Maricela Cruz, PhD, strives to create useful and widely applicable methodology to tackle real-world problems in public health. Her research primarily focuses on developing novel statistical methods to assess and evaluate the impact of complex health care interventions. In her research, she uses statistical techniques for interrupted time series, time series, correlated data, change point detection, longitudinal data and interventions research.
Dr. Cruz received her PhD in statistics from the University of California Irvine. During her time there, she was a National Science Foundation Graduate Research Fellowship awardee and Eugene Cota-Robles fellow. She worked with care delivery experts and practitioners to design and conduct statistical analyses that assessed health care interventions across multiple hospitals and hospital units. She developed two novel interrupted time series models that allow for complex correlation structures that can change post intervention and are adequate for single- and multi-unit continuous data. To communicate and encourage access to her methods by non-statisticians in the broader public health community, Dr. Cruz produced an application in R Shiny that analyzes interrupted time series data. As a graduate student, she was a summer associate at the RAND Corporation. While at RAND, she led a collaborative study examining the relationship between group cohesion and climate with alcohol use outcomes in a group therapy intervention for individuals with a first-time offense for driving under the influence.
At KPWHRI, Dr. Cruz works alongside researchers in behavioral health, mental health, and insurance design. She explores the relationship between weight change and diabetes measures and the built environment, aids in the development of suicide risk prediction algorithms, and evaluates interventions that encourage high-value use of health care services.
Buszkiewicz JH, Bobb JF, Kapos F, Hurvitz PM, Arterburn D, Moudon AV, Cook A, Mooney SJ, Cruz M, Gupta S, Lozano P, Rosenberg DE, Theis MK, Anau J, Drewnowski A. Differential associations of the built environment on weight gain by sex and race/ethnicity but not age. Int J Obes (Lond). 2021 Aug 27. doi: 10.1038/s41366-021-00937-9. [Epub ahead of print]. PubMed
Cruz M, Pinto M, Gillen DL, Ombao H. RITS: a toolbox for assessing complex interventions via interrupted time series models. BMC Med Res Methodol. 2021 Jul 8;21(1):143. doi: 10.1186/s12874-021-01322-w. PubMed
Coley RY, Walker RL, Cruz M, Simon GE, Shortreed SM. Clinical risk prediction models and informative cluster size: assessing the performance of a suicide risk prediction algorithm. Biom J. 2021 Oct;63(7):1375-1388. doi: 10.1002/bimj.202000199. Epub 2021 May 24. PubMed
Buszkiewicz JH, Bobb JF, Hurvitz PM, Arterburn D, Moudon AV, Cook A, Mooney SJ, Cruz M, Gupta S, Lozano P, Rosenberg DE, Theis MK, Anau J, Drewnowski A. Does the built environment have independent obesogenic power? urban form and trajectories of weight gain. Int J Obes (Lond). 2021 Sep;45(9):1914-1924. doi: 10.1038/s41366-021-00836-z. Epub 2021 May 11. PubMed
Bender M, Williams M, Cruz MF, Rubinson C. A study protocol to evaluate the implementation and effectiveness of the clinical nurse leader care model in improving quality and safety outcomes. Nurs Open. 2021 May 3. doi: 10.1002/nop2.910. Online ahead of print. PubMed
Models that are easier to explain, use could have better uptake in health care settings.
A new study finds that moving from low- to high-density neighborhoods might be related to reductions in weight gain.
New research suggests fast food and other aspects of built environments don’t affect weight, contrary to earlier findings.