Faculty Profile

Rafael  Meza, PhD

Rafael Meza, PhD

  • Associate Chair and Associate Professor, Epidemiology

  • Associate Professor of Global Public Health

  • Co-Leader, Cancer Epidemiology and Prevention Program, UM Rogel Cancer Center
  • M5533 SPH II
  • 1415 Washington Heights
  • Ann Arbor, Michigan 48109-2029
  • Language(s) Spoken:
    • English
    • Spanish

Dr. Meza is associate professor in the Department of Epidemiology at the University of Michigan. He received his BSc in applied mathematics from the Instituto Tecnologico Autonomo de Mexico (ITAM), and his PhD in applied mathematics from the University of Washington. After receiving his PhD, Dr. Meza completed a two-year postdoctoral fellowship at the Fred Hutchinson Cancer Research Center - and a three-year fellowship at the University of British Columbia Centre for Disease Control.

Dr. Meza's research interests lie at the interface of epidemiology, biostatistics and biomathematics. In particular, he is interested in cancer risk assessment and the analysis of cancer epidemiology data using mechanistic models of carcinogenesis. He is also interested in the mathematical modeling of chronic and infectious disease dynamics and its applications in disease prevention public health policy design. Dr. Meza is Coordinating Principal Investigator of the Cancer Intervention and Surveillance Modeling Network (CISNET) lung group, core member and co-leader of the Cancer Epidemiology and Prevention Program at the University of Michigan Rogel Cancer Center. He is also Honorary Professor at the Mexico National Institute of Public Health (INSP), and a member of Mexico’s National System of Investigators (SNI Level II).

Currently, Dr. Meza is developing models to evaluate the impact of screening and smoking cessation on lung cancer risk. Additional projects include the development of methodologies to investigate the effects of infectious disease dynamics on the risk of cancers with infectious disease etiology, particularly HPV-related cancers, assessing the acceptability of HPV screening in indigenous and minority populations in Latin America, and modeling the impact of policies on tobacco use.

PhD, Applied Mathematics, University of Washington, 2006

B.Sc., Applied Mathematics, ITAM (México), 2000

  • Cancer risk assessment, analysis of cancer epidemiology data using mathematical models of carcinogenesis, smoking and lung cancer risk, colon cancer epidemiology, public health policy modeling, mathematical modeling of infectious disease dynamics, contact network epidemiology, cancers with infectious disease etiology. Stochastic processes, applied probability, statistical inference and dynamical systems.

  • Comparative Modeling of Lung Cancer Prevention and  Control Policies
    Sponsor: NIH/NCI (U01CA199284 & U01CA152956)

     Lung cancer is the top cancer killer and smoking remains the leading preventable cause of death in the US. Furthermore, major disparities in smoking and lung cancer exist by education, income, and race/ethnicity. While tobacco control policies are the most effective strategies to prevent lung cancers, lung cancer computed tomography (CT) screening has also been shown to reduce lung cancer risk among heavy current and former smokers. The Cancer Intervention and Surveillance Modeling Network (CISNET) lung group develops and applies population models for lung cancer, quantifying the impact of tobacco control and CT screening on lung cancer and all-cause mortality. To date, this work has focused on the country as a whole and has yet to account for tobacco and lung cancer disparities by subgroup and region. This proposed work will extend existing CISNET lung models to investigate the synergistic impacts of tobacco control policies and lung cancer screening in the US and in middle-income nations, focusing on disparities in both smoking behavior and lung cancer risk. The smoking and lung cancer models will incorporate other factors that reflect different smoking risks such as race/ethnicity, education, income, and geographic location. This will allow for analyses of the effects of tobacco control policies on US smoking prevalence in relevant high-risk groups, and estimation of the impact of policies on health disparities in smoking and lung cancer outcomes.

  • Modeling the Policy Impact on Cigarette and Smokeless Use and on US Mortality
    Sponsor: NIH/NIDA (R01DA036497)

    We are conducting statistical analyses of the transitions to and from cigarette and smokeless tobacco use. The statistical analyses will consider the effect of tobacco control policies on the initiation, cessation, multiple product use and quantity smoked. We will apply the statistical analyses to three existing models, the SimSmoke tobacco control policy model and two natural history of disease models, the Michigan Lung Cancer Model and the Massachusetts General Hospital Lung Cancer Policy Model (LCPM) models. These models were developed as part of the National Cancer Institute's Cancer Intervention and Surveillance Modeling Network (CISNET). A key advance in CISNET has been the collaborative use of multiple models to address a common question using shared inputs, an approach cited for best modeling practices.

    The models will project smokeless tobacco and cigarette use in the US, incorporating multiple product use and the initiation, cessation, and switching between products. We will compare the US population impact of regulations such as: health warnings, retail point-of-sale restrictions and the regulation of product content. The models will consider the impact of  regulations on population smokeless tobacco and cigarette prevalence (in total and by age and gender) and on tobacco- attributable deaths.

  • From Mechanism to Population: Modeling HPV-Related Oropharyngeal Carcinogenesis
    Sponsor: NIH/NCI (U01CA182915) & UM MCubed

    While cervical and other genital cancers are primarily caused by Human Papilloma Virus (HPV) infections, recent studies have demonstrated that HPV is also associated with head and neck (HN) cancers. The prevalence of oral HPV infection among men and women aged 14 to 69 years in the US is about 7%, however, 90% of University of Michigan (UM) oropharyngeal squamous cancer (OPSC) patients carry high-risk HPV. Indeed, the incidence of HPV- associated OPSCs is increasing and OPSC has become the most common HPV-related cancer in the US. HPV has been shown to disrupt several key cancer pathways in oropharyngeal squamous cell lines, including p53 and Rb, but many open questions remain regarding oral HPV transmission epidemiology, infection and persistence, the mechanisms of HPV HN carcinogenesis, and the connection between the ongoing oral HPV epidemic and the rising OPSC incidence. The overarching goal of this proposal is to understand the mechanistic effects of HPV infection on the regulatory pathways of oropharyngeal carcinogenesis, and how these effects in turn shape the observed age-specific incidence and mortality of OPSCs. This problem is inherently multi-scale, as population level HPV transmission drives dynamic, ongoing changes to intracellular cancer regulatory pathways, which in turn drives population-level trends in cancer incidence and mortality. Thus, understanding the rising incidence in OPSC necessitates tying together both the population level processes of infectious disease and the population-level cancer incidence through the mechanistic interactions between HPV and carcinogenesis. Toward this goal, we will develop systems biology models of the main proliferation regulatory networks affected by HPV, and assess the consequences of HPV infection, integration and alternate transcripts on the dynamics of HPV-positive tumor cell proliferation. We will integrate these mechanistic infection and cancer models into multistage models of carcinogenesis to gauge the impacts of HPV infection on the population-level age-specific incidence and mortality of OPSC. We will use these integrated multi-scale cancer models in combination with population-level oral HPV transmission models to predict the effects of current HPV prevalence trends on future rates of OPSCs and the potential impact of vaccination and other prevention strategies. Our systems models will be based on multi-scale inference using mechanistic infection and cancer data.

  • Assessing the Potential of HPV Screening in Guatemala
    Sponsor: MCubed

    Cervical cancer is the most common cancer in Guatemalan women, leading to a significant number of unnecessary deaths and considerable social and economic costs. Although pap smear screening is an effective way of preventing cervical cancer, limited access to care, particularly among indigenous populations, as well as other socioeconomic barriers result in low screening rates in Guatemala. HPV is the cause of a majority of cervical cancers. Thus, HPV testing, and in particular self-collection HPV testing, has been proposed as an alternative to pap smears for developing countries. We propose to evaluate the feasibility of HPV self-collection testing in indigenous and Afro Caribbean populations in Guatemala. As a separate aim we will measure the follow-up and treatment rates of positive cervical cancer screens in the rural Mayan community of Santiago Atitlán, Guatemala and the Garifuna community of Livingston Izabal, as a way to reduce cervical cancer mortality in the region.

Society of Mathematical Biology

Society of Industrial and Applied Mathematics