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BSCD Summer Public Health Research Fellowship - 2024

The University of Chicago Medicine & Biological Sciences
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 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 $5500 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

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

Please submit all materials as PDF files. 

Expiration Date

April 1st, 2024

Please Note: If you are applying to multiple BSCD Fellowship Grants, please fill out the following BSCD Preference Form -  https://careeradvancement.wufoo.com/forms/bscd-research-2024/

Interviews

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.

FACULTY SPONSOR: Dezheng Huo (https://profiles.uchicago.edu/profiles/display/37017)

 

Topic 2: How have sleep habits changed with increased working from home?

For working adults, the need to get up in time to commute to work is one determinant of people sleeping shorter hours than they would like. With the pandemic, there has been a dramatic change in work and commuting, with increasing numbers of people working from home on at least some days. The Bureau of Labor Statistics collects annual population-based samples of the population that include 24-hour time diaries (ATUS: American Time Use Survey), which is a record of when each activity in the 24 hours stops and starts. “Sleep” is one of the activities, as are transportation and work. While these data are collected because of interest in labor economics, these data have sometimes been used to examine trends in sleep hours and determinants of sleep hours, such as unemployment. The project requires data science and statistical competencies. If successful, this is likely to result in a publication.

Undergraduate researchers can help with this research by participating in the following types of activities under faculty supervision:

  • Understand the data structure and download four years of the ATUS files. Prepare them for analysis and carry out statistical tests of differences in bedtimes, waketimes and sleep durations, by year and in terms of whether there is work with or without commuting time.
  • Conduct literature review of previous relevant studies.

REQUIREMENTS: A strong foundation in statistics including regression analysis, such as Statistics 224 Applied Regression and experience with computer programming. Programming experience in Stata preferred, but R is also acceptable.

CLASS LEVEL ELIGIBILITY: UChicago undergraduate students at all levels are eligible as long as they have the statistics prerequisite

FACULTY SPONSOR: Diane Lauderdale (http://health.bsd.uchicago.edu/PersonProfile/Diane-Lauderdale)


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 adapting a tobacco cessation curriculum to be specific to the CHW model of care (i.e., C.H.A.N.G.E. training). We aim to develop a scalable tailored training and curriculum for CHW when interacting with members in the community at high risk for tobacco use. We will conduct trainings with CHW who serve socially disadvantaged populations, and evaluate the trainings effectiveness on knowledge change and training adoption among CHWs. 

(2) Community Health Worker Perspectives on Healthy Lifestyle Counseling and Weight Stigma: The project uses focus groups to elicit CHW perspectives on the concept of weight stigma and how it may or may not affect the conversations they have with clients about healthy lifestyle. We will elicit perspectives on their current standard of care/approach to conversations about nutrition and exercise and ability to connect clients to needed resources and care and address barriers related to social determinants of health. 

              Undergraduate researchers can help with these research projects by participating in the following types of activities under faculty supervision: 

  • Assisting in preparation of training curriculum materials for the C.H.A.N.G.E. trainings 
  • Assisting in editing of curriculum based on data collected from trainings 
  • Assisting in organizing and conducting qualitative interviews with CHW in local community health organizations 
  • Transcription of qualitative interviews 
  • Conducting systematic reviews of the literature on the health disparities related to above projects 
  • RedCap management and analyses 
  • 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. 

FACULTY SPONSOR: Marcia Tan (https://health.uchicago.edu/faculty/marcia-tan-phd)

 

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.

FACULTY SPONSOR: Eric Polley (https://health.uchicago.edu/faculty/eric-polley-phd)

 

Topic 5:  Climate change and mental health

 Evidence suggests that elevated ambient temperatures can produce physiological stressors that precipitate poor mental health. Those experiencing other systemic, structural, and socioeconomic stressors may be at increased risk. The Departments of Public Health Sciences and Family Medicine are developing a new mixed-methods project to evaluate the impacts of temperature on mental health in a cohort of lower-income Black mothers who are residing in Chicago’s south side. Undergraduates can help with this research by participating in the following types of activities under faculty supervision:

  • Developing qualitative interview guides focused on climate and other intersecting psychosocial stressors;
  • Conducting systematic reviews of the literature on the impacts of temperature on mental health in high risk populations;
  • Supporting qualitative data collection and the management of qualitative data; 
  • Preparation of qualitative data for analysis.

REQUIREMENTS: Experience with qualitative data collection or analysis preferred

 

CLASS LEVEL ELIGIBILITY: Undergraduate students at all levels are eligible.

FACULTY SPONSOR: Kate Burrows (https://biologicalsciences.uchicago.edu/faculty/kate-burrows )

 

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.

FACULTY SPONSOR: Betsy Cliff https://biologicalsciences.uchicago.edu/faculty/betsy-cliff-phd

 

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.

FACULTY SPONSOR: Brandon Pierce

(http://health.bsd.uchicago.edu/PersonProfile/Brandon-Pierce)

 

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.

FACULTY SPONSOR: Jasmin Tiro (https://newfaculty.uchicago.edu/staff-directory/jasmin-tiro/)

 

Topic 9: Policies and Place: harnessing data science to understand and address racial/ethnic health inequities

Racial/ethnic health inequities are pervasive in the US and largely shaped by structural drivers and social determinants across regions in the US and federal to local policies. The Embodying Racism lab, led by Dr. Aresha Martinez-Cardoso, aims to use empirical data to document variations in racial/health inequities across places and use epidemiological methods to link policies to health outcomes. Undergraduate summer fellows will support a range of projects in the lab depending on their skills and interests including:

·      Developing a visual toolkit and map of local immigration policies across counties in the US

·      Compiling county-level data on the structural drivers of health inequities and linking to national birth outcomes data to study maternal and child health disparities

·      Cleaning and performing statistical analysis on hospital administrative data to understand how health insurance policies shape healthcare utilization among Latinx immigrants in Illinois

REQUIREMENTS: Students who are passionate about addressing racial/ethnic health inequities;  experience in quantitative data analysis in R, STATA, and Python; previous coursework or experience in research methods and statistics; capacity to develop skills in data science, web apps, and mapping tools.

CLASS LEVEL ELIGIBILITY: Undergraduate students at all levels are eligible-- sophomores and juniors preferred.

FACULTY SPONSOR: Aresha Martinez-Cardoso https://erlab.uchicago.edu/

 

Topic 10: Health Disparities and Chronic Diseases

There is an increasing global burden across high, posed by chronic conditions, such as cancer and diabetes. Chronic illness disproportionately affects segments of the population resulting in not only a higher prevalence of disease but also worse health outcomes as is observed in at-risk and traditionally underserved populations like those of specific racial/ethnic populations or of certain socioeconomic status/social class. Current projects within Public Health Sciences are evaluating clinical (e.g., electronic health records), surveys, and large administrative databases (e.g., insurance claims) to examine these health disparities as well as inform broader health policies to improve quality of care. 

Undergraduate researchers under faculty supervision can help with this research by participating in the following ways: 

·       Conducting systematic reviews of the literature on health disparities in prevention (e.g. disease screening), treatment, and health outcomes

·       Collecting and maintaining a database of health measures and assessments (e.g., clinical indicators and those related to social determinants of health) for a variety of healthcare systems

·       Researching and tracking policy development that may affect treatment for chronic conditions

·       Quantitative and qualitative data analysis

·       Extraction and preliminary data analysis of information from a variety of data sources (e.g. publicly available health surveys, interviews, insurance claims data, etc.)                                                                                    

SKILLS REQUESTED:

·       Prior experience working with data in statistical packages Stata, R, SAS, Python, or equivalent. 

·       Prior experience working with REDCap and Dedoose/NVivo qualitative analysis software.

CLASS LEVEL ELIGIBILITY: Undergraduate students at all levels are eligible

FACULTY SPONSOR:  Loren Saulsberry (http://health.bsd.uchicago.edu/PersonProfile/Loren-Saulsberry)