speech and hearing problems

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May is Better Hearing and Speech Month

NIDCD research builds connections for better hearing and speech

Click on the image or on this link-sharing page to use our social media sharing tools or embed the image in your website or blog. A Spanish-language version of this image is also available.

Each May, the National Institute on Deafness and Other Communication Disorders (NIDCD) joins the American Speech-Language-Hearing Association (ASHA) in observing Better Hearing and Speech Month to raise awareness about hearing and speech disorders . This year’s theme, “Building Connections,” couldn’t be more relevant, as pandemic-related restrictions have been limiting our social interactions over the past year, highlighting ways in which maintaining communication—and connections—are central to our well-being. In addition, scientific breakthroughs in hearing and speech—many of which were funded by the NIDCD—connect research in the lab to interventions that help people connect with each other through communication.    

Hearing, voice, speech, and language disorders exact a far-reaching toll on public health and individual well-being. At least 46 million people in the U.S. have a hearing or other communication disorder that affects how they engage with their environments. These disorders can occur at any age and in all ethnic and socioeconomic groups.

Hearing loss can be present at birth, happen instantly, or, most commonly, develop over time from excessive noise exposure, aging, or a combination of these factors. Approximately 15% of American adults (37.5 million) report some trouble hearing. This rate increases to 50% among those who are 75 and older.

Individuals with untreated hearing loss may become isolated from their families and communities. In older adults, untreated hearing loss is also associated with higher total health care costs, increased risk of dementia and cognitive decline, falls, depression, and lower quality of life. Hearing aids and other assistive devices can help people with hearing problems reconnect to others, but only about one in four adults (ages 20 and over) who could benefit from hearing aids has ever used them.

Speech disorders and related conditions also affect adults and children—with or without hearing problems. By the first grade, roughly 5% of children have noticeable speech disorders, usually marked by difficulty pronouncing specific sounds. Stuttering , a disorder affecting speech fluency, affects approximately 5 to 10% of children in the U.S. Stuttering may last a few weeks or several years, or it may persist into adulthood.

Research funded by the NIDCD has advanced our understanding of hearing, voice, speech, and language disorders, leading to improvements in diagnostic, treatment, and prevention strategies. To help people with hearing loss connect with others and with their environment, for example, the NIDCD has supported basic, clinical, and translational research on assistive device technology to improve hearing aids, cochlear implants, and other devices, as well as studies to improve access to affordable hearing health care .

Researchers are making similarly remarkable advances in assistive devices for people with speech disorders. One exciting example could help individuals who have lost their ability to express words from stroke or neurodegenerative diseases regain an ability to communicate. Early research is exploring how novel assistive devices could record and translate certain signals in the brain to enable the user to speak directly through a computer.  

This May, we encourage you to share our Better Hearing and Speech Month image (also available in a Spanish-language version ) and our science-based health information resources on hearing, voice, speech, and language disorders. These NIDCD pages provide more information:

• Adult Hearing Health Care
• Hearing, Ear Infections, and Deafness
• Voice, Speech, and Language

• Patient Care & Health Information
• Diseases & Conditions

Auditory processing disorder (APD)

Auditory processing disorder, also called APD, is a type of hearing loss caused by something affecting the part of the brain that processes how you hear. Ear damage causes other types of hearing loss.

APD is also sometimes called central auditory processing disorder (CAPD). It can happen in anyone. But it most often happens in children and older adults.

Many conditions can affect how well a person understands what they hear, such as attention-deficit/hyperactivity disorder (ADHD) or autism. But these conditions are different from auditory processing disorder, although they can appear with APD . APD also can happen with other types of hearing loss.

Auditory processing disorder has no cure. But treatments can help you hear better.

Symptoms of auditory processing disorder (APD) can be subtle. Symptoms can include having trouble with:

• Telling where sound is coming from.
• Understanding words that are spoken quickly or in a noisy room.
• Paying attention.
• Reading and spelling.
• Following directions unless they are short and simple.
• Learning a new language.
• Singing or enjoying music.
• Understanding and remembering spoken information.

If you have APD , you also might:

• Take longer to reply to someone who is talking to you.
• Often need others to repeat themselves.
• Not understand sarcasm or jokes.

APD is often seen with attention, language and learning issues like those seen in attention-deficit/hyperactivity disorder, or ADHD.

When to see a doctor

If you have trouble hearing or understanding what you hear, talk to a health care professional.

The cause of auditory processing disorder (APD) is sometimes unknown. APD can be linked to many conditions. In older adults, conditions might include stroke and head trauma. In children, APD can be linked to issues at birth, such as low birth weight or early birth, or repeated ear infections.

In typical hearing, the brain's auditory center takes the sound waves sent from the ears and turns them into sounds you know. But with auditory processing disorder (APD), the auditory part of the brain can't do this.

Risk factors

Factors that increase your risk of auditory processing disorder (APD) include:

• Head trauma.
• Lead poisoning.
• Seizure disorders.
• Issues linked to birth, such as an early birth, low birth weight or a pregnant person using alcohol, drugs or tobacco.
• Repeated ear infections, especially at a young age.

Complications

Auditory processing disorder (APD) complications include:

• Trouble understanding what people are saying.
• Trouble taking part in activities.
• Feeling isolated and lonely.
• Trouble reading and writing, in children.
• Trouble doing well in school.
• Feeling depressed.
• Auditory processing disorders. American Academy of Audiology. https://www.audiology.org/consumers-and-patients/hearing-and-balance/auditory-processing-disorders/. Accessed June 22, 2023.
• Understanding auditory processing disorders in children. American Speech-Language-Hearing Association. https://www.asha.org/public/hearing/understanding-auditory-processing-disorders-in-children/. Accessed June 22, 2023.
• Cifu DX, et al., eds. Auditory, vestibular and visual impairments. In: Braddom's Physical Medicine and Rehabilitation. 6th ed. Elsevier; 2021. https://www.clinicalkey.com. Accessed June 22, 2023.
• Liu P, et al. Electrophysiological screening for children with suspected auditory processing disorder: A systematic review. Frontiers in Neurology. 2021; doi:10.3389/fneur.2021.692840.
• Sardone R, et al. The age-related central auditory processing disorder: Silent impairment of the cognitive ear. Frontiers in Neuroscience. 2019; doi:10.3389/fnins.2019.00619.
• Central Auditory Processing Disorder. American Speech-Language-Hearing Association. https://www.asha.org/practice-portal/clinical-topics/central-auditory-processing-disorder/. Accessed June 22, 2023.
• Health Education & Content Services. Auditory processing disorder. Mayo Clinic; 2023.
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Am Fam Physician. 2024;109(4):361-362

As published by the USPSTF.

The full recommendation statement is available at https://www.uspreventiveservicestaskforce.org/uspstf/recommendation/speech-and-language-delay-and-disorders-in-children-age-5-and-younger-screening .

The USPSTF recommendations are independent of the U.S. government. They do not represent the views of the Agency for Healthcare Research and Quality, the U.S. Department of Health and Human Services, or the U.S. Public Health Service.

This series is coordinated by Joanna Drowos, DO, contributing editor.

A collection of USPSTF recommendation statements published in AFP is available at https://www.aafp.org/afp/uspstf .

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Hearing loss | chapter 2: definitions, identification, and professionals, definitions, identification, and professionals.

Author:  Dolly Bhargava, M. Spec. Ed.

Hearing plays an important role in student development and daily performance.  Hearing impairment occurs when there's a problem with or damage to one or more parts of the hearing mechanism.  It is identified as one of the ten most prevalent causes of disability in the United States.  The U.S. Department of Health and Human Services (1991) reported that five percent of children 18 years and under have a hearing loss.  A student with a hearing impairment is part of a heterogeneous group whose one common characteristic is some degree of hearing loss.  To effectively teach students with hearing impairment, teachers need to become familiar with hearing related concepts.  The purpose of this chapter is to briefly outline the nature of hearing impairment and provide you with useful current definitions.  The chapter will provide you with information on how we hear, how hearing is assessed, ways of identifying students with hearing impairments in the classroom and discuss their learning characteristics.  Some information on the several types of professionals who can be of assistance in diagnosing and educating students with a hearing impairment will also be provided.

The Individuals with Disabilities Education Act (IDEA), defines “hearing impairment” and “deafness” separately.  Hearing impairment is defined as an “impairment in hearing, whether permanent or fluctuating that adversely affects a child’s educational performance.”  Deafness is defined as a “hearing impairment that is so severe that the child is impaired in processing linguistic information through hearing, with or without amplification.”

Components of the Ear

Author:  Dolly Bhargava, M. Spec. Ed.

The organ of hearing is the ear and it is composed of three major parts: the outer, middle and inner ear.  It is important to understand the basic anatomy of each part and how it works before reading about the different types of hearing impairments.  Refer to  http://www.echalk.co.uk/Science/Biology/InteractiveDiagrams/Ear.htm  for a diagram of the ear.  The anatomy and contribution of each part of the ear has been described below:

Outer Ear  - Includes the external visible part of the ear (pinna) and the ear canal.

• Sound waves are received by the pinna from the environment.
• Sound waves travel through the ear canal.
• Sound waves make their way to the beginning of the middle ear called the ear drum (thin membrane that separates the outer ear from the middle ear).  (Dugan, 2003; Scheetz, 2000)

Includes the eardrum, ossicles or three tiny bones(hammer/malleus; anvil/incus; stirrup/stapes) and Eustachian tube.

• Sound waves reach the eardrum.
• The ear drum begins to vibrate and makes the three tiny bones / ossicles to move together.
• These bones help sound move into the inner ear.

The middle ear is filled with air and for optimal hearing the air pressure inside the middle ear and outside of the ear needs to be the same.  The Eustachian tube connects the middle ear to the back of the nose.  The Eustachian tube helps keeps the air pressure in the middle ear equal to the air pressure in the environment.  (Dugan, 2003; Fraser, 1996)

Inner Ear  - Includes a snail-shaped, fluid filled structure called the cochlea, tiny hair cells (cilia) and the auditory nerve, which travels from the inner ear to the brain.

• The vibration of the sound waves causes the cilia to move.
• This movement creates electrical impulses or signals that are sent to the brain via the auditory nerve.
• The hearing centers in the brain interpret these signals as sound and help give them meaning. (Lysons, 2003)

Some Definitions

A hearing impairment results when there is a problem in one or more components of the hearing mechanism.  When describing hearing impairment, three attributes are considered:

• Type of hearing loss -  part of the hearing mechanism that is affected
• Degree of hearing loss - range and volume of sounds that are not heard
• Configuration - range of pitches or frequencies at which the loss has occurred  

Type of Hearing Loss

Hearing impairment can be classified into four categories depending on the site of the problem in the ear - conductive, sensorineural, mixed, and central auditory processing disorder.

A. Conductive Hearing Loss

A conductive hearing impairment results from a blockage or damage to the outer or middle ear structures.  This problem interferes with the transmission of sound through the outer and middle ear to the inner ear, resulting in reduced loudness of the sound (quantity of sound). A conductive hearing loss can fluctuate according to the presence or severity of the blockage. During these periods of reduced hearing the student may miss out on learning, negatively affecting all aspects of development (Pagliano, 2005). Some of the common causes of conductive hearing impairment include excessive earwax (cerumen); middle ear infections (such as Otitis Media, where there is pus or fluid build-up or inflammation which causes pain and reduces the ability of the eardrum to vibrate); a hole or perforation of the ear drum; and otosclerosis (the eardrum and the ossicles have decreased mobility) (Scheetz, 2000). In many cases, a conductive hearing impairment can be corrected with medication, hearing aids or surgery.

B. Sensorineural Hearing Loss

A sensorineural hearing loss results from damage to the inner ear or the auditory nerve.  A sensorineural hearing loss can range from mild to profound and often affects the student’s ability to hear certain frequencies more than others.  Thus, even with amplification to increase the sound level, a student with sensorineural hearing loss may perceive distorted speech sounds, resulting in difficulty with understanding speech and interpreting various sounds (quantity and quality of sound). Some of its causes include genetic disorders (which can interfere with proper development of inner ear structures); injuries; complications during pregnancy or birth; infections or illnesses (such as mumps, measles, chickenpox) and medication side effects (Lysons, 1996). This type of hearing impairment is usually permanent and irreversible.  Sensorineural hearing impairments are frequently not medically or surgically treatable.  However, most people with a sensorineural loss find wearing hearing aids and using sound amplification technology to be of significant benefit (Dugan, 2003). 

C. Mixed Hearing Loss

A mixed hearing loss refers to a combination of conductive and sensorineural hearing loss, indicating that there may be damage in the outer or middle ear and the cochlea or auditory nerve.  Treatment depends on the severity of the conductive (i.e. hearing aid, medical or surgical intervention) and sensorineural (i.e. hearing aid) portions of the hearing impairment (Dugan, 2003).  

D. Central Auditory Processing Disorder

This form of hearing impairment occurs when the auditory centers of the brain are affected by injury, disease, tumor, heredity, birth trauma, head trauma or unknown causes. Although the outer, middle and inner parts of the ear deliver sound signals, these signals are unable to be processed and interpreted by the brain.  Thus, even though the person’s hearing may be normal, there are difficulties with understanding what is being said, resulting in learning problems.  Central auditory processing involves a variety of skills such as localizing the sound, attending to it, perceiving it accurately through auditory discrimination and auditory recognition to make sense of the sound (ASHA Task Force on Central Auditory Processing Consensus Development, 1996).  Difficulties in one or more of the above-listed behaviors may constitute a central auditory processing disorder. The use of assistive technology and environmental modifications to the listening environment can provide acoustic enhancement by amplifying the incoming speech signal, thereby improving the auditory signal to noise ratio, that is, the ratio between the signal and any unwanted background noise (Pagliano, 2005).  According to the ASHA Task Force on Central Auditory Consensus Development (1996) audiologists are best qualified to understand breakdowns in central auditory processing and to guide management of the condition.

Understanding these four categories of hearing impairment will be helpful to you as a teacher, as they suggest the teaching and learning frameworks that must be set in place so that each student can properly access the curriculum.

Degree of Hearing Loss

It is important to understand each specific type of hearing impairment in terms of the continuum of degree of hearing impairment.  The degree or severity of impairment ranges across mild, moderate, severe and profound.  Sometimes a hearing impairment may be borderline between two categories, such as “moderately severe” (Northern & Downs, 2002).  Having an understanding of the student’s degree of hearing loss is useful for determining the types of supports that will be required.

It is important to note that the parameters of these categories are not universally accepted and different authorities may assign different degrees of loss to each category.  However, for our purposes, degree of hearing loss will be classified using the descriptions given below.  First, it is important to understand that sound is measured by its loudness or intensity (measured in units called decibels, dB).  The greater the number of decibels the louder the sound.  For example, a 70 dB sound is much louder than a 30 dB sound.  A person without a hearing impairment can hear sounds ranging from 0 to 140 dB.  A whisper is around 30 dB.  Conversations are usually 45 to 50 dB and with background noise, about 60-65dB.  Sounds that are louder than 90 dB can be uncomfortable to hear. A loud rock concert might be as loud as 115 dB (Marschark, Lang, & Albertini, 2002).  Sounds that are 120 dB or louder can be painful and can result in temporary or permanent hearing loss. 

The following information has been collected from a range of sources: Dugan (2003), Lysons (1996) and Pagliano (2005):

A. Normal Hearing

• Threshold - Children with hearing thresholds (softest sound that the child can hear) for both ears that fall between 0 to 25dB are said to have hearing within normal limits.
• Can hear – The child can hear all speech sounds.

B. Mild Hearing Loss

• Threshold - Children with hearing thresholds between 26 to 40 dB have a mild hearing loss. 
• Can hear - The child with a mild hearing loss can hear sounds of 26 to 40 dB or louder.  He/She will be able to hear the vowel sounds  a, e, i, o, u  as well as most consonants clearly.
• Hearing difficulties - Consonants, especially voiceless consonants such as p, h, f, s and th are not heard clearly.  Speech that is especially faint, distant, rapid or with background noise present will be difficult to understand.
• Speech – A child with mild hearing loss learns speech through hearing.  The child may have some speech production difficulties.  The child will benefit from speech reading, favorable acoustics, hearing aids, and/or a personal FM system, and speech therapy (professional support for understanding and producing speech and language correctly).  Speech and language may develop normally in a child with a mild hearing loss. 

C. Moderate Hearing Loss

• Threshold - Children with hearing thresholds between 41 to 70 dB have a moderate hearing loss. 
• Can hear - The child with a moderate hearing loss can hear sounds of 41 to 70 dB or louder. 
• Hearing difficulties – The child will miss most of the speech sounds at conversational level, without a hearing aid or other technology to amplify sound.  Due to inaccurately hearing sounds and missing sounds completely, children with a moderate hearing loss may have a language delay (i.e. limited vocabulary, difficulty with grammatical rules, word meanings, multiple meaning of words, and word placement in a sentence).  The child will have difficulty understanding speech in group situations.
• Speech – Speech articulation of the student with moderate hearing loss may exhibit omitted and distorted consonants (Northern & Downs, 2002).  The student will require sound amplification by hearing aids, assistive devices such as personal FM systems and favorable classroom acoustics.  In conjunction with this, the student will require auditory training, speech reading, and speech therapy. 

D. Severe Hearing Loss

• Threshold - Children with hearing thresholds between 71 to 90 dB have a severe hearing loss. 
• Can hear - The child with a severe hearing loss can hear sounds of 71 to 90 dB or louder, such as a vacuum cleaner or lawn mower at close range. 
• Hearing difficulties – No sounds can be heard at normal conversation level, without a hearing aid or sound amplification technology.  The student may benefit from a cochlear implant. If a severe loss is not detected at birth, or shortly thereafter, language and speech will not develop spontaneously and learning difficulties may arise. 
• Speech – Students will use speech reading, hearing aids and/or sign language to communicate.  If a severe hearing loss is identified early, and the child is fitted with hearing aids, auditory training and speech therapy are provided, the child has a more positive outlook for speech and language development and overall learning.  He/She may also use technology such as text phones, and loop systems.

E. Profound Hearing Loss

• Threshold - Children with hearing thresholds from 91 dB or more have a profound hearing loss.   
• Can hear - The child with a severe hearing loss can hear sounds of 90 dB or louder such as a chain saw at close range or the vibrating component of loud sound.
• Hearing difficulties – No speech sounds can be heard without amplification by a hearing aid or other amplification technology.  The student may benefit from a cochlear implant.
• Speech – The student will use speech reading, hearing aids, and/or sign language to communicate.  He/She may also use technology such as text phones, FM systems and loop systems.  It is essential that the student has a favorable acoustical environment. The student may also benefit from the use of a sign language interpreter and/or a notetaker in the classroom.

Types of Hearing Loss

“Configuration” refers to the overall shape or pattern of the hearing loss, depending on the extent of hearing loss across the range of sounds.  To determine the configuration, an audiologist (a professional who works with people who have hearing, balance, and related ear problems) performs a test that involves using a machine called an “audiometer” to measure hearing.  The audiometer makes sounds (pure tones) which go into headphones over the child’s ears. 

Information about the child’s hearing abilities is plotted on an “audiogram,” a graph displaying intensity in relation to frequency.  The softest sounds the child can hear (hearing thresholds) are plotted on the audiogram.  The vertical axis of the graph shows the volume (intensity) of the sound (in decibels).  The dB are listed from top to bottom starting from –10 dB to 120 dB.  The greater the number of decibels the louder the sound.  The horizontal axis shows the pitch (frequency) of the sounds tested (measured in cycles per second or Hertz). The lower the frequency numbers the lower the pitch.  The frequency starts on the left side with 250 Hz and ranges up to 8000 Hz.  Usually frequencies of 250-8000 Hz are used in testing because this range represents most of the speech spectrum, although the human ear can detect frequencies from 20-20,000 Hz (Marschark et al., 2002).

For example of audiograms, refer to  https://www.hearinglink.org/your-hearing/hearing-tests-audiograms/what-d... .

There are three types of configurations: rising, sloping, and flat.  The shape of the configuration provides insight into the type of speech-understanding errors that a student may be experiencing and the nature of the hearing impairment.  A rising configuration means that a person can hear high pitch tones better than low pitch tones. A sloping configuration means that a person can hear low pitch tones better than high pitch tones.  Finally, a flat configuration means that a student is experiencing the same amount of hearing loss for low and high sounds (Schwartz, 1987).  For examples of the different configurations refer to the Internet site

http://www.asha.org/public/hearing/Configuration-of-Hearing-Loss/

Other descriptors associated with hearing loss as described in ASHA (n.d):

• Bilateral vs. Unilateral –  A bilateral hearing loss means hearing is affected in both ears, whereas a unilateral hearing loss means hearing in only one ear is affected.
• Fluctuating vs. Stable Hearing Loss -  Some hearing losses change - sometimes getting better, sometimes getting worse. Such a change commonly occurs in young children who have hearing loss as a result of Otitis Media, or fluid in the middle ear. Other hearing losses will remain the same year after year and are regarded as stable.
• Progressive vs. sudden hearing loss -  Progressive hearing loss is a hearing loss that becomes increasingly worse over time. A sudden hearing loss is one that has an acute or rapid onset and therefore occurs quickly, perhaps as a result of head trauma, or perhaps a tumor in the auditory nerve.
• Symmetrical vs. asymmetrical -  Symmetrical hearing loss means that the degree and configuration of hearing loss are the same in each ear. An asymmetrical hearing loss is one in which the degree and/or configuration of the loss is different for each ear.

To summarize, there is no “typical” student with a hearing impairment.  Although all have some degree of hearing loss, it is important to note that hearing loss affects each individual differently. The effect that a hearing impairment has on a student will depend on a number of factors beyond the attributes described above.  Other important factors include the age of onset, when it was detected, how the student manages the hearing impairment (for example, level of compliance with wearing their hearing aids), the student’s abilities, personality, and the quality and type of auditory intervention programs.  Hence, it is extremely important to consider each student as an individual and find out the specific type of loss in order to accommodate needs.

Identifying a Student with Hearing Loss

Niemann, Greenstein & David (2004) describe the impact of a hearing impairment where a child can see people talking but cannot understand what is being said.  This results in the child having difficulties understanding the world and in expressing personal needs, resulting in limited interactions and social isolation.  Thus, it is important to identify the hearing impairment as early as possible; otherwise the child will also miss out on important educational experiences (Pagliano, 2005).  Sometimes hearing impairment can go unidentified partly because it is not immediately visible.  A hearing impairment could be so mild that it may it may have gone unnoticed for many years (Smith, Polloway, Patton & Dowdy, 1998).  On the other hand, a hearing impairment may develop over time.  The student is often the last one to recognise or report a loss in hearing unless it has deteriorated significantly.  If the hearing impairment remains undetected, it can result in the student facing a substantial educational disadvantage.  The adverse affect of the hearing impairment can create challenges of a personal, academic and social nature for the student and interfere with reaching full potential.  Teachers need to be aware of the indicators that signal the possibility that a student has an undiagnosed hearing loss.  On a medical note, if the hearing problem is undetected and untreated, it can cause permanent loss of hearing and the long-term consequences for the quality of life can be serious.

Consider the case of Sue, a 10 year old girl who had undiagnosed conductive hearing impairment with a mild hearing loss in her left ear.  She thought most of her friends mumbled a lot.  Sue stated that she had to strain to listen to them and found it difficult following a conversation, especially in group situations.  She would often have to ask her friends to repeat themselves.  Often Sue wouldn’t realize that someone was calling her, especially if they were in another room or if it was a very noisy classroom.  She would often ask for the volume of the TV or stereo to be turned up or would sit closer to them to hear properly.  Without adult awareness, her impairment continued unrecognized.

Table 1 is a simple  Yes/No  checklist of the signs and symptoms that are indicative of whether a student in your class has a hearing impairment.  If any of these behaviours are present, teachers should consider suggesting to the student’s caregivers that they refer the student to a health professional for a formal hearing assessment (Fraser, 1996; Kuster, 1993).  Smith et. al. (1998) state “A teacher’s careful observations and referral can spare a student months or years of struggle and frustration” (p. 217).

Table 1: Identification of Students with Hearing Impairments

Learning characteristics.

Hearing is one of the most important senses.  It plays a vital part in the learning process.  It is held that more than 80% of education is received through hearing (Pagliano, 1994).   American Speech-Language-Hearing Association  (ASHA) stipulates that hearing impairment  can affect children in the following ways:

• It causes delay in the development of receptive and expressive spoken language skills. It also usually causes delay in general language acquisition and receptive and expressive communication. (However, in the minority of deaf children born to signing Deaf families, language and communication skills usually develop at a normal rate.)
• The language deficit can cause learning problems that result in reduced academic achievement.

* Communication difficulties may often lead to social isolation and poor self-concept.

* It may have an impact on vocational choices.

Hearing impairment can impose basic limitations on an individual in terms of

• access to spoken language
• access to environmental auditory experiences
• ease of interacting with a wide range of people, due to the above restrictions.

Difficulty in accessing spoken language may appears to be an obvious result of hearing impairment. On the contrary, there are many variables which affect just how much access a hearing impaired person has to spoken language.  As noted earlier, there are different levels of hearing loss; yet, even within these levels many layers exist.  For example, two different people may have the same type of hearing loss, the same level of hearing loss and all practical, physiological processes could be almost identical, but they may not have the same access to spoken language.  Person A may have acquired the hearing loss post-lingually, which has provided the opportunity to hear and the hearing nerves to be used.  Person B may have had to learn to listen without ever experiencing sound, and would have had to learn that sound itself has meaning before beginning to interpret what those sounds mean.

Clear access to speech is contingent upon many variables, including:

• background noise being at a minimum
• well-functioning and optimal hearing technology
• clear speech being expressed by the speaker

Hearing technology continues to improve but still has limitations.  One of the limitations is that background noise is not usually completely eliminated when a person with hearing impairment is listening to some source of sound.  This means that the important information (i.e., speech) has to be separated from the background noise during cognitive processing. 

Clear speech is not exaggerated speech, and the student with hearing impairment uses all the visual and auditory information available to aid understanding of the message.  If any of these are exaggerated or distorted, the student has to cognitively explore the possibilities of what was said before trying to repair their misunderstanding of the message.  Accents, speech impediments, prostheses (braces), moustaches and beards can all contribute to the challenge of understanding with ease.

Environmental auditory experiences enable hearing students to retrieve information about what is happening in their environment.  Hearing students are able to learn incidentally about the world around them and about important functions of language.  In contrast, students with a hearing impairment are rarely able to hear enough (without attending to a specific interaction) to learn incidentally from:

• Discussions and interactions between parents, teachers, other adults or peers
• Disagreements or resolutions between these same people
• Television or radio
• Conversations on the telephone.  

(Calderon & Greenberg, 2003, p. 179)

Most environmental sounds are very informative. Hearing sounds around the house can indicate where a person is and what that person might be doing.   For example, hearing water boiling or the microwave timer lets you know that someone is cooking in the kitchen. Environmental sounds can help you predict what might happen next. For example, hearing the phone ring tells you that someone will get up and move to another area, or start you wondering if that the phone call might be for you. When they hear such things, subconsciously, hearing people make predictions and assumptions without effort.  For person with hearing impairment, some of these environmental sounds may be heard and some may not, which in turn means that the appropriate assumptions and predictions may not be made.  A student with hearing impairment may not understand why the teacher and all the students are looking out the window, when they heard a car crash on the road outside, or why the teacher stops talking and looks at some other students in the room who have been whispering. 

Warnings of danger, such as smoke alarms, fire alarms and car horns are usually given by sound signals, and the student with a hearing impairment may not be aware of the location of the sounds or what they mean. Some of these auditory signals can be supplemented by a range of visual ones, such as flashing strobe lights.  These accommodations should be considered by schools with students who are hearing impaired.

All these factors are challenges to students with hearing impairments. While most will be aware of the challenges, they may not always be aware of the strategies they can employ to help them glean information to stay abreast of the happenings in their environment.  These strategies can be utilized and encouraged by teachers.  

The student with hearing impairment obviously has to consider events and make predictions more consciously than the average hearing student.  This can be extraordinarily tiring (Calderon & Greenberg, 2003) and can have the effect of causing the ability to do these very things to deteriorate.  On a given day some students may be easily able to understand what the teacher is saying even though there is construction work happening outside, but on another day, may not be able to understand at all. 

There are a number of factors that can contribute to ease of understanding, and these are covered in the  Accommodations  section of this web site.

The student’s ability to access spoken language, environmental auditory experiences, and ease of interaction with others will depend on the type and degree of hearing impairment, self-awareness and acceptance, and on the educational, personal and practical support available. 

How do students who are hearing impaired develop an understanding of concepts, communication, and social skills as well as the ability to independently participate in everyday activities?   The following areas are underscored by a variety of authors (Marschark, n.d.); Smith, Polloway, Patton & Dowdy (2004); and ASHA (n.d) as some of those that are affected by a hearing impairment:

Early relationships, experience and early learning opportunities  of the hearing impaired student will have made a lasting impact upon that student’s ongoing social, language, cognitive, and emotional development.

Communication and language  are greatly affected by hearing impairment.  Early delay in receptive and expressive communication skills may have an ongoing impact upon the student’s ability to understand, process and use the information being acquired throughout one’s educational life. Individual students with hearing impairment may have varying abilities in their communication abilities.  The student may have difficulties with reading or with subjects incorporating jargon specific to that subject, but not used much in everyday language. There may also be difficulties with hypothesizing or with seeing things from another person’s perspective.

Social skills  refer to those skills necessary for effective communication with other people.  They include both verbal and non-verbal behaviors. Some of the skills include eye contact, proximity, word choice, intonation, ability to read non-verbal cues, facial expressions, take the perspective of another, shift attention, and maintain topic of conversation (Disability News, 2001).  Social skills are crucial to our lives for personal, academic and vocational success. A student with a hearing impairment has difficulty with accessing auditory information and expressing ideas in a way that others can understand.  The Learning Disabilities Association of Ontario (1999) states that a student with hearing impairment may appear disoriented, distracted, or at times confused because of difficulties with accessing accurate auditory information.  The student may try to cope with difficult situations by copying peers or pretending to understand what is going on in the class while actually misunderstanding the situation.  The student with hearing impairment may appear to hear normally, when in fact the student can not hear speech sounds clearly enough and is misinterpreting the information. The student may have difficulties pronouncing speech sounds correctly,  poor vocal quality, or trouble explaining ideas clearly.  All of the above- stated factors can negatively impact social skill development as well as the ability to freely communicate, resulting in the student becoming shy, socially isolated, or displaying behaviors of concern (Learning Disabilities Association of Ontario, 1999).

It is important to be aware of potential areas of difficulty and to note when any confusion or misunderstandings begin to occur.  Rectifying confusion or misunderstandings early is imperative in order to ensure the foundational information is understood before placing more scaffolding information on top.  One suggestion is offering to give more information or background material to the student, parents, or the visiting support teacher, to preview or review the information being covered.  It can be helpful to have students act out or present information visually to enable clearer and deeper understanding of complex information.

Marschark (2002) suggests that these students “may have different knowledge, cognitive strategies and experiences” from hearing students and that we need to support the student with hearing impairment by utilizing skills and acknowledging areas of difficulty (p. 10). 

As discussed above, the student may lag behind in achievement in comparison to hearing peers due to the impact of hearing impairment on learning.  Therefore, the student may require skills in these areas to be specifically and explicitly taught, along with additional time and opportunities to practice these skills.

Professionals Who Diagnose and Treat Hearing Loss

This section presents information on the assessment process and the various team members that can assist the student.  A comprehensive evaluation of the student’s functional hearing needs to take place so that the student receives appropriate intervention and services (Lewis & Allman, 2000).  To fully evaluate the hearing condition, abilities and needs of the student, a two-fold approach is required, involving diagnosis and assessment.

Initially, the diagnosis of a hearing impairment is based upon a comprehensive evaluation by a multidisciplinary evaluation team, which includes a physician, an audiologist, and an otolaryngologist or otologist.  Table 2 below describes the roles of the various professionals involved in the hearing assessment process and information they can provide to educators to help the student. 

Hearing Assessments  

Audiological exam.

Once the referral to the appropriate professional has been made, an audiological examination is performed.  An audiological examination is conducted by an audiologist to determine:

• if a hearing loss is present;  
• which tones are affected;
• the degree of the hearing loss;
• the type of hearing loss (i.e. conductive, sensorineural, or mixed);
• the best method of treating the hearing loss; including selection of an appropriate hearing aid, if appropriate.

The audiologist then performs several tests in order to obtain an accurate measure of the child’s hearing abilities to determine the existence and extent of the hearing impairment. 

When these tests have been completed, the audiologist may conduct more specialized procedures, such as the evaluation of the mechanical functioning of the eardrum and bones of the middle ear (“intermittence teats”), and other measures to assess the function of the cochlea. Alternatively, if deemed necessary, the audiologist will make referrals to other professionals.

After reviewing the child’s information and test results, the audiologist will be able to describe a hearing loss as unilateral (affecting one ear) or bilateral (affecting both ears), the degree of hearing impairment (mild, moderate, severe or profound), and the type of loss (conductive, sensorineural or mixed). The audiologist will make recommendations regarding amplification and provide suggestions on how to best manage the hearing impairment.  It is important to remember that every student is an individual and as Scheetz (2000) emphasizes, “One must be careful not to pigeonhole or label someone based on this information” (p. 52).  To help the student we need to then individualize the supports that we provide by consulting with other professionals.

Below is information about some of the tests that are performed to assess hearing.  They include:

• Otoscopy.   Otoscopy is a physical examination of the ear that involves looking into the ear with an instrument called an “otoscope” (or “auriscope”) to examine the structures of the outer ear and the eardrum.  Otoscopy can help detect problems such as a hole (perforation) in the eardrum and infections of the middle ear (such as acute Otitis Media, an infection that produces pus, fluid, and inflammation within the middle ear). The nose, nasopharynx (space within the skull that is above the roof of the mouth, and behind the nose), and upper respiratory tract are also examined.
• Tympanometry.  Tympanometry measures the ability of the middle ear to conduct sound.  It is particularly useful in detecting fluid in the middle ear; Eustachian tube dysfunction (such as negative middle ear pressure); disruption of the ossicles (bones); tympanic membrane (ear drum) perforation; and otosclerosis (abnormal growth of bone in the middle ear).  To perform this test, a soft probe is placed into the ear canal and a small amount of pressure is applied.  The instrument then measures mobility of the tympanic membrane and its response to the pressure changes.  The results of the test are printed as a graph, called a “tympanogram” which can help identify middle ear problems .   For example ,  a flat line on the tympanogram may indicate that the eardrum is not mobile or not vibrating properly due to fluid in the middle ear (Hain, 2002).

The audiologist begins the examination of the child by first taking a case history.  The audiologist asks questions about the child’s medical conditions, hearing behaviors, hearing loss in the family, and any concerns of the child and the family.

Audiometry is the testing of a person’s ability to hear sounds at a range of frequencies. This includes air conduction tests, bone conduction tests, and speech audiometry tests.  An audiometric test is used to determine the types of sounds the child can and cannot hear. 

Air conduction tests involve the presentation of beeps and whistle-like sounds, called “pure tones,” through headphones in a soundproof room.  Sounds are of varying loudness (intensity measured in dB) and of different pitch (or frequencies measured in Hz)  The pure tones go via the air, down the ear canal, through the middle ear into the inner ear.  The child is asked to respond when he or she detects a sound (such as by raising a hand or pushing a button).  The loudness of each tone is reduced until the child can just hear the tone.  The softest sounds the child can hear constitute the hearing threshold.  This is marked on a graph called an “audiogram,” which can be used to identify and diagnose hearing impairment (Martin & Clark, 2003).

A bone conduction test involves using a skull vibrator such as a vibrating tuning fork.  It is placed behind the ear to measure the softest sounds that the child can hear to test the functioning of the inner ear.  The child is asked to respond when detecting a sound produced by the device.  As the sound travels through the bones of the skull to the inner ear (cochlea, auditory nerves), the sound sidesteps the outer and middle ear. 

Interpretation of Air Conduction and Bone Conduction Results

• If air conduction and bone conduction thresholds are within normal limits, then hearing is normal.
• If the air conduction thresholds indicate a loss and the bone conduction thresholds do not indicate a loss, then a conductive hearing loss is present.  In other words a person does not hear normally when sound has to go through the outer, middle and inner ear, but if the sound bypasses the conductive mechanism (i.e. the outer and middle ear), and goes directly to the inner ear, then hearing is normal.
• If there is a loss by both air- and bone - conduction and the abnormal thresholds are essentially similar, then a sensorineural hearing loss exists.  In other words, there is a hearing loss by air conduction, such as when the sound goes through the outer, middle and inner parts of the ear.  There is the same amount of hearing loss when the sound bypasses the outer and middle ear and goes directly to the inner ear. If there is a loss by both air conduction and bone conduction, but the loss by air conduction is worse than the loss by bone conduction, a mixed hearing loss exists.  Both conductive and sensorineural components are present.

Speech Audiometry

This involves presenting the child with a series of simple recorded syllables, words and sentences spoken at various volumes into the headphones.  The test is designed to assess speech threshold (i.e., when the child can first hear speech) and the child’s ability to understand speech (Scheetz, 2000). The test requires the child to repeat each word back to the audiologist as it is heard.  This provides information on the volume (quantity) of the speech sound that the child can hear and also the quality of the sound (clear vs. distorted) the child hears.  The level at which the child can repeat 50% of test materials correctly provides information about the type and degree of hearing impairment (Lysons, 1996).  This is useful to determine the candidacy for hearing aid and reaffirm the findings of the pure-tone audiometry test.  

Designing the Individual Educational Plan

Once a hearing impairment has been identified, the next step involves creating a plan of action, or the Individual Education Plan (IEP), that meets the abilities and needs of the student.  It is important to do this by using a team approach.  Table 3 below describes the various team members. The team can be comprised of a variety of educational specialists, such as a certified teacher and assessment professionals.  The team also includes a variety of other health professionals such as Occupational Therapists, Audiologists, and Speech and Language pathologists. The composition of the team will depend on the needs and abilities of your student.  Hence your team may not necessarily include all the people listed below.

In conclusion, teachers can work with a variety of professionals to plan an appropriate educational program that will help the student with hearing impairment participate successfully in the classroom. This chapter has provided an overview of hearing impairment by detailing information on how we hear, how hearing is assessed, ways of identifying students with hearing impairments in the classroom, and learning characteristics of students with hearing impairments.  Additional information has been provided on the types of professionals who can be of assistance in diagnosing and educating students with a hearing impairment.

continue to Chapter 3

• ASHA (n.d.) Types of Hearing Loss.  Retrieved February, 15, 2005 from  http://www.asha.org .
• ASHA Task Force on Central Auditory Processing Consensus Development (1996).  Central auditory processing: current status of research and implications for current practice.  American Journal of Audiology, 5 (2): 41-54.
• Calderon, R. & Greenberg, M. (2003).   Social and Emotional Development of Deaf Children: Family, school and program effects. In M. Marschark & P.E. Spencer (Eds) Oxford Handbook of Deaf Studies, Language and     Education. (pg 177 –189). Oxford ; New York : Oxford University Press.
• Disability News. (2001, April 2). I should have stayed in bed. Retrieved  April 1, 2005 from,  http://www.redwoods.edu/district/dsps/newsletter/Spring-01/htm . (no longer available)
• Dugan, M. B. (2003).  Living with hearing loss.  Washington, D. C.: Gallaudet University Press.
• Fraser, B. (1996).  Supporting children with hearing impairment in mainstream schools.  London : The Questions Publishing Company.
• Hain, T.C. (2002).  Hearing Testing.  Retrieved February, 15, 2005 from  http://www.tchain.com/otoneurology/testing/hearing_test.htm#tympanometry  (no longer available)
• Kuster, J. M. (1993).  Experiencing a day of conduction hearing loss.  Journal of school health, 63, 235-237.
• Learning Disabilities Association of Ontario. (1999). Literacy project: A listing of learning disability groupings. In An introduction to Learning and Employment Assessment Profile (LEAP) and Learning disabilities.    
• Lysons, K. (1996).  Understanding hearing loss.  London ; Bristol, Pa. : J. Kingsley Publishers.
• Marschark, M. (2000). Looking beyond the obvious: Assessing and understanding deaf learners.  Invited address to the 3rd International Conference Action Communication Formation Pour la Surdite [ACFOS], Paris.
• Marschark, M., Lang, H. G. & Albertini, J. A. (2002).  Educating deaf students : from research to practice.  Oxford ; New York : Oxford   University Press.
• Martin, F. N. & Clark, J. G. (2003).  Introduction to audiology (8th Ed). Boston : Allyn and Bacon.
• Niemann, S., Greenstein, D. & David, D. (2004).  Helping children who are deaf : family and community support for children who do not hear well.  Berkeley, Calif.: Hesperian Foundation.
• Northern, J. L. & Downs, M. P. (2002).  Hearing in children (5th Ed.). Philadelphia, PA : Lippincott Williams & Wilkins.
• Pagliano, P. (2005).  Using the senses.  In Ashman, A. & Elkins, J. (Eds.). Educating children with diverse abilities (2nd Ed) (pg 319-359). Frenchs Forest, N.S.W. : Pearson Education Australia.
• Scheetz, N. (2000).  Orientation to deafness (2nd Ed). Boston, MA :Allyn and Bacon.
• Schwatz, S. (Ed.) (1987). Choices in deafness: A parents guide. Kenington, MD: Woodbine House.
• Smith, T. C., Polloway, E. A., Patton, & J. R., Dowdy, C. A. (1998). Teaching students with special needs in inclusive settings (2nd Ed). Boston : Allyn and Bacon.
• US Dept of Health and Human Services (1991).  Healthy people 2000: National health promotion and disease prevention objectives.  DHHS Publication No. 91-50121.  Washington, DC: US Government Printing Office, Superintendent of Documents.
• Waldron, K. A.  (1996).  Introduction to a special education: The inclusive classroom.  Albany, NY: Delmar Publishers.

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Language and speech outcomes of children with hearing loss and additional disabilities: Identifying the variables that influence performance at 5 years of age

Linda cupples.

1 Department of Linguistics, Centre for Cognition and its Disorders, Macquarie University, Sydney, Australia

Teresa Y.C. Ching

2 The Hearing CRC, Melbourne, Australia

3 National Acoustic Laboratories, Sydney, Australia

Laura Button

4 RIDBC Renwick Centre (Royal Institute for Deaf and Blind Children/The University of Newcastle), Sydney, Australia

Vivienne Marnane

Jessica whitfield, miriam gunnourie, louise martin.

This study examined language and speech outcomes in young children with hearing loss and additional disabilities.

Receptive and expressive language skills and speech output accuracy were evaluated using direct assessment and caregiver report. Results were analysed first for the entire participant cohort, and then to compare results for children with hearing aids (HAs) versus cochlear implants (CIs).

Study sample

A population-based cohort of 146 5-year-old children with hearing loss and additional disabilities took part.

Across all participants, multiple regressions showed that better language outcomes were associated with milder hearing loss, use of oral communication, higher levels of cognitive ability and maternal education, and earlier device fitting. Speech output accuracy was associated with use of oral communication only. Average outcomes were similar for children with HAs versus CIs, but their associations with demographic variables differed. For HA users, results resembled those for the whole cohort. For CI users, only use of oral communication and higher cognitive ability levels were significantly associated with better language outcomes.

Conclusions

The results underscore the importance of early device fitting for children with additional disabilities. Strong conclusions cannot be drawn for CI users given the small number of participants with complete data.

Introduction

Children born with hearing loss form a heterogeneous population with respect to a range of variables that might impact on development of their speech and language skills. Recent research indicates that, at 3 years of age, relevant variables include gender, severity of hearing loss, maternal level of education, and for children with cochlear implants (CIs), age at switch-on ( Ching et al., 2013 ). Most importantly for present purposes, children who have a significant disability in addition to their hearing loss achieve poorer language outcomes than children with no additional disability ( Ching et al., 2013 ). Given that approximately 20–40% of deaf or hard-of-hearing (DHH) children have a significant additional disability ( Gallaudet Research Institute, 2011 ; Picard, 2004 ), it is crucially important to understand the achievements of this large subgroup of children in order to provide informed advice to parents and caregivers who are considering intervention options, such as hearing aids (HAs) or CIs.

Benefits of Audiological Intervention for DHH Children with Additional Disabilities

Most previous published studies of outcomes in DHH children with additional disabilities focused on the benefits of cochlear implantation (e.g., Beer et al., 2012 ; Dammeyer, 2009 ; Donaldson et al., 2004 ; Fukuda et al., 2003 ; Hamzavi et al., 2000 ; Holt & Kirk, 2005 ; Palmieri et al., 2012 ; Wakil et al., 2014 ; Waltzman et al., 2000 ). Findings generally showed that these children benefit from CIs, albeit more slowly and/or to a lesser degree than children who have a hearing loss but no additional disabilities (e.g., Beer et al., 2012 ; Holt & Kirk, 2005 ; Palmieri et al., 2014 ; Pyman et al., 2000 ; Waltzman, et al., 2000 ; Yang et al., 2004 ). Substantial individual variation is also evident, however, in the outcomes achieved by these children, especially on formal assessments of speech and language processing.

Waltzman et al. (2000) reported on 29 DHH children who were diagnosed with a diverse set of additional disabilities including delayed cognition, learning disability, motor and oral-motor delays, attention deficit disorder, autism spectrum disorder (ASD), and others. The children, who received their CIs between 1.9 and 12 years of age, were assessed on various auditory-linguistic measures prior to cochlear implantation and at subsequent yearly intervals from 1 to 8 years. Although there was a gradual increase from year to year in both the number of children able to complete assessments and the scores they obtained, there was also marked variability within the sample. At the children’s final postoperative assessments, scores ranged from below 10% to greater than 90% on all of the individual open-set speech recognition tasks. Hamzavi et al. (2000) reported on a smaller group of 10 DHH children with additional disabilities who received their CIs between 8 and 77 months of age. After postoperative periods ranging from 12 to 55 months, just 2 of the 10 children were able to recognise spoken words and sentences, with a further 3 children being able to produce and/or understand a few words. The remaining 5 children developed no speech perception or production skills during the study period. Donaldson et al. (2004) also reported marked variability in formal testing outcomes for a group of 7 children with ASD who were fitted with CIs between 3 and 9 years of age. After postoperative periods ranging from 6 to 60 months, 4 of the 7 children remained unable to complete formal assessments of expressive and receptive vocabulary, or open-set speech recognition of familiar words and phrases.

The highly variable nature of the outcomes achieved by DHH children with additional disabilities on formal assessments of speech and language might partly reflect the lack of suitability of these assessment methods for this population. Berrettini et al. (2008) raised this possibility along with the related concern that use of such methods “may result in inappropriate estimation of the effects of CIs in terms of quality of life and educational and social gains” (p. 200). Some support for this view comes from Donaldson et al.’s (2004) research examining outcomes in children with ASD. Although numerous missing scores made the results from children’s formal assessments difficult to interpret, parental ratings of communicative skills were much more convincing in showing consistent improvement during the postoperative period in aspects such as reacting to sound, vocalizing, making eye contact, responding to verbal requests, and attending to people.

By contrast with the growing body of literature on the benefits of CIs for DHH children with additional disabilities, few researchers have examined the benefits of HAs in this population. An exception is a study by Kaga et al. (2007) , which examined auditory outcomes in 28 children who were fitted with HAs between 1 and 5 years of age. Of the 28 participants, only 17 showed improved auditory behaviours in the months following HA fitting. Six of the remaining children could not adapt to using HAs, and five used their HAs but showed no improvement in auditory behaviours. Interpretation of these outcomes is complicated, however, since no information was provided regarding the children’s degree of hearing loss.

The studies described above provide support for the effectiveness of CIs for DHH children with additional disabilities considered as a group. They also reveal, however, that outcomes can vary markedly across individual participants and different assessment measures. Variability between participants might reflect the influence of uncontrolled demographic and cognitive variables, but investigation of this possibility has been hampered by the use of small, heterogeneous samples. Most previous studies in this area involved fewer than 30 participants who usually varied widely in the nature of their additional disabilities, age at implantation, age at assessment, and duration of device use.

Outcomes in Relation to Demographic Variables

Until recently, there has been limited evidence to suggest that differences in audiological and family-related demographic variables influence the outcomes achieved by DHH participants with additional disabilities. In a small group of 20 children, Meinzen-Derr et al. (2010) found that after controlling for variation in nonverbal cognitive ability, earlier age at diagnosis of hearing loss was associated with better receptive and expressive language outcomes on the Preschool Language Scale 4th Edition (PLS-4) ( Zimmerman et al., 2002 ). In the same study, however, they also reported that children’s language outcomes were not significantly associated with parental education or income, and that duration of CI use was negatively associated with language skill; that is, longer duration of CI use was linked to poorer language outcomes. They suggested that the latter, counterintuitive finding might reflect inclusion in their participant sample of several long-term implant users with poor language skills ( Meinzen-Derr et al., 2010 ). In a subsequent study, Meinzen-Derr et al. (2011) reported that neither age at CI switch-on nor degree of hearing loss accounted for significant unique variance in children’s PLS-4 outcomes after controlling for nonverbal cognitive ability.

Using a different outcome measure based on parental report, the Infant-Toddler Meaningful Auditory Integration Scale (IT-MAIS) ( Zimmerman-Phillips et al., 2000 ), Beer et al. (2012) also found a nonsignificant association between age at CI switch-on and outcomes. They concluded that “established early predictors of functional auditory benefit may not be as critical or meaningful in predicting benefit in deaf children with ADs” ( Beer et al., 2012 , p. 497). On the other hand, Palmieri et al. (2012) reported a positive association between duration of CI use and children’s degree of improvement on the language and communication scales of a new Deafness and Additional Disabilities Questionnaire (DADQ). The questionnaire, which is administered to parents in interview format, was designed by Palmieri et al. to collect information on five areas: (A) perceptual skills, (B) preferred communication mode, (C) communicative behaviors, (D) attention and memory skills, and (E) social interaction, behaviour control, and self government.

A larger and more recent study reported by Cupples et al. (2014) provides stronger evidence for a link between demographic variables and language outcomes in DHH children with additional disabilities. Cupples et al. investigated outcomes achieved by 119 3-year-olds on the directly administered PLS-4 and the caregiver reported Child Development Inventory (CDI) ( Ireton, 2005 ). These children were drawn from a population-based cohort who participated in the 3-year-old assessment phase of the Longitudinal Outcomes of Children with Hearing Impairment (LOCHI) study ( Ching et al., 2013 ). After controlling for a range of variables including gender, degree of hearing loss, audiological device (HA or CI), and communication mode (oral or mixed), Cupples et al. found that type of additional disability was the strongest predictor of children’s expressive and receptive language outcomes. A combined group of children with autism, cerebral palsy (CP), and/or developmental delay (DD) achieved consistently poorer outcomes than a combined group of children with other disabilities, including vision or speech output impairments, syndromes not entailing DD, and medical disorders. Better receptive and expressive language outcomes were also linked to higher levels of maternal education, especially for the group of children with autism, CP, and/or DD. For children with other disabilities the most important predictor of outcomes was degree of hearing loss.

A notable strength of the Cupples et al. (2014) study is the large number of included participants, which made it possible to evaluate the influence on children’s outcomes of a range of potentially important demographic variables, while simultaneously controlling for others. There was, however, one important measure that was not included in the assessment of LOCHI children at 3 years of age, namely, cognitive ability. Thus, while significantly poorer language outcomes in children with autism, CP, or DD compared to children with other types of additional disabilities might have been attributable to lower levels of cognitive ability, Cupples et al. had no objective empirical evidence with which to evaluate this assertion.

Outcomes in Relation to Cognitive Ability

Previous research has provided evidence of a positive association between children’s level of intellectual or cognitive functioning and their outcomes following cochlear implantation (e.g., Beer et al. 2012 ; Dettman et al., 2004 ; Edwards et al., 2006 ; Lee et al., 2010 ; Meinzen-Derr et al., 2010 ; Wiley et al., 2008 ; Wakil et al., 2014 ). Meinzen-Derr et al. (2010) reported on a group of 20 DHH children with additional disabilities who received their CIs at least 10 months earlier at ages ranging from 13.5 to 54 months. Receptive and expressive language skills were assessed using the PLS-4. The results from multiple regression analyses showed that nonverbal cognition was the strongest predictor of children’s postoperative language ability, accounting for 67% and 71% of variance in receptive and expressive language quotients respectively.

More recently, Wakil et al. (2014) reported retrospective, chart review data for a group of 21 DHH children with developmental delay who were aged between 1.6 and 11.7 years at the time of cochlear implantation. At the children’s most recent postoperative assessments, 10 out of 13 children with a severe developmental delay were unable to complete formal assessments of speech recognition and their scores on the IT-MAIS remained well below ceiling despite long term use of CIs (> 7 years). By contrast, 8 children with mild to moderate developmental delays achieved open-set speech recognition scores of between 48% and 94%.

Although it seems logical that degree of cognitive ability would predict outcomes in DHH children, not all previous studies have provided evidence consistent with this view. In an investigation of 23 DHH children with additional disabilities, Berrettini et al. (2008) reported that outcomes for a sub-group of 10 children with intellectual disability were similar to those obtained for the entire sample. Furthermore, within the intellectually disabled subgroup, there was no significant association between degree of intellectual disability and any of the post-implant outcome measures, which included assessments of auditory word recognition and communicative behaviour (based on a parent questionnaire). Cautious interpretation of these findings is warranted, however, because 8 of the 10 children with an intellectual disability were classified as mildly disabled.

In an earlier study, Holt and Kirk (2005) also failed to provide unequivocal support for the importance of cognitive ability in determining outcomes achieved by DHH children. Although some of their included assessment measures revealed a significant difference between children with mild intellectual disability and those without (e.g., in open-set sentence recognition), other comparisons either failed to reach significance (e.g., for the IT-MAIS) or only reached significance when children with intellectual disability were compared to a subgroup of control participants using either oral or total communication. By way of example, children with intellectual disability achieved lower receptive vocabulary scores than a control group of total communicators, but they did not differ in receptive vocabulary from a control group of oral communicators.

To summarise, previous research has shown that DHH children with additional disabilities tend to achieve poorer and more variable speech and language outcomes than children with hearing loss alone. A major focus of recent research has been to examine the benefits of cochlear implantation for this population of children. A secondary focus has been the extent to which audiological, cognitive, and demographic variables influence their performance on outcome measures. Although published findings are inconclusive due to the inclusion of small and heterogeneous participant samples in most studies, they suggest possible roles for type of additional disability, cognitive ability, level of maternal education, duration of CI use, and in some children, degree of hearing loss. Further research is needed to consider the benefits of HAs as well as CIs in larger samples of children.

The Current Study

The aim of the present investigation was to provide detailed information about the language and speech outcomes achieved by a large sample of 5-year-old DHH children with additional disabilities. All of the children were fitted with CIs or HAs. The inclusion of a sample of children with HAs distinguished this research from the majority of previous studies in the area, which have focused predominantly on outcomes for children with CIs. In line with recommendations from previous research, outcome measures included both formal assessments of language and speech development and more subjective measures based on parent report.

Research Questions and Hypotheses

Three research questions were addressed.

• How do the language and speech outcomes of 5-year-old DHH children with additional disabilities compare to those of typically developing peers without hearing loss?
• Which demographic variables are associated with language and speech outcomes in young DHH children with additional disabilities? Do different outcome measures reveal similar patterns of association? The specific demographic variables under consideration were both audiological (degree of hearing loss, type of sensory device, age at fitting of sensory devices), and child- and family-related (gender, nonverbal cognitive ability, maternal education, communication mode).
• Do similar demographic variables predict outcomes in DHH children with additional disabilities who use HAs or CIs?

Research question 1 was aimed primarily at documenting the extent to which participants exhibited delays in language and speech outcomes as assessed using standardised, norm-referenced tests. As such, no related hypothesis was proposed. With respect to research questions 2 and 3, we hypothesised that: a) demographic variables, including degree of hearing loss, cognitive ability and maternal level of education, would be associated with variation in children’s language and speech outcomes; and b) similar demographic variables would be important in predicting outcomes for children with HAs or CIs.

Participants

Participants were members of a population-based cohort taking part in the LOCHI study described earlier. They were children born with hearing loss between 2002 and 2007 in the Australian states of New South Wales, Victoria, and Queensland. All children who were diagnosed with hearing loss and presented at Australian Hearing, the government-funded hearing service provider for all children in Australia, before 3 years of age were invited to participate. As part of the LOCHI study, caregivers were asked to indicate whether or not their child had been diagnosed with a disability in addition to hearing loss by a qualified professional. A total of 180 children, approximately 39% of the total LOCHI population, had been diagnosed with an additional disability by the age of 5 years. Of these children, data were available for 146. The remaining 34 children were unavailable or unaided at the time of assessment, spoke a language other than English, or had withdrawn from the study. A wide range of additional disabilities was reported, as summarised in Table 1 . It is worth noting that these specific diagnoses are included for descriptive purposes. They do not relate directly to our primary aims, and were not formally validated as part of the study.

Mean nonverbal IQs (Wechsler Nonverbal full scale scores) - children grouped according to type of disability ( N = 146)

The classification system used here was devised by Cupples et al. (2014) . Although each child was allocated to a single category, developmental delay (DD) often occurred in combination with other named disabilities; for example, DD was present in 6 of the 18 children with ASD (category 1), and in 16 of the 25 children with CP (category 2). All 4 children with ASD and CP were diagnosed with DD (category 3). Categories 4 and 5 were groupings in which DD was reported either in combination with disabilities or syndromes other than ASD or CP (category 4), or as a child’s only disability (category 5). The remaining four categories encompassed disorders of vision, speech output (i.e., difficulties producing clear, fluent speech), a variety of syndromes that could not be assumed to entail DD, and a diverse set of medical disorders, often affecting major body organs or motor skills (see Table 1 ).

Also included in Table 1 are the mean standard scores achieved by participants in each disability category on the Wechsler Nonverbal Scale of Ability (WNV) ( Wechsler & Naglieri, 2006 ). Of the 146 participants, 102 were assessed on the WNV, with 15 scoring more than 1.5 SDs below the typical mean (i.e., standard scores < 78). Consistent with the allocation of participants to disability categories, the majority of these low-scoring participants (12 out of 15) were diagnosed with ASD, CP, and/or DD. Also consistent with the current classification system is the finding that children diagnosed with ASD, CP, and/or DD achieved significantly lower average standard scores on the WNV than children in the other four disability groups ( F [1,100] = 24.92, p < .001).

Table 2 presents relevant background data on the cohort of 146 participants, more than half of whom were boys. Audiological information was collected from the databases of Australian Hearing and relevant intervention agencies. Hearing loss is represented as a four-frequency-average in the better ear (4FAHL). Across the cohort, hearing loss at 5 years of age ranged from mild to profound ( M = 62.5, SD = 24.8, range = 15.0 – 123.8). The majority of children were HA users, with approximately one third using unilateral or bilateral CIs (see Table 2 ). Device use was associated with degree of hearing loss. Most children with mild or moderate losses (74 out of 80 or 92.5%) used HAs, as did 68.8% (22 out of 32) of children with a severe loss. By contrast, the majority of children with a profound loss (33 out of 34 or 97.1%) used a CI. On average, children had been diagnosed with a hearing loss at 6.7 months of age ( SD = 9.6, range = 0 – 48), and first fitted with HAs approximately 3.5 months later ( M = 10.2, SD = 11.2, range = 0 – 58). For children using CIs, devices were first switched-on between 5 and 58 months of age ( M = 20.0, SD = 13.1), which meant that, at the time of assessment, 46 of the 49 children with CIs (93.9%) had more than 12 months experience with their device and 44 (89.8%) had more than 24 months experience.

Participants’ background information ( N = 146)

Note : 4FAHL = the average of hearing threshold levels at 0.5, 1, 2, and 4 KHz in the better ear, represented non-linearly.

Most children in the cohort used oral communication only in early intervention, although a substantial minority used a combination of sign and speech (see Table 2 ). Only 3 children were reported to communicate using sign only in early intervention. In regard to spoken language, all children used English, with a small percentage (n = 15, 10.3%) using another spoken language as well.

Children’s socio-economic status was measured using the Index of Relative Socioeconomic Advantage and Disadvantage (IRSAD) from the Socio-Economic Index for Areas ( Australian Bureau of Statistics, 2006 ). Lower IRSAD scores indicate geographic areas with relatively fewer resources, whereas higher scores indicate geographic areas with relatively more resources. Scores are expressed as deciles. Most children in the current cohort lived in more advantaged areas, with 63% scoring 7 or above on the IRSAD ( Mdn = 7.0, range = 1 – 10). In regard to maternal education levels, children were evenly divided between those whose mothers had a university qualification, a diploma or certificate, or formal schooling of 12 years or less (see Table 2 ).

Evaluation Tools

Evaluation tools included assessments of receptive and expressive language, speech output, and nonverbal cognitive ability.

Directly administered assessments

Directly administered assessments included: the PLS-4 ( Zimmerman et al., 2002 ); Peabody Picture Vocabulary Test 4 th Edition (PPVT-4) ( Dunn & Dunn, 2007 ); Diagnostic Evaluation of Articulation and Phonology (DEAP) ( Dodd et al., 2002 ); and the WNV ( Wechsler & Naglieri, 2006 ).

The PLS-4 provides a formal assessment of children’s overall receptive and expressive language abilities. At 5 years of age, the test incorporates verbal tasks that enable children to demonstrate understanding of and ability to produce English language structures including semantics, morphology, and syntax. In recent studies, the PLS-4 has been used successfully with children who have hearing loss and additional disabilities (e.g., Cupples et al., 2014 ; Meinzen-Derr et al., 2010 , 2011 ).

The PPVT-4 was used to obtain a formal measure of children’s receptive vocabulary. This widely used test is based on a four-alternative, forced-choice, picture-selection format, and has been used successfully to assess children from a range of special populations.

The phonology subtest of the DEAP was included to provide a quantitative measure of children’s speech production ability. Single-word utterances are elicited using pictures, verbal cues, and/or imitation. For present purposes, children’s speech output is represented as a standard score based on the number of phonemes correctly produced (i.e., both consonants and vowels).

Nonverbal cognitive ability was assessed using the WNV. This assessment was designed specifically for linguistically diverse populations, including people with hearing loss. It contains four subtests, the results of which combine to provide a full-scale IQ score. For children ages 4;0 – 7;11 (years;months) the relevant subtests are matrices, coding, object assembly, and recognition.

Report-based evaluation

The CDI ( Ireton, 2005 ) was used to obtain a caregiver report of participants’ receptive and expressive language abilities. This questionnaire comprises 300 statements to which caregivers are asked to respond “yes” or “no” based on observations of their child’s behaviours. It contains eight scales, results for two of which are reported here: language comprehension (50 items) and expressive language (50 items).

The 5-year-old LOCHI test battery included assessments of language, speech, and nonverbal cognitive ability. Language and speech data were collected when children reached 5 years of age. Although there was some variation in age at testing across children and tasks ( range : 60 to 69 months for PLS-4, PPVT-4, and DEAP; and 59 to 69 months for CDI), just over 90% of language and speech assessments were conducted between 60 and 64 months of age. WNVs were administered between 56 and 98 months of age, with the majority (121 out of 146 or 82.9%) conducted within a 1-year age bracket, when children were between 60 and 72 months old.

A team of research speech pathologists directly assessed children in a location of best convenience for the families (including homes, schools, early intervention centres, childcare centres, or Australian Hearing offices). During evaluation, children wore their devices (HAs or CIs) at the settings prescribed by their audiologist. When possible, speech pathologists were blinded to children’s age of intervention and severity of hearing loss.

All language and speech assessments were administered in 1 to 2 sessions, with no systematic counterbalancing of presentation order. The Wechsler nonverbal cognitive assessment was administered in a separate session by a professional psychologist. Tasks were administered according to instructions in their respective manuals where each child’s abilities allowed. When direct assessments of language and speech could not be administered in spoken language only (e.g., for the three children who communicated using sign), assessments were either not administered or administered in nonstandard format, typically using a combination of speech and sign. In these cases, standard scores were not assigned.

Reliability

Inter-rater reliability was computed for the group of participants in the larger LOCHI study. Formal assessments were video/audio recorded, and randomly selected samples were subjected to a second, independent scoring by a member of the research speech pathologist team who was not involved in the initial test administration or scoring. Approximately 5–10% of PLS-4 and PPVT-4 assessments were double-scored. Agreement was uniformly high on test items administered for PLS-4 receptive (99.4%), PLS-4 expressive (98.4), and PPVT-4 (99.8%).

Statistical considerations and preliminary data analysis

Statistical analysis was conducted using standard scores for PLS-4, PPVT-4 and DEAP, developmental quotients for the CDI, and full scale IQ scores for the WNV. Standard scores on the PLS-4 and PPVT-4, and full scale IQ scores on the WNV have a mean of 100 and SD of 15. For the DEAP, standard scores have a mean of 10 and SD of 3. For the CDI, developmental quotients are computed by dividing language age by chronological age and multiplying by 100. A quotient of 70 or below represents impaired performance (i.e., at least 30% below age expectations). The use of these various standardised scores enabled us to fulfil our aims of: (1) documenting the extent to which this cohort of DHH children exhibited delays in language and speech outcomes on standardised, norm-referenced tests; (2) examining language and speech outcomes as a function of demographic and ability-related variables within the entire cohort; and (3) examining language and speech outcomes within subgroups of children with HAs or CIs.

In line with these primary research aims, the first step in data analysis was to examine the mean scores achieved by the cohort across the range of outcome measures. The second step was to examine the extent to which children’s language and speech outcomes were associated with each of a group of audiological variables (4FAHL, sensory device [HA or CI], age at fitting of HAs, age at CI switch-on), and child and family-related variables (gender, cognitive ability, maternal education, communication mode in early intervention) across the entire participant cohort. This second step was achieved using correlations and regression procedures. Participants were included in individual regression analyses only if their data were complete (i.e., if they had valid scores on the dependent variable and all predictor variables). Three regression models were fitted for each of the six dependent variables (PLS-4 receptive and expressive, CDI receptive and expressive, PPVT-4, DEAP phonemes correct). The first model included four categorical variables and one continuous variable. Categorical variables were gender, device type (HA or CI), communication mode in early intervention (oral or mixed), and maternal education, which was recoded as two binary variables using university level education as the reference category. The continuous variable was 4FAHL. In the second model, the continuous variable of age at intervention with the child’s current device was added to the set of predictors. This single variable was operationalised as age at first HA fitting for children using HAs, and age at first CI switch-on for children with CIs. In the third and final model the continuous variable of cognitive ability was added to the regression model.

The final set of analyses was designed to address the question of whether similar demographic variables are important in predicting outcomes for DHH children with additional disabilities who use HAs or CIs. Correlations and regression techniques as described above were used for this purpose omitting the predictor variable of device type.

In line with standard practice, a Type I error rate of α = .05 (two-tailed) was adopted for all statistical analyses, which were performed using SPSS version 22.0 ( IBM Corp., 2013 ).

Of the 146 participants, 29 who did not have valid scores on any of the language and speech outcome measures were omitted from all further analyses. Average hearing loss was more severe in these omitted participants than in those with at least one valid score (4FAHL = 74.1 vs. 59.6 respectively; t [144] = 2.89, p = .005), but there was no significant difference in maternal education ( χ 2 [2] = 3.18, p = .20). In cases where valid scores were not achieved, the major contributing factors were: participants’ inability to cope with the task demands; use of nonstandard administration (e.g., a combination of speech and sign), which precluded the use of standard scores; unavailability for testing; and, in the case of the CDI, a failure to return completed caregiver-report forms.

Table 3 shows the number of participants with valid scores on the individual language and speech outcome measures and the assessment of nonverbal cognitive ability. Also shown are participants’ mean scores and standard deviations on the individual assessment tasks. These scores were computed using all available data. Thus, means for each task are based on somewhat different participant subgroups. A second set of means was also computed, however, for participants who achieved valid scores on all tasks. Because these means showed the same pattern of results as those reported in Table 3 , they are not included here.

Mean language, speech, and cognitive outcomes (standardised scores) for all participants with valid scores

Note . CDI = Child Development Inventory developmental quotient; PLS-4 = Preschool Language Scale, 4 th edition, standard score; PPVT-4 = Peabody Picture Vocabulary Test, 4 th edition, standard score; DEAP = Diagnostic Evaluation of Articulation and Phonology, phonemes correct, standard score; WNV = Wechsler Non-Verbal Full Scale IQ.

As the data in Table 3 show, participants’ language and speech outcomes on the directly administered tasks (PLS-4, PPVT-4, DEAP) were, in general, approximately 1 to 2 SDs below the means for typically developing children without hearing loss. On the parent-report (CDI) measure, scores were more than 30% below age expectations, representing impaired performance. These marked delays stand in contrast to the children’s relatively good performance on the measure of nonverbal cognitive ability, which placed them, on average, within approximately 0.3 SDs of the typical mean (see Table 3 ). Despite this general pattern of delayed language and speech relative to cognitive ability, there was also variation according to the particular skills assessed. Whereas children scored 2 SDs below the typical mean in speech production (DEAP), their receptive vocabulary scores (PPVT-4) were relatively better, at just below 1 SD of the typical mean.

Associations between demographic variables and outcome measures

Table 4 presents the results of a bivariate correlational analysis conducted to address the second research question, which was aimed at identifying the demographic variables associated with children’s language and speech outcomes. As shown, four demographic variables were significantly correlated with the various outcome measures.

• Use of oral communication only rather than a mixed mode (oral/sign) was associated with better outcomes on all language and speech measures.
• Nonverbal cognitive ability was positively correlated with all language and speech measures.
• Higher levels of maternal education were associated with better receptive language scores on the PLS-4.
• More severe levels of hearing loss were associated with poorer outcomes on all language and speech measures except for the receptive language scale of the PLS-4.

Correlations between demographic variables and outcome measures for all children, with number of paired observations in parentheses

Note . Pearson’s r is reported for correlations involving two continuous variables, Spearman’s rho for correlations involving maternal education, and point-biserial correlations for associations between one dichotomous and one continuous variable; Gender (0 = male; 1 = female); Mode = Mode of Communication in early intervention (0 = oral; 1 = mixed); MatEd = Maternal Education (1 = university; 2 = diploma/certificate; 3 = ≤12 years formal schooling); 4FAHL = 4 Frequency Average Hearing Loss in the better ear; Device (0 = HA; 1 = CI); AgeHA = Age at Hearing Aid Fitting; AgeSO = Age at CI switch-on; WNV = Wechsler Non-Verbal Full Scale IQ; CDIRec = CDI Receptive Language (Language comprehension) developmental quotient; PLSRec = PLS-4 Receptive Language (Auditory comprehension) standard score; PPVT-4 = PPVT-4 Receptive Vocabulary standard score; CDIExp = CDI Expressive Language developmental quotient; PLSExp = PLS-4 Expressive Communication standard score; DEAP = DEAP Percent phonemes correct standard score.

None of the four remaining demographic variables (gender, device, age at HA fitting, or age at CI switch-on) was significantly associated with children’s language or speech outcomes.

Predicting outcomes as a function of demographic variables

Six multiple regressions were conducted to investigate in more detail the associations described above. The results are summarised in Table 5 . For each of the five language outcome measures (PLS-4 receptive and expressive, CDI receptive and expressive, and PPVT4), the full set of predictors accounted for significant total variance ranging from 41% to 57%. For the remaining measure, phonemes correct on the DEAP, the full set of predictors accounted for only 17% of variance, which was not significant. Four other main findings are noteworthy.

Multiple regression summaries for language and speech outcomes: All children

Note. Regression coefficients are for the final model containing all predictor variables; Mode = communication mode in early intervention; MatEd = Maternal education (coded as two binary variables using university education as the reference category); Device = HA versus CI.

First, better language outcomes on the PLS-4, CDI, and PPVT-4 were all associated with milder levels of hearing loss, use of oral communication, and higher levels of cognitive ability after controlling for the variance associated with other demographic variables. Second, children whose mothers completed postsecondary education achieved better outcomes on the directly administered language assessments (PLS-4 and PPVT-4) than children whose mothers had 12 years or less formal schooling. Third, age at intervention with the current device (HA or CI) accounted for significant unique variance in PLS-4 receptive language scores and CDI expressive language scores, reflecting better outcomes for children who received their devices earlier. Fourth, communication mode (oral vs. mixed) was the only variable to significantly predict speech output performance on the DEAP (see Table 5 ).

Predicting outcomes in children with HAs versus CIs

With respect to the study’s third aim, Table 6 presents mean standard scores for individual outcome measures and correlations between demographic variables and outcomes for subgroups of children fitted with HAs or CIs. Independent samples t-tests revealed no significant difference in mean outcomes between the participant subgroups (all p s > .20); however, a different pattern of associations was observed for children with HAs versus CIs. In particular, results for children with HAs resembled those for the cohort as a whole in that better outcomes were generally associated with higher levels of cognitive ability, use of oral communication, milder levels of hearing loss, and for the PLS-4, higher levels of maternal education. By contrast, for children with CIs, only use of oral communication was consistently associated with better language outcomes, and higher levels of cognitive ability were associated with better outcomes on the PLS-4 receptive language scale (see Table 6 ).

Mean language and speech outcome scores and correlations between demographic variables and outcome measures for children with HAs and CIs separately (number of paired observations in parentheses)

Note . Pearson’s r is reported for correlations between two continuous variables, Spearman’s rho for correlations involving maternal education, and point-biserial correlations for associations between one dichotomous and one continuous variable; Gender (0 = male; 1 = female); Mode = Mode of Communication in early intervention (0 = oral; 1 = mixed); MatEd = Maternal Education (1 = university; 2 = diploma/certificate; 3 = ≤12 years formal schooling); 4FAHL = 4 Frequency Average Hearing Loss in the better ear; Device (0 = HA; 1 = CI); AgeHA = Age at Hearing Aid Fitting; AgeSO = Age at CI switch-on; WNV = Wechsler Non-Verbal Full Scale IQ; CDIRec = CDI Receptive Language (Language comprehension) developmental quotient; PLSRec = PLS-4 Receptive Language (Auditory comprehension) standard score; PPVT-4 = PPVT-4 Receptive Vocabulary standard score; CDIExp = CDI Expressive Language developmental quotient; PLSExp = PLS-4 Expressive Communication standard score; DEAP = DEAP Percent phonemes correct standard score.

Outcomes for HA users were investigated further with multiple regression. The results are presented in Table 7 . Once again, the pattern of results resembled that for the cohort as a whole. The predictor variables combined accounted for significant total variance ranging from 24% for the DEAP to 65% for the PPVT-4. Better language outcomes on the PLS-4, CDI, and PPVT-4 were all associated with milder levels of hearing loss, use of oral communication, and higher levels of cognitive ability. Children whose mothers had completed postsecondary education achieved better language outcomes on the directly administered language assessments (PLS-4 and PPVT-4) than children whose mothers had 12 years or less formal schooling. Communication mode was the only variable to significantly predict speech output performance on the DEAP; and age at fitting of HAs accounted for significant unique variance in PLS-4 receptive language scores, reflecting better language outcomes in children who received their devices earlier.

Multiple regression summaries for language and speech outcomes: Children with HAs only

Note. Regression coefficients are for the final model containing all predictor variables; Mode = Communication mode in early intervention; MatEd = Maternal education (coded as two binary variables using university education as the reference category); Device = HA vs. CI

The aim of this investigation was to evaluate language and speech outcomes in a large sample of 5-year-old DHH children with additional disabilities. In line with recommendations in the literature, outcome measures involved both direct assessment and caregiver report (e.g., Berrettini et al., 2008 ; Edwards, 2007 ). A total of 146 children took part in the study, which was aimed at: (1) documenting their delays in language and speech outcomes relative to nonverbal cognitive ability using standardised, norm-referenced tests; and (2) identifying the demographic variables associated with language and speech outcomes both in the cohort as a whole and in children using different types of sensory devices – HAs or CIs.

As regards these research aims, we note first that children in this study achieved language and speech outcomes on directly administered measures of 1 to 2 SDs below average for hearing children of the same age. They were also more than 30% below age expectations on a caregiver-report measure of receptive and expressive language. By contrast, nonverbal cognitive ability was within approximately 0.3 SDs of the typical mean. There was no obvious difference between outcome measures in their sensitivity to the children’s difficulties, although receptive vocabulary appeared to be a relative strength.

Second, in the cohort as a whole, three demographic variables were consistently associated with language outcomes on the PLS-4, PPVT-4 and CDI: Children with less severe hearing losses achieved better outcomes, as did children with higher levels of nonverbal cognitive ability and those using oral communication only in early intervention. While these correlations are not indicative of a causal relationship, they are in the expected direction. Other relevant variables were maternal education, which predicted outcomes on two of the three directly administered assessments (PLS-4, PPVT-4), and age at fitting of the current device (HA or CI), which predicted PLS-4 receptive and CDI expressive outcomes. The sixth primary outcome measure, the DEAP, assessed children’s speech output accuracy. In this case, just a single demographic variable, communication mode, uniquely predicted performance. It is difficult to interpret this finding in a meaningful way, however, given that both variables essentially measure the same thing; that is, a child’s ability to use the oral modality for communication. Finally, when outcomes were considered for children with HAs only, the pattern of results was virtually identical to that for the cohort as a whole: The only exception was that age at fitting of HAs significantly predicted PLS-4 receptive language outcomes, but not CDI expressive outcomes.

Findings from the current study are noteworthy in several respects. First, they provide the strongest evidence yet that audiological, cognitive and demographic variables influence language outcomes in DHH children with additional disabilities. Thus, the current set of well-established, early predictor variables accounted for between 41 and 57% of variance in receptive and expressive language outcomes across the range of measures used. Second, the current findings reveal a marked similarity in the pattern of results obtained across the various language outcome measures, in particular the directly administered PLS-4 and the caregiver reported CDI. Cupples et al. (2014) reported similar consistency between these assessments when LOCHI children were 3 years of age.

A third noteworthy aspect relates to the increased number of demographic variables significantly associated with language outcomes in the current cohort of 5-year-old children as compared to the earlier, 3-year-old cohort. Despite the increased number of significant predictors identified in the current study, the two data sets reveal a high degree of consistency. Indeed, the only significant predictor to emerge at 5 years of age that was not identified in the 3-year-old data was age at device fitting. Of the remaining significant 5-year-old predictors: maternal education predicted 3-year-old PLS-4 outcomes; and degree of hearing loss and communication mode predicted the performance of a subgroup of 3-year-old children who were identified as having impairments to vision or speech output, various syndromes not entailing DD, or medical disorders ( Cupples et al., 2014 ). Finally, nonverbal cognitive ability was most likely reflected in the variable of “disability type” at 3 years of age, given the marked differences in WNV standard scores observed between children with different types of disabilities in the current study (see Table 1 ).

The stronger and more consistent associations observed between predictor variables and outcome measures at 5 years of age than at 3 years of age probably reflects an increase in the reliability and validity of the test results achieved by children as they get older and become more able to cope with the demands of direct assessment. As regards the positive influence of earlier device fitting on selected language outcomes at 5 years of age, we interpret this finding as confirming our previous suggestion that a stronger effect of early auditory stimulation on language development may emerge at an age older than 3 years ( Cupples et al., 2014 ). More importantly, this finding underscores the clinical need for early fitting of HAs or CIs in children with additional disabilities.

A question arises as to why previous studies have often failed to find evidence of associations between well-established early predictors and language outcomes in DHH children with additional disabilities (e.g., Beer et al., 2012 ; Meinzen-Derr et al., 2011 ). The answer is clear with respect to the role of age at HA fitting, since previous studies have focused more narrowly on the benefits of cochlear implantation. With respect to the other significant predictors identified here, the previous literature is dominated by investigations of small heterogeneous samples and individual cases, thus reducing generalisability and contributing to a pattern of mixed and inconsistent findings (e.g., Beer et al., 2012 ; Berrettini et al., 2008 ; Fukuda et al., 2003 ; Meinzen-Derr et al., 2010 , 2011 ; Wiley et al., 2008 ). By contrast, a major strength of the research reported here is the inclusion of a large and heterogeneous participant sample, comprising 146 children with a diverse range of additional disabilities. This large sample made it feasible to evaluate the unique influence of a range of demographic variables while controlling for others, an approach that was not possible in most previous studies. Despite the large number of children included in the current participant sample, however, missing data meant that the cohort of children with CIs was not large enough to enable use of multiple regression analyses to investigate the unique predictors of language and speech outcomes in that sub-group. The 9-year-old assessments conducted as part of the wider LOCHI study may enable us to address this shortcoming, as more participants develop the ability to cope with task requirements.

An advantage that follows from the large number of participants included in this research is enhanced generalisability of the findings to the population of DHH children with additional disabilities. Also noteworthy in this regard is that the current sample was drawn from an Australian, population-based cohort who participated in the 5-year-old assessment phase of the LOCHI study. The only criterion for inclusion in the current analyses was the presence of a diagnosed disability in addition to hearing loss by the age of 5 years. Also encouraging are the nature and distribution of disability types within our sample. At 39% the total percentage of children with reported additional disabilities is similar to that reported in the Annual Survey of Deaf and Hard of Hearing Children in the United States ( Gallaudet Research Institute, 2011 ). Also the range of reported disability types and their relative frequencies (although computed differently) are similar in revealing a large percentage of children with developmental delay (including intellectual disability and learning disability), and smaller percentages with other disabilities such as visual impairment (or deafblindness), orthopaedic impairment (including CP), and ASD.

In conclusion, this investigation demonstrates an important role for well-established early predictors of outcomes in DHH children with additional disabilities who use HAs or CIs. More specifically, severity of hearing loss, cognitive ability, and mode of communication all made a significant unique contribution to children’s receptive and expressive language outcomes on both directly administered and caregiver report measures. Maternal education and age at fitting of the child’s current audiological device also made a unique contribution to some, but not all, outcome measures. This pattern of results was obtained when data from the full participant sample were analysed, and when analyses were restricted to a cohort of children who were fitted with HAs. Through its focus on the effectiveness of HAs in particular for DHH children with additional disabilities, this research fills a significant gap in the literature. Moreover, it provides important clinical data regarding the potential benefits of early device fitting for this population.

Acknowledgments

We gratefully thank all the children, their families and their teachers for participation in this study. We are also indebted to the many persons who served as clinicians for the study participants or assisted in other clinical or administrative capacities at Australian Hearing, Hear and Say Centre, the Royal Institute for Deaf and Blind Children, the Shepherd Centre, and the Sydney Cochlear Implant Centre.

Declaration of interest

This work was partly supported by the National Institute on Deafness and Other Communication Disorders (Award Number R01DC008080). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute on Deafness and Other Communication Disorders or the National Institutes of Health.

We acknowledge the financial support of the Australian Government Department of Health, and the HEARing CRC, established and supported under the Cooperative Research Centres Program – an initiative of the Australian Government. We also acknowledge the support provided by New South Wales Department of Health, Australia; Phonak Ltd.; and the Oticon Foundation.

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Syracuse lab seeks to break isolation for stroke patients with aphasia (Guest Opinion by Ellyn Riley)

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Are ‘Forever Chemicals’ a Forever Problem?

The environmental protection agency says “forever chemicals” must be removed from tap water. but they lurk in much more of what we eat, drink and use..

This transcript was created using speech recognition software. While it has been reviewed by human transcribers, it may contain errors. Please review the episode audio before quoting from this transcript and email [email protected] with any questions.

From “The New York Times,” I’m Sabrina Tavernise. And this is “The Daily.”

[THEME MUSIC]

This month for the first time, the Environmental Protection Agency began to regulate a class of synthetic chemicals, known as forever chemicals, in America’s drinking water. But the chemicals, which have been linked to liver disease and other serious health problems, are in far more than just our water supply. Today, my colleague Kim Tingley explains.

It’s Wednesday, April 17.

So Kim, any time the EPA announces a regulation, I think we all sort of take notice because implicit in it is this idea that we have been exposed to something — something bad, potentially, lead or asbestos. And recently, the EPA is regulating a type of chemical known as PFAS So for those who don’t know, what are PFAS chemicals

Yeah, so PFAS stands for per and polyfluoroalkyl substances. They’re often called forever chemicals just because they persist so long in the environment and they don’t easily break down. And for that reason, we also use them in a ton of consumer products. They’re in makeup. They’re in carpet. They’re in nonstick cookware. They’re in food packaging, all sorts of things.

Yeah, I feel like I’ve been hearing about these chemicals actually for a very long time. I mean, nonstick pans, Teflon — that’s the thing that’s in my mind when I think PFAS.

Absolutely. Yeah, this class of chemicals has been around for decades. And what’s really important about this is that the EPA has decided, for the first time, to regulate them in drinking water. And that’s a ruling that stands to affect tens of millions of people.

So, help me understand where these things came from and how it’s taken so long to get to the point where we’re actually regulating them.

So, they really actually came about a long time ago. In 1938, DuPont, the people who eventually got us to Teflon, they were actually looking for a more stable kind of refrigerant. And they came upon this kind of chemical, PFAS. The thing that all PFAS chemicals have is a really strong bond between carbon atoms and fluorine atoms. This particular pairing is super strong and super durable.

They have water repellent properties. They’re stain resistant. They’re grease resistant. And they found a lot of uses for them initially in World War II. They were using them as part of their uranium enrichment process to do all these kinds of things. And then —

Well, good thing it’s Teflon.

In the 1950s is when they really started to come out as commercial products.

Even burned food won’t stick to Teflon. So it’s always easy to clean.

So, DuPont started using it in Teflon pans.

Cookware never needs scouring if it has DuPont Teflon.

And then another company, 3M also started using a kind of PFAS —

Scotchgard fabric protector. It keeps ordinary spills from becoming extraordinary stains.

— in one of their big products, Scotchgard. So you probably remember spraying that on your shoes if you want to make your shoes waterproof.

Use Scotchgard fabric protector and let your cup runneth over.

Right — miracle product, Scotchgard, Teflon. But of course, we’re talking about these chemicals because they’ve been found to pose health threats. When does that risk start to surface?

Yeah, so it’s pretty early on that DuPont and 3M start finding effects in animals in studies that they’re running in house.

Around the mid ‘60s, they start seeing that PFAS has an effect on rats. It’s increasing the liver and kidney weights of the rats. And so that seems problematic. And they keep running tests over the next decade and a half. And they try different things with different animals.

In one study, they gave monkeys really, really high levels of PFAS. And those monkeys died. And so they have a pretty strong sense that these chemicals could be dangerous. And then in 1979, they start to see that the workers that are in the plants manufacturing, working with these chemicals, that they’re starting to have higher rates of abnormal liver function. And in a Teflon plant, they had some pregnant workers that were working with these chemicals. And one of those workers in 1981 gave birth to a child who had some pretty severe birth defects.

And then by the mid 1980s, DuPont figures out that it’s not just their workers who are being exposed to these chemicals, but communities that are living in areas surrounding their Teflon plant, particularly the one in Parkersburg, West Virginia, that those communities have PFAS in their tap water.

Wow, so based on its own studies, DuPont knows its chemicals are making animals sick. They seem to be making workers sick. And now they found out that the chemicals have made their way into the water supply. What do they do with that information?

As far as we know, they didn’t do much. They certainly didn’t tell the residents of Parkersburg who were drinking that water that there was anything that they needed to be worried about.

How is that possible? I mean, setting aside the fact that DuPont is the one actually studying the health effects of its own chemicals, presumably to make sure they’re safe, we’ve seen these big, regulating agencies like the EPA and the FDA that exist in order to watch out for something exactly like this, a company that is producing something that may be harming Americans. Why weren’t they keeping a closer watch?

Yeah, so it goes kind of back to the way that we regulate chemicals in the US. It goes through an act called the Toxic Substances Control Act that’s administered by the EPA. And basically, it gives companies a lot of room to regulate themselves, in a sense. Under this act they have a responsibility to report to the EPA if they find these kinds of potential issues with a chemical. They have a responsibility to do their due diligence when they’re putting a chemical out into the environment.

But there’s really not a ton of oversight. The enforcement mechanism is that the EPA can find them. But this kind of thing can happen pretty easily where DuPont keeps going with something that they think might really be a problem and then the fine, by the time it plays out, is just a tiny fraction of what DuPont has earned from producing these chemicals. And so really, the incentive is for them to take the punishment at the end, rather than pull it out early.

So it seems like it’s just self-reporting, which is basically self-regulation in a way.

Yeah, I think that is the way a lot of advocacy groups and experts have characterized it to me, is that chemical companies are essentially regulating themselves.

So how did this danger eventually come to light? I mean, if this is in some kind of DuPont vault, what happened?

Well, there’s a couple different things that started to happen in the late ‘90s.

The community around Parkersburg, West Virginia, people had reported seeing really strange symptoms in their animals. Cows were losing their hair. They had lesions. They were behaving strangely. Some of their calves were dying. And a lot of people in the community felt like they were having health problems that just didn’t really have a good answer, mysterious sicknesses, and some cases of cancers.

And so they initiate a class action lawsuit against DuPont. As part of that class action lawsuit, DuPont, at a certain point, is forced to turn over all of their internal documentation. And so what was in the files was all of that research that we mentioned all of the studies about — animals, and workers, the birth defects. It was really the first time that the public saw what DuPont and 3M had already seen, which is the potential health harms of these chemicals.

So that seems pretty damning. I mean, what happened to the company?

So, DuPont and 3M are still able to say these were just a few workers. And they were working with high levels of the chemicals, more than a person would get drinking it in the water. And so there’s still an opportunity for this to be kind of correlation, but not causation. There’s not really a way to use that data to prove for sure that it was PFAS that caused these health problems.

In other words, the company is arguing, look, yes, these two things exist at the same time. But it doesn’t mean that one caused the other.

Exactly. And so one of the things that this class action lawsuit demands in the settlement that they eventually reach with DuPont is they want DuPont to fund a formal independent health study of the communities that are affected by this PFAS in their drinking water. And so they want DuPont to pay to figure out for sure, using the best available science, how many of these health problems are potentially related to their chemicals.

And so they ask them to pay for it. And they get together an independent group of researchers to undertake this study. And it ends up being the first — and it still might be the biggest — epidemiological study of PFAS in a community. They’ve got about 69,000 participants in this study.

Wow, that’s big.

It’s big, yeah. And what they ended up deciding was that they could confidently say that there was what they ended up calling a probable link. And so they were really confident that the chemical exposure that the study participants had experienced was linked to high cholesterol, ulcerative colitis, thyroid disease, testicular cancer, kidney cancer, and pregnancy induced hypertension.

And so those were the conditions that they were able to say, with a good degree of certainty, were related to their chemical exposure. There were others that they just didn’t have the evidence to reach a strong conclusion.

So overall, pretty substantial health effects, and kind of vindicates the communities in West Virginia that were claiming that these chemicals were really affecting their health.

Absolutely. And as the years have gone on, that was sort of just the beginning of researchers starting to understand all the different kinds of health problems that these chemicals could potentially be causing. And so since the big DuPont class action study, there’s really just been like this building and building and building of different researchers coming out with these different pieces of evidence that have accumulated to a pretty alarming picture of what some of the potential health outcomes could be.

OK, so that really kind of brings us to the present moment, when, at last, it seems the EPA is saying enough is enough. We need to regulate these things.

Yeah, it seems like the EPA has been watching this preponderance of evidence accumulate. And they’re sort of deciding that it’s a real health problem, potentially, that they need to regulate.

So the EPA has identified six of these PFAS chemicals that it’s going to regulate. But the concern that I think a lot of experts have is that this particular regulation is not going to keep PFAS out of our bodies.

We’ll be right back.

So, Kim, you just said that these regulations probably won’t keep PFAS chemicals out of our bodies. What did you mean?

Well, the EPA is talking about regulating these six kinds of PFAS. But there are actually more than 10,000 different kinds of PFAS that are already being produced and out there in the environment.

And why those six, exactly? I mean, is it because those are the ones responsible for most of the harm?

Those are the ones that the EPA has seen enough evidence about that they are confident that they are probably causing harm. But it doesn’t mean that the other ones are not also doing something similar. It’s just sort of impossible for researchers to be able to test each individual chemical compound and try to link it to a health outcome.

I talked to a lot of researchers who were involved in this area and they said that they haven’t really seen a PFAS that doesn’t have a harm, but they just don’t have information on the vast majority of these compounds.

So in other words, we just haven’t studied the rest of them enough yet to even know how harmful they actually are, which is kind of alarming.

Yeah, that’s right. And there’s just new ones coming out all the time.

Right. OK, so of the six that the EPA is actually intending to regulate, though, are those new regulations strict enough to keep these chemicals out of our bodies?

So the regulations for those six chemicals really only cover getting them out of the drinking water. And drinking water only really accounts for about 20 percent of a person’s overall PFAS exposure.

So only a fifth of the total exposure.

Yeah. There are lots of other ways that you can come into contact with PFAS. We eat PFAS, we inhale PFAS. We rub it on our skin. It’s in so many different products. And sometimes those products are not ones that you would necessarily think of. They’re in carpets. They’re in furniture. They’re in dental floss, raincoats, vinyl flooring, artificial turf. All kinds of products that you want to be either waterproof or stain resistant or both have these chemicals in them.

So, the cities and towns are going to have to figure out how to test for and monitor for these six kinds of PFAS. And then they’re also going to have to figure out how to filter them out of the water supply. I think a lot of people are concerned that this is going to be just a really expensive endeavor, and it’s also not really going to take care of the entire problem.

Right. And if you step back and really look at the bigger problem, the companies are still making these things, right? I mean, we’re running around trying to regulate this stuff at the end stage. But these things are still being dumped into the environment.

Yeah. I think it’s a huge criticism of our regulatory policy. There’s a lot of onus put on the EPA to prove that a harm has happened once the chemicals are already out there and then to regulate the chemicals. And I think that there’s a criticism that we should do things the other way around, so tougher regulations on the front end before it goes out into the environment.

And that’s what the European Union has been doing. The European Chemicals Agency puts more of the burden on companies to prove that their products and their chemicals are safe. And the European Chemicals Agency is also, right now, considering just a ban on all PFAS products.

So is that a kind of model, perhaps, of what a tough regulation could look like in the US?

There’s two sides to that question. And the first side is that a lot of people feel like it would be better if these chemical companies had to meet a higher standard of proof in terms of demonstrating that their products or their chemicals are going to be safe once they’ve been put out in the environment.

The other side is that doing that kind of upfront research can be really expensive and could potentially limit companies who are trying to innovate in that space. In terms of PFAS, specifically, this is a really important chemical for us. And a lot of the things that we use it in, there’s not necessarily a great placement at the ready that we can just swap in. And so it’s used in all sorts of really important medical devices or renewable energy industries or firefighting foam.

And in some cases, there are alternatives that might be safer that companies can use. But in other cases, they just don’t have that yet. And so PFAS is still really important to our daily lives.

Right. And that kind of leaves us in a pickle because we know these things might be harming us. Yet, we’re kind of stuck with them, at least for now. So, let me just ask you this question, Kim, which I’ve been wanting to ask you since the beginning of this episode, which is, if you’re a person who is concerned about your exposure to PFAS, what do you do?

Yeah. So this is really tricky and I asked everybody this question who I talked to. And everybody has a little bit of a different answer based on their circumstance. For me what I ended up doing was getting rid of the things that I could sort of spot and get rid of. And so I got rid of some carpeting and I checked, when I was buying my son a raincoat, that it was made by a company that didn’t use PFAS.

It’s also expensive. And so if you can afford to get a raincoat from a place that doesn’t manufacture PFAS, it’s going to cost more than if you buy the budget raincoat. And so it’s kind of unfair to put the onus on consumers in that way. And it’s also just not necessarily clear where exactly your exposure is coming from.

So I talk to people who said, well, it’s in dust, so I vacuum a lot. Or it’s in my cleaning products, so I use natural cleaning products. And so I think it’s really sort of a scattershot approach that consumers can take. But I don’t think that there is a magic approach that gets you a PFAS-free life.

So Kim, this is pretty dark, I have to say. And I think what’s frustrating is that it feels like we have these government agencies that are supposed to be protecting our health. But when you drill down here, the guidance is really more like you’re on your own. I mean, it’s hard not to just throw up your hands and say, I give up.

Yeah. I think it’s really tricky to try to know what you do with all of this information as an individual. As much as you can, you can try to limit your individual exposure. But it seems to me as though it’s at a regulatory level that meaningful change would happen, and not so much throwing out your pots and pans and getting new ones.

One thing about PFAS is just that we’re in this stage still of trying to understand exactly what it’s doing inside of us. And so there’s a certain amount of research that has to happen in order to both convince people that there’s a real problem that needs to be solved, and clean up what we’ve put out there. And so I think that we’re sort of in the middle of that arc. And I think that that’s the point at which people start looking for solutions.

Kim, thank you.

Here’s what else you should know today. On Tuesday, in day two of jury selection for the historic hush money case against Donald Trump, lawyers succeeded in selecting 7 jurors out of the 12 that are required for the criminal trial after failing to pick a single juror on Monday.

Lawyers for Trump repeatedly sought to remove potential jurors whom they argued were biased against the president. Among the reasons they cited were social media posts expressing negative views of the former President and, in one case, a video posted by a potential juror of New Yorkers celebrating Trump’s loss in the 2020 election. Once a full jury is seated, which could come as early as Friday, the criminal trial is expected to last about six weeks.

Today’s episode was produced by Clare Toeniskoetter, Shannon Lin, Summer Thomad, Stella Tan, and Jessica Cheung, with help from Sydney Harper. It was edited by Devon Taylor, fact checked by Susan Lee, contains original music by Dan Powell, Elisheba Ittoop, and Marion Lozano, and was engineered by Chris Wood.

Our theme music is by Jim Brunberg and Ben Landsverk of Wonderly.

That’s it for The Daily. I’m Sabrina Tavernise. See you tomorrow.

• April 22, 2024   •   24:30 The Evolving Danger of the New Bird Flu
• April 19, 2024   •   30:42 The Supreme Court Takes Up Homelessness
• April 18, 2024   •   30:07 The Opening Days of Trump’s First Criminal Trial
• April 17, 2024   •   24:52 Are ‘Forever Chemicals’ a Forever Problem?
• April 16, 2024   •   29:29 A.I.’s Original Sin
• April 15, 2024   •   24:07 Iran’s Unprecedented Attack on Israel
• April 14, 2024   •   46:17 The Sunday Read: ‘What I Saw Working at The National Enquirer During Donald Trump’s Rise’
• April 12, 2024   •   34:23 How One Family Lost $900,000 in a Timeshare Scam
• April 11, 2024   •   28:39 The Staggering Success of Trump’s Trial Delay Tactics
• April 10, 2024   •   22:49 Trump’s Abortion Dilemma
• April 9, 2024   •   30:48 How Tesla Planted the Seeds for Its Own Potential Downfall
• April 8, 2024   •   30:28 The Eclipse Chaser

Hosted by Sabrina Tavernise

Featuring Kim Tingley

Produced by Clare Toeniskoetter ,  Shannon M. Lin ,  Summer Thomad ,  Stella Tan and Jessica Cheung

With Sydney Harper

Edited by Devon Taylor

Original music by Dan Powell ,  Elisheba Ittoop and Marion Lozano

Engineered by Chris Wood

Listen and follow The Daily Apple Podcasts | Spotify | Amazon Music

The Environmental Protection Agency has begun for the first time to regulate a class of synthetic chemicals known as “forever chemicals” in America’s drinking water.

Kim Tingley, a contributing writer for The New York Times Magazine, explains how these chemicals, which have been linked to liver disease and other serious health problems, came to be in the water supply — and in many more places.

On today’s episode

Kim Tingley , a contributing writer for The New York Times Magazine.

Background reading

“Forever chemicals” are everywhere. What are they doing to us?

The E.P.A. issued its rule about “forever chemicals” last week.

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We aim to make transcripts available the next workday after an episode’s publication. You can find them at the top of the page.

Fact-checking by Susan Lee .

The Daily is made by Rachel Quester, Lynsea Garrison, Clare Toeniskoetter, Paige Cowett, Michael Simon Johnson, Brad Fisher, Chris Wood, Jessica Cheung, Stella Tan, Alexandra Leigh Young, Lisa Chow, Eric Krupke, Marc Georges, Luke Vander Ploeg, M.J. Davis Lin, Dan Powell, Sydney Harper, Mike Benoist, Liz O. Baylen, Asthaa Chaturvedi, Rachelle Bonja, Diana Nguyen, Marion Lozano, Corey Schreppel, Rob Szypko, Elisheba Ittoop, Mooj Zadie, Patricia Willens, Rowan Niemisto, Jody Becker, Rikki Novetsky, John Ketchum, Nina Feldman, Will Reid, Carlos Prieto, Ben Calhoun, Susan Lee, Lexie Diao, Mary Wilson, Alex Stern, Dan Farrell, Sophia Lanman, Shannon Lin, Diane Wong, Devon Taylor, Alyssa Moxley, Summer Thomad, Olivia Natt, Daniel Ramirez and Brendan Klinkenberg.

Our theme music is by Jim Brunberg and Ben Landsverk of Wonderly. Special thanks to Sam Dolnick, Paula Szuchman, Lisa Tobin, Larissa Anderson, Julia Simon, Sofia Milan, Mahima Chablani, Elizabeth Davis-Moorer, Jeffrey Miranda, Renan Borelli, Maddy Masiello, Isabella Anderson and Nina Lassam.

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