Patent Application
20240023836
The invention relates to occupational safety and health as it pertains to noise exposure and hearing. In particular, aspects of the invention provide methods to mathematically transform individual audiometric test data into a single numerical metric that summarizes the magnitude of hearing loss specifically toward early noise-induced hearing loss.
Noise-induced hearing loss (“NIHL”) represents one of the most prevalent and costly occupational diseases in the industrialized world. It is the most common occupational disease (illness) reported in the United States. NIHL affects all demographics and disproportionately impairs younger workers.
Work-related NIHL is a largely, if not completely, preventable disease. NIHL develops in response to cumulative exposure to excessive levels of noise in a wide variety of industries and occupations. The disease has a relatively rapid progression from onset of exposure to the time of detection, a point at which NIHL is irreversible. Past and current methods of preventing the disease have consisted of regulations limiting workers' exposure to noise and requiring them to wear personal protective equipment (“PPE”).
Occupational noise regulations include, without limitation, the United States Occupational Safety and Health Administration (“OSHA”) 29 CFR 1910.95 and the Mine Safety and Health Administration (“MSHA”) 30 CFR Part 62 (collectively the “Noise Standards”), and industry guidelines and comparable regulations in other nations (e.g., Canadian Standards Association Standard Z107.6-16 “Audiometric Testing for Use in Hearing Loss Prevention Programs” and Z1007-16 “Hearing Loss Prevention Program (HLPP) Management”). These specification-based regulations and guidelines require primary preventive measures consisting of engineering controls, work practice control measures, and use of PPE, namely hearing protective devices (“HPDs”) such as ear plugs and ear muffs, to reduce workers' noise exposure dose below the permissible exposure limit (“PEL”) of 90 decibels (dB). The decibel is a unit of measure of the intensity of a sound or the power level of an electrical signal by comparing it with a given level on a logarithmic scale.
The above-referenced regulations require the employer to institute a hearing conservation program (“HCP”) if an 8-hour time-weighted average sound level exposure equals or exceeds the “action level,” which under OSHA and MSHA is 85 decibels (dB). The purpose of the HCP as a form of secondary prevention is to accurately identify early NIHL and prevent it from becoming irreversible and advanced in workers occupationally exposed to noise. Employees in a HCP undergo training and audiometric testing (“audiogram”) before starting work in a noise-exposed job (to establish a hearing “baseline”) and periodically (typically annually) thereafter.
For screening audiograms conducted as part of HCPs, hearing levels (also commonly referred to as “thresholds”) at each sound frequency are recorded separately for each ear in decibels in interval (non-continuous) increments of 5 dB. At its earliest stages, NIHL impacts hearing thresholds (i.e., causes a decline in hearing) in the higher sound frequencies (4 kHz and 6 kHz, occasionally 3 kHz in selected industries or exposures). These frequencies are mostly outside the human speech range, and thus a subject's diminished ability to hear them does not impair regular hearing or otherwise cause symptoms.
NIHL has a characteristic, diagnostic audiometric pattern of a high frequency hearing loss “notch” with a peak threshold (also referred to as a hearing “loss”) at 4 kHz or 6 kHz (and occasionally at 3 kHz) with recovery (i.e., return toward 0 dB) at 8 kHz. As NIHL progresses in an individual with continued exposure to excessive noise, it impairs the ability to hear lower, speech-range sound frequencies (0.5, 1 and 2 kHz), and thus impacts speech perception. Beyond this point, with further advancement of NIHL, the audiogram shape typically deforms and loses its characteristic, diagnostic “notch” appearance. Advanced NIHL results in a variable amount of irreversible hearing loss, sometimes accompanied by tinnitus (ringing of the ears), that can cause permanent partial disability, significantly impair social life and work, and require the use of a hearing aid.
Clinical interpretation of individual audiograms is a complex process. For interpretation of serial screening audiograms, an objective, generally accepted or validated criterion (or set of criteria) does not exist to detect and measure the earliest audiometric signs of occupational NIHL, or to recognize and differentiate them from any other disease or condition that produces hearing loss. In practice, when a screening audiogram is interpreted or compared to a baseline or previous audiograms, it is typically “eye-balled,” i.e., reviewed without being subjected to any formal statistically analysis, for each employee, one-at-a-time, and then filed away for recordkeeping compliance purposes unless specific administrative action is required.
The Noise Standards do not even require a medical or hearing professional to clinically interpret audiograms, either individually or in aggregate, nor do they specify a particular method or criterion for their interpretation. Aside from the computation of a non-specific Standard Threshold Shift (as described below), neither the regulations nor any professional standards require or provide guidelines for statistical analysis of individual or aggregate trends among similar exposure groups (“SEG”s) of workers in audiometric hearing loss progression over time relative to measured or estimated workplace noise exposures.
Uncertainties in clinical sensitivity and specificity are a major limitation in the reliability and effectiveness of serial screening audiograms as a secondary preventive method for occupational NIHL. Studies have demonstrated that even when trained audiologists, otolaryngologists and occupational medicine physicians review an individual's screening audiogram results and test-to-test changes, their interpretation methodology is subjective and their interpretations vary widely in consistency. This inconsistency arises because the earliest audiometric abnormalities indicative of NIHL are difficult to distinguish from what constitutes a “normal” audiogram. The pattern and timing of audiometric progression from normal to abnormal for NIHL has many different presentations which are time- and exposure-dependent. No specific numerical criterion defines where audiometric normalcy ends and NIHL disease begins. Most audiologists and other hearing professionals consider hearing loss thresholds of ≤15 dB or ≤20 dB at any frequency as within the normal range for adults, and many organizations and regulatory agencies do not consider hearing to be “abnormal” until at least one frequency is ≥20 or 25 dB. A universally accepted absolute or relative (e.g., percent) criterion for a significant year-to-year (test-to-test) change does not exist. Intra-person test-to-test variability (+/−5 dB at any frequency) further impacts the ability to reliably distinguish a true positive test result (one indicating clinically relevant hearing loss) from a false positive test result (one with non-clinically relevant hearing loss with one or more elevated hearing levels reflecting normal variability).
As a result, an individual subject's serial audiograms may have small, seemingly minor or fluctuating year-to-year changes that represent the earliest stage of reversible NIHL disease which go unnoticed, whereas only when a large overall change from baseline occurs in a recognizable pattern may an irreversible noise-induced hearing loss in either or both ears be reliably detected and diagnosed.
Interpretation of an individual worker's series of screening audiograms is further complicated by the inherently variable natural history of NIHL disease progression which is dependent on individual health, work conditions, cumulative and peak noise levels, and work practices, including the use of HPDs. All individuals exposed to certain levels, types and duration of noise are susceptible to NIHL, but only some of those who have been adequately exposed will develop the disease. The onset of NIHL typically begins after 4-10 years of sufficiently high noise exposure and can occur even with regular use of HPDs. Non-occupational sources of noise exposure and certain medical conditions can contribute to hearing loss progression, or confound the audiometric diagnosis of early NIHL. Some employees may have an abnormal baseline audiogram reflecting pre-existing NIHL or other forms of hearing loss. Even when noise exposure affects both ears equally, the progression of audiometric changes may not be symmetrical.
Hearing tends to either stay the same or worsen in adults over time. Only in a few diseases or conditions (e.g., cerumen removal, or treatment of middle ear infection) does hearing improve significantly. In persons over 50 years of age, NIHL in its advanced stages can be difficult to audiometrically and clinically differentiate from presbycusis, a common, age-related cause of high frequency hearing loss. Numerous other variables related to noise exposure, employee, physician, and disease course also complicate the accurate diagnosis and causal assessment of NIHL on an individual basis.
As mentioned above, the Threshold Shift, i.e., STS (“Standard Threshold Shift” in the United States, and “Significant Threshold Shift” in Canada) is the conventional, regulatory-defined metric for occupational audiograms deemed to indicate irreversible hearing loss. Either or both ears can sustain a STS, simultaneously or separately, and a single individual can incur multiple STS occurrences over time. In the United States, OSHA and MSHA define a STS (“STS-OSHA,” in either ear) as the decline (as compared with baseline) of the arithmetic average of hearing threshold at 2, 3 and 4 kHz equal to or greater than 10 dB, as shown in the calculation below:
(L2 kHzCurrent+L3 kHzCurrent+L4 kHzCurrent)/3−(L2 kHzBaseline+L3 kHzBaseline+L4 kHzBaseline)/3≥10 dB
where L=threshold of detection of the sound at the given frequency, in dB.
An OSHA recordable STS has the added criterion:
(L2 kHzCurrent+L3 kHzCurrent+L4 kHzCurrent)/3≥25 dB.
Thus, a “recordable” STS (“STS-REC-OSHA”) requires at least one of the 2, 3 or 4 kHz hearing levels to be ≥25 dB. This criterion was added by OSHA to prevent false positives that are within the normal range of hearing. Whether or not such non-recordable STSs predict or are a sign of subsequent early noise-induced hearing loss is unknown.
Under the Canadian Standards Association Z107.6-16 (2016), a Significant Threshold Shift (“STS-CSA”), in either ear) is defined as:
[(L2 kHzCurrent+L3 kHzCurrent+L4 kHzCurrent)/3−(L2 kHzBaseline+L3 kHzBaseline+L4 kHzBaseline)/3>10 dB AND (L2 kHzcurrent+L3 kHzCurrent+L4 kHzCurrent)/3≥30 dB]
OR (L3 kHzCurrent−L3 kHzBaseline)≥15 dB
OR (L4 kHzCurrent−L4 kHzBaseline)≥15 dB.
Though the Noise Standards consider the STS to be an “early indicator of permanent hearing loss,” no scientific evidence has been published to validate the STS-OSHA (or STS-CSA) as an effective preventive metric. At the time the OSHA Noise Standard was instituted in the early 1980s, the regulatory STS-OSHA definition and its criterion (“cutoff”) value of ≥10 dB was promulgated by OSHA through consensus, and not through published scientific studies. Since that time (nearly 40 years), no scientific research has been conducted to validate or challenge this criterion.
The STS (for every Standard) is intended for individual worker determination, but not for measuring aggregate (SEG) trends toward or past the point of early NIHL. Thus, a STS in and of itself does not clearly demarcate a significant change in reversible NIHL, either individually or among SEGs or the entire population of employees in the HCP. Often, by the time a STS is detected for an individual, it is too late to prevent or reverse the disease process. Thus, the STS is a non-specific, lagging indicator of (irreversible) disease that relies upon and is limited to one-test (and one-ear)-at-a-time determinations, with or without subjective interpretations of the entire individual audiogram.
In conjunction with the limitations discussed above with regard to audiometric interpretation, the audiometric test performance characteristics—sensitivity and specificity—of the STS to detect of the earliest phase of NIHL have never been systematically analyzed or determined. Sensitivity is the probability an abnormal (“positive”) test correctly identifies the presence of the disease. The STS is problematic with regard to sensitivity because NIHL characteristically starts in the high frequency hearing range, but in many cases by the time it impacts the 2 kHz (speech) range—which the STS includes—hearing loss is already moderately advanced and irreversible. The 6 kHz threshold, in contrast, is much more sensitive to early NIHL changes, but it is not included in the STS definition. Specificity is the probability a normal (“negative”) test correctly identifies the absence of disease. The specificity of the STS is similarly inadequate because when NIHL advances to the point where a STS has occurred, the pattern may not be accurately differentiated from common diseases such as presbycusis or other less prevalent diseases associated with high frequency hearing loss. This common situation creates a false positive STS which must nonetheless be reported and medically evaluated. Further, the Noise Standards contain outdated age-adjustment formulas that unreliably filter out the effect of older age on STS values, thereby misattributing some noise-related hearing loss to age-related hearing loss (presbycusis) in older workers. Consequently, contestation of such cases for purposes of occupational injury recordability and workers' compensation claim adjudication consumes a substantial amount of resources that contribute to the large economic burden of NIHL.
While individual clinical diagnosis and screening for NIHL through audiograms is an important component to occupational disease control, a public health (i.e., population-based) approach represents the most effective method to reduce occupational disease risk through intervention. The need for and importance of quantitatively evaluating HCP effectiveness within a company by utilizing audiometric data was first recognized in the late 1980s. The term “audiometric database analysis” (“ADBA”) was proposed to describe a standardized, systematic method of aggregate statistical analysis of serial audiograms in individual employees. ADBA is described more fully in Hearing Conservation Programs: Practical Guidelines for Success, Julia Royster and Larry H. Royster © 1990. The purpose of ADBA is (1) early identification and measurement of aggregate trends among similarly exposed workers in a given workplace that would prevent threshold shifts, and (2) objective statistical evaluation of the overall effectiveness of the HCP among each group of workers, across departments, or for the entire facility.
After more than 30 years of professional efforts to develop a robust ADBA methodology for this purpose, no consensus-based criteria (e.g., the American National Standards Institute (“ANSI”) S12.13 Standard) or mathematical modeling methods have ever been scientifically validated or widely adopted. Even with the widespread use of computerization and availability of databases, internet, and automated information technology, no substantive methodological or technological advances have been developed or widely implemented for employers or other stakeholders to utilize audiometric data to statistically analyze aggregate audiometric trends over time to objectively measure risk for NIHL. Neither governmental agencies, nor any professional (medical, audiological, or industrial hygiene) organizations, nor any audiometry hardware or software manufacturers or distributors have developed or offer any audiometric analytical methods or tools to objectively measure HCP effectiveness. Thus, the de facto “standard of care” for HCP audiometric data remains limited to fulfilling the minimum recordkeeping and one-test-at-a-time audiometric STS requirements mandated by the OSHA and MSHA Noise Standards, or their international counterparts.
While billions of dollars have been spent over the past 40 years satisfying regulatory compliance requirements for noise, the incidence of NIHL continues unabated, making it one of the most prevalent occupational diseases. The magnitude and extent to which HCPs within companies or industries are effectively controlling the risk of NIHL within their worker populations remains largely unknown. The reasons why audiometric data remains largely unactionable include the aforementioned inherent complexity of the audiogram, reliance upon one-test-at-a-time expert interpretation, the STS metric as a crude lagging indicator of early disease, and the lack of an adequate ADBA methodology and system.
To make audiometric data actionable, there is a need for a method of calculating a metric that accurately summarizes individual audiometric results (data) for hearing loss specifically toward NIHL. To such a summary metric, post hoc statistical methods can be applied to analyze audiometric data trends within and among individuals, SEGs or any designated population within a company or organization. Widespread implementation of such population-based, statistical analytical approaches can transform compliance-driven, individual screening testing with limited preventive capability into medical surveillance processes that can be directly linked to corrective and prevention actions for individuals and groups of workers, as more fully set forth in Craner, J. “Medical Surveillance” (chapter 41) in Current Occupational and Environmental Medicine, Fifth Edition, LaDou J and Harrison R (Eds.), McGraw Hill Companies, Inc., 2014.
There is a further need for a computerized information management platform or tool, configurable to each organization (company, facility or other entity for which screening audiograms are conducted), to automate the process of managing audiometric data, including scheduling, data collection and organization, statistical analysis, interpretation, reporting, follow-up tasks, and documentation.
There is a still further need for a system and method of analyzing audiometric data to provide an indication of the onset of NIHL in a subject or a population at a point prior to the disease being irreversible, in order to predict and reduce the risk for development of NIHL through noise exposure controls and other preventive measures.
There remains yet a further need for a system and method of analyzing audiometric data to distinguish NIHL form other types of hearing loss.
Described herein are methods for predicting Noise Induced Hearing Loss (“NIHL”) in a subject. In particular, the summary metric and its derivations described herein can be statistically analyzed to identify and predict NIHL in its early, reversible stages and measure trends in noise-induced hearing loss among individual and similar exposure groups (“SEGs”) of workers to objectively measure the effectiveness of hearing conservation programs (“HCPs”) over time. The metric, its derivations and the statistical analyses can also be applied to any other cause or stage of progressive hearing loss that is monitored and detected through serial audiometric testing. As such, the methods and metrics of the invention provides an improved system and method of analyzing audiometric data to provide an indication of the onset of NIHL.
In certain aspects, the present invention relates to a method for predicting NIHL in a subject comprising the steps of 1) taking a baseline audiogram measurement of the subject, 2) taking subsequent audiogram measurements of the subject at set or varying intervals of time, 3) for each such audiogram measurement calculating a metric, termed the Weighted Hearing Level, W, reported in units of “dBw” (‘weighted decibels’ as defined herein), according to the following equation:
where
4) calculating the following derivations of the Weighted Hearing Level to further characterize and analyze audiometric findings and changes:
WTS=ΔdBw=dBw Current test−dBw Baseline test
W
L-R=|(dBw Left−dBw Right)/[(dBw Left+dBw Right)/2]|
The above-described metrics provide a method of identifying and predicting early NIHL in its reversible or pre-impairment stage in individual subjects. Other embodiments of the invention comprise a method of using the Weighted Hearing Level (dBw), the derived metrics, and the statistical analyses thereof to measure temporal trends and predict significant changes among individuals or within a similarly exposed or defined group (“population”) of employees, and estimating the effectiveness of interventions to prevent (irreversible) NIHL.
In certain other embodiments, the present invention relates to a method for preventing NIHL comprising the steps of 1) taking a baseline audiogram measurement of a subject, 2) taking subsequent audiogram measurements of the subject at set or varying intervals of time, 3) converting the baseline and each subsequent audiogram measurement to W (dBw, as illustrated above) and measuring the magnitude and direction of the WTS (ΔdBw, as defined above), 4) on an individual basis, examining WTS to see if it meets the predetermined level (cutoff) wherein NIHL is predicted, 5) utilizing W, WTS, rw, and WL-R from the population of audiometrically screened subjects to perform advanced statistical analyses of trends and population outcomes, and 6) incorporation of these methods into a HCP for the subject and/or for the population to which the subject belongs that has exposure to the noises inducing the hearing loss risk.
The invention further comprises calculating equipment with access to audiogram data, for which the calculating equipment is suitable for calculating W (dBw) and the derived metrics, as illustrated above, either by preprogrammed instruction code or by manual calculations by the equipment's user. A further embodiment of the invention comprises the calculating equipment being suitable for performing statistical operations on the resulting W (dBw) values and other derived metrics, either by preprogrammed instruction code or by manual calculations by the equipment's user. In particular embodiments the calculating equipment is able to perform parametric statistical operations and/or non-parametric statistical operations. In further embodiments, the calculating equipment incorporates an audiogram graphical display.
Other features and advantages of the present invention will be understood by reference to the detailed description and the examples that follow.
The present invention springs in part from the need for and development of a robust metric directed to transforming raw audiometric data for the purpose of objective, accurate, specific detection and prediction of particular types of progressive hearing deficiencies and diseases, such as early NIHL, that are diagnosed and characterized by a series of audiograms taken at intervals over a discrete time period, typically annually.
For purposes of this document and for clarity, all percentages referred to herein are percentages by weight (wt. %) or proportion of observations or number of persons, unless otherwise noted.
Ranges, if used, are used as shorthand to avoid having to list and describe each and every value within the range. Any value within the range can be selected, where appropriate, as the upper value, lower value, or the terminus of the range.
The term “about” refers to the variation in the numerical value of a measurement, e.g., temperature, weight, percentage, length, concentration, and the like, due to typical error rates of the device used to obtain that measure. In one embodiment, the term “about” means within 5% of the reported numerical value; preferably, the term “about” means within 3% of the reported numerical value.
As used herein, the singular form of a word includes the plural, and vice versa, unless the context clearly dictates otherwise. Thus, the references “a”, “an”, and “the” are generally inclusive of the plurals of the respective terms. Likewise, the terms “include”, “including” and “or” should all be construed to be inclusive, unless such a construction is clearly prohibited from the context. Similarly, the term “examples,” particularly when followed by a listing of terms, is merely exemplary and illustrative and should not be deemed to be exclusive or comprehensive.
The term “comprising” is intended to include embodiments encompassed by the terms “consisting essentially of” and “consisting of”. Similarly, the term “consisting essentially of” is intended to include embodiments encompassed by the term “consisting of”.
Various publications, including patents, published applications and scholarly articles, are cited throughout the specification. Each of these publications is incorporated by reference herein in its entirety.
As noted above, an audiogram is a test that measures the ability of a subject to hear a pure tone at standardized, ordinally ranked frequencies by air conduction. Suitable frequencies for use herein range from about 0.5 kilohertz (kHz) to about 10 kHz or more, e.g., 0.5 kHz, 0.6 kHz, 0.7 kHz, 0.8 kHz, 0.9 kHz, 1 kHz, 1.1 kHz, 1.2 kHz, 1.3 kHz, 1.4 kHz, 1.5 kHz, 1.6 kHz, 1.7 kHz, 1.8 kHz, 1.9 kHz, 2 kHz, 2.1 kHz, 2.2 kHz, 2.3 kHz, 2.4 kHz, 2.5 kHz, 2.6 kHz, 2.7 kHz, 2.8 kHz, 2.9 kHz, 3 kHz, 3.1 kHz, 3.2 kHz, 3.3 kHz, 3.4 kHz, 3.5 kHz, 3.6 kHz, 3.7 kHz, 3.8 kHz, 3.9 kHz, 4 kHz, 4.1 kHz, 4.2 kHz, 4.3 kHz 4.4 kHz, 4.5 kHz, 4.6 kHz, 4.7 kHz, 4.8 kHz, 4.9 kHz, 5 kHz, 5.1 kHz, 5.2 kHz, 5.3 kHz, 5.4 kHz, 5.5 kHz, 5.6 kHz, 5.7 kHz, 5.8 kHz, 5.9 kHz, 6 kHz, 6.1 kHz, 6.2 kHz, 6.3 kHz, 6.4 kHz, 6.5 kHz, 6.6 kHz, 6.7 kHz, 6.8 kHz, 6.9 kHz, 7 kHz, 7.1 kHz, 7.2 kHz, 7.3 kHz, 7.4 kHz, 7.5 kHz, 7.6 kHz, 7.7 kHz, 7.8 kHz, 7.9 kHz, 8 kHz, 8.1 kHz, 8.2 kHz, 8.3 kHz, 8.4 kHz, 8.5 kHz, 8.6 kHz, 8.7 kHz, 8.8 kHz, 8.9 kHz, 9 kHz, 9.1 kHz, 9.2 kHz, 9.3 kHz, 9.4 kHz, 9.5 kHz, 9.6 kHz, 9.7 kHz, 9.8 kHz, 9.9 kHz, 10 kHz, or more. Preferably, the range is between about 0.5 kHz and about 8 kHz. Suitable ordinally-ranked frequencies are, e.g., 0.5 kHz, 1, kHz, 2, kHz, 3 kHz, 4 kHz, 5 kHz, 6 kHz, 7 k Hz, and 8 kHz, it being understood that other ordinally-ranked frequency values can be selected for conducting audiograms. The hearing ability of the subject for each frequency, at various volumes (amplitudes), may be measured in each ear and recorded in decibels (dB). An audiogram may be generated by using an audiometer, which is a device regularly used by audiologists, physicians, and other hearing professionals for evaluating hearing acuity, according to specified methods for calibration and accuracy.
Audiometers can be a self-contained hardware unit comprising a sound generating unit connected to a pair of headphones, or software-based programs run through a computer connected to headphones. Preferably, audiograms are conducted in a sufficiently quiet environment, typically a soundproof booth designed for this purpose. Examples of audiometers include, without limitation, audiometers produced by Welch Allen, Benson Medical, Primus, Maico, Grason-Stadler, and others.
When audiograms are performed, the hearing levels, or thresholds, at each sound frequency are recorded separately for each ear in decibels in interval (i.e., non-continuous) increments of 5 dB. Individual audiogram raw data is typically reported in tabular format, and it is commonly graphically displayed as an inverted line with markers of the hearing thresholds on the y-axis for each of the measured frequencies along the x-axis. The hearing levels (results) can either be reported in a separate graph for each ear, or both ears can be plotted together on a single graph. The graphical reports typically use the convention of an “X” denoting the left ear and “O” denoting the right ear, with the minimum (typically 0 dB) threshold located at the top and the physiologically maximum threshold value (110 or 120 dB) at the bottom.
The raw audiometric data may then be transformed into robust metrics that can be further subjected to post hoc statistical analysis. For instance, the audiogram measurement data can be used to calculate the Weighted Hearing Level (W) metric, which summarizes the magnitude of hearing loss in an ear specifically toward NIHL, and is derived by mathematically transforming raw audiometric data from one ear into a single number (vector) that is expressed in dBw units which are equivalent to the units (dB) in which the raw data are obtained. In this document, the W is sometimes referred to herein as “W (dBw).”
In addition to W, provided herein are several derived metrics including the Weighted Threshold Shift (WTS), the Weighted Correlation Coefficient (rw), and the Weighted Left Right Laterality (WL-R). The WTS reflects the magnitude and direction of change in hearing in an ear (in dBw units) specifically toward (or away from) NIHL between an audiogram obtained at a given time (the “current” test) relative to the baseline audiogram. In this document, the WTS is sometimes referred to as “WTS (ΔdBw).” The rw measures the extent of a linear relationship between a change in the WTS (ΔdBw) and the early NIHL template pattern. A similar correlation coefficient (rB) can also be calculated for a baseline test's relationship to the early NIHL pattern. Finally, the WL-R measures the extent of asymmetry between Left and Right ears' (“sides”) Weighted Hearing Level in an individual audiogram.
The W and its derived metrics (WTS, rw, WL-R) can be subjected to post hoc statistical tests that would not otherwise be feasible using raw audiometric data, and which thereby allow audiometric data to be utilized to:
To determine the Weighted Hearing Level (W) for each ear (WRight and WLeft) from raw audiogram data, the following calculations may be performed:
A sample calculation of W for raw audiogram data (using the 4 kHzV1 template, see Table 3) is provided in Table 1 below.
To calculate the WTS (ΔdBw) for the current test in each ear:
Table 2 demonstrates calculations of W and WTS using the 4 kHzV1 template for a hypothetical audiogram (one ear) demonstrating a typical progression from normal (baseline) hearing to NIHL over 7 years. Table 3 enumerates the ƒi values for the 4 kHzV1 template. The raw audiometric data for the current test could also be plotted in graphical format, as illustrated in
The calculated values of WTS (in dBw units) using the 4 kHzV1 template are compared to the corresponding calculated values of STS (in dB) for the same set of audiograms. The results are further explained below.
The rw and WL-R are calculated by following the mathematical operations as defined by the equations described above. In connection with the above, calculations do not require rounding, or they can be rounded to 1 or 2 decimals. In one embodiment, W (in dBw units) can be reported to one decimal place. It is also noted that W can have a positive, negative or zero value. Finally, W is reported in equivalent units of decibels (dB) which reflect the mathematical weighting of the hearing levels at each frequency, and therefore are termed “dBw.”
It will be understood that, to calculate the WTS (ΔdBw) for a periodic (current) test after an initial (baseline) test, one would subtract the (initial) “baseline” test W from the current test W for each ear, i.e., WTSRight=WRight Current−WRight Baseline and WTSLeft=WLeft Current−WLeft Baseline, where “Left” means the left ear, and “Right’ means the right ear. The WTS (ΔdBw) value can be positive, negative, or zero. A positive WTS means the hearing loss has increased (worsened) toward NIHL. A negative WTS means the hearing loss has decreased (improved) or changed its shape away from NIHL. A zero (0) WTS means no change in either direction.
It will be understood that the calculation of rw for a periodic (current) test after an initial (baseline) test produces a value between −1.0 and +1.0, and can be reported to one, two or more decimal places. A rw value close to −1.0 means a negative correlation of the audiometric results with early NIHL. A rw value close to +1.0 means a positive correlation of the audiometric results with early NIHL. For example, if a current (non-baseline) audiogram has a high WTS (ΔdBw), but it has a low correlation with the NIHL template, it indicates a substantial hearing loss, but one that is not consistent with NIHL.
It will be understood that the calculation of WL-R for a subsequent (current) test after an initial (baseline) test is based upon the absolute value of a difference between ears, and thereby produces a value between zero (0) and +1.0, which can be reported to one, two or more decimal places. A WL-R value close to or equal to 0 means no difference between sides, and a WL-R value close to or equal to 1.0 means completely different between sides. The convention to subtract the Right value from the Left value, rather than vice versa, is arbitrary; and because either the numerator and/or the denominator could be negative values, absolute values (| . . . |) are used to express the relative difference in dBw between the two ears.
For screening audiograms, i normally is equal to seven (the count of frequencies of 0.5, 1, 2, 3, 4, 6, 8 kHz), or eight (when 0.25 kHz is also included). However, i can be equal to any number of frequencies that are measured in an audiogram. For example, if lower, intermediate and/or higher frequencies are also measured, i would be greater than seven (or eight), and if only six frequencies are measured, i would be six. The preferred range of i is between six and ten. A more preferred range is between seven and eight.
The Weighted Hearing Level (W) is designed to closely summarize the hearing loss based on the relative magnitude of hearing levels at each frequency that correspond to a typical pattern, or “template” of NIHL at its earliest, recognizable and distinguishable form (see Table 3 above). This template is represented mathematically by a series of expected or modeled relative hearing levels denoted by the symbol ƒi for each i frequency in the audiogram. The values of ƒi are unitless scalars that can be expressed as integers or to one or more decimal places. For purposes of simplicity, they are described herein and employed in the examples rounded to increments of 0.5. For early NIHL, the relative values of ƒi are established to resemble the expected, or hypothetical, pattern of NIHL's characteristic high frequency “notch” at the earliest audiometrically recognizable stage of disease, preferably at or before an STS has occurred (Table 1). However, the particular absolute or relative values or combinations of ƒi are not fixed, nor are they necessarily based on any particular empirical or consensus-based audiometric diagnostic criteria for the reasons discussed above; namely, 1) the shape of the NIHL audiogram is inherently variable among any two subjects even with its characteristic high frequency “notch” (which is typically “V” shaped with a single nadir frequency but can also be “U” shaped with two equal nadir frequencies); and 2) there is no single, absolute or universally accepted clinical definition of what audiometric pattern constitutes NIHL at its earliest detectable phase, or what that pattern is when a STS is detected.
The flexibility of the expected response functions ƒi allows for the W, WTS and other metrics to be used to evaluate common variants of NIHL, such as the 4 kHz peak (most common) or the 6 kHz peak, or the less common but recognized 3 kHz peak. The weighted expected response functions ƒi can be modified to fit the patterns typically observed at a given company, occupation or type of noise exposure. Several variations of expected response (ƒi) template patterns (indicating early NIHL) can be utilized, e.g., 4 kHzV, 6 kHzV, 4-6 kHzU, modelled after the most commonly recognized patterns of NIHL in its early stages with peak hearing loss at 4 kHz (V-shaped), 6 kHz (V-shaped), or both 4 and 6 kHz (U-shaped), respectively. For each such variation, sub-variations (“sub-variants”) reflecting asymmetry can also be used. Examples of three sub-variants of the 4 kHzV template (labelled as sub-variants 1, 2 and 3) are shown in Table 3 above. All three sub-variants have the same V-shaped pattern with subtle differences in notch symmetry: ƒi (4 KHzV1) is symmetrically weighted around the 4 kHz notch, whereas ƒi (4 kHzV2) and ƒi (4 KHz3V) are asymmetrically weighted around the 4 kHz notch.
In addition to the aforementioned purpose and functionality, attributes of the Weighted Hearing Level (dBw) as a metric for interpretation of audiometric data include the following advantages.
The WTS (ΔdBw) is defined as the arithmetic difference (“A”) between the dBw value of the current audiogram minus the dBw value of the baseline audiogram. The WTS is a readily calculated metric that measures the magnitude and direction of overall changes in hearing loss towards early NIHL in a subject from baseline test to current test, analogous to how the (non-specific, non-predictive) regulatory STS is defined. For an individual subject, the calculated WTS (ΔdBw) for each subsequent audiogram can be computed to evaluate, tabulate and plot changes over time. Like for the STS, an absolute WTS (ΔdBw) cutoff or criterion value could be established as a predictive tool, as well as to identify potential outliers or individuals who require investigation or intervention, such as exposure controls or work practices, or even temporary or permanent removal from excessive noise exposure. An appropriate, comparable criterion or “cutoff” for the WTS could be ≥10 dBw, which corresponds to the STS-OSHA- or STS-CSA-defined criterion of ≥10 dB.
In addition to the aforementioned purpose and functionality, attributes of the WTS (ΔdBw) as a metric for statistical analysis of audiometric data include the following:
Other derived summary statistics from the W (dBw) and WTS (ΔdBw), such as change in W (dBw) between the two most recent (periodic) tests or between any two tests, percent change in W (dBw) per unit time, or variation in W (dBw) as measured by the absolute value of a pairwise difference, can be similarly calculated and can then be analyzed and compared using a variety of either parametric or non-parametric statistical methods, as described below.
For all such analyses, the time period could be measured in calendar time (in years or months) or the relative duration between periodic tests (e.g., third year to fifth year) in the HCP, or duration of noise exposure or employment. All such analyses can evaluate effects in a given ear (Left or Right), or both ears by presenting the data from each ear and comparing them side-by-side, or by combining them into a single metric, such as an arithmetic average, as described above.
Short-term (year-to-year or test-to-test) changes in W (dBw) and WTS (ΔdBw) in an individual subject can be graphically plotted using standard statistical tools such as a control chart, also known as a Shewhart chart (see, e.g.,
For more slowly developing individual audiometric changes over a number of years, statistical tools such as a cumulative sum control chart, also known as a cusum chart, can be utilized to plot temporal distributions (see, e.g.,
Either the Shewhart chart or the cusum chart can utilize the entire employee population, the subject's SEG, or any other defined sub-population to compute the upper and lower control limits (UCL and LCL) or data bounds (UDB and LDB), respectively. In addition, individual trends for a particular worker can be directly compared to one or more applicable aggregate (group) trends through the statistical methods described below.
Table 2 above illustrates a classic hypothetical audiogram progression from normal hearing to the characteristic high frequency notch with a peak at 4 kHz. This hypothetical case demonstrates audiogram progression in one ear from “normal” at baseline (B) to NIHL over 7 years. The STS (Δ234 kHz average Current-Baseline≥10 dB) is detected at Year 6, whereas the WTS (WCurrent−WBaseline≥10 dBw) for the same audiogram is detected at the Year 3 test.
The real-world study (Examples 1 and 2) of the invention described below provides additional examples of how the application of the weighted model to generate a WTS reveals early NIHL significantly before NIHL is detected by a STS-OSHA or CSA-STS.
The W and its related, derived metrics (WTS, rw, WL-R) are most powerful in the analysis of aggregate audiometric data involving groups of workers (e.g., SEGs) to predict and measure trends that cannot otherwise be accomplished through interpretation of raw individual audiometric data or use of STS values.
Both parametric and non-parametric statistical tests can be applied to W and its related, derived metrics (WTS, rw, WL-R). Non-parametric methods have important advantages in the application to workplace populations because they obviate the need to assume a normal distribution of data, can be utilized with relatively small groups, are less impacted by missing values and uneven time intervals, and are not as susceptible to the effects of outliers.
A robust, non-parametric test for measuring gradual rates of progression of the response (hearing loss) over time is the Sen-Theil slope, also known as a Theil-Sen estimator or Sen slope estimator. This “runs test” estimates a (linear) slope as the median of all of the pairwise slopes between data points with respect to time. It is a useful summary statistic for identifying and quantifying an increasing or decreasing trend in such a way that occasional outliers do not adversely affect the calculation. The Sen slope is defined as the median of the pairwise slopes between points (e.g., test-to-test) for a subject over a defined time interval, where the denominator is typically a unit of time (e.g., years). The time interval between pairs of data points does not need to be constant—an advantage for samples that are not always collected at precise time intervals, or where there are missing values. The Sen slope can be tested for significance (i.e., whether the slope is different from zero or some other specified threshold), producing a z score and a corresponding p value (probability due to chance) which is reported with 95% upper and lower confidence intervals for the median slope. Thus, if trends in individual or aggregate dBw (and ΔdBw) values are increasing over a defined time period, the slope will be positive; if trends are decreasing over time, the slope will be negative. When the dBw data are natural log-transformed, Sen's slope can be interpreted as a proportional rate of change in NIHL per unit time (e.g., % ΔdBw per year), which explains not only whether a change is significant, but also how large it is.
The Sen slopes of all subjects tested in a group (e.g., each SEG) in a given time period can be pooled (compiled), and their distributions and measures of central tendency are measured by calculating the mean or median value for each group (SEG). The mean or median for each group can then be compared to one another for a given time period, or compared to themselves before versus after a noise exposure control intervention (e.g., reduce noise sources or upgrade HPDs) has been implemented, using post-hoc non-parametric tests and plotted graphically as follows. A standard statistical test such as analysis of variance (non-parametric one-way ANOVA by ranks tests to compare medians) yields a standard probability (p) value and confidence intervals (typically 95th percentile or 99th percentile), which can be interpreted to make decisions about the magnitude and direction of the findings.
The comparative findings can be graphically displayed as a box plot (also known as a box and whiskers plot, e.g., as shown in
For an individual subject, the Sen slope can be compared to values within the same individual or SEG, the entire population, or any defined sub-population (a “you are here” snapshot analysis) within a given time period or employment or exposure duration by plotting values as a histogram or smoothed density curve (see, e.g.,
Alternative statistical methods are available for these analyses. For example, an overall slope within an individual or group can also be calculated as a pooled Kendall's tau-beta. A parametric ANOVA could be utilized instead of its non-parametric counterpart. Other non-parametric methods for temporal trends of aggregate audiometric summary statistics (WTS) or for a given individual person's audiograms include the Wald-Wolfowitz or the Wallis-Moore runs test.
All of these statistical analyses can be applied to audiometric data for one ear (such as all left ears or all right ears) or both ears. Since hearing loss typically remains unchanged or progresses and only rarely improves in adults, and since the primary outcome of interest is whether and how much increase in hearing loss has occurred as reflected by W (dBw) or WTS (ΔdBw), the statistical test should be preferably be one-tailed, with the exception of the KW test and any other tests explicitly recommended to be two-tailed.
WTS (ΔdBw) trends over time, both among and across the members (workers) of each SEG and as well as for an individual person or ear (see above) within a SEG, can be analyzed with statistical tests to quantify the magnitude and direction by which audiometric results change over time.
The distribution of WTS (ΔdBw) or Sen slopes (median ΔdBw/Δtime) for a selected time period, duration of employment or noise exposure (e.g., duration of time in the HCP) can also be compared across SEGs using histograms or density plots for either or both ears (see, e.g.,
Most NIHL is a bilateral (both ear) process, whereas other forms of hearing loss can be asymmetric or involve only one ear. However, progression of NIHL is rarely perfectly symmetrical as measured by audiograms. By utilizing the WTS to compare ΔdBw in Right versus Left ears (sides) and comparing them in a scatter plot, criteria can be set to identify outliers (see, e.g.,
By comparing the WTS (ΔdBw) to the rw for a group such as the entire worker population or a particular SEG, and plotting the data as a scatter plot and then setting a criterion (i.e., a cutoff, depending on desired sensitivity and specificity), the proportion and subject identities of outlier audiograms indicative of significant changes toward early NIHL can be statistically identified (see, e.g.,
The aforementioned data collection, organization, calculations and statistical analyses are typically conducted using computerized systems and software designed for this purpose. In theory, these data could be managed manually on paper or manually recorded using desktop or web-based software applications such as spreadsheets, including Microsoft Excel, Google Sheets, Apache Open Office, Quattro Pro, and the like. The calculations could similarly be performed by hand or by using spreadsheets with customized formulas, and could be programmed to query, filter, and sort data to ensure the correct data are selected for the particular analysis. The statistical analysis methods such as non-parametric tests are available in some spreadsheets and specialized statistical software packages, particularly those that render graphical outputs such as SAS, SPSS, R, R-Shiny, and the like.
Because audiometric data, employee (subject) data including calculated time or exposure intervals, and exposure classification (SEG) data are dynamic, the use of a real-time, automated information management software application designed to manage all aspects of collecting, aggregating, organizing, analyzing, interpreting and reporting audiometric data seamlessly in conjunction with employee and other related health and safety data and documentation is essentially necessary to practically utilize the invention. For this automation to be effective, it must be configured according to rules and logic specific to the business processes and particular organization, and deliver output (such as analysis and reports) in an accessible format.
Any sophisticated data management platform can be used to perform the calculations and analyses described herein. These include, but are not limited to environmental health and safety (EHS) platforms such as Cority, Gensuite, Cintellate, Enablon, Pure Safety, VelocityEHS, and others; audiometry-specific software or services such as Benson Solo, CounselEAR, Examinetics XM Solutions, HearTrak, Noah, Shoebox, Sycle, Workplace Integra and others; or occupational medicine or general medicine software platforms such as Agility, Allscripts, Epic, GalenMD, Meditech, OHM and others. In certain embodiments, the webOSCAR™ technology platform is utilized (URL www.webOSCAR.com). webOSCAR is a technology platform developed by an occupational medicine physician and designed to manage health and safety data (including but not limited to audiometric data) for highly regulated, hazardous industries.
The application of this invention is not limited to detection and prediction of early NIHL. It can be readily adapted and modelled to evaluate hearing loss using applicable templates when such hearing loss is (1) characterized by a distinct or characteristic audiometric pattern in at least one phase of the disease process and (2) monitored for its progression by comparison of serial audiograms over time in a population of people. For example, templates can be developed to identify and predict moderate or advanced stages of hearing loss that develops after early NIHL has already developed. Though the focus of this disclosure is for prevention and screening for work-related hearing loss, particularly NIHL, the summary metric (W) and its derivatives (ΔdBw, rw, WL-R) can be constructed to perform a similar function for screening, detecting or predicting non-occupational hearing loss such as in motorcyclists, target shooters, or people who listen to music through devices such as mobile phones and earbuds.
The invention similarly can be applied to screen and predict other forms, causes or diseases of either sensorineural, conductive or mixed progressive hearing loss in any population, such as presbycusis in people over age 50, and otosclerosis in children by utilizing the rw in combination with the W and WTS to distinguish specific types of progressive hearing loss by their characteristic audiometric pattern. The invention could also be applied to people with any type of hearing disorder who wear hearing assistive devices (hearing aids) to measure improvement in functional hearing over time, and compare various types of devices. Finally, the invention could be applied in epidemiological studies to define what constitutes “normal” hearing levels or changes within a given population, such as school-age children or another demographic group.
The following examples are provided to describe the invention in greater detail. They are intended to illustrate, not to limit, the invention.
Raw data from a Benson Solo audiometer software database were exported and compiled in an Excel spreadsheet. Employee-specific data (names) were de-identified and assigned sequential ID numbers. Demographic information in the database included date of birth, gender, test type (baseline, periodic or repeat), job title, and hearing protector type. The demographic data were scrubbed and, where applicable, corrected to ensure formatting consistency.
Similar exposure group (SEG) classifications based upon noise dosimetry measurements obtained by an industrial hygienist were categorized as follows:
Incomplete or invalid audiometric data (27 tests, from the original data set of 1,647 tests) were excluded from data analysis. Baseline and periodic test types were computed based upon the number of tests and time intervals between tests for each subject. STS values (-OSHA, -OSHA-REC, and -CSA) were calculated from the raw data.
The following metrics were computed in the webOSCAR™ platform using the ‘4 kHzV1’ template (previously described) to describe the typical audiometric pattern and magnitude of early NIHL (ENIHL) for an ear with 4 kHz nadir (peak hearing loss), as described above:
Data were analyzed using a combination of SQL (Structured Query Language, Microsoft Corporation), SAS Version 9.4 software (SAS Institute, Cary, NC) and R 3.5.2 for Windows (https://www.r-project.org/). SQL and R are incorporated into the webOSCAR platform to automate the audiometric analytics.
This computer simulation measured the comparative sensitivity and specificity of the WTS (ΔdBw) and rw for identifying, predicting and measuring ENIHL in comparison to the STS (STS-OSHA and STS-CSA). Variant patterns and progressions of ‘normal’ hearing (“control” group), early NIHL (the 4 kHzV1 template), and typical presbycusis were simulated at relatively low total levels of peak hearing loss with a nadir (maximum loss in hearing level) at 4 kHz. The receiver operating characteristics (sensitivity and specificity) were plotted. The results corroborated the dBw metrics have superior sensitivity and specificity for detecting and predicting early NIHL over the STS, particularly when the correlation to early NIHL (rw) was applied.
Individual employee audiometric data metrics were analyzed using Shewhart and cusum control charts (described above).
Aggregate analyses with the following post hoc non-parametric statistics were utilized on the W, WTS and derived metrics to measure and compare short- and long-term trends and changes of individuals and groups (within and among SEGs) within the employee population as explained above: Sen slope; Kruskal-Wallis and Mann-Whitney tests. Other non-parametric tests utilized for this analysis included the Spearman rank correlation coefficient (a correlation to test if two set of values are directly or inversely correlated); and Wilcoxon Signed rank test (to measure the magnitude of differences between two sets of data where the measurement scale is at least interval to test symmetry (up or down) around 0.0 values, wherein a low p value (<0.05) indicates absence of symmetry (i.e., presence of asymmetry) and a high value indicates presence of symmetry.
To demonstrate the invention works as intended, and in particular to demonstrate how the sensitivity and specificity of the Weighted Hearing Level (W) and Weighted Threshold Shift (WTS) compare to the STS for detecting individual audiometric progression toward early NIHL (ENIHL), predicting employees and SEGs at increased risk for NIHL, and objectively measuring aggregate trends within and among SEGs to assess the effectiveness of the HCP, a two-part research study was conducted in 2018-2019. In this Example, the metrics and statistical methodology described herein were applied to the collective audiometric database from a large gold mining company located in North America (the “Company”) over the course of 6.25 years. A computerized simulation study with sensitivity and specificity analysis was also conducted to validate the methods.
Results are presented below. The employee demographics are summarized in Table 4. The subject population was predominantly comprised of males, and more than one quarter of subjects (27.2%) had at least one (1) baseline or periodic audiogram test at which the computed employee age is ≥50 years—the age threshold for increased risk for baseline or concomitant presbycusis, a potential confounder for STS determinations. Because employment start date was not captured, the duration of employment was not calculated.
Summarized in Table 5 is the test type distribution. Approximately two thirds (64.9%) of the 1,647 total valid audiogram tests conducted in the selected period were Baseline tests. Of the 1,067 employees, one third (33.3%) had only one (1) periodic test, 8.6% had two (2) consecutive periodic tests, and only 9 employees (0.8%) had three (3) consecutive tests. No (0) employees had four (4) or more consecutive tests. The mean (average) periodic test interval was 2.4 years, with approximately only one quarter (28.4%) occurring in the ‘annual’ interval range.
Tables 6A-6C summarize the statistics for the distribution of tests by exposure SEG, distribution of SEGs by job title and HPD assignment, and distribution of tests by hearing protection and HPD type. Over half (60.5%) of tests are reported in employees in MED and HIGH SEGs (see Table 6A). Consistent recording of Job Title or Level by SEG was not available. As shown in Table 6B, data on HPD usage by Employee and Job Title/Level were not systematically recorded. Moreover, nearly two thirds (61.0%) of tests are reported with associated use of hearing protection devices (see Table 6C).
In Table 7, the statistics for the distribution of baseline tests was summarized. Over half (60.1%) of baseline audiograms were ‘abnormal’ insofar as having a hearing loss of >25 dB in at least one frequency in either ear. Further, nearly one third (31.8%) of such abnormalities demonstrated moderate severity (40-50 dB) and nearly one fifth (18.4%) were severe. Of all these abnormal baseline tests, none (0.0%) demonstrated a conductive hearing loss pattern (e.g., middle ear damage or cerumen impaction). One quarter (25.1%) demonstrated a peak hearing loss at 4 kHz, less than half (10.5%) of which had concomitant loss at 8 kHz, and nearly that same proportion (13.1%-21.4%) had a “downsloping” pattern. Collectively these findings suggested that as many as 15-25% of baselines represented a pre-existing NIHL.
The STS outcomes are summarized in Table 8. A total of 117 audiograms (7.1% of periodic tests) had an STS (-OSHA or -CSA) in either ear, but only a small subset (12 audiograms, 0.7% of periodic tests) had bilateral STS. The distribution between right (64) and left (65) ears was equal. In addition, the total number of periodic audiograms that met the STS-OSHA criteria (which in contrast to STS-CSA does not include large individual high frequency changes) was 61 (3.7% of periodic tests) in either ear, with a similar low subset (6 audiograms, 0.4% of all periodic tests) of bilateral STS. When OSHA recordability criteria were applied, the total number of periodic audiograms that met the STS-OSHA-REC criteria was 40 (2.4% of periodic tests). Approximately 25% of periodic audiograms meeting the STS-OSHA definition thus occurred with relatively low absolute hearing levels in the higher (2, 3, and 4 kHz) frequencies.
For employees (subjects') with at least one (1) periodic test plus Baseline test (minimum 2 tests total), control charts were generated to plot individual dBw levels from test-to-test, in comparison to ‘control’ data (which for this project is derived from the ‘Nil’ SEG). These charts had the most value for employees with a higher number of consecutive periodic tests, as illustrated for Employee #1821 with the maximum number of periodic tests (3). As shown in Table 9, Employee 1821 exhibited a moderate progressive increase in ΔdBw in the LEFT ear indicating early NIHL, but no corresponding change in the RIGHT ear. For instance, the LEFT ear Correlations (rw) between the three (3) post-baseline audiograms and the NIHL template were 0.22, 0.3 and 0.63, respectively, which is consistent with developing progressive NIHL in the left ear. As the hearing loss increased, both the WTS (ΔdBw) and the rw values increased such that by post-baseline Test #2, the WTS (ΔdBw=11.9) crossed the 10 dBw threshold to flag as possible NIHL. By the third post-baseline observation, the WTS (ΔdBw=20.27) and the rw (0.63, >>0.5 threshold) were sufficiently high to confirm with high probability that this ear had sustained ENIHL—which the lagging STS indicators did not detect until Test #3. The Shewhart and Cusum charts illustrate these progressions (see
In comparison, in the RIGHT ear correlation (rw) values were negative or hovered around 0.0, and the WTS (ΔdBw) values decreased over time. These findings suggested that minimal hearing loss progression that did not follow an ENIHL pattern. The gradually increasing Laterality described this asymmetry in hearing loss between ears.
For employees with at least two consecutive periodic tests plus Baseline test (3 tests total), “You are Here” distributions were generated that indicated the individual's ENIHL metrics in comparison to peers (which for this project is derived from the entire study population with one or more periodic test). The charts depicted in
The aggregate comparative trends toward ENIHL across exposure groups was studied.
As can be seen in
Shown in
Shown in
Distributions of Trends toward Early NIHL
Shown in
Symmetry of Changes toward Early NIHL
Shown in
Scatter plot analysis between left and right ears was also performed on the employee population for their most recent periodic tests. As shown in
Shown in
This may include additional testing, verification that the subjects are making appropriate use of protective headgear, or in the extreme, transfer out of high-exposure environments. Further, by setting the ΔdBw criteria lower (e.g., to 10 dBw to correspond to STS thresholds), the sensitivity (i.e., more early true positives) increased with the risk of decreasing specificity (i.e., more early false positives).
The Company audiometric database contained a significant amount of valuable audiometric data that, upon robust analysis as described above, allowed important statistical findings to be made and conclusions to be reached at an aggregate and individual employee level. Based upon the foregoing data analysis, the Company's HCP appeared to be effective at preventing early NIHL, particularly among employees with moderate and high measured external noise exposures. More definitive conclusions about audiometric trends and HCP effectiveness, and more specific identification and handling of outliers to verify early NIHL cases, would benefit from a larger number of longitudinal audiograms (longer duration) and more consistent collection of periodic (annual) tests at recurring frequency intervals. With such consistently collected longitudinal data and the right platform to schedule, collect, analyze, and report them, these metrics and statistical methods can be readily incorporated into an Audiometric Best Practices to effectively manage noise-related risk.
In conjunction with existing on-site audiometric testing and EHS systems, these Audiometric Best Practices methods and technology can be implemented at all operations across the entire Company as a standard operating procedure. This approach would substantially improve corporate-wide program performance and outcomes related to measuring and controlling risk for occupational noise exposure, and would directly benefit and protect the many workers who wear hearing protection to prevent NIHL.
Various modifications of the invention, in addition to those described herein, will be apparent to those skilled in the art from the foregoing description. Such modifications are also intended to fall within the scope of the appended claims.
This claims the benefit of the filing date of U.S. Provisional Application No. 63/122,083, filed December 7, 2020, the entire content of which is incorporated by reference herein.
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/US2021/062073 | 12/6/2021 | WO |
| Number | Date | Country | |
|---|---|---|---|
| 63122083 | Dec 2020 | US |
