Depression among Older Adults in Indonesia: Prevalence, Role of Chronic Conditions and Other Associated Factors
Yvonne Suzy Handajani1, *, Elisabeth Schröder-Butterfill2, Eef Hogervorst3, Yuda Turana1, Antoninus Hengky4
Identifiers and Pagination:Year: 2022
E-location ID: e174501792207010
Publisher ID: e174501792207010
Article History:Received Date: 15/12/2021
Revision Received Date: 16/3/2022
Acceptance Date: 1/4/2022
Electronic publication date: 05/09/2022
Collection year: 2022
open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Depression is one of the most common illnesses worldwide, with a prevalence of 5.7% among older adults aged over 60. Depression is a severe health condition that can significantly affect the quality of life.
The objective of this study is to investigate the determinant factors of depression among older adults in Indonesia.
Data of 4236 adults of 60 years old and over were taken from the fifth wave of the Indonesian Family Life Survey (IFLS-5). Sociodemographic and multiple health-related variables collected through interviews and measurements were analyzed. Multivariate logistic regression was used to evaluate depression and its associated factors.
The prevalence of depression assessed using ten questions from the Center for Epidemiologic Studies Depression Scale (CES-D 10) was 16.3%. Significant associated factors for depression were moderate and low subjective economic status, living in Java or other regions outside Sumatra and Java, no life satisfaction, self-perceived as having poor health, having dependency (IADL scores), and experienced falls and insomnia. Among chronic conditions, stroke, arthritis, and hearing impairment were also more common in depressed older adults.
Predictors of depression identified in this study may be used to help prevent and improve depression in Indonesian older adults, especially those who live on Java. Improvement in healthcare, especially in the prevention and rehabilitation of stroke, arthritis, possible frailty (falls and dependency), hearing impairment, and insomnia, concurrent with early detection of depression in these chronic conditions, may help create a better quality of life among Indonesian older adults.
Depression is one of the most common illnesses worldwide, with a prevalence of 3.8% among the general population and 5.7% among older adults aged 60 and over . Depression is a severe health condition, especially if recurrent and of moderate to extreme intensity, which can significantly affect the person's quality of life . Depression becomes a burden for the person, family, and society due to lowered productivity and higher healthcare costs [3, 4]. The prevalence of depression in older adults may be underestimated because it tends to be unrecognized and untreated compared to younger individuals . In Indonesia, the number of older adults has increased over the past decades, with an estimated 16 million older adults (6.1% of the population) in 2019 . This sizeable aging population faces various challenges, including disability, multiple chronic diseases, frailty, and especially depression [2, 6, 7]. According to Indonesia's basic health research, the prevalence of mental health disorders in Indonesia has increased from 6% in 2013 to 9.8% in 2018. In the same study, depression among persons older than 15 years old in 2018 was estimated at 6.1%, with only 9% receiving treatment for the condition in Indonesia .
Depression prevalence was lower in individuals with higher socioeconomic status, better self-reported health, more social capital, and functional independence in multiple studies [9-14]. In addition, engaging in physical activity was found to act as a protective factor against depression [15-17]. Various studies have shown that depression increases with multimorbidity, frailty, and dementia. Chronic diseases, including hypertension, diabetes, stroke, heart disease, kidney disease, and liver disease, confer a risk of depression over time [9, 10, 18]. People with visual and auditory impairment also have a higher prevalence of depression than the average population [19-21].
There is limited research on depression and its associated factors among the older adult population in Indonesia, and some studies only involve small samples [22-24]. Due to increasing trends and the high disease burden, more studies regarding depression in Indonesia have become necessary. The Indonesia Family Life Survey provides large representative data from various regions in Indonesia; data analysis on older people from these surveys might help create better-targeted strategies to prevent and improve depression among older Indonesian adults. This study aims to investigate factors associated with depression among older adults in Indonesia.
2. MATERIALS AND METHODS
2.1. Study Design and Participants
Indonesia Family Life Survey (IFLS) is the only large-scale longitudinal survey available for Indonesia. The fifth wave of the IFLS was collected cross-sectionally from the end of 2014 to early 2015 and involved data related to individuals, families, households, and communities. The sampling method was a multistage stratified sampling design. The total number of participants included in the analysis was 4236 older adults aged 60 years and older residing in various regions of Indonesia . IFLS-5 involved participants from previous IFLS (IFLS 1 to 4) with recontact rate of 90.5%. These high re-interview rates were obtained in part because of committed tracking and interviewing of individuals who had moved or split off from the origin IFLS-1 households . IFLS involved participants stratified in provinces and urban/rural locations. Provinces were determined to maximize representation of the population, capture Indonesia's cultural and socioeconomic variations, and still be cost-effective. Therefore, this study included 13 out of 34 provinces comprising 83% of the population, which are four provinces on Sumatera (North Sumatera, West Sumatera, South Sumatra, and Lampung), all five of the Java provinces (Jakarta, West Java, Central Java, Daerah Istimewa Yogyakarta, and East Java), and four provinces covering the remaining major island groups (Bali, West Nusa Tenggara, South Kalimantan, and South Sulawesi). Within each of the 13 provinces, enumeration areas (EAs) were randomly chosen from a nationally representative sample frame used in the National Census. Within each EAs, households were randomly selected based upon National Census listings obtained from the regional national statistics center office. The household survey questionnaire was divided into several books depending on the participants’ age and research variables. We used books 3A and 3B questionnaire data, which contain individual-level sociodemographic and health data from adult respondents, and book US, which contains physical health assessments of the individuals. In IFLS-5, the obtained data were input electronically in the field after the interview by the trained interviewer to a computer-assisted personal interview system (CSPro). All of the available data of the participants were included.
Multiple validated and standardized sociodemographic and health variables were assessed and are defined in (Table 1) [27-33]. These variables were considered to be included because they involved variables usually associated with other health problems, particularly in older adults. Sociodemographic factors included age, sex, marital status, education level, residential status, region, and subjective economic status. Assessment of variables mostly used validated measurements to avoid bias. Body mass index (BMI) was calculated using measured height and body weight taken using standard instruments and classified using Asia Pacific Classification . Depression was assessed using the Center for Epidemiological Studies Depression Scale (CES-D) . Depression has a huge societal impact, making accurate measurement paramount. While several available measures exist, the Center for Epidemiological Studies Depression Scale (CESD) is a popular assessment tool with wide applicability in the general population. CES-D was shown to have high validity and reliability from exploratory factor analysis and confirmatory factor analyses with internal consistency reliability (Cronbach’s alpha = 0.84) . Life satisfaction, subjective health status, social capital, tobacco use, chronic conditions, and history of falls were obtained based on specific questions in the questionnaire. The functional ability was assessed using Katz’s Activity of Daily Living (ADL) and Lawton's Instrumental Activity of Daily Living (IADL) [28, 29]. The physical activity was assessed using International Physical Activity Questionnaire (IPAQ) short version (IPAQ-S7S) . The cognitive function was evaluated using the Telephone Survey of Cognitive Status (TICS) . Insomnia was assessed using Patient-reported Outcomes Measurement Information System (PROMIS) sleep disturbance and sleep impairment measure .
Table 1. Classification of socioeconomic and health variables.
|Variable||Assessment Question||Classification Group|
|Sociodemographic||Age in years||(1) 60-69 years
(2) 70-79 years
(3) ≥80 years
|Marital status||(1) Married or coinhabiting
(2) Never married, separated, widowed, or divorced
|Education status||(1) High (higher than junior high school)
|Residential status||(1) Urban
|Living region||(1) Sumatera
(3) Other regions
|Subjective economic status||Please, imagine a six-step ladder where on the bottom stand the poorest people and on the highest step stand the richest people. On which economic step are you today?”||(1) Rich (Step 5 and 6)
(2) Medium (Step 3 and 4)
(3) Low (Step 1 and 2)
|Depression||Depression was assessed using the Centers for Epidemiologic Studies Depression Scale (CES-D 10) question items .||(1) Depression (score higher than 10)
|Functional ability||Six items of Katz Activity Daily Living (ADL), included bathing, dressing, toileting, transference, continence, and feeding ; and
Six items of the Lawton Instrumental Activity of Daily Living (IADL), included shopping, food preparation, housekeeping, laundry, taking medications, and handling finance .
|Life satisfaction||Please, think about your life as a whole. How satisfied are you with it?||(1) High
|Subjective health status||In general, how is your health?||(1) Healthy
|Social capital||Presence of activities engaged in the past 12 months, including participation in a community meeting, doing voluntary labor, attending a neighborhood program, and participating in religious activities||(1) High (having at least one of the activities)
|Tobacco use||Have you ever chewed tobacco, smoked a pipe, smoked self-enrolled cigarettes, or smoked cigarettes/cigars?
Do you still have the habit or have you quit?
|(1) Never or former
(2) Current smoker
|Body mass index (BMI)||The body mass index was calculated using height and body weight taken using standard measures and classified using the Asia Pacific classification .||(1) Underweight
|Physical activity||Assessed using the International Physical Activity Questionnaire (IPAQ) short version (IPAQ-S7S) .||(1) High
|Cognitive function||Assessed with items from Telephone Survey Cognitive Status (TICS) . The items include awareness of date and day of the week, self-reported memory question, subtraction of 7s from 100, immediate and delayed word recall of 10 nouns with a total score of 34.||(1) Good cognitive function
(2) Possible dementia (score below 8)
|Chronic condition||Has a doctor/paramedic/ nurse/midwife ever told you that you had certain disease? These include hypertension, diabetes, stroke, heart disease, arthritis, hypercholesterolemia, cancer, and visual and hearing impairment.||(1) No
|Insomnia||Assessed using Patient-Reported Outcomes Measurement Information System (PROMIS) sleep disturbance measure and sleep impairment measure .||(1) Normal
(2) Insomnia (score higher than 20)
|Falls||In the last two years, have you ever fallen?||(1) No
2.3. Statistical Analysis
Comparisons of characteristics between groups of dependent variables were analyzed using bivariate analyses (binary logistic regression) to assess the association between the dependent and each independent variable. Variables with less than 0.2 p-values were fitted to the multivariable logistic regression. Odds ratios with 95% CI were calculated, and statistical significance was considered at p-value < 0.05 in the multivariable logistic regression to control for any confounding. The IBM SPSS software version 22 (IBM, New York, USA) was used to perform the analyses.
3.1. Characteristics of Older Adults
A total of 4236 older adults were involved in the analysis, and no missing data was found. The prevalence of depression assessed using ten questions from the Center for Epidemiologic Studies Depression Scale (CES-D 10) was found to be 16.3% in Indonesia (Table 2). The majority of the respondents (70.3%) were aged between 60 to 69, more than half (50.3%) were female, two-thirds (66.6%) were married or cohabiting, 85% had low education (<9 years), 40% reported a moderate subjective economic status and more than half (50.8%) lived in urban areas, mostly (78.2%) on Java conform census. The majority (82.2%) of the respondents reported satisfaction with life and self-perceived themselves as healthy (65.4%), most were active in social activities (82.3%), about a third of the respondents were smokers (33.3%), and many engaged in low physical activity (47.2%). From the self-reported history of medical illness, most respondents stated they had good cognitive function (86.0%), and less than 5% of respondents had experienced a stroke, heart disease, hearing impairment, or cancer. More than 10% of the respondents had arthritis, visual impairment, and hypertension, which was the most prevalent (27.6%). Almost half (46.6%) of the respondents experienced one or more chronic diseases. Furthermore, 10.5% of respondents had insomnia, 12.7% reported dependency in activities of daily life (ADL), and a quarter (25.6%) had dependency in instrumental activities of daily living (IADL), while 12.0% had experienced falls.
|Variables||Categories||Frequency (n)||Percentage (%)|
|Marital status||Married/ Coinhabiting||2820||66.6|
|Never married, separated,
|Subjective economic status||Rich||1120||26.4|
|Subjective health status||Healthy||2770||65.4|
|Tobacco use||Never, Former||2816||66.5|
|One or more||1975||46.6|
3.2. Factors Associated with Depression
The data showed that depression was significantly associated with having moderate or low subjective economic status, living in Java and other regions, no life satisfaction, being self-perceived as unhealthy, having had a stroke, reporting arthritis, hearing impairment and insomnia, dependency (IADL) and falls. However, depression had no significant association with age, sex, marital status, education, residential status, social capital, tobacco use/smoking, physical activity, BMI, cognitive function, heart disease, hypercholesterolemia, diabetes, hypertension, visual impairment, cancer, chronic conditions and dependency (as assessed by ADL).
Older adults with moderate and low subjective economic status were 1.34 times and 1.64 times more likely to have depression than older adults with high subjective economic status (AOR = 1.34, 95% CI: 1.05-1.76) and (AOR =1.64, 95% CI: 1.28-2.11). Respondents from Java and other regions outside Java and Sumatera were 1.80 times (95% CI: 1.33-2.44) and 2.25 times (95% CI: 1.52-3.31) more likely to have depression than respondents from Sumatera. People who were dissatisfied in life and self-perceived as unhealthy were 1.75 times (95% CI:1.42-2.17) and 1.85 times (95% CI:1.53-2.24) more likely to have depression. Moreover, older adults with a history of medical illness, such as stroke, arthritis, and hearing impairment, were 2.45 times (95% CI: 1.60 -3.74), 1.59 times (95% CI: 1.27-2.00), and 1.69 times (95% CI: 1.03-2.05) more likely to be depressed than those who did not experience this disorders. Furthermore, respondents who experienced insomnia, dependency (IADL), and falls tended to have depression 5.3 times (95% CI: 4.24- 6.62), 1.68 times (95% CI: 1.37-2.05), and 1.57 times (95% CI: 1.23-2.01) more than those who did not experience these issues (Table 3).
|Variables||Categories||Depression||Unadjusted Odd Ratio||p-value||Adjusted Odd Ratio||p-value|
|Age||60-69||2490 (70.2)||486 (70.3)||Reference||-||-||-|
|70-79||892 (25.2)||182 (26.3)||1.04 (0.87-1.26)||0.661||-||-|
|>= 80||163 (4.6)||23 (3.3)||0.73 (0.47-1.14)||0.161||-||-|
|Sex||Male||1789 (50.5)||316 (45.7)||Reference||-||-||-|
|Female||1756 (49.5)||375 (54.3)||1.21 (1.03-1.43)||0.022||-||-|
|Marital status||Married/ Coinhabiting||2383 (67.2)||437 (63.2)||Reference||-||-||-|
|Never married, separated, divorced, widowed||1162 (32.8)||255 (36.8)||1.2 (1.01-1.42)||0.039||-||-|
|Education||High||564 (15.9)||70 (10.1)||Reference||-||-||-|
|Low||2981 (84.1)||621 (89.9)||1.68 (1.29-2.19)||<0.001||1.12 (0.84-1.49)||0.45|
|Subjective economic status||Rich||997 (28.1)||123 (17.8)||Reference||-||-||-|
|Medium||1437 (40.5)||252 (36.5)||1.42 (1.13-1.79)||0.003||1.34 (1.05-1.76)||0.018|
|Poor||1110 (31.3)||316 (45.7)||2.3 (1.84-2.89)||<0.001||1.64 (1.28-2.11)||<0.001|
|Residential status||Urban||1791 (50.5)||362 (52.3)||Reference||-||-||-|
|Rural||1753 (49.5)||330 (47.7)||0.93 (0.79-1.1)||0.389||-||-|
|Region||Sumatera||474 (13.4)||62 (9)||Reference||-||-||-|
|Java||2771 (78.1)||544 (78.6)||1.51 (1.14-2)||0.004||1.8 (1.33-2.44)||<0.001|
|Others||301 (8.5)||86 (12.4)||2.2 (1.54-3.14)||<0.001||2.25 (1.52-3.31)||<0.001|
|Life satisfaction||Yes||3007 (84.8)||475 (68.6)||Reference||-||-||-|
|No||538 (15.2)||217 (31.4)||2.55 (2.12-3.07)||<0.001||1.75 (1.42-2.17)||<0.001|
|Subjective health status||Healthy||2455 (69.3)||314 (45.4)||Reference||-||-||-|
|Unhealthy||1090 (30.7)||377 (54.6)||2.07 (2.29-3.19)||<0.001||1.85 (1.53-2.24)||<0.001|
|Social capital||Yes||2925 (82.5)||563 (81.4)||Reference||-||-||-|
|No||620 (17.5)||129 (18.6)||1.08 (0.88-1.33)||0.471||-||-|
|Tobacco use||Never, Former||2374 (67)||442 (63.9)||Reference||-||-||-|
|Yes||1171 (33)||250 (36.1)||1.15 (0.99-1.36)||0.114||-||-|
|Physical activities||High||780 (22)||169 (24.5)||Reference||-||-||-|
|Moderate||1080 (30.5)||206 (29.8)||0.88 (0.7-1.1)||0.251||-||-|
|Low||1685 (47.5)||316 (45.7)||0.86 (0.7-1.06)||0.159||-||-|
|BMI||Underweight||897 (25.3)||204 (29.5)||1.24 (1.01-1.51)||0.038||-||-|
|Normal||1469 (41.4)||270 (39.1)||Reference||-||-||-|
|Overweight/ Obese||1179 (33.3)||217 (31.4)||1 (0.83-1.22)||0.976||-||-|
|Cognitive function||Good||3063 (86.4)||579 (83.8)||Reference||-||-||-|
|Possible Dementia||482 (13.6)||112 (16.2)||1.23 (0.99-1.54)||0.067||-||-|
|Stroke||No||3477 (98.1)||656 (94.9)||Reference||-||-||-|
|Yes||68 (1.9)||35 (5.1)||2.75 (1.81-4.16)||<0.001||2.45 (1.6-3.74)||<0.001|
|Heart diseases||No||3409 (96.2)||652 (94.4)||Reference||-||-||-|
|Yes||136 (3.8)||39 (5.6)||1.48 (1.03-2.14)||0.035||-||-|
|Hypercholesterolemia||No||3302 (93.1)||643 (93.1)||Reference||-||-||-|
|Yes||243 (6.9)||48 (6.9)||1.02 (0.74-1.4)||0.925||-||-|
|Hypertension||No||260 (73.3)||467 (67.6)||Reference||-||-||-|
|Yes||945 (26.7)||224 (32.4)||1.32 (1.12-1.57)||0.002||1.14 (0.95-1.37)||0.155|
|Diabetes||No||3301 (93.1)||651 (94.2)||Reference||-||-||-|
|Yes||244 (6.9)||40 (5.8)||0.84 (0.59-1.18)||0.31||-||-|
|Arthritis||No||3154 (89)||568 (82.2)||Reference||-||-||-|
|Yes||390 (11)||123 (17.8)||1.76 (1.41-2.19)||<0.001||1.59 (1.27-2)||<0.001|
|Visual impairment||No||3187 (89.9)||592 (85.7)||Reference||-||-||-|
|Yes||357 (10.1)||99 (14.3)||1.49 (1.17-1.89)||0.001||1.24 (0.96-1.59)||0.096|
|Hearing impairment||No||3484 (98.3)||666 (96.4)||Reference||-||-||-|
|Yes||61 (1.7)||25 (3.6)||2.12 (1.32-3.4)||0.002||1.69 (1.03-2.77)||0.036|
|Cancer||No||3519 (99.3)||690 (99.9)||Reference||-||-||-|
|Yes||26 (0.7)||1 (0.1)||0.16 (0.02-1.46)||0.104||-||-|
|Chronic conditions||No||1941 (54.8)||320 (46.3)||Reference||-||-||-|
|One or more||1604 (45.2)||371 (53.7)||1.4 (1.19-1.65)||<0.001||1.15 (0.96-1.39)||0.14|
|Insomnia||No||3324 (93.8)||469 (67.9)||Reference||-||-||-|
|Yes||221 (6.2)||222 (32.1)||7.13 (5.78-8.79)||<0.001||5.3 (4.24-6.62)||<0.001|
|ADL||Independent||3153 (88.9)||543 (78.6)||Reference||-||-||-|
|Dependent||392 (11.1)||148 (21.4)||2.2 (1.78-2.71)||<0.001||1.25 (0.97-1.61)||0.081|
|IADL||Independent||2735 (77.2)||418 (60.5)||Reference||-||-||-|
|Dependent||810 (22.8)||273 (39.5)||2.21 (1.86-2.62)||<0.001||1.68 (1.37-2.05)||<0.001|
|Falls||No||384 (10.8)||124 (17.9)||Reference||-||-||-|
|Yes||3161 (89.2)||567 (82.1)||1.81 (1.45-2.26)||<0.001||1.57 (1.23-2.01)||<0.001|
The prevalence of depression in our study was high (16.3%). This number was higher than that reported in the general population from the 2018 Indonesia Basic Health research (6,1%) . Studies of older adults from a Portuguese national cohort (11.8%), the South Africa nation-wide survey (4%), and a meta-analysis by Lim et al. (12.9%), all showed lower prevalence. In comparison, studies from a nationwide survey in South Korea (27.8%) and a rural area in Thailand (18.5%) showed higher prevalence [9, 10, 35-37]. Different assessment methods of depression may cause variations between these studies, and alternatively, different cultures and sampling methods could be responsible for discrepancies. Both studies in South Korea and Thailand used Geriatric Depression Scale (GDS), but a different cut-off was established, with the South Korean study using ≥8 as a cut-off while the Thailand study using ≥13. This may explain the higher prevalence of depression in the South Korean study. The South African study used the World Mental Health Survey version of the Composite International Diagnostic Interview, and Indonesia basic health research used the Mini International Neuropsychiatry Interview; both showed lower prevalence than other studies. A meta-analysis by Lim et al. also showed that self-reported assessments tend to have a higher score than clinical diagnostic interviews. Diagnostic interviews adopted stricter criteria to differentiate individuals with clinical depression from those with only depressive symptoms.
Among demographic factors, there was an association with living in Java and other regions, while being older was not associated with depression. Many studies have shown that older age confers a higher risk, but this was not found in a meta-analysis by Maier et al. . The insignificant result between older age and depression may be explained by the fact that older age is related to declining physical health and disability, which results in depression, and is not age-independent . However, there was no sex difference in depression in our study. However, many studies have shown that older women tend to have depression more commonly than men [18, 36]. Given bio-psychosocial aspects, women have a higher concentration of serotonin that plays a role in anxiety and stress; they also tend to have a dysregulated hypothalamic-pituitary-adrenal axis in response to stress compared to men; women are also more likely to have experienced sexual and emotional abuse, and in the Asian and many other cultures, women’s role is restricted to home duty and child care, which are associated with depression. Women were also reported to be more likely poorer or have less control over economic resources in the household .
Depression was also more prevalent in participants with functional dependency as measured by IADL, having moderate and low socioeconomic status, low life satisfaction, and being self-perceived as unhealthy. These findings are consistent with many studies, including a meta-analysis [9, 16, 38, 39]. Having a functional disability could increase the prevalence of having clinical depression and worsen the existing depression . The declining physical function creates uncertainty and hopelessness by being dependent in older adults [41, 42]. Being poor by the subjective judgment of the participant and/or from income/wealth aspects (objective poverty) predisposes older adults to depression. Having a low income may lead to poor living conditions and multiple serious life events, which increase the higher prevalence of depression. Men tend to be more affected by low socioeconomic status than women because, traditionally, men are the head of the family and responsible for family needs . On the other hand, women usually outlive men and then live in poorer households, so they are more likely to experience poverty in later life . However, as stated, we found no gender differences in depression. In a study done by Sasaki et al., subjective economic status had a stronger association than objective economic status. Some reasons for this phenomenon were health disparities and negative psychological consequences (stress-related dysregulation of the hypothalamic-pituitary-adrenal axis) due to differences in subjective economic status. Subjective economic status might represent a cognitive estimation of current economic status and extend it to the past and future prospects . Having dissatisfaction in life and self-perception as being unhealthy probably have a bidirectional relationship with depression.
Having a low physical activity or sedentary life has a bidirectional relationship with depression. Depressive individuals tend to be less physically active, even formerly active individuals . Having an active lifestyle has been associated with fewer depressive symptoms in other studies. The relation between depression and physical activity may be mediated by several variables, such as social relations and engagement, chronic conditions, and functional ability . Older adults who have a disability may also be unable to do certain activities. Despite these findings, our study did not show a significant association between physical activity and depression. A meta-analysis by Huang et al. showed that older adults with dementia had an increased probability of depression compared to those with good cognitive function . However, a systematic review by Maier et al. concludes that it is difficult to extrapolate these associations due to heterogeneous results between the studies . In a study by Carta et al., performing moderate exercise improves cognitive function in healthy older adults. Due to bidirectional relationships between physical activity and cognitive function with depression, improvement in either variable might improve depression in older adults .
Insomnia was associated with depression in our study. A longitudinal study by Li et al. also confirmed our findings . Two prospective cohort studies by Lee et al. and Baglioni et al. found that insomnia significantly increased depression in nondepressed older adults at baseline in the subsequent year [49, 50]. In the study by Lee et al., persistent insomnia increased depression risk rather than intermittent insomnia. A meta-analysis by Maier et al. also showed that insomnia, especially early insomnia, was significantly associated with depression . Our assessment of insomnia has a limitation in that we could not differentiate insomnia into multiple types for further analysis. Insomnia and depression have a complex relationship. Depression diagnoses usually have criteria for sleep disturbances, while insomnia could cause alterations in emotional response, resulting in depression. Other studies also show that insomnia endorses maladaptive sleep beliefs that increase the individual’s sense of hopelessness, which also results in depression .
Our findings regarding stroke showed an increased prevalence of depression significantly, which has also been demonstrated by other studies [52-55]. The prevalence of post-stroke depression was around 31.1%, with more women affected by it . The pathogenesis of poststroke depression itself is multifactorial, involving complex biological, social, and psychological mechanisms . The higher number of depression in stroke patients may also be mediated by long-term hospitalization, isolation, and problems adapting to new conditions, such as significant disability and communication difficulties . A longitudinal study by Thomas et al. showed that two-thirds of poststroke patients remained depressed at follow-up between 7 to 13 months and became even more depressed . Lower perceived control of recovery and difficulty in communication contributed to this phenomenon. One of the problems is that depressive symptoms usually also involve somatic complaints, which can also be affected by the presence of stroke, so it is difficult to determine an accurate diagnosis .
In our study, more older people with arthritis suffer from depression than those without arthritis. According to Mortality and Morbidity Weekly Report (MMWR), in 2018, around 22.7% of US adults were diagnosed with arthritis, and 22.5% and 12.1% were suffering from anxiety and depression . Based on age-specific prevalence, anxiety and depressive symptoms were more common in those between 18-44 years than in people over 65 years in our study. Visual and auditory impairment was significantly associated with depression in multiple studies, but in our study, only auditory impairment was associated with depression [19, 58, 59]. A study by Chou et al. found that visual impairment has a more robust impact than hearing impairment because it tends to be more obvious; it is also perceived as worse than hearing loss and wider restrictions . A national cohort study by Kim et al. showed that mild to moderate hearing loss has little impact on depression, but in our study, we did not classify the hearing impairment because it was self-reported . In treating patients with visual or hearing impairment, the healthcare providers often do not address the mental health aspects, including depression.
The prevalence of depression among older adults in Indonesia is still very high. Many factors contribute to depression, such as living in Java and other regions, being dependent, having moderate to low subjective economic status, having no life satisfaction, being self-perceived as unhealthy, and having experienced stroke, arthritis, hearing impairment, insomnia, dependency (IADL) and falls. The latter two could be indicative of frailty, a common syndrome in older people and associated with many of the health variables which have not been entered in multivariate regression analyses. Despite the inability to determine causality and the limitation that most of the assessments were self-reported, strong associations were found in this study. Predictors investigated in this study may help prevent and improve depression in Indonesian older adults, especially those living in Java. Improvement in healthcare, especially in the prevention and rehabilitation of stroke, arthritis, and hearing impairment, concurrent with early detection of depression in these chronic conditions, may help create a better quality of life among Indonesian older adults.
LIST OF ABBREVIATIONS
|ADL||= Activity Daily Living|
|AOR||= Adjusted Odds Ratio|
|CES-D 10||= Center for Epidemiologic Studies Depression Scale|
|EAs||= Enumeration Areas|
|GDS||= Geriatric Depression Scale|
|IADL||= Instrumental Activity in Daily Living|
|IFLS-5||= Indonesia Family Life Survey|
|MMWR||= Mortality and Morbidity Weekly Report|
|ADL||= Activity of Daily Living|
|IADL||= Instrumental Activity of Daily Living|
|IPAQ||= International Physical Activity Questionnaire|
|TICS||= Telephone Survey of Cognitive Status|
ETHICS APPROVAL AND CONSENT TO PARTICIPATE
The IFLS-5 surveys and their procedure were reviewed and approved by Institutional Review Board (IRBs) in the United States at Research and Development (RAND) Corporation and in Indonesia at the University of Gadjah Mada. The ethical clearance number from RAND’s Human Subjects Protection Committee (RAND’s IRB) was s0064-06-01-CR01. Informed consent was obtained from all study participants before the interview began.
HUMAN AND ANIMAL RIGHTS
No animals were used in this research. All human research procedures followed were in accordance with the Institutional Review Board (IRBs) in the United States at Research and Development (RAND) Corporation, in Indonesia at the University of Gadjah Mada, and the Helsinki Declaration of 1975.
CONSENT FOR PUBLICATION
All requirements of consent for adults were met and approved by IRBs before the study began (Available from: https://www.rand.org/well-being/social-and-behavioral-policy/data/FLS/IFLS.html). The IFLS-5 data request from RAND was registered by a co-author (Antoninus Hengky via firstname.lastname@example.org). The requested proof was also submitted.
STANDARDS OF REPORTING
STROBE guidelines were followed.
AVAILABILITY OF DATA AND MATERIALS
Data employed in this study are publicly available by registering requests at RAND (https://www.rand.org/ well-being/social-and-behavioral-policy/data/FLS/IFLS.html).
This study was funded by the Economic and Social Research Council (ESRC).
CONFLICT OF INTEREST
The authors declare that there is no conflict of interest regarding the publication of this article.
The authors gratefully acknowledge RAND for giving them access to the IFLS-5 data (https://www.rand.org/ well-being/social-and-behavioral-policy/data/FLS/IFLS.html), and the Economic and Social Research Council (ESRC), UK, for the financial support via its research project funding for Care Networks in Indonesia (Project ES/S013407/1).
|||World Health Organization [WHO].. depression 2012.who.int/news-room/fact-sheets/detail/depression|
|||Cahoon CG. Depression in older adults. Am J Nurs 2012; 112(11): 22-30.
|||Chang SM, Hong JP, Cho MJ. Economic burden of depression in South Korea. Soc Psychiatry Psychiatr Epidemiol 2012; 47(5): 683-9.
|||Donohue JM, Pincus HA. Reducing the societal burden of depression: A review of economic costs, quality of care and effects of treatment. PharmacoEconomics 2007; 25(1): 7-24.
|||Pramesona BA, Taneepanichskul S. Prevalence and risk factors of depression among Indonesia elderly: A nursing home-based cross-sectional study. Psychiatry Brain Res 2018; 30: 22-7.|
|||Hanum L, Utoyo DB, Jaya ES. Jaya, ES Indonesian older adults’ mental health: an overview. Psychol Res Urban Soc 2018; 1(2): 74-80.
|||Setiati S, Harimurti K, Dewiasty E, Istanti R. Predictors and scoring system for health-related quality of life in an Indonesian community-dwelling elderly population. Acta Med Indones 2011; 43(4): 237-42.
|||World Health Organization [WHO]. World Population Ageing 2019; WHO: Geneva. 2019.|
|||Setiati S, Laksmi PW, Aryana IGPS, et al. Frailty state among Indonesian elderly: prevalence, associated factors, and frailty state transition. BMC Geriatr 2019; 19(1): 182.
|||Carmona-Torres JM, Rodríguez-Borrego MA, Laredo-Aguilera JA, López-Soto PJ, Santacruz-Salas E, Cobo-Cuenca AI. Disability for basic and instrumental activities of daily living in older individuals. PLoS One 2019; 14(7): e0220157.
|||Ministry of Health Laporan nasional Riset Kesehatan Dasar (Riskesdas) 2018. labmandat.litbang.depkes.go.id/images/download/laporan/RKD/2018/Laporan_Nasional_RKD2018_FINAL.pdf|
|||Peltzer K, Phaswana-Mafuya N. Depression and associated factors in older adults in South Africa. Glob Health Action 2013; 6(1): 18871.
|||Sousa RD, Rodrigues AM, Gregório MJ, et al. Anxiety and depression in the portuguese older adults: Prevalence and associated factors. Front Med (Lausanne) 2017; 4: 196.
|||El-Gilany AH, Elkhawaga GO, Sarraf BB. Depression and its associated factors among elderly: A community-based study in Egypt. Arch Gerontol Geriatr 2018; 77: 103-7.
|||Wu S, Wang R, Zhao Y, et al. The relationship between self-rated health and objective health status: A population-based study. BMC Public Health 2013; 13(1): 320.
|||Ambresin G, Chondros P, Dowrick C, Herrman H, Gunn JM. Self-rated health and long-term prognosis of depression. Ann Fam Med 2014; 12(1): 57-65.
|||Cao W, Li L, Zhou X, Zhou C. Social capital and depression: Evidence from urban elderly in China. Aging Ment Health 2015; 19(5): 418-29.
|||Lee H, Lee JA, Brar JS, Rush EB, Jolley CJ. Physical activity and depressive symptoms in older adults. Geriatr Nurs 2014; 35(1): 37-41.
|||Ku PW, Fox KR, Chen LJ, Chou P. Physical activity and depressive symptoms in older adults: 11-year follow-up. Am J Prev Med 2012; 42(4): 355-62.
|||Strawbridge WJ, Deleger S, Roberts RE, Kaplan GA. Physical activity reduces the risk of subsequent depression for older adults. Am J Epidemiol 2002; 156(4): 328-34.
|||Maier A, Riedel-Heller SG, Pabst A, Luppa M. Risk factors and protective factors of depression in older people 65+. A systematic review. PLoS One 2021; 16(5): e0251326.
|||Chou KL. Combined effect of vision and hearing impairment on depression in older adults: Evidence from the English Longitudinal Study of Ageing. J Affect Disord 2008; 106(1-2): 191-6.
|||Li CM, Zhang X, Hoffman HJ, Cotch MF, Themann CL, Wilson MR. Hearing impairment associated with depression in US adults, National Health and Nutrition Examination Survey 2005-2010. JAMA Otolaryngol Head Neck Surg 2014; 140(4): 293-302.
|||Kim HJ, Kim BH, Kim OS. The effect of visual and hearing impairment on depression and cognitive function in community-dwelling elderly : The Korean longitudinal study of aging 2008. Korean J Adult Nurs 2011; 23(6): 584-94.|
|||Strauss J, Witoelar F, Sikoki B. The Fifth Wave of the Indonesia Family Life Survey: Overview and Field Report 2016; Vol. 1|
|||RAND Corporation. The IFLS Study Design rand.org/well-being/social-and-behavioral-policy/data/FLS/IFLS/study.html|
|||Andresen EM, Malmgren JA, Carter WB, Patrick DL. Screening for depression in well older adults: evaluation of a short form of the CES-D (Center for Epidemiologic Studies Depression Scale). Am J Prev Med 1994; 10(2): 77-84.
|||Katz S, Ford AB, Moskowitz RW, Jackson BA, Jaffe MW. Studies of illness in the aged: the index of ADL: A standardized measure of biological and psychosocial function. JAMA 1963; 185(12): 914-9.
|||Lawton MP, Brody EM. Assessment of older people: Self-maintaining and instrumental activities of daily living. Gerontologist 1969; 9(3 Part 1): 179-86.
|||Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. Lancet 2004; 363(9403): 157-63.
|||Craig CL, Marshall AL, Sjöström M, et al. International physical activity questionnaire: 12-country reliability and validity. Med Sci Sports Exerc 2003; 35(8): 1381-95.
|||Pengpid S, Peltzer K. Prevalence and associated factors of frailty in community-dwelling older adults in Indonesia, 2014–2015. Int J Environ Res Public Health 2019; 17(1): 10.
|||Yu L, Buysse DJ, Germain A, et al. Development of short forms from the PROMIS™ sleep disturbance and Sleep-Related Impairment item banks. Behav Sleep Med 2012; 10(1): 6-24.
|||Karim J, Weisz R, Bibi Z, ur Rehman S. Validation of the eight-item center for epidemiologic studies depression scale (CES-D) among older adults. Curr Psychol 2015; 34(4): 681-92.
|||Lim GY, Tam WW, Lu Y, Ho CS, Zhang MW, Ho RC. Prevalence of Depression in the Community from 30 Countries between 1994 and 2014. Sci Rep 2018; 8(1): 2861.
|||Charoensakulchai S, Usawachoke S, Kongbangpor W, et al. Prevalence and associated factors influencing depression in older adults living in rural Thailand: A cross-sectional study. Geriatr Gerontol Int 2019; 19(12): 1248-53.
|||Park JH, Kim KW, Kim MH, et al. A nationwide survey on the prevalence and risk factors of late life depression in South Korea. J Affect Disord 2012; 138(1-2): 34-40.
|||Cong L, Dou P, Chen D, Cai L. Depression and associated factors in the elderly cadres in Fuzhou, China: A community-based study. Int J Gerontol 2015; 9(1): 29-33.
|||Ahmad NA, Abd Razak MA, Kassim MS, et al. Association between functional limitations and depression among community-dwelling older adults in Malaysia. Geriatr Gerontol Int 2020; 20(S2)(Suppl. 2): 21-5.
|||Chen CM, Mullan J, Su YY, Griffiths D, Kreis IA, Chiu HC. The longitudinal relationship between depressive symptoms and disability for older adults: A population-based study. J Gerontol A Biol Sci Med Sci 2012; 67(10): 1059-67.
|||Kim BJ, Liu L, Nakaoka S, Jang S, Browne C. Depression among older Japanese Americans: The impact of functional (ADL & IADL) and cognitive status. Soc Work Health Care 2018; 57(2): 109-25.
|||Schröder-Butterfill E, Fithry TS. Care dependence in old age: preferences, practices and implications in two Indonesian communities. Ageing Soc 2014; 34(3): 361-87.
|||Back JH, Lee Y. Gender differences in the association between socioeconomic status (SES) and depressive symptoms in older adults. Arch Gerontol Geriatr 2011; 52(3): e140-4.
|||Sasaki Y, Shobugawa Y, Nozaki I, et al. Association between depressive symptoms and objective/subjective socioeconomic status among older adults of two regions in Myanmar. PLoS One 2021; 16(1): e0245489.
|||Roshanaei-Moghaddam B, Katon WJ, Russo J. The longitudinal effects of depression on physical activity. Gen Hosp Psychiatry 2009; 31(4): 306-15.
|||Huang CQ, Wang ZR, Li YH, Xie YZ, Liu QX. Cognitive function and risk for depression in old age: A meta-analysis of published literature. Int Psychogeriatr 2011; 23(4): 516-25.
|||Carta MG, Cossu G, Pintus E, et al. Moderate exercise improves cognitive function in healthy elderly people: Results of a randomized controlled trial. Clin Pract Epidemiol Ment Health 2021; 17(1): 75-80.
|||Li MJ, Kechter A, Olmstead RE, Irwin MR, Black DS. Sleep and mood in older adults: Coinciding changes in insomnia and depression symptoms. Int Psychogeriatr 2018; 30(3): 431-5.
|||Lee E, Cho HJ, Olmstead R, Levin MJ, Oxman MN, Irwin MR. Persistent sleep disturbance: A risk factor for recurrent depression in community-dwelling older adults. Sleep 2013; 36(11): 1685-91.
|||Baglioni C, Battagliese G, Feige B, et al. Insomnia as a predictor of depression: A meta-analytic evaluation of longitudinal epidemiological studies. J Affect Disord 2011; 135(1-3): 10-9.
|||Sadler P, McLaren S, Jenkins M. A psychological pathway from insomnia to depression among older adults. Int Psychogeriatr 2013; 25(8): 1375-83.
|||Whyte EM, Mulsant BH, Vanderbilt J, Dodge HH, Ganguli M. Depression after stroke: A prospective epidemiological study. J Am Geriatr Soc 2004; 52(5): 774-8.
|||Thomas SA, Lincoln NB. Factors relating to depression after stroke. Br J Clin Psychol 2006; 45(1): 49-61.
|||Schöttke H, Giabbiconi CM. Post-stroke depression and post-stroke anxiety: prevalence and predictors. Int Psychogeriatr 2015; 27(11): 1805-12.
|||Kowalska J, Bojko E, Szczepańska-Gieracha J, Rymaszewska J, Rożek-Piechura K. Occurrence of depressive symptoms among older adults after a stroke in the nursing home facility. Rehabil Nurs 2016; 41(2): 112-9.
|||Dafer RM, Rao M, Shareef A, Sharma A. Poststroke Depression. Top Stroke Rehabil 2008; 15(1): 13-21.
|||Guglielmo D, Hootman JM, Boring MA, et al. symptoms of anxiety and depression among adults with arthritis — United States, 2015–2017. MMWR Morb Mortal Wkly Rep 2018; 67(39): 1081-7.
|||Hayman KJ, Kerse NM, La Grow SJ, Wouldes T, Robertson MC, Campbell AJ. Depression in older people: Visual impairment and subjective ratings of health. Optom Vis Sci 2007; 84(11): 1024-30.
|||Court H, McLean G, Guthrie B, Mercer SW, Smith DJ. Visual impairment is associated with physical and mental comorbidities in older adults: A cross-sectional study. BMC Med 2014; 12(1): 181.
|||Kim SY, Kim HJ, Park EK, Joe J, Sim S, Choi HG. Severe hearing impairment and risk of depression: A national cohort study. PLoS One 2017; 12(6): e0179973.