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| ORIGINAL ARTICLE |
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| Year : 2023 | Volume
: 5
| Issue : 1 | Page : 63-70 |
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Reducing the Treatment Gap for Psychiatric Disorders – The Role of Accredited Social Health Activists in South India
Shivam Gakkhar1, P Lakshmi Nirisha2, Gajanan Sabhahit3, Patley Rahul3, Nithesh Kulal1, Nisha R Harshitha1, N Manjunatha1, Jagadisha Thirthalli1, Naveen C Kumar1, Adarsha Alur Manjappa4, Rajani Parthasarthy5, Prabhat Kumar Chand1, Sanjeev Arora6, Suresh Badamath1
1 Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bengaluru, Karnataka, India
2 Department of Psychiatry, AIIMS Raipur, National Institute of Mental Health and Neurosciences, Bengaluru, Karnataka; Department of Psychiatry, AIIMS, Raipur, Chhattisgarh, India
3 Department of Psychiatry, GS-NIMHANS Mental Health Programme (Project), National Institute of Mental Health and Neurosciences, Bengaluru, Karnataka, India
4 Department of Health and Family Welfare, District Health Officer, Karnataka, India
5 Department of Health and Family Welfare, Government of Karnataka, India
6 University of New Mexico Health Sciences Center, Albuquerque, NM, USA
| Date of Submission | 13-Mar-2023 |
| Date of Acceptance | 13-Mar-2023 |
| Date of Web Publication | 26-Apr-2023 |
Correspondence Address:
Dr. Naveen C Kumar
Department of Psychiatry,,National Institute of Mental Health and Neurosciences, Bengaluru - 560 029, Karnataka
India
 Source of Support: None, Conflict of Interest: None
DOI: 10.4103/wsp.wsp_14_23
Objective: The objective of this study was to evaluate the effectiveness of mental health work carried out by accredited social health activists (ASHAs) on reduction of the “treatment gap” for severe mental disorders (SMD), common mental disorders (CMDs), and substance use disorders (SUDs) in rural communities. Methods: This study is an offshoot of a larger randomized controlled trial designed to comprehensively compare the effectiveness of two methods of training and empowering grassroots-level workers in mental health. Three primary health centers (PHCs) were selected (simple random sampling) as the study group (SG). Thirty-five ASHAs were trained and mentored (National Institute of Mental Health and Neurosciences-Extension of Community Health Outcomes model of skilled capacity building using digital technology) for a period of 18 months in identifying/counseling/referral of commonly prevalent mental health problems in the community. Control group (CG) PHCs' ASHAs (n = 36) received “training as usual” (i.e. 1 day in person classroom training session). Both the groups were regularly contacted by the research team to monitor for progress. Reduction in “treatment gap” was evaluated using pre–post design for SG and CG separately, and the same was compared between SG and CG. Results: A total of 35,023 adults were screened, and positives were identified, counseled, and referred for care and treatment. Treatment gap for SMDs and SUDs reduced significantly both in SG and CG (SMDs: 10% vs. 38%, respectively; P = 0.03 for both; SUDs: 51% vs. 70% respectively; P < 0.001 for both) while it increased for CMDs (13% vs. 14% P < 0.01 and 0.09, respectively). Comparatively speaking, SG fared better for SUDs (P < 0.05), and CG did better for SMDs (P < 0.05). It was unequivocal for CMDs (P = 0.48). Conclusion: ASHAs could be effectively empowered to carry out mental health work resulting in meaningful reduction of treatment gap for the priority mental illnesses including SMD and SUDs.
Keywords: Accredited social health activists, common mental disorders, National Institute of Mental Health and Neurosciences-Extension of Community Health Outcomes model, priority mental illnesses, severe mental disorders, substance use disorders, treatment gap
How to cite this article:
Gakkhar S, Nirisha P L, Sabhahit G, Rahul P, Kulal N, Harshitha NR, Manjunatha N, Thirthalli J, Kumar NC, Manjappa AA, Parthasarthy R, Chand PK, Arora S, Badamath S. Reducing the Treatment Gap for Psychiatric Disorders – The Role of Accredited Social Health Activists in South India. World Soc Psychiatry 2023;5:63-70 |
How to cite this URL:
Gakkhar S, Nirisha P L, Sabhahit G, Rahul P, Kulal N, Harshitha NR, Manjunatha N, Thirthalli J, Kumar NC, Manjappa AA, Parthasarthy R, Chand PK, Arora S, Badamath S. Reducing the Treatment Gap for Psychiatric Disorders – The Role of Accredited Social Health Activists in South India. World Soc Psychiatry [serial online] 2023 [cited 2023 Jun 10];5:63-70. Available from: https://www.worldsocpsychiatry.org/text.asp?2023/5/1/63/374615 |
| Introduction | |  |
Accredited social health activists (ASHAs) are an all-female cadre of community health workers (CHWs) who, as part of the National Health Mission, are assigned responsibilities of creating awareness about health along with its social determinants and for mobilizing the community for health planning at local level, ultimately leading to increased existing health services usage and accountability.[1],[2] They are also responsible for providing a minimum level of curative care as required and feasible for that level in addition to making timely referrals to higher centers. Many programs and studies have shown that ASHAs can be trained to effectively provide a larger range of health-care services including mental health.[3],[4],[5],[6]
The concept of “Treatment Gap”
”Treatment gap” refers to the absolute difference between the total prevalence of any disorder and the total number of individuals affected by that disorder who are receiving treatment.[7] Alternatively, it can also be defined as the percentage of individuals who warrant treatment but are not receiving it. The determination of the treatment gap in a population is linked to the period prevalence of the disorder, the time frame of the service utilization analysis, and the demographic variations of the study sample with reference to the target population. As an indicator of the adequacy of public mental health systems, this concept is strongly promoted by the World Health Organization (WHO) and is commonly used in the context of low- and middle-income countries (LMICs).[8]
Treatment gap – Findings in National Mental Health Survey of India, 2016
The treatment gap associated with mental health is very large in most countries, more so in LMICs including India. There are several reasons for this wide gap, which range from availability to affordability and are influenced by several factors. As per NMHS 2016,[9] treatment gap in mental health was >80% in India, ranging between 70% and 92% for different disorders.[10] This is in line with a WHO study done in developing countries, where the treatment gap (the number of people with disease who do not receive treatment) for mental disorders was found to be 76%–85%.[11]
To bridge this large treatment gap, there is a need for building a workforce that has the knowledge, expertise, and self-efficacy to provide mental health services in their communities. Increasing the quantity, quality, and coverage of training capacities across the country in an equitable and widespread manner has the potential to achieve this objective. Strengthening of the capacity of general health services which can provide basic mental health care at the primary level can also help significantly in bridging this gap.
We studied the reduction in treatment gap of psychiatric disorders that occurred due to early identification and prompt referral by ASHAs from 6 primary health centers (PHCs) in Ramanagara District in South India who took part in implementation research aimed at comparing the effectiveness of hybrid training and “training as usual (TAU)” in mental health. Hybrid model included classroom sessions and online sessions along with continuous mentoring.
| Methods | | |
This study is an offshoot of a randomized controlled trial[12] funded by the Indian Council of Medical Research (ICMR) which was designed to compare the effectiveness of the National Institute of Mental Health and Neurosciences (NIMHANS)-Virtual Knowledge Network-Extension of Community Health Outcomes (ECHO) model[13] of training [Figure 1] as compared to TAU. The detailed methodology is described elsewhere.[12] Aspects relevant to this study are mentioned briefly. | Figure 1: Hub and spokes ECHO model of training (hub was at NIMHANS/Ramanagara where training and continuous monitoring and mentoring was provided. ASHAs were the spokes who received training). ECHO: Extension of Community Health Outcomes, NIMHANS: National Institute of Mental Health and Neurosciences, ASHAs: Accredited social health activists
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Settings
The study site was Ramanagara, a district headquarters in Karnataka state of India [Figure 2] which is located around 50 km (30 miles) from NIMHANS, Bengaluru. It has 61 PHCs in total. The total study period was 3 years (October 2018 to September 2021). Six PHCs of the district were chosen for the RCT and through simple random sampling. 3 PHCs were chosen as the study group (SG) and the other 3 as the control group (CG). Consenting 35 ASHAs were included in the SG-PHCs and 36 in the CG-PHCs. The population of adults where SG-ASHAs were serving was 22,623. CG-ASHAs were serving 12,400 adults in their respective PHCs (Office of the Registrar General and Census Commissioner, 2011).[14]
Components of training
Onsite training
ASHAs belonging to both the groups received a single day (6 h) onsite classroom teaching which comprised overview about mental illnesses, identifying and referring using “Symptoms in Others,”[15] a simple to administer tool which is used for screening severe mental illnesses.
Online training
The SG-ASHAs were trained using the NIMHANS-ECHO model of skilled capacity building for a period of 18 months, with each online session lasting between 60 and 90 min. The time interval between two sessions was between 4 and 6 weeks. Each session commenced with discussion of cases by ASHAs, followed by case vignette-based topic discussions. The next was the didactic session. At the end of each session, there was discussion regarding take-home points.
Hence, training for SG-PHC ASHAs contained both the offline and online content whereas the CG-PHC ASHAs received only routine training onsite classroom (one engagement offline, which is a routine matter of any District Mental Health Program [DMHP] activity).
Mentoring and monitoring
SG-PHC ASHAs and CG-PHC ASHAs were contacted through telephone calls at least once in 2 weeks for monitoring the progress of identifying cases and for providing guidance in dealing with various mental health-related issues which they were coming across in their communities. A research nurse and a research social worker carried out this task.
Assessment
House-to-house surveys
As part of the study, the ASHAs of both the groups conducted and completed 2 door-to-door surveys over the study period. The first one was carried out in around May 2019 using “Symptoms in Others” tool. We identified significant limitations of this tool as it did not screen for common mental disorders (CMDs) such as depression/somatization and substance use disorders (SUDs). A second tool was designed during the same period to address this deficit which is the Mental Health Screening and Counselling Tool (MERIT).[16] The tool contains 11 categorical questions which aids CHWs to screen for possible severe mental disorder (SMD), CMD, and SUD. It is a quick screening method which results in identification of persons having mental health issues. It is a simple to administer and valid (manuscript under preparation) instrument which requires only 5 min for its application. In our study, people who were positively screened after administration of MERIT by ASHAs were subsequently referred to PHCs or DMHP clinics for further assessment and treatment. MERIT was used for 2nd round of door-to-door survey during the period from November 2020 to March 2021. Each screen positive (by ASHAs) was considered to be a potential “case.”[17]
Treatment gap and its reduction
First, the expected prevalence of psychiatric disorders in the areas covered by 6 PHCs was calculated by extrapolating figures of the National Mental Health Survey of India [Table 1]. This was done for SMDs, CMDs, and SUDs separately. | Table 1: Expected number of patients with severe mental disorders, common mental disorders, and substance use disorders in the areas covered by 6 primary health centers of the study (prevalence calculated as per the National Mental Health Survey of India, 2016)
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Baseline treatment gap (for the above categories of disorders separately) was also extrapolated from the NMHS figures. As defined in NMHS, the gap is the number of people with active disease who were on treatment (had access to treatment by way of identification, care, and referral whichever is applicable) or on inadequate treatment. By applying the treatment gap percentages upon “prevalence” figures of SMDs, CMDs, and SUDs, two categories of numbers were derived for each category of disorders: (a) baseline identified cases and (b) baseline unidentified cases.
For calculating the postsurvey treatment gap (treatment gap that remained following the two door-to-door surveys), the below-mentioned steps were adopted: as and when an ASHA identified potential “cases,” they were counseled about the possibility of the presence of a mental health issue needing further care and were promptly referred to the nearest public health facilities including PHC, CHC, taluk hospital, or district hospital staffed by health-care providers. The gap (treatment/access gap) was deemed to be closed for those individuals, as they were “covered” with regard to treatment access. After two rounds of house-to-house surveys, we calculated the numbers of potential patients that were identified (and referred; postsurvey identified cases). Next, the number of postsurvey unidentified cases was computed by subtracting the number of postsurvey-treated cases from the expected prevalent figures (derived from extrapolated NMHS numbers)
The difference between the baseline treatment gap and the postsurvey treatment gap gave us the “change in treatment gap” following study interventions. We also compared the performance of SG-ASHAs and CG-ASHAs in reducing the gap for the three categories of disorders. These details are given in [Table 2], [Table 3], [Table 4]. | Table 2: Comparison of reduction in treatment gap for potential cases of severe mental disorders
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 | Table 3: Comparison of reduction in treatment gap for potential cases of common mental disorders
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 | Table 4: Comparison of reduction in treatment gap for potential cases of Substance use disorders
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For the study, we included only SMDs, CMDs (including depression, somatization, and anxiety disorders), and SUDs. Patients with childhood mental health disorders and epilepsy were excluded.
Statistical analysis
Statistical analysis was carried out using version 23 of SPSS software; single-group Chi-square test was used to analyze the reduction in treatment gap in SG and CG separately (pre–post design). Comparison of reduction in treatment gap between SG and CG was carried out using Pearson's Chi-square test. P value was set at 0.05 as statistically significant.
| Results | | |
A total of 6275 households were screened in the villages under the 6 PHCs, containing a population of 35,023 adult population (≥18 years to 59 years of age) which was calculated as per population of the Ramanagara District population census in 2011; out of this, 22,623 adults were in SG and 12,400 adults were in CG. The expected number of patients with SMDs, CMDs, and SUDs in the study population (prevalence calculated as per the NMHS 2016) is given in [Table 1].
Reduction in treatment gap
Severe mental disorders
SG-PHC ASHAs identified 61 new potential cases out of likely 180, covering almost one-third of the expected number of cases. Consequently, the treatment gap decreased from 76% (as per NMHS, 2016) to 66% resulting in reduction of 10% (P = 0.03), by the end of the second survey. CG-PHC ASHAs identified 61 new potential cases, thereby reducing the treatment gap by a considerable 38% (P = 0.031926), i.e. half the NMHS value. Comparatively, CG-PHC ASHAs fared significantly better than SG-PHC ASHAs (P < 0.05) [Figure 3]. | Figure 3: Treatment gap for SMD in the study population and its change after training. SMD: severe mental disorder
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Common mental disorders (depression, anxiety disorders, and somatization disorders)
SG-PHC ASHAs identified only 96 potential patients, covering only 4% of the expected number were identified (and referred). The treatment gap hence showed a negative shift, increasing from 83% (as shown in NMHS 2016) to 96% (P < 0.01). In the CG-PHC group, 33 out of a likely 1314 cases (3%) were identified. Again, a negative trend was seen as the treatment gap increased to 97% in comparison to 83%, though nonsignificantly (P = 0.09). Comparative figures showed that the gap had increased significantly more in the CG-ASHAs (P < 0.05) [Figure 4]. | Figure 4: Treatment gap for CMD in the study population and its change after training. CMD: Common mental disorders
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Substance use disorders
ASHAs were able to identify a staggering 873 potential cases out of 1041 expected in SG-PHCs, which meant that more than four-fifth of all likely cases were identified. The treatment gap was reduced to 16% from 86%, a significant reduction of 70% (P < 0.01). In CG-PHCs, 373 out of likely 570 cases were identified by ASHAs. This meant that the gap reduced to 35% (P < 0.001) from the earlier 86%. Comparatively, SG-PHC ASHAs fared significantly better than the CG-PHC ASHAs (P < 0.05) [Figure 5]. | Figure 5: Treatment gap for SUD in the study population and its change after training. SUD: Substance use disorder
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| Discussion | | |
This study demonstrates that involving ASHAs could result in meaningful reduction in treatment gap (defined in a pragmatic manner) for potential psychiatric disorders, in rural communities. These disorders cover priority mental illnesses including SMD and SUD. The same was not found to be true with regard to CMDs. For, SMDs, CG ASHAs fared better. An additional important finding is that extended periods of mentoring (using digital means) resulted in better identification of potential SUD cases. These results, however, should be viewed with important methodological caveats, including (a) definition of “treatment gap” used in the study and (b) methods used to arrive at baseline prevalence of psychiatric disorders and (c) the measurement instruments used in case identification in NMHS vis-à-vis our study. The findings are by no means definitive but give important clues and directions to proceed further. We would like to discuss each of these issues in the following paragraphs.
The concept of treatment gap though is a well-established indicator of adequacy of services in the public health sector, does not have a uniform definition, and is understood from multiple angles.[7],[8] While one approach is mentioned above, the other is to view it from the lens of “health services coverage.”[18] The total number of people for whom there is scope of providing service shows the service capacity and is indicative of the service potential. Alternatively, the total number of people who have already received the service tells us about the service output and is an indicator of the actual performance of the service. Hence, the coverage in relation to service capacity is defined as potential coverage and that is related to service output as actual coverage. We chose the latter definition because of its practical applicability. Another issue is about the need for local refinement and adaptability of the concept of “treatment gap” for countries such as India. India is such a diverse country with innumerable explanatory models and pathways to care for mental ailments. There is a need to take the original concept given by Tanahashi[18] and modify it based on ground realities, to get more accurate measure of “treatment gap” lest we run the risk of overestimating the gap.
One must also be aware of the different measurement instruments used in NMHS and in this study. NMHS used MINI while our study used MERIT. Another difference is the qualification of the assessors. NMHS used degree/postgraduate holders while this study engaged ASHAs with matriculation qualification. These issues too may come in the way of reaching to firm conclusion.
It may be noted here that comparing “change in treatment gap” was one of the secondary objectives of the research project.[13] Similar was the consideration for calculating the baseline prevalence figures. As there was no previous epidemiological survey in the local area, the only available nationally representative study had to be fallen upon. Resource constraints precluded us from expanding the scope of both the above concepts. We believe, however, that the results give us a meaningful direction for pursuing further prospective evaluations. Most importantly, the study shows that ASHAs can be gainfully empowered to do mental health work without additionally burdening them.
One must be aware that just because a person is identified with a problem and referred for care, it would not solve the problem. By doing so, one climbs only the first among the multiple steps and only the “access gap” would be covered. It is only the beginning of a complex process, particularly in the mental health space. In any case, there is no uniform, accepted definition of “treatment gap.” We propose our definition as one of the several conceptualizations. This becomes even more relevant when newer definition of “psychosocial care gap” is looped into the scenario. As mentioned above, involving field-level workers (FLWs; ASHAs in this case) can be a useful entry point to achieve more ideal goals in providing mental health care at a public health scale.
Differential impact on case-finding
While the SG ASHAs fared better than CG ASHAs with regard to SUDs and CMDs, the reverse was true when it came to SMDs. One hypothetical explanation for this differential counterintuitive finding is that SMDs are one of the easiest health conditions to identify even with minimal training. Obvious symptoms such as disorganized behavior, delusions, and hallucinations which are very common in SMDs and can be easily detected by screening.
For CMDs, there was a negative shift in the treatment gap in both the groups. This can be attributed to the fact that ASHAs (of both SG and CG) were not able to identify the presenting symptoms of CMDs such as depression, anxiety disorders, and somatization disorder during their screening processes at houses. Considering the notoriously deceptive nature and subjectivity involved in picking up CMDs, screening by FLWs may not be an ideal method to manage them. Health and wellness centers or the PHCs may be a better place to target CMDs. There are numerous studies to show that a substantial proportion of patients presenting to PHCs do in fact have undetected CMDs. In a study by Nambi et al.,[19] the prevalence of depressive, anxiety, and somatization disorders among all patients who came to PHCs ranged between 17% and 46%. Srinivasan et al.[20] in their study on women aged 18–59 years in a rural setting reported the prevalence of depression, anxiety, and stress to be 15%, 10.6%, and 5%, respectively. Training primary care doctors may be the more definitive way to tackle them.[21],[22],[23]
For SUDs, SG PHCs showed a staggering 70% reduction in the gap. This was, on the one hand, surprising while underlining the importance of involving FLWs (such as ASHAs) in identification of SUDs in the community. This is one aspect that needs serious consideration by the policymakers. Moreover, since door-to-door surveys were conducted, SUD identification became easy due to close-knit community environment and common drinking practices. In SUD patients, there is high internalized stigma[24] than SMDs and many do not reach out to primary care facilities. Alcohol use has cultural acceptance in many communities[25] and hence may have been readily revealed to ASHAs (who belong to the same community) while they were interviewing. Many recent training strategies have focused on nonspecialist health workers, including lay counselors and CHWs for delivering psychological interventions for many disorders including alcohol use disorders in LMICs.
Several studies[6],[15],[26],[27],[28],[29] done around the world have shown that FLW-delivered interventions lead to symptom reduction and bridging of treatment gap. Training FLWs to identify and enhance the delivery of evidence-based practices also helps to address mental health disparities. One similar model is developed by NIMHANS ECHO for capacity building in the practice of addiction treatment, by linking primary care professionals with specialists for remote supervision by leveraging mobile technology.[30]
Barnett et al.[31] in their systematic review assessing the effectiveness of FLWs in delivering mental health care concluded that it is an extremely promising approach to improve care and accessibility in underserved communities. It has also been shown to decrease the mental health-care-related costs and traveling expenses of families of persons with mental illness (PMI) significantly.[32] Moreover, attitude of ASHA workers toward patients, especially those with SMI, has been found to be influenced positively.[33] The sustainability of the knowledge and skills gained through these online short-training sessions remains doubtful, and hence, continuous monitoring and effective supervision is needed.[34]
Technology-driven initiatives for capacity building are an effective strategy to encounter the shortage of human resources in mental health in India. It is a new path and a welcome step, but it also comes with its own set of challenges. In this project, we encountered many such issues, such as dedicated space availability for setting up Hub studio, weak Internet connection (especially in remote areas), and limited literacy with respect to digital medium among the grassroots FLWs who belong to villages.[35]
Meaningful reduction in the treatment gap is possible by sustained engagement of grassroots-level health workers of the public health system (mode for engagement can either be digital or in-person). In this study, as compared to the routine one-day classroom training, the longitudinal hybrid model led to increased reduction in treatment gap of mental health disorders in community. More initiatives along the same lines would be a welcome step in dealing with limited human resources and high prevalence of mental health disorders in the community.
| Conclusion | | |
SG PHCs' ASHAs identified more proportion of SUDs and the reduction in the treatment gap was statistically significant, whereas CG did well in identifying SMDs. ASHAs may not be the right candidates for identifying CMDs and community health officers at HWCs or PHCs can be a better option. The use of digital technology in training of FLWs regarding the identification and primary care of mental health disorders is a challenging but effective way of significantly reducing the treatment gap.
Acknowledgments
The researchers would like to thank the patients and their respective families, the health-care professionals of the state of Karnataka including the health administrators, DMHP teams, primary care doctors, ASHA workers, and ANMs for their contribution in effective implementation of the trials. This work is supported by the ICMR under Capacity Building Projects for National Mental Health Program, ICMR-NMHP. We thank Dr. Soumya Swaminathan (then Secretary, Department of Health Research, DHR), Dr. Balram Bhargav (current Secretary DHR), Professor V. L. Nimgaonkar, Professor Smita N. Deshpande, Dr. Ravinder Singh, and Dr. Harpreet Singh. We thank the faculty of “Cross Fertilized Research Training for New Investigators in India and Egypt” (D43 TW009114, HMSC File No. Indo-Foreign/35/M/2012-NCD-1, funded by Fogarty International Centre, NIH). We are also thankful to the National Coordinating Unit of ICMR for NMHP Projects for their constant support and guidance. We thank Data Management Unit of ICMR for designing the database. The content of this manuscript is solely the responsibility of the authors and does not necessarily represent the official views of NIH or ICMR, and they 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.
Financial support and sponsorship
Nil.
Conflicts of interest
There are no conflicts of interest.
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[Figure 1], [Figure 2], [Figure 3], [Figure 4], [Figure 5]
[Table 1], [Table 2], [Table 3], [Table 4]
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