Investigating Socioeconomic Barriers to Cochlear Implantation.

OBJECTIVE: To explore socioeconomic disparities in cochlear implant evaluation (CIE) referrals and cochlear implantation. STUDY DESIGN: Retrospective chart review. SETTING: Tertiary referral academic center. METHODS: Adult patients (n = 271) with an audiogram performed between 2015 and 2019 with a pure-tone average of at least 60 dB and word recognition score of 60% or less in the better-hearing ear or no word recognition score performed were included to determine if socioeconomic factors influenced the rate of referral to CIE and cochlear implantation. RESULTS: There were 122 insured patients referred to CIE where 84 were considered cochlear implant (CI) candidates and 73 were implanted. In multivariate regression analysis, non-English-speaking patients were referred to CIE at lower rates ( p < 0.01) than English-speaking patients. Patients who met the CI candidacy criteria with private insurance ( p = 0.03) or Medicare with private insurance supplement ( p = 0.03) had higher rates of cochlear implantation than those with Medicare or Medicaid. Of the uninsured patients (n = 22), 3 were referred to CIE and 2 were considered CI candidates. No uninsured patients received a CI. CONCLUSIONS: Primary language spoken was associated with a disparity in rates of CIE referral. Insurance type did influence rate of cochlear implantation once patients completed CIE and were considered CI candidates. Additional research is needed to implement strategies for more inclusive treatment.

Socioeconomic Disparities in Pediatric Single-Sided Deafness.

OBJECTIVE: To explore socioeconomic disparities in pediatric single-sided deafness (SSD) treatment. STUDY DESIGN: Retrospective chart review. SETTING: Tertiary referral academic center. METHODS: The charts of 190 pediatric patients with SSD were reviewed for demographic and clinical characteristics. Socioeconomic variables included race and insurance status. ZIP codes were used to obtain additional socioeconomic data from the American Community Survey, including mean and median income, percentage of families below the poverty level, and employment status. Socioeconomic status (SES) was classified by insurance status and income. Treatment outcomes were analyzed by socioeconomic variables. RESULTS: There were 105 males and 85 females with a mean follow-up of 55.2 months and a mean age at diagnosis of 4.4 years. Sixty-three percent of children received treatment at last follow-up. Thirty-five percent of children had public insurance and 65% had private insurance. Treatment rates were similar in the private and public insurance groups (60.6% vs 66.7%, P = .42), but device type was different between groups (P = .02). Consistent device use was associated with private insurance (47.5% vs 38.9%, P = .003) and high SES (94.4% vs 80%, P = .04) on univariate but not on multivariate analysis. Aided audiometry results were similar between SES groups. No association was found between sex, race, income level, poverty level, or employment status and treatment outcomes. CONCLUSION: Insurance type and SES were not associated with SSD treatment outcomes in children, although device use may be higher in children with private insurance and higher SES. Further research should focus on strategies to reduce barriers to treatment and improve adherence.

Development of a Low-Cost, Noninvasive, Portable Visual Speech Recognition Program.

OBJECTIVES: Loss of speech following tracheostomy and laryngectomy severely limits communication to simple gestures and facial expressions that are largely ineffective. To facilitate communication in these patients, we seek to develop a low-cost, noninvasive, portable, and simple visual speech recognition program (VSRP) to convert articulatory facial movements into speech. METHODS: A Microsoft Kinect-based VSRP was developed to capture spatial coordinates of lip movements and translate them into speech. The articulatory speech movements associated with 12 sentences were used to train an artificial neural network classifier. The accuracy of the classifier was then evaluated on a separate, previously unseen set of articulatory speech movements. RESULTS: The VSRP was successfully implemented and tested in 5 subjects. It achieved an accuracy rate of 77.2% (65.0%-87.6% for the 5 speakers) on a 12-sentence data set. The mean time to classify an individual sentence was 2.03 milliseconds (1.91-2.16). CONCLUSION: We have demonstrated the feasibility of a low-cost, noninvasive, portable VSRP based on Kinect to accurately predict speech from articulation movements in clinically trivial time. This VSRP could be used as a novel communication device for aphonic patients.