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Prevalence of depressive symptoms, anxiety, sleep & substance use disorders among older adults in LMICs: A systematic review & meta-analysis
Present address: #Department of Epidemiology, ICMR-National Institute for Research in Bacterial Infections, Kolkata, India
For correspondence: Prof Indranil Saha, Department of Epidemiology, ICMR-National Institute for Research in Bacterial Infections, Kolkata 700 010. West Bengal, India e-mail: drsahaindranil@gmail.com
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Received: ,
Accepted: ,
Abstract
Background & objectives
The global population is ageing, with an increase in older adults in lower middle-income countries (LMICs). Fragmented data from LMICs indicate the need for the estimation of mental health disorders to guide policies, resource allocation, and sustainable mental health strategies. This systematic review and meta-analysis calculated the overall prevalence of depressive, anxiety, sleep and substance use disorders in older adults aged ≥60 yr in LMICs.
Methods
The protocol was registered in PROSPERO (CRD42024503470). A systematic search across PubMed, Scopus, and Cochrane Central was conducted (Dec 2023-Jan 2024), focusing on cross-sectional studies published in English. Quality assessments were conducted using the AXIS tool. A random-effects model was used to estimate pooled prevalence; heterogeneity was calculated using I2 statistics.
Results
Out of 4,204 articles, 92 studies were included, with the majority from India (n=29), Iran (n=11) and Nigeria (n=9). Pooled prevalence of depressive disorders was 38.76 per cent [95% confidence interval (CI): 34.25–43.28%; n=76; I2=96.89%], sleep disorders 33.58 per cent (95% CI: 27.03–40.14%; n=20; I2=95.76%), anxiety disorders 27.76 per cent (95% CI: 13.74–41.79%; n=13; I2=96.62%), and substance use disorders 26.36 per cent (95% CI: 14.23–38.49%; n=9; I2=96.86%). Depressive disorders were the highest in Africa, while sleep, anxiety, and substance use disorders were at higher rates in Asia.
Interpretation & conclusions
The findings emphasise the high burden of mental health disorders among older adults in LMICs, which indicate the need for strategic management. Early diagnosis, treatment, and integration of mental health into primary care, along with capacity building of the health care workers, are urgently needed.
Keywords
Anxiety disorders
depressive disorders
lower middle-income countries
older adults
sleep disorders
substance use disorders
Changes in the life expectancy of humans are due to the global advancement of healthcare and technology1. Consequently, the number of older adults is increasing2. The World Health Organization (WHO) projects that the population aged 60 or more will increase from 1 billion in 2020 to 1.4 billion by 20303, most of whom will be residing in low- and middle-income countries by 20504. Shifts in this demographic lead to increased concerns about the mental health disorders of older adults resulting from feelings of loneliness, emotional neglect, and a host of abusive experiences5.The above factors, often coupled with the physiological and psychological changes typical of old age, enhance older adults’ vulnerability to mental health-related issues6,7.
Studies from low-and middle-income countries reveal varied rates of depressive disorders (0.5 to 62.7%) and anxiety disorders (0.2 to 32.2%) among older adults, with a pooled depressive disorders prevalence of 10.5 per cent8. In India, older adults demonstrated a higher prevalence of both lifetime depressive disorders (6.9%) and current depressive disorders (3.5%) as compared to younger adults. The most common anxiety disorders among this demographic were specific phobias (1.7%) and agoraphobia (1.6%)9. Insomnia, prevalent among the elderly, is linked to increased mortality and reduced quality of life10-12. Studies have shown that in India, 37 per cent of older adults experience insomnia, with a higher prevalence among women (41%) compared to men (32%)13. In India, 27.7 per cent of older adults have substance use disorders, including 4.1 per cent affected by alcohol use disorder, exacerbated by limited access to mental health services, social isolation, economic stress, and trauma14. Studies from South Africa have shown lifetime prevalence of anxiety disorders to be 17 per cent, depressive disorders to be 6.5 per cent, substance use disorders to be 11 per cent among the 65 or above age group15. Published data from South East Asian countries like Singapore, Malaysia, and Thailand have shown the prevalence rate of depressive disorders to be 5.7 per cent among older adults16.
Although, knowledge of different mental health outcomes among older adults is necessary for policymakers and planners to plan for the allocation of resources and required service provisions in the catchment areas of these regions, no comprehensive systematic review and meta-analysis (SRMA) has pooled the prevalence of depressive, anxiety, sleep and substance use disorders in older adults across lower middle-income countries (LMIC) contexts. Such knowledge of prevalence can be used in filling existing gaps in mental health services and in training and distributing mental health professionals across the regions. This SRMA were performed to estimate the overall prevalence of depressive, anxiety, sleep and substance use disorders in older adults aged 60 years or above in accordance with WHO criteria3, residing in LMICs as defined by World Bank for the 2023 fiscal year17.
Materials & Methods
This study was undertaken by the department of Mental Health & Ageing, Indian Council of Medical Research- Centre for Ageing & Mental Health, Kolkata, India from December 1, 2023 to January 10, 2025. This SRMA was registered in the PROSPERO database18 (CRD42024503470). The study was carried out following the PRISMA 2020 guidelines19 and the protocol has been published20.
Study selection criteria
The study selection criteria emphasized cross-sectional studies, original field research, published in English, focusing on individuals aged 60 yr or above in LMICs, and addressing depressive disorders, anxiety disorders, sleep disorders and substance use disorders as defined by ICD-10 criteria21. Randomised controlled trials (RCTs), case-control studies and cohort studies were excluded from this study. Studies without access or without full-text versions were not considered. Article categories such as commentaries, conference proceedings, and editorials were excluded from this study.
Literature search
The research question was initially deconstructed into distinct components following PICOS format: P (Population), I (Intervention), C (Comparator), O (Outcome), and S (Settings) format. The ‘Population (P)’ component encompassed the concept of ‘old age’ while concepts such as ‘mental health’, ‘depressive disorders’, ‘sleeping disorders’, ‘anxiety’ and ‘substance use’ were categorized as concepts under the ‘Outcome (O)’ component. As per the research question, ‘Intervention (I)’ and ‘Comparator (C)’ were not applicable. Additionally, considering the objective of identifying articles in LMICs, ‘Lower Middle-income countries’ were assigned to the ‘Settings (S)’ component in the search strategy. For each concept, relevant ‘MeSH term’ and text terms were identified separately. The final search strategy was constructed by employing appropriate Boolean Operators such as ‘OR’ and ‘AND’ to link the concepts in search engines based on the research questions. Three databases, viz. PubMed, Scopus, and Cochrane Central were searched to retrieve articles. The initial search was conducted in December 2023, and the final search was conducted in January 2024 (Supplementary Tables I and II).
Data abstraction
Articles were searched from three different databases imported into Rayyan software and underwent a deduplication process to remove duplicates. Two reviewers (DD and MKS) performed title and abstract [tiab] screening, and then full-text screening. The first stage excluded records based on titles and abstracts, but in the second step, all potentially relevant studies were examined in full texts to make the final inclusion or exclusion decision. If there are conflicting views, a third reviewer (AP) was consulted to resolve conflicts.
Assessment of methodological quality for individual studies
An independent author extracted data using a data extraction form based on the Cochrane data extraction form22 which was verified by another author. This form comprised study identification details and all relevant information required for the study. The methodological quality of the cross-sectional studies was assessed by the AXIS tool23.
Statistical analysis
Stata software, version 17 (StataCorp, College Station, Texas, USA) was used for meta-analysis. Pooled prevalence with 95 per cent Confidence Intervals (CI) was measured for depressive, anxiety, sleep and substance use disorders independently. Subgroup analysis was conducted based on gender, study settings, and continent, as the previous literature indicated considerable variations in the prevalence of mental health disorders across these subgroups. To stabilize the variances, Freeman-Tukey double arcsine transformation was applied24. Given the anticipated variability across studies conducted in multiple LMIC contexts, a random-effects model was used for all meta-analysis. I2 values were calculated to estimate the heterogeneity, which was considered low if <25 per cent, moderate between 25 to 75 per cent, and high if >75 per cent. Potential sources of heterogeneity were explored through subgroup analysis. Leave-one-cut sensitivity analysis was conducted by sequentially removing each study and recalculating the overall effect estimate to assess the robustness and influence of individual studies on the pooled results. Additionally, meta-regression analysis was carried out using study settings and continent as covariates to investigate heterogeneity further. Funnel plot and Egger’s test evaluated the publication bias. Studies with missing outcome data were included in the qualitative synthesis but excluded from quantitative meta-analysis if the missing information precluded calculation of effect estimates. Sensitivity analyses were conducted, where possible, to assess the potential impact of missing data on the overall findings.
Results
General characteristics of the studies
4,204 articles were found using the initial search carried out on three databases. After tiab and full-text screening, 92 articles were finally considered for this study25-116. Details of the article selection process are displayed in a PRISMA flow chart (Fig. 1).
- Flowchart for literature search and study selection following the PRISMA guidelines.
Among the 92 studies, the majority were from India (29 studies)30,32,33,36,39-41,44,45,47,49,51,52,54-56,60,61,64,66-68,70-72,75,76,78,80, followed by Iran (11 studies)29,35,42,43,59,63,77,88,98,112,114, Nigeria (9 studies)31,34,81,83,84,90,92,93,116, Nepal (8 studies)26,53,79,87,89,91,96,103, Pakistan (7 studies)57,65,74,82,99,113,115 and Egypt (6 studies)62,69,73,94,101,102. Five articles were retrieved each from Sri Lanka86,97,106,107,109 and Vietnam50,58,105,108,111, whereas three articles were retrieved from Indonesia28,38,110. Two articles were retrieved each from Tanzania46,85, Bangladesh37,48, Ghana100,104. One article was retrieved each from Bahrain95, Lebanon25, and Myanmar27 (Table I)24-115. Of the 49 studies in which the study area was specified, 17 were located in rural areas and 17 in urban areas and 15 in both the areas (Table I). The first study was conducted in 1995, and the last one in 2022. The duration was unspecified in 23 studies (Table I). Two studies were done over a 24-month period, which is more extended than the other studies. Age group information was not available for one study. The sample sizes varied widely, ranging from 42 to 31,464 participants. Selected studies had different study settings, i.e., community-based (64 studies), hospital-based (16 studies), old-age home-based (5 studies), and health centre-based (7 studies) (Table I). The most frequently used instruments to evaluate mental health disorders were the Geriatric Depression Scale (GDS), the International Classification of Diseases-10 (ICD-10), and the Centre for Epidemiological Studies Depression Scale (CESD). The instruments were administered offline in the selected studies (Table I).
| First author | Study area | Study period | Age of the study population (yr) | Sample size (M% & F%) | Study settings | Urban/rural setting | Instruments used |
|---|---|---|---|---|---|---|---|
| Karam et al24, 2016 | Lebanon | … | 60 & above | 593 (48.4 & 51.6) | Community | Both | CIDI |
| Samadarshi et al25, 2022 | Nepal | … | 60 & above | 405 (49.6 & 50.4) | Community | Rural | GDS-15 |
| Cho et al26, 2021 | Myanmar | Dec – Jan 2019 | 60 & above | 655 | Health centre | Rural | GAI, GDS |
| Margaretha et al27, 2021 | Indonesia | Jun 2020 | 60 & above | 102 (39.2 & 60.8) | Community | … | SDQ, GDS |
| Mazloomzadeh et al28, 2021 | Iran | 2017 | 60 – 80 | 228 | Health centre | Urban | GHQ |
| Ashe et al29, 2019 | India | Jul 2018 – May 2019 | 60 & above | 354 (70.3 & 29.7) | Community | Urban | GDS |
| Amoo et al30, 2020 | Nigeria | … | 60 & above | 532 (35.3 & 64.7) | Community | Urban | SDQ, GHQ |
| Shinde et al31, 2023 | India | 24 months | 60 – 80 | 220 (44.5 & 55.5) | Community | Urban | Predesigned questionnaire |
| Nakulan et al32, 2015 | India | Jun – Nov 2011 | 65 & above | 220 (42.2 & 57.7) | Community | Rural | ICD-10 |
| Gureje et al33, 2007 | Nigeria | Nov 2003 – Aug 2004 | 65 & above | 1897 (46.6 &53.4) | Community | … | DSM-IV |
| Raeisvandi et al34, 2023 | Iran | Feb – Mar 2020 | 60 & above | 301 (45.8 & 55.2) | Health centre | … | DASS |
| Shetty et al35, 2023 | India | 2015 – 2017 | 60 & above | 1832 (41.3 & 58.7) | Community | Both | GDS |
| Akter et al36, 2023 | Bangladesh | Aug 2021 | 60 - 80 | 200 (50.5 & 49.5) | Old age home | … | Predesigned questionnaire |
| Santoso et al37, 2023 | Indonesia | Oct 2014 – Apr 2015 | 75 & above | 543 (50.1 & 49.9) | Community | Both | CESD, PROMIS |
| Hossaain et al38, 2022 | India | 2017 – 2018 | 60 & above | 30366 (47.1 & 52.9) | Community | Both | CESD |
| Ansari et al39, 2022 | India | 2017 – 2018 | 60 & above | 31464 (48 & 52) | Community | Both | CESD |
| Jesudoss et al40, 2023 | India | Apr – Dec 2019 | 60 & above | 100 (50 & 50) | Old age home | … | PSQI |
| Azizabadi et al41, 2022 | Iran | 2016 - 2018 | 60 & above | 4010 | Community | … | CESD, HADS |
| Abbasi et al42, 2022 | Iran | 2018 | 60 – 94 | 400 (49 & 51) | Health centre | … | SCL-90-R |
| Sindhu et al43, 2022 | India | Dec 2020 – May 2021 | 60 & above | 339 (36.9 & 63.1) | Community | Rural | GAD-7, GDS-15 |
| Jagadeesan et al44, 2022 | India | May 2018 – Mar 2019 | 60 & above | 459 (55.8 & 44.2) | Community | Urban | GDS |
| Mlaki et al45, 2021 | Tanzania | Apr – Sep 2010 | 70 & above | 296 (31.8 & 68.2) | Community | Rural | DSM-IV |
| Kumari et al46, 2021 | India | … | 60 & above | 425 (43.5 & 56.4) | Health centre | Rural | GDS |
| Alam et al47, 2021 | Bangladesh | … | … | 400 (50 & 50) | Community | … | GDS |
| Bharti et al48, 2021 | India | … | 60 & above | 52 | Health centre | Both | Predesigned questionnaire |
| Nguyet et al49, 2021 | Vietnam | Jul – Oct 2019 | 60 & above | 376 (33.5 & 66.5) | Community | Rural | PHQ-9 |
| Pathak et al50, 2020 | India | 24 months | 60 & above | 209 | Community | Urban | GDS |
| Vimala et al51, 2020 | India | Jun 2019 – Jan 2020 | 60 & above | 120 (46 & 54) | Community | … | GDS |
| Yadav et al52, 2020 | Nepal | Jan – Apr 2018 | 60 & above | 794 (50.4 & 49.6) | Community | Rural | GDS-15 |
| Dahale et al53, 2020 | India | May – Dec 2016 | 60 & above | 1574 (44.5 & 55.5) | Health centre | … | ISI |
| Paul et al54, 2019 | India | … | 60 & above | 162 (58.6 & 41.4) | Community | Rural | GDS SF |
| Nayak et al55, 2019 | India | Apr – Jun 2017 | 60 & above | 244 (48 & 52) | Community | Urban | GDS-15, GAD-7 |
| Farazdaw et al56, 2019 | Pakistan | Feb – Jun 2013 | 60 & above | 152 (38.8 & 61.2) | Hospital | … | DSMMD |
| Dao-Tran et al57, 2018 | Vietnam | Aug 2014 – Jan 2015 | 60 & above | 440 (0 & 100) | Community | Both | GSDS |
| Dehghankar et al58, 2018 | Iran | … | 60 & above | 400 (48.5 & 51.5) | Community | … | PSQI |
| Sangma et al59, 2018 | India | Oct 2014 – Sep 2016 | 60 & above | 240 (44.6 & 55.4) | Community | Urban | GDS |
| Boyanagari et al60, 2018 | India | Jan – Jun 2017 | 60 & above | 570 (61.6 & 38.4) | Community | Both | GDS |
| El-Gilany et al61, 2018 | Egypt | Oct – Dec 2016 | 60 & above | 474 (49.8 & 50.2) | Community | Both | GDS-SF |
| Hosseini et al62, 2017 | Iran | … | 60 & above | 1416 | Community | … | Predesigned questionnaire |
| Spoorthi et al63, 2018 | India | Jun - Jul 2016 | 60 & above | 216 (48.1 & 51.9) | Community | Urban | GDS |
| Alvi et al64, 2017 | Pakistan | … | 60 & above | 624 | Community | … | GDS-15 |
| Sengupta et al65, 2015 | India | Jun – Nov 2011 | 60 & above | 3038 (45.5 & 54.5) | Community | Both | GDS |
| D’souza et al66, 2015 | India | Aug – Nov 2014 | 60 & above | 210 (50.5 & 49.5) | Community | Urban | GDS |
| Gambhir et al67, 2014 | India | Jun 2009 – May 2011 | 60 & above | 504 (60.3 & 39.7) | Hospital | … | GDS, Sleep Disorder Protocol |
| Ayoub et al68, 2014 | Egypt | … | 60 & above | 380 (59.7 & 40.3) | Community | … | ISI |
| Sinha et al69, 2013 | India | Feb – Mar 2012 | 60 & above | 103 (56.3 & 43.7) | Community | Rural | GDS-15 |
| Swarnalatha70, 2013 | India | Apr – Sep 2019 | 60 & above | 400 (50 & 50) | Community | Rural | GDS |
| Tiwari et al71, 2013 | India | … | 60 & above | 2146 (47.4 & 52.6) | Community | Rural | ICD-10 |
| Esmayel et al72, 2013 | Egypt | Mar – Apr 2013 | 65 & above | 200 (56 & 44) | Hospital | … | GDS |
| Bhamani et al73, 2013 | Pakistan | Jul – Sep 2008 | 60 & above | 953 (53.1 & 46.9) | Community | Urban | GDS-15 |
| Dhar74, 2013 | India | Aug – Dec 2011 | 60 & above | 204 (52.9 & 47.1) | Community | … | GDS |
| Reddy et al75, 2012 | India | Jun 2011 – Jan 2012 | 60 & above | 800 (50 & 50) | Community | Rural | GDS |
| Mousavi et al76, 2012 | Iran | Jan – Mar 2009 | 65 & above | 772 (32.1 & 67.9) | Hospital | … | DSM-IV R |
| Seby et al77, 2011 | India | Aug 2002 – May 2003 | 65 & above | 202 (50.9 & 49.1) | Community | Urban | GHQ-12, GDS-15, CAGE |
| Kumar et al78, 2011 | Nepal | Apr – May 2009 | 65 & above | 42 | Hospital | … | BDI-II, BAI |
| Rajkumar et al79, 2009 | India | … | 65 & above | 1000 (45.4 & 54.6) | Community | Rural | GMS |
| Gureje et al80, 2009 | Nigeria | Nov 2003 – Aug 2004 | 65 & above | 2152 (46.2 & 53.8) | Community | … | CIDI |
| Taqui et al81, 2007 | Pakistan | … | 65 & above | 400 (78 & 22) | Hospital | … | GDS |
| Uwakawe82, 2000a | Nigeria | Aug – Oct 1995 | 60 & above | 164 (66.5 & 33.5) | Community | Rural | ICD-10 |
| Uwakawe83, 2000b | Nigeria | Oct 1995 – Feb 1996 | 60 & above | 106 (61.3 & 38.7) | Hospital | … | ICD-10 |
| Adams et al84, 2021 | Tanzania | Jun – July 2019 | 60 & above | 304 (49 & 51) | Community | … | GDS-15 |
| Rajapakshe et al85, 2019 | Sri Lanka | May – Jul 2015 | 60 & above | 1283 (47.2 & 52.8) | Community | Urban | GDS-15 |
| Thapa et al86, 2020 | Nepal | … | 60 & above | 794 (52.1 & 47.9) | Community | … | DASS-21 |
| Chegini et al87, 2022 | Iran | … | 60 & above | 583 (47.9 & 52.1) | Community | … | GDS |
| Manandhar et al88, 2019 | Nepal | Jan – Feb 2019 | 60 - 85 | 439 (45.8 & 54.2) | Community | … | GDS-15 |
| Akosile et al89, 2018 | Nigeria | … | 60 - 98 | 206 (43.7 & 56.3) | Community | … | GDS |
| Devkota et al90, 2019 | Nepal | Jan – Feb 2018 | 60 & above | 124 (45.2 &54.8) | Community | Urban | GDS-15 |
| Damagum et al91, 2022 | Nigeria | Jul – Sep 2018 | 60 & above | 392 (29.3 & 70.7) | Hospital | … | GDS |
| Adayonfo et al92, 2019 | Nigeria | Aug – Nov 2018 | 60 & above | 392 (38 & 62) | Hospital | … | SMAST |
| El-Sherbiny et al93, 2016 | Egypt | Sep 2014 – feb 2015 | 60 – 90 | 2219 (52.5 & 47.5) | Community | Both | GDS LF |
| Asokan et al94, 2019 | Bahrain | … | 65 - 90 | 571 (57.8 & 42.2) | Community | … | GDS-15 |
| Shrestha et al95, 2020 | Nepal | Jul 2019 – Feb 2020 | 60 & above | 280 (39 & 61) | Hospital | … | GDS-15 |
| Abeysekera et al96, 2021 | Sri Lanka | Aug – Nov 2019 | 60 & above | 310 (65.2 & 34.8) | Old age home | … | GDS, PSQI |
| Taheri Tanjanai et al97, 2017 | Iran | … | 60 & above | 1350 (47.5 & 52.5) | Community | … | GDS-SF |
| Ganatra et al98, 2008 | Pakistan | Jul 2006 | 65 & above | 402 (69.7 & 30.3) | Health centre | … | GDS-15 |
| Kugbey et al99, 2018 | Ghana | … | 65 & above | 262 (38.5 & 61.5) | Community | … | GDS, |
| Aly et al100, 2018 | Egypt | May 2016 – Mar 2017 | 60 & above | 1027 (48 & 52) | Community | Both | GDS-15 |
| Ahmed et al101, 2014 | Egypt | Jan – Mar 2014 | 65 & above | 240 (35 & 65) | Old age home | … | GDS-15, HAS |
| Khattri et al102, 2006 | Nepal | … | 65 & above | 79 | Hospital | … | GDS |
| Nakua et al103, 2023 | Ghana | Jun 2021 | 65 & above | 418 (32 & 68) | Community | Both | GDS |
| Nguyen et al104, 2023 | Vietnam | Nov 2021 – Jul 2022 | 60 & above | 1004 (33 & 67) | Hospital | Both | GDS |
| Malhotra et al105, 2010 | Sri Lanka | … | 60 & above | 993 (44.8 & 55.2) | Community | Both | GDS-15 |
| Khaltar et al106, 2017 | Sri Lanka | Jul – Aug 2013 | 60 & above | 778 (38.7 & 61.3) | Community | Urban | GDS-15 |
| Hoang Lan et al107, 2020 | Vietnam | Jun – Dec 2018 | 60 & above | 110 (40 & 60) | Community | Urban | GDS-15 |
| Sivayokan et al108, 2022 | Sri Lanka | Nov 2020 - Mar 2021 | 60 & above | 122 (47.5 & 52.5) | Hospital | … | GDS |
| Gunawan et al109, 2022 | Indonesia | Aug – Sep 2019 | 65 & above | 116 (34.5 & 65.5) | Hospital | … | GDS |
| Do et al110, 2022 | Vietnam | Oct – Nov 2021 | 60 & above | 495 (31.3 & 68.7) | Community | Rural | GDS |
| Nazemi et al111, 2013 | Iran | 2010 - 2012 | 60 & above | 244 (46.7 & 53.3) | Hospital | … | GDS |
| Mubeen et al112, 2012 | Pakistan | Mar 2009 – Feb 2010 | 60 & above | 284 (73.9 & 26.1) | Community | … | GDS |
| Mokhber et al113, 2011 | Iran | … | 60 - 106 | 1565 (46 & 54) | Community | Rural | GDS |
| Fatima et al114, 2019 | Pakistan | Jul – Dec 2018 | 65 & above | 367 (45 & 55) | Community | … | GDS-15 |
| Ogunbode et al115, 2014 | Nigeria | Apr 2013 – Jan 2015 | 60 & above | 843 (40.3 & 59.7) | Old age home | Urban | CIDI-3 |
CIDI, composite international diagnostic interview; GDS-15, geriatric depression scale-15; GAI, geriatric anxiety inventory; SDQ, socio demographic questionnaire; GHQ, general health questionnaire; ICD-10, International classification of diseases-10; DSM-IV, diagnostic and statistical manual of mental disorders-IV; DASS, depression, anxiety, and stress scale; PSQI, Pittsburgh sleep quality index; CESD, centre for epidemiological studies depression scale; PROMIS, patient-reported outcomes measurement information system; HADS, hospital anxiety and depression scale; SCL-90-R, symptoms check-list-90-revised; PHQ-9, patient health questionnaire-9; ISI, insomnia severity index; GDS SF, geriatric depression scale- short form; DSMMD, diagnostic and statistical manual of mental disorders; GSDS, general sleep disturbance scale; DSM-IV-R, diagnostic and statistical manual of mental disorders-IV-revised; CAGE, cut, annoyed, guilty and eye; BDI-II, beck depression inventory-II; BAI, beck anxiety inventory; GMS, geriatric mental state; SMAST, short Michigan alcoholism screening test; GDS LF, geriatric depression scale- long form; HAS, Hamilton anxiety scale
Methodological quality of the studies
All cross-sectional studies (n=92) described the study’s aims, and 98.9 per cent (n=91) identified the target population. 96.7 per cent (n=89) justified their sample size, while 86.9 per cent (n=80) presented clear statistical significance or precision estimates. Basic data were sufficiently detailed in 91.3 per cent (n=84) of the articles, and all presented results according to their earlier stated methods. Measures to address and categorise non-responders were mentioned in 19.6 per cent (n=18) of the studies. However, 90.2 per cent (n=83) of the articles described the study’s limitations, and 76.1 per cent (n=70) addressed ethics clearance and/or informed consent (Supplementary Table III).
Studies included in meta-analysis
Selected articles comprised of 116,776 participants. Many studies reported various mental health disorders in this population, i.e., depressive, sleep, anxiety, and substance use disorders (Table II)24-115. The prevalence of depressive disorders ranged from 4.51 per cent to 90.16 per cent, with scores fluctuating between 6.16±3.40 and 21.4±8.03. Sleep disorders were observed in 1.7 per cent to 80 per cent of the cases, with scores ranging from 13.21±2.16 to 36.28±22.68. Anxiety disorders showed a prevalence range from 0.9 per cent to 100 per cent, and scores varied from 6.58±6.27 to 13±4. Prevalence of substance use disorders ranged from 0.75 per cent to 94.6 per cent (Table II).
| First author, yr of publication |
Age (yr) Mean±SD |
Anxiety disorders Mean±SD (%) |
Depressive disorders Mean±SD (%) |
Sleep disorders Mean±SD (%) |
Substance use disorders (%) |
Other mental health disorders |
|---|---|---|---|---|---|---|
| Karam et al24, 2016 | … | (12.3) | (8.6) | … | … | Burden of mental health disorder: 17.40; Bipolar disorder: 0.3; Post Traumatic Stress Disorder: 3.9 |
| Samadarshi et al25, 2022 | … | … | (68.04) | … | … | … |
| Cho et al26, 2021 | 70.2 | (39.4) | (35.57) | … | … | … |
| Margaretha et al27, 2021 | 66.69±5.57 | … | (23.53) | … | … | … |
| Mazloomzadeh et al28, 2021 | 68.03±5.89 | 13±4 | 9.56±3.47 | … | … | Total mental health: 56.93±10.27; Somatic symptoms: 15.19±2.86; Social function disorder: 19.41±2.46 |
| Ashe et al29, 2019 | … | … | (81.07) | … | … | Anorexia: 57.3 |
| Amoo et al30, 2020 | 71.4±8.86 | … | (4.51) | … | (0.75) | Mental health disorder: 9.77; Dementia: 3.38; Mild cognitive impairment: 2.07 |
| Shinde et al31, 2023 | … | … | (48.6) | … | … | … |
| Nakulan et al32, 2015 | … | … | 21.4±8.03 (39.1) | … | … | … |
| Gureje et al33, 2007 | … | … | (26.2) | … | … | … |
| Raeisvandi et al34, 2023 | 69.86±7.2 | (35.5) | (45.5) | … | … | Stress: 40.2 |
| Shetty et al35, 2023 | … | … | (38.42) | … | … | Cognitive Impairment: 49.13 |
| Akter et al36, 2023 | 68.13±5.83 | … | (100) | … | … | … |
| Santoso et al37, 2023 | 79.4 | … | (14.5) | (41.2) | … | Cognitive impairment: 61.1 |
| Hossaain et al38, 2022 | … | … | (100) | … | … | … |
| Ansari et al39, 2022 | … | … | (28.97) | (36.33) | (8.77) | … |
| Jesudoss et al40, 2023 | 70.5±7.61 | … | … | 13.21 ± 2.16 (66) | … | … |
| Azizabadi et al41, 2022 | … | (5.79) | (11.55) | … | … | … |
| Abbasi et al42, 2022 | 67.39±6.89 | 6.58±6.27 (53.5) | 12.02±10.29 (60.5) | … | … | Psychoticism: 2.48 ± 4.07 |
| Sindhu et al43, 2022 | 64.46±4.72 | (8.25) | (22.41) | … | … | … |
| Jagadeesan et al44, 2022 | … | … | (31.6) | … | … | Mini Mental State Examination Mild deficit: 34.2 |
| Mlaki et al45, 2021 | … | … | (16.21) | … | … | … |
| Kumari et al46, 2021 | 67.47±6.43 | … | (29.1) | … | … | Cognitive impairment: 36 |
| Alam et al47, 2021 | 72.3±8.1 | … | (83.75) | … | … | … |
| Bharti et al48, 2021 | 72.4 | (17) | - | (50) | … | Forgetfulness: 5.7 |
| Nguyet et al49, 2021 | 71.7±7.9 | … | 13.6±3.6 (26.1) | … | … | … |
| Pathak et al50, 2020 | … | … | (42.1) | … | … | … |
| Vimala et al51, 2020 | … | … | (21) | … | … | Cognitive impairment: 35 |
| Yadav et al52, 2020 | 69.9 | … | (55.8) | … | … | … |
| Dahale et al53, 2020 | 68.6±6.3 | … | -- | (11.16) | … | … |
| Paul et al54, 2019 | 68.7±5.5 | … | (52.5) | … | … | … |
| Nayak et al55, 2019 | … | (100) | (70.49) | … | (94.6) | Cognitive impairment: 86.47 |
| Farazdaw et al56, 2019 | 65.68±5.86 | … | … | (42.1) | … | … |
| Dao-Tran et al57, 2018 | … | … | … | 36.28±22.68 (38.9) | … | … |
| Dehghankar et al58, 2018 | 67.48±7.09 | -- | … | (80) | … | … |
| Sangma et al59, 2018 | 68.7±7.7 | --- | (29.6) | … | … | … |
| Boyanagari et al60, 2018 | … | … | (6.66) | … | … | … |
| El-Gilany et al61, 2018 | 67.3±7.1 | … | (44.4) | … | … | Stress: 39.3 |
| Hosseini et al62, 2017 | … | … | … | (24.8) | … | … |
| Spoorthi et al63, 2018 | 68.73 | … | (41) | … | … | … |
| Alvi et al64, 2017 | 73 | … | (22.3) | … | … | … |
| Sengupta et al65, 2015 | … | … | (8.92) | … | … | Cognitive impairment: 8.8 |
| D’souza et al66, 2015 | 66.86±6.35 | … | (51.9) | … | … | … |
| Gambhir et al67, 2014 | … | … | (42.86) | (31.94) | (32.34) | … |
| Ayoub et al68, 2014 | 67±5.7 | … | … | (33.42) | … | … |
| Sinha et al69, 2013 | … | … | (42.72) | … | … | … |
| Swarnalatha70, 2013 | … | … | (47) | … | … | … |
| Tiwari et al71, 2013 | 67.8±5.9 | … | (7.6) | (1.7) | (4) | Mild cognitive impairment: 4.6; Psychosis (F20-F29): 0.6; Mood disorder (F30-F39): 7.6; Neurotic, stress related & somatoform disorders (F40-F48): 2; Mental retardation (F70-F79): 0.2; Alzheimer’s disease: 2.4; Vascular dementia: 0.4; Organic amnestic syndrome: 0.1 |
| Esmayel et al72, 2013 | … | … | (72) | … | … | Cognitive impairment: 30 |
| Bhamani et al73, 2013 | … | … | (40.6) | … | … | … |
| Dhar74, 2013 | … | … | (59.8) | … | … | … |
| Reddy et al75, 2012 | … | … | 6.16±3.4 (47) | (36) | … | … |
| Mousavi et al76, 2012 | 76.8±8.05 | … | … | (39.25) | … | Cognitive impairment: 23.7 |
| Seby et al77, 2011 | 73.9 | (6.4) | (16.3) | … | (4) | Cognitive impairment: 18.81; Psychiatric morbidity: 26.7; Dementia: 14.9; Bipolar disorder: 2.5; Schizophrenia spectrum disorder: 1.5 |
| Kumar et al78, 2011 | 69.1±4.8 | (76.1) | (56.1) | … | … | … |
| Rajkumar et al79, 2009 | 72.54±5.87 | … | (12.7) | … | … | … |
| Gureje et al80, 2009 | … | … | … | (30.67) | … | … |
| Taqui et al81, 2007 | 69±5.1 | … | (19.5) | … | … | … |
| Uwakawe82, 2000a | 72±12 | (1.21) | (18.3) | (14) | (57.93) | Obsessive Compulsive Disorder: 0.6; psychotic disorder, unspecified: 1.2; Undifferentiated somatoform disorder: 1.8 |
| Uwakawe83, 2000b | 69.9±8.4 | (0.9) | (22.6) | (4.7) | (32.07) | … |
| Adams et al84, 2021 | … | … | (44.4) | … | … | … |
| Rajapakshe et al85, 2019 | 66.15±4.02 | … | (13.9) | … | … | … |
| Thapa et al86, 2020 | … | (18.1) | (15.4) | … | … | Stress: 12.1 |
| Chegini et al87, 2022 | 67.87±5.86 | … | (22.5) | … | … | … |
| Manandhar et al88, 2019 | 70.9±8.6 | … | (56) | … | … | … |
| Akosile et al89, 2018 | 69.7±6.69 | … | (49.59) | … | … | … |
| Devkota et al90, 2019 | 70.26±7.69 | … | (55.64) | … | … | … |
| Damagum et al91, 2022 | … | … | (22.45) | … | … | … |
| Adayonfo et al92, 2019 | 70±7.4 | … | -- | (20.92) | (10.20) | … |
| El-Sherbiny et al93, 2016 | 68.8±7.4 | … | (74.49) | … | … | … |
| Asokan et al94, 2019 | … | … | (50.61) | … | … | … |
| Shrestha et al95, 2020 | 71.36±8.06 | … | (45.7) | … | … | … |
| Abeysekera et al96, 2021 | 74.97 ± 8.85 | … | (76.5) | (86.1) | … | … |
| Taheri Tanjanai et al97, 2017 | 69.7±7 | … | (36.7) | … | … | … |
| Ganatra et al98, 2008 | 70.6 | … | (22.9) | … | … | … |
| Kugbey et al99, 2018 | … | … | (37.8) | … | … | … |
| Aly et al100, 2018 | … | … | (62.7) | … | … | … |
| Ahmed et al101, 2014 | 64.8±2.5 | (44.2) | (67.5) | … | … | … |
| Khattri et al102, 2006 | … | … | (53.2) | … | … | Cognitive impairment: 5 |
| Nakua et al103, 2023 | 69.9±8.8 | … | (42.1) | … | … | … |
| Nguyen et al104, 2023 | 70.8±7.3 | … | (15.54) | … | … | … |
| Malhotra et al105, 2010 | … | … | (26.68) | … | … | … |
| Khaltar et al106, 2017 | … | … | (31.84) | … | … | … |
| Hoang Lan et al107, 2020 | 66.9±6.2 | … | (25.5) | … | … | … |
| Sivayokan et al108, 2022 | 68.3±5.7 | … | (44.26) | … | … | Cognitive impairment: 80.3 |
| Gunawan et al109, 2022 | 71.2±2.86 | … | (56.9) | … | … | … |
| Do et al110, 2022 | … | … | (28.68) | … | … | … |
| Nazemi et al111, 2013 | 75.8±8.7 | … | (90.16) | … | … | … |
| Mubeen et al112, 2012 | 68.44±7.59 | … | (41.2) | … | … | … |
| Mokhber et al113, 2011 | 70.14±7.57 | … | (78) | … | … | … |
| Fatima et al114, 2019 | … | … | (37.06) | … | … | … |
| Ogunbode et al115, 2014 | … | … | … | (27.52) | … | … |
Depressive disorders
Random-effect model indicated a pooled prevalence of 38.76 per cent (95% CI: 34.25 to 43.28%) for Depressive disorders, with an I2 value of 96.89 per cent, indicating high heterogeneity among the included 76 studies (comprising of 76,864 participants) (Fig. 2). The funnel plot asymmetry (Supplementary Fig.1) and Egger’s test (P=0.02) revealed statistically significant evidence of small-study effects (potential publication bias). The subgroup analysis for depressive disorders revealed that males had a prevalence of 37.13 per cent (I2 = 99.16%) while females recorded 46.12 per cent (I2 = 99.12%) (Supplementary Fig. 2). Studies conducted within old age homes showed the highest prevalence at 87.85 per cent, with other study settings showing lower prevalence-for example, hospital-based studies recorded a prevalence of 44.65 per cent, Health centre-based reported a prevalence of 38.63 per cent, and community-based studies observed a 35.99 per cent prevalence of depressive disorders (Supplementary Fig. 3). When stratified by continent, studies from Africa and Asia reported a prevalence of 38.89 per cent and 38.64 per cent of depressive disorders, respectively (Supplementary Fig. 3).
- Forest plot showing the pooled prevalence of Depressive disorders (n=76).
Sleep disorders
The meta-analysis included 20 studies with 44,455 participants and found a pooled prevalence of 33.58 per cent for sleep disorders (95% CI: 27.03 to 40.14%) which was associated with an I2 value of 95.76 per cent (Fig. 3). suggesting considerable heterogeneity among the included studies. Although, funnel plot showed asymmetry (Supplementary Fig. 1), but Egger’s test (P=0.21) did not show any publication bias. Subgroup analysis also reflects the variance by gender; where males had 28.25 per cent (I2 = 86.38%) and females showed a prevalence of 36.21 per cent (I2 = 85.68%) (Supplementary Fig. 4). Among various study settings, old age homes reported the highest prevalence of sleep disorders at 48.56 per cent. Other settings reported lower prevalence, such as 33.48 per cent in community-based studies, 29.11 per cent in hospital-based studies, and 28.41 per cent in health centre-based studies. Studies from Africa observed a prevalence of 25.03 per cent, whereas Asian studies reported a prevalence of 38.35 per cent in sleep disorders (Supplementary Fig. 3).
- Forest plot showing the pooled prevalence of sleep disorders (n=20), anxiety disorders (n=13), substance use disorders (n=9).
Anxiety disorders
Overall, pooled anxiety disorders prevalence was estimated as 27.76 per cent (95% CI: 13.74 to 41.79%) among 13 studies and 7,902 participants, with I2 value of 96.62 per cent (Fig. 3). The heterogeneity of the studies was significantly high. Asymmetry within the funnel plot pointed possible publication bias (Supplementary Fig. 1), but there was no statistically significant evidence (P=0.39). Gender-wise subgroup analysis proved that the prevalence for anxiety disorders among males was 12.20 per cent (I2 = 70.68 %) and for females, was 16.52 per cent with I2 = 86.63 per cent (Supplementary Fig. 4). Among settings of study settings, in health centres, the highest number of anxiety disorders prevalence was reported up to 40 per cent. Hospital-based studies reported a prevalence of 37.63 per cent, while studies in the community reported a lower prevalence of 20.69 per cent (Supplementary Fig. 3). According to continents, anxiety disorders prevalence was particularly low in African studies at 1.11 per cent, whereas Asia showed a significantly higher prevalence at 32.47 per cent (Supplementary Fig. 3).
Substance use disorders
The estimated pooled prevalence of substance use disorders was 26.36 per cent among 9 studies including 35,754 participants (95% CI: 14.23 to 38.49%; Fig. 3). The I2 value was also substantial at 96.86 per cent, implying high heterogeneity among the studies. From the subgroup analysis by gender, it was noticed that 17.37 per cent males were affected, whereas 3.20 per cent females were affected (Supplementary Fig. 4). Among study settings, the community-based ones had a substance use prevalence of 27.41 per cent, while the corresponding prevalence in hospital-based studies stood at 24.27 per cent (Supplementary Fig. 3). Regionally, the total prevalence is at 24.45 per cent. Studies in Asia have shown a little higher prevalence of 28.22 per cent (Supplementary Fig. 3).
Validity of the results and assessment of heterogeneity
The leave-one-out sensitivity analysis showed that no single study significantly altered the overall effect estimate, indicating that the meta-analysis results were robust and not unduly influenced by any individual study. Additionally, subgroup and sensitivity analysis did not significantly reduce the heterogeneity. In meta-regression analysis, the study settings and continent did not statistically (P>0.05) explain the observed heterogeneity.
Discussion
The findings of the review reveal a substantial burden of different mental health disorders, i.e. depressive, sleep, anxiety, and substance use disorders. The high prevalence observed may reflect a combination of factors, including social isolation, age-related physiological and psychological changes.
The present meta-analysis reported a pooled prevalence of depressive disorders among older adults at 38.76 per cent (95% CI: 34.25 to 43.28%). This was much higher than the global prevalence as reported by another study at 31.74 per cent (95% CI: 27.90 to 35.59%)117. Developed countries presented a lower prevalence of 17.05 per cent (95% CI: 11.61 to 22.50%), whereas developing countries had a much higher percentage of 40.78 per cent (95% CI: 36.33 to 45.24%)117. Such differences may be seen due to several reasons, including limited mental health services, social stigma, and other socio-cultural factors that may result in underreporting or underdiagnosis of depressive disorders. A worldwide SRMA of 83 studies reported a prevalence of depressive disorders at 27 per cent (95% CI: 24 to 29%)118. A global review involving patients aged 75 and older, consisting of 24 studies, revealed that the prevalence of depressive disorders is 17.1 per cent (95% CI: 9.7 to 26.1%)119. A meta-analysis of 120 studies from South Asian countries found that the prevalence among older adults reached 42 per cent (95% CI: 38 to 46%)8. A meta-analysis encompassing 51 studies conducted in India reveals a prevalence of depressive disorders at 34.4 per cent (95% CI: 29.3 to 39.7%). This is also consistent with our study, wherein Depressive disorders are more prevalent among females120. Prevalence in Ethiopia was reported to be 41.85 per cent (95% CI: 33.52 to 50.18%)121. A meta-analysis conducted in China reported a pooled prevalence of 38.6 per cent (95% CI: 31.5 to 46.3%)122, very similar to our findings. In the present study, participants from institutional settings had a higher prevalence of depressive disorders than participants from the community settings, which aligns with the findings of other studies123,124. This could be due to factors like limited family interaction, loss of autonomy, etc., which are the established contributors to depressive disorders.
This study established a pooled prevalence of 33.58 per cent for sleep disorders (95% CI: 27.03 to 40.14%). A meta-analysis of 252 studies from 36 countries determined that obstructive sleep apnoea is the most common sleep disorder globally, affecting 46 per cent of all individuals. Poor quality sleep occurred in 40 per cent, other sleep problems in 37 per cent, insomnia in 29 per cent, and excessive daytime sleepiness in 19 per cent of cases125. The prevalence of poor sleep quality in 2,195 rural older adults living in Yanlou Town, China, was 33.8 per cent in a cross-sectional survey. These prevalences varied by gender, with 26.3 per cent reporting among males and 39.2 per cent reporting among females126, which is consistent with the present study’s findings since higher prevalence was seen in females. The Shanghai Aging Study of 1,086 residents indicated the burden of poor sleep quality as 41.5 per cent (95% CI: 38.6% to 44.5%), with higher rates in older females (45.8%, 95% CI: 41.9 to 49.7%) than old males (35.8%, 95% CI: 31.4 to 40.1%). Moreover, prevalence increased with age. Whereas 32.1 per cent (95% CI: 27.8 to 36.4%) of persons aged 60-69 yr were affected, among those aged 80 years and older, it was 52.5 per cent (95% CI: 45.9 to 59.1%)127.
Pooled prevalence of anxiety disorders in this was estimated as 27.76 per cent (95% CI: 13.74 to 41.79%). The global prevalence of anxiety disorders in the elderly population was found to be 16.5 per cent (95% CI: 11.1% to 22.8%) in 47 studies128. A cross-sectional study among 462 older adults of Turkey showed the prevalence of anxiety disorders at 17.1 per cent129. A survey of 803 participants conducted in Thailand found the prevalence of anxiety disorders at 6.4 per cent among older adults130. A cross-sectional study that involved 1173 older adults in China revealed a prevalence of 32.74 per cent higher than the pooled prevalence of this study131. A cross-sectional study carried out in Egypt involving 756 older adults reported a prevalence of 38 per cent, which is higher than the pooled prevalence found in this study. One study from a high-income country like England showed prevalence of anxiety disorders to be 2.6 per cent , including 1863 older adults132. A cross-sectional study among 1204 older adults of South Korea showed the prevalence of anxiety disorders as 38.1 per cent133. High prevalence in some regions could indicate unmet mental health needs, while lower prevalences in high-income countries such as England may reflect more accessible support systems. In the present study the prevalence of anxiety disorders in Asian studies was significantly higher.
Overall assessed prevalence of substance use disorders in older adults was found to be 26.36 per cent (95% CI: 14.23 to 38.49%) in present study. A high proportion of the literature on substance use disorders discussed alcohol use disorders134. It was found that the prevalence of DSM-IV defined alcohol abuse was 1.2 per cent, and for alcohol dependence was 0.24 per cent among older adults aged 65 years and above in the general population135. Another study reported 20 per cent of older people experience any substance use disorders among 28 participants136. Furthermore, in another study surveying 12413 participants aged 65 years and above, with 9 per cent of them reporting unhealthy alcohol usage habits, males had a higher percentage of unhealthy drinking behaviour at 16 per cent as compared to females at 4 per cent137, which resonates with observations made in this review. According to the findings of this study, it was also noted that higher rates of substance use prevalence occurred among aged adults in Asia.
Most LMICs currently don’t have specific policies focused on the mental health of older adults. Although India’s National Mental Health Policy (2014)137 considered older adults as a vulnerable population, it failed to provide targeted strategies or special services. WHO Comprehensive Mental Health Action Plan 2013-2030138 and the Mental Health Gap Action Programme (mhGAP)139 provided guidelines that incorporated suggestions for enhancing mental health care, including older adults. Yet, implementation in LMICs is limited due to resource constraints and lack of accessible services. This SRMA will bridge this gap by the pooled prevalence estimates of mental health disorders in older adults. These results can guide national and global stakeholders by bringing attention to the burden and service demands of this group.
This present study has some limitations. Studies were retrieved from 16 countries only, which highlights a gap and the need for research in the remaining countries. Several factors may have contributed to the high heterogeneity observed in the pooled prevalence of the mental health disorders reported in this study. Differences in distribution of age, gender composition across studies, multiple study settings, methodological variations such as employment of various assessment tools, varying sample size, and sampling strategies likely played a significant role. The incorporation of studies conducted over a wide span of time may have introduced variations, reflecting changes in mental health awareness, access to care, and the implementation of interventions over time. These factors may have confounded to the observed heterogeneity and differences and, in turn, may limit the generalizability of the pooled prevalence. Moreover, subgroup and meta-regression could not fully explain the observed heterogeneity. Although publication bias was observed in the analysis of pooled prevalence of depressive disorders, its assessment in the context of substance use disorders was not conducted due to the small number of included studies (n=9), as the statistical power of Egger’s test and funnel plot asymmetry may be unreliable with few studies, potentially limiting the detection of bias140-142. Exclusion of non-English studies, unavailable studies, inability to search other databases due to limited resources, and grey literature could have further influenced the external validity of the findings.
This SRMA show high prevalence of depressive, sleep, anxiety, and substance use disorders among older adults in LMICs. There are some differences in terms of gender; females have a higher combined prevalence for several reported mental health disorders. Moreover, variations in prevalence-based study contexts (i.e., community, old-age homes, health centres, and hospitals) explain the effects of environmental and social factors. Thus, strategic planning is required for early diagnosis and subsequent treatment of studied mental health problems. There’s a need to integrate mental health services into the geriatric health care services. Time has come to integrate this care into primary health care level as well along with subsequent capacity building of the health care workers. Thus, this SRMA has given an initial insight to both policymakers and health care service providers to address the mental health needs of this vulnerable population through culturally appropriate information, community-based programmes, and enhanced access to mental health services.
Financial support & sponsorship
The study received funding support by the Indian Council of Medical Research, India and Forte, Sweden as a funding agency of this Indo Swedish collaborative project (Project no.: 2022-17316).
Conflicts of Interest
None.
Use of Artificial Intelligence (AI)-Assisted Technology for manuscript preparation
The authors confirm that there was no use of AI-assisted technology for assisting in the writing of the manuscript and no images were manipulated using AI.
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