جستجوی مقالات مرتبط با کلیدواژه "forecast" در نشریات گروه "پزشکی"

Health indicators are often used for a variety of purposes, including program management, re-source allocation, monitoring of country progress, performance-based payment, and global reporting.Real pro-gress in health towards the United Nations Millennium Development Goals and other national health priorities is vitally dependent on stronger health systems. We aimed to analyse the progress of “birth related indicators” of selected countries of Balkan and Eastern Europe and to forecast their values in the future.

This research report article represents a descriptive data analysis of selected health indicators, ex-tracted from European Health for All database (HFA-DB) and EuroStat. Indicators of interest were analysed for 17 countries in observational period from 1990 to 2019. The data were analysed usinga linear trend estimate and median operation and interquartile range 25th–75th percentile were used for better comparison of each country. Forecasting analysis to year 2025 was performed by combining Excel analysis and SPSS program.

Number of all live births to mothers aged under 20 is decreasing in almost all examined countries,while live births to mother over 35 is mostly increasing. Total fertility rate is also mainly decreasing in almost all countries of interest for our investigation, as well as the crude birth rate.Estimated infant mortality per 1000 live births is decreasing in all observed countries.

Population aging is becoming more pronounced, while current birth-related indicators have nega-tive tendencies; this problem will obviously continue over time.

Background  Health impact assessment (HIA) is a widely used process that aims to identify the health impacts, positive or negative, of a policy or intervention that is not necessarily placed in the health sector. Most HIAs are done prospectively and aim to forecast expected health impacts under assumed policy implementation. HIAs may quantitatively and/or qualitatively assess health impacts, with this study focusing on the former. A variety of quantitative modelling methods exist that are used for forecasting health impacts, however, they differ in application area, data requirements, assumptions, risk modelling, complexities, limitations, strengths, and comprehensibility. We reviewed relevant models, so as to provide public health researchers with considerations for HIA model choice.Methods  Based on an HIA expert consultation, combined with a narrative literature review, we identified the most relevant models that can be used for health impact forecasting. We narratively and comparatively reviewed the models, according to their fields of application, their configuration and purposes, counterfactual scenarios, underlying assumptions, health risk modelling, limitations and strengths.Results  Seven relevant models for health impacts forecasting were identified, consisting of (i) comparative risk assessment (CRA), (ii) time series analysis (TSA), (iii) compartmental models (CMs), (iv) structural models (SMs), (v) agentbased models (ABMs), (vi) microsimulations (MS), and (vii) artificial intelligence (AI)/machine learning (ML). These models represent a variety in approaches and vary in the fields of HIA application, complexity and comprehensibility. We provide a set of criteria for HIA model choice. Researchers must consider that model input assumptions match the available data and parameter structures, the available resources, and that model outputs match the research question, meet expectations and are comprehensible to end-users.Conclusion  The reviewed models have specific characteristics, related to available data and parameter structures, computational implementation, interpretation and comprehensibility, which the researcher should critically consider before HIA model choice.

The outbreak of COVID-19 is rapidly spreading around the world and became a pandemic disease. For help to better planning of interventions, this study was conducted to forecast the number of daily new infected cases with COVID-19 for next thirty days in Iran.

The information of observed Iranian new cases from 19th Feb to 30th Mar 2020 was used to predict the number of patients until 29th Apr. Artificial Neural Networks (ANN) and Auto-Regressive Integrated Moving Average (ARIMA) models were applied for prediction. The data was prepared from daily reports of Iran Ministry of Health and open datasets provided by the JOHN Hopkins. To compare models, dataset was separated into train and test sets. Mean Squared Error (MSE) and Mean Absolute Error (MAE) was the comparison criteria.

Both algorithms forecasted an exponential increase in number of newly infected patients. If the spreading pattern continues the same as before, the number of daily new cases would be 7872 and 9558 by 29th Apr, respectively by ANN and ARIMA. While Model comparison confirmed that ARIMA prediction was more accurate than ANN.

COVID-19 is contagious disease, and has infected many people in Iran. Our results are an alarm for health policy planners and decision-makers, to make timely decisions, control the disease and provide the equipment needed.

Coronavirus, the cause of severe acute respiratory syndrome (COVID-19), is rapidly spreading around the world. Since the number of corona positive patients is increasing sharply in Iran, this study aimed to forecast the number of newly infected patients in the coming days in Iran.

The data used in this study were obtained from daily reports of the Iranian Ministry of Health and the datasets provided by the Johns Hopkins University including the number of new infected cases from February 19, 2020 to March 21, 2020. The autoregressive integrated moving average (ARIMA) model was applied to predict the number of patients during the next thirty days.

The ARIMA model forecasted an exponential increase in the number of newly detected patients. The result of this study also show that if the spreading pattern continues the same as before, the number of daily new cases would be 3574 by April 20.

Since this disease is highly contagious, health politicians need to make decisions to prevent its spread; otherwise, even the most advanced and capable health care systems would face problems for treating all infected patients and a substantial number of deaths will become inevitable

Iran’s western provinces have higher suicide rate compared to the other provinces of the country. Although suicide rates fluctuate over time, suitable statistical models can describe their underlying stochastic dynamics. This study was conducted to explore the fluctuations of the monthly suicide rates in the most populated western province of Iran using exponential
smoothing state space model to compute the forecasts. For this reason, the monthly frequencies of completed suicides were converted to rates per 100,000 and a state‑space approach was identifed and ftted to the monthly suicide rates. Diagnostic checks were performed to determine the adequacy of the ftted model. A signifcant seasonal variation was detected in completed suicide with a peak in August. Diagnostic checks and the time series graph of the observed monthly suicide rates against predicted values from the ftted model showed that in the study period (from March 2006 to September 2013), the observed and predicted values were in agreement. Thus, the model was used to obtain the short‑term forecasts of the monthly suicide rates. In this study, we had no signifcant trend but seasonal variations in the suicide rates that were identifed. However, additional data from other parts of the country with longer duration are needed to visualize the reliable trend of suicide and identify seasonality of suicide across the country

Accurate economic forecast has important effects on governmental policy and economic planning, and it can help policymakers to make decisions for future and create new infrastructures for the development of new forecasting methods. This study calculated total health expenditure, public health expenditure and out of pocket (OOP) payment for 2016-2020. 

Autoregressive Integrated Moving Average Process (ARIMA) is one of the most important forecasting models. In this study, five-year values were forecasted using EViews8 software according to health expenditures in Iran from 1971 to 2015.

Applying annual data for total health expenditure, resulted in the ARIMA (1,1,1) model being the most appropriate to predict these costs. The results of this study indicate that total health expenditures will reach from about 1228338 billion IRR in 2016 to 2698346 billion IRR in 2020 and the amount of out of pocket (OOP) will become more than 41% of total health expenditure in 2020.

Total health expenditures in 2020 will become more than two halves in 2016. These expenditures indicated there is a need for continued governmental support of this sector during the upcoming years.

Bipolar disorder (BD) is a major public health problem. In time series count data there may be over dispersion, and serial dependency. In such situation some models that can consider the dependency are needed. The purpose current research was to use Poisson hidden Markov model to forecast new monthly BD instances.
In current study the dataset including the frequency of new instances of BD from October 2008 to March 2015 in Hamadan Province, the west of Iran were used. We used Poisson hidden Markov with different number of conditions to determine the best model according to Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). Then we used final model to forecast for the next 24 months.
Poisson hidden Markov with two states were chosen as the final model. Each component of dependent mixture model explained one of the states. The results showed that the new BD cases is increase over time and due to forecasting results number of patients for the next 24 months comforted in state two with mean 85.15. The forecast interval was approximately (56, 100).
As the Poisson hidden Markov models was not used to forecast the future states in other prior researches, the findings of this study set forward a forecasting strategy as an alternative to common methods, by considering its deficiencies.

 Shortage of physicians particularly in specialty levels is considered as an important issue in Iran health system. Thus, in an uncertain environment, long‑term planning is required for health professionals as a basic priority on a national scale.

This study aimed to estimate the number of required neurosurgeons using system dynamic modeling. SETTING AND DESIGN: System dynamic modeling was applied to predict the gap between stock and number of required neurosurgeons in Iran up to 2020.

A supply and demand simulation model was constructed for neurosurgeons using system dynamic approach. The demand model included epidemiological, demographic, and utilization variables along with supply model‑incorporated current stock of neurosurgeons and flow variables such as attrition, migration, and retirement rate. STATISTICAL ANALYSIS USED: Data were obtained from various governmental databases and were analyzed by Vensim PLE Version 3.0 to address the flow of health professionals, clinical infrastructure, population demographics, and disease prevalence during the time.

It was forecasted that shortage in number of neurosurgeons would disappear at 2020. The most dominant determinants on predicted number of neurosurgeons were the prevalence of neurosurgical diseases, the rate for service utilization, and medical capacity of the region.

Shortage of neurosurgeons in some areas of the country relates to maldistribution of the specialists. Accordingly, there is a need to reconsider the allocation system for health professionals within the country instead of increasing the overall number of acceptance quota in training positions.

To report a scientific forecast of the number of published dental articles in the next 20 years.
On October 12, 2016, to find all dental articles, PubMed was searched via the query “1800/1/1”[PDAT]: “2015/12/31”[PDAT] AND jsubsetd [text]. Relevant limitations were applied to find dental clinical trials, review articles, and free full-text dental articles. Consequently, all PubMed records were exported to a CSV file. To forecast the future dental research output using existing time-based data, the Exponential Triple Smoothing algorithm was used, which is an advanced machine learning algorithm. Data were analyzed by Microsoft Office Excel 2016.
Seventy-five (1940–2015) years of human attempts to publish dental articles were explored and 572490 records were found, from which 27244 (4.75%) articles were free full-text, 19238 (3.36%) were clinical trials, and 31853 (5.56%) were reviews. Researchers will publish 19195 dental articles in 2036, among which 917 (4.77%) articles will be clinical trials, 1474 (7.67%) will be review articles, and 5482 (28.55%) will be free full-text articles.
Changes may be because of the quantity of research funds. The number of all types of dental articles will increase with an acceptable rate over the next 20 years. Of more interest, the number of free full-text articles will grow more rapidly than other article types.

Congenital hypothyroidism (CH) is one of the most common endocrine diseases and is a major cause of preventable mental retardation. Early diagnosis of CH can help prevent future diseases. Although time series techniques are often utilized to forecast future status, they are inadequate to deal with count data with overdispersion. The aim of this study was to apply Poisson hidden Markov model to forecast new monthly cases of CH disease.
Methods & Materials:This study was based on the monthly frequency of new CH cases in Khuzestan province of Iran, from 2008 to 2014. We applied stationary Poisson hidden Markov with ifferent states to determine the number of states for the model. According to the model, with the specified state, new CH cases were forecast for the next 24 months.
The Poisson hidden Markov with two states based on Akaike information criterion was chosen for the data. The results of forecasting showed that the new CH cases for the next 2 years comforted in state two with the frequency of new cases at 6-18. The forecast mode and median for all months were 12 and 13, respectively.
Our estimates indicated that state of frequency of CH case is invariant during the forecast time. Forecast means for the next 2 years were from 13 to 14 new CH cases. Furthermore, forecasting intervals were observed between 7 and 25 new cases. These estimates are valid when the general fertility rate and crude birth rate were been fixed.