A Group-based Dual Trajectory of Depression and Anxiety in Older Adults: An 8-year Follow-up of Population-based Study

A Group-based Dual Trajectory of Depression and Anxiety in Older Adults: An 8-year Follow-up of Population-based Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A Group-based Dual Trajectory of Depression and Anxiety in Older Adults: An 8-year Follow-up of Population-based Study Yanzhao Cheng, Lilian Thorpe, Rasel Kabir, Hyun Ja Lim This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-263107/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Background : Depression and anxiety are common mental health conditions for elderly population. Understanding the trajectory developments of them will help us implementing treatments and interventions. Aims: This study aims to identify depression and anxiety trajectories in the elderly, evaluate the interrelationship of these conditions, and recognize trajectory-predicting characteristics. Methods : Group-based dual trajectory modeling (GBDTM) was applied to the data of 3,983 individuals, aged 65 years or older who participated in the Korean Health Panel Study between 2008 and 2015. Logistic regression was used to identify the association between characteristics and trajectory groups. Results : Four trajectory groups from GBDTM were identified in both the depression and anxiety outcomes. Depression has: “low-flat (87.0%)”, “low-to-middle (8.8%)”, “low-to-high (1.3%)” and “high-stable (2.8%)” trajectory groups. Anxiety has: “low-flat (92.5%)”, “low-to-middle (4.7%)”, “high-to-low (2.2%)” and “high-curve (0.6%)” trajectory groups. Interrelationship between depression and anxiety were identified. Members of the high-stable depression group were more likely to have “high-to-low” or “high-curved” anxiety trajectories. Female sex, the presence of more than three chronic diseases, and having income generating activity were significant factors in depression and anxiety. Conclusions : Dual trajectory analysis of depression and anxiety in older adults shows that when one condition is present, the probability of the other is increased. Sex, having more chronic disease, and income generating activity might be at increased risks for both depression and anxiety. Health policy decision-makers can use our findings in developing strategies for prevention of both depression and anxiety in older adults. Geriatrics & Gerontology depressive disorder trajectory anxiety longitudinal studies elderly Figures Figure 1 Figure 2 Introduction Along with the difficulties of aging, elderly individuals are known to be at risk of both depression and anxiety disorders [ 1 ]. Recently, research focused on studying the comorbidity of depression and anxiety in elders has increased [ 2 ]. One likely reason is the aging population, with the proportion of elderly increasing every year in almost every country [ 3 ]. In South Korea, concerns related to aging are substantial, as individuals 65 years or older constitute approximately 14% of the overall population, more than twice the proportion of people age 14 or younger [ 4 ]. Another reason for combined interest in these conditions is that a variety health conditions may influence mental health in the elderly population, potentially generating more than one illness. For example, an elderly person who suffers one or more chronic disease has a greater chance of developing late-onset depression and/or anxiety [ 5 , 6 , 7 ]. Although it has been suggested that comorbidity between depression and anxiety is less in elders compared to the younger adults [ 8 ], it is still of considerable concern. Beekman found that 47.5% of individuals age 65 years or older who suffer from depression also have a comorbid anxiety disorder [ 9 ]. A Korean study included 1204 elderly, showed 22.8% of them had comorbid anxiety and depression [ 10 ]. In terms of psychopathology, elderly patients suffering both conditions simultaneously are regard as more severe cases compared to patients with only one disorder [ 1 ]. Depression comorbid with anxiety has been shown to result in more severe somatic symptoms compared to patients with only depression [ 11 , 12 ]. Moreover, poorer social function and higher suicide rate persist in the comorbid depression and anxiety patient versus the patient with only depression or anxiety [ 13 , 14 ]. The common risk factors for both depression and anxiety in the elderly have been described in three broader categories: biological, psychological and social [ 15 ], including the influences of physical illness, disability, bereavement, and others. Nevertheless, longitudinal studies suggest that there are some risk factor differences between elderly depression and anxiety. Among biological risk factors, cognitive functional impairment and visual defect relate to depression but not anxiety, whereas hypertension is risk factor only in anxiety [ 9 , 16 , 17 , 18 , 19 , 20 ]. Also, psychological risk factors have no difference between depression and anxiety [ 9 , 17 , 20 ]. Evaluating social risk factors, marital status and social network size have been correlated with only depression [ 21 , 22 , 23 ]. In contrast, risk factors associated with anxiety but not depression include being childless, traumatic life events, and low income [ 9 , 16 , 17 , 18 , 19 , 20 , 24 , 25 ]. The trajectory analysis of both depression and anxiety were usually developed based on youth and adolescence [ 26 , 27 , 28 , 29 ]. To study the trajectory classes in elderly, researchers often focused on only depression [ 30 , 31 , 32 , 33 , 34 , 35 ]. However, depression and anxiety trajectories were rarely studied simultaneously. Only three studies developed the trajectories for both depression and anxiety in elderly [ 36 , 37 , 38 ], while these two studies used separate trajectory models to develop depression and anxiety trajectories. The present study seeks to examine the longitudinal association between depression and anxiety in the elder population with a joint trajectory modeling method. The aims of our study are (i) to describe the trajectories of depression and anxiety, based on heterogeneity, simultaneously for comparison; (ii) to evaluate the relationship between depression and anxiety trajectory groups; and (iii) to identify predictors that may associate with the depression and anxiety trajectory groups. Methods Data and Sample This study utilized a subset of a longitudinal survey called the Korea Health Panel Study (KHPS). The KHPS samples, collected by the Korean Institute for Health and Social Affairs in conjunction with the National Health Insurance Service, were selected and weighted from 2005 Korean Population and Housing Census. The KHPS began in 2008 and incorporated a total of 24,616 participants from 7387 households. In 2014, KHPS was strengthened with the addition of more the 2520 household to mitigate attrition. Using computer assisted personal interviews, trained staff collected the data with three aspect: household, individual, and case-based sections. Comprehensive assessments on the use of healthcare services, cost of healthcare, and potentially influential factors have been conducted annually since 2008. Further details of the KHPS administration and mission are available on the KHPS website [ 39 ]. Overall, 3983 elderly who were 65 or older were studied. The additional subjects in year 2014 and 215 were moved to year 2008 and 2009. The follow-up participation rates among this study’s sample were 91.0%, 63.4%, 60.6%, 56.5%, 53.4%, 50.0% and 46.4% from year 2009 to 2015, respectively. The average number of responses was 5.2 times. Measures The data collection methods for the KHPS involved the investigators visiting the target households and using computer-assisted personal interviewing (CAPI) technique. Covariates included sex, age, education, marital status, residential area, number of members in the household, household composition type, housing type, current chronic disease status, private health insurance, household income quantile, and household expense. The main dichotomous outcomes of depression and anxiety were collected from medical expenses, including prescription drug receipts or medical institutions/pharmacies, potentially leading to inadequate recognition of our sample outcomes. If there is any annually using of inpatient treatment, outpatient treatment, or emergency-service utilization from the case-based survey relevant to depression or anxiety, the outcome is assigned to 1, otherwise, the outcome is assigned to 0. Statistical Analysis The analysis commenced with basic descriptive statistics to characterize the study sample. General group-based trajectory modeling (GBTM) with single repeated measurement outcome, also called latent class growth modeling, has the ability to identify unobserved heterogeneous subgroups within the sample population and develop trajectories based on the trends within them [ 40 ]. The individuals are placed into the most likely subgroups relying on the largest posterior probability (i.e. the probability that an individual with a particular outcome pattern belongs to a particular group among those suggested by the model) [ 41 ]. To estimate the model, maximum likelihood estimation is used, and thus the model has the ability to handle the missing, unbalanced data [ 42 ]. Group-based dual trajectory modeling (GBDTM) determines the trajectories of two associated outcomes, such as depression and anxiety in our study [ 43 ]. The relationship between the two outcomes has been labeled as “comorbidity” or “heterotypic continuity” [ 44 ]. “Comorbidity” recognizes multiple illness states occurring at the same time [ 45 ]. “Heterotypic continuity” recognizes that two outcomes may linked within an individual but do not occur simultaneously, introducing the possibly of causality [ 46 ]. In GBDTM, the linkage of trajectory groups identified within the respective two associated outcomes relies on the conditional probabilities. For example, since depression and anxiety are diagnosed as co-current events in our study, the conditional probabilities for both depression given anxiety and anxiety given depression were considered [ 47 ]. Then, this conditional probability represents the likelihood of a person having depression if anxiety is present, and vice versa [ 44 ]. To select the most appropriate number of trajectories, separate GBTM for depression and anxiety with the number of trajectory groups from two to five were tried. All the trajectories from the model were assumes to be linear. Bayesian Information Criterion (BIC) [ 48 ], Akaike Information Criterion (AIC) [ 49 ], and the average posterior probability [ 50 ] were utilized to evaluate the model fit. First, the separate GBTM for depression and anxiety were conducted. Then, based on the result from the separate GBTM, GBDTM was developed. SAS programming 9.4 and Proc Traj package was utilized for trajectory modeling and logistic regression. Significant level of this study was set at α = 0.05. Results 3983 participants, who were age 65 or older from KHPS, were investigated during the study period. In this sample, 57% were female and the average age at baseline measurement was 72.4 (SD ± 6) years. Among these participants, 63% had never received any education or only finished elementary school; 62.5% lived with their spouse, and 1.6% lived alone. The majority (83%) reported that their income level was lower than the median income level of the complete survey sample, and 37% were still undertaking income generating activities. Only 38.2% lived in metro-cities, and 57.4% resided in a detached house. Based on model selection criteria, four trajectory groups was identified as the best fit for both the depression and anxiety outcomes from GBDTM (Table 1 ). Duel trajectory modeling, with results for the four trajectory groups specified within each outcome, is presented with depression: “low-flat (87.0%)”, “low-to-middle (8.8%)”, “low-to-high (1.3%)” and “high-stable (2.8%)” trajectory groups (Figs. 1 A) and anxiety “low-flat (92.5%)”, “low-to-middle (4.7%)”, “high-to-low (2.2%)” and “high-curved (0.6%)” trajectory groups (Figs. 1 B). The characteristics of the sample are described according to the respective trajectory groups from GBDTM (Tables 2 and 3 ). Table 1 Goodness of fit to select the optimal number of trajectory group for depression and anxiety Number of trajectories Depression Anxiety BIC AIC PP BIC AIC PP 2 -2534.8 -2530.7 0 -1525.8 -1521.7 0 3 -2403.8 -2397.2 0 -1484.2 -1477.6 0 4 -2363.7 -2354.7 0.83 -1476.3 -1467.2 1 5 -2365.3 -2353.8 0.17 -1489.8 -1478.3 0 BIC = Bayesian information criterion, AIC = Akaike information criterion, PP = Posterior probability Conditional probabilities from GBDTM provided a straightforward view of the association between depression and anxiety in the dual trajectory model. The most likely anxiety trajectory regardless of depression trajectory was low-flat, with probabilities ranging from 95.7% probability in the low-flat depression group to 68.5% in the high-stable depression group (Fig. 2 A). However, 22.9% of low-to-middle depression trajectory participants were in the low-to-middle anxiety trajectory group and 21.1% of the high-stable depression group followed a high-to-low anxiety trajectory; respective proportions for these anxiety trajectories were highest among these respective depression trajectories. Persistent anxiety trajectories (high-curved) were only observed among respondents with low-to-middle or high-stable trajectory depression patterns and in small proportions (Fig. 2 A). From the conditional probability of depression given anxiety (Fig. 2 B), the proportions with low-flat depression were again high in the low-flat anxiety group (90.0%), but less so among the other anxiety trajectories (high-to-low, 56.5%; low-to-middle, 48.9%; and high-curved, 23.9%). Individuals within the low-to-middle anxiety trajectory were almost as likely to follow a low-to-middle depression trajectory (43.0%) as a low-stable one (48.9%). Similarly, participants in the persistent anxiety group (high-curved) also frequently showed a low-to-middle depression trajectory (59.5%). Although participants in the high-to-low anxiety group did most often follow a low-flat depression trajectory (56.5%), the high-stable depression trajectory was also relatively common in this group (27.0%) and in the high-curved group (16.6%). The low-to-high depression trajectory was generally infrequent in all the groups (< 4% in all anxiety trajectories). Univariate (Table 4 and Table 5 ) and multivariate logistic regression utilized the low-flat group as reference in both the depression and anxiety trajectory outcomes. In multivariate analyses of depression trajectory groups (Table 6 ), individuals within the low-to-high depression trajectory were more likely to be in the low-to-middle anxiety trajectory group (OR = 5.80, 95% CI: 1.83–19.68, p-value = 0.005) than the low-flat depression group and more likely to be not participating in income generating activity (OR = 2.79, 95% CI: 1.15–6.80, p-value = 0.024). Similarly, members of the low-to-middle depression group were also more likely to be in the low-to-middle anxiety trajectory group (OR = 9.26, 95% CI: 5.63–15.22, p-value < 0.0001) or the high-curved anxiety trajectory group than the low-flat depression group (OR = 25.91, 95% CI: 7.45–90.15, p-value < 0.0001), be female (OR = 1.51, 95% CI: 1.07–2.12, p-value = 0.018) and have multiple chronic diseases (OR = 3.96, 95% CI: 1.83–8.59, p-value = 0.0005). Participants in the high-stable depression trajectory group were more often in the low-to-middle (OR = 10.65, 95% CI: 5.30–21.38, p-value < 0.0001) or high-curved anxiety trajectories (OR = 17.31, 95% CI: 3.04–98.68, p-value < 0.0001) and were more likely to be female (OR = 1.87, 95% CI: 1.02–3.14, p-value = 0.042), have multiple chronic diseases (OR = 4.18, 95% CI: 1.01–17.31, p-value = 0.049), and not have income generating activity (OR = 1.91, 95% CI: 1.02–3.59, p-value = 0.044) than the low-flat depression group. When anxiety trajectory groups were used as the outcome, the depression trajectory groups were similarly associated with the anxiety trajectories (Table 7 ). In the multivariate logistic analysis, female sex was an adjusted predictor for low-to-middle anxiety group membership (OR = 1.73, 95% CI: 1.09–2.76, p-value = 0.021) and high-to-low anxiety group membership (OR = 2.17, 95% CI: 1.28–3.69, p-value = 0.025) versus the low-flat anxiety trajectory. Not taking income generating activity was another predictor of high-to-low anxiety group membership over low-flat membership (OR = 2.17, 95% CI: 1.28–3.69, p-value = 0.025). Discussion In this longitudinal study, data gathered from the KHPS included 3983 Korea elderly people with potential to receive care for depression and/or anxiety symptoms within an 8 year follow up period. To examine the tendency to suffer these two mental health problems simultaneously, which may change over time, GBDTM was applied to identify trajectories of co-occurring depression and anxiety. Among the four groups recognized for their differing probabilities of depression, a large majority showed no depression and were generally unlikely to experience anxiety concomitantly. However, slightly more than 10% did experience depression during the follow up period, with most of these individuals showing a gradual increase in depression probability. Among individuals following this trajectory, 20% also experienced a moderately increase in anxiety risk over time. Regarding anxiety, also recognized to follow four trajectories, the vast majority of respondents did not experience this condition and were also free of depression, although 5% again saw a slow increase in anxiety propensity over time. This was accompanied by an increasing depression tendency in just under half the cases. In general, female sex, not taking income generating activity, and membership in a trajectory suggesting risk for the alternate condition independently predicted a more vulnerable risk trajectory than “low-flat” for both depression and anxiety. Our presentation of four trajectory groups aligns with other depression trajectories studies focused on the elderly [ 30 , 31 , 32 , 33 , 35 , 51 ]. However, researchers have only infrequently identified older adult anxiety trajectories: a six-year cohort study of depression and anxiety trajectories in older adults by Holmes et al, which only involved two trajectories (a stable anxiety trajectory (82%) and an elevated anxiety trajectory (18%)) [ 36 ] and two studies recognizing three anxiety trajectory groups in older adults [ 37 , 38 ]. In our study, four anxiety trajectory groups were identified. The anxiety trajectory shapes of these latter studies are different than our study as one focused on depressed older patients and the other on musculoskeletal pain patients. Among our four depression trajectory groups, no decreasing trajectory was found for depression. The high-stable depression group is thought to have less likelihood of recovery in our older population as this life stage is more likely to include reduced life-satisfaction, low income and living quality, and poor health conditions [ 52 , 53 , 54 ]. The low-to-high depression group had an intense increase in depression occurrence from 2009 to 2013 but only contained 31 older adults. The markedly increased probability may have been precipitated by sudden serious events, such as the loss of spouse, physical incapacity, etc. However, among anxiety trajectories, a declining trajectory and a curved shape trajectory showed evidence of a decreasing risk. One explanation may be a reduction in stressful income generating activity as time going by. A possible second explanation for this observe decline is that individuals adapt or cope to their anxious feeling and no longer seek treatment. A third explanation may be that other more pressing medical conditions emerge, eclipsing anxiety management; as such anxiety may still have been present but not identified [ 55 ]. The high-curve anxiety trajectory only involves 13 elderly, which is extremely small. However, since the constant high anxiety probability of these subjects, this group is important and cannot be replaced by other trajectory groups. The association between depression and anxiety were clearly identified from the conditional probabilities of the trajectories and the logistic regression odds ratios. The current finding that the low-to-high and low-to-middle depression group also had risk of being in the low-to-middle anxiety group suggests that older adults with an increasing trend of depression over time also have a greater chance of increasing anxiety, consistent other research [ 56 , 57 ]. Moreover, low-to-middle depression group members made up a high proportion in the high-curved anxiety group, suggesting that older patients who had severe anxiety may suffer mild depression as well. High-stable depression group members were more likely to have anxiety risk following the high-to-low and, less frequently, the high-curved anxiety trajectory; individuals in this particular overlap have serious mental health conditions and require more attention [ 2 ]. The association between depression and anxiety status is also supported by the inverse of these findings; individuals in this study who did not have one of the study conditions tended not to have the other also. Our evaluation of demographic risk factors shows varying alignment with the literature. In the majority of the depression and anxiety studies, sex does have an association with these conditions, suggesting older females generally at greater risk [ 58 , 59 ]. Our study’s findings in this regard are consistent with results from other trajectory studies [ 34 , 35 , 36 , 60 ]. Nevertheless, other studies have found no sex-specific differences when investigating depression and anxiety [ 38 , 61 ]. This inconsistency may be related to different economic circumstances, social cultural factors, psychosocial gender roles, or other population differences. In our study, age was a significant univariate influence of depression only, accordant with [ 36 ]. Level of education was not a significant predictor of either outcome, which is consistent with some studies [ 36 , 62 , 63 ], but not others [ 30 , 31 , 32 , 33 , 34 , 35 , 38 , 64 ]. In our study, this lack of relationship may be attributable to the relatively low education level in our respondents overall. Social factors are also known to influence mental health. Some studies suggest that older adults living along or those having no partner within an isolated social environment have a higher risk of depression or anxiety [ 60 , 65 , 66 , 67 , 68 , 69 ]. However, living alone and marital status did not relate to the outcomes in our study, which is consistent with other studies [ 32 , 33 , 34 , 37 , 70 ]. The risk behaviors of smoking or excessive drinking might also increasing the risk of depression and anxiety [ 31 , 32 , 67 , 71 ]. Nevertheless, this association was not identified in our study or in the work of others [ 35 , 65 ]. Studies show that homeownership reduces the risk of depression and anxiety [ 65 , 72 ], but this association did not remain in the multivariate analysis. Income generating activity, however, did predict both depression and anxiety trajectory groups, suggesting that people in later life who were still working and having financial security may have better mental health. Chronic diseases (heart disease, stroke, diabetes asthma, cancer, arthritis, osteoporosis etc.) are understandably difficult challenges for the elderly that may impact mental health. In studying the relationship between depression, anxiety, and chronic disease, Clarke and Kay reviewed 159 papers between 1995 and 2007, finding that depression was correlated with nearly all chronic diseases [ 73 ]. However, anxiety only associated with heart disease, stroke, and diabetes mellitus. Moreover, the depression and anxiety patients who were diagnosed with heart disease, stroke, cancer, and arthritis were difficult to treat [ 73 ]. In our study, the older adults who have more than three chronic disease were more likely to develop depression. However, in anxiety trajectory groups, chronic disease was only significant in “high-to-low” group from the univariate analysis, but not in the multivariate analysis. Studies have showed that older adults with physical illness or disability usually positive correlate with depression and anxiety [ 65 , 74 , 75 , 76 ], but physical/mental disability were not observed to predict these outcomes in the multivariate analysis. Several limitations should be considered in this study. First, these outcomes were collected from medical expenses and prescription drug receipts or from medical institutions/pharmacies, potentially leading to inadequate recognition of outcomes in our sample. This is particularly true in the context of other chronic disease conditions [ 77 ]. Another limitation was the low prevalence of anxiety across the survey period; this limited predictor evaluation, particularly in the poorly populated trajectories such as the “high-curved” group (n = 13). Third, although the current study employed data from a large elderly subsample of the KHPS dataset, around 35% of the outcome measurements were missing, which might result in bias even though the maximum likelihood estimation and GBDTM were used. Fourth, the variables included in this study did not contain all the potentially important health and psychosocial aspects that may be associated with depression and anxiety, such as stressful life events and social/family support information. And lastly, these observations are all made within the specific cultural context of Korean elderly, which may not be generalizable to all contexts. In summary, four trajectory groups of both depression and anxiety were generated among the elderly of the KHPS dataset. The large majority of older adults belong to the low-flat trajectory group for both depression (87.0%) and anxiety (92.5%), which suggests that most older adults do not identify depression and anxiety as problems. However, among those who do, an interrelationship between these diagnoses, particularly in those with anxiety, is evident from GBDTM. Female sex, the presence of 3 or more chronic diseases, and involvement in income generating activity are additional predictors for a concerning depression trajectory group, and with the exception of chronic diseases, for the anxiety trajectories as well. The findings of this study can be used to assist health policy decision-makers in identifying individuals at risk for comorbid depression and anxiety and aid in devising supports for older individuals at risk of deteriorating mental health. Declarations Ethics approval and consent to participate This study does not require ethics approval because the data is publicly available. Consent for publication Not applicable Availability of data and materials The datasets analysed during the current study are available in the Korea Health Panel Study repository: https://www.khp.re.kr:444/eng/main.do . Competing interests The authors declare that they have no competing interests. Funding The authors declare that they have no funding support for this study. Authors' contributions YC performed the statistical analysis and wrote the first draft of the manuscript. RK performed data management. LT provided clinical input to the study. HL conceived the original study idea, lead the study and wrote the manuscript. All authors read and approved the final manuscript. Acknowledgements The authors thank all the study participants for generously joining this survey. The authors also thank all the research staff who did data collection and recruitment of participants. The authors thank the reviewers for their helpful comments. References Byrne GJ, Pachana NA. Anxiety and depression in the elderly: do we know any more?. Current opinion in psychiatry. 2010 Nov 1;23(6):504-9. Lenze EJ. Comorbidity of depression and anxiety in the elderly. Current Psychiatry Reports. 2003 Feb 1;5(1):62-7. WPA. World Population Aging. Electronic format 2017. 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European Psychiatry. 2017 Jul;44:134-40. KHPS. Korea Health Panel Study 2016. Available at: https://www.khp.re.kr:444/eng/main.do . Accessed 16 February 2021. Nagin DS. Analyzing developmental trajectories: a semiparametric, group-based approach. Psychological methods. 1999 Jun;4(2):139. Huang DY, Lanza HI, Anglin MD. Association between adolescent substance use and obesity in young adulthood: a group-based dual trajectory analysis. Addictive behaviors. 2013 Nov 1;38(11):2653-60. Jones BL, Nagin DS, Roeder K. A SAS procedure based on mixture models for estimating developmental trajectories. Sociological methods & research. 2001 Feb;29(3):374-93. Nagin DS, Tremblay RE. Analyzing developmental trajectories of distinct but related behaviors: a group-based method. Psychological methods. 2001 Mar;6(1):18. Nagin D. Group-based modeling of development Harvard University Press. Cambridge, Mass. 2005. Kessler RC, McGonagle KA, Zhao S, Nelson CB, Hughes M, Eshleman S, Wittchen HU, Kendler KS. Lifetime and 12-month prevalence of DSM-III-R psychiatric disorders in the United States: results from the National Comorbidity Survey. Archives of general psychiatry. 1994 Jan 1;51(1):8-19. Caspi A, Roberts BW. Personality development across the life course: The argument for change and continuity. Psychological inquiry. 2001 Apr 1;12(2):49-66. Wiesner M, Kim HK. Co-occurring delinquency and depressive symptoms of adolescent boys and girls: a dual trajectory modeling approach. Developmental psychology. 2006 Nov;42(6):1220. Schwarz GJ. The annals of statistics. Estimating the dimension of a model. 1978;6:461-4. Akaıke H. A new look at the statistical model identification. IEEE Trans. Automatic Control. 1974 Dec;19:716-23. Kass RE, Wasserman L. A reference Bayesian test for nested hypotheses and its relationship to the Schwarz criterion. Journal of the american statistical association. 1995 Sep 1;90(431):928-34. Xiang X, Cheng J. Trajectories of major depression in middle‐aged and older adults: A population‐based study. International journal of geriatric psychiatry. 2019 Oct;34(10):1506-14. Dew MA, Whyte EM, Lenze EJ, Houck PR, Mulsant BH, Pollock BG, Stack JA, Bensasi S, Reynolds III, MD CF. Recovery from major depression in older adults receiving augmentation of antidepressant pharmacotherapy. American Journal of Psychiatry. 2007 Jun;164(6):892-9. You KS, Lee HO, Fitzpatrick JJ, Kim S, Marui E, Lee JS, Cook P. Spirituality, depression, living alone, and perceived health among Korean older adults in the community. Archives of Psychiatric Nursing. 2009 Aug 1;23(4):309-22. Jang Y, Small BJ, Haley WE. Cross-cultural comparability of the Geriatric Depression Scale: comparison between older Koreans and older Americans. Aging & Mental Health. 2001 Feb 1;5(1):31-7. AAPG. Anxiety and Older Adults: Overcoming Worry and Fear 2019. Available at: https://www.aagponline.org/index.php?src=gendocs&ref=anxiety . Accessed 17 February 2021. Wetherell JL, Gatz M, Craske MG. Treatment of generalized anxiety disorder in older adults. Journal of consulting and clinical psychology. 2003 Feb;71(1):31. Bassil N, Ghandour A, Grossberg GT. How anxiety presents differently in older adults. Curr Psychiatry. 2011 Mar;10:65-72. McLean CP, Asnaani A, Litz BT, Hofmann SG. Gender differences in anxiety disorders: prevalence, course of illness, comorbidity and burden of illness. Journal of psychiatric research. 2011 Aug 1;45(8):1027-35. Girgus JS, Yang K, Ferri CV. The gender difference in depression: are elderly women at greater risk for depression than elderly men?. Geriatrics. 2017 Dec;2(4):35. El-Gilany AH, Elkhawaga GO, Sarraf BB. Depression and its associated factors among elderly: A community-based study in Egypt. Archives of gerontology and geriatrics. 2018 Jul 1;77:103-7. Taylor MG, Lynch SM. Trajectories of impairment, social support, and depressive symptoms in later life. The Journals of Gerontology Series B: Psychological Sciences and Social Sciences. 2004 Jul 1;59(4):S238-46. Norris FH, Murrell SA. Prior experience as a moderator of disaster impact on anxiety symptoms in older adults. American Journal of Community Psychology. 1988 Oct;16(5):665-83. Hong SI, Hasche L, Bowland S. Structural relationships between social activities and longitudinal trajectories of depression among older adults. The Gerontologist. 2009 Feb 1;49(1):1-1. Andreescu C, Chang CC, Mulsant BH, Ganguli M. Twelve-year depressive symptom trajectories and their predictors in a community sample of older adults. International psychogeriatrics/IPA. 2008 Apr;20(2):221. Kang HJ, Bae KY, Kim SW, Shin IS, Yoon JS, Kim JM. Anxiety symptoms in Korean elderly individuals: a two-year longitudinal community study. International psychogeriatrics. 2016 Mar 1;28(3):423. Chong MY, Chen CC, Tsang HY, Yeh TL, Chen CS, Lee YH, Tang TC, Lo HY. Community study of depression in old age in Taiwan: prevalence, life events and socio-demographic correlates. The British Journal of Psychiatry. 2001 Jan;178(1):29-35. Mehta KM, Simonsick EM, Penninx BW, Schulz R, Rubin SM, Satterfield S, Yaffe K. Prevalence and correlates of anxiety symptoms in well‐functioning older adults: findings from the health aging and body composition study. Journal of the American Geriatrics Society. 2003 Apr;51(4):499-504. Brown JW, Liang J, Krause N, Akiyama H, Sugisawa H, Fukaya T. Transitions in living arrangements among elders in Japan: does health make a difference?. The Journals of Gerontology Series B: Psychological Sciences and Social Sciences. 2002 Jul 1;57(4):S209-20. Won MR, Choi YJ. Are Koreans prepared for the rapid increase of the single-household elderly? Life satisfaction and depression of the single-household elderly in Korea. The Scientific World Journal. 2013 Oct;2013. Cole MA. Sex and marital status differences in death anxiety. OMEGA-Journal of Death and Dying. 1979 Oct;9(2):139-47. Kirchner JE, Zubritsky C, Cody M, Coakley E, Chen H, Ware JH, Oslin DW, Sanchez HA, Durai UN, Miles KM, Llorente MD. Alcohol consumption among older adults in primary care. Journal of General Internal Medicine. 2007 Jan 1;22(1):92-7. Chiao C, Weng LJ, Botticello AL. Social participation reduces depressive symptoms among older adults: an 18-year longitudinal analysis in Taiwan. BMC public health. 2011 Dec;11(1):1-9. Clarke DM, Currie KC. Depression, anxiety and their relationship with chronic diseases: a review of the epidemiology, risk and treatment evidence. Medical Journal of Australia. 2009 Apr;190:S54-60. Knight BG, Nordhus IH, Satre DD. Psychotherapy with older adults. Handbook of Psychology. 2003 Apr 15:453-68. Brenes GA, Penninx BW, Judd PH, Rockwell E, Sewell DD, Wetherell JL. Anxiety, depression and disability across the lifespan. Aging and Mental Health. 2008 Jan 1;12(1):158-63. Hermans H, Evenhuis HM. Factors associated with depression and anxiety in older adults with intellectual disabilities: results of the healthy ageing and intellectual disabilities study. International journal of geriatric psychiatry. 2013 Jul;28(7):691-9. Manela M, Katona C, Livingston G. How common are the anxiety disorders in old age?. International Journal of Geriatric Psychiatry. 1996 Jan;11(1):65-70. Tables Due to technical limitations, tables (2-7) docx is only available as a download in the Supplemental Files section. Additional Declarations No competing interests reported. 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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-263107","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":13489507,"identity":"db18d2eb-ff4d-44f4-baa2-eb642bf61a58","order_by":0,"name":"Yanzhao Cheng","email":"","orcid":"","institution":"University of Saskatchewan","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yanzhao","middleName":"","lastName":"Cheng","suffix":""},{"id":13489508,"identity":"ec324ad4-f9c9-4f47-88d5-e01349a8f4b8","order_by":1,"name":"Lilian Thorpe","email":"","orcid":"","institution":"University of Saskatchewan","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Lilian","middleName":"","lastName":"Thorpe","suffix":""},{"id":13489509,"identity":"1aa7a03e-5f6f-49c0-b785-1c95337d060b","order_by":2,"name":"Rasel Kabir","email":"","orcid":"","institution":"University of Saskatchewan","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Rasel","middleName":"","lastName":"Kabir","suffix":""},{"id":13489510,"identity":"22889fd2-a9d7-48d4-9aa8-2a38084da0f6","order_by":3,"name":"Hyun Ja Lim","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAv0lEQVRIiWNgGAWjYBACAzBZIMHAD2YcIFqLgQSDZAOJWoDoALFazCVyzB4wGFhEG99If/jxxxkGef4GAlosZ+SYGwAdlrvtRo6xNM8NBsMZhGwyuJFjJgHVwiDN8IEhgaDj4Fo2z0h//PMHUIs80Vo2SCSYSQAdlmBAUMuZZ2USCUAtM868MbPmOSNhuJGgluPJ2yQ+VNTl9renP77545iNvBwhLQwCCQxAL8OBBCH1QMBP0NBRMApGwSgY8QAAb8U/5Ee8Q3EAAAAASUVORK5CYII=","orcid":"","institution":"University of Saskatchewan","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Hyun","middleName":"Ja","lastName":"Lim","suffix":""}],"badges":[],"createdAt":"2021-02-21 05:13:57","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-263107/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-263107/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":6584721,"identity":"4b5624cd-7b2f-49ea-a5ea-d95e8265e81b","added_by":"auto","created_at":"2021-03-03 23:15:14","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":97399,"visible":true,"origin":"","legend":"Depression and Anxiety Trajectories. The solid line indicates the observed value; the dot line the dashed line indicates the predicted value. A and B are depression and anxiety trajectories in group-based dual trajectory modeling.","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-263107/v1/df17208075efd55feedea5c5.jpg"},{"id":6585076,"identity":"2fa7c57f-9b23-473e-bb5d-a519217196f0","added_by":"auto","created_at":"2021-03-03 23:18:14","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":61958,"visible":true,"origin":"","legend":"Conditional probability of anxiety given depression (A). Conditional probability of depression given anxiety (B).","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-263107/v1/c69814e3227b6c3561e3877d.jpg"},{"id":13675257,"identity":"08dd1423-0db4-42b8-9e65-7823bfc23ee0","added_by":"auto","created_at":"2021-09-17 11:25:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":368472,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-263107/v1/3ba20611-ca61-4584-8ae8-89e08f393cc2.pdf"},{"id":6585075,"identity":"138f79b2-c22b-433c-8ad6-43a7aec46c57","added_by":"auto","created_at":"2021-03-03 23:18:14","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":36047,"visible":true,"origin":"","legend":"","description":"","filename":"Tables2to7.docx","url":"https://assets-eu.researchsquare.com/files/rs-263107/v1/d45fc735437d49f60b9f1976.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Group-based Dual Trajectory of Depression and Anxiety in Older Adults: An 8-year Follow-up of Population-based Study","fulltext":[{"header":"Introduction","content":" \u003cp\u003eAlong with the difficulties of aging, elderly individuals are known to be at risk of both depression and anxiety disorders [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Recently, research focused on studying the comorbidity of depression and anxiety in elders has increased [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. One likely reason is the aging population, with the proportion of elderly increasing every year in almost every country [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In South Korea, concerns related to aging are substantial, as individuals 65 years or older constitute approximately 14% of the overall population, more than twice the proportion of people age 14 or younger [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Another reason for combined interest in these conditions is that a variety health conditions may influence mental health in the elderly population, potentially generating more than one illness. For example, an elderly person who suffers one or more chronic disease has a greater chance of developing late-onset depression and/or anxiety [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAlthough it has been suggested that comorbidity between depression and anxiety is less in elders compared to the younger adults [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], it is still of considerable concern. Beekman found that 47.5% of individuals age 65 years or older who suffer from depression also have a comorbid anxiety disorder [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. A Korean study included 1204 elderly, showed 22.8% of them had comorbid anxiety and depression [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. In terms of psychopathology, elderly patients suffering both conditions simultaneously are regard as more severe cases compared to patients with only one disorder [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Depression comorbid with anxiety has been shown to result in more severe somatic symptoms compared to patients with only depression [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Moreover, poorer social function and higher suicide rate persist in the comorbid depression and anxiety patient versus the patient with only depression or anxiety [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe common risk factors for both depression and anxiety in the elderly have been described in three broader categories: biological, psychological and social [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], including the influences of physical illness, disability, bereavement, and others. Nevertheless, longitudinal studies suggest that there are some risk factor differences between elderly depression and anxiety. Among biological risk factors, cognitive functional impairment and visual defect relate to depression but not anxiety, whereas hypertension is risk factor only in anxiety [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Also, psychological risk factors have no difference between depression and anxiety [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Evaluating social risk factors, marital status and social network size have been correlated with only depression [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. In contrast, risk factors associated with anxiety but not depression include being childless, traumatic life events, and low income [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe trajectory analysis of both depression and anxiety were usually developed based on youth and adolescence [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. To study the trajectory classes in elderly, researchers often focused on only depression [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. However, depression and anxiety trajectories were rarely studied simultaneously. Only three studies developed the trajectories for both depression and anxiety in elderly [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], while these two studies used separate trajectory models to develop depression and anxiety trajectories. The present study seeks to examine the longitudinal association between depression and anxiety in the elder population with a joint trajectory modeling method. The aims of our study are (i) to describe the trajectories of depression and anxiety, based on heterogeneity, simultaneously for comparison; (ii) to evaluate the relationship between depression and anxiety trajectory groups; and (iii) to identify predictors that may associate with the depression and anxiety trajectory groups.\u003c/p\u003e "},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n\u003ch2\u003eData and Sample\u003c/h2\u003e\n\u003cp\u003eThis study utilized a subset of a longitudinal survey called the Korea Health Panel Study (KHPS). The KHPS samples, collected by the Korean Institute for Health and Social Affairs in conjunction with the National Health Insurance Service, were selected and weighted from 2005 Korean Population and Housing Census. The KHPS began in 2008 and incorporated a total of 24,616 participants from 7387 households. In 2014, KHPS was strengthened with the addition of more the 2520 household to mitigate attrition. Using computer assisted personal interviews, trained staff collected the data with three aspect: household, individual, and case-based sections. Comprehensive assessments on the use of healthcare services, cost of healthcare, and potentially influential factors have been conducted annually since 2008. Further details of the KHPS administration and mission are available on the KHPS website [\u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eOverall, 3983 elderly who were 65 or older were studied. The additional subjects in year 2014 and 215 were moved to year 2008 and 2009. The follow-up participation rates among this study\u0026rsquo;s sample were 91.0%, 63.4%, 60.6%, 56.5%, 53.4%, 50.0% and 46.4% from year 2009 to 2015, respectively. The average number of responses was 5.2 times.\u003c/p\u003e\n\u003ch2\u003eMeasures\u003c/h2\u003e\n\u003c/div\u003e\n\u003cp\u003eThe data collection methods for the KHPS involved the investigators visiting the target households and using computer-assisted personal interviewing (CAPI) technique.\u003c/p\u003e\n\u003cp\u003eCovariates included sex, age, education, marital status, residential area, number of members in the household, household composition type, housing type, current chronic disease status, private health insurance, household income quantile, and household expense. The main dichotomous outcomes of depression and anxiety were collected from medical expenses, including prescription drug receipts or medical institutions/pharmacies, potentially leading to inadequate recognition of our sample outcomes. If there is any annually using of inpatient treatment, outpatient treatment, or emergency-service utilization from the case-based survey relevant to depression or anxiety, the outcome is assigned to 1, otherwise, the outcome is assigned to 0.\u003c/p\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\n\u003cp\u003eThe analysis commenced with basic descriptive statistics to characterize the study sample.\u003c/p\u003e\n\u003cp\u003eGeneral group-based trajectory modeling (GBTM) with single repeated measurement outcome, also called latent class growth modeling, has the ability to identify unobserved heterogeneous subgroups within the sample population and develop trajectories based on the trends within them [\u003cspan class=\"CitationRef\"\u003e40\u003c/span\u003e]. The individuals are placed into the most likely subgroups relying on the largest posterior probability (i.e. the probability that an individual with a particular outcome pattern belongs to a particular group among those suggested by the model) [\u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e]. To estimate the model, maximum likelihood estimation is used, and thus the model has the ability to handle the missing, unbalanced data [\u003cspan class=\"CitationRef\"\u003e42\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eGroup-based dual trajectory modeling (GBDTM) determines the trajectories of two associated outcomes, such as depression and anxiety in our study [\u003cspan class=\"CitationRef\"\u003e43\u003c/span\u003e]. The relationship between the two outcomes has been labeled as \u0026ldquo;comorbidity\u0026rdquo; or \u0026ldquo;heterotypic continuity\u0026rdquo; [\u003cspan class=\"CitationRef\"\u003e44\u003c/span\u003e]. \u0026ldquo;Comorbidity\u0026rdquo; recognizes multiple illness states occurring at the same time [\u003cspan class=\"CitationRef\"\u003e45\u003c/span\u003e]. \u0026ldquo;Heterotypic continuity\u0026rdquo; recognizes that two outcomes may linked within an individual but do not occur simultaneously, introducing the possibly of causality [\u003cspan class=\"CitationRef\"\u003e46\u003c/span\u003e]. In GBDTM, the linkage of trajectory groups identified within the respective two associated outcomes relies on the conditional probabilities. For example, since depression and anxiety are diagnosed as co-current events in our study, the conditional probabilities for both depression given anxiety and anxiety given depression were considered [\u003cspan class=\"CitationRef\"\u003e47\u003c/span\u003e]. Then, this conditional probability represents the likelihood of a person having depression if anxiety is present, and vice versa [\u003cspan class=\"CitationRef\"\u003e44\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eTo select the most appropriate number of trajectories, separate GBTM for depression and anxiety with the number of trajectory groups from two to five were tried. All the trajectories from the model were assumes to be linear. Bayesian Information Criterion (BIC) [\u003cspan class=\"CitationRef\"\u003e48\u003c/span\u003e], Akaike Information Criterion (AIC) [\u003cspan class=\"CitationRef\"\u003e49\u003c/span\u003e], and the average posterior probability [\u003cspan class=\"CitationRef\"\u003e50\u003c/span\u003e] were utilized to evaluate the model fit. First, the separate GBTM for depression and anxiety were conducted. Then, based on the result from the separate GBTM, GBDTM was developed. SAS programming 9.4 and Proc Traj package was utilized for trajectory modeling and logistic regression. Significant level of this study was set at \u0026alpha;\u0026thinsp;=\u0026thinsp;0.05.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e3983 participants, who were age 65 or older from KHPS, were investigated during the study period. In this sample, 57% were female and the average age at baseline measurement was 72.4 (SD\u0026thinsp;\u0026plusmn;\u0026thinsp;6) years. Among these participants, 63% had never received any education or only finished elementary school; 62.5% lived with their spouse, and 1.6% lived alone. The majority (83%) reported that their income level was lower than the median income level of the complete survey sample, and 37% were still undertaking income generating activities. Only 38.2% lived in metro-cities, and 57.4% resided in a detached house.\u003c/p\u003e\n\u003cp\u003eBased on model selection criteria, four trajectory groups was identified as the best fit for both the depression and anxiety outcomes from GBDTM (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Duel trajectory modeling, with results for the four trajectory groups specified within each outcome, is presented with depression: \u0026ldquo;low-flat (87.0%)\u0026rdquo;, \u0026ldquo;low-to-middle (8.8%)\u0026rdquo;, \u0026ldquo;low-to-high (1.3%)\u0026rdquo; and \u0026ldquo;high-stable (2.8%)\u0026rdquo; trajectory groups (Figs.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eA) and anxiety \u0026ldquo;low-flat (92.5%)\u0026rdquo;, \u0026ldquo;low-to-middle (4.7%)\u0026rdquo;, \u0026ldquo;high-to-low (2.2%)\u0026rdquo; and \u0026ldquo;high-curved (0.6%)\u0026rdquo; trajectory groups (Figs.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eB). The characteristics of the sample are described according to the respective trajectory groups from GBDTM (Tables\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eGoodness of fit to select the optimal number of trajectory group for depression and anxiety\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eNumber of trajectories\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eDepression\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eAnxiety\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eBIC\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eAIC\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePP\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eBIC\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eAIC\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePP\u003c/strong\u003e\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\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-2534.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-2530.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-1525.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-1521.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-2403.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-2397.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-1484.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-1477.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-2363.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-2354.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.83\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-1476.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-1467.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-2365.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-2353.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-1489.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-1478.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"7\"\u003eBIC\u0026thinsp;=\u0026thinsp;Bayesian information criterion, AIC\u0026thinsp;=\u0026thinsp;Akaike information criterion, PP\u0026thinsp;=\u0026thinsp;Posterior probability\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConditional probabilities from GBDTM provided a straightforward view of the association between depression and anxiety in the dual trajectory model. The most likely anxiety trajectory regardless of depression trajectory was low-flat, with probabilities ranging from 95.7% probability in the low-flat depression group to 68.5% in the high-stable depression group (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA). However, 22.9% of low-to-middle depression trajectory participants were in the low-to-middle anxiety trajectory group and 21.1% of the high-stable depression group followed a high-to-low anxiety trajectory; respective proportions for these anxiety trajectories were highest among these respective depression trajectories. Persistent anxiety trajectories (high-curved) were only observed among respondents with low-to-middle or high-stable trajectory depression patterns and in small proportions (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA).\u003c/p\u003e\n\u003cp\u003eFrom the conditional probability of depression given anxiety (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eB), the proportions with low-flat depression were again high in the low-flat anxiety group (90.0%), but less so among the other anxiety trajectories (high-to-low, 56.5%; low-to-middle, 48.9%; and high-curved, 23.9%). Individuals within the low-to-middle anxiety trajectory were almost as likely to follow a low-to-middle depression trajectory (43.0%) as a low-stable one (48.9%). Similarly, participants in the persistent anxiety group (high-curved) also frequently showed a low-to-middle depression trajectory (59.5%). Although participants in the high-to-low anxiety group did most often follow a low-flat depression trajectory (56.5%), the high-stable depression trajectory was also relatively common in this group (27.0%) and in the high-curved group (16.6%). The low-to-high depression trajectory was generally infrequent in all the groups (\u0026lt;\u0026thinsp;4% in all anxiety trajectories).\u003c/p\u003e\n\u003cp\u003eUnivariate (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e and Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e) and multivariate logistic regression utilized the low-flat group as reference in both the depression and anxiety trajectory outcomes. In multivariate analyses of depression trajectory groups (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e), individuals within the low-to-high depression trajectory were more likely to be in the low-to-middle anxiety trajectory group (OR\u0026thinsp;=\u0026thinsp;5.80, 95% CI: 1.83\u0026ndash;19.68, p-value\u0026thinsp;=\u0026thinsp;0.005) than the low-flat depression group and more likely to be not participating in income generating activity (OR\u0026thinsp;=\u0026thinsp;2.79, 95% CI: 1.15\u0026ndash;6.80, p-value\u0026thinsp;=\u0026thinsp;0.024). Similarly, members of the low-to-middle depression group were also more likely to be in the low-to-middle anxiety trajectory group (OR\u0026thinsp;=\u0026thinsp;9.26, 95% CI: 5.63\u0026ndash;15.22, p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) or the high-curved anxiety trajectory group than the low-flat depression group (OR\u0026thinsp;=\u0026thinsp;25.91, 95% CI: 7.45\u0026ndash;90.15, p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), be female (OR\u0026thinsp;=\u0026thinsp;1.51, 95% CI: 1.07\u0026ndash;2.12, p-value\u0026thinsp;=\u0026thinsp;0.018) and have multiple chronic diseases (OR\u0026thinsp;=\u0026thinsp;3.96, 95% CI: 1.83\u0026ndash;8.59, p-value\u0026thinsp;=\u0026thinsp;0.0005). Participants in the high-stable depression trajectory group were more often in the low-to-middle (OR\u0026thinsp;=\u0026thinsp;10.65, 95% CI: 5.30\u0026ndash;21.38, p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) or high-curved anxiety trajectories (OR\u0026thinsp;=\u0026thinsp;17.31, 95% CI: 3.04\u0026ndash;98.68, p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and were more likely to be female (OR\u0026thinsp;=\u0026thinsp;1.87, 95% CI: 1.02\u0026ndash;3.14, p-value\u0026thinsp;=\u0026thinsp;0.042), have multiple chronic diseases (OR\u0026thinsp;=\u0026thinsp;4.18, 95% CI: 1.01\u0026ndash;17.31, p-value\u0026thinsp;=\u0026thinsp;0.049), and not have income generating activity (OR\u0026thinsp;=\u0026thinsp;1.91, 95% CI: 1.02\u0026ndash;3.59, p-value\u0026thinsp;=\u0026thinsp;0.044) than the low-flat depression group.\u003c/p\u003e\n\u003cp\u003eWhen anxiety trajectory groups were used as the outcome, the depression trajectory groups were similarly associated with the anxiety trajectories (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e). In the multivariate logistic analysis, female sex was an adjusted predictor for low-to-middle anxiety group membership (OR\u0026thinsp;=\u0026thinsp;1.73, 95% CI: 1.09\u0026ndash;2.76, p-value\u0026thinsp;=\u0026thinsp;0.021) and high-to-low anxiety group membership (OR\u0026thinsp;=\u0026thinsp;2.17, 95% CI: 1.28\u0026ndash;3.69, p-value\u0026thinsp;=\u0026thinsp;0.025) versus the low-flat anxiety trajectory. Not taking income generating activity was another predictor of high-to-low anxiety group membership over low-flat membership (OR\u0026thinsp;=\u0026thinsp;2.17, 95% CI: 1.28\u0026ndash;3.69, p-value\u0026thinsp;=\u0026thinsp;0.025).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this longitudinal study, data gathered from the KHPS included 3983 Korea elderly people with potential to receive care for depression and/or anxiety symptoms within an 8 year follow up period. To examine the tendency to suffer these two mental health problems simultaneously, which may change over time, GBDTM was applied to identify trajectories of co-occurring depression and anxiety. Among the four groups recognized for their differing probabilities of depression, a large majority showed no depression and were generally unlikely to experience anxiety concomitantly. However, slightly more than 10% did experience depression during the follow up period, with most of these individuals showing a gradual increase in depression probability. Among individuals following this trajectory, 20% also experienced a moderately increase in anxiety risk over time. Regarding anxiety, also recognized to follow four trajectories, the vast majority of respondents did not experience this condition and were also free of depression, although 5% again saw a slow increase in anxiety propensity over time. This was accompanied by an increasing depression tendency in just under half the cases. In general, female sex, not taking income generating activity, and membership in a trajectory suggesting risk for the alternate condition independently predicted a more vulnerable risk trajectory than \u0026ldquo;low-flat\u0026rdquo; for both depression and anxiety.\u003c/p\u003e\n\u003cp\u003eOur presentation of four trajectory groups aligns with other depression trajectories studies focused on the elderly [\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e51\u003c/span\u003e]. However, researchers have only infrequently identified older adult anxiety trajectories: a six-year cohort study of depression and anxiety trajectories in older adults by Holmes et al, which only involved two trajectories (a stable anxiety trajectory (82%) and an elevated anxiety trajectory (18%)) [\u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e] and two studies recognizing three anxiety trajectory groups in older adults [\u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e]. In our study, four anxiety trajectory groups were identified. The anxiety trajectory shapes of these latter studies are different than our study as one focused on depressed older patients and the other on musculoskeletal pain patients.\u003c/p\u003e\n\u003cp\u003eAmong our four depression trajectory groups, no decreasing trajectory was found for depression. The high-stable depression group is thought to have less likelihood of recovery in our older population as this life stage is more likely to include reduced life-satisfaction, low income and living quality, and poor health conditions [\u003cspan class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e54\u003c/span\u003e]. The low-to-high depression group had an intense increase in depression occurrence from 2009 to 2013 but only contained 31 older adults. The markedly increased probability may have been precipitated by sudden serious events, such as the loss of spouse, physical incapacity, etc. However, among anxiety trajectories, a declining trajectory and a curved shape trajectory showed evidence of a decreasing risk. One explanation may be a reduction in stressful income generating activity as time going by. A possible second explanation for this observe decline is that individuals adapt or cope to their anxious feeling and no longer seek treatment. A third explanation may be that other more pressing medical conditions emerge, eclipsing anxiety management; as such anxiety may still have been present but not identified [\u003cspan class=\"CitationRef\"\u003e55\u003c/span\u003e]. The high-curve anxiety trajectory only involves 13 elderly, which is extremely small. However, since the constant high anxiety probability of these subjects, this group is important and cannot be replaced by other trajectory groups.\u003c/p\u003e\n\u003cp\u003eThe association between depression and anxiety were clearly identified from the conditional probabilities of the trajectories and the logistic regression odds ratios. The current finding that the low-to-high and low-to-middle depression group also had risk of being in the low-to-middle anxiety group suggests that older adults with an increasing trend of depression over time also have a greater chance of increasing anxiety, consistent other research [\u003cspan class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e57\u003c/span\u003e]. Moreover, low-to-middle depression group members made up a high proportion in the high-curved anxiety group, suggesting that older patients who had severe anxiety may suffer mild depression as well. High-stable depression group members were more likely to have anxiety risk following the high-to-low and, less frequently, the high-curved anxiety trajectory; individuals in this particular overlap have serious mental health conditions and require more attention [\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e]. The association between depression and anxiety status is also supported by the inverse of these findings; individuals in this study who did not have one of the study conditions tended not to have the other also.\u003c/p\u003e\n\u003cp\u003eOur evaluation of demographic risk factors shows varying alignment with the literature. In the majority of the depression and anxiety studies, sex does have an association with these conditions, suggesting older females generally at greater risk [\u003cspan class=\"CitationRef\"\u003e58\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e59\u003c/span\u003e]. Our study\u0026rsquo;s findings in this regard are consistent with results from other trajectory studies [\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e60\u003c/span\u003e]. Nevertheless, other studies have found no sex-specific differences when investigating depression and anxiety [\u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e61\u003c/span\u003e]. This inconsistency may be related to different economic circumstances, social cultural factors, psychosocial gender roles, or other population differences. In our study, age was a significant univariate influence of depression only, accordant with [\u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e]. Level of education was not a significant predictor of either outcome, which is consistent with some studies [\u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e62\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e63\u003c/span\u003e], but not others [\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e64\u003c/span\u003e]. In our study, this lack of relationship may be attributable to the relatively low education level in our respondents overall.\u003c/p\u003e\n\u003cp\u003eSocial factors are also known to influence mental health. Some studies suggest that older adults living along or those having no partner within an isolated social environment have a higher risk of depression or anxiety [\u003cspan class=\"CitationRef\"\u003e60\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e65\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e66\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e67\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e68\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e69\u003c/span\u003e]. However, living alone and marital status did not relate to the outcomes in our study, which is consistent with other studies [\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e70\u003c/span\u003e]. The risk behaviors of smoking or excessive drinking might also increasing the risk of depression and anxiety [\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e67\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e71\u003c/span\u003e]. Nevertheless, this association was not identified in our study or in the work of others [\u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e65\u003c/span\u003e]. Studies show that homeownership reduces the risk of depression and anxiety [\u003cspan class=\"CitationRef\"\u003e65\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e72\u003c/span\u003e], but this association did not remain in the multivariate analysis. Income generating activity, however, did predict both depression and anxiety trajectory groups, suggesting that people in later life who were still working and having financial security may have better mental health.\u003c/p\u003e\n\u003cp\u003eChronic diseases (heart disease, stroke, diabetes asthma, cancer, arthritis, osteoporosis etc.) are understandably difficult challenges for the elderly that may impact mental health. In studying the relationship between depression, anxiety, and chronic disease, Clarke and Kay reviewed 159 papers between 1995 and 2007, finding that depression was correlated with nearly all chronic diseases [\u003cspan class=\"CitationRef\"\u003e73\u003c/span\u003e]. However, anxiety only associated with heart disease, stroke, and diabetes mellitus. Moreover, the depression and anxiety patients who were diagnosed with heart disease, stroke, cancer, and arthritis were difficult to treat [\u003cspan class=\"CitationRef\"\u003e73\u003c/span\u003e]. In our study, the older adults who have more than three chronic disease were more likely to develop depression. However, in anxiety trajectory groups, chronic disease was only significant in \u0026ldquo;high-to-low\u0026rdquo; group from the univariate analysis, but not in the multivariate analysis. Studies have showed that older adults with physical illness or disability usually positive correlate with depression and anxiety [\u003cspan class=\"CitationRef\"\u003e65\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e74\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e75\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e76\u003c/span\u003e], but physical/mental disability were not observed to predict these outcomes in the multivariate analysis.\u003c/p\u003e\n\u003cp\u003eSeveral limitations should be considered in this study. First, these outcomes were collected from medical expenses and prescription drug receipts or from medical institutions/pharmacies, potentially leading to inadequate recognition of outcomes in our sample. This is particularly true in the context of other chronic disease conditions [\u003cspan class=\"CitationRef\"\u003e77\u003c/span\u003e]. Another limitation was the low prevalence of anxiety across the survey period; this limited predictor evaluation, particularly in the poorly populated trajectories such as the \u0026ldquo;high-curved\u0026rdquo; group (n\u0026thinsp;=\u0026thinsp;13). Third, although the current study employed data from a large elderly subsample of the KHPS dataset, around 35% of the outcome measurements were missing, which might result in bias even though the maximum likelihood estimation and GBDTM were used. Fourth, the variables included in this study did not contain all the potentially important health and psychosocial aspects that may be associated with depression and anxiety, such as stressful life events and social/family support information. And lastly, these observations are all made within the specific cultural context of Korean elderly, which may not be generalizable to all contexts.\u003c/p\u003e\n\u003cp\u003eIn summary, four trajectory groups of both depression and anxiety were generated among the elderly of the KHPS dataset. The large majority of older adults belong to the low-flat trajectory group for both depression (87.0%) and anxiety (92.5%), which suggests that most older adults do not identify depression and anxiety as problems. However, among those who do, an interrelationship between these diagnoses, particularly in those with anxiety, is evident from GBDTM. Female sex, the presence of 3 or more chronic diseases, and involvement in income generating activity are additional predictors for a concerning depression trajectory group, and with the exception of chronic diseases, for the anxiety trajectories as well. The findings of this study can be used to assist health policy decision-makers in identifying individuals at risk for comorbid depression and anxiety and aid in devising supports for older individuals at risk of deteriorating mental health.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study does not require ethics approval because the data is publicly available.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets analysed during the current study are available in the Korea Health Panel Study repository: \u003ca href=\"https://www.khp.re.kr:444/eng/main.do\"\u003ehttps://www.khp.re.kr:444/eng/main.do\u003c/a\u003e.\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\u003eThe authors declare that they have no funding support for this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYC performed the statistical analysis and wrote the first draft of the manuscript. RK performed data management. LT provided clinical input to the study. HL conceived the original study idea, lead the study and wrote the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank all the study participants for generously joining this survey. The authors also thank all the research staff who did data collection and recruitment of participants. The authors thank the reviewers for their\u0026nbsp;helpful comments.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eByrne GJ, Pachana NA. Anxiety and depression in the elderly: do we know any more?. Current opinion in psychiatry. 2010 Nov 1;23(6):504-9.\u003c/li\u003e\n\u003cli\u003eLenze EJ. Comorbidity of depression and anxiety in the elderly. Current Psychiatry Reports. 2003 Feb 1;5(1):62-7.\u003c/li\u003e\n\u003cli\u003eWPA. World Population Aging. 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Handbook of Psychology. 2003 Apr 15:453-68.\u003c/li\u003e\n\u003cli\u003eBrenes GA, Penninx BW, Judd PH, Rockwell E, Sewell DD, Wetherell JL. Anxiety, depression and disability across the lifespan. Aging and Mental Health. 2008 Jan 1;12(1):158-63.\u003c/li\u003e\n\u003cli\u003eHermans H, Evenhuis HM. Factors associated with depression and anxiety in older adults with intellectual disabilities: results of the healthy ageing and intellectual disabilities study. International journal of geriatric psychiatry. 2013 Jul;28(7):691-9.\u003c/li\u003e\n\u003cli\u003eManela M, Katona C, Livingston G. How common are the anxiety disorders in old age?. International Journal of Geriatric Psychiatry. 1996 Jan;11(1):65-70.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eDue to technical limitations, tables (2-7) docx is only available as a download in the Supplemental Files section.\u003c/p\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":"bmc-geriatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bgtc","sideBox":"Learn more about [BMC Geriatrics](http://bmcgeriatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bgtc/default.aspx","title":"BMC Geriatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"depressive disorder, trajectory, anxiety, longitudinal studies, elderly ","lastPublishedDoi":"10.21203/rs.3.rs-263107/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-263107/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: Depression and anxiety are common mental health conditions for elderly population. Understanding the trajectory developments of them will help us implementing treatments and interventions.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eAims:\u003c/strong\u003e This study aims to identify depression and anxiety trajectories in the elderly, evaluate the interrelationship of these conditions, and recognize trajectory-predicting characteristics.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: Group-based dual trajectory modeling (GBDTM) was applied to the data of 3,983 individuals, aged 65 years or older who participated in the Korean Health Panel Study between 2008 and 2015.\u0026nbsp;Logistic regression was used to identify the association between characteristics and trajectory groups.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: Four trajectory groups from GBDTM were identified in both the depression and anxiety outcomes. Depression has: “low-flat (87.0%)”, “low-to-middle (8.8%)”, “low-to-high (1.3%)” and “high-stable (2.8%)” trajectory groups. Anxiety has: “low-flat (92.5%)”, “low-to-middle (4.7%)”, “high-to-low (2.2%)” and “high-curve (0.6%)” trajectory groups. Interrelationship between depression and anxiety were identified. Members of the high-stable depression group were more likely to have “high-to-low” or “high-curved” anxiety trajectories. Female sex, the presence of more than three chronic diseases, and having income generating activity were significant factors in depression and anxiety.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e: Dual trajectory analysis of depression and anxiety in older adults shows that when one condition is present, the probability of the other is increased. Sex, having more chronic disease, and income generating activity might be\u0026nbsp;\u003cem\u003eat increased risks\u003c/em\u003e for both depression and anxiety. Health policy decision-makers can use our findings in developing strategies for prevention of both depression and anxiety in older adults.\u003c/p\u003e","manuscriptTitle":"A Group-based Dual Trajectory of Depression and Anxiety in Older Adults: An 8-year Follow-up of Population-based Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-03-03 23:15:12","doi":"10.21203/rs.3.rs-263107/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2021-06-02T14:47:09+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2021-06-01T12:22:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"a8b84bab-a62f-4f5a-8670-f800eb6cf1ce","date":"2021-03-24T15:46:53+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2021-03-18T04:19:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"c4f85cbb-b6a2-4553-91f1-0826c8789a26","date":"2021-03-09T13:10:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"2da55317-aa02-4d9f-97de-aba04d223433","date":"2021-02-28T18:38:57+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2021-02-24T16:41:37+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2021-02-24T16:30:16+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2021-02-24T16:20:50+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2021-02-24T09:27:32+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Geriatrics","date":"2021-02-21T05:09:27+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-geriatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bgtc","sideBox":"Learn more about [BMC Geriatrics](http://bmcgeriatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bgtc/default.aspx","title":"BMC Geriatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"dd6c9efe-bd39-4d9b-abdf-2b53833896ce","owner":[],"postedDate":"March 3rd, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":2738937,"name":"Geriatrics \u0026 Gerontology"}],"tags":[],"updatedAt":"2021-09-22T08:29:11+00:00","versionOfRecord":[],"versionCreatedAt":"2021-03-03 23:15:12","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-263107","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-263107","identity":"rs-263107","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}