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Purpose. This study investigates the relationship between patterns of health behaviors and the use of cancer-screening tests while controlling for sociodemographic and health system factors. Design. Cross-sectional analysis of the 2000 National Health Interview (NHIS). Setting. Nationally representative sample. Subjects. Adults 50 years and older. Measures. Use of cancer-screening tests, health behaviors, sociodemographic factors, and health system factors from self-reported responses from the NHIS. Sixteen health behavior patterns were identified based on lifestyle recommendations for physical activity, tobacco use, alcohol consumption, and fruit and vegetable consumption. Results. Health behavior patterns, age, educational attainment, usual source of care, and health insurance were significantly associated with the use of breast, cervical, and colorectal cancer screening (p < .05). Approximate B2 for the four models ranged from .067 for colorectal cancer screening in women to .122 for cervical cancer screening. Having a usual source of care was the strongest correlate of screening; the magnitude of associations for health behavior patterns and demographic variables and screening was similar and much smaller than those for usual source of care. Conclusion. These findings demonstrate relationships between patterns of multiple health behaviors and use of recommended cancer-screening tests, even when accounting for factors known to influence test use. This suggests potential for addressing cancer screening in the context of multiple behavior change interventions once barriers to health care access are removed.

<p class="Default">Addressing minority health and health disparities has been a missing piece of the puzzle in Big Data science. This article focuses on three priority opportunities that Big Data science may offer to the reduction of health and health care disparities. One opportunity is to incorporate standardized information on demographic and social determinants in electronic health records in order to target ways to improve quality of care for the most disadvantaged popula­tions over time. A second opportunity is to enhance public health surveillance by linking geographical variables and social determinants of health for geographically defined populations to clinical data and health outcomes. Third and most impor­tantly, Big Data science may lead to a better understanding of the etiology of health disparities and understanding of minority health in order to guide intervention devel­opment. However, the promise of Big Data needs to be considered in light of significant challenges that threaten to widen health dis­parities. Care must be taken to incorporate diverse populations to realize the potential benefits. Specific recommendations include investing in data collection on small sample populations, building a diverse workforce pipeline for data science, actively seeking to reduce digital divides, developing novel ways to assure digital data privacy for small populations, and promoting widespread data sharing to benefit under-resourced minority-serving institutions and minority researchers. With deliberate efforts, Big Data presents a dramatic opportunity for re­ducing health disparities but without active engagement, it risks further widening them.</p><p class="Default"><em>Ethn.Dis;</em>2017;27(2):95-106; doi:10.18865/ed.27.2.95.</p>