Background: The COVID-19 pandemic posed a danger to global public health because of the unprecedented physical, mental, social, and environmental impact affecting quality of life (QoL). The study aimed to find the changes in QoL among COVID-19 recovered individuals and explore the determinants of change more than 1 year after recovery in low-resource settings. Methods: COVID-19 patients from all eight divisions of Bangladesh who were confirmed positive by reverse transcription-polymerase chain reaction from June 2020 to November 2020 and who subsequently recovered were followed up twice, once immediately after recovery and again 1 year after the first follow-up. The follow-up study was conducted from November 2021 to January 2022 among 2438 individuals using the World Health Organization Quality of Life Brief Version (WHOQOL-BREF). After excluding 48 deaths, 95 were rejected to participate, 618 were inaccessible, and there were 45 cases of incomplete data. Descriptive statistics, paired-sample analyses, generalized estimating equation (GEE) analysis, and multivariable logistic regression analyses were performed to test the mean difference in participants' QoL scores between the two interviews. Results: Most participants (n = 1710, 70.1%) were male, and one-fourth (24.4%) were older than 46. The average physical domain score decreased significantly from baseline to follow-up, and the average scores in psychological, social, and environmental domains increased significantly at follow-up (P < 0.05). By the GEE equation approach, after adjusting for other factors, we found that older age groups (P < 0.001), being female (P < 0.001), having hospital admission during COVID-19 illness (P < 0.001), and having three or more chronic diseases (P < 0.001), were significantly associated with lower physical and psychological QoL scores. Higher age and female sex [adjusted odd ratio (aOR) = 1.3, 95% confidence interval (CI) 1.0–1.6] were associated with reduced social domain scores on multivariable logistic regression analysis. Urban or semi-urban people were 49% less likely (aOR = 0.5, 95% CI 0.4–0.7) and 32% less likely (aOR = 0.7, 95% CI 0.5–0.9) to have a reduced QoL score in the psychological domain and the social domain respectively, than rural people. Higher-income people were more likely to experience a decrease in QoL scores in physical, psychological, social, and environmental domains. Married people were 1.8 times more likely (aOR = 1.8, 95% CI 1.3–2.4) to have a decreased social QoL score. In the second interview, people admitted to hospitals during their COVID-19 infection showed a 1.3 times higher chance (aOR = 1.3, 95% CI 1.1–1.6) of a decreased environmental QoL score. Almost 13% of participants developed one or more chronic diseases between the first and second interviews. Moreover, 7.9% suffered from reinfection by COVID-19 during this 1-year time. Conclusions: The present study found that the QoL of COVID-19 recovered people improved 1 year after recovery, particularly in psychological, social, and environmental domains. However, age, sex, the severity of COVID-19, smoking habits, and comorbidities were significantly negatively associated with QoL. Events of reinfection and the emergence of chronic disease were independent determinants of the decline in QoL scores in psychological, social, and physical domains, respectively. Strong policies to prevent and minimize smoking must be implemented in Bangladesh, and we must monitor and manage chronic diseases in people who have recovered from COVID-19.
Keywords: Quality of life; Health-related quality of life; Reverse transcription-polymerase chain reaction; COVID-19; Bangladesh
Supplementary Information The online version contains supplementary material available at https://doi.org/10.1186/s40249-023-01125-9.
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COVID-19 caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been a burden for global health systems, and it has led to personal and social consequences from its onset [[
In the last decade, QoL has been explored mainly in research specializing in non-communicable and chronic diseases. HRQoL is dynamic, subjective, and multi-dimensional. These dimensions include physical, social, psychological, and environmental considerations [[
QoL is a strong predictor of endurance in general health and well-being [[
This follow-up study focuses on COVID-19 patients confirmed by reverse transcription-polymerase chain reaction (RT-PCR) from June 2020 to November 2020 and who subsequently recovered. A baseline cross-sectional assessment of QoL using the WHOQOL-BREF [[
Data were collected using the structured questionnaire prepared during the baseline interview (1st interview), with some modifications. Once the revised questionnaire was finalized, the data collection team was given a list of division enrollees. The 20-person study team conducted over-the-phone interviews with the participants. We assigned interviewers to each division based on location to overcome linguistic obstacles. Before initiating the interviews, we assured the interviewees that questions could be skipped if they felt uncomfortable answering. The quality assurance team was assigned to ensure data accuracy, regular data monitoring, adherence to protocols, and overall research integrity.
The pre-tested structured questionnaire used during the first interview was slightly modified to include two questions about the vaccination against COVID-19 and the incidence of reinfection between the first and second interviews. The final questionnaire consisted of a socio-demographic profile, personal history, presence of comorbidities, COVID-19 vaccination, and reinfection history. We used the WHOQOL-BREF scale for quality-of-life assessment.
To assess the QoL of COVID-19-positive patients, we used a Bangla-validated version WHOQOL-BREF quality of life assessment questionnaire [[
The WHOQOL-BREF scores were converted to a scale of 100 based on the guideline (
Table 1 illustrates our study participants' demographic and clinical characteristics (n = 2438). The average age of the participants at inclusion was 38.1 ± 2.3 years, and the majority were aged more than 46 years (24.4%), male (70.1%), and living in the urban areas (74.5%) of the country. We observed a significant improvement in participants' QoL in every domain as well as individuals' overall perception of QoL and their health (as assessed by Q1 and Q2) except the physical domain (Fig. 1). The average physical domain score decreased significantly from baseline to follow-up, whereas the mean scores in psychological, social, and environmental domains increased significantly at follow-up (P < 0.05) (Additional file 1: Fig. S2).
Table 1 Demographic characteristics and participant clinical profiles
Characteristics Category Frequency ( Percentage (%) Age < 26 317 13.0 26–30 499 20.5 31–35 418 17.1 36–40 367 15.0 41–45 242 9.9 46+ 595 24.4 Gender Male 1710 70.1 Female 728 29.9 Division Barishal 98 4.0 Chattogram 340 13.9 Dhaka 1217 49.9 Khulna 149 6.1 Mymensingh 129 5.3 Rajshahi 216 8.9 Rangpur 146 6.0 Sylhet 143 5.9 Residence Rural 315 12.9 Urban 1816 74.5 Semi-urban 307 12.6 Educational status No formal education 57 2.3 Primary 171 7.0 Up to SSC 270 11.1 Up to HSC 598 24.5 Graduation 920 37.7 Post-graduation 422 17.3 Employment status Service 1417 58.1 Business 350 14.4 Farmer 27 1.1 Housewife 307 12.6 Student 173 7.1 Unemployed 88 3.6 Others 76 3.1 Monthly family income in BDT ( ≤ 20 000 595 24.4 20 001–40 000 1012 41.5 40 001–60 000 450 18.5 > 60 000 381 15.6 Marital status Single 438 18.0 Married 1945 79.8 Separated 4 0.2 Divorced 18 0.7 Widowed/widower 33 1.3 Health care worker No 2067 84.8 Yes 371 15.2 Smoke No 1616 66.3 Yes 533 21.9 Past smoker 289 11.8 Hypertension No 2000 82.0 Yes 438 18.0 Diabetes mellitus No 2045 83.9 Yes 393 16.1 Heart diseases No 2263 92.8 Yes 175 7.2 Asthma No 2173 89.1 Yes 265 10.9 Chronic kidney disease No 2360 96.8 Yes 78 3.2 Cancer No 2208 96.5 Yes 81 3.5
BDT Bangladesh Taka, USD United States Dollar
Graph: Fig. 1Pattern of changes in overall quality of life and health satisfaction over the study period
Table 2 describes the inter and intra-interview change of the participants' QoL across different variables over the period. The physical domain scores decreased significantly among those aged ≥ 36 years, educated, employed, and married, irrespective of sex, living area, and hospital admission. The psychological domain score increased significantly at follow-up in participants aged < 46 years (P < 0.001), living in urban/semi-urban areas (P < 0.001), participants having graduation (or above) (P < 0.002), single (not-married) individuals (P < 0.02), health care workers (HCWs) (P < 0.001), and persons having a history of hospital admission (P < 0.001). The social domain scores improved significantly in participants aged < 36 years (P < 0.001), irrespective of sex (P < 0.001), living area (P < 0.001), marital status (P < 0.001), and smoking history (P < 0.001), in individuals with education (P < 0.001), among employed participants (P < 0.001), among those having a monthly family income of < 60 000 Bangladesh Taka (BDT) (P < 0.001), and among those without a history of hospital admission (P < 0.001). Participants with a history of hospital admission showed significant declines (P < 0.001) in social domain scores. For the environmental domain, the average scores increased significantly (P < 0.001) for all the variables except the participants who were uneducated, unemployed, living in rural areas, and had a previous smoking history.
Table 2 Comparison of quality of life between baseline and follow-up interviews
Variable Physical Psychological Social Environmental 1st visit 2nd visit 1st visit 2nd visit 1st visit 2nd visit 1st visit 2nd visit Mean ( Mean ( Mean ( Mean ( Mean ( Mean ( Mean ( Mean ( Overall domain score 69.2 (14.2) 66.7 (14.5) < 0.001 64.3 (15.3) 65.1 (14.0) 0.02 61.5 (19.4) 65.8 (17.5) < 0.001 62.9 (12.8) 65.9 (11.5) < 0.001 Age < 26 71.7 (14.2) 71.9 (14.1) 0.80 66.1 (15.6) 69.6 (13.9) 0.001 44.2 (18.1) 53.4 (17.5) < 0.001 63.1 (13.6) 66.4 (11.0) < 0.001 26–35 70.5 (14.0) 69.5(13.6)a 0.07 65.9 (14.9) 67.1 (13.5)a 0.04 61.5 (20.0)a 68.6 (17.7)a < 0.001 63.1 (12.8) 66.3 (11.4) < 0.001 36–45 70.6(13.4) 65.5 (14.7)ab < 0.001 64.4 (15.1) 64.0 (14.5)ab 0.59 68.8 (15.9)ab 69.3 (15.3)a 0.54 63.4 (12.1) 65.9 (12.4) < 0.001 ≥ 46 64.2 (14.0)abc 60.8 (13.9)abc < 0.001 60.6 (15.4)abc 60.8 (13.3)abc 0.80 63.5 (16.6)ac 64.3 (16.2)abc 0.33 62.1 (13.0) 64.9 (10.9) < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 0.38 0.15 Gender Male 69.7 (14.3) 67.5 (14.4) < 0.001 65.2 (15.4) 66.0 (13.9) 0.06 60.9 (19.3) 66.0 (17.4) < 0.001 62.9 (12.8) 65.9 (11.3) < 0.001 Female 68.0 (13.9) 64.8 (14.7) < 0.001 62.1 (15.1) 62.9 (14.2) 0.18 62.8 (19.7) 65.1 (17.7) 0.01 62.8 (12.7) 65.8 (11.9) < 0.001 0.01 < 0.001 < 0.001 < 0.001 0.02 0.26 0.79 0.84 Residence Rural 71.5 (13.5) 67.5 (15.7) < 0.001 66.9 (13.4) 62.8 (15.6) < 0.001 61.5 (17.6) 64.5 (16.9) 0.01 61.9 (12.6) 63.2 (10.9) 0.17 Urban/semi-urban 68.8 (14.3) 66.6 (14.4) < 0.001 63.9 (15.6) 65.5 (13.8) < 0.001 61.5 (19.7) 65.9 (17.6) < 0.001 63.1 (12.8) 66.3 (11.5) < 0.001 0.002 0.28 0.001 0.002 0.99 0.16 0.12 < 0.001 Education No or primary education 67.5 (14.9) 65.7 (16.2) 0.19 61.6 (15.4) 59.5 (13.6) 0.08 60.4 (17.6) 60.8 (17.3) 0.78 62.0 (14.9) 61.8 (11.6) 0.88 Up to HSC 68.6 (14.2) 66.3 (14.7) < 0.001 63.8 (15.0) 64.5 (14.6)a 0.28 58.5 (19.7) 63.5 (17.3) < 0.001 61.9 (12.22) 64.7 (10.9)a < 0.001 Graduate/above 69.8 (14.0) 67.1 (14.1) < 0.001 65.0 (15.5)a 66.5 (13.5)ab 0.002 63.7 (19.1)b 68.1 (17.3)ab < 0.001 63.8 (12.7)b 67.3 (11.6)ab < 0.001 0.02 0.24 0.01 < 0.001 < 0.001 < 0.001 0.002 < 0.001 Employment status Unemployed 65.7 (14.9) 64.6 (16.5) 0.62 60.8 (15.0) 61.9 (14.2) 0.59 57.9 (19.5) 59.8 (19.4) 0.44 62.7 (12.2) 64.7 (10.6) 0.29 Employed 69.3 (14.1) 66.8 (14.5) < 0.001 64.4 (15.3) 65.2 (14.0) 0.02 61.7 (19.4) 65.9 (17.4) < 0.001 62.9 (12.8) 65.9 (11.5) < 0.001 0.02 0.18 0.03 0.03 0.08 0.001 0.87 0.33 Monthly family income (BDT) ≤ 20 000 67.3 (14.8) 65.9 (14.6) 0.06 61.4 (15.5) 64.6 (13.7) < 0.001 55.9 (21.0) 64.2 (17.4) < 0.001 61.9 (14.9) 63.9 (10.2) 0.01 20 001–40 000 69.4 (13.7)a 67.7 (14.6) 0.004 65.0 (14.2)a 65.7 (13.9) 0.20 61.9 (18.0)a 66.0 (17.4) < 0.001 61.6 (12.0) 65.6 (11.4)a < 0.001 40 001–60 000 70.3 (14.0)a 67.5 (14.5) < 0.001 66.7 (16.0)a 66.0 (14.1) 0.44 62.6 (19.1)a 66.6 (17.7) < 0.001 64.6 (12.1)b 67.6 (11.6)ab < 0.001 > 60 000 70.2 (14.4)a 64.1 (13.9)bc < 0.001 63.8 (16.5)c 63.1 (14.7)bc 0.46 67.9 (18.4)abc 66.5 (17.6) 0.23 66.1 (11.1)ab 67.7 (13.0)ab 0.07 0.002 < 0.001 < 0.001 0.01 < 0.001 0.08 < 0.001 < 0.001 Marital status Single 70.9 (14.9) 70.2 (15.0) 0.33 65. (15.9) 67.7 (14.6) 0.02 38.3 (12.9) 51.4 (18.7) < 0.001 63.3 (13.1) 66.8 (11.6) < 0.001 Married 68.7 (13.9) 65.8 (14.3) < 0.001 63.9 (15.1) 64.5 (13.8) 0.15 67.4 (16.1) 69.4 (15.1) < 0.001 62.8 (12.7) 65.6 (11.5) < 0.001 0.002 < 0.001 0.01 < 0.001 < 0.001 < 0.001 0.45 0.04 Healthcare workers No 69.2 (14.4) 66.3 (14.5) < 0.001 64.5 (15.5) 64.5 (14.1) 0.95 61.5 (19.2) 65.2 (17.4) < 0.001 63.0 (13.0) 65.5 (11.5) < 0.001 Yes 68.9 (13.3) 68.5 (14.5) 0.65 62.5 (14.0) 68.3 (13.3) < 0.001 61.7 (20.4) 68.9 (17.5) < 0.001 62.5 (11.6) 67.7 (11.6) < 0.001 0.75 0.01 0.02 < 0.001 0.884 < 0.001 0.51 < 0.001 Hospital admission No 70.8 (14.0) 68.3(14.0) < 0.001 66.1 (14.8) 66.2 (13.9) 0.93 62.9 (19.5) 66.5 (17.5) < 0.001 62.8 (12.2) 66.3 (11.5) < 0.001 Yes 65.7 (13.9) 63.2 (15.0) < 0.001 60.3 (15.7) 62.9 (14.0) < 0.001 68.5 (18.8) 64.3 (17.3) < 0.001 63.1 (13.9) 65.0 (11.5) 0.003 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 0.003 0.60 0.01 Smoking status No 69.1 (14.) 66.5 (14.3) < 0.001 64.2 (14.8) 64.9 (13.9) 0.11 62.2 (19.5) 65.7 (17.6) < 0.001 62.8 (12.6) 65.9 (11.2) < 0.001 Yes 68.32 (14.4) 66.5 (14.8) 0.02 61.9 (15.9)a 65.3 (13.6) < 0.001 58.7 (19.4)a 64.9 (17.4) < 0.001 62.2 (13.6) 65.5 (11.9) < 0.001 Past smoker 71.0 (14.4)b 68.1 (15.4) 0.01 68.8 (16.1)ab 65.9 (15.4) 0.02 63.1 (18.2)b 67.7 (16.8) < 0.001 64.9 (12.5)ab 66.0 (12.4) 0.26 0.03 0.23 < 0.001 0.48 < 0.001 0.08 0.01 0.73
Scores were expressed as mean ± SDSD Standard deviation, BDT Bangladesh Taka, HCW Healthcare worker P value was determined using one-way ANOVA with post-hoc analysis by Duncan multiple range test
Of all, 13.1% of participants developed one or more chronic diseases between the first and second interviews, and 7.9% were re-infected from COVID-19 during the follow-up period (Additional file 1: Fig. S3). In the physical domain, the participants who did not have chronic diseases observed a significant decline (P < 0.001) in their average QoL score between baseline and follow-up assessment. In contrast, the average score in the psychological domain increased among participants with all chronic diseases except for cancer at the follow-up. For the other two fields, the average QoL scores improved significantly (P < 0.001) for almost all chronic diseases, irrespective of the presence or absence of the disease. However, the different domain scores were significantly lower (P < 0.05) among those with chronic conditions than those without at baseline and follow-up (Additional file 2: Table S1).
In the generalized estimating equation approach, after adjusting for other factors, we found that the age groups ≥ 26 years, females, hospital admission during COVID-19 illness, and chronic diseases were significantly associated with a lower physical (P < 0.001) and psychological QoL (P < 0.001) score (Table 3). Higher education and income also positively improved QoL scores in the social and environmental domains. In contrast, three or more comorbidities degraded the participants' QoL in those domains (P < 0.001). After adjusting for all factors and their intra-group variations, a significant reduction in physical domain scores (ß = − 1.5, P < 0.001) and a significant increase in other domain scores were noted in follow-up interviews compared with baseline scores (ß = 1.8, P < 0.001; ß = 3.6, P < 0.001; ß = 3.2, P < 0.001).
Table 3 Factors associated with quality of life scores after adjusting for intra-individual changes between two interviews and for other factors by generalized estimating equation model
Variable Categories Difference in physical score Difference in psychological score Difference in social score Difference in environmental score Coefficient Coefficient Coefficient Coefficient Age < 26 (Ref) 1 1 1 1 26–35 − 2.2 0.01 − 2.1 0.01 − 0.3 0.69 − 0.8 0.27 36–45 − 3.4 < 0.001 − 3.9 < 0.001 − 0.1 0.86 − 0.8 0.28 ≥ 46 − 5.5 < 0.001 − 3.7 < 0.001 − 1.1 0.23 − 1.0 0.22 Gender Male (Ref) 1 1 1 1 Female − 2.2 < 0.001 − 3.1 < 0.001 − 0.4 0.46 0.2 0.57 Residence Rural (Ref) 1 1 1 1 Urban/semi urban − 0.7 0.31 − 0.1 0.87 0.5 0.41 0.9 0.09 Education No/primary education (Ref) 1 1 1 1 Up to HSC − 1.4 0.09 1.4 0.07 1.6 0.06 0.7 0.35 Graduation/above − 0.8 0.32 2.3 0.01 2.1 0.01 2.2 0.002 Employment status Unemployed (Ref) 1 1 1 1 Employed 2.7 0.03 4.2 < 0.001 − 0.0 0.99 0.9 0.31 Income < 20 000 (Ref) 1 1 1 1 20 001–40 000 1.7 0.002 2.5 < 0.001 2.5 < 0.001 0.8 0.09 40 001–60 000 2.2 < 0.001 3.3 < 0.001 2.9 < 0.001 3.3 < 0.001 > 60 000 2.3 0.001 2.8 < 0.001 6.8 < 0.001 4.8 < 0.001 Marital status Unmarried (Ref) 1 1 1 1 Married 0.2 0.74 0.1 0.84 29.4 < 0.001 − 0.8 0.18 HCW No (Ref) 1 1 1 1 Yes 1.0 0.08 1.0 0.11 0.7 0.27 − 0.2 0.71 Hospital admission No (Ref) 1 1 1 1 Yes − 2.9 < 0.001 − 2.7 < 0.001 − 2.9 < 0.001 0.2 0.69 Smoking status Not smoker (Ref) 1 1 1 1 Smoker − 0.2 0.74 − 1.4 0.01 − 1.7 0.002 − 0.1 0.81 Past smoker 0.7 0.36 1.4 0.08 − 0.6 0.37 0.8 0.19 Number of chronic diseases 0 (Ref) 1 1 1 1 1 − 4.0 < 0.001 − 3.0 < 0.001 − 0.9 0.07 − 0.3 0.56 2 − 6.9 < 0.001 − 5.9 < 0.001 − 3.6 < 0.001 − 1.1 0.11 ≥ 3 − 9.5 < 0.001 − 9.3 < 0.001 − 5.9 < 0.001 − 2.9 < 0.001 Follow-up First (Ref) 1 1 1 1 Second − 1.5 < 0.001 1.8 < 0.001 3.6 < 0.001 3.2 < 0.001
On multivariable logistic regression analysis (Table 4), we observed that increasing age was significantly associated decline (26–35 years: aOR = 1.5, 95% CI 1.0–2.2; 36–45 years: aOR = 1.9, 95% CI 1.2–2.9; ≥ 46 years: aOR = 2.1, 95% CI 1.4–3.3) in social domain QoL, and females were 1.30 times more likely (aOR = 1.3, 95% CI 1.0–1.6) to have deteriorated social QoL than males during follow-up. Participants living in the urban or semi-urban areas were 49% less likely (aOR = 0.5, 95% CI 0.38–0.7) and 32% less likely (aOR = 0.7, 95% CI 0.5–0.9) to have a declined QoL in the psychological domain and the social domain, respectively, than rural people. Those who earned more than 60 000 BDT/month witnessed 1.5, 1.9, 2.2, and 1.5 times lower QoL than those with an income of less than 20 000 BDT in physical (aOR = 1.5, 95% CI 1.1–2.0), psychological (aOR = 1.9, 95% CI 1.4–2.5), social (aOR = 2.2, 95% CI 1.6–2.9), and environmental domains (aOR = 1.5, 95% CI 1.1–2.0), respectively. Participants admitted to hospitals during their COVID-19 infection showed a 1.32 times higher chance of a decreased environmental QoL score than those who did not (aOR = 1.3, 95% CI 1.1–1.6). People with three or more chronic diseases were 46% (aOR = 0.5, 95% CI 0.4–0.8) and 42% (aOR = 0.6, 95% CI 0.4–0.9) less likely to have a decreased QoL score in physical and psychological domains, respectively, than those without chronic diseases. The incidence of chronic diseases was associated with a 1.4 times higher chance of having a reduced physical domain score between two interviews (aOR = 1.4, 95% CI 1.0–1.8). Lastly, participants with a history of COVID-19 reinfection had 1.5 times and 1.7 times higher chance of having reduced QoL scores in psychological (aOR = 1.5, 95% CI 1.1–2.1) and social (aOR = 1.7, 95% CI 1.2–2.4) domains, respectively.
Table 4 Logistic regression model to identify factors that are associated decline in Quality of Life score from baseline to follow-up interview
Variable Categories Physical Psychological Social Environmental a 95% a 95% a 95% a 95% Age < 26 (Ref) 1 1 1 1 26–35 1.1 0.8–1.5 1.1 0.8–1.5 1.5 1.0–2.2 0.9 0.7–1.4 36–45 1.4 0.9–1.9 1.3 0.9–1.9 1.9 1.2–2.9 0.9 0.7–1.4 ≥ 46 1.4 0.9–2.1 1.4 0.9–2.0 2.1 1.4–3.3 0.8 0.5–1.2 Gender Male (Ref) 1 1 1 1 Female 1.1 0.8–1.3 1.1 0.9–1.4 1.3 1.0–1.6 1.2 0.9–1.5 Residence Rural (Ref) 1 1 1 1 Urban/semi urban 0.8 0.7–1.1 0.5 0.4–0.7 0.7 0.5–0.9 0.8 0.6–1.1 Educational status No or primary education (Ref) 1 1 1 1 Up to HSC 1.4 1.0–1.9 0.8 0.6–1.1 0.7 0.5–1.0 0.9 0.7–1.3 Graduate/above 1.2 0.9–1.7 0.7 0.5–1.0 0.8 0.6–1.2 0.9 0.6–1.3 Employment status Unemployed (Ref) 1 1 1 1 Employed 1.1 0.7–1.7 1.1 0.6–1.8 0.6 0.4–1.0 0.9 0.6–1.6 Monthly family income in BDT < 20 000 (Ref) 1 1 1 1 20 000–40 000 0.9 0.7–1.1 1.3 0.9–1.6 1.2 0.9–1.5 0.8 0.7–1.0 40 001–60 000 1.1 0.8–1.4 1.4 1.1–1.9 1.4 1.0–1.9 1.1 0.8–1.5 > 60 000 1.5 1.1–2.0 1.9 1.4–2.5 2.2 1.6–2.9 1.5 1.1–2.0 Marital status Single (Ref) 1 1 1 1 Married 1.2 0.9–1.5 1.0 0.8–1.4 1.8 1.3–2.4 1.2 0.9–1.6 HCW No (Ref) 1 1 1 1 Yes 0.7 0.6–0.9 0.5 0.4–0.7 0.8 0.6–1.0 0.8 0.6–1.0 Hospital admission No (Ref) 1 1 1 1 Yes 1.2 0.9–1.4 0.9 0.8–1.2 0.8 0.7–1.0 1.3 1.1–1.6 Smoking status Not smoker (Ref) 1 1 1 1 Smoker 0.9 0.7–1.2 0.9 0.8–1.2 0.9 0.7–1.2 1.1 0.8–1.4 Past smoker 0.9 0.7–1.2 1.4 1.0–1.9 0.9 0.7–1.3 1.2 0.8–1.6 Number of chronic diseases 0 (Ref) 1 1 1 1 1 0.7 0.5–0.8 0.8 0.6–0.9 0.9 0.7–1.1 1.0 0.8–1.3 2 0.9 0.7–1.3 0.7 0.5–1.1 1.1 0.7–1.5 0.9 0.7–1.4 ≥ 3 0.5 0.4–0.8 0.6 0.4–0.9 0.7 0.4–1.0 0.9 0.7–1.4 New chronic disease No (Ref) 1 1 1 1 Yes 1.4 1.0–1.8 1.1 0.9–1.5 1.1 0.9–1.5 1.1 0.8–1.5 COVID-19 vaccination Yes (Ref) 1 1 1 1 No 0.9 0.8–1.1 1.1 0.9–1.4 1.2 0.9–1.5 1.1 0.91.3 COVID-19 re-infection No (Ref) 1 1 1 1 Yes 0.9 0.6–1.2 1.5 1.1–2.1 1.7 1.2–2.4 0.8 0.5–1.1
Before the widespread global vaccination, the COVID-19 pandemic was responsible for the deaths of millions and had devastating economic consequences. The aftermath of the pandemic might continue to affect people directly through its long-term physical and psychological sequels and indirectly through its negative socio-economic impacts [[
There was, on average, a statistically significant decline in the physical domain score and a substantial increase in the participants' psychological, social, and environmental domain scores from baseline to follow-up interview. However, this varied across different participants' characteristics. Taking the intra-individual variations between the two interviews into account, we found that higher age, female sex, history of hospital admission during COVID-19, smoking, and a higher number of chronic diseases were associated with a lower score in different domains. On the other hand, higher education, employment, and marriage were associated with higher scores in various domains. This is similar to previous studies on QoL of COVID-19 patients conducted during their active illness or between 1 and 6 months after recovery, where older age, female sex, hospitalization history, unemployed, and comorbidities were reported to be associated with low levels of QoL [[
The multivariate logistic regression analysis also revealed that after adjusting for other variables, the decline in the physical domain occurred mainly in participants from the highest income category (> 60 000 BDT) and participants other than HCWs. Interestingly, the reduction was significant in those who did not have comorbidities during the first interview but who later developed chronic disease. This indicates that the average decline in the physical domain scores in the adult groups (36–45 and ≥ 46 years), as found in the bivariate analysis, was because of the new onset of chronic disease within one and a half years after COVID-19 infection. On the other hand, people from higher socio-economic categories were more likely to have insufficient physical activity [[
Although the average psychological domain score improved in all patients, participants who suffered reinfection by SARS-CoV-2 between the first and second interviews were significantly more likely to decline the score. Moreover, the odds of the decline were higher in those residing in rural areas and having a higher income (> 40 000 BDT). COVID-19 can lead to a general deterioration of the affected person's mental health [[
The WHOQOL-BREF instrument measures social domain scores based on participants' perceptions of their relationships, sex life, and support from friends. According to our findings, predominantly older adults and females were affected by this shift in perspective. After multivariable adjustments, other domains remained unaffected by the participant's age or sex. Besides, rural residence, a higher monthly income (40 001–60 000, and > 60 000 BDT), being married, and reinfection of COVID-19 between the first and second interviews was independently associated with a decline in the social domain. This finding is in line with previous evidence, as female sex and older age were reported to be associated with low QoL in many studies conducted on the mental health impact of COVID-19 [[
In the environmental domain, besides higher monthly income, another independent predictor of the score was the history of hospital admission due to COVID-19, an indicator of severe disease. Health and social care accessibility and availability, which are essential components of the environmental domain [[
Our analysis revealed that the new onset of chronic diseases after recovery from COVID-19 was a significant negative determinant of QoL among the sufferers. A recent study exploring the QoL among type 2 DM patients found a very low average score in all four domains, which supports our assumption [[
Regrettably, our study could not compare the QoL scores between individuals who have not been infected with COVID-19 and those who have recovered from the disease. Nevertheless, earlier investigations conducted among a healthy population in Bangladesh indicate that adolescents and adults had an average QoL score of 80–90 between 2005 and 2007 [[
Our study highlighted the fact that COVID-19 pandemic and the drastic control measures taken during that period, had long time consequences among the persons affected by the disease. Although many individuals had been adapting well with time, a considerable number experienced a decline in their quality of life. Nonetheless, authorities and policy makers could take the determinants of decline in consideration and plan necessary actions to reverse the process of decline. Especially, risk of re-infection could be a major mediator of decline in QoL among recovered victims of the disease. Lessons learnt from COVID could be applied for unforeseen emergence of diseases in the future. Rather than applying nonchalant or drastic measures, applying dynamic control mechanisms based on realistic unbiased calculations [[
This study had some limitations. First, many participants were lost from follow-up. Second, an evaluation of the effect of socio-cultural determinants like health service availability, economic security, rehabilitative measures, and health-seeking behavior on the QoL could not be done. Third, the impacts of persistent and debilitating symptoms after COVID-19 were not explored. Fourth, there were no true controls to compare the QoL scores with that of individuals who did not suffer from COVID-19. However, our study was one of the few which reported the QoL of COVID-19 after an extended duration and described possible implications for policy-level strategies to prevent further demise and rehabilitate these individuals to total health.
The present study found that the QoL of COVID-19 recovered people improved over more than 1 year after recovery, particularly in psychological, social, and environmental domains. However, age, sex, the severity of COVID-19, smoking habits, and comorbidities were significantly associated with reduced QoL. Events of reinfection and the emergence of chronic disease were independent determinants of the decline in QoL scores in psychological, social, and physical domains, respectively. Based on our study findings, we have the following recommendations: (
Not applicable.
The manuscript was reviewed and accepted by all contributors. Conceptualization and design: MDHH, MUR, MASK, MHN, MH, KD; data collection: MML, SA, MAH, TR, SYB, AAS, TZM, SKS, RMM, NA, SSSDB, IC, SS, MA, SAB, FAZ, SH, AE, HB, NN, MMAH, SR, KMTH, MLR, MHN; data curation: MDHH, MUR, MH, MLR, MHN; data analysis: MDHH, MUR, MASK, MLR, MHN, KD; draft manuscript preparation: all authors; review and editing: MDHH, KD; final manuscript preparation: MDHH, KD; supervision: MDHH; critical review: KD.
Open access funding provided by Mid Sweden University. This study did not receive any funds from the public or any donor agency.
The data underlying the results presented in this study will be provided on reasonable request to Dr. Delwer H. Hawlader. Email: mohammad.hawlader@northsouth.edu.
The ethical review committee (ERC)/institutional review board (IRB) of North South University provided ethical approval for this project (2020/OR-NSU/IRB-No. 0801). All procedures were carried out per the Helsinki Declaration of 1964 and subsequent revisions or comparable ethical norms. Informed verbal consent was taken before inclusion of the study participants.
Not aapplicable.
The authors declare that they have no competing interests.
Graph: Additional file 1: Figure S1. Flow chart of participant selection and data collection. Figure S2. Pattern of change in score in physical, psychological, social and environmental domains of quality of life. Figure S3. Pattern of changes in overall quality of life and health satisfaction over the period. Figure S4. Onset of new chronic disease and percentage of re-infection among the recovered COVID-19 participants during second follow-up.
Graph: Additional file 2: Table S1. Comparison of quality of life between baseline and follow-up interviews in relation to presence or absence of individual chronic diseases.
• ANOVA
- Analysis of Variance
- aOR
- Adjusted odd ratio
• BDT
- Bangladesh Taka
- CI
- Confidence interval
• CKD
- Chronic kidney disease
• GEE
- Generalized estimating equation
• HCW
- Health care worker
• HRQoL
- Health related quality of life
• QoL
- Quality of Life
• RT-PCR
- Reverse transcription-polymerase chain reaction
• SARS
- Severe acute respiratory syndrome
- SARS-CoV-2
- Severe acute respiratory syndrome coronavirus 2
- SD
- Standard deviation
• WHO
- World Health Organization
- WHOQOL-BREF
- World Health Organization quality of life brief version
By Mohammad Delwer Hossain Hawlader; Md Utba Rashid; Md Abdullah Saeed Khan; Mowshomi Mannan Liza; Sharmin Akter; Mohammad Ali Hossain; Tajrin Rahman; Sabrina Yesmin Barsha; Alberi Afifa Shifat; Mosharop Hossian; Tahmina Zerin Mishu; Soumik Kha Sagar; Ridwana Maher Manna; Nawshin Ahmed; Sree Shib Shankar Devnath Debu; Irin Chowdhury; Samanta Sabed; Mashrur Ahmed; Sabrina Afroz Borsha; Faraz Al Zafar; Sabiha Hyder; Abdullah Enam; Habiba Babul; Naima Nur; Miah Md. Akiful Haque; Shopnil Roy; K. M. Tanvir Hassan; Mohammad Lutfor Rahman; Mohammad Hayatun Nabi and Koustuv Dalal
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