Pub Date : 2023-12-01DOI: 10.4103/indianjpsychiatry.indianjpsychiatry_829_23
D. Dinakaran, Arpitha Krishna, A. R. Elangovan, S. Amudhan, Selvi Muthuswamy, C. Ramasubramanian, Palanimuthu T. Sivakumar, Girish N. Rao, G. Gururaj, Mathew Varghese, V. Benegal
Existing psychiatric epidemiological studies from Tamil Nadu with methodological limitations and variations had under-reported the prevalence of mental morbidity. Robust data from a representative population-based epidemiological study are not readily available to guide mental health programs in Tamil Nadu. This study aimed to estimate the prevalence, correlates, and treatment gap of mental morbidity in the state of Tamil Nadu using data from National Mental Health Survey (NMHS) of India, 2015–2016. NMHS in Tamil Nadu was conducted in 60 clusters of 4 districts (Trichy, Tirunelveli, Thoothukudi, and Namakkal) using a door-to-door survey and multistage sampling proportionate to rural, urban nonmetro, and urban metro population. Mini-International Neuropsychiatric Interview (M.I.N.I version 6) and Fagerstrom nicotine dependence scale were administered on a representative adult (aged ≥18 years) sample to assess the mental morbidity. Prevalence and 95% confidence intervals (CIs) were estimated after weighing the sample for survey design. A total of 3059 adults from 1069 households were interviewed. The overall weighted prevalence of lifetime and current mental morbidity was 19.3% (95% CI: 19.0%–19.6%) and 11.8% (95% CI: 11.6%–12.0%) respectively. Participants who were men (largely contributed by substance-use disorders), aged 40–49 years, from rural areas, and from lower income quintile had higher prevalence of mental morbidity. The treatment gap was 94.2% for any mental health problem. Common mental disorders (depression, anxiety, and substance-use) accounted for most of the morbidity. The burden and treatment gap for mental health morbidity is high in Tamil Nadu. The findings call for urgent policy level and systemic action to strengthen mental health program in Tamil Nadu.
{"title":"Epidemiological analysis of mental health morbidity in Tamil Nadu","authors":"D. Dinakaran, Arpitha Krishna, A. R. Elangovan, S. Amudhan, Selvi Muthuswamy, C. Ramasubramanian, Palanimuthu T. Sivakumar, Girish N. Rao, G. Gururaj, Mathew Varghese, V. Benegal","doi":"10.4103/indianjpsychiatry.indianjpsychiatry_829_23","DOIUrl":"https://doi.org/10.4103/indianjpsychiatry.indianjpsychiatry_829_23","url":null,"abstract":"\u0000 \u0000 Existing psychiatric epidemiological studies from Tamil Nadu with methodological limitations and variations had under-reported the prevalence of mental morbidity. Robust data from a representative population-based epidemiological study are not readily available to guide mental health programs in Tamil Nadu.\u0000 \u0000 \u0000 \u0000 This study aimed to estimate the prevalence, correlates, and treatment gap of mental morbidity in the state of Tamil Nadu using data from National Mental Health Survey (NMHS) of India, 2015–2016.\u0000 \u0000 \u0000 \u0000 NMHS in Tamil Nadu was conducted in 60 clusters of 4 districts (Trichy, Tirunelveli, Thoothukudi, and Namakkal) using a door-to-door survey and multistage sampling proportionate to rural, urban nonmetro, and urban metro population. Mini-International Neuropsychiatric Interview (M.I.N.I version 6) and Fagerstrom nicotine dependence scale were administered on a representative adult (aged ≥18 years) sample to assess the mental morbidity. Prevalence and 95% confidence intervals (CIs) were estimated after weighing the sample for survey design.\u0000 \u0000 \u0000 \u0000 A total of 3059 adults from 1069 households were interviewed. The overall weighted prevalence of lifetime and current mental morbidity was 19.3% (95% CI: 19.0%–19.6%) and 11.8% (95% CI: 11.6%–12.0%) respectively. Participants who were men (largely contributed by substance-use disorders), aged 40–49 years, from rural areas, and from lower income quintile had higher prevalence of mental morbidity. The treatment gap was 94.2% for any mental health problem. Common mental disorders (depression, anxiety, and substance-use) accounted for most of the morbidity.\u0000 \u0000 \u0000 \u0000 The burden and treatment gap for mental health morbidity is high in Tamil Nadu. The findings call for urgent policy level and systemic action to strengthen mental health program in Tamil Nadu.\u0000","PeriodicalId":13345,"journal":{"name":"Indian Journal of Psychiatry","volume":"32 4","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138624684","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-01DOI: 10.4103/indianjpsychiatry.indianjpsychiatry_845_23
S. Suhas, B. Arvind, G. Sukumar, Pradeep S Banandur, Lakshmi P. Nirisha, C. Kumar, V. Benegal, Girish N. Rao, Mathew Varghese, G. Gururaj
A staggering 85% of the global population resides in low- and middle-income countries (LAMICs). India stands as an exemplary pioneer in the realm of mental health initiatives among LAMICs, having launched its National Mental Health Program in 1982. It is imperative to effectively evaluate mental health systems periodically to cultivate a dynamic learning model sustained through continuous feedback from mental healthcare structures and processes. The National Mental Health Survey (NMHS) embarked on the Mental Health Systems Assessment (MHSA) in 12 representative Indian states, following a pilot program that contextually adapted the World Health Organization's Assessment Instrument for Mental Health Systems. The methodology involved data collection from various sources and interviews with key stakeholders, yielding a set of 15 quantitative, 5 morbidity, and 10 qualitative indicators, which were employed to encapsulate the functional status of mental health systems within the surveyed states by using a scorecard framework. The NMHS MHSA for the year 2015–16 unveiled an array of indices, and the resultant scorecard succinctly encapsulated the outcomes of the systems' evaluation across the 12 surveyed states in India. Significantly, the findings revealed considerable interstate disparities, with some states such as Gujarat and Kerala emerging as frontrunners in the evaluation among the surveyed states. Nevertheless, notable gaps were identified in several domains within the assessed mental health systems. MHSA, as conducted within the framework of NMHS, emerges as a dependable, valid, and holistic mechanism for documenting mental health systems in India. However, this process necessitates periodic iterations to serve as critical indicators guiding the national mental health agenda, including policies, programs, and their impact evaluation.
{"title":"A bird's eye view of the mental health systems in India","authors":"S. Suhas, B. Arvind, G. Sukumar, Pradeep S Banandur, Lakshmi P. Nirisha, C. Kumar, V. Benegal, Girish N. Rao, Mathew Varghese, G. Gururaj","doi":"10.4103/indianjpsychiatry.indianjpsychiatry_845_23","DOIUrl":"https://doi.org/10.4103/indianjpsychiatry.indianjpsychiatry_845_23","url":null,"abstract":"\u0000 \u0000 A staggering 85% of the global population resides in low- and middle-income countries (LAMICs). India stands as an exemplary pioneer in the realm of mental health initiatives among LAMICs, having launched its National Mental Health Program in 1982. It is imperative to effectively evaluate mental health systems periodically to cultivate a dynamic learning model sustained through continuous feedback from mental healthcare structures and processes.\u0000 \u0000 \u0000 \u0000 The National Mental Health Survey (NMHS) embarked on the Mental Health Systems Assessment (MHSA) in 12 representative Indian states, following a pilot program that contextually adapted the World Health Organization's Assessment Instrument for Mental Health Systems. The methodology involved data collection from various sources and interviews with key stakeholders, yielding a set of 15 quantitative, 5 morbidity, and 10 qualitative indicators, which were employed to encapsulate the functional status of mental health systems within the surveyed states by using a scorecard framework.\u0000 \u0000 \u0000 \u0000 The NMHS MHSA for the year 2015–16 unveiled an array of indices, and the resultant scorecard succinctly encapsulated the outcomes of the systems' evaluation across the 12 surveyed states in India. Significantly, the findings revealed considerable interstate disparities, with some states such as Gujarat and Kerala emerging as frontrunners in the evaluation among the surveyed states. Nevertheless, notable gaps were identified in several domains within the assessed mental health systems.\u0000 \u0000 \u0000 \u0000 MHSA, as conducted within the framework of NMHS, emerges as a dependable, valid, and holistic mechanism for documenting mental health systems in India. However, this process necessitates periodic iterations to serve as critical indicators guiding the national mental health agenda, including policies, programs, and their impact evaluation.\u0000","PeriodicalId":13345,"journal":{"name":"Indian Journal of Psychiatry","volume":"4 4","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138624474","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-01DOI: 10.4103/indianjpsychiatry.indianjpsychiatry_824_23
Pavithra Jayasankar, S. Suhas, Lakshmi P. Nirisha, Sharad Philip, N. Manjunatha, Girish N. Rao, G. Gururaj, Mathew Varghese, V. Benegal
Generalized anxiety disorder (GAD) is one of the common anxiety disorders leading to impairment and burden. However, GAD remains the least studied anxiety disorder. There is a need for nationally representative epidemiological data of GAD to understand the current burden and plan the mental health policies and programs to attain their unmet needs. Hence, this study focuses on epidemiology, socio-demographic correlates, disability, and treatment gap of GAD from India's National Mental Health Survey (NMHS) 2016. NMHS 2016 was a nationally representative epidemiological survey of adult respondents from 12 states of India. NMHS is a multi-stage, stratified, random cluster sampling with random selection based on probability proportional to size at each stage. The Mini-International Neuropsychiatric Interview 6.0.0 used to diagnose psychiatric disorders. Sheehan disability scale was used to assess the disability. The current weighted prevalence of GAD was estimated. Association between GAD and socio-demographic factors was done using Firth's penalized logistic regression. The treatment gap and disability in GAD also calculated. The current weighted prevalence of GAD is 0.57%. The male gender and higher education groups have significantly lesser odds with current GAD. Urban metro and the married group have significantly higher odds with current GAD. The most common comorbid psychiatric disorders are depression (15.8%) followed by agoraphobia (9.4%). Among respondents with current GAD in the past 6 months across three domains, around 2/5th has mild and moderate disability, 1/10th has a severe disability, and 1/20th has an extreme disability. The overall treatment gap of current GAD is 75.7%. NMHS 2016 has provided valuable insights into the epidemiology and burden of GAD among the general population. The available findings provide a glimpse of the current scenario in GAD to aid policymakers in targeting interventions.
{"title":"Current prevalence and determinants of generalized anxiety disorder from a nationally representative, population-based survey of India","authors":"Pavithra Jayasankar, S. Suhas, Lakshmi P. Nirisha, Sharad Philip, N. Manjunatha, Girish N. Rao, G. Gururaj, Mathew Varghese, V. Benegal","doi":"10.4103/indianjpsychiatry.indianjpsychiatry_824_23","DOIUrl":"https://doi.org/10.4103/indianjpsychiatry.indianjpsychiatry_824_23","url":null,"abstract":"\u0000 \u0000 Generalized anxiety disorder (GAD) is one of the common anxiety disorders leading to impairment and burden. However, GAD remains the least studied anxiety disorder. There is a need for nationally representative epidemiological data of GAD to understand the current burden and plan the mental health policies and programs to attain their unmet needs. Hence, this study focuses on epidemiology, socio-demographic correlates, disability, and treatment gap of GAD from India's National Mental Health Survey (NMHS) 2016.\u0000 \u0000 \u0000 \u0000 NMHS 2016 was a nationally representative epidemiological survey of adult respondents from 12 states of India. NMHS is a multi-stage, stratified, random cluster sampling with random selection based on probability proportional to size at each stage. The Mini-International Neuropsychiatric Interview 6.0.0 used to diagnose psychiatric disorders. Sheehan disability scale was used to assess the disability. The current weighted prevalence of GAD was estimated. Association between GAD and socio-demographic factors was done using Firth's penalized logistic regression. The treatment gap and disability in GAD also calculated.\u0000 \u0000 \u0000 \u0000 The current weighted prevalence of GAD is 0.57%. The male gender and higher education groups have significantly lesser odds with current GAD. Urban metro and the married group have significantly higher odds with current GAD. The most common comorbid psychiatric disorders are depression (15.8%) followed by agoraphobia (9.4%). Among respondents with current GAD in the past 6 months across three domains, around 2/5th has mild and moderate disability, 1/10th has a severe disability, and 1/20th has an extreme disability. The overall treatment gap of current GAD is 75.7%.\u0000 \u0000 \u0000 \u0000 NMHS 2016 has provided valuable insights into the epidemiology and burden of GAD among the general population. The available findings provide a glimpse of the current scenario in GAD to aid policymakers in targeting interventions.\u0000","PeriodicalId":13345,"journal":{"name":"Indian Journal of Psychiatry","volume":"14 3","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138624930","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-01DOI: 10.4103/indianjpsychiatry.indianjpsychiatry_826_23
S. Suhas, Pavithra Jayasankar, Rahul Patley, N. Manjunatha, Girish N. Rao, G. Gururaj, Mathew Varghese, V. Benegal
Social anxiety disorder (SAD), also termed as social phobia, is a disabling psychiatric condition with limited epidemiological research on it in India. This study, using data from the National Mental Health Survey (NMHS), 2016, is the first to explore its current prevalence and associated factors in India. The NMHS in India used a comprehensive population-based study with subjects selected through a multistage stratified random cluster sampling technique across 12 states. The study included 34,802 adults interviewed with the Mini-International Psychiatric Interview 6.0.0. Firth penalized logistic regression (FPLR) was used to estimate covariate odds ratios (ORs), and the treatment gap for SAD and disability measured using Sheehan's disability scale was calculated. The study found a 0.47% prevalence of SAD, with an average age of 35.68 years (standard deviation (SD) = 15.23) among those affected. Factors, such as male gender, unemployment, and living in urban areas, were associated with higher odds of SAD, while the elderly had lower odds. A significant proportion of individuals with SAD experienced disability in work (63%), social life (77%), and family life (68%). They spent a median of ₹ 2500 per month on treatment and had a high rate of comorbid psychiatric disorders (58%). The treatment gap was substantial at 82%. A considerable portion of India's population (approximately >65 lakhs) is affected by SAD. Surprisingly, the NMHS 2016 report indicates a higher risk of SAD among males compared with females, a trend that warrants further investigation. SAD in India is linked to significant disability and a considerable treatment gap, emphasizing the need for innovative approaches to address this large, affected population, especially in light of the scarcity of mental health professionals.
{"title":"Nationally representative epidemiological study of social anxiety disorder from India","authors":"S. Suhas, Pavithra Jayasankar, Rahul Patley, N. Manjunatha, Girish N. Rao, G. Gururaj, Mathew Varghese, V. Benegal","doi":"10.4103/indianjpsychiatry.indianjpsychiatry_826_23","DOIUrl":"https://doi.org/10.4103/indianjpsychiatry.indianjpsychiatry_826_23","url":null,"abstract":"\u0000 \u0000 Social anxiety disorder (SAD), also termed as social phobia, is a disabling psychiatric condition with limited epidemiological research on it in India. This study, using data from the National Mental Health Survey (NMHS), 2016, is the first to explore its current prevalence and associated factors in India.\u0000 \u0000 \u0000 \u0000 The NMHS in India used a comprehensive population-based study with subjects selected through a multistage stratified random cluster sampling technique across 12 states. The study included 34,802 adults interviewed with the Mini-International Psychiatric Interview 6.0.0. Firth penalized logistic regression (FPLR) was used to estimate covariate odds ratios (ORs), and the treatment gap for SAD and disability measured using Sheehan's disability scale was calculated.\u0000 \u0000 \u0000 \u0000 The study found a 0.47% prevalence of SAD, with an average age of 35.68 years (standard deviation (SD) = 15.23) among those affected. Factors, such as male gender, unemployment, and living in urban areas, were associated with higher odds of SAD, while the elderly had lower odds. A significant proportion of individuals with SAD experienced disability in work (63%), social life (77%), and family life (68%). They spent a median of ₹ 2500 per month on treatment and had a high rate of comorbid psychiatric disorders (58%). The treatment gap was substantial at 82%.\u0000 \u0000 \u0000 \u0000 A considerable portion of India's population (approximately >65 lakhs) is affected by SAD. Surprisingly, the NMHS 2016 report indicates a higher risk of SAD among males compared with females, a trend that warrants further investigation. SAD in India is linked to significant disability and a considerable treatment gap, emphasizing the need for innovative approaches to address this large, affected population, especially in light of the scarcity of mental health professionals.\u0000","PeriodicalId":13345,"journal":{"name":"Indian Journal of Psychiatry","volume":"113 7","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138608538","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-01DOI: 10.4103/indianjpsychiatry.indianjpsychiatry_825_23
Pavithra Jayasankar, S. Satish, H. Suchandra, N. Manjunatha, Girish N. Rao, G. Gururaj, Mathew Varghese, V. Benegal
Panic disorder (PD) is one of the most common and debilitating anxiety disorder. Individuals with PD seek frequent healthcare and emergency services leading to frequent work absenteeism and economic burden. However, its prevalence patterns in the Indian context are poorly understood. Hence, this article discusses the epidemiology, disability, and treatment gap from India's National Mental Health Survey 2016. National Mental Health Survey 2016 was a nationally representative epidemiological survey of adult respondents from 12 states of India. Mini International Neuropsychiatric Interview 6.0.0 is used to diagnose psychiatric disorders. Sheehan disability scale was used to assess the disability. The current weighted prevalence of PD was estimated. Association between PD and its sociodemographic correlates was done using Firth penalized logistic regression. The treatment gap and disability in PD were also calculated. The lifetime and current weighted prevalence of PD was 0.5% (95% confidence interval 0.49-0.52) and 0.3% (95% confidence interval 0.28-0.41), respectively. The male gender and unemployed have significantly lesser odds with current PD. The elderly, Urban metro, and the married/separated group have significantly higher odds with current PD. The most common comorbid psychiatric disorder is agoraphobia (42.3%) and depression (30.9%) followed by Generalized Anxiety Disorder (10%). Among respondents with current PD in the past 1 month across three domains, around 80% had a disability of any severity and 20%-25% had marked disability. The overall treatment gap of current PD is 71.7%. It is the first study reporting prevalence from a nationally representative sample from the general population of India. The survey has shed light on the epidemiology and the challenges faced by those with PD which emphasizes the urgency of bridging the treatment gap. These findings are paramount to the development of more inclusive and effective mental health policies and interventions to tackle the current burden due to PD.
{"title":"Panic disorder: Epidemiology, disability, and treatment gap from nationally representative general population of India","authors":"Pavithra Jayasankar, S. Satish, H. Suchandra, N. Manjunatha, Girish N. Rao, G. Gururaj, Mathew Varghese, V. Benegal","doi":"10.4103/indianjpsychiatry.indianjpsychiatry_825_23","DOIUrl":"https://doi.org/10.4103/indianjpsychiatry.indianjpsychiatry_825_23","url":null,"abstract":"\u0000 \u0000 Panic disorder (PD) is one of the most common and debilitating anxiety disorder. Individuals with PD seek frequent healthcare and emergency services leading to frequent work absenteeism and economic burden. However, its prevalence patterns in the Indian context are poorly understood. Hence, this article discusses the epidemiology, disability, and treatment gap from India's National Mental Health Survey 2016.\u0000 \u0000 \u0000 \u0000 National Mental Health Survey 2016 was a nationally representative epidemiological survey of adult respondents from 12 states of India. Mini International Neuropsychiatric Interview 6.0.0 is used to diagnose psychiatric disorders. Sheehan disability scale was used to assess the disability. The current weighted prevalence of PD was estimated. Association between PD and its sociodemographic correlates was done using Firth penalized logistic regression. The treatment gap and disability in PD were also calculated.\u0000 \u0000 \u0000 \u0000 The lifetime and current weighted prevalence of PD was 0.5% (95% confidence interval 0.49-0.52) and 0.3% (95% confidence interval 0.28-0.41), respectively. The male gender and unemployed have significantly lesser odds with current PD. The elderly, Urban metro, and the married/separated group have significantly higher odds with current PD. The most common comorbid psychiatric disorder is agoraphobia (42.3%) and depression (30.9%) followed by Generalized Anxiety Disorder (10%). Among respondents with current PD in the past 1 month across three domains, around 80% had a disability of any severity and 20%-25% had marked disability. The overall treatment gap of current PD is 71.7%.\u0000 \u0000 \u0000 \u0000 It is the first study reporting prevalence from a nationally representative sample from the general population of India. The survey has shed light on the epidemiology and the challenges faced by those with PD which emphasizes the urgency of bridging the treatment gap. These findings are paramount to the development of more inclusive and effective mental health policies and interventions to tackle the current burden due to PD.\u0000","PeriodicalId":13345,"journal":{"name":"Indian Journal of Psychiatry","volume":" 18","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138613020","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-01DOI: 10.4103/indianjpsychiatry.indianjpsychiatry_839_23
Rohit Garg, B. Chavan, Subhash Das, Sonia Puri, Arvind Banavaram, V. Benegal, Girish N. Rao, Mathew Varghese, G. Gururaj
There is no data on the treatment gap and health care utilization for mental disorders from Punjab. The present study reports on the same by using the data collected during the National Mental Health Survey. Multisite, multistage, stratified, random cluster sampling study conducted in four districts, namely Faridkot, Moga, Patiala, and Ludhiana (for urban metro areas). Data were collected from October 2015 to March 2016. Mini International Neuropsychiatric Interview 6.0.0 and Adapted Fagerstrom Nicotine Dependence Scale were used to diagnose mental and behavioral disorders and tobacco use disorder, respectively. Pathways Interview Schedule of the World Health Organization was applied to persons having any disorder to assess treatment gap and health care utilization. Exploratory focused group discussions (FGDs) were conducted to understand the community perceptions regarding mental and behavioral disorders. The treatment gap for mental and behavioral disorders was 79.59%, and it was higher for common mental disorders than severe mental disorders and higher for alcohol and tobacco use disorders as compared to opioid use disorders. The median treatment lag was 6 months. Only seven patients out of 79 were taking treatment from a psychiatrist, and the average distance traveled by the patient for treatment was 37.61 ± 45.5 km. Many attitudinal, structural, and other barriers leading to high treatment gaps were identified during FGDs in the community, such as stigma, poor knowledge about mental health, deficiency of psychiatrists, and distance from the hospital. Vertical as well as horizontal multisectoral integration is required to reduce the treatment gap and improve healthcare utilization. Increasing mental health literacy, providing high-quality mental health services at the primary-healthcare level and human resources development are the need of the hour.
没有关于旁遮普精神疾病治疗差距和保健利用情况的数据。本研究利用全国心理健康调查期间收集的数据，报告了同样的情况。在Faridkot、Moga、Patiala和Ludhiana四个区(城市都市区)进行了多地点、多阶段、分层、随机整群抽样研究。数据收集时间为2015年10月至2016年3月。采用Mini International Neuropsychiatric Interview 6.0.0和adaptive Fagerstrom尼古丁依赖量表分别诊断精神行为障碍和烟草使用障碍。采用世界卫生组织的路径访谈表对患有任何疾病的人进行评估，以评估治疗差距和卫生保健利用情况。进行探索性焦点小组讨论(fgd)以了解社区对精神和行为障碍的看法。精神和行为障碍的治疗差距为79.59%，普通精神障碍的治疗差距高于严重精神障碍，酒精和烟草使用障碍的治疗差距高于阿片类药物使用障碍。中位治疗滞后为6个月。79名患者中只有7人接受了精神科医生的治疗，平均路程为37.61±45.5公里。在社区的fgd期间，发现了许多导致治疗差距很大的态度、结构和其他障碍，例如耻辱、对精神卫生的了解不足、缺乏精神科医生以及距离医院很远。需要进行纵向和横向的多部门整合，以缩小治疗差距并提高医疗保健利用率。提高精神卫生知识普及程度、在初级保健一级提供高质量的精神卫生服务以及开发人力资源是当前的需要。
{"title":"Treatment gap for mental and behavioral disorders in Punjab","authors":"Rohit Garg, B. Chavan, Subhash Das, Sonia Puri, Arvind Banavaram, V. Benegal, Girish N. Rao, Mathew Varghese, G. Gururaj","doi":"10.4103/indianjpsychiatry.indianjpsychiatry_839_23","DOIUrl":"https://doi.org/10.4103/indianjpsychiatry.indianjpsychiatry_839_23","url":null,"abstract":"\u0000 \u0000 There is no data on the treatment gap and health care utilization for mental disorders from Punjab. The present study reports on the same by using the data collected during the National Mental Health Survey.\u0000 \u0000 \u0000 \u0000 Multisite, multistage, stratified, random cluster sampling study conducted in four districts, namely Faridkot, Moga, Patiala, and Ludhiana (for urban metro areas). Data were collected from October 2015 to March 2016.\u0000 \u0000 \u0000 \u0000 Mini International Neuropsychiatric Interview 6.0.0 and Adapted Fagerstrom Nicotine Dependence Scale were used to diagnose mental and behavioral disorders and tobacco use disorder, respectively. Pathways Interview Schedule of the World Health Organization was applied to persons having any disorder to assess treatment gap and health care utilization. Exploratory focused group discussions (FGDs) were conducted to understand the community perceptions regarding mental and behavioral disorders.\u0000 \u0000 \u0000 \u0000 The treatment gap for mental and behavioral disorders was 79.59%, and it was higher for common mental disorders than severe mental disorders and higher for alcohol and tobacco use disorders as compared to opioid use disorders. The median treatment lag was 6 months. Only seven patients out of 79 were taking treatment from a psychiatrist, and the average distance traveled by the patient for treatment was 37.61 ± 45.5 km. Many attitudinal, structural, and other barriers leading to high treatment gaps were identified during FGDs in the community, such as stigma, poor knowledge about mental health, deficiency of psychiatrists, and distance from the hospital.\u0000 \u0000 \u0000 \u0000 Vertical as well as horizontal multisectoral integration is required to reduce the treatment gap and improve healthcare utilization. Increasing mental health literacy, providing high-quality mental health services at the primary-healthcare level and human resources development are the need of the hour.\u0000","PeriodicalId":13345,"journal":{"name":"Indian Journal of Psychiatry","volume":" 18","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138617946","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-01DOI: 10.4103/indianjpsychiatry.indianjpsychiatry_835_23
N. Manjunatha, D. Dinakaran, S. Sarkhel, C. Kumar
{"title":"The National Mental Health (?Psychiatric) Survey (2015-2016): A superb acceleration in public mental health scenario of India","authors":"N. Manjunatha, D. Dinakaran, S. Sarkhel, C. Kumar","doi":"10.4103/indianjpsychiatry.indianjpsychiatry_835_23","DOIUrl":"https://doi.org/10.4103/indianjpsychiatry.indianjpsychiatry_835_23","url":null,"abstract":"","PeriodicalId":13345,"journal":{"name":"Indian Journal of Psychiatry","volume":"16 2","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138625049","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-01DOI: 10.4103/indianjpsychiatry.indianjpsychiatry_837_23
R. Bhandary, Soyuz John, Anil Kumar M. Nagaraj, Samir K. Praharaj, C. Rao, Muralidhar M. Kulkarni, Sheena K. Agarwal
The National Mental Health Survey 2016 (NMHS 2016) was a large epidemiological study, one of its kind, conducted by the National Institute of Mental Health and Neuro Sciences (NIMHANS), Bengaluru to overcome the shortcomings of the previous surveys. The detailed report of the study is available in two parts- ‘mental health systems’ and ‘prevalence, pattern and outcomes’. Though done comprehensively, there are some inevitable limitations. The private sector, a substantial health care provider in the country was not a participant in the survey. Though MINI version 6.0 is a standard and structured instrument, it does not cover many commonly encountered mental illnesses like somatoform disorders. Further, the methodology of the survey makes it difficult for an accurate calculation of the prevalence of individual major psychiatric disorders. The survey has been appraised using a standard checklist for prevalence studies. The detailed qualitative data has not been shared in the report. The contribution of the traditional indigenous systems of healthcare and accessibility of services in rural areas have not been elaborated. Thus, the need for a comprehensive and culturally sensitive assessment tool, involvement of the private sector, and enhancing funding provision to improve the infrastructure are emphasized as future directions for the subsequent phases of the survey.
{"title":"A close critical look of India's National Mental Health Survey 2016","authors":"R. Bhandary, Soyuz John, Anil Kumar M. Nagaraj, Samir K. Praharaj, C. Rao, Muralidhar M. Kulkarni, Sheena K. Agarwal","doi":"10.4103/indianjpsychiatry.indianjpsychiatry_837_23","DOIUrl":"https://doi.org/10.4103/indianjpsychiatry.indianjpsychiatry_837_23","url":null,"abstract":"The National Mental Health Survey 2016 (NMHS 2016) was a large epidemiological study, one of its kind, conducted by the National Institute of Mental Health and Neuro Sciences (NIMHANS), Bengaluru to overcome the shortcomings of the previous surveys. The detailed report of the study is available in two parts- ‘mental health systems’ and ‘prevalence, pattern and outcomes’. Though done comprehensively, there are some inevitable limitations. The private sector, a substantial health care provider in the country was not a participant in the survey. Though MINI version 6.0 is a standard and structured instrument, it does not cover many commonly encountered mental illnesses like somatoform disorders. Further, the methodology of the survey makes it difficult for an accurate calculation of the prevalence of individual major psychiatric disorders. The survey has been appraised using a standard checklist for prevalence studies. The detailed qualitative data has not been shared in the report. The contribution of the traditional indigenous systems of healthcare and accessibility of services in rural areas have not been elaborated. Thus, the need for a comprehensive and culturally sensitive assessment tool, involvement of the private sector, and enhancing funding provision to improve the infrastructure are emphasized as future directions for the subsequent phases of the survey.","PeriodicalId":13345,"journal":{"name":"Indian Journal of Psychiatry","volume":" 2","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138611253","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-01DOI: 10.4103/indianjpsychiatry.indianjpsychiatry_846_23
Sukanto Sarkar, Aniruddha Basu, Sucharita Mandal, Pavithra Jayashankar, Pradeep K. Saha, Raghunath Misra, Debasish Sinha, R. Neogi, Soumyadeep Saha, V. Benegal, Girish N. Rao, Mathew Varghese, G. Gururaj
West Bengal, situated in eastern India, comprising 19 districts as of 2016 and consisting of 9.13 crore population, had been one of the participating states in the National Mental Health Survey, 2015–16. To estimate the prevalence and pattern of mental disorders in a representative population in West Bengal. Based upon a multi-stage stratified random cluster sampling with probability proportionate to each stage, 2646 eligible individuals were interviewed. Standard validated instruments in Bengali like socio-demographic profiles and Mini International Neuropsychiatric Interview (MINI) version 6 were used by trained data collectors with quality monitoring as per a standardized protocol. The current prevalence of mental illness in the state of West Bengal is 13.07% (12.9–13.24 95% CI), which is more than the current national average of 10.56% (10.51–10.61 95% CI). The prevalence of severe mental illness of 2.32% and suicide risk of 1.75% (1.68–1.81 95% CI) is higher than the national average. The common mental illness prevalence is 11.29 (11.13–11.45 95% CI), which is similar to the national weighted average. In West Bengal, severe mental illness is more concentrated in the rural areas in contrast to the national trend. Also, the prevalence of alcohol use disorder is 3.04 (2.96–3.13 95% CI) and epilepsy is 0.03 (0.27–0.29 95% CI), which is less than the national average. The prevalence of mental disorders in the state of West Bengal is higher than the national average, and for severe mental illness, the prevalence is the highest as compared to the national average.
{"title":"Prevalence and pattern of mental disorders in the state of West Bengal: Findings from the National Mental Health Survey of India 2016","authors":"Sukanto Sarkar, Aniruddha Basu, Sucharita Mandal, Pavithra Jayashankar, Pradeep K. Saha, Raghunath Misra, Debasish Sinha, R. Neogi, Soumyadeep Saha, V. Benegal, Girish N. Rao, Mathew Varghese, G. Gururaj","doi":"10.4103/indianjpsychiatry.indianjpsychiatry_846_23","DOIUrl":"https://doi.org/10.4103/indianjpsychiatry.indianjpsychiatry_846_23","url":null,"abstract":"\u0000 \u0000 West Bengal, situated in eastern India, comprising 19 districts as of 2016 and consisting of 9.13 crore population, had been one of the participating states in the National Mental Health Survey, 2015–16.\u0000 \u0000 \u0000 \u0000 To estimate the prevalence and pattern of mental disorders in a representative population in West Bengal.\u0000 \u0000 \u0000 \u0000 Based upon a multi-stage stratified random cluster sampling with probability proportionate to each stage, 2646 eligible individuals were interviewed. Standard validated instruments in Bengali like socio-demographic profiles and Mini International Neuropsychiatric Interview (MINI) version 6 were used by trained data collectors with quality monitoring as per a standardized protocol.\u0000 \u0000 \u0000 \u0000 The current prevalence of mental illness in the state of West Bengal is 13.07% (12.9–13.24 95% CI), which is more than the current national average of 10.56% (10.51–10.61 95% CI). The prevalence of severe mental illness of 2.32% and suicide risk of 1.75% (1.68–1.81 95% CI) is higher than the national average. The common mental illness prevalence is 11.29 (11.13–11.45 95% CI), which is similar to the national weighted average. In West Bengal, severe mental illness is more concentrated in the rural areas in contrast to the national trend. Also, the prevalence of alcohol use disorder is 3.04 (2.96–3.13 95% CI) and epilepsy is 0.03 (0.27–0.29 95% CI), which is less than the national average.\u0000 \u0000 \u0000 \u0000 The prevalence of mental disorders in the state of West Bengal is higher than the national average, and for severe mental illness, the prevalence is the highest as compared to the national average.\u0000","PeriodicalId":13345,"journal":{"name":"Indian Journal of Psychiatry","volume":" 41","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138620900","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-01DOI: 10.4103/indianjpsychiatry.indianjpsychiatry_841_23
Vijender Singh, Roshan F. Sutar, Suruchi Gupta, Abhijit P. Pakhare, A. Kokane, B. A. Aravind, G. Gururaj, Mathew Varghese, V. Benegal, Girish N. Rao
Disability associated with mental illness has a disproportionate impact on the work, social, and family responsibilities of an individual toward society. The evidence for disability in mental illnesses would help the clinician, caregivers, policymakers, and various stakeholders to come up with sustainable solutions not only to help fill the existing gaps in care but also to develop new avenues as per the specific needs of the population of Madhya Pradesh (MP). To estimate the burden of disability related to mental illnesses in the state of MP. A multi-site cross-sectional study was conducted in 2015–16 as part of National Mental Health Survey among adults above 18 years of age. Samples were selected using multi-stage, stratified, random cluster sampling based on probability proportionate to size. Six tehsils with one urban metro out of four districts from a total of 50 districts were selected in the state of MP. The Sheehan Disability Scale and socio-economic impact of illness (from selected questions from WHO-Disability Assessment Schedule-2.0) were used to assess mental morbidity and the subjective reporting of disability. The weighted prevalence of disability (n = 1011) was found as 10.2%, 13.1%, and 13.9%, respectively, in work/school, social life, and family/home domains. The weighted prevalence of moderate to extreme disability in the same domains was, respectively, 5.1%, 6.7%, and 7.3%. The presence of common mental disorders (CMDs) increases the odds of self-reported disability in work [odds ratio (OR) 2.48, 95% CI 1.35 to 4.59], social life (OR 2.74, 95% CI 1.50 to 5.07), and family domains (OR 3.03, 95% CI1.62 to 5.74). When combined with common mental disorders, tobacco use disorder further escalates the odds of self-reported disability in all three domains [OR 7.10, confidence interval (CI) 3.15 to 16.37; 4.93, CI 2.19 to 11.28; and 7.10, CI 2.78 to 19.25]. Currently, non-working persons had a higher disability in social life and family life domains (P = 0.003 and P = 0.021), respectively. We report a substantial magnitude of disability in social, work, and family life domains. Participants having CMDs, female gender, and those non-working had more disabilities and would require targeted interventions.
与精神疾病相关的残疾对个人对社会的工作、社会和家庭责任有不成比例的影响。精神疾病致残的证据将有助于临床医生、护理人员、政策制定者和各种利益相关者提出可持续的解决方案，不仅有助于填补现有的护理空白，还可以根据中央邦(MP)人口的具体需求开发新的途径。估计MP州与精神疾病相关的残疾负担。作为全国18岁以上成年人心理健康调查的一部分，2015-16年进行了一项多地点横断面研究。样本采用基于概率与大小成比例的多阶段分层随机聚类抽样。中央邦从总共50个选区的4个选区中选出了6个拥有城市地铁的选区。使用Sheehan残疾量表和疾病的社会经济影响(来自世卫组织残疾评估表-2.0的选定问题)来评估精神发病率和残疾的主观报告。在工作/学校、社会生活和家庭/家庭领域，加权残疾患病率(n = 1011)分别为10.2%、13.1%和13.9%。相同领域中中度至重度残疾的加权患病率分别为5.1%、6.7%和7.3%。常见精神障碍(cmd)的存在增加了工作中自我报告残疾的几率[比值比(OR) 2.48, 95% CI 1.35至4.59]，社会生活(OR 2.74, 95% CI 1.50至5.07)和家庭领域(OR 3.03, 95% CI1.62至5.74)。当与常见精神障碍合并时，烟草使用障碍进一步增加了所有三个领域中自我报告残疾的几率[OR 7.10，置信区间(CI) 3.15至16.37;4.93, CI 2.19 ~ 11.28;7.10, CI 2.78 ~ 19.25]。目前，非工作人员在社会生活和家庭生活领域的残疾程度较高(P = 0.003和P = 0.021)。我们报告了社会、工作和家庭生活领域中大量的残疾。患有慢性疾病、女性和非工作的参与者有更多的残疾，需要有针对性的干预。
{"title":"A study of disability and socio-economic impact of mental morbidities from the state of Madhya Pradesh, India","authors":"Vijender Singh, Roshan F. Sutar, Suruchi Gupta, Abhijit P. Pakhare, A. Kokane, B. A. Aravind, G. Gururaj, Mathew Varghese, V. Benegal, Girish N. Rao","doi":"10.4103/indianjpsychiatry.indianjpsychiatry_841_23","DOIUrl":"https://doi.org/10.4103/indianjpsychiatry.indianjpsychiatry_841_23","url":null,"abstract":"\u0000 \u0000 Disability associated with mental illness has a disproportionate impact on the work, social, and family responsibilities of an individual toward society. The evidence for disability in mental illnesses would help the clinician, caregivers, policymakers, and various stakeholders to come up with sustainable solutions not only to help fill the existing gaps in care but also to develop new avenues as per the specific needs of the population of Madhya Pradesh (MP).\u0000 \u0000 \u0000 \u0000 To estimate the burden of disability related to mental illnesses in the state of MP.\u0000 \u0000 \u0000 \u0000 A multi-site cross-sectional study was conducted in 2015–16 as part of National Mental Health Survey among adults above 18 years of age. Samples were selected using multi-stage, stratified, random cluster sampling based on probability proportionate to size. Six tehsils with one urban metro out of four districts from a total of 50 districts were selected in the state of MP. The Sheehan Disability Scale and socio-economic impact of illness (from selected questions from WHO-Disability Assessment Schedule-2.0) were used to assess mental morbidity and the subjective reporting of disability.\u0000 \u0000 \u0000 \u0000 The weighted prevalence of disability (n = 1011) was found as 10.2%, 13.1%, and 13.9%, respectively, in work/school, social life, and family/home domains. The weighted prevalence of moderate to extreme disability in the same domains was, respectively, 5.1%, 6.7%, and 7.3%. The presence of common mental disorders (CMDs) increases the odds of self-reported disability in work [odds ratio (OR) 2.48, 95% CI 1.35 to 4.59], social life (OR 2.74, 95% CI 1.50 to 5.07), and family domains (OR 3.03, 95% CI1.62 to 5.74). When combined with common mental disorders, tobacco use disorder further escalates the odds of self-reported disability in all three domains [OR 7.10, confidence interval (CI) 3.15 to 16.37; 4.93, CI 2.19 to 11.28; and 7.10, CI 2.78 to 19.25]. Currently, non-working persons had a higher disability in social life and family life domains (P = 0.003 and P = 0.021), respectively.\u0000 \u0000 \u0000 \u0000 We report a substantial magnitude of disability in social, work, and family life domains. Participants having CMDs, female gender, and those non-working had more disabilities and would require targeted interventions.\u0000","PeriodicalId":13345,"journal":{"name":"Indian Journal of Psychiatry","volume":"13 4","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138625212","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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