Quality of life among patients with the common chronic disease during COVID-19 pandemic in Northwest Ethiopia: A structural equation modelling

Tadesse Awoke Ayele; Habtewold Shibru Fanta; Malede Mequanent Sisay; Tesfahun Melese Yilma; Melkitu Fentie; Telake Azale; Tariku Belachew; Kegnie Shitu; Tesfa Sewunet Alamneh

doi:10.1371/journal.pone.0278557

. 2022 Dec 6;17(12):e0278557. doi: 10.1371/journal.pone.0278557

Quality of life among patients with the common chronic disease during COVID-19 pandemic in Northwest Ethiopia: A structural equation modelling

Tadesse Awoke Ayele ¹, Habtewold Shibru Fanta ², Malede Mequanent Sisay ¹, Tesfahun Melese Yilma ³, Melkitu Fentie ⁴, Telake Azale ⁵, Tariku Belachew ⁶, Kegnie Shitu ⁵, Tesfa Sewunet Alamneh ^1,^*

Editor: Filipe Prazeres⁷

PMCID: PMC9725128 PMID: 36472997

Abstract

Background

Improving Quality of Life (QoL) for patients with chronic diseases is a critical step in controlling disease progression and preventing complications. The COVID-19 pandemic has hampered chronic disease management, lowering patients’ quality of life. Thus, we aimed to assess the quality of life and its determinants in patients with common chronic diseases, in Northwest Ethiopia during the COVID-19 pandemic.

Methods

A cross-sectional study was conducted among 1815 randomly selected chronic patients with common chronic diseases. A standardized WHOQOL BREF tool was used, and electronic data collection was employed with the kobo toolbox data collection server. Overall QoL and the domains of Health-Related Quality of life (HRQoL) were determined. Structural equation modelling was done to estimate independent variables’ direct and indirect effects. Path coefficients with a 95% confidence interval were reported.

Results

About one in third, (33.35%) and 11.43% of the study participants had co-morbid conditions and identified complications, respectively. The mean score of QoL was 56.3 ranging from 14.59 and 98.95. The environmental domain was the most affected domain of HRQoL with a mean score of 52.18. Age, psychological, and environmental domains of HRQoL had a direct positive effect on the overall QoL while the physical and social relationships domains had an indirect positive effect. On the other hand, the number of medications taken, the presence of comorbidity, and complications had a direct negative impact on overall QoL. Furthermore, both rural residency and the presence of complications had an indirect negative effect on overall QoL via the mediator variables of environmental and physical health, respectively.

Conclusion

The quality of life was compromised in chronic disease patients. During the COVID-19 pandemic, the environmental domain of HRQoL was the most affected. Several socio-demographic and clinical factors had an impact on QoL, either directly or indirectly. These findings highlighted the importance of paying special attention to rural residents, patients with complications, patients taking a higher number of medications, and patients with comorbidity.

Background

The novel coronavirus Severe Acute Respiratory Syndrome (SARS-2 COVID-19) outbreak has resulted in a dramatic loss of human life and poses an unprecedented challenge to the world, particularly in developing countries [1]. The pandemic is continuing to cause over 3.5 million deaths in the world and aggravated hunger and poverty. Ethiopia had approximately 274,601 total cases and 4,260 deaths as of June 17, 2021 [2]. Following the confirmation of the first case, Ethiopia implemented various prevention strategies. These include closing schools and universities, avoiding large crowds, mandating mask wear, and restricting access to hospitals. Some patients were afraid to go to the hospital for treatment. COVID-19 has been linked to several adverse effects in patients with chronic diseases, including treatment delays, discontinuation, morbidity, and mortality [3–5].

However, chronic diseases such as HIV/AIDS, cardiovascular disease, cancer, diabetes, and chronic respiratory conditions (e.g., asthma and chronic obstructive pulmonary disease) continue to be the world’s leading causes of death and disability [6–9]. About 41 million people died each year due to chronic illness, equivalent to 71% of all deaths globally, and over 85% of premature deaths occur in low- and middle-income countries [10]. These illnesses are associated with a decrease in patients’ Quality of Life as well as significant socio-economic implications. Moreover, these conditions are associated with a decrease in patients’ Quality of Life (QoL) as well as significant socio-economic implications [11].

The COVID-19 pandemic has had several impacts on the community psychosocial, health care system, and economy of the countries. Particularly for chronic disease patients, this pandemic increases the risk of severe illness and death [12, 13]. Control measures taken during the pandemic like the restriction of movement and mobilization of health professionals would also compromise chronic disease management and affects their QoL [14]. However, in patients with chronic disease, a complete cure is not possible; instead, supportive measures have been provided to control disease progression and prevent disease-related complications, with the ultimate goal of improving QoL [15, 16].

Despite COVID-19 having a tremendous effect on patients with chronic disease, there is limited evidence on QoL and its factors among chronic disease patients. Previous studies have been assessed using univariate analysis that only looks at the direct effect of the factors on the dependent variable but not the indirect effect. While, QOL is a broad and multidimensional concept that includes domains related to physical, mental, emotional and social functioning [17].

Therefore, this study aimed at assessing the QoL of patients with common chronic diseases during the COVID-19 pandemic periods and its factors using Structural equation modelling (SEM). The study will provide evidence to policymakers and program planners to help them make decisions and will be useful for evidence-based interventions in support of the World Health Organization’s (WHO) Sustainable Development Goal (SDG) target of a one-third reduction in premature deaths from the noncommunicable disease by 2030 [18].

Methods

Study design and setting

A cross-sectional study design was employed in public Hospitals that provide chronic care in Ethiopia, in 2021. The source population were all patients with common chronic diseases (HIV/AIDS, (Diabetes Mellitus) DM, cardiovascular disease (CVD), and respiratory diseases) that are on follow-up at the hospitals in Amhara regional state were the source population. Whereas patients who had chronic care appointments and follow up during the data collection period were the study population. All patients with common chronic disease conditions, aged at least 18 years who have been on medication for greater than or equal to 2 years were included. Those who repeated their visit during the data collection period were excluded from the study. The study included all hospitals (referral and district) with chronic care centres in the Amhara region.

Source and study population

The source population included all patients with common chronic diseases (HIV/AIDS, DM, CVD, and respiratory diseases) who were being followed up on at hospitals in Amhara regional state. The study population consisted of patients who had chronic care appointments and follow-ups during the data collection period. All patients with common chronic disease conditions who were at least 18 years old and had been on medication for at least 2 years were included. Those who revisited during the data collection period were not included in the study.

Sample size and sampling procedures

Regarding sample size estimation, there is no single formula used to estimate sample size for studies based on the Structural Equation Modelling (SEM) analysis approach. Several factors are considered during a sample size calculation, including the indicator-to-latent variable ratio, model complexity, assumption violations (i.e. multivariate normal distribution), and indicator reliability [19]. In point of this, the minimum required sample size for SEM is to be at least 200, 10 observations per observed variable and 20 cases per parameter estimated but none of this can’t fit all in a different situation [10, 19, 20]. As a result, we decided to use a formula that can provide a maximum sample size to compute estimates even when the data do not conform to the multivariate normality assumption. Thus, $n = \frac{p (p + 1)}{2}$ , where p is 45 (the number of observed variables in the model). Accordingly, the sample size was 1355 including a 10% non-response rate and a design effect of 2.

In the Amhara region, study samples were drawn from both referral and district hospitals. First, stratification was performed based on hospital status (referral vs district). The hospitals were chosen at random. Finally, participants in the study were chosen using a systematic random sampling technique with disease type (HIV/AIDS, DM, CVD, respiratory illnesses, and cancer) in the selected hospitals while taking the proportion of disease categories into account (Fig 1).

Data collection and measurements

The data were collected by using an interviewer-administered semi-structured questionnaire adapted from the WHO Quality of Life-BREF (WHOQOL-BREF) tool and developed after a review of various works of literature. Following training, health professionals and medical doctors with experience in chronic disease follow-up participated in data collection. An electronic questionnaire form was developed by the Kobo toolbox. The tool’s validity and reliability were checked after the pretest. Possible changes to the data collection tool were made. The collected data was centrally reviewed daily for completeness and consistency. Following the retrieval of the appointment logbook and patient chart, patients were interviewed.

The instrument was divided into four sections: socio-demographic variables (age, gender, residence, marital status, literacy status), clinical factors (duration of follow-up, frequency of follow-up, number of medications, mental health problems, presence of co-morbidity and complication), behavioural factors (alcohol use and medication adherence), and the WHOQOL-BREF tool. The WHOQOL-BREF consists of 26 items designed to assess four domains of HRQoL as well as the overall perception of quality of life and general health. These are the Physical Health Domain (PHD) (7 items), the Psychological Health Domain (PSHD) (6 items), the Social Relationship Domain (SRD) (3 items), and the Environmental Health Domain (EHD) (8 items), the overall perception of QoL (2 items). Each item on the WHOQOL-BREF is assigned a score from 1 to 5, based on a five-point Likert scale [21]. To make the domains comparable, the domain raw score was calculated by multiplying the mean score of all items in each domain by 4, which ranged from 4 to 20 points. Then the raw score was linearly transformed to domain scores out of 100 by the formula; $d o m a i n s c o r e = (r a w s c o r e - 4) * \frac{100}{16}$ . Finally, the overall QoL was computed by the average of the four domain scores [22, 23]. Furthermore, the presence of common mental health problems was assessed using the SRQ-20 tool that is validated in Ethiopia with high internal consistency, Cronbach’s alpha = 0.92 [24, 25]. To assess medication adherence, the Morisky Medication Adherence Scale (MMAS-8) was used, which consists of eight yes/no questions with a score ranging from 0 to 8. The internal consistency of the MMAS was checked out by Cronbach’s alpha and was 0.72 [26, 27].

Operational definition

Chronic diseases

Chronic diseases are broadly defined as conditions that last 1 year or more and require ongoing medical attention or limited activities of daily living or both [9]. In our setup, major chronic diseases under organized follow-up are cardiovascular diseases (hypertension, Chronic Kidney Disease (CKD), and cardiac illnesses), cancer, Respiratory disorder (COPD and Asthma), diabetes, chronic liver diseases, and HIV/AIDS [28].

Data management and statistical analysis

All data were analyzed using R. For numerical variables, descriptive statistics such as mean, median, interquartile range, and standard deviation was used. For categorical variables, frequencies and percentages were used.

Following descriptive data exploration, SEM was used to assess the direct and indirect effects of factors on patients’ overall QoL. The SEM was made up of two parts: the measurement model and the structural model. The measurement components evaluate the relationship between a latent variable and its indicators or items, whereas the structural components primarily indicate the relationship between the latent variables. It also provided causality between the system’s dependent and independent variables [29]. The analysis began with the theoretical model (Fig 2) [30], and iterative modifications were made by adding paths or including mediator variables, if theoretically supported, and comparing by Root Mean Error Approximation (RMSA) as the absolute measure of the model fitness index and by information criteria as the measure of model parsimony. Finally, an overidentified model with an RMSA close to 0.05 and the smallest information criterion was kept. The effect of each exogenous or mediating variable on the respective dependent variable was represented diagrammatically by the path coefficient and a single-headed arrow, and the correlation among error terms was represented by double-headed arrows. To determine statistical significance, a confidence level of 95% and a p-value less than 0.05 were used.

Ethical consideration

Ethical clearance was secured from the institutional review board of the University of Gondar. The supportive letter was obtained from the Amhara public health institute and permission was obtained from the medical director of each hospital. Participants of the study were informed about the purpose, objectives, and their right to participate or not participate in the research. The right of participants to withdraw from the study at any time, without any precondition was disclosed unequivocally. Written consent was obtained from each participant before data collection. Moreover, to guarantee confidentiality code numbers were used rather than personal identifiers.

Results

Background characteristics

Out of 1815 patients, more than half (55.37%) were female. Their median age was 48 years with an Inter Quartile Range (IQR) of 22 (37–59) years. Most of the study participants, 1,262 (69.53%) were urban dwellers. More than one-fourth (28.0%) of them were not able to read and write. Nearly one-in-four patients (23.63%) have been following for more than 10 years with a median duration of 6 years (IQR = 3–10 years). About one in third, 602 (33.35%) of the patients had identified co-morbid conditions and 205 (11.43%) of the study participants had identified complications (Table 1).

Table 1. Background characteristics of patients with common chronic disease in Amhara region, Ethiopia, 2021.

Variables	Category	Number	%
Age	Median ± IQR	48 ± 22
Sex	Female	1,005	55.37
	Male	810	44.63
Residence	Urban	1,262	69.53
	Rural	553	30.47
Marital status	Single	218	12.01
	Married	1,187	65.40
	Divorced	107	5.90
	Separated	115	6.34
	Widowed	188	10.36
Literacy status	Unable to read and write	508	27.99
	Able to read and write	1123	72.01
Education status	Primary education	326	39.28
	Secondary education	298	35.90
	Diploma	206	24.82
Medication adherence	Low	310	17.08
	medium	977	53.83
	high	528	29.09
Alcohol use	No	1,550	85.40
	Yes	265	14.60
Duration of follow up	< 2 years	373	20.45
	2–5 years	534	29.28
	6–10 years	486	26.64
	>10 years	431	23.63
Frequency of follow up	Weekly	25	1.37
	Every two/three week	91	5.00
	Monthly	554	30.42
	Every two month	402	22.08
	Every 3–5 months	585	32.13
	Every 6 or more months	164	9.01
Presence of co-morbidity	No	1,203	66.65
	Yes	602	33.35
Presence of complication	No	1,588	88.57
	Yes	205	11.43

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Regarding the chronic disease category, the three common types of chronic disease included in the study were HIV/AIDS (27.69%), hypertension (23.41%) and diabetics (22.7%) (Fig 3).

Self-rated quality of life and perceived health satisfaction

More than half (52.62%) of the patients reported that their quality of life was good. Regarding perceived satisfaction with their health, 52.23% (948) were satisfied. On the other hand, one in four patients (26.23%) was not satisfied with their health (Table 2).

Table 2. Self-rated quality of life and health status satisfaction among chronic disease patients in Amhara region, Ethiopia (n = 1, 815).

Self-rated quality of life		Satisfaction with health status
Response category	Frequency, n (%)	Response category	Frequency, n (%)
Very poor	32 (1.76)	Very dissatisfied	31 (1.71)
Poor	445 (24.52)	Dissatisfied	445 (24.52)
Neutral	383 (21.10)	Neutral	391 (21.54)
Good	857 (47.22)	Satisfied	847 (46.67)
Very good	98 (5.40)	Very satisfied	101 (5.56)

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Quality of life using the WHOQOL-BREF tool

The overall QoL mean score was 56.3 (±14.5). In terms of overall QoL, approximately 906 (49.92%) of the respondents scored below the mean, with minimum and maximum mean scores of 14.59 and 98.95, respectively. Cronbach alpha was used to assess the internal reliability of each domain, and all domains achieved good reliability (α ≥ 0.7). Respondents scored the highest in the social domain (62.93 ± 15.43) of the four HRQoL domains. Patients, on the other hand, scored the lowest in the environmental domain (52.18 ± 14.96) (Table 3).

Table 3. Quality of life descriptive results among chronic disease patients in Amhara region, Ethiopia (n = 1, 815), 2021.

Domains	Cronbach’s α	Median	Mean ± SD	95% CI
Physical	0.85	57.14	55.89 ± 18.09	(55.06, 56.73)
Psychological	0.84	58.33	58.78 ± 17.46	(57.98, 59.59)
Social relationships	0.70	66.67	62.93 ± 15.43	(62.22, 63.64)
Environmental	0.81	53.13	52.18 ± 14.96	(51.49, 52.88)
Overall QoL	-	57.29	56.26 ± 14.51	(55.59,56.93)

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Causal factors for quality of life among chronic disease patients

Fig 4 depicts the final model, which contains both the measurement and structural components of structural equation modelling. When compared to other fitted models, these models had an RMSA of 0.08 and lower Akaike information criteria and Bayesian information criterion values, so they were chosen as the relatively fitted model. Some variables, such as marital status, education, current alcohol use, and follow-up duration, were excluded from the final model because their estimated contributions were not statistically significant at an alpha level of 0.05.

In the final model, all the path coefficients in the diagram were statistically significant at an alpha level of 0.05. Accordingly, this model included 8 exogenous observed variables (age, sex, residence, medication adherence, kinds of medication in numbers, presence of comorbidity, presence of complications, and mental health problem), four mediator latent variables (the four domains of QoL), one endogenous latent variable (QoL), and 26 endogenous observed variables (items of QoL). The exogenous observed variables, namely age, sex, residence, medication, kinds of medication, presence of comorbidity, mental health problem, and presence of complications were, directly and indirectly, related to QoL through the mediator variables of physical health domain, psychological health domain, social relationship domain and environmental health domain.

Specifically, age (adjusted β = 0.054, 95%CI;0.026, 0.082), psychological (adjusted β = 0.80, 95%CI;0.62, 0.98), environmental (adjusted β = 0.23, 95%CI;0.06, 0.39) had a direct positive effect on QoL. Besides, the physical and social relation domain (adjusted β = 0.184, 95%CI, 0.051, 0.339) had a positive indirect effect on QoL. However, kinds of medication (adjusted β = -0.03, 95%CI;-0.060, -0.004), presence of comorbidity (adjusted β = -0.033, 95%CI; -0.062, -0.002), and presence of complication (adjusted β = -0.023,95%CI;-0.056, -0.003) had a direct negative effect on QoL. Moreover, rural residency (adjusted β = -0.006, 95%CI;-0.016,-0.0005) and the presence of complication (adjusted β = - 0.045, 95%CI;-0.069, -0.024) had an indirect negative effect on the QoL via the mediator variable environmental and physical health domains respectively. In addition, the psychological (adjusted β = 0.80, 95%CI; 0.62, 0.98) and environmental (adjusted β = 0.23, 95%CI; 0.06, 0.39) domains of health had a direct positive effect where as the physical (adjusted β = 0.69, 95%CI; 0.43, 1.02) and social (adjusted β = 0.18, 95%CI; (0.05, 0.34) domains of health had an indirect positive effect on the QoL (Table 4).

Table 4. The direct, indirect, and total effects of sociodemographic and clinical factors on quality of life among common chronic disease patients in Amhara region, Ethiopia, derived from the SEM, 2021.

Characteristics	Category	Direct effect (95%CI)	Indirect effect (95%CI)	Total effect (95%CI)
DV: QoL
Physical domain		-	0.69 (0.43, 1.02)
Psychological domain		0.80 (0.62, 0.98)	-
Social relationship domain		-	0.184 (0.051, 0.339)
Environmental domain		0.23 (0.06, 0.39)	-
Age		0.054 (0.026,0.082)	-
Residence	Urban	0	0
	Rural	-	-0.006 (-0.016, -0.0005)
Mental health problem	No	0	0
	Yes	-	-0.045 (-0.069, -0.024)
Presence of complication	No	0	0
	Yes	-0.023 (-0.056, -0.003)	-
Presence of comorbidity	No	0	0
	Yes	-0.033(-0.062, -0.002)	-
Kinds of medication (number)		-0.03(-0.060, -0.004)	-
*DV^: physical health domain**
Medication adherence		0.63 (0.61, 0.64)	-
Mental health problem	No	0	0
	Yes	-0.13 (-0.17, -0.09)	-
DV: Psychological domain
Age		-	0.083 (0.065, 0.103)
Sex	Female	0	0
	Male	-	0.028 (0.016, 0.041)
Mental health problem	No	0	0
	Yes	-0.051(-0.069, -0.033)	-0.098 (-0.13, - 0.071)	-0.149 (-0.199, -0.104)
Social relationship domain		0.35 (0.29, 0.41)	-
Physical domain		0.65 (0.59, 0.71)	-
DV: Social relation domain
Age		0.21 (0.19, 0.24)	-
Sex	Female	0	0
	Male	0.077 (0.053, 0.10)	-
Physical domain		0.90 (0.89, 0.92	-
Medication adherence		-	0.57 (0.54, 0.59
Mental health problem	No	0	0
	Yes	-	-0.082 (-0.11, -0.06
DV: Environmental domain
Residence	Urban	0	0
	Rural	-0.016 (—0.033, -0.001)	-
Social relation domain		0.80 (0.74,0.87)	-
Physical domain		0.19 (0.13, 0.26)	0.73 (0.66,0.80)	0.92 (0.73, 1.06)
Age			0.19 (0.14, 0.21)
Sex	Female	0	0
	Male		0.06 (0.04, 0.09)
Mental health problem	No	0	0
	Yes	-	-0.025 (-0.044, -0.011)
Medication adherence		-	0.12 (0.08, 0.17)

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*DV = Dependent Variable

Discussion

This study aimed to assess the quality of life with its domains and casual factors among patients with the common chronic disease during the COVID-19 pandemic period. This study estimates the mean score of the domains of HRQoL and overall QoL for patients with common chronic diseases during the COVID-19 pandemic. The study found that patients with common chronic diseases had compromised QoL in all domains, especially in the environmental health domain. The finding was in agreement with previous studies done in Ethiopia among diabetic and heart failure patients [23, 31]. This could be explained by the COVID-19 pandemic creating enormous direct or indirect economic, financial, psychological, and institutional burdens to the society next to the second world war [32, 33]. The control measures for COVID-19 infection like moment restriction or lockdown and social distancing also highly compromise environmental health factors like physical security, financial resources and healthcare facilities [34]. Moreover, it led to the generation of a massive amount of medical waste that affects the air condition and water quality across the globe which in turn affects the QoL [35].

This study also identifies the associated factors of QoL. The study revealed that being a rural resident was associated with lower HRQoL in a social relation domain and overall QoL as compared with their counterparts. This finding was in agreement with a study done at Ethiopia [36]. This could be justified by the fact that rural dwellers might have poor social relationships, support and limited availability and accessibility of health facilities that play a great role in chronic disease follow-up and management [37]. Thus, individuals with limited accessibility and availability of health facilities had poor disease control and management which in turn compromised the quality of life [38].

Consistent with a study done in Egypt [22], this study showed that the age of the patients had a direct positive relationship with QoL. This could be linked with as age increases the patients might hold fewer responsibilities to think about with regard to their work and their families as compared with younger patients [39]. In addition, as age increases patients might build good social relationships and gain better social support from society and their families [40].

This study highlighted that the presence of comorbidity had an inverse relationship with QoL. The result of this study agreed with previous studies conducted in Ethiopia, Iran, and Malaysia [23, 41, 42]. The possible justification for this finding could be the presence of comorbidity makes the patient become dependent on many different drugs. Thus, this patient needs extra money to afford these drugs and the demand for healthcare services [43]. On the other side, taking many medications contributes to impaired QoL due to their side effects or drug interactions of the different drugs [44].

In addition to comorbidity, the presence of an identified complication/complications also inverse relationship with QoL, which was supported by previous studies done in Kenya, Saudi Arabia, and the United States of America that show the presence of an identified complications was negatively associated QoL [23, 45, 46]. This might be explained by the presence of complications is an indicator of poor treatment control and disease follow-up [47]. While disease control and strict follow are recommended in patients with chronic disease with the goal of enhancing QoL.

Medication adherence had a positive relationship with the Physical domain of WHO BREF while the number of medications taken for controlling the disease had an inverse relation with the overall QoL [48, 49]. This could be justified by patients taking the medication adherently may be associated with relieving signs and symptoms of their underlying disease in the short term and helps to manage their underlying disease condition in the long term period, thus resulting in better social, physical functioning and improved their QoL [50, 51].

The current study used a standardized tool, and data were collected by trained and experienced nurses and medical doctors under close and supportive supervision. The respondents were also informed about the importance of the study and the confidentiality of personal data to gain the trust of respondents and minimize the nonresponse rate. But this study was not free of limitations. The study includes different medical conditions; thus, the quality of life and its factors might be different for each disease entity attention should be given while interpreting the findings of the study. Moreover, since the study was facility based there might be a risk of social desirability bias. Also, the application of SEM for latent variables like quality of life is considered the main strength of the study but the model only accommodate binary variables as in the measurement component which might cause potential loss of information.

Conclusions

The quality of life of patients with common chronic diseases was compromised during the COVID-19 pandemic. The environmental domain of health was the most affected domain of health-related quality of life. The socio-demographic variables (age, sex, and residence), clinical factors (kinds of medications, presence of co-morbidity, complication, and mental health problem), and medication adherence from the behavioural factors had either direct or indirect significant relation with QoL. Therefore, program planners and policymakers should give special emphasis to rural residents, patients with complications, taking higher numbers of medications, and co-morbidity. Further, improving the patient’s behaviour on medication adherence had a para-amount importance for enhancing their QoL.

Supporting information

S1 Table. WHOQOL BRIEF tool description.

(DOCX)

Click here for additional data file.^{(20.9KB, docx)}

Acknowledgments

The authors are grateful to the federal ministry of health (MoH) for sponsoring this research. We would like to thank the University of Gondar, Amhara Health Bureau and Amhara Public Health Institute for the technical support and facilitation they provide during the study. We also thank the study participants for providing the information during the interview.

List of abbreviation

CVD: Cardio-vascular disease
DM: Diabetes Mellitus
HRQoL: Health-Related Quality of life
IQR: Inter Quartile Range
QoL: Quality of Life
RMSA: Root Mean Error Approximation
SEM: Structural Equation Modelling
SDG: Sustainable Development Goal
WHO: World Health Organization’s

Data Availability

Data cannot be shared publicly because of the the ethical issue. Data are available from the university of Gondar Ethics Committee (contact via cmhsshirb2022@gmail.com) for researchers who meet the criteria for access to confidential data.

Funding Statement

This study was funded by Ethiopian Ministry of Health with grant number of 34/49/1142. However, the funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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PLoS One. doi: 10.1371/journal.pone.0278557.r001

Decision Letter 0

Filipe Prazeres

26 Apr 2022

PONE-D-21-23936Quality of life among patients with common chronic disease during COVID-19 Pandemic in Northwest Ethiopia; A Structural Equation ModelingPLOS ONE

Dear Dr. Alamneh,

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Reviewer #1: This is a cross sectional study to understand the determinants that affects QOL in patients with chronic diseases during COVID pandemic in Ethiopia. My comments are as follows:

The study design:

Although the authors mentioned in both discussion and conclusion that the quality of life was compromised during COVID-19, this conclusion cannot be drawn from the existing data. This is a cross sectional study - unless the current data can be compared to similar data prior to the COVID-19 outbreak, we cannot know if the QOL actually worsened during COVID-19.

Furthermore, the determinants that may predict QOL appeared to be similar to pre-COVID19 era. i.e. poorer QOL is found in people with disease complications. What's the new information then?

Results:

1/ The age of the participants are relatively young (they were all supposed to have chronic diseases). Why is that so? Most of the patients with chronic diseases are usually eldery. (your IQR was 37-59).

2/ some of the results are difficult to read. for example, "age.... had direct posetive effect on QOL". Does this mean younger or older age? The same goes with the other presentations.

3/ "kinds of medication.... had direct negative effective on QOL". How do you put different kinds of medications into a statistical model? Furthermore, what are the medications that are associated with low QOL?

4/ I will be very careful to use the word "Causal factors" because this is a cross-sectional study, many causal relationships cannot be established. For example, it is likely that people with good drug compliance has less problems with their diseases and thereby a better QOL. However, it may also be true that people with good QOL are less troubled by their problems and thereby remember to take their medications. Actually this is one of the limitations that should be discussed.

Discussion:

1/ most of the discussion were concentrated to explain the results. But some of the results may not need very detailed discussion. For example, it is well understood why presence of co-morbidities is linked to poorer QOL. There should be multiple reasons behind. Rather, the discussion can point out how we can use the results. Any policies that need to be changed according to your results? Any more research that needs to be done?

2/ The authors correctly pointed out that the study population is heterogeneous - it included multiple chronic diseases. Then, the authors can see if the results are the same when analyzing only a subgroup of patients (e.g. only include patients with HIV or patients with hypertension). This will make your results more robust

3/ The findings may actually apply to pre-COVID era. Can the data be compared to other studies in the pre-COVID era to show that there is indeed a worsening of environmental QOL, for example?

Minor:

There are multiple spelling and grammar mistakes. The manuscript may benefit from language editing.

Reviewer #2: /// Overall ///

I don't have background on direct and indirect effects, the authors may want to clarify these to all readers. Overall, I understand the the direct effect was determined via univariate model(s) whilst the indirect one was from a mixed multivariate model(s), then, why you have to call it direct and indirect?

Typo are found in the text, please proofread carefully

//// Detail suggestions////

line 32: the range of mean score QOL seem to be large, author may consider to report both mean and median

line 161: the full term of CKD should be written before the abbreviation. Please check through all abbreviations.

line 171, 231: typo

line 205: reference of reliability thresholds should be cited properly.

line 262-266: also many literature have proved that QOL decrease by age (as their physicals health getting poorer), you may want to add some discussion on this phenomenon.

/// Tables and Figures ///

Figure 2 and 4: the author may want to add some notes to explain what are q1-q25, PHD, QOL, EHD, SRD, PSHD

Figure 3: typo

Reviewer #3: Abstract and Introduction- Need to rephrase and construct for better sentences

Line 54-58 Need to check the sentences, the facts are contradicting with each other.

Line 96- suggest for ‘Facility based cross sectional study design’ to ‘A cross sectional study design’…

Methods- study setting

It is important to highlight during the study period, how many existing healthcare facilities are used for COVID-19, converted as hybrid healthcare facilities and healthcare facilities are not used for COVID-19. Not need to mention too detail about the burden of disease for COVID-19.

Can you explain about the semi-structured questionnaire used for your study?

What is the proportion of face-to-face interview before you switch to the electronic form?

Why were the validity and reliability not conducted for SRQ-20 tool and Morisky Medication Adherence Scale (MMAS-8)?

The chronic diseases are self-reported or confirmed with the medical records?

There are few typos error

How do you overcome information and recall bias in this study?

Should separate the categories able to read and write and the education level and re-analyze the data.

Can the patient the differentiate the symptoms of the disease and complications of the disease?

How did you ask the common mental disorders for this study? (This is a diagnosis)

What was measured for ‘Kind of medication’?

**********

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Reviewer #2: No

Reviewer #3: No

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PLoS One. 2022 Dec 6;17(12):e0278557. doi: 10.1371/journal.pone.0278557.r002

Author response to Decision Letter 0

4 Jul 2022

June 2022

Rebuttal letter

Manuscript ID: PONE-D-21-23936

Title: Quality of life among patients with common chronic disease during COVID-19 Pandemic in Northwest Ethiopia; A Structural Equation Modelling

Tadesse Awoke Ayele, HabtewoldShibru, Malede Mequanent Sisay, TesfahunMelese, MelkituFentie, TelakeAzale, Tariku Belachew, Kegnie Shitu, and Tesfa SewunetAlamneh*

PLOS ONE

Dear Editor and reviewer,

We would like to thank for your consideration and suggestion for the betterment our manuscript and make it more informative. We tried to amend the format of the manuscript according to the journal guidelines and address the questions raised by reviewer on the manuscript. Our point-by-point responses for each comment and questions are described in detail on the following pages. Further, the details of changes were shown by track changes in the supplementary document attached.

Editor’s comment

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf andhttps://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

Authors’ response: Thank you dear editors for your concern. We tried to adjust the format according to the journal requirements.

2. We note that the grant information you provided in the ‘Funding Information’ and ‘Financial Disclosure’ sections do not match. When you resubmit, please ensure that you provide the correct grant numbers for the awards you received for your study in the ‘Funding Information’ section.

Authors’ response: Thank you dear editors for your concern. We have putted the grant numbers in the funding information section.

3. your Data Availability statement, you have not specified where the minimal data set underlying the results described in your manuscript can be found. PLOS defines a study's minimal data set as the underlying data used to reach the conclusions drawn in the manuscript and any additional data required to replicate the reported study findings in their entirety. All PLOS journals require that the minimal data set be made fully available. For more information about our data policy, please see

http://journals.plos.org/plosone/s/data-availability.

http://journals.plos.org/plosone/s/data-availability#loc-recommended-repositories. Any potentially identifying patient information must be fully anonymized.

http://journals.plos.org/plosone/s/data-availability#loc-unacceptable-data-access-restrictions. Note that it is not acceptable for the authors to be the sole named individuals responsible for ensuring data access. We will update your Data Availability statement to reflect the information you provide in your cover letter

Authors’ response: Thank you dear editors for your concern. We have putted the appropriate data availability statement on the online submission.

4. Your ethics statement should only appear in the Methods section of your manuscript. If your ethics statement is written in any section besides the Methods, please move it to the Methods section and delete it from any other section. Please ensure that your ethics statement is included in your manuscript, as the ethics statement entered the online submission form will not be published alongside your manuscript.

Authors’ response: Thank you dear editors for your concern. We have putted it the method part according to your recommendation.

Response to reviewers #1

1. This is a cross sectional study to understand the determinants that affects QOL in patients with chronic diseases during COVID pandemic in Ethiopia. My comments are as follows:

The study design:

Furthermore, the determinants that may predict QoL appeared to be like pre-COVID19 era. i.e., poorer QoL is found in people with disease complications. What's the new information then?

Authors’ response: Thank you dear reviewer for your concern. Of course, we didn’t make a before and after pandemic comparisons since we don’t have a data collected before the pandemic. The aim of this study was to inform the readers that the health-related quality of life of adults living with common chronic disease is compromised during the pandemic regardless of the pre-pandemic status. We used during the pandemic to refer the time or period of the study. The nobility of this study is quantifying the quality of life and its determinants in COVID 19 pandemic. It as identifies the direct indirect factors of quality of life.

Results:

2. The age of the participants are relatively young (they were all supposed to have chronic diseases). Why is that so? Most of the patients with chronic diseases are usually elderly. (Your IQR was 37-59).

Authors’ response: Thank you dear reviewer for your concern It is because of that nearly half of the study participants are adults with HIV/AIDS: it can occur more among sexually active individuals (younger age). Moreover, nearly 70% participants are within the age group of 40 or above with is plausible with the current understanding of in relation to chronic medical conditions.

3. some of the results are difficult to read. for example, "age.... had direct positive effect on QOL". Does this mean younger or older age? The same goes with the other presentations.

Authors’ response: Thank you dear reviewer for your concern. We had thought that the term “direct and positive” could respond such kind of question. Now, revisions had been made accordingly to make it more clear for readers (See the last paragraph of the result section of the revised manuscript)

4. "kinds of medication.... had direct negative effective on QOL". How do you put different kinds of medications into a statistical model? Furthermore, what are the medications that are associated with low QOL?

Authors’ response: Thank you for your question. What we do mean by kind of medication is to refer how many types of medications a patient was taking. E.g., Participant x may say “they were taking glipizide, hydrocortisone, and prednisolone for the treatment of their condition. In this case, the data collectors recorded 3 in response to the how many kinds of medications a patient taking? Thus, we were not intended to assess the effect of a medicine on the quality of life of patient, instead we hypothesised and tested how increased number in kind of medications would interact with the quality of life of the patients.

5. I will be very careful to use the word "Causal factors" because this is a cross-sectional study, many causal relationships cannot be established. For example, it is likely that people with good drug compliance have less problems with their diseases and thereby a better QOL. However, it may also be true that people with good QOL are less troubled by their problems and thereby remember to take their medications. This is one of the limitations that should be discussed.

Authors’ response: Thank you dear reviewer for your concern. Yes, casual inferences are not passible with a cross-sectional study. It is one of the limitations of such studies. We have acknowledged this in limitations of the study

Discussion:

6. most of the discussion were concentrated to explain the results. But some of the results may not need very detailed discussion. For example, it is well understood why presence of co-morbidities is linked to poorer QOL. There should be multiple reasons behind. Rather, the discussion can point out how we can use the results. Any policies that need to be changed according to your results? Any more research that needs to be done?

Authors response: Thank you for your comment. Revisions have been made accordingly (See the discussion section of revised manuscript)

7. The authors correctly pointed out that the study population is heterogeneous - it included multiple chronic diseases. Then, the authors can see if the results are the same when analysing only a subgroup of patients (e.g., only include patients with HIV or patients with hypertension). This will make your results more robust

Authors response: Thank you for your comment. We have tried to do a subgroup analysis. However, the sample in the groups subgroups was insufficient to run the subgroup analysis as structural equation modelling with a complex model requires a larger sample size. The model for the subgroups becomes unidentified model or not well converged.

8. The findings may apply to pre-COVID era. Can the data be compared to other studies in the pre-COVID era to show that there is indeed a worsening of environmental QOL, for example?

Authors response: Thank you for your question. To compare our finding with findings reported by studies conducted prior to COVID-19, there should be a study done with the same population and study area, at least. Unfortunately, to the best of the investigator’s knowledge, there are no studies done before the pandemic assessing QoL among these population in the study area.

Minor:

9. There are multiple spelling and grammar mistakes. The manuscript may benefit from language editing.

Authors response: Thank you dear reviewer for your concern. English language review and editing were made by an English language professional, and revisions were made accordingly.

Response to reviewers #2

1. I don't have background on direct and indirect effects, the authors may want to clarify these to all readers. Overall, I understand the direct effect was determined via univariate model(s) whilst the indirect one was from a mixed multivariate model(s), then, why you must call it direct and indirect?

Authors Response: Thank you so much for your questions.

The direct effect of one event on another can be defined and measured by holding constant all intermediate variables between the two or more outcomes. Indirect effects present a conceptual mediation effect because we cannot be isolated by holding certain variables constant. Our SEM examines how the quality of life (QOL) is affected through multiple mediators to predict a set of outcomes, such as physical, psychological, social, and environmental health. We want to see if the indirect effect through a set of the variables (e.g. sociodemographic variables --> Var 1--> Var 2--> QoL) was a significant proportion of the main effect (sociodemographic variables --> outcome).

2. Typo are found in the text, please proofread carefully

Authors Response: Thank you for your feedback, we performed detailed proofreading, checking spelling, grammar, sentence structure, and terminology with the help of language experts.

3. line 32: the range of mean score QOL seem to be large, author may consider reporting both mean and median

Authors Response: Thank you dear reviewer for your concern. We have tested its distribution and ii was not normal. As you know in the case of asymmetric distribution, the median with IQR is recommended.

4. line 161: the full term of CKD should be written before the abbreviation. Please check through all abbreviations.

Authors Response: Thank you dear reviewer for your concern. We made correction

5. line 171, 231: typo

Authors Response: Thank you dear reviewer for your concern, we mad correction as per your comment

6. line 205: reference of reliability thresholds should be cited properly.

line 262-266: also, many literatures have proved that QOL decrease by age (as their physicals health getting poorer), you may want to add some discussion on this phenomenon.

Authors Response: Thank you dear reviewer for your concern, revisions have been made.

7. Figure 2 and 4: the author may want to add some notes to explain what are q1-q25, PHD, QOL, EHD, SRD, PSHD

Figure 3: typo

Authors Response: Thank you dear reviewer for your concern, we include the details in the revised document.

Response to reviewers #3

1. Abstract and Introduction- Need to rephrase and construct for better sentences

Authors Response: Thank you dear reviewer for your concern, we tried to paraphrase it.

2. Line 54-58 Need to check the sentences, the facts are contradicting with each other.

Authors Response: Thank you dear reviewer for your concern, revisions have been made.

3. Line 96- suggest for ‘Facility based cross sectional study design’ to ‘A cross sectional study design’…

Authors Response: Thank you dear reviewer for your concern, revisions have been made.

4. Methods- study setting

Can you explain about the semi-structured questionnaire used for your study?

Authors Response: Thank you dear reviewer for your concern, Semi-structured questionnaire means we have used both closed and open-ended questions.

5. What is the proportion of face-to-face interview before you switch to the electronic form?

Authors Response: Thank you dear reviewer for your concern, we employed face-to-face interview with electronic data collection methods instead of paper.

6. Thank you, dear reviewer, for your concern, why were the validity and reliability not conducted for SRQ-20 tool and Morisky Medication Adherence Scale (MMAS-8)?

Authors Response: Thank you dear reviewer for your concern, this tool is already widely used and validate for our country.

7. The chronic diseases are self-reported or confirmed with the medical records?

Authors Response: Thank you dear reviewer for your concern, all diseases and outcomes are confirmed by appropriate physicians and are already recorded in the patients’ medical chart.

8. There are few typos error

Authors Response: Thank you dear reviewer for your concern, revisions have been made.

9. How do you overcome information and recall bias in this study?

Authors Response: it is not totally free from biases, but We tried to probe the patients by raising different scenario. We also crosscheck with their medical records

10. Should separate the categories able to read and write and the education level and re-analyze the data.

Authors Response: Thank you for your concerns. First, we tried to fit a model that includes with educational level. However, the major limitation of SEM is that the determinants should be either continuous variable or binary if it is categorical.

11. Can the patient the differentiate the symptoms of the disease and complications of the disease?

Authors Response: Thank you dear reviewer for your concern. We conduct the chart review and take from their chart since all diseases and outcomes which are confirmed by physicians are already recorded in the patients’ medical chart.

12. How did you ask the common mental disorders for this study? (This is a diagnosis)

Authors Response: Thank you dear reviewer for your concern. We used self-reported common mental disorders SRQ-20 tool and finally coding was conducted based on the guideline of the tool.

13. What was measured for ‘Kind of medication’?

Authors Response: Thank you dear reviewer for your concern. We asked them how many kinds of drugs they took, and its unit was in numbers.

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PLoS One. doi: 10.1371/journal.pone.0278557.r003

Decision Letter 1

Filipe Prazeres

15 Aug 2022

PONE-D-21-23936R1Quality of life among patients with common chronic disease during COVID-19 Pandemic in Northwest Ethiopia; A Structural Equation ModelingPLOS ONE

Dear Dr. Alamneh,

Please submit your revised manuscript by Sep 29 2022 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

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We look forward to receiving your revised manuscript.

Kind regards,

Filipe Prazeres, MD, MSc, Ph.D.

Academic Editor

PLOS ONE

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Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

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Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #3: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #3: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #3: No

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #3: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #3: Yes

**********

6. Review Comments to the Author

Reviewer #3: Refer to the attachment file.

1. Please put the Cronbach Alpha value for SRQ-20 tool and Morisky Medication Adherence Scale (MMAS-8), and cited the papers for tools validation conducted in Ethiopia(as references).

2. For descriptive analysis (table 1), ‘Unable to read and write’ and ‘Able to read and write’ should be put under new variable e.g ‘Literacy status’. The 2 categories mentioned above are not compatible with the existing categories in the variable ‘education status’

For SEM analysis, you need to decide how to make the 3 categories for education status into binary. (same with marital status that have more than 2 categories)

3.The words ‘common mental disorder’ in the narrative of the results, tables, discussion and conclusion are not appropriate, because SRQ-20 tool is a screening tool for depression, anxiety symptoms and psychosomatic complaints.

Please change ‘common mental disorder’ with ‘mental health problem’.

4.The use of ‘Kind of medication’ is not clear, unless you define it in the operational definition.

It is suggested the variable for ‘Kind of medication’ is change to ‘how many medication’.

**********

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Reviewer #3: No

**********

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PLoS One. 2022 Dec 6;17(12):e0278557. doi: 10.1371/journal.pone.0278557.r004

Author response to Decision Letter 1

24 Aug 2022

August 2022

Rebuttal letter

Submission ID: PONE-D-21-23936R1

Title: Quality of life among patients with common chronic disease during COVID-19 Pandemic in Northwest Ethiopia; A Structural Equation Modelling

PLOS ONE

Tadesse Awoke Ayele, Habtewold Shibru, Malede Mequanent Sisay, Tesfahun Melese, Melkitu Fentie, Telake Azale, Tariku Belachew, Kegnie Shitu, and Tesfa Sewunet Alamneh*

Dear Editor and reviewer,

Response to Reviewer

1. Please put the Cronbach Alpha value for SRQ-20 tool and Morisky Medication Adherence Scale (MMAS-8) and cited the papers for tools validation conducted in Ethiopia (as references).

Author’s response: thank you dear reviewer and we appreciate your comment. We have included it.

2. For descriptive analysis (table 1), ‘Unable to read and write’ and ‘Able to read and write’ should be put under new variable e.g., ‘Literacy status’. The 2 categories mentioned above are not compatible with the existing categories in the variable ‘education status’

Author’s response: thank you dear reviewer and we appreciate your comment. We have changed to literacy status.

3. The words ‘common mental disorder’ in the narrative of the results, tables, discussion, and conclusion are not appropriate, because SRQ-20 tool is a screening tool for depression, anxiety symptoms and psychosomatic complaints.

Please change ‘common mental disorder’ with ‘mental health problem’.

Author’s response: thank you dear reviewer and we appreciate your comment. We have updated it.

4. The use of ‘Kind of medication’ is not clear unless you define it in the operational definition. It is suggested the variable for ‘Kind of medication’ is change to ‘how many medication’.

Author’s response: thank you dear reviewer and we appreciate your comment. It was supposed to indicate how many medications were taken. As you mentioned, it is ambiguous, and we changed it to kind of medication according to make it clear.

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PLoS One. doi: 10.1371/journal.pone.0278557.r005

Decision Letter 2

Filipe Prazeres

19 Sep 2022

PONE-D-21-23936R2Quality of life among patients with common chronic disease during COVID-19 Pandemic in Northwest Ethiopia: A Structural Equation ModellingPLOS ONE

Dear Dr. Alamneh,

Please submit your revised manuscript by Nov 03 2022 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

We look forward to receiving your revised manuscript.

Kind regards,

Filipe Prazeres, MD, MSc, Ph.D.

Academic Editor

PLOS ONE

Journal Requirements:

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #3: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #3: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #3: No

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #3: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #3: Yes

**********

6. Review Comments to the Author

Reviewer #3: 1. For descriptive analysis (table 1), you only changed the ‘education status’ to ‘Literacy status’ and the categories remained the same and also their frequency and percentage.

(i) for the new variable ‘Literacy status’, the 2 categories under it are ‘Unable to read and write’ and ‘Able to read and write’. The total percentage of these 2 categories is 100%. Do not include the categories under variable ‘education status’ in this variable ‘ literacy status’.

(ii) Please maintain the variable ‘education status’. The 3 categories in this variable are Primary education, Secondary education and Diploma. Please ensure the total percentage of these 3 categories is 100%.

2. Please address my comment in the previous revision:

For SEM analysis, you need to decide how to make the 3 categories for education status into binary. (same with marital status that have more than 2 categories)

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

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Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #3: No

**********

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PLoS One. 2022 Dec 6;17(12):e0278557. doi: 10.1371/journal.pone.0278557.r006

Author response to Decision Letter 2

17 Oct 2022

October 2022

Rebuttal letter

Submission ID: PONE-D-21-23936R1

Title: Quality of life among patients with common chronic disease during COVID-19 Pandemic in Northwest Ethiopia; A Structural Equation Modelling

PLOS ONE

Tadesse Awoke Ayele, Habtewold Shibru, Malede Mequanent Sisay, Tesfahun Melese, Melkitu Fentie, Telake Azale, Tariku Belachew, Kegnie Shitu, and Tesfa Sewunet Alamneh*

Dear Editor and reviewer,

We would like to thank for your consideration and suggestion for the betterment our manuscript and make it more informative. We undergone editing of the manuscript to address the questions raised by reviewer. Our point-by-point responses for both comments are described in detail on the following pages. Further, the details of changes were shown by track changes in the supplementary document attached.

Response to Reviewer

1. For descriptive analysis (table 1), you only changed the ‘education status’ to ‘Literacy

status’ and the categories remained the same and their frequency and percentage.

(i) for the new variable ‘Literacy status’, the 2 categories under it are ‘Unable to read and

write’ and ‘Able to read and write’. The total percentage of these 2 categories is 100%. Do

not include the categories under variable ‘education status’ in this variable ‘literacy status’.

(ii) Please maintain the variable ‘education status’. The 3 categories in this variable are

Primary education, Secondary education, and Diploma. Please ensure the total percentage of these 3 categories are 100%.

Author’s response: thank you dear reviewer and we appreciate your effort for the betterment of our work. We have tried to address your comment by including literacy status and educational level. As you mentioned it the literacy variable has 2 categories who can read and write and unable to read and write. Able to read and write individual were further classified to the three categories that you strike it. The frequency and detail editing are found in the revised version.

2. Please address my comment in the previous revision:

For SEM analysis, you need to decide how to make the 3 categories for education status into

binary. (Same with marital status that have more than 2 categories).

Author’s response: thank you dear reviewer and we appreciate your comment. After you suggestion we used to literacy status (which is binary in nature) instead of educational level after assuming all educated individual can read and write. We assumed that separated, widowed, and divorced individuals were married at certain time but it might cause loss of information since there might be heterogeneity among married, separated, widowed, and divorced individuals. Due this, we include the drawbacks of SEM which only consider either continuous or binary variable in the measurement model as a limitation in our work.

Attachment

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PLoS One. doi: 10.1371/journal.pone.0278557.r007

Decision Letter 3

Filipe Prazeres

21 Nov 2022

Quality of life among patients with common chronic disease during COVID-19 Pandemic in Northwest Ethiopia: A Structural Equation Modelling

PONE-D-21-23936R3

Dear Dr. Alamneh,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Kind regards,

Filipe Prazeres, MD, MSc, Ph.D.

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

PLoS One. doi: 10.1371/journal.pone.0278557.r008

Acceptance letter

Filipe Prazeres

25 Nov 2022

PONE-D-21-23936R3

Quality of life among patients with the common chronic disease during COVID-19 Pandemic in Northwest Ethiopia: A Structural Equation Modelling

Dear Dr. Alamneh:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

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Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

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on behalf of

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PLOS ONE

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Table. WHOQOL BRIEF tool description.

(DOCX)

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Data Availability Statement

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PERMALINK

Quality of life among patients with the common chronic disease during COVID-19 pandemic in Northwest Ethiopia: A structural equation modelling

Tadesse Awoke Ayele

Habtewold Shibru Fanta

Malede Mequanent Sisay

Tesfahun Melese Yilma

Melkitu Fentie

Telake Azale

Tariku Belachew

Kegnie Shitu

Tesfa Sewunet Alamneh

Roles

Abstract

Background

Methods

Results

Conclusion

Background

Methods

Study design and setting

Source and study population

Sample size and sampling procedures

Fig 1. Schematic presentation of sampling procedures (note that; RH and referral hospital, DH: District hospital).

Data collection and measurements

Operational definition

Chronic diseases

Data management and statistical analysis

Fig 2. Theoretical model of quality-of-life measurement (S1 Table).

Ethical consideration

Results

Background characteristics

Table 1. Background characteristics of patients with common chronic disease in Amhara region, Ethiopia, 2021.

Fig 3. Type of chronic disease in patients on chronic disease follow-up at Amhara region, Ethiopia, 2021.

Self-rated quality of life and perceived health satisfaction

Table 2. Self-rated quality of life and health status satisfaction among chronic disease patients in Amhara region, Ethiopia (n = 1, 815).

Quality of life using the WHOQOL-BREF tool

Table 3. Quality of life descriptive results among chronic disease patients in Amhara region, Ethiopia (n = 1, 815), 2021.

Causal factors for quality of life among chronic disease patients

Fig 4. Causal factors for quality of life among common chronic disease patients in Amhara region, Ethiopia, derived from the SEM, 2021.

Table 4. The direct, indirect, and total effects of sociodemographic and clinical factors on quality of life among common chronic disease patients in Amhara region, Ethiopia, derived from the SEM, 2021.

Discussion

Conclusions

Supporting information

Acknowledgments

List of abbreviation

Data Availability

Funding Statement

References

Decision Letter 0

Filipe Prazeres

Roles

Author response to Decision Letter 0

Decision Letter 1

Filipe Prazeres

Roles

Author response to Decision Letter 1

Decision Letter 2

Filipe Prazeres

Roles

Author response to Decision Letter 2

Decision Letter 3

Filipe Prazeres

Roles

Acceptance letter

Filipe Prazeres

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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