Abstract
Introduction:
Perioperative pain may have deleterious effects for all patients. We aim to examine disparities in pain management for children in the perioperative period to understand whether any racial and ethnic groups are at increased risk of poor pain control
Methods:
Medical records from children ≤18 years of age who underwent surgery from May 2014 to May 2018 were reviewed. The primary outcome was total intraoperative morphine equivalents. The secondary outcomes were intraoperative non-opioid analgesic administration and first conscious pain score. The exposure was race and ethnicity. The associations of race and ethnicity with outcomes of interest were modelled using linear or logistic regression, adjusted for preselected confounders and covariates. Bonferroni corrections were made for multiple comparisons.
Results:
21,229 anesthetics were included in analyses. In the adjusted analysis, no racial and ethnic group received significantly more or less opioids intraoperatively than non-Hispanic (NH) whites. Asians, Hispanics, and Pacific Islanders were estimated to have significantly lower odds of receiving non-opioid analgesics than NH whites: odds ratio (OR)=0.83 (95% confidence interval (CI): 0.70,0.97); OR=0.84 (95% CI: 0.74,0.97) and OR=0.53 (95% CI: 0.33,0.84) respectively. Asians were estimated to have significantly lower odds of reporting moderate-to-severe pain on awakening than NH whites: OR=0.80 (95% CI: 0.66,0.99).
Conclusions:
Although children of all races and ethnicities investigated received similar total intraoperative opioid doses, some were less likely to receive non-opioid analgesics intraoperatively. Asians were less likely to report moderate-severe pain upon awakening. Further investigation may delineate how these differences lead to disparate patient outcomes and are influenced by patient, provider, and system factors.
Keywords: Pediatric anesthesia, Healthcare disparities, Race and ethnicity, Perioperative analgesia
Introduction
Disparities in healthcare and pain management for children and adults of different races and ethnicities have been reported in multiple settings, and often adversely affect outcomes.[1-5] The management of adult perioperative pain has important implications, as both too little and too much opioid analgesic administration have negative consequences, such as delayed recovery, chronic pain, and increased healthcare costs.[6-16] However, research on variability in the perioperative anesthetic and analgesic management in children of different races and ethnicities is limited and offers inconclusive results about disparities in pain medication administration and its impact. Separate single site studies of children undergoing tonsillectomy/adenoidectomy, elective ambulatory surgery, and emergency appendectomies found either small or no differences in intraoperative analgesic administration, recovery room analgesic administration, and opioid related side effects between White and non-White patients.[17-20]
Six million children undergo surgery each year in the United States[21-23] The study of perioperative care of children is important for all pediatric specialists, including anesthesiologists, surgeons, and pediatricians. These physicians are all tasked with perioperative care, including education about preoperative anxiety, assessment of postoperative physiologic decline, postoperative pain management,[24-29] and management in the intensive care unit and inpatient floors. Thus, understanding the intraoperative anesthetic and analgesic management and its impact postoperatively is important for all pediatricians.
Before determining any effect on outcomes, it is necessary to first examine if perioperative analgesic delivery disparities occur. Using a large, diverse perioperative database from a tertiary, pediatric hospital, we investigated whether racial and ethnic disparities occur in delivery of intraoperative analgesics and whether there were differences in pain levels upon regaining consciousness in the recovery room. The use of a multimodal analgesic plan that includes both opioid and non-opioid analgesics is the current standard for perioperative acute pain management.[30] Despite literature indicating health services disparities in adult anesthetic care, the evidence to date with regards to pediatric populations is inconclusive.[31-35] Thus, our primary hypothesis was that White non-Hispanic children would not receive higher doses of opioid analgesics intraoperatively than children of other races or ethnicities. We further hypothesized that white children would not be more likely to receive intraoperative non-opioid analgesics than racial/ethnic minority children, and that the latter would not have higher the first awake pain scores in the post-anesthesia care unit (PACU) than white children.
Methods
This retrospective cohort study was part of a quality improvement project at Lucile Packard Children’s Hospital Stanford (LPCH, Stanford, CA), a tertiary care pediatric hospital, examining racial and ethnic differences in pediatric anesthetic care. The Stanford University Institutional Review Board waived requirement for written informed consent.
Study sample
Inclusion:
Children included were ≤18 years of age who underwent surgery and general anesthesia at LPCH from May 4, 2014 to May 31, 2018 (inclusive). SAP Business Objects BI Platform 4.2 (Version: 14.2.5.2618; Walldorf, Germany) was used to create a patient dataset from the electronic medical record (EMR) system (Epic Systems, Verona, WI).
Exclusion:
Surgeries were excluded if: the patient was transferred from the OR suite to the intensive care unit; the patient’s American Society of Anesthesiologists (ASA) physical status classification ≥4; surgery length was >5 hours (these clinical scenarios might require different clinical decision-making); surgical length was impossible (e.g. 0 minutes); the patient received regional anesthesia; the patient received an opioid infusion except remifentanil (total opioid administered from the infusion was not available in the EMR); the cases were from radiation oncology, anesthesiology pain service, radiology, and cardiology (these procedures are typically associated with minimal peri-procedural nociception and pain); race or ethnicity was unknown or declined to be stated; the racial or ethnic group size was <100 individuals; either the medical/surgical service they were booked under or the anesthesiologist staffing the case had >10 cases total during the study period; patient weight was not in the EMR at the time of their procedure (excluded from primary outcome analysis); and the patient did not have a first conscious pain score recorded in the EMR (excluded from the secondary outcome analysis, first conscious pain score).
Exposure
Primary exposure was the following races and ethnicities: Asian, Black, Hispanic, Other, Pacific Islander, NH white. This data is collected during the patient intake and scheduling process at LPCH; patients or parents self-identify. We studied individual minorities (rather than an aggregated, single ‘minority’ category) given literature that minorities have different healthcare system experiences.[36, 37]
Outcomes
The primary outcome of interest was total intraoperative morphine equivalent dose (milligram per kilogram of bodyweight – mg kg−1). Opioid medications administered intraoperatively were converted to morphine equivalent units,[9, 38] summed to calculate the total intraoperative opioid dose per patient, and divided by patient weight at the time of surgery. Remifentanil was not included in this total due to its very short half-life.
There were two secondary outcomes. One secondary outcome was the use of intraoperative non-opioid analgesic medications (≥ 1 non-opioid analgesic versus none). The administration of one or more non-opioid pain medications (any dose, except as below) preoperatively (given in the preoperative intake or holding rooms just prior to surgery) or intraoperatively were included. These medications were chosen based on evidence for efficacy in adults and/or children and LPCH institutional anesthesia culture: celecoxib, clonidine, dexmedetomidine, acetaminophen, gabapentin, indomethacin, ketamine, ketorolac, magnesium, pregabalin, dexamethasone (≥0.1 mg kg−1), and lidocaine (any intravenous infusion).[39] The other secondary outcome was the first conscious pain score, defined as the first pain score after a documented postanesthetic recovery consciousness score >0.[40] The pain score (at LPCH, the following 0 – 10 pain scales are used for patients of different ages: Face, Legs, Activity, Cry, Consolability (FLACC) scale for patients 2 months to 3 years of age; Wong-Baker FACES scale for patients 3 – 10 years of age; Numeric Rating Scale for patients ≥10 years of age) was classified as either <4 or ≥4, reflecting the well-defined differentiation between mild and moderate pain[41-43] and the threshold below which pain medications are not given by LPCH PACU nursing staff.
Other variables
To isolate the impact of race and ethnicity on the primary outcome and the use of non-opioid analgesia intraoperatively, we added the following covariates and confounders to our model, identified based on the adult and pediatric literature:[9, 44, 45] length of surgery, history of obstructive sleep apnea (OSA) (binary: yes or no), weight status (binary: overweight[46, 47] or not), history of chronic pain (binary: yes or no), and year of surgery (continuous), age (continuous), ASA classification (categorical: 1, 2 or 3), surgical service (categorical), staff anesthesiologist (categorical), and the intraoperative use of remifentanil (binary: yes or no). To isolate the impact of race and ethnicity on the secondary outcome of pain score, we adjusted for the following covariates and confounders: length of surgery (continuous), history of chronic pain (binary), the use of a language interpreter (binary: yes or no), surgical service (categorical), age (continuous), and sex (binary: male or female). Intraoperative opioids were considered an intermediary variable and not included. Using the Institute of Medicine’s definition of disparities, we did not adjust for insurance status or socioeconomic status as these are considered potential mediators of disparities.[48] Given the concern for collinearity between the covariates of ‘use of a language interpreter’ and ‘primary language,’ we a priori elected to adjust for the former since English proficiency (rather than primary language) has been linked to poor health outcomes.[49]
In the absence of height data at the time of surgery for most patients in the LPCH EMR and therefore body mass index, the Centers for Disease Control and Prevention’s percentile guidelines for overweight were applied to weight percentile to classify patients as overweight or not. Overweight was defined as ≥85th percentile for age and sex.[46, 47] A history of OSA was established by searching the medical record for at least two mentions of any of the International Classification of Diseases (ICD) codes for OSA at least 90 days apart in the five years prior to surgery. For a history of chronic pain, a single recorded instance of one chronic pain ICD code in the five years prior to surgery was sufficient. We identified ICD codes for acute pain. If a patient had the same acute pain ICD code at least 3 months apart in the five years prior to surgery, that patient was classified as having a history of chronic pain.[50]
Statistical analysis
Statistical analyses were planned a priori. Case demographics and characteristics were reported as frequency by race and ethnicity. The significance of unadjusted differences across races and ethnicities were tested using chi-squared tests for categorical variables and ANOVA for continuous variables. The association between patient race and ethnicity and total intraoperative opioids was modelled using linear regression, adjusted for covariates and confounders. The association of race and ethnicity with use of intraoperative non-opioid analgesic medications (≥ 1 non-opioid analgesic versus none) was modelled using a logistic regression model, adjusted for covariates and confounders. The association of race and ethnicity with the first conscious pain score (<4 or ≥4) was modelled using logistic regression, adjusted for covariates and confounders.
Based on prior studies assessing disparities in pediatric, anesthesia, and pediatric anesthesia health services, NH whites were the reference and other races and ethnicities were compared individually to NH whites. Results are presented as adjusted regression coefficients for the primary outcome, and adjusted odds ratios (ORs) for the secondary outcomes, with 95% confidence intervals (CIs). P-values and 95%CIs were adjusted for multiple testing in the following way using Bonferroni corrections: the primary outcome for multiple comparisons across the races, i.e. 5 tests, and the secondary outcomes together for multiple comparisons across the secondary outcomes and the races, i.e. 10 tests.
Predicted probabilities for the secondary outcomes for each race and ethnicity are calculated from their respective adjusted models. Whether including race and ethnicity in each model yielded a significant improvement in the fit of the model was tested using the likelihood ratio test (LRT). P-values are reported as two-tailed and p < 0.05 was considered significant (after adjustment for multiple comparisons). Analyses were performed using R (R Core Team, Vienna, Austria)) and Stata 15 (StataCorp LLC, College Station, TX).
Post Hoc Analysis
In post-hoc analysis, each group served as a reference for the other racial and ethnic groups.
Sample size and power
All available data that met inclusion criteria were analyzed. A sample size calculation using G*Power[51] showed that in order to be able to detect a partial R squared of 0.01 for race in multiple linear regression of total intraoperative opioids at the 0.05 significance level and 95% power, a total sample size of 1,289 was required. To simply detect a clinically meaningful difference in mean total intraoperative opioid amounts between NH white and another race and ethnicity of 0.02 mg kg−1 morphine equivalents[52, 53] with 95% power at the 0.05 significance level, using observed values for mean and standard deviation of the NH white cases, the required sample size was 12,250.
Results
There were 32,149 cases in the full dataset. After exclusions (Figure 1) there were 21,229 individual surgeries (66% of the original dataset) included in the analyses.
Figure 1. Flowchart of included and excluded patients.
LPCH – Lucile Packard Children’s Hospital Stanford; n – number; OR – operating room; ICU – intensive care unit; ASA – American Society of Anesthesia physical status classification
A breakdown of the data set by case demographics and characteristics appears in Table 1. The majority (57.8%) of patients were male, mean age was 7.9 years (standard deviation 5.2), median surgery length was 0.57 hours (interquartile range 0.30,1.08). 4,091 (19.3%) cases required an interpreter. Hispanic was the most common race or ethnicity with 8,261 cases (38.9%), followed by 7,332 NH white (34.5%), 3,938 Asian (18.6%), 957 Other (4.5%), 444 Black (2.1%) and 297 Pacific Islander (1.4%).
Table 1:
Patient characteristics by race and ethnicity
| Patient First Race | p- values |
||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Asian | Black | Hispanic | Other | Pacific Islander | non-Hispanic White | All races and ethnicities | |||||||||
| Sex | n | (%) | n | (%) | n | (%) | n | (%) | n | (%) | n | (%) | n | (%) | |
| Female | 1618 | (41.09) | 176 | (39.64) | 3461 | (41.90) | 431 | (45.04) | 140 | (47.14) | 3135 | (42.76) | 8961 | (42.21) | 0.061 |
| Male | 2320 | (58.91) | 268 | (60.36) | 4800 | (58.10) | 526 | (54.96) | 157 | (52.86) | 4197 | (57.24) | 12268 | (57.79) | |
| Age, years | |||||||||||||||
| mean (SD) | 7.38 | (5.02) | 8.71 | (5.39) | 7.76 | (5.24) | 7.73 | (5.29) | 10.04 | (5.67) | 8.19 | (5.24) | 7.89 | (5.23) | <0.001 |
| Interpreter Needed? | |||||||||||||||
| No | 3542 | (89.94) | 440 | (99.10) | 4683 | (56.69) | 916 | (95.72) | 294 | (98.99) | 7263 | (99.06) | 17138 | (80.73) | <0.001 |
| Yes | 396 | (10.06) | <10 | (<2.25) | 3578 | (43.31) | 41 | (4.28) | <10 | (<3.37) | 69 | (0.94) | 4091 | (19.27) | |
| History of OSA? | |||||||||||||||
| No | 3833 | (97.33) | 431 | (97.07) | 7949 | (96.22) | 953 | (99.58) | 290 | (97.64) | 7086 | (96.64) | 20542 | (96.76) | <0.001 |
| Yes | 105 | (2.67) | 13 | (2.93) | 312 | (3.78) | <10 | (<1.04) | <10 | (<3.37) | 246 | (3.36) | 687 | (3.24) | |
| Overweight? | |||||||||||||||
| No | 3432 | (87.15) | 300 | (67.57) | 5759 | (69.71) | 722 | (75.44) | 154 | (51.85) | 5815 | (79.31) | 16182 | (76.23) | <0.001 |
| Yes | 501 | (12.72) | 144 | (32.43) | 2492 | (30.17) | 234 | (24.45) | 142 | (47.81) | 1507 | (20.55) | 5020 | (23.65) | |
| Weight unknown | <10 | (<0.25) | 0 | (0.00) | 10 | (0.12) | <10 | (<1.04) | <10 | (<3.37) | 10 | (0.14) | 27 | (0.13) | |
| History of chronic pain? | |||||||||||||||
| No | 3780 | (95.99) | 394 | (88.74) | 7812 | (94.56) | 925 | (96.66) | 263 | (88.55) | 6777 | (92.43) | 19951 | (93.98) | <0.001 |
| Yes | 158 | (4.01) | 50 | (11.26) | 449 | (5.44) | 32 | (3.34) | 34 | (11.45) | 555 | (7.57) | 1278 | (6.02) | |
| Year of surgery | |||||||||||||||
| 2014 | 578 | (14.68) | 69 | (15.54) | 1238 | (14.99) | 63 | (6.58) | 52 | (17.51) | 1177 | (16.05) | 3177 | (14.97) | <0.001 |
| 2015 | 955 | (24.25) | 126 | (28.38) | 1946 | (23.56) | 131 | (13.69) | 82 | (27.61) | 1948 | (26.57) | 5188 | (24.44) | |
| 2016 | 982 | (24.94) | 104 | (23.42) | 1976 | (23.92) | 233 | (24.35) | 79 | (26.60) | 1736 | (23.68) | 5110 | (24.07) | |
| 2017 | 1039 | (26.38) | 121 | (27.25) | 2257 | (27.32) | 340 | (35.53) | 67 | (22.56) | 1768 | (24.11) | 5592 | (26.34) | |
| 2018 | 384 | (9.75) | 24 | (5.41) | 844 | (10.22) | 190 | (19.85) | 17 | (5.72) | 703 | (9.59) | 2162 | (10.18) | |
| ASA rating | |||||||||||||||
| 1 | 1389 | (35.27) | 114 | (25.68) | 2542 | (30.77) | 432 | (45.14) | 68 | (22.90) | 2485 | (33.89) | 7030 | (33.12) | <0.001 |
| 2 | 1717 | (43.60) | 225 | (50.68) | 3742 | (45.30) | 407 | (42.53) | 137 | (46.13) | 3293 | (44.91) | 9521 | (44.85) | |
| 3 | 832 | (21.13) | 105 | (23.65) | 1977 | (23.93) | 118 | (12.33) | 92 | (30.98) | 1554 | (21.19) | 4678 | (22.04) | |
| Patient disposition | |||||||||||||||
| Hospital Outpatient Surgery | 2923 | (74.23) | 315 | (70.95) | 5828 | (70.55) | 676 | (70.64) | 231 | (77.78) | 5358 | (73.08) | 15331 | (72.22) | <0.001 |
| Inpatient | 376 | (9.55) | 61 | (13.74) | 1046 | (12.66) | 121 | (12.64) | 26 | (8.75) | 782 | (10.67) | 2412 | (11.36) | |
| Unknown | 0 | (0.00) | 0 | (0.00) | <10 | (0.01) | 0 | (0.00) | 0 | (0.00) | 0 | (0.00) | <10 | (<0.05) | |
| Surgery Admit | 639 | (16.23) | 68 | (15.32) | 1386 | (16.78) | 160 | (16.72) | 40 | (13.47) | 1192 | (16.26) | 3485 | (16.42) | |
| Intraoperative remifentanil | |||||||||||||||
| No | 3537 | (89.82) | 406 | (91.44) | 7354 | (89.02) | 877 | (91.64) | 241 | (81.14) | 6500 | (88.65) | 18915 | (89.10) | <0.001 |
| Yes | 401 | (10.18) | 38 | (8.56) | 907 | (10.98) | 80 | (8.36) | 56 | (18.86) | 832 | (11.35) | 2314 | (10.90) | |
| Surgery length, hours | |||||||||||||||
| median (IQR) | 0.57 | (0.30, 1.12) | 0.62 | (0.30, 1.15) | 0.58 | (0.30, 1.13) | 0.57 | (0.32, 1.02) | 0.58 | (0.33, 1.05) | 0.55 | (0.30, 1.05) | 0.57 | (0.30, 1.08) | 0.059 |
| Service | |||||||||||||||
| Audiology | 47 | (1.19) | <10 | (<2.25) | 111 | (1.34) | <10 | (<1.04) | <10 | (<3.37) | 37 | (0.50) | 208 | (0.98) | <0.001 |
| Dental | 95 | (2.41) | 15 | (3.38) | 282 | (3.41) | 20 | (2.09) | 12 | (4.04) | 156 | (2.13) | 580 | (2.73) | |
| Dermatology | 54 | (1.37) | <10 | (<2.25) | 111 | (1.34) | <10 | (<1.04) | <10 | (<3.37) | 57 | (0.78) | 234 | (1.10) | |
| Gastroenterology | 327 | (8.30) | 46 | (10.36) | 547 | (6.62) | 119 | (12.43) | 21 | (7.07) | 866 | (11.81) | 1926 | (9.07) | |
| General | 676 | (17.17) | 77 | (17.34) | 1387 | (16.79) | 147 | (15.36) | 21 | (7.07) | 1177 | (16.05) | 3485 | (16.42) | |
| Hand | 32 | (0.81) | <10 | (<2.25) | 38 | (0.46) | <10 | (<1.04) | <10 | (<3.37) | 33 | (0.45) | 114 | (0.54) | |
| Interventional Radiology | <10 | (<0.25) | <10 | (<2.25) | <10 | (<0.12) | <10 | (<1.04) | 0 | (0.0) | <10 | (<0.14) | 10 | (0.05) | |
| Nephrology | 67 | (1.70) | <10 | (<2.25) | 95 | (1.15) | <10 | (<1.04) | 27 | (9.09) | 101 | (1.38) | 306 | (1.44) | |
| Neurology | 70 | (1.78) | <10 | (<2.25) | 23 | (0.28) | <10 | (<1.04) | 0 | (0.0) | 61 | (0.83) | 162 | (0.76) | |
| Neurosurgery | 78 | (1.98) | <10 | (<2.25) | 230 | (2.78) | 12 | (1.25) | <10 | (<3.37) | 262 | (3.57) | 592 | (2.79) | |
| Obstetrics/Gynecology | 18 | (0.46) | <10 | (<2.25) | 39 | (0.47) | 17 | (1.78) | <10 | (<3.37) | 51 | (0.70) | 128 | (0.60) | |
| Oncology/Hematology | 140 | (3.56) | 13 | (2.93) | 246 | (2.98) | 14 | (1.46) | <10 | (<3.37) | 168 | (2.29) | 586 | (2.76) | |
| Ophthalmology | 192 | (4.88) | 22 | (4.95) | 448 | (5.42) | 58 | (6.06) | <10 | (<3.37) | 288 | (3.93) | 1014 | (4.78) | |
| Orthopedics | 389 | (9.88) | 47 | (10.59) | 867 | (10.50) | 132 | (13.79) | 31 | (10.44) | 784 | (10.69) | 2250 | (10.60) | |
| Otolaryngology | 1076 | (27.32) | 116 | (26.13) | 2566 | (31.06) | 268 | (28.00) | 120 | (40.40) | 2120 | (28.91) | 6266 | (29.52) | |
| Plastics | 270 | (6.86) | 19 | (4.28) | 544 | (6.59) | 47 | (4.91) | 13 | (4.38) | 387 | (5.28) | 1280 | (6.03) | |
| Pulmonology | 24 | (0.61) | <10 | (<2.25) | 71 | (0.86) | <10 | (<1.04) | 0 | (0.0) | 73 | (1.00) | 175 | (0.82) | |
| Rheumatology | <10 | (<0.25) | <10 | (<2.25) | 16 | (0.19) | <10 | (<1.04) | 0 | (0.0) | 38 | (0.52) | 66 | (0.31) | |
| Transplant | 17 | (0.43) | <10 | (<2.25) | 83 | (1.00) | <10 | (<1.04) | <10 | (<3.37) | 47 | (0.64) | 164 | (0.77) | |
| Urology | 356 | (9.04) | 49 | (11.04) | 553 | (6.69) | 76 | (7.94) | 26 | (8.75) | 623 | (8.50) | 1683 | (7.93) | |
| Total | 3938 | (100.00) | 444 | (100.00) | 8261 | (100.0) | 957 | (100.00) | 297 | (100.00) | 7332 | (100.0) | 21229 | (100.00) | |
IQR, interquartile range; OSA, obstructive sleep apnea; SD, standard deviation
Primary Outcome
21,202 cases met inclusion/exclusion criteria for the primary outcome. The mean total intraoperative morphine equivalents was 0.15 mg kg−1 (standard deviation 0.28 mg kg−1); the unadjusted values did not differ significantly across races and ethnicities (p=0.234) (Table 2). In the adjusted analysis (Figure 2a has regression model results), each race and ethnicity was estimated to receive fewer opioids intraoperatively than NH whites (none of these results was statistically or clinically significant). Including race and ethnicity in the model did not significantly improve the fit of the model (LRT p=0.29).
Table 2.
Primary and secondary outcomes by race and ethnicity
| Patient Races and Ethnicities | p-value | |||||||
|---|---|---|---|---|---|---|---|---|
| Asian | Black | Hispanic | Other | Pacific Islander |
White non- Hispanic |
All races | ||
| Mean (SD) | Mean (SD) | Mean (SD) | Mean (SD) | Mean (SD) | Mean (SD) | Mean (SD) | ||
| Total intraoperative morphine equivalent dose (mg kg−1) | 0.14 (0.26) | 0.15 (0.23) | 0.15 (0.29) | 0.14 (0.17) | 0.12 (0.13) | 0.15 (0.31) | 0.15 (0.28) | 0.234 |
| mg kg−1 (95% CI) |
mg kg−1 (95% CI) |
mg kg−1 (95% CI) |
mg kg−1 (95% CI) |
mg kg−1 (95% CI) |
mg kg−1 (95% CI) |
|||
| Predicted amount * | 0.14 (0.13,0.15) | 0.15 (0.12,0.18) | 0.15 (0.14,0.16) | 0.14 (0.12,0.16) | 0.13 (0.09,0.18) | 0.15 (0.14,0.16) | ||
| Non-opioid analgesics | n (%) | n (%) | n (%) | n (%) | n (%) | n (%) | n (%) | |
| 0 | 886 (22.50) | 105 (23.65) | 1673 (20.25) | 179 (18.70) | 82 (27.61) | 1522 (20.76) | 4447 (20.95) | 0.001 |
| 1 or more | 3052 (77.50) | 339 (76.35) | 6588 (79.75) | 778 (81.30) | 215 (72.39) | 5810 (79.24) | 16782 (79.05) | |
| % (95% CI) | % (95% CI) | % (95% CI) | % (95% CI) | % (95% CI) | % (95% CI) | |||
| Predicted % witd 1 or more * | 78.1 (76.7, 79.6) | 77.5 (73.2, 81.8) | 78.4 (77.4, 79.4) | 80.8 (78.0, 83.7) | 72.4 (66.5, 78.3) | 80.3 (79.3, 81.4) | ||
| First conscious pain score |
n (%) | n (%) | n (%) | n (%) | n (%) | n (%) | n (%) | |
| <4 | 3209 (86.06) | 358 (84.24) | 6527 (83.14) | 767 (83.10) | 226 (83.70) | 5766 (83.14) | 16853 (83.71) | 0.002 |
| ≥4 | 520 (13.94) | 67 (15.76) | 1324 (16.86) | 156 (16.90) | 44 (16.30) | 1169 (16.86) | 3280 (16.29) | |
| % (95% CI) | % (95% CI) | % (95% CI) | % (95% CI) | % (95% CI) | % (95% CI) | |||
| Predicted % with ≥4 * | 14.3 (12.7, 15.8) | 16.1 (11.4, 20.8) | 16.5 (15.3, 17.7) | 17.0 (13.8, 20.4) | 16.2 (10.4, 22.1) | 17.0 (15.8, 18.3) | ||
SD, standard deviation; mg kg−1, milligram per kilogram; n, number; CI, confidence interval
CIs adjusted for multiple comparisons using Bonferroni method
Figure 2. Results of the regression models for primary and secondary outcomes of interest (CIs and p-values corrected for multiple comparisons using Bonferroni method).
a. The results of the regression model for the primary outcome of interest, the difference in the intraoperative morphine equivalent dose for each race and ethnicity included. NH white is the reference group.
b. The results of the regression model for the secondary outcome of interest, the use of intraoperative non-opioid analgesic medications (≥ 1 non-opioid analgesic versus none) for each race and ethnicity included. NH white is the reference group.
c. The results of the regression model for the secondary outcome of interest, the first conscious pain score (<4 or ≥4) for each race and ethnicity included. NH white is the reference group.
a and b controlled for: length of surgery, history of OSA, weight status (overweight or not), history of chronic pain, year of surgery, age, ASA classification, surgical service, staff anesthesiologist, and the intraoperative use of remifentanil (yes or no). c controlled for: length of surgery, history of chronic pain, the use of a language interpreter, surgical service, age, and sex.
Secondary Outcomes
21,229 met inclusion/exclusion criteria for the intraoperative adjunct secondary outcome. 16,782 cases (79.1%) received ≥1 non-opioid analgesic intraoperatively; this ranged from 72.4% of Pacific Islanders to 81.3% of Others. The percentages were statistically significantly different across races and ethnicities (p=0.001). (Table 2) After adjusting for confounders and covariates (Figure 2b) Asians, Hispanics, and Pacific Islanders were each estimated to have significantly lower odds of receiving non-opioid pain medications than NH whites: OR=0.83 (95% CI: 0.70,0.97); OR=0.84 (95% CI: 0.74,0.97) and OR=0.53 (95% CI: 0.33,0.84) respectively. Blacks were also estimated to have lower odds of receiving non-opioid pain medications, but not significantly: OR=0.78 (95% CI: 0.53,1.16). Others were estimated to have increased odds of receiving non-opioid pain medications but not significantly: OR=1.05 (95% CI: 0.78,1.40). Including race and ethnicity significantly improved the fit of the model (LRT p<0.001). Predicted probabilities are shown in Table 2.
20,133 cases met inclusion/exclusion criteria for the first conscious pain score secondary outcome. The first conscious pain score was ≥4 for 16.3% of all patients (Table 2). The percentage of patients with first conscious pain score ≥4 ranged from 13.9% for Asians to 16.9% for Others, and was significantly different across races before adjustment (p=0.002). After adjustment for confounders and covariates (Figure 2c), Asians were estimated to have significantly lower odds of reporting pain scores ≥4 on awakening than NH whites: OR=0.80 (95%CI: 0.66, 0.99). None of the other races and ethnicities showed a significant difference from NH white: Black OR=0.93 (95% CI: 0.57, 1.251); Hispanic OR=0.96 (95% CI: 0.81, 1.15); Other OR=1.00 (95% CI: 0.72, 1.40) and Pacific Islander OR=0.94 (95% CI: 0.52, 1.71). Including race and ethnicity significantly improved the fit of the model (LRT p=0.01). Predicted probabilities are shown in Table 2.
Post-hoc Analysis
In the post-hoc analysis comparing each race to every other race for each outcome (adjusted for multiple comparisons using Bonferroni corrections), the only significant finding was for the intraoperative adjuncts outcome, in which Pacific Islander was significantly different from Other race: OR=0.51 (95% CI: 0.28, 0.91).
Discussion
As an initial step in determining whether perioperative analgesic delivery disparities affect outcomes, we examined intraoperative pain medication administration and first awake pain scores among minority children. Our study did not find a statistically significant difference in the total amount of opioid analgesics administered intraoperatively to pediatric patients of different races and ethnicities. However, Asians, Hispanics, and Pacific Islanders were found to have significantly lower odds of receiving non-opioid analgesics intraoperatively than NH whites. Asians had significantly lower odds of reporting moderate or severe pain in the PACU upon regaining consciousness than the reference group.
Previous studies have reported that 24 - 66% of hospitalized children report moderate-to-severe pain,[54-57] and pain after ambulatory surgery is one of the most commonly reported problem by patients and caregivers.[58] While understudied in children, increased pain after surgery has significant negative consequences for all patients, including undesirable behavioral changes, delayed recovery, increased length of stay, and chronic pain.[59-62] Meta-analyses have demonstrated the benefits of perioperative use of non-opioid analgesics to include decreased postoperative opioid consumption, pain, nausea and vomiting, anti-emetic use, and emergence agitation among various pediatric surgical populations.[63-69] Thus, use of multimodal analgesia—regional anesthesia, non-opioid analgesics, opioid analgesics, and non-pharmacologic options—is generally recommended for children who undergo surgery.[70-72] Furthermore, literature has suggested that opioid-sparing techniques in enhanced recovery pathways is both safe and effective[73, 74] Together, these data suggest that all children should benefit from multimodal analgesics in the perioperative setting.
Our result for total intraoperative opioid dose administered is consistent with prior studies that did not observe a significant difference in intraoperative weight-based fentanyl or morphine doses between Black and White patients undergoing emergency appendectomies,[19] or in the perioperative opioid administration between Spanish speaking Hispanic patients and English speaking non-Latino White patients undergoing tonsillectomy and adenoidectomy.[17] Differing from the results reported here, the same two retrospective studies reported no difference in non-opioid analgesic administration between Caucasian and Latino or Black patients who underwent a single type of surgery. Our findings may be different due to the study designs, races included (Asians, Pacific Islanders, and Others were not included), sample size and therefore power, the number of non-opioid analgesics included, the number of surgeries included, and controlling for confounders and covariates in our adjusted model, or may represent true differences in practice.
It is not apparent why Asians, Hispanics, and Pacific Islanders would be significantly less likely to receive non-opioid pain medications than NH whites in our analysis. However, the magnitude of the differences is small (in our model the predicted percentage of Asians receiving non-opioid pain medications was 78.1%, compared to 80.3% of NH whites), and the majority of patients in each group received one or more non-opioid pain medications, but either disparate patient responses to nociception intraoperatively or provider practices led to these differences.
It is likewise not clear why Asians would be significantly less likely to have moderate or severe pain on regaining consciousness. The magnitude of this difference is relatively small, the predicted percentage of Asians was 14.3% compared to 17.0% of NH whites, and it may be of limited clinical consequence.[75] The finding is either a result of patient differences or provider bias in evaluating patients where the FLACC pain scale was used to determine pain scores. Our finding is inconsistent with previous studies which assessed racial and ethnic differences in pain sensitivity and found that adult African-Americans, Asians, and Hispanics have higher pain sensitivity compared to non-Hispanic Whites.[76, 77] In addition, a small study of healthy children exposed to pressure and radiant heat pain stimuli found greater pain sensitivity among Asians compared to NH whites.[78] However, the nociceptive stimuli in a healthy volunteer study may not be directly comparable to that of children who have undergone surgery.
Strengths of our study are the large number of patients and the racial and ethnic diversity. However, our findings may be restricted in generalizability given that is a retrospective study at a single academic tertiary children’s hospital. It is important to note, however, that the race and ethnicity demographics of children having surgery at our institution parallel those within the state of California. Data from 2018 show that of children in California from 0-17 years of age, 52% are Hispanic/Latino, 27% are White, 11% are Asian American, 5% are African American/black, 5% are multiracial, and <1% are American Indian/Alaska Native or Native Hawaiian/Pacific Islander.[79] Another limitation is the inherent inaccuracies in race and ethnicity data, including the potential for bias from those patients who declined to state race or ethnicity (139 cases), the verbal capture of this information at our institution, and the imprecision of ‘Other’ category. Thirdly, the exclusion of certain cases due to missing data could also introduce bias. However, these were only a small number of cases. Finally, many patients at LPCH are brought to the PACU under deep sedation or general anesthesia. This study did not identify time to awakening in the PACU. Serum concentrations of intraoperative analgesics could have dropped below the minimum effective concentration for analgesia, and the use of first conscious pain score as a surrogate for adequacy of intraoperative analgesia would be misleading.
Conclusion
We present an analysis on the association of race and ethnicity and the administration of intraoperative opioid analgesic in a dataset of 21,229 pediatric patients at a single, academic children’s hospital; our results do not show a difference for NH whites versus other racial and ethnic groups. While the size of the differences is small and may not be clinically significant, differences were found in the secondary outcomes investigated. Asians, Hispanics, and Pacific Islanders had significantly lower odds of receiving non-opioid analgesics intraoperatively than NH whites. Also, Asians had significantly lower odds of reporting moderate or severe pain upon regaining consciousness compared to NH whites. Additional investigation is needed to examine why these differences are present, if there are additional influencing factors, and if pediatric patient outcomes are affected by these differences.
Funding Source:
Dr. Rosenbloom’s work was supported by the National Institutes of Health T32 GM007592 grant (Research Training for Anesthetists). All other support was provided from institutional and/or departmental sources.
Abbreviations:
- LPCH
Lucile Packard Children’s Hospital Stanford
- EMR
electronic medical record
- OR
operating room
- ASA
American Society of Anesthesiologists physical status classification
- FLACC
Face, Legs, Activity, Cry, Consolability pain scale
- PACU
post-anesthesia care unit
- OSA
obstructive sleep apnea
- ICD
International Classification of Diseases
- LTR
likelihood ratio test
- OR
odds ratio
- CI
confidence interval
- ICU
intensive care unit
- IQR
interquartile range
- SD
standard deviation
- NH whites
non-Hispanic whites
Footnotes
Financial Disclosure Statement: The authors have indicated they have no financial relationships relevant to this article to disclose.
Conflict of Interest Statement: All authors have indicated they have no potential conflicts of interest to disclose.
Publisher's Disclaimer: This Author Accepted Manuscript is a PDF file of a an unedited peer-reviewed manuscript that has been accepted for publication but has not been copyedited or corrected. The official version of record that is published in the journal is kept up to date and so may therefore differ from this version.
Contributor Information
Christine G. Jette, Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, United States.
Julia M. Rosenbloom, Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States.
Ellen Wang, Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, United States.
Elizabeth De Souza, Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, United States.
T. Anthony Anderson, Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, United States.
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