A cross-sectional study of factors predicting readmission in Thais with coronary artery disease

Abstract

Background

Coronary artery disease is a major cause of morbidity and mortality with high readmission rates. Hospital readmissions for coronary artery disease contribute to rising healthcare costs and are a marker of quality of care. Despite this, prior studies have found that readmission rates vary widely.

Aims

This study aims to determine the impact of social support, depression, comorbidities, symptom severity, quality of life and readmission among coronary artery disease patients in Thailand.

Methods

A total of 321 coronary artery disease patients from tertiary care hospitals across all regions of Thailand were recruited for this study. Data were analysed using multiple regression analysis.

Results

The coefficient for social support (beta = −0.22) was found to be significant (p < 0.05), whereas comorbidity, symptom severity, depression and quality of life were not significant. Thus, social support was found to be the most significant predictive factor for readmission.

Conclusions

Accordingly, when designing effective nursing interventions, nurses should promote social support interventions for coronary artery disease patients to improve the quality of care, decrease readmission rates and improve patients' quality of life.

Keywords

coronary artery disease descriptive-correlational readmission social support Thai

Introduction

Coronary artery disease (CAD) is a chronic medical with high morbidity and mortality rates (Pelle et al., 2008; Sethares and Elliott, 2004; Volz et al., 2011). This group of patients tends to be readmitted to hospital due to recurrence of the condition (Gheorghiade et al., 2013; Ryan et al., 2014; Desai et al., 2009). Thus, the readmission rate is recognised as a marker of quality of care (QOC) and poses a significant financial burden on rising healthcare costs (Coffey et al., 2012; Hasan et al., 2009; Lundback et al., 2017; Sud et al., 2017; Dharmarajan, 2016). Although readmission is a marker of QOC, prior studies have found that readmission rates vary between 15% and 30%, within 30–60-day post-discharge period and between 25% and 50% within 6 months after the first hospitalisation, increasing to 80% within 1 year. This accounts for approximately 70% of the healthcare costs of CAD patients in the United States (US) (Giamouzis et al., 2011; Dharmarajan, 2016; Dordunoo et al., 2017). Various reasons are given for readmission, such as other medical problems, non-adherence to health recommendations (e.g., medication adherence, diet, fluid restriction or emotional factors), mental health factors (e.g., pain, anxiety, depression, substance abuse or cognitive disorders), environment and inadequate discharge planning (Annema et al., 2009; Desai et al., 2009; Coffey et al., 2012; Hasan et al., 2009; Eastwood et al., 2017; Jencks et al., 2009). Thus, readmission remains an important issue in health systems that indicates hospital-level quality and efficiency of care. Frequent readmission after discharge is increasingly recognized as a marker of inpatient QOC and is a significant contributor to rising healthcare costs (Desai et al., 2009; Dharmarajan, 2016; Hasan et al., 2009; Jencks et al., 2009). The role of nurses in the care of CAD patients includes providing routine 24-hour care, contributing to the discharge process and assisting patients and their families in making lifestyle adjustments. It is essential to consider the patient as part of a family structure so family needs can be assessed and met through individual or group sessions using appropriate resources. Thus, a study of the factors predicting readmission is needed for patients in this group.

Review of the literature

In Thailand, readmission has been documented as a QOC marker in CAD. However, previous studies examining readmission have focused on a single disease or condition (Jenghua and Jedsadayanmata, 2011; Kiatchoosakun et al., 2012). For example, research has been conducted on the readmission of heart failure (HF) patients. One study examined the rate and predictors of early readmission (within 30 days of discharge) among Thai HF patients by reviewing the electronic medical database. It found the early readmission rate for HF patients was 14.1%, when the length of stay in hospital was more than 5 days. In CAD patients it found that a history of diabetes mellitus, renal failure and digoxin were all factors associated with readmission. The most common cause of readmission was angina pectoris and chronic renal failure (Jenghua and Jedsadayanmata, 2011; Kiatchoosakun et al., 2012).

In addition, several interventions have been conducted to reduce the readmission rates of CAD patients such as fluid restriction intervention, discharge planning, advice and counselling intervention and medication regimen intervention, amongst others (Annema et al., 2009; Ryan et al., 2014; Jenghua and Jedsadayanmata, 2011). However, the readmission rate in CAD patients remains high. Moreover, most studies on readmission have been conducted in the US, but the factors related to readmission among CAD patients may vary across cultures and populations. In Thailand, the limited research conducted on the factors related to readmission has tended to confirm those in the US. However, because Thai cultural characteristics are different from the US, it is reasonable to suspect that research findings might differ too, such as comorbidities, symptom severity, depression, social support and living behaviour, which are all known to be factors related to readmission (Annema et al., 2009; Coffey et al., 2012; Hasan et al., 2009; Jencks et al., 2009).

In healthcare, quality has been defined as ‘the degree to which the health service increases the likelihood of desired health outcomes and are consistent with current professional knowledge’. There are three components of a quality care model (Duffy, 2009: 28). The first is the structure, which refers to the context or condition in which care is provided. Factors in this component are the institutional resources, provider credentials and patient characteristics. This study focuses on the patient characteristics because we wished to emphasise the manipulated factors related to patients. The second component is the process, which refers to the actions carried out for the patient, including both the technical and interpersonal aspects of care. The third component is the outcomes, which are the consequences of the healthcare process. Again, this study focuses on CAD patient characteristics because we aimed to manipulate the factors related to the patients before developing a process to improve the outcome. Thus, the following factors have been documented related to readmission among CAD patients: social support, depression, comorbidities, symptom severity and quality of life (QOL) (Annema et al., 2009; Betihavas et al., 2012; Carel, 2004).

The relationship among variables can be explained as follows: (a) social support significantly improved QOL and was linked to a decrease in readmission in CAD patients (Bennett et al., 2011; Volz et al., 2011); (b) patients had greater depressive symptoms, reflecting the high rate of readmission (Betihavas et al., 2012; Pelle et al., 2008); (c) symptom severity was consistently related to readmission, with a higher severity of symptoms related to high readmission (Carel, 2004; Giamouzis et al., 2011); (d) comorbidity had the greatest association with readmission and is a precipitant for patients' early readmission (Coffey et al., 2012; Hasan et al., 2009); and (d) CAD is a chronic condition and has an unstable course of illness with unpredictable exacerbations and progression of symptoms, resulting in poor QOL and is linked to frequency of readmission (O'Loughlin et al., 2010; Sethares and Elliott, 2004). Thus, the study aims to determine the impact of social support, depression, comorbidities, symptom severity, QOL and readmission.

Methods

Design

This cross-sectional study aimed to determine the impact of social support, depression, comorbidities, symptom severity and QOL on readmission among CAD patients in Thailand.

Sample and setting: this study was conducted to examine which potential factors could help predict readmission among CAD patients in Thailand. The sample size was derived from the rule of thumb of multiple regressions, where the maximum ratio of the observed to independent variable is 20:1 for a valid statistical power (Hair et al., 2010). This study has five variables. The sample size of 100 patients is the minimum to meet statistic power analysis of multiple regressions. Thus, a total of 321 CAD patients is a valid statistical power of multiple regressions. The participants were recruited from tertiary care hospitals from all regions of Thailand. All potential participants who met the inclusion criteria were approached and asked to participate in the study. The inclusion criteria were: (a) Thai CAD patients who had recently been readmitted to a hospital inpatient cardiologic department; (b) aged 20 years old or more; (c) no cognitive impairment and no other disease complications, such as heart attack and cardiogenic shock; and (d) willingness to participate in this study.

Instrument: six instruments were used in this study and all were tested and shown to have acceptable validity and reliability. They were:

Readmission, which refers to the number of CAD patients with repeated hospitalisation within 12 months of discharge from index hospitalisation, which was collected from their medical record.

Comorbidity, which refers to the presence of one or more additional conditions co-occurring with CAD in the patient and was determined from medical records.

Symptom severity, which was measured using the Canadian Cardiovascular Society Classification for classifying angina, where Class IV – unable to do any activities with or without angina at rest; Class III – have symptoms with daily living activities, and so experience moderate limitation; Class II – experiencing angina only during vigorous physical activity and so a slight limitation; and Class I – only experiencing angina during strenuous or prolonged physical activity (Sangareddi et al., 2004).

Multidimensional Scale of Perceived Social Support (Zimet et al., 1988). This instrument was developed for those undergoing a diagnostic coronary angiography. The 12-item scale was designed to assess three aspects of perceived social support (from friends, family and significant others). Participants were asked to respond to 12 items on a Likert scale from 1 (disagree very strongly) to 7 (agree very strongly), with a total score ranging from 12–84. A higher score represents a higher level of social support. A score between 12 and 36 would be a low level of social support, from 37–60 would be mild social support and from 61–84 would be a higher level of social support. This had a high internal reliability (Cronbach's alpha = 0.89) (Phromsornt et al., 2019).

Cardiac Depression Scale (CDS) (Hare and Davis, 1996; Oldridge, 1997). The CDS is a 26-item self-rating scale used specifically for medical cardiac patients in Australia. It consists of two dimensions: Dimension 1 (sleep, uncertainty, mood, hopelessness and inactivity) and Dimension 2 (anhedonia and cognition). The CDS has been validated in adult cardiac inpatients and had satisfactory concurrent validity with the Beck Depression Inventory. The CDS has a high internal consistency (Cronbach's alpha = 0.91) and test-retest reliability coefficient of 0.94 in the Chinese version (Wang et al., 2008). Participants were asked to respond to 26 items on a Likert scale from 1 (strongly disagree) to 7 (strongly agree), with a total score ranging from 26–182. A high score represents a high level of depression. The score less than 90 is defined as no depression, score range from 90–100 is defined as mild to moderate depression, and score more than 100 is defined as strong depression. The Cronbach's alpha coefficient of Thai version was 0.82 (Polsook and Aungsuroch, 2019).

Quality of Life Index-Cardiac version IV which was developed to measure QOL in terms of cardiovascular patients' satisfaction with life. This was translated into Thai by Saengsiri (2003) and comprised 70 items in two parts. The first part (35 items) measured the patient's satisfaction with various aspects of life, whereas the second measured the importance of those aspects. This tool covered the four domains of (a) health and functioning, (b) social and economic, (c) psychological /spiritual and (d) family. Answers were based on a Likert scale ranging from 0–5, with a final score range from 0–30. A lower score indicates a poor QOL (score 1–10). A score 11–20 is defined as a low QOL and score between 21 and 30 is defined as a high QOL. The internal consistency the Thai version using Cronbach's alpha was 0.91.

Ethical consideration: this study was approved by the ethics committee of the tertiary care hospitals from all regions of Thailand prior to data collection. Those internal review boards (IRB) included the Ethics Committee of Hatyai Hospital (approval 85), the Ethics Clearance Committee on Human Right Related to Research Involving Human Subjects Faculty of Medicine Ramathibodi Hospital, Mahidol University (2558/716), Research Ethics Committee Faculty of Medicine Chiang Mai University (2558-03518), IRB of the Faculty of Medicine, Chulalongkorn University (074/59) and the Khon Kaen University Ethics Committee for Human Research (00001189).

The benefits and risks of the intervention and the protection of human rights were presented in non-technical terms to the patient and their consent to participate in the study was obtained. All subjects agreed to participate and signed an informed consent form after being given a written description and further verbal information about the research project. Participants were asked to complete questionnaires. During data collection, participants were able to leave the study at any time.

Data collection: after approval from the ethics committee, the researcher explained and clarified the study objectives, data collection procedures and expected outcomes and benefits of the study to the physicians and nurses of each cardiology inpatient department in the selected hospitals. Research assistants were trained to conduct questionnaire administration, informed consent procedures, the participant information sheet and patient recruitment: (a) screening patients for eligibility by research assistant, (b) participants who met the inclusion criteria were inviting to participate, (c) informed consent process, explaining study information, assuring participants' rights, (d) collecting data after obtaining written consent from participants and explaining it would take 30–45 minutes to complete all the questionnaires.

Statistical analysis: this study's dependent variable is readmission and independent variables are social support, depression, comorbidities, symptom severity and QOL. The data were analysed using IBM SPSS 22. Descriptive statistics delineated the characteristics of the sample and examined the demographic variables, then Pearson's product correlation coefficient was used to analyse the correlation between variables. The predictive ability of potential factors was analysed using stepwise multiple regression. Statistical significance was p < 0.05.

Results

Characteristics of the participants

A total of 321 CAD patients agreed to participate in the study. A slight majority of the participants were male (59.2%) and the mean age of all participants was 63 ± 14.3 years. Most participants were married (71.7%) and nearly half were unemployed (48.3%). Almost half of the participants had National Health Security support (48.9%). The highest proportion of participants had primary school education (45.5%), followed by higher education (24.9%) then high school (24.0%). Most participants had a salary of less than 10,000 baht per month (64.8%). With respect to readmissions, one-third of the participants had been readmitted once (40.2%), whereas two and three readmissions were found in 35.5% and 11.2% pf patients, respectively (Table 1).

Table 1.

Demographic characteristics of the CAD patients of this study (n = 321).

Characteristic	Number	Percentage
Age
20–40	23	7.2
41–60	101	31.4
≥61	197	61.4
Gender
Male	190	59.2
Female	131	40.8
Marital status
Single	23	7.2
Married	230	71.7
Widowed	57	17.7
Divorced	11	3.4
Education level
No education	18	5.6
Primary school	146	45.5
High school	77	24.0
Higher education	80	24.9
Financial status
≤10,000 baht/month	208	64.8
10,001–20,000 baht/month	62	19.3
20,001–30,000 baht/month	24	7.5
30,001–40,000 baht/month	11	3.4
≥40,001 baht/month	16	5.0
Occupation
Unemployed	155	48.3
Employee	50	15.6
Employee of the government	5	1.6
Government pension	38	11.8
State enterprise	6	1.9
Business	27	8.4
Government official	18	5.6
Agriculture	22	6.8
Type of healthcare coverage
Universal Coverage Scheme (30-Baht Scheme)	157	48.9
Social security	29	9.0
Pay for themselves	6	1.9
Government coverage	128	39.9
Health insurance	1	0.3
Readmission
1 time	129	40.2
2 times	114	35.5
3 times	36	11.2
4 times	20	6.2
≥5 times	22	6.9

CAD: Coronary artery disease.

Description of predictive variables

Most participants had a high level of social support (60.4%) and slightly more than half were not depressed (51.7%). Nearly one-quarter of participants had no comorbidity (24.0%), followed by two (16.5%) and four comorbidities (12.7%). For symptom severity, one-third of the participants were Class II (35.7%), followed by Class III (28.9%), Class I (18.6%) and Class IV (16.9%). The majority of participants had a high QOL (76.9%).

Predictors of readmission

The mean score of readmission was 2.2 ± 1.9. Four variables were found to be significant. Social support and QOL were significantly negatively associated with readmission (r = −0.23 and −0.15, respectively; p < 0.05), whereas depression and symptom severity were significantly positively correlated with readmission (r = 0.14 and 0.12, respectively; p < 0.05). The overall test for the significance of the regression (Table 2), with an overall F ratio of 5.299 (p < 0.00), supported that together comorbidity (beta = 0.093), symptom severity (beta = 0.061), depression (beta = 0.070), social support (beta = −0.225) and QOL (beta = −0.013) were significantly correlated with readmission. However, individually, only the coefficient for social support was significant (p < 0.05), whereas comorbidity, symptom severity, depression and the QOL were not significant (Table 3). This study indicated no multicollinearity in this sample where the lowest tolerance was 0.683 and the variance inflation factor was less than 10 with a range of 1.063–1.464 (Table 3). The overall regression model is shown in Table 4. Multiple R (0.279) and R² (0.078) are presented, followed by the adjusted R² (0.063) and the standard error of estimate (1.804). From Table 4, the comorbidity, symptom severity, depression, social support and QOL were correlated with readmission (R² = 0.078), but these five variables explained only 7.8% of the variance (p < 0.05).

Table 2.

ANOVA.

Model	Sum of squares	df	Mean square	F	Sig.
1 regression	12.783	1	12.783	3.712	0.055^a
Residual	1098.457	319	3.443
Total	1111.240	320
2 regressions	16.995	2	8.498	2.470	0.086^b
Residual	1094.244	318	3.441
Total	1111.240	320
3 regressions	29.640	3	9.880	2.896*	0.035^c
Residual	1081.600	317	3.412
Total	1111.240	320
4 regressions	86.076	4	21.519	6.633**	0.000^d
Residual	1025.164	316	3.244
Total	1111.240	320	17.242
5 regressions	86.211	5		5.299**	0.000^e
Residual	1025.029	315	3.254
Total	1111.240	320

Predictors: (constant), comorbidity.

Predictors: (constant), comorbidity and symptom severity.

Predictors: (constant), comorbidity, symptom severity and depression.

Predictors: (constant), comorbidity, symptom severity, depression and social support.

Predictors: (constant), comorbidity, symptom severity, depression, social support and QOL.

*p < .05, ** p < .01. ANOVA: analysis of variance; QOL: quality of life.

Table 3.

Regression coefficients.

Model	Unstandardised coefficients		Standardised coefficients	t	Sig.	95% confidence interval for B		Collinearity statistics
Model	B	Std. Error	Beta	t	Sig.	Lower bound	Upper bound	Tolerance	VIF
Constant	3.206	1.018		3.147	0.002**	1.202	5.210
Comorbidity	0.031	0.018	0.093	1.676	0.095	−0.005	0.067	0.941	1.063
Symptom severity	0.116	0.108	0.061	1.079	0.281	−0.096	0.328	0.920	1.087
Depression	0.005	0.004	0.070	1.119	0.264	−0.004	0.014	0.743	1.345
Social support	−0.028	0.007	−0.225	−3.869	0.000**	−0.042	−0.014	0.868	1.153
QOL	0.000	0.003	−0.013	−0.204	0.839	−0.006	0.005	0.683	1.464

**p < .01.

QOL: quality of life; VIF: variance inflation factor.

Table 4.

Model summary.

Model	R	R ²	Adjusted R²	Std. error of the estimate	Change statistics
Model	R	R ²	Adjusted R²	Std. error of the estimate	R² change	F change	df1	df2	Sig. F change
1	0.107^a	0.012	0.008	1.85565	0.012	3.712	1	319	0.055
2	0.124^b	0.015	0.009	1.85500	0.004	1.224	1	318	0.269
3	0.163^c	0.027	0.017	1.84716	0.011	3.706	1	317	0.055
4	0.278^d	0.077	0.066	1.80116	0.051	17.396	1	316	0.000**
5	0.279^e	0.078	0.063	1.80390	0.000	0.042	1	315	0.839

Predictors: (constant), comorbidity.

Predictors: (constant), comorbidity and symptom severity.

Predictors: (constant), comorbidity, symptom severity and depression.

Predictors: (constant), comorbidity, symptom severity, depression and social support.

Predictors: (constant), comorbidity, symptom severity, depression, social support and QOL.

**p < .01.

QOL: quality of life.

Discussion

According to the findings of this study, the five factors of comorbidity, symptom severity, depression, social support and QOL explained 7.8% of the variance (p < 0.05) in readmission among CAD patients. However, only social support was found to be significant at the p < 0.05 level and was negatively correlated with readmission because most participants were married (71.7%), so had someone to take care them, resulting in lower readmission rates. Additionally, in Thailand people usually have a close extended family and most participants lived with family members, thus it is possible family members participated in the care and support of these patients (Polsook et al., 2013; Polsook et al., 2016). This result concurs with other studies that show social support is negatively correlated with readmission. For example, social support has been shown to be related to decreased readmission and mortality rates among CAD patients (Lu et al., 2016; Tang et al., 2018) and CAD patients with a high level of social support had lower readmission rates, whereas adult chronic CAD patients with a lack of social support had an increased risk of readmission (Bennett et al., 2011; Tang et al., 2018).

The other four variables of this study (comorbidity, symptom severity, depression and QOL) were not significant. Comorbidity did not predict readmission because nearly one-third of the participants in this study reported no comorbidity (24.0%), which meant participants did not have the complexity of other health conditions. Thus, readmission is at a lower rate in this study. This result is similar to other studies, where more comorbidities had a positive correlation with readmission. For example, more comorbidities were found to be a significant predictor of readmission (Hasan et al., 2009) and had the greatest association with readmission (Coffey et al., 2012) or early readmission in CAD patients (Gheorghiade et al., 2013; Betihavas et al., 2012; Kwok et al., 2018). Additionally, more comorbidities were shown to reflect poor health and the burden associated with an increasing readmission (Benbassat and Taragin, 2000; Kansagara et al., 2011). For symptom severity, more than one-third of participants (35.8%) were Class II: a slight limitation of physical activity but comfortable at rest. Ordinary physical activity results in fatigue, palpitation and dyspnoea (shortness of breath), so they do not have severe clinical symptoms (Sangareddi et al., 2004). Thus, the participants can participate in any activity without severe clinical symptoms. This finding supports previous studies showing symptom severity was consistently related to readmission and a higher severity of symptoms was related to a higher rate of readmission (Giamouzis et al., 2011; Carel, 2004).

In our study most participants did not exhibit depressive symptoms (51.7%) because they had a high level of social support, no comorbidity and could participate in activities without severe clinical symptoms. So, the participants had lower readmission rates. This result is supported by other studies, where no depressive symptoms or a lower level of depression symptoms were associated with lower readmission rates (Pelle et al., 2008). Additionally, post-myocardial infarction, no depression and increased adoption of secondary prevention measures led to a decrease in hospital admission, where depression was related to readmission (Myers et al., 2012). Moreover, depressive symptoms were demonstrated to have an adverse effect on readmission (Volz et al., 2011; Dhital et al., 2018), where CAD patients who developed severe depressive symptoms tended to have an increased chance of hospital readmission and a lower QOL.

The majority of participants had a high QOL. As shown by the researchers mentioned above, high social support meant patients had a spouse or family member helping them, so they had lower readmission rates. The results of this study contrast with a previous study, where a low QOL predicted readmission. Indeed, QOL is recognised as a significant predictor of readmission for CAD patients (Dhital et al., 2018; Heo et al., 2009; O'Loughlin et al., 2010), where CAD patients who experience physical and emotional symptoms that result in a poor QOL are linked to a higher frequency of readmission (Kim et al., 2019; Sethares and Elliott, 2004; O'Loughlin et al., 2010). Likewise, CAD was found to be a chronic condition with an unstable course and unpredictable exacerbations and progression of symptoms, which resulted in a poor QOL and was linked to an increased frequency of readmission (Kim et al., 2019; Sethares and Elliott, 2004; O'Loughlin et al., 2010).

The limitations of this study include that the data were based on self-reports, which could have caused overestimated or underestimated values. Based on these findings, a longitudinal study should be conducted to assess the changes in these variables and readmission in CAD patients to provide a more causal explanation regarding readmission in CAD patients and its predictors.

Conclusion and implications for practice

Social support was found to be a significant predictive factor for readmission. Based on the findings of the study, social support should be promoted to enhance the ongoing care of CAD patients. Although depression is not associated with hospital readmission in this study, the mediating effects of anxiety and depression contributing to the QOL of patients with CAD are well established (Lee et al., 2014). It would be beneficial for all CAD patients to be screened for depression so proper intervention can be provided to avoid hospital readmission. This information about the results of this study will help nurse as a health care team to solve hospital readmission among CAD patients. This was a cross-sectional study, but future studies are needed with an experimental/quasi-experimental design with intervention and control groups that promote social support. This would then show if this variable was real and effective in decreasing readmission in this group to improve QOC and decrease readmission rates and healthcare costs, while improving the patient's QOL.

Footnotes

Key points for policy,practice and/or research

Social support was found to be a significant predictive factor for readmission. Nurses should promote social support to enhance care of CAD patients.

Nurses, as part of a multidisciplinary team, should screen for depression in CAD patients for the early detection of depression symptoms. This information about the results of this study will help nurses in the health care team to solve hospital readmission among CAD patient.

More studies are needed with an experimental/quasi-experimental design with intervention and control groups that promote social support intervention.

For an effective decrease in readmission in this group, policy makers should promote social support to improve QOC and decrease readmission rates and healthcare costs while improving the patient's QOL.

Acknowledgements

The authors thank the participants for their time and effort. Additionally, the authors would like to thank the targeted hospitals for facilitating for data collection process.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Ethics approvals

This study was approved by the ethics committee of the tertiary care hospitals from all regions of Thailand prior to data collection. Those IRBs included the Ethics Committee of Hatyai hospital (approval 85), the Ethics Clearance Committee on Human Right Related to Research Involving Human Subjects Faculty of Medicine Ramathibodi Hospital, Mahidol University (2558/716), Research Ethics Committee Faculty of Medicine Chiang Mai University (2558-03518), IRB of the Faculty of Medicine, Chulalongkorn University (074/59) and the Khon Kaen University Ethics Committee for Human Research (00001189).

Funding

The author(s) disclose receipt of the following financial support for the research, and/or publication of this article: This work was supported by the Ratchadaphiseksomphot Endowment Fund, Chulalongkorn University (grant CU-GR_60_38_36_03).

ORCID iD

Rapin Polsook

Rapin Polsook is an Assistant Professor at the Faculty of Nursing, Chulalongkorn University. She has published articles related to cardiovascular disease. She is a reviewer and member of the editorial team of the Nursing Journal.

Yupin Aungsuroch is an Associate Professor at the Faculty of Nursing, Chulalongkorn University. She is a former Dean. She has published articles on administrative issues and is an editor and reviewer of many journals.

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