Identifying key variables in African American adherence to colorectal cancer screening: The application of data mining

BMC Public Health

Volumn 14, Issue 1, 2014, Pages

  Thompson, Vetta L Sanders ^a   Lander, Sean ^b   Xu, Shuyu ^b   Shyu, Chi Ren ^b  

^a WASHINGTON UNIVERSITY IN ST LOUIS   (United States)

^b UNIVERSITY OF MISSOURI   (United States)

Author keywords

African American; Cancer screening; Colorectal cancer

Indexed keywords

AFRICAN AMERICAN; AGED; COLONOSCOPY; COLORECTAL NEOPLASMS; CULTURAL FACTOR; DATA MINING; EARLY DIAGNOSIS; ETHNOLOGY; FEMALE; HEALTH SURVEY; HUMAN; MALE; MIDDLE AGED; PATIENT ATTITUDE; STATISTICAL MODEL; STATISTICS AND NUMERICAL DATA; TELEPHONE; UNITED STATES; UTILIZATION;

AFRICAN AMERICANS; AGED; COLONOSCOPY; COLORECTAL NEOPLASMS; CULTURAL CHARACTERISTICS; DATA MINING; EARLY DETECTION OF CANCER; FEMALE; HEALTH SURVEYS; HUMANS; LOGISTIC MODELS; MALE; MIDDLE AGED; PATIENT ACCEPTANCE OF HEALTH CARE; TELEPHONE; UNITED STATES;

EID: 84964314480     PISSN: None     EISSN: 14712458     Source Type: Journal    
DOI: 10.1186/1471-2458-14-1173     Document Type: Article

References (46)

1. American Cancer Society: Cancer Facts & Figures 2012. Atlanta: American Cancer Society; 2012.
2. American Cancer Society: Colorectal Cancer Facts & Figures 2011-2013. Atlanta: American Cancer Society; 2011.
3. Horner MJ, Ries LAG, Krapcho M, Neyman N, Aminou R, Howlader N, Altekruse SF, Feuer EJ, Huang L, Mariotto A, Miller BA, Lewis DR, Eisner MP, Stinchcomb DG, Edwards BK: SEER Cancer Statistics Review, 1975-2006. Bethesda, MD: National Cancer Institute; 2009. http://seer.cancer.gov/csr/1975-2006/, based on November 2008 SEER data submission, posted on the SEER web site.
4. Byers T, Levin B, Rothenberger D, Dodd G, Smith R: American Cancer Society Guidelines for screening and surveillance for early detection of colorectal polyps and cancer: update 1997. American cancer society detection and treatment advisory group on colorectal cancer. CA-Cancer J Clin 1997, 47:154-160.
5. National Cancer Institute: What you Need to Know About Cancer of the Colon and Rectum. Washington, DC: U.S: Department of Health and Human Services, National Institutes of Health; 2006.
6. Centers for Disease Control and Prevention. Top 10 Cancers Among Men. 2012. http://apps.nccd.cdc.gov/uscs/toptencancers.aspx. Retrieved November 24, 2014.
7. U.S. Preventive Services Task Force: Screening for colorectal cancer: U. S. Preventive Services Task Force recommendation. Ann Intern Med 2008. 149(9):627-637. doi:10.7326/0003-4819-149-9-200811040-00243.
8. U. S. Department of Health and Human Services: Healthy People 2010 Objectives. 1st edition. Washington, DC: US Government Printing Office; 2000.
9. Thompson Sanders VL, Harris J, Joo S: "Measuring Cultural Attitudes Relevant to Cancer Screening and Behavior". Washington, DC: Poster presented at the 2011 Association for Psychological Science; 2011. May 28.
10. Thompson VLS, Kalesan B, Wells A, Williams S-L, Caito N: Comparing the use of evidence and culture in targeted colorectal cancer communication for African Americans. Patient Educ and Couns 2010, 81(S1):S22-S33. doi:10.16/j.pec.2010.07.019. PMC2988986.
11. Han J, Kamber M, Pei J: Data Mining: Concepts and Techniques. San Francisco, CA: Morgan Kaufmann Publishers; 2011.
12. Agrawal R, Imieliński T, Swami A: Mining association rules between sets of items in large databases. ACM SIGMOD Record 1993, 22:207-216.
13. Das G, Lin KI, Mannila H, Renganathan G, Smyth P: Rule discovery from time series. KDD Proc 1998, 98:16-22.
14. Miller HJ, Han J: Geographic Data Mining and Knowledge Discovery. London: Taylor & Francis; 2009.
15. Xu S, Shyu CR: Efficient selection of association rules from lymphedema symptoms data using a graph structure. AMIA Annu Symp Proc 2010, 2010:912-916.
16. Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay E: Scikit-learn: machine learning in Python. JMLR 2011, 12:2825-2830.
17. Thomas TM: Machine Learning. 1st edition. New York, NY, USA: McGraw-Hill, Inc.; 1997.
18. Cortes C, Vapnik V: Support-vector networks. Mach Learn 1995, 20:273. Doi:10.1007/BF00994018 DOI:10.1007%2FBF00994018.
19. Breiman L: "Random Forests". Mach Learn 2001, 45(1):5-32.
20. Vernon SW, Myers RE, Tilley BC: Development and validation of an instrument to measure factors related to colorectal cancer screening adherence. Cancer Epidemiol Biomarkers Prev 1997, 6:825-832.
21. Chaiken S, Eagly AH: Communication modality as a determinant of message persuasiveness and message comprehensibility. J Pers Soc Psychol 1976, 34:605-614.
22. Mandelson MT, LaCroix AZ, Anderson LA, Nadel MR, Lee NC: Comparison of self-reported fecal occult blood testing with automated laboratory records among older women in a health maintenance organization. Am J Epidemiol 1999, 150:617-621.
23. Vernon SW, Meissner H, Klabunde C, Rimer BK, Ahnen DJ, Bastani R, Mandelson MT, Nadel MR, Sheinfeld-Gorin S, Zapka J: Measures for ascertaining use of colorectal cancer screening in behavioral, health services, and epidemiologic research. Cancer Epidemiol Biomarkers Prev 2004, 13:898-905.
24. Baier M, Calonge N, Cutter G, McClatchey M, Schoentgen S, Hines S, Marcus A, Ahnen D: Validity of self-reported colorectal cancer screening behavior. Cancer Epidemiol Biomarkers Prev 2000, 9:229.
25. Vernon SW, Tiro JA, Vojvodic RW, Coan S, Diamond PM, Greisinger A, Fernandez ME: Reliability and validity of a questionnaire to measure colorectal cancer screening behaviors: Does mode of survey administration matter? Cancer Epidemiol Biomarkers Prev 2008, 17:758-767.
26. Landis JR, Koch GG: The measurement of observer agreement for categorical data. Biometrics 1977, 33:159-174.
27. McQueen A, Tiro JA, Vernon SW: Construct validity and invariance of four factors associated with colorectal cancer screening across gender, race, and prior screening. Cancer Epidemiol Biomarkers Prev 2008, 17:2231-2237.
28. Fishbein M, Ajzen I: Predicting and Changing Behavior: The Reasoned Action Approach. New York: Psychology Press (Taylor & Francis); 2010.
29. Office of Behavior and Social Science Research: Progress and Promise in Research on Social and Cultural Dimensions of Health: A Research Agenda. Washington D.C: National Institute of Health; 2004.
30. Mattis JS: African American women's definitions of spirituality and religiosity. J Black Psychol 2000, 26:101-122.
31. Hiatt RA, Pasick RJ, Pérez-Stable EJ, McPhee SJ, Engelstad L, Lee M, Sabogal F, Carol ND, Onofrio SS: Pathways to early cancer detection in the multiethnic population of the San Francisco bay area. Health Educ Behav 2008, 23:S10-S27.
32. Powe BD: Fatalism among elderly African Americans: effects on colorectal cancer screening. Cancer Nurs 1995, 18:385-392.
33. Powe BD: Cancer fatalism among elderly African American women. J Psychosoc Oncol 2001, 19:85-95.
34. Thompson Sanders VL: Factors affecting the level of African American identification. J Black Psychol 1990, 17:14-23.
35. Sellers R, Smith M, Shelton J, Rowley S, Chavous T: Multidimensional model of racial identity: a reconceptualization of African American racial identity. Pers Soc Psychol Rev 1998, 1:18-39.
36. Resnicow K, Soler RE, Braithwaite RL, Selassie MB, Smith M: Development of a racial and ethnic identity scale for African American adolescents: the survey of black life. J Black Psychol 1999, 25:171-188.
37. Yancey AK, Aneshensel CS, Driscoll AK: The assessment of ethnic identity in a diverse urban youth population. J Black Psychol 2001, 27:190-208.
38. Thompson Sanders VL, Bazile A, Akbar M: (2004). African Americans' perceptions of psychotherapy and psychotherapists. Prof Psychol-Res Pract 2004, 35:19-26.
39. Jagers RJ, Mock LO: The communalism scale and collectivisticindividualistic tendencies: some preliminary findings. J Black Psychol 1995, 21(2):153-167.
40. National Health Interview Survey: National Health Interview Survey, Adult Access to Health Care and Utilization, 2007. 2005. http://www.cdc.gov/nchs/nhis/about-nhis.htm.
41. Agrawal R, Mannila H, Srikant R, Toivonen H, Verkamo AI: Fast discovery of association rules. Advances in Knowledge Discovery and Data Mining. 1996:307-328.
42. Borthakur D: The hadoop distributed file system: architecture and design. Hadoop Proj Website 2007, 11:21.
43. Zaharia M, Chowdhury M, Franklin MJ, Shenker S, Stoica I: Spark: cluster computing with working sets. In Proceedings of the 2nd USENIX conference on Hot topics in cloud computing. Boston, MA: USENIX Association; 2010:10-10.
44. Bay SD, Pazzani MJ: Detecting group differences: mining contrast sets. Data Min Knowl Disc 2001, 5(3):213-246.
45. Thompson Sanders V, Cavazos-Rehg P, Rutten LF, Hesse BW, Moser RP, Kreps GL: Health Information National Trends Survey: Implications for Addressing Cancer Health Disparities through Public Health Surveillance. In Building the Evidence Base in Cancer Communication: Hints. Cresskill, NJ: Hampton Press; 2010.
46. Dreier M, Borutta B, Seidel G, Kreusel I, Töppich J, Bitzer EM, Dierks ML, Walter U: Development of a comprehensive list of criteria for evaluating consumer education materials on colorectal cancer screening. BMC Public Health 2013, 13(1):843. doi:10.1186/1471-2458-13-843.