Association of social isolation and depressive symptoms with workplace productivity loss: A multi- ethnic Asian study

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We aim to examine the association between social isolation and depressive symptoms with productivity loss. Methods The National Healthcare Group (NHG) Population Health Index (PHI) study is a population-based study on community-dwelling employed adults aged ≥21 years, residing in the Central and Northern of Singapore. The severity of depressive symptoms and social isolation were assessed using the 9-item Patient Health Questionnaire (PHQ-9) and Lubben Social Network Scale-6 (LSNS-6) respectively. Productivity loss was assessed using the Work Productivity and Activity Impairment Questionnaire (WPAI). We used Generalised Linear Models, with family gamma, log link for the analysis. Models were adjusted for socio-demographic variables (including age, gender, ethnicity, employment status, housing type) and self-reported chronic conditions (including the presence of diabetes, hypertension, and dyslipidemia). Results There were 2,605 working (2,143 full-time) adults in this study. The median reported percentage of unadjusted productivity loss was 0.0%, 10.0% and 20.0% for individuals with social isolation, depressive symptoms, and both, respectively. In the regression analysis, mean productivity loss scores were 2.81 times (95% Confidence Interval: 2.12, 3.72) higher in participants with depressive symptoms than those without. On the other hand, social isolation was not found to be associated with productivity loss scores (1.17, 95% Confidence Interval: 0.96, 1.42). The interaction term of depressive symptoms with social isolation was statistically significant, with an effect size of 1.89 (95% Confidence Interval: 1.04, 3.44). It appears that productivity loss was amplified when social isolation and depressive symptoms are concomitant. Conclusions Our results suggested associations between depressive symptoms and social isolation with productivity loss. The findings highlighted the potential impact of depressive symptoms and social isolation on work performance and draw attention to the importance of having a holistic work support system that promotes mental wellbeing, social connectedness and work productivity. Health sciences/Health care Health sciences/Health occupations Productivity Economic impact Mental health Depression Social isolation Figures Figure 1 Introduction The global prevalence of major depressive disorder (MDD) was estimated to be 3.2% in 2021, an increase of 27.6% from 2020 due the coronavirus disease-2019 (COVID-19) pandemic, with reasons attributed to reduced mobility and social interactions [ 1 ]. The Singapore Mental Health Study conducted in 2016 had reported a 12-month and lifetime prevalence of major depressive disorder to be 2.3% and 6.3%, respectively [ 2 ]. The actual Singapore prevalence of depression is likely higher accounting for underdiagnoses and underreporting. Studies have cited reasons for low prevalence figures such as a tendency to underreport negative health outcomes and mood problems among Asian populations due to stigmatisation [ 3 – 5 ]. It was additionally found that depressive symptoms among Asians were more likely to present as somatic symptoms and a reduction in willingness to socialise, and are hence less likely identified as depressive disorders [ 6 ]. In 2021, it was reported that Singapore residents were more likely to seek help from informal support network comprising of family and friends (69.1%) than from healthcare providers (58.3%), highlighting the importance of building strong social networks and social interactions in the backdrop of the rising prevalence of depression [ 7 ]. The lack of social relationships and a poor social network indicates the extent to which an individual is socially isolated [ 8 ]. There were 26.3% of adults identified to be isolated according to the Population Health Index (PHI) survey, conducted in Singapore in 2016 [ 9 ]. With numerous social restrictions put in place at the height of COVID-19, associations between social isolation and depression have been identified across diverse groups [ 10 – 13 ]. The impact of depressive symptoms and social isolation appears deleterious as individuals with both were found to have poorer health outcomes, reporting worse depressive symptoms, lower remission rates and poorer social function compared to those with depressive symptoms but higher perceived social support[ 14 ]. While associations have been found between work disability caused by depression, with social isolation [ 15 – 17 ], the association between workplace productivity and social isolation among individuals with depressive symptoms is not well studied. Health-related productivity loss (HRPL) can provide information of the indirect economic burden of health problems. It allows for a comprehensive evaluation of both direct and indirect costs of a medical condition or benefit from intervention. The economic burden of mental disorders in Singapore is well recognised and it has been estimated to approximately cost S $ 3,900 per person per year [ 18 ]. On the other hand, individuals who are socially isolated are three times more likely to experience occupational burnout, which is linked to absenteeism and poorer work performance [ 19 ]. However, it is unknown if social isolation and depressive symptoms impacts workplace productivity. Hence, we aim to examine the association of social isolation and depressive symptoms with HRPL. These findings could potentially highlight the importance of building social and mental health support at the workplace. We hypothesise that working adults reporting both depressive symptoms and social isolation would incur more HRPL. Methods Study Design Study data was from the Population Health Index (PHI) Survey study initiated by the National Healthcare Group (NHG) to establish a better understanding of current health states of residents residing in the area. The PHI study was approved by the ethics review committee of the National Healthcare Group (NHG) Domain Specific Review Board (Reference Number: 2015/00269), and all research was performed in accordance with relevant guidelines and regulations. Written informed consent was obtained from all individual participants after they were informed of the study objectives and confidentiality of the collected data was maintained throughout the conduct of the study. The specific data used in this study were taken from the Phase 2 PHI, a cross-sectional survey conducted between November 2018 and June 2019. The survey questionnaire was administered by trained interviewers via face-to-face interviews among community-dwelling adults aged ≥ 21 years and residing in the Central and Northern of Singapore. The sampling design and participant recruitment processes have been described elsewhere [ 20 ]. Participants were included in this analysis if they met the following two criteria: 1) employed (either full-time or part-time), and 2) cognitively sound and responded to the survey independently. There were no missing data for any variables. Measures The PHI questionnaire consisted of socio-demographics, lifestyle, medical history, and a set of validated measures. The following socio-demographic characteristics and self-reported chronic conditions from the survey were used as covariates in this study to account for any potential confounding effects: age, gender (female or male), ethnicity (Chinese, Malay, Indian or Others), employment status (full-time or part-time), housing type (Public 1–2 room, Public 3-room, Public 4-room, or Public 5-room and bigger and private properties), region of residence (residing in Central or Northern Singapore) and presence of self-reported diabetes, hypertension and dyslipidemia. Explanatory variables Depressive symptoms The severity of depressive symptoms was assessed by the 9-item Patient Health Questionnaire (PHQ-9). The PHQ-9 was developed to make symptom-based diagnosis of depression and additionally grade the severity of depressive symptoms [ 21 ]. Each question item was scored on a 4-point ordinal scale and an aggregate depressive symptom score was derived from summing the scores of each item. A higher score indicates greater severity of depressive symptoms. Individuals with a score ranging from 0 to 4 was categorised as “without depressive symptoms” and those with a score of 5 to 27 was considered as “with depressive symptoms” [ 21 ]. Social isolation The magnitude of social isolation was estimated by the Lubben Social Network Scale-6 (LSNS-6). The LSNS-6 presents a composite scale for measuring social connectedness and examining the relationship of social connectedness to health outcomes [ 22 ]. The LSNS-6 consists of a set of three questions that measure social connectedness with family members and another set of three questions that measure social connectedness with friends. Each item was scored on a 5-point Likert scale, and the scores were added up to form a total score. Higher scores indicate greater levels of social connectedness and lower risk of social isolation. It has been suggested that a higher cut-off score may be more appropriate to identify social isolation in Asian populations due to greater reliance on social relationships [23; 24]. For this study, participants with a score ranging from 17 to 30 was classified as “without social isolation” and those with a score of 0 to 16 was deemed as “socially isolated”. Outcome variable The loss in workplace productivity due to health reasons, or more commonly known as HRPL, consists of workplace health-related absenteeism and presenteeism. HRPL was measured by the Work Productivity and Activity Impairment Questionnaire (WPAI). The WPAI was developed as a quantitative measure that measures work impairment as distinct from impairment in other activities of daily living [ 25 ]. The WPAI has been previously used to measure the general health impact of individuals with rheumatoid arthritis and major depressive disorder [26; 27]. The WPAI consists of six questions, of which the number of work days missed and the degree of health-related productivity loss (using a 0 to 10 Visual Analogue Scale) were measured. Absenteeism, defined as missed time from work, (percentage of work time missed due to health problems = \(\frac{Hours absent due to health}{\left[Hours absent due to health+Actual worked hours\right]} x 100\) ). Presenteeism, defined as reduced performance while at work, (percentage of work impaired due to health problems = \(\left(\frac{Impairment rating}{10}\right) x\frac{Actual worked hours}{\left[Hours absent due to health+Actual worked hours\right]} x 100\) ) on a 10-point rating scale [ 28 ]. To derive the total HRPL (with a score ranging from 0-100), we calculated the percentage of total work impaired due to health problems by summing the percent of absenteeism and percent of presenteeism. Statistical analysis To ascertain differences in HRPL among working adults, we used Generalised Linear Model (GLM), with family gamma, log link for the analysis due to the known distribution characteristics of our outcome (non-negative values, right skewed distribution). The coefficients were exponentiated to describe the arithmetic mean ratios between the groups. In the first model, depressive symptoms and social isolation were both added as independent variables to determine their effects on HRPL respectively. In the second model, we tested for significant interactions between the presence of depressive symptoms and social isolation, with productivity loss. Both models were adjusted for socio-demographic variables (including age, gender, ethnicity, employment status, housing type) and self-reported chronic conditions (including the presence of diabetes, hypertension, and dyslipidemia). Results Socio-demographics Among the 4,005 participants who were surveyed for the PHI study, 2,605 employed participants (65.0%) were identified for this analysis on working adults. Table 1 presents the socio-demographics of the 2,605 participants. Of which 1,273 (48.9%) were female, the mean age was 47.3 years and 30.9% were aged below 40. There were 727 (27.9%) participants having at least one self-reported chronic condition. Most of the participants were Chinese (73.4%), living in public 4-room flat and bigger (69.7%), and working full-time (82.3%). More than half (54.1%) of participants were residing in Northern Singapore. There were 1,137 participants (43.6%) identified to be at high risk of social isolation, 130 participants (5.0%) having mild-severe depressive symptoms, and 82 participants (3.1%) experienced co-occurring social isolation and depressive symptoms. Table 1 Socio-demographics Variables Employed (n = 2,605) Mean Age (SD) 47.3 (13.2) < 40 years, n (%) 806 (30.9%) 40 years and above, n (%) 1,799 (69.1%) Gender, N (%) Female 1,273 (48.9%) Male 1,332 (51.1%) Ethnicity, N (%) Chinese 1,913 (73.4%) Malay 318 (12.2%) Indian 265 (10.2%) Others 109 (4.2%) Housing type, N (%) 1–2 room flat 168 (6.4%) 3-room flat 622 (23.9%) 4-room flat 1,060 (40.7) 5-room flat and larger/private housing 755 (29.0%) Employment type, N (%) Full-time 2,143 (82.3%) Part-time 462 (17.7) Region of residence, N (%) Central 1,196 (45.9%) Northern 1,409 (54.1%) With Diabetes, N (%) 253 (9.7%) With Hypertension, N (%) 467 (17.9%) With Dyslipidaemia, N (%) 464 (17.8%) Depressive symptoms, N (%) None-minimal 2,475 (95.0%) Mild 108 (4.2%) Moderate 17 (0.6%) Moderately Severe 3 (0.1%) Severe 2 (0.1%) Social isolation, N (%) Low risk 776 (29.8%) Moderate 692 (26.6%) High risk 597 (22.9%) Isolated 540 (20.7%) SD: Standard deviation; IQR: Interquartile range Unadjusted Productivity Loss The median percentage of productivity loss reported due to missed work days (absenteeism) was 0.0% for individuals with social isolation, depressive symptoms and both respectively (Fig. 1 ). Presenteeism appeared to be a bigger component of HRPL, with the median percentage of work productivity affected while at work of 0.0%, 5.0% and 10.0% for individuals with social isolation, depressive symptoms, and both, respectively. The median reported percentage of overall HRPL was 0.0%, 10.0% and 20.0% for individuals with social isolation, depressive symptoms, and both, respectively. Association between productivity loss with depressive symptoms and social isolation The results from multiple linear regression analyses are shown in Table 2 . Mean HRPL scores were up to 2.81 times (95% Confidence Interval (CI): 2.12, 3.72) higher in participants with depressive symptoms than those without. On the other hand, social isolation was not found to be associated with HRPL scores (1.17, 95% CI: 0.96, 1.42). Table 2 Adjusted Productivity Losss for all employed participants Variables Mean ratio (95% CI) Model 1: Employed participants (N = 2,605) Model 2: Employed participants (N = 2,605), with interaction term Depressive Symptoms (Reference: PHQ-9 score < 5) 2.81 (2.12, 3.72)*** 1.81 (1.08, 3.06)* Social Isolation (Reference: LSNS ≥ 17) 1.17 (0.96, 1.42) 1.14 (0.93, 1.39) Depressive Symptoms##Social Isolation -- 1.89 (1.04, 3.44)* Aged below 40 years (Reference: Age ≥ 40 years) 1.38 (1.10, 1,73)** 1.37 (1.10, 1.72)** Female (Reference: Male) 1.22 (1.00, 1.48)* 1.22 (1.00, 1.48)* Ethnicity (Reference: Chinese) Malay 2.01 (1.56, 2.57)*** 2.01 (1.57, 2.57)*** Indian 1.33 (0.97, 1.83) 1.35 (0.98, 1.86) Others 1.22 (0.74, 2.01) 1.20 (0.72, 2.00) Housing type (Reference: 1–2 room) 3-room 1.10 (0.73, 1.63) 1.10 (0.74, 1.64) 4-room 0.96 (0.65, 1.43) 0.96 (0.65, 1.43) 5-room and larger/private housing 0.90 (0.60, 1.36) 0.90 (0.59, 1.35) Employed Full-time (Reference: Part-time) 1.29 (1.00, 1.67)* 1.30 (1.01, 1.69)* Northern region (Reference: Central region) 1.69 (1.37, 2.10)*** 1.69 (1.36, 2.09)*** Diabetes (Reference: No diabetes) 1.28 (0.92, 1.78) 1.28 (0.92, 1.78) Hypertension (Reference: No Hypertension) 1.04 (0.79, 1.38) 1.05 (0.79, 1.39) Dyslipidaemia (Reference: No Dyslipidaemia) 1.52 (1.14, 2.04)** 1.52 (1.13, 2.04)** *p<0.05; **p<0.0;1 ***p<0.001 CI: Confidence Interval; PHQ−9: Patient Health Questionnaire−9; LSNS−6: Lubben Social Network Scale−6 Upon including the interaction term in model 2, we observed that participants with depressive symptoms had 1.81 times (95% CI: 1.08, 3.06) higher HRPL scores as compared to those without. Socially isolated participants were not found to be associated with HRPL scores (1.14, 95% CI: 0.93, 1.39). The interaction between depressive symptoms and social isolation was statistically significant (p-value = 0.037), with an effect size of 1.89 (95% CI: 1.04, 3.44). From both GLM results, depressive symptoms were associated with productivity loss among all working adults, while we did not find evidence for the association between social isolation and productivity loss. The non-exponentiated coefficients (See Supplementary Table 1, Additional File 1) present similar trends and the positive interaction term suggest incremental marginal effects on productivity loss. Discussion Main findings In this multi-ethnic community-dwelling working adult population, we observed association between workplace productivity loss and social isolation concomitant with depressive symptoms. The interaction term suggested that HRPL is amplified when both co-occurs, which is in alignment with poorer health outcomes (e.g., remission, survival, biological and functional outcomes) that were previously reported among socially isolated individuals with depressive symptoms [14; 29; 30]. Our findings highlighted the potential impact of depressive symptoms and social isolation on work performance and draw attention to the importance of having a holistic work support system that promotes mental wellbeing, social connectedness and work productivity. Presence of depressive symptoms was associated with productivity loss, however, the same was not observed for presence of social isolation. Our findings suggest that psychological symptoms and experiences potentially have a stronger relationship with work productivity than social connectedness. The interplay of complex psychosocial factors warrants future research and intervention strategies targeted at individuals with depressive symptoms who are at risk of social isolation. Additionally, females were observed to be associated with productivity loss. This may be explained by gender differences in occupations and comorbidities [ 33 ]. Ethnicity was also observed to be associated with productivity loss (Malay ethnicity was significantly associated with productivity loss compared to Chinese ethnicity). It could potentially be due to a higher prevalence of chronic conditions among the Malay ethnicities [ 2 ], hence strengthening the association with productivity loss respectively. Being employed full-time (reference: employed part-time) was found to be associated with productivity loss, probably because those working full-time were subjected to longer working hours, hence reporting greater presenteeism [ 34 ]. With entitlement to more paid sick leaves, there is also a tendency for full-timer employees to take additional days off from work [ 35 ]. Those of a younger age was found to be associated with productivity loss in this study. This phenomenon corroborates with the literature where the work performance of older workers outdoes that of the younger workers, due to reasons such as greater job experience and self-assurance to perform better at work [ 36 ]. Residents residing in the Northern region are younger (mean age of 45.5 versus 49.4) and more are employed full-time (84.7% versus 79.4%), likely explaining for their higher productivity loss. Lastly, having dyslipidaemia incurs greater productivity loss, likely due to the higher risks of cardiovascular diseases, impacting productivity [ 37 ]. It appears that there are multiple factors associated with productivity loss, and further sub-group analyses should be done given a larger study population in the future. In this study, we observed that individuals with depressive symptoms and social isolation were 10.0% less productive at work due to their health problems in the past seven days (presenteeism). We postulate that the association between poor mental health and presenteeism has driven HRPL in our study. In a Singaporean study it was estimated that individuals with depressive symptoms and/or anxiety had their productivity at work reduced by 40% [ 38 ]. In another Malaysian study, mental health was reported to be a predictor of presenteeism, and physical health as a predictor of absenteeism [ 39 ]. Our findings underscore the need to focus more efforts on enhancing at-work productivity and putting in place policies to encourage employees to take time-off from work for their mental health when needed. Strengths and Limitations As a population-based study, the primary strength of this study is its representativeness of the population in Central and Northern regions of Singapore. Singapore is a multi-ethnic Asian population and the associations presented in this study are likely observable in the rest of the Asian countries. Other strengths of this study included the use of standardised questionnaire. This study was able to evaluate multiple factors associated with productivity loss, allowing for sub-group analyses and adjustment for various socio-economic factors. Lastly, the magnitude of productivity loss was based on self-reported data with a recall period of 7 days. Although the main outcome of interest is subject to recall errors, the extent of recall bias was possibly minimised by the short recall period. This study, however, is not without limitations. The study design is a cross-sectional study, and we are unable to ascertain the temporal sequence of the explanatory factors and main outcome of interest. There had been a 5-year Finnish cohort study that had established Major Depressive Disorder as a predictor of poor work outcomes among psychiatric subjects [ 15 – 17 ]. In another 12-year American cohort study among older adults, it was found that depressive symptoms predicted social isolation, while social isolation was not a predictor of depressive symptoms [ 40 ]. The temporal sequence of depressive symptoms, social isolation and work productivity loss altogether need further investigation. Our study only considered productivity loss at the workplace, whereas other studies also looked at activity impairment in unpaid work activities. Additionally, the prevalence of depressive symptoms reported in our study is possibly underreported due to existing stigma. Hence, the actual productivity loss associated with depressive symptoms could be much higher. As this study only included working adults, those that were non-employed were excluded from the analysis. It is unclear if 40.7% of those socially isolated and 49.8% of those with depressive symptoms were not employed in this study due to social isolation and depressive symptoms. Although this study did not conduct qualitative interviews to further investigate the psychosocial reasons for productivity loss, existing qualitative findings suggest that a work-culture which values employees’ well-being, alongside supportive peers and employers, and assisted return-to-work arrangements do influence return-to-work outcome of employees with depression [ 41 ]. Further research could be a cohort study with mixed-methods approach to better understand the relationship between depressive symptoms, social isolation, and workplace productivity loss. Implications To our knowledge, this is the first study exploring the association of depressive symptoms and social isolation, with workplace productivity loss in community-dwelling working adult population in Singapore. Our results corroborate with the existing literature that have shown associations between poorer work adjustments and absenteeism, with depressive symptoms and social isolation [ 15 – 17 ]. Our findings on the association between productivity loss and psychosocial factors (productivity loss of up to 1.89 times more) echo the importance of managing social isolation and depressive symptoms at the workplace. More structural support (workplace policies) and social support (from peers and employers) are needed for the promotion of workplace productivity and well-being. Conclusion This study suggests an association between productivity loss and depressive symptoms, and the association is stronger when working adults were simultaneously reported to be socially isolated. We observed a greater magnitude of reduced productivity at work as compared to missed time from work. With the increasing focus on well-being at the workplace, our findings reiterate the importance of building a supportive work environment and forging strong social networks, as mental well-being and work productivity are closely interconnected. Abbreviations MDD Major Depressive Disorder COVID-19 Coronavirus disease-2019 PHI Population Health Index HRPL Health-related productivity loss NHG National Healthcare Group PHQ-9 Patient Health Questionnaire-9 LSNS-6 Lubben Social Network Scale-6 WPAI Work Productivity and Activity Impairment Questionnaire GLM Generalised Linear Model CI Confidence Interval Declarations Ethics approval and consent to participate The PHI study was approved by the ethics review committee of the National Healthcare Group Domain Specific Review Board (Reference Number: 2015/00269). Written informed consent was obtained from all individual participants after they were being informed about the study objectives. Consent for publication Written informed consent was obtained from all individual participants after they were being informed about the study objectives. Availability of data and materials The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding This work was supported by National Healthcare Group Pte Ltd in the form of salaries for all authors. The funder had no role in / influence on study design, data collection and analysis, decision to publish, or preparation of the manuscript. Authors’ contributions J.H.W.Y. analysed and interpreted the data and wrote the manuscript. W.F.Y. interpreted the data and revised the manuscript. L.G. conceived the study and revised the manuscript. C.W.Y. conceived the study and revised the manuscript. M.J.P. conceived the study, interpreted the data, and revised the manuscript. 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J Gen Intern Med , 16 (9), 606-613. https://doi.org/10.1046/j.1525-1497.2001.016009606.x Lubben, J., Blozik, E., Gillmann, G., Iliffe, S., von Renteln Kruse, W., Beck, J. C., & Stuck, A. E. (2006). Performance of an abbreviated version of the Lubben Social Network Scale among three European community-dwelling older adult populations. Gerontologist , 46 (4), 503-513. https://doi.org/10.1093/geront/46.4.503 Chang, Q., Sha, F., Chan, C. H., & Yip, P. S. F. (2018). Validation of an abbreviated version of the Lubben Social Network Scale ("LSNS-6") and its associations with suicidality among older adults in China. PLoS One , 13 (8), e0201612. https://doi.org/10.1371/journal.pone.0201612 Chang, Q., Chan, C. H., & Yip, P. S. F. (2017). A meta-analytic review on social relationships and suicidal ideation among older adults. Soc Sci Med , 191 , 65-76. https://doi.org/10.1016/j.socscimed.2017.09.003 Reilly, M. C. (2008). Development of the work productivity and activity impairment (WPAI) questionnaire. New York: Reilly Associates . Trivedi, M. H., Morris, D. W., Wisniewski, S. R., Lesser, I., Nierenberg, A. A., Daly, E., Kurian, B. T., Gaynes, B. N., Balasubramani, G. K., & Rush, A. J. (2013). Increase in work productivity of depressed individuals with improvement in depressive symptom severity. Am J Psychiatry , 170 (6), 633-641. https://doi.org/10.1176/appi.ajp.2012.12020250 Zhang, W., Bansback, N., Boonen, A., Young, A., Singh, A., & Anis, A. H. (2010). Validity of the work productivity and activity impairment questionnaire--general health version in patients with rheumatoid arthritis. Arthritis Res Ther , 12 (5), R177. https://doi.org/10.1186/ar3141 Boles, M., Pelletier, B., & Lynch, W. (2004). The relationship between health risks and work productivity. J Occup Environ Med , 46 (7), 737-745. https://doi.org/10.1097/01.jom.0000131830.45744.97 Spaderna, H., Zittermann, A., Reichenspurner, H., Ziegler, C., Smits, J., & Weidner, G. (2017). Role of Depression and Social Isolation at Time of Waitlisting for Survival 8 Years After Heart Transplantation. J Am Heart Assoc , 6 (12). https://doi.org/10.1161/JAHA.117.007016 Hafner, S., Zierer, A., Emeny, R. T., Thorand, B., Herder, C., Koenig, W., Rupprecht, R., Ladwig, K. H., & Investigators, K. S. (2011). Social isolation and depressed mood are associated with elevated serum leptin levels in men but not in women. Psychoneuroendocrinology , 36 (2), 200-209. https://doi.org/10.1016/j.psyneuen.2010.07.009 Gohar, B., Lariviere, M., Lightfoot, N., Wenghofer, E., Lariviere, C., & Nowrouzi-Kia, B. (2020). Meta-analysis of nursing-related organizational and psychosocial predictors of sickness absence. Occup Med (Lond) , 70 (8), 593-601. https://doi.org/10.1093/occmed/kqaa144 Wong, D. F., & Leung, G. (2008). The functions of social support in the mental health of male and female migrant workers in China. Health Soc Work , 33 (4), 275-285. https://doi.org/10.1093/hsw/33.4.275 Laaksonen, M., Mastekaasa, A., Martikainen, P., Rahkonen, O., Piha, K., & Lahelma, E. (2010). Gender differences in sickness absence--the contribution of occupation and workplace. Scand J Work Environ Health , 36 (5), 394-403. https://doi.org/10.5271/sjweh.2909 Lee, D. W., Lee, J., Kim, H. R., & Kang, M. Y. (2020). Association of long working hours and health-related productivity loss, and its differential impact by income level: A cross-sectional study of the Korean workers. J Occup Health , 62 (1), e12190. https://doi.org/10.1002/1348-9585.12190 Ahn, T., & Yelowitz, A. (2016). Paid sick leave and absenteeism: The first evidence from the US. SSRN , 2740366 . Viviani, C. A., Bravo, G., Lavalliere, M., Arezes, P. M., Martinez, M., Dianat, I., Braganca, S., & Castellucci, H. I. (2021). Productivity in older versus younger workers: A systematic literature review. Work , 68 (3), 577-618. https://doi.org/10.3233/WOR-203396 Ferrara, P., Di Laura, D., Cortesi, P. A., & Mantovani, L. G. (2021). The economic impact of hypercholesterolemia and mixed dyslipidemia: A systematic review of cost of illness studies. PLoS One , 16 (7), e0254631. https://doi.org/10.1371/journal.pone.0254631 Chodavadia, P., Teo, I., Poremski, D., Fung, D. S. S., & Finkelstein, E. A. (2023). Prevalence and economic burden of depression and anxiety symptoms among Singaporean adults: results from a 2022 web panel. BMC Psychiatry , 23 (1), 104. https://doi.org/10.1186/s12888-023-04581-7 Wee, L. H., Yeap, L. L. L., Chan, C. M. H., Wong, J. E., Jamil, N. A., Swarna Nantha, Y., & Siau, C. S. (2019). Anteceding factors predicting absenteeism and presenteeism in urban area in Malaysia. BMC Public Health , 19 (Suppl 4), 540. https://doi.org/10.1186/s12889-019-6860-8 Luo, M. (2023). Social Isolation, Loneliness, and Depressive Symptoms: A Twelve-Year Population Study of Temporal Dynamics. J Gerontol B Psychol Sci Soc Sci , 78 (2), 280-290. https://doi.org/10.1093/geronb/gbac174 Corbiere, M., Renard, M., St-Arnaud, L., Coutu, M. F., Negrini, A., Sauve, G., & Lecomte, T. (2015). Union perceptions of factors related to the return to work of employees with depression. J Occup Rehabil , 25 (2), 335-347. https://doi.org/10.1007/s10926-014-9542-5 Additional Declarations No competing interests reported. Supplementary Files AdditionalFile1.docx Cite Share Download PDF Status: Published Journal Publication published 27 Sep, 2024 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 26 Jun, 2024 Reviews received at journal 25 Jun, 2024 Reviews received at journal 02 Jun, 2024 Reviewers agreed at journal 31 May, 2024 Reviewers agreed at journal 31 May, 2024 Reviewers invited by journal 24 May, 2024 Editor assigned by journal 24 May, 2024 Editor invited by journal 24 May, 2024 Submission checks completed at journal 24 May, 2024 First submitted to journal 22 May, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4463422","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":309594464,"identity":"a38919ac-7086-464b-8a38-a43efd43ef05","order_by":0,"name":"Joey Wei Yee Ha","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA90lEQVRIiWNgGAWjYBAC9gYgwdh2mMHgMPMBZqK0MMK1HG9LIElLGoPBmTMGRGppbz4mwdhmw2BwI+fb44JfDPL8YgeYP37Bp6XnWJoE4zYJBvsbuduNZ/YxGM6cncAmLYNPy4wcM7AWgxu526R5exgSDG4nsDFL4NWS/w2i5f6bZzAtzJ/xaRGckcMmwfgPZEsOmzTPD7AWBskPeLRI8xwztmA8B9KSZibN2yAB9EtimzQeHQx87M0PbzC2gbQkP5Pm+WMjzy+dfPjjD3x6GBhYpP8wMNQ3gH3WBvIEYwMzD34tzEgO/wOhGAnYMgpGwSgYBSMLAAD6i0xP4OUDxwAAAABJRU5ErkJggg==","orcid":"","institution":"National Healthcare Group, Singapore","correspondingAuthor":true,"prefix":"","firstName":"Joey","middleName":"Wei Yee","lastName":"Ha","suffix":""},{"id":309594465,"identity":"d6b219f1-9828-4f0e-8654-aec7640ed603","order_by":1,"name":"Wan Fen Yip","email":"","orcid":"","institution":"National Healthcare Group, Singapore","correspondingAuthor":false,"prefix":"","firstName":"Wan","middleName":"Fen","lastName":"Yip","suffix":""},{"id":309594466,"identity":"d48256e5-ddaf-4874-9335-76533e567c11","order_by":2,"name":"Lixia Ge","email":"","orcid":"","institution":"National Healthcare Group, Singapore","correspondingAuthor":false,"prefix":"","firstName":"Lixia","middleName":"","lastName":"Ge","suffix":""},{"id":309594467,"identity":"e407aa65-8c24-41e9-9b3e-4b394551b79e","order_by":3,"name":"Chun Wei Yap","email":"","orcid":"","institution":"National Healthcare Group, Singapore","correspondingAuthor":false,"prefix":"","firstName":"Chun","middleName":"Wei","lastName":"Yap","suffix":""},{"id":309594468,"identity":"eadcc2ac-11bc-4e74-a6c9-a60944c7e2a8","order_by":4,"name":"Michelle Jessica Pereira","email":"","orcid":"","institution":"National Healthcare Group, Singapore","correspondingAuthor":false,"prefix":"","firstName":"Michelle","middleName":"Jessica","lastName":"Pereira","suffix":""}],"badges":[],"createdAt":"2024-05-23 01:00:44","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4463422/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4463422/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-024-73272-4","type":"published","date":"2024-09-27T15:57:05+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":58076828,"identity":"59105add-fe31-4560-9abf-596926ee698d","added_by":"auto","created_at":"2024-06-10 22:22:32","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":19129,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eUnadjusted Median Productivity Loss (Interquartile range)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4463422/v1/e71e7c6a79e539f05f828deb.png"},{"id":65627122,"identity":"f52bfa7c-b9ee-4457-b8f1-6deedd8f8be2","added_by":"auto","created_at":"2024-09-30 16:12:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":789292,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4463422/v1/d632956f-a165-4ad9-aa33-55fca07dd9ea.pdf"},{"id":58076829,"identity":"dc513bca-b7c6-4cf4-8aa8-2938eb46cbc5","added_by":"auto","created_at":"2024-06-10 22:22:32","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":16485,"visible":true,"origin":"","legend":"","description":"","filename":"AdditionalFile1.docx","url":"https://assets-eu.researchsquare.com/files/rs-4463422/v1/b806b52e2e038eb26b14e8b7.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association of social isolation and depressive symptoms with workplace productivity loss: A multi- ethnic Asian study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe global prevalence of major depressive disorder (MDD) was estimated to be 3.2% in 2021, an increase of 27.6% from 2020 due the coronavirus disease-2019 (COVID-19) pandemic, with reasons attributed to reduced mobility and social interactions [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The Singapore Mental Health Study conducted in 2016 had reported a 12-month and lifetime prevalence of major depressive disorder to be 2.3% and 6.3%, respectively [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The actual Singapore prevalence of depression is likely higher accounting for underdiagnoses and underreporting. Studies have cited reasons for low prevalence figures such as a tendency to underreport negative health outcomes and mood problems among Asian populations due to stigmatisation [\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. It was additionally found that depressive symptoms among Asians were more likely to present as somatic symptoms and a reduction in willingness to socialise, and are hence less likely identified as depressive disorders [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. In 2021, it was reported that Singapore residents were more likely to seek help from informal support network comprising of family and friends (69.1%) than from healthcare providers (58.3%), highlighting the importance of building strong social networks and social interactions in the backdrop of the rising prevalence of depression [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe lack of social relationships and a poor social network indicates the extent to which an individual is socially isolated [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. There were 26.3% of adults identified to be isolated according to the Population Health Index (PHI) survey, conducted in Singapore in 2016 [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. With numerous social restrictions put in place at the height of COVID-19, associations between social isolation and depression have been identified across diverse groups [\u003cspan additionalcitationids=\"CR11 CR12\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The impact of depressive symptoms and social isolation appears deleterious as individuals with both were found to have poorer health outcomes, reporting worse depressive symptoms, lower remission rates and poorer social function compared to those with depressive symptoms but higher perceived social support[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. While associations have been found between work disability caused by depression, with social isolation [\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], the association between workplace productivity and social isolation among individuals with depressive symptoms is not well studied.\u003c/p\u003e \u003cp\u003e Health-related productivity loss (HRPL) can provide information of the indirect economic burden of health problems. It allows for a comprehensive evaluation of both direct and indirect costs of a medical condition or benefit from intervention. The economic burden of mental disorders in Singapore is well recognised and it has been estimated to approximately cost S\u003cspan\u003e$\u003c/span\u003e3,900 per person per year [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. On the other hand, individuals who are socially isolated are three times more likely to experience occupational burnout, which is linked to absenteeism and poorer work performance [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. However, it is unknown if social isolation and depressive symptoms impacts workplace productivity. Hence, we aim to examine the association of social isolation and depressive symptoms with HRPL. These findings could potentially highlight the importance of building social and mental health support at the workplace. We hypothesise that working adults reporting both depressive symptoms and social isolation would incur more HRPL.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eStudy Design\u003c/p\u003e \u003cp\u003eStudy data was from the Population Health Index (PHI) Survey study initiated by the National Healthcare Group (NHG) to establish a better understanding of current health states of residents residing in the area. The PHI study was approved by the ethics review committee of the National Healthcare Group (NHG) Domain Specific Review Board (Reference Number: 2015/00269), and all research was performed in accordance with relevant guidelines and regulations. Written informed consent was obtained from all individual participants after they were informed of the study objectives and confidentiality of the collected data was maintained throughout the conduct of the study.\u003c/p\u003e \u003cp\u003eThe specific data used in this study were taken from the Phase 2 PHI, a cross-sectional survey conducted between November 2018 and June 2019. The survey questionnaire was administered by trained interviewers via face-to-face interviews among community-dwelling adults aged\u0026thinsp;\u0026ge;\u0026thinsp;21 years and residing in the Central and Northern of Singapore. The sampling design and participant recruitment processes have been described elsewhere [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eParticipants were included in this analysis if they met the following two criteria: 1) employed (either full-time or part-time), and 2) cognitively sound and responded to the survey independently. There were no missing data for any variables.\u003c/p\u003e \u003cp\u003eMeasures\u003c/p\u003e \u003cp\u003eThe PHI questionnaire consisted of socio-demographics, lifestyle, medical history, and a set of validated measures. The following socio-demographic characteristics and self-reported chronic conditions from the survey were used as covariates in this study to account for any potential confounding effects: age, gender (female or male), ethnicity (Chinese, Malay, Indian or Others), employment status (full-time or part-time), housing type (Public 1\u0026ndash;2 room, Public 3-room, Public 4-room, or Public 5-room and bigger and private properties), region of residence (residing in Central or Northern Singapore) and presence of self-reported diabetes, hypertension and dyslipidemia.\u003c/p\u003e \u003cp\u003eExplanatory variables\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eDepressive symptoms\u003c/h2\u003e \u003cp\u003eThe severity of depressive symptoms was assessed by the 9-item Patient Health Questionnaire (PHQ-9). The PHQ-9 was developed to make symptom-based diagnosis of depression and additionally grade the severity of depressive symptoms [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Each question item was scored on a 4-point ordinal scale and an aggregate depressive symptom score was derived from summing the scores of each item. A higher score indicates greater severity of depressive symptoms. Individuals with a score ranging from 0 to 4 was categorised as \u0026ldquo;without depressive symptoms\u0026rdquo; and those with a score of 5 to 27 was considered as \u0026ldquo;with depressive symptoms\u0026rdquo; [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eSocial isolation\u003c/h2\u003e \u003cp\u003eThe magnitude of social isolation was estimated by the Lubben Social Network Scale-6 (LSNS-6). The LSNS-6 presents a composite scale for measuring social connectedness and examining the relationship of social connectedness to health outcomes [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The LSNS-6 consists of a set of three questions that measure social connectedness with family members and another set of three questions that measure social connectedness with friends. Each item was scored on a 5-point Likert scale, and the scores were added up to form a total score. Higher scores indicate greater levels of social connectedness and lower risk of social isolation. It has been suggested that a higher cut-off score may be more appropriate to identify social isolation in Asian populations due to greater reliance on social relationships [23; 24]. For this study, participants with a score ranging from 17 to 30 was classified as \u0026ldquo;without social isolation\u0026rdquo; and those with a score of 0 to 16 was deemed as \u0026ldquo;socially isolated\u0026rdquo;.\u003c/p\u003e \u003cp\u003eOutcome variable\u003c/p\u003e \u003cp\u003eThe loss in workplace productivity due to health reasons, or more commonly known as HRPL, consists of workplace health-related absenteeism and presenteeism. HRPL was measured by the Work Productivity and Activity Impairment Questionnaire (WPAI). The WPAI was developed as a quantitative measure that measures work impairment as distinct from impairment in other activities of daily living [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The WPAI has been previously used to measure the general health impact of individuals with rheumatoid arthritis and major depressive disorder [26; 27]. The WPAI consists of six questions, of which the number of work days missed and the degree of health-related productivity loss (using a 0 to 10 Visual Analogue Scale) were measured. Absenteeism, defined as missed time from work, (percentage of work time missed due to health problems = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\frac{Hours absent due to health}{\\left[Hours absent due to health+Actual worked hours\\right]} x 100\\)\u003c/span\u003e\u003c/span\u003e). Presenteeism, defined as reduced performance while at work, (percentage of work impaired due to health problems = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\left(\\frac{Impairment rating}{10}\\right) x\\frac{Actual worked hours}{\\left[Hours absent due to health+Actual worked hours\\right]} x 100\\)\u003c/span\u003e\u003c/span\u003e) on a 10-point rating scale [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. To derive the total HRPL (with a score ranging from 0-100), we calculated the percentage of total work impaired due to health problems by summing the percent of absenteeism and percent of presenteeism.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eTo ascertain differences in HRPL among working adults, we used Generalised Linear Model (GLM), with family gamma, log link for the analysis due to the known distribution characteristics of our outcome (non-negative values, right skewed distribution). The coefficients were exponentiated to describe the arithmetic mean ratios between the groups. In the first model, depressive symptoms and social isolation were both added as independent variables to determine their effects on HRPL respectively. In the second model, we tested for significant interactions between the presence of depressive symptoms and social isolation, with productivity loss. Both models were adjusted for socio-demographic variables (including age, gender, ethnicity, employment status, housing type) and self-reported chronic conditions (including the presence of diabetes, hypertension, and dyslipidemia).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eSocio-demographics\u003c/p\u003e\n\u003cp\u003eAmong the 4,005 participants who were surveyed for the PHI study, 2,605 employed participants (65.0%) were identified for this analysis on working adults.\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e presents the socio-demographics of the 2,605 participants. Of which 1,273 (48.9%) were female, the mean age was 47.3 years and 30.9% were aged below 40. There were 727 (27.9%) participants having at least one self-reported chronic condition. Most of the participants were Chinese (73.4%), living in public 4-room flat and bigger (69.7%), and working full-time (82.3%). More than half (54.1%) of participants were residing in Northern Singapore. There were 1,137 participants (43.6%) identified to be at high risk of social isolation, 130 participants (5.0%) having mild-severe depressive symptoms, and 82 participants (3.1%) experienced co-occurring social isolation and depressive symptoms.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSocio-demographics\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"2\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEmployed (n\u0026thinsp;=\u0026thinsp;2,605)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean Age (SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e47.3 (13.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;40 years, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e806 (30.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e40 years and above, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,799 (69.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender, N (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,273 (48.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,332 (51.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEthnicity, N (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChinese\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,913 (73.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMalay\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e318 (12.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e265 (10.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e109 (4.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHousing type, N (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u0026ndash;2 room flat\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e168 (6.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3-room flat\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e622 (23.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4-room flat\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,060 (40.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5-room flat and larger/private housing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e755 (29.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEmployment type, N (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFull-time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2,143 (82.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePart-time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e462 (17.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eRegion of residence, N (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCentral\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,196 (45.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNorthern\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,409 (54.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eWith Diabetes, N (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e253 (9.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eWith Hypertension, N (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e467 (17.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eWith Dyslipidaemia, N (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e464 (17.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDepressive symptoms, N (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNone-minimal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2,475 (95.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMild\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e108 (4.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17 (0.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModerately Severe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3 (0.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSevere\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2 (0.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSocial isolation, N (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow risk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e776 (29.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e692 (26.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh risk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e597 (22.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIsolated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e540 (20.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\"\u003e\u003csup\u003eSD: Standard deviation; IQR: Interquartile range\u003c/sup\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eUnadjusted Productivity Loss\u003c/p\u003e\n\u003cp\u003eThe median percentage of productivity loss reported due to missed work days (absenteeism) was 0.0% for individuals with social isolation, depressive symptoms and both respectively (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Presenteeism appeared to be a bigger component of HRPL, with the median percentage of work productivity affected while at work of 0.0%, 5.0% and 10.0% for individuals with social isolation, depressive symptoms, and both, respectively. The median reported percentage of overall HRPL was 0.0%, 10.0% and 20.0% for individuals with social isolation, depressive symptoms, and both, respectively.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eAssociation between productivity loss with depressive symptoms and social isolation\u003c/p\u003e\n\u003cp\u003eThe results from multiple linear regression analyses are shown in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. Mean HRPL scores were up to 2.81 times (95% Confidence Interval (CI): 2.12, 3.72) higher in participants with depressive symptoms than those without. On the other hand, social isolation was not found to be associated with HRPL scores (1.17, 95% CI: 0.96, 1.42).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAdjusted Productivity Losss for all employed participants\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eMean ratio (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModel 1: Employed participants (N\u0026thinsp;=\u0026thinsp;2,605)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModel 2: Employed participants (N\u0026thinsp;=\u0026thinsp;2,605), with interaction term\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDepressive Symptoms\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(Reference: PHQ-9 score\u0026thinsp;\u0026lt;\u0026thinsp;5)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.81 (2.12, 3.72)***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.81 (1.08, 3.06)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSocial Isolation (Reference: LSNS\u0026thinsp;\u0026ge;\u0026thinsp;17)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.17 (0.96, 1.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.14 (0.93, 1.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDepressive Symptoms##Social Isolation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.89 (1.04, 3.44)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAged below 40 years\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(Reference: Age\u0026thinsp;\u0026ge;\u0026thinsp;40 years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.38 (1.10, 1,73)**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.37 (1.10, 1.72)**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale (Reference: Male)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.22 (1.00, 1.48)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.22 (1.00, 1.48)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEthnicity (Reference: Chinese)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMalay\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.01 (1.56, 2.57)***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.01 (1.57, 2.57)***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.33 (0.97, 1.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.35 (0.98, 1.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.22 (0.74, 2.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.20 (0.72, 2.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHousing type (Reference: 1\u0026ndash;2 room)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3-room\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.10 (0.73, 1.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.10 (0.74, 1.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4-room\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.96 (0.65, 1.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.96 (0.65, 1.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5-room and larger/private housing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.90 (0.60, 1.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.90 (0.59, 1.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEmployed Full-time (Reference: Part-time)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.29 (1.00, 1.67)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.30 (1.01, 1.69)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNorthern region (Reference: Central region)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.69 (1.37, 2.10)***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.69 (1.36, 2.09)***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiabetes (Reference: No diabetes)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.28 (0.92, 1.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.28 (0.92, 1.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHypertension (Reference: No Hypertension)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.04 (0.79, 1.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.05 (0.79, 1.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDyslipidaemia\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(Reference: No Dyslipidaemia)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.52 (1.14, 2.04)**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.52 (1.13, 2.04)**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\"\u003e\u003csup\u003e*p\u0026lt;0.05; **p\u0026lt;0.0;1 ***p\u0026lt;0.001\u003c/sup\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\"\u003e\u003csup\u003eCI: Confidence Interval; PHQ\u0026minus;9: Patient Health Questionnaire\u0026minus;9; LSNS\u0026minus;6: Lubben Social Network Scale\u0026minus;6\u003c/sup\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eUpon including the interaction term in model 2, we observed that participants with depressive symptoms had 1.81 times (95% CI: 1.08, 3.06) higher HRPL scores as compared to those without. Socially isolated participants were not found to be associated with HRPL scores (1.14, 95% CI: 0.93, 1.39). The interaction between depressive symptoms and social isolation was statistically significant (p-value\u0026thinsp;=\u0026thinsp;0.037), with an effect size of 1.89 (95% CI: 1.04, 3.44).\u003c/p\u003e\n\u003cp\u003eFrom both GLM results, depressive symptoms were associated with productivity loss among all working adults, while we did not find evidence for the association between social isolation and productivity loss. The non-exponentiated coefficients (See Supplementary Table\u0026nbsp;1, Additional File 1) present similar trends and the positive interaction term suggest incremental marginal effects on productivity loss.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eMain findings\u003c/p\u003e \u003cp\u003eIn this multi-ethnic community-dwelling working adult population, we observed association between workplace productivity loss and social isolation concomitant with depressive symptoms.\u003c/p\u003e \u003cp\u003eThe interaction term suggested that HRPL is amplified when both co-occurs, which is in alignment with poorer health outcomes (e.g., remission, survival, biological and functional outcomes) that were previously reported among socially isolated individuals with depressive symptoms [14; 29; 30]. Our findings highlighted the potential impact of depressive symptoms and social isolation on work performance and draw attention to the importance of having a holistic work support system that promotes mental wellbeing, social connectedness and work productivity.\u003c/p\u003e \u003cp\u003ePresence of depressive symptoms was associated with productivity loss, however, the same was not observed for presence of social isolation. Our findings suggest that psychological symptoms and experiences potentially have a stronger relationship with work productivity than social connectedness. The interplay of complex psychosocial factors warrants future research and intervention strategies targeted at individuals with depressive symptoms who are at risk of social isolation.\u003c/p\u003e \u003cp\u003eAdditionally, females were observed to be associated with productivity loss. This may be explained by gender differences in occupations and comorbidities [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Ethnicity was also observed to be associated with productivity loss (Malay ethnicity was significantly associated with productivity loss compared to Chinese ethnicity). It could potentially be due to a higher prevalence of chronic conditions among the Malay ethnicities [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], hence strengthening the association with productivity loss respectively. Being employed full-time (reference: employed part-time) was found to be associated with productivity loss, probably because those working full-time were subjected to longer working hours, hence reporting greater presenteeism [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. With entitlement to more paid sick leaves, there is also a tendency for full-timer employees to take additional days off from work [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Those of a younger age was found to be associated with productivity loss in this study. This phenomenon corroborates with the literature where the work performance of older workers outdoes that of the younger workers, due to reasons such as greater job experience and self-assurance to perform better at work [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Residents residing in the Northern region are younger (mean age of 45.5 versus 49.4) and more are employed full-time (84.7% versus 79.4%), likely explaining for their higher productivity loss. Lastly, having dyslipidaemia incurs greater productivity loss, likely due to the higher risks of cardiovascular diseases, impacting productivity [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. It appears that there are multiple factors associated with productivity loss, and further sub-group analyses should be done given a larger study population in the future.\u003c/p\u003e \u003cp\u003eIn this study, we observed that individuals with depressive symptoms and social isolation were 10.0% less productive at work due to their health problems in the past seven days (presenteeism). We postulate that the association between poor mental health and presenteeism has driven HRPL in our study. In a Singaporean study it was estimated that individuals with depressive symptoms and/or anxiety had their productivity at work reduced by 40% [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. In another Malaysian study, mental health was reported to be a predictor of presenteeism, and physical health as a predictor of absenteeism [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Our findings underscore the need to focus more efforts on enhancing at-work productivity and putting in place policies to encourage employees to take time-off from work for their mental health when needed.\u003c/p\u003e \u003cp\u003eStrengths and Limitations\u003c/p\u003e \u003cp\u003eAs a population-based study, the primary strength of this study is its representativeness of the population in Central and Northern regions of Singapore. Singapore is a multi-ethnic Asian population and the associations presented in this study are likely observable in the rest of the Asian countries. Other strengths of this study included the use of standardised questionnaire. This study was able to evaluate multiple factors associated with productivity loss, allowing for sub-group analyses and adjustment for various socio-economic factors. Lastly, the magnitude of productivity loss was based on self-reported data with a recall period of 7 days. Although the main outcome of interest is subject to recall errors, the extent of recall bias was possibly minimised by the short recall period.\u003c/p\u003e \u003cp\u003eThis study, however, is not without limitations. The study design is a cross-sectional study, and we are unable to ascertain the temporal sequence of the explanatory factors and main outcome of interest. There had been a 5-year Finnish cohort study that had established Major Depressive Disorder as a predictor of poor work outcomes among psychiatric subjects [\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. In another 12-year American cohort study among older adults, it was found that depressive symptoms predicted social isolation, while social isolation was not a predictor of depressive symptoms [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. The temporal sequence of depressive symptoms, social isolation and work productivity loss altogether need further investigation. Our study only considered productivity loss at the workplace, whereas other studies also looked at activity impairment in unpaid work activities. Additionally, the prevalence of depressive symptoms reported in our study is possibly underreported due to existing stigma. Hence, the actual productivity loss associated with depressive symptoms could be much higher. As this study only included working adults, those that were non-employed were excluded from the analysis. It is unclear if 40.7% of those socially isolated and 49.8% of those with depressive symptoms were not employed in this study due to social isolation and depressive symptoms. Although this study did not conduct qualitative interviews to further investigate the psychosocial reasons for productivity loss, existing qualitative findings suggest that a work-culture which values employees\u0026rsquo; well-being, alongside supportive peers and employers, and assisted return-to-work arrangements do influence return-to-work outcome of employees with depression [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Further research could be a cohort study with mixed-methods approach to better understand the relationship between depressive symptoms, social isolation, and workplace productivity loss.\u003c/p\u003e \u003cp\u003eImplications\u003c/p\u003e \u003cp\u003eTo our knowledge, this is the first study exploring the association of depressive symptoms and social isolation, with workplace productivity loss in community-dwelling working adult population in Singapore. Our results corroborate with the existing literature that have shown associations between poorer work adjustments and absenteeism, with depressive symptoms and social isolation [\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Our findings on the association between productivity loss and psychosocial factors (productivity loss of up to 1.89 times more) echo the importance of managing social isolation and depressive symptoms at the workplace. More structural support (workplace policies) and social support (from peers and employers) are needed for the promotion of workplace productivity and well-being.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study suggests an association between productivity loss and depressive symptoms, and the association is stronger when working adults were simultaneously reported to be socially isolated. We observed a greater magnitude of reduced productivity at work as compared to missed time from work. With the increasing focus on well-being at the workplace, our findings reiterate the importance of building a supportive work environment and forging strong social networks, as mental well-being and work productivity are closely interconnected.\u003c/p\u003e"},{"header":"Abbreviations ","content":"\u003cp\u003eMDD Major Depressive Disorder\u003c/p\u003e\n\u003cp\u003eCOVID-19 Coronavirus disease-2019\u003c/p\u003e\n\u003cp\u003ePHI Population Health Index\u003c/p\u003e\n\u003cp\u003eHRPL Health-related productivity loss\u003c/p\u003e\n\u003cp\u003eNHG National Healthcare Group\u003c/p\u003e\n\u003cp\u003ePHQ-9 Patient Health Questionnaire-9\u003c/p\u003e\n\u003cp\u003eLSNS-6 Lubben Social Network Scale-6\u003c/p\u003e\n\u003cp\u003eWPAI Work Productivity and Activity Impairment Questionnaire\u003c/p\u003e\n\u003cp\u003eGLM Generalised Linear Model\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCI Confidence Interval\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe PHI study was approved by the ethics review committee of the National Healthcare Group Domain Specific Review Board (Reference Number: 2015/00269).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWritten informed consent was obtained from all individual participants after they were being informed about the study objectives.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWritten informed consent was obtained from all individual participants after they were being informed about the study objectives.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by National Healthcare Group Pte Ltd in the form of salaries for all authors. The funder had no role in / influence on study design, data collection and analysis, decision to publish, or preparation of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJ.H.W.Y.\u0026nbsp;analysed and interpreted the data and wrote the manuscript. W.F.Y. interpreted the data and revised the manuscript. L.G. conceived the study and revised the manuscript. C.W.Y. conceived the study and revised the manuscript. M.J.P. conceived the study, interpreted the data, and revised the manuscript. \u0026nbsp;All authors have read and approved the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eCollaborators, C.-M. D. (2021). 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Union perceptions of factors related to the return to work of employees with depression. \u003cem\u003eJ Occup Rehabil\u003c/em\u003e,\u003cem\u003e 25\u003c/em\u003e(2), 335-347. https://doi.org/10.1007/s10926-014-9542-5 \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Productivity, Economic impact, Mental health, Depression, Social isolation","lastPublishedDoi":"10.21203/rs.3.rs-4463422/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4463422/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe association between health-related productivity loss (HRPL) with social isolation and depressive symptoms is not well studied. We aim to examine the association between social isolation and depressive symptoms with productivity loss.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe National Healthcare Group (NHG) Population Health Index (PHI) study is a population-based study on community-dwelling employed adults aged ≥21 years, residing in the Central and Northern of Singapore.\u003c/p\u003e\n\u003cp\u003eThe severity of depressive symptoms and social isolation were assessed using the 9-item Patient Health Questionnaire (PHQ-9) and Lubben Social Network Scale-6 (LSNS-6) respectively. Productivity loss was assessed using the Work Productivity and Activity Impairment Questionnaire (WPAI). We used Generalised Linear Models, with family gamma, log link for the analysis. Models were adjusted for socio-demographic variables (including age, gender, ethnicity, employment status, housing type) and self-reported chronic conditions (including the presence of diabetes, hypertension, and dyslipidemia).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere were 2,605 working (2,143 full-time) adults in this study. The median reported percentage of unadjusted productivity loss was 0.0%, 10.0% and 20.0% for individuals with social isolation, depressive symptoms, and both, respectively. In the regression analysis, mean productivity loss scores were 2.81 times (95% Confidence Interval: 2.12, 3.72) higher in participants with depressive symptoms than those without. On the other hand, social isolation was not found to be associated with productivity loss scores (1.17, 95% Confidence Interval: 0.96, 1.42). The interaction term of depressive symptoms with social isolation was statistically significant, with an effect size of 1.89 (95% Confidence Interval: 1.04, 3.44). It appears that productivity loss was amplified when social isolation and depressive symptoms are concomitant.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur results suggested associations between depressive symptoms and social isolation with productivity loss. The findings highlighted the potential impact of depressive symptoms and social isolation on work performance and draw attention to the importance of having a holistic work support system that promotes mental wellbeing, social connectedness and work productivity.\u003c/p\u003e","manuscriptTitle":"Association of social isolation and depressive symptoms with workplace productivity loss: A multi- ethnic Asian study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-10 22:22:27","doi":"10.21203/rs.3.rs-4463422/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-06-26T12:16:05+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-25T09:13:12+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-02T12:07:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"159427253938524685908048355594013977","date":"2024-06-01T00:38:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"15206125932154795531852975456843877621","date":"2024-05-31T12:48:09+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-05-24T10:50:14+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-05-24T10:43:47+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-05-24T10:37:57+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-05-24T10:30:46+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-05-23T00:59:20+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f73be5f9-26b8-4b9a-ac4c-7543585dbdc0","owner":[],"postedDate":"June 10th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":32711079,"name":"Health sciences/Health care"},{"id":32711080,"name":"Health sciences/Health occupations"}],"tags":[],"updatedAt":"2024-09-30T16:00:06+00:00","versionOfRecord":{"articleIdentity":"rs-4463422","link":"https://doi.org/10.1038/s41598-024-73272-4","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2024-09-27 15:57:05","publishedOnDateReadable":"September 27th, 2024"},"versionCreatedAt":"2024-06-10 22:22:27","video":"","vorDoi":"10.1038/s41598-024-73272-4","vorDoiUrl":"https://doi.org/10.1038/s41598-024-73272-4","workflowStages":[]},"version":"v1","identity":"rs-4463422","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4463422","identity":"rs-4463422","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}