Predict to Prevent: Dynamic Spatiotemporal Analyses of Opioid Overdose to
Guide
Pre-Emptive Public Health Responses (1R01DA054267-01A1; MPIs: Stopka, Bauer)
The Tufts University School of Medicine’s
Department of Public Health and Community Medicine invites applications for a Postdoctoral Fellowship in spatial epidemiology,
biostatistical, and geostatistical analyses focused on the opioid overdose crisis.
Opioid overdose (OD) fatalities have reached
crisis levels in all socioeconomic and geographic communities in the US and have been exacerbated by the COVID-19 pandemic. By utilizing a
first-of-its-kind statewide Public Health Data Warehouse (PHD) with multiple linked administrative datasets and state-of-the-art Bayesian
spatiotemporal models, we are in a unique position to help fill in the fundamental gaps in the field’s ability to rapidly identify
current OD patterns, predict future OD epidemics, and evaluate the effectiveness of public health and clinical interventions.We aim to hire
a Postdoctoral Fellow to join our multidisciplinary team as we begin to develop a new population health analytic framework to support opioid
OD control in Massachusetts that can be generalizable to other parts of the country. We aim to: 1) Develop a Bayesian multilevel
spatiotemporal model to identify individual, interpersonal, community, and societal factors that contribute to opioid OD; 2) develop an
efficient Bayesian spatiotemporal model to identify time-space OD clusters and extend the model to construct a dynamic predictive model;
and, 3) evaluate and predict policy and intervention effects through model-based simulation studies to provide practical guidance and
decision-making support to public health officials. We will develop visualization tools, analytical approaches, and related code, in
collaboration with the Massachusetts Department of Public Health (MDPH) and our Community Advisory Board (CAB), to enhance data warehouse
capabilities and improve dissemination of findings.
This Fellowship provides opportunities to work with Dr. Thomas Stopka at the
Tufts University School of Medicine, as well as investigators from The University of Texas-Houston, Boston University, the University of
Massachusetts-Lowell, and MDPH. The Fellowship includes opportunities to contribute to Bayesian spatiotemporal and geostatistical modeling
to identify and characterize overdose hotspots, predict future OD spikes across space and time, and assess the effectiveness of individual
and combined interventions aimed at preventing ODs.
We would particularly welcome applicants with extensive experience
conducting research focused on the opioid overdose epidemic in the U.S., with broad expertise in substance use and misuse, spatial
epidemiology, Bayesian spatiotemporal modeling in SAS and R, and data science. Candidates will have opportunities to apply these skill sets
while collaborating on a study funded by the National Institute on Drug Abuse.
This is a three-year appointment beginning during the
Summer of 2022. There is a possibility of renewal for two additional years.
Qualifications
·
Completion of a PhD (in epidemiology, biostatistics, or a health-related field), DrPH, or similar doctoral degree.
· Research
experience in the social sciences and data science
· Coursework in advanced statistics, GIS, spatial epidemiology and Bayesian
spatiotemporal analyses required
· Extensive experience with statistical software packages (e.g., SAS, R) to manage and
analyze massively linked quantitative data
· Ability to conduct complex multi-level Bayesian spatiotemporal and geostatistical
analyses, and to develop multivariable statistical models
· Ability to create static and dynamic (i.e., online interactive)
GIS maps and develop and manage a public-facing online data dashboard
· Experience working with ArcGIS, SatScan, CrimeStat,
QGIS are also desirable
· A strong publication track record is highly desirable
· Ability to work well under
pressure, understand and follow policies and procedures, accommodate change, and meet deadlines
· Ability to take
responsibility for assignments, work independently, and as part of a team
· Ability to handle confidential materials with
discretion
Trainees must be citizens or a non-citizen national of the United States or have been lawfully admitted for permanent
residence at the time of appointment.
Interested candidates should submit a letter of interest, curriculum vitae, and a list of three
recommenders (name and contact information), as well as a brief statement (1-2 pages) describing their background and research training.
These materials should be submitted via Interfolio at: http://apply.interfolio.com/107649 by July 15, 2022 or until the position is filled.
Tufts University,
founded in 1852, prioritizes quality teaching, highly competitive basic and applied research, and a commitment to active citizenship
locally, regionally, and globally. Tufts University has also committed to becoming an anti-racist institution and prides itself on the
continuous improvement of diversity, equity and inclusion work. Current and prospective employees of the university are expected to have and
continuously develop skill in, and disposition for, positively engaging with a diverse population of faculty, staff, and students.
Tufts University is an Equal Opportunity/Affirmative Action Employer. We are committed to increasing the diversity of our faculty
and staff and fostering their success when hired. Members of underrepresented groups are welcome and strongly encouraged to apply. See the
University’s Non-Discrimination statement and policy here https://oeo.tufts.edu/policies-procedures/non-discrimination/. If you are an applicant with a disability
who is unable to use our online tools to search and apply for jobs, please contact us by calling the Office of Equal Opportunity (OEO) at
617-627-3298 or at [email protected]. Applicants can learn
more about requesting reasonable accommodations at https://oeo.tufts.edu/
For technical support please visit https://support.interfolio.com/ or email: [email protected] or phone: 877-997-8807.
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