ABSTRACT
【Objective】 To construct a prediction model of severe obstructive sleep apnea (OSA) risk in the general population by using nomogram in order to explore the independent risk factors of severe OSA and guide the early diagnosis and treatment. 【Methods】 We retrospectively enrolled patients who had been diagnosed by polysomnography and divided them into training and validation sets at the ratio of 7∶3. Patients were divided into severe OSA group and non-severe OSA group according to apnea hypopnea index (AHI)>30. Variables entering the model were identified by least absolute shrinkage and selection operator regression model (Lasso), and logistic regression (LR) method. Then, multivariable logistic regression analysis was used to establish the nomogram, and the area under the receiver operating characteristic curve (AUC) was used to evaluate the discriminative properties of the nomogram model. Finally, we conducted decision curve analysis (DCA) of nomogram model, STOP-Bang questionnaire and Berlin questionnaire to assess clinical utility. 【Results】 Through single factor and multiple factor logistic regression analyses, the independent risk factors for severe OSA were screened out, including moderate and severe sleepiness, family history of hypertension, history of smoking, drinking, snoring, history of suffocation, sedentary lifestyle, male, age, body mass index (BMI), waist and neck circumference. Lasso logistic regression identified smoke, suffocation time, snoring time, waistline, Epworth sleepiness scale (ESS) and BMI as predictive factors for inclusion in the nomogram. The AUC of the model was 0.795 [95% confidence interval (CI): 0.769-0.820] . Hosmer-Lemeshow test indicated that the model was well calibrated (χ2=3.942, P=0.862). The DCA results on the visual basis confirmed that the nomogram had superior overall net benefits within a wide, practical threshold probability range which displayed the nomogram was higher than that of STOP-Bang questionnaire and Berlin questionnaire, which is clinically useful. The Clinical Impact Curve (CIC) analysis showed the clinical effectiveness of the prediction model when the threshold probability was greater than 82% of the predicted score probability value. The prediction model determined that the high-risk population with severe OSA was highly matched with the actual population with severe OSA, which confirmed the high clinical effectiveness of the prediction model. 【Conclusion】 The model performed better than STOP-Bang questionnaire and Berlin questionnaire in predicting severe OSA and can be applied to screening. And it can be helpful to the early diagnosis and treatment of OSA in order to reduce social burden.
ABSTRACT
Background Subways are typical congregate settings and may facilitate aerosol transmission of viruses. However, quantified transmission probability estimates are lacking. Purpose To model spread and diffusion of respiratory aerosols in subways by simulation and calculation of infection probabilities. Methods The internal environment of carriages of Shanghai Metro Line 10 was used to establish a study scene. The movement of tiny particles was simulated using the turbulent model. Trend analysis of infection probabilities and viral quantum doses was conducted in a closed subway carriage scene by a quantum emission-infection probability model. Results Under a typical twelve-vent air conditioning configuration, respiratory droplet aerosols within a subway carriage dispersed rapidly throughout various regions due to airflow, with limited short-term diffusion to other carriages. Concurrently, owing to the uncertainty of airflow patterns, the airflow might circulate and converge within carriages, causing delayed outward dispersion or hindered dispersion of droplet aerosols upon entry into these zones. Passengers boarding the carriage could exacerbate the formation of these zones. When the air conditioning system functioned adequately (air exchange rate=23.21 h−1), the probability of a virus carrier transmitting the virus to other passengers within the same carriage via aerosol transmission was approximately 3.8%. However, in the event of air conditioning system failure (air exchange rate=0.5 h−1), this probability escalated dramatically to 30%. Furthermore, a super-spreader (with virus spreading exceeding 90% of the average) elevated the infection probability to 14.9%. Additionally, due to the complexity of turbulence within the carriage, if local diffusion occurred in 1/2 zones of a carriage, the anticipated infection probability would increase to 8.9%, or during the morning or evening rush hours leading to elevated aerosol concentrations, the infection probability would rise to 4.7%. The subway transmission probability for common coronaviruses diminished to as low as 0.9%. Conclusion Combined computational fluid dynamics and infection probability analysis reveals that in the prevalent twelve-vent air conditioning configurations, despite being a major transportation hub with substantial spatial-temporal overlap, the internal space of subway carriages exhibits a certain level of resistance to virus aerosol transmission owing to built-in ventilation capabilities. However, turbulence and passenger positioning may lead to localized hovering of droplet aerosols, thereby increase the risk of virus transmission. Furthermore, super-spreaders, poor operational status of built-in air conditioning system, and high passenger volume at morning or evening peak hours exert profound effects on virus transmission and infection probability.
ABSTRACT
Background Non-occupational carbon monoxide (CO) poisoning is a public health problem that seriously affect people’s health and lives. Objective To describe the prevalence of non-occupational CO poisoning during 2007—2018 in Shanghai, analyze its epidemiological characteristics and potential influencing factors, and explore effective prevention and control measures. Methods Daily reported non-occupational CO poisoning cases and meteorological factors from 2007 to 2018 were collected in Shanghai, epidemiological characteristics were analyzed by descriptive epidemiology methods, and a distributed lag nonlinear model was used to assess the association between temperature and non-occupational CO poisoning. Results A total of 2264 non-occupational CO poisoning events and 3866 cases from 2007 to 2018 were reported in Shanghai, including 59 death cases. More than half of the poisoning cases were female (56.3%), and young adults accounted for more cases than any other age group (54.8%). The poisoning events mainly occurred in winter (from December to next February); however, cases reported in summer increased in recent years. The peak period of the events was from 20:00 to 24:00. Households (85.2%) and restaurants (8.0%) were the common places of non-occupational CO poisoning events, and the main cause was improper use of gas water heater (36.9%). A nonlinear curve was found between daily average temperature of current day and the occurrence of non-occupational CO poisoning. Temperature was negatively associated with the risk of non-occupational CO poisoning when the temperature was lower than 9.6 ℃, while a positive association was found during 9.7-26.0 ℃. Conclusion Winter is a high season for non-occupational CO poisoning in Shanghai, rising cases reported in summer is also worthy of attention. Supervision should be strengthened to ban sales of unqualified gas water heaters, and health education on CO poisoning prevention and control should be conducted through multiple channels, in order to reduce the incidence of CO poisoning.
ABSTRACT
Objective:To analyze the correlation between obstructive sleep apnea (OSA) and attention deficit hyperactivity disorder (ADHD).Methods:The clinical Data, polysomnography (PSG) and cognitive function examination results of 112 OSA children admitted to Department of Otorhinolaryngology Head and Neck Surgery of the Second Affiliated Hospital of Xi′an Jiaotong University from January 2019 to June 2020 were retrospectively analyzed. According to the severity of OSA, the children were divided into mild, moderate and severe OSA groups, and the basic demographic characteristics, sleep parameters and ADHD occurrence were analyzed. According to the results of ADHD examination, the children were divided into ADHD group and non-ADHD group, and the basic demographic characteristics and sleep parameters were analyzed. Taking these parameters as independent variables, binary Logistic regression analysis was conducted to establish the model equation for predicting the risk of OSA associated ADHD among children.Results:Grouped by OSA severity, among the three groups, apnea-hypopnea index (AHI) [3.70 (2.84, 5.47) vs 8.59 (7.50, 9.54) vs 19.48 (15.83, 25.23)], obstructive apnea index (OAI) [1.31 (0.93, 1.82) vs 3.03 (1.54, 4.41) vs 11.69 (8.53, 15.42)], obstructive apnea-hypopnea index (OAHI) [2.82 (1.81, 3.64) vs 6.17 (5.58, 7.26) vs 15.68 (13.12, 21.25)], and respiratory event-related arousal index [0.50 (0.25, 1.05) vs 1.25 (0.70, 2.23) vs 2.40 (1.60, 4.70)] increased, minimum pulse oxygen saturation (SpO 2) [90.00 (88.00, 92.00) vs 87.00 (83.00, 90.25) vs 81.00 (76.00, 85.00)] decreased, the differences were statistically significant (all P<0.05). The non-rapid eye movement (NREM)1 period time ratio of the severe OSA group was significantly longer than that of the mild OSA group, while the average SpO 2 was significantly lower than that of the mild OSA group; the NREM3 period time ratio of the moderate and severe OSA group was significantly less than that of the mild OSA group; the arousal index of the severe OSA group was significantly greater than the mild or moderate OSA group. There were no statistically significant differences among the three groups in gender, age, body mass index, sleep efficiency, rapid eye movement (REM) period time ratio, and NREM2 period time ratio (all P>0.05). Mild OSA group had 10 cases of ADHD (17.54%), moderate OSA group had 7 cases (23.33%) of ADHD, severe OSA group had 9 cases of ADHD (36.00%), and the difference was not statistically significant. Grouped by ADHD examination, the AHI, OAI, OAHI, and NREM1 period time ratios of the ADHD group were significantly higher than those of the non-ADHD group, while the sleep efficiency, minimum SpO 2 and NREM3 period time ratio were significantly lower than those of the non-ADHD group. The Logistic regression analysis suggested that ADHD was correlated with sleep efficiency, minimum SpO 2, and NREM3 period time.The established Logistic regression equation was: X=15.670+0.061×(sleep efficiency)-0.212×(minimum SpO 2)-0.144×(NREM3 period time ratio), the sensitivity and specificity of the model prediction were 84.6% and 79.1% respectively when the area under the receiveroperating characteristic curves was 0.867. Conclusions:OSA and ADHD in children have a certain correlation. Sleep structure disturbance and intermittent hypoxia may be important reasons. The predictive model equations obtained by PSG in this study can be used to assess the risk of ADHD in children with OSA.