On the Difficulty Predicting Word Recognition Performance After Cochlear Implantation.

HYPOTHESIS: Preimplantation word scores cannot reliably predict postimplantation outcomes. BACKGROUND: To date, there is no model based on preoperative data that can reliably predict the postoperative outcomes of cochlear implantation in the postlingually deafened adult patient. METHODS: In a group of 228 patients who received a cochlear implant between 2002 and 2021, we tested the predictive power of nine variables (age, etiology, sex, laterality of implantation, preimplantation thresholds and word scores, as well as the design, insertion approach, and angular insertion depth of the electrode array) on postimplantation outcomes. Results of multivariable linear regression analyses were then interpreted in light of data obtained from histopathological analyses of human temporal bones. RESULTS: Age and etiology were the only significant predictors of postimplantation outcomes. In agreement with many investigations, preimplantation word scores failed to significantly predict postimplantation outcomes. Analysis of temporal bone histopathology suggests that neuronal survival must fall below 40% before word scores in quiet begin to drop. Scores fall steeply with further neurodegeneration, such that only 20% survival can support acoustically driven word scores of 50%. Because almost all cochlear implant implantees have at least 20% of their spiral ganglion neurons (SGNs) surviving, it is expected that most cochlear implant users on average should improve to at least 50% word recognition score, as we observed, even if their preimplantation score was near zero as a result of widespread hair cell damage and the fact that ~50% of their SGNs have likely lost their peripheral axons. These "disconnected" SGNs would not contribute to acoustic hearing but likely remain electrically excitable. CONCLUSION: The relationship between preimplantation word scores and data describing the survival of SGNs in humans can explain why preimplantation word scores obtained in unaided conditions fail to predict postimplantation outcomes.

Mindfulness-Based Stress Reduction for Symptom Management in Older Individuals with HIV-Associated Neurocognitive Disorder.

The growing number of people aging with HIV represents a group vulnerable to the symptom burdens of HIV-associated neurocognitive disorder (HAND). Among younger groups, Mindfulness-Based Stress Reduction (MBSR) has been shown to help people living with HIV manage HIV-related and other life stress, and although there is some theoretical and empirical evidence that it may be effective among those with cognitive deficits, the approach has not been studied in older populations with HAND. Participants (n = 180) 55 years or older with HIV and cognitive impairment were randomly assigned to either an 8-week MBSR arm or a waitlist control. We assessed the impact of MBSR compared to a waitlist control on psychological outcomes [stress, anxiety, depression, and quality of life (QOL)] and cognitive metrics (e.g., speed of information processing, working memory, attention, impulsivity) measured at baseline, immediately post intervention (8 weeks) and one month later (16 weeks). Intent to treat analyses showed significant improvement in the MBSR group compared to control on symptoms of depression from baseline to 8 weeks, however, the difference was not sustained at 16 weeks. The MBSR group also showed improvement in perceived QOL from baseline to 16 weeks compared to the waitlist control group. Cognitive performance did not differ between the two treatment arms. MBSR shows promise as a tool to help alleviate the symptom burden of depression and low QOL in older individuals living with HAND and future work should address methods to better sustain the beneficial impact on depression and QOL.

Evidence of cochlear neural degeneration in normal-hearing subjects with tinnitus.

Tinnitus, reduced sound-level tolerance, and difficulties hearing in noisy environments are the most common complaints associated with sensorineural hearing loss in adult populations. This study aims to clarify if cochlear neural degeneration estimated in a large pool of participants with normal audiograms is associated with self-report of tinnitus using a test battery probing the different stages of the auditory processing from hair cell responses to the auditory reflexes of the brainstem. Self-report of chronic tinnitus was significantly associated with (1) reduced cochlear nerve responses, (2) weaker middle-ear muscle reflexes, (3) stronger medial olivocochlear efferent reflexes and (4) hyperactivity in the central auditory pathways. These results support the model of tinnitus generation whereby decreased neural activity from a damaged cochlea can elicit hyperactivity from decreased inhibition in the central nervous system.

Optimizing campus-wide COVID-19 test notifications with interpretable wastewater time-series features using machine learning models.

During the COVID-19 pandemic, wastewater surveillance of the SARS CoV-2 virus has been demonstrated to be effective for population surveillance at the county level down to the building level. At the University of California, San Diego, daily high-resolution wastewater surveillance conducted at the building level is being used to identify potential undiagnosed infections and trigger notification of residents and responsive testing, but the optimal determinants for notifications are unknown. To fill this gap, we propose a pipeline for data processing and identifying features of a series of wastewater test results that can predict the presence of COVID-19 in residences associated with the test sites. Using time series of wastewater results and individual testing results during periods of routine asymptomatic testing among UCSD students from 11/2020 to 11/2021, we develop hierarchical classification/decision tree models to select the most informative wastewater features (patterns of results) which predict individual infections. We find that the best predictor of positive individual level tests in residence buildings is whether or not the wastewater samples were positive in at least 3 of the past 7 days. We also demonstrate that the tree models outperform a wide range of other statistical and machine models in predicting the individual COVID-19 infections while preserving interpretability. Results of this study have been used to refine campus-wide guidelines and email notification systems to alert residents of potential infections.

Framework for converting mechanistic network models to probabilistic models.

There are two prominent paradigms for the modelling of networks: in the first, referred to as the mechanistic approach, one specifies a set of domain-specific mechanistic rules that are used to grow or evolve the network over time; in the second, referred to as the probabilistic approach, one describes a model that specifies the likelihood of observing a given network. Mechanistic models (models developed based on the mechanistic approach) are appealing because they capture scientific processes that are believed to be responsible for network generation; however, they do not easily lend themselves to the use of inferential techniques when compared with probabilistic models. We introduce a general framework for converting a mechanistic network model (MNM) to a probabilistic network model (PNM). The proposed framework makes it possible to identify the essential network properties and their joint probability distribution for some MNMs; doing so makes it possible to address questions such as whether two different mechanistic models generate networks with identical distributions of properties, or whether a network property, such as clustering, is over- or under-represented in the networks generated by the model of interest compared with a reference model. The proposed framework is intended to bridge some of the gap that currently exists between the formulation and representation of mechanistic and PNMs. We also highlight limitations of PNMs that need to be addressed in order to close this gap.

Identification of system-level features in HIV migration within a host.

OBJECTIVE: Identify system-level features in HIV migration within a host across body tissues. Evaluate heterogeneity in the presence and magnitude of these features across hosts. METHOD: Using HIV DNA deep sequencing data generated across multiple tissues from 8 people with HIV, we represent the complex dependencies of HIV migration among tissues as a network and model these networks using the family of exponential random graph models (ERGMs). ERGMs allow for the statistical assessment of whether network features occur more (or less) frequently in viral migration than might be expected by chance. The analysis investigates five potential features of the viral migration network: (1) bi-directional flow between tissues; (2) preferential migration among tissues in the same biological system; (3) heterogeneity in the level of viral migration related to HIV reservoir size; (4) hierarchical structure of migration; and (5) cyclical migration among several tissues. We calculate the Cohran's Q statistic to assess heterogeneity in the magnitude of the presence of these features across hosts. The analysis adjusts for missing data on body tissues. RESULTS: We observe strong evidence for bi-directional flow between tissues; migration among tissues in the same biological system; and hierarchical structure of the viral migration network. This analysis shows no evidence for differential level of viral migration with respect to the HIV reservoir size of a tissue. There is evidence that cyclical migration among three tissues occurs less frequent than expected given the amount of viral migration. The analysis also provides evidence for heterogeneity in the magnitude that these features are present across hosts. Adjusting for missing tissue data identifies system-level features within a host as well as heterogeneity in the presence of these features across hosts that are not detected when the analysis only considers the observed data. DISCUSSION: Identification of common features in viral migration may increase the efficiency of HIV cure efforts as it enables targeting specific processes.

Estimating contact network properties by integrating multiple data sources associated with infectious diseases.

To effectively mitigate the spread of communicable diseases, it is necessary to understand the interactions that enable disease transmission among individuals in a population; we refer to the set of these interactions as a contact network. The structure of the contact network can have profound effects on both the spread of infectious diseases and the effectiveness of control programs. Therefore, understanding the contact network permits more efficient use of resources. Measuring the structure of the network, however, is a challenging problem. We present a Bayesian approach to integrate multiple data sources associated with the transmission of infectious diseases to more precisely and accurately estimate important properties of the contact network. An important aspect of the approach is the use of the congruence class models for networks. We conduct simulation studies modeling pathogens resembling SARS-CoV-2 and HIV to assess the method; subsequently, we apply our approach to HIV data from the University of California San Diego Primary Infection Resource Consortium. Based on simulation studies, we demonstrate that the integration of epidemiological and viral genetic data with risk behavior survey data can lead to large decreases in mean squared error (MSE) in contact network estimates compared to estimates based strictly on risk behavior information. This decrease in MSE is present even in settings where the risk behavior surveys contain measurement error. Through these simulations, we also highlight certain settings where the approach does not improve MSE.

Impact of antiretroviral therapy intensification with C-C motif chemokine receptor 5 antagonist maraviroc on HIV-associated neurocognitive impairment.

OBJECTIVES: Chemokine receptor CCR5 is the principal co-receptor for entry of M-tropic HIV virus into immune cells. It is expressed in the central nervous system and may contribute to neuro-inflammation. The CCR5 antagonist maraviroc (MVC) has been suggested to improve HIV-associated neurocognitive impairment (NCI). DESIGN: A double-blind, placebo-controlled, 48-week, randomized study of MVC vs. placebo in people with HIV (PWH) on stable antiretroviral therapy (ART) for more than one year in Hawaii and Puerto Rico with plasma HIV RNA less than 50 copies/ml and at least mild NCI defined as an overall or domain-specific neuropsychological z (NPZ) score less than -0.5. METHODS: Study participants were randomized 2 : 1 to intensification of ART with MVC vs. placebo. The primary endpoint was change in global and domain-specific NPZ modeled from study entry to week 48. Covariate adjusted treatment comparisons of average changes in cognitive outcome were performed using winsorized NPZ data. Monocyte subset frequencies and chemokine expression as well as plasma biomarker levels were assessed. RESULTS: Forty-nine participants were enrolled with 32 individuals randomized to MVC intensification and 17 to placebo. At baseline, worse NPZ scores were seen in the MVC arm. Comparison of 48-week NPZ change by arm revealed no differences except for a modest improvement in the Learning and Memory domain in the MVC arm, which did not survive multiplicity correction. No significant changes between arms were seen in immunologic parameters. CONCLUSION: This randomized controlled study found no definitive evidence in favor of MVC intensification among PWH with mild cognitive difficulties.

Predicting neural deficits in sensorineural hearing loss from word recognition scores.

The current gold standard of clinical hearing assessment includes a pure-tone audiogram combined with a word recognition task. This retrospective study tests the hypothesis that deficits in word recognition that cannot be explained by loss in audibility or cognition may reflect underlying cochlear nerve degeneration (CND). We collected the audiological data of nearly 96,000 ears from patients with normal hearing, conductive hearing loss (CHL) and a variety of sensorineural etiologies including (1) age-related hearing loss (ARHL); (2) neuropathy related to vestibular schwannoma or neurofibromatosis of type 2; (3) Ménière's disease; (4) sudden sensorineural hearing loss (SSNHL), (5) exposure to ototoxic drugs (carboplatin and/or cisplatin, vancomycin or gentamicin) or (6) noise damage including those with a 4-kHz "noise notch" or reporting occupational or recreational noise exposure. Word recognition was scored using CID W-22 monosyllabic word lists. The Articulation Index was used to predict the speech intelligibility curve using a transfer function for CID W-22. The level at which maximal intelligibility was predicted was used as presentation level (70 dB HL minimum). Word scores decreased dramatically with age and thresholds in all groups with SNHL etiologies, but relatively little in the conductive hearing loss group. Discrepancies between measured and predicted word scores were largest in patients with neuropathy, Ménière's disease and SSNHL, intermediate in the noise-damage and ototoxic drug groups, and smallest in the ARHL group. In the CHL group, the measured and predicted word scores were very similar. Since word-score predictions assume that audiometric losses can be compensated by increasing stimulus level, their accuracy in predicting word score for CHL patients is unsurprising. The lack of a strong age effect on word scores in CHL shows that cognitive decline is not a major factor in this test. Amongst the possible contributions to word score discrepancies, CND is a prime candidate: it should worsen intelligibility without affecting thresholds and has been documented in human temporal bones with SNHL. Comparing the audiological trends observed here with the existing histopathological literature supports the notion that word score discrepancies may be a useful CND metric.

Coronavirus disease 2019 vaccination is protective of clinical disease in solid organ transplant recipients.

BACKGROUND: Clinical effectiveness of coronavirus disease 2019 (COVID-19) vaccination in solid organ transplant recipients (SOTRs) is not well documented despite multiple studies demonstrating sub-optimal immunogenicity. METHODS: We reviewed medical records of eligible SOTRs at a single center to assess vaccination status and identify cases of symptomatic COVID-19 from January 1 to August 12, 2021. We developed a Cox proportional hazards model using the date of vaccination and time since transplantation as a time-varying covariate with age and gender as potential time-invariant confounders. Survival curves were created using the parameters estimated from the Cox model. RESULTS: Among 1904 SOTRs, 1362 were fully vaccinated (96% received mRNA vaccines) and 542 were either unvaccinated (n = 470) or partially vaccinated (n = 72). There were 115 cases of COVID-19, of which 12 occurred in fully vaccinated individuals. Cox regression with the date of vaccination and time since transplantation as the time-varying co-variates showed that after baseline adjustment for age and sex, being fully vaccinated had a significantly lower hazard for COVID-19, hazard ratio (HR) = 0.29 and 95% confidence interval ([CI] 0.09, 0.91). CONCLUSION: We found that 2-dose mRNA COVID-19 vaccination was protective of symptomatic COVID-19 in vaccinated versus unvaccinated SOTRs. TWEET: COVID-19 vaccination was associated with a significantly lower hazard for symptomatic COVID-19 (HR 0.29; 95% CI 0.09, 0.91) among 1904 SOT recipients at a single center from January 1 to August 12, 2021.

Primary Neural Degeneration in Noise-Exposed Human Cochleas: Correlations with Outer Hair Cell Loss and Word-Discrimination Scores.

Animal studies suggest that cochlear nerve degeneration precedes sensory cell degeneration in both noise-induced hearing loss (NIHL) and age-related hearing loss (ARHL), producing a hearing impairment that is not reflected in audiometric thresholds. Here, we investigated the histopathology of human ARHL and NIHL by comparing loss of auditory nerve fibers (ANFs), cochlear hair cells and the stria vascularis in a group of 52 cases with noise-exposure history against an age-matched control group. Although strial atrophy increased with age, there was no effect of noise history. Outer hair cell (OHC) loss also increased with age throughout the cochlea but was unaffected by noise history in the low-frequency region (<2 kHz), while greatly exacerbated at high frequencies (≥2 kHz). Inner hair cell (IHC) loss was primarily seen at high frequencies but was unaffected by noise at either low or high frequencies. ANF loss was substantial at all cochlear frequencies and was exacerbated by noise throughout. According to a multivariable regression model, this loss of neural channels contributes to poor word discrimination among those with similar audiometric threshold losses. The histopathological patterns observed also suggest that, whereas the low-frequency OHC loss may be an unavoidable consequence of aging, the high-frequency loss, which produces the classic down-sloping audiogram of ARHL, may be partially because of avoidable ear abuse, even among those without a documented history of acoustic overexposure.SIGNIFICANCE STATEMENT As regenerative therapeutics in sensorineural hearing loss enter clinical trials, it becomes critical to infer which cochlear pathologies are present in addition to hair cell loss. Here, by analyzing human autopsy material, we show that acoustic injury accelerates age-related primary neural degeneration, but not strial degeneration, neither of which can be inferred from audiometric thresholds. It exacerbates outer hair cell (OHC) loss only in the high-frequency half of the cochlea, suggesting that the apical loss is age-related, whereas the basal loss is partially noise induced, and therefore avoidable. Statistical analysis suggests that neural loss helps explain differences in word-recognition ability among individuals with similar audiometric thresholds. The surprising correlation between neural loss and OHC loss in the cochlea's speech region also implicates neural loss in the well-known decline in word scores as thresholds deteriorate with age.

Investigation of patient-sharing networks using a Bayesian network model selection approach for congruence class models.

A Bayesian approach to conduct network model selection is presented for a general class of network models referred to as the congruence class models (CCMs). CCMs form a broad class that includes as special cases several common network models, such as the Erdos-Rényi-Gilbert model, stochastic block model, and many exponential random graph models. Due to the range of models that can be specified as CCMs, our proposed method is better able to select models consistent with generative mechanisms associated with observed networks than are current approaches. In addition, our approach allows for incorporation of prior information. We illustrate the use of this approach to select among several different proposed mechanisms for the structure of patient-sharing networks; such networks have been found to be associated with the cost and quality of medical care. We found evidence in support of heterogeneity in sociality but not selective mixing by provider type or degree.

Envelope following responses predict speech-in-noise performance in normal-hearing listeners.

Permanent threshold elevation after noise exposure or aging is caused by loss of sensory cells; however, animal studies show that hair cell loss is often preceded by degeneration of the synapses between sensory cells and auditory nerve fibers. Silencing these neurons is likely to degrade auditory processing and may contribute to difficulties understanding speech in noisy backgrounds. Reduction of suprathreshold ABR amplitudes can be used to quantify synaptopathy in inbred mice. However, ABR amplitudes are highly variable in humans, and thus more challenging to use. Since noise-induced neuropathy preferentially targets fibers with high thresholds and low spontaneous rate and because phase locking to temporal envelopes is particularly strong in these fibers, measuring envelope following responses (EFRs) might be a more robust measure of cochlear synaptopathy. A recent auditory model further suggests that modulation of carrier tones with rectangular envelopes should be less sensitive to cochlear amplifier dysfunction and, therefore, a better metric of cochlear neural damage than sinusoidal amplitude modulation. In this study, we measure performance scores on a variety of difficult word-recognition tasks among listeners with normal audiograms and assess correlations with EFR magnitudes to rectangular versus sinusoidal modulation. Higher harmonics of EFR magnitudes evoked by a rectangular-envelope stimulus were significantly correlated with word scores, whereas those evoked by sinusoidally modulated tones did not. These results support previous reports that individual differences in synaptopathy may be a source of speech recognition variability despite the presence of normal thresholds at standard audiometric frequencies.NEW & NOTEWORTHY Recent studies suggest that millions of people may be at risk of permanent impairment from cochlear synaptopathy, the age-related and noise-induced degeneration of neural connections in the inner ear. This study examines electrophysiological responses to stimuli designed to improve detection of neural damage in subjects with normal hearing sensitivity. The resultant correlations with word recognition performance are consistent with a contribution of cochlear neural damage to deficits in hearing in noise abilities.

Evaluation of Severe Acute Respiratory Syndrome Coronavirus 2 Transmission Mitigation Strategies on a University Campus Using an Agent-Based Network Model.

Universities are faced with decisions on how to resume campus activities while mitigating severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) risk. To provide guidance for these decisions, we developed an agent-based network model of SARS-CoV-2 transmission to assess the potential impact of strategies to reduce outbreaks. The model incorporates important features related to risk at the University of California San Diego. We found that structural interventions for housing (singles only) and instructional changes (from in-person to hybrid with class size caps) can substantially reduce the basic reproduction number, but masking and social distancing are required to reduce this to at or below 1. Within a risk mitigation scenario, increased frequency of asymptomatic testing from monthly to twice weekly has minimal impact on average outbreak size (1.1-1.9), but substantially reduces the maximum outbreak size and cumulative number of cases. We conclude that an interdependent approach incorporating risk mitigation, viral detection, and public health intervention is required to mitigate risk.

Evaluating the power of the causal impact method in observational studies of HCV treatment as prevention.

Objectives: The causal impact method (CIM) was recently introduced for evaluation of binary interventions using observational time-series data. The CIM is appealing for practical use as it can adjust for temporal trends and account for the potential of unobserved confounding. However, the method was initially developed for applications involving large datasets and hence its potential in small epidemiological studies is still unclear. Further, the effects that measurement error can have on the performance of the CIM have not been studied yet. The objective of this work is to investigate both of these open problems. Methods: Motivated by an existing dataset of HCV surveillance in the UK, we perform simulation experiments to investigate the effect of several characteristics of the data on the performance of the CIM. Further, we quantify the effects of measurement error on the performance of the CIM and extend the method to deal with this problem. Results: We identify multiple characteristics of the data that affect the ability of the CIM to detect an intervention effect including the length of time-series, the variability of the outcome and the degree of correlation between the outcome of the treated unit and the outcomes of controls. We show that measurement error can introduce biases in the estimated intervention effects and heavily reduce the power of the CIM. Using an extended CIM, some of these adverse effects can be mitigated. Conclusions: The CIM can provide satisfactory power in public health interventions. The method may provide misleading results in the presence of measurement error.

Accounting for Confounding by Time, Early Intervention Adoption, and Time-Varying Effect Modification in the Design and Analysis of Stepped-Wedge Designs: Application to a Proposed Study Design to Reduce Opioid-Related Mortality.

Background: Stepped-wedge designs (SWDs) are currently being used in the investigation of interventions to reduce opioid-related deaths in communities located in several states. However, these interventions are competing with external factors such as newly initiated public policies limiting opioid prescriptions, media awareness campaigns, and COVID-19 social distancing mandates. Furthermore, control communities may prematurely adopt components of the intervention as they become available. The presence of time-varying external factors that impact study outcomes is a well-known limitation of SWDs; common approaches to adjusting for them make use of a mixed effects modeling framework. However, these models have several shortcomings when external factors differentially impact intervention and control clusters. Methods: We discuss limitations of commonly used mixed effects models in the context of proposed SWDs to investigate interventions intended to reduce opioid-related mortality, and propose extensions of these models to address these limitations. We conduct an extensive simulation study of anticipated data from SWD trials targeting the current opioid epidemic in order to examine the performance of these models in the presence of external factors. We consider confounding by time, premature adoption of components of the intervention, and time-varying effect modificationâ€" in which external factors differentially impact intervention and control clusters. Results: In the presence of confounding by time, commonly used mixed effects models yield unbiased intervention effect estimates, but can have inflated Type 1 error and result in under coverage of confidence intervals. These models yield biased intervention effect estimates when premature intervention adoption or effect modification are present. In such scenarios, models incorporating fixed intervention-by-time interactions with an unstructured covariance for intervention-by-cluster-by-time random effects result in unbiased intervention effect estimates, reach nominal confidence interval coverage, and preserve Type 1 error. Conclusions: Mixed effects models can adjust for different combinations of external factors through correct specification of fixed and random time effects; misspecification can result in bias of the intervention effect estimate, under coverage of confidence intervals, and Type 1 error inflation. Since model choice has considerable impact on validity of results and study power, careful consideration must be given to choosing appropriate models that account for potential external factors.

Mindfulness-based stress reduction for HIV-associated neurocognitive disorder: Rationale and protocol for a randomized controlled trial in older adults.

The symptom burden of HIV-associated neurocognitive disorder (HAND) is high among older individuals, and treatment options are limited. Mindfulness-based stress reduction (MBSR) has potential to improve neurocognitive performance, psychosocial wellbeing, and quality of life, but empirical studies in this growing vulnerable population are lacking. In this trial, participants (N = 180) age 55 and older who are living with HIV infection, are on combination antiretroviral therapy with suppressed viral loads, and yet continue to experience behavioral and cognitive symptoms of HAND, are randomized to MBSR or to a waitlist control arm that receives MBSR following a 16-week period of standard care. Primary outcomes (attention, executive function, stress, anxiety, depression, everyday functioning, quality of life) and potential mediators (affect, mindfulness) and moderators (social support, loneliness) are assessed at baseline and weeks 8, 16, and 48 in both groups, with an additional assessment at week 24 (post-MBSR) in the crossover control group. Assessments include self-report and objective measures (e.g., neuropsychological assessment, neurological exam, clinical labs). In addition, a subset of participants (n = 30 per group) are randomly selected to undergo fMRI to evaluate changes in functional connectivity networks and their relationship to changes in neuropsychological outcomes. Forthcoming findings from this randomized controlled trial have the potential to contribute to a growing public health need as the number of older adults with HAND is expected to rise.

Accounting for external factors and early intervention adoption in the design and analysis of stepped-wedge designs: Application to a proposed study design to reduce opioid-related mortality.

BACKGROUND: Stepped-wedge designs (SWDs) are currently being used to investigate interventions to reduce opioid overdose deaths in communities located in several states. However, these interventions are competing with external factors such as newly initiated public policies limiting opioid prescriptions, media awareness campaigns, and social distancing orders due to the COVID-19 pandemic. Furthermore, control communities may prematurely adopt components of the proposed intervention as they become widely available. These types of events induce confounding of the intervention effect by time. Such confounding is a well-known limitation of SWDs; a common approach to adjusting for it makes use of a mixed effects modeling framework that includes both fixed and random effects for time. However, these models have several shortcomings when multiple confounding factors are present. METHODS: We discuss the limitations of existing methods based on mixed effects models in the context of proposed SWDs to investigate interventions intended to reduce mortality associated with the opioid epidemic, and propose solutions to accommodate deviations from assumptions that underlie these models. We conduct an extensive simulation study of anticipated data from SWD trials targeting the current opioid epidemic in order to examine the performance of these models under different sources of confounding. We specifically examine the impact of factors external to the study and premature adoption of intervention components. RESULTS: When only external factors are present, our simulation studies show that commonly used mixed effects models can result in unbiased estimates of the intervention effect, but have inflated Type 1 error and result in under coverage of confidence intervals. These models are severely biased when confounding factors differentially impact intervention and control clusters; premature adoption of intervention components is an example of this scenario. In these scenarios, models that incorporate fixed intervention-by-time interaction terms and an unstructured covariance for the intervention-by-cluster-by-time random effects result in unbiased estimates of the intervention effect, reach nominal confidence interval coverage, and preserve Type 1 error, but may reduce power. CONCLUSIONS: The incorporation of fixed and random time effects in mixed effects models require certain assumptions about the impact of confounding by time in SWD. Violations of these assumptions can result in severe bias of the intervention effect estimate, under coverage of confidence intervals, and inflated Type 1 error. Since model choice has considerable impact on study power as well as validity of results, careful consideration needs to be given to choosing an appropriate model that takes into account potential confounding factors.

Age-Related Hearing Loss Is Dominated by Damage to Inner Ear Sensory Cells, Not the Cellular Battery That Powers Them.

Age-related hearing loss arises from irreversible damage in the inner ear, where sound is transduced into electrical signals. Prior human studies suggested that sensory-cell loss is rarely the cause; correspondingly, animal work has implicated the stria vascularis, the cellular "battery" driving the amplification of sound by hair cell "motors." Here, quantitative microscopic analysis of hair cells, auditory nerve fibers, and strial tissues in 120 human inner ears obtained at autopsy, most of whom had recent audiograms in their medical records, shows that the degree of hearing loss is well predicted from the amount of hair cell loss and that inclusion of strial damage does not improve the prediction. Although many aging ears showed significant strial degeneration throughout the cochlea, our statistical models suggest that, by the time strial tissues are lost, hair cell death is so extensive that the loss of battery is no longer important to pure-tone thresholds and that audiogram slope is not diagnostic for strial degeneration. These data comprise the first quantitative survey of hair cell death in normal-aging human cochleas, and reveal unexpectedly severe hair cell loss in low-frequency cochlear regions, and dramatically greater loss in high-frequency regions than seen in any aging animal model. Comparison of normal-aging ears to an age-matched group with acoustic-overexposure history suggests that a lifetime of acoustic overexposure is to blame.SIGNIFICANCE STATEMENT This report upends dogma about the causes of age-related hearing loss. Our analysis of over 120 autopsy specimens shows that inner-ear sensory cell loss can largely explain the audiometric patterns in aging, with minimal contribution from the stria vascularis, the "battery" that powers the inner ear, previously viewed as the major locus of age-related hearing dysfunction. Predicting inner ear damage from the audiogram is critical, now that clinical trials of therapeutics designed to regrow hair cells are underway. Our data also show that hair cell degeneration in aging humans is dramatically worse than that in aging animals, suggesting that the high-frequency hearing losses that define human presbycusis reflect avoidable contributions of chronic ear abuse to which aging animals are not exposed.