Trend in the admissions of patients with non-COVID-19 respiratory symptoms during COVID-19 pandemic and its impact on hospital finances in Surabaya, Indonesia

Anang Endaryanto; Arlina Dewi; Kusbaryanto; Ricardo Adrian Nugraha

doi:10.1016/j.heliyon.2023.e15122

. 2023 Mar 31;9(4):e15122. doi: 10.1016/j.heliyon.2023.e15122

Trend in the admissions of patients with non-COVID-19 respiratory symptoms during COVID-19 pandemic and its impact on hospital finances in Surabaya, Indonesia

Anang Endaryanto ^a,^b,^∗, Arlina Dewi ^a, Kusbaryanto ^a, Ricardo Adrian Nugraha ^c

PMCID: PMC10065813 PMID: 37035376

Abstract

Background

COVID-19 cases surge, it has a crucial impact on healthcare systems, with rapidly increasing demand for healthcare resources in hospitals and intensive care units (ICUs) in Indonesia and worldwide. It is necessary to quantify the extent to which the effects of the COVID-19 pandemic on the hospital admissions, and clinical and financial outcomes of patients with non-COVID-19 respiratory symptoms.

Objective

To determine whether the COVID-19 pandemic changed the hospitalisation of child and adult patients with non-COVID-19 respiratory conditions and whether these changes affected the patient's disease condition, clinical outcomes, and hospital finances.

Methods

A retrospective cohort study was conducted from May 1, 2018 (before the COVID-19 pandemic) until December 31, 2021. Total sampling was done to compare hospital admission of patients with non-COVID-19 respiratory symptoms before versus during the COVID-19 pandemic. The results were analyzed using SPSS 26.0 and SmartPLS.v.3.2.9.

Results

There was a reduction in hospitalisations for respiratory disorders unrelated to COVID-19 during the pandemic by 55.3% in children and 47.8% in adult patients. During the pandemic, the average hospital revenue per patient of child and adult patients increased significantly, but the profit per patient decreased. Pathway analysis showed that in children, the COVID-19 Pandemic changed disease severity and complexity (β = 0.132, P < 0.001), as well as clinical outcomes (β = 0.029, P < 0.05). In adults, the COVID-19 pandemic improves disease severity and complexity (β = -0.020, P < 0.001), as well as clinical outcomes (β = −0.013, P < 0.001). COVID-19 pandemic increases care charges (in children with β = 0.135, P < 0.001; and in the adult patients with β = 0.110, P < 0.001), worsens hospital financial outcomes relating to child (β = −0.093, P < 0.001) and adult patient (β = −0.073, P < 0.001). In adult patients, seasonal variations moderate the impact of the COVID-19 pandemic on improving disease conditions (β = −0.032, P=<0.001). The child structural model effectively predicted clinical outcomes (Q² = 0.215) and financial outcomes (Q² = 0.462). The adult structural model effectively predicted clinical outcomes (Q² = 0.06) and financial outcomes (Q² = 0.472).

Conclusion

The conclusions are that the number of non-COVID respiratory patients decreased during the COVID-19 pandemic (47.8% in adult patients, 55.3% in child patients). Disease severity and complexity increased in child patients but decreased in adult patient. Costs of care and insurance payments increased. Since the insurance payments did not increase as much as the cost of care, hospital profit decreased.

Keywords: COVID-19 pandemic, Adult patients, Child patients, Clinical outcome, Costs of care, Disease condition, Financial outcome, Respiratory symptoms

1. Introduction

It has almost been three years since Indonesia and the world is struggling with the COVID-19 pandemic. Mortality and morbidity of respiratory diseases, whether related to COVID-19 or not, are serious problems. Unless treated properly, it can cause a long-standing hypoxia [1,2]. Long-standing hypoxia can damage brain cells and induce cerebral atrophy and abnormal brain development in children [3]. Likewise, in adults, long-standing hypoxia can also inhibit myelinogenesis and causes functional cognitive deficits in the brain [4], consequently decreasing quality of life [5].

Hypoxia due to the non-COVID-19 respiratory infection and non-communicable respiratory diseases in Indonesia remains unchanged and tends to increase, but it was neglected, primarily due to the COVID-19 pandemic. A major reason for neglected patients with non-COVID-19 respiratory disorders is the similarity of symptoms between COVID-19 patients and non-COVID-19 patients, causing non-COVID-19 patients to follow procedures to ensure that it is not COVID-19, and this is a time-consuming [6]. In addition, there is a tendency for health professionals to be less alert to children with respiratory problems because they are more worried about the presence of COVID-19 in adults, and children are considered more resistant to COVID-19. The early detection delay of non-COVID-19 diseases can delay diagnosis, impair the effectiveness of therapy, and increase mortality [7,8]. Delay in to detect of respiratory diseases like tuberculosis earlier in hospitalised patients will increase the length of stay and implicates the increased risk of nosocomial transmission, worsening patients' clinical and the hospital’s financial outcomes [9,10].

Globally, there has been a significant reduction in respiratory illness hospitalisation rates during the COVID-19 pandemic. Fear of patients and their families infected with COVID-19 in hospitals and fear of doctors exposing COVID-19 during procedures in the airways leads to increased delays in diagnosis, resulting in a more complex and severe disease [11,12]. The fear of being exposed to COVID-19 in hospitals influences the perceived need for hospitalisation [7,13,14]. In the COVID-19 era, clinicians mostly avoid using invasive diagnostic test kits such as spirometry, invasive bronchoscopy, and thoracoscopy due to fear of spreading COVID-19 [6]. Consequently, non-covid respiratory diseases were underdiagnosed among those who presented to the medical system.

On the one hand, this finding may prevent exposure to aggravating factors due to self-confinement behaviour. On the other hand, this finding may also prevent avoiding visiting hospitals due to fear of COVID-19 infection [14]. Inadequate or delayed treatment of respiratory diseases, in addition to causing long-term effects, may cause short-term effects, especially in increasing the severity and length of hospitalisation, mortality, treatment costs, and wasting hospital resources [15]. Therefore, it is necessary to increase serious attention to managing the medical conditions of non-COVID-19 patients [9,10].

This study aims to determine whether the COVID-19 pandemic changed the hospitalisation of child and adult patients with non-COVID-19 respiratory conditions and whether these changes affected the patient's disease condition (severity and complexity of the disease), clinical outcomes (readmission <30 days, average length of stay, and mortality rate), and financial outcomes (insurance payment and profit). The future disruption of health services in non-COVID-19 respiratory illness due to the COVID-19 pandemic must be anticipated, mitigated, and minimised based on valid evidence.

With valid evidence from this research, a national program to minimize the impact on children's lives in the future (childhood/adolescence/adult/old age) due to undertreatment because of exigencies of the pandemic situation in child care during the COVID-19 pandemic can be immediately designed and implemented. In addition, by knowing the similarities and differences between child and adult patients who experience non-COVID-19 respiratory problems during the COVID-19 pandemic in terms of complexity, severity, and disease outcomes, both clinical and financial, referral and primary health care programs based on age group can be arranged to anticipate the negative impact of the COVID-19 pandemic on child and adult patients who experience these problems.

The main contribution expected from this research is to provide scientific information about the care of child and adult patients with non-COVID-19 respiratory problems, who may be marginalised and neglected during the COVID-19 pandemic because the world of health only focuses on the fight against COVID-19.

2. Methods

2.1. Study design, sample size, data collection, and data source

We conducted a retrospective cohort study. We sampled all child and adult patients with non-COVID-19 respiratory problems who were admitted to the Dr. Soetomo Academic General Hospital from May 2018 to December 2021. This includes patients whose primary diagnosis was non-respiratory, and whose hospitalisations were paid for by public insurance included in the study (Fig. 2). Patients without respiratory problems or those with self-funded or private insurance were excluded. The total number of non-COVID-19 patients admitted to the hospital during the study is estimated at 28,000. The study was conducted according to the Declaration of Helsinki guidelines and approved for exempt review by the Research Ethics Committee of Dr. Soetomo General Academic Hospital, Surabaya, Indonesia (2022), with reference number 0813/LOE/301.4.2/I/2022.

Fig. 2 — Identify suitable samples of child and adult patients with non-COVID-19 respiratory disorders before and during the pandemic in Dr. Soetomo General Academic Hospital, Surabaya, East Java, Indonesia 2018–2021.

2.2. Variables

The independent variable was the COVID-19 pandemic. The primary dependent variables were the clinical outcome of care (readmission <30 days, average length of stay, and in-hospital mortality) and financial outcome of care (insurance payment and profit). The intervening variables were disease severity and complexity and total charges for care. The confounding variable was the month of hospital admission (reflecting seasonal distribution). This study also explored the month of hospital admission as a moderating variable for the impact of the COVID-19 pandemic on clinical and financial outcomes. In this study, all items of variables were in the reflective measurement [16].

Disease severity was classified into mild, moderate, and severe based on INA-CBGs (Indonesia Case-Based Groups) version 4.0 [17]. The complexity of the disease was reflected by the number of diagnoses (comorbidities) in the patient. Readmission less than 30 days was the percentage of patients readmitted less than 30 days from discharge from the hospital. Length of stay (LOS) is the duration (in days) of care of the patient treated for 1 episode of treatment. Hospital mortality is the percentage of patient deaths per period (before or during the COVID-19 Pandemic) during hospitalisation. The total charges for care (based on hospital rates) was the full-service charge, including non-surgical and surgical procedures, consultations, experts, nursing services, radiological examinations, laboratory examinations, blood services, medical rehabilitation, accommodation and rooms, use of drugs (symptomatic, antibiotics, drugs, chronic disease, chemotherapy), medical devices, consumable materials, and equipment rental. Insurance payment was the hospital's total income from claim fees paid to hospitals by National Health Insurance. NHI did not institute any special payment policies during the COVID-19 pandemic. During the COVID-19 pandemic, the Indonesian government, through the Ministry of Health, had a special policy to pay all costs to the hospital for the COVID-19 testing. Profit was the excess of insurance payments overcharges.

2.3. Statistical analysis

We used SPSS version 26 to test the normality distribution using the Kolmogorov–Smirnov test with Lilliefors-correction of all variables and compare values for all variables during the COVID-19 pandemic with previous. The comparison uses Student's t-test for normally distributed continuous data, which is variance homogeneous; Welch’s t-test for normally distributed continuous data, which is variance heterogenous; Mann-Whitney U test if the data is not normally distributed; Fisher's exact test for two by 2 tables of categorical data; or Chi-square test for n by k tables of categorical data. P-values of all the statistical tests are only descriptive. Furthermore, to analyse the relationship pattern between variables in our structural model (Fig. 1), we used Structural Equation Modelling (SEM). We used AMOS or Smart-PLS software (the choice depends on the distribution and the level of measurement of our research data) to test all the research hypotheses in Fig. 1. Because the data were not normally distributed, and the level of measurement was an interval, we used Smart-PLS software.

Firstly, we performed Confirmatory Factor Analysis (CFA) with uni-dimensionality for each scale and reliability-validity of each construct. We look at the uni-dimensionality for each scale with factor loading, and if it shows a value less than 0.500 [18], we will remove it. Because the items from our variables (construct) were reflective, the reliability indicators were loading factors, Cronbach's alpha, composite reliability, and average extracted variance (AVE). Construct reliability was achieved when the factor loading value was greater than 0.700 [19], Cronbach's alpha, and composite reliability were equal to or greater than 0.600.

The validity indicators were average extracted variance (AVE), Fornell–Larcker criteria (square root of the AVE for each variable was higher than the variance of the variable divided by other variables) [19], Heterotrait – Monotrait Ratio of Correlations by Henseler [20] for discriminant validity, and cross loading factors. Validity was achieved when the mean of extracted variance (AVE) for convergent validity was higher than 0.500 [19], Heterotrait – Monotrait Ratio of Correlations (HTMT) value was less than 0.900 [21]. Cross factor loading of items must be higher with each variable than with other variables.

After that, we also had to assess whether there was a collinearity problem in the model by looking at the correlation matrix and variance inflation factor (VIF). If the value of VIF was less than 3.30 [18], the model had no collinearity problem. The general threshold of VIF was 5 [19]. If there were no problems with multicollinearity, PLS-SEM was better for estimating the path coefficients among the latent variables of the structural model [22]. To assess the explanatory power of the structural model, we measured R² values. R² values 0.75, 0.50, and 0.25 were considered substantial, moderate, and mild, respectively [23].

To assess individual predictors and their role in the model, we measure Effect size (f²). The f² values of 0.35, 0.15, and 0.02 refer to the large, medium, and small effect of an independent variable [24]. To assess and predict relevancy and accuracy in the model, we measure Q². Q² value was greater than zero indicating a model-relevant and accurate [23].

3. Result

Among all the patients with non-COVID-19 respiratory disorders hospitalised in the Dr. Soetomo Academic General Hospital (n = 33,529), 65.7% (22,051/33,526) were patients with respiratory diseases hospitalised before the COVID-19 pandemic and 34.2% (11,478/33,526) during COVID-19 Pandemic. Among the 22,051 patients before the COVID-19 pandemic, 18,964 patients (86.0%) were covered by National Health Insurance. And among the 11,478 patients during the COVID-19 pandemic, 10,101 (88.0%) patients were covered by National Health Insurance. A total of 444 patients (116 child and 328 adult patients) were excluded because they tested positive for COVID-19 during hospitalisation. Before the COVID-19 pandemic, child and adult patients whose primary diagnosis was non-respiratory were 2064 and 13,292, respectively. During the COVID-19 pandemic, child and adult patients whose primary diagnosis was non-respiratory were 1041 and 7142 patients, respectively. Fig. 2 shows the results of the sample identification procedure.

3.1. Impact of the COVID-19 pandemic

We obtained data from 4684 child patients and 23,937 adult patients with non-COVID-19 respiratory disorders during the 44-month observation period, as shown in Table 1. These include data from 3237 child patients 22 months before the COVID-19 pandemic and 1447 child patient in 22 months during the COVID-19 pandemic, 15,727 adult patients within 22 months before the COVID-19 pandemic, and 8210 adult patients, within 22 months during the COVID-19 pandemic.

Table 1.

Characteristics of child and adult patients with non-COVID-19 respiratory disorders before and during the COVID-19 pandemic in Dr. Soetomo General Academic Hospital, Surabaya, East Java, Indonesia 2018–2021.

Characteristics	Child patients (n = 4684)					Adult patients (n = 23,937)
Characteristics	Before pandemic n = 3237		During pandemic n = 1447		P	Before pandemic n = 15,727		During pandemic n = 8210		P
Age (years), mean (SD)	4.0	(5.53)	4.8	(6.02)	0.001^(a)	52.2	(14.97)	50.9	(14.72)	<0.001 ^(a)
Sex
Male, n (%)	1739	(53.7)	825	(57.0)	0.020 ^(a)	7396	(47.0)	3783	(46.1)	0.083^(b)
Female, n (%)	1498	(46.3)	622	(43.0)		8331	(53.0)	4427	(53.9)
Primary Diagnosis (%)
Respirology, n (%)	1173	(36.2)	406	(27.5)	<0.001 ^(b)	2435	(15.5)	1068	(13.0)	<0.001 ^(c)
Cardiology, n (%)	95	(2.9)	38	(2.6)		2021	(12.9)	764	(9.3)
Dermatology, n (%)	10	(0.3)	3	(0.2)		47	(0.3)	23	(0.3)
Endocrinology, n (%)	35	(1.1)	17	(1.2)		822	(5.2)	363	(4.4)
Gastroenterology, n (%)	59	(1.8)	39	(2.6)		522	(3.3)	256	(3.1)
Hepatology, n (%)	28	(0.9)	11	(0.7)		296	(1.9)	132	(1.6)
Immunology, n (%)	132	(4.1)	83	(5.6)		1089	(6.9)	508	(6.2)
Labor, n (%)	35	(1.1)	13	(0.9)		1131	(7.2)	815	(9.9)
Neonatology, n (%)	766	(23.7)	409	(27.7)		0	(0.0)	0	(0.0)
Nephro-urology, n (%)	55	(1.7)	28	(1.9)		595	(3.8)	162	(2.0)
Neurology, n (%)	170	(5.3)	65	(4.4)		436	(2.8)	170	(2.1)
Non-COVID Infection, n (%)	344	(10.6)	72	(4.9)		997	(6.3)	367	(4.5)
Oncology, n (%),	229	(7.1)	200	(13.5)		4999	(31.8)	3453	(42.1)
Ophthalmology, n (%)	9	(0.3)	6	(0.4)		33	(0.2)	13	(0.2)
Orthopedics, n (%)	63	(1.9)	32	(2.2)		195	(1.2)	61	(0.7)
Psychiatry, n (%)	2	(0.1)	1	(0.1)		16	(0.1)	5	(0.1)
Traumato-toxicology, n (%)	6	(0.2)	6	(0.4)		82	(0.5)	49	(0.6)
Others	26	(0.8)	18	(1.2)		11	(0.1)	1	(0.0)
Comorbidity or complexity
0, n (%)	332	(10.3)	89	(6.2)	<0.001 ^(b)	405	(2.6)	198	(2.4)	<0.001 ^(c)
1, n (%)	393	(12.1)	127	(8.8)		1484	(9.4)	784	(9.5)
2, n (%)	432	(13.3)	168	(11.6)		2866	(18.2)	1545	(18.8)
3, n (%)	433	(13.4)	179	(12.4)		2594	(16.5)	1340	(16.3)
4, n (%)	432	(13.3)	190	(13.1)		2287	(14.5)	1039	(12.7)
5, n (%)	384	(11.9)	159	(11.0)		1922	(12.2)	862	(10.5)
6, n (%)	303	(9.4)	142	(9.8)		1544	(9.8)	737	(9.0)
7, n (%)	229	(7.1)	107	(7.4)		1079	(6.9)	564	(6.9)
8, n (%)	117	(3.6)	85	(5.9)		693	(4.4)	401	(4.9)
9, n (%)	72	(2.2)	79	(5.5)		410	(2.6)	294	(3.6)
10, n (%)	47	(1.5)	30	(2.1)		211	(1.3)	173	(2.1)
>10, n (%)	63	(1.9)	92	(6.4)		232	(1.5)	271	(3.3)
Severity level
Mild, n (%)	721	(22,3)	265	(18,3)	<0.001 ^(b)	1916	(12,2)	988	(12,0)	<0.001 ^(c)
Moderate, n (%)	937	(28,9)	372	(25,7)		5846	(37,2)	3934	(47,9)
Severe, n (%)	1579	(48,8)	810	(56,0)		7965	(50,6)	3288	(40,0)

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Note: The bivariate comparison test uses a). Mann-Whitney U test; b). Fisher's exact test; c). Chi-square test.

The number of cases of child patients with non-COVID-19 respiratory disorders before the COVID-19 pandemic was 3237 and a decrease of 55.3%–1447. The number of cases of adult patients before the COVID-19 pandemic was 15,727 and fell to 8210 (a reduction of 47.8%).

Cases of respiratory disorders unrelated to COVID-19 without comorbidity in child patients, as shown in Table 1, before the COVID-19 pandemic was 333 (10.3%), and during the COVID-19 pandemic was 89 (6.2%). Cases of respiratory disorders unrelated to COVID-19 without comorbidity in adult patients before the COVID-19 pandemic were 405 (2.6%), and during the COVID-19 pandemic, it was 198 (2.4%). Most child patients had comorbidity varying from 0 to 5 (52.2%) before the COVID-19 pandemic, and from 1 to 6 (57.3%) during the COVID-19 pandemic. Most adult patients had comorbidity varying from 2 to 5 (61.5%), before the COVID-19 pandemic, and from 2 to 5 (58.3%) during the COVID-19 pandemic.

The highest severity percentage in child patients increased by 7.2% during the COVID-19 pandemic. In adult patients, the moderate severity level increased by 10.7%, and the highest severity (severe) decreased by 10.6% during the COVID-19 pandemic.

The percentages of readmission <30 days of child patients hospitalised within 22 months before the COVID-19 pandemic, as shown in Table 2, were 4.5% (145/3237 cases) and were similar to readmission <30 days during the COVID-19 pandemic [4.6% (66/1447 cases)]. Readmission <30 days of adult patients hospitalised within 22 months before the COVID-19 pandemic were 18.4% (2899/15,727 cases) and increased during COVID-19 pandemic [22.4% (1838/8210 cases)].

Table 2.

The impact of the COVID-19 pandemic on hospital costs of care, the clinical and financial outcome in patients with non-COVID-19 respiratory disorders in Dr. Soetomo General Academic Hospital, Surabaya, East Java, Indonesia 2018–2021.

Characteristics	Child Patients n = 4684			Adult Patients n = 23,937
	Before Pandemic n = 3237	During Pandemic n = 1447	P	Before Pandemic n = 15,727	During Pandemic n = 8210	P
Clinical outcome
Readmission (%)
<30 days (n, %)	145 (4.5)	66 (4.6)	0.477(a)	2899(18.4)	1838(22.4)	<0.001(a)
>30 days (n, %)	3092 (95.5)	1381 (95.4)		12,828(81.6)	6372(77.6)
Average Length of Stay [aLOS] (days)
Mean (SD)	12.6 (14.23)	15.5 (16.53)	<0.001(b)	8.2 (7.99)	7.9 (7.27)	0.001(b)
Min to Max	1.0 to 152.0	1.0 to 160.0		1.0 to 159.0	1.0 to 90.0
Mortality (%)
Death, n (%)	859 (26.5)	414 (28.6)	0.075(a)	4779(30.4)	2062(25.1)	<0.001(a)
Survive, n (%)	2378(73.5)	1033 (71.4)		10,948(69.6)	6148(74.9)
Charges for care/patient (US$)
Total	4,200,455.3	3,237,660.0		16,795,196.0	11,321,784.3
Mean (SD)	1297.6 (2051.95)	2237.5 (3209.56)	<0.001(b)	1067.9 (1637.10)	1379.0 (1943.48)	<0.001(b)
Min to Max	10.4 to 26,418.9	13.2 to 36,361.9		0.0 to 47,904.2	4.9 to 37,757.0
Financial outcome/patient (US$)
Insurance payment
Total	4,388,750.4	2,329,145.1		17,190,006.5	9,213,496.8
Mean (SD)	1355.9 (1832.94)	1609.5 (2063.63)	<0.001(b)	1092.8 (1390.66)	1122.7 (1389.71)	<0.001(b)
Min to Max	120.0 to 12141.6	120.0 to 14,165.2		110.8 to 14,176.6	120.0 to 14,165.2
Profit
Total	188,423.0	−908,642.8		391,152.2	−2,104,629.0
Mean (SD)	58.2 (1579.38)	−628.0 (2526.05)	<0.001(b)	24.9 (1351.04)	−256.3 (1396.97)	<0.001(b)
Min to Max	−17,456.0 to 9925.0	−31,669.3 to 9925.0		−47,319.0 to 11,714.8	−23,591.8 to 9935.9

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Note: The bivariate comparison test uses a). Fisher's exact test; (b). Mann-Whitney U test.

The average length of stay (aLOS) in child patients hospitalised within 22 months (Table 2) before the COVID-19 pandemic were 12.6 (SD 14.23) days and increased to 15.5 (SD 16.53) days within 22 months during the COVID-19 pandemic. In adult patients hospitalised, aLOS tended to decrease from 8.2 (SD 7.99) to 7.9 (SD 7.27) days.

The percentage in-hospital mortality of child patients hospitalised within 22 months before the COVID-19 pandemic, as shown in Table 2, was 26.5% (859/3237 cases) and similar to in-hospital mortality during the COVID-19 pandemic [28.6% (414/1447 cases)]. The in-hospital mortality of adult patients hospitalised within 22 months before the COVID-19 pandemic was 30.4% (4779/15,727 cases) and decreased during COVID-19 pandemic [25.1% (2062/8210 cases)].

The mean charges for care/patient of child patients hospitalised within 22 months before the COVID-19 pandemic, as shown in Table 2, was US$ 1297.6 (SD US$ 2051.95) and increased to US$ 2237.5 (SD US$ 3209.56) within 22 months during the COVID-19 pandemic. In adult patients, the mean charges for care/patient increased from US$ 1067.9 (SD US$ 1637.10) to US$ 1379.0 (SD US$ 1943.48). The total charge of child patients hospitalised within 22 months before the COVID-19 pandemic, as shown in Table 2, was US$ 4,200,455.3 and decreased 22.9% to 3,237,660.0 within 22 months during the COVID-19 pandemic. In adult hospitalised patients, the total charges for care/patient decreased by 32.6% (from 16, 795, 196.0 to US$ 11, 321, 784.3).

The mean insurance payment/patient of child patients hospitalised within 22 months before the COVID-19 pandemic, as shown in Table 2, was US$ 1355.9 (SD US$ 1832.94) and increased to US$ 1609.5 (SD US$ 2063.63) within 22 months during the COVID-19 pandemic. In adult patients, the mean insurance payment/patient increased from US$ 1092.8 (SD US$ 1390.66) to US$ 1122.7(SD US$ 1389.71). The total insurance payment from National Health Insurance for non-COVID-19 respiratory cases hospitalised at Dr. Soetomo General Academic Hospital during the study period (22 months during the COVID-19 pandemic, compared to 22 months before the COVID-19 pandemic) in child patients decreased by 46.9% (from US$ 4,388,750.4 to US$ 2,329,145.1), and in adult patients decreased by 46.4% (from US$ 17, 190, 006.5 to US$ 9,213,496.8).

The mean profit/patient from National Health Insurance for non-COVID-19 respiratory cases hospitalised at Dr. Soetomo General Academic Hospital during the study period, as shown in Table 2, in child patients before the COVID-19 pandemic was US$ 58.2 (SD US$1579.38) and decreased to US$ −628.0 (SD US$ 2526.05) during the COVID-19 pandemic. In adult patients, the profit/patient decreased from US$ 24.9 (SD US$ 1351.04) to US$ −256.3 (SD US$ 1396.9). The total profit from National Health Insurance for non-COVID-19 respiratory cases hospitalised at Dr. Soetomo General Academic Hospital during the study period (22 months during the COVID-19 pandemic, compared to 22 months before the COVID-19 pandemic) in child patients decreased by 82.2% (from US$ 188,423.0 to US$ −908,642.8) and decreased by 638.1% in adult patients (from US$ 391,152.2 to US$ - 2,104,629.0).

There were 64 types of respiratory disorders in child patients during this study. During the COVID-19 pandemic, the number of cases in eight types of diagnosis increased, and the number of cases in the eight types of diagnosis decreased. The types of disorders that were relatively more common (by more than 1% of total cases increased) among child patients (Fig. 3A) were perinatological cases (newborn respiratory distress increased by 6.9%), and oncological cases (secondary malignant neoplasm of respiratory tract increased 3.1%, P < 0.001). The types of disorders that were relatively less common (by more than 1% of total cases decreased) among child patients were (Fig. 3A) were neonatal respiratory conditions (decreased by 3.6%), and acute tonsillitis (decreased by 1.4%).

Fig. 3 — 3A and 3B. Cases with non-COVID-19 respiratory problems (in percentage) based on diagnosis during the pandemic compared to before in Dr. Soetomo General Academic Hospital, Surabaya, East Java, Indonesia 2018–2021. A). Child patients; B). Adult patients.

There were 66 types of respiratory disorders in adult patients during this study. During the COVID-19 pandemic, the number of cases in 8 types of diagnosis increased and in 12 types of diagnosis decreased. The types of disorders that were relatively more common (by more than 1% of total cases increased) among adult patients (Fig. 3B) were oncological cases (respiratory tract malignant neoplasms increased by 8.7%; bronchial/lung malignant neoplasms increased 2.3%), respiratory system disease complicating pregnancy (increased 2.7%), and respiratory failure (increased 1.6%). The types of disorders that were relatively less common (by more than 1% of total cases decreased) among adult patients were pneumonia-unspecified organisms (decreased by 8.2%), pleural effusion (decreased by 2.7%), and pulmonary edema (decreased 1.6%).

Distribution of increase or decrease at least US$5 of charges/patient that were relatively more common among child patients (Fig. 4A) were non-surgical procedures (increased US$ 326.6); drug (increased US$110.9); accommodation (increased US $99.4), transfusion (increased US$90.4); laboratory (increased US$80.5; nursing (increased US$71.7); medical device (increased US$70.2); surgical procedure (increased US$39.9); consultation (increased US$35.5); equipment rental (increased US$30.0); chemotherapy drug (increased US$7.6). Distribution of increase or decrease of charge/patient that were relatively more common (by more than US$5 increased/decreased) among adult patients (Fig. 4B) were non-surgical procedures (increased US$109.0); accommodation (increased US$18.5); transfusion (increased US$21.2); laboratory (increased US$49.1); nursing (increase US$8.2); medical device (increased US$5.8); surgical procedure (increased US$30.1); consultation (increased US$18.8); equipment rental (increased US$24.01); chemotherapy drug (increased US$43.6); and consumable materials (decreased US$27.4).

In child patients during the COVID-19 pandemic (Fig. 5B), bronchopulmonary dysplasia in the perinatal period resulted in the highest loss (US$2599.7). It also produced the highest loss (US$2612.2) before the COVID-19 pandemic (Fig. 5A). Cases in child patients such as pneumonitis, acute laryngitis, tracheitis, rhinitis (vasomotor or allergic), and bronchiectasis consistently yield profit before (Fig. 5A) and during the COVID-19 pandemic (Fig. 5B). Cases in child patients such as cough, acute nasopharyngitis (common cold), asthma, other respiratory disorders, and acute bronchitis resulted in profits during the COVID-19 pandemic. In contrast, before the COVID-19 pandemic, they did not (Fig. 5A). In adult patients, cough and emphysema resulted in the highest loss during the COVID-19 pandemic (Fig. 6B). Status asthmaticus resulted in profit of US$1075.3 during the COVID-19 pandemic (Fig. 6B). It did not produce a profit before the COVID-19 pandemic (Fig. 6A).

Fig. 6 — 6A and 6B. The most common diagnoses produced the greatest losses (or profits) for the hospital associated with hospitalisation during the pandemic compared to before in Dr. Soetomo General Academic Hospital, Surabaya, East Java, Indonesia 2018–2022. A). Adult patients before COVID-19 pandemic; B). Adult patients during COVID-19 pandemic.

Fig. 7A and B showed trends in the severity and complexity of respiratory cases by month of hospital admission before and during the COVID-19 pandemic. The number of child cases decreased from 3237 to 1447 (Table 1) during the COVID-19 pandemic, with predominantly severe cases (Fig. 7A). Fig. 7B also shows that adult patients reduced considerably during the COVID-19 pandemic, with moderate cases predominant. Fig. 8A and B shows the disease complexity trend by month of hospital admission before and during the COVID-19 pandemic. During the COVID-19 pandemic, the higher complexity of cases became more dominant in child and adult patients.

Fig. 8 — 8A and 8B. Case complexity based on the month of hospital admission in Dr. Soetomo General Academic Hospital, Surabaya, East Java, Indonesia 2018–2021. A). Child patient; B). Adult patients.

3.2. Measurement model assessment

Because the data distribution of the dependent variable was not normally distributed, we used Partial Least Squares (PLS-SEM) with Smart-PLS software v.3.2.9 to assess the measurement model and the structural model of the conceptual framework. In the child structural model, items of “the month of hospital admission” construct except “June,” surgical procedure, consultation, expert, surgical procedure, consultation, expert, support, radiology, transfusion, rehabilitation, accommodation, intensive care, drug, consumable materials, equipment rental, a chemotherapy drug, readmission <30 days, mortality, insurance payments profits were removed from the model (Table 3, Fig. 9A) due to the factor loading value being less than 0.700. In the adult structural model, items of “the month of hospital admission” construct except “July,” surgical procedure, consultation, expert, surgical procedure, consultation, expert, support, radiology, transfusion, rehabilitation, accommodation, intensive care, consumable materials, equipment rental, a chemotherapy drug, readmission <30 days, mortality, insurance payments profits were removed from the model (Table 4, Fig. 9B) due to the factor loading value being less than 0.700. Construct reliability was achieved because the factor loading value was more significant than 0.700 [19]. Cronbach's alpha and composite reliability were equal to or greater than 0.600 (Table 5 and Table 6). Validity was achieved due to the mean of extracted variance (AVE) for convergent validity being higher than 0.500, Heterotrait – Monotrait Ratio of Correlations (HTMT) value was less than 0.900 (Table 6, Table 7, Table 8), cross factor loading of items was higher with each variable than with other variables (Table 9, Table 10), and the correlation matrix and variance inflation factor (VIF) was less than the general threshold of VIF (Table 11, Table 12). Because there were no problems with multicollinearity, the structural model was better for estimating the path coefficients (Table 13, Table 14). The measurement of our model meets all the criteria for a good fit and is suitable for Hypothesis testing (Table 15, Table 16).

Table 3.

Construct and items construct descriptive statistic in child structural model.

No	Constructs	Items of constructs	Mean	SD	Skew-ness	1st Loading Factor	2nd Loading Factor
1	Pandemic status	Pandemic Status	0.309	0.462	0.827	1.000	1.000
2	Patient's condition	1. Severity	2.300	0.794	−0.587	0.816	0.844
2	Patient's condition	2. Complexity	5.157	2.970	0.883	0.915	0.928
3	The month of hospital admission	1. January	0.069	0.253	3.403	−0.048	removed
		2. February	0.078	0.269	3.139	−0.470	removed
		3. March	0.079	0.269	3.134	0.098	removed
		4. April	0.088	0.283	2.920	−0.012	removed
		5. May	0.093	0.291	2.802	−0.144	removed
		6. June	0.095	0.294	2.755	−0.664	1.000
		7. July	0.072	0.259	3.302	0.330	removed
		8. August	0.076	0.265	3.207	0.0173	removed
		9. September	0.081	0.273	3.064	0.178	removed
		10. October	0.086	0.280	2.958	0.179	removed
		11. November	0.085	0.278	2.988	−0.037	removed
		12. December	0.098	0.298	2.701	0.447	removed
4	Charges for care	1. Non-Surgical Procedure	352.734	781.436	6.187	0.713	0.824
		2. Surgical Procedure	106.529	380.882	8.198	0.535	removed
		3. Consultation	38.403	261.077	17.282	0.252	removed
		4. Expert	3.228	13.063	12.452	0.216	removed
		5. Nursing	64.046	146.594	4.691	0.721	0.788
		6. Support	1.130	5.909	17.839	0.127	removed
		7. Radiology	36.481	85.827	5.870	0.559	removed
		8. Laboratory	189.629	303.060	4.244	0.803	0.793
		9. Tranfusion	110.378	378.026	8.344	0.346	removed
		10. Rehabilitation	4.753	29.843	26.062	0.373	removed
		11. Accomodation	114.648	232.951	6.217	0.635	removed
		12. Intensive Care	16.331	75.584	7.794	0.385	removed
		13. Drug	215.869	505.692	12.848	0.678	removed
		14. Medical Device	182.499	426.059	6.983	0.706	0.721
		15. Comsumable materials	28.042	152.016	24.728	0.211	removed
		16. Equipment Rental	116.971	381.660	7.047	0.676	removed
		17. Chemotherapy Drug	6.315	59.654	15.525	−0.004	removed
		18. Total chare of care	1587.984	2505.510	3.802	0.965	0.965
5	Clinical outcome	1. Readmission <30 days	0.045	0.207	4.388	−0.049	removed
		2. In-hospital mortality	0.728	0.445	1.026	0.352	removed
		3. aLOS	13.484	15.035	2.884	0.933	1.000
6	Financial outcome	1. Insurance payments	1434.222	1910.359	2.769	0.648	1.000
6	Financial outcome	2. Insurancepayments profits	−153.762	1947.743	−2.726	0.651	removed

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Fig. 9 — 9A and 9B. Model results: The P-values of the path coefficients of child and adult patients with non-COVID-19 respiratory disorders before and during the COVID-19 pandemic in Dr. Soetomo General Academic Hospital, Surabaya, East Java, Indonesia 2018–2021.

Table 4.

Construct and items construct descriptive statistic in adult structural model

No	Constructs	Items of constructs	Median	SD	Skew-ness	1^stLoading Factor	2^ndLoading Factor
1	Pandemic status	Pandemic Status	0.000	0.000	0.662	1.000	1.000
2	Patient's condition	1. Severity	-0.030	0.498	-0.401	0.867	0.872
2	Patient's condition	2. Complexity	0.028	0.468	0.401	0.883	0.979
3	The month of hospital admission	1. January	-0.242	0.981	3.087	0.192	removed
		2. February	-0.172	0.848	2.058	0.530	removed
		3. March	-0.235	0.969	2.919	0.245	removed
		4. April	-0.231	0.976	3.106	0.218	removed
		5. May	-0.287	0.988	2.647	0.155	removed
		6. June	-0.261	0.984	3.060	-0.178	removed
		7. July	-0.198	0.671	1.006	-0.741	1.000
		8. August	-0.285	0.993	2.958	-0.118	removed
		9. September	-0.294	0.997	2.971	-0.071	removed
		10. October	-0.314	0.998	2.764	-0.063	removed
		11. November	-0.307	0.987	2.583	-0.164	removed
		12. December	-0.337	0.999	2.525	0.044	removed
4	Charges for care	1. Non-Surgical Procedure	-0.039	0.671	6.812	0.741	0.849
		2. Surgical Procedure	-0.186	0.902	3.458	0.431	removed
		3. Consultation	-0.043	0.927	8.179	0.375	removed
		4. Expert	-0.276	0.969	12.552	0.245	removed
		5. Nursing	0.040	0.583	3.091	0.812	0.857
		6. Support	-0.245	0.994	24.886	0.110	removed
		7. Radiology	-0.258	0.893	3.082	0.450	removed
		8. Laboratory	-0.114	0.615	0.130	0.788	0.861
		9. Tranfusion	-0.142	0.932	10.092	0.364	removed
		10. Rehabilitation	-0.021	0.983	34.129	0.181	removed
		11. Accomodation	-0.165	0.897	4.906	0.441	removed
		12. Intensive Care	0.025	0.882	14.519	0.472	removed
		13. Drug	-0.032	0.675	11.464	0.738	0.762
		14. Medical Device	0.002	0.793	12.027	0.609	0.721
		15. Comsumable materials	-0.012	0.990	61.487	0.138	removed
		16. Equipment Rental	0.044	0.734	20.108	0.679	removed
		17. Chemotherapy Drug	-0.349	0.988	4.718	-0.157	removed
		18. Chronic Disease Drug	-0.013	1.000	90.678	-0.002	removed
		19. Total charges for care	-0.031	0.281	23.237	0.896	0.869
5	Clinical outcome	1. Readmission < 30 days	-0.340	0.869	1.229	0.494	removed
		2. In-hospital mortality	0.345	0.899	0.447	0.439	removed
		3. aLOS	0.004	0.586	0.328	-0.811	1.000
6	Financial outcome	1. Insurance payments	-0.054	0.680	0.584	0.734	1.000
6	Financial outcome	2. Insurance payments profits	-0.068	0.852	0.584	-0.524	removed

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Table 5.

Reliability and validity results in child structural model.

Constructs	Reliability			Convergent Validity (AVE) & Discriminant Validity (square root of AVE)
Constructs	Cronbach's Alpha	rho_A	Composite Reliability	Charges for care	Clinical Outcome	Financial Outcome	Month of hospital admission	Pandemic Status	Pandemic status * Month of admission (CC)	Pandemic status * Month of admission (CO)	Pandemic status * Month of admission (FO)	Pandemic status * Month of admission (PC)	Patient Condition
Charges for care	0.877	0.893	0.912	0.822
Clinical Outcome	1.000	1.000	1.000	0.805	1.000
Financial Outcome	1.000	1.000	1.000	0.638	0.439	1.000
Month of hospital admission	1.000	1.000	1.000	−0.005	0.011	−0.001	1.000
Pandemic Status	1.000	1.000	1.000	0.192	0.090	0.061	0.038	1.000
Pandemic status * Month of admission (CC)	1.000	1.000	1.000	−0.004	−0.003	−0.018	0.115	0.030	1.000
Pandemic status * Month of admission (CO)	1.000	1.000	1.000	−0.004	−0.003	−0.018	0.115	0.030	1.000	1.000
Pandemic status * Month of admission (FO)	1.000	1.000	1.000	−0.004	−0.003	−0.018	0.115	0.030	1.000	1.000	1.000
Pandemic status * Month of admission (PC)	1.000	1.000	1.000	−0.004	−0.003	−0.018	0.115	0.030	1.000	1.000	1.000	1.000
Patient Condition	0.737	0.780	0.881	0.459	0.465	0.439	0.011	0.132	−0.012	−0.012	−0.012	−0.012	0.888

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Note: CC=Charges for care; CO=Clinical Outcome; FO=Financial Outcome.

Table 6.

Reliability and validity results in adult structural model.

Constructs	Reliability			Convergent Validity (AVE) & Discriminant Validity (square root of AVE)
Constructs	Cronbach's Alpha	rho_A	Composite Reliability	Charges for care	Clinical Outcome	Financial Outcome	Month of hospital admission	Pandemic Status	Pandemic status * Month of admission (CC)	Pandemic status * Month of admission (CO)	Pandemic status * Month of admission (FO)	Pandemic status * Month of admission (PC)	Patient Condition
Charges for care	0.901	0.909	0.926	0.846
Clinical Outcome	1.000	1.000	1.000	0.660	1.000
Financial Outcome	1.000	1.000	1.000	0.672	0.353	1.000
Month of hospital admission	1.000	1.000	1.000	−0.022	−0.004	−0.016	1.000
Pandemic Status	1.000	1.000	1.000	0.105	−0.018	0.010	−0.077	1.000
Pandemic status * Month of admission (CC)	1.000	1.000	1.000	0.002	0.006	0.008	−0.266	−0.056	1.000
Pandemic status * Month of admission (CO)	1.000	1.000	1.000	0.002	0.006	0.008	−0.266	−0.056	1.000	1.000
Pandemic status * Month of admission (FO)	1.000	1.000	1.000	0.002	0.006	0.008	−0.266	−0.056	1.000	1.000	1.000
Pandemic status * Month of admission (PC)	1.000	1.000	1.000	0.002	0.006	0.008	−0.266	−0.056	1.000	1.000	1.000	1.000
Patient Condition	0.695	0.695	0.868	0.292	0.245	0.252	−0.030	−0.016	−0.018	−0.018	−0.018	−0.018	0.875

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Note: CC=Charges for care; CO=Clinical Outcome; FO=Financial Outcome.

Table 7.

Heterotrait-Monotrait Ratio (HTMT) in child structural model.

Constructs	Charges for care	Clinical Outcome	Financial Outcome	Month of hospital admission	Pandemic Status	Pandemic status * Month of admission (CC)	Pandemic status * Month of admission (CO)	Pandemic status * Month of admission (FO)	Pandemic status * Month of admission (PC)
Charges for care
Clinical Outcome	0.854
Financial Outcome	0.678	0.439
Month of hospital admission	0.007	0.011	0.001
Pandemic Status	0.206	0.090	0.061	0.038
Pandemic status * Month of admission (CC)	0.011	0.003	0.018	0.115	0.030
Pandemic status * Month of admission (CO)	0.011	0.003	0.018	0.115	0.030	1.000
Pandemic status * Month of admission (FO)	0.011	0.003	0.018	0.115	0.030	1.000	1.000
Pandemic status * Month of admission (PC)	0.011	0.003	0.018	0.115	0.030	1.000	1.000	1.000
Patient Condition	0.553	0.530	0.510	0.013	0.145	0.013	0.013	0.013	0.013

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Note: CC=Charges for care; CO=Clinical Outcome; FO=Financial Outcome.

Table 8.

Heterotrait-Monotrait Ratio (HTMT) in adult structural model.

Constructs	Charges for care	Clinical Outcome	Financial Outcome	Month of hospital admission	Pandemic Status	Pandemic status * Month of admission (CC)	Pandemic status * Month of admission (CO)	Pandemic status * Month of admission (FO)	Pandemic status * Month of admission (PC)
Charges for care
Clinical Outcome	0.694
Financial Outcome	0.703	0.353
Month of hospital admission	0.023	0.004	0.016
Pandemic Status	0.106	0.018	0.010	0.077
Pandemic status * Month of admission (CC)	0.007	0.006	0.008	0.266	0.056
Pandemic status * Month of admission (CO)	0.007	0.006	0.008	0.266	0.056	1.000
Pandemic status * Month of admission (FO)	0.007	0.006	0.008	0.266	0.056	1.000	1.000
Pandemic status * Month of admission (PC)	0.007	0.006	0.008	0.266	0.056	1.000	1.000	1.000
Patient Condition	0.364	0.293	0.303	0.035	0.079	0.021	0.021	0.021	0.021

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Note: CC=Charges for care; CO=Clinical Outcome; FO=Financial Outcome.

Table 9.

Cross Loadings in child structural model.

Construct	Pandemic Status	Patient Condition	Month of hospital admission	Charges for care	Clinical Outcome	Financial Outcome	Pandemic status * Month of admission (CC)	Pandemic status * Month of admission (CO)	Pandemic status * Month of admission (FO)	Pandemic status * Month of admission (PC)
PandemicStatus	1.000	0.132	0.038	0.192	0.090	0.061	0.030	0.030	0.030	0.030
Severity	0.065	0.853	0.011	0.322	0.332	0.375	−0.008	−0.008	−0.008	−0.008
Complexity	0.157	0.921	0.008	0.475	0.476	0.404	−0.012	−0.012	−0.012	−0.012
March	0.038	0.011	1.000	−0.005	0.011	−0.001	0.115	0.115	0.115	0.115
NonSurgicalProcedure	0.193	0.372	−0.012	0.824	0.610	0.496	−0.016	−0.016	−0.016	−0.016
Nursing	0.226	0.355	−0.006	0.788	0.664	0.433	−0.010	−0.010	−0.010	−0.010
Laboratory	0.123	0.444	0.002	0.793	0.723	0.531	0.011	0.011	0.011	0.011
MedicalDevic	0.076	0.266	−0.005	0.721	0.490	0.499	0.000	0.000	0.000	0.000
TotalCareCosts	0.173	0.428	0.000	0.965	0.787	0.641	−0.005	−0.005	−0.005	−0.005
aLOS	0.090	0.465	0.011	0.805	1.000	0.439	−0.003	−0.003	−0.003	−0.003
Insurance_payment	0.061	0.439	−0.001	0.638	0.439	1.000	−0.018	−0.018	−0.018	−0.018
Pandemic Status * Month of hospital admission (PC)	0.030	−0.012	0.115	−0.004	−0.003	−0.018	1.000	1.000	1.000	1.000
Pandemic Status * Month of hospital admission (CO)	0.030	−0.012	0.115	−0.004	−0.003	−0.018	1.000	1.000	1.000	1.000
Pandemic Status * Month of hospital admission (FO)	0.030	−0.012	0.115	−0.004	−0.003	−0.018	1.000	1.000	1.000	1.000
Pandemic Status * Month of hospital admission (CC)	0.030	−0.012	0.115	−0.004	−0.003	−0.018	1.000	1.000	1.000	1.000

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Note: CC=Charges for care; CO=Clinical Outcome; FO=Financial Outcome.

Table 10.

Cross Loadings in adult structural model.

Construct	Pandemic Status	Patient Condition	Month of hospital admission	Charges for care	Clinical Outcome	Financial Outcome	Pandemic status * Month of admission (PC)	Pandemic status * Month of admission (CO)	Pandemic status * Month of admission (FO)	Pandemic status * Month of admission (CC)
PandemicStatus	1.000	−0.016	−0.077	0.105	−0.018	0.010	−0.056	−0.056	−0.056	−0.016
Severity	−0.072	0.872	−0.010	0.226	0.204	0.253	−0.011	−0.011	−0.011	0.872
Complexity	0.044	0.879	−0.041	0.284	0.224	0.189	−0.020	−0.020	−0.020	0.879
March	−0.077	−0.030	1.000	−0.022	−0.004	−0.016	−0.266	−0.266	−0.266	−0.030
NonSurgicalProcedure	0.146	0.241	−0.024	0.849	0.509	0.612	0.004	0.004	0.004	0.241
Nursing	0.071	0.223	−0.021	0.857	0.537	0.581	0.006	0.006	0.006	0.223
Laboratory	0.099	0.385	−0.025	0.861	0.650	0.531	−0.008	−0.008	−0.008	0.385
MedicalDevic	0.025	0.165	−0.008	0.762	0.478	0.455	−0.001	−0.001	−0.001	0.165
TotalCareCosts	0.084	0.205	−0.015	0.896	0.610	0.641	0.008	0.008	0.008	0.205
aLOS	−0.018	0.245	−0.004	0.660	1.000	0.353	0.006	0.006	0.006	0.245
Insurance_payment	0.010	0.252	−0.016	0.672	0.353	1.000	0.008	0.008	0.008	0.252
Pandemic Status * Month of hospital admission (PC)	−0.056	−0.018	−0.266	0.002	0.006	0.008	1.000	1.000	1.000	−0.018
Pandemic Status * Month of hospital admission (CO)	−0.056	−0.018	−0.266	0.002	0.006	0.008	1.000	1.000	1.000	−0.018
Pandemic Status * Month of hospital admission (FO)	−0.056	−0.018	−0.266	0.002	0.006	0.008	1.000	1.000	1.000	−0.018
Pandemic Status * Month of hospital admission (CC)	−0.056	−0.018	−0.266	0.002	0.006	0.008	1.000	1.000	1.000	−0.018

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Note: CC=Charges for care; CO=Clinical Outcome; FO=Financial Outcome.

Table 11.

Collinearity statistics (VIF) Inner VIF Values in child structural model.

Construct	Patient Condition	Charges for care	Clinical Outcome	Financial Outcome
Pandemic Status	1.002	1.020	1.020	1.060
Patient Condition		1.018	1.018	1.317
Month of hospital admission	1.015	1.015	1.015	1.016
Charges for care				3.027
Clinical Outcome				2.984
Financial Outcome
Pandemic status * Month of admission (PC)	1.014
Pandemic status * Month of admission (CO)			1.014
Pandemic status * Month of admission (CC)		1.014
Pandemic status * Month of admission (FO)				1.014

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Note: CC=Charges for care; CO=Clinical Outcome; FO=Financial Outcome.

Table 12.

Collinearity statistics (VIF) Inner VIF Values in adult structural model.

Construct	Patient Condition	Charges for care	Clinical Outcome	Financial Outcome
Pandemic Status	1.012	1.013	1.013	1.039
Patient Condition		1.002	1.002	1.102
Month of hospital admission	1.086	1.087	1.087	1.087
Charges for care				1.880
Clinical Outcome				1.806
Financial Outcome
Pandemic status * Month of admission (PC)	1.083
Pandemic status * Month of admission (CO)			1.084
Pandemic status * Month of admission (CC)		1.084
Pandemic status * Month of admission (FO)				1.084

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Note: CC=Charges for care; CO=Clinical Outcome; FO=Financial Outcome.

Table 13.

Path Coefficients, Mean, SD, T-Values, P-Values, and Decision of Hypotheses in the child structural model.

No of H	Child Path	Beta	Mean	SD	T -Values	95% CI	P -Values	Decision
1.	Pandemic Status - > Clinical Outcome	0.029	0.030	0.013	2.183	0.004 to 0.055	0.029	Supported
2.	Pandemic Status - > Financial Outcome	−0.093	−0.094	0.010	9.447	−0.110 to-0.073	0.000	Supported
3.	Pandemic Status - > Patient Condition	0.132	0.133	0.014	9.107	0.102 to 0.159	0.000	Supported
4.	Pandemic Status - > Charges for care	0.135	0.135	0.014	9.950	0.106 to 0.162	0.000	Supported
5.	Patient Condition - > Charges for care	0.441	0.442	0.010	45.086	0.420 to 0.458	0.000	Supported
6.	Patient Condition - > Clinical Outcome	0.461	0.461	0.010	46.636	0.443 to 0.480	0.000	Supported
7.	Patient Condition - > Financial Outcome	0.225	0.224	0.015	15.203	0.199 to 0.256	0.000	Supported
8.	Charges for care - > Financial Outcome	0.785	0.786	0.028	28.437	0.725 to 0.834	0.000	Supported
9.	Month of hospital admission - > Charges for care	−0.014	−0.014	0.011	1.342	−0.035 to 0.007	0.180	Rejected
10.	Month of hospital admission - > Patient Condition	0.007	0.006	0.014	0.537	−0.019 to 0.034	0.592	Rejected
11.	Month of hospital admission - > Clinical Outcome	0.005	0.005	0.013	0.366	−0.020 to 0.030	0.714	Rejected
12.	Month of hospital admission - > Financial Outcome	0.008	0.008	0.011	0.747	−0.011 to 0.029	0.455	Rejected
13.	Clinical Outcome - > Financial Outcome	−0.289	−0.288	0.030	9.729	−0.346 to 0.230	0.000	Supported
14.	Pandemic status * Month of admission (PC) - > Patient Condition	−0.016	−0.016	0.014	1.109	−0.041 to 0.012	0.268	Rejected
15.	Pandemic status * Month of admission (CC) - > Charges for care	−0.002	−0.002	0.011	0.155	−0.024 to 0.020	0.877	Rejected
16.	Pandemic status * Month of admission (CO) - > Clinical Outcome	0.001	0.000	0.012	0.059	−0.021 to 0.024	0.953	Rejected
17.	Pandemic status * Month of admission (FO) - > Financial Outcome	−0.011	−0.010	0.009	1.170	−0.030 to 0.007	0.242	Rejected

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Note: H=Hypotheses; CC=Charges for care; CO=Clinical Outcome; FO=Financial Outcome.

Table 14.

Path Coefficients, Mean, SD, T-Values, P-Values, and Decision of Hypotheses in the adult structural model.

No of H	Adult Path	Beta	Mean	SD	T -Values	95% CI	P -Values	Decision
1.	Pandemic Status - > Clinical Outcome	−0.013	−0.013	0.006	2.175	−0.026 to −0.002	0.030	Supported
2.	Pandemic Status - > Financial Outcome	−0.073	−0.073	0.005	13.563	−0.084 to −0.062	0.000	Supported
3.	Pandemic Status - > Patient Condition	−0.020	−0.020	0.007	2.925	−0.033 to −0.005	0.004	Supported
4.	Pandemic Status - > Charges for care	0.110	0.110	0.006	16.974	0.096 to 0.120	0.000	Supported
5.	Patient Condition - > Charges for care	0.293	0.294	0.006	45.273	0.279 to 0.304	0.000	Supported
6.	Patient Condition - > Clinical Outcome	0.245	0.245	0.006	38.808	0.233 to 0.257	0.000	Supported
7.	Patient Condition - > Financial Outcome	0.068	0.068	0.006	11.510	0.056 to 0.080	0.000	Supported
8.	Charges for care - > Financial Outcome	0.776	0.778	0.017	46.849	0.737 to 0.804	0.000	Supported
9.	Month of hospital admission - > Charges for care	−0.001	−0.002	0.007	0.207	−0.014 to 0.014	0.836	Rejected
10.	Month of hospital admission - > Patient Condition	−0.039	−0.039	0.007	5.963	−0.052 to −0.027	0.000	Supported
11.	Month of hospital admission - > Clinical Outcome	0.005	0.005	0.006	0.789	−0.007 to 0.017	0.431	Rejected
12.	Month of hospital admission - > Financial Outcome	−0.002	−0.002	0.005	0.470	−0.012 to 0.008	0.639	Rejected
13.	Clinical Outcome - > Financial Outcome	−0.177	−0.178	0.016	10.763	−0.208 to −0.143	0.000	Supported
14.	Pandemic status * Month of admission (PC) - > Patient Condition	−0.032	−0.032	0.007	4.719	−0.047 to −0.020	0.000	Supported
15.	Pandemic status * Month of admission (CC) - > Charges for care	0.015	0.014	0.009	1.568	−0.001 to 0.035	0.118	Rejected
16.	Pandemic status * Month of admission (CO) - > Clinical Outcome	0.012	0.011	0.007	1.695	−0.001 to 0.026	0.091	Rejected
17.	Pandemic status * Month of admission (FO) - > Financial Outcome	0.004	0.005	0.006	0.742	−0.007 to 0.015	0.459	Rejected

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Note: H=Hypotheses; CC=Charges for care; CO=Clinical Outcome; FO=Financial Outcome.

Table 15.

Discriminant Validity R square, f square, and Q square in child structural model.

Constructs	R² Adjusted	Q²	f²
Constructs	R² Adjusted	Q²	Charges for care	Clinical Outcome	Financial Outcome	Patient Condition
Pandemic Status			0.023	0.001	0.015	0.018
Patient Condition	0.017	0.013	0.248	0.267	0.072
Month of hospital admission			0.000	0.000	0.000	0.000
Charges for care	0.228	0.152			0.381
Clinical Outcome	0.216	0.215			0.052
Financial Outcome	0.466	0.462
Pandemic status * Month of admission (CC)			0.000
Pandemic status * Month of admission (CO)				0.000
Pandemic status * Month of admission (FO)					0.000
Pandemic status * Month of admission (PC)						0.000

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Note: CC=Charges for care; CO=Clinical Outcome; FO=Financial Outcome.

Table 16.

Discriminant Validity R square, f square, and Q square in the adult structural model.

Constructs	R² Adjusted	Q²	f²
Constructs	R² Adjusted	Q²	Charges for care	Clinical Outcome	Financial Outcome	Patient Condition
Pandemic Status			0.013	0.000	0.010	0.000
Patient Condition	0.002	0.001	0.095	0.064	0.008
Month of hospital admission			0.000	0.000	0.000	0.001
Charges for care	0.097	0.068			0.611
Clinical Outcome	0.060	0.060			0.033
Financial Outcome	0.475	0.472
Pandemic status * Month of admission (CC)			0.000
Pandemic status * Month of admission (CO)				0.000
Pandemic status * Month of admission (FO)					0.000
Pandemic status * Month of admission (PC)						0.001

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Note: CC=Charges for care; CO=Clinical Outcome; FO=Financial Outcome.

As shown in Table 15 (structural modelling in children), the power of the structural model to explain the variance of clinical outcomes (R²) was 0.216 (mild), and to explain the variance of financial outcomes (R²) was 0.466 (moderate). This children structural model effectively predicted clinical outcomes (Q² = 0.215) and financial outcomes (Q² = 0.462). As shown in Table 16 (structural modelling in adult), the power of the structural model to explain the variance of clinical outcomes (R²) was 0.06 (mild), and to explain the variance of financial outcomes (R²) was 0.472 (moderate). This adults structural model effectively predicted clinical outcomes (Q² = 0.06) and financial outcomes (Q² = 0.472).

3.3. Results of structural modelling of the COVID-19 pandemic in child patients

From the structural modelling of the COVID-19 pandemic in children (Fig. 6A, Table 13), our study showed that the COVID-19 pandemic had: (1). Indirect effects on clinical outcome (β = 0.029, P < 0.05) by increasing aLOS; (2). The indirect impact on financial outcomes (β = −0.093, P < 0.001) by decreasing the insurance payment; (3). Direct effect on child patient’s condition (β = 0.132, P < 0.001) by increasing severity and complexity; (4). Direct impact on charges for care (β = 0.135, P < 0.001) by increasing the total charges for care, the laboratory, the medical device, the charge of nursing, and the nonsurgical procedure, respectively.

From the child structural model (Fig. 6A, Table 13), our study showed that: (1). Patient condition had a direct effect on charges for care (β = 0.441, P < 0.001) by increasing total charges for care, charges for the laboratory, charges for medical device, charges for nursing, and charge of the nonsurgical procedure, respectively; (2). The patient condition had a direct effect on clinical outcome (β = 0.461; P < 0.001) by increasing aLOS; (3) the Patient condition has a direct impact on financial outcomes (β = 0.255, P < 0.001) by increasing the insurance payment; (4). Charges for care has a direct impact on financial outcomes (β = 0.785, P < 0.001) by increasing the insurance payment; (5). The month of hospital admission had no direct effect on charges for care (β = -0.014, P = 0.180); patient’s condition (β = 0.007, P = 0.592); clinical outcome (β = 0.005, P = 0.714), and financial outcome (β = 0.008, P = 0.455), respectively.

3.4. Results of structural modelling of COVID-19 pandemic in adult patients

From the structural modelling of COVID-19 pandemic in adults (Fig. 6B, Table 14), our study showed that COVID-19 pandemic had: (1). Indirect effects on clinical outcome (β = −0.013, P < 0.001) by decreasing aLOS; (2). The indirect impact on financial outcomes (β = −0.073, P < 0.001) by reducing the insurance payment; (3). Direct effect on adult patient’s condition (β = -0.020, P < 0.001) by decreasing severity and complexity; (4). Direct impact on charges for care (β = 0.110, P < 0.001) by increasing the total charges for care, the laboratory, the medical device, the nursing, and the nonsurgical procedure charges, respectively.

From the adult structural model (Fig. 6B, Table 14), our study showed that: (1). Patient condition had a direct effect on charges for care (β = 0.293, P < 0.001) by increasing total charges for care, charges for the laboratory, charges for medical device, charges for nursing, and charge of the nonsurgical procedure, respectively; (2). The patient condition had a direct effect on clinical outcome (β = 0.245; P < 0.001) by increasing aLOS; (3) Patient condition has a direct impact on financial outcomes (β = 0.293, P < 0.001) by increasing the insurance payment; (4). Charges for care has a direct impact on financial outcomes (β = 0.776, P < 0.001) by increasing the insurance payment; (5). The month of hospital admission had no direct effect on charges for care (β = -0.001, P = 0.836); clinical outcome (β = 0.005, P = 0.431); financial outcome (β = −0.002, P = 0.639) respectively; (5). The month of hospital admission (July) had a direct effect patient’s condition (β = -0.029, P < 0.001) by decreasing severity and complexity.

3.5. Moderation by the month of hospital admission

Regarding the month of hospital admission, in the child structural model (Fig. 6A, Table 13), our study showed that the month of hospital admission did not moderate the effect of the COVID-19 pandemic on patient condition (β = 0.007, P = 0.268), charges for care (β = −0.014, P = 0.180), clinical outcomes (β = 0.005, P = 0.714), and financial outcomes (β = −0.011, P = 0.242), respectively. Regarding the month of hospital admission, the adult structural model (Fig. 6B, Table 14), our study showed that the month of hospital admission moderated the effect of the COVID-19 pandemic on patient condition (β = −0.032, P=<0.001) by decreasing severity and complexity, and did not moderate the impact of the COVID-19 pandemic on the charges for care (β = 0.015, P = 0.118), clinical outcomes (β = 0.012, P = 0.091), and financial outcomes (β = 0.004, P = 0.459), respectively.

4. Discussion

Dr. Soetomo General Academic Hospital, where this research was conducted, is an Academic Medical Center (AMC) referral tertiary hospital with the largest number of beds (1670 beds) and residents (1890 doctors) in Indonesia [25]. The hospital – located in Surabaya, Java Island, Indonesia - has become the highest referral for patients from eastern Indonesia. As many as 30–40% of patients in this hospital come from Surabaya, and the rest (60–70%) come from all districts/cities in Eastern Indonesia.

In the first, second, and third waves (September 2020, January 2021, and July 2021), there was a drastic decrease in the number of child and adult patients with non-COVID-19 respiratory disorders (Fig. 7, Fig. 8, Fig. 9B). In child patients, whose number decreased by 55.3%, there was an increase in the severity and complexity of the disease, an increase in aLOS, unchanged readmission <30 days, and unchanged mortality (Table 1, Table 2). In adult patients, whose number decreased by 47.8%, there was a decrease in disease severity, an increase in disease complexity, a reduction in aLOS, an increase in readmission <30 days, and a decrease in mortality (Table 1, Table 2).

ECIEN -2020 study [26] reports that during the COVID-19 pandemic, a reduction of 33.0% of total hospitalisations and 48.0% of all patients with non-COVID-19 respiratory diseases during the COVID-19 pandemic. In 2020, a child aged <18 years in Taiwan with childhood community-acquired pneumonia (CAP) was 15.7/10,000. During the COVID-19 pandemic, CAP decreased by 72.3% [27]. It turns out that interventions for controlling COVID-19 (prevention of droplet transmission and direct contact) could prevent seasonal influenza and CAP [28]. Study results in adult patients showed that elderly pneumonia cases decreased in hospitals that did not accept COVID-19 patients [29].

Our study showed a change in the distribution of the type of diagnosis and the number of cases in child and adult patients. Perinatal and oncology cases have the highest incremental for child and adult patients (Fig. 3A and B). The cases of hospitalisations for respiratory tuberculosis for child and adult patients have decreased in absolute and relative terms (Fig. 3A and B). The results of our study are similar to those of a study in Japan [30], which reported that the number of hospitalisations with non-COVID-19 respiratory diseases, particularly pneumonia and asthma, decreased dramatically. In contrast, the number of hospitalisations for chemotherapy (oncology) and hemodialysis (nephrology) were less affected.

Some research results can help us understand why oncology cases experienced the highest increase during the COVID-19 pandemic [[31], [32], [33], [34], [35]]. Due to low immunity after chemotherapy, many cancer patients (especially lung cancer) often have the same pulmonary symptoms as COVID-19, so it is necessary to determine whether these symptoms are due to COVID-19 [33]. In addition, during the COVID-19 pandemic, oncology doctors and patients feel safer as the global oncology community puts safety first for cancer patients who need essential care. A series of international guidelines and recommendations are provided to manage cancer patients with specific protocols [34].

The reduction in hospitalisations for respiratory tuberculosis in child and adult patients (Fig. 3A and B) should be noted. Increased mortality and transmission of respiratory tract tuberculosis in the community can thwart the success of the national tuberculosis program [36,37]. Disturbances in health services or programs for tuberculosis patients affected treatment adherence.

In child patients during the COVID-19 pandemic, there is an increase in the severity and complexity of the disease without accompanying increased mortality (Table 1, Table 2, Fig. 8A). In adult patients, during the COVID-19 pandemic, there was a decrease in disease severity and an increase in disease complexity, accompanied by a decrease in mortality (Table 1, Table 2, Fig. 8B).

The severity of the disease is strongly related to the outcome. Patients' reluctance to be hospitalised during the COVID-19 pandemic often occurs in non-surgical emergencies [31] and cancer [32]. Even the amount patient with emergency conditions such as myocardial infarction and stroke decreased during the COVID-19 pandemic [31]. Why has the severity of hospitalised adult patients decreased during the COVID-19 pandemic (Table 1)? In Indonesia, the family of adult patients with severe diseases is afraid if the patient dies positively of COVID-19. When a die with positive for COVID-19, so funeral must be conducted by the burial procedure patient COVID-19 by ministry guidelines health, so the family patient cannot treat the patient's corpse based on his culture and religion.

In child patients during the COVID-19 pandemic, there was an increase in aLOS with no change in readmission <30 days. In adult patients, there was a decrease in aLOS accompanied by increased readmission <30 days (Table 1, Table 2). During the COVID-19 pandemic, patients experience fear and are forced to self-isolate for long periods [38], resulting in many non-COVID-19 patients delaying health care [39]. Previous studies reported that child patients with non-COVID-19 respiratory problems are scared to be exposed to COVID-19 in hospitals, affecting their absolute need for hospitalisation [7,13,14].

A study shows that postponing hospitalisation increases LOS and worsens the clinical outcome [40]. However, the ECIEN-2020 study [26] demonstrated that LOS in hospitals (7.5 days in 2019 vs 5.2 days in 2020) in child patients with unrelated COVID-19 respiratory distress was relatively the same as before the COVID-19 pandemic. We report the same results as the study in an academic medical centre hospital in Indonesia [41], which shows an increase in aLOS in child patients with non-COVID-19 respiratory problems 19 during the COVID-19 pandemic.

In child patients, the COVID-19 pandemic is not accompanied by changes in mortality (Table 1, Table 2). In adult patients, the COVID-19 pandemic was accompanied by decreased mortality (Table 1, Table 2). Unlike our study, previous studies in adult patients with unrelated COVID -19 have shown that mortality has almost doubled, from 7.7% in 2019 to 13.0% in the year 2020 [42], which is the current mortality of COVID-19 patients of 9.27%. Most of the patients (91.1%) who died during the lockdown were more than 70 years old, and the leading causes of death were nervous system disorders, blood circulation, and breathing. These three system disturbances globally contribute to more than 60% of deaths [43].

The results of our study are the same as those at other AMC hospitals in Indonesia [41], which show no change in mortality in respiratory cases unrelated to COVID -19 in child patients. Other [44] equal to the result in our research, there is a decrease in the number of deaths due to respiratory disorders unrelated to COVID-19 during the COVID-19 pandemic, like asthma, COPD, pneumonia, acute bronchitis, seasonal influenza, and pneumococcal infections invasive. Previous studies [45] report that respiratory problems unrelated to COVID-19 in adult patients increased mortality. A previous study reported that readmission <30 days during the COVID-19 pandemic is not different from before the COVID-19 pandemic [46]. An AMC hospital study showed that workload did not affect readmission <30 days [47].

Why readmission <30 days in hospitalised adult patients increased during the COVID-19 pandemic (Table 1) could connect with a decrease in LOS. In our research, an adult patient's complexity tends to increase during the COVID-19 pandemic (Fig. 8B). In complex and severe adult patients, their families are afraid if the patient dies in the hospital because the funeral procedure will be different from the usual procedure based on culture and religion. In addition, the COVID-19 pandemic period makes patients want to discharge from the hospital faster [47], with the result that recovery is not complete and is at risk increase readmission <30 days.

Research shows that health care utilisation and direct medical financing changed during the COVID-19 pandemic [48]. During the COVID-19 pandemic, patients are hesitant to go to the hospital for fear of being exposed to viral pneumonia that causes COVID-19 or because of the lockdown policy [15,49,50]. As a result, the disease is very severe at hospital admission and requires ICU, so maintenance costs increase [51].

Indonesia's National Health Insurance system protects many inpatients, especially the poorest people, from overspending. As much as 71% of citizens of Indonesia is National Health Insurance participant [52]. In our study, the number of patients covered by National Health Insurance before the COVID-19 pandemic was 86.0% (18,964/22,051), and during the COVID-19 pandemic, 88.0% (10,101/11,478). The total payment of National Health Insurance at hospitals during this research period from care to child and adult patients decreased by 46.9% and 46.4%. It happened because, during the COVID-19 pandemic, the number of child and adult patients decreased by 55.3% and 47.8% (Table 1).

If the profit decreases, the hospital burden will get heavier [15,50]. The COVID-19 pandemic makes the use of health services by child and adult patients decreases by 64% and 38%, respectively [53], with the consequence of lateness in all activity of hospital services, such as diagnosis, treatment, and elective procedures [54,55]. In our study, the average insurance payment/patient in child and adult patients during the COVID-19 pandemic increased by 18.9% and 2.7%. In our study, the average profit in child and adult patients decreased by 582.2% and 638.1%, respectively.

In the Philippines, monthly insurance claims from March to September 2020 decreased by almost 60% compared to previous years. Claim tertiary referral hospital insurance decreased to 30%. Less-urgent procedures have substantially decreased insurance payments compared to chemotherapy, vaginal delivery, and cesarean section [56]. An Iranian study reported that hospital revenues fell 9%, with operational costs increasing to 70% [57].

A cross-sectional multicenter study in children admitted to 49 tertiary hospitals in the US reported that the cost of care for bronchiolitis, pulmonary edema, respiratory failure, and asthma was lower during the COVID-19 pandemic. However, the cost for upper respiratory tract infections and nonbacterial gastroenteritis is higher [46]. In our study, the higher costs were due to additional examinations, treatment procedures, and staff protection against exposure to COVID-19. Consistent improvement in child and adult patients in financing occurs for non-surgical procedures, accommodation, laboratories, medical devices, consultation, and equipment rental (Fig. 4A and B). The addition of testing to be aware of exposure to COVID-19 will increase laboratory and accommodation costs because add LOS.

Although epidemiological studies of influenza transmission have identified absolute humidity and temperature as climatologic predictors [58], the child structural model (Fig. 6A, Table 13) showed no direct effect of the month on patient conditions, clinical outcomes, maintenance costs, or financial outcomes. In adult patients, however, admission in July directly affected the patient's condition by decreasing disease severity and complexity.

Influenza virus transmission depends on absolute humidity and temperature. In Indonesia, respiratory disease increases in the rainy months (December, January, and February) and in the first transitional seasons (March, April, and May). After it decreased in the dry season months (June, July, and August) and the transition of the second season (September, October–November). Respiratory disease associated with indoor air pollution [59]. Even though the month of hospitalisation (month of July) is related to drop severity and complexity, however results of our research show that the month of hospitalisation did not moderate the impact of the COVID-19 pandemic on care charges, clinical outcomes and financial outcomes.

During this research, the criteria for payment by an insurance company national remains the same. However, the utilisation rate for unrelated COVID-19 health services fell by almost 25% during the COVID-19 pandemic, despite the skyrocketing utilisation of COVID-19 health services. Participant insurance national active participation (indicated by the number of paying participants) also declined due to the economic recession and dues avoidance. However, if people are affected by the economic recession, income decreases, or they lose work, subsidised insurance premiums, or local government [60]. So, the number of people eligible for insurance payments remained unchanged over the study period.

The limitations of this study are: (1). The design used is retrospective; (2). The research subjects came from only one hospital (the largest teaching hospital in Indonesia with 1691 beds); (3). Data is only taken from patients who are financed by national health insurance. To consolidate our findings, further international, prospective, multicenter studies should be conducted, covering more hospitals, and focusing on the impact of the COVID-19 pandemic on child and adult patients unrelated to COVID -19.

The strengths of this research are: (1). Number of subjects observed is large, comparing the condition of the COVID-19 pandemic with the previous one with a long observation period; 2). The research was conducted at the largest tertiary hospital in Indonesia and became the highest referral for patients from eastern Indonesia; 3). The research use path analysis with a structural equation model (SEM) to explore the relationship between research variables. PLS-SEM is a precise, robust, powerful tool [61,62]. In addition, for consistent path modelling results and driving predictive targets, PLS-SEM is the right choice for researchers [63].

The study found that during the COVID-19 pandemic, the severity and complexity of respiratory illnesses unrelated to COVID-19 increased in child patients and decreased in adult patients (Table 1). This finding is important because the more severe and complex the respiratory disorders suffered in childhood, the children's intelligence and quality of life will decrease in the future [1,2]. For this reason, the rehabilitation program for children must be the next priority in dealing with the negative impacts of the COVID-19 pandemic. Likewise, the trend of the increasing proportion of oncology and perinatal hospitalisation cases during the COVID-19 pandemic may reflect that early treatment of oncology and perinatology cases requires more attention during and after the COVID-19 pandemic.

From this study, we get information about the similarities and differences in the impact of the COVID-19 pandemic on child and adult patients with non-COVID-19 respiratory disorders, as follows.

1)
The COVID-19 pandemic affected disease severity and complexity in child and adult patients. What is different is that in child patients, the effect is positive (worsening), while in adult patients, the effect is negative (improving).
2)
The COVID-19 pandemic affected clinical outcomes in child and adult patients. What is different is that in child patients, the effect is positive (worsening), while in adult patients, the effect is negative (improving).
3)
The COVID-19 pandemic decreased hospital profits from both child and adult patients.
4)
The structural model produces the same evidence for child and adult patients, worsening the patient's condition has a positive effect on the high charges for care, the high charges for care has a positive effect on improving financial outcomes, and worsening clinical outcomes hurt financial outcomes.
5)
The structural model produces different evidence between child and adult patients regarding seasonal variations (the month of hospital admission): children's disease conditions are not influenced by seasonal/monthly changes (the month of hospital admission = July), and seasonal variations do not moderate the impact of the COVID-19 pandemic in child patients; the disease condition of adult patients is influenced by seasonal/monthly changes (the month of hospital admission = July), and seasonal/monthly changes moderate the impact of the COVID-19 pandemic on improving patient conditions.

From research, we know that payment insurance should increase as the severity of disease increase and the charges for care, but this is different in child patients. Because increasing the severity and complexity of the disease will increase the average Length of Stay (aLOS), which reduces financial outcomes.

The novelty of this study is to measure impact of the COVID-19 pandemic on clinical and financial outcomes. Previous literature has also discussed the impact of the COVID-19 pandemic. Still, the literature does not have estimates of the magnitude of the COVID-19 pandemic “effect size” on clinical and financial outcomes in a structural model [9,30,41,45,56,60,[64], [65], [66], [67], [68], [69], [70], [71], [72], [73], [74], [75], [76], [77], [78], [79], [80], [81], [82]]. Previous publications have also not provided information on the relationship between the COVID-19 pandemic, patient condition, cost risk, and clinical and financial outcomes, as well as the effect of the season as a confounder in two structural models, the child model and the adult model. From the results of this study, the impact of disruption to health services due to the COVID-19 pandemic in the future is more manageable, mitigated, and minimised based on valid evidence.

This study shows that the impact of the COVID-19 pandemic on human well-being outweighs the implications directly related to SARS-CoV-2 infection. Insurance payments that do not accommodate the severity and complexity of the disease causing the COVID-19 pandemic impact on clinical outcomes are not always accommodated in financial outcomes. The results of this study are expected to add more facts that can explain the secondary effects of the COVID-19 pandemic.

In the future, it is hoped that policymakers will be able to increase the health system's resilience and reduce the effects of sudden fluctuations in activity resulting from disasters by establishing mechanisms to adjust insurance payment systems quickly. It is necessary to protect the hospital from lost revenue and to reasonably pay for services that change and services that require additional expenses. Some payment adjustments are likely to be temporary.

5. Conclusion

The conclusions are that the number of non-COVID respiratory patients decreased during the COVID-19 pandemic (47.8% in adult patients, 55.3% in child patients). Disease severity and complexity increased in child patients but decreased in adult patients. Costs of care and insurance payments increased. Since the insurance payments did not increase as much as the cost of care, hospital profit decreased.

Author contribution statement

Anang Endaryanto: Conceived and designed the experiments; Performed the experiments; Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data; Wrote the paper.

Arlina Dewi; Kusbaryanto: Conceived and designed the experiments.

Ricardo Adrian Nugraha: Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data; Wrote the paper.

Data availability statement

Data included in article/supplementary material/referenced in article.

Acknowledgements

We would like to express special gratitude to our subjects and their parents and advisers who allowed us to do this research. We would like to thank Budi Santoso as Dean of Faculty of Medicine Universitas Airlangga and Joni Wahyuhadi as Director of Dr. Soetomo General Academic Hospital for their support in finishing this manuscript.

Contributor Information

Anang Endaryanto, Email: a.endaryanto.psc21@mail.umy.ac.id.

Arlina Dewi, Email: arlinadewi@umy.ac.id.

Kusbaryanto, Email: kusbaryanto@umy.ac.id.

Ricardo Adrian Nugraha, Email: ricardo.adrian.nugraha-2019@fk.unair.ac.id.

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PERMALINK

Trend in the admissions of patients with non-COVID-19 respiratory symptoms during COVID-19 pandemic and its impact on hospital finances in Surabaya, Indonesia

Anang Endaryanto

Arlina Dewi

Kusbaryanto

Ricardo Adrian Nugraha

Abstract

Background

Objective

Methods

Results

Conclusion

1. Introduction

2. Methods

2.1. Study design, sample size, data collection, and data source

Fig. 2.

2.2. Variables

2.3. Statistical analysis

Fig. 1.

3. Result

3.1. Impact of the COVID-19 pandemic

Table 1.

Table 2.

Fig. 3.

Fig. 4.

Fig. 5.

Fig. 6.

Fig. 7.

Fig. 8.

3.2. Measurement model assessment

Table 3.

Fig. 9.

Table 4.

Table 5.

Table 6.

Table 7.

Table 8.

Table 9.

Table 10.

Table 11.

Table 12.

Table 13.

Table 14.

Table 15.

Table 16.

3.3. Results of structural modelling of the COVID-19 pandemic in child patients

3.4. Results of structural modelling of COVID-19 pandemic in adult patients

3.5. Moderation by the month of hospital admission

4. Discussion

5. Conclusion

Author contribution statement

Data availability statement

Acknowledgements

Contributor Information

References

Associated Data

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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