Depression is a leading cause of disability worldwide and is a major contributor to the overall global burden of disease. It is also one of the most common geriatric psychiatric disorders and a major risk factor for disability and mortality in elderly patients. Even though depression is a common mental health problem in the elderly population, it is undiagnosed in half of the cases. Several studies showed different and inconsistent prevalence rates in the world. Hence, this study aimed to fill the above gap by producing an average prevalence of depression and associated factors in old age.
Objective
This study aims to conduct a systematic review and meta-analysis to provide a precise estimate of the prevalence of depression and its determinants among old age.
Method
A comprehensive search of PubMed, Scopus, Web of sciences, Google Scholar, and Psych-info from database inception to January 2020. Moreover, the reference list of selected articles was looked at manually to have further eligible articles. The random-effects model was employed during the analysis. Stata-11 was used to determine the average prevalence of depression among old age. A sub-group analysis and sensitivity analysis were also run. A graphical inspection of the funnel plots and Egger’s publication bias plot test were checked for the occurrence of publication bias.
Result
A search of the electronic and manual system resulted in 1263 articles. Nevertheless, after the huge screening, 42 relevant studies were identified, including, for this meta-analysis, n = 57,486 elderly populations. The average expected prevalence of depression among old age was 31.74% (95% CI 27.90, 35.59). In the sub-group analysis, the pooled prevalence was higher among developing countries; 40.78% than developed countries; 17.05%), studies utilized Geriatrics Depression Scale-30(GDS-30); 40.60% than studies that used GMS; 18.85%, study instrument, and studies having a lower sample size (40.12%) than studies with the higher sample; 20.19%.
Conclusion
A high prevalence rate of depression among the old population in the world was unraveled. This study can be considered as an early warning and advised health professionals, health policymakers, and other pertinent stakeholders to take effective control measures and periodic care for the elderly population.
Hinweise
Publisher's Note
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Abkürzungen
ADL
Activities of daily living
AOR
Adjusted odds ratio
CDEP
Community-dwelling elderly people
CES-D
Center for Epidemiologic Studies Depression Scale
CI
Confidence interval
CIDI-SF
Composite International Diagnostic Interview Short Form
CSDS
Clinically significant depressive symptoms
CS
Cross-sectional
DASS-21
Depression, Anxiety, and Stress Scale
DSM-III
Diagnostic and Statistical Manual of Mental Disorders
EMI
Elderly medical inpatients
GD
Geriatrics depression
GDS
Geriatric Depression Scale
GMS
Geriatric Mental State Schedule
HADS
Hospital Anxiety and Depression Scale
KICA-dep
Kimberley Indigenous Cognitive Assessment of Depression
MCS
Mental Component Summary
NR
Not reported
PRISMA
Preferred Reporting Items for Systematic Reviews and Meta-analysis
UK
United Kingdom
USA
United States of America
WHO
World Health Organization
Background
The elderly people are matured and experienced persons of any community. Their experience, wisdom, and foresight can be useful for development and progress; they are a valuable asset for any nation [1]. Despite their invaluable wisdom and insight, the aging of the world's population is causing extensive economic and social consequences globally [2]. The aging population has increased rapidly over the last decades owing to two significant factors, namely, the reduction in mortality and fertility rates and improved quality of life, leading to an increase in life expectancy worldwide [3‐5]. Globally, the number and proportion of people aged 60 years and older in the population are increasing. In 2019, the number of people aged 60 years and older was 1 billion. This number will increase to 1.4 billion by 2030 and 2.1 billion by 2050. By 2050, 80% of all older people will live in low- and middle-income countries [6‐8].
A high geriatric population leads to high geriatric psychiatric problems [9]. The elderly, in general, face various challenges that are associated with physical and psychological changes commonly associated with the aging process [10]. The incidence of mental health problems is expected to increase among adults in general as well as in older populations in particular [11].
Anzeige
Depression is a leading cause of disability worldwide and is a major contributor to the overall global burden of disease [12]. It is also one of the most common geriatric psychiatric disorders [13] and a major risk factor for disability and mortality in older patients [14]. Even though depression is a common mental health problem in the elderly population, it is undiagnosed in about 50% of cases. The estimates for the prevalence of depression in the aging differ greatly [15‐17]. WHO estimated that the global depressive disorder among older adults ranged between 10 and 20% [18‐21]. Among all mentally ill individuals, 40% were diagnosed to have a depressive disorder [22]. People with depressive disorder have a 40% greater chance of premature death than their counterparts [20].
Most of the time, the clinical picture of depression in old age is masked by memory difficulties with distress and anxiety symptoms; however, these problems are secondary to depression [23‐25]. Numerous community-based studies showed that older adults experienced depression-related complications [26‐30]. Depression amplifies the functional disabilities caused by physical illness, interferes with treatment and rehabilitation, and further contributes to a decline in the physical functioning of a person [31, 32]. It also has an economic impact on older adults due to its significant contribution to the rise of direct annual livelihood costs [33]. Hence, improvement of mental health among people in late life is considered to be medically urgent to prevent an increase in suicides in a progressively aging society.
Although real causes of depression remain not clear, psychological, social, and biological processes are thought to determine the etiology of depression and comorbid psychiatric diagnoses (e.g., anxiety and various personality disorders) [34]. Social scientists, postulating the psychosocial theory, posited that depression could be caused by a lack of interpersonal and communication skills, social support, and coping mechanisms [35]. Old biological theories stated depression is caused by a lack of monoamines in the brain. However, recent theories underscore the role of Brain-derived neurotrophic factor (BDNF) in the pathogenesis of depression [36]. In general, depression in the elderly is the result of a complex interaction of social, psychological, and biological factors [37, 38].
Different factors associated with geriatric depression, such as female sex [39‐47], increasing age [37, 40, 41, 44, 46‐49], being single or divorced [42], religion [50], lower educational attainment [39‐42, 44], unemployment [38, 42], low income [37, 39, 40, 42, 44, 46, 51, 52], low self-esteem [53], childhood traumatic experiences [54], loneliness or living alone [40, 50, 51, 55], social deprivation [45, 46, 56], bereavement [39, 43, 57, 58], presence of chronic illness or poor health status [37, 39, 43‐46, 49, 50, 56, 59‐64], lack of health insurance [42], smoking habit [48], cognitive impairment [39, 43‐47, 61] and a history of depression [43, 44, 47].
Anzeige
Compared with other health services, evidence of depressive disorders tends to be relatively poor. Therefore, the level of its burden among older adults is not well addressed in the world. Lack of adequate evidence about depression in older adults may be a factor that contributes to poor or inconsistent mental health care at the community level [21, 65]. In addition to the poor setting for mental health care services, there are no up-to-date systematic reviews and meta-analysis studies conducted that could vividly show the global prevalence and determinants of depression among old age. Several studies also revealed different and inconsistent prevalence rates in the world. Therefore, this systematic review and meta-analysis aimed to summarize the existing evidence on the prevalence of depression among old age and to formulate possible suggestions for clinicians, the research community, and policymakers.
Methods
Search process
A systematic search of the literature in September 2020 using both international [PubMed, Scopus, Web of sciences, Google Scholar, Psych-info, and national scientific databases] was conducted to identify English language studies, published between August 1994 and January 2020, that examined the prevalence of depression among old age. We searched English keywords of “epidemiology” OR “prevalence” OR “magnitude” OR “incidence” AND “factor” OR “associated factor” OR “risk” OR “risk factor” OR “determinant”, “depression”, “depressive disorder” OR “major depressive disorder” AND “old age” OR “elderly” OR “geriatrics”, “community”, “hospital” and “global”. In addition, the reference lists of the studies were manually checked to obtain further studies.
Inclusion and exclusion criteria
Original quantitative studies that examined the prevalence and determinants of depression among old age were included. The included studies were randomized controlled trials, cohort, case–control, cross-sectional, articles written in English, full-text articles, and published between August 1994 and January 2020. The exclusion criteria were studies which published as review articles, qualitative studies, brief reports, letter to the editor or editorial comments, working papers articles published in a language other than English, researches conducted in non-human subjects, and studies having duplicate data with other studies. The literature search was conducted based on the PRISMA (preferred reporting items for systematic reviews and meta-analyses) guideline [66]. All articles were independently reviewed by four researchers against inclusion and exclusion criteria. Any initial disagreement was resolved.
Data extraction and appraisal of study quality
After eliminating the duplicates, four investigators reviewed study titles and abstracts for eligibility. If at least one of them considered an article as potentially eligible, the full texts were assessed by the same reviewers. Any disagreements were resolved by discussion. Detailed information on the country, data source, study population, and results were extracted from each included study into a standardized spreadsheet by two authors and checked by the other two authors. EndNote X7.3.1 was used to organize the identified articles. Two investigators independently assessed the risk of bias of each of the included studies. The quality of studies included in the final analysis was evaluated with the Newcastle Ottawa quality assessment checklist [67]. The components of the quality assessment checklist include study participants and setting, research design, recruitment strategy, response rate, representativeness of the sample, the convention of valid measurement, reliability of measurement, and appropriate statistical analyses.
Statistical analysis
The data were analyzed with STATA 12.0 [68]. Prevalence standard errors were calculated using the standard formula for proportions: sqrt [p*(1 – p)/n]; The heterogeneity across the studies in proportion of depression in the elderly population and the contribution of studies attributing to total heterogeneity was estimated by the I2 statistic. The point estimates from each study were combined using a random-effects meta-analysis model to obtain the overall estimate with the DerSimonian–Laird method. Sources of heterogeneity across studies were examined with meta-regression. Publication bias and small study effects were assessed with the Egger test.
Results
Search result
The search procedure primarily obtained n = 1263 results, which after reading the title and abstract, full-text, and the application of the inclusion and exclusion criteria were reduced to n = 42. The selection process is shown in Fig. 1.
Fig. 1
Articles search flow diagram
×
Characteristics of the study subjects
A total of 42 studies [38, 42, 50, 57, 69‐105] studied our outcome of interest; A total sample size of fifty-seven thousand four hundred and eighty-six (57,486) elderly populations were included in the present study. The geographical province of studies was assessed. We found: Six studies in India [72, 86, 94, 95, 98, 102], five studies in China [50, 77, 84, 89], three studies in Turkey [71, 82, 105], three studies in Nepal [76, 90, 97], three studies in Thailand [70, 75, 83], two studies in the USA [91, 100], two studies in Australia [57, 99], two studies in Malaysia [42, 96], two studies in Ethiopia [81, 93], one study in German [103], one study in the UK [104], one study in Norway [85], one study in Italy [79], one study in Japan [87], one study in Mexico [78], one study in Brazil [92], one study in Finland [74], one study in Singapore [101], one study in Saudi Arabia [69], one study in the United Arab Emirates [80], one study in Ghana [88], one study in Sudan [73] and one study in Egypt [38]. Most of the studies in the present analysis were cross-sectional [38, 42, 50, 57, 69‐79, 81, 82, 84‐90, 92, 93, 95‐98, 101‐103, 105] and four studies were Cohort [85, 94, 99, 104].
Sixteen studies [70, 73, 74, 81, 86, 88, 90, 92‐94, 97, 98, 102‐105] used Geriatric Depression Scale-15 (GDS-15), 12 studies [38, 69, 71, 72, 75‐77, 82, 84, 89, 96] used Geriatric Depression Scale-30 (GDS-30), four studies [50, 80, 83, 101] used Geriatric Mental State Schedule (GMS) and ten studies [42, 57, 78, 79, 85, 87, 91, 95, 99, 100] used others (ICD-10, CIDI, DASS-21, KICA, CES-D, Euro-D, DSM-III, MCS and HADS) tools to measure depression in old age (Table 1).
Table 1
Characteristics of study participants among the elderly populations
Author, year of publication
Country
Study design
Sample size
Tools with cut off points
Sampling technique
Response rate
Characteristics of respondents
Overall prevalence (%)
Boman et al. 2015
Anland, Finnish
CS
1452
GDS-15 ≥ 5
NR
93.5%
F ≥ 65 years
11.2
Güzel et al. 2020
Burdur, Turkey
CS
770
GDS-30 ≥ 14
Cluster sampling method
NR
M & F
≥ 65 years
51.8
Swarnalatha N et al. 2013
Chittoor District, India
CS
400
GDS-15 > 5
Random sampling
100%
M & F
≥ 60 years
47
Ashe et al. 2019
Cuttack district, India
CS
354
GDS-30 ≥ 10
Simple random sampling
97.5%
M & F
> 60 years
81.1
Girma et al. 2016
Harar, Ethiopia
CS
344
GDS-15 ≥ 5
Systematic random sampling technique
97.7%
M & F
> 60 years
28.5
Mirkena et al. 2018
Ambo, Ethiopia
CS
800
GDS-15 ≥ 5
Multi-stage sampling technique
94.8%
M & F
≥ 60 years
41.8
He et al. 2016
Rural China
CS
509
GDS-30 ≥ 11
NR
96.8%
M & F
> 65 years
36.94
Cong et al. 2015
Fuzhou, China
CS
1910
GDS-30 ≥ 11
Randomly selected
98.0%
M & F
> 60 years
10.5
Feng et al. 2014
Xinjiang, China
CS
1329
GMS ≥ 3
Multistage stratified random sampling
91.3%
M & F
> 60 years
10.61
Kugbey et al. 2018
Ghana
CS
262
GDS-15 ≥ 5
Stratified random sampling
100%
M & F
> 65 years
37.8
Rajkumar et al. 2009
Southern Indian, Tamil Nadu
CS
978
ICD-10
NR
97.75%
M & F
> 65 years
12.7
Choulagai P S et al. 2013
Kathmandu Valley, Nepal
CS
78
GDS-30 ≥ 10
Purposively selected
100%
M & F
> 60 years
51.3
Simkhada et al. 2017
Kathmandu, Nepal
CS
300
GDS-15 ≥ 5
Randomly selected
99.0%
M & F
> 60 years
60.6
Manandhar et al. 2019
Kavre district, Nepal
CS
439
GDS-15 ≥ 6
Randomly selected
95.4%
M & F
≥ 60 years
53.1
Arslantas et al. 2014
Middle Anatolia, Turkey
CS
203
GDS-30 ≥ 13
NR
80.8%
M & F
≥ 65 years
45.8
Yaka et al. 2014
Turkey
CS
482
GDS-15 ≥ 8
Cluster sampling method
100%
M & F
≥ 65 years
18.5
Charoensakulchai et al. 2019
Thailand
CS
416
GDS-30 ≥ 13
NR
100%
M & F
> 60 years
18.5
Forlani et al. 2012
Bologna, Italy
CS
359
ICD-10
Randomly chosen sample
100%
M & F
> 74 years
25.1
Wilson et al. 2007
UK
Cohort
376
GDS-15 ≥ 5
NR
100%
M & F
80 to 90 years
21
Steffens et al. 2009
USA
Cohort
775
CIDI-SF ≥ 5
Stratified sampling method
90.5%
M & F
> 71 years
11.19
Manaf et al. 2016
Perak, Malaysia
CS
230
DASS-21 ≥ 5
Convenient sampling
100%
M & F
> 60 years
27.8
Almeida et al. 2014
Kimberley
and Derby, Australia
CS
235
KICA-dep ≥ 9
NR
94.0%
M & F
> 45 years
7.7
Weyerer et al. 2008
German
CS
3242
GDS-15 ≥ 6
NR
100%
M & F
> 75 years
9.7
Jadav et al. 2017
Vadodara, Gujarat, India
CS
176
GDS-15 > 5
Simple random sampling
88%
M & F
> 60 years
34.1
Sinha et al. 2013
Tamil Nadu, India
CS
103
GDS-15 ≥ 5
Universal sampling technique
100%
M & F
≥ 60 years
42.7
Kaji et al. 2010
Japan
CS
10,969
CES-D ≥ 16
Stratified sampling design
100%
M & F
> 50 years
31.2
Ferna´ndez et al. 2014
Mexico
CS
7867
CES-D ≥ 5
NR
NR
M & F
> 60 years
35.6
AL-shammari et al. 1999
Saudi Arabia
CS
7970
GDS-30 ≥ 10
Stratified two-stage sampling technique
98.8%
M & F
> 60 years
39
Sidik et al. 2004
Sepang, Malaysia
CS
223
GDS-30 > 10
Simple random sampling
84.8%
M & F
> 60 years
7.6
Subramaniam et al. 2016
Singapore
CS
2565
GMS ≥ 1
Stratified sampling design
NR
M & F
> 60 years
17.1
Assil et al. 2013
Sudan
CS
300
GDS-15 ≥ 5
Systematic
random sampling
100%
M & F
> 60 years
41.0
Haseen et al. 2011
Rural, Thailand
CS
1001
Euro-D scale-12 ≥ 5
NR
100%
M & F
> 60 years
27.5
Ghubash et al. 2004
United Arab Emirates
CS
610
GMS-A3 ≥ 3
Selected by randomly
90.3%
M & F
> 60 years
20.2
Abdo et al. 2011
Zagazig District, Egypt
CS
290
GDS-30 ≥ 10
Multistage random sampling technique
100%
M & F
> 60 years
46. 6
Snowdon et al. 1994
Sydney, Australia
Cohort
146
DSM-III
Random sample
69%
M & F
> 65 years
12.5
McCall et al. 2002
USA
CS
617
MCS ≥ 42
Simple random sampling
61.7%
M & F
> 65 years
25
Li et al. 2016
China, CDEP
CS
4901
GDS-30 ≥ 11
Consecutively selected
NR
M & F
> 60 years
11.6
Mendes et al. 2008
Brazil, Inpatients
CS
189
GDS-15 > 6
Randomly selected
100%
M & F
> 60 years
56.1
Li et al. 2016
China, EMI
CS
2373
GDS-30 ≥ 11
Consecutively selected
NR
M & F
> 60 years
18.1
Prashanth et al. 2015
India, Outpatient
Cohort
51
GDS-15 ≥ 5
NR
100%
M & F
> 60 years
58.8
Helvik et al. 2010
Norway, Medical inpatients
CS
484
HADS ≥ 8
NR
100%
M & F
> 65 years
10.3
Anantapong et al. 2017
Thailand, Outpatients
CS
408
GDS-15 > 5
Convenience sampling
100%
65–99 years
9.6
CDEP: community-dwelling elderly people; CES-D: Center for Epidemiologic Studies Depression Scale; CIDI-SF: Composite International Diagnostic Interview Short Form; CS: cross-sectional; DASS-21: Depression, Anxiety, and Stress Scale; DSM-III: diagnostic and Statistical Manual of Mental Disorders; EMI: elderly medical inpatients; GDS: Geriatric Depression Scale; GMS: Geriatric Mental State Schedule; HADS: Hospital Anxiety and Depression Scale; KICA-dep: Kimberley Indigenous Cognitive Assessment of Depression; MCS: mental component summary; NR: not reported; UK: United Kingdom; USA: United States of America
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Quality of included studies
The quality of 42 studies [38, 42, 50, 57, 69‐105] was assessed with the modified Newcastle Ottawa quality assessment scale. This scale divides the total quality score into 3 ranges; a score of 7 to 10 as very good/good, a score of 5 to 6 as having satisfactory quality, and a quality score less than 5 as unsatisfactory. The majority (28 from the 42 studies) had scored good quality, nine had a satisfactory quality, and four of the studies had unsatisfactory quality.
The prevalence of depression among old age
The reported prevalence of elderly depression among 42 studies [38, 42, 50, 57, 69‐105] included in this study ranges from 7.7% in a study from Malaysia and Australia [57, 96] to 81.1% in India [72]. The average prevalence of depression among old age using the random effect model was found to be 31.74% (95% CI 27.90, 35.59). This average prevalence of depression was with the heterogeneity of (I2 = 100%, p value = 0.000) from the difference between the 42 studies (Fig. 2).
Fig. 2
Forest plot for the prevalence of depression
×
Subgroup analysis of the prevalence of depression among old age
A subgroup analysis was done considering the economic status of countries, the study instrument and the sample size of each study. The cumulative prevalence of depression in elderly population among developing countries; 40.78% [38, 42, 69‐73, 75, 76, 78, 81‐83, 86, 88, 90, 92‐98, 101, 102, 105] was higher than the prevalence in developed countries; 17.05% [50, 57, 74, 77, 79, 80, 84, 85, 87, 89, 91, 99, 100, 103, 104] (Fig. 3). The average prevalence of depression was 40.60% in studies that used GDS-30 [38, 69, 71, 72, 75‐77, 82, 84, 89, 96] which is higher than the prevalence in studies that utilized GDS-15;35.72% [70, 73, 74, 81, 86, 88, 90, 92‐94, 97, 98, 102‐105], GMS;18.85% [50, 80, 83, 101] and other tools;19.91% [42, 57, 78, 79, 85, 87, 91, 95, 99, 100] (Fig. 4). Moreover, studies which had a sample size of below 450 [38, 42, 57, 70‐73, 75, 76, 79, 81, 86, 88, 90, 92, 94, 96‐99, 102, 104] provided higher prevalence of depression; 40.12% than those who had a sample size ranges from 450 to 999 [74, 80, 82, 84, 85, 91, 93, 95, 100, 105]; 25.38% and above 1000 [50, 69, 74, 77, 78, 83, 87, 89, 101, 103]; 20.19% (Fig. 5).
Fig. 3
Sub-group analysis of depression based on economic status of countries
Fig. 4
Sub-group analysis of depression based on study instruments
Fig. 5
Sub-group analysis of depression based on sample size of studies
×
×
×
Sensitivity analysis
The sensitivity analysis was performed to identify whether one or more of the 42 studies had out-weighted the average prevalence of depression among old age. However, the result showed that there was no single influential study, since the 95% CI interval result was obtained when each of the 42 studies was excluded at a time (Fig. 6).
Fig. 6
Sensitivity analysis for the prevalence of depression among old age
×
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Publication bias
There was no significant publication bias detected and Egger's test p value was (p = 0.644) showing the absence of publication bias for the prevalence of depression among old age. This was also supported by asymmetrical distribution on the funnel plot for a Logit event rate of prevalence of depression among old age against its standard error (Fig. 7).
Fig. 7
Funnel plot for publication bias for depression
×
Factors associated with depression among old age
Among 42 studies [38, 42, 50, 57, 69‐105] included in the present meta-analysis, only 32 [38, 42, 50, 57, 69, 72, 73, 75, 77‐81, 83, 84, 86‐98, 101‐105] reported about the associated factors for depression among old age. Our qualitative synthesis for the sociodemographic factors associated with depression in elderly populations showed that female gender [38, 69, 72, 75, 80, 86, 89, 93, 98, 102, 105], age older than 75 years [38, 69, 101, 102], being single, divorced or widowed [38, 42, 69, 80, 81, 87, 89, 98, 105], being unemployed [69, 86, 96, 105], retired [95], no educational background [75, 81, 86, 89, 90, 97, 102] OR low level of education [69, 81, 84, 91, 92, 105], low level of income [69, 72, 78, 80, 94, 95, 105], substance use [75, 81, 103], poverty [95, 102], cognitive impairment [81, 103], presence of physical illness, such as diabetes, heart diseases, stroke and head injury [42, 50, 57, 72, 77, 81, 83, 84, 86‐89, 95, 97, 106], living alone [88, 102, 104], disturbed sleep [77, 89], lack of social support [73, 77, 87], dependent totally for the activities of daily living [50, 79, 91, 92, 97, 102, 103], living with family [42, 93], history of a serious life events, such as death in family members, conflict in family, chronic illness in family members and those who had 3 or more serious life events [72, 83, 96], poor daily physical exercise [89] and exposure to verbal and/or physical abuse were strongly and positively associated with depression [90] (Table 2).
Table 2
Associated factors for depression among elderly populations
Factor category
Associated factors
AOR
95% CI
Strength of association
Author, year of publication
Demography
> 80 years
NR
NR
NR
Swarnalatha et al. 2013
Females
NR
NR
NR
Illiterates
NR
NR
NR
Socioeconomic status
Those who were below the poverty line
NR
NR
NR
Those who were living alone
NR
NR
NR
Economic dependency
Those who were economically partially dependent
NR
NR
NR
ADL
Those depended totally for the activities of daily living
NR
NR
NR
Sociodemographic characteristics
Female gender
4.75
2.1, 10.7
Strong
Ashe et al. 2019
Socioeconomic status
Low socioeconomic class
9.36
3.69, 23.76
Strong
Health conditions and comorbidities
Diabetes mellitus
2.76
1.27, 5.98
Moderate
Hypertension
2.15
1.06, 4.36
Moderate
Life events
Death in family members
5.52
2.08, 14.65
Strong
Conflicts in family
5.78
2.55, 13.09
Strong
Chronic illness in family members
6.77
1.47, 31.13
Strong
Socio-demographic characteristics
Not married
10.1
3.89, 26.18
Strong
Girma et al. 2016
Those with no formal education
3.6
1.45, 9.07
Strong
Elderly who attended primary school
0.28
0.1, 0.78
Weak
Substance use and clinical related
Those who had chronic illness
3.47
1.5, 7.7
Strong
Elderly with cognitive impairments
2.77
1.18, 6.47
Moderate
Substance use
2.6
1.07, 6.28
Moderate
Socio-demographic characteristics
Female sex
1.72
1.12, 2.66
Weak
Mirkena et al. 2018
Trading
2.44
1.32, 4.57
Moderate
Living with children
3.19
1.14, 8.93
Strong
Retirement
3.94
2.11, 7.35
Strong
Characteristics of the participants
Frequency of children’s visits
NR
NR
NR
He et al. 2016
Living situation
NR
NR
NR
Physical activity
NR
NR
NR
Number of chronic diseases
NR
NR
NR
Education level
NR
NR
NR
Demographic characteristics
Lack of social engagement
0.313
0.134, 0.731
Weak
Cong et al. 2015
Low family support
0.431
0.292, 0.636
Weak
Chronic disease
2.378
1.588, 3.561
Moderate
Disturbed sleep
1.822
1.187, 2.798
Weak
Behaviors and life events
Religious belief
3.92
1.18, 13.03
Strong
Feng et al. 2014
Suffering from more chronic diseases
1.70
1.42, 2.04
Weak
Lack of ability to take self-care
2.20
1.09, 4.48
Moderate
Socio-demographic characteristics
Religion (Non-Christians)
5.67
2.10, 15.27
Strong
Kugbey et al. 2018
Living arrangement (Alone)
2.36
1.16, 4.83
Moderate
Chronic illness (Not having chronic illness)
0.25
0.13, 0.47
Weak
Socio-demographic and psychosocial profiles
Low income
1.78
1.08, 2.91
Weak
Rajkumar et al. 2009
Experiencing hunger
2.58
1.56, 4.26
Moderate
History of cardiac illnesses
4.75
1.96, 11.52
Strong
Transient ischemic attack
2.43
1.17–5.05
Moderate
Past head injury
2.70
1.36, 5.36
Moderate
Diabetes
2.33
1.15, 4.72
Moderate
Having more confidants
0.13
0.06, 0.26
Weak
Socio-demographic characteristics
Illiteracy
2.01
1.08, 3.75
Moderate
Simkhada et al. 2017
Physical immobility
5.62
1.76, 17.99
Strong
The presence of physical health problems
1.97
1.03, 3.77
Weak
Not having any time spent with family members
3.55
1.29, 9.76
Strong
Not being considered in family decision-making
4.02
2.01, 8.04
Strong
Socio-demographic characteristics
Rural habitation
1.6
1.1, 2.4
Weak
Manandhar et al. 2019
Illiteracy
2.1
1.1, 4.0
Moderate
Family support
Limited time provided by families
1.8
1.1, 2.9
Weak
Exposure to verbal and/or physical abuse
2.6
1.4, 4.8
Moderate
Sociodemographic–economic characteristics
Female gender
NR
NR
NR
Yaka et al. 2014
Being single or divorced
NR
NR
NR
Lower educational status
NR
NR
NR
Low income
NR
NR
NR
Unemployment
NR
NR
NR
Lack of health insurance
NR
NR
NR
Baseline characteristics and family relationship
Female sex
2.78
1.54, 7.49
Moderate
Charoensakulchai et al. 2019
Illiteracy
2.86
1.19, 6.17
Moderate
Current smoker
4.25
2.12, 10.18
Strong
Imbalanced family type (low attachment, low cooperation and poor alignment between each member)
4.52
2.14, 7.86
Strong
Sociodemographic characteristics
Not having a main daily activity in men
3.01
1.00, 9.13
Strong
Forlani et al. 2012
Health-Related Variables
Stroke in men
7.25
2.19, 24.06
Strong
Sociodemographic characteristics
Not living close to friends and family
2.540
1.442, 4.466
Moderate
Wilson et al. 2007
Poor satisfaction with living accommodation
0.840
0.735, 0.961
Weak
Poor satisfaction with finances
0.841
0.735, 0.961
Weak
Subsequent development of clinically significant depressive symptoms was associated with base line increased scores in depression
1.68
1.206, 2.341
Weak
Socio-demographic characteristics
Single elderly
3.27
1.66, 6.44
Strong
Manaf et al. 2016
Living with family
4.98
2.05, 12.10
Strong
Poor general health status
2.28
1.20, 4.36
Moderate
Clinical characteristics
Heart problems
3.3
1.2, 8.8
Strong
Almeida et al. 2014
ADL
Functional impairment
2.9
2.26, 3.78
Moderate
Weyerer et al. 2008
Socio-demographic characteristics
Smoking
1.6
1.03, 2.36
Weak
Multi-domain mild cognitive impairment
2.1
1.30, 3.43
Moderate
Socio-demographic characteristics
Female gender
10.64
5.09–21.82
Strong
Jadav et al. 2017
Unemployed/retired
7.37
2.49, 21.79
Strong
Illiterate
4.17
1.99, 8.72
Strong
Clinical related
Respiratory problems
5.47
2.63, 11.37
Strong
Socio-demographic characteristics
Female sex
NR
NR
NR
Sinha et al. 2013
Widowhood
NR
NR
NR
Problems related to social environment
Having no one to talk to (Mild to moderate depression)
3.3
2.5, 4.4
Strong
Kaji et al. 2010
Having no one to talk to (Severe depression)
5.0
3.6, 6.9
Strong
Problems with primary support group
Separation/divorce(Mild to moderate depression)
2.8
1.4, 5.3
Moderate
Health/illness/care of self(Severe depression)
0.8
0.6, 0.9
Weak
Socioeconomic characteristics
Socioeconomic deprivation at municipal levels
1.16
1.04, 1.30
Weak
Ferna´ndez et al. 2014
Socio-demographic characteristics
Poor education
NR
NR
NR
Al-Shammari et al. 1999
Unemployment
NR
NR
NR
Divorced or widowed status
NR
NR
NR
Old age
NR
NR
NR
Being a female
NR
NR
NR
Living in a remote rural area with poor housing arrangements
NR
NR
NR
Limited accessibility within the house and poor interior conditions
NR
NR
NR
Limited privacy, such as having a particular room specified for the elderly
NR
NR
NR
Lower incomes inadequate for personal needs as well as depending on charity or other relatives
NR
NR
NR
Socio demographic Profile
Unemployment
NR
NR
NR
Sidik et al. 2004
Socio-demographic Status
Aged 75 to 84 years
2.1
1.1, 3.9
Moderate
Subramaniam et al. 2016
Those of Indian ethnicity
4.1
1.1, 14.9
Strong
Those of Malay ethnicity
5.2
3.1, 8.7
Strong
Other Health Conditions
Those who had a history of depression diagnosis by a doctor
3.2
1.9, 5.4
Strong
Socio-demographic characteristics
Being retired
3.88
1.27, 11.76
Strong
Assil et al. 2013
Having social problems
3.27
1.45, 7.41
Strong
Having living problems
2.19
1.19, 3.94
Moderate
Physical illness
Those who had 4 or more infirmity
2.08
NR
Moderate
Haseen et al. 2011
Disability Assessment
Those who had medium disability
3.12
NR
Strong
Serious life events
Those who had 3 or more serious life events
5.25
NR
Strong
Socio-demographic characteristics
Female gender
1.8
NR
Weak
Ghubash et al. 2004
Insufficient income
3.8
NR
Strong
Being single, separated, divorced or widowed
2.1
NR
Moderate
Socio-demographic Characteristics
Age ≥ 75 years
5.08
2.21, 11.89
Strong
Abdo et al. 2011
Being female
2.56
1.55, 4.24
Moderate
Not married
4.47
2.52, 7.97
Strong
Having previous death event among the surrounding
7.68
3.57, 16.93
Strong
Respondent characteristics
Years of education
0.87
NR
Weak
McCall et al. 2002
Difficulties performing activities of daily living
1.72
NR
Weak
Enrolled in medicaid
2.67
NR
Moderate
Socio-demographic variables
Being female
Residing in rural or suburb
1.25
2.31
1.02, 1.54
1.88, 2.86
Weak
Moderate
Li et al. 2016
Currently not married or not
living with spouse
1.45
1.17, 1.80
Weak
Poor physical health
5.23
3.97, 6.88
Strong
Poor daily physical exercise
1.79
1.39, 2.29
Weak
Poor sleep quality
2.76
2.14, 3.56
Moderate
Socio-demographic variables
Low educational level
5.9
1.5, 22.6
Strong
Mendes-Chiloff et al. 2008
Death
5.5
1.7, 17.1
Strong
ADL
Dependence regarding basic ADL
5.1
2.2, 11.0
Strong
Socio-demographic variables
Illiterate or elementary school
1.68
1.2, 2.29
Weak
Li et al. 2016
Poor physical health
4.49
(3.15, 6.38
Strong
Poor daily physical exercise
1.51
1.07, 2.11
Weak
Poor sleep quality
3.25
2.33, 4.53
Strong
Socio-demographic
Financial fears regarding future
NR
NR
NR
Prashanth et al. 2015
Income insufficiency
NR
NR
NR
AOR: Adjusted Odds Ratio; CI: Confidence Interval; NR: Not Reported
Discussion
As to the researcher’s knowledge, this review and meta-analysis on the prevalence and determinants of depression among old age are the first of their kind in the world. Therefore, the knowledge generated from this meta-analysis on the pooled prevalence and associated factors for depression among old age could be important evidence to different stakeholders aiming to plan policy in the area. The average prevalence of depression among old age using the random effect model was found to be 31.74%. A subgroup analysis was done considering the economic status of countries, the study instrument, and the sample size of each study.
In the present systematic review and meta-analysis, the existing available information varies by the region, where the study was conducted, data collection tools used to screen depression, and the sample size assimilated in the study. Sixty-two percent (n = 26) of the studies were found in developing countries. About 38% (n = 16) of the incorporated studies utilized GDS-15 to screen depression, around 28% (n = 12) studies used GDS-30 to screen depression, ten percent (n = 4) studies used GMS to screen depression, whereas the rest utilized other tools. More than half (n = 22) of the included studies utilized a sample size of below 450.
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The result of this meta-analysis revealed that depression in the elderly populations in the world was high (31.74%). This pooled prevalence of depression among old age in the world (31.74%; 95% CI 27.90 to 35.59%) was higher than a global systematic review and meta-analysis study on 95,073 elderly populations aged > 75 years and 24 articles in which a pooled prevalence of depression was 17.1% (95% CI 9.7 to 26.1%) [107], a global systematic review and meta-analysis study on 41 344 outpatients and 83 articles in which a pooled prevalence of depression was 27.0% (95% CI: 24.0% to 29.0%) [108], WHO reports on mental health of older adults over 60 years old with 7% prevalence of depression in the general older population [106], a Brazilian systematic review and meta-analysis study on 15,491 community-dwelling elderly people average age 66.5 to 84.0 years and 17 articles with a pooled prevalence rates of 7.0% for major depression, 26.0% for CSDS (clinically significant depressive symptoms), and 3.3% for dysthymia [109] and an Iranian meta-analysis study on 3948 individuals aged 50 to 90 years and 13 articles with a pooled prevalence of severe depression was 8.2% (95% CI 4.14 to 6.3%) [110]. The reason for such a high prevalence of depression in the globe would be due to the difference in sample size, study subjects, the severity of depression, study area, study instruments, and the means of administration of the tools employed in the studies [111].
In contrast to our current systematic review and meta-analysis study, the pooled prevalence of depression was lower than a Chinese Meta-Analysis of Observational Studies on 36,791 subjects and 46 articles with a pooled prevalence of depression was 38.6% (95% CI 31.5–46.3%) [112], and an Indian systematic review and meta-analysis study on 22,005 study subjects aged 60 years and above, and 51 articles with a pooled prevalence of depression was 34.4% (95% CI 29.3 to 39.6) [113]. The reason for the discrepancy might be due to the wide coverage of the study and the higher sample size utilized in the present study. Furthermore, differences could be due to the poor health care coverage and significant population makes a destitute life both in China and India. In addition, both China and India have a rapidly aging population. Old age causes enforced retirement which may lead to marginalizing older people. Elders are regarded as incompetent and less valuable by potential employers. This attitude serves as a social stratification between the young and old and can prevent older men and women from fully participating in social, political, economic, cultural, spiritual, civic, and other activities [114‐116].
A significant regional variation on the pooled prevalence of depression in the elder population was observed in this review and meta-analysis study. The aggregate prevalence of depression in elderly population among developing countries; 40.78% [38, 42, 69‐73, 75, 76, 78, 81‐83, 86, 88, 90, 92‐98, 101, 102, 105] was higher than the prevalence in developed countries; 17.05% [50, 57, 74, 77, 79, 80, 84, 85, 87, 89, 91, 99, 100, 103, 104]. The huge variation might be due to absolute poverty, economic reform programs, limited public health services, civil unrest, and sex inequality are very common in developing countries [117].
Likewise, the greater pooled prevalence of depression in elderly population was observed in studies using a sample size below 450 study subjects (40.12%) [38, 42, 57, 70‐73, 75, 76, 79, 81, 86, 88, 90, 92, 94, 96‐99, 102, 104] than the pooled prevalence of depression in elders that used a sample size of 450–999 (25.38%) [74, 80, 82, 84, 85, 91, 93, 95, 100, 105], and above 1000 (20.19%) [50, 69, 74, 77, 78, 83, 87, 89, 101, 103]. The reason could be a smaller sample size increases the probability of a standard error thus providing a less precise and reliable result with weak power.
Regarding the associated factors; being female, age older than 75 years, being single, divorced or widowed, being unemployed, retired, no educational background, low level of education, low level of income, lack of social support, living with family, current smoker, presence of physical illness, such as diabetes, heart diseases, stroke, and head injury, poor sleep quality, physical immobility and a history of serious life events, such as a death in family members, conflict in the family, chronic illness in family members and those who had 3 or more serious life events were found to have a strong and positive association with depression among old age.
Difference between included studies in the meta-analysis
This meta-analysis study was obtained to have a high degree of heterogeneity between the studies incorporated in pooling the prevalence of depression in the elderly population of the world. The analysis of subgroups for detection of sources of heterogeneity was done and the economic status of the country, where the study was done, data collection instruments, and sample size were identified to contribute to the existing variation between the studies incorporated in the analysis. Besides, a sensitivity analysis was performed using the random-effects model to identify the effect of individual studies on the pooled estimate. No significant changes in the pooled prevalence were found on the removal of a single study.
Limitations should be considered when interpreting the results of this study. Screening tools cannot take the place of a comprehensive clinical interview for confirmatory diagnosis of depression. Nevertheless, it is a useful tool for public health programs. Screening provides optimum results when linked with confirmation by mental health experts, treatment, and follow-up. As this meta-analysis included studies done using screening tools, a further meta-analysis done with diagnostic tools will help to assess the true burden of depression and to determine the need for pharmacological and non-pharmacological interventions. Furthermore, because of the lack of access to the full text of some studies, the researchers failed to include these research findings.
Conclusion
This review and meta-analysis study obtained a pooled prevalence of depression in the elderly population in the world to be very high, 31.74% (95% CI 27.90, 35.59). This pooled effect size of depression in the elderly population in the world obtained is very important as it showed aggregated evidence of the burden of depression in the targeted population. Since the high prevalence of depression among the old population in the world, this study can be considered as an early warning and advice to health professionals, health policymakers, and other pertinent stakeholders to take effective control measures and periodic assessment for the elderly population.
Acknowledgements
None.
Declarations
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not Applicable.
Competing interests
The authors have no competing interests to declare.
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