Increased risk of major depressive disorder in sleep apnea patients in Taiwan

Anesthesia for cesarean delivery and subsequent depression: A nationwide retrospective cohort study

BACKGROUND

Postpartum depression is a major psychiatric disorder associated with maternal suicide and child developmental disturbances. In this study, we aimed to investigate whether general anesthesia for cesarean delivery is associated with a higher rate of new-onset depression after delivery than neuraxial anesthesia.

METHODS

This is a nationwide retrospective cohort study using data retrieved from the National Health Insurance Research Database between 2014 and 2018. Women who had cesarean delivery under general or neuraxial anesthesia were enrolled. After 1:4 propensity score matching, there were 4544 and 18,176 women under the general and neuraxial anesthesia groups, respectively. The primary outcome was new-onset depression diagnosed after delivery in a time-to-event analysis setting.

RESULTS

After propensity-score matching, the rate of new-onset depression diagnosed within 1 year was 1.10 % (50/4488) and 0.86 % (157/18176) after cesarean delivery under general and neuraxial anesthesia, respectively. For depression diagnosed within 90 days of delivery, significant difference between the two groups was noted (0.51 % vs. 0.30 %, P = 0.031). In the time-to-event analysis with Cox regression model, women who delivered under general anesthesia were associated with significantly higher risk of postpartum depression within 90 days (Hazard ratio [HR], 1.71; 95 % CI, 1.05-2.79) compared with those under neuraxial anesthesia.

LIMITATIONS

The observational design only allows asserting association, rather than establishing causality between exposure and outcomes.

CONCLUSIONS

Women who underwent cesarean delivery under general anesthesia had a higher risk of subsequent depression within 90 days than those under neuraxial anesthesia. Early screening for depressive disorders might facilitate timely management.

Pulmonary alveolar proteinosis in Taiwan

BACKGROUND/PURPOSE

Pulmonary alveolar proteinosis (PAP) is rare disease manifested as alveolar macrophage dysfunction and abnormal accumulation of surfactant protein in the alveoli. In this nationwide, population-based study, we investigated the epidemiology of PAP in Taiwan, and discovered the comorbidities and prognostic factors of PAP.

METHODS

From the National Health Insurance Research Database (NHIRD), we obtained comprehensive information about all patients of PAP in Taiwan between 1995 and 2013. The incidence, baseline characteristics comorbidities, and prognostic factors of PAP were investigated.

RESULTS

The annual incidence rate of PAP was around 0.79 (range: 0.49-1.17) patients per million people after 2000, and the prevalence rate was 7.96 patients per million people by the end of 2013. In total, 276 patients of PAP were identified, including 177 (64%) and 99 (36%) patients with primary and secondary PAP, respectively. The median age of diagnosis was 53.8 years. The median survival was 9.6 years after the initial PAP diagnosis, and the 5-year survival rate was 65.96%. Twenty (7%) patients received whole lung lavage (WLL) within three months after the diagnosis had significantly better survival compared to the others. Multivariable Cox regression analyses showed that elder age, secondary PAP, and malignancy were associated with poorer survival, while WLL within 3 months of diagnosis might greatly improve the survival.

CONCLUSION

We demonstrated the epidemiology of PAP in Taiwan, showing several poor prognostic factors and the potential effectiveness of WLL. Further prospective studies based on registry are warranted to improve the diagnosis and treatment of PAP.

Increased incidence of alcohol use disorder and alcohol-related psychiatric disorders in patients with obstructive sleep apnea: A nationwide population-based cohort study

BACKGROUND

Obstructive sleep apnea (OSA) and alcohol-related diseases (ARDs), including alcohol use disorder, alcohol-related psychiatric disorders, alcoholic liver disease, alcoholic polyneuropathy alcoholic cardiomyopathy, and alcoholic gastritis, are both highly prevalent conditions. Alcohol consumption is associated with a higher risk of sleep apnea. However, whether OSA increases the risk of ARD has not, as yet, been studied comprehensively. Our study aimed to determine whether OSA increases the subsequent risk of ARD.

METHODS

This study utilized the data from Taiwan's National Health Insurance Database between 2000 and 2015. We identified 7722 individuals newly diagnosed with OSA and randomly selected sex-, age-, and index date-matched (1:3) 22,166 controls without OSA, with a total of 29,888 subjects. We used the Fine and Gray's survival analysis to estimate the effects of OSA on ARD.

RESULTS

The OSA cohort had an adjusted hazard ratio of subsequent ARDs as 1.486 (95% Confidence Interval: 1.301-1.698), when comparing the cohort without OSA. The Kaplan-Meier analysis showed that the cumulative incidence of ARDs was significantly higher in the OSA cohort than in the controls in the first year of follow-up, till the end of the follow-up. A post-hoc analysis showed that OSA was associated with alcohol use disorder, alcohol-related psychiatric disorders, and alcoholic liver disease, but not alcoholic polyneuropathy, alcoholic cardiomyopathy, and alcoholic gastritis. The use of psychoactive medication, including the sedative-hypnotics, antidepressants or antipsychotics were associated with a lower risk of ARDs.

CONCLUSIONS

Our study demonstrates that the OSA patients are at a higher risk of developing ARDs.

Shift work, clinically significant sleep disorders and mental health in a representative, cross-sectional sample of young working adults

Mental health conditions confer considerable global disease burden in young adults, who are also the highest demographic to work shifts, and of whom 20% meet criteria for a sleep disorder. We aimed to establish the relationship between the combined effect of shift work and sleep disorders, and mental health. The Raine Study is the only longitudinal, population-based birth cohort in the world with gold-standard, Level 1 measurement of sleep (polysomnography, PSG) collected in early adulthood. Participants (aged 22y) underwent in-laboratory PSG and completed detailed sleep questionnaires. Multivariable adjusted robust linear regression models were conducted to explore associations with anxiety (GAD7) and depression (PHQ9), adjusted for sex, health comorbidities, and work hours/week. Data were from 660 employed young adults (27.3% shift workers). At least one clinically significant sleep disorder was present in 18% of shift workers (day, evening and night shifts) and 21% of non-shift workers (p = 0.51); 80% were undiagnosed. Scores for anxiety and depression were not different between shift and non-shift workers (p = 0.29 and p = 0.82); but were higher in those with a sleep disorder than those without (Md(IQR) anxiety: 7.0(4.0-10.0) vs 4.0(1.0-6.0)), and depression: (9.0(5.0-13.0) vs 4.0(2.0-6.0)). Considering evening and night shift workers only (i.e. excluding day shift workers) revealed an interaction between shift work and sleep disorder status for anxiety (p = 0.021), but not depression (p = 0.96), with anxiety scores being highest in those shift workers with a sleep disorder (Md(IQR) 8.5(4.0-12.2). We have shown that clinical sleep disorders are common in young workers and are largely undiagnosed. Measures of mental health do not appear be different between shift and non-shift workers. These findings indicate that the identification and treatment of clinical sleep disorders should be prioritised for young workers as these sleep disorders, rather than shift work per se, are associated with poorer mental health. These negative mental health effects appear to be greatest in those who work evening and/or night shift and have a sleep disorder.

Sleep disorders and non-sleep circadian disorders predict depression: a systematic review and meta-analysis of longitudinal studies

Patients with depression often suffer from sleep disorders and non-sleep circadian disorders. However, whether they precede and predict subsequent depression is unclear. We conducted a meta-analysis of studies on sleep disorders and non-sleep circadian disorders. We found insomnia, hypersomnia, short and long sleep duration, obstructive sleep apnea, restless legs syndrome and eveningness orientation at baseline all led to subsequent depression. Those with propensity to late meal patterns, heightened levels of cortisol in awakening response and low robustness of rest-activity rhythm at baseline had higher risks for later depression. Among insomnia subtypes, difficulty initiating sleep and difficulty maintaining sleep predicted future depression. Notably, persistent insomnia at baseline contributed to more than two-fold risk of incident depression compared to insomnia. Moreover, insomnia symptom numbers showed dose-dependent relationship with the incident depression. In conclusion, different types of sleep disorders and non-sleep circadian disorders were proven to be risk factors of subsequent depression, and mechanisms underlying the relationship between sleep disorders, non-sleep circadian disorders and subsequent depression should be further elucidated in the future.

Risk of pneumothorax in pneumoconiosis patients in Taiwan: a retrospective cohort study

OBJECTIVES

This study was conducted to explore the association between pneumoconiosis and pneumothorax.

DESIGN

Retrospective cohort study.

SETTING

Nationwide population-based study using the Taiwan National Health Insurance Database.

PARTICIPANTS

A total of 2333 pneumoconiosis patients were identified (1935 patients for propensity score (PS)-matched cohort) and matched to 23 330 control subjects by age and sex (7740 subjects for PS-matched cohort).

PRIMARY AND SECONDARY OUTCOME MEASURES

The incidence and the cumulative incidence of pneumothorax.

RESULTS

Both incidence and the cumulative incidence of pneumothorax were significantly higher in the pneumoconiosis patients as compared with the control subjects (p<0.0001). For multivariable Cox regression analysis adjusted for age, sex, residency, income level and other comorbidities, patients with pneumoconiosis exhibited a significantly higher risk of pneumothorax than those without pneumoconiosis (HR 3.05, 95% CI 2.18 to 4.28, p<0.0001). The male sex, heart disease, peripheral vascular disease, chronic pulmonary disease and connective tissue disease were risk factors for developing pneumothorax in pneumoconiosis patients.

CONCLUSIONS

Our study revealed a higher risk of pneumothorax in pneumoconiosis patients and suggested potential risk factors in these patients. Clinicians should be aware about the risk of pneumothorax in pneumoconiosis patients.

Performance Assessment of Certain Machine Learning Models for Predicting the Major Depressive Disorder among IT Professionals during Pandemic times

Major depressive disorder (MDD) is the most common mental disorder in the present day as all individuals' lives, irrespective of being employed or unemployed, is going through the depression phase at least once in their lifetime. In simple terms, it is a mood disturbance that can persist for an individual for more than a few weeks to months. In MDD, in most cases, the individuals do not consult a professional, and even if being consulted, the results are not significant as the individuals find it challenging to identify whether they are depressed or not. Depression, most of the time, cooccurs with anxiety and leads to suicide in few cases, among the employees, who are about to handle the pressure at work and home and mostly unnoticing such problems. This is why this work aims to analyze the IT employees who are mostly working with targets. The artificial neural network, which is modeled loosely like the brain, has proved in recent days that it can perform better than most of the classification algorithms. This study has implemented the multilayered neural perceptron and experimented with the backpropagation technique over the data samples collected from IT professionals. This study aims to develop a model that can classify depressed individuals from those who are not depressed effectively with the data collected from them manually and through sensors. The results show that deep-MLP with backpropagation outperforms other machine learning-based models for effective classification.