Abstract
This study examines the association of unmet healthcare needs (UHNs) with health-related productivity loss (HRPL), work ability, and work engagement among Korean workers. We analyzed 6,633 workers from two waves (August 2023 and September 2024) of the Korean Work, Sleep, and Health Study, a nationwide panel survey. UHNs were assessed based on self-reported experiences of forgoing necessary medical care. HRPL, work ability, and work engagement were measured using validated instruments. The generalized estimating equation models to repeated measurements applied to examine associations between UHNs and labor-related outcomes, adjusting for sociodemographic factors. Workers with UHNs had a 9.517 percentage point higher HRPL, 3.348-fold higher odds of poor work ability, and 1.506-fold higher odds of low work engagement compared with those without UHNs. Among causes of UHNs, financial constraints were associated with the largest increases in HRPL, poor work ability and low work engagement. These findings suggest that inadequate access to healthcare not only affects individual health but also has substantial implications for labor outcomes. Policy interventions to reduce UHNs, particularly for financially vulnerable workers, may improve both health and labor outcomes.
Keywords: Unmet healthcare needs, Health services accessibility, Work ability, Health-related productivity loss, Work engagement
Introduction
Equitable access to healthcare according to needs is fundamental for achieving health equity, ensuring that all individuals have the opportunity to attain the highest level of health1). However, disparities persist owing to factors such as socioeconomic status, geographic location, and educational level. Individuals from lower-income backgrounds, rural areas, or those with limited education often face significant barriers in accessing necessary health services, leading to poor health outcomes and exacerbating health disparities. Unmet healthcare needs (UHNs) refer to situations in which individuals perceive that they have not received necessary medical services2). This concept serves as a critical indicator of healthcare accessibility and health equity. Contextual reasons contributing to UHNs include financial constraints, limited availability of services, and personal barriers, such as time constraints or lack of information.
UHNs are influenced by a range of factors spanning demographic, socioeconomic, geographic, and personal domains3, 4). Demographically, high incidences of UHNs are observed among women, individuals with lower income and education levels, and those lacking health insurance. Elderly populations, particularly those with functional or cognitive impairments, also face increased challenges in accessing necessary healthcare services. Economically, high medical costs and substantial out-of-pocket expenses can deter individuals from seeking care, even in countries with universal health coverage5). Geographically, disparities in the distribution of healthcare resources lead to variations in access, with rural residents often experiencing greater unmet needs than their urban counterparts. Personal factors, including time constraints, limited health literacy, and distrust in medical institutions, further contribute to the prevalence of UHNs.
It has been consistently reported that UHNs are associated with adverse health outcomes. Previous research has indicated that UHNs can deteriorate overall health6) and reduce quality of life7), increasing the likelihood of multimorbidity8), cognitive impairment9), functional decline10), cancer11), and mortality. Moreover, individuals with UHNs are at a higher risk of depression12), anxiety13), and even suicidal behaviours14), particularly among vulnerable populations such as older adults and those with pre-existing mental health conditions.
Thus, UHNs can significantly affect labour-related variables. When employees cannot access necessary medical services, their health may deteriorate, leading to increased absenteeism and presenteeism, which in turn elevates health-related productivity loss (HRPL). This decline in health can also impair work ability, as individuals struggle to perform tasks effectively. Moreover, physical and mental discomfort stemming from untreated health issues can decrease work engagement, motivation, and commitment to job responsibilities. Therefore, addressing UHNs could be crucial for maintaining workforce productivity and overall organizational performance.
However, while previous research has established that UHNs negatively affect health outcomes, their influence on workplace performance and labor productivity remains scarce. Hence, this study aimed to analyze the association between UHNs and labor outcomes using three key measures: HRPL, work ability, and work engagement. By evaluating these, this study seeks to provide evidence that can inform workplace health policy and occupational health interventions. This is particularly relevant in Korea, where the absence of a universal public sickness benefit system may leave many workers without adequate support during illness, potentially amplifying the negative impact of UHNs on labor outcomes. Insights into the relationship between UHNs and labor-related outcomes may guide the development of targeted strategies to reduce healthcare barriers among workers, particularly those in vulnerable situations.
Methods
Study population
Data from a nationwide panel study, the Korean Work, Sleep, and Health Study (KWSHS), launched in 2022 to examine the association between working conditions, sleep patterns, and health outcomes among Korean workers were used in this study15). The KWSHS comprises five biannual online surveys conducted between July 2022 and September 2024. The target population was Korean workers aged 19–70 yr from various occupational sectors. This study utilized data from only the August 2023 and September 2024 surveys, which assessed UHNs, resulting in an interval of approximately one year between the two waves. The surveys collected sociodemographic information (gender, age, educational level, occupation, and household income) and included measures of UHNs, HRPL, work ability, and work engagement. We included only individuals who perceived a need for medical treatment or examination within the past year at the time of the surveys. Respondents with incomplete answers to key survey items were excluded from the analysis. Of the 5,553 participants who responded to the August 2023 survey, 4,411 were included, while 4,467 of the 5,783 respondents from the September 2024 survey were included. Of the 6,633 participants included in this study, 2,245 were included in both the August 2023 and September 2024 surveys, whereas 4,388 responded to only one of the two surveys. A detailed participant flow chart is provided in Fig. 1, illustrating the selection process for both the repeated measures and cross-sectional analyses.
Fig. 1.
Flowchart of study participant selection and analysis.
Independent variable: UHNs
UHNs were assessed using the following question: “In the past year, have you ever needed treatment or an examination at a hospital but did not receive it?” Participants who responded “Yes, I did not receive it at least once” were classified as having UHNs. Those who reported having UHNs were asked to select the relevant reasons from the following ten options: 1) financial burden, including medical and transportation costs; 2) financial loss due to missing work; 3) difficulty finding time; 4) unavailability of appointments or inconvenient operating hours; 5) uncertainty about which hospital or department to visit; 6) inconvenient transportation or long travel distances; 7) mobility issues or health conditions limiting access; 8) complexity or difficulty of appointment procedures; 9) being denied medical care; and 10) other reasons. Multiple responses and non-responses were permitted. The reasons for the analysis were categorized as financial constraints (1 and 2), time constraints (3 and 4), and other causes (5–10).
Dependent variables: HRPL, work ability, and work engagement
HRPL was measured using the general health version of the Work Productivity and Activity Impairment Questionnaire16). The reliability and validity of this questionnaire are well established17). The Korean version, available online, underwent a standardized translation process including independent translation, back-translation, and expert review18). This questionnaire assesses the impact of health on work productivity and daily activities through six items: employment status, work hours missed due to health and other reasons, actual working hours, impact of health on work productivity, and its influence on daily activities. Responses were used to calculate overall productivity loss, encompassing both absenteeism and presenteeism. Absenteeism was defined as the percentage of work hours missed due to health problems in the past 7 d. Presenteeism referred to reduced productivity while at work, attributed to health issues, and was calculated as the percentage of working performance lost over the same period. HRPL was expressed as a percentage, representing the total proportion of work hours lost owing to health-related absences and productivity reductions in the past 7 d.
Work ability was assessed using the work ability index (WAI)19), a widely used tool in occupational health research20). The WAI has been validated across multiple countries21) and has demonstrated high reliability in the Korean population22). The questionnaire evaluated various aspects of work ability, health status, and mental well-being. Participants rated their current work ability, assessed their ability in relation to job demands, reported any physician-diagnosed diseases, estimated work impairment, recorded days absent due to illness, predicted their work ability over the next 2 yr, and reflected on their mental capabilities. The total WAI score was calculated by summing the section scores, with certain items weighted based on job demands. Job demands were classified as physically or mentally demanding or both. Only those diseases diagnosed by a physician were included in the scoring system. The total WAI score ranged from 7 to 49, with higher scores indicating greater workability. According to the WAI, the scores are categorized as poor (7–27), moderate (28–36), good (37–43), or excellent (44–49). In this study, work ability was dichotomized into ‘poor’ (7–27) and ‘not poor’ (28–49) and analyzed as a binary outcome variable.
Work engagement was assessed using the Ultrashort Version of the Utrecht Work Engagement Scale (UWES-3), an efficient tool for measuring work engagement23). The validity of the UWES-3 has been reported in multiple countries24) and has also been utilized in the Korean Working Conditions Survey25). The UWES-3 consists of three items: (1) “At my work, I feel bursting with energy” (vigor), (2) “I am enthusiastic about my job” (dedication), and (3) “I am immersed in my work” (absorption). Each item is rated on a Likert scale ranging from 0 (never) to 6 (always), with a total score ranging from 0 to 18. For analysis, work engagement was categorized as ‘low’ if the total score was ≤7, corresponding to the lowest quantile. Work engagement was analyzed as a binary outcome variable.
Other variables
Variables related to sociodemographic characteristics included gender, age, educational level, occupation, and household income. These variables were used for subgroup analysis and covariate adjustment. Age was categorized as below or above 60 yr when it was treated as a binary variable, reflecting Korea’s statutory retirement age. Educational level was classified as ‘high school or below’ and ‘college or above’. Occupations were categorized as white-collar (managers, professionals, and office workers), pink-collar (service and sales workers), and blue-collar (workers in agriculture, forestry, fishery, crafts, machine operation, assembly-line work and manual labor). Household income, encompassing both labor and financial income, was adjusted using an equalized scale by dividing total income by the square root of the number of household members26). It was then categorized into quintiles, ranging from the lowest-income group (Q1) to the highest-income group (Q5), among all survey participants.
Statistical analysis
Data were collected repeatedly throughout the study, with each participant providing one or two responses. Initially, the cohort characteristics in each survey were analyzed based on UHNs. Additionally, among participants who reported reasons for having UHNs, the number and proportion of respondents selecting each specific reason were summarized based on the individual’s first response to avoid duplication. The proportion was calculated by excluding respondents who did not provide a response regarding the causes of UHNs, and multiple responses were counted for each relevant cause.
The primary analyses were performed using generalized estimating equation models with exchangeable correlation structures to account for repeated measurements and to examine differences based on UHNs. Mean differences in HRPL with 95% confidence intervals (CIs) were calculated, along with odds ratios (ORs) and 95% CIs for ‘poor’ work ability and ‘low’ work engagement, comparing individuals with and without UHNs. Analyses were conducted using two models: an unadjusted model and a sociodemographically adjusted model that accounted for gender, age, educational level, occupation, and household income.
Similarly, mean differences in HRPL and ORs for ‘poor’ work ability and ‘low’ work engagement were estimated among individuals who reported financial constraints as the cause of UHNs compared with those without UHNs or whose UHNs were not due to financial constraints. The same analyses were conducted separately for individuals who identified time constraints as the cause of UHNs and for those who attributed UHNs to other constraints, with each group compared with individuals without UHNs or whose UHNs were not due to the respective constraints.
Supplementary Table 1 presents separate analyses for each of the ten specific causes of UHNs. For each cause, individuals who reported the respective reason were compared with those without UHNs or with UHNs due to other causes. Each comparison was conducted independently to estimate the mean difference in HRPL and the odds ratios for poor work ability and low work engagement. These cause-specific contrasts were considered exploratory, and interpretation emphasized effect sizes with 95% confidence intervals. Statistical analyses were performed using R version 4.4.0, with a two-tailed p-value <0.05 considered statistically significant.
Results
Table 1 shows the baseline characteristics of the study population in each survey. In both survey waves, the overall workforce had a higher proportion of men; however, UHNs were significantly more prevalent among women than among men. Participants who reported having UHNs exhibited a higher mean HRPL, greater proportion of poor work ability, and higher proportion of low work engagement than those without UHNs. Younger age groups reported a high prevalence of UHNs.
Table 1. Baseline characteristics of the study population in each survey.
| August 2023 | September 2024 | ||||||||
|---|---|---|---|---|---|---|---|---|---|
| Unmet health needs |
Unmet health needs |
||||||||
| Overall | No | Yes | p | Overall | No | Yes | p | ||
| n (%) | 4,411 | 2,771 (62.8) | 1,640 (37.2) | 4,467 | 2,999 (67.1) | 1,468 (32.9) | |||
| Gender (%) | |||||||||
| Men | 2,333 | 1,611 (69.1) | 722 (30.9) | <0.001 | 2,302 | 1,686 (73.2) | 616 (26.8) | <0.001 | |
| Women | 2,078 | 1,160 (55.8) | 918 (44.2) | 2,165 | 1,313 (60.6) | 852 (39.4) | |||
| Age (%) | |||||||||
| 20–29 | 698 | 412 (59.0) | 286 (41.0) | <0.001 | 683 | 430 (63.0) | 253 (37.0) | <0.001 | |
| 30–39 | 871 | 520 (59.7) | 351 (40.3) | 893 | 571 (63.9) | 322 (36.1) | |||
| 40–49 | 1,070 | 643 (60.1) | 427 (39.9) | 1,041 | 704 (67.6) | 337 (32.4) | |||
| 50–59 | 1,159 | 751 (64.8) | 408 (35.2) | 1,155 | 780 (67.5) | 375 (32.5) | |||
| 60+ | 613 | 445 (72.6) | 168 (27.4) | 695 | 514 (74.0) | 181 (26.0) | |||
| Educational level (%) | |||||||||
| Elementary to high school | 560 | 344 (61.4) | 216 (38.6) | 0.495 | 590 | 383 (64.9) | 207 (35.1) | 0.236 | |
| College or above | 3,851 | 2,427 (63.0) | 1,424 (37.0) | 3,877 | 2,616 (67.5) | 1,261 (32.5) | |||
| Occupation (%) | |||||||||
| Blue-collar | 632 | 422 (66.8) | 210 (33.2) | 0.083 | 644 | 441 (68.5) | 203 (31.5) | 0.046 | |
| Pink-collar | 486 | 304 (62.6) | 182 (37.4) | 550 | 344 (62.5) | 206 (37.5) | |||
| White-collar | 3,293 | 2,045 (62.1) | 1,248 (37.9) | 3,273 | 2,214 (67.6) | 1,059 (32.4) | |||
| Household income (%) | |||||||||
| Q1 | 874 | 553 (63.3) | 321 (36.7) | 0.105 | 887 | 569 (64.1) | 318 (35.9) | 0.063 | |
| Q2 | 885 | 523 (59.1) | 362 (40.9) | 969 | 633 (65.3) | 336 (34.7) | |||
| Q3 | 959 | 623 (65.0) | 336 (35.0) | 837 | 570 (68.1) | 267 (31.9) | |||
| Q4 | 834 | 523 (62.7) | 311 (37.3) | 904 | 630 (69.7) | 274 (30.3) | |||
| Q5 | 859 | 549 (63.9) | 310 (36.1) | 870 | 597 (68.6) | 273 (31.4) | |||
| Work ability (%) | |||||||||
| Moderate to excellent | 4,228 | 2,710 (64.1) | 1,518 (35.9) | <0.001 | 4,267 | 2,921 (68.5) | 1,346 (31.5) | <0.001 | |
| Poor | 183 | 61 (33.3) | 122 (66.7) | 200 | 78 (39.0) | 122 (61.0) | |||
| Work engagement (%) | |||||||||
| High | 3,396 | 2,231 (65.7) | 1,165 (34.3) | <0.001 | 3,359 | 2,329 (69.3) | 1,030 (30.7) | <0.001 | |
| Low | 1,015 | 540 (53.2) | 475 (46.8) | 1,108 | 670 (60.5) | 438 (39.5) | |||
| Health-related productivity loss (mean (SD)) | 31.33 (26.72) | 27.22 (25.98) | 38.28 (26.54) | <0.001 | 31.61 (27.10) | 28.68 (26.51) | 37.59 (27.31) | <0.001 | |
Table 2 shows the cross-sectional distribution of the causes of UHNs among 2,643 respondents who provided a response regarding the cause of UHNs, based on each participant’s first available survey response. UHNs due to time constraints were the most common (81.4%), followed by financial constraints (29.9%). Additionally, not knowing which hospital or department to visit accounted for 9.1% of responses.
Table 2. Causes of unmet healthcare needs.
| Causes of unmet healthcare needs | n | % | |
|---|---|---|---|
| Financial constraints | 789 | 29.9 | |
| Financial burden, including medical and transportation costs | 613 | 23.2 | |
| Financial loss from missing work | 301 | 11.4 | |
| Time constraints | 2,151 | 81.4 | |
| Difficulty finding time | 1,971 | 74.6 | |
| Unavailability of appointments or inconvenient operating hours | 548 | 20.7 | |
| Other causes | 612 | 23.2 | |
| Not knowing which hospital or department to visit | 240 | 9.1 | |
| Inconvenient transportation or long distances | 171 | 6.5 | |
| Mobility issues or health conditions limiting access | 25 | 0.9 | |
| Complexity or difficulty of appointment procedures | 137 | 5.2 | |
| Being denied medical care | 9 | 0.3 | |
| Other reasons | 113 | 4.3 | |
Table 3 presents the longitudinal associations of HRPL, work ability, and work engagement with UHNs among 6,633 observations (4,411 from the August 2023 survey and 4,467 from the September 2024 survey, including 2,245 participants with repeated measurements). As shown in Table 3(A), individuals with UHNs had a 9.517 higher percentage point HRPL (95% CI: 8.355–10.679 percentage points, sociodemographically adjusted model) than those without UHNs. When UHNs were due to time constraints, the difference was 8.978 percentage points (95% CI: 7.749–10.207 percentage point, sociodemographically adjusted model). The gap was even greater when financial constraints were the cause, reaching 11.467 percentage points (95% CI: 9.620–13.314 percentage points, sociodemographically adjusted model). As shown in Table 3(B), individuals with UHNs had 3.348 times higher odds (95% CI: 2.696–4.158, sociodemographically adjusted model) of ‘poor’ work ability compared with those without UHNs. The OR was 2.931 (95% CI: 2.380–3.611, sociodemographically adjusted model) when UHNs were due to time constraints, whereas financial constraints were associated with a higher OR of 3.232 (95% CI: 2.541–4.112, sociodemographically adjusted model). As shown in Table 3(C), individuals with UHNs had 1.506 times higher odds (95% CI: 1.357–1.670) of experiencing ‘low’ work engagement than those without UHNs. The OR was 1.499 (95% CI: 1.346–1.669, sociodemographically adjusted model) for UHNs due to time constraints, whereas financial constraints were associated with a slightly higher OR of 1.526 (95% CI: 1.309–1.779, sociodemographically adjusted model). All results in Table 3 are statistically significant across the unadjusted and sociodemographically adjusted analyses, with consistent trends observed across models. Moreover, to strengthen the robustness of our conclusions, we conducted a sensitivity analysis restricted to participants who completed both survey waves (Supplementary Table 1). The results were consistent with those presented in Table 3, showing that UHNs were associated with greater HRPL, ‘poor’ work ability, and ‘low’ work engagement. We believe this additional analysis strengthens the robustness of our conclusions.
Table 3. Health-related productivity loss, work ability, and work engagement according to unmet healthcare needs.
| (A) Health-related productivity loss | ||
|---|---|---|
| Unmet healthcare needs | Unadjusted | Sociodemographically adjusted |
| Mean difference (95% CI) | Mean difference (95% CI) | |
| Any causes | 10.004 (8.839–11.169) | 9.517 (8.355–10.679) |
| Financial constraints | 12.783 (10.921–14.645) | 11.467 (9.620–13.314) |
| Time constraints | 9.674 (8.449–10.899) | 8.978 (7.749–10.207) |
| Other causes | 7.863 (5.681–10.045) | 8.060 (5.929–10.191) |
| (B) Poor work ability | ||
| Unmet healthcare needs | Unadjusted | Sociodemographically adjusted |
| Odds ratio (95% CI) | Odds ratio (95% CI) | |
| Any causes | 3.475 (2.808–4.301) | 3.348 (2.696–4.158) |
| Financial constraints | 3.574 (2.826–4.519) | 3.232 (2.541–4.112) |
| Time constraints | 2.999 (2.441–3.686) | 2.931 (2.380–3.611) |
| Other causes | 3.287 (2.537–4.260) | 3.240 (2.486–4.224) |
| (C) Low work engagement | ||
| Unmet healthcare needs | Unadjusted | Sociodemographically adjusted |
| Odds ratio (95% CI) | Odds ratio (95% CI) | |
| Any causes | 1.577 (1.427–1.743) | 1.506 (1.357–1.670) |
| Financial constraints | 1.675 (1.448–1.937) | 1.526 (1.309–1.779) |
| Time constraints | 1.615 (1.456–1.792) | 1.499 (1.346–1.669) |
| Other causes | 1.342 (1.133–1.590) | 1.385 (1.162–1.650) |
** Sociodemographically adjusted model is adjusted for gender, age, educational level, occupation, and household income.
(A) Mean difference in health-related productivity loss of individuals with unmet healthcare needs compared with those without unmet healthcare needs. (B) Odds ratio of ‘poor’ work ability of individuals with unmet healthcare needs compared with those without unmet healthcare needs. (C) Odds ratio of ‘low’ work engagement of individuals with unmet health needs compared with those without unmet healthcare needs.
Detailed results based on the specific causes of UHNs are presented in Supplementary Table 2. Notably, when mobility issues or health conditions limiting access and being denied medical care were the reasons for UHNs, the increases in HRPL, ‘poor’ workability odds, and ‘low’ work engagement odds were greater compared with other causes.
Discussion
To the best of our knowledge, this is the first study to examine the associations of UHNs with HRPL, work ability, and work engagement. Our findings indicated that workers with UHNs experience a significant increase in HRPL. Additionally, UHNs negatively affect work ability and engagement, suggesting that insufficient access to healthcare can impair an individual’s capacity to perform job-related tasks and remain committed to their work. Among the causes of UHNs, financial constraints were most strongly associated with HRPL and work engagement. Workers with limited financial resources were more likely to forgo necessary medical care, potentially leading to worsened health conditions that can hinder their job performance27).
Deterioration in health due to UHNs can lead to increased absenteeism and presenteeism. Absenteeism refers to employees missing work because of health issues, whereas presenteeism describes a scenario in which employees are present at work but operate at a reduced capacity because of health problems28). Both phenomena diminish individual productivity and can result in substantial economic losses for organizations. For instance, a study analyzing health risk appraisal responses from over one million participants found that HRPL were estimated cost U.S. employers over $260 billion annually, often surpassing direct medical expenditures29).
The mechanisms underlying these losses in productivity are multifaceted. Physiologically, untreated or inadequately managed health conditions can lead to chronic pain, fatigue, and other debilitating symptoms that hinder employees’ ability to perform tasks efficiently29,30,31). The lack of appropriate medical care adversely affects workers’ physical and mental health32), leading to a decline in their capacity to effectively perform job tasks. Psychologically, stress and anxiety stemming from unresolved health issues can impair cognitive function, decision making, and concentration, further diminishing work performance33). This health-related discomfort and distress diminish employees’ engagement levels, weakening their dedication and enthusiasm toward organizational objectives34). Reduced engagement can affect team dynamics and overall workplace morale. Research indicates that low work engagement is associated with decreased work ability and increased absence due to sickness35). Therefore, UHNs can lead to a cascade of negative outcomes, including deteriorated health, diminished work ability and work engagement, and increased labor productivity losses.
This study had several limitations. First, reliance on self-reported survey data introduces the potential for response and common method bias, as both UHNs and work-related outcomes were subjectively assessed by participants. This raises the possibility of inflated associations due to shared method variance—a concern aligned with Kristensen’s “triviality trap”, which warns against drawing conclusions from analyses based on similarly perceived exposure and outcome36). Moreover, UHN was measured through a single self-reported item that, although widely used in national and international health surveys6, 14), lacks formal validation and does not distinguish between work-related and non-work-related healthcare needs. This limitation may introduce interpretive ambiguity in assessing its relationship with labor outcomes. Second, although this study accounted for several demographic and occupational factors, other contextual variables, such as workplace health policies, employer-provided healthcare benefits, and job security, were not fully explored. In particular, re-employed older workers beyond the statutory retirement age may face different working conditions (e.g., reduced pay or job security), which could influence both their likelihood of experiencing UHNs and the extent to which these unmet needs affect labor outcomes. Third, the interval between the two surveys was relatively short (1 yr), which may not be sufficient to fully capture the change in variables. Therefore, longitudinal studies with extended follow-up periods are needed to better capture the long-term effects of UHNs on labor outcomes, while also considering changes in workplace policies, healthcare benefits, and job security as potential moderating factors. Fourth, this study focused exclusively on workers in Korea. Given Korea’s unique labor market characteristics, including high work intensity, long working hours, and a growing proportion of non-regular employment, caution is needed when generalizing these findings to other countries. Fifth, geographical factors such as regional disparities in healthcare resource distribution and differences between urban and rural areas were not included in our analysis. Future studies incorporating detailed geographical information could provide in-depth insights into regional disparities in UHNs and their impact on labor outcomes. Finally, the cause-specific analyses involved multiple contrasts and were exploratory; thus, the potential for type I error inflation should be considered, and emphasis is placed on effect sizes and confidence intervals rather than dichotomous significance.
In conclusion, this study showed a significant association between UHNs and labor-related outcomes, including increased HRPL, poor work ability, and low work engagement. The findings suggest that UHNs not only affect individual health but also have broader economic and organizational consequences. Given the substantial burden posed by UHNs, particularly among economically vulnerable workers, addressing these unmet needs is crucial for enhancing workforce well-being and labor outcomes. From a policy perspective, integrated healthcare strategies should be developed to reduce barriers to medical access, particularly for low-income workers and those in precarious employment. In settings where formal sickness benefit systems are limited or inconsistently applied, expanding preventive measures, such as implementation of flexible or national-level sick leave policies, employer-provided healthcare benefits, and improved insurance coverage, could play a critical role in improving both individual health outcomes and overall workforce efficiency.
Declaration of Generative AI and AI-assisted Technologies in the Writing Process
During the preparation of this work, the authors used ChatGPT-4o in order to refine language. After using this tool/service, the authors reviewed and edited the content as needed and take full responsibility for the content of the publication.
Author Contributions
Mo-Yeol Kang: Writing − original draft. Writing − Review & Editing, Supervision, Methodology, Conceptualization. Heejoo Ko: Formal analysis, Writing − original draft, Visualization. Seong-Sik Cho: Methodology, Resources, Project administration. Dong-Wook Lee: Writing − Review & Editing. Jaesung Choi: Writing − Review & Editing.
Ethics Statement
This study was conducted in compliance with the principles of the Declaration of Helsinki. Informed consent was obtained from all the participants, and anonymity and confidentiality were ensured. The study protocol was approved by the Institutional Review Board of Dong-A University (IRB No. 2-1040709-AB-N-01-202202-HR-017-06).
Funding
This work was supported by National Research Foundation of Korea (NRF-2022R1F1A1066498).
Conflict of Interest
None declared.
Supplementary
Funding Statement
This work was supported by National Research Foundation of Korea (NRF-2022R1F1A1066498).
Data Availability
The KWSHS data are available upon reasonable request, provided that researchers meet specific criteria.
References
- 1.Marmot M, Commission on Social Determinants of Health (2007) Achieving health equity: from root causes to fair outcomes. Lancet 370, 1153–63. [DOI] [PubMed] [Google Scholar]
- 2.Carr W, Wolfe S. (2019) Unmet needs as sociomedical indicators. In: Sociomedical health indicators, Elinson J and Siegmann AE (Eds.), 33–46, Routledge, Abingdon. [Google Scholar]
- 3.Shi L, Stevens GD. (2005) Vulnerability and unmet health care needs. The influence of multiple risk factors. J Gen Intern Med 20, 148–54. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Yilmaz F, Mete AH, Turkon BF, Boz C. (2024) How enabling factors determine unmet healthcare needs? A panel data approach for countries. Eval Program Plann 107, 102492. [DOI] [PubMed] [Google Scholar]
- 5.Bloom DE, Khoury A, Subbaraman R. (2018) The promise and peril of universal health care. Science 361, eaat9644. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Smolić Š, Blaževski N, Fabijančić M. (2024) The impact of unmet healthcare needs on the perceived health status of older Europeans during COVID-19. Int J Public Health 69, 1607336. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Ju YJ, Kim TH, Han KT, Lee HJ, Kim W, Ah Lee S, Park EC. (2017) Association between unmet healthcare needs and health-related quality of life: a longitudinal study. Eur J Public Health 27, 631–7. [DOI] [PubMed] [Google Scholar]
- 8.Li C, Zhou R, Yao N, Cornwell T, Wang S. (2020) Health care utilization and unmet needs in Chinese older adults with multimorbidity and functional impairment. J Am Med Dir Assoc 21, 806–10. [DOI] [PubMed] [Google Scholar]
- 9.Stein J, Pabst A, Luck T, Lühmann D, Heser K, Jessen F, Bickel H, Mösch E, Pentzek M, Fuchs A, Wiese B, Mamone S, König HH, Brettschneider C, Werle J, Scherer M, Maier W, Weyerer S, Riedel-Heller SG, AgeCoDe and AgeQualiDe Study Group (2017) Unmet care needs in the oldest old primary care patients with cognitive disorders: results of the AgeCoDe and AgeQualiDe study. Dement Geriatr Cogn Disord 44, 71–83. [DOI] [PubMed] [Google Scholar]
- 10.Bergeot J, Jusot F. (2023) The impact of unmet health care needs on self-assessed health and functional limitations during the first wave of the COVID-19 pandemic. In: Social, health, and economic impacts of the COVID-19 pandemic and the epidemiological control measures, 31, De Gruyter, Berlin/Boston. [Google Scholar]
- 11.Quintal C, Moura Ramos L, Antunes M, Lourenço Ó. (2023) Unmet healthcare needs among the population aged 50+ and their association with health outcomes during the COVID-19 pandemic. Eur J Ageing 20, 12. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Eimontas J, Gegieckaitė G, Zamalijeva O, Pakalniškienė V. (2022) Unmet healthcare needs predict depression symptoms among older adults. Int J Environ Res Public Health 19, 8892. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Kim H, Huang Z, Qin F, Arokianathan F, Kamiya Y, Wang D, Hiraoka S. (2024) P209 Work productivity and unmet needs of treated ulcerative colitis patients: a Japan National Health and Wellness Survey study. J Crohns Colitis 18, i535. [Google Scholar]
- 14.Lee H, Lee W. (2023) Association between unmet healthcare needs and suicidal behaviors among the Korean population. Gen Hosp Psychiatry 84, 158–64. [DOI] [PubMed] [Google Scholar]
- 15.Cho SS, Min J, Ko H, Kang MY. (2025) Data profile: Korean Work, Sleep, and Health Study (KWSHS). Ann Occup Environ Med 37, e3. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Reilly MC, Zbrozek AS, Dukes EM. (1993) The validity and reproducibility of a work productivity and activity impairment instrument. PharmacoEconomics 4, 353–65. [DOI] [PubMed] [Google Scholar]
- 17.Zhang W, Bansback N, Boonen A, Young A, Singh A, Anis AH. (2010) Validity of the work productivity and activity impairment questionnaire—general health version in patients with rheumatoid arthritis. Arthritis Res Ther 12, R177. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Reilly Associates. WPAI Translations. http://www.reillyassociates.net/WPAI_Translations.html. Accessed October 3, 2024.
- 19.Ilmarinen J. (2007) The work ability index (WAI). Occup Med (Lond) 57, 160. [Google Scholar]
- 20.El Fassi M, Bocquet V, Majery N, Lair ML, Couffignal S, Mairiaux P. (2013) Work ability assessment in a worker population: comparison and determinants of Work Ability Index and Work Ability score. BMC Public Health 13, 305. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.de Zwart BC, Frings-Dresen MH, van Duivenbooden JC. (2002) Test-retest reliability of the Work Ability Index questionnaire. Occup Med (Lond) 52, 177–81. [DOI] [PubMed] [Google Scholar]
- 22.Yang DJ, Kang D, Kim YK, Kim YH, Yang YA, Cha SM, Eom IK, Kim JE. (2013) Reliability of self-administered Work Ability Index questionnaire among Korean workers. Ergonomics 56, 1652–7. [DOI] [PubMed] [Google Scholar]
- 23.Schaufeli WB, Shimazu A, Hakanen J, Salanova M, De Witte H. (2017) An ultra-short measure for work engagement. Eur J Psychol Assess 35, 577–91. [Google Scholar]
- 24.Merino-Soto C, Lozano-Huamán M, Lima-Mendoza S, Calderón de la Cruz G, Juárez-García A, Toledano-Toledano F. (2022) Ultrashort version of the Utrecht work engagement scale (UWES-3): a psychometric assessment. Int J Environ Res Public Health 19, 890. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Choi M, Suh C, Choi SP, Lee CK, Son BC. (2020) Validation of the work engagement scale-3, used in the 5th Korean working conditions survey. Ann Occup Environ Med 32, e27. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Stand DW, Rising WIK. (2011) An overview of growing income inequalities in OECD countries: main findings. In: Divided We Stand: Why Inequality Keeps Rising, 21–45, OECD Publishing, Paris. [Google Scholar]
- 27.Kim Y, Kim S, Jeong S, Cho SG, Hwang SS. (2019) Poor people and poor health: examining the mediating effect of unmet healthcare needs in Korea. J Prev Med Public Health 52, 51–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Johns G. (2008) Absenteeism and presenteeism: not at work or not working well. The Sage handbook of organizational behavior 1, 160–77. [Google Scholar]
- 29.Mitchell RJ, Bates P. (2011) Measuring health-related productivity loss. Popul Health Manag 14, 93–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Pizzi LT, Carter CT, Howell JB, Vallow SM, Crawford AG, Frank ED. (2005) Work loss, healthcare utilization, and costs among US employees with chronic pain. Dis Manag Health Outcomes 13, 201–8. [Google Scholar]
- 31.Daneshmandi H, Choobineh AR, Ghaem H, Alhamd M, Fakherpour A. (2017) The effect of musculoskeletal problems on fatigue and productivity of office personnel: a cross-sectional study. J Prev Med Hyg 58, E252–8. [PMC free article] [PubMed] [Google Scholar]
- 32.Jones MK, Latreille PL, Sloane PJ, Staneva AV. (2011) Work-related health in Europe: are older workers more at risk? Flinders University, Adelaide. [DOI] [PubMed] [Google Scholar]
- 33.Staal MA. (2004) Stress, cognition, and human performance: a literature review and conceptual framework. NASA, Washington, DC. [Google Scholar]
- 34.Herbert M. (2011) An exploration of the relationships between psychological capital (hope, optimism, self-efficacy, resilience), occupational stress, burnout and employee engagement. Stellenbosch University, Stellenbosch. [Google Scholar]
- 35.Rongen A, Robroek SJ, Schaufeli W, Burdorf A. (2014) The contribution of work engagement to self-perceived health, work ability, and sickness absence beyond health behaviors and work-related factors. J Occup Environ Med 56, 892–7. [DOI] [PubMed] [Google Scholar]
- 36.Kristensen TS. (1996) Job stress and cardiovascular disease: a theoretic critical review. J Occup Health Psychol 1, 246–60. [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The KWSHS data are available upon reasonable request, provided that researchers meet specific criteria.