Volume 33, Issue 124 (June 2020)
IJN 2020, 33(124): 27-40 |
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Shahraki M. The Determinants of Nursing Workforce Demand and Predicting the Number of the Required Nurses in the Public Hospitals of Iran (2018-2025). IJN 2020; 33 (124) :27-40
URL: http://ijn.iums.ac.ir/article-1-3190-en.html
URL: http://ijn.iums.ac.ir/article-1-3190-en.html
Faculty of Management and Human Sciences, Chabahar Maritime University, Chabahar, Iran (Corresponding author) Tel: +98-05431272241 Email: shahraki@cmu.ac.ir
Abstract: (4089 Views)
Background & Aims: The optimal and appropriate ratio of nurses is essential to an efficient healthcare system. In addition to decreasing the quality of health care, the shortage of nursing staff adversely affects the physical and mental characteristics of nurses. On the other hand, the supply surplus of nurses leads to high costs of medical service provision and waste of resources. In case of the surplus or shortage of nurses that could lead to the inefficiency of the healthcare system, adopting appropriate policies and proper planning to maintain equilibrium in the supply and demand of nurses are paramount. The present study aimed to evaluate the influential factors in the demand of nurses, predict the number of the required nurses, and determine the surplus or shortage of nurses in the public hospitals in Iran during 2018- 2025.
Materials & Methods: This analytical study aimed to determine the required nurses and the surplus/shortage of nurses in the hospitals affiliated to Iran University of Medical Sciences during 2018- 2025. To determine the number of the required nurses, the nurse demand function was initially estimated based on the most important influential factors using the autoregressive distributed lag (ARDL) method during 1994- 2017. The obtained results were used to predict the number of the required nurses during 2018- 2025. Before the estimation of the model, the stationary of the variables had to be ensured, for which the augmented Dickey-Fuller (ADF) test was used. The nurse short-term demand function was defined by selecting the optimal lags based on the Schwarz criterion (SIC) in the ARDL method, as follows:
: natural logarithm of the number of nurses per 1,000 population;
: natural logarithm of the number of nurses per 1,000 population with a one-time lag;
: natural logarithm of the number of nurses per 1,000 population with a two-time lag;
: natural logarithm of the gross domestic product (GDP) per capita based on the purchasing power parity;
: the ratio of people aged more than 65 years to those aged 14-65 years;
: the ratio of the out-of-pocket payments for health expenditures to the total health expenditures;
: the ratio of the out-of-pocket payments for health expenditures to the total health expenditures with a one-time lag;
: the number of hospital beds per 1,000 population;
: the coefficients of the model variables
To estimate the long-term demand function of nurses, the presence of long-term correlations had to be ensured, for which the F-test was used. If the F statistic value was higher than the critical value of the upper bound, the null hypothesis that there is no long-term correlation would be rejected, and if the F statistic value was less than the lower bound, the null hypothesis could not be rejected. Finally, if the F statistic value was between the two bounds, the result would be uncertain. To determine the surplus or shortage of nurses during 2018- 2025, the difference between the predicted values of the supply and demand of nurses was used. To predict the supply of nurses, the autoregressive integrated moving average (ARIMA) method was used based on the Box-Jenkins methodology in four steps of identification, estimation, diagnostic checking, and forecasting. The required data were the annual time series that were collected for the period of 1994- 2017. In addition, data on the GDP per capita, ratio of the out-of-pocket payments for health expenditures to the total health expenditures, and ratio of the people aged more than 65 years to those aged 14- 65 years were obtained from the World Bank databases, and the data on the number of nurses and hospital beds were extracted from the statistical yearbooks of the Statistics Center of Iran. The required models and tests were estimated in the EViews software version 10.
Results: The number of the nurses in the public hospitals per 1,000 population in 1994 was 0/207, while it was 1.12 in 2016 with the mean of 0/55±0/26 during this period. The natural logarithm of the GDP per capita during this period had an upward trend, with the mean value of 9/63 ± 0/13 per person. In addition, the mean ratio of the people aged more than 65 years to those aged 14- 65 years in this period was 7/33 ± 0/5, and the mean of the out-of-pocket payment for health expenditures to the total health expenditures was 53/53 ± 6/36. Before estimating the nurse demand function, the stationary of the variables had to be ensured using the ADF test, and the results showed that all the variables were non-stationary at the level, while they were stationary at the first difference. After determining the stationary of the variables, the short-term demand function of nurses was estimated using the ARDL method, and the results of the short-term nurse demand function indicated that the natural logarithmic coefficient of the number of nurses per 1,000 population with a one-time lag was 0/46 (i.e., 1% increase in the demand of this year would increase the demand of the next year by 0/46%). On the other hand, the natural logarithmic coefficient of GDP per capita was equal to 0/874. The coefficients of the ratio of the people aged more than 65 years to those aged 14- 65 years and the ratio of the out-of-pocket payments for health expenditures to the total health expenditures in the previous year were 0/37 and -0/015, respectively. To estimate the long-term demand function, the presence of a long-term correlation was initially evaluated using the F-test, and the nurse long-term demand function was estimated using the ARDL method. The F statistic value was 9/38, which was higher than the upper bound value at the significance of 5%; therefore, the null hypothesis regarding the lack of a long-term correlation was rejected. Furthermore, the obtained results indicated that the coefficients of the natural logarithmic of GDP per capita, ratio of the people aged more than 65 years to those aged 14- 65 years, and ratio of the out-of-pocket payments for health expenditures to the total health expenditures were 1/77, 0/76, and -0/0332, respectively. To determine the surplus or shortage of nurses during 2018- 2025, the difference between the predicted values for the supply and demand of nurses was used, and the obtained results showed that the predicted value of nurse demand was higher than the predicted value of nurse supply during 2018- 2025. In addition, the mean predicted values of the supply and demand of nurses during this period were 1/1622 and 1/3254 nurses per 1,000 population, respectively, which indicated the shortage of nurses by 0/17 per 1,000 population.
Conclusion: According to the results, the GDP and ratio of the people aged more than 65 years to those aged 14-65 years had a positive impact on the nurse demand, while the ratio of the out-of-pocket payments for health expenditures to the total health expenditures had a negative impact on this variable. Furthermore, a shortage of nurses is expected by 2025, and there is an urgent need for effective policies and proper planning to control this issue. In this regard, increased GDP and employment rates, strong incentives, and flexible employment contracts are proposed to prevent the early retirement of nurses.
Materials & Methods: This analytical study aimed to determine the required nurses and the surplus/shortage of nurses in the hospitals affiliated to Iran University of Medical Sciences during 2018- 2025. To determine the number of the required nurses, the nurse demand function was initially estimated based on the most important influential factors using the autoregressive distributed lag (ARDL) method during 1994- 2017. The obtained results were used to predict the number of the required nurses during 2018- 2025. Before the estimation of the model, the stationary of the variables had to be ensured, for which the augmented Dickey-Fuller (ADF) test was used. The nurse short-term demand function was defined by selecting the optimal lags based on the Schwarz criterion (SIC) in the ARDL method, as follows:
To estimate the long-term demand function of nurses, the presence of long-term correlations had to be ensured, for which the F-test was used. If the F statistic value was higher than the critical value of the upper bound, the null hypothesis that there is no long-term correlation would be rejected, and if the F statistic value was less than the lower bound, the null hypothesis could not be rejected. Finally, if the F statistic value was between the two bounds, the result would be uncertain. To determine the surplus or shortage of nurses during 2018- 2025, the difference between the predicted values of the supply and demand of nurses was used. To predict the supply of nurses, the autoregressive integrated moving average (ARIMA) method was used based on the Box-Jenkins methodology in four steps of identification, estimation, diagnostic checking, and forecasting. The required data were the annual time series that were collected for the period of 1994- 2017. In addition, data on the GDP per capita, ratio of the out-of-pocket payments for health expenditures to the total health expenditures, and ratio of the people aged more than 65 years to those aged 14- 65 years were obtained from the World Bank databases, and the data on the number of nurses and hospital beds were extracted from the statistical yearbooks of the Statistics Center of Iran. The required models and tests were estimated in the EViews software version 10.
Results: The number of the nurses in the public hospitals per 1,000 population in 1994 was 0/207, while it was 1.12 in 2016 with the mean of 0/55±0/26 during this period. The natural logarithm of the GDP per capita during this period had an upward trend, with the mean value of 9/63 ± 0/13 per person. In addition, the mean ratio of the people aged more than 65 years to those aged 14- 65 years in this period was 7/33 ± 0/5, and the mean of the out-of-pocket payment for health expenditures to the total health expenditures was 53/53 ± 6/36. Before estimating the nurse demand function, the stationary of the variables had to be ensured using the ADF test, and the results showed that all the variables were non-stationary at the level, while they were stationary at the first difference. After determining the stationary of the variables, the short-term demand function of nurses was estimated using the ARDL method, and the results of the short-term nurse demand function indicated that the natural logarithmic coefficient of the number of nurses per 1,000 population with a one-time lag was 0/46 (i.e., 1% increase in the demand of this year would increase the demand of the next year by 0/46%). On the other hand, the natural logarithmic coefficient of GDP per capita was equal to 0/874. The coefficients of the ratio of the people aged more than 65 years to those aged 14- 65 years and the ratio of the out-of-pocket payments for health expenditures to the total health expenditures in the previous year were 0/37 and -0/015, respectively. To estimate the long-term demand function, the presence of a long-term correlation was initially evaluated using the F-test, and the nurse long-term demand function was estimated using the ARDL method. The F statistic value was 9/38, which was higher than the upper bound value at the significance of 5%; therefore, the null hypothesis regarding the lack of a long-term correlation was rejected. Furthermore, the obtained results indicated that the coefficients of the natural logarithmic of GDP per capita, ratio of the people aged more than 65 years to those aged 14- 65 years, and ratio of the out-of-pocket payments for health expenditures to the total health expenditures were 1/77, 0/76, and -0/0332, respectively. To determine the surplus or shortage of nurses during 2018- 2025, the difference between the predicted values for the supply and demand of nurses was used, and the obtained results showed that the predicted value of nurse demand was higher than the predicted value of nurse supply during 2018- 2025. In addition, the mean predicted values of the supply and demand of nurses during this period were 1/1622 and 1/3254 nurses per 1,000 population, respectively, which indicated the shortage of nurses by 0/17 per 1,000 population.
Conclusion: According to the results, the GDP and ratio of the people aged more than 65 years to those aged 14-65 years had a positive impact on the nurse demand, while the ratio of the out-of-pocket payments for health expenditures to the total health expenditures had a negative impact on this variable. Furthermore, a shortage of nurses is expected by 2025, and there is an urgent need for effective policies and proper planning to control this issue. In this regard, increased GDP and employment rates, strong incentives, and flexible employment contracts are proposed to prevent the early retirement of nurses.
Type of Study: Research |
Subject:
nursing
Received: 2020/03/9 | Accepted: 2020/06/8 | Published: 2020/06/8
Received: 2020/03/9 | Accepted: 2020/06/8 | Published: 2020/06/8
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