Name: Sophie Greer
Class Year: 2023
Major: Neuroscience
Hometown: Brookline, MA
Internship Organization: The Center for Population Health Research at the Lankenau Institute for Medical Research
Job Title: Undergraduate Intern
Location: Wynnewood, PA
What鈥檚 happening at your internship? We would love to hear what kind of work you are doing!
I have been researching how stroke, pulmonary hypertension (PH), and diabetes correlate with demographics such as race, sex, and income level to affect the length of time that a patient will spend in the hospital and the chance that a patient will die in the hospital. I also researched the efficacy of marijuana as a chronic pain treatment and the chance that chemotherapy patients will develop neuropathy. I use Stata (a computer program used for data analysis) and Excel; I taught myself Stata as part of my training and have improved my ability to create graphs and tables using Excel considerably, which I am proud of! I have just finished analyzing a national data set (of over 7 million hospital records and over 110,000 patients!) as part of our study on the prevalence of pulmonary hypertension as a secondary condition in patients with a primary diagnosis of cerebral infarction. This is an under-researched area since most research on this topic is about patients who develop pulmonary hypertension first, before their strokes. For this study, our research questions were: 鈥渨hat are the risk factors for PH in patients with a cerebral infarction (CI),鈥 鈥渋s there a difference in hospital mortality between CI patients with PH vs. no PH,鈥 and 鈥渋s there a difference in LOS between CI patients with PH vs. no PH?鈥 I made the table shown below using Stata and Excel. I will be presenting about this study next week!
I analyzed data from a study on the efficacy of medical marijuana as a treatment for chronic pain in cancer patients. For this experiment, we compared the pain patients felt in their hands and feet between certain intervals over the course of the 4-month-long study. Additionally, I cleaned data for a test that determines whether someone will have neuropathy during chemotherapy (cleaning data means preparing it for analysis)
Also, I taught myself a programming language called SQL through an online course and have a certificate to show for it! Lastly, I also completed training in research ethics before working with the data, which will last for a few years, so I will be all set to work with other data sets for a few years after I graduate from college. This will come in handy, because I plan to use those years to gain more research experience before graduate school.
I am incredibly grateful to have the chance to participate in this internship. I am enjoying the work, mission, and people here at Lankenau and couldn鈥檛 have done it without my summer funding award! Thank you 魅影直播!
Why did you apply for this internship?
I have been passionate about neuroscience since middle school and learned of the data science and public health fields through my time at 魅影直播. I have wanted to go into a field that combines these areas for a few years now and felt that participating in an internship in biostatistics would help me refine my career goals.
What is something you have learned from your internship that you didn鈥檛 expect?
Through cultivating relationships with my coworkers and supervisors, I am learning more and more about which style of communication I do best with and how I communicate with others. I have always known that I have been a direct person but this internship has confirmed for me that I do best with clear, direct, specific instructions.
Additionally, I used to think that I was an introvert who could only work on her own. Through this internship, I have learned that given the 鈥渞ight鈥 type of communication, I actually really enjoy working with others! I have learned that I can work really well in a team as long as my teammates and I can communicate in the same way.
Can you give us three adjectives and three nouns that describe your internship experience?
Intellectually stimulating, Fulfilling, Impactful
Research, Statistics, Health
Table 1: In Model 1, we found that patients with CI and PH had a 29.7% longer stay in the hospital than patients without PH, on average. In Model 2, we found that when we took age, sex, and race into account, that percentage lowered slightly to 28.4%. In Model 3, we took age, sex, race, insurance type, income level, comorbidities, Elixhauser index score (measure of how many co-occuring conditions someone has), hospital location, and whether or not patients had had a major surgery while in the hospital into account and we learned that the average patient with CI and PH had a hospital stay that was 10.5% longer than their counterpart without PH. To get these percentages, we log-transformed the LOS variable, so we raised e to the power of each beta coefficient (尾), subtracted one, and multiplied by 100: (e尾 - 1) * 100.
Visit the Summer Internship Stories page to read more about student internship experiences.