Factors associated with the non-retention of health sector employees recruited in Iran: a survival analysis

Abstract

Background

Health staff are an essential component of the health system and a significant factor in improving health outcomes. As a result, without a suitable number of trained and supported workforces, health-related goals will not be achieved. As a result, one of the most significant tasks of the government and healthcare human resource management in healthcare organizations is focusing on development and maintenance of personnel.

Method

This study was a longitudinal and historical cohort study. Data was collected on the 40 medical universities and healthcare organizations under the jurisdiction of Iran Ministry of Health and Medical Education from 2008 to 2018 from the human resources database. In this study the COX regression and survival analysis was used to assess the factors of staff maintenance, retention rate, and the risk of turnover.

Result

46,939 health workforces were included in the study, of which 13,328 (28.4%) were men and 33,611 (71.6%) were women. Based on the finding of cox regression model, the retention of staff were statistically different between male and female. Employees whose current work place and birthplace were similar had a significantly higher probability of retention. Accordingly, the cox regression result showed, the risk of employee turnover for single personnel was higher than the married ones.

Conclusion

an applicable policy for increasing maintenance among workforce recruitment, could be considering the native born professionals instead of non-natives born which reduce the costs of employee turnover, including re-hiring, initial and on-the-job training, housing, and other extra living expenses away from home and family.

Introduction

Human resources are the core of any country’s health system and the most significant aspect of health care systems, and a critical component in health policies [1]. Human resources in the health sector have unique characteristics that make them difficult to manage, such as higher education, expensive specialized courses, providing vital services to customers, and services that cannot be easily replaced in a short period of time. These factors all contribute to the need for strategic human resource management in the health sector [2]. The World Health Organization’s Middle East Regional Office (EMRO) examined human resources in the health sector from a different perspective, emphasizing factors such as supply and demand imbalances, unequal geographical distribution in urban and rural areas, and an imbalance in the number of different professional groups. It reveals more facets of the difficulties confronting the development of the healthcare sector. Developing a workforce and human resources for health is a strategic need, not a choice [3].

Health professionals are an essential component of the health-care system and play a significant role in improving health outcomes. As a result, without a sufficient number of trained and supported workers, health-related goals will not be achieved [4]. One of the barriers to achieving health goals is a lack of a trained and motivated personnel, as well as a lack of factors affecting the longevity of health care workers, which is critical for the health system’s effectiveness [5]. As a result, one of the most essential roles of the government and the health human resources management is to keep employees in health care organizations. A more motivated workforce, a healthier population, improved health outcomes, and increased access to health care services will result from adequate attention to the development and maintenance of human resources required for health sector programs [6]. Because of the importance of the issue of retention, various governments have implemented a variety of laws and methods to protect and sustain human resources [7]. One of these policies that has been widely employed in Iran in recent years is the policy of native born professional recruitment in government recruitment, which has been applied in the recruitment of human resources by the Ministry of Health and Medical Education in recent years. The philosophy of this policy is to reduce the costs of employee turnover, including re-hiring, initial and on-the-job training, housing, and other extra living expenses away from home and family [8].

Several developing countries also have low retention rates. For example, in Guinea [9], 69% of officially developed health personnel were kept after one year, 85% in Cameroon [10] after a three-year follow-up, and 78.2% in Thahiland [11].

A cross-sectional study that was done in Southwest Ethiopia, indicated that there were a significant relationship between sex, marital status, education, and accommodation conditions with the turnover intention among health workforces and the prevalence of turnover intention among healthcare workers in this country was 58.09 [12]. Some studies found there was a significant association between age and intend to remain in job, married employees were more intended to stay in work place than single employee [13, 14].

Medical doctor’s turnover in Iraq was reported to be 55.2% [15]. The turnover intention of health workers in sub-Saharan Africa (51.96%) [16], Saudi Arabia (40%) [17], China (30.4%) [18], and Taiwan (14.5%) [19] and in Iran (32.7%) [20]. The probability of turnover for health professional employee in Mashhad in one year, two year and five years of employment were 12%, 16%, and 27% respectively [21].

Given the lack of competent professionals in Iran, and the fact that staff turnover imposes direct and indirect costs such as the hiring process, onboarding and on-the-job training, improper use of the employment quota license, and being a time-consuming process, it is critical for managers in Iran to understand and predict how long employees will stay in the organizations, and what factors influence this workplace turnover [22]. Therefore, this study aims to determine the factors associated with the retention longevity of recruited employees at the Iran Ministry of Health and Medical Education and related Medical Universities.

Method

We used a longitudinal and historical cohort study that included 46,939 hired employees at the medical universities and healthcare organizations under the jurisdiction of Iran Ministry of Health and Medical Education from 2008 to 2018. The employee’s status, including replacement, workplace change, occupation, long-term leave, laying off, leave without payment and demographic profiles of the employees, date of employment and date of turnover were sent to the universities in a researcher-made checklist along with the instructions for collecting information through a circular. Checklist items prepared after multiple talks in specialized and expert meetings according to research objectives, effective elements in the recruitment of employees in the Ministry of Health of Iran.

In the next stage, the quality of the information sent by the universities was evaluated, and the existing deficiencies and problems were corrected and completed by providing appropriate feedback to the universities. Finally, after the follow-ups, the information of 40 universities of medical sciences had the necessary quality and their information was included in the study. We tried to minimize the amount of missing data. To ensure the quality of the collected data and minimize the amount of data lost (Missing), 1) a step-by-step guide was prepared to complete the forms. also2) representative data from each university were selected to appraise the accuracy and precision of data collection and its process. 3) In the next step, the final information completed by the universities was evaluated, and the existing shortcomings and problems were eliminated by providing appropriate feedback to the universities. 4) Finally, a data expert cleaned and standardized all data.

Univariable cox regression was used to investigate the factors affecting personnel retention and the Multi-variable analysis was used to alter the confounding effects of variables with less than 0.2 p-values calculated in the univariate model, the Cox proportional hazards model using forward method was used to simultaneously investigate the association between independent variables and personnel retention. Statistical analysis was carried out in SPSS.

Result

In this study, 46,939 employees working at 40 Medical Universities between the years 2008 to 2018 were investigated that 13,328(28.4%) were men and 33,611(71.6%) were women. The average age of employees hired during the last ten years was 35.37 ± 5.24. Regarding educational levels: 8466 (18%) had less than a bachelor’s degree, 34,278 (73%) had a bachelor’s degree, 2938 (6.3%) had an upper bachelor’s degree, and 1257 (2.7%) had medical degrees. Also, 21,686 (46.2%) of the employees were nurses, 375 (0.8%) were assistance nurses, 5351 (11.4%) were other nursing group jobs, 3145 (6.7%) midwives, 751 (1.6%) were general practitioners, 375 (0.8%) specialist physicians, 9575 (20.4%) other health care jobs and 5632 (12%) non-healthcare jobs.

Based on the findings of the survival analysis, the probability of turnover for native-born professionals in 2008 was 1.6 and this percentage increased to 8.2 in 2018 and this percentage was 2.8 in 2008 and 17 in 2018 for non-native born professionals.

Table 1 shows the factors associated with probability of non-retention using the COX model. In this table, three models are reported. In the first model, each of the variables was entered into Cox regression one by one. The hazard ratio and P-value has been reported for them. As shown in model 1, gender (95% confidence interval: 1.11–1.30; HR = 1.21), non-native born employment (95% confidence interval: 1.82–2.12; HR = 1.96), bachelor’s degree (95% confidence interval) Confidence: 0.72–0.89 (HR = 0.79), and higher than bachelor’s degree (95% confidence interval: 0.69–0.97; HR = 0.82) all have a significant relationship with non-retention rate. Furthermore, the rate of non-retention in the recruitment workforce before the localization policy is higher than after the implementation of the localization policy, which is statistically significant (95% confidence interval: 1.03–1.20; HR = 1.12). In the second model, the factors whose P-value is less than 0.2 in the first model were included in the analysis. The obtained results indicated that there is a significant difference between non-retention and gender (95% confidence interval: 1.22–1.03; HR = 1.12). There is a substantial correlation between non-native born employment (95% confidence interval: 1.81–2.10; HR = 1.95), bachelor’s degree (95% confidence interval: 0.76–0.93; HR = 0.84) and non-retention in the workforces was higher prior to the implementation of the localization strategy, which is statistically significant (95% confidence interval: 1.02–1.19; HR = 1.11). In the third model, to exclude the effect of the confounding variable, we enter all the variables combined in a stepwise regression analysis, as can be shown between non-retention and male gender (95% confidence interval: 1.23–1.03; HR = 1.13), priority non-native born employment (95% confidence interval: 1.82–2.10; HR = 1.95), and localization policy (95% confidence interval: 1.19–1.02; HR = 1.12) remain significantly related.

Table 1.

The results of multivariate Cox regression to investigate the factors affecting the non-retention rate of the recruited workforce between 2008 and 2018

Variables	Model 1		Model 2		Model 3
	Hazard Ratio (95% CI)	P-value	Hazard Ratio (95% CI)	P-value	Hazard Ratio (95% CI)	P-value
Sex
Female	reference	reference	reference	reference	reference	reference
male	1.21 (1.11–1.30)	< 0.001	1.12 (1.03–1.22)	0.007	1.13 (1.03–1.23)	0.006
Recruitment priority
Native born	reference	reference	reference	reference	reference	reference
Non-native born	1.96 (1.82–2.12)	< 0.001	1.95 (1.81–2.10)	< 0.001	1.95 (1.82–2.10)	< 0.001
Length of commitment
0–5	reference	reference	reference	reference	reference	reference
6–10	1.08 (1.06–1.10)	< 0.001	1.06 (1.03–1.07)	< 0.001	1.05 (1.03–1.07)	< 0.001
> 10	1.02 (0.87–1.17)	0.820	-	-	0.986 (0.85–1.14)	0.856
Level of education
Under license	reference	reference	reference	reference	reference	reference
license	0.79 (0.79–0.82)	< 0.001	0.84 (0.76–0.93)	0.001	0.83 (0.74–0.92)	0.001
Up license	0.82 (0.69–0.97)	0.02	0.85 (0.71–1.02)	0.07	0.84 (0.71–1.01)	0.06
Medicine	0.85 (0.67–1.08)	0.19	0.90 (0.70–1.15)	0.41	0.84 (0.66–1.07)	0.21
Service unit
Hospital	1 (0.92–1.08)	0.998	-	-	0.85 (0.82–1.37)	0.51
headquarters	0.938 (0.85–1.02)	0.15	0.92 (0.84–1.01)	0.10	0.79 (0.49–1.28)	0.36
Health care service center	1.09 (0.98–1.22)	0.09	0.96 (0.85–1.09)	0.59	0.84 (0.51–1.37)	0.48
other	1 (0.92–1.08)	0.99	-	-	-	-
Type of personnel
Medical Group	1.01 (0.95–1.07)	0.719	-	-	0.48 (0.48–1.06)	0.73
Nursing group	1.003 (0.98–1.02)	0.762	-	-	0.99 (0.62–1.36)	0.69
Other healthcare	0.97 (0.94-1.49)	0.97	-	-	0.92 (0.62–1.36)	0.69
Non-health care	1.28 (1.06–1.29)	< 0.001	1.19 (1.06–1.34)	0.003	1.15 (0.78–1.70)	0.46
local/regional strategies
After applying the policy	reference	reference	reference	reference	reference	reference
Before applying the policy	1.12 (1.03–1.20)	0.004	1.11 (1.02–1.19)	0.002	1.12 (1.02–1.19)	< 0.001

Table 2 displays the results of a Cox regression to analyze the association between being native born and retention by classifying the effective components. As reported in the table, there is a relationship between being native born and retention with gender male (95% confidence interval: 1.62–2.11; HR = 1.85)/ female (95% confidence interval: 1.84–2.21; HR = 2.02), lower educational levels From bachelor’s degree (95% confidence interval: 1.46–2.03; HR = 1.72)/ bachelor’s degree (95% confidence interval: 1.84–2.21; HR = 2.02)/ higher than bachelor’s degree (95% confidence interval: 1.41–2.59; HR = 1.91) / Medicine (95% confidence interval: 1.68–4.11; HR = 2.62), duration of commitments between zero and five years (95% confidence interval: 1.99–1.65; HR = 1.81)/ six to ten years (95% confidence interval: 2.09–2.72; HR = 2.39), hospital (95% confidence interval: 1.89–2.28; HR = 2.07)/ headquarters (95% confidence interval: 1.46–2.01; HR = 1.71)/ health centers (95% confidence interval) Confidence: 2.25–1.49; 1.83 h=)/other (95% confidence interval: 8.9–1.28; 3.39 h) and rich regions of the country (95% confidence interval: 1.74–2.34; 2.02 h)/ semi-rich (95%) Confidence interval: 2.19–1.81; HR = 1.99) and non-privileged (95% confidence interval: 3.54–1.85; HR = 2.56) there is a statistically significant relationship. In such a way that non-native born personnel in non-privileged areas have not been permanent 2.56 times more than native born workforces, that is, most of them have left their workplaces, and this value is 2.02 for privileged areas and 1.99 for semi-privileged areas. In fact, in general, non-native born workers have left their workplaces almost twice as often as native workers which is shown separately in the table below.

Table 2.

The effective factors on the retention rate of the native born recruited workforce by the Cox Model

Effective factors	0.95% CI	HR	P-value
areas
Privileged	1.74–2.34	2.02	< 0.001
Semi-Privileged	1.81–2.19	1.99	< 0.001
Under-served	1.85–3.54	2.56	< 0.001
Sex
Male	1.62–2.11	1.85	< 0.001
Female	1.84–2.21	2.02	< 0.001
Level of education
Under license	1.46–2.03	1.72	< 0.001
license	1.84–2.21	2.02	< 0.001
Up license	1.41–2.59	1.91	< 0.001
Medicine	1.68–4.11	2.62	< 0.001
Duration of obligations
0–5	1.65–1.19	1.81	< 0.001
6–10	2.09–2.72	2.39	< 0.001
> 10	0.22–2.76	0.796	0.720
Service unit
Hospital	1.89–2.28	2.07	< 0.001
headquarters	1.46–2.01	1.71	< 0.001
Health care service center	1.49–2.25	1.83	< 0.001
other	1.28–8.09	3.39	0.01

Table 3 displays the native born employees’ retention rate from 2008 to 2018. Based on the result, the retention rate of the native born workforces in the first year was 98.4%, implying that in the first year, the risk of turnover of the native born recruited workforces was 1.6%. The rate in the second year following recruitment (2009) is 97.7%, and the risk of turnover was 2.3%, and in the same way, the rate of retention and risk of turnover of native born workforces have been reported until the last year. The retention rate declined by approximately 0.5–1%, and in 2017, the retention rate of native born workforces was 92.4%.

Table 3.

The retention rate of the native-born recruited workforce during the years 2008–2018

Time(Year)	Estimation (retention estimation rate)	SE	Number (Persons non-retention)	Hazard (risk level for non-retention)
2008	98.4	0.001	526	1.6
2009	97.7	0.001	734	2.3
2010	96.9	0.001	918	3.1
2011	96.1	0.001	1074	3.9
2012	95.4	0.001	1222	4.6
2013	94.7	0.002	1336	5.3
2014	94	0.002	1436	6
2015	93.5	0.002	1510	6.5
2016	93	0.002	1542	7
2017	92.4	0.002	1567	7.6
2018	91.8	0.003	1574	8.2

Table 4 shows the retention rate of the non-native born workforce from 2008 to 2018 decreased by 1.4% yearly. The retention rate in the first year after employment was 97.2, and the risk of non-retention turnover was 2.8%. Similarly, until the years 2018, the retention rate decreased to 83% and the rate of turnover increased to 17%, which was showed that the retention rate of the native born recruited workforce was 8% higher than the non-native born workforce.

Table 4.

The retention rate of non-native recruited workforces over the years 2008–2018

Time(Year)	Estimation (retention estimation rate)	SE	Number (Persons non-retention)	Hazard (risk level for non-retention)
2008	97.2	0.001	374	2.8
2009	96	0.002	506	4
2010	94.7	0.002	622	5.3
2011	93	0.002	761	7
2012	91.7	0.003	862	8.3
2013	90.2	0.003	963	9.8
2014	88.8	0.003	1050	11.2
2015	87.3	0.004	1124	12.7
2016	85.8	0.004	1167	14.2
2017	84.3	0.005	1195	15.7
2018	83	0.007	1201	17

Discussion

Turnover among health workers is a pressing issue that requires prompt attention [23]. The purpose of our study was to examine the turnover and the factors related to the non-retention of newly hired employees in the medical universities and healthcare organizations under the jurisdiction of Iran Ministry of Health and Medical Education. Over the research period, Iran Ministry of Health experienced a turnover rate of 10% for newly hired employees.

Survival analysis showed the probability of non-retention for a native born recruited workforce individual within one year, five years, and ten years of employment were 1.6%, 4.6%, and 8.2% respectively and 91.8% of newly hired employees had never left their workplace. This probability for non-native born recruited workforce were 2.8%, 8.3%, and 17% and the 83% of them were remained in their workplace.

In this study, the retention of all healthcare personnel was about 90%, and no significant relationship was seen between the retention of the medical, nursing, and other healthcare groups. However, the non-heath care group had shorter retention than the healthcare groups. In the studies conducted so far in different countries in the field of health sector workforce retention, the retention rate has been different, so this rate among nurses in 21 hospitals in Australia was 71.9% [24], in the Southwest of Ethiopia in 5 years was 50% [25], 92% [26] in Niagara country, 67.2% retention of doctors in Japan [27]. This different retention rate is due to different types and levels of working and living conditions, welfare services, job security, available facilities and other factors between countries. In Iran, in a study conducted in Isfahan, the retention rate of public hospital nurses was reported to be 95.8% [28] another study in Iran indicated 52.2% of nurses tended to leave their job and 40% of them had very high tendency to leave their jobs [29], the score of health care workers in china was 2.15 ± 1.03 and 19.5% of respondents reported a higher turnover intention [30]. However, studies of this scope have yet to be conducted to investigate the retention of the health sector’s human resources in Iran. A study that conducted in Mashhad, Iran was found the turnover rate of health workforces who worked less than 5 years were 27% and this proportion for those who worked less than 10 years were 40% [21]. Considering a lot of time and cost spent on education and training, healthcare professionals are only sometimes replaceable. For the significance of this field, especially in terms of the health of society, proper measures must be implemented for the retention of these workforces. Retention of health workers refers to efforts, rules, and overall strategies for keeping employees in practice and preventing attrition [31].

Our study showed that the retention rate of native born recruits at the end of the 10th year after applying the local recruitment policy was 91.8% for native born recruits. In comparison, this rate for non-native recruits was 83%, which shows that the retention rate of native born recruits is about 8% higher. A study conducted in Iceland, Ireland, Norway, Scotland, Sweden and Greenland showed that there was a significant relationship(x² = 3.98, p < 0.05) between having a rural background and intending to stay with the same rurally located organization [32]. A study conducted in North Carolina among primary care physicians showed that the average length of tenure in rural and urban areas was relatively short, however, being about 4.5 years [33].

This study observed a positive and significant relationship between gender and retention rate; namely, women had more retention. This result aligned with studies conducted among hospital employees in southwest Ethiopia [25] and Iran [34]. The retention rate of women among family doctors in Kermanshah was also higher than men [35]. In China there was a higher rate of turnover intention for male [36]. A significant relationship was observed between the duration of commitments and the retention rate. In this regard, people whose duration of commitments was up to 5 years had more retention than those with higher duration of commitments.

We note that non-native born workforces had left their workplaces 2.56 times more than native born workforces in underserved areas. This finding implies that governments should pay more attention to the recruitment of native and local applicants in order to absorb the required forces of the health system in underserved areas. Most low and middle-income countries continue to face challenges in retaining health workers in rural and difficult-to-access locations, which has a negative influence on health care delivery [37]. However, for the sake of justice, the development of infrastructure and amenities in these areas is a top priority.

Our cross-country results strongly confirm that more duration of the binding commitments, determined for recruited people, is not an influential factor for longer retention. The forces with 6 to 10 years of involuntary commitment had less retention rate than those with commitments between 0 and 5 years. Especially the negative effect of the long duration of commitments on retention was more noticeable in disadvantaged areas, so people who worked in underserved areas without any commitment compared to people in privileged areas who had commitments 1.28 times more left their place of employment. Results show that the effect of regional deprivation is more decisive than commitment in the retention of recruits. Therefore, interventions for creating suitable and sufficient facilities are needed to increase the retention of recruits in underserved areas. There is a global issue in which isolated and rural places have considerably less human resources for health that their population [38]. Universal health coverage will be impossible to accomplish if certain people continue to have insufficient access to qualified healthcare workers [39]. Consistent with the results of our study, some studies show a variety of factors, including rural/urban upbringing, age, family composition and gender, are essential to staying in rural areas. Norway estimated that 20% stay in rural areas. Nigeria anecdotally stated that people only stay in rural areas after completing the requirement [40].

The results of this study showed that the 100% local recruitment policy was adequate, and the retention rate of the workforce was higher after implementing this policy. A Canadian study showed that for shortage and misdistribution of the physician supply that is made challenging for residents to access primary medical care, the basic solution is to ensure that locally trained physicians and recruit them to work in their area a fundamental policy [41].

In summary, this analysis represents one of the first attempts to measure the effectiveness of a policy for human health resources at the national level. Findings of this kind help policymakers to strengthen human resources for health through data and evidence-based decisions and policies.

Limitations

The present study had some limitations. It is difficult to collect data related to the movements of forces in the last ten years due to the weaknesses of the information systems, and there will be errors in the data. We tried to reduce data limitations by compiling standard forms, monitoring data collection, checking and correcting several times and finally, data cleaning and standardization. Also, there needed to be more studies related to the retention rate and the effectiveness of retention solutions. We used the entire experience and studies available in this field by increasing the scope of the search and broadening the definitions.

Conclusion

For nearly three decades, the importance of human resources has been emphasized as the most valuable asset to the health systems and for their success in achieving universal health coverage. Today human resource managers believe that selecting and appointing competent people according to the correct positions is a way to show understanding of this valuable asset. Therefore it is recommended to establish laws for attracting and maintaining human resources in required job positions and geographical areas. The evidence shows that interventions involving strong coercion are associated with lower rural retention than interventions involving less coercion. Interventions such as preferential hiring of rural human resources and distributed training in rural areas have been more linked to increased rural health professional retention. Policymakers seeking to retain rural residents in the medium and long term should strengthen rural training pathways while limiting highly coercive interventions.

Abbreviations

CI

Confidence Interval

HR

Hazard Ratio

SE

Standard Deviation

Author contributions

Data gathering: F.G, F.Y. Analysis: M.KH, F.G, R.KH, and M.B. Methodology: B.R, B.T, M.B, R.K, T.K, M.KH, F.G. Assessment: M.S, B.R, B.T, R.KH, M.B and T.K. Writing Original draft: F.G, RKH, and M.B. Writing review & editing: B.R, B.T, M.S, R.KH, M.B, T.K, F.G, M.KH and F.Y. Also all the authors reviewed the draft and approved the final version of the manuscript.

Funding

Not applicable.

Data availability

The datasets collected and/or analyzed during this study are not publicly available due to privacy and confidentiality considerations, but are available from the corresponding author on reasonable request.

Declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.