This study examined the influence of social, socioeconomic, and medical histories on sepsis-related mortality. The study retrospectively analyzed data from over 5,000 patients with sepsis from 2010-2015 at Barnes Jewish Hospital. Contrary to expectations, African American patients, those with a history of recreational drug use, and those from zip codes with higher incomes and education levels had lower mortality. Further analysis is needed to understand these unexpected results and identify relationships between pre-admission variables and in-hospital mortality.

1. Examining the Influence of Social,
Socioeconomic (SES), and Medical
Histories on Sepsis-Related Mortality
Sai Dodda, Student Pharmacist Class
of 2021

2. Background and Study Objective
• Sepsis is a leading cause of hospital mortality
– In 2014, 35% of hospitalizations that resulted in death
• Previously identified variables include: race, co-
morbidities, SES (household income), and BMI
• Goal: Understand the relationships between
pre-admission variables (SES, medical, and
social histories) and in-hospital mortality

3. Methods
• Study Design: Retrospective cohort study
• Setting: Barnes Jewish Hospital from 2010-2015.
• Participants: A patients’ first case of sepsis was extracted from the electronic health record
based on ICD-9-CM coding
• Variables:
– Demographics
– Comorbidities
– Social histories (recreational drugs)
– SE variables based on residential zip code - American Community Survey
• Median Household Income
• Percentage of Individuals Below Poverty Level
• Percent High School Graduate or Higher
• Percent Unemployment
– Number of Hospitals Per Zip Code - Center for Medicare & Medicaid Services
– Number of Pharmacies Per Zip Code - Google Maps

4. Analysis
• Evaluate differences between survivors and non-
survivors relative to pre-admission variables
• Analytic methods
– Descriptive Statistics
– Inferential Statistics
• Chi-Square Analysis
• Independent Samples T-test

5. Flow of Participants

6. Study Population
Age, mean ± standard deviation (SD) 61.3 ± 15.9
BMI 32.1 ± 10.4)
Race
- Black
- Caucasian
- Unknown
- Others
1879 (31.5%)
3671 (61.6%)
305 (5.1%)
104 (1.7%)
Male 3276 (55%)
Median Household Income (per zip code)
- Less than $40,000
- $40,000 to $79,999
- Above $80,000
2376 (39.9%)
3160 (53%)
423 (7.1%)
Percentage of Individuals Below Poverty Level, mean ± SD 16.8 ± 11.4
Percent High School Graduate or Higher, mean ± SD 87.4 ± 7.3
Unemployment Percent, mean ± SD 9.4 ± 6.2
Number of Pharmacies, mean ± SD 5.31 ± 4.0
Charlson Score, mean ± SD 6.4 ± 3.2

7. Results
Variable Alive (n=3709) Dead (n=2250) Difference (CI)
Age, mean ± SD 59.7 ± 16.1 64.1 ± 15.1 -4.4 (-5.3, -3.6)
BMI, mean ± SD 32.3 ± 10.4 31.9 ± 10.2 0.4 (-0.13, 0.97)
Race – Black 1216 (32.8%) 663 (29.5%) 3.4% (0.9, 6.3)
Race – Caucasian 2233 (60.2%) 1438 (63.9%) -3.7% (-6.2,-1.1)
Cannabis 89 (2.4%) 31 (1.4%) 1.2% (0.3, 2.0)
Cocaine 101 (2.7%) 37 (1.6%) 1.1% (0.3, 1.8)
Opioid 99 (2.7%) 34 (1.5%) 1.2% (0.4, 2.0)
Tobacco Use Disorder 733 (19.8%) 334 (14.8%) 5.0% (2.7, 11.0)
Charlson, mean ± SD 5.8 ± 3.1 7.4 ± 3.1 -1.6 (-1.7, -1.4)

8. Results
Alive (n=3709) Dead (n=2250) Difference (CI)
Median Household Income 46998 ± 19927 48271 ± 20569 -$1273 (-2330,-217)
Percentage of Individuals Below
Poverty Level
19.4 ± 11.4 18.6 ± 11.3 0.8% (0.2, 1.4)
Percent High School Graduate or
Higher
87.2 ± 7.4 87.7 ± 7.2 -0.44% (-0.82,-0.06)
Unemployment Percent 9.6 ± 6.1 9.3 ± 6.2 -0.3 (-0.62,0.02)
Number of Pharmacies 5.3 ± 4.0 5.4 ± 4.0 0.1 (-0.11,0.31)
All comparisons per the patient’s residential zip code

9. Discussion/Conclusion
• Contrary results from what is expected in terms of SES, race,
and social history
– African American race patients tend to have less mortality
– Presence of recreational drug use corresponds to less mortality
– Higher household income, lower poverty, and higher education per
zip code correspond to less mortality
• Next Steps
– Evaluation of the contrary results
– Regression Analysis
– Cluster Modeling
– Apply this data to other outcomes (hospital length of stay, type and
number of organ dysfunctions, type of infection)

10. Acknowledgements
• Dr. Scott Micek, PharmD, BCPS, FCCP
• Dr. Tiffany Osborne, MD, MPH
• Marin H. Kollef, MD, FACP, FCCP
• Dr. Randi Foraker, PhD, MA, FAHA