Conflict of Interest Disclosure Maximizing auditory development outcomes in children with hearing loss Ryan McCreery, PhD ryan.mccreery@boystown.org Research presented here was supported by the National Institutes of Health, National Institute for Deafness and Communication Disorders (R01 DC009560, R01 DC013591) Other research support: Phonak Oticon Andrew Hetzel Research Fund Pediatric Audiology Biostatistics, Linguistics, & Psychology Acknowledgements Beth Walker, PhD Meredith Spatford, AuD Child Language Ruth Bentler, PhD Jacob Oleson, PhD Project Management Lenore Holte, PhD Bruce Tomblin, PhD Patricia Roush, AuD Mary Pat Moeller, PhD Overview Factors that affect outcomes Previous research on outcomes What do we know? Cumulative auditory experience Aided speech audibility Hearing aid use Our findings on outcomes Auditory development Communication development Duration Demographic Malleable Age of identification Age of amplification SES Additional disabilities Aided audibility Hearing aid use 1
Age of identification Yoshinaga-Itano et al. 1998 Earlier is better Before 6 months vs. after 6 months (Moeller, 2000; Yoshinaga-Itano et al. 1998) Supported widespread adoption of universal newborn hearing screening and early intervention programs Moeller, 2000 Invariance assumption Family involvement Circle High Triangle Average Square Below average Duration of HA use Duration of HA use Age of HA fitting Age of HA fitting Time Prior to newborn hearing screening Age at HA FIT: After UNHS Age of amplification Later-amplified THEN Less hearing loss Average age of ID Overall 2 years Late group 3.5 + years 120 100 Number 80 60 40 100 32 Later-amplified NOW Early compared to pre-unhs Delayed onset hearing loss Mild hearing loss 20 0 13 8 11 7 10 4 15 AGE Age at HA Fit (Months) 2
What now? Predictors of language at Age 3 Duration variables may not explain Demographic factors Degree of hearing loss Socioeconomic status Additional disabilities Cochlear implants Primary language other than English Significant Gender (Girls > Boys) Additional disability (-) Degree of hearing loss (-) Age of cohlear implantation (-) Socioeconomic status (+) Communication mode (Oral > not oral) Not significant Age of first HA fitting Ching et al. 2013 How can we use demographic factors? Understand populations who may be at higher risk for delays Targeted intervention? Important for understanding how research relates to real world Supports public policy How does OCHL build on previous research? Demographic variables included: Degree of hearing loss Socioeconomic status Addition of malleable factors Aided audibility Hearing aid use Language input Intervention / Service provision Key differences Children with bilateral hearing loss who wear hearing aids Did not include children with: Cochlear implants Additional disabilities From homes where English was not primary Effects of hearing loss on development Outcomes may be different than general population 3
Who are the OCHL participants? Distribution of Better Ear Hearing SUBJECTS TOTAL HH 317 NH 117 Degree of hearing loss (Better ear pure tone average, BEPTA) 21 Sample Characteristics HH NH Cumulative auditory experience influences outcomes Number of subjects 317 117 Hearing (PTA) 25-75 db HL < 20 db HL Age ranges 0;6 to 7;3 at entry Consistency of auditory access Early intervention services Outcomes Nonverbal IQ Maternal education Language use Additional disabilities Within the average range Matched but > US sample Spoken English in the home No autism; no major vision, cognitive, or motor disabilities Audibility & Hearing aid use We hypothesize that access to speech will predict success for children who are HH Findings are mixed re: relationship between PTA and outcomes Most previous research looked at audiometric thresholds (i.e., PTA) as a predictor of success Fitzpatrick et al., 2007 Wake et al., 2005 Delage & Tuller, 2007 Davis et al., 1986 Moeller, 2000 Ramkalawan & Davis, 1992 Gilbertson & Kamhi, 1995 4
FULL NO Problems with Pure Tone Average (PTA) Why has previous research on audibility been limited? Focus on degree of hearing loss Hearing aid verification data is difficult to collect in children Limited previous research Stiles et al. 2012 (+Vocabulary) Sininger et al. 2010 (Could not analyze due to close relationship with degree of hearing loss) Research questions Can we assume that auditory experience is invariant? What are the factors that cause variability in: Aided audibility Hearing aid use Determining how close HA fittings are to target? The characteristics of hearing aid fittings in infants and young children (McCreery, Bentler, & Roush, 2013) RMS = root-meansquare Compare DSL target SII to measured SII 0-1, with 1 = completely audible RMS error to DSL target at 4 frequencies RMS error < 5 db = optimal HA fitting Can we assume children are fit to target? Target vs. Actual (RMS error) Fitting data compared to DSL targets RMS error < 5 db is a good fit Calculate RMS error of deviations from target at 5., 1, 2, and 4 khz 5
How can you measure RMS error? Accuracy of Verification methods Probe microphone real ear measures RMS error= 5.67 db (SD = 3.95 db) Functional gain (aided soundfield) RMS error=7.92 db (SD = 4.67 db) McCreery, Bentler, Roush, 2013 Actual Hearing aid fit quality Take home message: Auditory experience varies based on the quality of the hearing aid fitting. What predicts audibility? PTA (p < 0.001, β = 0.663) Fit-to-target (p < 0.001, β = 0.553) aka RMS error <5 db good fitting McCreery, et al., in preparation McCreery, Bentler, Roush, 2013 Changes in aided audibility over time threshold change & quality of HA fitting relate to audibility over time Better thresholds & match-to-target Worse thresholds & match-to-target 6
What contributes to change in thresholds over time? Changes in middle ear status (normal abnormal) Etiology NOT related to: Age PTA Aided SII MPO Hours of HA use Previous studies of HA use Parent report 75-80% of parents report that children wear their devices Most of the time or always (Ching et al. 2013) Not reported What do audiologists believe about HA use? Components of auditory experience Audiologists overwhelmingly felt that children were wearing their hearing aids for all waking hours Audibility Hearing aid use Auditory exposure Do children who are HH vary the amount of time they wear their HAs? How did we measure amount of daily HA use? Subjective Objective Which factors predict daily HA use time in children who are hard of hearing? Walker et al., 2013 272 children with hearing aids How consistently do children wear HAs in different settings? Hearing aid questionnaire average # of hours per day consistency of use across contexts: in the car, meal times, book sharing, etc. Hearing aid data logging 7
Parent report of use (hours) What factors predict the amount children wear HAs? 16 14 12 10 8 6 4 2 Hours of Use (n=272) Weekday Weekend 0 0.5 1 1.5 2 3 4 5 6 7 8 Age (years) Walker et al., 2013 Significant: Age Better-ear PTA Maternal education level Site Not significant: Gender Age at HA fitting Length of HA experience Data logging by age group Walker, et al., in preparation Actual use differs from parent report Does variability predict outcomes? Auditory development questionnaires Aided speech recognition Language Hypothesis: Children with better audibility and more hearing aid use will have better outcomes OCHL Auditory Development Measures Auditory development outcomes by visit (number of subjects at each age) 12 mn 18 mn 2 yr 3 yr 4 yr 5 yr 6 yr 7 yr 8 yr 9 yr Examples of complexity Hearing aid use Audibility, language, speech recognition Questionnaires LittlEARS 56 85 104 PEACH 15 46 81 SSQ 144 125 39 Word recognition measures Open Closed 165 ESP 72 148 LNT - Easy 3 179 43 LNT - Hard 174 44 PBK 45 188 165 93 54 CASPA 89 38 24 PEACH = Parent's Evaluation of Aural/Oral Performance of Children SSQ = Speech Spatial and Qualities of Hearing Questionnaire ESP = Early Speech Perception Test; LNT = Lexical Neighborhood Test PBK = Phonemically-Balanced Kindergarten Word Lists CASPA = Computer-Assisted Speech Perception Assessment 8
LittlEARS PEACH SSQ Predictors Measure Audibility HA Use Language Mat Ed LittlEars + + + PEACH + + SSQ + *Controlling for pure tone average Open and Closed Set Task Aided Word Recognition 2 year-olds 4-8 yearolds 9
Computer Assisted Speech Perception Assessment (CASPA) Predictors Measure Audibility HA Use Language Other Open/Closed + + + LNT-Hard + + Age CASPA + + + Working memory, Age *Controlling for pure tone average and maternal education level 7-9 yearolds Language scores as a function of audibility Highest SII 10 point difference (2/3 of a SD) Conclusions Cumulative auditory experience affects outcomes Audibility Hearing aid use Cumulative auditory experience can be influenced by clinical practices Lowest SII Tomblin et al., in review How can we promote consistency of HA use? How can we maximize audibility? Emphasize link between auditory stimulation and language Find times when initial use is most practical Parent-parent connections Professional collaboration (B-3 and audiologists) Use of validated, evidence-based prescriptive methods Probe microphone verification 10