جستجوی مقالات مرتبط با کلیدواژه « Time Series » در نشریات گروه « پزشکی »

Cardiovascular diseases are among the most important causes of death worldwide. The present study aimed to evaluate the trend of changes in the death rate due to cardiovascular diseases, with an emphasis on short- and long-term effective variables.

In this descriptive-analytical (time series) study, all deaths due to cardiovascular diseases registered in the health registration system of death cases by the Health Vice-Chancellor of Urmia University of Medical Sciences from 2018 to 2021 were analyzed. A total of 27,146 cases of death due to cardiovascular causes were recorded and included in our study. SPSS, Minitab, and SAS software were utilized for data analysis.

The rate of death due to cardiovascular causes in this study was 30.51% during the investigated period. The univariate time series model (ARMA 1, 2) was deemed the most suitable fit model for cardiovascular death data. Also, age and education were identified as effective factors in the rate of cardiovascular deaths.

The trend of cardiovascular deaths has not been rising. It has increased with age and lower education levels over time. This rate has been further exacerbated during the COVID-19 pandemic.

In low- and middle-income countries, a large proportion of road users include pedestrians, cyclists, and motorcyclists, and nearly half of road traffic fatalities occur among motorcyclists. This study aimed to examine the pattern of motorcyclists' death due to accidents in East Azerbaijan, Iran between 2006 and 2021 and present a forecast.

We used death data due to motorcycle accidents of Legal Medicine Department between 2006 and 2021. For time series analysis, the Box-Jenkins model was used and three stages of identification, estimation, and diagnosis were successively performed and repeated several times to achieve the best prediction model. The Box-cox transformation method was used to stabilize the variance, and the first-order seasonal differential method with a period of 12 was used to control the seasonality. Due to seasonal variations, the Seasonality Auto-Regressive Integrated Moving Average model: SARIMA (p, d, q) (P, D, Q)s was employed and the death trend was predicted for 36 months. The candidate models were compared based on Log-likelihood, AIC, and BIC indices. STATA 17 was used for data analysis.

About 18.6% of all accident deaths are attributed to motorcycle accidents. The death rate for all causes of accidents and motorcycle accidents were 23.13 and 4.30 per 100,000 population, respectively. Seven models were considered as candidates. The SARIMA (0, 0, 0) (1, 1, 1)12 model was selected as the best model due to better fit and used to predict the number and trend of motorcycle accident deaths. Motorcycle accident deaths are predicted to decrease gradually in the next 36 months, from June 2021 to May 2024, affected by seasonal changes.

The trend of death due to motorcycle accidents from 2006 to 2021 in East Azerbaijan was declining, and it is predicted to decrease slightly in the next three years as well. As this reduction may be attributed to many factors, it is recommended to investigate effective factors in future studies.

Investigating the temporal variations and forecasting the trends in drug-related deaths can help prevent health problems and develop intervention programs. The recent policy in Iran is strongly focused on deterring drug use and replacing illicit drugs with legal ones. This study aimed to investigate drug-related deaths in Iran in 2014-2016 and forecast the death toll by 2019.

In this longitudinal study, Box-Jenkins time series analysis was used to forecast drug-related deaths. To this end, monthly counts of drug-related deaths were obtained from March 2014 to March 2017. After data processing, to obtain stationary time series and examine the stability assumption with the Dickey-Fuller test, the parameters of the Autoregressive Integrated Moving Averages (ARIMA) model were determined using autocorrelation function (ACF) and partial autocorrelation function (PACF) graphs. Based on Akaike statistics, ARIMA (0, 1, 1) was selected as the best-fit model. Moreover, the Dickey-Fuller test was used to confirm the stationarity of the time series of transformed observations. The forecasts were made for the next 36 months using the ARIMA (0,1,2) model and the same confidence intervals were applied to all months. The final extracted data were analyzed using R software, Minitab, and SPSS-23.

According to the Iranian Ministry of Health and the Legal Medicine Organization, there were 8883 drug-related deaths in Iran from March 2014 to March 2017. According to the time series findings, this count had an upward trend and did not show any seasonal pattern. It was forecasted that the mean drug-related mortality rate in Iran would be 245.8 cases per month until 2019.

This study showed a rising trend in drug-related mortality rates during the study period, and the modeling process for forecasting suggested this trend would continue until 2019 if proper interventions were not instituted

The present study examines the association between ambient air pollution and harmful consequences at birth in Yazd, Iran during 2017-2020.

This time series study by the autoregressive (AR) and moving average (MA) or ARMA model was conducted in Yazd, Iran. Birth information including fetal sex, birth weight, birth height, and head circumference as well as preterm birth (PTB) and abortion was collected from mother and birth cohort databases. Data on air pollutants concentrations in the corresponding gestational period were obtained from fixed air monitors of Yazd Municipal Environmental Monitoring Center. The time series model statistical test was performed to find the relation between ambient air pollution and harmful consequences at birth.

2131 singleton live births were monitored for 3 years. In ARMA models, the ratio of girl births to total births (Coef: 7.943, 95% CI: 2.797, 13.089), preterm delivery (Coef: 2.915, 95% CI: 0.224, 5.606), and spontaneous abortion (Coef: 44.751, 95% CI: 26.872, 62.629) was associated with NO2 exposure. Distributed mismatch models also suggested associations between the Air Quality Index (AQI) in pregnant women with a sex-premature birth relationship (Coef: 0.001, 95% CI: 0.000, 0.001).

Exposure to air pollution, even at low levels, may increase the risk of sex ratio in singletons, premature birth, and spontaneous abortion. However, the results of the present study could not definitively show the relationship between air quality and other birth problems. More research studies are required to investigate the present findings.

We aimed to investigate the relationship between air pollution and the Infant mortality rate (IMR) during nearly ten years in Tehran, Iran.

This study is a retrospective cohort case using time series analysis. Air pollution monitoring data during the study period (2009-2018) were collected from the information of 23 Air Quality Control Centers in different areas of Tehran. For this purpose, the daily measures of PM10, PM2.5, O3, CO, SO2, NO2 were obtained. Data on infant mortality was obtained from the National Statistics Office of Iran and mortality registered in Tehran's main cemetery during the study period. Distributed lag linear and non-linear models were used.

A total of 23,206 infant deaths were reported during the study period. Following an increase of 10 ug/m3 in PM10 in an early day of exposure, the risk of mortality increased significantly (RR=1.003, 95%CI:1.001-1.005). There is a pick on lag 5-10 that shows a very strong and immediate effect of cold temperature which means that cold temperatures increase the risk of mortality at an early time. At cold temperate, (var=0 and lag 0) risk of infant mortality was significantly higher than reference temperature (19˚C) (RR=1.1295, %CI: 1.01-1.25).

The results show the adverse effects of PM10 exposure on infant mortality in Tehran, Iran. Accordingly, a steady decline in PM10 levels in Tehran may have greater benefits in reducing the Infant mortality rate.

 Bladder cancer is one of the most common cancers worldwide and also in Iran. Understanding of bladder cancer epidemiology is of great value for policymakers and assists in the prevention and early detection of the disease.

 The aim of the present study was to report national and subnational incidence trends of bladder cancer in Iran between 2003 and 2015.

 This study investigated the age-standardized incidence rates (ASIRs) per annum of bladder cancer from 2003 to 2015 in Iran using the data from the cancer registration system of the Ministry of Health and Medical Education of Iran. The crude incidence rates were calculated by dividing incident cases by the country and province population, which were provided by the Statistic Center of Iran. Age standardization was performed using the WHO standard population, and ASIRs were compared by age, sex, and province.

 The ASIR of bladder cancer increased from 8.35 in 2003 to 13.57 in 2015 in men. The ASIR of bladder cancer also showed a mild increase in females, 2.12 in 2003 versus 2.86 in 2015. The province of Yazd had the highest rate of bladder cancer in men, and West Azerbaijan had the highest rate for women (15.13 and 7.79 per 100,000), while Sistan va Baluchestan and Ilam had the lowest ASIRs for men and women (3.01 and 0.96 per 100,000, respectively).

 The increasing trend of bladder cancer incidence in Iran, despite the clear decreasing global trend accompanying to ongoing aging of the population highlights the diseases as a potential health problem in the upcoming years in Iran. Therefore, it is necessary for health organizations to implement effective research and control programs in the country to prevent further increases in disease burden.

Visceral leishmaniasis (VL) is a neglected infection currently occurring in some regions of Europe, Asia, Africa, and America. This study was an attempt to determine the temporal patterns of VL from January 2000 to December 2019 in the Ardabil Province of north-western Iran using the Markov Switching Models (MSM). This descriptive study used monthly data of 602 VL cases during the study period. The data were provided by the Leishmaniasis National Surveillance System (LNSS), the Iran Meteorological Organization (IMO), and Space Agency (SA), and two states were considered for such modelling. Given the Akaike and Bayesian information criterion, the two-state MSM with a five-month lag is an appropriate model. The MSM showed that the probability of staying in the non-epidemic state is 67%, (P11), while that of staying in an epidemic state is 93% (P22). The mean absolute percentage error (MAPE) was 31.63%, and the portmanteau test (Q=19.03, P=0.66) for the residuals of the selected model revealed that the data were completely modelled. The total VL cases in the next 24 months forecasted 14 cases. The MSM has a relatively acceptable predictive power and is useful in planning future interventions with more information about different stages of the epidemic it provides to policymakers for early warning of epidemics.

There are different mathematical models describing the propagation of an epidemic. These models can be divided into phenomenological, compartmental, deep learning, and individual-based methods. From other viewpoints, we can classify them into macroscopic or microscopic, stochastic or deterministic, homogeneous or heterogeneous, univariate or multivariate, parsimonious or complex, or forecasting or mechanistic. This paper defines a novel univariate bi-partite time series model able to describe spreading a communicable infection in a population in terms of the relative increment of the cumulative number of confirmed cases. The introduced model can describe different stages of the first wave of the outbreak of a communicable disease from the start to the end.

The outcome of the model is relative increment, and it has five positive parameters: the length of the first days of spreading and the relative increment in these days, the potent of the mildly decreasing trend (after the significant decrease), and the adjusting coefficient to adapt this trend to the initial pattern, and the fixed ratio of the mean to the variance.

We use it to describe the propagation of various disease outbreaks, including the SARS (2003), the MERS (2018), the Ebola (2014-2016), the HIV/AIDS (1990-2018), the Cholera (2008-2009), and the COVID-19 epidemic in Iran, Italy, the UK, the USA, China and four of its provinces; Beijing, Guangdong, Shanghai, and Hubei (2020). In all mentioned cases, the model has an acceptable performance. In addition, we compare the goodness of this model with the ARIMA models by fitting the propagation of COVID-19 in Iran, Italy, the UK, and the USA.

The introduced model is flexible enough to describe a broad range of epidemics. In comparison with ARIMA time series models, our model is more initiative and less complicated, it has fewer parameters, the estimation of its parameters is more straightforward, and its forecasts are narrower and more accurate. Due to its simplicity and accuracy, this model is a good tool for epidemiologists and biostatisticians to model the first wave of an epidemic.

Forecasting methods are used in various fields including the health problems. This study aims to use the Artificial Neural Network (ANN) method for predicting coronavirus disease 2019 (COVID-19) cases in Iran.

This is a descriptive, analytical, and comparative study to predict the time series of COVID-19 cases in Iran from May 2020 to May 2021. An ANN model was used for forecast​ing, which had three Input, output, and intermediate layers. The network training was conducted by the Levenberg-Marquardt algorithm. The forecasting accuracy was measured by calculating the mean absolute percentage error.

The mean absolute error of the designed ANN model was 6 and its accuracy was 94%.

The ANN has high accuracy in forecasting the number of COVID-19 cases in Iran. The outputs of this model can be used as a basis for decisions in controlling the COVID-19.

Measles is a feverish condition labeled among the most infectious viral illnesses in the globe. Despite the presence of a secure, accessible, affordable and efficient vaccine, measles continues to be a worldwide concern.

This epidemiologic study used machine learning and time series methods to assess factors that placed people at a higher risk of measles. The study contained the measles incidence in Markazi Province, the center of Iran, from Apr 1997 to Feb 2020. In addition to machine learning, zero-inflated negative binomial regression for time series was utilized to assess development of measles over time.

The incidence of measles was 14.5% over the recent 24 years and a constant trend of almost zero cases were observed from 2002 to 2020. The order of independent variable importance were recent years, age, vaccination, rhinorrhea, male sex, contact with measles patients, cough, conjunctivitis, ethnic, and fever. Only 7 new cases were forecasted for the next two years. Bagging and random forest were the most accurate classification methods.

Even if the numbers of new cases were almost zero during recent years, age and contact were responsible for non-occurrence of measles. October and May are prone to have new cases for 2021 and 2022.

Over 150,000 confirmed cases, around 140 countries, and about 6,000 death occurred owing to coronavirus disease 2019 (COVID-19) pandemic in China, Italy, Iran, and South Korea. Iran reported its first confirmed cases of COVID-19 in Qom City on 19 February 2020 and has the third-highest number of COVID-19 deaths after China and Italy and the highest in Western Asia.

We applied a two-part model of time series to predict the spread of COVID-19 in Iran through addressing the daily relative increments. All of the calculations, simulations, and results in our paper were carried out by using MatLab R2015b software. The average, upper bound, and lower bound were calculated through 100 simulations of the fitted models.

According to the prediction, it is expected that by 15 April 2020, the relative increment (RI) falls to the interval 1.5% to 3.6% (average equal to 2.5%). During the last three days, the RI belonged to the interval of 12% to 15%. It is expected that the reported cumulative number of confirmed cases reaches 71,000 by 15 April, 2020. Moreover, 80% confidence interval was calculated as 35K and 133K.

The screening of suspected people, using their electronic health files, helps isolate the patients in their earlier stage, which in turn helps decrease the period of transmissibility of the patients. Considering all issues, the best way is to apply the model with no modification to model the probable increasing or decreasing acceleration of spreading.

Road traffic accident is one of the most important causes of disability and death in the young population. A significant number of people injured in road traffic accidents die after they arrive at the hospital.
This study aimed to assess the trend of mortality in road traffic accidents and forecast it for the coming years using time series modeling.
This study investigated the trend of road traffic accidents and their victims in Najafabad, Iran, between 2011 and 2017. The ARIMA time series model was fitted on the obtained data and the best model was selected based on the least mean square error. Moreover, the model’s goodness of fit was investigated by residuals ACF and PACF plots as well as Ljung-Box chi-square statistics.
The trend analysis and ARIMA models were investigated, and the results showed a descending trend of fatalities due to traffic accident during 2011-2017. Afterwards, some models were fitted and ARIMA was selected (0, 1, 1), because it had the lowest mean square error value. By fitting the best model, the trend of traffic accident mortality was forecasted for five years (2018 to 2022). Finally, the forecasted values showed that future traffic accident mortalities had a decreasing trend.
The trend of mortality due to road traffic injuries declined, indicating a decreasing trend in deaths for the upcoming years. Therefore, the interventions that have been applied in recent years may be considered as useful.