Analysis of the impact of setting up plasmapheresis station on the collection volume of voluntary blood donation based on panel vector autoregressive model / 中国输血杂志

【Objective】 To analyze the dynamic relationship between the setting up of plasmapheresis station and the volume of voluntary blood donation collected using panel vector autoregressive model, so as to provide scientific reference for the management policies of blood stations and plasmapheresis stations in China. 【Methods】 The data collected from blood stations in seven administrative regions of Guangyuan, Sichuan Province from 2011 to 2021, as well as plasma collection data from two plasmapheresis stations in the region within two years since their operation, were collected. A panel vector autoregressive model was constructed. Impulse response analysis and variance decomposition analysis were used to analyze the impact and time lag effects of simulated plasmapheresis station settings on the collection volume of voluntary blood donation. Covariance analysis was used to explore whether the establishment of plasmapheresis station had an impact on the volume of voluntary blood donation collected after excluding the impact of initial value differences. 【Results】 The pulse response results showed that after the plasmapheresis station was set up, there was a negative impact effect on the voluntary blood donation collection volume at the first stage, and its impact began to rise after the second stage, reached the highest value in the third stage, and then began to decline. After the seventh stage, it tended to be stable. However, within the 10 stage range, the confidence interval for the response strength of voluntary blood donation collection volume always included 0, indicating that the response of blood collection volume to the plasmapheresis station setting in the region was not statistically significant. The results of variance decomposition showed that the contribution of collection volume of voluntary blood donation to their own impact reached 94.3%. In terms of the contribution of plasmapheresis station factors, the number of plasma donors has a relatively greater impact on the volume of voluntary blood donation collected(2.2%). Covariance analysis showed that after removing the initial confounding factors, whether to establish a plasmapheresis station had no significant impact on blood donation volume in the two groups of regions (P>0.05). 【Conclusion】 The establishment of a new plasmapheresis station will have a certain impact on blood collection volume of blood stations in the region in a short term, but in the long term, it may not directly affect the voluntary blood donation collection in the region.

Dynamic relationship between changes of mortality rate and urban development level in Shanghai / 上海预防医学

ObjectiveTo analyze the dynamic response relationship between urban development and mortality rate in Shanghai， and to predict the trend of mortality rate changes. MethodsBy analyzing the total mortality rate （TMR）， gross domestic product （GDP） and socio-demographic index （SDI） in Shanghai from 1978 to 2017， a vector autoregressive （VAR） model was constructed to evaluate the impact of urban development on the mortality rate. ResultsThe fitted R2 of the VAR model was 0.92. The short-term effect of GDP on the improvement of death level was negative， while the long-term effect was positive， and the SDI was negative regardless of the short-term and long-term effects. By the tenth year， GDP and SDI contributed 10.61% and 27.25% to TMR changes， respectively. The model predicted that the mortality rate in Shanghai would be 9.17 per thousand by 2030. ConclusionLong-term economic growth can effectively promote a decline in population mortality. However， as the economy develops vigorously， the adverse effects of declining birth rates and population aging on population health during the era of high-level population development should not be ignored.

The predictive value of time series forecasting model in prehospital emergency medical services demand in Guangzhou / 中华急诊医学杂志

Objective:To study the value of autoregressive integrated moving average (ARIMA) and autoregressive (AR) models in predicting the daily number of ambulances in prehospital emergency medical services demand in Guangzhou.Methods:Matlab simulation software was used to analyze the emergency dispatching departure records in Guangzhou from January 1, 2021 to December 31, 2021. A time series for the number of ambulances per day was calculated. After identifying the time series prediction model, ARIMA(1,1,1), AR(4) and AR(7) models were obtained. These models were used to predict the number of ambulances per day. ARIMA(1,1,1) model divided the time series into the training set and test set. Prony method was used for parameter calculation, and the demands of number of ambulances of the next few months were forecasted. AR(4) and AR(7) models used uniformity coefficient to forecast the demands of number of ambulances on that very day.Results:ARIMA(1,1,1), AR(4) and AR(7) can effectively predict the number of ambulances per day. The prediction fitting error of ARIMA (1,1,1) decreased with the extension of prediction time. The mean absolute percentage error (MAPE) of forecast results of daily vehicle output of emergency dispatching within two months was less than 6% and the predicted results were almost within the 95% confidence interval. The residual analysis of the model verified that the model was significantly effective.Conclusions:ARIMA model can make a long-term within two months and effective prediction fitting of the daily vehicle output of emergency dispatching, and AR model can make a short-term and effective prediction of the daily vehicle output of emergency dispatching.

Empirical Research on the Influential Factors of Chinese Crude Drug Price Based on SVAR Model / 中国药房

OBJECTIVE：To explore th e influential factors of the price fluctuation of Chinese crud drug ，and to provide reference for the healthy development of tranditional Chinese medicine industry in China. METHODS ：Based on the relevant statistics from 1992 to 2019，a structural vector autoregressive model was established. After data stationarity test ，cointegration relationship test ，model estimation and stability test ，impulse response function and variance decomposition were used to investigate the impact of supply ，demand，cost of implantation and production and inflation on the price of Chinese crud drug. RESULTS & CONCLUSIONS：There was a long-term equilibrium relationship among the price of Chinese crud drug ，planting area of Chinese crud drug ，output of Chinese patent medicine ，market turnover of Chinese crud drug ，export volume of Chinese crud drug ，price index of agricultural means of production and consumer price index. The price of Chinese crud drug had the greatest impact and contribution rate on itself ，followed by planting area of Chinese crud drug ，export volume of Chinese crud drug and price index of agricultural means of production. However ，the output of Chinese patent medicine ，the turnover of Chinese crud drug market and consumer price index had weak influence. It is suggested to make full use of Internet information technology ，strengthen the price monitoring and early warning of Chinese crud drug ，promote the supply side structural reform of tranditional Chinese medicine industry，improve the scientific and technological level of Chinese crud drug planting and promote large-scale production of Chinese herbal medicine so as to stabilize the price of Chinese crud drug.

Human Activity Recognition Based on Features Fusion / 医用生物力学

Objective To establish a human activity recognition （HAR）model based on human activity signals obtained by built-in sensors of the mobile phone, so as to support daily physical state assessment, special population monitoring and other biomedical researches. Methods The mobile signal was collected using the mobile phone built-in sensor, and the public data set UCI HAR and WISDM were used as experimental data. The HAR model was established by using the feature extraction method combined with convolutional neural network and autoregressive model. Results The models all achieved more than 90% recognition accuracy in the self-collected dataset, UCI HAR and WISDM. Conclusions The introduction of autoregressive model can avoid the manual design eigenvalues and effectively reduce the computational complexity of large-scale stacked convolutional layers. The research findings prove that the method based on feature fusion can effectively recognize human activity.

Application and comparison of residual autoregressive model and Holt’s two-parameter exponential smoothing model in infant mortality prediction in some countries along the Belt and Road Initiative / 中华疾病控制杂志

@# Objective To explore the application of residual autoregressive model and Holt's two-parameter exponential model in the prediction of infant mortality rate in some countries along the “Belt and Road” (China-Indo-China Peninsula Economic Corridor). Methods The time series data of infant mortality rate in Vietnam, Laos, Cambodia, Myanmar, Thailand, Singapore, Malaysia, and China for 1978-2013 were used as training set to fit residual autoregressive model and Holt's two-parameter exponential model. The 2014-2016 data was used as the validation set to compare the performance of model prediction. Results The akaike information criterion (AIC) value of the residual autoregressive model was superior to Holt's two-parameter exponential model. Both prediction models showed high accuracy, and most evaluation indicators (absolute error and relative error) of residual autoregressive prediction model were smaller than Holt's two-parameter exponential model. The residual autoregressive models of Laos, Myanmar and Cambodia were better than the Holt’s two-parameter exponential model for the infant mortality rate(IMR) prediction in different years. Conclusions The residual autoregressive model and Holt's two-parameter exponential model performed well in infant mortality rate prediction in some countries along the China-Indo-china Peninsula Economic Corridor. The residual autoregressive model has better fitting effect. The residual autoregressive model for infant mortality prediction is superior to the Holt two-parameter exponential model in most countries in most years.

Application of ARIMA-NAR combined model in predicting bacillary dysentery / 第二军医大学学报

Objective To explore the application of autoregressive integrated moving average (ARIMA) model, and ARIMA combined nonlinear autoregressive (ARIMA-NAR) model in predicting bacterial dysentery (BE) incidence. Methods Data of BE monthly incidences from Jan. 2004 to Feb. 2015 in Jiangsu Province were used as fitting samples, the 15-month data from Mar. 2015 to May 2016 were used in the prediction phase. ARIMA model and ARIMA-NAR model were established and the effects of two models were compared according to mean absolute error (MAE), mean square error (MSE), and mean absolute percentage error (MAPE), in which lower values suggested higher prediction accuracy. Results In the fitting phase, the MAE, MSE and MAPE of the ARIMA model were 0. 177 5, 0. 081 4 and 0. 184 7, respectively, while those of the ARIMA-NAR model were 0. 094 1, 0. 029 5 and 0. 104 6, respectively. In the prediction phase, the MAE, MSE and MAPE of the ARIMA model were significantly higher than those of the ARIMA-NAR model. Conclusion ARIMA-NAR combined model is superior to ARIMA model in predicting the time series of BE incidence in Jiangsu Province, suggesting that ARIMA-NAR model can be used to predict the incidence of BD.

Transaction costs in beans market in Brazil / Custos de transação no mercado de feijão no Brasil

This paper aimed to evaluate the presence of transaction costs in the beans market in Brazil. Therefore, threshold autoregressive (TAR) models were used to check co-integration and the existence of transaction costs in the Brazilian beans market. The results confirmed the presence of transaction costs in the beans market, which are mainly related to the freight component of production, since the markets are often far away from the producing regions.

O objetivo deste estudo foi avaliar a existência de custos de transação no mercado de feijão no Brasil. Para tanto foram utilizados modelos de threshold autoregressivo para verificar a cointegração e a existência de custos de transação no mercado brasileiro de feijão. Os resultados confirmam a presença de custos de transação no mercado de feijão, os quais estão relacionados principalmente aos custos de frete, considerando que os mercados consumidores podem estar distantes dos locais de produção.

Deprivation and Mortality at the Town Level in Busan, Korea: An Ecological Study / 예방의학회지

OBJECTIVES: Busan is reported to have the highest mortality rate among 16 provinces in Korea, as well as considerable health inequality across its districts. This study sought to examine overall and cause-specific mortality and deprivation at the town level in Busan, thereby identifying towns and causes of deaths to be targeted for improving overall health and alleviating health inequality. METHODS: Standardized mortality ratios (SMRs) for all-cause and four specific leading causes of death were calculated at the town level in Busan for the years 2005 through 2008. To construct a deprivation index, principal components and factor analysis were adopted, using 10% sample data from the 2005 census. Geographic information system (GIS) mapping techniques were applied to compare spatial distributions between the deprivation index and SMRs. We fitted the Gaussian conditional autoregressive model (CAR) to estimate the relative risks of mortality by deprivation level, controlling for both the heterogeneity effect and spatial autocorrelation. RESULTS: The SMRs of towns in Busan averaged 100.3, ranging from 70.7 to 139.8. In old inner cities and towns reclaimed for replaced households, the deprivation index and SMRs were relatively high. CAR modeling showed that gaps in SMRs for heart disease, cerebrovascular disease, and physical injury were particularly high. CONCLUSIONS: Our findings indicate that more deprived towns are likely to have higher mortality, in particular from cardiovascular disease and physical injury. To improve overall health status and address health inequality, such deprived towns should be targeted.

Critical analysis of autoregressive and fast Fourier transform markers of cardiovascular variability in rats and humans

The autonomic nervous system plays an important role in physiological and pathological conditions, and has been extensively evaluated by parametric and non-parametric spectral analysis. To compare the results obtained with fast Fourier transform (FFT) and the autoregressive (AR) method, we performed a comprehensive comparative study using data from humans and rats during pharmacological blockade (in rats), a postural test (in humans), and in the hypertensive state (in both humans and rats). Although postural hypotension in humans induced an increase in normalized low-frequency (LFnu) of systolic blood pressure, the increase in the ratio was detected only by AR. In rats, AR and FFT analysis did not agree for LFnu and high frequency (HFnu) under basal conditions and after vagal blockade. The increase in the LF/HF ratio of the pulse interval, induced by methylatropine, was detected only by FFT. In hypertensive patients, changes in LF and HF for systolic blood pressure were observed only by AR; FFT was able to detect the reduction in both blood pressure variance and total power. In hypertensive rats, AR presented different values of variance and total power for systolic blood pressure. Moreover, AR and FFT presented discordant results for LF, LFnu, HF, LF/HF ratio, and total power for pulse interval. We provide evidence for disagreement in 23 percent of the indices of blood pressure and heart rate variability in humans and 67 percent discordance in rats when these variables are evaluated by AR and FFT under physiological and pathological conditions. The overall disagreement between AR and FFT in this study was 43 percent.

A Comparative Analysis of Heart Rate Variability of Electrocardiogram and Pulse-wave Using Time Series / 대한의료정보학회지

The estimation of power spectrum based on the heart rate variability is studied to evaluate the variation of the tension and physiological workloads by the activity of the autonomic system as evaluation parameter. In this paper, we made an attempt to apply the pulse wave to estimate the variation of physiological condition quantitatively instead of the evaluation of power spectrum of HRV through electrocardiogram in the past by comparing and analyzing time series data for the similarity of the heart rate of electrocardiogram and pulse wave. For the similarity of two signals, we can get the likeness by calculating an average, a variance, a correlation coefficient, and the power peak and the shape of power spectral density from the time series data. In the experimental result, it is shown that same subject have a similar variation of time series and power spectrum density for electrocardiogram and pulse wave. As a result, it is expected to estimate a change of the tension and physical condition quantitatively through the evaluation of a power ~pectrum of HRV by Pulse Wave in the future.

A Low Frequency Oscillation in Pulmonary Circulation and Its Dynamic Relation to the Low Frequency Oscillation of Systemic Circulation : Power Spectrum and Phase Estimation by Autoregressive Algorithm and Cross Spectral Analysis

BACKGROUND: Low frequency oscillation of systemic artery pressure was known as the marker of sympathetic modulation. Recently the low frequency oscillation of pulmonay artery pressure in pulmonary hypertensive patient was reported. But no further study about its quantitative relationshop and phasic coupling between the low frequency oscillation of pulmonary artery pressure and systemic artery pressure. Power spectral analysis with autoregressive algorithm and cross spectral analysis are powerful tool for investigation these relationship. METHOD: Analog signals of simultaneous measured left pulmonary and femoral artery pressure in thirty one patients with ventricular septal defect were digitized and stored. After modeling each time series with autoregressive algorithm, power spectral density function was obtained by calculation the frequency response function of each model, and then low frequency power was computed. Cross spectral density function provided squared coherence and phase spectrum. Phase between the low frquency oscillation of the two signal was measured from the phase spectrum when the squared coherence is above 0.5. RESULTS: The advantage of using autoregressive model was that the power spectral density function was continous and sharp spectral peak was usually found. In patients with Rp/Rsor=0.25, there was no significant difference between the low frequency power of pulmonary artery pressure(384+/-461) and that of the systemic artery pressure(752+/-1241). In patients with Rp/Rs>or=0.25, it was more probable that low frequency oscillation of pulmonary and systemic artery pressure was timely coherent(sqaured coherence>0.5) than in patient with Rp/Rs<0.25. And their phase difference was 0~1.96 radian. CONCLUSION: Autoregressive algorithm is a more powerful tool for spectral analysis than the method of conventional spectrum estimation. When pulmonary vascular resistance remains low, the low frequency oscillation of pulmonary artery pressure was negligible compared to systemic artery pressure. But as pulmonary vascular resistance elevates, the low frequency power of pulmonary artery pressure is much the same as that of systemic artery pressure, and there is a explicit time realtionship that pulmonary artery pressure leads the systemic artery pressure about 0~3 seconds in the low frequency range.

The application of the extraction of 40Hz ASSR with parametric-model method to monitoring the depth of anesthesia / 医疗卫生装备

It is very significative to perform real-time and effective monitor of the depth of anesthesia during the clinical operation. With the introduction of muscle-relaxing agents in general anesthesia, the two major indices to judge the depth of anesthesia have been obscured, which makes it more difficult to judge the depth of anesthesia. In recent years it has been found that 40Hz Auditory Steady State Response (ASSR) has a very good characteristic to monitor the depth of anesthesia. This article makes use of parametric-model method to extract 40Hz ASSR, which has a higher speed than Moving Time Average (MTA) when tracing the transition process of the patient from being conscious to being unconscious.