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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
 
Stroke. Author manuscript; available in PMC 2017 April 1.
Published in final edited form as:
PMCID: PMC4811684
NIHMSID: NIHMS755082

Racial and socioeconomic disparities in gastrostomy tube placement after intracerebral hemorrhage in the US

Abstract

Background and Purpose

Percutaneous endoscopic gastrostomy (PEG) tubes are widely used for enteral feeding of patients after intracerebral hemorrhage (ICH). We sought to determine whether PEG placement after ICH differs by race and socioeconomic status.

Methods

Patient discharges with ICH as the primary diagnosis from 2007 to 2011 were queried from the Nationwide Inpatient Sample. Logistic regression was used to evaluate the association between race, insurance status, and household income with PEG placement.

Results

Of 49,946 included ICH admissions, a PEG was placed in 4,464 (8.94%). Among PEG recipients, 47.2% were minorities and 15.6% were Medicaid enrollees, while 33.7% and 8.2% of patients without a PEG were of a race other than white and enrolled in Medicaid, respectively (P<0.001). Compared to whites, the odds of PEG were highest among Asians/Pacific Islanders (OR 1.62, 95% CI 1.32-1.99) and blacks (OR 1.42, 95%, CI 1.28-1.59). Low household income (OR 1.25, 95% CI 1.09-1.44 in lowest compared to highest quartile) and enrollment in Medicaid (OR 1.36, 95% CI 1.17-1.59 compared to private insurance) were associated with PEG placement. Racial disparities (minorities versus whites) were most pronounced in small/medium-sized hospitals (OR 1.77, 95% CI 1.43-2.20 vs. OR 1.31, 95% CI 1.17-1.47 in large hospitals; p-value for interaction 0.011), and in hospitals with low ICH case volume (OR 1.58, 95% CI 1.38-1.81 vs. OR 1.29, 95% CI 1.12-1.50 in hospitals with high ICH case volume; p-value for interaction 0.007).

Conclusion

Minority race, Medicaid enrollment, and low household income are associated with PEG placement after ICH.

Keywords: intracerebral hemorrhage, disparities, socioeconomic status, gastrostomy tube, PEG, feeding tube

Introduction

Spontaneous intracerebral hemorrhage (ICH) accounts for about 15-20% of all strokes and is a leading cause of long-term disability in the United States1. Dysphagia is a common sequela after ICH, contributing significantly to overall morbidity and disability2-4. Prediction of recovery from dysphagia in stroke patients is unreliable and lacking objective criteria5,6. Thus, patients perceived unlikely to recover adequate swallowing function in a timely manner commonly undergo time-limited enteral feeding via a percutaneous endoscopic gastrostomy (PEG).

To date, however, the utility of enteral feeding via PEG in stroke patients has not been validated7. Although success rates greater than 95% have been reported for placement of PEG tubes, procedure-related complications range from 9% to 17%, including bleeding, local infection, peritonitis, perforation, and aspiration; major complications occur in 1%-3% of cases8,9. Other than direct procedure-related complications, PEG placement may be associated with poor long-term outcomes and increased mortality10,11.

Due to lack of reliable and objective criteria, the decision to proceed with and timing of PEG placement is difficult, and may be influenced by a variety of factors including cultural perceptions of health care providers, caregiver experience with the health care system, and economic factors such as income and access to care. We and others have previously identified black race as a risk factor for PEG tube placement in ICH patients in single center studies12,13, but it is unclear whether this observation is representative of common practice across the United States, or whether PEG placement after ICH is more common in other minority populations as well. An association between PEG placement practices and indicators of socioeconomic status (SES), such as income and insurance status, in ICH patients has not previously been investigated, either as an independent risk factor for PEG placement or as an explanation for observed differences by race.

In the present study we aimed to determine whether PEG placement decisions after ICH differ by race, level of income, and insurance status in a nationwide dataset of inpatient hospital admissions. In addition, we evaluated whether observed differences vary across different subgroups of geographical regions and hospital characteristics.

Methods

Data source

Data were obtained from the Nationwide Inpatient Sample (NIS), part of the Healthcare Cost and Utilization Project (HCUP), sponsored by the Agency for Healthcare Research and Quality14. The NIS is the largest all-payer inpatient database in the US, representing a 20% stratified sample of all admissions to non-federal US hospitals. NIS captures information regarding demographics, hospital characteristics, primary and secondary diagnoses and procedures, comorbidities, and case severity measures on several million hospital discharges each year. All diagnoses and procedures are recorded using International Classification of Diseases version 9 Clinical Modification (ICD9-CM) codes. Detailed information on the design and contents of the NIS is available at http://www.hcup-us.ahrq.gov. Because NIS data are publicly available and contains no personal identifying information, this study was exempt from institutional review board approval.

Case selection

Using the NIS, we identified adult cases with primary diagnosis of non-traumatic ICH by using ICD9-CM code 431 between 2007 and 2011. This ICD9-CM code is estimated to have a positive predictive value of >89%15,16. We excluded cases with a secondary ICD9-CM code of traumatic brain injury (851-854), arteriovenous malformation (437.3), malignant brain tumor (191.x), skull fracture (800-801), concussion (850), and those undergoing aneurysm clipping and coiling to restrict our population to those with primary ICH. In addition, we excluded patients enrolled in a clinical trial (ICD9-CM code V70.7). The unit of observation in NIS is discharge after hospitalization. This harbors the potential for double counting of individual patients who are transferred to another acute care facility and have a discharge record both at the initial hospital of presentation and the hospital to which they are transferred. In order to prevent oversampling of transferred patients who may potentially have multiple acute care inpatient records pertaining to the same ICH event, cases transferred to another hospital were excluded while cases “transferred in” were included in our analysis.

Primary exposures and outcome of interest

The primary exposures of interest were race/ethnicity, primary insurance payer status and median household income of the residential zip code for each patient resides. The primary outcome of interest was placement of a PEG as identified by ICD9-CM procedure code 43.11.

Comorbidity and severity adjustment

We calculated the Charlson comorbidity index, a weighted score of 17 different comorbidities validated for outcome adjustment for analyses of administrative data sets using ICD9-CM codes17,18, for each patient. The presence of dysphagia was identified by ICD9-CM codes 787.20-787.24, and 787.29. Case severity was determined using the all patient refined diagnosis-related groups (APR-DRGs), a 4-point ordinal scale (minor, moderate, major, and extreme risk of mortality) derived from age, primary and secondary diagnoses, and procedures19. The APR-DRG algorithm is a validated and reliable indicator of mortality, and is commonly used as a severity indicator in studies relating to hemorrhagic stroke20,21.

Statistical analysis

Comparisons of sociodemographic, hospital-level, and clinical characteristics among patients with and without PEG tube were made using Chi2 and Wilcoxon rank-sum tests for categorical and continuous variables, respectively. Univariate logistic regression was performed to determine the unadjusted association of PEG tube placement and race, primary insurance payer status, and median household income per zip code. Multivariable models were adjusted for age, sex, hospital characteristics (teaching status, bed size, location, region, and annual volume of ICH cases), discharge quarter, weekend admission, modified Charlson Comorbidity Index, APR-DRG severity subclass, hypertension, diabetes mellitus, dyslipidemia, coronary artery disease, congestive heart failure, atrial fibrillation, valvular disease, anemia, thrombocytopenia, alcohol abuse, drug abuse, chronic kidney disease, transfusion of blood products, performance of cerebral angiography, craniotomy/craniectomy, hydrocephalus, withdrawal of care status, and death. For the primary analysis, observations with missing information on the primary exposures of interest were excluded; since the variable race had substantial missingness (16.5%), sensitivity analysis including imputed values for race via multiple imputation via chained equations (MICE) was performed. We used a Generalized Estimation Equations (GEE) approach to account for clustering of patients within hospitals. Statistical analysis was performed using STATA version 13 (Stata Statistical Software: Release 13. College Station, TX). A p-value of <0.05 was considered statistically significant. 95% confidence intervals are reported. Statistical interactions between race and income and race and insurance status on PEG placement were explored. In addition, we explored interactions between race and hospital characteristics, such as hospital region, location, bed size, annual ICH case volume, and teaching status.

Results

Patient characteristics

Among the 49,946 cases that met all inclusion criteria (Figure 1), 4,464 underwent PEG placement (8.94%). Patient and hospital characteristics of subjects with and without PEG are summarized in Table 1. Patients who received a PEG tube were younger (median 68 vs.73 years, p<0.001), and more likely to be male (55.2% vs.49.1%, p<0.001) than were ICH patients who did not receive a PEG tube. While only 33.7% of patients without PEG placement were of a minority race, 47.2% of all PEG tube recipients were of a race other than white (p<0.001). Among black patients 13.2% received a PEG tube, compared to 7.3% receiving a PEG tube among white patients (p<0.001). The proportion of Medicaid patients among PEG tube recipients was significantly higher compared to those not receiving a PEG tube (15.6% vs. 8.2%, p<0.001). Similarly, the percentage of patients living in a zip code of the lowest quartile of median household income (less than 39,000-41,000 USD per year) in those receiving a PEG tube was higher than in those without PEG tube (34.1% vs. 28.6%, p<0.001). Further baseline characteristics are presented in Table 1.

Figure 1
Flow diagram indicating selection of the study population. *Not mutually exclusive.
Table 1
Baseline characteristics of the study population stratified by PEG status.

Minority race is associated with increased risk of PEG placement

In univariate analysis, the odds of PEG tube placement were significantly higher for patients of a minority race compared to whites: OR 1.95, 95% CI 1.81-2.10 in blacks, OR 1.46, 95% CI 1.32-1.62 in Hispanics, OR 1.73, 95% CI 1.52-1.98 in Asians/Pacific Islanders, and OR 1.68, 95% CI 1.46-1.95 in subjects identified as a racial group not part of any of the aforementioned categories (Table 2). These results persisted after multivariate adjustment (Table 2). Taken together, minority patients had 1.41 times higher odds of PEG placement compared to whites (95% CI 1.27-1.56). Although the proportion of patients with the diagnosis dysphagia was only minimally higher in minority patients (11.5% vs 10.3% in whites, p=0.001), 42.3% of all minority patients with dysphagia received a PEG tube, while only 32.4% of white patients with dysphagia underwent PEG placement (p<0.001). Since there was substantial missing data on race (16.5%), we performed a sensitivity analysis by repeating the primary analysis after multiple imputation via chained equations (MICE) of the missing values for race; analysis of the imputed dataset yielded similar results as complete-case analysis with regard to effect size, direction, and statistical significance (Supplemental Table I).

Table 2
Multivariable analysis for racial and socioeconomic determinants of PEG placement after ICH.

When stratified by hospital region, racial disparities were least prominent in the South (OR 1.29, 95 CI 1.10-1.51 vs. OR 1.65, 95% CI 1.29-2.11 in the Northeast; p-value for interaction 0.040, Table 3). Furthermore, disparities by race were most pronounced in small/medium-sized hospitals (OR 1.77, 95% CI 1.43-2.20 vs. OR 1.31, 95% CI 1.17-1.47 in large hospitals; p-value for interaction 0.011), and hospitals with a ICH case volume below the median (OR 1.58, 95% CI 1.38-1.81 vs. OR 1.29, 95% CI 1.12-1.50 in hospitals with ICH case volume above the median; p-value for interaction 0.007). Racial disparities in PEG placement after ICH did not differ by hospital location (rural vs. urban) or hospital teaching status (Table 3).

Table 3
Racial disparities in PEG placement after ICH stratified by hospital region, location, bed size, ICH case volume, and teaching status.

Increased risk of PEG placement in Medicaid enrollees and low income patients

Medicaid patients had almost 2-fold higher odds of receiving a PEG tube compared to patients with private insurance in univariate analysis (OR 1.94, 95% CI 1.74-2.16). Patients living in a zip code where the median household income was in the lowest quartile had significantly elevated odds of PEG placement compared to the highest income quartile (OR 1.35, 95% CI 1.24-1.47). These discrepancies were insufficiently explained by individual patient or hospital characteristics, since the observed differences remained in multivariable analysis (Table 2). Enrollment in Medicaid was associated with higher odds of PEG placement in adjusted models (OR 1.36, 95% CI 1.17-1.59 compared to private insurance), while self-payers had a lower probability of PEG placement (OR 0.76, 95% CI 0.64-0.91) when compared to private insurance. Similarly, living in a zip code where the median household income is in the lowest quartile remained significantly associated with higher odds of PEG placement (OR 1.25, 95% CI 1.09-1.44 compared to the highest income quartile). Results in the dataset with imputed values for observations for which information on race was missing were similar (Supplemental Table I).

The proportion of patients with dysphagia did not significantly differ by income quartile (10.6% in the lowest quartile, 10.4% in quartile 2, 11.2% in quartile 3, and 10.6% in the highest income quartile, p=0.182). However, among all patients with dysphagia, the proportion of patients undergoing PEG placement was 40.1% in the lowest income quartile, 35.6% in quartile 2, 33.1% in quartile 3, and 34.9% in the highest income quartile (p=0.001). The odds of PEG placement among Medicaid enrollees and low income patients was similar among whites and non-whites: OR 1.33, 95% CI 1.04-1.70 vs. OR 1.34, 95% CI 1.12-1.60 among Medicaid enrollees (p-value for interaction 0.678), and OR 1.20, 95% CI 1.00-1.43 vs. OR 1.34, 95% CI 1.12-1.60 among patients in the lowest income quartile (p-value for interaction 0.603).

Discussion

In the present study we report higher PEG placement rates in minorities, Medicaid enrollees, and low income patients after ICH, despite similar incidence of dysphagia. These differences were insufficiently explained by medical comorbidities, case severity, and other hospital characteristics captured in the NIS database. Racial disparities were most pronounced in small/medium-sized hospitals, hospitals with lower yearly ICH case volume, and in the Northeast geographical region of the US.

Racial differences in the incidence of dysphagia after combined ischemic and hemorrhagic stroke have been described in Asians but not consistently in other minority groups22, and it is presently unclear whether racial or socioeconomic differences exist in dysphagia incidence specifically after ICH. While we observed disparities in PEG placement by race and socioeconomic status, the incidence of dysphagia did not substantially differ among the different racial groups or quartiles of income in our study population. In administrative databases, dysphagia is largely undercoded and extraction of information on dysphagia based on ICD9-CM coding has low sensitivity23; however, specificity is high, and it is unlikely that dysphagia is differentially undercoded among various racial groups and surrogates or SES. Although the time-to-PEG did not differ among the various groups (median 11 days in minorities vs. 10 days in whites; median 10 days in the low income quartile and all other quartiles combined), it is possible that differential access to repeated speech-language-pathology (SLP) evaluations may account for differences in detection of early recovery. Beyond the initial SLP evaluation, minority (and low SES) patients may not receive the same number of follow-up SLP visits as their white counterparts24,25. This may preclude timely detection of signs and symptoms of swallowing recovery, and thus diminish the chance to avert PEG placement. Since SLP evaluations are not coded in the NIS, we were unable to further investigate this as a potential explanation for the observed differences in PEG placement. However, in our study length of stay was longer for minority patients compared to whites (median 12 vs 8 days), precluding the possibility that white patients were given more time to recover swallowing function during their hospitalization. Our findings are consistent with a recent study describing racial disparities in PEG placement after ischemic stroke26; however, some of the observed disparities may at least in part be due to residual confounding by education level, another commonly employed surrogate of SES. Since the NIS does not collect information on level of education, we were unable to further test this hypothesis in our dataset.

Although placement of a PEG tube is relatively common after stroke, there is no consensus regarding necessity, timing, and objective criteria for PEG placement in ICH patients; this uncertainty of when and in whom to place a PEG is evidenced by the fact that current US and European ICH management guidelines do not provide specific recommendations regarding the role of PEG tubes27,28. Thus, decisions on PEG placement largely depend on perceptions of necessity among providers, patients, family members, and health care decision makers, and may be influenced by the providers’ perceived family support structure and home environment of the patient29,30. In addition, providers may oversell the benefits of a PEG when counseling minority and low SES patients; paradoxically, this may be attributable to a fear of being perceived as suggesting limits on interventions or less aggressive care to such families, even if this may be the more appropriate approach31. Explaining and implementing alternatives to feeding via PEG may be time-consuming and laced with uncertainty, adding to the physical and psychosocial burden of family members and caretakers32,33. This may result in a biased perception of counseling on PEG tubes, particularly, in a race-discordant or SES-discordant physician-patient relationship where trust issues may impede a balanced interpretation of counseling efforts34,35. Physicians might spent more time with white than with minority patients when counseling about PEG tube placement, thus differentially affecting its perceived necessity36,37. Lastly, a paternalistic view of medical treatments and the physician-patient relationship may prevail in certain cultures38,39, and may result in increased willingness to accept a PEG when suggested by providers.

Medicaid enrollees had higher odds of PEG placement compared to the privately insured. This is consistent with our finding of higher PEG rates in low compared to high income patients, since Medicaid is a program specifically designed to provide health coverage for the poor and families/individuals with limited resources. Of note, self-payers had decreased odds of PEG placement. While a substantial proportion of patients without insurance coverage have limited resources and low income, self-payers also include some middle class families and high income patients, i.e. about 15% of patients with an annual household income of 50,000-75,000 and about 8% of patients with a household income >75,000 USD are self-payers40. Our finding is in-line with other reports indicating lower health care usage in self-payers in general, at least in part explained by self-payers being more cognizant of their healthcare expenditures41.

Our study has several limitations. The NIS does not contain clinical and physiological data on ICH volume or location, intraventricular extension, level of consciousness, or associated laboratory parameters, which may potentially confound the described associations of race and indicators of SES with PEG placement12. We attempted to mitigate this shortcoming by adjusting all regression models for the Charlson Comorbiditity Index, a validated measure of patient comorbidities in ischemic stroke and ICH18,42. Miscoded and missing data may occur in large administrative datasets reliant on ICD9-CM coding; however, it is unlikely that there is differential miscoding by race or SES. Using ICD9-CM code 431 for identifying primary ICH cases has high sensitivity and positive predictive value15,16,43. To address missingness for race, we performed a sensitivity analysis after multiple imputation by chained equations (MICE) for the race variable.

Despite these limitations, our study identifies minority race, Medicaid insurance coverage, and low-income as risk factors for PEG placement after ICH. These differences were not accounted for by medical comorbidities, measures of case severity, and hospital characteristics captured in the NIS dataset. Our findings emphasize that race and socioeconomic disparities appear to influence decision-making regarding PEG tube placement after ICH. Further studies are needed to clarify whether the reason for the observed differences are related to physician-patient communication, patient preference, or both.

Supplementary Material

Supplemental Table I

Acknowledgments

Sources of Funding

Dr Faigle is supported by an institutional KL2 grant from the Johns Hopkins Institute for Clinical and Translational Research (ICTR), which is funded in part by Grant Number KL2TR001077 from the National Center for Advancing Translational Sciences (NCATS) a component of the National Institutes of Health (NIH), and the NIH Roadmap for Medical Research.

Footnotes

Disclosures

Disclosures: None.

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