Cleft palate with or without cleft lip is a common congenital abnormality that predisposes children to a myriad of negative health outcomes.1–5 In addition to debilitating effects on speech, hearing, and appearance, oral clefts can cause children to experience psychosocial problems, including poor academic achievement and increased anxiety and depression.6–8 Given these adverse outcomes, early and proper treatment of cleft palate is imperative. This care is costly, however, to both the health system and affected families, and can be challenging in resource-low settings. Recent studies have suggested that socioeconomic status, for example, influences both the risk of and treatment for orofacial clefts.9,10
The effects of other influences within the biopsychosocial model warrant further study in the context of cleft repair. These factors, which include racial and ethnic identification, have been shown to intersect with health systems in a way that can both create and perpetuate health disparities. Minority ethnic groups, for example, have been shown to experience earlier onset of disease, poorer quality of care, and increased mortality for a variety of diseases.11 Disparities extend across the lifespan, and have even been shown recently in pediatric surgery. In studies of craniosynostosis, for example, race was identified as a cause of delayed age at the time of care and higher overall hospital charges.12,13
Although differences in cleft palate incidence based on race have been documented—higher rates have been reported in Asian and American Indian populations, whereas lower rates have been reported in African-derived populations14—the effects of race on overall palate care have not yet been reported. The purpose of this study was to elucidate the impact of race on patient demographics, admission characteristics, hospital costs, and short-term complications following both primary and revision cleft palate repair. (Note that use of the word “race” in this article reflects identification as coded within the studied database, which may not accurately represent the racial and ethnic designations preferred by individual patients. The authors advocate for improvements in patient identification practices that allow for more accurate representation of diverse patient populations.)
PATIENTS AND METHODS
Data Source and Patient Population
The Kids’ Inpatient Database is a large national database in the Healthcare Cost and Utilization Project of the Agency for Healthcare Research and Quality.15 This database is released every 3 years, and offers unique insight into both socioeconomic variables (i.e., insurance and hospital characteristics) and hospital characteristics (i.e., bedsize, location, charges). The Kids’ Inpatient Database collected data from 2784 hospitals in 2000 for a total of 7,291,032 weighted pediatric discharges, and grew to 4121 hospitals in 2009 and a total of 7,370,203 weighted discharges. Same-day discharges are included in the database.
The Kids’ Inpatient Databases released in the years 2000, 2003, 2006, and 2009 were analyzed for this study. These databases reported age in days. Patients were sorted by International Classification of Diseases, Ninth Revision, Clinical Modification diagnosis codes along with an International Classification of Diseases, Ninth Revision, procedure code for surgical repair. Eleven International Classification of Diseases, Ninth Revision, diagnoses codes were chosen, covering all cleft palate with or without cleft lip diagnoses and validated by other studies.16 Trauma, malignancy, and emergent operations were excluded from analyses.
Total cleft palate operations were identified by International Classification of Diseases, Ninth Revision, procedure code 27.62 (correction of cleft palate) or 27.63 (revision of cleft palate). Primary cleft palate operations were isolated by excluding revision operations, which were isolated by International Classification of Diseases, Ninth Revision, procedure code 27.63.
Race was defined by the Healthcare Cost and Utilization Project into six categories: white, black, Hispanic, Asian/Pacific Islander, Native American, and other. Because of low numbers in the Native American category, this was combined with “other” in analysis.
Patient Characteristics and Outcomes
Demographic, hospital, socioeconomic, charge, and length of stay data were collected directly from the Kids’ Inpatient Database. Comorbidities were defined by the Deyo International Classification of Diseases, Ninth Revision, modification to the Charlson Comorbidity Index.17,18 All postoperative complications were identified by International Classification of Diseases, Ninth Revision, Clinical Modification diagnosis or procedure codes in any one of the 13 complication categories (Table 1).
Standard STATA SVY methodology for analyzing nationwide estimates was used with Stata version 13.0 (StataCorp, LP, College Station, Texas).13,19,20 All perioperative and hospital characteristics were compared between white, black, Hispanic, Asian/Pacific Islander, and other by univariate and multivariable analysis within individual years and in total years as a combined data set. A post hoc Pearson chi-square test was used for categorical variables, and the Wald test was used for continuous variables. Multivariable regressions were performed to control for variables contributing to total complications and cost. A value of p < 0.05 was set as significant. Bonferroni correction for multiple comparisons yielded an adjusted value of p = 0.0042 for demographic and admission characteristics (Tables 2 and 3).
There were 3464 white, 1428 Hispanic, 413 black, 398 Asian or Pacific Islander, and 470 patients of other races captured overall. Among the total and revision cohorts, black patients were most likely to be female and white patients were most likely to be male (p = 0.009 and p = 0.002, respectively). White patients had primary cleft repair at a significantly earlier age than nonwhite patients (p = 0.002) (Table 2).
Hispanic patients were the most likely to be diagnosed with cleft palate and cleft lip, whereas black and other patients were most likely to be diagnosed with cleft palate only (p = 0.037). At primary admission, black patients had higher Charlson Comorbidity Index scores (p = 0.004) and were more likely to be comorbid with a chronic pulmonary disorder (p < 0.001). Asian patients were most likely to be comorbid with congenital heart disease (p = 0.032).
In all cohorts, black and Hispanic patients were most likely be in the lowest income quartile, whereas Asian/Pacific Islanders were most likely to be in the highest quartile, followed by whites (p < 0.001, p < 0.001, and p < 0.001, respectively). In total and primary cohorts, the Midwest treated more white patients, the South treated more black patients, the West treated more Hispanic patients, and the Northeast treated more other patients (p < 0.001 and p < 0.001).
Black patients were most likely to have emergent admissions among total (7.3 percent; p = 0.005) and primary (8.0 percent; p = 0.003) repair, whereas all other races had a less than 2.6 percent rate of emergent admissions. Black patients also experienced hospitals of larger bedsize in all cohorts (p < 0.001, p < 0.001, and p = 0.034), and had increased length of stay following primary repair (p = 0.018). In the revision cohort, white patients had more concurrent procedures (p < 0.001). Revision cases were composed of more white patients and fewer black and Hispanic patients than primary cases (p < 0.001). Overall, black patients were most likely to pay with Medicaid, whereas Asian/Pacific Islander and white patients were more likely to use private insurance (p < 0.001) (Table 3).
In total, Hispanic patients accumulated the highest hospital charges ($19,069 per patient), followed by black patients ($18,984), whereas white patients had the lowest costs ($14,885; p = 0.0194). Similarly, among primary patients, black patients accumulated the most charges ($19,769), followed by Hispanic patients ($19,390). White patients accumulated the lowest costs ($14,947; p = 0.032) (Fig. 1).
Multivariable regressions were tabulated in the total group, including all significantly different variables, to determine charge (p < 0.001). After controlling for race, Charlson Comorbidity Index score, region, elective/nonelective, payer, diagnosis, and income quartile, length of stay (p < 0.001) and age (p < 0.001) significantly contributed to monetary charges.
Short-Term Postoperative Complications
Overall, white patients had significantly fewer total complications than nonwhite patients (p = 0.033). Black patients had more total complications than nonblack patients (p = 0.039), including higher rates of postoperative fistula (p = 0.020) and unspecified complications (p = 0.005). Blacks had more total complications among primary repairs (p = 0.001), and more nonspecific complications among revision repairs (p = 0.003). Among the primary cohort, Asian/Pacific Islanders experienced higher rates of accidental puncture (p = 0.031) and fistula (p < 0.001). Other patients had the highest rates of wound disruption across all cohorts (p = 0.013) (Fig. 2).
Multivariable regression for total complications was performed, including all significantly different factors. Both length of stay (p < 0.001) and patient age at the time of surgery (p = 0.020) contributed to increased total complications after controlling for race, Charlson Comorbidity Index score, insurance type, income, region, elective/nonelective, and diagnosis.
Cleft palate repairs are widely performed operations with established indications. Although palatoplasties are considered “elective,” adverse events accompanying unrepaired palates (e.g., feeding problems, failure to thrive, ear infections, speech/airway problems, and psychosocial issues) have prompted providers to recommend early treatment for all patients.2,7 Despite of this, our national analysis shows differences in both the treatment of and short-term outcomes following cleft palate repair for different racial and ethnic populations.
Inherent in this analysis is a discussion of short-term outcomes. Overall, black patients experienced higher total rates of postoperative complications in both primary and revision repair, whereas white patients experienced the fewest complications. Asian/Pacific Islander patients experienced relatively high rates of accidental puncture and fistula following primary repair, and other patients had high rates of wound disruption.
Increased complication rates in certain patient populations may stem from numerous sources. Socioeconomic disparities, reflected in this study by lower zip code incomes and higher Medicaid use among black and Hispanic patients, often lead to poor pediatric outcomes because of increased environmental stress, lower birth weights, delayed access to care, and limited availability of health resources.21–23 Preoperative comorbidities, which were higher in black patients, may lead to increased case complexity and thus more postoperative complications. Anatomical differences—black and other patients were less likely to present with the cleft lip—may influence both surgical complexity and treatment timelines, as earlier referral of patients with cleft lip may improve downstream retention for palate repair. This may explain why Hispanic patients, despite socioeconomic indicators similar to those of black patients, had relatively low complication rates. It is also important to consider the effects of provider bias and discrimination, which have been shown to substantially influence a variety of health outcomes.24,25
Although all of these factors may contribute collectively to poor operative outcomes, none of them was found to be an independent predictor of complication rates—multivariable analysis did not yield significance for race, insurance, income, Charlson Comorbidity Index score, or diagnoses. Rather, length of stay and patient age at the time of surgery were the only independent predictors of increased complications and costs. The association between length of stay and complications and costs, however, likely reflects length of stay as a result of—not the cause of—postoperative complications. Although increased hospital length of stay may increase risks for certain complications, it is unlikely that these risks alone would cause the significant association found in this study.
Patient age at the time of surgery, however, may play an important role in the creation of outcome disparities. Delayed surgical repair can present with multiple operative complexities; older patients often have wide clefts compelling extensive tissue rearrangement, adherent mucoperiosteal flaps requiring longer and bloodier dissections, and vertical palatal shelves complicating paring incisions.26 Concurrent premaxillary setback may be indicated if the premaxilla has excessive protrusion. Large clefts are also common predictors for fistula, and may explain increased specific fistula rates in black and Asian patients.27
Causes of delayed care among minority populations include location of residence, cleft type, receipt of maternity care coordination services, and publicly funded insurance, among other factors.28–30 In addition, Saha et al. reported black patients were the only minority group in which the majority of individuals would often delay treatment to see practitioners of their own race, often as a result of medical distrust.31 Among Asian patients, delays in age at presentation may stem from high adoption rates of children of Asian descent, which can be as high as 12 percent of cleft lip–cleft palate patients at some centers.32 Earlier surgical referral by primary care physicians, increased diversity among surgeons providing palatal repair, and multidisciplinary efforts to overcome disparities in health access may facilitate earlier care for minority cleft patients, in turn improving operative outcomes.
Increased total complications and subsequent length of stay also contributed to cost differences observed in this study. Overall, white patients accrued the lowest costs, whereas black and Hispanic patients accumulated $3000 to $4000 more per operation. Although these numbers do not reflect current pricing for palate operations, they do suggest an effect of race on the costs associated with cleft palate repair. Shweikeh et al. had similar findings for craniosynostosis, in which there was a $10,000 difference between whites and nonwhites.13 Together, these results suggest broader patterns of cost disparity that warrant further analysis in the context of ethnicity and race.
Overall, the findings of this study are consistent with previously demonstrated health inequalities that affect minority populations, especially black patients, in the United States. Particularly concerning is the extension of these disparities to early stages of life at which cleft palate repair occurs. Poor health early in life not only predisposes to poor health later in life, but can also lead to a variety of unfavorable outcomes, including decreased employment opportunities and earning potential.33 Thus, early adverse health outcomes can be particularly devastating—and early disparities are especially concerning. Surgeons and medical personnel treating palatal deformities should be aware of existing disparities and advocate for increased health equity. Earlier referral of minority patients for surgical repair and increased diversity among craniofacial surgeons may play an especially important role in facilitating earlier repairs and thereby ameliorating disparities in care.
Our study design was limited by its retrospective nature. Any changes in payment and treatment patterns over recent years are not included in the data, which captures patients from 2000 to 2009. Although we attempted to limit and stratify patients rigorously by International Classification of Diseases, Ninth Revision diagnosis/procedures codes and age, inaccurate coding accounts for cohort errors. Furthermore, the National Inpatient Sample database captures only data from each admission, precluding conclusions on long-term follow-up. Nonetheless, the results of the study sufficiently demonstrate differences in the treatment of cleft palate repair.
Patient race may play a significant role in both primary and revision cleft palate repair. White patients had fewer complications, shorter lengths of stay, and lower costs following repair compared with patients from racial minority groups. Although many factors may contribute to disparities in care, delayed age at treatment may be particularly impactful, predisposing patients to more adverse sequelae, increased length of stay, and higher hospital costs. Efforts should be made to improve early surgical referral of minority patients and to eliminate other potential sources of disparity in cleft palate repair.
1. Parker SE, Mai CT, Canfield MA, et al.; National Birth Defects Prevention Network. Updated National Birth Prevalence estimates for selected birth defects in the United States, 2004-2006. Birth Defects Res A Clin Mol Teratol. 2010;88:1008–1016.
2. Watkins SE, Meyer RE, Strauss RP, Aylsworth AS. Classification, epidemiology, and genetics of orofacial clefts. Clin Plast Surg. 2014;41:149–163.
3. Sabbagh HJ, Hassan MH, Innes NP, Elkodary HM, Little J, Mossey PA. Passive smoking in the etiology of non-syndromic orofacial clefts: A systematic review and meta-analysis. PLoS One 2015;10:e0116963.
4. Leslie EJ, Marazita ML. Genetics of cleft lip and cleft palate. Am J Med Genet C Semin Med Genet. 2013;163:246–258.
5. Sasaki Y, Taya Y, Saito K, Fujita K, Aoba T, Fujiwara T. Molecular contribution to cleft palate production in cleft lip mice. Congenit Anom (Kyoto) 2014;54:94–99.
6. Wehby GL, Collet B, Barron S, Romitti PA, Ansley TN, Speltz M. Academic achievement of children and adolescents with oral clefts. Pediatrics 2014;133:785–792.
7. Wehby GL, Collett BR, Barron S, Romitti P, Ansley T. Children with oral clefts are at greater risk for persistent low achievement in school than classmates. Arch Dis Child. 2015;100:1148–1154.
8. Wehby GL, Tyler MC, Lindgren S, Romitti P, Robbins J, Damiano P. Oral clefts and behavioral health of young children. Oral Dis. 2012;18:74–84.
9. Lupo PJ, Danysh HE, Symanski E, Langlois PH, Cai Y, Swartz MD. Neighborhood-based socioeconomic position and risk of oral clefts among offspring. Am J Public Health 2015;105:2518–2525.
10. Smillie I, Yong K, Harris K, Wynne DM, Russell CJ. Socioeconomic influence on orofacial cleft patient care. Scott Med J. 2015;60:70–74.
11. Williams DR, Priest N, Anderson NB. Understanding associations among race, socioeconomic status, and health: Patterns and prospects. Health Psychol. 2016;35:407–411.
12. Brown ZD, Bey AK, Bonfield CM, et al. Racial disparities in health care access among pediatric patients with craniosynostosis. J Neurosurg Pediatr. 2016;18:269–274.
13. Shweikeh F, Foulad D, Nuño M, Drazin D, Adamo MA. Differences in surgical outcomes for patients with craniosynostosis in the US: Impact of socioeconomic variables and race. J Neurosurg Pediatr. 2016;17:27–33.
14. Dixon MJ, Marazita ML, Beaty TH, Murray JC. Cleft lip and palate: Understanding genetic and environmental influences. Nat Rev Genet. 2011;12:167–178.
15. Thorne CH, Chung KC, Gosain A, et al. Grabb and Smith’s Plastic Surgery. 2014.Philadelphia: Wolters Kluwer/Lippincott Williams & Wilkins.
16. Hayes C, Werler MM, Willett WC, Mitchell AA. Case-control study of periconceptional folic acid supplementation and oral clefts. Am J Epidemiol. 1996;143:1229–1234.
17. Deyo RA, Cherkin DC, Ciol MA. Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases. J Clin Epidemiol. 1992;45:613–619.
18. Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: Development and validation. J Chronic Dis. 1987;40:373–383.
19. Lin Y, Pan IW, Harris DA, Luerssen TG, Lam S. The impact of insurance, race, and ethnicity on age at surgical intervention among children with nonsyndromic craniosynostosis. J Pediatr. 2015;166:1289–1296.
20. Lin Y, Pan IW, Mayer RR, Lam S. Complications after craniosynostosis surgery: Comparison of the 2012 Kids’ Inpatient Database and Pediatric NSQIP Database. Neurosurg Focus 2015;39:E11.
21. McLaren L. Socioeconomic status and obesity. Epidemiol Rev. 2007;29:29–48.
22. Adler NE, Ostrove JM. Socioeconomic status and health: What we know and what we don’t. Ann N Y Acad Sci. 1999;896:3–15.
23. Schreier HM, Chen E. Socioeconomic status and the health of youth: A multilevel, multidomain approach to conceptualizing pathways. Psychol Bull. 2013;139:606–654.
24. Hall WJ, Chapman MV, Lee KM, et al. Implicit racial/ethnic bias among health care professionals and its influence on health care outcomes: A systematic review. Am J Public Health 2015;105:e60–e76.
25. Gee GC. A multilevel analysis of the relationship between institutional and individual racial discrimination and health status. Am J Public Health 2002;92:615–623.
26. Murthy J. Management of cleft lip and palate in adults. Indian J Plast Surg. 2009;42(Suppl):S116–S122.
27. Braunstein JB, Sherber NS, Schulman SP, Ding EL, Powe NR. Race, medical researcher distrust, perceived harm, and willingness to participate in cardiovascular prevention trials. Medicine (Baltimore) 2008;87:1–9.
28. White RB. Services for children with congenital facial clefts through a state Crippled Children’s Service Program. Cleft Palate J. 1981;18:116–121.
29. Cassell CH, Meyer RE, Farel AM. Predictors of referral to the North Carolina Child Service Coordination Program among infants with orofacial clefts. Cleft Palate Craniofac J. 2007;44:45–51.
30. Abbott MM, Kokorowski PJ, Meara JG. Timeliness of surgical care in children with special health care needs: Delayed palate repair for publicly insured and minority children with cleft palate. J Pediatr Surg. 2011;46:1319–1324.
31. Saha S, Taggart SH, Komaromy M, Bindman AB. Do patients choose physicians of their own race? Health Aff (Millwood) 2000;19:76–83.
32. Swanson JW, Smartt JM Jr, Saltzman BS, et al. Adopted children with cleft lip and/or palate: A unique and growing population. Plast Reconstr Surg. 2014;134:283e–293e.
33. Currie J. Healthy, wealthy, and wise: Socioeconomic status, poor health in childhood, and human capital development. J Econ Lit. 2009;47:87–122.