Mental health status and quality of life among Thai people after the COVID-19 outbreak: a cross-sectional study – Nature.com

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Scientific Reports volume 14, Article number: 25896 (2024)
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The coronavirus disease 2019 (COVID-19) pandemic has had a profound impact on individuals’ mental health and well-being worldwide. This study investigated the prevalences of and association between mental health and quality of life (QOL) in Thailand after the COVID-19 outbreak. A cross-sectional study was conducted among Thai individuals aged ≥ 20 years across all regions. Descriptive statistics, chi-square tests, and multiple logistic regression analyses were performed to examine the association between mental health and QOL. A total of 1,133 participants (mean age: 35.1 ± 17.2 years) completed the survey. The prevalence of depression with PHQ-9 instrument was 19.4%. Depression was significantly associated with those who have had family members die from COVID-19 infection (adjusted odds ratio [AOR] 2.27, 95% confidence interval [CI] [1.13–4.52]). The percentages of depression, anxiety, and stress with DASS-21 instrument were 32.4%, 45.4%, and 24.1%, respectively. Smokers and alcohol consumption had approximately 1.5-time higher risk of stress compared with non-smokers and no alcohol consumption (AOR = 1.50, 95% CI [1.01–2.24], AOR = 1.48, 95% CI [1.09–2.02], respectively). An association was observed between socioeconomic factors such as job and income loss and mental health outcomes. Depression, anxiety, and stress were significantly negatively associated with QOL. This study demonstrates a strong association between mental health and QOL among Thai people after the COVID-19 outbreak. The findings underscore the need for interventions targeting lifestyles, including those addressing alcohol consumption and smoking, especially among those who have had family members die from COVID-19 infection and mental health support services that can address depression, anxiety, and stress to improve the overall well-being of the population.
The coronavirus disease 2019 (COVID-19) pandemic has had a profound impact on global health, economies, and social well-being1,2,3. Beyond the immediate physical health consequences, the pandemic has also taken a toll on individuals’ mental health4,5. Worldwide, the COVID-19 pandemic led to extensive government-imposed lockdowns, which had a notably negative effect on mental health6,7. In Thailand, mental health was also impacted by the economic challenges and policies implemented during the pandemic8,9,10. Studies have reported increased rates of psychological distress, anxiety, and depression among populations worldwide, highlighting the need to examine the mental health implications of the pandemic in different contexts11,12. Thailand also experienced the devastating effects of the COVID-19 outbreak 13,14. The government implemented strict public health measures including lockdowns, travel restrictions, and social distancing to curb the spread of the virus15. These measures, although essential for public safety, led to economic instability, social isolation, and significant disruptions in daily life, potentially exacerbating mental health issues among the Thai population16,17.
Depression, a common mental health disorder that is characterized by persistent feelings of sadness, loss of interest or pleasure, and a range of emotional and physical symptoms, is usually assessed using the Patient Health Questionnaire (PHQ-9)9. The prevalence of depression, which can have a substantial impact on individuals’ overall well-being and quality of life (QOL), was 20.5% during the COVID-19 pandemic among Thai older adults 16. The association between depression and QOL has been widely recognized in various populations and health contexts18. Previous studies have shown that gender, social factors, income level, loneliness, hospital location, physical comorbidities, and the severity of COVID-19 were significantly linked to anxiety and depression during or after the pandemic19,20,21,22,23. Most studies on the association between depression and QOL among Thai people were conducted during the COVID-19 pandemic16,24. However, a limited amount of research has focused on understanding the association between depression and QOL specifically among Thai individuals after the COVID-19 outbreak25.
The Depression Anxiety Stress Scales (DASS-21) have emerged as a reliable instrument for assessing the three core components of psychological distress: depression, anxiety, and stress26. This tool provides a comprehensive evaluation of emotional states and has been widely used in studies that have examined the psychological impact of various stressors including public health emergencies such as the COVID-19 pandemic27,28. Moreover, understanding the relationship between mental health outcomes and QOL is essential for informing targeted interventions and support strategies29. A decline in QOL can have far-reaching consequences and can affect individuals’ overall well-being, social functioning, and productivity30. By examining the interplay between mental health indicators and QOL, this study seeks to identify potential avenues for intervention and support that might mitigate the adverse effects on the Thai population after the COVID-19 pandemic. However, no study has yet examined all mental health indicators, including depression, anxiety, and stress, after the pandemic in Thailand. This is crucial for exploring the risk factors and burden of these mental health issues, which can provide valuable insights for policymakers to help with prevention and control efforts.
Therefore, this cross-sectional study investigates the association between mental health and QOL among Thai people after the COVID-19 outbreak. By examining the impact of depression on various QOL domains including physical health, psychological well-being, social relationships, and environmental factors, this study provides comprehensive insights into the interplay between mental health and overall well-being in the post-pandemic context. In addition, the study examines the association between depression, anxiety, and stress after the outbreak. Investigating the association between mental health and QOL among Thai people is crucial for several reasons. The relationship can provide valuable insights into the mental health implications of the pandemic within the Thai cultural and socioeconomic context, allowing for a better understanding of the unique challenges faced by the population. Moreover, this study can inform policymakers, healthcare professionals, and mental health practitioners who seek to develop effective strategies such as early screening among at-risk populations to mitigate the negative effects of mental health problems and enhance QOL among Thai individuals affected by the COVID-19 pandemic.
This cross-sectional study was conducted between February 2023 and July 2023 among a representative sample of Thai individuals aged 20 years and above residing in Thailand’s Bangkok, Chiang Rai, Chon Buri, Tak, Nakhon Si Thammarat, and Khon Kaen provinces (Fig. 1), which represent the country’s attractive localities for tourists and are representative of each region with a high population density31,32. To gather participants from all provinces, we employed stratified random sampling with proportion allocation based on population distribution. Individuals who met the inclusion criteria were subsequently identified and invited to join the study. The study used the PHQ-933 to investigate the prevalence of depression, the DASS-2134 to investigate depression, anxiety, and stress, and the World Health Organization Quality of Life-BREF (WHOQOL-BREF)35 to examine the factors associated with QOL. The study included a comprehensive analysis of sociodemographic factors, lifestyle characteristics, and mental health problems to better elucidate the contextual factors that contribute to mental health problems and QOL in Thailand after the COVID-19 outbreak.
Map of Thailand depicting Bangkok, Chiang Rai, Chon Buri, Tak, Nakhon Si Thammarat, and Khon Kaen using QGIS.
Data were gathered through an online self-administered questionnaire via a Google Form link and email, specifically designed for this study. We collected data from a sample residing in Bangkok, Chiang Rai, Chon Buri, Tak, Nakhon Si Thammarat, and Khon Kaen provinces in Thailand. The questionnaire included sections on participants’ sociodemographic and socioeconomic characteristics, such as gender, age, marital status, education, occupation, monthly income, job loss, income loss, social media use, underlying diseases, type of housing, living alone, housing setting, and risk behaviors, including smoking and alcohol consumption. It also covered the history of COVID-19 infection, including reinfection and whether family members had died from COVID-19, as well as mental health aspects like depression, stress, and anxiety, and quality of life, including physical health, mental health, social relationships, and environmental health.
Out of 1,200 distributed questionnaires, 1,133 were filled out and returned, yielding a response rate of 94.4%. A total of 1,133 participants met the inclusion criteria (see Supplementary Information File): (1) Thai nationality, (2) age 20 years and above, (3) residing in Bangkok, Chiang Rai, Chon Buri, Tak, Nakhon Si Thammarat, and Khon Kaen provinces for at least six months, and (4) voluntary participation. No participants met the exclusion criteria, which were: (1) inability to access the internet, and (2) illiteracy.
Depression: The Thai version of the PHQ-9 was used to assess depression symptoms among the participants36. The PHQ-9 is a commonly utilized and validated instrument comprising nine questions that align with the diagnostic criteria for major depressive disorder. Each question is rated on a 4-point Likert scale, from 0 (not at all) to 3 (nearly every day), where higher scores signify greater severity of depressive symptoms. The total scores range from 0 to 27, with higher overall scores reflecting more severe depressive symptoms33. PHQ-9 scores indicating levels of depression symptoms are as follows: no symptoms (less than 5 points), mild (5–9 points), moderate (10–14 points), moderately severe (15–19 points), and severe (20 points or more). A cut-off point of 10 points or more indicates the presence of depressive symptoms (moderate, moderately severe, and severe cases) 37. Cronbach’s α was employed to assess the internal consistency of the questionnaire, with acceptable values ranging from 0.70 to 0.9538. The value obtained in this study was 0.83.
The 21-item DASS-21 was developed by Lovibond and Lovibond (1995)39 and was translated into Thai40. The DASS-21 has been widely validated and applied in numerous studies worldwide 34 to assess the emotional states of participants in three mental health domains: depression (7 items), anxiety (7 items), and stress (7 items). Each domain is rated on a 4-point scale: “did not apply to me at all” (0 points), “applied to me to some degree or some of the time” (1 point), “applied to me to a considerable degree or a good part of the time” (2 points), and “applied to me very much or most of the time” (3 points). The final scores for each mental health symptom were calculated by summing the relevant items and then doubling the total. The severity levels were categorized as follows: depression (normal: 0–9; mild: 10–13; moderate: 14–20; severe: 21–27; extremely severe: 28 or more); anxiety (normal: 0–7; mild: 8–9; moderate: 10–14; severe: 15–19; extremely severe: 20 or more); and stress (normal: 0–14; mild: 15–18; moderate: 19–25; severe: 26–33; extremely severe: 34 or more). Participants were categorized as normal, mild, moderate, severe, or extremely severe for each symptom based on their scores. Those who scored in the mild to extremely severe range were classified as having mental health symptoms. In this study, Cronbach’s α was 0.91.
Quality of Life: The WHOQOL-BREF questionnaire was utilized to assess the participants’ QOL35. The WHOQOL-BREF is a reliable and validated instrument consisting of 26 items that cover four domains: physical health, psychological well-being, social relationships, and environmental factors. The questionnaire includes three negative questions and 23 positive questions. Positive questions are rated on a 5-point Likert scale: “never” (1 point), “rarely” (2 points), “sometimes” (3 points), “often” (4 points), and “always” (5 points). Negative questions (items 2, 9, and 11) are also rated on a 5-point Likert scale but in reverse: “never” (5 points), “rarely” (4 points), “sometimes” (3 points), “often” (2 points), and “always” (1 point). The total scores for physical health are segmented into three levels: poor (7–16 points), moderate (17–26 points), and good (27–35 points). Mental health scores are categorized into three levels: poor (6–14 points), moderate (15–22 points), and good (23–30 points). Social relationship scores are divided into three levels: poor (3–7 points), moderate (8–11 points), and good (12–15 points). Environmental health scores are segmented into three levels: poor (8–18 points), moderate (19–29 points), and good (30–40 points). Overall quality of life is categorized into three levels: poor (26–60 points), moderate (61–95 points), and high (96–130 points). The Thai version of the WHOQOL-BREF used in the study was completely developed by the World Health Organization (WHO) and is freely available 41. Cronbach’s α in this study was 0.92.
The WHO estimates a 25% rise in the global rates of anxiety and depression since the onset of the COVID-19 pandemic11,42. In Thailand, the prevalence is similar to the global rate, approximately 25%8. A prior sample size calculation conducted using G*Power software (version 3.1) considered a moderate effect size (Cohen’s f = 0.25) with a margin of error of 0.035, a power of 0.80, and a significance level of 0.05. Based on these parameters, the estimated sample size needed to assess the prevalence of mental health and its associated factors was approximately 600 participants.
Descriptive statistics, such as frequencies, percentages, means, and standard deviations, were employed to summarize the sociodemographic characteristics of participants as well as their scores related to mental health and quality of life. The chi-squared (χ2) test was utilized to compare mental health and factors associated with quality of life. Additionally, univariate logistic regression analysis was conducted to further examine these associations and identify independent factors associated with depression43. The odds ratios along with their corresponding 95% confidence intervals (CIs) were preserved. Variables demonstrating p-values < 0.05 in the univariate analysis were retained and incorporated into a multiple logistic regression analysis. This analysis yielded adjusted odds ratios (AORs) after accounting for potential confounding factors, encompassing sociodemographic, socioeconomic, and lifestyle characteristics. Variables with p-values < 0.05 were considered statistically significant. SPSS (version 28; IBM Corp., Armonk, NY, USA) was used to perform all statistical analyses.
The study was conducted in accordance with the guidelines of the Declaration of Helsinki. Before the commencement of this study, ethical approval was granted by the Human Research Ethics Committee of Chulabhorn Royal Academy, Thailand (EC-054/2565). Additionally, the study received approval from the general community. All of the participants in the study provided signed informed consent before completing the questionnaires.
A total of 1,133 participants completed the online questionnaire and were included in the analysis. The mean age was 35.1 ± 17.2 years; the standard deviation ranged from 20 to 88 years. The sociodemographic characteristics of the participants are presented in Table 1. Most of the individuals were women (70.3%). Overall, 66.8%, 21.4%, and 11.8% of individuals were aged 20–40 years, 41–60 years, and over 60 years. Most of the individuals were in Bangkok (Center) (33.4%), followed by Khon Kaen (Northeastern) (27.2%), Chiang Rai (Northern) (14.7%), Chon Buri (Eastern) (10.7%), Nakhon Si Thammarat (Southern) (9.7%), and Tak (Western) (4.3%). Most of the individuals were unmarried (65.4%), had a bachelor’s degree (66.3%), and earned between 10,000 and 30,000 THB monthly (42.6%). Approximately 84.4% of individuals experienced job loss during the COVID-19 pandemic, whereas 47.2% faced income loss. Approximately 53.8% of individuals used social media for 0–6 h daily, and 74.5% did not have an underlying disease. Roughly 85.5% of individuals did not smoke, and 51.2% consumed alcohol. Among the participants, 66.5% lived in urban areas, and 33.5% lived alone. Approximately 54.5% were previously diagnosed with and 6.1% experienced reinfection. Moreover, 3.7% had lost family members to COVID-19 infection. The prevalence of depression as determined by the PHQ-9 was 19.4%. Among these individuals, 50.8% exhibited no symptoms, whereas 29.8% showed mild symptoms. According to the DASS-21, the prevalences of depression, anxiety, and stress were 32.4%, 45.4%, and 24.1%, respectively. QOL across various dimensions (physical, mental, social, and environmental) varied from poor to good, with significant proportions falling into the moderate-to-good range (98.8%, 97.2%, 98.1%, and 97.4% for physical, mental, social, and environmental dimensions, respectively).
The responses to the questionnaire about depression based on the PHQ-9 revealed that 64.9% of the respondents experienced trouble sleeping or falling/staying asleep. Approximately 61.5% felt little interest or pleasure in performing activities, and 55.0% felt tired or had limited energy. Overall, 55.3% of the respondents experienced poor appetite or overeating, and 49.0% experienced trouble concentrating on activities such as reading the newspaper or watching television (Table 2).
The responses to the questionnaire about depression based on the DASS-21 revealed that 51.2% of the respondents felt unable to be enthusiastic about anything; 48.2% also felt downhearted and sad. In total, 43.7% felt that they did not have anything to which to look forward (Table 3).
The DASS-21 for anxiety showed that 55.0% of the respondents were aware of dryness of the mouth, 47.1% were worried about situations in which they might panic and “make fools of themselves,” and 41.7% were aware of a change in the heart rhythm (e.g., heart rate increase, heart missing a beat) in the absence of physical exertion (Table 3).
The DASS-21 for stress showed that 56.5% of the respondents were rather irritable, 54.2% found it difficult to relax, and 53.7% tended to overreact (Table 3).
The responses to the questionnaire about depression based on the WHOQOL-BREF revealed that 7.8% experienced extreme pain, 5.6% experienced an extreme amount of negative feelings, 3.8% experienced poor sleep, 3.8% experienced having no satisfaction with sex, and 3.4% experienced no satisfaction from having financial resources (Table 4).
A notable association was observed between the prevalence of depression and risk factors. Multiple logistic regression analysis showed that adults aged over 60 years had an approximately 80% lower risk of depression compared with those aged 20–40 years (AOR = 0.20, 95% CI [0.08–0.51]; Table 5). Divorced people had an approximately 3.4-time higher risk of depression compared with unmarried people (AOR = 3.42, 95% CI [1.56–7.51]). Moreover, non-government employees had an approximately 61% lower risk of developing depression compared with the unemployed (AOR = 0.39, 95% CI [0.17–0.88]). The use of over 6 h of social media daily was associated with an approximately 1.6-time higher risk of depression compared with 0–6 h of social media use (AOR = 1.59, 95% CI [1.14–2.23]). Smokers had an approximately 1.5-time higher risk of depression compared with non-smokers (AOR = 1.53, 95% CI [1.14–2.23]). Having family members die from COVID-19 infection was associated with an approximately 2.3-time higher risk of depression compared with not having family members die from COVID-19 (AOR = 2.27, 95% CI [1.13–4.52]).
A notable association was observed between risk factors and the prevalence of depression, anxiety, and stress. Multiple logistic regression analysis showed that adults aged over 60 years had a roughly 50–62% lower risk of depression (AOR = 0.42, 95% CI [0.23–0.78]), anxiety (AOR = 0.38, 95% CI [0.25–0.57]), and stress (AOR = 0.44, 95% CI [0.27–0.98]) compared with those aged 20–40 years (Table 6). Divorced people had an approximately 2.4-time higher risk of stress compared with unmarried people (AOR = 2.44, 95% CI [1.22–4.87]). Those with a bachelor’s degree had an approximately 1.5-time higher risk of anxiety compared with those with a lower education (below bachelor’s degree; AOR = 1.48, 95% CI [1.03–2.11]). Job loss was associated with an approximately 1.5-time higher risk of depression (AOR = 1.45, 95% CI [1.01–2.11]) and anxiety (AOR = 1.50, 95% CI [1.03–2.19]) compared with no job loss. Income loss was associated with an approximately 1.5-time higher risk of anxiety compared with no income loss (AOR = 1.51, 95% CI [1.12–2.03]). The use of social media for over 6 h daily was associated with an approximately 1.4–1.7-time higher risk of depression, anxiety, and stress compared with 0–6 h of use (AOR = 1.36, 95% CI [1.03–1.80], AOR = 1.67, 95% CI [1.28–2.17], and AOR = 1.53, 95% CI [1.13–2.07], respectively). Smokers had an approximately 1.5-time higher risk of stress compared with non-smokers (AOR = 1.50, 95% CI [1.01–2.24]). Alcohol consumption was associated with an approximately 1.3–1.5-time higher risk of depression (AOR = 1.36, 95% CI [1.03–1.80]), anxiety (AOR = 1.31, 95% CI [1.02–1.68]), and stress (AOR = 1.48, 95% CI [1.09–2.02]) compared with no alcohol consumption.
A notable association was observed between QOL and risk factors. Multiple logistic regression analysis showed that adults aged over 60 years had approximately 50% fewer social relationships compared with those aged 20–40 years old (AOR = 0.52, 95% CI [0.30–0.90]; Table 7). Married people had approximately 1.4 times the level of good QOL from an environmental perspective compared with unmarried people (AOR = 1.43, 95% CI [1.01–2.03]). People with higher education (above bachelor’s degree) had approximately two times better QOL from physical health (AOR = 2.00, 95% CI [1.14–3.49]) and environmental (AOR = 2.01, 95% CI [1.17–3.44]) perspectives compared with those lower education (below bachelor’s degree). Non-governmental (AOR = 2.57, 95% CI [1.25–5.26]) and government (AOR = 2.65, 95% CI [1.24–5.67]) employees had approximately 2.6–2.7 times the level of good QOL based on physical health compared with the unemployed. Job loss was associated with a 43% poor environmental perspective compared with no job loss (AOR = 0.57, 95% CI [0.38–0.85]). Based on the PHQ-9, people with depression had roughly 38–60% lower QOL in all domains except for social relationships (physical health, AOR = 0.40, 95% CI [0.24–0.67]; psychological well-being, AOR = 0.43, 95% CI [0.27–0.71]; and environmental factors, AOR = 0.62, 95% CI [0.40–0.96]) compared with those without depression. Depression based on the DASS-21 was associated with roughly 50% lower QOL in all domains (physical health, AOR = 0.45, 95% CI [0.29–0.69]; psychological well-being, AOR = 0.21, 95% CI [0.14–0.33]; social relationships (AOR = 0.50, 95% CI [0.34–0.73]), and environmental factors (AOR = 0.46, 95% CI [0.31–0.69]) compared with no depression. Anxiety based on the DASS-21 was associated with a roughly 30% lower QOL in all domains except psychological well-being (physical health, AOR = 0.50, 95% CI [0.35–0.70]; social relationships, AOR = 0.71, 95% CI [0.51–0.98]; and environmental factors, AOR = 0.71, 95% CI [0.51–0.99]) compared with no anxiety. Stress based on the DASS-21 was associated with roughly 37% lower overall QOL compared with no stress (AOR = 0.63, 95% CI [0.41–0.96]).
To the best of our knowledge, this was the first cross-sectional study to assess the mental health problems, including depression, anxiety, and stress across all representative regions in Thailand after the COVID-19 outbreak. This study investigated the association between mental health problems and QOL among Thai individuals following the COVID-19 outbreak. The study provides valuable insights into the mental health challenges faced by the Thai population and sheds light on the relationship between mental health and overall QOL in the post-pandemic context in Thailand.
The prevalence of depression among Thai individuals as assessed by the PHQ-9 was 19.4%, similar to that in other studies conducted before, during, and after the COVID-19 pandemic, including a prevalence of 12.0% in the District of Columbia, Puerto Rico, and the U.S. Virgin Islands, and 19.5% in Korea44,45. Among respondents who were depressed, 50.8% exhibited no symptoms, and 29.8% displayed mild symptoms. A comparison of these findings with data from previous studies indicates potential shifts in mental health after the COVID-19 pandemic compared with during the COVID-19 outbreak14. Some studies have shown a lower prevalence of depression (approximately 10%) during the COVID-19 pandemic compared with that in our study9. Other studies have indicated that older adults were more significantly affected than the general population, with approximately 50% of older adults experiencing depression during the COVID-19 outbreak16,46. The prevalence of depression indicates that a significant proportion of individuals experienced mild symptoms. This finding is consistent with those of a similar study in the United States47 and underscores the substantial impact of the COVID-19 outbreak on mental health in Thailand. The pandemic, which was characterized by widespread fear, uncertainty, and social disruption, likely contributed to increased rates of depression among the population. These findings align with those of previous studies that have highlighted the adverse psychological consequences of pandemics and other public health emergencies. This study is expected to inform mental health practitioners who seek to develop effective strategies such as early screening of at-risk populations to mitigate the negative effects of depression during and after the COVID-19 pandemic.
As indicated by the responses to the PHQ-9 regarding depression, our study findings provide notable insights into the mental health challenges faced by Thai individuals following the COVID-19 outbreak. Specifically, similar to the findings of previous studies35,36, a considerable proportion of respondents reported experiencing sleep disturbances (64.9%), diminished interest or pleasure in activities (61.5%), feelings of fatigue or low energy (55.0%), changes in appetite (55.3%), and difficulty concentrating on tasks (49.0%). Comparing these results with findings from previous studies on mental health in Thailand, particularly pre-COVID-19, reveals potentially significant shifts in patterns of mental well-being48. Although prior research has shown similar trends in some aspects of depression such as sleep disturbances and fatigue, the magnitude of these issues post COVID-19 appears to be notably higher49, suggesting that the pandemic exacerbated existing mental health challenges among the Thai population.
The study presents findings on the association between depression and various risk factors and suggests that adults over 60 years have a significantly lower risk of depression compared with those aged 20–40 years. This finding aligns with those of previous studies that have indicated a decrease in the prevalence of depression among older adults50,51. Older age is often associated with increased resilience, adaptation to stressors, and better coping mechanisms, all of which may contribute to a lower risk of depression. In contrast, older adults exhibit higher rates of depression than younger adults in many studies52,53. Divorced individuals had a significantly higher risk of depression compared with unmarried people. This finding is consistent with those of numerous prior studies that have highlighted the impact of marital dissolution on mental health54,55. Divorce can lead to feelings of loneliness, loss, and reduced social support, all of which are associated with an increased risk of depression. Unemployed individuals had a higher risk of depression compared with non-government employees. This finding aligns with the results of previous studies that have identified unemployment as a significant risk factor for depression44,56. However, accounting for factors such as job stability, satisfaction, and social support among the unemployed is essential. Excessive use of social media (over 6 h daily) was associated with a higher risk of depression compared with moderate usage (0–6 h daily). This result is consistent with the findings from a growing body of literature that links heavy social media use to adverse mental health outcomes57. Excessive social media consumption can lead to social comparison, cyberbullying, sleep disturbances, and reduced real-life social interactions, all of which may contribute to depressive symptoms. Smokers had a higher risk of depression compared with non-smokers. This finding is consistent with the results of previous research that suggests a bidirectional relationship between smoking and depression 58,59. Smoking may be a coping mechanism for individuals who experience depressive symptoms but can exacerbate mood disorders through physiological and psychological mechanisms. Individuals who had lost family members to COVID-19 infection had a higher risk of depression compared with those who had not experienced such losses. This finding is in line with the results of emerging research that highlights the COVID-19 pandemic’s profound psychological impact—including grief, bereavement, and increased rates of mental health disorders such as depression60,61.
Additionally, according to the DASS-21, the prevalences of depression, anxiety, and stress were 32.4%, 45.4%, and 24.1%, respectively, similar to those of other studies conducted during the COVID-19 pandemic62,63. In addition to the results of previous studies, the prevalence of depressive, anxiety, and stress symptoms among Thai people remained high after the COVID-19 outbreak8. The elevated rates of depression, anxiety, and stress observed among the Thai population compared with pre-pandemic levels underscore the multifaceted impact of the pandemic on mental well-being64. The overall trends indicate a substantial impact on mental health and emphasize the need for interventions targeting lifestyles, including those addressing alcohol consumption and smoking and support systems to address these issues. Further longitudinal studies may provide insights into the persistence and trajectory of these mental health challenges over time.
We revealed several findings regarding the association between mental health (depression, anxiety, and stress) and various risk factors. In our study, adults aged over 60 years exhibited a significantly lower risk of depression, anxiety, and stress compared with those aged 20–40 years65. A previous study did not include a direct comparison of age groups, but its results are consistent with the notion that older age may be a protective factor against mental health issues66. In this study, divorced individuals had a substantially higher risk of stress compared with unmarried individuals. These findings are consistent with those of a previous study that has addressed marital status as a risk factor for stress; this finding underscores the importance of considering social support and relationship status in understanding mental health outcomes67. Moreover, we observed that individuals with a bachelor’s degree had a higher risk of anxiety compared with those with lower levels of education. This result is consistent with the finding in a previous study that assessed anxiety by education level; the finding suggests a potential link between higher education and increased anxiety levels that could be further explored in future research68,69. Consistent with the findings of previous studies70,71, job loss was associated with a higher risk of depression and anxiety. The excessive use of social media (over 6 h daily) was associated with a higher risk of depression, anxiety, and stress. Similarly, previous studies have investigated the association between social media use and mental health outcomes, making this a finding that highlights the potential impact of digital technology on mental well-being72,73. In this study, both smoking and alcohol consumption were associated with an increased risk of stress and other mental health issues— similar to the results of other studies74,75. This finding underscores the importance of addressing substance abuse in mental health interventions. The DASS-21 is used for depression screening76, while the PHQ-9 is a reliable and valid measure of depression severity77. Additionally, the DASS-21 has been reported to have a sensitivity of 69.2% and a specificity of 75.5%78, while the PHQ-9 has shown the highest sensitivity (100%) and specificity (84%)79. A previous study using the PHQ-9 found that individuals who had lost family members to COVID-19 exhibited increased severity of depression60. Our study showed similar results, with a significant association between moderate to severe depression (as measured by the PHQ-9) and individuals who had lost family members to COVID-19, but no significant association when using the DASS-21.
Furthermore, the analysis of QOL across various dimensions revealed variability from poor to good, with substantial proportions falling into the moderate-to-good range (physical 98.8%, mental 97.2%, social 98.1%, and environmental 97.4%), similar to findings in other studies80,81. Whereas most individuals reported moderate-to-good QOL across these dimensions, the distribution highlights the need for interventions targeting lifestyles to address areas of concern and support overall well-being in the post-pandemic era80,82. To facilitate the development of effective strategies that can mitigate adverse effects and promote resilience within the Thai community, continued monitoring and research are essential for tracking changes in mental health and QOL trends over time.
This study highlights significant associations between QOL and various risk factors. Individuals in the study aged over 60 years old had fewer social relationships compared with those aged 20–40 years, similar to the result of previous studies83,84. This finding may have implications for understanding the social dynamics and support systems across age demographics. Married individuals exhibited a better QOL from an environmental perspective compared with unmarried individuals. Similarly, a previous study suggested that marital status influences QOL, emphasizing the importance of supportive relationships in enhancing well-being85. People with higher education levels (above bachelor’s degree) demonstrated better QOL in terms of physical health and environmental perspectives compared with those with lower education levels (below bachelor’s degree). This finding aligns with the notion that higher education is associated with better health outcomes and overall QOL, as suggested in previous studies86,87,88. Non-government employees and government employees had better QOL associated with physical health compared with the unemployed, similar to the findings of previous studies89,90. Losing a job was associated with a poor environmental perspective in this study. This finding is consistent with that of a previous study that indicated that unemployment or job loss can negatively impact various aspects of QOL including the environment91. Individuals experiencing job loss may face social stigma or reduced social networks, which can impact their sense of identity and belonging within environmental communities. Individuals with depression as measured by PHQ-9 and DASS-21 exhibited significantly lower QOL across all domains compared with those without depression, similar to the findings of previous studies92,93. Correspondingly, individuals with anxiety and stress as measured by DASS-21 showed lower QOL across domains compared with those without these conditions, consistent with the results of a previous study94. These findings underscore the profound impact of mental health on overall QOL and further support the emphasis of previous studies on the association between mental health and QOL. Our study revealed a strong negative association between mental health problems and all QOL domains including physical health, psychological well-being, social relationships, and environmental factors. The negative impact of mental health problems on psychological well-being, social relationships, and environmental factors highlights the broader psychological and social consequences of the condition. This study reinforces and expands upon previous findings regarding the association between QOL and various risk factors including demographic, socioeconomic, and mental health factors. Understanding these associations is crucial for developing targeted interventions that are aimed at improving overall well-being and enhancing QOL across diverse populations in the post-pandemic context.
In addition to mental health interventions, a multidimensional approach is needed to improve QOL among Thai individuals. Efforts should be directed toward enhancing physical health, promoting psychological well-being, fostering social connections, and improving environmental factors. Initiatives may include providing accessible and affordable healthcare services, promoting healthy lifestyle behaviors, strengthening social support networks, and creating supportive physical and social environments. The study’s findings contribute to the existing literature on mental health problems and QOL, particularly in the context of the post COVID-19 pandemic.
The study had several limitations. First, the use of stratified random sampling limited to the country’s attractive localities using the proportion allocation method may limit the generalizability of the findings to the entire Thai population. Second, the cross-sectional design of the study precludes establishing causal relationships between mental health and QOL. Longitudinal studies are warranted to provide a more comprehensive understanding of the dynamic relationship between these variables over time. Qualitative studies could provide in-depth insights into the lived experiences of individuals with depression and further elucidate the specific factors that influence QOL in the post-pandemic context. Third, the self-report nature of the data introduces the potential for response bias. Fourth, most of the participants are women, so the results may be less representative of all sexes. Fifth, the sample could not represent individuals at the national level because it was drawn from residents of Bangkok, Chiang Rai, Chon Buri, Tak, Nakhon Si Thammarat, and Khon Kaen provinces, which are attractive localities for tourists and representative of each region with high population density. Finally, the generalizability of the findings may be limited to those who have access to the internet, are wealthier, or are better educated because the study used online platforms for data collection.
In conclusion, this study highlights the significant burden of mental health problems among Thai individuals after the COVID-19 outbreak and its strong negative association with QOL. The results highlight the necessity of enhancing QOL through specific actions and mental health assistance to tackle depression, anxiety, and stress, especially among smokers and alcohol consumers and those who have lost jobs and income and those who have had family members die from COVID-19 infection. Efforts should not solely concentrate on mental health but also on the enhancement of physical well-being, social connections, and environmental conditions to improve overall QOL. This study is expected to inform policymakers, healthcare professionals, and mental health practitioners who seek to develop effective strategies such as early screening of at-risk populations to mitigate negative mental health effects and enhance QOL among Thai individuals who were affected by the COVID-19 pandemic.
Data is provided within the supplementary information file.
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This research project is supported by Chulabhorn Royal Academy (Project code ISF05-009/2565). We thank Anahid Pinchis from Edanz (www.edanz.com/ac) for editing a draft of this manuscript.
This research project is supported by Chulabhorn Royal Academy (Contract No. ISF05-009/2565).
Princess Srisavangavadhana College of Medicine, Chulabhorn Royal Academy, Bangkok, 10210, Thailand
Wiriya Mahikul & Pisinee Narayam
School of Health Science, Mae Fah Luang University, Chiang Rai, 57100, Thailand
Peeradone Srichan
Department of Medical Science, School of Medicine, Walailak University, Nakhon Si Thammarat, 80160, Thailand
Udomsak Narkkul
Faculty of Public Health, Burapha University, Chonburi, 20131, Thailand
Ingfar Soontarawirat
Department of Community Medicine, Faculty of Medicine, Chiang Mai University, Chiang Mai, 50200, Thailand
Amornphat Kitro
Environmental and Occupational Medicine Excellent Center, Faculty of Medicine, Chiang Mai University, Chiang Mai, 50200, Thailand
Amornphat Kitro
One Health Research Unit, Mahasarakham University, Maha Sarakham, 44000, Thailand
Natapol Pumipuntu
Faculty of Veterinary Sciences, Mahasarakham University, Maha Sarakham, 44000, Thailand
Natapol Pumipuntu
Faculty of Public Health, Thammasat University, Lampang, 25190, Thailand
Sayambhu Saita
Research Unit in One Health and Ecohealth, Thammasat University, Thammasat University, Pathum Thani, 12120, Thailand
Sayambhu Saita
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Conceptualization: W.M.; Data curation: all authors.; Formal analysis: W.M. and P.N.; Funding acquisition: W.M.; Investigation: all authors.; Methodology: W.M.; Project administration: W.M.; Resources: W.M., P.S., U.N., I.S., A.K., N.P., S.S.; Software: W.M. and P.N.; Validation: W.M.; Visualization: W.M.; Writing–original draft: W.M.; Writing–review & editing: all authors.
Correspondence to Wiriya Mahikul.
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Mahikul, W., Srichan, P., Narkkul, U. et al. Mental health status and quality of life among Thai people after the COVID-19 outbreak: a cross-sectional study. Sci Rep 14, 25896 (2024). https://doi.org/10.1038/s41598-024-77077-3
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DOI: https://doi.org/10.1038/s41598-024-77077-3
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