National Institute of Mental Health and Neurosciences

autophagic responses. Here, we critically discuss current methods of assessing autophagy and the information they can, or cannot, provide. Our ultimate goal is to encourage intellectual and technical innovation in the field.

Traumatic brain injury (TBI), according to the World Health Organization, will surpass many diseases as the major cause of death and disability by the year 2020. With an estimated 10 million people affected annually by TBI, the burden of mortality and morbidity that this condition imposes on society, makes TBI a pressing public health and medical problem. The burden of TBI is manifest throughout the world, and is especially prominent in Low and Middle Income Countries which face a higher preponderance of risk factors for causes of TBI and have inadequately prepared health systems to address the associated health outcomes. Latin America and Sub Saharan Africa demonstrate a higher TBI-related incidence rate varying from 150-170 per 100,000 respectively due to RTIs compared to a global rate of 106 per 100,000. As highlighted in this global review of TBI, there is a large gap in data on incidence, risk factors, sequelae, financial costs, and social impact of TBI. This should be addressed through planning of comprehensive TBI prevention programs in LMICs through well-established surveillance systems. Greater resources for research and prioritized interventions are critical to promote evidence-based policy for TBI.

Abstract Over the past few decades, neuroimaging has become a ubiquitous tool in basic research and clinical studies of the human brain. However, no reference standards currently exist to quantify individual differences in neuroimaging metrics over time, in contrast to growth charts for anthropometric traits such as height and weight 1 . Here we assemble an interactive open resource to benchmark brain morphology derived from any current or future sample of MRI data ( http://www.brainchart.io/ ). With the goal of basing these reference charts on the largest and most inclusive dataset available, acknowledging limitations due to known biases of MRI studies relative to the diversity of the global population, we aggregated 123,984 MRI scans, across more than 100 primary studies, from 101,457 human participants between 115 days post-conception to 100 years of age. MRI metrics were quantified by centile scores, relative to non-linear trajectories 2 of brain structural changes, and rates of change, over the lifespan. Brain charts identified previously unreported neurodevelopmental milestones 3 , showed high stability of individuals across longitudinal assessments, and demonstrated robustness to technical and methodological differences between primary studies. Centile scores showed increased heritability compared with non-centiled MRI phenotypes, and provided a standardized measure of atypical brain structure that revealed patterns of neuroanatomical variation across neurological and psychiatric disorders. In summary, brain charts are an essential step towards robust quantification of individual variation benchmarked to normative trajectories in multiple, commonly used neuroimaging phenotypes.

. In vitro, B.1.617.2 is sixfold less sensitive to serum neutralizing antibodies from recovered individuals, and eightfold less sensitive to vaccine-elicited antibodies, compared with wild-type Wuhan-1 bearing D614G. Serum neutralizing titres against B.1.617.2 were lower in ChAdOx1 vaccinees than in BNT162b2 vaccinees. B.1.617.2 spike pseudotyped viruses exhibited compromised sensitivity to monoclonal antibodies to the receptor-binding domain and the amino-terminal domain. B.1.617.2 demonstrated higher replication efficiency than B.1.1.7 in both airway organoid and human airway epithelial systems, associated with B.1.617.2 spike being in a predominantly cleaved state compared with B.1.1.7 spike. The B.1.617.2 spike protein was able to mediate highly efficient syncytium formation that was less sensitive to inhibition by neutralizing antibody, compared with that of wild-type spike. We also observed that B.1.617.2 had higher replication and spike-mediated entry than B.1.617.1, potentially explaining the B.1.617.2 dominance. In an analysis of more than 130 SARS-CoV-2-infected health care workers across three centres in India during a period of mixed lineage circulation, we observed reduced ChAdOx1 vaccine effectiveness against B.1.617.2 relative to non-B.1.617.2, with the caveat of possible residual confounding. Compromised vaccine efficacy against the highly fit and immune-evasive B.1.617.2 Delta variant warrants continued infection control measures in the post-vaccination era.

OBJECTIVE: To estimate the prevalence of frontotemporal dementia (FTD) and other degenerative early-onset dementias in a geographically defined population. BACKGROUND: Early-onset dementia (at age <65 years) results in high psychiatric morbidity and caregiver burden. Prevalence figures are available for early-onset AD but not for FTD, a dementia that is almost invariably of early onset. METHODS: Case ascertainment was by review of case records of three specialist clinic databases and inpatient admissions at a university hospital in Cambridge, United Kingdom, for patients with dementia who were <65 years of age, living in Cambridge City or East or South Cambridgeshire (population 326,019) on May 30, 2000. All the relevant health services in the area were also contacted for potential cases. Diagnosis of various dementias was based on published criteria. All patients with potential FTD were examined by the study investigators and underwent structural neuroimaging. The 1998 population estimates for the area were used to calculate age and sex prevalence with confidence intervals for AD, FTD, and other causes of dementia. RESULTS: A total of 108 patients (66 men and 42 women) with dementia with onset before they were 65 years of age were identified, of whom 60 were <65 years on the census date, giving an overall prevalence of 81 (95% CI, 62.8 to 104.5) per 100,000 in the 45- to 64-year age group. The prevalences of early-onset FTD and AD were the same: 15 per 100,000 (8.4 to 27.0) in the 45- to 64-year-old population. The mean age at onset of FTD was 52.8 years and there was a striking male preponderance (14:3). It is possible case ascertainment methods resulted in a relative underrepresentation of some forms of dementia. CONCLUSIONS: Frontotemporal dementia is a more common cause of early-onset dementia than previously recognized and appears to be more common in men.

Most research is conducted on convenience and purposive samples that may be randomly or nonrandomly drawn. A convenience sample is the one that is drawn from a source that is conveniently accessible to the researcher. A purposive sample is the one whose characteristics are defined for a purpose that is relevant to the study. The findings of a study based on convenience and purposive sampling can only be generalized to the (sub)population from which the sample is drawn and not to the entire population. This article explains the concepts involved with the help of examples of both good and bad sampling practice. Database studies and studies with enriched designs are cited as special examples of convenience and purposive sampling. Issues related to the internal and external validity of convenience and purposive samples are explained. The importance of good sampling techniques in the design and interpretation of research is understated; this must change.

As a feature of many chronic health problems, stigma contributes to a hidden burden of illness. Health-related stigma is typically characterized by social disqualification of individuals and populations who are identified with particular health problems. Another aspect is characterized by social disqualification targeting other features of a person's identity-such as ethnicity, sexual preferences or socio-economic status-which through limited access to services and other social disadvantages result in adverse effects on health. Health professionals therefore have substantial interests in recognizing and mitigating the impact of stigma as both a feature and a cause of many health problems. Rendering historical concepts of stigma as a discrediting physical attribute obsolete, two generations of Goffman-inspired sociological studies have redefined stigma as a socially discrediting situation of individuals. Based on that formulation and to specify health research interests, a working definition of health-related stigma is proposed. It emphasizes the particular features of target health problems and the role of particular social, cultural and economic settings in developing countries. As a practical matter, it relates to various strategies for intervention, which may focus on controlling or treating target health problems with informed health and social policies, countering the disposition of perpetrators to stigmatize, and supporting those who are stigmatized to limit their vulnerability and strengthen their resilience. Our suggestions for health studies of stigma highlight needs for disease- and culture-specific research that serves the interests of international health.

In randomized controlled trials (RCTs), endpoint scores, or change scores representing the difference between endpoint and baseline, are values of interest. These values are compared between experimental and control groups, yielding a mean difference between the experimental and control groups for each outcome that is compared. When the mean difference values for a specified outcome, obtained from different RCTs, are all in the same unit (such as when they were all obtained using the same rating instrument), they can be pooled in meta-analysis to yield a summary estimate that is also known as a mean difference (MD). Because pooling of the mean difference from individual RCTs is done after weighting the values for precision, this pooled MD is also known as the weighted mean difference (WMD). Sometimes, different studies use different rating instruments to measure the same outcome; that is, the units of measurement for the outcome of interest are different across studies. In such cases, the mean differences from the different RCTs cannot be pooled. However, these mean differences can be divided by their respective standard deviations (SDs) to yield a statistic known as the standardized mean difference (SMD). The SD that is used as the divisor is usually either the pooled SD or the SD of the control group; in the former instance, the SMD is known as Cohen's d, and in the latter instance, as Glass' delta. SMDs of 0.2, 0.5, and 0.8 are considered small, medium, and large, respectively. SMDs can be pooled in meta-analysis because the unit is uniform across studies. This article presents and explains the different terms and concepts with the help of simple examples.

During the past few months, mental health has received public and media attention in an unprecedented manner. This was related to the release of the report of the National Mental Health Survey (NMHS).[1] Media referred to the findings of the report as follows: “India needs to talk about mental illness;”[2] “Every sixth Indian needs mental health help;”[3] “8% of people in Karnataka have mental illness;”[4] “Mental problems more in 30–49 age group or over 60; low income linked to occurrence of mental disorders;”[5] and “urban areas to be most affected”[2] were some of the headlines in the mass media. The NMHS[1] will be a milestone in understanding the epidemiology of mental disorders in the country. It is against this importance of the survey, the current commentary places the survey in the historical context, describes the methodology of the survey, salient findings and discusses the implications of the survey. Psychiatric epidemiology has been an area of great interest among all the leading psychiatrists. The other similar area of interest is the psychiatric classifications. In both these fields, there are more “failures” than successes in the past 60 years. There have been great times and not so great times in the Indian psychiatric epidemiology. It has been well reviewed.[67] One of the first studies, a massive plan by Govindaswamy as quoted by[67] in the 1st year of the All India Institute of Mental Health, Bengaluru, was so ambitious in identifying the causative factors that it did not get off the planning stage. The next major, a milestone, was the Agra study.[8] This study is significant for the size of the studied population (nearly 30,000 in one study center, as compared to 30,000 + in the National Mental Health survey (NMHS), which is in the focus of the current review) and the identification of factors contributing to mental disorders (rural–urban differences, migrancy, etc.). There were a number of small size studies in the 1960s and 1970s. The most important of these was the “The Great Universe of Kota”[9] considered later. There was a recognition for the need for a large-scale multicentered epidemiological study in 1976 and the Indian Council of Medical Research Department of Science and Technology (DST) project came up.[7] This was a four-centered epidemiological project in Bengaluru, Baroda, Calcutta, and Patiala. Initially, the study aimed to “estimate the prevalence of psychiatric morbidity at different selected centers and investigate the sociodemographic correlates.” However, midway in the project, the objective was shifted to an interventional study, “develop and evaluate methods designed to reach, and offer services to the sick population.”[7] There was a lull in general population, psychiatric epidemiological studies till the early 2000. The next major multicentered study using the World Mental Health Survey (WMHS) occurred in the beginning of the current century. It has had challenges is clear from the fact that it is being published only after 12 years of completion of the study (published in the current issue).[10] The background is important to understand as the NMHS is the most expensive (around Rs. 5 crores), and most ambitious general population epidemiological study undertaken in the country to date. For this reason, it is important to understand the findings in detail and draw lessons for the future. As a scientific discipline, epidemiology has an important place in health sciences. It was Morris who described the seven purposes of epidemiology. These are: (i) understanding the magnitude of the mental disorders; (ii) the causative factors; (iii) calculating the morbid risk; (iv) monitoring the historical trends; (v) completion of clinical picture; (vi) identification of new syndromes; and (vii) treatment utilization in the community.[11] NATIONAL MENTAL HEALTH SURVEY METHODOLOGY The NMHS was undertaken in 12 states across six regions of India (North [Punjab and Uttar Pradesh]; South [Tamil Nadu and Kerala]; East [Jharkhand and West Bengal]; West [Rajasthan and Gujarat]; Central [Madhya Pradesh and Chhattisgarh]; and North-East [Assam and Manipur]). In each state, the dedicated team of investigators included mental health and public health professionals. A uniform and standardized methodology was adopted for the NMHS. A pilot study was undertaken in the district of Kolar, the Public Health Observatory of National Institute of Mental Health and Neuro Sciences (NIMHANS). The Master Protocol for the study was drafted based on the results from the pilot study and finalized after deliberations with the National Technical Advisory Group and the National Expert Panel and discussions with the state teams. A detailed operational guidelines document was developed to conduct the survey. The report provides detailed account of the methodology adopted for the study (pages 7–83) and results (pages 84–132) and presents recommendations (pages 138–144). The methodology adopted was multistage, stratified, random cluster sampling technique, with random selection based on probability proportionate to size at each stage; all individuals 18 years and above in the selected households were interviewed. A subsample was included in four states to examine feasibility of methodology for understanding mental morbidity among adolescents (13–17 years). The total sample is 34,802 with about 3000 (range: 2479–3508) in each of the states. The NMHS 2015–2016 interviewed 39,532 individuals across 720 clusters from eighty taluks in 43 districts of the 12 selected states. The response rate was 91.9% at household level and 88.0% at individual level. Across the states, the population interviewed were similar to the state population characteristics and also representative of the country as per Census 2011. Both quantitative and qualitative methods were employed. A set of ten instruments including Mini International Neuropsychiatric Interview (M.I.N.I 6.0) was utilized to identify the cases, household information, sociodemographic details, tobacco use, screening for epilepsy, screening for intellectual disability (ID), screening for autism spectrum disorder, disability assessment, health-care utilization, and socioeconomic impact of illness. After a rigorous 8-week training and microplanning effort, field data collectors undertook door-to-door interviews. The training was participatory and the different methods included classroom sessions, training in the hospital (observation and demonstration of interviews), and training in the community (both supervised and independent) and hands-on training in data collection on tablets. Information was captured on handheld devices, and strict protocols were established for data transfer and management with access controlled mechanisms. To ensure quality apart from rigorous training, weekly and fortnightly review and problem-solving meeting were held both locally and with the NIMHANS team. Data received from all states were examined for errors periodically and regularly and feedback provided to the state team during fortnightly e-reviews. More than 200 such e-meetings were held during the survey period. The weighted estimates for the lifetime prevalence and current prevalence were derived for conditions included in the International Classification of Disease, Tenth Revision, Diagnostic Criteria for Research. RESULTS Of the large amount of findings, three of them are vital for understanding of the prevalence, pattern of mental disorders, and the treatment utilization. First of this is the lifetime and current prevalence for the different states [Table 1].Table 1: Crude prevalence rates of mental disorders, tobacco use and suicidal risk (Adapted from Table 13 (p.79) from NMHS[1])The overall weighted prevalence for any mental morbidity was 13.7% lifetime and 10.6% current mental morbidity. What is striking is the differing prevalence rates across the centers. The lowest lifetime prevalence of 8.1% in Assam and the highest lifetime prevalence of 19.9% in Manipur have been reported, though they share common background. Similar is the striking 2-fold difference in the lifetime prevalence between Punjab and Uttar Pradesh (18.1% and 8.7%). The same 2-fold difference is seen in the current prevalence rates. Another significant finding is the vast difference in tobacco use (5.4%–39.6%). Furthermore, significant finding are the differences in the reported suicidal risk (2.2%–12.2%). A number of tables provide the sociodemographic factors and prevalence rates. Currently, only aggregated data are available and not center-specific data. It is interesting to see wide variation in the prevalence rates of different disorders across the states. Explanation for these differences is not currently available [Table 2].Table 2: Distribution of mental morbidity across the states, from NMHS, Table 22, Page 121[1]The age group between 40 and 49 years was predominantly affected (psychotic disorders, bipolar affective disorders [BPADs]), depressive disorders, and neurotic and stress-related disorders. The prevalence of substance use disorders was highest in the 50–59 years age group (29.4%). The gender prevalence of psychotic disorders was near similar (lifetime: male: 1.5%; female: 1.3%; current: male: 0.5%; female: 0.4%). While there was a male predominance in alcohol use disorders (9.1% vs. 0.5%) and for BPAD (0.6% vs. 0.4%), a female predominance was observed for depressive disorders (both current [female: 3.0%; male: 2.4%] and lifetime [female: 5.7%; male: 4.8%]) for neurotic and stress-related disorders. Residents from urban metro had a greater prevalence across the different disorders. Persons from lower income quintiles were observed to have a greater prevalence of one or more mental disorders. An individual's risk of suicide in the past 1 month was observed to be 0.9% (high risk) and 0.7% (moderate risk); it was highest in the 40–49 years age group, greater among females, and those from urban metros. The most striking findings calling for greater analysis is the wide variations in the rates of specific disorders across the states. For example, under “neurotic and stress disorders,” there is a near 10-fold difference between Assam and Jharkhand and Manipur and Rajasthan. It is not clear whether this is a reflection of the social differences, cultural factors, or other variables. A detailed analysis can only give the answers. ID screener positivity rate was 0.6%; it was greater among the younger age group, among males, and those from urban metro areas. The prevalence of morbidity among adolescents was 7.3% with a similar distribution between males and females (male: 7.5%; female: 7.1%) but was higher in urban metro areas. The current prevalence of anxiety disorders was 3.6%, and depressive disorders was 0.8%. The third important finding relates to treatment gap. This has serious implications for the organization of services [Table 3].Table 3: Treatment patterns and care characteristics (Adapted from Table 36 (Page 126) from NAMS[1]Treatment gap for mental disorders ranged between 70% and 92% for different disorders: common mental disorder - 85.0%; severe mental disorder - 73.6%; psychosis - 75.5%; BPAD - 70.4%; alcohol use disorder - 86.3%; and tobacco use - 91.8%. The median duration for seeking care from the time of the onset of symptoms varied from 2.5 months for depressive disorder. Of all the findings, the most important from public health point is the long duration of illness of severe mental disorders. In majority of the cases, a government facility was the most common source of care. At least half of those with a mental disorder reported disability in all three domains of work, social, and family life and was relatively less among alcohol use disorder. Greater disability was reported among persons with epilepsy, depression, and BPAD. These data are the most important for planning of services. However, as the twelve states where the survey was conducted varied widely in terms of the mental health resources (Kerala and Madhya Pradesh are at two extremes), it would have been valuable to see the treatment gap in the different states and across specific mental disorders. It is important to know the contribution of the following factors to the treatment gap, namely, level of mental health infrastructure, the distance (people have to travel to treatment centers, the integration of mental health care in general health care in the study area, the level of stigma attached to mental disorders, the availability of alternative sources of help, and income level of the nonusers of services). COMMENTARY In preparation for the current book review, I had communicated with Prof. G. Gururaj and Prof. Mathew Verghese, of the NMHS team, for clarifications on a number of areas of the report. I am thankful for their prompt response. The summary of the response to the same is as follows: “All states are working on state-specific reports in their specific context to bring out state prevalence data along with current status of mental health services. These are in progress and likely to be released in the next few months;” “data from NMHS is available only on different sources for care and has to be analysed.” “Information on how long the person utilised each source is specifically unavailable as various care sources got combined. This will be analysed later for different diagnostic categories;” “being a descriptive study, we have been able to show the associations between different sociodemographic and illness-related variables. Risk factor assessments and quantifications were not part of the study;” “broad conclusions (about literacy) have been drawn on this issue and future analysis will provide more details.” Regarding the 2-fold difference, in the current morbidity, (6.6%–13.4%) across the participating states, “this needs to be analysed further.” We look forward to the detailed analysis to understand the full implications of the survey. It is relevant to recall the historic and milestone epidemiological study (The Great Universe of Kota)[9] carried out nearly 45 years back, to understand the approaches used and the findings of the various parameters relating mental disorders/psychiatric symptoms. This study of 1233 adults in one center examined the relationships between “case rate” and sex, education, income, marital status, caste, and social change. Further, it presented psychiatric symptom distribution in relation to sexes, castes, and the occurrence of somatic symptoms and possession states. The study also gave information about the ability to cope, social functioning, consultation pattern, role of social stress, and the need for treatment. This report could provide a framework for the analysis of state level data and reporting of findings. With such an analysis, there would be greater possibility of understanding the associations of sociodemographic and other factors and prevalence rates and treatment utilization rates across the states. This type of analysis is awaited, as noted by the NMHS team and state centers. The NMHS is important for the following reasons: First, this survey in 12 centers provides an important reference point about mental disorders in the community. Taking the earlier WMHS (reported in this issue of the journal)[11] covering ten states, we have a rich baseline to plan services at the level of prevention, promotion, and treatment of mental disorders. Second, the methodology and the use of information technology used for the survey is a major methodological advance in psychiatric epidemiology in the country. Third, it has provided insights into the prevalence of different mental disorders in 12 centers and some sociodemographic associations of these disorders. Fourth, the current level of treatment utilization and the large treatment gap among all the disorders is a cause of concern. This is especially so, as India completes 35 years of the National Mental Health Programme, formulated in August 1982. There is much cause for concern, for the relative failure to educate the population, decrease stigma, and empower the primary health-care personnel to initiate care at their level to reach the population in need of services. It is not sufficient to call for more of the same measures implemented during the past few decades. To that extent, the survey provides a wake-up call to redesign mental health care in the country. The issues relating the mental health systems have been covered in the second part of the NMHS as well as by the National Human Rights Report of 2016. These will be covered in the next issue of IJP. There are some areas of concern, arising from the survey. The limited scope of the NMHS[1] and the WMHS[11] calls for comment. Traditionally, the purpose of epidemiology is to address seven aspects of understanding health and illness conditions [Box 1].Box 1: Recent epidemiological studies in IndiaIt is significant that only two of the seven purposes of an epidemiological survey have been fulfilled. Of these two aspects also, there is limited information. The NMHS was possibly not powered to address severe mental disorders and both the surveys for the understanding of the biosocial variables related to mental disorders. Currently, the treatment-seeking information is available to the total sample and not for the individual disorders at each of the centers. With widely varying levels of mental health-care infrastructure, it would have added much value to know the impact of the differing levels of mental health care in the community and the treatment gaps. The identification of etiological factors has occurred at a very limited level. The study being conducted in a large number of sites with wide variations in potential etiological factors such as level of poverty, literacy, status of the two sexes, the cultural beliefs and practices relating to distress, disease and help-seeking behavior, and comorbidities has been lost. There are many reasons for this failure of the survey. The sample sizes chosen for the individual centers were small to bring out the differential contribution of these factors. The more than two fold difference between the twelve states surveyed (prevalence rate has not been broken down across the diagnostic groups and participating centers), to see the possible correlation with the prevalence rates of the syndromes and the etiological factors in the different study centers. This limitation of the NMHS and WMNH survey is especially important as preventive, promotive efforts can only come from such an identification etiological associations and possible causation. The lack of reference to objectives relating to the syndromal description and identification new syndromes suggests that the survey was not geared to look for this aspect of distribution of mental disorders. It seems to have decided on what to find and found them (the much talked about “suitcase syndrome” - you find what you have put into the suitcase). CONCLUSION In India, psychiatric epidemiology remains a challenge. This was noted succinctly in 1976 by Carstairs and Kapur[9] as follows: “Cross-cultural comparisons for determining the relative significance of environmental factors in the development of mental disorder. However, researchers engaged in such comparisons face two major problems: that of establishing comparable definitions of psychiatric symptoms across cultures and the lack of suitable techniques for the relevant also share with psychiatric in general the of and methods of a in a population and of those in greater need of attention from the It was against this background that the study was The NMHS team - NIMHANS and state centers are to be for of this massive There is need for analysis of the state reports and to these aspects in future studies in the sample and This survey be in and by all mental health and public health of the country.

Online surveys are growing in popularity, perhaps because they are an easy, convenient, and inexpensive means of data collection. Online surveys commonly suffer from two serious methodological limitations: the population to which they are distributed cannot be described, and respondents with biases may select themselves into the sample. Research is of value only when the findings from a sample can be generalized to a meaningful population. When the population addressed by the survey cannot be described, and when the sample is contaminated by respondents with biases, findings from online surveys cannot be generalized and may therefore mislead.

BACKGROUND: Many people with mental, neurological and substance-use disorders (MNS) do not receive health care. Non-specialist health workers (NSHWs) and other professionals with health roles (OPHRs) are a key strategy for closing the treatment gap. OBJECTIVES: To assess the effect of NSHWs and OPHRs delivering MNS interventions in primary and community health care in low- and middle-income countries. SEARCH METHODS: We searched the Cochrane Central Register of Controlled Trials (CENTRAL) (including the Cochrane Effective Practice and Organisation of Care (EPOC) Group Specialised Register) (searched 21 June 2012); MEDLINE, OvidSP; MEDLINE In Process & Other Non-Indexed Citations, OvidSP; EMBASE, OvidSP (searched 15 June 2012); CINAHL, EBSCOhost; PsycINFO, OvidSP (searched 18 and 19 June 2012); World Health Organization (WHO) Global Health Library (searched 29 June 2012); LILACS; the International Clinical Trials Registry Platform (WHO); OpenGrey; the metaRegister of Controlled Trials (searched 8 and 9 August 2012); Science Citation Index and Social Sciences Citation Index (ISI Web of Knowledge) (searched 2 October 2012) and reference lists, without language or date restrictions. We contacted authors for additional studies. SELECTION CRITERIA: Randomised and non-randomised controlled trials, controlled before-and-after studies and interrupted-time-series studies of NSHWs/OPHR-delivered interventions in primary/community health care in low- and middle-income countries, and intended to improve outcomes in people with MNS disorders and in their carers. We defined an NSHW as any professional health worker (e.g. doctors, nurses and social workers) or lay health worker without specialised training in MNS disorders. OPHRs included people outside the health sector (only teachers in this review). DATA COLLECTION AND ANALYSIS: Review authors double screened, double data-extracted and assessed risk of bias using standard formats. We grouped studies with similar interventions together. Where feasible, we combined data to obtain an overall estimate of effect. MAIN RESULTS: The 38 included studies were from seven low- and 15 middle-income countries. Twenty-two studies used lay health workers, and most addressed depression or post-traumatic stress disorder (PTSD). The review shows that the use of NSHWs, compared with usual healthcare services: 1. may increase the number of adults who recover from depression or anxiety, or both, two to six months after treatment (prevalence of depression: risk ratio (RR) 0.30, 95% confidence interval (CI) 0.14 to 0.64; low-quality evidence); 2. may slightly reduce symptoms for mothers with perinatal depression (severity of depressive symptoms: standardised mean difference (SMD) -0.42, 95% CI -0.58 to -0.26; low-quality evidence); 3. may slightly reduce the symptoms of adults with PTSD (severity of PTSD symptoms: SMD -0.36, 95% CI -0.67 to -0.05; low-quality evidence); 4. probably slightly improves the symptoms of people with dementia (severity of behavioural symptoms: SMD -0.26, 95% CI -0.60 to 0.08; moderate-quality evidence); 5. probably improves/slightly improves the mental well-being, burden and distress of carers of people with dementia (carer burden: SMD -0.50, 95% CI -0.84 to -0.15; moderate-quality evidence); 6. may decrease the amount of alcohol consumed by people with alcohol-use disorders (drinks/drinking day in last 7 to 30 days: mean difference -1.68, 95% CI -2.79 to -0.57); low-quality evidence).It is uncertain whether lay health workers or teachers reduce PTSD symptoms among children. There were insufficient data to draw conclusions about the cost-effectiveness of using NSHWs or teachers, or about their impact on people with other MNS conditions. In addition, very few studies measured adverse effects of NSHW-led care - such effects could impact on the appropriateness and quality of care. AUTHORS' CONCLUSIONS: Overall, NSHWs and teachers have some promising benefits in improving people's outcomes for general and perinatal depression, PTSD and alcohol-use disorders, and patient- and carer-outcomes for dementia. However, this evidence is mostly low or very low quality, and for some issues no evidence is available. Therefore, we cannot make conclusions about which specific NSHW-led interventions are more effective.

BACKGROUND: Transcranial direct current stimulation has shown promising clinical results, leading to increased demand for an evidence-based review on its clinical effects. OBJECTIVE: We convened a team of transcranial direct current stimulation experts to conduct a systematic review of clinical trials with more than 1 session of stimulation testing: pain, Parkinson's disease motor function and cognition, stroke motor function and language, epilepsy, major depressive disorder, obsessive compulsive disorder, Tourette syndrome, schizophrenia, and drug addiction. METHODS: Experts were asked to conduct this systematic review according to the search methodology from PRISMA guidelines. Recommendations on efficacy were categorized into Levels A (definitely effective), B (probably effective), C (possibly effective), or no recommendation. We assessed risk of bias for all included studies to confirm whether results were driven by potentially biased studies. RESULTS: Although most of the clinical trials have been designed as proof-of-concept trials, some of the indications analyzed in this review can be considered as definitely effective (Level A), such as depression, and probably effective (Level B), such as neuropathic pain, fibromyalgia, migraine, post-operative patient-controlled analgesia and pain, Parkinson's disease (motor and cognition), stroke (motor), epilepsy, schizophrenia, and alcohol addiction. Assessment of bias showed that most of the studies had low risk of biases, and sensitivity analysis for bias did not change these results. Effect sizes vary from 0.01 to 0.70 and were significant in about 8 conditions, with the largest effect size being in postoperative acute pain and smaller in stroke motor recovery (nonsignificant when combined with robotic therapy). CONCLUSION: All recommendations listed here are based on current published PubMed-indexed data. Despite high levels of evidence in some conditions, it must be underscored that effect sizes and duration of effects are often limited; thus, real clinical impact needs to be further determined with different study designs.

Knowledge, attitude, and practice (KAP) surveys are popular in health care because they provide useful information and appear easy to design and execute. There are subtleties, however, in such surveys that early career researchers need to be aware of. This article does not provide a detailed review of the subject, nor does it address theory; rather, it provides practical guidance on matters such as identifying the need for the survey; defining the target population; preparing the questions that address knowledge, attitudes, and practice; preparing options for the answers to the items in the questionnaire; deciding how to score the instrument and analyze the results; and validating the instrument. Specific examples are presented to help readers understand and apply the guidance in various contexts.

Hereditary spastic paraplegias (HSPs) are neurodegenerative motor neuron diseases characterized by progressive age-dependent loss of corticospinal motor tract function. Although the genetic basis is partly understood, only a fraction of cases can receive a genetic diagnosis, and a global view of HSP is lacking. By using whole-exome sequencing in combination with network analysis, we identified 18 previously unknown putative HSP genes and validated nearly all of these genes functionally or genetically. The pathways highlighted by these mutations link HSP to cellular transport, nucleotide metabolism, and synapse and axon development. Network analysis revealed a host of further candidate genes, of which three were mutated in our cohort. Our analysis links HSP to other neurodegenerative disorders and can facilitate gene discovery and mechanistic understanding of disease.

Cytokine storm is an acute hyperinflammatory response that may be responsible for critical illness in many conditions including viral infections, cancer, sepsis, and multi-organ failure. The phenomenon has been implicated in critically ill patients infected with SARS-CoV-2, the novel coronavirus implicated in COVID-19. Critically ill COVID-19 patients experiencing cytokine storm are believed to have a worse prognosis and increased fatality rate. In SARS-CoV-2 infected patients, cytokine storm appears important to the pathogenesis of several severe manifestations of COVID-19: acute respiratory distress syndrome, thromboembolic diseases such as acute ischemic strokes caused by large vessel occlusion and myocardial infarction, encephalitis, acute kidney injury, and vasculitis (Kawasaki-like syndrome in children and renal vasculitis in adult). Understanding the pathogenesis of cytokine storm will help unravel not only risk factors for the condition but also therapeutic strategies to modulate the immune response and deliver improved outcomes in COVID-19 patients at high risk for severe disease. In this article, we present an overview of the cytokine storm and its implications in COVID-19 settings and identify potential pathways or biomarkers that could be targeted for therapy. Leveraging expert opinion, emerging evidence, and a case-based approach, this position paper provides critical insights on cytokine storm from both a prognostic and therapeutic standpoint.

Reducing the risk of dementia can halt the worldwide increase of affected people. The multifactorial and heterogeneous nature of late-onset dementia, including Alzheimer's disease (AD), indicates a potential impact of multidomain lifestyle interventions on risk reduction. The positive results of the landmark multidomain Finnish Geriatric Intervention Study to Prevent Cognitive Impairment and Disability (FINGER) support such an approach. The World-Wide FINGERS (WW-FINGERS), launched in 2017 and including over 25 countries, is the first global network of multidomain lifestyle intervention trials for dementia risk reduction and prevention. WW-FINGERS aims to adapt, test, and optimize the FINGER model to reduce risk across the spectrum of cognitive decline-from at-risk asymptomatic states to early symptomatic stages-in different geographical, cultural, and economic settings. WW-FINGERS aims to harmonize and adapt multidomain interventions across various countries and settings, to facilitate data sharing and analysis across studies, and to promote international joint initiatives to identify globally implementable and effective preventive strategies.

Online gaming has greatly increased in popularity in recent years, and with this has come a multiplicity of problems due to excessive involvement in gaming. Gaming disorder, both online and offline, has been defined for the first time in the draft of 11th revision of the International Classification of Diseases (ICD-11). National surveys have shown prevalence rates of gaming disorder/addiction of 10%-15% among young people in several Asian countries and of 1%-10% in their counterparts in some Western countries. Several diseases related to excessive gaming are now recognized, and clinics are being established to respond to individual, family, and community concerns, but many cases remain hidden. Gaming disorder shares many features with addictions due to psychoactive substances and with gambling disorder, and functional neuroimaging shows that similar areas of the brain are activated. Governments and health agencies worldwide are seeking for the effects of online gaming to be addressed, and for preventive approaches to be developed. Central to this effort is a need to delineate the nature of the problem, which is the purpose of the definitions in the draft of ICD-11.

< 0.05 and other values of P, examines arguments for and against the concept of statistical significance, and suggests other and better ways for analyzing data and for presenting, interpreting, and discussing the results.

White matter hyperintensities (WMHs) are frequently seen on brain magnetic resonance imaging scans of older people. Usually interpreted clinically as a surrogate for cerebral small vessel disease, WMHs are associated with increased likelihood of cognitive impairment and dementia (including Alzheimer's disease [AD]). WMHs are also seen in cognitively healthy people. In this collaboration of academic, clinical, and pharmaceutical industry perspectives, we identify outstanding questions about WMHs and their relation to cognition, dementia, and AD. What molecular and cellular changes underlie WMHs? What are the neuropathological correlates of WMHs? To what extent are demyelination and inflammation present? Is it helpful to subdivide into periventricular and subcortical WMHs? What do WMHs signify in people diagnosed with AD? What are the risk factors for developing WMHs? What preventive and therapeutic strategies target WMHs? Answering these questions will improve prevention and treatment of WMHs and dementia.

OBJECTIVE: Human rabies has been endemic in India since time immemorial, and the true incidence of the disease and nationwide epidemiological factors have never been studied. The main objectives of the present study were to estimate the annual incidence of human rabies in India based on a community survey and to describe its salient epidemiological features. METHODS: The Association for Prevention and Control of Rabies in India (APCRI) conducted a national multi-center survey with the help of 21 medical schools during the period February-August 2003. This community-based survey covered a representative population of 10.8 million in mainland India. Hospital-based data were also obtained from the 22 infectious diseases hospitals. A separate survey of the islands of Andaman, Nicobar, and Lakshadweep, reported to be free from rabies, was also undertaken. RESULTS: The annual incidence of human rabies was estimated to be 17,137 (95% CI 14,109-20,165). Based on expert group advice, an additional 20% was added to this to include paralytic/atypical forms of rabies, providing an estimate of 20,565 or about 2 per 100000 population. The majority of the victims were male, adult, from rural areas, and unvaccinated. The main animals responsible for bites were dogs (96.2%), most of which were stray. The most common bite sites were the extremities. The disease incubation period ranged from two weeks to six months. Hydrophobia was the predominant clinical feature. Many of the victims had resorted to indigenous forms of treatment following animal bite, and only about half of them had sought hospital attention. Approximately 10% of these patients had taken a partial course of either Semple or a cell culture vaccine. The islands of Andaman, Nicobar, and Lakshadweep were found to be free of rabies. CONCLUSION: Human rabies continues to be endemic in India except for the islands of Andaman, Nicobar, and Lakshadweep. Dogs continue to be the principal reservoir. The disease is taking its toll on adult men and children, the majority from rural areas, due to lack of awareness about proper post-exposure immunization. The keys to success in the further reduction of rabies in India lies in improved coverage with modern rabies vaccines, canine rabies control, and intensifying public education about the disease.