Background Mental disorders and self-harm are currently estimated to account for almost 8.5% of all disability adjusted life-years worldwide. The prevalence of depression, anxiety and substance use disorders is estimated to be ~10%. Earlier research has shown that India has high suicide rates, with Andhra Pradesh having one of the highest suicide rates in the country at around 37.5/100,000 people. To address care access barriers, solutions that involve delivery of mental healthcare by primary care physicians and community-level healthcare workers are being increasingly sought. In India, two studies have shown that basic mental health care delivery is possible by training lay health workers. However, such interventions involve recruiting additional resources which may affect scalability and generalisability in resource poor primary care settings. Mobile health (mHealth) technologies offer an unprecedented opportunity to address access and treatment gaps for common health conditions. SMART Mental Health was conceived as an implementation platform for India’s national strategies and policies to improve mental health services. It builds on a pilot project that was conducted between 2014-18 across 42 villages in Andhra Pradesh, and the results of which showed that a technology-enabled mental health services delivery model for common mental disorders (CMD) was feasible and acceptable for rural populations and indicated beneficial results in increasing uptake of mental health services, improvement in stigma perceptions related to mental health, and improvement in depression and anxiety scores for those who were screened positive for those conditions. The complex intervention involved using an anti-stigma campaign and an electronic decision support system (EDSS) that allowed identification, diagnosis and treatment of CMD by primary healthcare workers. Aim and Objective The study aims to evaluate the feasibility, clinical effectiveness and cost-effectiveness of a multifaceted primary healthcare worker intervention. We hypothesise that: (1) a community-based anti-stigma campaign will address barriers to accessing mental health care and lead to significant improvements in community behaviours toward mental disorders; and (2) a mobile device based decision support system will improve the management of adults at high risk of CMDs and lead to significant improvements in the proportion achieving remission for depression, anxiety and suicide risk. Study Design The key phases of the study are: 1 Intervention optimisation - the implementation challenges identified in the pilot study will be addressed and the intervention will be adapted for expansion to North India in collaboration with the All India Institute for Medical Sciences; 2 Effectiveness testing - a parallel-group cluster randomised, controlled trial will be conducted involving 44 primary health care centres in rural Andhra Pradesh and the Haryana region (~165,000 adults screened with a random sample of the baseline adult population (n = 1,936) and all people identified at high risk of CMDs (n = 1,936) followed over 12 months; and 3 Sustainability assessment – detailed process and economic evaluations of the trial will be conducted and the intervention will be offered to both trial arms for up to 9 months as part of a non-randomised evaluation. Study Duration The study will be conducted in 44 PHCs and 2-5 randomly selected villages associated with each of these PHCs over a 4-year period in Haryana and Andhra Pradesh. Baseline Data Collection: ASHAs will screen until they have identified 150 ‘high-risk’ individuals/PHC for common mental disorders (CMD). This will form the ‘high-risk’ cohort. A second cohort of 110 adults/PHC not at high risk for CMD will be identified by selecting a random sample from the screened population. This would form the ’non high-risk’ cohort. Baseline data collection will also involve a detailed interview of all high-risk and non high-risk individuals. Following baseline data collection, detailed intensive training will be provided to primary care health workers in the intervention group, while those in control group will receive basic training. Rescreening of the 150 high-risk individuals in each PHC will be done to identify the final set of ‘high-risk’ cohort. Allowing for 25% natural remission over 3-4 months from the time the individuals were screened initially, we expect that 110 adults would still be at high-risk. This cohort of 110 adults/PHC will form the final set of ‘high-risk’ cohort, who would be randomized for intervention phase along with the 110 non-high-risk cohort/PHC. Thus, the total number of adults included in the intervention will be 7500 (3750 in control and equal number in intervention arm). Assuming that about 25% may be lost to followup during the intervention, at the end of intervention there should be 88 high-risk and 88 non high-risk adults in each PHC as indicated by power calculation. Randomisation: Randomisation will be conducted at the level of the primary health centre (PHC). Allocation of PHCs (including two villages per PHC) to intervention or control will use a web-based allocation sequence and will be stratified by geographic region, population size. The villages would need to have at least a collective population of 6300 and have adequate number of ASHAs as designated by government regulations. The intervention and control villages will be non-contiguous to avoid contamination. Intervention: The key components of the intervention will be: 1) an anti-stigma campaign to increase awareness of CMDs in the community and reduce stigma perceptions related to help-seeking; and 2) implementation of the enhanced mobile technology based decision support system for people identified at high risk of CMDs. Participants will be electronically referred via the mHealth platform and also provided with a paper referral card to take to the doctor. The PHC doctor will review participants as part of a village health camp visit or the patient will visit the PHC. Complex cases will be
referred to a psychiatrist in the next tier of the public health system. The
psychiatrist will also conduct case reviews via phone and/or Skype with the
doctors to enhance their management skills. The PHC doctors will see patients
either at the outpatient clinics of their PHC’s or at health camps or via
teleconsultation (for those patients who are unable to attend either
outpatients or health camps).
Outcome The mHealth component will be assessed in the ‘high-risk’ cohort and the anti-stigma component will be assessed in ‘non-high-risk’ cohorts at 12 months. To assess the mHealth/ task-sharing component the primary outcome will be the mean difference in PHQ-9 scores at end of trial. This will involve the ‘high-risk’ cohort. To assess the impact of the anti-stigma campaign the difference in mean behaviour scores at end of trial using the Mental Health Knowledge, Attitude and Behaviour (KAB) scale will be assessed in both combined ‘high risk’ and ‘non-high-risk’ cohort and separately within the groups. Sample size: For people with or at high risk of CMDs, sample sizes of 1936 in intervention and 1936 in control (total 3872), obtained by sampling 22 clusters with an average of 88 subjects each in intervention group and 22 clusters with an average of 88 subjects each in control group at the end of trial, will provide 90% power to detect a standardised mean difference in PHQ-9 scores between the group of at least 2 points. Assuming a standard deviation of 5.0, this corresponds to an effect size of 0.4. These calculations further assume an intracluster correlation coefficient considered is 0.15, a coefficient of variation of cluster sizes of 0.65 and a two-sided significance level of 0.05. In the non high-risk population, sample sizes of 1936 in intervention and 1936 in control (total 3872), obtained by sampling 22 clusters with an average of 88 subjects each in intervention group and 22 clusters with an average of 88 subjects each in control group at the end of trial, will provide >90% power to detect a standardised mean difference of 0.3 in mean behaviour scores between the intervention and control arms. This assumes a conservative ICC of 0.05 (0.01 in pilot and 0.04 in similar studies), and a 2-sided significance level of 0.05. It also assumes a mean behaviour score of 2 (SD 1) at baseline, and a 20% relative improvement in the control group (score of 1.6) by 12 months based on pilot and published data, which would correspond to a 35% improvement in the intervention group (score of 1.3) and a between-group difference of 0.3 points. The combined high-risk and non high-risk sample will also enable us to estimate differences in the behaviour score as outlined, in both high risk and non-high-risk groups, separately Data collection- Baseline, 3 month, 6 month, 12 month and Post intervention: Around 165,000 adults in total (6300 per PHC) will be screened at baseline to achieve the required sample size. Both ‘high-risk’ and ‘non high-risk’ cohorts will be re-interviewed at 3, 6, 12 months. This will constitute the period for which the cRCT will be conducted. Outcome data will be collected by trained interviewers, blinded to intervention allocation. Participants will be interviewed for approximately one hour in their house and the same questionnaires will be administered at follow-up, including questions on mental health services use and quality of life. Following the cRCT, the components of the intervention will be rolled out in the post-intervention phase to the control arm too, and both intervention and control arm will be monitored but less intensively. This should be akin to what would be expected in a scaled-up implementation programme. The key outcomes mentioned above will be assessed after 12 months of post-intervention in both arms and the outcomes will be compared across both arms. Process evaluation and economic evaluation: Qualitative information about the experiences of the community participants, ASHAs, doctors, and other key stakeholders about the intervention will also be gathered using focus group discussions and/or in-depth interviews (see process evaluation below). For participants in the intervention arm, the electronic database behind the mHealth system will capture usage analytic data about each follow-up visit made by the ASHA and doctor and track any changes in treatment and/or diagnosis Data Analyses Suitable descriptive and analytic statistics will be conducted and models will be developed to understand factors associated with the outcome variables. Qualitative data will be analysed using appropriate techniques to identify key concepts.
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