Impact statement
Rural communities in India often lack access to mental health support due to remote services and a lack of specialists. This study demonstrates that Community Health Workers (CHWs) can effectively screen for mental health concerns at scale, using the MERIT-Plus tool, across diverse rural areas. Screening over 200,000 people, the research highlights who is identified, common concerns and real-world screening accuracy. Results show that high specificity helps prioritize care for those most in need, though further strategies are needed to address sensitivity gaps. Practical challenges and solutions from this work can guide similar efforts elsewhere. Overall, the model supports integrating mental health screening into primary care systems in India and similar settings.
Introduction
Mental health disorders remain a significant but underreported public health challenge in rural India, where nearly two-thirds of the population resides and treatment gaps exceed 70% despite expansions of the District Mental Health Programme (DMHP) and other national initiatives (Mathur et al., Reference Mathur, Chawla and Chadda2024; Maulik et al., Reference Maulik, Daniel, Devarapalli, Kallakuri, Kaur, Ghosh, Billot, Mukherjee, Sagar, Kant, Chatterjee, Essue, Raman, Praveen, Thornicroft, Saxena, Patel and Peiris2024). While the National Mental Health Survey (NMHS) of India suggests the prevalence of mental disorders in rural settings is slightly lower than in urban areas – lifetime prevalence rates near 12% – this finding may underestimate the true burden due to stigma, poor mental health literacy, lack of accessible services and limited community data (Gururaj et al., Reference Gururaj, Varghese, Benegal, Rao, Pathak, Singh, Mehta, Ram, Shibukumar, Kokane, Lenin Singh, Chavan, Sharma, Ramasubramanian, Dalal, Saha, Deuri, Giri, Kavishvar, Sinha, Thavody, Chatterji, Akoijam, Das, Kashyap, Ragavan, Singh and Misra2016; Rajkumar et al., Reference Rajkumar, Julia, Sri Lakshmi K, Ranjana, Manjima, Devi, Rukmini, Christina, Romate, Allen, Abraham and Jacob2022). This highlights the persistent treatment gap and underscores the urgent need for community-based case finding.
Promising models, such as the Taluk (sub-district administrative area) Mental Health Programs (TMHP) in regions like Thirthahalli and Turuvekere taluks of Karnataka, have demonstrated successful approaches employing trained Community Health Workers (CHWs) for early identification, linkage to care and follow-up of individuals with severe mental disorders (Manjunatha et al., Reference Manjunatha, Kumar, Chander, Sadh, Gowda, Vinay, Shashidhara, Parthasarathy, Rao, Math and Thirthalli2019). These programs emphasize specialist supervision alongside active engagement of local CHWs, resulting in improved detection and management of mental health disorders in underserved populations. NAMAN ([NIMHANS – AHT Mental Health Action Program for Rural Communities]) extends CHWs’ roles to address common mental and substance use disorders, as well as prevention, promotion, treatment and rehabilitation. It uses task-sharing and local care to improve rural mental health with limited resources.
Globally, low- and middle-income regions, including South-East Asia, bear a disproportionate share of the mental health burden, compounded by limited infrastructure, workforce shortages and sociocultural barriers to care (Thara and Padmavati, Reference Thara and Padmavati2013; World Health Organization, 2023; Lemon et al., Reference Lemon, Svob, Bonomo, Dhungana, Supanya, Sittanomai, Diatri, Haider, Javed, Chandra, Herrman, Hoven and Sartorius2024). Earlier community-based epidemiological studies, such as the pioneering work by Rao et al. in rural South India, have highlighted variable prevalence patterns and persistent operational challenges similar to those faced by contemporary mental health screening initiatives in similarly underserved rural contexts (Sathyanarayana Rao et al., Reference Sathyanarayana Rao, Darshan, Tandon, Raman, Karthik, Saraswathi, Das, Harsha, Krishna and Ashok2014).
Community-based screenings, particularly those conducted through house-to-house approaches, offer feasible and reliable methodologies for reaching remote, underrepresented populations and identifying mental health disorders and patterns of mental health problems (Kessler et al., Reference Kessler, Aguilar-Gaxiola, Alonso, Chatterji, Lee, Ormel, Üstün and Wang2009; OECD, 2023). Evidence from South Asia reveals that such screenings, combined with local stakeholder engagement (e.g., Accredited Social Health Activist workers, faith healers), can reduce stigma, increase early detection and catalyze sustainable care models (Mental Health in the South-East Asia, n.d.; Thara and Padmavati, Reference Thara and Padmavati2013; Willmot et al., Reference Willmot, Sharp, Amir Kassim and Parkinson2023). At the same time, there is increasing recognition of the need for community-level data to inform locally relevant mental health responses (World Health Organization, 2022b).
Despite progress, research examining the operational, real-world implementation and scalability challenges of large-scale community-based mental health screening – particularly barriers such as terrain inaccessibility, seasonal migration and local mistrust – remains limited in rural regions of India. Conducted under NAMAN, this study aimed to examine patterns of mental health disorders identified through community screening, estimate population-level screen-positive rates, evaluate the performance of CHW-led screening relative to specialist assessments and describe the major operational challenges encountered during large-scale community mental health screening and the practical strategies adopted during implementation.
Methodology
Overview of NAMAN
NAMAN is a community-based mental health program designed to address the mental health treatment gap in rural areas through a holistic (inclusive of the modern bio-psycho-social approaches and the traditional approaches such as Ayurveda, Yoga, etc.) and comprehensive program (a life-span approach targeting everyone, starting with perinatal women to the geriatric population, with promotive, preventive, curative and rehabilitative approaches). It covers the entire population of two diverse taluks (in terms of geography and sociocultural aspects) in Belur (a sub-district of Hassan, Karnataka, South India) and Munsyari (a sub-district of Pithoragarh, Uttarakhand, North India). It decentralizes mental health care through a hub-and-spoke model anchored at the National Institute of Mental Health and Neurosciences (NIMHANS) in Bengaluru and supported by a sub-hub at the All India Institute of Medical Sciences (AIIMS) in Rishikesh. NAMAN embeds mental healthcare within existing rural public healthcare delivery systems. It employed dedicated CHWs to decentralize mental health care. NAMAN was implemented in four phases: (a) Resource Building, (b) Situational Analysis – which includes screening the population for mental health disorders, (c) Interventions and (d) Impact Evaluation.
The central hub at the NIMHANS, Bengaluru, India, an Institute of National Importance specializing in mental health and neurosciences, provided specialist oversight, coordination, quality assurance and site-specific monitoring and mentoring for Belur, as well as overall supervision across both sites. The sub-hub at AIIMS Rishikesh provided focused mentoring and monitoring tailored to the operational and geographic challenges of Munsyari. At the taluk level, multidisciplinary teams (a Medical Officer or a Psychiatrist, a psychologist and a social worker) were based at hospitals in Block Headquarters. The taluk team acted as the implementation agent, providing clinical services, supervising CHWs, managing referrals and ensuring continuity of care. In line with revised DMHP guidelines (MoHFW, 2015), two CHWs were recruited per Primary Health Centre (PHC), with routine supervision to ensure data quality and culturally appropriate service delivery (Detailed quality assurance procedures and supervision mechanisms are described in subsequent sections).
Role of CHWs in NAMAN
CHWs, each with at least a higher secondary education, play a crucial and foundational role in NAMAN. In addition to screening, they act as trusted liaisons within their communities – educating households about both the program and mental health, referring individuals to taluk-level clinics or PHC centers for further assessment, treatment, rehabilitation and leading prevention and promotion activities across different community sectors. Their responsibilities include organizing health promotion initiatives, such as yoga sessions and providing Ayurveda-based dietary and lifestyle advice. CHWs also offer ongoing support, particularly for high-risk cases such as those involving suicidality and work to overcome logistical and cultural obstacles through gender-sensitive approaches and strong community involvement. By working locally, they ensure care is culturally relevant and help reduce stigma and practical barriers, effectively connecting the community with formal mental health services.
Recruitment and training of CHWs
A total of 32 CHWs were recruited from local communities at both study sites – 20 in Belur and 12 in Munsyari. Selection criteria extended beyond basic educational qualifications to include residency within the community, proficiency in the local language, knowledge of the area and willingness to participate in fieldwork.
CHWs completed approximately 30 h of classroom-based training over a 30-day period, typically involving daily one-hour onsite sessions led by the Taluk team under the supervision of the Hub/Sub-hub teams. In addition, CHWs participated in practical field demonstrations facilitated by the Taluk teams and accompanied Accredited Social Health Activists (ASHAs), who serve as key CHWs in India’s public healthcare system (National Health Mission, 2026), during community visits to observe household engagement, screening processes, referral systems and follow-up protocols. The structured, multi-module training program encompassed: (a) introduction to community mental health and the role of CHWs in mental healthcare delivery, (b) administration of the MERIT Plus (Mental HEalth ScReenIng Tool Plus) (Nirisha et al., Reference Nirisha, Suhas, Malathesh, Kulal, Harshitha, Sabahit, Manjunatha, Thirthalli, Kumar, Manjappa, Parthasarathy, Chand, Arora and Math2026), including protocol for screening, scoring, interpretation and referrals, (c) survey methodology, effective household engagement, socio-demographic data collection and procedures for follow-up, (d) communication and interviewing techniques, including rapport-building, obtaining informed consent, culturally sensitive interviewing and the use of scripted engagement materials (for prevention and promotion activities) (e) digital data entry using the Customer Relationship Management (CRM) application, with attention to data privacy and operational troubleshooting, (f) standard operating procedures (SOPs) for addressing missed households, refusal management, suicidality, escalation pathways and field safety and (g) identification and fundamental management strategies for common mental health conditions including brief supportive counseling, suicide-risk response and referral mechanisms. To ensure thorough coverage, all households within the designated geographic zones were included. Each CHW was allocated specific village clusters within their respective PHC areas.
Supervision, refresher training, quality assurance and operational support
Monthly site supervision was conducted jointly by the Taluk multidisciplinary teams and the Hub/Sub-hub teams to review field progress, interview quality, proforma completeness, adherence to SOPs, data accuracy, household coverage targets, operational issues and mitigation plans. Common challenges included incomplete documentation, handling uncooperative participants, referral uncertainties and recognizing subtle psychiatric symptoms. To address these, bi-monthly refresher training sessions were held by the Taluk and Hub/Sub-hub teams, informed by feedback and observations. Remote support (primarily by phone) addressed software issues and ambiguous psychiatric cases outside MERIT Plus (Nirisha et al., Reference Nirisha, Suhas, Malathesh, Kulal, Harshitha, Sabahit, Manjunatha, Thirthalli, Kumar, Manjappa, Parthasarathy, Chand, Arora and Math2026) coverage. CHWs escalated cases involving refusal of consent, hostile behavior, suicidality or missed appointments to the Taluk team for further intervention. In addition, operational challenges encountered during program implementation were documented through routine supervisory visits, field monitoring, refresher training sessions and regular interactions between CHWs, the Taluk multidisciplinary teams and the Hub/Sub-hub teams. These observations were collated throughout program implementation and are presented descriptively to illustrate the operational challenges encountered and the adaptive strategies adopted.
Community engagement and field procedures
CHWs built trust and program awareness by meeting local leaders, teachers, self-help groups and health and religious workers when starting in a new village. For locked or missed households, they made up to three return visits.
Management of suicidality
When suicidality was identified, CHWs promptly notified the Taluk team and the family, explained the risks, recommended an immediate referral, advised restricting access to sharp objects and followed up over 2 days through visits or calls. A further one-week follow-up was conducted to monitor treatment adherence and progress.
House-to-house screening process and the screening tool
The screening was thorough and systematic, encompassing everyone from both taluks. CHWs began interviews by introducing themselves, explaining the NAMAN initiative and requesting verbal informed consent. The Mental HEalth ScReenIng Tool for community health workers in India (MERIT) is a short, family-informant-based instrument developed for CHWs to identify key mental health concerns – including substance use, anxiety, depressive or somatoform symptoms, severe mental illness and suicidal risk. Each household screening takes about five minutes. Although MERIT was developed and validated before this study, its publication occurred later (Nirisha et al., Reference Nirisha, Suhas, Malathesh, Kulal, Harshitha, Sabahit, Manjunatha, Thirthalli, Kumar, Manjappa, Parthasarathy, Chand, Arora and Math2026). MERIT was selected because it is a brief tool that covers several important mental health disorders, unlike most existing tools which are disorder-specific or meant for individual-level use. Validation studies showed MERIT has moderate sensitivity (62.5%), high specificity (97.5%), strong concurrent validity (κ = 0.79) and good inter-rater reliability (κ = 0.74). These results support its role as a conservative community screening and triage tool, rather than a diagnostic one.
For this study, however, a modified version called “MERIT Plus” was used, which included extra questions about obsessive-compulsive, developmental and memory disorders. This modified “MERIT Plus” tool has not yet been independently validated. It was administered to a single responsible adult in each household, allowing for efficient screening of all household members. However, because the screening depends on proxy informants rather than direct interviews, it may overlook internalizing symptoms and subtle mental health problems. This method was deemed practical for large-scale screening in rural areas with limited resources. Data were entered electronically using a digital CRM system, facilitating real-time recording, unique participant ID creation, centralized oversight, referral monitoring and rapid error correction. Individuals who screened positive were directed to the Taluk Mental Health Clinic or local PHCs for further assessment and care. For households that were missed or did not respond initially, up to three follow-up attempts were made. Additional outreach ensured access for homeless and migrant populations.
Data collection and management
Screening took place in Belur (46,424 households; 157,614 individuals, May – Dec 2024) and Munsyari (9,723 households; 44,315 individuals, Nov 2024–Apr 2025). Surveys used village and household lists from PHCs and local records. CHWs conducted sequential door-to-door visits with weekly targets of 120 households. Digital documentation reduced duplication and missing data, while backup strategies protected against network issues. Mop-up rounds and quality checks ensured thorough case identification.
Agreement and quality-monitoring exercise
To assess agreement between CHW-administered and mental health professionals-administered screening, a sub-study was conducted alongside the main activity. The quality monitoring team included a Medical Officer or Psychiatrist, a psychologist or social worker from the taluk and Mental Health Professionals (MHPs) – qualified psychiatrists, clinical psychologists, psychiatric social workers and psychiatric nurses from the NIMHANS hub and AIIMS Rishikesh sub-hub teams – who (either one or two of them visited one household) independently reassessed households and individuals using the same MERIT-Plus screening instrument.
In Belur, between October 28 and November 3, 2024, CHWs screened 1,807 households. All 41 households with positive screens and a random 10% sample (192 households) of those with only negative screens, were independently reassessed by the validators using MERIT Plus. Similarly, in Munsyari, from January 27 to February 4, 2025, CHWs screened 479 households; six households tested positive, all of which were verified. Additionally, 15% (70 households) of screen-negative households were randomly selected and reassessed. Random subsampling of screen-negative households (10%–15%) served as a practical approach to estimate missed cases and evaluate screening reliability, remaining feasible for rural fieldwork and consistent with prior Indian community mental health surveys (Thirthalli et al., Reference Thirthalli, Venkatesh, Kishorekumar, Arunachala, Venkatasubramanian, Subbakrishna and Gangadhar2009; Kumar et al., Reference Kumar, Kalaivanan, Suresha, Gowda, Harisha, Rahul, Philip, Varshney, Basavaraju, Manjunatha, Math and Thirthalli2026).
Validators, blinded to the initial CHW screening results, re-administered MERIT-Plus to the same family members in these chosen households, measuring agreement and consistency between CHW-led screenings and independent professional reassessments under field conditions rather than against a standard diagnostic evaluation. Apparent sensitivity, apparent specificity and agreement statistics were calculated.
Ethical considerations
Ethical approval was obtained from NIMHANS (No. NIMHANS/EC (BEH. SC. DIV.) MEETNG/2024, dated 15-04-2024) and AIIMS Rishikesh (Letter No- AIIMS/IEC/25/04, Date: 17.01.2025) before beginning the house-to-house survey and quality checks. Ethical standards were upheld throughout. In each household, an eligible adult who was present and consented provided information for the whole family. Verbal informed consent was digitally recorded as accepted or refused. If someone refused, their reason was documented, and if needed, another attempt was made with a social worker from the Taluk team. Data were entered into secure systems only after all consent attempts had been completed. Privacy, confidentiality and non-duplication were carefully managed: previously screened negative individuals who visited the OPD were not counted again and received referral cards in accordance with SOPs. CHWs also offered short, supportive counseling and helped refer individuals in high-risk cases such as suicidality, following established protocols and under the oversight of the Taluk team.
Outcome measures
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(a) Population-level screen-positive rates and patterns of mental health disorders identified through CHW-led community screening.
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(b) Performance of CHW screening compared with specialist psychiatric assessments.
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(c) Operational, contextual and implementation-related challenges encountered during the house-to-house screening.
Statistical analysis
Data entry was performed in the CRM application, and descriptive analysis was conducted in Microsoft Excel. STATA version 16 software was used to analyze inter-rater reliability, apparent sensitivity, apparent specificity, Kappa statistics and screen-positive rates.
Results
Screening outcomes
Community screening achieved wide population coverage in both Belur and Munsyari (Tables 1 and 2), with balanced gender representation and most screen-positive individuals were in the working-age group (18–59 years). Among adults (>18 years), the overall screen-positive rate for mental health disorders was 2.42% in Belur and 1.31% in Munsyari, while detection among children and adolescents (<18 years) remained low (<0.4%) across both sites (Table 3).
Population coverage and demographic characteristics of the screened population
Table 1. Long description
The table consists of three columns. The first column lists demographic categories, while the second and third columns provide data for Belur taluk (May to December 2024) and Munsyari taluk (November 2024 to April 2025) respectively.
* Screening Totals:
- Number of households screened: 46,424 in Belur; 9,723 in Munsyari.
- Total individuals screened: 157,614 in Belur; 44,315 in Munsyari.
- Individuals screened positive: 3,191 in Belur; 474 in Munsyari.
* Age Group Distribution:
- 0 to 17 years: 30,853 (19 percent) in Belur; 10,729 (24 percent) in Munsyari.
- 18 to 59 years: 102,052 (65 percent) in Belur; 27,669 (63 percent) in Munsyari.
- Greater than 60 years: 24,709 (16 percent) in Belur; 5,917 (13 percent) in Munsyari.
* Gender Distribution:
- Male: 79,588 (50 percent) in Belur; 23,052 (52 percent) in Munsyari.
- Female: 78,016 (50 percent) in Belur; 21,260 (48 percent) in Munsyari.
- Transgender: 10 (less than 0.01 percent) in Belur; 3 (less than 0.01 percent) in Munsyari.
* Annual Family Income in Rupees:
- Less than 1 lakh: 39,565 (85.22 percent) in Belur; 8,286 (85.22 percent) in Munsyari.
- 1 to 5 lakh: 6,506 (14.01 percent) in Belur; 1,170 (12.03 percent) in Munsyari.
- 5 to 10 lakh: 334 (0.72 percent) in Belur; 244 (2.50 percent) in Munsyari.
- Greater than 10 lakh: 19 (0.04 percent) in Belur; 23 (0.23 percent) in Munsyari.
* Values are presented as n (%). Percentages were calculated based on the total screened population at each site.
Demographic distribution of age-specific screen-positive rates in Belur and Munsyari
Table 2. Long description
The table is divided into two main sections: Belur and Munsyari. Each section includes columns for Females, Males, Total screened positive, Total population screened, and Age-specific screen-positive rate in percent.
* 0 to 17 years: In Belur, 49 females and 66 males were positive out of 30,853 screened (0.37 percent). In Munsyari, 12 females and 21 males were positive out of 10,729 screened (0.31 percent).
* 18 to 59 years: In Belur, 942 females and 1,051 males were positive out of 102,052 screened (1.95 percent). In Munsyari, 179 females and 176 males were positive out of 27,669 screened (1.28 percent).
* Greater than 60 years: In Belur, 607 females and 476 males were positive out of 24,709 screened (4.38 percent). In Munsyari, 46 females and 40 males were positive out of 5,917 screened (1.45 percent).
* Total: Belur had 1,598 females and 1,593 males positive out of 157,614 screened (2.02 percent). Munsyari had 237 females and 237 males positive out of 44,315 screened (1.07 percent).
* Age-specific screen-positive rates were calculated using the total screened population within each age group.
Distribution of screened positives
Table 3. Long description
The table is structured with Screening domains as the first column, followed by two main geographic headers: Belur and Munsyari. Each geographic header is subdivided into three columns: less than 18 years number screened positive (%), greater than 18 years number screened positive (%), and Overall number screened positive (%).
Data rows include:
* Tobacco abuse: Belur (9, 1,024, 1,033); Munsyari (-, 48, 48).
* Alcohol abuse: Belur (2, 780, 782); Munsyari (-, 79, 79).
* Anxiety: Belur (9, 754, 763); Munsyari (3, 256, 259).
* Developmental problems / difficulty in school: Belur (77, 53, 130); Munsyari (29, 8, 37).
* Memory problems: Belur (35, 183, 218); Munsyari (1, 62, 63).
* Obsessive / compulsive symptoms: Belur (1, 177, 178); Munsyari (-, 15, 15).
* Others: Belur (12, 346, 358); Munsyari (2, 16, 18).
* Disorganized / violent / fearful behavior: Belur (9, 182, 191); Munsyari (6, 97, 103).
* Suicidal risk: Belur (1, 39, 40); Munsyari (-, 7, 7).
* Sadness / somatoform symptoms: Belur (11, 798, 809); Munsyari (-, 63, 63).
* Grand total: Belur (166, 4,336, 4,502); Munsyari (41, 651, 692).
A note indicates that the total number of disorders exceeds the number of individuals because some individuals screened positive for more than one disorder.
# Note: The total number of mental health disorders (4,502 & 692) exceeds the number of individuals screened positive (3,191 & 474), because some individuals screened positive for more than one mental health disorder.
* Percentages were calculated using the corresponding screened population within each age category (<18 years and ≥ 18 years) at each site.
Substance use and anxiety-related symptoms were the most common screen-positive findings in Belur, whereas anxiety-related symptoms predominated in Munsyari. Less common mental health problems across both sites included developmental problems, memory-related symptoms, obsessive-compulsive symptoms and suicidality. Multiple mental health disorders were identified in some individuals.
Agreement and quality-monitoring exercise
In Belur, CHW-led screening demonstrated moderate agreement (κ = 0.52), with a sensitivity of 45.3% and a specificity of 98.2%. The quality monitoring team estimated a higher screen-positive rate (9.5%) compared with 2.4% identified through CHW screening. Agreement was highest for psychosis (κ = 0.75; sensitivity 60%), whereas anxiety (κ = 0.32; sensitivity 28.6%) and tobacco use (κ = 0.38; sensitivity 26.3%) showed lower agreement and sensitivity (Tables 4 and 5).
Agreement between CHW screening and quality monitoring assessments in Belur and Munsyari
Table 4. Long description
The table is divided into two sections: 4A. Household-level screening agreement and 4B. Individual-level screening agreement.
Section 4A (Household-level):
- Belur Q M team: C H W positive results include 31 true positives and 9 false positives (Total 40). C H W negative results include 33 false negatives and 159 true negatives (Total 192). Grand total is 232.
- Munsyari Q M team: C H W positive results include 5 true positives and 0 false positives (Total 5). C H W negative results include 14 false negatives and 52 true negatives (Total 66). Grand total is 71.
Section 4B (Individual-level):
- Belur Q M team: C H W positive results include 34 true positives and 13 false positives (Total 47). C H W negative results include 41 false negatives and 703 true negatives (Total 744). Grand total is 791.
- Munsyari Q M team: C H W positive results include 5 true positives and 0 false positives (Total 5). C H W negative results include 21 false negatives and 291 true negatives (Total 312). Grand total is 317.
Footnote: C H W stands for community health worker; Q M stands for quality monitoring.
* CHW, community health worker; QM, quality monitoring.
Quality monitoring mental health disorders-wise summary (Inter-rater reliability)
Table 5. Long description
The table is divided into two sections: 5A for Belur (N = 791) and 5B for Munsyari (N = 317). Columns include Screening domains, C H W response, Q M No, Q M Yes, Kappa (95% C I) (Agreement), Apparent sensitivity (%), Apparent specificity (%), C H W screen-positive rate (%), and Q M screen-positive rate % (95% C I).
Section 5A (Belur) highlights:
* Overall: Kappa 0.52 (Moderate), Sensitivity 45.33%, Specificity 98.20%.
* Alcohol abuse: Kappa 0.49 (Moderate), Sensitivity 42.85%.
* Tobacco abuse: Kappa 0.38 (Fair), Sensitivity 26.31%.
* Anxiety: Kappa 0.32 (Fair), Sensitivity 28.57%.
* Disorganized/violent/fearful behavior: Kappa 0.75 (Substantial), Sensitivity 60.00%.
* Other domains like Sadness symptoms and Suicidal risk are marked N A for agreement due to low cell counts.
Section 5B (Munsyari) highlights:
* Overall: Kappa 0.30 (Fair), Sensitivity 19.23%, Specificity 100%.
* Anxiety: Kappa 0.45 (Moderate), Sensitivity 33.34%.
* Disorganized/violent/fearful behavior: Kappa 0.80 (Substantial), Sensitivity 66.67%.
* Developmental delays and Difficulty in school: Kappa 0.67 (Substantial), Sensitivity 50.00%.
* Several domains including Alcohol, Tobacco, and Somatisation are marked N A for agreement statistics.
Cohen’s κ interpretation: ≤0.00, no agreement; 0.01–0.20, slight; 0.21–0.40, fair; 0.41–0.60, moderate; 0.61–0.80, substantial; 0.81–1.00, almost perfect agreement (McHugh, Reference McHugh2012).
NA (Not applicable): Indicates that agreement statistics were not estimable because of zero cell counts or very low screen-positive frequencies.
Calculated screened positive rate: Screen-positive rate refers to the proportion of individuals screening positive among the total population screened.
* CHW, community health worker; QM, quality monitoring.
In Munsyari, overall agreement was fair (κ = 0.30), with low sensitivity (19.2%) but high specificity (100%). Psychosis again showed the highest agreement (κ = 0.80; sensitivity 66.7%), whereas anxiety demonstrated comparatively lower agreement (κ = 0.45; sensitivity 33.3%). Several mental health disorders with very low screen-positive rates could not be reliably assessed for agreement.
Fieldwork challenges and solutions
The implementation of household screening at scale presented a range of field-level challenges – environmental, logistical and social – that directly impacted workflow and safety. Table 6 summarizes these challenges and the practical solutions adopted.
Fieldwork challenges and solutions
Table 6. Long description
The table consists of three columns: Issue, Description, and Solutions implemented.
* Reluctant or hard-to-reach households: Working-age adults were unavailable during the day, elderly provided incomplete info, and some were hesitant to disclose sensitive data. Solutions included gender-matched C H W deployment, repeated visits, and trust-building through local leaders.
* Language and cultural barriers: Diverse languages among migrants and reluctance to discuss alcohol or tobacco. Solutions involved engaging local influencers like Gram Pradhan, teachers, and A S H A s, plus collaboration with faith leaders.
* Geographic and weather barriers: Semi-mountainous terrain and monsoon rains delayed access. Solutions included workload redistribution and flexible scheduling.
* Connectivity and digital access constraints: Mobile signal blackouts limited coordination. Solutions included an offline-enabled mobile application with 48-hour synchronization requirements and scheduled check-ins at signal hotspots.
* Wildlife threats: Encounters with elephants, leopards, and wild dogs. Solutions included route optimization, pairing C H W s for safety, and coordination with the Forest Department.
* Safety and mental health risks: Aggression from individuals with severe mental illness and fear of violence. Solutions included well-being workshops, counseling, and regular debriefings.
Discussion
This study demonstrates that community-based mental health screening can be feasibly and acceptably delivered in rural and remote Indian settings through trained CHWs. The observed adult screen-positive rates for mental health disorders (1.31%–2.42%) were substantially lower than nationally weighted diagnostic estimates reported in the NMHS 2015–2016 (10.6%) and diagnostically intensive village-level studies using structured interviews such as the MINI (Sathyanarayana Rao et al., Reference Sathyanarayana Rao, Darshan, Tandon, Raman, Karthik, Saraswathi, Das, Harsha, Krishna and Ashok2014; Gururaj et al., Reference Gururaj, Varghese, Benegal, Rao, Pathak, Singh, Mehta, Ram, Shibukumar, Kokane, Lenin Singh, Chavan, Sharma, Ramasubramanian, Dalal, Saha, Deuri, Giri, Kavishvar, Sinha, Thavody, Chatterji, Akoijam, Das, Kashyap, Ragavan, Singh and Misra2016). While the latter surveys were designed to estimate diagnostic prevalence under research conditions, NAMAN employed a brief, CHW-led screening approach embedded within routine population surveys, prioritizing feasibility, coverage and early identification. Accordingly, the lower observed screen-positive rates were expected and should be interpreted as conservative, lower-bound indicators of population mental health needs rather than true diagnostic prevalence. The household-informant – based design enabled rapid population coverage in resource-limited and geographically challenging settings; however, reliance on proxy respondents may limit recognition and reporting of internalizing symptoms in other family members, particularly for common mental disorders, thereby contributing to lower observed screen-positive rates. These findings highlight the importance of strengthening CHW training, supervision and repeated community engagement strategies to improve recognition of less overt mental health concerns. Similar capacity-building approaches for frontline mental health workers have also been highlighted in recent Indian implementation models (Sangwan et al., Reference Sangwan, Kansara, Ali, Khetan, Huda, Dhikav and Babu2025). Such under-recognition may delay identification and referral, particularly for internalizing and substance use problems, contributing to persistent untreated morbidity in rural populations with limited specialist access. This is consistent with the earlier MERIT validation study, which noted that common mental disorders may be more difficult for CHWs to identify in community settings than severe mental disorders and substance use disorders because symptoms often overlap with normal stress, emotional distress and everyday functioning (Nirisha et al., Reference Nirisha, Suhas, Malathesh, Kulal, Harshitha, Sabahit, Manjunatha, Thirthalli, Kumar, Manjappa, Parthasarathy, Chand, Arora and Math2026).
Despite lower absolute screen-positive rates, NAMAN identified a broad spectrum of symptom clusters across substance use, anxiety-spectrum and somatoform domains, predominantly among working-age adults. In Belur, the relative distribution and rank ordering of symptom clusters showed reasonable directional concordance with those reported in national studies. Compared with Belur, Munsyari demonstrated lower screen-positive rates for tobacco and alcohol use, whereas anxiety-related and somatic symptoms were more commonly identified. This divergence likely reflects contextual and cultural factors such as acceptance of alcohol use and its brewing in households, the remote Himalayan terrain, geographically dispersed hamlets, seasonal male migration, close-knit traditional communities and historically limited specialist mental health services in Munsyari. In such settings, mental health disorders may be underreported because of stigma and confidentiality concerns during household interviews, whereas psychological distress may be more readily expressed through somatic and anxiety-related complaints, consistent with recent qualitative findings (Choudhary et al., Reference Choudhary, Sangwan, Kumar, Khetan, Huda, Arora, Gill, Valecha and Babu2026). NAMAN findings should therefore be viewed as programmatically actionable indicators of unmet mental health needs that complement, but do not replace, diagnostic assessments (Sathyanarayana Rao et al., Reference Sathyanarayana Rao, Darshan, Tandon, Raman, Karthik, Saraswathi, Das, Harsha, Krishna and Ashok2014; Gururaj et al., Reference Gururaj, Varghese, Benegal, Rao, Pathak, Singh, Mehta, Ram, Shibukumar, Kokane, Lenin Singh, Chavan, Sharma, Ramasubramanian, Dalal, Saha, Deuri, Giri, Kavishvar, Sinha, Thavody, Chatterji, Akoijam, Das, Kashyap, Ragavan, Singh and Misra2016; Lemon et al., Reference Lemon, Svob, Bonomo, Dhungana, Supanya, Sittanomai, Diatri, Haider, Javed, Chandra, Herrman, Hoven and Sartorius2024; Szücs et al., Reference Szücs, Van Der Lubbe, Arias, Valderas, Hay, Bisignano, Morgan, Acharya, Adnani, Apostol, Aslam, Asri, Aung, Aurizki, Baig, Bermudez, Cenderadewi, Danpanichkul, Efendi, Estrada, Fauk, Fridayani, Garcia, Hasan, Ibrahim, Ilyas, Ismail, Jamaluddin, Jamora, Jonas, Kanmodi, Khan, Khotimah, Kim, Kurniasari, Kustanti, Kusuma, Laksono, Lau, Loreche, Ma, Maravilla, Marzo, Maude, Porntaveetus, Rahmawaty, Rodriguez, Roy, Salami, Samodra, Sreeramareddy, Subramaniyan, Sukaew, Sulistiyorini, Sumpaico-Tanchanco, Wicaksana, Wong, Younis, Zuniga, Murray, Santomauro and Ng2025).
Using the MERIT-Plus tool, this program demonstrates that task-sharing with CHWs is a practical approach for identifying mental health disorders in low-resource areas. Importantly, the CHW model used in this study was not a newly created parallel workforce but aligned with the revised DMHP framework, which already proposes dedicated CHWs for community mental health activities (Ministry of Health and Family Welfare, 2015). This model should also not be interpreted as adding further responsibilities to existing ASHAs, who already carry substantial public health responsibilities (National Health Mission, 2026). Programs such as NAMAN can therefore provide important field-level experience regarding large-scale recruitment, training, supervision and deployment of such CHWs in rural settings. These apparent sensitivity and apparent specificity estimates should be interpreted carefully, as agreement assessments involved re-administration of the same screening tool under field conditions. Although the formal publication of MERIT occurred later, the tool had already undergone psychometric evaluation and field testing prior to implementation in the present study. While the tool showed high specificity, the notably lower sensitivity for internalizing symptoms likely reflects the inherent challenges of household-informant screening for less overt mental health disorders in real-world rural settings, including stigma, cultural barriers and limited disclosure (Ministry of Health and Family Welfare, 2015; Mathur et al., Reference Mathur, Chawla and Chadda2024; Nirisha et al., Reference Nirisha, Suhas, Malathesh, Kulal, Harshitha, Sabahit, Manjunatha, Thirthalli, Kumar, Manjappa, Parthasarathy, Chand, Arora and Math2026). CHWs, however, performed strongly in identifying severe disorders such as psychosis, showing that trained non-specialists can detect high-risk mental health disorders that often go unnoticed in rural populations. In places where specialists are scarce and treatment gaps exceed 70%, these results show that CHW-led screening plays an important role in early identification.
The barriers faced across both sites – mountain terrain, extreme weather, elderly-dominant households, migration, language barriers and stigma – are similar to those reported across India, South-East Asia and other low- and middle-income countries (Thara and Padmavati, Reference Thara and Padmavati2013; Sivakumar et al., Reference Sivakumar, James, Basavarajappa, Parthasarathy, Naveen Kumar and Thirthalli2019; Szücs et al., Reference Szücs, Van Der Lubbe, Arias, Valderas, Hay, Bisignano, Morgan, Acharya, Adnani, Apostol, Aslam, Asri, Aung, Aurizki, Baig, Bermudez, Cenderadewi, Danpanichkul, Efendi, Estrada, Fauk, Fridayani, Garcia, Hasan, Ibrahim, Ilyas, Ismail, Jamaluddin, Jamora, Jonas, Kanmodi, Khan, Khotimah, Kim, Kurniasari, Kustanti, Kusuma, Laksono, Lau, Loreche, Ma, Maravilla, Marzo, Maude, Porntaveetus, Rahmawaty, Rodriguez, Roy, Salami, Samodra, Sreeramareddy, Subramaniyan, Sukaew, Sulistiyorini, Sumpaico-Tanchanco, Wicaksana, Wong, Younis, Zuniga, Murray, Santomauro and Ng2025). NAMAN used several practical solutions: village-level awareness, support from local leaders, gender-matched CHW deployment, flexible field planning and continuous quality monitoring. These strategies closely follow global best practices in task-shifting, stigma reduction and community engagement, as recommended by the World Health Organization (WHO) and the Mental Health Gap Action Programme (mhGAP) (World Health Organization, 2022a; Willmot et al., Reference Willmot, Sharp, Amir Kassim and Parkinson2023). Additionally, although MERIT Plus incorporated contextually relevant modifications to the original MERIT tool, the modified version has not yet undergone independent validation.
In conclusion, this study adds critical evidence to the growing body of research that large-scale community screening initiatives are pivotal to understanding and addressing rural mental health burdens. By realistically capturing what is possible through non-specialist task-sharing, the NAMAN approach highlights both the promise and the gaps that must be addressed. It provides a feasible, scalable and replicable model for integrating community-based mental health screening and referral within rural primary-care systems in India, South-East Asia and similar low-resource international contexts, laying the groundwork for policy, practice and future research on community-based mental health interventions.
Lessons learned and recommendations
NAMAN demonstrates that CHW-led community mental health screening is feasible and scalable in rural and remote settings. Despite moderate sensitivity, the approach achieved high specificity and enabled early identification and referral of individuals who may otherwise have remained undetected in underserved communities with large treatment gaps and limited specialist services. The use of locally recruited CHWs, structured training, digital data systems and ongoing supervision supported implementation across geographically and culturally distinct settings.
Key challenges included stigma, confidentiality concerns, difficult terrain and under-recognition of less overt internalizing symptoms. These were addressed in part through community engagement, gender-sensitive staffing, flexible field procedures and routine supervision. Similar implementation and training-related challenges have also been described in Indian primary-care mental health settings (Sangwan et al., Reference Sangwan, Kansara, Matoria, Ali, Khetan, Singh, Thakor, Huda and Babu2024). Strengthening CHW training and supervisory support may further improve sensitivity for internalizing conditions.
For implementers, strengthening CHW training, supervision and referral pathways may improve continuity of care. For policymakers, strengthening local governance mechanisms and recruiting and training dedicated CHWs for mental health work within primary care systems may support earlier identification and reduce treatment gaps in underserved rural populations. For researchers, future studies should focus on improving screening sensitivity and evaluating long-term outcomes across diverse low-resource settings.
Open peer review
To view the open peer review materials for this article, please visit http://doi.org/10.1017/gmh.2026.10300.
Data availability statement
The data supporting the findings of this study are available from the corresponding author upon reasonable request and appropriate institutional approval.
Acknowledgments
Our sincere gratitude to the trustees and staff of Ashraya Hastha Trust (AHT), Bengaluru, for their constant support and guidance. We extend our sincere thanks to Dr. Naveen Bhat (Former Mission Director- National Health Mission), Sri Harsh Gupta (Principal Secretary, Dept. of Health and Family Welfare, Govt of Karnataka), Dr. Rajani Parthasarathy (Deputy Director, Mental Health, Dept. of Health and Family Welfare Services, Government of Karnataka), Smt. Sathyabhama (Former Deputy Commissioner, Hassan), Smt. K. S. Latha Kumari (Deputy Commissioner and District Magistrate, Hassan), Sri Venkateshwara Rathod (District Health Officer, Hassan), Dr. P. Nagesh (District Leprosy Officer, Hassan), Dr. Vijay (Taluk Health Officer, Belur), K.P. Narayan and Raje Gowda (Former Block Education Officer’s, Belur taluk), Bhagyamma S. (Block Education Officer, Belur taluk), Shivamariyappa K.G. (Block Resource person, BRC Office, Belur taluk), Dr. Sudha, (Former Administrative Medical Officer, Belur taluk Hospital), Dr. Sathish, (Administrative Medical Officer, Belur taluk Hospital), Smt. Mamata (Former Tahsildar, Belur) and Mr. Shridhar Kankanawadi (Tahsildar, Belur).
We extend our sincere thanks to Dr. Vineetha Shah (DGHS, Uttarakhand), Dr. Sujata Singh, Assistant Director, Training, Department of Medical Health and Family Welfare, Uttarakhand, Bhagirathi Jagpangi (Chief Executive Officer, SMHA, Uttarakhand), Dr. Sumit Deb Burman (Joint Director, SMHA, Uttarakhand), Dr. Mayank Badola (Former Assistant Director, SMHA, Uttarakhand), Smt. Reena Joshi (Former District Magistrate, Pithoragarh), Dr. Heera Singh Hayanki (Former Chief Medical Officer, Pithoragarh), Dr. Santhosh Singh Nabiyal (CMO Pithoragarh), Dr. Lalit Bhat (Nodal officer, Mental health, Pithoragarh), Digambar Lal Arya (Block Education Officer, Munsyari), Ms. Neha Kumari (Block Development Officer, Munsyari), Dr. Gaurav (Medical officer Incharge, CHC Munsyari) and Mr. Ram Prasad (Former Tehsildar, Munsyari). We extend our sincere thanks to the District Administrations and the various departments including Health and Family Welfare, Women and Child Development, Social Welfare, Police, Education, Excise, Youth Welfare, Empowerment of Differently Abled, School and College administration, District Disaster Management Agencies and District Child Protection Unit, both in Belur and Munsyari.
We are thankful to Sri Vaidya Rajesh Kotecha (Secretary, Ministry of Ayurveda, Yoga & Naturopathy, Unani, Siddha, Sowa-Rigpa and Homoeopathy (AYUSH), Government of India), Mr. Shrinivasulu, IFS (former AYUSH Commissioner, Dept. of AYUSH Health and Family Welfare, Government of Karnataka), Sri Vipin Singh (IFS AYUSH Commissioner, Government of Karnataka), Dr. Veenalatha, former District AYUSH Officer, Hassan) Dr. Musa Byari (District AYUSH Officer, Hassan), Dr. Geetha S (AYUSH Nodal Officer, Belur). We also extend our thanks to Mr. Yogesh C.M. (AYUSH Volunteer, Shri Raghvendra Yoga Kendra, Belur), Mr. N. Shambu Gowada (AYUSH Volunteer, Member of Rotary Club, Belur) and Shri Patanjali Yoga Shikshana Samiti (SPYSS) in Belur for their support in promoting wellness practices in the community.
We are also thankful to Dr. Vijay Kumar Jogdande, IAS (Additional Secretary/ The Director, The Department of AYUSH & AYUSH Education, Government of Uttarakhand), Shri Deependra Kumar Chaudhari (Secretary, The Department of AYUSH & AYUSH Education, Government of Uttarakhand), Dr. Jyotsana Sanwal (Former District AYUSH Officer, Pithoragarh), Dr. Chandrakala Bhaisoda (District AYUSH Officer, Pithoragarh) Dr. Usha Bhatt (AYUSH Nodal Officer, Pithoragarh).
Both in Belur and Munsyari, our gratitude is due to Officers-in-charge of Primary Health Centres, Community Health Centers, Ayushman Aarogya Mandirs, Medical Officers, Community Health Officers (CHOs), Accredited Social Health Activists (ASHA) and Medical Officers of Belur and Munsyari taluks. We are thankful to the CHOs, ASHAs and Anganwadi workers for their essential role in community outreach.
We extend our heartfelt gratitude to the International Institute of Information Technology, Bengaluru (IIIT-B) team for their invaluable support and technical expertise in developing and maintaining e-SAMANVAYA. Likewise, gratitude is due to administrators of NIMHANS and AIIMS Rishikesh for facilitating the smooth conduct of NAMAN.
A special thanks to the local non-governmental organizations and self-help groups in Belur and Munsyari for their active engagement and collaborative efforts in promoting mental health awareness and promotive practices.
We extend our sincere gratitude to the people of Belur and Munsyari for their active participation in NAMAN.
Finally, we disclose that AI-based tools (ChatGPT, OpenAI) were used for assistance with editing and phrasing during manuscript preparation; the intellectual content, interpretations and final decisions remain solely the responsibility of the authors.
Author contributions
M.R. contributed to the conceptualization of the study, the literature review, the interpretation of findings, the drafting and editing of the manuscript and the overall revision of the manuscript. S.D. contributed to manuscript preparation, drafting and editing. S.M. contributed to data analysis and interpretation. C.N.K. and J.T. contributed to manuscript review, critical editing and overall supervision of the study. All authors reviewed and approved the final manuscript.
Funding statement
The Registered Charitable Trust Ashraya Hastha Trust, based at Krutaarth, No. 2, 1st floor, 15th Cross, Patalamma Temple Road, 3rd Block, Jayanagar, Bangalore – 560,011, provided funding for the NAMAN.
Competing interests
The content of this manuscript is solely the responsibility of the authors and does not necessarily represent the official views of the respective state governments, state departments or implementing partner organizations who had no role in the design and conduct of the study; collection, management, analysis and interpretation of the data; preparation, review or approval of the manuscript; and decision to submit the manuscript for publication.
Ethics and approvals
Ethical approval was obtained from the Institutional Ethics Committees of NIMHANS and AIIMS Rishikesh. Details regarding consent procedures and ethical safeguards are described in the Methods section under “Ethical Considerations”.
AI statement
During manuscript preparation, the authors used ChatGPT and Copilot (OpenAI, GPT-5.5; accessed in January 2026 and between May and June 2026) for language editing, grammatical refinement and assistance with improving clarity and organization of the text. The tool was accessed through the ChatGPT web interface. No AI tools were used for data analysis, interpretation of findings or generation of scientific conclusions. All outputs were critically reviewed, revised and verified by the authors, who take full responsibility for the final content of the manuscript.
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