Effects of Long-Haul COVID on the Health-Related Quality of Life Among Recovered Hospitalized Patients

doi:10.21203/rs.3.rs-1478232/v1

People with long-haul COVID-19 could experience various health problems, from milder to more severe ones. This study aimed to identify the effect of long-haul COVID-19, specifically on the Quality of Life domains, experienced by COVID-19 patients who have been discharged. Data collection was carried out online, using data from DKI Jakarta hospitalized patients who have been confirmed with and recovered from SARS-CoV-2 infections. We selected patients who were at least a month away from the onset of COVID-19. Logistic regression technique was used to analyse the data. The questionnaire used in this study contained questions regarding long-haul COVID 19 symptoms and domains of Quality of Life, which were measured by WHOQOL-BREF. Prior to the data collection, we tested the questionnaire with 30 recovered patients who were hospitalized outside DKI Jakarta. Of the 172 recovered inpatients who filled out the questionnaire correctly and aged 18 years or more selected by random sampling, 30.2% had long-haul COVID 19. Respondents who only experienced one long-haul COVID-19 symptom had a proportion of 23.8 %, while those who experienced more than one long-haul COVID-19 symptom had a proportion of 6.4%. This study showed that the long-haul effects of COVID-19 affected almost all domains in Quality of Life except the environmental one. Age, gender, and marital status were covariates for the association between long-haul COVID 19 and the Quality of Life domains. In conclusion, coordination between health service providers and public health officers is essential to obtain an adequate response of post surveillance for recovered COVID-19 patients.

Long-Haul COVID 19

Domains of Quality of Life

Good Health and Wellbeing

Long-haul COVID, also known as post-COVID syndrome, post-acute sequelae of COVID-19 (PASC), or chronic COVID-19 syndrome (CCS), is a condition characterized by long-term sequelae appearing or persisting after the typical convalescence period of COVID-19 (1). Long-haul COVID can affect nearly every organ system with a wide range of symptoms that are commonly discussed, including fatigue, headaches, shortness of breath, anosmia, parosmia, muscle weakness, low fever, and cognitive dysfunction. The sequelae include nervous system and neurocognitive disorders, mental health disorders, metabolic disorders, cardiovascular disorders, gastrointestinal disorders, malaise, fatigue, musculoskeletal pain, and anemia (2–5).

Long-haul COVID tends to occur more frequently in survivors who have more severe symptoms during infection (6,7). The effectiveness of treatment measures to treat this condition is still being sought. Conditions associated with health-related quality of life have become an important issue in medical and psychological research. The World Health Organization (WHO) defines quality of life as individuals’ perceptions of their position in life in the context of the culture and value systems in which they live and in relation to their goals, expectations, standards, and concerns (8). WHO has developed the WHOQOL-100 questionnaire to assesses people’s quality of life. It is a multidimensional evaluation of individuals’ perceptions of their health status, psychosocial status, and other aspects of their lives (9). WHO has also created a simplified version of WHOQOL-100 called WHOQOL-BREF (10). The WHOQOL-BREF questionnaire can be completed independently by the respondent or with the assistance of a trained interviewer (8,10).

WHOQOL-BREF demonstrated good discriminant validity, content validity, internal consistency, and retest reliability, as well as a high correlation with WHOQOL-100 domain scores (10–12). The Indonesian version of the WHOQOL-BREF instrument showed a near-perfect match in two general items and a good agreement in four domains. Therefore, it can be concluded that WHOQOL-BREF is a consistent and stable instrument to measure the quality of life of Indonesian people in general (13–15).

Further in-depth studies are needed to examine the health problems of COVID-19 patients who have been declared cured and discharged from the hospital. Thus, this research aimed to identify the effects of long-haul COVID symptoms on the quality of life of recovered patients in hospitals in Jakarta. The results of this research are expected to strengthen post-hospitalization supervision of recovered patients.

This research on long-haul COVID had secured an ethical permit and a data collection permit from the DKI Health Office for six months. The research was conducted in DKI Jakarta, the capital of Indonesia and the center of the COVID-19 spread in the country. Due to conditions that did not allow for the data to be collected directly, data collection was conducted online using a database from reports of COVID-19 patients from various hospitals and health centers. The respondents were randomly selected from a list of COVID-19 patients who had recovered. This list was provided by the Provincial Health Office of DKI Jakarta. Several difficulties, such as a slow pace of data collection because some patients who had recovered from COVID-19 refused to fill out online surveys, were encountered during the implementation. Eventually, only 281 respondents were obtained. The database of COVID-19 patient reports from various hospitals and health centers were also not well organized, and some of the data reported were incomplete. After data cleaning, making sure that the respondents met the inclusion criteria (aged 18 or more, hospitalized, and interviewed a minimum of 28 days after being hospitalized for COVID-19), and properly filling out the form, the total number of respondents was 172.

Many terms are used to describe and assess long-haul COVID’s symptoms. Long-haul COVID, as the main independent variable in this study, is characterized by a history of confirmed SARS-CoV-2 infection and symptoms that are not present before becoming sick. Other independent variables, including gender, age, length of treatment, length from the onset of the disease to the interview, marital status, occupation, and respirator use, were determined as covariates. WHOQOL-BREF was utilized to measure the dependent variable of this research: quality of life.

The four major domains assessed in WHOQOL-BREF are (1) the physical domain with seven items, (2) psychological health with six items, (3) social relationships with three items (personal relationships, sexual activity, social support), and (4) environmental health with eight items. The responses to the questions are on a 1–5 Likert scale, where 1 represents “disagree” or “not at all” and 5 represents “completely agree” or “extremely.”

Based on data from 172 participants in this research, Table 1 shows that the mean length (days) of treatment was 15.17 ± 8, while the mean length (days) of the gap from the baseline to the interview was 109.22 ± 69.71. Out of four quality-of-life domains, the lowest mean score (69.31 ± 12.31) was identified in the physical domain, while the highest score (78.29 ± 16.08) was recorded in the social domain. The mean scores of the psychological and environmental domains were 74.89 ± 11.70 and 73.60 ± 13.30, respectively.

Table 1

Descriptive statistics of Length of Treatment, Length from Onset to Interview, Quality of Life domains,
Variable	Mean	SD	Min.	Max.
Length of Treatment (day)	15,17	8,00	7	42
Length from Onset to Interview (day)	109,22	69,72	29	309
WHOQOL Physical Domain Score	69,31	12,31	35,71	100
WHOQOL Psychological Domain Score	74,44	11,60	45,83	100
WHOQOL Social Domain Score	78,29	16,08	16,67	100
WHOQOL Environmental Domain Score	73,62	13,53	43,75	100

Demographic characteristics

Table 2 describes the demographic characteristics of the research participants. The numbers of males (48.3%) and females (51.7%) were almost equal. The majority were aged 18–39 years (58.1%), married or living with partners (66.9%), and working in the private sector (55.2%). Out of the 172 participants, only 20 (11.6%) used respiratory aids during their COVID-19 treatment. Also, almost one-third (30.2%) experienced long-haul COVID symptoms, with 23.8% having one symptom and 6.4% having two or more symptoms.

Table 2

The Demographic characteristics, History of treatments and The Long-Haul COVID 19
No	Variables	Frequencies (%)
1	Gender
	Male	83 (48.3%)
	Female	89 (51.7%)
2	Age Group
	18–39 years old	100 (58.1%)
	≥ 40 years old	72 (41.9%)
3	Marital Status
	Married/Living with Partner	115 (66.9%)
	Divorced/ Single	57 (33.1%)
4	Occupation
	Civil Servant & Police & Army	41 (23.8%)
	Private Sector	95 (55.2%)
	Unemployed	36 (20.9%)
5	Length of Treatment
	≤ 13 days (< 2 weeks)	71 (41.3%)
	≥ 14 days (≥ 2 weeks)	101 (58.7%)
6	Respiratory Used
	No	152 (88.4%)
	Yes	20 (11.6%)
7	Length from Onset (to Interview)
	≤ 3 months	92 (53.5%)
	> 3 months	80 (46.5%)
8	Long-Haul COVID 19
	No	120 (69.8%)
	Yes	52 (30.2%)
9	Number of Long-Haul COVID 19
	No	120 (69.8%)
	1 Symptom	41 (23.8%)
	> 1 Symptom	11 (6.4%)

Further information on long-haul COVID is presented in Table 3. From the 11 symptoms that were collected, most of the participants experienced fatigue (16.3%), chest pain (7%), coughing (4.1%), breathing trouble (2.9%), and digestive disorder and headache (2.3%). One to two participants experienced other symptoms, such as ageusia, anosmia, memory loss, nausea, and joint pain.

Table 3

The list of The Long-Haul COVID 19 symptomps
No	Variables	Frequencies (%)
1	Coughing
	No	165 (95.9%)
	Yes	7 (4.1%)
2	Digestive Disorder
	No	168 (97.7%)
	Yes	4 (2.3%)
3	Ageusia
	No	169 (98.3%)
	Yes	3 (1.7%)
4	Anosmia
	No	170 (98.8%)
	Yes	2 (1.2%)
5	Headache
	No	168 (97.7%)
	Yes	4 (2.3%)
6	Memory Loss
	No	171 (99.4%)
	Yes	1 (0.6%)
7	Nausea
	No	170 (98.8%)
	Yes	2 (1.2%)
8	Joint Pain
	No	171 (99.4%)
	Yes	1 (0.6%)
9	Fatigue
	No	144 (83.7%)
	Yes	28 (16.3%)
10	Breathing Trouble
	No	167 (97.1%)
	Yes	5 (2.9%)
11	ChestPain
	No	160 (93.0%)
	Yes	12 (7.0%)

Table 4 presents a comparison of the average scores for each quality-of-life domain according to sociodemographic factors, treatment history, and long-haul COVID symptoms. This comparison showed a difference between the average scores in the social domain; the score was higher for the male group than the female group (p-value of 0.03). In the psychological domain, the average score was higher for the 18–39 years age group than the 40 years or older age group (p-value of 0.03). The average psychological domain score was also higher for participants who were married or living with a partner than those from the single or divorced group (p-value of 0.007). On the duration of treatment, there was no significant difference between the average scores in all quality-of-life domains. However, regarding the use of breathing aids during the COVID-19 treatment, the psychological and environmental domains showed a significant average difference with p-values of 0.01 and 0.02, respectively. The other variables were not proven to be significant.

Table 4

The quality of life domain scores for sociodemographic and health-related predictors
Variables	Frequencies (%)	Mean ± SD[1]
		Physical Domain Score				Psychological Domain Score				Social Domain Score				Environmental Domain Score
		Score	95% CI		P value	Score	95% CI		P value	Score	95% CI		P value	Score	95% CI		P value
		Score	Lower	Upper	P value	Score	Lower	Upper	P value	Score	Lower	Upper	P value	Score	Lower	Upper	P value
Gender
Male	83 (48.3%)	70.22 ± 11.99	67,60	72,84	0,35	74.99 ± 12.03	72,37	77,63	0,54	81.02 ± 15.30	77,68	84,37	0.03*	74.96 ± 12.67	72,20	77,73	0,21
Female	89 (51.7%)	68.46 ± 12.61	65,80	71,11	0,35	73.92 ± 11.23	71,56	76,29	0,54	75.75 ± 16.46	72,28	79,22	0.03*	72.37 ± 14.23	69,37	75,37	0,21
Age Group
18 – 39 years old	100 (58.1%)	70.86 ± 12.56	68,37	73,35	0,052	76.04 ± 11.41	73,78	78,31	0.03*	79.25 ± 16.65	75,95	82,55	0,36	73.72 ± 13.88	70,97	76,48	0,91
≥ 40 years old	72 (41.9%)	67.16 ± 11.71	64,41	69,91	0,052	72.22 ± 11.58	69,50	74,94	0.03*	79.97 ± 15.29	73,37	80,56	0,36	73.48 ± 13.11	70,40	76,56	0,91
Marital Status
Married/Living with Partner	115 (66.9%)	68.91 ± 11.59	66,77	71,05	0,55	73.26 ± 10.99	71,23	75,29	0,06	80.87 ± 13.87	78,31	83,43	0.007*	73.45 ± 12.74	71,09	75,81	0,82
Divorced/ Single	57 (33.1%)	70.11 ± 13.73	66,47	73,75	0,55	76.83 ± 12.50	73,51	80,14	0,06	73.10 ± 18.89	68,08	78,11	0.007*	73.96 ± 15.11	69,95	77,97	0,82
Occupation
Civil Servant & Police & Army	41 (23.8%)	68.90 ± 12.81	64,86	72,95	0,51	73.88 ± 10.91	70,44	77,32	0,94	81.71 ± 14.34	77,18	86,23	0,30	75.54 ± 13.96	71,13	79,94	0,44
Private Sector	95 (55.2%)	70.19 ± 11.39	67,87	72,51		74.65 ± 11.07	72,39	76,90		77.19 ± 15.95	73,94	80,44		72.47 ± 13.07	69,81	75,13
Unemployed	36 (20.9%)	67.46 ± 14.10	62,69	72,23		74.54 ± 13.86	69,85	79,23		77.31 ± 18.10	71,19	83,44		74.48 ± 14.28	69,65	79,31
Length of Treatment
1-13 days (<2 weeks)	71 (41.3%)	69.67 ± 11.94	66,84	72,49	0,75	74.29 ± 10.08	71,91	76,68	0,89	80.16 ± 15.39	76,52	83,81	0,20	73.86 ± 12.92	70,80	76,92	0,85
≥ 14 days (≥2 weeks)	101 (58.7%)	69.06 ± 12.62	66,57	71,55	0,75	74.55 ± 12.61	72,06	77,03	0,89	76.98 ± 16.50	73,72	80,24	0,20	73.45 ± 14.00	70,69	76,22	0,85
Respiratory Used
No	152 (88.4%)	68.75 ± 12.08	66,81	70,68	0,10	73.63 ± 11.46	71,79	75,47	0.01*	77.63 ± 16.62	74,97	80,30	0,14	72.78 ± 13.53	70,61	74,95	0.02*
Yes	20 (11.6%)	73.57 ± 13.53	67,24	79,90	0,10	80.62 ± 11.00	75,47	85,77	0.01*	83.33 ± 10.12	78,60	88,07	0,14	80.00 ± 11.97	74,40	85,60	0.02*
Length from Onset (to Interview)
≤ 3 months	92 (53.5%)	67.90 ± 12.14	65,38	70,41	0,11	73.41 ± 11.32	71,07	75,76	0,21	76.54 ± 14.40	73,56	79,52	0,12	72.86 ± 13.15	70,14	75,59	0,43
>3 months	80 (46.5%)	70.94 ± 12.38	68,18	73,69	0,11	75.62 ± 11.88	72,98	78,27	0,21	80.31 ± 17.70	76,37	84,25	0,12	74.49 ± 13.98	71,38	77,61	0,43
Long-Haul COVID 19
No	120 (69.8%)	70.12 ± 12.41	67,88	72,36	0,19	74.99 ± 11.29	72,96	77,04	0,34	78.89 ± 15.84	76,03	81,75	0,46	73.52 ± 13.68	71,04	75,99	0,88
Yes	52 (30.2%)	67.44 ± 11.99	64,10	70,78	0,19	73.16 ± 12.30	69,73	76,58	0,34	76.92 ± 16.72	72,27	81,57	0,46	73.86 ± 13.29	70,16	77,56	0,88
Number of Long-Haul COVID 19
No	120 (69.8%)	70.12 ± 12.41	67,88	72,36	0,37	74.99 ± 11.29	72,96	77,04	0,49	78.89 ± 15.84	76,03	81,75	0,70	73.52 ± 13.68	71,04	75,99	0,45
1 Symptom	41 (23.8%)	66.99 ± 10.91	63,54	70,43		72.56 ± 11.71	68,86	76,26		76.42 ± 16.86	71,10	81,75		72.64 ± 12.61	68,66	76,62
> 1 Symptom	11 (6.4%)	69.15 ± 15.91	58,46	79,85		75.38 ± 14.73	65,48	85,27		78.79 ± 16.82	67,49	90,09		78.41 ± 15.34	68,11	88,71

In Table 5, the quality-of-life variables were divided into two categories (good and poor), as the scores were not normally distributed. Table 5 shows that female participants, those aged 40 years or more, divorced/single participants, and unemployed participants tend to have a poorer quality of life in the physical domain. People with two or more symptoms of long-haul COVID also tended to have a poor quality of life in the physical domain compared to those without long-haul symptoms (OR = 2.54; 95% CI = 0.68–9.46).

Table 5

The association of Quality of Life domain category related by sociodemographic and health-related predictors
Variables	Frequencies (%)		Physical Domain Category				OR (95%CI)			Psychological Domain Category					Social Domain Category							OR (95%CI)	Environmental Domain Category
Variables	Frequencies (%)		Good (%)		Poor (%)					Good (%)		Poor (%)			Good (%)					Poor (%)			Good (%)			Poor (%)
Gender
Male	83 (48.3%)		68 (81.9%)		15 (18.1%)		1 (Ref)	74 (89.2%)			9 (10.8%)		1 (Ref)			75 (90.4%)			8 (9.6%)			1 (Ref)			72 (86.7%)		11 (13.3%)		1 (Ref)
Female	89 (51.7%)		67 (75.3%)		22 (24.7%)		1.49 (0.71–3.11)	79 (88.8%)			10 (11.2%)		1.04 (0.40–2.70)			71 (79.8%)			18 (20.2%)			2.38 (0.97–5.81)			66 (74.2%)		23 (25.8%)		2.28 (1.03–5.04)*
Age Group (2 categories)
18–39 years old	100 (58.1%)		82 (82.0%)		18 (18.0%)		1 (Ref)	93 (93.0%)			7 (7.0%)		1 (Ref)			84 (84.0%)			16 (16.0%)			1 (Ref)			79 (79.0%)		21 (21.0%)		1 (Ref)
≥ 40 years old	72 (41.9%)		53 (73.6%)		19 (26.4%)		1.63 (0.79–3.39)	60 (83.3%)			12 (16.7%)		2.66 (0.99–7.13)			62 (86.1%)			10 (13.9%)			0.85 (0.36–1.99)			59 (81.9%)		13 (18.1%)		0.83 (0.38–1.79)
Marital Status
Married/Living with Partner	115 (66.9%)		93 (90.3%)		22 (19.1%)		1 (Ref)	101 (87.8%)			14 (12.2%)		1 (Ref)			103 (89.6%)			12 (10.4%)			1 (Ref)			96 (83.5%)		19 (16.5%)		1 (Ref)
Divorced/ Single	57 (33.1%)		42 (73.7%)		15 (26.3%)		1.51 (0.71–3.20)	52 (91.2%)			5 (8.8%)		0.69 (0..24–2.03)			43 (75.4%)			14 (24.6%)			2.79 (1.19–6.53)			42 (73.7%)		15 (26.3%)		1.80 (0.84–3.89)
Occupation
Civil Servant & Police & Army	41 (23.8%)		31 (75.6%)		10 (24.4%)		1 (Ref)	36 (87.8%)			5 (12.2%)		1 (Ref)			36 (87.8%)			5 (12.2%)			1 (Ref)			33 (80.5%)		8 (19.5%)		1 (Ref)
Private Sector	95 (55.2%)		79 (83.2%)		16 (16.8%)		0.63 (0.26–1.53)	85 (89.5%)			10 (10.5%)		0.85 (0.27–2.65)			80 (84.2%)			15 (15.8%)			1.35 (0.46–3.99)			76 (80.0%)		19 (20.0%)		1.03 (0.41–2.59)
Unemployed	36 (20.9%)		25 (69.4%)		11 (30.6%)		1.36 (0.50–3.73)	32 (88.9%)			4 (11.1%)		0.90 (0.22–3.64)			30 (83.3%)			6 (16.7%)			1.44 (0.40–5.19)			29 (80.6%)		7 (19.4%)		0.99 (0.32–3.08)
Length of Treatment
1–13 days (< 2 weeks)	71 (41.3%)		57 (80.3%)		14 (19.7%)		1 (Ref)	66 (93.0%)			5 (7.0%)		1 (Ref)			61 (85.9%)			10 (14.1%)			1 (Ref)			60 (84.5%)		11 (15.5%)		1 (Ref)
≥ 14 days (≥ 2 weeks)	101 (58.7%)		78 (77.2%)		23 (22.8%)		1.20 (0.57–2.53)	87 (86.1%)			14 (13.9%)		2.12 (0.73–6.19)			85 (84.2%)			16 (15.8%)			1.15 (0.49–2.70)			78 (77.2%)		23 (22.8%)		1.61 (0.73–3.56)
Respiratory Used
No	152 (88.4%)		118 (77.6%)		34 (22.4%)		1 (Ref)	134 (88.2%)			18 (11.8%)		1 (Ref)			126 (82.9%)			26 (17.1%)			1 (Ref)			119 (78.3%)		33 (21.7%)		1 (Ref)
Yes	20 (11.6%)		17 (85.0%)		3 (15.0%)		0.61 (0.17–2.21)	19 (95.0%)			1 (5.0%)		0.39 (0.05–3.11)			20 (100.0%)			0 (0.0%)			-			19 (95.0%)		1 (5.0%)		0.19 (0..02–1.47)
Length from Onset (to Interview)
≤ 3 months	92 (53.5%)		67 (72.8%)		25 (27.2%)		1 (Ref)	81 (88.0%)			11 (12.0%)		1 (Ref)			76 (82.6%)			16 (17.4%)			1 (Ref)			74 (80.4%)		18 (19.6%)		1 (Ref)
> 3 months	80 (46.5%)		68 (85.0%)		12 (15.0%)		0.47 (0.22–1.02)	72 (90.0%)			8 (10.0%)		0.82 (0.31–2.15)			70 (87.5%)			10 (12.5%)			0.68 (0.29–1.59)			64 (80.0%)		16 (20.0%)		1.03 (0.48–2.18)
Long- Haul COVID 19
No	120 (69.8%)		98 (81.7%)		22 (18.3%)		1 (Ref)	110 (91.7%)			10 (8.3%)		1 (Ref)			105 (87.5%)			15 (12.5%)			1 (Ref)			96 (80.0%)		24 (20.0%)		1 (Ref)
Yes	52 (30.2%)		37 (71.2%)		15 (28.8%)		1.81 (0.85–3.85)	43 (82.7%)			9 (17.3%)		2.30 (0.87–6.06)			41 (78.8%)			11 (21.2%)			1.88 (0.80–4.43)			42 (80.8%)		10 (19.2%)		0.95 (0.42–2.17)
Number of Long-Haul COVID 19
No		120 (69.8%)		98 (81.7%)		22 (18.3%)	1 (Ref)		110 (91.7%)		10 (8.3%)			1 (Ref)			105 (87.5%)	15 (12.5%)			1 (Ref)			96 (80.0%)				24 (20.0%)		1 (Ref)
1 Symptom		41 (23.8%)		30 (73.2%)		11 (26.8%)	1.63 (0.71–3.75)		34 (82.9%)		7 (17.1%)			2.26 (0.80–6.40)			32 (78.0%)	9 (22.0%)			1.97 (0.79–4.92)			33 (80.5%)				8 (19.5%)		0.97 (0.39–2.37)
> 1 Symptom		11 (6.4%)		7 (63.6%)		4 (36.4%)	2.54 (0.68–9.46)		9 (81.8%)		2 (18.2%)			2.44 (0.46–12.89)			9 (81.8%)	2 (18.2%)			1.56 (0.31–7.90)			9 (81.8%)				2 (18.2%)		0.89 (0.18–4.39)

In the psychological domain, participants aged 40 years or more and participants with 14 or more days of treatment had a twofold increased risk for a poor quality of life than their counterparts. Participants with two or more long-haul COVID symptoms also tended to have a higher risk for a poor quality of health in the psychological domain, with an odds ratio of 2.44 (95% CI = 0.46–12.89), than those with no symptoms.

In the social domain, people with one long-haul COVID symptom have a higher risk, almost twofold, than those with no symptoms (OR = 1.97; 95% CI = 0.79–4.92). In the environmental domain, those with long-haul COVID symptoms tended to be protective a poor quality of life than those with no symptoms. Thus, these initial results must be analyzed further using a multivariate analysis, as seen in Table 6. A multivariate analysis with four models was presented in this research. Each model has one dependent variable from each quality-of-life domain.

Table 6

Results of Multivariable analysis of Quality of Life Domains and Gender, Age Group, Marital Status, Occupation and Having Long-Haul COVID 19
No	Variables	Physical Domain	Psychological Domain	Social Domain	Environmental Domain
No	Variables	AOR (95% CI)	AOR (95% CI)	AOR (95% CI)	AOR (95% CI)
1	Gender (Female/ Male)	1.50 (0.66–3.39)	1.70 (0.55–5.22)	2.30 (0.88–6.05)	2.23 (0.95–5.22)
2	Age Group (≥ 40 years old/ 18–39 years old)	1.85 (0.79–4.35)	3.68 (1.17–11.61)	1.26 (0.45–3.50)	1.14 (0.46–2.80)
3	Marital Status (Divorced & Single/ Married & Living With Partner)	1.55 (0.68–3.53)	0.80 (0.24–2.63)	2.48 (0.99–6.18)	1.63 (0.72–3.71)
4a	Occupation (Private Sector / Civil Servant & Police & Army)	0.62 (0.24–1.56)	0.94 (0.29–3.10)	1.28 (0.41–4.00)	0.98 (0.38–2.56)
4b	Occupation (Unemployed / Civil Servant & Police & Army)	0.91 (0.29–2.89)	0.55 (0.11–2.77)	0.90 (0.21–3.96)	0.70 (0.19–2.53)
5	Long-Haul COVID 19 (Yes/ No)	1.93 (0.88–4.23)	2.62 (0.96–7.14)	2.09 (0.85–5.12)	0.99 (0.43–2.30)

After adjustments with all independent variables, the results showed that people with long-haul COVID tended to be at a greater risk of having a poor quality of life in the physical, psychological, and social domains, with odds ratios of 1.93 (95% CI = 0.88–4.23), 2.62 (95% CI = 0.96–7.14), and 2.09 (95% CI = 0.85–5.12), respectively. With an odds ratio of 0.99, the effect of long-haul COVID symptoms on the quality of life in the environmental domain was not conclusive.

Limitations of the research

One limitation of this research is that the symptoms reported by the subjects were obtained through online questionnaires due to the government fieldwork requirements. Therefore, these symptoms are subjective, that is, based on the perception of each individual. Moreover, many the participants are relatively young (under 40 years). Thus, persistent variation in symptoms according to age remains unknown. Since the patients are young and have a lower epidemiological propensity for chronic diseases and other degenerative symptoms, nevertheless some of those have complained of symptoms long-haul COVID

Strength of the research

Many people experience prolonged symptoms, poor health, and reduced functions for months even though they are not hospitalized for SARS-CoV-2 infection. This study explored the health condition of COVID-19 patients after they had been discharged from the hospital. It emphasizes the necessity of post-hospital surveillance and sustainable health services as a recovery response from a quality-of-life perspective.

Long-haul COVID and quality of life

Quality of life is currently defined as a multidimensional concept consisting of several domains that people consider and evaluate differently according to the importance, they attach to them in their lives (17). In post-COVID patients, it is interesting to explore the relationship of the four domains in WHOQOL BREF as a quality-of-life instrument (8). This study is important to determine the post-hospital care steps should be taken to promote a holistic health-related quality of life.

Furthermore, the epidemiological and immunological studies of recovered COVID-19 patients can be utilized to monitor their health status for possible future complications (2,16). Observational investigations in larger cohorts will help us understand the deep prognosis and pathogenesis of the COVID-19 disease. Studies of this kind will help determine whether patients recovering from COVID-19 require post-acute care to recover from further infection or multi-organ damage (18). SARS-CoV-2 mainly affects people who are immunocompromised and have previous medical conditions (problems in the lungs, kidneys, heart, and digestive tract) (19,20). Our research showed that almost one-third (30.2%) experienced long-haul symptoms of COVID-19, with 23.8% having one symptom and 6.4% having two or more symptoms. Most of them experienced fatigue, chest pain, coughing, breathing trouble, and digestive disorder and headache (16.3%, 7%, 4.1%, 2.9%, and 2.3%, respectively).

After the quality-of-life variable was divided into two categories (good and poor), the results indicate that the long-haul symptoms affect all domains except the environmental one. Patients with Long-haul COVID symptoms and more than one symptom have approximately twice the risk of having a poor quality of life than those without symptoms. In addition, the risk of having a bad quality of life varies greatly depending on the number of long-haul Covid as the predominance factors. In particular, the use of respirators did not show an increased risk for a poor quality of life in any of the domains. However, respondents aged 40 years or over are more at risk of having a poor quality of life in the physical and psychological aspects. Women, unmarried, or divorced respondents are also at risk of having a poor quality of life in the physical domain. A hospital stay of more than 14 days is also more likely to lead to a poor quality of life in the psychological domain. In the social domain, an unmarried or divorced status and the type of work seems to affect the quality of life as well. In the environmental domain, female respondents, those with a single or divorced status, and those who stayed in the hospital for more than 14 days are at risk of having a bad quality of life.

The results of the multivariate analysis indicate that long-haul COVID patients tend to have a greater risk for a poor quality of life in the physical, psychological, and social domains, with odds ratios of 1.93 (95% CI = 0.88–4.23), 2.62 (95% CI = 0.96–7.14), and 2.09 (95% CI = 0.85–5, respectively (12). The environmental domain has an odds ratio of 0.99, which indicates a positive factor in the quality of life. The significant impact of the emergence of long-haul COVID on recovered patients confirmed the association between long-haul COVID and the WHOQOL-BREF quality-of-life domains (21). The findings of this study are essential to clarify the steps for post-hospital care that must be taken to promote a holistics and health-related quality of life (22,23).

Those who have recovered from COVID-19 still experience health problems, such as long-haul COVID-19 symptoms. Many of these issues are associated with a reduced quality of life. If long-haul COVID is left untreated, patients will continue to experience long-term health complications. Therefore, robust post-hospital surveillance is necessary to identify the need for post-hospital health-related services.

PASC – Post-acute sequelae of COVID-19

CCS – Chronic COVID syndrome

COVID-19 – is an infectious disease caused by the SARS-CoV-2 virus.

SARS-CoV-2 – is a new type of coronavirus that has not previously been identified in humans.

WHO – is World Health Organization, WHO's primary role is to direct international health within the United Nations' system and to lead partners in global health responses

CI – Confidence Interval, in statistics, refers to the probability that a population parameter will fall between a set of values for a certain proportion of times.

Ethics approval and consent to participate

This study obtained ethical clearance from Universitas Indonesia’s Ethics Committee, with Ethical Approval No. 593/UN2.F10.D11/PPM.00.02/2020.

Patient consent for publication/informed consent

Informed consent was given before administering the questionnaire.

Availability of data and materials

The data sets used and/or analyzed in this study are available from the corresponding author Indang Trihandini ([email protected]) upon request.

Competing interests

The authors declare no competing interests.

Funding

This research was supported by the International Indexed Publication Grant (PUTI) for Q2 Fiscal Year 2020 (Grant No. NKB-1626/UN2.RST/HKP.05.00/2020, Universitas Indonesia).

Authors’ contributions

I.T. conceptualized the research design. I.T., M.M., D.A.K.S., and C.P.E. collected the data, contributed to the data analysis, and interpreted the results. I.T., M.M., and C.P.E. prepared the manuscript’s draft. I.T. was a major contributor in writing the manuscript, especially in the Methodology, Discussion, and Conclusion sections. All authors read and approved the final draft.

Acknowledgments

The authors would like to thank the respondents who filled out the online questionnaire and the Health Agency of DKI Jakarta for their approval and assistance in the data collection.

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No competing interests reported.

Effects of Long-Haul COVID on the Health-Related Quality of Life Among Recovered Hospitalized Patients

Status:

Version 1

Abstract

Introduction

Research Method

Results

Discussion

Conclusion

Abbreviations

Declarations

References

Additional Declarations

Status:

Version 1