Direct medical cost and cost analysis of COVID-19 in Iran: A multicenter cross-sectional study.

Background: Although our daily life and economics were severely affected by COVID-19, cost analysis of the disease has not been conducted in Iran. Hence, we aimed to perform a cost analysis study and then estimate direct medical costs of COVID-19.
Methods: A cross-sectional study was performed in Tehran and recorded medical files from March 1, 2020, to September 1, 2020, were examined. A predefined electronic form was developed and all required variables were included. All people whose both first and final diagnoses were COVID-19 positive and were admitted in governmental hospitals were considered for inclusion. Using stratified random sampling method, 400 medical records were evaluated to gather all data. STATA 14 was used for data analysis.
Results: We evaluated 400 medical records and the age of patients ranged from 22 to 71 years. The mean cost of COVID-19 was 1434 USD. Of 400 patients, 129 of them had underlying disease and statistical significance was observed in people who had underlying diseases than people who did not have underlying disease.
Conclusion: Beds and medications were the most important factors that added to the costs. COVID-19 has undoubtedly imposed a high financial burden on the health system. It is highly recommended that patients with positive test result be strictly encouraged to stay at home and adhere to safety protocols. Copyright:

INTRODUCTION

A pneumonia outbreak of unknown etiology was reported in Wuhan, China, in December 2019.[12] It was reported that the agent initially spread from a seafood market[3] and probably from a rat.[4] After the outbreak, the World Health Organization (WHO) identified a novel coronavirus to be the reason of the outbreak.[25] Till February 12, 2020, there were 43,103 confirmed cases out of which 42,708 of them (i.e., 99.1%) were Chinese.[2] Causative agent was extracted via throat swab by the Chinese Centers for Diseases Control and then was named as severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2).[6] The disease was named COVID-19 by the WHO[6] and then WHO declared SARS-CoV-2 as a public health emergency in January 31, 2020,[7] when the viruses started spreading all over the world.[1] Consequentially, the outbreak was declared a pandemic on March 12, 2020, by the WHO[18] and cases are detected in all countries and regions.[9] The first cases were found in Qom, Iran.[10]

Epidemiology of COVID-19

Droplets and close contacts are the most common ways of disease transmission and so were aerosols.[4] Incubation period ranged from 1 to 14 days,[7] with an average of 5.5 days.[11] Asymptomatic patients were also capable of transmitting the disease.[7] R0 varied in different studies. It ranged from 2.47 to 2.86,[12] from 2.2 to 3.6,[13] and from 2 to 3.3.[14] It is estimated that case fatality rate (CFR) in Western Pacific region, European region, Southeast Asian region, American Region, African Region, and globally ia 4.12%, 9.48%, 3.87%, 4.08%, 5.23%, 3.74%, and 7.05%, respectively,[15] and people with underlying chronic diseases such as cardiovascular diseases (10/5%), diabetes (7/3%), and chronic respiratory diseases (6/3%) have a higher CFR in comparison with people without preexisting conditions.[16] The symptoms of disease include fever, cough, respiratory problems, pneumonia, and even death in severe cases.[17] The most common symptoms are fever, weakness, cough, and diarrhea.[11] Breathing difficulties also have been reported in more than half of the cases.[11]

Economic impacts

Daily life, businesses, schools, life style, and global economics were severely affected by COVID-19.[18] COVID-19 even has adverse psychological and mental effects.[1819] More new widespread outbreaks undoubtedly imposed a high financial burden on the economy of countries. Reports show that influenza pandemic resulted in a mortality rate of 700,000 and imposed a financial burden of about 570 billion dollars on countries.[20] Ebola outbreak imposed an expenditure of 53/19 billion dollars on the west of Africa in 2014.[21] It was estimated that the impact of SARS and influenza on macroeconomic was 30-100 billion dollars and 40 billion dollars in China, respectively.[22] Drug demand, export and import, tourism, and so on can be badly affected by epidemics. Microeconomic costs include costs of individuals and family, firms, schools, health centers, and health and governmental workforce.[23] The costs take into account mortality and morbidity costs as well. Families may have to spend on some expenses in the diagnosis and treatment of COVID-19, which are not covered by the government and insurance companies.[24] Even when governments and insurance companies cover the costs, people still may have some additional expenses such as copayments, transporting cost, and other indirect costs.[24] COVID-19 effects out-of-pocket too. Moreover, COVID-19 has some negative impacts on macroeconomic index such as unemployment rate, recession, aggregated demand, aggregated supply, production, and consumption.[25] Undoubtedly, after the great economic hit in 1930, COVID-19 will be the next most devastating economic crisis in America.[1] Based on the OECD reports, GNP growth would diminish at 0.5% in 2020.[26] Economics of a country can be affected in countless ways during disease outbreaks. The calculation of financial burden, direct and indirect costs of disease, and economic burden are conducted by health economists.[27] While the prevalence and the incidence of disease are yet high and ongoing in the world and Iran, cost analysis of the diseases still has not been evaluated to determine the contributions to cost in Iran. Most of the conducted studies evaluated macroeconomics effects of COVID-19 or they used aggregated data. Hence, in the present study, we aimed to carry out an overall cost analysis of COVID-19 and determine the costs contributed by all services.

METHODS

The aim of this study was to analyze the direct medical costs of COVID-19 from payer perspective in Tehran, Iran. Payer perspective means total cost paid by all individuals and organizations, to be precise, people, government, nongovernmental organizations, insurance companies, and so forth. This is the first ever study conducted for the aforementioned aim so far in Iran. Hence, a descriptive analytical cross-sectional study was performed in Tehran. Target population included all people referred to the hospitals and had both their first and final diagnoses COVID-19 positive. Recorded medical files from start of disease in Iran (March 1, 2020) till September 1, 2020, were examined. The studied variables were age, gender, length of stay (LOS) (the date and hour from admission to discharge) kind of referral to the hospitals (elective or emergency), encounter type (outpatient or inpatients), basic insurance (social security organization [SSO], health insurance, army, no insurance), underlying diseases, outcome (death, discharge against medical advice, improvement, relative improvement, other), total cost, copayment, share of insurance companies, visit charges, laboratory, computerized tomography (CT) scan, radiography, and so forth (exchange rate for 1 USD = 42,000 Riyals).[28] A predefined electronic form was developed and all required variables were included. All information was compiled from Hospital Intelligence Management.

Inclusion and exclusion criteria

All people whose both first and final diagnoses were COVID-19 positive and were admitted in governmental hospitals affiliated with SBMU were considered for inclusion. On the other hand, people who came to hospitals to receive other medical services (people who had traumas) were excluded. In addition, people who underwent surgery, people whose first or final diagnosis were not COVID-19, patients admitted in specialized hospitals, incomplete medical records, hospitals with <30 COVID-19 medical records in a month were excluded.

To maximize the sample size, the value of P and q were assumed 0.5. The value of d and Z1 − a/2 were 0.05 and 1.96, respectively. The sample size was obtained to be equal to 384 medical records using the aforementioned variables; then, we circled the whole digits upward and the sample size obtained 400.

Sampling method

Stratified random sampling method was used to gather all data. To do so, first, we found all COVID-19 medical records for all hospitals. Then, the share of contribution to COVID-19 during all months for all hospitals was determined. In other words, the strata variable was hospitals. In detail, the share of every hospital was determined through dividing the total medical files of this hospital by the total medical files of all hospitals. The sample size was represented by multiplying the number obtained by 400. Hence, the share of all hospitals was attained through the aforementioned method. Finally, medical records were chosen through simple random method from subgroups. In detail, all documents were opted randomly and all of which had the same chance to be chosen.

Statistical analysis

Both descriptive and analytical statistics were used. All variables were summarized in tables. Normality was checked through both Shapiro−Wilk test and normal probability plots. The test shows that data were not normal. Nonparametric tests including Kruskal−Wallis test and Mann−Whitney test were utilized. P ≤ 0.05 and 95% confidence interval were considered as the level of significance. A multiple logistic regression model was also developed to determine the variables that mostly impacted costs on medical records. Total cost was assumed as a dependent variable. An amount more than the determined mean cost was considered as high-cost medical record and less than the mean cost was considered low-cost medical records. Thus, for making binary variables, more than and less than mean cost for the model was utilized. Furthermore, age, gender, underlying disease, LOS, and kind of refer to hospital were considered as the independent variables. P ≤0.1 was assumed for level of significance. STATA 14 (STATA Corporation, College Station, TX, USA) was used for analysis.[29]

RESULTS

Totally, based on both inclusion and exclusion criteria, 400 medical records were evaluated using proportionate stratified random sampling and aforementioned variables were compiled via an electronic form. Shapiro−Wilk test shows that data were not normal (P = 0.00). Thus, nonparametric tests were used.

The age of the patients ranged from 22 to 71 with a mean of 55.99 (median was 56 and mode was 66). LOS ranged from 1 h to 593 h (mean was 128.38 h, median was 98 h, and mode was 1 h). Most of the patients were male and it was revealed that there is no significant statistical difference between cost of COVID-19 for males and females (P = 0.34). Mann−Whitney test also showed that there is a significant statistical difference between inpatients and outpatients (P = 0.00). People who came to hospitals as emergency referrals cost more in comparison to people who came to hospitals as elective referrals and this difference was significant (P = 0.00). Of 400 patients, 129 of them had underlying disease and statistical significance was observed between people who had underlying diseases and people who did not have underlying disease (P = 0.00) [Table 1].

Table 1

Frequency and cost of variables

Variable	Subgroups	Frequency (%)	Mean	SD	Median	Interquartile	P
Gender	Male	233 (58.3)	1441.19	1674.24	874.01	1453.82	0.34
	Female	167 (41.7)	1424.19	3183.52	772.15	942.17
Encounter type	Inpatient	351 (87.7)	1621.50	2522.58	983.67	1287.25	0.00
	Outpatient	49 (12.3)	100.63	69.21	73.26	54.89
Refer to hospitals	Emergency	338 (84.4)	383.72	611.93	126.69	377.47	0.00
	Elective	62 (15.2)	1625.73	2571.05	985.22	1314.7
Underlying disease	Yes	129 (32.2)	2350.03	3655.54	1526.43	2248.86	0.00
	No	271 (67.8)	998.55	1311.45	610.34	908.34
Insurance	SSO	228 (56.9)	1527.19	3050.16	738.43	1255.19	0.40
	Health	104 (26.1)	1458.03	1340.46	931.33	1374.19
	Army	17 (4.3)	915.00	679.26	895.62	555.92
	Uninsured	51 (12.7)	1137.92	1068.40	766.19	1151.93

SD: Standard deviation, SSO: Social security organization

The commonly used insurance was SSO and no statistically significant difference was discovered among people with different basic insurance (P = 0.37).

Intensive care unit (ICU), coronary care unit (CCU), and pediatric intensive care unit (PICU) beds were the most expensive items for both inpatients and electives [Table 2].

Table 2

Detailed cost of services based on encounter type/kind of refers to hospital

	Inpatient (USD)		Outpatient (USD)		Elective (USD)		Emergency (USD)
	Mean (SD)	Median (IQR)	Mean (SD)	Median (IQR)	Mean (SD)	Median (IQR)	Mean (SD)	Median (IQR)
Tests	182.28 (122.68)	156.11 (123.51)	54.55 (21.81)	56.01 (18.69)	180.12 (124.83)	155.77 (128.54)	91.58 (76.56)	63.57 (55.46)
Bed (general)	227.37 (276.62)	147.36 (270.26)	-	-	225.82 (281.27)	138.89 (280.07)	58.34 (121.14)	0.00 (49.12)
One/two capacity bed	53.52 (186.13)	-	-	-	53.42 (188.66)	-	12.28 (54.38)	-
ICU, CCU, PICU	368.59 (792.89)	0.00 (227.95)	-	-	375.41 (804.95)	0.00 (227.95)	28.49 (161.19)	-
Heart tests	15.16 (27.15)	0.00 (17.89)	1.66 (4.80)	-	14.26 (27.06)	0.00 (17.89)	9.56 (18.04)	0.00 (12.73)
Nursing	32.04 (40.41)	19.15 (36.15)	-	-	32.20 (41.27)	18.42 (38.32)	5.53 (10.03)	0.00 (5.89)
Drug	2.57 (2.66)	1.88 (2.66)	0.26 (0.69)	0.00 (0.24)	2.55 (2.73)	1.86 (2.97)	0.88 (1.14)	0.24 (1.82)
Consultation	41.04 (66.67)	14.59 (45.09)	1.10 (3.50)	0.00	41.45 (67.51)	14.59 (49.87)	3.95 (10.06)	0.00
Supplementary tests	11.09 (24.30)	3.75 (8.48)	5.32 (13.44)	1.75 (4.24)	10.06 (19.30)	3.75 (8.48)	12.33 (39.27)	0.53 (5.46)
Consumed drugs in the wards	211.05 (310.09)	111.97 (225.48)	3.93 (9.63)	0.00 (3.40)	207.93 (311.34)	111.01 (227.78)	65.20 (174.59)	2.32 (49.08)
Radiography	3.61 (8.72)	0.00 (3.66)	0.37 (1.88)	-	3.45 (8.72)	-	1.99 (50.03)	-
Sonography	5.94 (14.71)	-	1.48 (6.41)	-	6.16 (15.01)	-	1.31 (4.84)	-
CT scan	21.57 (31.67)	16.23 (28.37)	8.29 (12.82)	0.00 (22.51)	21.70 (32.06)	8.12 (28.37)	9.83 (14.86)	0.00 (22.51)
Tools consumed in wards	73.82 (106.48)	39.54 (69.32)	6.77 (13.44)	3.32 (7.58)	72.48 (105.94)	39.13 (69.01)	28.26 (70.07)	4.72 (12.05)
ECG/EEG	5.61 (8.63)	2.95 (5.91)	1.08 (2.11)	0.00 (2.95)	5.34 (8.45)	2.95 (5.91)	3.62 (7.07)	1.48 (3.75)
Visit (EMG)	4.18 (10.40)	-	1.92 (4.45)	-	4.34 (10.57)	0.00 (6.35)	1.09 (3.27)	-
Visit (inpatient)	122.14 (108.26)	99.73 (128.11)	3.96 (7.06)	0.00 (7.43)	121.04 (111.12)	99.73 (143.23)	33.85 (51.78)	0.00 (68.96)
Visit (outpatient)	0.93 (2.32)	-	2.25 (3.17)	0.00 (3.55)	0.98 (2.38)	-	1.74 (2.89)	0.00 (3.55)
Other	41.36 (94.29)	7.81 (43.73)	6.71 (22.39)	0.00 (0.61)	42.74 (95.88)	8.75 (43.78)	6.80 (19.75)	-
Total cost	1621 (2522.59)	983.67 (1287.25)	100.63 (69.21)	73.26 (54.89)	1625 (2571.05)	985.22 (1314.7)	383.72 (611.93)	126.69 (377.47)

SD: Standard deviation, IQR: Interquartile range, ICU: Intensive care unit, PICU: Pediatric ICU, CCU: Coronary care unit, CT: Computerized tomography, ECG: Electrocardiogram, EEG: Electroencephalogram, EMG: Electromyography

The total costs are categorized into six groups including bed (ICU, CCU, PICU, general bed, and VIP beds), diagnosis (laboratory tests, sonography, CT scan, electroencephalogram, and electrocardiogram), visit and consultation (emergency, inpatient, outpatient, and consultation), drug (drug, drug and tools consumed in wards), nursing, and other costs. Of total share, 45% of total costs were allocated for beds [Figure 1 and Table 3].

Figure 1

Categorized direct medical costs of COVID-19

Table 3

Categorized direct medical costs of coronavirus disease-2019 according to kind of refer to hospital and encounter type

	Inpatient (USD)		Outpatient (USD)		P	Elective (USD)		Emergency (USD)		P
	Mean (SD)	Median (IQR)	Mean (SD)	Median (IQR)		Mean (SD)	Median (IQR)	Mean (SD)	Median (IQR)
Diagnosis	245.26 (162.88)	197.13 (43.29)	72.76 (32.84)	68.48 (33.75)	0.00	241.09 (162.29)	192.39 (142.08)	130.24 (137.96)	70.70 (81.4)	0.00
Visit and consultation	183.09 (153.74)	140.58 (167.1)	9.80 (9.19)	7.75 (11.3)	0.00	183.21 (157.50)	140.58 (174.33)	41.10 (55.63)	13.03 (65.41)	0.00
Bed	649.49 (772.64)	392.95 (598.21)	-	-	-	654.65 (787.64)	392.95 (612.36)	99.12 (202.16)	0.00 (98.24)	0.00
Nursing	32.04 (40.41)	19.15 (36.15)	-	-	-	32.20 (41.27)	18.42 (38.32)	5.52 (10.03)	0.00 (5.89)	0.00
Consumed drug and tool	287.45 (391.16)	158.26 (273.54)	10.96 (22.76)	4.72 (12.1)	0.00	282.96 (391.10)	157.17 (279.3)	94.35 (243.53)	8.66 (73.10)	0.00
Other	41.36 (94.29)	7.81 (43.73)	6.71 (22.39)	0.00 (0.61)	0.00	42.74 (95.88)	8.75 (43.78)	6.80 (19.75)	-	0.00

SD: Standard deviation, IQR: Interquartile range

The results of logistic regression show that females were less likely to bear high expenses (more than mean cost) in comparison with males (odds ratio [OR] = 0.33). People with underlying diseases in comparison with people who do not have underling diseases were more likely (OR = 4.25) to bear high expenses [Table 4].

Table 4

Results of logistic regression for high-cost medical records

Variables	Categories	OR	P	CI
Age		0.80	0.57	0.42-1.53
Gender	Male*
	Female	0.33	0.02	0.15-0.71
Underlying diseases	No*
	Yes	4.25	0.00	1.92-9.38
LOS		9.45	0.00	5.49-16.29
	_cons	0.00	0.00	0.00-0.03

*Reference group. OR: Odds ratio, CI: Confidence interval, LOS: Length of stay

DISCUSSION

The primary aim of this study was to determine the cost of different services delivered to COVID-19 patients. Like many other cost analysis studies,[3031323334] nonparametric tests were employed. The mean cost of COVID-19 for both men and women was 1434 USD. In another study conducted by Bartsch et al.,[35] the cost per case ranged from 2837 to 3205 USD. It is widely proven that developed countries like the United States (gross domestic product [GDP] per capita in the US was 65,118 USD in 2019) spend more on their health systems.[36] On the contrary, developing countries such as Iran (GDP per capita was 5520 USD in Iran in 2017) spend less on their health systems.[37] Other reasons for this difference may be attributed to different health systems and methods. While the mentioned study used modeling to estimate the costs, we conducted a real study.

The findings of our study depicted that the most part of cost (40%) belongs to bed (ICU, CCU, PICU, general bed, and VIP beds). Drugs and equipment's were second to impact costs. Since there has been no confirmedly approved drug for COVID-19 to date, people have to stay in hospitals to receive supportive cares. This could potentially put up bed and medicine costs. Hospital stays impose hospitalization and drug cost on both health system and people. It is suggested that taking diagnostic tests in health centers (regional health centers that give preventive services) will prevent beds from being occupied until a definite diagnosis is made. Li (2020)[38] stated that drug consumption accounted for the most part of the total costs. Given the disease affects many organs of the body, it is imperative to prescribe a wide range of different drugs for patients. Unfortunately, insurance companies refuse to cover these costs, so patients and sometimes hospitals end up bearing the burden of excessive costs. In fact, lack of one united decision-making body makes insurance companies to not cooperate with the situation. Since the beginning of COVID-19 in the country, numerous and controversial directives have been ordered to cover the costs, which caused chaos among patients and hospitals.

Inpatients had higher cost than outpatients because inpatients occupied the hospitals longer than outpatients. Consequentially, this put up drug costs, cost of bed, and cost of the tests. It was revealed that there is a significant correlation between costs and the encounter type. Long duration of hospital stays and various medicines and services delivered to inpatients is clearly the cause.

According to our findings, patients with underlying diseases had borne higher costs compared to other patients. A possible explanation for this is that underlying diseases impose extra costs by making new condition more severe or worsening it. In other words, preexisting disease already requires drug maintenance, and this puts up costs more than with the case of COVID-19 alone. Underlying diseases also exacerbate disease complications. Eventually, they affect both the patient and health system by imposing high costs on them. Similarly, Li (2020)[38] found that patients with underlying diseases bore higher costs than patients without underlying disease.

Findings show that there was a positive correlation between total cost and age which are consistent with the results of another study.[38] It might be explained in this way that elders need extra special care and treatments which in turn increase the costs. Our study also showed a significant correlation between costs and LOS. This may be explained by the fact that increased LOS increased different costs such as bed costs, drug costs, nursing, and other related cost.

While there is no specific confirmed drug to treat COVID-19, the existing diagnostic tests and supportive interventions impose a high financial burden on health system. Moreover, COVID-19−related economic evaluation studies have not been performed till now. Henceforth, more economic evaluation studies are imperative to detect the most cost-effective intervention to cut down costs of healthcare system. It appears that one of the most effective ways to reduce costs is to increase public education on prevention and precaution, as well as make personal protective equipment such as face masks and disinfectants widely available. This study recommends facilitating a specific infrastructure on a national level to strengthen the pharmaceutical industry in the country so that the country does not face a drug shortage during crises. Further, taking into account the high financial burden resulting from the pandemic, it is suggested that government support insurance companies to cover the high costs of COVID-19.

It is suggested that government invests on prevention strategies rather than treatment approaches to control the diseases. In other words, considering the highly-contagious nature of the disease and the prolonged length of the pandemic, government should focus on public education such as wearing face masks, avoiding crowded public places and traveling by public transportation, and calling off ceremonies and get-togethers and promote social distancing and practice personal hygiene.

Our study had several strengths in comparison to other studies. First, data were collected from 17 public hospitals that were located in different geographical areas of the city. Second, a reliable and valid formula was used to determine the sample size. Third, because of the large sample size, the results of this study can be generalized to similar communities.

Limitations

Some limitations were inevitable. We evaluated direct medical costs of COVID-19, while economic burden studies are comprehensive ones. Some variables such as bed cost and nursing costs have the value of zero for outpatients. This might lead to misleading inferences. Hence, it is vital to interpret the results cautiously.

Furthermore, the current study was carried out before medications like Redmesivir, Favipiravir and so on were introduced. So, the total direct medical cost of COVID-19 may exceed more than what was estimated.

CONCLUSION

Our study demonstrated that COVID-19 inflicted a huge financial burden on the health system as it is counted as the main responsible body in the country. Beds and medications were the most important factors that added to the costs. It is highly recommended that patients with positive test result be strictly encouraged to stay at home and adhere to safety protocols. This will not only reduce costs for people and health system but also would decrease the risk of disease transmission. In addition, for the increased costs of medicines, it is recommended to further conduct economic evaluation studies to identify the most cost-effective interventions and drugs.

Research quality and ethics statement

The study did not require institutional review board / ethics committee approval as it utilized de-identified data from an administrative database. The authors followed the applicable EQUATOR Network (http://www.equator-network.org/) guidelines, during the conduct of this research project.[3839]

Authors observed all ethical issues including plagiarism, informed consent, double submission, double publication, and misconduct.

Financial support and sponsorship

No.

Conflicts of interest

There are no conflicts of interest.