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.
Lozano PM, Bobb JF, Kapos FP, Cruz M, Mooney SJ, Hurvitz PM, Anau J, Theis MK, Cook A, Moudon AV, Arterburn DE, Drewnowski A. Residential density is associated with BMI trajectories in children and adolescents: Findings from the Moving to Health study. AJPM Focus. 2024 Mar 15;3(3):100225. doi: 10.1016/j.focus.2024.100225. eCollection 2024 Jun. PubMed
Cruz M, Wei A, Hardin J, Radunskaya A. Long-term averages of the stochastic logistic map. J Differ Equ Appl.1-29. https://doi.org/10.1080/10236198.2024.2316379.
Rosenberg DE, Cruz MF, Mooney SJ, Bobb JF, Drewnowski A, Moudon AV, Cook AJ, Hurvitz PM, Lozano P, Anau J, Theis MK, Arterburn DE. Neighborhood built and food environment in relation to glycemic control in people with type 2 diabetes in the moving to health study. Health Place. 2024;86:103216. doi: 10.1016/j.healthplace.2024.103216. Epub 2024 Feb 23. PubMed
Simon GE, Cruz M, Boggs JM, Beck A, Shortreed SM, Coley RY. Predicting outcomes of antidepressant treatment in community practice settings. Psychiatr Serv. 2023 Dec 5:appips20230380. doi: 10.1176/appi.ps.20230380. [Epub ahead of print]. PubMed
Yeung L, Cruz M, Tsiao E, Watkins JB, Sullivan SD. Drug use and spending under a formulary informed by cost effectiveness. J Manag Care Spec Pharm. 2023 Nov;29(11):1175-1183. doi: 10.18553/jmcp.2023.29.11.1175. PubMed
Study uses geographic data to track change over time.
Models that are easier to explain, use could have better uptake in health care settings.
JSM TV, Aug. 6, 2024