The relationship between obstructive sleep apnea (OSA) and chronic rhinosinusitis (CRS) still remains unclear. This retrospective cohort study aimed to investigate the relationship between OSA and subsequent CRS using a population-based dataset. The study used data from the Taiwan Longitudinal Health Insurance Database 2005. We selected 971 patients with OSA for the study cohort and 4855 patients without OSA for the comparison cohort. Each patient was tracked for 5 years to determine those who were subsequently diagnosed with CRS. Stratified Cox proportional hazard regression analyses were performed to examine the association of OSA with subsequent CRS. The results revealed that 161 (2.76%) of the total sampled patients were subsequently diagnosed with CRS. Subsequent incidences of CRS were found in 64 (6.59%) patients with OSA and 97 (2.00%) patients without OSA. The adjusted hazard ratio (HR) of subsequent CRS for patients with OSA was 3.18 (95% confidence interval: 2.27~4.45) compared to those without OSA. Furthermore, the HR for CRS was similar for subjects with OSA for both genders (with an adjusted HR of 3.44 for males and 2.63 for females). We concluded that patients with OSA had a higher risk of subsequent CRS compared to patients without OSA regardless of sex.

This study aimed to investigate the annual incidence of parapharyngeal and retropharyngeal abscess (PRPA) based on 10-year population-based data. Patients with PRPA were identified from the Taiwan Health Insurance Research Database, a database of all medical claims of a randomly selected, population-representative sample of over two million enrollees of the National Health Insurance system that covers over 99% of Taiwan’s citizens. During 2007–2016, 5779 patients received a diagnosis of PRPA. We calculated the population-wide incidence rates of PRPA by sex and age group (20–44, 45–64, and >64) as well as in-hospital mortality. The annual incidence rate of PRPA was 2.64 per 100,000 people. The gender-specific incidence rates per 100,000 people were 3.34 for males and 1.94 for females with a male:female gender ratio of 1.72. A slight increase in incidence rates among both genders over the study period was noted. Age-specific rates were lowest in the 20–44 age group with a mean annual incidence of 2.00 per 100,000 people, and the highest rates were noted in the age groups of 45–64 and >64 years with mean annual incidences of 3.21 and 3.20, respectively. We found that PRPA is common in Taiwan, males and older individuals are more susceptible to it, and incidence has increased in recent years.

Key Points: Question: Can the traditional Chinese version of the hearing handicap inventory for elderly screening (HHIE-S) checklist screen for age-related hearing loss (ARHL) in elderly individuals? Findings: In this cross-sectional study of 1696 Taiwanese patients who underwent annual government-funded geriatric health checkups, the Chinese version of the HHIE-S had a sensitivity of 76.9% and a specificity of 79.8% with a cutoff score greater than 6 for identifying patients with disabled hearing loss (defined as a PTA > 40 dB). Meaning: The traditional Chinese version of the HHIE-S is an effective test to detect ARHL and can improve the feasibility of large-scale hearing screening among elderly individuals. Purpose: The traditional Chinese version of the hearing handicap inventory for elderly screening (TC-HHIE-S) was translated from English and is intended for use with people whose native language is traditional Chinese, but its effectiveness and diagnostic performance are still unclear. The purpose of this study was to evaluate the validity and reliability of the traditional Chinese version of the HHIE-S for screening for age-related hearing loss (ARHL). Methods: A total of 1696 elderly people underwent the government’s annual geriatric medical examination at community hospitals. In this cross-sectional study, we recorded average conducted pure-tone averages (PTA) (0.5 kHz, 1 kHz, 2 kHz, 4 kHz), age, sex, and HHIE-S data. Receiver operating characteristic (ROC) curve analysis was used to identify the best critical point for detecting hearing impairment, and the validity of the structure was verified by the agreement between the TC-HHIE-S and PTA results. Results: The HHIE-S scores were correlated with the better-ear pure-tone threshold averages (PTAs) at 0.5–4 kHz (correlation coefficient r = 0.45). The internal consistency of the total HHIE-S score was excellent (Cronbach’s alpha = 0.901), and the test-retest reliability was also excellent (Spearman’s correlation coefficient = 0.60, intraclass correlation coefficient = 0.75). In detecting disabled hearing loss (i.e., PTA at 0.5–4 kHz > 40 dB), the HHIE-S cutoff score of > 6 had a sensitivity of 76.9% and a specificity of 79.8%. Conclusions: The traditional Chinese version of the HHIE-S is a valid, reliable, and efficient tool for large-scale screening for ARHL.

Background: Tinnitus due to hyperactivity across neuronal ensembles along the auditory pathway is reported. We hypothesized that trigeminal neuralgia patients may subsequently suffer from tinnitus. Using nationwide, population-based data and a retrospective cohort study design, we investigated the risk of tinnitus within 1 year following trigeminal neuralgia. Methods: We used the Taiwan National Health Insurance Research Dataset, a claims database, to identify all patients diagnosed with trigeminal neuralgia from January 2001 to December 2014, 12,587 patients. From the remaining patients, we identified 12,587 comparison patients without trigeminal neuralgia by propensity score matching, using sex, age, monthly income, geographic region, residential urbanization level, and tinnitus-relevant comorbidities (hyperlipidemia, diabetes, coronary heart disease, hypertension, cervical spondylosis, temporomandibular joint disorders and injury to head and neck and index year). All study patients (n = 25,174) were tracked for a one-year period to identify those with a subsequent diagnosis of tinnitus over 1-year follow-up. Results: Among total 25,174 sample patients, the incidence of tinnitus was 18.21 per 100 person-years (95% CI = 17.66 ~ 18.77), the rate being 23.57 (95% CI = 22.68 ~ 24.49) among patients with trigeminal neuralgia and 13.17 (95% CI = 12.53 ~ 13.84) among comparison patients. Furthermore, the adjusted Cox proportional hazard ratio for tinnitus in the trigeminal neuralgia group was 1.68 (95% CI = 1.58 ~ 1.80) relative to the comparison cohort. Conclusions: We found a significantly increased risk of tinnitus within 1 year of trigeminal neuralgia diagnosis compared to those without the diagnosis. Further studies in other countries and ethnicities are needed to explore the relationship between trigeminal neuralgia and subsequent tinnitus.

Objectives: The elderly are disproportionately affected by age-related hearing loss (ARHL). Despite being a well-known tool for ARHL evaluation, the Hearing Handicap Inventory for the Elderly Screening version (HHIE-S) has only traditionally been used for direct screening using self-reported outcomes. This work uses a novel integration of machine learning approaches to improve the predicted accuracy of the HHIE-S tool for ARHL in older adults. Methods: We employed a dataset that was gathered between 2016 and 2018 and included 1,526 senior citizens from several Taipei City Hospital branches. 80% of the data were used for training (n = 1220) and 20% were used for testing (n = 356). XGBoost, Gradient Boosting, and LightGBM were among the machine learning models that were only used and assessed on the training set. In order to prevent data leakage and overfitting, the Light Gradient Boosting Machine (LGBM) model—which had the greatest AUC of 0.83 (95% CI 0.81–0.85)—was then only used on the holdout testing data. Results: On the testing set, the LGBM model showed a strong AUC of 0.82 (95% CI 0.79–0.86), far outperforming conventional techniques. Notably, several HHIE-S items and age were found to be significant characteristics. In contrast to traditional HHIE research, which concentrates on the psychological effects of hearing loss, this study combines cutting-edge machine learning techniques—specifically, the LGBM classifier—with the HHIE-S tool. The incorporation of SHAP values enhances the interpretability of the model's predictions and provides a more comprehensive comprehension of the significance of various aspects. Conclusions: Our methodology highlights the great potential that arises from combining machine learning with validated hearing evaluation instruments such as the HHIE-S. Healthcare practitioners can anticipate ARHL more accurately thanks to this integration, which makes it easier to intervene quickly and precisely.