Forecasting health expenditures in Iran using the ARIMA model (2016-2020)

Ramezanian, Maryam; Haghdoost, Ali Akbar; Mehrolhassani, Mohammad Hossein; Abolhallaje, Masoud; Dehnavieh, Reza; Najafi, Behzad; Fazaeli, Ali Akbar

doi:10.47176/mjiri.33.24

Cited by 18 publications

(9 citation statements)

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“…The promotion of health is a moral obligation, including the social and economic categories. Thus, every health treatment service planning should be part of the pervasive attitude of healthcare policy to make part of the integrated plan of sustainable development [ 2 ]. Most of the world’s countries have faced rising health treatment section expenses during the recent decade [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

Hospital efficiency measurement in the west of Iran: data envelopment analysis and econometric approach

Nayer

Fazaeli

Hamidi

2022

Cost Eff Resour Alloc

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Objective Measuring hospital efficiency is one of the way how to use resources.The optimal hospital performance is the goals of healthcare policymakers. This study aimed to the current study was conducted to evaluate the efficiency the current study was conducted to evaluate the efficiency and assess the association between hospital size and hospital area population with technical efficiency in public hospitals. Methods In this descriptive-analytical study, the statistical population consisted of 15 public hospitals in the west of Iran. First, the data envelopment analysis (DEA) method was used to evaluate technical efficiency. inputs included staff and beds, and outputs consisted of the number of surgeries, the number of patients, and the average length of stay. Then, according to the public ownership of all hospitals, their educational and therapeutic activities, as well as their size and population were considered as the environmental factor affecting efficiency. Thus, regression was applied to measure their effects on efficiency. Results The average technical efficiency of the studied hospitals, the average management efficiency, and the average efficiency of the scale were 0.935, 0.961, and 0.987, respectively. Out of the total evaluated hospitals, six and nine hospitals had an efficiency of less than one and one, respectively. Moreover, the size of the hospital and the population as the environment variable were significant in the Tobit model. Our regression demonstrated that although the size of the hospital is positively associated with its technical efficiency, the hospital population negatively affects hospital efficiency. Conclusion According to the size and area population of the hospitals, they decrease their inputs to maximize their efficacy by optimizing their surplus amounts. Tobit regression analysis concludes that hospital size and population covered by the hospital significant effect on hospitals' efficiency.

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Section: Introductionmentioning

confidence: 99%

Hospital efficiency measurement in the west of Iran: data envelopment analysis and econometric approach

Nayer

Fazaeli

Hamidi

2022

Cost Eff Resour Alloc

Self Cite

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“…In that report, the source and method of forecasts for 2019 remain unknown, and some may assess the results as optimistic and some others pessimistic. Furthermore, Ramezanian et al [ 47 ] have used the autoregressive integrated moving average (ARIMA) forecasting model to project the health figure of 2020 in Iran. The results showed that the total health spending by government and households will increase 75% during the years 2016–2020.…”

Section: Discussionmentioning

confidence: 99%

Forecasting Iran national health expenditures

Kazemian¹,

Abdi²,

Amiri³

2022

Journal of Education and Health Promotion

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BACKGROUND: Forecasting the future trend of health expenditures is an important step toward sustainable financing of health-care systems. This study aims to develop a conceptual framework for forecasting Iran health spending growth. MATERIALS AND METHODS: At first, we concentrated on the general model and conceptual framework of health expenditure projection by reference to a broad literature review and smart classifications of the origins of health spending and indicators. At the second step, we developed a time series modeling for econometric estimation and forecasting national health expenditure without restrictive assumptions except for current laws and regulatory environment. In the third step, we tested the accuracy of model by forecasting Iran real per capita health expenditures (2017–2025). RESULTS: The results of the literature review represented a distinct classification of the origins of health spending and indicators, applicable to any health system and health spending projection model. Furthermore, the model of expenditure forecasting shows the power of certainty of no spurious estimation, assessment of the normal state of a health system, and test of the accuracy of forecasting results. The projection by the Iranian health system database showed that the real per capita health spending will grow 43 percent till 2025 in the absence of any unforeseen disturbance in the future. CONCLUSIONS: The presented model provides estimates that are compatible with actual trends of health spending and can be applied to forecast health expenditure in the near future. The forecasted image of Iran's health spending growth implies that health authorities need to concentrate more on the growth rate of the health budget and its fiscal space in the near future.

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“…It is the most commonly used prediction techniques in the evaluation and monitoring epidemiological surveillance [ 20 – 26 ]. The ARIMA model consists of auto regressive (AR) model, moving average (MA) model, seasonal auto regressive integrated moving average (SARIMA) model and etc [ 17 , 27 ]. If the data of research showed evidence of seasonal tendency, the seasonal auto regressive integrated moving average (SARIMA) model should be used [ 28 ].…”

Section: Methodsmentioning

confidence: 99%

The research of ARIMA, GM(1,1), and LSTM models for prediction of TB cases in China

Zhao¹,

Zhang²,

Cao

et al. 2022

PLoS ONE

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Background and objective Tuberculosis (Tuberculosis, TB) is a public health problem in China, which not only endangers the population’s health but also affects economic and social development. It requires an accurate prediction analysis to help to make policymakers with early warning and provide effective precautionary measures. In this study, ARIMA, GM(1,1), and LSTM models were constructed and compared, respectively. The results showed that the LSTM was the optimal model, which can be achieved satisfactory performance for TB cases predictions in mainland China. Methods The data of tuberculosis cases in mainland China were extracted from the National Health Commission of the People’s Republic of China website. According to the TB data characteristics and the sample requirements, we created the ARIMA, GM(1,1), and LSTM models, which can make predictions for the prevalence trend of TB. The mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) were applied to evaluate the effects of model fitting predicting accuracy. Results There were 3,021,995 tuberculosis cases in mainland China from January 2018 to December 2020. And the overall TB cases in mainland China take on a downtrend trend. We established ARIMA, GM(1,1), and LSTM models, respectively. The optimal ARIMA model is the ARIMA (0,1,0) × (0,1,0)12. The equation for GM(1,1) model was X(k+1) = -10057053.55e(-0.01k) + 10153178.55 the Mean square deviation ratio C value was 0.49, and the Small probability of error P was 0.94. LSTM model consists of an input layer, a hidden layer and an output layer, the parameters of epochs, learning rating are 60, 0.01, respectively. The MAE, RMSE, and MAPE values of LSTM model were smaller than that of GM(1,1) and ARIMA models. Conclusions Our findings showed that the LSTM model was the optimal model, which has a higher accuracy performance than that of ARIMA and GM (1,1) models. Its prediction results can act as a predictive tool for TB prevention measures in mainland China.

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Forecasting health expenditures in Iran using the ARIMA model (2016-2020)

Abstract: Background policymakers t calculated total Methods: Au study, five-yea Results: App predict these co to 2698346 bil 2020. Conclusion: need for contin

Cited by 18 publications

References 0 publications

Hospital efficiency measurement in the west of Iran: data envelopment analysis and econometric approach

Hospital efficiency measurement in the west of Iran: data envelopment analysis and econometric approach

Forecasting Iran national health expenditures

The research of ARIMA, GM(1,1), and LSTM models for prediction of TB cases in China

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