










































AGORA International Journal of Economical Sciences, http://univagora.ro/jour/index.php/aijes 

ISSN 2067-3310, E-ISSN 2067-7669 

Vol. 18, No. 2 (2024), pp. 300-319 

 

300 

 

THE EXPERIENCE OF ILLNESS: EMOTIONAL AND ECONOMIC 

IMPACTS 

 

P. TERESIUTE, D. VASILIAUSKAITE 

 

Paulina Teresiute1, Deimante Vasiliauskaite2 

1Faculty of Medicine, Vilnius University, Lithuania 

https://orcid.org/0009-0000-9152-5715 E-mail: paulina.teresiute@mf.stud.vu.lt  
2Business School, Vilnius University, Lithuania 

https://orcid.org/0000-0002-8107-1964 E-mail: deimante.vasiliauskaite@vm.vu.lt   

 

Abstract: This study examines the complex emotional and economic consequences of illness, 

focusing on individuals' subjective experiences and their broader societal impact. Employing 

a multi-level methodological approach, it combines theoretical insights with wavelet transform 

analysis to investigate long-term sickness trends in the UK. The findings highlight a significant 

increase in economically inactive individuals due to long-term sickness, driven by factors such 

as mental health conditions, chronic diseases, and the COVID-19 pandemic. The emotional 

aspects of illness are explored through their influence on relationships, personal identity, and 

societal attitudes, underscoring the necessity of patient-centered care that addresses both 

psychological and social needs. The economic analysis reveals the substantial burden of illness 

manifested in decreased labor force participation, rising healthcare costs, and productivity 

losses. Wavelet transform analysis captures both short-term variations and long-term patterns, 

offering valuable insights into the dynamic, non-stationary nature of sickness-related economic 

inactivity. This research emphasizes the critical need for proactive healthcare policies, 

workplace accommodations, and robust mental health support to reduce the adverse effects of 

illness on individuals and economies. By integrating advanced analytical methods and 

interdisciplinary perspectives, the study enhances our understanding of the intricate 

connections between illness, emotional health, and economic resilience. 

Keywords: economic impact of illness; emotional impact; experience of illness. 

 

INTRODUCTION 

The world is facing a persistent global mental health crisis exacerbated by the COVID-

19 pandemic, rising living costs, and increasing global challenges. These factors have 

intensified pre-existing issues and created more significant uncertainty for many. According to 

the World Health Organization, approximately 15% of the global working-age population 

experiences mental illness, impacting not only individual well-being and relationships but also 

the workplace. Depression and anxiety are estimated to cost the global economy $1 trillion 

annually, equating to 12 billion lost workdays. A recent Deloitte study highlights the significant 

mental health challenges faced by Gen Z and millennials, who comprise a large portion of the 

workforce. The research found that 40% of Gen Zs and 35% of millennials report feeling 

stressed or anxious most of the time, with nearly half experiencing workplace burnout. 

https://orcid.org/0009-0000-9152-5715
mailto:paulina.teresiute@mf.stud.vu.lt
https://orcid.org/0000-0002-8107-1964
mailto:deimante.vasiliauskaite@vm.vu.lt


Paulina TERESIUTE, Deimante VASILIAUSKAITE 

 

301 

 

The experience of illness is a highly relevant topic with profound implications for 

healthcare, policy, and society. Exploring this subject can deepen our understanding of how 

illness affects individuals physically, emotionally, and socially beyond what traditional 

biomedical models capture. It can also highlight the diversity of personal responses to illness 

and the role of cultural, psychological, and social factors in shaping those experiences.  

From a scientific perspective, studying the experience of illness can lead to improved 

patient-centered care, as it helps healthcare professionals recognize and respond to patients' 

unique needs and perspectives. It can inform interventions that address not only the symptoms 

but also the psychosocial aspects of illness, improving overall quality of life for patients. 

The experience of illness is a multifaceted phenomenon encompassing many personal, 

social, and cultural dimensions. It is not merely about the physical symptoms but also involves 

the individual's subjective experience, which includes changes in self-identity, time perception, 

and existential meaning, as highlighted by Havi Carel's phenomenological analysis (Carel, 

2015). The diagnosis process plays a crucial role in shaping the illness experience, as it involves 

navigating complex power dynamics and social interactions that can significantly impact how 

patients perceive and manage their conditions (Jeske et al., 2023). Moreover, the experience of 

illness is deeply intertwined with the search for meaning as individuals and caregivers grapple 

with questions about the purpose of life amidst suffering. This search for meaning can lead to 

either despair or a heightened sense of awareness and responsibility, serving as a therapeutic 

resource (Bruzzone, 2021). The social construction of illness further complicates this 

experience, as it is influenced by cultural norms and societal values, which shape how 

individuals interpret and respond to their conditions (Skrzypek, 2014). Illness narratives, which 

are personal stories about living with illness, provide valuable insights into these experiences, 

though they must be critically examined for their epistemological properties to ensure they 

accurately reflect the realities of the individuals' experiences (Lucius-Hoene et al., 2018). 

Additionally, the patient experience is a complex construct that includes determinants such as 

the quality of healthcare services and the politics of healthcare, which influence patient 

satisfaction and engagement (Zakkar, 2019). Overall, understanding the experience of illness 

requires a holistic approach that considers the interplay of personal, social, and systemic 

factors, as well as the narratives and meanings individuals ascribe to their experiences (Locock 

et al. (2017), Cipolletta, (2020), Palmeira & Gewehr (2018)). 

The experience of illness is a profoundly personal and often life-altering journey, 

shaped by a range of physical, emotional, and psychological challenges. Unlike clinical 

definitions that focus on biological abnormalities and diagnostic criteria, the experience of 

illness is subjective, encompassing how individuals perceive, interpret, and react to their health 

conditions. This viewpoint enlightens the lived reality of patients, revealing how illness 

interrupts daily routines, reshapes identities, and affects relationships (Shrout et al., 2024). 

Studying the experience of illness uncovers symptoms and treatments and the intricate 

connections between the body, mind, and society. While healthcare professionals focus on 

diagnosis and treatment, the experience of illness highlights how individuals interpret their 

suffering and adapt to the changes it brings. This process involves many emotions—from fear 

and uncertainty to resilience and acceptance—and emphasizes the significant influence of 

culture, family, and personal beliefs on how illness is understood. Acknowledging the 



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experience of illness is crucial for healthcare providers, as it bridges the divide between clinical 

care and patient-centered support, promoting empathy and comprehensive approaches that 

consider both medical and personal dimensions. 

This paper aims to identify theoretical aspects of the emotional and economic impacts 

of illness and create a methodology to value the effect of COVID-19 on illness and stress.  

This paper is organized as follows: Section 2 gives insights into the theoretical basis of 

understanding illness's emotional and economic impact. Section 3 analysing the research 

methodology focusing on the idea of wavelet transformation. Section 4 describes the results 

and discussion, and finally, we present our conclusions and recommendations. 

 

LITERATURE REVIEW  

Emotional impact 

Illness significantly impacts relationships, often transforming them in complex ways. 

Long-term illness can lead to positive and negative social network changes, as individuals may 

experience increased support from family and friends. At the same time, some relationships 

may become distant or dissolve due to the strain of illness. Long-term illnesses can lead to 

shifts in social networks, with some relationships becoming more supportive while others 

diminish, reflecting the complex nature of social support during illness (Espvall & Dellgran, 

2016). Chronic illness, mainly when concealable, can challenge couples' communication and 

relational well-being, often resulting in lower satisfaction and increased burden. However, 

when couples engage in open communication and dyadic coping strategies, they can enhance 

their relational satisfaction and closeness (Shrout et al., 2023). Emotionally focused therapy 

has been shown to help couples enrich their emotional quality, allowing them to express deep-

seated emotions and meet each other's needs despite the challenges posed by chronic illness 

(Chawla & Kafescioglu, 2012). The stress of illness can also lead to role changes within 

relationships, where partners may need to take on additional responsibilities, potentially 

leading to feelings of resentment and grief on both sides (Campling & Sharpe, 2008). 

Communication-debilitating illnesses further complicate relationships by necessitating 

adjustments in how individuals interact with loved ones, often requiring new strategies to 

maintain relational bonds (Bute et al., 2007). In young adults, chronic illness can disrupt 

normative relationship development, leading to insecure attachments and lower relationship 

satisfaction compared to healthy peers (Cole & Karantzas, 2006). Family dynamics are also 

affected, as serious illness can lead to protective behaviors that isolate family members from 

one another when mutual support is most needed. Chronic illness in children also affects family 

dynamics, with older children experiencing weaker family relationships, although these 

relationships can be strengthened by better health and reduced anxiety or depressive symptoms 

(Cox et al., 2021). Despite these challenges, sharing personal narratives and life challenges can 

help individuals with chronic illnesses redefine their identities and foster healthier relationships 

with family and friends (Huang et al., 2018). Chronic illness profoundly transforms 

relationships, highlighting the resilience and adaptability of individuals and their social 

networks as they navigate evolving challenges and roles. 

Individuals experiencing illness frequently exhibit diverse emotional responses 

characterized by complexity and nuance. Common emotional responses include feelings of 



Paulina TERESIUTE, Deimante VASILIAUSKAITE 

 

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depression, anxiety, fatigue, and a desire to limit social interactions, collectively known as the 

sickness response, which is an adaptive behavior aimed at conserving energy and improving 

survival during illness (Lekander, 2022). This response is functionally similar to classical 

emotions like fear and can be exacerbated by low-grade inflammation, contributing to 

symptoms in both somatic and mental health contexts (Lekander, 2022). Emotional reactions 

to illness are not only individual but also relational, affecting family dynamics and manifesting 

in themes such as denial versus acceptance, despair versus hope, and isolation versus 

connection (McDaniel et al., 2000). The phenomenological perspective, as discussed by 

Madeira et al. (2019), introduces the concept of the "uncanny" in illness, where individuals 

experience a disconcerting shift in their sense of being, leading to feelings of fear and loss of 

control (Madeira et al., 2019). The severity of the illness does not always correlate with the 

intensity of psychological reactions, as even minor illnesses require lifestyle adjustments and 

can lead to significant emotional distress, including anxiety and depression (G. G. Lloyd, 

2007). A lack of empathy from others can further exacerbate the emotional burden of illness, 

leading to feelings of isolation and frustration (Havi, 2008). The author reflects on the 

emotional toll of experiencing rapid physical decline at a young age, noting the anxiety and 

dread that accompany each deterioration in health. This includes concerns about losing the 

ability to engage in once-manageable activities, leading to a shrinking world and a sense of 

helplessness (Havi, 2008). While fear and sadness are common, they are often normal 

emotional responses to the threat posed by illness, and understanding these emotions can aid 

in better patient adaptation and management (Bowman, 2001). The paper concludes that the 

traditional psychopathological paradigm, which primarily emphasizes anxiety and depression 

as indicators of patient reactions to acute illness, is insufficient due to the considerable 

variability in how these emotions are measured and interpreted across different populations. It 

calls for a shift in perspective, advocating for recognizing emotional responses to illness as 

normal reactions rather than pathological ones, and suggests that future research should adopt 

this approach to enhance the understanding of patient experiences. (Bowman, 2001) 

Individuals facing illness often experience a complex spectrum of emotions, including fear, 

sadness, anger, guilt, and isolation, alongside moments of hope, gratitude, and acceptance. 

These emotions, shaped by personal and social contexts, evolve and reflect the profound 

psychological impact of living with illness. 

Understanding the emotional responses to illness is crucial for improving patient care 

and support, as it directly influences patient outcomes and satisfaction. Recognizing and 

addressing patients' emotional reactions, such as fear, anger, sadness, joy, and compassion, can 

enhance the provider-patient relationship and facilitate better health outcomes (Naidorf (2024), 

Beale (2017). By recognizing patients' emotions, addressing their concerns, and involving them 

in treatment decisions, healthcare providers can enhance patient outcomes and create a more 

compassionate and collaborative healthcare environment (Naidorf, 2024). Emotional responses 

can profoundly influence a patient's capacity to understand and act on health information, 

frequently posing obstacles to accessing healthcare services (Beale, 2017). Integrating 

technology, including intelligent support systems and biofeedback sensors, offers valuable 

tools for evaluating and managing patients' emotional states, delivering emotional support and 

practical assistance in their daily lives (Maj et al., (2024), Wilson et al. (2016). The system will 



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facilitate communication between patients and doctors, allowing for preliminary diagnoses and 

tailored treatment plans based on collected data. It will also provide patients with health reports 

and treatment recommendations to aid their recovery (Maj et al., 2024). Moreover, 

incorporating psychotherapeutic teaching into medical education can enhance communication 

skills among healthcare providers, enabling them to understand better and address the 

emotional aspects of illness (Groves, 2015). Emotional support is vital to patient satisfaction 

but is frequently underemphasized in healthcare settings. By recognizing and implementing 

practical strategies for providing emotional support, healthcare organizations can enhance 

patient satisfaction and elevate the overall quality of care. (Adamson et al., 2012) 

Moreover, the emotional impact of illness and care, particularly in chronic conditions 

like advanced kidney disease, emphasizes the need for healthcare providers to acknowledge 

and address patients' emotional experiences. Neglecting these aspects can foster mistrust and 

isolation, adversely affecting the care experience (O’Hare et al., 2018). Patients often struggle 

with feelings of personal responsibility for their illness, commonly blaming themselves for 

their kidney disease and believing it was preventable. This tendency toward self-blame 

underscores the importance of healthcare providers addressing the emotional dimensions of 

chronic illness to enhance overall patient care and support (O’Hare et al., 2018). Illness requires 

adaptations in communication and roles, making effective management and open dialogue 

essential for sustaining and strengthening relationships in the face of challenges associated with 

chronic and mental health conditions (Bute et al., 2007). A comprehensive understanding of 

patients' emotional responses to illness can lead to more effective, empathetic, and patient-

centered care, ultimately enhancing the healing process and patient satisfaction (Picton, 2011). 

Addressing and understanding the emotional aspects of illness are crucial for improving patient 

care and satisfaction. By incorporating empathy, clear communication, innovative 

technologies, and comprehensive provider training, healthcare systems can develop a more 

compassionate, patient-focused approach that fully acknowledges the emotional impact of 

illness. This approach enhances the provider-patient relationship, supports better health 

outcomes, builds trust, and elevates the overall quality of care. 

 

Economic impact 

The economic impact of illness is multifaceted, affecting individuals, households, and 

broader economies in various ways. Mental health illnesses, for instance, impose a significant 

economic burden, as evidenced by studies from countries like Canada, where costs are 

projected to increase six-fold over the next 30 years, potentially exceeding A$2.8 trillion 

(Doran & Kinchin, 2017). In Nigeria, mental health issues are prevalent and pose a threat to 

economic stability without robust policy interventions (Owoeye, 2024).   

Chronic illnesses also have profound economic implications, particularly in low- and 

middle-income countries. In Nigeria, chronic illnesses lead to substantial direct and indirect 

costs, with many households spending over 10% of their income on health, often resorting to 

borrowing or selling assets to cope (Okediji et al., 2017). 

Similarly, catastrophic health expenditures in Korea are rising, especially among low-

income groups, highlighting the inadequacy of current health safety nets. Despite universal 

health coverage in Thailand, severe illness significantly increases out-of-pocket expenses and 



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reduces household labor income by nearly a third, forcing reliance on informal financial 

support (Neelsen et al., 2015). The impact of illness on employment is also notable; in 

Australia, illness-related early retirement results in significant income loss, increased 

government support payments, and reduced tax revenue, amounting to billions annually 

(Schofield et al., 2011). In rural India, adult illness reduces workforce participation and 

earnings, though households attempt to mitigate these effects through increased labor 

participation by non-sick members (Alam et al., 2018). Furthermore, illnesses like 

hypercholesterolemia contribute to the economic burden of cardiovascular diseases, with direct 

costs ranging significantly, underscoring the need for updated research and public health 

strategies (Ferrara et al., 2021). Overall, the economic impact of illness is substantial, 

necessitating comprehensive policy responses to mitigate these effects and support affected 

individuals and economies. 

Mental health illnesses contribute significantly to the economic burden across various 

countries, impacting both direct and indirect costs. In South America, mental health conditions, 

along with other noncommunicable diseases, are projected to cost approximately 7.3trillion 

from 2020 to 2050, equating to about 47.3 trillion from 2020 to 2050, equating to about 42.5 

trillion, with indirect costs, such as lost productivity, being particularly substantial (Ferranna 

et al., 2023). In developed countries, mental health issues account for around 4% of GDP, with 

significant productivity losses, as mental illness is prevalent among working-age populations 

(Frank, (2022), Doran & Kinchin (2020)). In Canada, the economic costs of mental illness are 

expected to increase six-fold over the next 30 years, highlighting the growing financial impact 

(Layard, 2016). In Germany, mental illnesses accounted for societal costs of 146 billion euros 

in 2015, representing 4.8% of GDP, with a significant portion attributed to direct health costs 

(Doran & Kinchin, 2017). The economic burden is not limited to direct healthcare expenses 

but extends to lost productivity, social exclusion, and reduced educational attainment, which 

are common across various regions, including Europe and Nigeria  (Lambert et al., (2023), 

Agboola et al. (2018)). Mental disorders such as depression, anxiety, and schizophrenia are 

particularly burdensome, necessitating increased investments in mental health care and cost-

effective interventions to mitigate these impacts (Turk & Albreht, 2010). The economic 

implications of mental health are profound, affecting individuals, families, and national 

economies, and underscore the need for comprehensive policy responses to address these 

challenges effectively (Razzouk, 2017). 

The economic costs associated with mental health illnesses are substantial and 

multifaceted, encompassing both direct and indirect expenses across various countries. Direct 

costs primarily include healthcare expenditures such as hospital stays, medication, and therapy, 

which in Germany alone amounted to 44.4 billion euros in 2015, representing a significant 

portion of the country's GDP (Lambert et al., 2023). Indirect costs, however, often surpass 

direct costs and include loss of productivity, unemployment, and social exclusion, which 

collectively contribute to a global financial burden estimated at 2.5 trillion annually, projected 

to rise to 2.5 trillion annually, projected to rise to 6 trillion by 2030 (Frank (2022), Sowers et 

al. (2019)). In the United States, mental illnesses affect about 20% of the population, with 

social costs growing more rapidly than healthcare costs, highlighting the broader societal 

impact (R. Frank & Glied, 2023). In South America, the macroeconomic burden of 



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noncommunicable diseases, including mental health conditions, is estimated to reduce GDP by 

about 4% over the period from 2020 to 2050 (Ferranna et al., 2023) (Ferranna et al., 2023). The 

indirect costs are further exacerbated by factors such as poverty, low educational levels, and 

gender inequity, which are prevalent in many regions (Razzouk, 2017). In Nigeria, the 

economic burden on households is significant, with many individuals unable to afford even 

essential goods due to the high costs associated with managing mental illnesses (Agboola et 

al., 2018). These costs burden individuals, families, and national economies, necessitating 

increased investments in mental health care and implementing cost-effective interventions to 

mitigate these impacts (Razzouk (2017), Martini & Attallah (2019). The complexity of 

estimating these costs is compounded by variations in definitions, populations studied, and 

incomplete data, yet improved methodologies are helping to provide a clearer picture of the 

economic impact of mental disorders (Trautmann et al., 2016). Overall, the economic costs of 

mental health illnesses are profound, affecting not just healthcare systems but also the broader 

economic and social fabric of societies worldwide.  

 

METHODOLOGY  

The methodologies used to estimate the economic burden of mental health illnesses 

vary significantly across different regions, reflecting diverse approaches and challenges. One 

common method is the human capital approach, which considers both direct costs, such as 

healthcare expenses, and indirect costs, including productivity losses due to disability or death 

(Frank, 2022). This approach is complemented by the value of the statistical life (VSL) method, 

which assesses the economic impact of mental health by evaluating trade-offs between risks 

and capital, similar to methods used for other major diseases like cardiovascular conditions 

(Frank, 2022). In Canada, a comprehensive measure of the economic burden incorporates 

medical resource use, productivity losses, and reductions in health-related quality of life 

(HRQOL), highlighting the significant role of indirect costs (Lim et al., 2008). The economic 

evaluation frameworks often used in these studies include cost-of-illness studies, which are 

crucial for informing resource allocation decisions by comparing costs and outcomes to assess 

efficiency (Patel, 2018). However, these evaluations face methodological challenges, such as 

determining the appropriate perspective (e.g., societal or health service) and the measurement 

of costs and outcomes, with debates surrounding the use of quality-adjusted life years (QALYs) 

in mental health studies (McCrone, 2011). In Europe, the economic burden of mental disorders 

like depression is primarily driven by indirect costs, such as productivity losses, which account 

for a significant portion of the total costs. Variations in definitions further compound the 

complexity of estimating these costs, populations studied, and cost components, necessitating 

more standardized approaches to achieve consensus (Jacobs et al., 2018). Additionally, the 

multidisciplinary nature of mental health interventions requires consideration of diverse cost 

categories, including social care, informal care, and education, which are often impacted by 

mental health issues (Shearer et al., 2016). Overall, while methodologies differ, there is a 

consensus on the substantial economic burden posed by mental health illnesses, underscoring 

the need for improved methods and increased investment in mental health care (Razzouk, 

2017), Trautmann et al. (2016)).  

Our research design is placed in Figure 1.  



Paulina TERESIUTE, Deimante VASILIAUSKAITE 

 

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Figure 1. Methodological framework  

 
Source: Done by authors 

 

Wavelet transforms are typically categorized into two main types: 

1. Continuous Wavelet Transform (CWT): Provides a highly detailed time-frequency 

representation by continuously scaling and shifting the wavelet. The results are often visualized 

as a scalogram, which shows the signal’s energy distribution across time and scales (Mallat 

(1999), Torrence & Compo (1998)). 

2. Discrete Wavelet Transform (DWT): Uses discrete scales and translations for 

computational efficiency, making it widely used in practical applications like signal denoising 

and compression. 

Advantages of Wavelet Transform Analysis 

 Time-Frequency Localization: Captures transient features and patterns that 

occur only at specific times. 

 Multiscale Analysis: Decomposes signals into components at different 

resolutions, allowing simultaneous analysis of global trends and local details. 

 Noise Robustness: Effectively separates noise from meaningful signal 

components, enhancing data interpretability (Mallat, 1999). 

Wavelet Transform Analysis is a robust mathematical technique used to decompose a 

signal into components localized in both time and frequency domains. Unlike Fourier analysis, 

which provides a global frequency representation, wavelet analysis can reveal transient and 

time-varying features within a signal. This makes it particularly useful for analyzing non-

stationary data across various scientific and engineering domains. Wavelet analysis usually 

consists of transforms, variance decomposition, thresholding, and outlier detection.  

 

RESULTS AND DISCUSSION:  

Overview of the UK situation. 

For our analysis, we have taken the UK as a case because in this country people unable 

to work due to long-term illness peaked a lot. Overall, it is estimated that nearly one-fifth of 

the UK’s working-age population has a condition that limits their ability to work. The think 

tank suggests that this issue has grown so severe that it now poses a significant threat to the 

country's economic potential. 

1 step

Overview of UK
trends: *The number of
people in the UK
unable to work due to
long-term illness (from
2000 Q1 to 2024 Q3)

*The number of
workers reporting
work-related stress,
depression or anxiety
(period 2008/09-
2022/23)

2 step

Descriptive statistics

One-way Tabulation

3 step

Wavelet Transform 
Analysis:
*Orthogonal transform

*Overlap

*MRA as seasonal 
adjustment 



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In early 2024, the number of people in the UK unable to work due to long-term illness 

peaked at 2.82 million, though this figure declined slightly in subsequent months. This marks 

a significant rise from just over 2 million in 2019. Before 2022, the previous high of 2.38 

million occurred in late 2021 and early 2022. At that time, caregiving for family members was 

the leading cause of inactivity. Since late 2021, however, long-term and temporary illnesses 

have become the dominant factors, comprising 32.2% of the economically inactive population 

by Q3 2024. 

 

Figure 2. The number of people in the UK unable to work due to long-term illness  

 
Source: Done by authors using data from Statista 

 

The reasons behind this increase remain uncertain if we want to focus on the causes of 

rising long-term illness. As of 2022, mental health conditions were the leading causes, with 

313,000 citing mental illness and 282,000 naming depression-related issues. The COVID-19 

pandemic also likely played a role; in April 2022, 1.8 million people reported experiencing 

Long Covid. While long-term sickness remains most prevalent among those over 50, younger 

groups have seen notable increases. Between 2019 and 2022, the number of individuals aged 

16–34 on long-term sick leave rose by 140,000, compared to a 32,000 increase for those aged 

35–49. The UK labor market has remained tight in recent years, favoring job seekers. In 2022, 

unemployment hit its lowest level since the 1970s, with job vacancies peaking at 1.3 million in 

May. Although wage growth was robust during this time, high inflation led to real-term pay 

reductions between late 2021 and mid-2023. By December 2023, redundancies reached their 

highest levels since April 2021 at 116,000, signaling potential cooling in 2024. Despite this, 

the labor market remains resilient by historical standards. 

 

Figure 3. Number of workers reporting work-related stress, depression or anxiety in Great 

Britain from 2008/09 to 2022/23 (in 1,000s) 

 
Source: Done by authors using data from Statista 

414 439 402 430 488 442 488
526 595 602

828 823 914 875

0

500

1.000

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From 2008/09 to 2022/23, the number of workers in Great Britain reporting work-

related stress, depression, or anxiety has shown a significant upward trend. In 2008/09, 

approximately 415,000 workers reported such conditions. This figure increased over the years, 

reaching around 602,000 in 2014/15. A notable rise occurred during the COVID-19 pandemic, 

with reports peaking at approximately 914,000 in 2021/22. In 2022/23, the number slightly 

decreased to about 875,000 workers. These statistics underscore the escalating concern of 

work-related mental health issues in the UK workforce over the past decade and a half.   

 

Descriptive Statistics and One Way Tabulation  

We start our quantitative analysis by calculating the main descriptive statistics 

parameters. Analyzing the data from 2000 Q1 until 2024 Q3, we can see that the maximum 

number of economically inactive people due to long-term sickness in the United Kingdom was 

at the highest level in 2024 Q1 and reached 2 820 (in 1,000s) while the quarterly average was 

2227,404 (more data of descriptive statistics in Table 1).  

 

Table 1. Results of Descriptive Statistics. 

Mean 2227,404 

Median 2209 

Maximum 2820 

Minimum 1959 

Std. Dev. 198,3388 

Skewness 1,30528 

Kurtosis 4,730509 

Jarque-Bera 40,46496 

Probabibility 0 

Observations 99 

Source: Done by authors using Eviews and data from Statista 

 

The descriptive statistics provide an overview of the patterns and characteristics of 

economically inactive individuals due to long-term sickness, offering insights into the UK's 

health and labor market challenges. The data reveals trends, variability, and anomalies that are 

critical for understanding the broader socio-economic implications. 

The mean value of 2227.404 thousand people represents the average number of 

economically inactive individuals due to long-term sickness per quarter across the dataset. The 

median value of 2209 thousand people is slightly below the mean, indicating that the dataset 

includes some higher values (outliers) that increase the mean. This reflects periods where 

sickness-related inactivity spiked, likely during major health crises or demographic shifts. The 

dataset spans from a minimum of 1959 thousand people to a maximum of 2820 thousand 

people, a range of 861 thousand. This range highlights the variability in inactivity levels over 

time, likely influenced by underlying factors such as chronic illness prevalence, healthcare 

accessibility, demographic trends, and external shocks like the COVID-19 pandemic. 

The standard deviation of 198.34 thousand people reflects moderate variation around 

the mean, indicating that while inactivity levels were generally consistent, there were notable 

fluctuations during the observed period. The positive skewness of 1.30528 suggests that the 



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distribution is right-skewed, with a few quarters experiencing significantly higher levels of 

inactivity. This likely reflects acute events, such as pandemics or systemic health crises. The 

kurtosis value of 4.730509 indicates a leptokurtic distribution, with a sharper peak and heavier 

tails than a normal distribution. This suggests that while most observations are close to the 

mean, extreme values (outliers) are more common than in a normal distribution. The Jarque-

Bera test statistic of 40.46496 with a probability of 0 indicates a significant departure from 

normality. This non-normality can be attributed to the presence of outliers, skewness, and 

kurtosis in the dataset. With 99 observations, the dataset covers a substantial period, likely 

capturing long-term trends and episodic events affecting economic inactivity. 

The distribution indicates a relatively stable baseline of economically inactive 

individuals due to long-term sickness, with the mean and median close in value. However, the 

positive skew and leptokurtic nature suggest the influence of episodic events, such as the 

COVID-19 pandemic, that temporarily elevated inactivity levels. The variability in the data 

highlights the dual challenge for policymakers: addressing the steady baseline of long-term 

sickness through healthcare improvements and chronic disease management while preparing 

for and mitigating the impact of acute health crises. The high mean value reflects a significant 

and persistent economic burden on the labor market and public resources. This underscores the 

need for targeted interventions to reduce long-term sickness-related inactivity, including 

preventive healthcare, workplace accommodations, and mental health support programs. 

The data's non-normal distribution and right skew call for further investigation into the 

drivers of extreme values. Time-series methods, such as wavelet analysis, could be employed 

to identify periodicity, trends, and the impact of specific events (e.g., pandemic waves, policy 

changes). The descriptive statistics highlight the substantial and variable burden of long-term 

sickness on economic inactivity in the UK. The findings emphasize the importance of sustained 

efforts to improve public health infrastructure and resilience, addressing both chronic and acute 

factors influencing labor market participation. 

In the next step, we will conduct a one-way tabulation, which offers a detailed dataset 

summary by presenting the frequency distribution of a single variable. This analysis is 

particularly useful for understanding data distribution, as it helps identify how data points are 

spread across different categories or values. It also assists in detecting outliers by spotting 

values that occur infrequently, which may indicate anomalies. The results of a one-way 

tabulation can be found in Table 2.  

 

Table 2. Results of One Way Tabulation. 

Value Count Percent Cumulative 

count 

Cumulative 

Percent 

[1800,2000) 8 8,08 8 8,08 

[2000,2200) 38 38,38 46 46,46 

[2200,2400) 44 44,44 90 90,91 

[2400,2600) 1 1,01 91 91,92 

[2600,2800) 5 5,05 96 96,97 

[2800,3000) 3 3,03 99 100,00 

Total 99 100,00 99 100,00 

Source: Done by authors using Eviews and data from Statista 



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Based on the results in Table 2 the tabulated results provide a detailed distribution of 

the quarterly number of economically inactive individuals due to long-term sickness, 

highlighting both the concentration and variability within the dataset. This analysis is critical 

for understanding patterns and trends in economic inactivity caused by health conditions. The 

majority of observations (82.82%) fall within the [2000,2400) range, with the [2200,2400) 

category being the most prevalent (44.44%). This concentration indicates that, over the 

observed period, the typical number of inactive individuals remained relatively stable. The 

lower range [1800,2000) accounts for only 8.08%, suggesting that values below 2000 were 

rare, potentially reflective of either improved health or a smaller affected population in earlier 

years. The upper ranges [2400,2600), [2600,2800), and [2800,3000) collectively represent 

9.09% of the dataset. These values likely correspond to significant disruptions, such as the 

COVID-19 pandemic that temporarily increased long-term sickness rates. 

The scarcity of values in the [2400,2600) (1.01%) and [2800,3000) (3.03%) ranges 

highlights that deviations from the central trend, whether due to health improvements or crises, 

were limited in duration and frequency. By the [2200,2400) range, the cumulative percentage 

reaches 90.91%, indicating that most periods were characterized by relatively moderate levels 

of long-term sickness-related inactivity. This suggests that fluctuations occurred but were 

generally confined to predictable limits. The clustering of data within the [2000,2400) range 

reflects temporal stability in the number of individuals inactive due to sickness, which may 

align with long-term trends in chronic illness prevalence, healthcare access, and demographic 

factors. The relatively few observations in higher ranges, especially [2800,3000), may signify 

the influence of significant but short-lived events like the COVID-19 pandemic, which 

temporarily elevated inactivity rates. This finding highlights the importance of monitoring and 

mitigating such disruptions. The relatively consistent mid-range values suggest that targeted 

policies to improve healthcare access and manage chronic conditions could significantly reduce 

the burden of long-term sickness. However, the occurrence of high-end values underscores the 

need for preparedness to address unexpected health crises that can strain the labor market.  

In conclusion, this distribution analysis provides valuable insights into the dynamics of 

long-term sickness-related economic inactivity in the UK. It underscores the importance of a 

dual approach: addressing the steady baseline of chronic illness while preparing for and 

mitigating the effects of health crises on the labor force. 
 

Wavelet Transform Analysis 

Wavelet analysis typically begins with a wavelet transform of the time series of interest, 

a process conceptually similar to a Fourier transform. In this step, the time series is broken 

down into its spectral (frequency) components across different scales. In wavelet analysis, the 

concept of scale is analogous to frequency in Fourier analysis. This process re-expresses the 

time series data from its time-domain representation to its frequency-domain behavior. This 

transformation helps identify the signal's activity's most prominent scales (or frequencies). 

We made Wavelet Transform Analysis based on decomposition: Orthogonal transform 

–DWT, filter class – Haar.  Wavelet transform analysis can be practically applied to study the 

number of economically inactive people due to long-term sickness by examining how the 

patterns and trends in this data evolve over time, particularly when complex, non-stationary 

factors like economic conditions, health crises, or policy changes influence these patterns.  



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312 

 

Figure 4. Wavelet transform analysis: Orthogonal transform  

 

 

 

 
Figure 4. indicates a summary of the wavelet transformation performed. The first plot 

in the output is a plot of the original series and the padded values in case a dyadic adjustment 

was applied. The last 7 plots are, respectively, the wavelet coefficients. At the first scale of 

wavelet decomposition, the frequency spectrum is effectively divided into two equal parts: low 

and high-frequency components. The low-frequency portion corresponds to the scaling 

coefficients (VV), while the high-frequency portion corresponds to the wavelet coefficients 

(WW). Notably, the spectra associated with the wavelet coefficients are significantly less 

pronounced than those of the scaling coefficients, suggesting that the number of economically 

inactive people due to long-term sickness in the United Kingdom series may be non-stationary. 

Additionally, the wavelet plot includes two dashed red lines, which denote the ±1 

standard deviation range for the coefficients at that scale. This visualization aids in identifying 

wavelet coefficients that should be shrunk to zero as part of wavelet shrinkage applications, 

highlighting the insignificant coefficients. Coefficients that exceed a specific threshold—here, 

the standard deviation—are retained, while the rest are shrunk to zero. From this, it becomes 

clear that most wavelet coefficients at scale 1 can be disregarded, providing further evidence 

that high-frequency components in the number of economically inactive people due to long-

term sickness in the United Kingdom series are not prominent. 



Paulina TERESIUTE, Deimante VASILIAUSKAITE 

 

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Figure 5. Wavelet transform analysis: Overlap  

  

 

 
Although wavelet decomposition is not a formal statistical test, it provides an excellent 

method for identifying which scales (or frequencies) dominate the behavior of the underlying 

series. This analysis is not restricted to the first scale. To illustrate this, we will repeat the 

abovementioned process using the maximum overlap discrete wavelet transform (MODWT) 

with the Daubechies (daublet) filter of length 6. The transform will be applied up to the 

maximum possible scale, indicating which and how many wavelet coefficients are influenced 

by boundary conditions.  

 

Figure 6. Wavelet transform analysis: MRA as seasonal adjustment  

 

 



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314 

 

Wavelet analysis helps identify patterns and uncover trends in long-term sickness-

related inactivity (e.g., seasonal effects, economic cycles). The results reveal that certain times 

of the year show spikes in inactivity due to sickness, which may be linked to flu seasons or 

other health-related phenomena. From our analysis, we see that the COVID-19 pandemic had 

a huge impact on UK number of economically inactive people due to long-term sickness. At 

the same time, we see a significant change in the number of workers reporting work-related 

stress, depression or anxiety in the UK. By analyzing low-frequency components, wavelet 

transforms identify broader trends, such as a gradual increase or decrease in long-term sickness 

rates over decades, potentially linked to aging populations or healthcare accessibility. 

Analyzing short-term fluctuations, high-frequency components are used to detect abrupt 

changes, such as those caused by significant events (e.g., a pandemic, economic downturns, or 

policy changes like stricter sick leave regulations). Wavelet-based models help forecast future 

inactivity rates by understanding past patterns, aiding policymakers and healthcare providers 

in preparing for potential increases in long-term sickness. Our results show that the level after 

the COVID-19 pandemic has changed significantly and can affect future trends considerably. 

It is essential to initiate changes in social security or healthcare policies which can help to 

create shifts in inactivity rates. Wavelet analysis can help pinpoint when these changes 

occurred and assess their immediate and long-term effects. 

The main findings of this research identify that an increase in economically inactive 

people due to long-term sickness over the last years from 2020 can be due to aging populations 

or chronic diseases; extreme weather conditions, which can be related to climate change 

challenges, but the most probable is the COVID-19 pandemic highlighting the need for targeted 

healthcare interventions. On the other side, we think that one of the reasons, but not the main 

factor, can be the attitude towards sickness, which has changed after the COVID-19 pandemic 

because, before it, many people continued working even after being sick. By understanding 

these dynamics, policymakers can design better health and labor policies, allocate resources 

more effectively, and implement preventive measures to mitigate future risks associated with 

long-term sickness and economic inactivity. The UK must take serious decisions to manage 

this problem as it can start affecting economic trends and increase health problems.  

 

CONCLUSIONS 

The UK has witnessed a substantial rise in the number of economically inactive 

individuals due to long-term sickness, with the highest levels recorded in early 2024. This 

increase, from approximately 2 million in 2019 to over 2.8 million in 2024, highlights a 

pressing issue affecting the nation’s workforce. The trend suggests that a combination of 

factors, including aging populations and the increasing prevalence of chronic diseases, drive 

this growth. Notably, the data indicates a demographic shift in long-term sickness, with 

younger populations also exhibiting higher rates of inactivity. This phenomenon underscores 

the need for comprehensive health monitoring systems and proactive intervention strategies to 

manage the impacts of these changes on economic productivity.  Wavelet transform analysis 

was instrumental in decomposing the time-series data of economically inactive individuals, 

revealing complex, multi-scale patterns of change over time. This method effectively captured 

short-term fluctuations linked to transient factors such as seasonal health issues and acute 



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economic disruptions by isolating high-frequency components. Simultaneously, low-frequency 

components illustrated the underlying long-term trends, enabling a clearer understanding of 

persistent factors like chronic illnesses and systemic changes in health and labor market 

conditions. This dual analysis provided nuanced insights, allowing policymakers and 

researchers to identify the causes of sickness-related inactivity and the optimal time frames for 

implementing policy interventions. 

The COVID-19 pandemic was a significant catalyst for the rise in long-term sickness 

rates, with lasting repercussions beyond the immediate health crisis. The pandemic's direct 

impacts, such as Long COVID symptoms affecting an estimated 1.8 million people in April 

2022, compounded pre-existing health challenges. Moreover, the pandemic induced broader 

societal shifts, including heightened awareness and sensitivity toward illness and a greater 

propensity for individuals to prioritize health over economic activity. This marked change in 

attitudes likely contributed to a reduction in the normalization of working while unwell, 

suggesting that COVID-19 has permanently altered societal and workplace norms regarding 

health and productivity. 

Mental health conditions have emerged as a leading cause of long-term sickness-related 

economic inactivity, particularly among younger demographics. Between 2019 and 2022, 

individuals aged 16–34 reported a sharp increase in mental health-related sickness, reflecting 

a growing vulnerability among younger workers to conditions such as anxiety, depression, and 

work-related stress. This trend aligns with broader findings that mental health challenges have 

surged globally, fueled by post-pandemic recovery pressures, economic uncertainty, and 

workplace burnout. These findings underscore the urgency of integrating mental health support 

into employment policies, including initiatives for stress management, flexible working 

conditions, and workplace mental health programs, to prevent further deterioration of the labor 

force's well-being. The rising levels of long-term sickness-related economic inactivity present 

a critical challenge to the UK’s economic potential. A smaller active labor force, increased 

healthcare expenditures, and social security demands could constrain economic growth. 

Policymakers must prioritize investments in healthcare infrastructure and social safety nets to 

address the root causes of long-term sickness. Simultaneously, labor market policies should 

incentivize businesses to adopt flexible and inclusive employment practices, enabling 

individuals with chronic or mental health conditions to remain economically active. Addressing 

these challenges will require cross-sector collaboration, integrating healthcare, social policy, 

and economic strategies to mitigate the adverse effects of long-term illness on both individuals 

and the broader economy. 

Wavelet-based models demonstrated their utility as a forecasting tool, highlighting both 

immediate and long-term trends in sickness-related inactivity. By analyzing historical patterns, 

these models can predict future surges in inactivity rates, aiding in strategic resource allocation. 

For example, the models identified the COVID-19 pandemic as a pivotal event that disrupted 

historical trends and set a new trajectory for inactivity levels. Policymakers can use these 

insights to anticipate potential shocks, such as future pandemics or climate-related health 

crises, and proactively design interventions to reduce their impact. This approach ensures 

public health and economic resilience by enabling a rapid, informed response to emerging 

challenges. 



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To develop effective solutions for the rising rates of long-term sickness, further research 

must explore the interplay between various contributing factors, including demographic shifts, 

climate change, mental health, and socioeconomic conditions. Longitudinal studies could 

provide deeper insights into how these elements interact and evolve over time. Additionally, a 

focus on regional disparities and vulnerable populations would ensure that policies address the 

specific needs of diverse groups. Policymakers should emphasize preventive healthcare 

measures, such as regular health screenings, mental health education, and community-based 

wellness programs, to reduce the incidence of long-term sickness. Furthermore, economic 

policies must consider the financial implications of inactivity and incentivize businesses to 

proactively support employee health and well-being. 

 

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