Estimating the Economic Burden of COVID-19: Findings from Southern Iran

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RESEARCH ARTICLE

Estimating the Economic Burden of COVID-19: Findings from Southern Iran

The Open Public Health Journal 02 Dec 2024 RESEARCH ARTICLE DOI: 10.2174/0118749445351894241031101616

Abstract

Background and Objective

COVID-19 is an acute respiratory disease that poses a significant threat to public health. Recognizing the impact and costs associated with this disease can assist policymakers in making informed decisions about the allocation of resources. Therefore, this study estimated the economic burden of COVID-19 from a societal perspective, focusing on patients admitted to the referral hospital for this disease in southern Iran in 2022.

Methods

This descriptive and analytical study employed a random sampling method to select patients admitted to hospitals affiliated with Jiroft University of Medical Sciences in southern Kerman, Iran. The sampling period spanned from March 2020 to February 2021. In total, 450 patients were included in this study. A researcher-made checklist was used as the data collection tool. The data were then analyzed using a t-test, ANOVA, and Pearson correlation coefficient. The analysis was performed using SPSS23, with the significance level set at 0.05.

Results

The study estimated the total direct cost, total indirect cost, total cost of the studied patients, and the economic burden of COVID-19 disease as 600,211.89, 193,667.28, 793,879.17, and 4,186,389.48 dollars, respectively. A statistically significant correlation was found between length of stay (r=0.858, p<0.001), type of insurance (F=2.853, P=0.023), and total direct cost.

Conclusion

Based on these results, it is evident that COVID-19 has imposed a substantial economic burden. The study findings emphasize the need for decision-makers to take measures and implement effective interventions to address COVID-19. This can be achieved by controlling risk factors, drawing upon successful experiences from other countries, and following recommendations provided by international organizations. By reducing the number of infections, the economic burden associated with the disease can be alleviated.

Keywords: Cost of illness, Health care costs, Health economics, Hospital costs, COVID-19, Respiratory Disease.

1. INTRODUCTION

The rapid spread of COVID-19 across various parts of the world has raised significant concerns in numerous countries as they encounter a large number of COVID-19 cases [1]. Iran officially declared the outbreak of this disease on February 18, 2020, and it is currently experiencing its seventh wave. Given the virus’s ability to spread rapidly, the reported incidence and mortality rates continue to be alarming [2].

While the primary and most crucial aspect of an epidemic is the suffering and loss of human lives, its impact extends beyond the healthcare system and affects all sectors of the country [3]. The economic sector is one of the areas profoundly affected by epidemics. Previous studies have emphasized the economic consequences of epidemics or pandemics, demonstrating their significant effects on the overall economy. Estimates suggest that the total economic loss caused by epidemics such as influenza amounts to approximately 500 billion dollars annually, equivalent to 0.6% of global income [4].

The economic impact of epidemic diseases varies across income groups, with middle and low-income countries experiencing more severe consequences than high-income countries [ 5 ]. The joint report by the World Health Organization and World Bank highlights the significant impact of epidemics. For instance, a report from the United Nations Development Program in 2017 estimated that the Zika virus epidemic in Latin America and the Caribbean from 2015 to 2017 incurred costs ranging from 7 to 18 billion dollars [ 4 ].

In recent decades, healthcare costs worldwide have increased significantly [ 6 ]. Consequently, health policymakers and planners have recognized the importance of understanding the economic costs associated with diseases. This understanding is crucial for evaluating the allocation of healthcare resources across different disease categories and for assessing the potential benefits of public health interventions [ 7 ]. These costs can arise directly from the diagnostic and therapeutic services provided to patients or indirectly from the loss of productivity and quality of life [ 6 ].

Illness costs can be categorized into two main groups: direct and indirect costs. Direct costs refer to the expenses incurred directly in providing healthcare services to patients. This includes both direct medical and non-medical costs. On the other hand, indirect costs pertain to the economic impact of the patient’s lost productivity due to illness [8].

Direct costs are further divided into two subcategories: direct clinical and direct non clinical costs. Direct clinical costs contain expenses related to hospitalization, surgeries, outpatient visits, radiation therapy, and chemo- therapy/immunotherapy. Direct non clinical costs, borne by patients and their families, include transportation costs to healthcare centers and expenses related to comple-mentary and alternative medicines [9].

European data demonstrate substantial macro- economic consequences of the COVID-19 pandemic, with an average decline in GDP of 7.4% in 2020. These effects vary among countries within the region. Without appropriate policy measures, it is estimated that the pandemic will disproportionately affect lower-income individuals, leading to increased poverty rates and overall inequality [10]. The US Council of Economic Advisers also projected that in the absence of economic support policies, low income individuals would experience an average loss of over 10% of their disposable income in the first quarter of 2020.

Initially, there was a scarcity of cost data regarding the burden of COVID-19, as is the case with any new disease. However, owing to the profound economic burden of this disease, numerous studies have recently focused on the medical costs of COVID-19 in different regions. Given the increasing number of these studies and their significant implications for healthcare budgets and the global economy, it is crucial to understand and consolidate these data to provide policymakers with a comprehensive understanding of the overall burden of COVID-19 [11].

A study conducted by Bartsch et al. (2020) in the US revealed that the infection rate of COVID-19 resulted in direct medical costs amounting to 163.4 billion dollars. The average direct medical cost for a symptomatic patient, including infection and one-year post hospital discharge, was $3,994. For hospitalized COVID-19 patients, the average cost was $18,579, considering the infection and one-year post-hospital discharge. Interestingly, the costs of hospitalization for COVID-19 patients were relatively similar across all age groups [11].

In the study by Gedik et al. (2020), the average clinical cost for patients with COVID-19 was reported to be $881.75 ± 667.31. For patients requiring Intensive Care Unit (ICU) treatment, the average cost was $2924 ± 2347.14 [12].

Based on a study by Darab (2021) conducted in Iran, the total direct medical costs for COVID-19 patients in a referral hospital in Fars province were estimated to be $1,791,172. Among the cost items, the highest cost was attributed to hospitalization, totaling $735,510. The average direct medical cost was estimated to be $3,755. The highest indirect costs were associated with the loss of productivity due to premature death, amounting to $10,190, whereas the lowest indirect costs were related to the loss of income due to hospitalization, totaling $378 [13].

To assess the economic burden of a disease, it is necessary to calculate both direct costs associated with resource allocation to medical care and indirect costs resulting from the economic resources and opportunities lost due to the disease and its treatment [9]. Gathering information on direct and indirect costs provides health policymakers with an understanding of the economic burden imposed by COVID-19 on governments, health insurance systems, and individuals. Such studies generate evidence to guide the rational allocation of limited resources to the healthcare sector during decision making processes [14]. Additionally, these studies can identify diseases that are more valuable in reducing the overall disease burden [15].

Several studies have been conducted in Iran to calculate the economic burden of various diseases, including cardiovascular diseases [16], eye diseases [17], hepatitis B [18], breast cancer [9], thalassemia [19], hepatitis C infection [20], and road accidents [21]. However, limited research has been conducted to evaluate the economic burden of this disease, which has become an epidemic in society. Therefore, this study aimed to calculate the economic burden of COVID-19 in patients hospitalized in 2022.

According to what was said, in this research, we looked for the answers to the following questions:

What are the direct and indirect costs of hospitalized patients with Covid-19?

What is the economic burden of COVID-19 in patients hospitalized?

The findings of this research, while helping to expand knowledge about the costs of the COVID-19 pandemic, have developed the awareness of health system managers and policy makers about the economic burden of such pandemics and can be a basis for planning to prepare for future similar crises.

2. METHODS

2.1. Design and Setting

This descriptive-analytical and microeconomic eva- luation study used a random sampling method among patients hospitalized in hospitals affiliated with Jiroft University of Medical Sciences in Southern Kerman, Iran, between March 2020 and February 2021. The hospitals included in this study were Imam Hossein, Imam Khomeini, Ayatollah Kashani, 12 Farvardin, Seyyed al-Shohada, and Shohada.

2.2. Participants

According to the following formula, the estimated sample size was 384 individuals. However, to enhance the validity of the results, we increased the sample size to 450 individuals. This figure was chosen based on the number of inpatients in the studied hospitals and the number of inpatients in different months, using a stratified simple random sampling approach.

The sample size of required patients in each of Imam Hossein, Imam Khomeini, Ayatollah Kashani, 12 Farvardin, Seyyed al-Shohada, and Shohada hospitals was estimated as 33, 208, 62, 89, 30, and 28, respectively.

n=Z2pq/d2

P=q= 0.5

Z= 1.96

d= 0.05

2.3. Instruments

The data collection tool used in this study was a checklist developed by the researchers. This checklist includes various demographic characteristics of the patients, such as age, sex, length of stay, discharge method, and type of insurance, as well as direct costs associated with visits, consultations, nursing services, pharmaceutical services, CT scans, ECG, laboratory tests, dialysis, radiology procedures, ultrasound, MRI, ward equipment, hoteling, and bed charges. Furthermore, the checklist incorporated relevant information needed to estimate indirect costs, such as the per capita GDP of Iran, the average daily wage rate, and the average number of days patients stayed in the hospital.

2.4. Procedures and Statistical Analysis

To collect information about direct costs, the researchers (ARY) referred to the accounting unit of the studied hospitals and obtained patients’ invoices from the Health Information System (HIS) within the desired time period. The checklist of direct costs was completed based on these invoices.

For calculating indirect costs, the human capital approach was employed as follows:

2.4.1. Estimating the Economic Loss in Potential Production due to Mortalities

The economic loss in potential production due to mortalities was determined by multiplying the GDP per capita by the average years of life lost due to premature death. In this study, Iran’s GDP per capita in 2021 was estimated based on the Central Bank report, and the average years of life lost due to premature death were calculated using the number of patients who died between the ages of 15 and 65 years.

2.4.2. Estimating the Economic Burden of Lost Productivity due to Illness and Disability

The economic burden of lost productivity due to illness and disability was calculated by multiplying the average days of hospitalization by the average daily wage rate and the total number of patients.

It should be noted that the average daily wage rate in 2021 was determined to be 15.16 dollars, according to a report by the Ministry of Cooperation, Labor, and Social Welfare of Iran.

2.4.3. Indirect Costs Related to the COVID-19

The indirect cost was estimated by adding the economic loss in potential production due to mortalities to the economic burden of lost productivity due to illness and disability. The economic burden of COVID-19 was then calculated using the following formula:

Economic burden of COVID-19 = total mean cost per person (total direct costs + total indirect costs) divided by the total number of studied patients (450) multiplied by the number of patients with COVID-19 in the studied period [13].

Demographic and cost information of the patients was collected confidentially and imported into SPSS23 without disclosing the patients’ names. The data were analyzed using descriptive and inferential statistical methods, including Pearson correlation coefficient, T-test, and ANOVA, with a significance level of α=0.05.

3. RESULTS

3.1. Demographic Characteristics of the Study Patients

The mean age of the patients included in the study was 55.88 ± 19.11 years, with the majority (18.89%) falling within the age range of 61 to 70 years. The majority of the patients were male (51.3 percent), and a significant portion had health insurance (69.8 percent). On average, the patients stayed in the hospital for a duration of 5.56 ± 4.14 days, with the majority (49.55%) being hospitalized for less than 5 days. Furthermore, 87.34% of patients recovered and were discharged from the hospital. Table 1 shows the frequency distribution of study patients.

3.2. Frequency of Services Provided to the Study Patients

Based on the findings, the highest number of services provided to the study patients was related to pharmaceutical services, with an average of more than 9,000 drugs administered (equivalent to an average of 22.05 drugs per patient) (Table 2).

Table 1.
Demographic characteristics of the study patients (n=450).
Variable Group Number Percent
Sex Female 219 48.7
Male 231 51.3
Age (year) < 20 3 0.67
20-30 35 7.78
31-40 79 17.56
41-50 72 16
51-60 68 15.11
61-70 85 18.89
71-80 52 11.55
81-90 40 8.89
>90 16 3.55
Length of stay (day) <5 223 49.55
5-10 189 42
11-15 25 5.56
16-20 6 1.34
> 20 7 1.55
Type of insurance Therapeutic services 314 69.8
Social security 106 23.6
Armed forces 14 3.1
Others 8 1.8
Without insurance 8 1.8
Discharge method Recovered 393 87.34
Passed away 36 8
Transferred 7 1.56
Discharged with personal consent 14 3.1
Table 2.
Frequency of services provided to the study patients (n=450).
Type of Service Frequency Mean Standard Deviation
Visit 3276 7.28 8.60
Consultation 270 0.60 1.24
Nursing 459 1,02 3.44
CT scan 400 0.89 0.71
ECG 513 1.14 1.42
Laboratory 2799 6.22 6.60
Medications 9922 22.05 17.50
Radiography 99 0.22 0.65
Ultrasound 58 0.13 0.41
MRI 5 0.01 0.07
Dialysis 18 0.04 0.52
Ward equipment 5668 12.59 12.96
Hoteling and bed 2484 5.52 4.03

Table 3.
Portions of primary and supplementary insurances, subsidies, patient contributions, and non-subsidized expenses in the total direct costs of study patients (N=450).
Category Amount Mean SD Percent Out of the Total Costs
Primary insurance 503458.21 1118.79 1425.02 83.88
Subsidy 27008.46 60.01 109.21 4.50
Supplementary insurance 4459.33 9.90 81.67 0.74
Patient 56219.69 124.93 235.85 9.37
Non-subsidized expenses 9066.18 20.14 169.20 1.51
Total 600211.89 1333.80 1315.02 100
Note: * The figures are denominated in US dollars.
Fig. (1).

Portions of primary and supplementary insurances, subsidies, patient contributions, and non-subsidized expenses in the total direct costs of study patients.

3.3. Direct Costs of all Study Patients

Based on these findings, the total direct cost was calculated to be $600,211.89. Out of this total amount, primary insurance accounted for 83.88% ($503,458.21), subsidies accounted for 4.50% ($27,008.46), supple- mentary insurance accounted for 0.74% ($4,459.33), patients contributed 9.37% ($56,219.70), and 1.51% ($9,066.18) were non-subsidized expenses. These figures are summarized in Table 3 and Fig. (1).

The results showed that among the types of services, pharmaceutical services accounted for the most direct costs, with 40.74% of the total costs ($244,578.16) (Tables 4 and 5).

3.4. The Relationship between the Direct Costs and Demographic Variables of the Study Patients

Based on the data presented in Table 6, a statistically significant correlation was found between age and the costs associated with visits, consultations, nursing services, CT scans, ECG, ultrasound, MRI, ward equipment, and hoteling and bed. This indicates that as age increased, the costs associated with these services increased (p < 0.05). Additionally, a statistically significant relationship was observed between the length of stay and both the total direct cost and direct costs categorized by service type. As the length of stay increased, the direct costs associated with specific services and the total direct cost increased (p < 0.05).

The findings indicated a statistically significant relationship between the type of insurance and the costs of visits, dialysis, and total direct costs (p < 0.05). Additionally, a statistically significant relationship was observed between the method of discharge, costs of consultation, nursing services, CT scans, laboratory tests, ultrasound, MRI, ward equipment, and hoteling and bed (p < 0.05).

Table 4.
Portions of primary and supplementary insurances, subsidies, patients, and non-subsidized expenses in the direct costs of study patients based on type of service.
Type of Service Total Amount Total Primary Insurance Total Subsidy Total Supplementary Insurance Total Patient Contribution Total Non-subsidized Expenses Percent out of Total Cost
Visit 82105.05 77269.20 1218.39 159.97 2967.17 503.18 13.67
Consultation 9081.04 9527.99 161.99 16.04 313.85 67.97 1.51
Nursing 9018.74 8467.36 231.85 30.32 821.47 146.72 1.50
CT scan 11672.33 10462.81 244.23 37.50 748.82 162.33 1.94
ECG 2158.84 1996.31 44.5942619 3.93 117.72 20.67 0.36
Laboratory 39550.31 35428.60 1027.23 149.72 3081.43 573.20 6.59
Medication 244578.16 192173.09 12861.20 2668.44 28238.85 0 40.74
Radiography 648.51 593.45 15.85 0.59 43.53 4.24 0.10
Ultrasound 1330.21 1224.95 32.47 12.86 59.91 8.10 0.22
MRI 111.11 101.18 1.24 2.48 6.20 0 0.01
Dialysis 1192.23 1158.41 12.45 0 21.36 0 0.19
Ward equipment 17587.30 5151.94 6171.03 675.69 5643.03 11.82 2.93
Hoteling and beds 153990.51 136444.32 2938.98 585.73 12597.39 2445.45 25.65
Table 5.
Mean (SD) portions of primary and supplementary insurances, subsidies, patient contributions, and non-subsidized expenses in the direct costs of study patients based on type of service (n=450).
Type of Service Mean (SD)
Total Amount Primary Insurance Subsidy Supplementary Insurance Patient Contribution Non-subsidized Expenses
Visit 182.45
(196.36)
171.70
(188.29)
2.70
(5.96)
0.35
(2.54)
6.60
(13.03)
1.11
(9.62)
Consultation 20.18
(44.40)
21.17
(55.52)
0.36
(1.46)
0.035
(0.46)
0.69
(2.91)
0.15
(2.60)
Nursing 20.04
(2.67)
18.81
(33.41)
0.51
(1.16)
0.06
(0.54)
1.82
(5.65)
0.32
(4.02)
CT scan 25.93
(24.40)
23.25
(20.37)
0.54
(0.88)
0.08
(0.66)
1.66
(3.49)
0.36
(3.32)
ECG 4.79
(6.25)
4.43
(5.85)
0.09
(0.20)
0.008
(0.07)
0.26
(0.49)
0.04
(0.41)
Laboratory 87.88
(64.69)
78.73
(59.30)
2.28
(3.92)
0.33
(2.24)
6.84
(13.94)
1.27
(11.98)
Medication 543.50
(729.17)
427.05
(525.75)
28.58
(68.23)
5.94
(68.01)
62.75
(147.07)
0
(0)
Radiography 1.44
(4.23)
1.31
(3.89)
0.03
(0.12)
0.001
(0.02)
0.09
(0.42)
0.009
(0.20)
Ultrasound 2.95
(11.86)
2.72
(11.01)
0.07
(0.34)
0.02
(0.36)
0.13
(0.66)
0.01
90.38)
MRI 0.24
(2.6)
0.22
(2.37)
0.002
(0.05)
0.005
(0.11)
0.01
(0.17)
0
(0)
Dialysis 2.64
(34.08)
2.57
(33.72)
0.02
(0.48)
0
(0)
0.04
(0.63)
0
(0)
Ward equipment 39.08
(92.70)
11.44
(41.06)
13.71
(36.10)
1.50
(20.68)
12.54
(26.76)
0.02
(0.55)
Hoteling and bed 342.20
(476.33)
303.21
(428.15)
8.75
(20.34)
1.30
(11.03)
27.99
(77.09)
5.43
(67.11)
Note: * The figures are denominated in US dollars.
Table 6.
Relationship between demographic variables and direct costs of the study patients.
Demographic Variable Direct Costs Pearson Coefficient Correlation P-value
Age Visit 0.096 0.042
Consultation 0.317 0
Nursing services 0.109 0.02
CT scan 0.127 0.007
ECG 0.115 0.014
Laboratory 0.198 0
Medication -0.025 0.6
Radiography 0.031 0.5
Ultrasound 0.141 0.003
MRI 0.111 0.019
Dialysis 0.044 0.35
Ward equipment 0.135 0.004
Hoteling and bed 0.101 0.032
Total direct cost 0.081 0.08
Length of stay Visit 0.868 0
Consultation 0.305 0
Nursing services 0.855 0
CT scan 0.29 0
ECG 0.129 0.006
Laboratory 0.688 0
Medication 0.504 0
Radiography 0.148 0.002
Ultrasound 0.199 0
MRI 0.107 0.023
Dialysis 0.264 0
Ward equipment 0.541 0
Hoteling and bed 0.859 0
Total direct cost 0.858 0
Demographic variable Direct costs F-value P-value
Type of insurance Visit 2.627 0.034
Consultation 1.363 0.246
Nursing services 0.931 0.446
CT scan 1.849 0.118
ECG 1.298 0.27
Laboratory 1.548 0.187
Medication 2.331 0.055
Radiography 0.444 0.777
Ultrasound 0.814 0.517
MRI 0.071 0.991
Dialysis 1.256 0
Ward equipment 1.074 0.369
Hoteling and bed 1.364 0.245
Total direct cost 2.853 0.023
Method of discharge Visit 1.002 0.406
Consultation 1.206 0
Nursing services 2.665 0.032
CT scan 3.108 0.015
ECG 1.885 0.112
Laboratory 4.053 0.003
Medication 0.482 0.749
Radiography 0.552 0.698
Ultrasound 3.964 0.004
MRI 4.361 0.002
Dialysis 0.224 0.925
Method of discharge
Ward equipment 7.529 0
Hoteling and bed 2.431 0.047
Total direct cost 1.517 0.196
Demographic variable Direct costs T value P-value
Sex Visit 1.107 0.552
Consultation 0.448 0.532
Nursing services 0.037 0.271
CT scan 1.568 0.158
ECG 0.761 0.192
Laboratory 0.911 0.495
Medication 0.81 0.643
Radiography 0.378 0.245
Ultrasound 0.166 0.639
MRI 0.95 0.057
Dialysis 1.181 0.019
Ward equipment 0.505 0.067
Hoteling and bed 0.067 0.371
Total direct cost 0.973 0.728
Note: * Significance level of 0.05.

Moreover, a statistically significant correlation was found between sex and dialysis costs when examining the relationship between demographic variables and direct costs. Specifically, the mean cost of dialysis was higher in men (4.49 ± 46.46) than in women (0.71 ±10.38).

3.4.1. Indirect Costs of all Study Patients

3.4.1.1. The Economic Loss in Potential Production due to Mortalities

The economic loss in potential production due to mortalities ($155,736.96) = GDP per capita ($9,733.56) × Average years lost due to premature death (16 years).

Given that out of the 36 patients who died, only seven people were of productive age (15-65 years), the average years lost due to premature death were calculated based on the age information of these seven people.

According to a report by the Central Bank, the GDP per capita of Iran in 2021 was considered 9733.56 dollars.

3.4.1.2. The Economic Burden of Lost Productivity due to Illness and Disability

The economic burden of lost productivity due to illness and disability ($37,930.32) = average hospital stays (5.56 days) × average daily wage rate ($15.16) × total number of patients (N= 450)

3.4.2. Indirect Costs

Indirect costs ($193,667.28) = the economic loss in potential production due to mortalities ($155,736.96) + the economic burden of lost productivity due to illness and disability ($37,930.32).

3.4.2.1. The Total Cost (Direct and Indirect) of the Study Patients

Total cost (direct and indirect) of the study patients ($793,879.17) = total direct costs ($600,211.89) + total indirect costs ($193,667.28)

3.4.2.2. Estimating the Economic Burden of Patients with COVID-19 Hospitalized in the Study Hospitals in a One-year Period (from the patient's point of view)

Economic burden of patients with COVID-19 hospitalized in the study hospitals ($4186389.48) = total mean cost per person (direct costs ($600211.89) + indirect costs ($193667.28) divided by the total number of study patients (450)) × the number of patients with COVID-19 in the study hospitals (N=2373)

The number of patients with COVID-19 in the study hospitals was 2373 people.

4. DISCUSSION

The COVID-19 pandemic has had a significant impact on the Iranian health system and society, resulting in a heavy economic burden [22]. This study aimed to estimate the economic burden of COVID-19 in hospitals affiliated with Jiroft University of Medical Sciences in southern Kerman, Iran.

The total direct medical costs for COVID-19 patients in this study amounted to $600,211.89, with a mean cost of $1333.80 ± 1315.02 per patient. Darab et al. (2021) conducted a study at a referral hospital for COVID-19 patients in Iran and found that the total costs and mean direct medical costs per patient were $1,791,172 and $3,755, respectively [13]. Bartsch et al. (2020) estimated the mean direct medical costs per COVID-19 patient in the US to be $3045 [23]. Cleary et al. (2021) estimated the hospitalization cost for chronically ill patients in South Africa to be $7600 [24]. Jin et al. (2020) found that the hospitalization cost for critically ill patients with COVID-19 in China was approximately $9,000, with hospital beds being one of the largest cost components [25].

Kabir et al. (2021) conducted a study in a referral hospital for COVID-19 patients in Iran and reported that the mean direct medical cost for each chronically ill patient was $5,117 [22]. Yusefi et al. (2022) estimated the total direct medical costs of COVID-19 patients in a referral hospital in Iran to be $1,037,992.15 [26]. Mojtabaeian and Monfared (2022) estimated the mean direct cost per COVID-19 patient in Iran to be 57,339,107 Rials [27].

It is important to note that direct medical costs can vary depending on factors such as the number of patients, disease severity, length of hospital stay, and length of ICU stay [28, 29]. The treatment costs for COVID-19 patients are generally higher than those for other infectious diseases, owing to the possibility of hospitalization and higher mortality rates [23]. The differences in total costs and mean direct medical costs across different studies may be attributed to differences in the range of medical diagnostic services, medications, and patient's insurance conditions.

The results indicated that pharmaceutical services accounted for the highest proportion of direct medical costs among the different types of services, amounting to 40.74% ($244,578.16) of the total costs. Numerous studies have shown that medication expenses constitute the largest portion of direct medical costs [30, 31]. Darab et al. (2021) found that pharmaceutical costs accounted for 34% of the total cost for COVID-19 patients [13]. Similarly, Sadeghifar et al. (2021) reported that pharmaceutical services represented the largest portion (30%) of direct medical costs for COVID-19 patients [32]. Kabir et al. (2021) also identified drug costs as a major component (32%) of COVID-19 patient expenses [22]. In Yusefi et al.’s study (2022), the cost of medicines constituted the largest out-of-pocket expense for COVID-19 patients (50.23%) (26). Jin et al. (2020) found that medication costs constituted the largest proportion of the total costs for COVID-19 patients [25].

Another significant finding of this study was the relationship between the length of hospital stay and both the total direct cost and direct costs associated with different types of services. It was found that reducing the length of hospital stay could lead to various benefits, including increased revenue, cost reduction, decreased variations in clinical procedures, improved quality of care, and increased profitability. Prolonged and unnecessary hospital stays not only waste human resources and depreciate equipment but also result in additional costs for the patients.

Several studies have demonstrated that reducing the length of hospital stay can effectively lower costs without compromising health outcomes or the quality of care. As a result, policymakers, hospital managers, and third party payers (such as insurers) often prioritize strategies aimed at reducing hospital stays. These stakeholders emphasize the importance of containing hospital costs and maximizing efficient and effective utilization of limited hospital resources [33].

Another significant finding of the study was the statistically significant relationship between age, and the costs associated with various services such as visits, consultations, nursing services, CT scans, ECG, ultrasound, MRI, ward equipment, and hoteling and bed. The results indicated that as age increased, the costs of these services also increased significantly (p < 0.05).

This finding is consistent with a study conducted by Richards et al. (2022), which found that, on average, older patients with COVID-19 incurred higher costs than younger age groups [11]. However, it is important to note that this finding contradicts the results of a study by Holy et al. (2020), which reported that older age groups (≥ 84 years) were associated with lower costs than other age groups [34]. The difference in findings between these studies may be attributed to insurance coverage for COVID-19 patients.

This study found a statistically significant relationship between insurance type, costs associated with visits, dialysis, and total direct costs (p < 0.05). According to Kabir et al. (2021), primary insurance covers an average of 82% of the total costs for COVID-19 patients [22]. Similarly, Sadeghifar et al. (2021) reported that primary insurance covered 80% of the total costs, whereas supplementary insurance covered 2% [32].

The study by Ghiasvand et al. (2010) revealed that having supplementary insurance coverage at the 10% level was statistically significant in preventing the occurrence of financially burdensome medical costs. However, social health insurance plans in Iran have not been effective in reducing such costs. The ineffectiveness of social health insurance plans in supporting patients may be attributed to the design and implementation of insurance plans without considering important factors such as the socioeconomic status of households, demographic characteristics, and the pattern and epidemiology of diseases in Iran [35].

The COVID-19 pandemic not only resulted in direct costs but also imposed significant indirect costs. These indirect costs include potential reductions in productivity due to absenteeism during hospitalization and recovery and decreased productivity resulting from premature death. Additionally, income lost due to illness, obligatory stay-at-home measures, or death of the workforce due to COVID-19 can be considered indirect costs [13].

According to Kabir et al. (2021), the total mean indirect cost for each COVID-19 patient was estimated to be 959,577,714 Rials [22]. In a study by Darab et al. (2021), the reduction in productivity among COVID-19 patients was found to be twice as much as the direct medical costs per patient. The mean indirect cost per person in the above study was estimated to be $11,634 [13]. Different indirect costs across different studies may be attributed to differences in the number of deaths among working-age individuals due to COVID-19.

The findings revealed that COVID-19 placed a significant economic burden on the study hospitals, amounting to $4,186,389.48. Jin et al. (2021) conducted a study in China to examine the economic burden of COVID-19, reporting the total health costs and social costs associated with the disease as $0.62 billion and $383.02 billion US dollars, respectively [36]. Fusco et al. (2021) conducted a study on the consequences and economic burden of 173,942 hospitalized patients with COVID-19 in the US, estimating the mean hospital costs to be $12,046 [37]. Additionally, Lui et al. (2021) conducted a study in the US, which indicated that the economic burden of COVID-19, considering years of life lost due to illness, disability, and death, amounted to $119.62 billion US dollars [38]. Bartsch et al. (2020) conducted a study in the US, estimating the mean direct medical costs per person during the pandemic to be $3,045 [23]. Claxton et al. (2020) also conducted a study on the costs of COVID-19, reporting mean hospital costs of $9,763 for patients without mechanical ventilation and $34,223-88,114 for those requiring mechanical ventilation [39].

When comparing the study results with those of similar studies, it is evident that COVID-19 has imposed a substantial economic burden on various countries. This trend poses a significant challenge to healthcare systems, particularly in underdeveloped and low-income countries, potentially pushing them towards collapse [ 40 ]. Therefore, it is crucial to focus on managing and controlling new COVID-19 cases to prevent their spread.

Regarding the limitations of the study, it is important to note that due to the limited access to socioeconomic information of patients, further research is recommended to explore the impact of lost production and productivity across different income groups.

CONCLUSION

The investigation of the economic dimensions of COVID-19 in this study yielded substantial evidence supporting the implementation of socio-economic policies pertaining to COVID-19 and similar epidemics. The study findings, along with the estimated economic burden, indicate that COVID-19 has the potential to exert unprecedented economic pressure on the Iranian health system in terms of allocating financial resources to healthcare centers. Therefore, it is strongly recommended that disease control be prioritized through preventive measures and subsequently implemented cost control policies.

Also, the implementation of additional preventive measures, such as large scale vaccination in order to reduce the incidence and thus reduce direct and indirect medical costs is a vital issue that health managers and policy makers should consider.

AUTHORS’ CONTRIBUTIONS

R.F.: Designed the study and prepared the initial draft; R.F. and A.R.Y.: Contributed to data collection and data analysis; S.B. and J.B.: Supervised the whole study and finalized the article.

LIST OF ABBREVIATIONS

ICU = Intensive Care Unit
HIS = Health Information System

ETHICS APPROVAL AND CONSENT TO PARTICIPATE

This study was approved by the Jiroft University of Medical Sciences Ethics Committee, southern Iran with the ID number IR.JMU.REC.1400.048. All the methods were carried out in accordance with relevant guidelines and regulations.

HUMAN AND ANIMAL RIGHTS

All procedures performed in studies involving human participants were in accordance with the ethical standards of institutional and/or research committees and with the 1975 Declaration of Helsinki, as revised in 2013.

CONSENT FOR PUBLICATION

Not applicable.

STANDARDS OF REPORTING

STROBE guidelines were followed.

AVAILABILITY OF DATA AND MATERIALS

All the data is presented as a part of tables or figures. Additional data can be requested from the corresponding author [A.Y].

FUNDING

This work was supported by Jiroft University of Medical Sciences, Jiroft, Iran with grant number: 606.

CONFLICT OF INTEREST

The authors declare no conflict of interest, financial or otherwise.

ACKNOWLEDGEMENTS

This article is part of a research project that has been approved by the Vice-Chancellor for Research and Technology at Jiroft University of Medical Sciences, with reference No. 606. The researchers would like to thank the Vice-Chancellor for Research and Technology at Jiroft University of Medical Sciences and the managers of the hospitals involved in the study. Their invaluable financial and spiritual support has made this research possible.

REFERENCES

1
Chen S, Yang J, Yang W, Wang C, Bärnighausen T. COVID-19 control in China during mass population movements at new year. Lancet 2020; 395(10226): 764-6.
2
Doshmangir L, Ahari AM, Qolipour K, Azami S, Kalankesh L, Doshmangir P, et al. East Asia's strategies for effective response to COVID-19: Lessons learned for Iran. Manage Strat Health Syst 4(4): 370-3.2019;
3
Chu IYH, Alam P, Larson HJ, Lin L. Social consequences of mass quarantine during epidemics: A systematic review with implications for the COVID-19 response. J Travel Med 2020; 27(7): taaa192.
4
Delivorias A, Scholz N. Economic impact of epidemics and pandemics. 2020. Available from: https://www.europarl.europa.eu/RegData/etudes/BRIE/2020/646195/EPRS_BRI(2020)646195_EN.pdf
5
Yusefi AR, Barfar E, Faryabi R, Bahmaei J, Movahed E. Investigating the share of Covid-19 patients from treatment direct costs. Open Public Health J 2023; 16(1): 1-9.
6
Kalantari H, Davari M, Akbari M, et al. The estimation of direct medical costs of treating patients with chronic hepatitis B and C in iran. Int J Prev Med 2012; 3(3): 191-6.
7
Kim SG, Hahm MI, Choi KS, Seung NY, Shin HR, Park EC. The economic burden of cancer in Korea in 2002. Eur J Cancer Care (Engl) 2008; 17(2): 136-44.
8
Lee Y-R, Cho B, Jo M-W, Ock M, Lee D, Lee D, et al. Measuring the economic burden of disease and injury in Korea, 2015. J Korean Med Sci 34(Suppl 1): e80.2019;
9
Mosadeghrad AM, Pourreza A, Akbarpour N. Economic burden of autism spectrum disorders in Iran. Tehran Univ Med J 2019; 76(10): 665-71.
10
Conte A, Lecca P, Sakkas S, Salotti S. The territorial economic impact of COVID-19 in the EU. A Rhomolo analysis. JRC Res Rep 2020.
11
Richards F, Kodjamanova P, Chen X, et al. Economic burden of COVID-19: a systematic review. Clinicoecon Outcomes Res 2022; 14: 293-307.
12
Gedik H. The cost analysis of inpatients with COVID-19. Acta Med Mediter 2020; 36(1): 3289-92.
13
Ghaffari Darab M, Keshavarz K, Sadeghi E, Shahmohamadi J, Kavosi Z. The economic burden of coronavirus disease 2019 (COVID-19): Evidence from Iran. BMC Health Serv Res 2021; 21(1): 132.
14
Daroudi R, Akbari Sari A, Nahvijou A, Kalaghchi B, Najafi M, Zendehdel K. The economic burden of breast cancer in Iran. Iran J Public Health 2015; 44(9): 1225-33.
16
Raghfar H, Sargazi N, Mehraban S, Akbarzadeh MA, Vaez Mahdavi MR, Vahdati Manesh Z. The economic burden of coronary heart disease in Iran: A bottom-up approach in 2014. J Ardabil Univ Med Sci 2018; 18(3): 341-56.
17
Hatef E, Mohammadi SF, Alinia C, et al. National burden of eye diseases in Iran, 1990–2010; findings from the global burden of diseases study 2010. Middle East Afr J Ophthalmol 2016; 23(1): 89-95.
18
Keshavarz K, Kebriaeezadeh A, Alavian SM, et al. Economic burden of hepatitis B virus-related diseases: Evidence from iran. Hepat Mon 2015; 15(4): e25854.
19
Esmaeilzadeh F, Azarkeivan A, Emamgholipour S, et al. Economic burden of thalassemia major in Iran, 2015. J Res Health Sci 2016; 16(3): 111-5.
20
Mohammadzadeh M, Derafshi H, Ghari T. The estimation of economic burden of hepatitis C virus infection in Iran. Iran J Public Health 2018; 47(10): 1575-82.
21
Rezaei S, Arab M, Karami Matin B, Akbari Sari A. Extent, consequences and economic burden of road traffic crashes in Iran. J Inj Violence Res 2014; 6(2): 57-63.
22
Kabir MJ, Heidari A, Moeini S, Khatirnamani Z, Kavian Telouri F, Eimery M. Calculation of direct medical costs and indirect costs in patients with Covid-19 hospitalized in the intensive care unit in golestan province. Manage Strat Health Syst 2022; 6(4): 308-16.
23
Bartsch SM, Ferguson MC, McKinnell JA, et al. The potential health care costs and resource use associated with COVID-19 in the United States: A simulation estimate of the direct medical costs and health care resource use associated with COVID-19 infections in the United States. Health Aff (Millwood) 2020; 39(6): 927-35.
24
Cleary SM, Wilkinson T, Tamandjou Tchuem CR, Docrat S, Solanki GC. Cost‐effectiveness of intensive care for hospitalized COVID-19 patients: Experience from South Africa. BMC Health Serv Res 2021; 21(1): 82.
25
Jin H, Wang H, Li X, Zheng W, Ye S, Zhang S, et al. Estimating the cost-of-illness associated with the COVID-19 outbreak in China from January to March 2020. MedRxiv 2020.
26
Yusefi AR, Mehralian G, Khodamoradi A, et al. Out-of-pocket payments for treatment of COVID-19 in Iran. Cost Eff Resour Alloc 2022; 20(1): 12.
27
Mojtabaeian SM, Monfared F. Diagnostic and treatment costs of patients diagnosed or suspected of COVID-19 disease in eastern Iran. Navidno 25(82): 27-37.2022;
28
Warren DK, Shukla SJ, Olsen MA, et al. Outcome and attributable cost of ventilator-associated pneumonia among intensive care unit patients in a suburban medical center. Crit Care Med 2003; 31(5): 1312-7.
29
Cheung AM, Tansey CM, Tomlinson G, et al. Two-year outcomes, health care use, and costs of survivors of acute respiratory distress syndrome. Am J Respir Crit Care Med 2006; 174(5): 538-44.
30
Rotstein Z, Hazan R, Barak Y, Achiron A. Perspectives in multiple sclerosis health care: Special focus on the costs of multiple sclerosis. Autoimmun Rev 2006; 5(8): 511-6.
31
Miltenburger C, Kobelt G. Quality of life and cost of multiple sclerosis. Clin Neurol Neurosurg 2002; 104(3): 272-5.
32
Sadeghifar J, Karimi F, Bayar M, Veisi M, Soleymani Y, Radabadi M, et al. Investigating the pattern of direct medical costs of Covid-19 patients in hospitals of Ilam province. SRPH J Med Sci Healthc Manag 3(4): 1-8.2021;
33
Ameri H, Adham D, Panahi M, Khalili Z, Fasihi A, Moravveji M, et al. Predictors for duration of stay in hospitals. J Health 2015; 6(3): 256-65.
34
Holy C, Shah S, Elangovanraaj N, Krishnan D, Gupta S, Trivedi P, et al. PIN17 healthcare resource utilization and cost in patients with inpatient mortality due to COVID-19 infection. Value Health 23(Suppl 2): S546.2020;
35
Ghiasvand H, Hadian M, Maleki MR. Relationship between health insurance and catastrophic medical payment in hospitals affiliated to Iran University of Medical Science; 2009. Econ Res : 207-24.46(1)2011;
36
Jin H, Wang H, Li X, et al. Economic burden of COVID-19, China, January–March, 2020: A cost-of-illness study. Bull World Health Organ 2021; 99(2): 112-24.
37
Di Fusco M, Shea KM, Lin J, et al. Health outcomes and economic burden of hospitalized COVID-19 patients in the United States. J Med Econ 2021; 24(1): 308-17.
38
Lui B, Zheng M, White RS, Hoyler M. Economic burden of lives lost due to COVID-19 in New York State. J Comp Eff Res 2021; 10(11): 893-7.
39
Claxton G, Damico A, Rae M, Young G, McDermott D, Whitmore H. Health benefits in 2020: Premiums in employer-sponsored plans grow 4 percent; Employers consider responses to pandemic. Health Aff (Millwood) 2020; 39(11): 2018-28.
40
Nie D, Shen F, Wang J, et al. Changes of air quality and its associated health and economic burden in 31 provincial capital cities in China during COVID-19 pandemic. Atmos Res 2021; 249: 105328.