University of Puerto Rico
Rio Piedras Campus
Deanship of Academic Affairs
Honors Study Program
Years of Potential Life Lost in Puerto Rico due to COVID-19
YPLL in PR due to COVID-19
Mortalidad en PR por COVID
3 credit hours
|Humberto Ortiz-Zuazaga, PhD
|Monday and Tuesday 1:00 - 3:00 PM
|or by appointment
This course will give participants an introduction to data science in public health. We will use the publically available data on mortality in Puerto Rico and open source tools for data science to investigate the impacts of the COVID-19 pandemic on public health in Puerto Rico. Years of Potential Life Lost, or YPLL, is a public health measure that seeks to quantify the effects of a process on society.
Through case studies and project based learning, the student will learn about challenges and opportunities in the use of public mortality data, data science concepts, and how to present research findings on the societal impacts of COVID-19 in a way to inform public discourse.
The student will work independently on their YPLL research proposal under the supervision of the researcher. There will be online weekly meetings to discuss the progress made and approaches to advance the completion of the proposal. At the end of the semester the student will submit and defend a research proposal to the Honors Study Program.
By the end of the semester the student is expected to achieve the following objectives:
- Be able to manage research data sets
- Apply data science principles to clean, normalize and analize data sets
- Communicate results of experiments and analysis in written and oral forms
- Understand reproducibility issues in science, and the tools to manage reproducibility
- Understand ethical issues in the collection, dissemination and analysis of public health data, and health disparities
- Understand how public health measures can help understand complex phenomena
- Prepare and defend a research proposal on Years of Potential Life Lost
One contact hour per week, by agreement. Due to the COVID-19 pandemic, meetings will likely be virtual.
- Introduction to data science, data provenance, repositories, the R and bioconductor programming environments. Reproducibility.
- Literature review of Years of Potential Life Lost and public health. Identification of data sets for analysis and comparison with Puerto Rico.
- Analysis of mortality data from Puerto Rico and comparable jurisdictions. Comparison of YPLL across years, across causes of death, and across jurisdictions.
- Preparation and defense of research proposal.
- Presentation of research results, dissemination of results via research repositories.
Alternative Teaching Methods
Certification No. 112 (2014-2015) of the Governing Board defines a classroom course as a course in which 75% or more of the hours of instruction require the physical presence of the students and the teacher in the classroom. This means that 25% of a classroom course could be offered without requiring the physical presence of the students and the teacher in the classroom. If necessary, this course will be able to complete up to 25% of the contact hours (11.25 hours) on a non-face-to-face basis by alternative methods such as: videoconferences, instructional modules, discussion forums and others. If so, the calendar/agenda will be modified to include the topics that will be covered by alternative methods.
The student will need a computer with internet access to participate in virtual lab meetings and prepare analyses and reports.
Irizarry, Rafael A. Introduction to data science: Data analysis and prediction algorithms with R. CRC Press, 2019. ISBN 9780367357986 https://rafalab.github.io/dsbook/
We will use the R software environment and the bioconductor project to analize and present results.
The course will primarilly be structured as a Course-based Undergraduate Research experience. At the start of the semester, several brief meetings will present materials and techniques. The student will then work independently on research problems where they will apply the techniques and prepare a written report. At the end of the semester the student will present their research proposal to the Honors Program and defend it.
Students work will be evaluated on a 100% basis with the standard curve. Work will be graded with letter grades A, B, C, D, or F.
- Participation in lab meetings, 25% final grade
- Written proposal and defense, 50% final grade
- Lab reports, 25% final grade
REGULATION ON DISCRIMINATION BY SEX AND GENDER IN THE FORM OF SEXUAL VIOLENCE
The University of Puerto Rico prohibits discrimination based on sex, sexual orientation, and gender identity in any of its forms, including that of sexual harassment. According to the Institutional Policy Against Sexual Harassment at the University of Puerto Rico, Certification Num. 130, 2014-2015 from the Board of Governors, any student subjected to acts constituting sexual harassment, must tum to the Office of the Student Ombudsperson, the Office of the Dean of Students, and/or the Coordinator of the Office of Compliance with Title IX for an orientation and/or a formal complaint.
The University of Puerto Rico complies with all state and federal laws and regulations related to discrimination, including “The American Disabilities Act” (ADA law) and Law #51 from the Puerto Rico Commonwealth (Estado Libre Asociado de Puerto Rico). Every student has the right to request and receive reasonable accommodation and Vocational Rehabilitation Services (VRS). Those students with special needs that require some type of particular assistance or accommodation shall explicitly communicate it directly to the professor. Students who are receiving VRS services shall communicate it to the professor at the beginning of the semester so that appropriate planning and the necessary equipment may be requested according to the Disabilities Persons Affairs Office (Oficina de Servicios a Estudiantes con Impedimentos –OSEI) from the Students’ Deanship office. Any other student requiring assistance or special accommodation shall also communicate directly with the professor. Reasonable accommodations requests or services DO NOT exempt the student from complying and fulfilling academic and course related requirements and responsibilities.
The University of Puerto Rico promotes the highest standards of academic and scientific integrity. Article 6.2 of the UPR Students General Bylaws (Board of Trustees Certification 13, 2009-2010) states that academic dishonesty includes, but is not limited to: fraudulent actions; obtaining grades or academic degrees by false or fraudulent simulations; copying the whole or part of the academic work of another person; plagiarizing totally or partially the work of another person; copying all or part of another person answers to the questions of an oral or written exam by taking or getting someone else to take the exam on his/her behalf; as well as enabling and facilitating another person to perform the aforementioned behavior. Any of these behaviors will be subject to disciplinary action in accordance with the disciplinary procedure laid down in the UPR Students General Bylaws.
To ensure user data integrity and security, hybrid and distance education courses are offered through the institutional learning management system, which employs secure connection and authentication protocols. The system authenticates the users’ identity with the username and password of their institutional accounts. Users are responsible for keeping their password secure and not sharing with others.
Humberto Ortiz-Zuazaga, Roberto Arce-Corretjer, Juan M. Solá-Sloan, José G. Conde. SalHUD – A Graphical Interface to Public Health Data in Puerto Rico. International Journal of Environmental Research and Public Health. Vol 13, No 1, 18, 2015. doi:10.3390/ijerph13010018
Stephen J. Elledge. 2.5 Million Person-Years of Life Have Been Lost Due to COVID-19 in the United States medRxiv 2020.10.18.20214783; doi: https://doi.org/10.1101/2020.10.18.20214783
Ramirez de Arellano, A.B. The death divide: Differentials in premature mortality by gender in Puerto Rico. Bol. Asoc. Med. P. R. 1992, 84, 11–14.
Huber, W., Carey, V., Gentleman, R. et al. Orchestrating high-throughput genomic analysis with Bioconductor. Nat Methods 12, 115–121 (2015). https://doi.org/10.1038/nmeth.3252
R Core Team (2017). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.
Irizarry, Rafael A. Introduction to data science: Data analysis and prediction algorithms with R. CRC Press, 2019.