Racial and Socioeconomic Disparities in Appendicitis Steven L. Lee, MD Chief of Pediatric Surgery, Harbor-UCLA Associate Clinical Professor of Surgery and Pediatrics David Geffen School of Medicine at UCLA
I have no financial disclosures.
Background Acute appendicitis Most common surgical emergency Time sensitive condition Risk of appendiceal perforation (AP) increases after 24 hours Delay in treatment increases risk of AP AP has higher morbidity
Background Poor access to healthcare Delay in surgical care Leads to increased AP rates AP rates: marker for access to surgical care Jablonski et al, Population Health Metrics, 2005 Gadmonski et al, HSR, 2001 Poor access = increased AP rates
Background Minorities have higher AP rates than whites Ponsky et al, JAMA 2004 Guagliardo et al, Acad Emerg Med 2003 Smink et al, Pediatrics 2005 Patients with public insurance have higher AP rates Pieracci et al, J Am Coll Surg 2007 Gadmonski et al, HSR 2001 Bratton et al, Pediatrics 2000 Braveman et al, NEJM 1994
Question Does equal access to healthcare eliminate racial and socioeconomic disparities in patients with perforated appendicitis?
Retrospective review Methods Southern California Kaiser Permanente Discharge database Patients (<18 years) with appendicitis 1998-2007
Methods Southern California Kaiser Permanente 12 Medical centers 3.5 million members (1% of US population) Single provider system All members have equal access to healthcare
Methods Study outcomes AP rate Length of hospitalization (LOH) Independent variables Age and gender Race Median per-capita Income Education level
Methods Race White, Black, Hispanic, Asian Median per-capita income Zip code of residence US Census database High, Medium, Low Education level Percentage of households with high school diplomas
Methods Statistical analyses Chi-squared analysis Multivariable linear and logistic regression
Results Gender AP Rate p Female (n = 2767) 30% Male (n = 4480) 29% Age < 5 years (n = 398) 60% 5-12 years (n = 3982) 30% > 12 years (n = 2867) 25% 0.33 < 0.0001
Results Race AP Rate p White (n = 1863) 27% Black (n = 364) 24% Hispanic (n = 4758) 31% 0.002 Asian (n =262) 33%
Results Income AP Rate p Low (<12K, n = 1640) 29% Medium (12-24k, n = 4257) 31% 0.004 High (>24K, n = 1350) 26% Education Low (<50%, n = 1290) 28% Medium (50-75%, n = 3244) 31% 0.29 High (>75%, n = 2713) 29%
Results Multivariable Analysis: AP Rate Gender OR (95% CI) p Female (n = 2767) Reference Reference Male (n = 4480) 0.94 (0.84, 1.04) 0.22 Age < 5 years (n = 398) Reference Reference 5-12 years (n = 3982) 0.29 (0.23, 0.36) < 0.0001 > 12 years (n = 2867) 0.22 (0.18, 0.27) < 0.0001
Results Multivariable Analysis: AP Rate Race OR (95% CI) p White (n = 1863) Reference Reference Black (n = 364) 0.87 (0.67, 1.14) 0.31 Hispanic (n = 4758) 1.11 (0.98, 1.27) 0.10 Asian (n =262) 1.27 (0.96, 1.69) 0.09
Results Multivariable Analysis: AP Rate Income OR (95% CI) p Low (<12K, n = 1640) Reference Reference Medium (12-24k, n = 4257) 1.00 (0.83, 1.21) 0.99 High (>24K, n = 1350) 0.79 (0.61, 1.01) 0.06 Education Low (<50%, n = 1290) Reference Reference Medium (50-75%, n= 3244) 1.16 (0.95, 1.43) 0.15 High (>75%, n = 2713) 1.29 (1.01, 1.64) 0.04
Results Gender LOH p Female (n = 2767) 2.9 ± 2.8 Male (n = 4480) 2.8 ± 2.9 Age 0.12 < 5 years (n = 398) 5.1 ± 4.0 5-12 years (n = 3982) 2.9 ± 2.7 > 12 years (n = 2867) 2.4 ± 2.7 < 0.0001
Results Race LOH p White (n = 1863) 2.8 ± 2.9 Black (n = 364) 3.2 ± 3.7 Hispanic (n = 4758) 2.8 ± 2.8 0.03 Asian (n =262) 2.8 ± 2.7
Results Income LOH p Low (<12K, n = 1640) 2.8 ± 2.7 Medium (12-24k, n = 4257) 2.9 ± 3.0 <0.0001 High (>24K, n = 1350) 2.5 ± 2.6 Education Low (<50%, n = 1290) 2.7 ± 2.7 Medium (50-75%, n = 3244) 2.9 ± 2.9 0.52 High (>75%, n = 2713) 2.8 ± 2.9
Results Multivariable Analysis: LOH Gender PE (95% CI) p Female (n = 2767) Reference Reference Male (n = 4480) -0.13 (-0.29, 0.03) 0.28 Age < 5 years (n = 398) Reference Reference 5-12 years (n = 3982) -2.24 (-2.59, -1.89) < 0.0001 > 12 years (n = 2867) -2.63 (-2.99, -2.28) < 0.0001
Results Multivariable Analysis: LOH Race PE (95% CI) p White (n = 1863) Reference Reference Black (n = 364) 0.46 (0.08, 0.84) 0.004 Hispanic (n = 4758) -0.03 (-0.23, 0.17) 0.41 Asian (n =262) 0.04 (-0.40, 0.47) 0.83
Results Multivariable Analysis: LOH Income PE (95% CI) p Low (<12K, n = 1640) Reference Reference Medium (12-24k, n = 4257) 0.09 (-0.20, 0.38) 0.73 High (>24K, n = 1350) -0.38 (-0.76, -0.01) 0.008 Education Low (<50%, n = 1290) Reference Reference Medium (50-75%, n = 3244) 0.12 (-0.19, 0.44) 0.41 High (>75%, n = 2713) 0.29 (-0.08, 0.66) 0.40
Results Similar study in adult patients Similar results
Conclusions In a setting of equal healthcare access: Minority and lower socioeconomic status did not correlate with higher AP rates Minority and lower socioeconomic status did not correlate with a clinically longer LOH
Limitations Data from discharge abstract data Income and education levels based on zip codes Racial diversity of these studies may not reflect the US population Lowest socioeconomic level not addressed Unemployed and uninsured
Lowest Socioeconomic Patients Public (n = 682) Private (n = 7220) Per-Capita Income $11,600 $15,900 <0.0001 AP Rate 42% 30% < 0.0001 LOH 3.9 ± 3.3 2.9 ± 3.4 < 0.0001 p
Lowest Socioeconomic Patients Multivariable Analysis Public (n = 682) Private (n = 7220) AP Rate 1.8 (1.5, 2.2) Reference < 0.0001 LOH 0.6 Reference < 0.0001 p
Public vs. Private Hospitals Increased AP rate and LOH at public hospital Higher cost $2,150 per patient Surgical procedures- global reimbursement Within the public hospital AP rate and LOH Similar across all races and income levels
Conclusions Lower socioeconomic background at public hospitals AP disproportionately higher at public hospital Higher AP rate led to longer LOH and increased costs No racial or socioeconomic disparities exist within the public hospital
Summary Poor access to surgical care leads to higher AP rates Minorities and public insurance Racial and socioeconomic disparities were eliminated within a setting of equal access to care Disparities between systems exist due to prehospital factors
Future Direction Perform a prospective collection of data Look at other disease process Use of seatbelts Influence health care initiatives to decrease racial and socioeconomic disparities in healthcare