Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2023 May 16.
Published in final edited form as: Prev Med. 2021 Jun 22;150:106695. doi: 10.1016/j.ypmed.2021.106695

Using big data to gauge effectiveness of breast cancer awareness month

D Gathers a,*, VS Pankratz c, M Kosich c, B Tawfik b
PMCID: PMC10187946  NIHMSID: NIHMS1895382  PMID: 34166676

Abstract

Breast Cancer Awareness Month (BCAM) has been used for decades to increase awareness and screening for breast cancer, but its geographic reach and effectiveness is difficult to judge. Using Internet Search Interest (ISI) could allow for better evaluation of BCAM effects. Using Google Trends, we evaluated the ISI for “breast cancer” and “mammogram” for each state and metropolitan area from 2006 to 2019. The ISI represents population level Google internet searches relative to the highest number of searches for the United States over a given period, with a max number of 100. The ISI for each term in October (BCAM) was compared against all other months during this period, across states and across major metropolitan regions. ISI was 2.34 times higher (95% Confidence Interval [CI]: 2.10–2.61, P < .001) in BCAM than the average for all other months combined. Geographically categorized data revealed that there were significant differences in the ISI for “breast cancer” and for “mammogram” among the 50 states, and among major metropolitan areas (P < .001 for each). ISI suggests that BCAM is effective at increasing breast cancer related internet searches, with significant heterogeneity across states and metro areas. Google Trends is a publicly available free tool that can be used to assess penetrance of awareness campaigns in a time sensitive and location specific manner for future targeting of populations with low breast cancer awareness. Future research is needed to assess relationships between preventive outcomes and ISI scores.

Keywords: Breast cancer, Mammogram, Google Trends, Internet search interest, Preventive, Surveillance

1. Introduction

1.1. Cancer burden

Cancer is the second leading cause of death globally, responsible for an estimated 9.6 million total deaths in 2018 and breast cancer specifically culminating in 2.09 million total cases with 627,000 deaths (Ferlay et al., 2013). The total of all health care costs for cancer in the US in 2012 was greater than $80 billion with 13% of all cancer treatment costs in the United States attributed to breast cancer (Soni, 2015; Mariotto et al., 2011). Cancer burden and cost can be reduced through early detection and screening (Kakushadze et al., 2017).

1.2. Cancer awareness month

Cancer Awareness Months (CAMs) are health promotion tools designed to offer enhanced, directed opportunities to increase public knowledge about cancer prevention through screening initiatives as well as improve access to care and treatment (Cancer Awareness Months and Days, 2020). Several months of the year have been designated as “Cancer Awareness Months” for variable cancer types and, since 1984, October has been recognized in the U.S. as National Breast Cancer Awareness Month (BCAM). Events typically associated with BCAM include walks, sporting events, lectures, display of posters, distribution of communication materials and ‘wear pink’ events (Jacobsen and Jacobsen, 2011). In addition, it has been shown that effective use of social media by medical professionals and organizations can serve as an important tool in implementing and disseminating critical prevention, screening, and treatment messages to the community in real-time (Xu et al., 2016). BCAMs public awareness campaigns have led to increased rates of cancer screening (Karabay et al., 2018). It has been found that Google’s search volume for breast cancer is higher in October and that October is the most likely month that women undergo screening mammography (Hernandez-Felix et al., 2019; Stat Bite, 2005). Recently, internet searches for breast cancer screening during BCAM have been shown to surpass searches for prostate cancer screening during prostate cancer awareness month, which could suggest increased comparative efficacy or penetration of BCAM campaigns (Johnson et al., 2021).

1.3. Google trends in healthcare

Although BCAMs have been used for decades to increase breast cancer awareness and screening, their geographic reach is difficult to judge (Jacobsen and Jacobsen, 2011; The Lancet Oncology, Null, 2007). Padilla et al. found significant heterogeneity in breast cancer screening rates among census clusters of variable socioeconomic status in the Lyon Metropole area, France (Padilla et al., 2019). Another recent observational study revealed wide geographic variation in breast cancer screening across the United States, compared between two distinct years, 2008 and 2012 (Feng et al., 2016). Similarly, we investigated spatial variation in breast cancer screening, further defined by state and metropolitan area over a continuous period, with specific comparisons between BCAM and the other months of the year. Google Trends data allows analysis of location-specific population level Google searches over discrete periods, defined as Internet Search Interest (ISI) (FAQ about Google Trends Data, 2010). In 2009, Google Flu Trends was used to track the influenza epidemic and predicted the spread earlier than the CDC (Ginsberg et al., 2009; Google Trends, 2009). Systematic review of the use of Google Trends in healthcare data has shown it to provide valuable insights into regional human behavioral patterns with real time availability, such as interest in developing surgical procedures, seasonality in seeking mental health information and geographic correlation with ISI for CPR (The Use of Google Trends in Health Care Research, 2014; Osuka et al., 2018). In this study, we collected the ISI for “breast cancer” and “mammogram” for each month then compared data by state and metro area for 2006–2019, hypothesizing that ISI for these terms would increase significantly during BCAM. That data was compared against the ISI for October for each year, which is BCAM.

2. Methods

2.1. Data collection

Google Trends is a freely accessible tool that allows users to analyze population level internet searches (The Use of Google Trends in Health Care Research, 2014). It provides access to a largely unfiltered sample of actual search requests made through Google. The search results are anonymized, categorized and aggregated into a measurable ratio, the ISI (FAQ about Google Trends Data, 2010). The ISI is a representative ratio including the total query volume for a particular search term in a given geographic region divided by the total number of queries in that region at a point in time, with a maximum representative number of 100 so the 100 only occurs once for the entire data extraction period (Choi and Varian, 2012). Google Trends was used to obtain and extract ISI data for “breast cancer” and “mammogram” for each available state and metropolitan area in the United States between 2006 and 2019 using parameters of “all categories” and “web search” without the addition of specific categories or filters for media outlet or cancer type. Monthly data per year for mammogram was obtained for each state and metropolitan area. ISI data using the search term “breast cancer” was obtained for each state and metropolitan area for BCAM of each year of the study period.

2.2. Statistical analysis

Search scores for “breast cancer” and for “mammogram” were obtained then means and standard deviations were calculated across the years of the study for each month of the year. To compare ISI values during BCAM to those from other months, we used linear models approaches, on log-transformed values to meet statistical modeling assumptions, to estimate and compare the relative magnitude of the ISI values, with adjustment for year-to-year differences. The between-group differences estimated on a logarithmic scale were back-transformed to the original scale for reporting, resulting in estimates of between-group ratios rather than differences. We similarly used linear models approaches to estimate and compare ISI results during BCAM across states and across major metropolitan regions. We performed global tests to determine if there were any significant differences among states or across metro regions and applied Fisher’s Protected LSD method to control familywise type I error rates. This method controls the multiplicity of testing problem by requiring that the global test be significant before corresponding pairwise comparisons are considered (Snedecor and Cochran, 1967; Westfall et al., 2011). Therefore, when global tests were significant, we sorted the state or metro predicted averages from largest to smallest and used the results of pairwise comparisons to merge each state or metro area into the prior group if its value was not significantly different from the average value of the states or metro areas in the immediately adjacent group. This resulted in groups of states or metro areas where the average score within each group was statistically significantly different from each of the individual scores from each of the states or metro regions outside of the groups. Two-sided tests with P < .05 were used to declare significance. Analyses were performed using the SAS and R statistical software packages.

3. Results

Internet search interest (ISI) values extracted from Google Trends for each month of the year from 2006 through 2019 illustrate that searches for “Breast Cancer” were markedly higher during the BCAM of October and that searches for “mammogram” gradually increased over the study period (Fig. 1). Averages for each month across the time period of this study further illustrate the heightened number of searches for “breast cancer” during the BCAM of October (Fig. 2), with an average (standard deviation [SD]) ISI of 76.4 (13.9), as compared to the average 32.7 (6.8) observed across all other months. Formal comparisons demonstrated that search interest was 2.34 times higher (95% Confidence Interval [CI]: 2.10–2.61, P < .001) in BCAM than the average for all other months combined. This interest appears to begin to increase during the month of September. Although ISI in September was only 0.57 times that of October (95% CI: 0.50–0.65, P < .001), its ISI values were 1.39 times higher than in the other 10 months of the year (95% CI: 1.26–1.53, p < .001). ISI for searches for “mammogram” were also higher during BCAM (Fig. 2), although to a lesser degree than for “Breast Cancer.” The average (SD) ISI for “mammogram” was 59.4 (19.0) during BCAM, as compared to 40.5 (12.9) for all other months. Formal comparisons found that search interest for “mammogram” was 1.47 times higher (95% CI: 1.24–1.75, P < .001) during BCAM than for the average for all other months combined.

Fig. 1.

Fig. 1.

Open in a new tab

Sample Google Trends output data for “mammogram” and for “breast cancer”.

Fig. 2.

Fig. 2.

Open in a new tab

Frequency of “Breast Cancer” and “mammogram” searches, as measured by the ISI from 2006 to 2019, by month of the year. Mean estimates are shown with plus or minus one standard deviation error bars.

There were significant differences in the ISI for the “breast cancer” and “mammogram” search terms among the states and among the metro areas categorized in Google Trends (p < .001 for each, Fig. 3). Five distinct groups were identified which had significant differences in breast cancer ISI among/between groups, with non-significant ISI differences within groups. Four such groups were identified for the mammogram ISI (Table 1). States clustered into Group 1 for the search term “breast cancer” (including MS, DE, WV, MD, CT, AL, etc) had a mean ISI 89 compared to Group 5 with mean ISI 54 (UT). Metropolitan clustered Group 1 for the search term “breast cancer” (including Greenwood, MS) had an ISI of 92.7 compared to Group 6 (including metropolitan areas specific to ID, UT and CA) had an ISI of 39.8.

Fig. 3.

Fig. 3.

Open in a new tab

Frequency of search terms by state, 2006–2019.

Table 1.

Weighted averages and standard deviations of ISI results, combined within groups of states with similar scores for “breast cancer” and “mammogram” search terms, measured during BCAM from 2006 through 2019. Groups were defined such that mean ISI values were not significantly different for states in the same group but were significantly different among different groups. Metropolitan groups detailed in supplementary data.

Search term Group States Mean
ISI		 SD
ISI
States Breast cancer 1 AL, CT, DE, MD, MS, WV 89.4 6.8
2 AR, DC, FL, GA, IA, IL, IN, KY, LA, MA, ME, MI, MN, MO, NC, NH, NJ, NY, OH, PA, RI, SC, SD, TN, WI 79.5 5.8
3 AK, AZ, CO, KS, ND, NE, NM, OK, TX, VT 71.2 6.6
4 CA, HI, ID, MT, NV, OR, VA, WA, WY 64.0 8.1
5 UT 54.9 5.2
Mammogram 1 AR, DE, FL, GA, IN, KY, MO, NC, OH, PA, SC, TN, WV 78.5 13.2
2 AL, AZ, CT, DC, HI, ID, IL, KS, LA, MA, MD, ME, MI, MN, MS, MT, ND, NE, NH, NJ, OK, RI, SD, TX, UT, VT 68.2 12.7
3 AK, CO, IA, NM, NV, NY, OR, VA, WA, WI, WY 60.2 12.7
4 CA 50.4 10.2
Open in a new tab

4. Discussion

4.1. Principal results

The BCAM of October was associated with significantly higher search interest for “breast cancer” as compared to the rest of the year, with a greater than 2-fold higher ISI than the average of all other months. In addition, BCAM was also associated with significantly higher search interest for “mammogram” as compared to the rest of the year, although to a lesser extent than the “breast cancer” search term. The average “mammogram” ISI value for BCAM was slightly less than 1.5-fold higher than the average ISI for the remaining months of the year. This suggests that women have heightened interest in screening, in addition to their interest in breast cancer, during BCAM. This could ultimately lead to improved screening and detection outcomes due to BCAM. In general, mammography ISI showed a steady uptrend over the study period (Fig. 1).

Temporal variation was evident over the entire study period of 2006 to 2019, although the “breast cancer” ISI was consistently higher in the months of September and October. Overall, breast cancer ISI was highest in October. Breast cancer ISI is noticeably higher during the BCAMs that followed the years after the U.S. Preventive Services Task Force (USPSTF) updated breast cancer screening recommendations in 2009 (Fig. 2). At that time, the USPSTF recommended biennial mammography for women aged 50–74 years and shared decision-making/selective screening for women aged 40–19 years for breast cancer screening, ultimately suggesting that women under 50 are not required to have regular mammograms (Wernli et al., 2017). This created a degree of controversy in the community that could have presumably led to less public clarity and therefore, increased public interest surrounding guidelines for cancer screening and prevention that did not exist prior to the change. Concurrently, the Affordable Care Act of 2010 increased access to preventive services for women, allowing breast cancer mammography screenings every 1–2 years for women over 40 as well as breast cancer genetic testing and breast cancer chemoprevention counseling for women at higher risk (Preventive Care Benefits for Women, 2019). Subsequently, this Medicaid expansion also led to a decreased number of uninsured patients and a reduced incidence of advanced stage breast cancer possibly due to more screening (Blanc et al., 2020).

In addition to these patterns, observed in data averaged across the United States, there were significant geographical variations in BCAM ISI for both “breast cancer” and “mammogram” search terms. Geographic data at the state level revealed a significant difference in breast cancer ISI between clustered Group 1 with mean ISI 89 (MS, DE, WV, MD, CT, AL) and Group 5 with mean ISI 54 (UT). State programs could presumably have a great deal of local impact, as Delaware in 2018 had the highest percentage of the population with up-to-date mammograms and the state offers completely free cancer care for residents (DPH Disease Information, 2010). In contrast, Mississippi has the one of the lowest breast cancer screening rates among older women in the U.S. and the highest breast cancer death rates in African American women suggesting that public awareness may not always translate into preventive action due to the multiple factors of cost, availability, transportation, and other factors that go into successful screening (Houston, 2009). California maintained the lowest ISI for mammogram at the state and metro level. It is worthwhile to note that all 6 states in Group 1 (highest breast cancer ISI) overlapped similarly into the Group 1 or Group 2 cluster for mammogram ISI. There was a more dramatic degree of variability in the means among the clustered groups for both breast cancer and mammogram ISI.

A closer look at metropolitan areas in Groups 1 and 2 (highest ISI for breast cancer) reveal that Rochester, MN is home to the Mayo Clinic, one of the US News designated “Best Hospitals for Cancer” multiple years in a row. The Early Cancer Therapeutics Group offers access to Phase 1 clinical trials and works in conjunction with the Minnesota Cancer Clinical Trials Network to improve cancer outcomes through greater access to cancer clinical trials for prevention and treatment (Masonic Cancer Center, 2021). Proximity to these resources could affect public perception of readily available treatments, clinical trials and positive outcomes, therefore increasing ISI. Similarities between Greenwood and Greenville, MS can be found in demographic census data from 2014 to 2018 (U.S. Census Bureau, 2018). Both cities are comprised of greater than 75% ethnic minorities and have 80% high school graduates with a median household income of $27–37,000 annually. Given that healthcare literacy is historically low among those with similar demographic data and the paucity of data available for BCAM in MS, the reason for the high ISI for these two locations is not readily evident (Cutilli and Bennett, 2009). Among the lowest BCAM ISI was Salt Lake City, UT (ISI 43) with a population that is predominately Caucasian, highly educated, and a median household income of $43–56,000 annually (U.S. Census Bureau, 2018). Although age-adjusted cancer mortality rate in Utah has generally decreased over the last 30 years, from 2014 to 2018, Salt Lake County Health District had the highest breast cancer mortality rate of 22.6 deaths per 100,000 women which may be due to low screening rates (Public Health Indicator Based Information System (IBIS), 2020). Locally, the Utah Cancer Action Network (UCAN) works collaboratively with the Utah Cancer Control Program (UCCP) to provide preventive education for residents and community stakeholders, as well as free to low-cost clinical breast exams and mammograms to the under insured. Unlike Rochester, Salt Lake City has a breast screening program but a very low ISI and breast cancer mortality which may suggest this program is less effective than the program in Rochester. Mass media campaigns have been shown to produce positive changes or prevent negative changes in health-related behaviors across large populations, but these data suggest variable efficacy of these programs. Mammogram ISI Group 1 mirrored the findings at the state level for mammogram ISI, with several states in WV and AL maintaining the highest ISI at the metropolitan level.

4.2. Limitations

Several limitations exist within this study. ISI does not represent the total number of searches but rather, relative search volumes, we were therefore unable to determine if the absolute search numbers related to breast cancer changed over time. In addition, Google Trends data captures relative change to a maximum, therefore a low ISI during BCAM for a specific region could represent a relatively high ISI for all the other months of a particular year, and vice versa. 72% percent of Americans in 2020 were seeking personalized health care information online, mostly using Google searches. However, for those who did not use internet searches, generalizability of these results was reduced (Anderson, 2016; Kontos et al., 2014). This analysis focused on Google data, which accounted for greater than 90% of the search engine market on average from published data including the period from January 2009 to December 2019 but did not include other search engines (Search Engine Market Share Worldwide, 2017). In addition, as the demographics of specific individuals searching for healthcare information online were not included in the Google Trends data extraction, a disparity may exist between those who have the acumen and ability to access the internet and those who do not. Individuals of advanced age, limited capabilities or economic disadvantage may not be well represented in this study. Further limiting was the inability to correlate ISI with the number of screening mammograms actually performed. If participation in or investigation of BCAM led directly to a patient undergoing screening mammography, that mammography may have been scheduled several months later, noted to be discussed with primary care clinician at the next visit or restricted by time, cost and availability. Finally, we were unable to clearly correlate state specific BCAM successes and failures based on available information regarding specific resources and outcomes.

4.3. Comparison with prior work

A related Google Trends study performed in Malaysia found that not only did breast cancer ISI increase during BCAM, but that there were differences in ISI between rural and more urban areas (Mohamad and Kok, 2019). Of interest, it was found that targeted breast cancer awareness programs were not directed at the most active internet users in Malaysia, those aged below 34 years old. We encountered a similar limitation, as we were unable to completely ascertain how much of the ISI values obtained was from the population most likely to benefit from breast cancer screening.

Kluger and Bouchard used Google Trends to evaluate searches for melanoma in Finland compared to other European countries increased in the summer and decreased during the winter between 2010 and 2018. This was in contrast with searches for breast and prostate cancer, which peaked in October for breast cancer and November for prostate cancer (Kluger and Bouchard, 2019). Finally, this work expands upon the investigation by Cohen et al. into the impact of various CAMs on national public interest, as we attempted to evaluate public interest related to a specific type of malignancy, organized by state and metropolitan area as opposed to a more broad, national level (Cohen et al., 2020). This expanded investigation into location specific ISI may allow for future opportunities to strengthen local public health initiatives.

5. Conclusions

Breast Cancer Awareness month was associated with significantly higher ISI for both “breast cancer” and “mammogram” as compared to the rest of the year, with a notable variability in geographic distribution. Location specific ISI for BCAM may be a critically important key to survival, as BCAM has led to the increased public awareness and documented increase in breast cancer screening which decreases mortality and ultimately saves lives (Jacobsen and Jacobsen, 2011; The Lancet Oncology, Null, 2007; Wald et al., 2009; Duffy et al., 2010). Using these data would help to evaluate chronological and temporal opportunities in a geographically specific fashion to optimize screening and prevention in each community via a publicly available free tool. Future directions include correlating BCAM ISI with outcomes and state level information.

Supplementary Material

Appendix A

Acknowledgments

This research was partially supported by the UNM Comprehensive Cancer Center Biostatistics Shared Resource, funded by UNMCCC Support Grant NCI P30CA118100.

Abbreviations:

BCAM

Breast cancer awareness month

CAM

Cancer awareness month

ISI

Internet search interest

Footnotes

Declaration of Competing Interest

None.

Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.ypmed.2021.106695.

References

  1. Anderson JG, 2016. Consumers of e-health: patterns of use and barriers. Soc. Sci. Comput. Rev 10.1177/0894439303262671. [DOI] [Google Scholar]
  2. Blanc JML, Heller DR, Friedrich A, Lannin DR, Park TS, 2020. Association of medicaid expansion under the affordable care act with breast cancer stage at diagnosis. JAMA Surgery 155 (8), 752–758. 10.1001/jamasurg.2020.1495. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Cancer Awareness Months and Days, 2020. Cancer Health. Retrieved September 7, 2020, from. https://www.cancerhealth.com/page/cancer-awareness-months-days.
  4. Choi H, Varian H, 2012. Predicting the present with google trends. Econ. Rec 88 (S1), 2–. [Google Scholar]
  5. Cohen SA, Cohen LE, Tijerina JD, 2020. Dec 10. The impact of monthly campaigns and other high-profile media coverage on public interest in 13 malignancies: a Google Trends analysis. Ecancermedicalscience 14, 1154. 10.3332/ecancer.2020.1154. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Cutilli CC, Bennett IM, 2009. Understanding the health literacy of America results of the National Assessment of adult literacy. Orthop. Nurs. Nation. Assoc. Orthop. Nurs 28 (1), 27–34. 10.1097/01.NOR.0000345852.22122.d6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. DPH Disease Information, 2010. Cancer—Delaware Health and Social Services—State of Delaware. Retrieved September 7, 2020, from. https://www.dhss.delaware.gov/dhss/dph/dpc/cancer.html.
  8. Duffy SW, Tabar L, Olsen AH, Vitak B, Allgood PC, Chen THH, Yen AMF, Smith RA, 2010. Absolute numbers of lives saved and overdiagnosis in breast cancer screening, from a randomized trial and from the breast screening Programme in England. J. Med. Screen 17 (1), 25–30. 10.1258/jms.2009.009094. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. FAQ about Google Trends Data, 2010. Trends Help. Retrieved September 7, 2020, from. https://support.google.com/trends/answer/4365533?hl=en.
  10. Feng Xue, Tan Xi, Alenzi Ebtihag O., Rai Pragya, Chang Jongwha, December 2016. Spatial and temporal variations of screening for breast and colorectal cancer in the United States, 2008 to 2012. Medicine 95 (51), e5656. 10.1097/MD.0000000000005656. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Ferlay J, Soerjomataram I, Ervik M, Dikshit R, Eser S, Mathers C, et al. , 2013. GLOBOCAN 2012 v1.0, Cancer Incidence and Mortality Worldwide: IARC CancerBase No. 11 Lyon. International Agency for Research on Cancer, France. [Google Scholar]
  12. Ginsberg J, Mohebbi MH, Patel RS, Brammer L, Smolinski MS, Brilliant L, 2009. Detecting influenza epidemics using search engine query data. Nature 457, 1012–1014. [DOI] [PubMed] [Google Scholar]
  13. Google Trends, 15 November 2009. A web-based tool for real-time surveillance of disease outbreaks. Herman Anthony Carneiro, Eleftherios Mylonakis. Clin. Infect. Dis 49 (10), 1557–1564. 10.1086/630200. [DOI] [PubMed] [Google Scholar]
  14. Hernandez-Felix JH, Meneses Medina MI, Martos Armendariz EO, Rivera Aguilar M, Camargo VR, Huitzil Melendez FD, 2019. Evaluating the impact of cancer awareness months in Mexico using internet search engine analytics. J. Clin. Oncol 37 (15_suppl), e18056. 10.1200/JCO.2019.37.15_suppl.e18056. [DOI] [Google Scholar]
  15. Houston GA, 2009. Breast cancer in Mississippi: what can we do? J. Miss. State Med. Assoc 50 (9), 299–301. [PubMed] [Google Scholar]
  16. Jacobsen GD, Jacobsen KH, 2011. Health awareness campaigns and diagnosis rates: evidence from National Breast Cancer Awareness Month. J. Health Econ 30 (1), 55–61. 10.1016/j.jhealeco.2010.11.005. [DOI] [PubMed] [Google Scholar]
  17. Johnson BS, Shepard S, Torgeson T, et al. , February 13, 2021. Using Google Trends and twitter for prostate cancer awareness: A comparative analysis of prostate cancer awareness month and breast cancer awareness month. Cureus 13 (2), e13325. 10.7759/cureus.13325. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Kakushadze Z, Raghubanshi R, Yu W, 2017. Estimating cost savings from early cancer diagnosis. Data 2 (3), 30. 10.3390/data2030030. [DOI] [Google Scholar]
  19. Karabay O, Hasbahceci M, Kadioglu H, 2018. Impact of breast cancer awareness month on detection of breast cancer in a private hospital. J. Int. Med. Res 46 (2), 619–625. 10.1177/0300060517699988. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Kluger N, Bouchard LJ, 2019. A comparative study of Google search trends for melanoma, breast cancer and prostate cancer in Finland. Dermatology 235, 346–347. 10.1159/000498987. [DOI] [PubMed] [Google Scholar]
  21. Kontos E, Blake KD, Chou W-YS, Prestin A, 2014. Predictors of eHealth usage: insights on the digital divide from the health information National Trends Survey 2012. J. Med. Internet Res 16 (7), e172 10.2196/jmir.3117. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Mariotto AB, Robin Yabroff K, Shao Y, Feuer EJ, Brown ML, 2011. Projections of the cost of cancer Care in the United States: 2010–2020. JNCI J. Nat. Cancer Inst 103 (2), 117–128. 10.1093/jnci/djq495. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Masonic Cancer Center, 2021. Minnesota Cancer Clinical Trials Network. https://www.cancer.umn.edu/mncctn.
  24. Mohamad Mazlyfarina, Kok Hui Sin, 2019. Using google trends data to study public interest in breast cancer screening in Malaysia. Asian Pac. J. Cancer Prev 20 (5), 1427–1432. 10.31557/APJCP.2019.20.5.1427. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Osuka H, Hall AJ, Wikswo ME, Baker JM, Lopman BA, 2018. Temporal relationship between healthcare-associated and nonhealthcare-associated norovirus outbreaks and Google Trends data in the United States. Infect. Control Hosp. Epidemiol 39 (3), 355–358. 10.1017/ice.2017.322. [DOI] [PubMed] [Google Scholar]
  26. Padilla C, Michel P. Soler, Vieira V, November 2019. Mapping variation in breast cancer screening: Where to intervene? Eur. J. Pub. Health 29 (Supplement_4). 10.1093/eurpub/ckz185.382 ckz185.382. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Preventive Care Benefits for Women, 2019. HealthCare.Gov. Retrieved September 7, 2020, from. https://www.healthcare.gov/preventive-care-women/.
  28. Public Health Indicator Based Information System (IBIS), 2020. Utah’s Public Health Data Resource. https://ibis.health.utah.gov/ibisph-view/indicator/complete_profile/CanDth.html.
  29. Search Engine Market Share Worldwide, 2017. StatCounter Global Stats. Retrieved September 7, 2020, from. https://gs.statcounter.com/search-engine-market-share.
  30. Snedecor GW, Cochran WG, 1967. Statistical Methods, 6. Iowa State University Press, Ames, Iowa, pp. 240–285. [Google Scholar]
  31. Soni A, April 2015. Trends in the Five Most Costly Conditions among the U.S. Civilian Noninstitutionalized Population, 2002 and 2012. Statistical Brief #470. Agency for Healthcare Research and Quality, Rockville, MD. http://www.meps.ahrq.gov/mepsweb/data_files/publications/st470/stat470.shtml. [PubMed] [Google Scholar]
  32. Stat Bite, 19 October 2005. Effect of breast cancer awareness month on mammography use. J. Natl. Cancer Inst 97 (20), 1493. 10.1093/jnci/dji368. [DOI] [PubMed] [Google Scholar]
  33. The Lancet Oncology, Null, 2007. Breast-cancer awareness: too much of a good thing? The lancet. Oncology 8 (12), 1041. 10.1016/S1470-2045(07)70347-9. [DOI] [PubMed] [Google Scholar]
  34. The Use of Google Trends in Health Care Research, 2014. A Systematic Review. ResearchGate. Retrieved September 7, 2020, from. https://www.researchgate.net/publication/267269093. [Google Scholar]
  35. U.S. Census Bureau, 2018. State Specific Data. Retrieved from. https://www.census.gov.
  36. Wald NJ, Law MR, Duffy SW, 2009. Breast screening saves lives. BMJ 339. 10.1136/bmj.b2922. [DOI] [PubMed] [Google Scholar]
  37. Wernli KJ, Arao RF, Hubbard RA, Sprague BL, Alford-Teaster J, Haas JS, Henderson L, Hill D, Lee CI, Tosteson ANA, Onega T, 2017. Change in breast cancer screening intervals since the 2009 USPSTF guideline. J. Women’s Health (2002) 26 (8), 820–827. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Westfall PH, Tobias RD, Wolfinger RD, 2011. Multiple Comparisons and Multiple Tests Using SAS. SAS Institute. [Google Scholar]
  39. Xu S, Markson C, Costello KL, Xing CY, Demissie K, Llanos AA, 2016. Leveraging social media to promote public health knowledge: example of cancer awareness via twitter. JMIR Public Health Surveill. 2 (1) 10.2196/publichealth.5205. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Appendix A
NIHMS1895382-supplement-Appendix_A.docx (23.8KB, docx)

RESOURCES