SCCM presentation
- 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
- 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