In the wake of rising number of SARS-CoV-2 cases, the Government of India had placed mass-quarantine measures, termed as "lockdown" measures from end-March 2020. The subsequent phase-wise relaxation from July 2020 led to a surge in the number of cases. This necessitated an understanding of the true burden of SARS-CoV-2 in the community. Consequently, a sero-epidemiological survey was carried out in the central Indian city of Ujjain, Madhya Pradesh. This article details the processes of data acquisition, compilation, handling, and information derivation from the survey. Information on socio-demographic and serological variables were collected from 4,883 participants using a multi-stage stratified random sampling method. Appropriate weightage was calculated for each participant as sampling fraction derived from Primary Sampling Unit (PSU), Secondary Sampling Unit (SSU) and Tertiary Sampling Unit (TSU). The weightage was then applied to the data to adjust the findings at population level. The comprehensive and robust methodology employed here may act as a model for similar future endeavours. At the same time, the dataset can also be relevant for researchers in fields such as data science, epidemiology, virology and earth modelling.
Background: About 14% of the global mental health burden is contributed by India. However, there exists a disparity in mental health patterns, utilization, and prioritization among various Indian states. The state of Madhya Pradesh is a low performer among Indian states, ranking lower than the national average on the Human Development Index, Hunger Index, and Gross Domestic Product (GDP). The state also performes poorly on other health-related indicators. Objectives of Study: To estimate the prevalence and patterns of mental illnesses in the state of Madhya Pradesh, India. Material and Methods: This study used the multistage, stratified, random cluster sampling technique, with selection probability proportionate to size at each stage. A total of 3240 individuals 18 years and older were interviewed. The mixed-method study that was employed had both quantitative and qualitative components. The Mini International Neuropsychiatric Interview along with 10 other instruments were used. Results: The overall weighted prevalence for any mental illness was 13.9%, with 16.7% over the lifetime. The treatment gap for all of the mental health problems is very high (91%), along with high suicidal risk and substance use in the state. Conclusions: This study provides evidence of the huge burden of mental, behavioral, and substance use disorders as well as the treatment gap in Madhya Pradesh. This information is crucial for developing an effective prevention and control strategy. The high treatment gap in the state calls for coordinated efforts from all stakeholders, including policy makers, political leaders, health care professionals, and the society at large to give mental health care its due priority. These findings also highlight the need for multi-pronged interventions rooted in health policy directed at reducing the treatment gap in the short term and disease burden in the long run.
BACKGROUND: Previous attempts of Mental Health Systems Assessment in India were restricted in scope and scale. Information on all aspects of mental health systems (leadership/governance, legislation, financing, service delivery, workforce, access to essential medicines, information systems, intersectoral activities, and monitoring and evaluation) was scarcely available. The National Mental Health Survey-Mental Health Systems Assessment (NMHS-MHSA), a unique endeavor, assessed the performance of mental health systems and services through health systems assessment framework. The present paper discusses the design and methodology adopted under NMHS-MHSA along with emphasizing its implication for India and other LMICs. METHODS: NMHS-MHSA was undertaken in 12 Indian states by contextually adapting WHO-AIMS instrument. Data was collated from several secondary sources including interviews of key stakeholders. Utilizing the data a set of 15-quantitative, 5-morbidity and 10-qualitative indicators were developed to summarize the functional status of mental health systems in the surveyed states. This information was authenticated through state level stakeholder's consultation and consensus building workshops following which a state mental health systems report card with indicators was developed. CONCLUSION: The process and robust method of data compilation enabled NMHS-MHSA to be a reliable and comprehensive method for assessing mental health systems at the state level. It's envisaged that the assessment provides requisite impetus for strengthening mental health program and mental health systems in India. Being less resource intensive, low -and middle- income countries can adopt NMHS-MHSA tool and methodology to assess their mental health systems with contextual modifications.
Background: Recognizing the need for good quality, scientific and reliable information for strengthening mental health policies and programmes, the National Mental Health Survey (NMHS) of India was implemented by National Institute of Mental Health and Neurosciences (NIMHANS), Bangalore, in the year 2015–2016. Aim: To estimate the prevalence, socio-demographic correlates and treatment gap of mental morbidity in a representative population of India. Methods: NMHS was conducted across 12 Indian states where trained field investigators completed 34,802 interviews using tablet-assisted personal interviews. Eligible study subjects (18+ years) in households were selected by a multi-stage, stratified, random cluster sampling technique. Mental morbidity was assessed using MINI 6. Three-tier data monitoring system was adopted for quality assurance. Weighted and specific prevalence estimates were derived (current and lifetime) for different mental disorders. Mental morbidity was defined as those disorders as per the International Statistical Classification of Diseases, Tenth Revision Diagnostic Criteria for Research (ICD-10 DCR). Multivariate logistic regression was conducted to examine risk for mental morbidity by different socio-demographic factors. Survey was approved by central and state-level institutional ethical committees. Results: The weighted lifetime prevalence of 'any mental morbidity' was estimated at 13.67% (95% confidence interval (CI) = 13.61, 13.73) and current prevalence was 10.56% (95% CI = 10.51, 10.61). Mental and behavioural problems due to psychoactive substance use (F10–F19; 22.44%), mood disorders (F30–F39; 5.61%) and neurotic and stress-related disorders (F40–F48; 3.70%) were the most commonly prevalent mental morbidity in India. The overall prevalence was estimated to be higher among males, middle-aged individuals, in urban-metros, among less educated and in households with lower income. Treatment gap for overall mental morbidity was 84.5%. Conclusion: NMHS is the largest reported survey of mental morbidity in India. Survey estimated that nearly 150 million individuals suffer from one or the other mental morbidity in India. This information is to be used for planning, delivery and evaluating mental health programming in the country.
