

 

 CCAM

04/2005

News anchor Peter Jennings’ lung cancer diagnosis revealed32

03/2009

Radio host Don Imus’ and actor David Prowse’s prostate cancer diagnoses revealed38,93

10/2006



















BCAM

03/2014

10/2007

03/2016

Administrator Elizabeth Garrett dies of colon cancer29

10/2011

USPSTF PSA testing recommendations change94

10/2008

08/2005

News anchor Peter Jennings dies of lung cancer33

 

Actor Dana Reeve’s lung cancer diagnosis revealed34

10/2009











CCAM

07/2013



Fish oil linked to prostate cancer40

 

Talk show host Michael Parkinson’s prostate cancer diagnosis revealed41

 

Musician Nile Rodgers’ prostate cancer-free status revealed42

10/2010

03/2017

10/2011

02/2018

No known event

10/2012

02/2020

Radio host Rush Limbaugh’s lung cancer diagnosis revealed35 

 

Actor Shannen Doherty lung cancer recurrence revealed35

10/2013

 

03/2018

10/2014

11/2014

Prostate cancer drug manufacturer Dendreon files for bankruptcy43

10/2015

10/2016

02/2021

Radio host Rush Limbaugh and actor Dustin Diamond die of lung cancer36

 

Politician Bob Dole’s lung cancer diagnosis revealed37

01/2018

Musician Hugh Masekela dies of prostate cancer95

 

Politician Mitt Romney’s prostate cancer treatment revealed44

10/2017

03/2019

10/2018

Former football player Mark Gastineau reveals colon cancer diagnosis30

10/2019

10/2019

Rod Stewart’s diagnosis, treatment, and prostate cancer-free status revealed45

 

Chris Matthews’ prostate cancer treatment revealed46

10/2020

08/2020

Actor Chadwick Boseman dies of colon cancer31

Data peaks most strongly correlated with CAM in breast cancer (15/16 peaks) and colon cancer (6/7 peaks) and least strongly in lung cancer (0/5 peaks) and prostate cancer (0/6 peaks). Data peaks correspond to the black dots in Figure 1a.

Summary

Google Trends data from between 2004 and 2021 (Fig. 1) demonstrate that ISI for related cancer terms is significantly higher during BCAMs (43.943; p<0.001) and CCAMs (4.126; p=0.004) (Fig. 2). No significantly increased ISI was found for “lung cancer” during LCAM (p=0.573). ISI for related cancer screening terms was found to be significantly related to the occurrence of BCAMs, CCAMs, and LCAMs. Concerning cancer screening terms, this interaction denoted significantly increased ISI during CAMs in the case of “mammogram” (13.827; p<0.001), “colonoscopy” (3.606; p=0.012), and “lung cancer screening” (8.554; p=0.009). However, “FIT” (-2.801; p=0.049) and “FOT” (-2.909; p=0.041) experienced significantly decreased ISI during CCAM. 

Prostate cancer terms were excluded from pairwise difference analysis as the global ANOVA test, performed to prevent type I errors, found no significant interaction of PCAM and related search terms (Table 1). Additionally, differences in search term impacted ISI across all cancer types. Year affected the ISI of terms relating to every cancer type but lung cancer. BCAM and LCAM were found to affect the ISI of related terms, but this was not the case for CCAM and PCAM. 

Peaks in breast and colon cancer term ISI correlated most often with their associated CAM, and peaks in lung and prostate cancer term ISI correlated most often with news of celebrity diagnoses, treatments, and deaths for lung and prostate cancer (Table 2).29–46 Significant interaction was found between August and September 2020, the month of Chadwick Boseman’s death and the following month respectively, and ISI for colon cancer terms (p<0.001). No other peak-based interaction analysis was performed as this was an unexpected finding and would be extrapolative. The USPSTF recommendation changes for prostate cancer in 2012 (p=0.451) and lung cancer in 2013 (p=0.902) did not significantly change ISI for either collection of cancer and cancer screening terms.  

Significant differences were found in all terms with sufficient data across state and metro areas (Fig. 3). Five groups of state and metro areas were created for each search term, with significant differences in ISI present across but not within each group. The best states for cancer and cancer screening awareness generally, as reflected by average grouping, were Connecticut, West Virginia, and Pennsylvania (Fig. 4). Bluefield-Beckley-Oak Hill WV, Tri-Cities TN-VA, Charleston-Huntington WV, and Rochester MN-Mason City IA-Austin MN were the best metro areas.

Discussion

Key Results

BCAM was the only CAM of those studied found to be effective in its goal to increase both cancer awareness and cancer screening awareness across all examined measures, thereby satisfying each examined individual belief component of the Health Behavior Model. The increased “colon cancer” and “colonoscopy” ISI during CCAM indicates that some measures of awareness are effectively promoted. However, the significant decrease in ISI for “FIT” and “FOT” could mean that this increased awareness is being negated by unintended, counterproductive effects of CCAM. Similarly, higher searches for “lung cancer screening” during LCAM indicate that the month is partially successful in promoting screening uptake, but the unchanged interest for “lung cancer” itself and “CAT scan” screening leaves substantial room for improvement. As PCAM was not associated with changes in ISI generally, much needs to change for this month to attain its potential awareness benefit. The correlation of prostate and lung ISI with news of celebrity cancer diagnoses, treatments, and deaths may indicate a new avenue of social news for cancer awareness groups to focus screening efforts. Additionally, the relative contributions of cancer awareness campaign strategies, cancer incidence, and other geographic contingencies to ISI trends across states and metro areas serve as directions for future research.

Strengths of Study Design

ISI data provides instant scalability and comparability regarding geographic data and timespan, allowing for collection and analysis of consistently procured data across subregional and metro areas. The speed of collection of ISI data preserves its relevance post-publication by reflecting the impact of recent changes in screening types, screening recommendations, and CAM awareness campaigns within our findings. The high accessibility of Google Trends data aids replication and comparison of findings across the literature. 

Clinical Implications

The use of Google Trends ISI data to estimate cancer and screening awareness has several advantages over traditional methods of cancer surveillance such as the Behavioral Risk Factor Surveillance System and the National Health Interview Survey.22 These methods tend to be slower, costlier, and rely on self-reporting.22 Notably, traditional methods often present data at an interval too infrequent and with too much lag time to evaluate CAM effectiveness.47,48 This study design seeks to overcome these limitations through publicly-available ISI data, utilizing a cancer screening surveillance method with the advantage of speed and granularity of time. By not relying on self-reported data, ISI reduces the risk that temporal misconceptions and socially pressured responses will factor into results.22,49 This method shows that Google Trends can be used in real-time with no cost to evaluate increased interest related to cancer-related programs at the national, state and metro level. 

 

 

The strength of BCAM at generating ISI for both cancer and cancer screening may be explained by the fact that breast cancer is the most commonly diagnosed cancer and has the largest survivor population.2,50 Breast cancer’s lower median age at diagnosis may contribute, since younger adults are more likely to use the internet for health information seeking.51,52 Similarly, this data may reflect breast cancer’s higher incidence in women,2 who are more likely to search for health information than men.53  Inversely, this may  account for the lack of CAM-associated ISI for prostate cancer and screening.2,50,54,55

 

In the case of CCAM, increased ISI in “colon cancer” but not consistently for colon cancer screening terms may reflect patient perceptions of higher screening costs and associations of screening with embarrassment, disgust, and violations of privacy.19,56–65 As ISI for “colonoscopy” was significantly higher than for “FIT” and “FOT”, CCAM may be effective at increasing interest for colonoscopy screenings at the expense of other colon cancer screening types. This may be because colon cancer awareness campaigns have often made colonoscopies the single focus of screening promotion.66–68 This leaves out mention of FIT and FOT, underrecognized forms of colon cancer screening.69,70

 

Lung cancer’s CAM-independent cancer term ISI may be related to social determinants of health, including income and education disparities.71 Although the incidence of lung cancer is higher in those of lower socioeconomic status, it has been shown that some of these individuals are less likely to use online resources for health information seeking due to complexity and lack of interest.54,72–74 Stigmatizing biases that frame lung cancer prevention as a matter of personal responsibility could explain the reduced ISI in lung cancer found in our study.75–78 If cultural stigma is indeed inhibiting lung cancer awareness, the success of BCAM indicates potential for change.79,80 Before feminist and charitable organizations led to mainstream American cultural destigmatization of breast cancer in the early 1990s, traditional American patriarchal norms labeled the disease a fault of women’s behaviours and physiology in addition to associating it with taboo topics such as sexuality and death.79–81 If American breast cancer organizations have managed to raise awareness by deconstructing stigmatizing social norms, overcoming other cancer stigmas could lead to similar benefits for the causes of colon, lung, and prostate cancer awareness.

 

PCAM’s apparent lack of impact on associated ISI could be related to the fact that, unlike breast, colon and lung cancers, the USPSTF does not clearly recommend prostate cancer screening.39,70,82,83 The lower ISI could  be the result of inadequate knowledge of the disease,84 as well as patient privacy concerns.85 

 

Limitations

Our research methods have several limitations. ISI for various cancers and their screenings does not serve as a comprehensive indicator of screening use, though ISI in cancer screening has been shown to correlate with cancer screening use.22 The decision to rely on ISI data instead of screening rates was based on data accessibility and the delay between patients contacting their health care systems and the actual cancer screening test. As younger adult populations of higher socioeconomic status are most likely to search for health information online, these data may underestimate the cancer awareness of other populations.25,86 Additionally, while ISI screening terms were selected by researchers based in part on conceptions of common usage, selected terms may not represent the queries most often searched to refer to each cancer and cancer screening type.87 The presence of screening terms with multiple meanings, such as “FIT” and “PSA,” may  obscure data trends since searches are not amenable to acronym stratification.88 It should be noted that Google is not the only search engine or online platform with cancer-related information on the internet and thus may not be representative of ISI generally.89

 

Google Trends’ data presents limitations of data transparency, as ISI sample sizes are not publicly available, making the inclusion of sampling error a possibility.26,90 Stephens-Davidowitz and Varian,91 in a guide for social science research published by Google, state that they do not expect that researchers will need more than a single sample. However they offer no corroborating data and the accuracy of this claim has been challenged in other literature.26,91,92 However, the potential of sampling error is reduced given the inclusion of 17 CAM ISI samples in national data and the longer timespan observed. Data comparison is limited because ISI data for a given term is presented as a sampled index of relative search volume over time or region, preventing simultaneous comparison of ISI across all terms or across time and region.22,24

 

Conclusion

BCAM and CCAMs were significantly associated with increased cancer ISI during their respective months as compared with ISI during nonadjacent months of the year, and there was no change in prostate and lung cancer ISI during their CAMs. Searches for “mammogram,” “colonoscopy,” and “lung cancer screening” were associated with significant increase in ISI during respective CAMs, while ISI for “FIT” and “FOT” were negatively associated with CCAM. In each CAM examined, significant geographical variability was noted. This indicates potential for intra- and inter-comparison of cancer awareness campaign strategies across regions and cancer types to improve screening rates locally and generally, thereby improving rates of early detection and reducing cancer burden. Future directions include international CAM evaluation and comparative evaluation of various CAM campaign strategies to increase screening rates.

Acknowledgements

This research was partially supported by UNM Comprehensive Cancer Center Support Grant NCI P30CA118100 and the Biostatistics shared resource.

References

  1. Ritchie H, Roser M. Causes of Death. Our World Data. Published online February 14, 2018. Accessed June 14, 2021. https://ourworldindata.org/causes-of-death
  2. American Cancer Society. Cancer Facts & Figures 2021. Am Cancer Soc. Published online 2021:72.
  3. Smith RA, Andrews KS, Brooks D, et al. Cancer screening in the United States, 2019: A review of current American Cancer Society guidelines and current issues in cancer screening. CA Cancer J Clin. 2019;69(3):184-210. doi:https://doi.org/10.3322/caac.21557
  4. Etzioni R, Urban N, Ramsey S, et al. The case for early detection. Nat Rev Cancer. 2003;3(4):243-252. doi:10.1038/nrc1041
  5. Cookson MS. Prostate Cancer: Screening and Early Detection. Cancer Control. 2001;8(2):133-140. doi:10.1177/107327480100800203
  6. Kakushadze Z, Raghubanshi R, Yu W. Estimating Cost Savings from Early Cancer Diagnosis. Data. 2017;2(3):30. doi:10.3390/data2030030
  7. Pell C. S.J.Res.301 – 101st Congress (1989-1990): A joint resolution designating October 1990 as “National Breast Cancer Awareness Month”. Published October 11, 1990. Accessed June 12, 2021. https://www.congress.gov/bill/101st-congress/senate-joint-resolution/301
  8. Breaux JB. S.Res.108 – 106th Congress (1999-2000): A resolution designating the month of March 2000, as National Colorectal Cancer Awareness Month. Published November 19, 1999. Accessed June 18, 2021. https://www.congress.gov/bill/106th-congress/senate-resolution/108
  9. Burns CR. S.Res.138 – 107th Congress (2001-2002): A resolution designating the month of September 2001 as “National Prostate Cancer Awareness Month”. Published August 3, 2001. Accessed June 12, 2021. https://www.congress.gov/bill/107th-congress/senate-resolution/138
  10. Chambliss S. S.Res.620 – 109th Congress (2005-2006): A resolution designating November 2006 as “National Lung Cancer Awareness Month”. Published November 16, 2006. Accessed June 18, 2021. https://www.congress.gov/bill/109th-congress/senate-resolution/620
  11. Jacobsen GD, Jacobsen KH. Health awareness campaigns and diagnosis rates: Evidence from National Breast Cancer Awareness Month. J Health Econ. 2011;30(1):55-61. doi:10.1016/j.jhealeco.2010.11.005
  12. Gathers D, Pankratz VS, Kosich M, Tawfik B. Using big data to gauge effectiveness of breast cancer awareness month. Prev Med. 2021;150:106695. doi:10.1016/j.ypmed.2021.106695
  13. Glynn RW, Kelly JC, Coffey N, Sweeney KJ, Kerin MJ. The effect of breast cancer awareness month on internet search activity – a comparison with awareness campaigns for lung and prostate cancer. BMC Cancer. 2011;11(1):442. doi:10.1186/1471-2407-11-442
  14. Rosenkrantz AB, Prabhu V. Public Interest in Imaging-Based Cancer Screening Examinations in the United States: Analysis Using a Web-Based Search Tool. Am J Roentgenol. 2016;206(1):113-118. doi:10.2214/AJR.15.14840
  15. Vernon E, Gottesman Z, Warren R. The value of health awareness days, weeks and months: A systematic review. Soc Sci Med. 2021;268:113553. doi:10.1016/j.socscimed.2020.113553
  16. Patel MS, Halpern JA, Desai AS, Keeter MK, Bennett NE, Brannigan RE. Success of Prostate and Testicular Cancer Awareness Campaigns Compared to Breast Cancer Awareness Month According to Internet Search Volumes: A Google Trends Analysis. Urology. 2020;139:64-70. doi:10.1016/j.urology.2019.11.062
  17. Phillips CA, Barz Leahy A, Li Y, Schapira MM, Bailey LC, Merchant RM. Relationship Between State-Level Google Online Search Volume and Cancer Incidence in the United States: Retrospective Study. J Med Internet Res. 2018;20(1). doi:10.2196/jmir.8870
  18. Cooper CP, Mallon KP, Leadbetter S, Pollack LA, Peipins LA. Cancer Internet Search Activity on a Major Search Engine, United States 2001-2003. J Med Internet Res. 2005;7(3):e413. doi:10.2196/jmir.7.3.e36
  19. Pantel HJ, Kleiman DA, Kuhnen AH, Marcello PW, Stafford C, Ricciardi R. Has National Colorectal Cancer Awareness Month increased endoscopy screening rates and public interest in colorectal cancer? Surg Endosc. 2021;35(1):398-405. doi:10.1007/s00464-020-07413-x
  20. Glanz K, Rimer BK, Viswanath K. The Health Belief Model. In: Health Behavior and Health Education. 4th ed. Jossey-Bass; 2008:45-62.
  21. Health Information National Trends Survey. Trends in Cancer Information Seeking.
  22. Schootman M, Toor A, Cavazos-Rehg P, et al. The utility of Google Trends data to examine interest in cancer screening. BMJ Open. 2015;5(6):e006678. doi:10.1136/bmjopen-2014-006678
  23. Wehner MR, Nead KT, Linos E. Correlation Among Cancer Incidence and Mortality Rates and Internet Searches in the United States. JAMA Dermatol. 2017;153(9):911-914. doi:10.1001/jamadermatol.2017.1870
  24. Google. FAQ about Google Trends data – Trends Help. Google Trends Help. Published 2021. Accessed June 11, 2021. https://support.google.com/trends/answer/4365533?hl=en
  25. Arora VS, McKee M, Stuckler D. Google Trends: Opportunities and limitations in health and health policy research. Health Policy. 2019;123(3):338-341. doi:10.1016/j.healthpol.2019.01.001
  26. Steegmans J. The Pearls and Perils of Google Trends A Housing Market Application. Published online August 2019.
  27. Westfall PH, Tobias RD, Wolfinger RD. Multiple Comparisons and Multiple Tests Using SAS. SAS Institute; 2011.
  28. Pagel C, Ramnarayan P, Ray S, Peters MJ. A Novel Method to Identify the Start and End of the Winter Surge in Demand for Pediatric Intensive Care in Real Time*. Pediatr Crit Care Med. 2015;16(9):821-827. doi:10.1097/PCC.0000000000000540
  29. Wilensky J. President Elizabeth Garrett dies of colon cancer at age 52. Cornell Chronicle. Published March 7, 2016. Accessed August 11, 2021. https://news.cornell.edu/stories/2016/03/president-elizabeth-garrett-dies-colon-cancer-age-52
  30. ABC, Inc., WPVI-TV Philadelphia. Former NFL star Mark Gastineau shares battle with colon cancer – 6abc Philadelphia. 6 ABC. Published March 19, 2019. Accessed August 11, 2021. https://6abc.com/nfl-player-colon-cancer-healthcheck-mark-gastineau/5206157/
  31. Rao S. ‘Black Panther’ star Chadwick Boseman dies at 43 after battling colon cancer – The Washington Post. Washington Post. Published August 29, 2020. Accessed August 11, 2021. https://www.washingtonpost.com/arts-entertainment/2020/08/28/chadwick-boseman-dies-after-cancer-battle/
  32. CNN. CNN.com – Peter Jennings has lung cancer – Apr 6, 2005. CNN.com. Published April 7, 2005. Accessed August 11, 2021. http://www.cnn.com/2005/SHOWBIZ/TV/04/05/jennings.cancer/
  33. CNN. CNN.com – Peter Jennings dies of lung cancer – Aug 8, 2005. CNN.com. Published August 8, 2005. Accessed August 11, 2021. https://www.cnn.com/2005/SHOWBIZ/TV/08/07/jennings.obit/
  34. BBC News. The trials of actress Dana Reeve. BBC News. http://news.bbc.co.uk/2/hi/entertainment/4155550.stm. Published August 16, 2005. Accessed August 11, 2021.
  35. Miller RW. Rush Limbaugh and Shannen Doherty have advanced cancer. What does that mean? What is stage IV? USA TODAY. Published February 5, 2020. Accessed August 11, 2021. https://www.usatoday.com/story/news/health/2020/02/05/rush-limbaugh-shannen-doherty-cancer-stage-4-cancer-always-fatal/4665043002/
  36. Puente M. Rush Limbaugh, conservative radio titan, dies of lung cancer at age 70. USA TODAY. Published February 17, 2021. Accessed August 11, 2021. https://www.usatoday.com/story/entertainment/celebrities/2021/02/17/rush-limbaugh-conservative-radio-host-has-died-lung-cancer-70/5998621002/
  37. Stoddart M. Former Sen. Bob Dole announces he has stage 4 lung cancer. ABC News. Published February 18, 2021. Accessed August 11, 2021. https://abcnews.go.com/Politics/sen-bob-dole-announces-stage-lung-cancer/story?id=75971520
  38. CNN. Don Imus battles prostate cancer – CNN.com. CNN.com. Published March 16, 2009. Accessed August 11, 2021. http://www.cnn.com/2009/SHOWBIZ/03/16/imus.prostate.cancer/
  39. US Preventive Services Task Force, Grossman DC, Curry SJ, et al. Screening for Prostate Cancer: US Preventive Services Task Force Recommendation Statement. JAMA. 2018;319(18):1901. doi:10.1001/jama.2018.3710
  40. Reinberg S. Too Much Fish Oil Might Boost Prostate Cancer Risk, Study Says – WebMD. WebMD. Published July 10, 2013. Accessed August 11, 2021. https://www.webmd.com/prostate-cancer/news/20130710/too-much-fish-oil-might-boost-prostate-cancer-risk-study-says
  41. Angela. Legendary Michael “Parky” Parkinson Diagnosed With Prostate Cancer. Pedestrian. Published July 8, 2013. Accessed August 11, 2021. https://www.pedestrian.tv/entertainment/legendary-michael-parky-parkinson-diagnosed-with-prostate-cancer/
  42. Blake J. Nile Rodgers given the all-clear from prostate cancer. BBC News. https://www.bbc.com/news/newsbeat-23501402. Published July 30, 2013. Accessed August 11, 2021.
  43. Pollack A. Dendreon, Maker of Prostate Cancer Drug Provenge, Files for Bankruptcy. DealBook. Published November 10, 2014. Accessed August 10, 2021. https://dealbook.nytimes.com/2014/11/10/dendreon-maker-of-prostate-cancer-drug-provenge-files-for-bankruptcy/
  44. Edelman A, Haake G, Hunt K. Mitt Romney was treated for prostate cancer last summer. NBC News. Published January 8, 2018. Accessed August 10, 2021. https://www.nbcnews.com/politics/politics-news/mitt-romney-was-treated-prostate-cancer-last-summer-n835846
  45. BANG! Showbiz. Sir Rod Stewart had intensive cancer treatment, his wife Lady Penny Lancaster-Stewart reveals. NZ Herald. Published October 10, 2019. Accessed August 11, 2021. https://www.nzherald.co.nz/entertainment/sir-rod-stewart-had-intensive-cancer-treatment-his-wife-lady-penny-lancaster-stewart-reveals/FKRI7YZ6U7P34JLJNTUPKJWYRI/
  46. McCullough M. MSNBC host Chris Matthews had prostate cancer surgery. What comes next? https://www.inquirer.com. Published October 15, 2019. Accessed August 10, 2021. https://www.inquirer.com/health/chris-matthews-msnbc-prostate-cancer-surgery-20191015.html
  47. CDC. CDC – BRFSS – BRFSS Frequently Asked Questions (FAQs). Behavioral Risk Factor Surveillance System. Published January 2, 2018. Accessed November 1, 2021. https://www.cdc.gov/brfss/about/brfss_faq.htm
  48. CDC. NHIS – About the National Health Interview Survey. National Center for Health Statistics. Published September 16, 2020. Accessed November 1, 2021. https://www.cdc.gov/nchs/nhis/about_nhis.htm
  49. Cronin KA, Miglioretti DL, Krapcho M, et al. Bias Associated With Self-Report of Prior Screening Mammography. Cancer Epidemiol Biomarkers Prev. 2009;18(6):1699-1705. doi:10.1158/1055-9965.EPI-09-0020
  50. American Cancer Society. Cancer Treatment & Survivorship Facts & Figures 2019-2021. Published online 2019.
  51. Karami S, Young HA, Henson DE. Earlier age at diagnosis: Another dimension in cancer disparity? Cancer Detect Prev. 2007;31(1):29-34. doi:10.1016/j.cdp.2006.11.004
  52. Wong C, Britt H, Henderson J, Harrison C. Patient use of the internet for health information. Aust Fam Physician. 2014;43(12):875-877.
  53. Manierre MJ. Gaps in knowledge: Tracking and explaining gender differences in health information seeking. Soc Sci Med. 2015;128:151-158. doi:10.1016/j.socscimed.2015.01.028
  54. Mayer DK, Terrin NC, Kreps GL, et al. Cancer survivors information seeking behaviors: A comparison of survivors who do and do not seek information about cancer. Patient Educ Couns. 2007;65(3):342-350. doi:10.1016/j.pec.2006.08.015
  55. Xiao N, Sharman R, Rao HR, Upadhyaya S. Factors influencing online health information search: An empirical analysis of a national cancer-related survey. Decis Support Syst. 2014;57:417-427. doi:10.1016/j.dss.2012.10.047
  56. Perisetti A, Khan H, George NE, et al. Colorectal cancer screening use among insured adults: Is out-of-pocket cost a barrier to routine screening? World J Gastrointest Pharmacol Ther. 2018;9(4):31-38. doi:10.4292/wjgpt.v9.i4.31
  57. Gonzalez P, Castaneda SF, Mills PJ, Talavera GA, Elder JP, Gallo LC. Determinants of Breast, Cervical and Colorectal Cancer Screening Adherence in Mexican–American Women. J Community Health. 2012;37(2):421-433. doi:10.1007/s10900-011-9459-2
  58. Jones SMW, Schuler TA, Padamsee TJ, Andersen MR. Financial Anxiety is Associated With Cancer Screening Adherence in Women at High Risk of Breast Cancer. Ann Behav Med. 2021;(kaab010). doi:10.1093/abm/kaab010
  59. Vernon SW. Participation in Colorectal Cancer Screening: A Review. JNCI J Natl Cancer Inst. 1997;89(19):1406-1422. doi:10.1093/jnci/89.19.1406
  60. Brandt HM, Dolinger HR, Sharpe PA, Hardin JW, Berger FG. Relationship of colorectal cancer awareness and knowledge with colorectal cancer screening. Colorectal Cancer. 2012;1(5):383-396. doi:10.2217/crc.12.45
  61. Consedine NS, Ladwig I, Reddig MK, Broadbent EA. The many faeces of colorectal cancer screening embarrassment: Preliminary psychometric development and links to screening outcome. Br J Health Psychol. 2011;16(3):559-579. doi:https://doi.org/10.1348/135910710X530942
  62. Gimeno Garcia AZ, Hernandez Alvarez Buylla N, Nicolas-Perez D, Quintero E. Public Awareness of Colorectal Cancer Screening: Knowledge, Attitudes, and Interventions for Increasing Screening Uptake. ISRN Oncol. 2014;2014:1-19. doi:10.1155/2014/425787
  63. Reynolds LM, Bissett IP, Consedine NS. Emotional predictors of bowel screening: the avoidance-promoting role of fear, embarrassment, and disgust. BMC Cancer. 2018;18(1):518. doi:10.1186/s12885-018-4423-5
  64. Reynolds LM, Consedine NS, Pizarro DA, Bissett IP. Disgust and Behavioral Avoidance in Colorectal Cancer Screening and Treatment: A Systematic Review and Research Agenda. Cancer Nurs. 2013;36(2):122-130. doi:10.1097/NCC.0b013e31826a4b1b
  65. Kiviniemi MT, Jandorf L, Erwin DO. Disgusted, Embarrassed, Annoyed: Affective Associations Relate to Uptake of Colonoscopy Screening. Ann Behav Med. 2014;48(1):112-119. doi:10.1007/s12160-013-9580-9
  66. Cram P, Fendrick AM, Inadomi J, Cowen ME, Carpenter D, Vijan S. The Impact of a Celebrity Promotional Campaign on the Use of Colon Cancer Screening: The Katie Couric Effect. Arch Intern Med. 2003;163(13):1601. doi:10.1001/archinte.163.13.1601
  67. Itzkowitz SH, Winawer SJ, Krauskopf M, et al. New York Citywide Colon Cancer Control Coalition: A public health effort to increase colon cancer screening and address health disparities. Cancer. 2016;122(2):269-277. doi:10.1002/cncr.29595
  68. Dillard AJ, Main JL. Using a Health Message With a Testimonial to Motivate Colon Cancer Screening: Associations With Perceived Identification and Vividness. Health Educ Behav. 2013;40(6):673-682. doi:10.1177/1090198112473111
  69. Carnahan LR, Jones L, Brewer KC, et al. Race and Gender Differences in Awareness of Colorectal Cancer Screening Tests and Guidelines Among Recently Diagnosed Colon Cancer Patients in an Urban Setting. J Cancer Educ. 2021;36(3):567-575. doi:10.1007/s13187-019-01666-4
  70. U.S. Preventive Services Task Force. Screening for Colorectal Cancer: U.S. Preventive Services Task Force Recommendation Statement. Ann Intern Med. 2021;149(9):627. doi:10.7326/0003-4819-149-9-200811040-00243
  71. Clegg LX, Reichman ME, Miller BA, et al. Impact of socioeconomic status on cancer incidence and stage at diagnosis: selected findings from the surveillance, epidemiology, and end results: National Longitudinal Mortality Study. Cancer Causes Control. 2009;20(4):417-435. doi:10.1007/s10552-008-9256-0
  72. Lee C joo, Ramírez AS, Lewis N, Gray SW, Hornik RC. Looking Beyond the Internet: Examining Socioeconomic Inequalities in Cancer Information Seeking Among Cancer Patients. Health Commun. 2012;27(8):806-817. doi:10.1080/10410236.2011.647621
  73. Sidorchuk A, Agardh EE, Aremu O, Hallqvist J, Allebeck P, Moradi T. Socioeconomic differences in lung cancer incidence: a systematic review and meta-analysis. Cancer Causes Control. 2009;20(4):459. doi:10.1007/s10552-009-9300-8
  74. Perez SL, Kravitz RL, Bell RA, Chan MS, Paterniti DA. Characterizing internet health information seeking strategies by socioeconomic status: a mixed methods approach. BMC Med Inform Decis Mak. 2016;16(1):107. doi:10.1186/s12911-016-0344-x
  75. Cataldo JK, Jahan TM, Pongquan VL. Lung cancer stigma, depression, and quality of life among ever and never smokers. Eur J Oncol Nurs. 2012;16(3):264-269. doi:10.1016/j.ejon.2011.06.008
  76. Chapple A, Ziebland S, McPherson A. Stigma, shame, and blame experienced by patients with lung cancer: qualitative study. BMJ. 2004;328(7454):1470. doi:10.1136/bmj.38111.639734.7C
  77. Dirkse D, Lamont L, Li Y, Simonič A, Bebb G, Giese–Davis J. Shame, guilt, and communication in lung cancer patients and their partners. Curr Oncol. 2014;21(5):e718-e722. doi:10.3747/co.21.2034
  78. LoConte NK, Else-Quest NM, Eickhoff J, Hyde J, Schiller JH. Assessment of guilt and shame in patients with non-small-cell lung cancer compared with patients with breast and prostate cancer. Clin Lung Cancer. 2008;9(3):171-178. doi:10.3816/CLC.2008.n.026
  79. Brinker N, Rodgers J. Promise Me – How a Sisters Love Launched the Global Movement to End Breast Cancer. Crown Archetype; 2010.
  80. Sulik GA. The Development of Pink Ribbon Culture. In: Pink Ribbon Blues: How Breast Cancer Culture Undermines Women’s Health. Oxford University Press; 2010:27-56.
  81. King S. Pink Ribbons Inc: breast cancer activism and the politics of philanthropy. Int J Qual Stud Educ. 2004;17(4):473-492. doi:10.1080/09518390410001709553
  82. US Preventive Services Task Force, Krist AH, Davidson KW, et al. Screening for Lung Cancer: US Preventive Services Task Force Recommendation Statement. JAMA. 2021;325(10):962. doi:10.1001/jama.2021.1117
  83. Siu AL. Screening for Breast Cancer: U.S. Preventive Services Task Force Recommendation Statement. Ann Intern Med. 2016;164(4):279. doi:10.7326/M15-2886
  84. Necku JG, Anaba EA, Abuosi AA. Prostate cancer awareness and attitude toward early detection among male soldiers in Ghana: a cross-sectional study. Afr J Urol. 2019;25(1):5. doi:10.1186/s12301-019-0004-3
  85. James LJ, Wong G, Craig JC, et al. Men’s perspectives of prostate cancer screening: A systematic review of qualitative studies. PLOS ONE. 2017;12(11):e0188258. doi:10.1371/journal.pone.0188258
  86. Koch-Weser S, Bradshaw YS, Gualtieri L, Gallagher SS. The Internet as a Health Information Source: Findings from the 2007 Health Information National Trends Survey and Implications for Health Communication. J Health Commun. 2010;15(sup3):279-293. doi:10.1080/10810730.2010.522700
  87. Nuti SV, Wayda B, Ranasinghe I, et al. The Use of Google Trends in Health Care Research: A Systematic Review. PLOS ONE. 2014;9(10):e109583. doi:10.1371/journal.pone.0109583
  88. Mavragani A, Ochoa G. Google Trends in Infodemiology and Infoveillance: Methodology Framework. JMIR Public Health Surveill. 2019;5(2):e13439. doi:10.2196/13439
  89. Ripberger JT. Capturing Curiosity: Using Internet Search Trends to Measure Public Attentiveness. Policy Stud J. 2011;39(2):239-259. doi:10.1111/j.1541-0072.2011.00406.x
  90. Behnen P, Kessler R, Kruse F, Gómez JM, Schoenmakers J, Zerr S. Experimental Evaluation of Scale, and Patterns of Systematic Inconsistencies in Google Trends Data. In: Koprinska I, Kamp M, Appice A, et al., eds. ECML PKDD 2020 Workshops. Communications in Computer and Information Science. Springer International Publishing; 2020:374-384. doi:10.1007/978-3-030-65965-3_25
  91. Stephens-Davidowitz S, Varian H. A Hands-on Guide to Google Data. Published online March 7, 2015.
  92. Behnen P, Kessler R, Kruse F, Schoenmakers J, Zerr S, Gómez J. White Paper: Evidence, Scale, and Patterns of Systematic Inconsistencies in Google Trends Data.; 2020. doi:10.13140/RG.2.2.26974.66880
  93. CNN. Darth Vader actor battles prostate cancer – CNN.com. CNN.com. Published March 18, 2009. Accessed August 11, 2021. https://www.cnn.com/2009/SHOWBIZ/Movies/03/18/darth.vader.cancer/index.html
  94. Harris G. U.S. Panel Says No to Prostate Screening for Healthy Men. The New York Times. https://www.nytimes.com/2011/10/07/health/07prostate.html. Published October 6, 2011. Accessed August 11, 2021.
  95. Chutel L. Hugh Masekela, South Africa’s legendary trumpeter, has died. Quartz. Published January 23, 2018. Accessed August 10, 2021. https://qz.com/africa/1186524/hugh-masekela-south-african-trumpeter-has-died-after-battle-with-prostate-cancer/

About the Author

Alex Vanderwiel

Alex Vanderwiel is a student in the Biology Department at Macalester College. Vanderwiel’s research areas include health surveillance and cancer.

Diamone Gathers, MD

Dr. Diamone Gathers is a Chief Resident in the Department of Internal Medicine and a board

certified, practicing internist at the University of New Mexico in Albuquerque. Her research areas include racial and socioeconomic disparities, cancer prevention, and survivorship. She studied medicine at Virginia Commonwealth University and subsequently completed her Internal Medicine residency at the University of New Mexico.

V. Shane Pankratz, PhD

Dr. Shane Pankratz is a professor in the Department of Internal Medicine at the University of New Mexico, and the Director of the Biostatistics Shared Resource of the UNM Comprehensive Cancer Center. He is a biostatistician with research interests in survival analysis and longitudinal data analysis methods, particularly with application to cancer epidemiology and cancer clinical trials. He received his formal training at Rice University.

Mikaela Kosich, MPH

Mikaela Kosich is a research scientist in the Biostatistics Shared Resource at the University of New Mexico Comprehensive Cancer Center. Her research interests include health equity, environmental epidemiology, statistical methods in epidemiology. Her training was completed at the University of Pittsburgh.

Bernard Tawfik, MD

Dr. Bernard Tawfik is Assistant Professor in Department of Internal Medicine, Division of  Hematology Oncology and a board certified, practicing medical oncologist at the University of New Mexico Cancer Comprehensive (UNM CCC) Center, an NCORP-MU site. His research areas include cancer care delivery, quality improvement and health disparities.

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