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

BackgroundThis paper reports on an effort to identify a streamlined set of issues important for colorectal cancer communication and interventions with older African Americans.MethodsAfrican American (N = 1,021), 683 women and 338 men, 50 to 75 years completed a telephone survey addressing demographics, colorectal cancer screening, cancer attitudes, and cancer related cultural attitudes. Several data analytics methods were applied and evaluated. Among them, results from associative data mining identified key variables and logistic regression was used to confirm associations to screening adherence.ResultsSets of co-occurring variables identified by associative data mining methods are extracted to further study differences between adherent and non-adherent groups. Logistic regressions suggested four variables were significantly associated with adherence: healthcare provider colonoscopy recommendation, prevention services at the place health care is usually sought, a history of colitis, and a history of polyps.ConclusionsThe findings suggest a streamlined set of issues and concerns that may be used by providers advising patients or developing colorectal cancer intervention strategies for older African Americans. The data suggest the continued importance of healthcare provider recommendation to screen. It is important that providers give a clear recommendation to screen regardless of the test ultimately selected and should advise all patients that family history and the absence of symptoms or colitis do not eliminate the value of screening.

[1]  Heikki Mannila,et al.  Verkamo: Fast Discovery of Association Rules , 1996, KDD 1996.

[2]  E. Bitzer,et al.  Development of a comprehensive list of criteria for evaluating consumer education materials on colorectal cancer screening , 2013, BMC Public Health.

[3]  B. Tilley,et al.  Development and validation of an instrument to measure factors related to colorectal cancer screening adherence. , 1997, Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology.

[4]  T. Byers,et al.  American Cancer Society guidelines for screening and surveillance for early detection of colorectal polyps and cancer: Update 1997 , 1997 .

[5]  Jiawei Han,et al.  Geographic Data Mining and Knowledge Discovery , 2001 .

[6]  Maria E Fernandez,et al.  Reliability and Validity of a Questionnaire to Measure Colorectal Cancer Screening Behaviors: Does Mode of Survey Administration Matter? , 2008, Cancer Epidemiology Biomarkers & Prevention.

[7]  Bernadette Mazurek Melnyk,et al.  Screening for colorectal cancer: U.S. Preventive Services Task Force recommendation statement. , 2008, Annals of internal medicine.

[8]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[9]  Maysa Akbar,et al.  African Americans' Perceptions of Psychotherapy and Psychotherapists , 2004 .

[10]  Petra Perner,et al.  Data Mining - Concepts and Techniques , 2002, Künstliche Intell..

[11]  A. LaCroix,et al.  Comparison of self-reported fecal occult blood testing with automated laboratory records among older women in a health maintenance organization. , 1999, American journal of epidemiology.

[12]  B. Rimer,et al.  Measures for ascertaining use of colorectal cancer screening in behavioral, health services, and epidemiologic research. , 2004, Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology.

[13]  Heikki Mannila,et al.  Fast Discovery of Association Rules , 1996, Advances in Knowledge Discovery and Data Mining.

[14]  T. Byers,et al.  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. , 1997, CA: a cancer journal for clinicians.

[15]  Tabbye M. Chavous,et al.  Multidimensional Model of Racial Identity: A Reconceptualization of African American Racial Identity , 1998, Personality and social psychology review : an official journal of the Society for Personality and Social Psychology, Inc.

[16]  Heikki Mannila,et al.  Rule Discovery from Time Series , 1998, KDD.

[17]  Screening for Colorectal Cancer: U.S. Preventive Services Task Force Recommendation , 2008, Annals of Internal Medicine.

[18]  I. Ajzen,et al.  Predicting and Changing Behavior: The Reasoned Action Approach , 2009 .

[19]  Robin E. Soler,et al.  Development of a Racial and Ethnic Identity Scale for African American Adolescents: The Survey of Black Life , 1999 .

[20]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[21]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[22]  R. Hiatt,et al.  Pathways to Early Cancer Detection in the Multiethnic Population of the San Francisco Bay Area , 1996 .

[23]  Jasmin A. Tiro,et al.  Construct Validity and Invariance of Four Factors Associated with Colorectal Cancer Screening across Gender, Race, and Prior Screening , 2008, Cancer Epidemiology Biomarkers & Prevention.

[24]  R. Jagers,et al.  The Communalism Scale and Collectivistic-Individualistic Tendencies: Some Preliminary Findings , 1995 .

[25]  A. Yancey,et al.  The Assessment of Ethnic Identity in a Diverse Urban Youth Population , 2001 .

[26]  Anjanette Wells,et al.  Comparing the use of evidence and culture in targeted colorectal cancer communication for African Americans. , 2010, Patient education and counseling.

[27]  J. Mattis African American Women’s Definitions of Spirituality and Religiosity , 2000 .

[28]  D. Ahnen,et al.  Validity of self-reported colorectal cancer screening behavior. , 2000, Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology.

[29]  J. R. Landis,et al.  The measurement of observer agreement for categorical data. , 1977, Biometrics.

[30]  Tomasz Imielinski,et al.  Mining association rules between sets of items in large databases , 1993, SIGMOD Conference.

[31]  Scott Shenker,et al.  Spark: Cluster Computing with Working Sets , 2010, HotCloud.

[32]  AgrawalRakesh,et al.  Mining association rules between sets of items in large databases , 1993 .

[33]  B. Powe,et al.  Cancer Fatalism Among Elderly African American Women , 2001 .

[34]  Chi-Ren Shyu,et al.  Efficient selection of association rules from lymphedema symptoms data using a graph structure. , 2010, AMIA ... Annual Symposium proceedings. AMIA Symposium.

[35]  V. Thompson Factors Affecting the Level of African American Identification , 1990 .

[36]  Stephen D. Bay,et al.  Detecting Group Differences: Mining Contrast Sets , 2001, Data Mining and Knowledge Discovery.

[37]  S. Chaiken,et al.  Communication modality as a determinant of message persuasiveness and message comprehensibility. , 1976 .

[38]  B. Powe,et al.  Fatalism among elderly African Americans: Effects on colorectal cancer screening , 1995, Cancer nursing.