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BSCD Summer Public Health Research Fellowship - 2023

BSCD Undergraduate Summer Fellowship in Public Health Research

The University of Chicago Department of Public Health Sciences (PHS) seeks to engage college students in mentored research projects in public health. PHS is a multidisciplinary department in the Biological Sciences Division that includes core public health fields including biostatistics, epidemiology, health economics, health services research, health behavior, and global health. The department mission is to improve the health of human populations and reduce disparities by expanding knowledge of factors that affect health, by advancing diverse methods for carrying out such research, and by training the next generation of innovative public health scholars and professionals. PHS faculty design and implement observational and experimental studies in both community and clinical settings, and develop and implement complex analytic methods to understand the determinants of health, the efficacy of interventions, and the structure and financing of health care at the population level.

A primary objective of the BSCD Undergraduate Summer Fellowship is to provide undergraduate students an immersive research experience through close interactions with faculty, research teams, and research projects. Projects will focus on interdisciplinary topics that bring biostatistical, quantitative and qualitative methods to improve understanding of complex problems in population health and develop new solutions.
The Fellowship covers a $5000 stipend, plus the $350 Student Life fee for the summer research period. 
Duties and Responsibilities 
Fellowships will be 10 weeks in duration and based in Chicago. Fellows will work with their faculty mentors on research projects. Project descriptions are provided below. Applicants need to identify interest in working on one or more of the projects in their application. Projects typically involve data analysis using a computer except where noted.
Requirements vary based on the project. Please see the project descriptions. 
Class Level Eligibility 
Eligibility varies based on the project. Please see the project descriptions. 
Required Materials
Applications should include the following: 
·      Statement of Interest or Cover Letter: Approx. 250 words. Please state here the project (or multiple projects), to which you are applying (see Project Descriptions below for full list).
·      Resume or CV
·      Unofficial Transcript
Expiration Date
April 9th, 2023
Please Note: If you are applying to multiple BSCD Fellowship Grants, please fill out the following BSCD Preference Form -
Shortlisted candidates will be interviewed by a faculty panel.
Topic 1: Breast Cancer Health Disparity
Breast cancer is the most common malignancy affecting women in the U.S. and the world. There is a gap in breast cancer mortality between African Americans and European Americans. We have an on ongoing program aimed at understanding the socioeconomic, biological, genomic, and health care delivery factors affecting racial disparity, and developing intervention to eliminate disparity and improve health outcomes of all breast cancer patients. We have conducted surveys to inquiry quality of life among breast cancer patients during Covid-19 pandemic and will conduct another yearly survey on quality of life after the peak of another contagious variant. Undergraduate researchers can help with this research by participating in the following types of activities under faculty supervision:
  • Epidemiological questionnaire development, interview, and data entry;
  • Conducting systematic reviews of the literature; and
  • Data clean and analysis.
REQUIREMENTS: Experience manipulating datasets using a statistical package or programming language (e.g. R, Stata, SAS, Python) is preferred. 
CLASS LEVEL ELIGIBILITY: UChicago Undergraduate students at all levels are eligible.
Topic 2: Encouraging bicycling in low- and middle-income countries for health and climate benefits
Large-scale shifts towards non-motorized transportation, such as walking and cycling, are vital to reducing and managing non-communicable diseases and transport-related carbon emissions in low- and middle-income countries (LMICs). Promoting such a shift necessitates research on the current state of bicycling and whether the roads are safe for bicycling. Bicyclists and pedestrians in LMICs are highly vulnerable to road traffic injuries. We are conducting mixed-methods research to study the use, access, risks, and barriers to bicycling in LMICs, build tools to audit current road infrastructure, and inform public policy on urban planning.
Undergraduate researchers can help with this research by participating in the following types of activities under faculty supervision:
·      Extracting and tabulating information from public data sources (household and education surveys, newspaper reports, police records) on the prevalence and barriers to bicycling and walking
·      Thematic qualitative coding and analysis of interview, newspaper, and audio-visual data
·      Mapping district and street-level information in areas with high levels of bicycling (e.g., help identify clusters of specific institutions, zones, or infrastructural features relevant to bicycling)
REQUIREMENTS: Prior experience with qualitative research tools (e.g., NVivo), data manipulation tools, or mapping tools (e.g., QGIS, ArcGIS, or Google Earth) with an interest in conducting spatial analysis would be valuable.
CLASS LEVEL ELIGIBILITY: UChicago undergraduate students at all levels are eligible.

Topic 3: Developing Health Intervention Trainings for Community Health Workers
(1) Tobacco Cessation Curriculum Development Project: Although individuals with low-income have a high prevalence of smoking, this population is less likely to receive assistance with quitting. Community health workers (CHW), who work directly with tobacco-related disparity populations, have an increasing role in tobacco cessation programs. However, existing tobacco cessation trainings are costly and time-consuming. Our research group is conducting a needs assessment to understand tobacco cessation practices in CHW, and we aim to develop a tailored training and curriculum for CHW when interacting with members in the community at high risk for tobacco use.
(2) Training CHW to Increase Advance Care Planning among Black Women with Breast Cancer Project: Although breast cancer mortality rates have declined significantly in recent decades, black women, who have a lower incidence of the disease, still die from breast cancer at disproportionate rates compared to white women. Because of the increased likelihood of morbidity and mortality after a breast cancer diagnosis, engaging in advanced care planning (ACP) is critical for black women to plan for future medical treatment and end-of-life care. For our study, we will examine current practices of discussing ACP with black women with breast cancer and examine strategies to facilitate the integration of CHW in ACP with black women with breast cancer.
Undergraduate researchers can help with these research projects by participating in the following types of activities under faculty supervision:
·      Assisting in the creation and editing of curriculum based on data collected from focus groups
·      Assisting in organizing and conducting focus groups with CHW in local community health organizations
·      Transcribing data from CHW focus groups
·      Conducting systematic reviews of the literature on the health disparities related to above projects
·      Conducting descriptive data analyses
REQUIREMENTS: Experience with database management (e.g., REDCap) is required, and experience manipulating datasets using a statistical package (e.g. SAS) is preferred. 
CLASS LEVEL ELIGIBILITY:  Undergraduate students at all levels are eligible.
Topic 4: Explainable machine learning predictions in public health
As machine learning prediction methods are increasingly being used in public health and medicine, there is a need to improve methods for interpretability and explainability of the predictions. While most machine learning predictors are deterministic mathematical functions, they often include a large numbers of parameters and are difficult to directly interpret relative to a linear regression or classification tree predictor. This project will focus on increasing awareness on methods for feature importance and their role in explainable machine learning. Some of the proposed methods include:
·      Accumulated Local Effects [1]
·      Permutation feature importance [2]
·      Local surrogate models [3]
Using a series of case studies and vignettes, we will explore how these methods are similar and where they differ in the explanation they would provide for a given predictor. For example, in the case with a feature with low prevalence, but high association with the outcome, this feature will appear highly statistically significant in the regression model with a large beta coefficient, but will likely not appear as a top feature using many permutation feature importance metrics. Understanding how these approaches differ will help guide how the methods should be used when attempting to explain predictors.         
[1]: Apley DW, Zhu J, “Visualizing the effects of predictor variables in black box supervised learning models.” Journal of the Royal Statistical Society: Series B (Statistical Methodology) 82.4 (2020)
[2]: Breiman L, “Random Forests.” Machine Learning 45(1):5-32 (2001)
[3]: Marco Tulio R, Singh S, Guestrin C, “Why should I trust you?: Explaining the predictions of any classifier.” Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. ACM (2016)
REQUIREMENTS: Experience simulating datasets and training machine learning algorithms using a statistical computing language (e.g. R or Python) is required. 
CLASS LEVEL ELIGIBILITY: Undergraduate students at all levels are eligible.

Topic 5: HIV Prevention and Care
The Chicago Center for HIV Elimination maintains a large database of electronic medical record data for patients at the University of Chicago tested for HIV or treated for HIV. The database is used to understand patterns in HIV testing and prevention as well as health outcomes among people with HIV. Undergraduate researchers can help with this research by participating in the following types of projects using this electronic database.
·      Identifying missed opportunities for HIV testing 
·      Examining patterns in care outcomes (e.g., viral suppression, immunization status, health screenings) among patients with HIV
·      Examining patterns in PrEP use among patients in different departments
REQUIREMENTS Experience manipulating datasets using a statistical package (e.g. R, Stata, SAS) is required. 
CLASS LEVEL ELIGIBILITY Undergraduate students at all levels are eligible.
FACULTY SPONSORS: Moira McNulty and Jessica Ridgway 

Topic 6: Medical care costs and health insurance
The United States spends more per capita than any other country in the world on health care. That high spending is often shifted to individuals. Medical bills are a leading cause of bankruptcy and many low- to middle-income Americans say they forego medical care because of costs. Researchers in Public Health Sciences have ongoing projects related to health care spending including estimating the effects of high patient costs on health care access, describing disparities in access to care due to cost, and understanding how costs impact individuals and health systems.
Undergraduate researchers can help with this research by participating in the following activities under faculty supervision:
·      Analyzing and interpreting medical spending data, especially medical claims datasets, to look for patterns by insurance type and patient costs
·      Extracting information from public data sources to describe differences across income in financial burden of health care costs or health impact of high costs
·      Conducting systematic reviews of literature on insurance plan structures and their effect on patient costs
·      Generating data on uncompensated care at hospitals and large clinics and health provider efforts to alleviate uncompensated care
REQUIREMENTS: Basic understanding of the U.S. health care system and experience manipulating datasets using a statistical package (e.g. R, Stata, SAS) are preferred but not required. 
CLASS LEVEL ELIGIBILITY Undergraduate students at all levels are eligible.

Topic 7: Role of Genetics in How Environment Affects Cancer Risk
Risk for cancer and other complex diseases is influenced by inherited genetic risk factors as well as lifestyle and environmental exposures. Ongoing research in the Department of Public Health Sciences is focused on understanding how genetic variation influences or alters the effects of environmental exposures and biomarkers on human health and biology. Areas of ongoing research include (1) telomere length as a biomarker of aging and cancer risk, (2) methods for assessing causal relationships among risk factors, biomarkers, and disease, (3) genome-wide association studies, and (4) susceptibility to the effects of environmental exposure to arsenic, a known carcinogen. Long term goals are to reveal biological mechanisms of disease susceptibility, identify potential targets for pharmacological intervention, and provide strategies for identifying high-risk individuals. Undergraduate students having taken statistical coursework can participate in conducting statistical analyses of genetic and environmental data to understand the determinants of health outcomes in the context of large epidemiological datasets.
REQUIREMENTS: Prior coursework in statistics or epidemiology and some experience using statistical software are required. Prior coursework in genetics is preferred, but not essential.
CLASS LEVEL ELIGIBILITY: Undergraduate students at all levels are eligible.

Topic 8: Cancer Prevention to Eliminate Disparities
Preventing and eliminating cancer disparities is key to achieving optimal and equitable health for all populations. The availability of screening tests to detect breast, cervical, colorectal and lung cancer early, the HPV vaccine to prevent HPV-related cancers, and programs to help patients stop smoking are great public health accomplishments; however, there are segments of the population that still do not receive the full benefits of these behaviors. All of these health behaviors require individuals to interact with health care provider teams and systems. Effective interventions must take into account the local community and policy context and must be easy to implement and sustain. Further, as new technologies (e.g., home-based HPV self-sampling) prove effective and are incorporated into clinical guidelines, the need for appropriate and effective communications to transfer knowledge from “bench to bedside” will be even greater in order to maximize the potential of these new technologies in reducing cancer morbidity and mortality. Public Health Sciences has a developing program to understand multilevel determinants of these cancer prevention behaviors and design and evaluate interventions promoting them in Chicagoland populations. Undergraduate researchers can help with this research by participating in the following types of activities under faculty supervision:
- Acquiring and extracting information from public data sources on the burden of various cancer types.
- Conducting systematic reviews of the literature on the effectiveness of interventions promoting various cancer prevention behaviors
- Developing surveys and semi-structured interview guides and fielding these data collection tools in local communities to understand factors influencing behavioral adoption 
REQUIREMENTS: Experience developing data collection tools in Redcap and manipulating datasets using a statistical package (e.g. R, Stata, SAS) is required. 
CLASS LEVEL ELIGIBILITY: Undergraduate students at all levels are eligible.

Topic 9: Impact of biased data on epidemiologic modeling performance in the context of COVID-19 pandemic
The COVID-19 pandemic has been a hard lesson for many countries that were unprepared to respond to the public health crisis in a timely and efficient way. Three years later, effective vaccines and evolving herd immunity are allowing countries, including the US, to resume normal lives and learn from the successes and failures of the pandemic response. Among many public health measures employed during the crisis, modeling played a crucial role in epidemic forecasting. While models can be extremely useful, they also need to be handled with care so that they don’t unintentionally provide a false sense of confidence in what the future holds. Models are informed by data. Even the most sophisticated models could fail to meet their objectives if the data used for their calibration is severely biased. As part of the NSF-funded Robust Epidemic Surveillance and Modeling (RESUME) project, we aim to investigate the impact of biased data on the performance of modeling studies in terms of parameter estimation, forecasting, and decision making. 
Undergraduate researchers can help with this research by participating in the following types of activities under faculty supervision:
·   Conducting computer simulation studies by incorporating a wide range of assumptions about the relationship between the “observed data” (case counts, hospitalizations, deaths) and the “ground truth”
·   Calibrating model parameters to “observed data” and characterizing bias in parameter estimates resulting from biased observations 
·   Characterizing bias in model-based forecasting resulting from biased observations and parameter estimates
REQUIREMENTS: Coding skills and experience using the R statistical computing language or Python is required; experience applying statistical estimation techniques to complex data is desired, but not required.
CLASS LEVEL ELIGIBILITY: UChicago undergraduate students at all levels are eligible.