Electronic Health Records As a Platform for Audiological Research: Data Validity, Patient Characteristics, and Hearing-Aid Use Persistence Among 731,213 U.S. Veterans.

OBJECTIVES: This article presents a summary of audiological, general health, and hearing aid (HA) outcome data in a large sample of U.S. Veterans receiving HAs. The current article also provides the foundation for a series of papers that will explore relationships between a wide range of factors and HA outcomes. DESIGN: The patient sample is all (n = 731,213) patients for whom HAs were ordered between April 2012 and October 2014 through the U.S. Veterans Health Administration Remote Order Entry System. For these patients, Veterans Affairs electronic health records (EHRs) stored in various databases provided data on demographics, received diagnostic and procedure codes (2007 to 2017), audiometry, self-reported outcomes up to 6 months postfitting, and HA battery orders (to 2017). Data cleaning and preparation was carried out and is discussed with reference to insights that provide potential value to other researchers pursuing similar studies. HA battery order data over time was used to derive a measure of long-term HA use persistence. Descriptive statistics were used to characterize the sample, comparative analyses against other data supported basic validity assessment, and bivariate analyses probed novel associations between patient characteristics and HA use persistence at 2 years postfitting. RESULTS: Following extensive cleaning and data preparation, the data show plausible characteristics on diverse metrics and exhibit adequate validity based on comparisons with other published data. Further, rates of HA use persistence are favorable when compared against therapy persistence data for other major chronic conditions. The data also show that the presence of certain comorbid conditions (Parkinson's disease, diabetes, arthritis, and visual impairment) are associated with significantly lower HA use persistence, as are prior inpatient admissions (especially among new HA recipients), and that increasing levels of multimorbidity, in general, are associated with decreasing HA use persistence. This is all despite the fact that deriving relevant audiological care-process variables from the available records was not straightforward, especially concerning the definition of the date of HA fitting, and the use of battery ordering data to determine long-term HA use persistence. CONCLUSIONS: We have shown that utilizing EHRs in audiology has the potential to provide novel insights into clinical practice patterns, audiologic outcomes, and relations between factors pertaining to hearing and to other health conditions in clinical populations, despite the potential pitfalls regarding the lack of control over the variables available and limitations on how the data are entered. We thus conclude that research using EHRs has the potential to be an integral supplement to population-based and epidemiologic research in the field of audiology.

Bayesian Applications in Auditory Research.

Purpose This article presents a basic exploration of Bayesian inference to inform researchers unfamiliar to this type of analysis of the many advantages this readily available approach provides. Method First, we demonstrate the development of Bayes' theorem, the cornerstone of Bayesian statistics, into an iterative process of updating priors. Working with a few assumptions, including normalcy and conjugacy of prior distribution, we express how one would calculate the posterior distribution using the prior distribution and the likelihood of the parameter. Next, we move to an example in auditory research by considering the effect of sound therapy for reducing the perceived loudness of tinnitus. In this case, as well as most real-world settings, we turn to Markov chain simulations because the assumptions allowing for easy calculations no longer hold. Using Markov chain Monte Carlo methods, we can illustrate several analysis solutions given by a straightforward Bayesian approach. Conclusion Bayesian methods are widely applicable and can help scientists overcome analysis problems, including how to include existing information, run interim analysis, achieve consensus through measurement, and, most importantly, interpret results correctly. Supplemental Material https://doi.org/10.23641/asha.7822592.

Global estimates of mortality associated with long-term exposure to outdoor fine particulate matter.

Exposure to ambient fine particulate matter (PM2.5) is a major global health concern. Quantitative estimates of attributable mortality are based on disease-specific hazard ratio models that incorporate risk information from multiple PM2.5 sources (outdoor and indoor air pollution from use of solid fuels and secondhand and active smoking), requiring assumptions about equivalent exposure and toxicity. We relax these contentious assumptions by constructing a PM2.5-mortality hazard ratio function based only on cohort studies of outdoor air pollution that covers the global exposure range. We modeled the shape of the association between PM2.5 and nonaccidental mortality using data from 41 cohorts from 16 countries-the Global Exposure Mortality Model (GEMM). We then constructed GEMMs for five specific causes of death examined by the global burden of disease (GBD). The GEMM predicts 8.9 million [95% confidence interval (CI): 7.5-10.3] deaths in 2015, a figure 30% larger than that predicted by the sum of deaths among the five specific causes (6.9; 95% CI: 4.9-8.5) and 120% larger than the risk function used in the GBD (4.0; 95% CI: 3.3-4.8). Differences between the GEMM and GBD risk functions are larger for a 20% reduction in concentrations, with the GEMM predicting 220% higher excess deaths. These results suggest that PM2.5 exposure may be related to additional causes of death than the five considered by the GBD and that incorporation of risk information from other, nonoutdoor, particle sources leads to underestimation of disease burden, especially at higher concentrations.

Short-Term Elevation of Fine Particulate Matter Air Pollution and Acute Lower Respiratory Infection.

RATIONALE: Nearly 60% of U.S. children live in counties with particulate matter less than or equal to 2.5 µm in aerodynamic diameter (PM2.5) concentrations above air quality standards. Understanding the relationship between ambient air pollution exposure and health outcomes informs actions to reduce exposure and disease risk. OBJECTIVES: To evaluate the association between ambient PM2.5 levels and healthcare encounters for acute lower respiratory infection (ALRI). METHODS: Using an observational case-crossover design, subjects (n = 146,397) were studied if they had an ALRI diagnosis and resided on Utah's Wasatch Front. PM2.5 air pollution concentrations were measured using community-based air quality monitors between 1999 and 2016. Odds ratios for ALRI healthcare encounters were calculated after stratification by ages 0-2, 3-17, and 18 or more years. MEASUREMENTS AND MAIN RESULTS: Approximately 77% (n = 112,467) of subjects were 0-2 years of age. The odds of ALRI encounter for these young children increased within 1 week of elevated PM2.5 and peaked after 3 weeks with a cumulative 28-day odds ratio of 1.15 per +10 µg/m3 (95% confidence interval, 1.12-1.19). ALRI encounters with diagnosed and laboratory-confirmed respiratory syncytial virus and influenza increased following elevated ambient PM2.5 levels. Similar elevated odds for ALRI were also observed for older children, although the number of events and precision of estimates were much lower. CONCLUSIONS: In this large sample of urban/suburban patients, short-term exposure to elevated PM2.5 air pollution was associated with greater healthcare use for ALRI in young children, older children, and adults. Further exploration is needed of causal interactions between PM2.5 and ALRI.

Short-Term Exposure to Fine Particulate Matter Air Pollution Is Preferentially Associated With the Risk of ST-Segment Elevation Acute Coronary Events.

BACKGROUND: Air pollution is associated with greater cardiovascular event risk, but the types of events and specific persons at risk remain unknown. This analysis evaluates effects of short-term exposure to fine particulate matter air pollution with risk of acute coronary syndrome events, including ST-segment elevation myocardial infarction, non-ST-segment elevation myocardial infarction, unstable angina, and non-ST-segment elevation acute coronary syndrome. METHODS AND RESULTS: Acute coronary syndrome events treated at Intermountain Healthcare hospitals in urban areas of Utah's Wasatch Front were collected between September 1993 and May 2014 (N=16 314). A time-stratified case-crossover design was performed matching fine particulate matter air pollution exposure at the time of each event with referent periods when the event did not occur. Patients served as their own controls, and odds ratios were estimated using nonthreshold and threshold conditional logistic regression models. In patients with angiographic coronary artery disease, odds ratios for a 10-µg/m(3) increase in concurrent-day fine particulate matter air pollution >25 µg/m³ were 1.06 (95% CI 1.02-1.11) for all acute coronary syndrome, 1.15 (95% CI 1.03-1.29) for ST-segment elevation myocardial infarction, 1.02 (95% CI 0.97-1.08) for non-ST-segment elevation myocardial infarction, 1.09 (95% CI 1.02-1.17) for unstable angina, and 1.05 (95% CI 1.00-1.10) for non-ST-segment elevation acute coronary syndrome events. Excess risk from fine particulate matter air pollution exposure was not observed in patients without angiographic coronary artery disease. CONCLUSIONS: Elevated fine particulate matter air pollution exposures contribute to triggering acute coronary events, especially ST-segment elevation myocardial infarction, in those with existing seriously diseased coronary arteries but not in those with nondiseased coronary arteries.

Microarrays and microneedle arrays for delivery of peptides, proteins, vaccines and other applications.

INTRODUCTION: Peptide and protein microarray and microneedle array technology provides direct information on protein function and potential drug targets in drug discovery and delivery. Because of this unique ability, these arrays are well suited for protein profiling, drug target identification/validation and studies of protein interaction, biochemical activity, immune responses, clinical prognosis and diagnosis and for gene, protein and drug delivery. AREAS COVERED: The aim of this review is to describe and summarize past and recent developments of microarrays in their construction, characterization and production and applications of microneedles in drug delivery. The scope and limitations of various technologies in this respect are discussed. EXPERT OPINION: This article offers a review of microarray/microneedle technologies and possible future directions in targeting and in the delivery of pharmacologically active compounds for unmet needs in biopharmaceutical research. A better understanding of the production and use of microarrays and microneedles for delivery of peptides, proteins and vaccines is needed.

Recent advances in prodrugs as drug delivery systems.

Prodrugs are a class of drug derivatives with little or no pharmacological activity that are converted in vivo to therapeutically active compounds. The primary utility of a prodrug approach is to improve pharmaceutical properties. Because it does not alter the primary structure of the parent drug, the synthesis of prodrugs is usually much less difficult than the synthesis of analogs. The derived physicochemical properties of the resulting derivatives can be carefully tailored by means of structural modification of the promoiety. However, sufficient levels of intrinsic activity of the parent drug need to be assured through in vivo cleavage of the prodrug. The prodrug approach has been successfully applied to a wide variety of drugs. This article briefly discusses advances in strategies for development of prodrugs and their mechanisms of drug release.