Understanding the burden and pattern of mental disorders as well as mapping the existing resources for delivery of mental health services in India, has been a felt need over decades. Recognizing this ...necessity, the Ministry of Health and Family Welfare, Government of India, commissioned the National Mental Health Survey (NMHS) in the year 2014-15. The NMHS aimed to estimate the prevalence and burden of mental health disorders in India and identify current treatment gaps, existing patterns of health-care seeking, service utilization patterns, along with an understanding of the impact and disability due to these disorders. This paper describes the design, steps and the methodology adopted for phase 1 of the NMHS conducted in India. The NMHS phase 1 covered a representative population of 39,532 from 12 states across 6 regions of India, namely, the states of Punjab and Uttar Pradesh (North); Tamil Nadu and Kerala (South); Jharkhand and West Bengal (East); Rajasthan and Gujarat (West); Madhya Pradesh and Chhattisgarh (Central) and Assam and Manipur (North East). The NMHS of India (2015-16) is a unique representative survey which adopted a uniform and standardized methodology which sought to overcome limitations of previous surveys. It employed a multi-stage, stratified, random cluster sampling technique, with random selection of clusters based on Probability Proportionate to Size. It was expected that the findings from the NMHS 2015-16 would reveal the burden of mental disorders, the magnitude of the treatment gap, existing challenges and prevailing barriers in the mental-health delivery systems in the country at a single point in time. It is hoped that the results of NMHS will provide the evidence to strengthen and implement mental health policies and programs in the near future and provide the rationale to enhance investment in mental health care in India. It is also hoped that the NMHS will provide a framework for conducting similar population based surveys on mental health and other public health problems in low and middle-income countries.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Comorbidities in alcohol dependence syndrome (ADS) are often associated with poor treatment outcomes and high service utilization. Deliberate self-harm (DSH) in India is a silent epidemic. There is a ...shortage of research on individuals with ADS, psychiatric comorbidities, and DSH.
To study DSH and psychiatric comorbidity in persons with ADS.
A cross-sectional study of 1-year duration is conducted in out patient department/indoor patient department of a tertiary care hospital.
Eighty-one individuals with ADS were selected purposefully. A semi-structured pro forma, the severity of alcohol dependence questionnaire, Mini-International Neuropsychiatric Interview (MINI), and Deliberate Self-Harm Inventory were administered for assessment. The statistical analysis used is as follows: correlation statistics and logistic regression.
Most subjects were married, belonged to upper-middle socioeconomic status, had at least secondary education, and had moderate alcohol dependence severity. Almost 3/4
of the subjects had at least one lifetime psychiatric disorder. A past episode of major depressive disorder was found in 47%. Nearly 20% of study subjects had a history of DSH, which was significantly associated with unmarried status, and earlier age of alcohol dependence. Marital status, educational level, and age of onset of alcohol dependence explain 64.3% of the variance in DSH attempts. Suicidality was significantly related to the presence of DSH.
The study found a high prevalence of psychiatric disorders and a history of DSH in individuals with ADS. The study confirmed the association between DSH and suicidality and indicated a specific subpopulation of ADS who need thorough clinical assessments to recognize these psychiatric comorbidities and DSH.
Background: Schizophrenia is a major psychiatric disorder with diverse clinical presentations and comorbidities. Comorbid nicotine use worsens the clinical symptomatology, predisposes the individuals ...to other chronic illnesses, and is associated with poorer outcomes in schizophrenia. It is thus clinically essential to assess the presence of tobacco use, the severity of nicotine dependence, and its correlation with psychopathology in patients with schizophrenia.
Aims and Objectives: (i) To describe the pattern of tobacco use and severity of nicotine dependence in schizophrenia patients. (ii) To compare the socio-demographic variables and symptom severity of schizophrenia between tobacco users and non-users. (iii) To determine the correlation between the severity of nicotine dependence and clinical and nicotine related variables in patients of schizophrenia.
Materials and Methods: It was a cross-sectional observational study conducted in a tertiary care level psychiatric hospital in the northeast of India. The study included 100 male schizophrenia patients. Fagerstrom Test for Nicotine Dependence (FTND) and FTND - Smokeless Tobacco, and the Positive and Negative Syndrome Scale five-factor model was used for assessment of the severity of nicotine dependence and symptoms of Schizophrenia, respectively.
Results: 70% of the study sample used some form of tobacco. The severity of nicotine dependence was significantly higher in patients using both forms of tobacco. Tobacco use was significantly associated with lower educational status, employment status, and positive symptoms of Schizophrenia. Finally, the severity of nicotine dependence was significantly correlated with the severity of positive and emotional symptoms of schizophrenia.
Conclusion: Higher positive and emotional symptoms of schizophrenia predicted severity of nicotine use. Study needs to be replicated in larger population of patients suffering from schizophrenia.
The National Mental Health Survey (NMHS) of India was undertaken with the objectives of (1) estimating the prevalence and patterns of various mental disorders in representative Indian population and ...(2) identifying the treatment gap, healthcare utilisation, disabilities and impact of mental disorders. This paper highlights findings pertaining to depressive disorders (DD) from the NMHS.
Multisite population-based cross-sectional study. Subjects were selected by multistage stratified random cluster sampling technique with random selection based on probability proportionate to size at each stage.
Conducted across 12 states in India (representing varied cultural and geographical diversity), employing uniform, standardised and robust methodology.
A total of 34 802 adults (
18 years) were interviewed.
Prevalence of depressive disorders (ICD-10 DCR) diagnosed using Mini International Neuropsychiatric Interview V.6.0.
The weighted prevalence of lifetime and current DD was 5.25% (95% CI: 5.21% to 5.29%, n=34 802) and 2.68% (95% CI: 2.65% to 2.71%, n=34 802), respectively. Prevalence was highest in the 40-59 age groups (3.6%, n=10 302), among females (3.0%, n=18 217) and those residing in cities with population >1 million (5.2%, n=4244). Age, gender, place of residence, education and household income were found to be significantly associated with current DD. Nearly two-thirds of individuals with DD reported disability of varying severity, and the treatment gap for depression in the study population was 79.1%. On an average, households spent INR1500/month (~US$ 23.0/month) towards care of persons affected with DD.
Around 23 million adults would need care for DD in India at any given time. Since productive population is affected most, DD entails considerable socioeconomic impact at individual and family levels. This is a clarion call for all the concerned stakeholders to scale up services under National Mental Health Programme in India along with integrating care for DD with other ongoing national health programmes.
Background: Depression and impulsivity are etiologically linked to alcohol dependence (AD) and are known to affect course and outcomes. The relationship between impulsivity and depressive symptoms ...has been investigated only in a few studies of individuals with AD. Aim: This study aimed to explore the association between impulsivity and depressive symptoms in patients with AD. Materials and Methods: Our study was conducted in the inpatient setup of a tertiary care psychiatry institute. The study design is cross-sectional. The Barratt Impulsiveness Scale (BIS-11) and stop signal task (SST) were used to assess levels of global impulsivity and behavioral impulsivity, respectively, among 60 recently detoxified inpatients with AD. The Hamilton Depression Rating Scale (HAM-D) was used to measure depressive symptoms. The results were analyzed to examine the association of depressive symptoms with impulsivity. Pearson’s coefficient of correlation or Spearman’s rank correlation and linear regression analysis were performed to explore the association between quantitative variables. Results: Patients with higher HAM-D scores were found to have significantly higher score on all three subscales of the BIS-11. The attention impulsivity subscale had the strongest correlations (r = 0.53, P < 0.001). Depressive symptoms were more strongly correlated with cognitive impulsivity (r = 0.54, P < 0.0001) compared with motor impulsivity and were not significantly associated with behavioral impulsivity. Adjusting for other variables, cognitive impulsivity was found to be the strongest predictor of the severity of depressive symptoms. Conclusions: The study showed a strong association between impulsivity and depressive symptoms in individuals with AD. This relationship may apply more to cognitive impulsivity, reflecting the role of impulsive decisions compared with impulsive actions.
Background: Neurological soft signs (NSS) are subtle motor and sensory deficits that are frequently found in various psychiatric disorders including schizophrenia. NSS in schizophrenia are frequently ...associated with impairment in cognitive abilities and deterioration in neuropsychological performance (NP).
Objective: We aimed to study the correlation between NSS and NP in persons with schizophrenia.
Methods: Sixty individuals of whom thirty had schizophrenia according to the International Classification of Diseases 10th Revision and the rest thirty were matched controls were selected based on inclusion and exclusion criteria. Demographic and clinical details were obtained and tests for the assessment of NSS and neuropsychological assessment were administered. Comparison based on scores obtained in these scales was made in both the groups.
Results: NSS were present in 100% of patients with schizophrenia and in 16.6% of controls in the control group. There was a statistically significant difference between the two groups in neuropsychological assessment. In Group 1, NSS showed a significant negative correlation with Tower of London, Stroop Color-Word Test, Digit Vigilance Test, and Digit Symbol Substitution Test. However, there was no correlation between NP and NSS in Group 2.
Conclusion: NSS were more in persons with schizophrenia compared to healthy normal controls. Furthermore, there is a negative correlation between NSS and NP in persons with schizophrenia, which is differing from the control group. We may conclude that the presence of NSS predicts the poor NP, and also contributes to poor cognitive abilities of persons with schizophrenia.
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. Keywords: Mental Health systems, Services, Methodology, Performance, Progress, India
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.
Abstract Background: The thyroid hormone’s significant impact on the treatment of depressive and other mood disorders is well documented. Even minor hypothyroidism can modify the progression and ...treatment results of major depressive disorder. This research aims to explore the correlation between initial thyroid levels and the treatment outcome in bipolar mood disorder using lithium, a well-established treatment method. Methodology: The study involved 45 bipolar mood disorder patients diagnosed according to the Diagnostic and Statistical Manual 5 criteria and admitted to a tertiary care teaching institute in Northeast India. The patients received lithium treatment, with injectable lorazepam used for immediate agitation control when necessary. On the 1 st day, a semi-structured questionnaire and Brief Psychiatric Rating Scale (BPRS) 24-item scale were used to evaluate symptoms, and samples were collected for a thyroid profile, including T3, T4, FT3, FT4, and thyroid-stimulating hormone (TSH). The BPRS scale was used again on the 30 th day to assess treatment response. Results: The BPRS subscale showed the highest treatment response for grandiosity and the lowest for depression. Factors such as age, illness duration, substance use, and family history of mood disorder were inversely correlated with the BPRS score’s decrease. Initial thyroid levels were identified as a predictor of treatment response, with baseline T4 levels showing a significant positive correlation with treatment response, while baseline TSH levels showed a negative correlation. Conclusion: Lithium proved to be an effective treatment for bipolar mood disorder, particularly for the manic subtype. Initial T4 and TSH levels were found to significantly predict treatment response, with T4 showing a positive correlation and TSH showing a negative correlation.
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