SUMMARYThis analysis, designed and executed by Ribble Rivers Trust, identifies areas across England with the greatest levels of depression in adults (aged 18+). Please read the below information to gain a full understanding of what the data shows and how it should be interpreted.ANALYSIS METHODOLOGYThe analysis was carried out using Quality and Outcomes Framework (QOF) data, derived from NHS Digital, relating to depression in adults (aged 18+).This information was recorded at the GP practice level. However, GP catchment areas are not mutually exclusive: they overlap, with some areas covered by 30+ GP practices. Therefore, to increase the clarity and usability of the data, the GP-level statistics were converted into statistics based on Middle Layer Super Output Area (MSOA) census boundaries.The percentage of each MSOA’s population (aged 18+) with depression was estimated. This was achieved by calculating a weighted average based on:The percentage of the MSOA area that was covered by each GP practice’s catchment areaOf the GPs that covered part of that MSOA: the percentage of registered patients that have that illness The estimated percentage of each MSOA’s population with depression was then combined with Office for National Statistics Mid-Year Population Estimates (2019) data for MSOAs, to estimate the number of people in each MSOA with depression, within the relevant age range.Each MSOA was assigned a relative score between 1 and 0 (1 = worst, 0 = best) based on:A) the PERCENTAGE of the population within that MSOA who are estimated to have depressionB) the NUMBER of people within that MSOA who are estimated to have depressionAn average of scores A & B was taken, and converted to a relative score between 1 and 0 (1= worst, 0 = best). The closer to 1 the score, the greater both the number and percentage of the population in the MSOA that are estimated to have depression, compared to other MSOAs. In other words, those are areas where it’s estimated a large number of people suffer from depression, and where those people make up a large percentage of the population, indicating there is a real issue with depression within the population and the investment of resources to address that issue could have the greatest benefits.LIMITATIONS1. GP data for the financial year 1st April 2018 – 31st March 2019 was used in preference to data for the financial year 1st April 2019 – 31st March 2020, as the onset of the COVID19 pandemic during the latter year could have affected the reporting of medical statistics by GPs. However, for 53 GPs (out of 7670) that did not submit data in 2018/19, data from 2019/20 was used instead. Note also that some GPs (997 out of 7670) did not submit data in either year. This dataset should be viewed in conjunction with the ‘Health and wellbeing statistics (GP-level, England): Missing data and potential outliers’ dataset, to determine areas where data from 2019/20 was used, where one or more GPs did not submit data in either year, or where there were large discrepancies between the 2018/19 and 2019/20 data (differences in statistics that were > mean +/- 1 St.Dev.), which suggests erroneous data in one of those years (it was not feasible for this study to investigate this further), and thus where data should be interpreted with caution. Note also that there are some rural areas (with little or no population) that do not officially fall into any GP catchment area (although this will not affect the results of this analysis if there are no people living in those areas).2. Although all of the obesity/inactivity-related illnesses listed can be caused or exacerbated by inactivity and obesity, it was not possible to distinguish from the data the cause of the illnesses in patients: obesity and inactivity are highly unlikely to be the cause of all cases of each illness. By combining the data with data relating to levels of obesity and inactivity in adults and children (see the ‘Levels of obesity, inactivity and associated illnesses: Summary (England)’ dataset), we can identify where obesity/inactivity could be a contributing factor, and where interventions to reduce obesity and increase activity could be most beneficial for the health of the local population.3. It was not feasible to incorporate ultra-fine-scale geographic distribution of populations that are registered with each GP practice or who live within each MSOA. Populations might be concentrated in certain areas of a GP practice’s catchment area or MSOA and relatively sparse in other areas. Therefore, the dataset should be used to identify general areas where there are high levels of depression, rather than interpreting the boundaries between areas as ‘hard’ boundaries that mark definite divisions between areas with differing levels of depression.TO BE VIEWED IN COMBINATION WITH:This dataset should be viewed alongside the following datasets, which highlight areas of missing data and potential outliers in the data:Health and wellbeing statistics (GP-level, England): Missing data and potential outliersLevels of obesity, inactivity and associated illnesses (England): Missing dataDOWNLOADING THIS DATATo access this data on your desktop GIS, download the ‘Levels of obesity, inactivity and associated illnesses: Summary (England)’ dataset.DATA SOURCESThis dataset was produced using:Quality and Outcomes Framework data: Copyright © 2020, Health and Social Care Information Centre. The Health and Social Care Information Centre is a non-departmental body created by statute, also known as NHS Digital.GP Catchment Outlines. Copyright © 2020, Health and Social Care Information Centre. The Health and Social Care Information Centre is a non-departmental body created by statute, also known as NHS Digital. Data was cleaned by Ribble Rivers Trust before use.COPYRIGHT NOTICEThe reproduction of this data must be accompanied by the following statement:© Ribble Rivers Trust 2021. Analysis carried out using data that is: Copyright © 2020, Health and Social Care Information Centre. The Health and Social Care Information Centre is a non-departmental body created by statute, also known as NHS Digital.CaBA HEALTH & WELLBEING EVIDENCE BASEThis dataset forms part of the wider CaBA Health and Wellbeing Evidence Base.

BackgroundThe rise of depression, anxiety, and suicide rates has led to increased demand for telemedicine-based mental health screening and remote patient monitoring (RPM) solutions to alleviate the burden on, and enhance the efficiency of, mental health practitioners. Multimodal dialog systems (MDS) that conduct on-demand, structured interviews offer a scalable and cost-effective solution to address this need.ObjectiveThis study evaluates the feasibility of a cloud based MDS agent, Tina, for mental state characterization in participants with depression, anxiety, and suicide risk.MethodSixty-eight participants were recruited through an online health registry and completed 73 sessions, with 15 (20.6%), 21 (28.8%), and 26 (35.6%) sessions screening positive for depression, anxiety, and suicide risk, respectively using conventional screening instruments. Participants then interacted with Tina as they completed a structured interview designed to elicit calibrated, open-ended responses regarding the participants' feelings and emotional state. Simultaneously, the platform streamed their speech and video recordings in real-time to a HIPAA-compliant cloud server, to compute speech, language, and facial movement-based biomarkers. After their sessions, participants completed user experience surveys. Machine learning models were developed using extracted features and evaluated with the area under the receiver operating characteristic curve (AUC).ResultsFor both depression and suicide risk, affected individuals tended to have a higher percent pause time, while those positive for anxiety showed reduced lip movement relative to healthy controls. In terms of single-modality classification models, speech features performed best for depression (AUC = 0.64; 95% CI = 0.51–0.78), facial features for anxiety (AUC = 0.57; 95% CI = 0.43–0.71), and text features for suicide risk (AUC = 0.65; 95% CI = 0.52–0.78). Best overall performance was achieved by decision fusion of all models in identifying suicide risk (AUC = 0.76; 95% CI = 0.65–0.87). Participants reported the experience comfortable and shared their feelings.ConclusionMDS is a feasible, useful, effective, and interpretable solution for RPM in real-world clinical depression, anxiety, and suicidal populations. Facial information is more informative for anxiety classification, while speech and language are more discriminative of depression and suicidality markers. In general, combining speech, language, and facial information improved model performance on all classification tasks.

This dataset contains health outcome (depressive symptoms defined by CES-D 10), neighborhood greenery (percent tree cover within 500m and 2000m from residences), historical HOLC grades, and sociodemographic factors (age, race/ethnicity, marital status, education, employment status, income, use of depression medication) for 3555 Sister Study participants. This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: Please submit data request through https://sisterstudy.niehs.nih.gov/English/coll-data.htm. Format: The Sister Study data are released in SAS format. This dataset is associated with the following publication: Tsai, W., M. Nash, D. Rosenbaum, S. Prince, A. D'Aloisio, M. Mehaffey, D. Sandler, T. Buckley, and A. Neale. Association of Redlining and Natural Environment with Depressive Symptoms in Women in the Sister Study. ENVIRONMENTAL HEALTH PERSPECTIVES. National Institute of Environmental Health Sciences (NIEHS), Research Triangle Park, NC, USA, 131(10): 107009, (2022).

ABSTRACT OBJECTIVE Evaluating characteristics of unpaid domestic work and its association with mental disorders, exploring gender differences. METHODS We analyzed cross-sectional data from the second wave of an urban population cohort (n = 2,841) aged 15 and older from a medium-sized city in Bahia (BA). The representative population sample was randomly selected in subsequent multiple steps. We interviewed the survey participants at their homes. This study analyzed sociodemographic, occupational, unpaid domestic work and mental illness data, stratified by sex (gender). We investigated the association between the work-family-personal time conflict, the effort-reward imbalance in domestic and family work and the occurrence of common mental disorders, such as generalized anxiety disorder and depression. We estimated prevalence, prevalence ratios and their respective 95% confidence intervals. RESULTS Among the participants, the unpaid domestic activities were performed by 71.3% of men and 95.2% of women, who were responsible for the investigated activities, except for minor repairs. The percentages of paid work were higher among men (68.1% versus 47.2% among women). The distribution of stressors and conflict experiences showed an inverse situation between genders: men depicted the highest high percentage of low work-family-personal time conflict (39.0%), while among women, the highest percentage was of high conflict (40.0%); 45.8% of the men reported low effort-reward imbalance in domestic and family work, while only 28.8% of women reported low imbalance. The investigated mental disorders were more prevalent among women, who showed a significant association between work-family-personal time conflict and common mental disorders, as well as depression; among men, conflict was positively associated with common mental disorders. The effort-reward imbalance, in turn, was strongly related to CMD (Common Mental Disorders), generalized anxiety disorder and depression among women. Amid men, this discrepancy was only associated to depression. CONCLUSIONS Domestic work persists as a mostly feminine assigned activity. The stressful situations of unpaid domestic work and the work-family-personal time conflict were more strongly associated with adverse effects on the female mental health.

This dataset contains health outcome (depressive symptoms defined by CES-D 10), neighborhood greenery (percent tree cover within 500m and 2000m from residences), historical HOLC grades, and sociodemographic factors (age, race/ethnicity, marital status, education, employment status, income, use of depression medication) for 3555 Sister Study participants. This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: Please submit data request through https://res1sisterstudyd-o-tniehsd-o-tnihd-o-tgov.vcapture.xyz/English/coll-data.htm. Format: The Sister Study data are released in SAS format. This dataset is associated with the following publication: Tsai, W., M. Nash, D. Rosenbaum, S. Prince, A. D'Aloisio, M. Mehaffey, D. Sandler, T. Buckley, and A. Neale. Association of Redlining and Natural Environment with Depressive Symptoms in Women in the Sister Study. ENVIRONMENTAL HEALTH PERSPECTIVES. National Institute of Environmental Health Sciences (NIEHS), Research Triangle Park, NC, USA, 131(10): 107009, (2022).

Percentage of persons aged 15 years and over by perceived mental health, by gender, for Canada, regions and provinces.

Table 27. Had at Least One Major Depressive Episode in the Past Year among Persons Aged 18 or Older, by State and Substate Regions: Percentages, Annual Averages Based on 2010, 2011, and 2012 NSDUHs

The World Health Organization estimates that almost 300 million people suffer from depression worldwide. Depression is the most common mental health disorder and shows racial disparities in disease prevalence, age of onset, severity of symptoms, frequency of diagnosis, and treatment utilization across the United States. Since depression has both social and genetic risk factors, we propose a conceptual model wherein social stressors are primary risk factors for depression, but genetic variants increase or decrease individual susceptibility to the effects of the social stressors. Our research strategy incorporates both social and genetic data to investigate variation in symptoms of depression (CES-D scores). We collected data on financial strain (difficulty paying bills) and personal social networks (a model of an individual’s social environment), and we genotyped genetic variants in five genes involved in stress reactivity (HTR1a, BDNF, GNB3, SLC6A4, and FKBP5) in 135 African Americans residing in Tallahassee, Florida. We found that high financial strain and a high percentage of people in one’s social network who are a source of stress or worry were significantly associated with higher CES-D scores and explained more variation in CES-D scores than did genetic factors. Only one genetic variant (rs1360780 in FKBP5) was significantly associated with CES-D scores and only when the social stressors were included in the model. Interestingly, the effect of FKPB5 appeared to be strongest in individuals with high financial strain such that participants with a T allele at rs1360780 in FKBP5 and high financial strain had the highest mean CES-D scores in our study population. These results suggest that material disadvantage and a stressful social environment increases the risk of depression, but that individual-level genetic variation may increase susceptibility to the adverse health consequences of social stressors.

London Quality Outcomes Framework (QOF) Depression Scores - The percentage of patients with diabetes and/or heart disease for whom case finding for depression has been undertaken on one occasion during the previous 15 months - In those patients with a new diagnosis of depression, recorded between the preceding 1 April and 31 March, the percentage of patients who have had an assessment of severity at the outset of treatment using an assessment tool validated for use in primary care. - In those patients with a new diagnosis of depression and assessment of severity recorded between the preceding 1 April to 31 March, the percentage of patients who have had a further assessment of severity 5–12 weeks (inclusive) after the initial recording of the assessment of severity. Data source: NHS Information Centre This data is being published by NHS London alongside other datasets related to depression. The aim is to develop a resource to support the public’s knowledge of this common condition, to raise awareness of treatments and how health services are meeting needs, and to support health policy and commissioning.

This table presents the 2008 to 2010 National Survey on Drug Use and Health (NSDUH) estimates of past year major depressive episode among those aged 18 or older by State and substate regions.

SUMMARYThis analysis, designed and executed by Ribble Rivers Trust, identifies areas across England with the greatest levels of obesity, inactivity and inactivity/obesity-related illnesses. Please read the below information to gain a full understanding of what the data shows and how it should be interpreted.The analysis incorporates data relating to the following:Obesity/inactivity-related illnesses (asthma, cancer, chronic kidney disease, coronary heart disease, depression, diabetes mellitus, hypertension, stroke and transient ischaemic attack)Excess weight in children and obesity in adults (combined)Inactivity in children and adults (combined)The analysis was designed with the intention that this dataset could be used to identify locations where investment could encourage greater levels of activity. In particular, it is hoped the dataset will be used to identify locations where the creation or improvement of accessible green/blue spaces and public engagement programmes could encourage greater levels of outdoor activity within the target population, and reduce the health issues associated with obesity and inactivity.ANALYSIS METHODOLOGY1. Obesity/inactivity-related illnessesThe analysis was carried out using Quality and Outcomes Framework (QOF) data, derived from NHS Digital, relating to:- Asthma (in persons of all ages)- Cancer (in persons of all ages)- Chronic kidney disease (in adults aged 18+)- Coronary heart disease (in persons of all ages)- Depression (in adults aged 18+)- Diabetes mellitus (in persons aged 17+)- Hypertension (in persons of all ages)- Stroke and transient ischaemic attack (in persons of all ages)This information was recorded at the GP practice level. However, GP catchment areas are not mutually exclusive: they overlap, with some areas covered by 30+ GP practices. Therefore, to increase the clarity and usability of the data, the GP-level statistics were converted into statistics based on Middle Layer Super Output Area (MSOA) census boundaries.For each of the above illnesses, the percentage of each MSOA’s population with that illness was estimated. This was achieved by calculating a weighted average based on:The percentage of the MSOA area that was covered by each GP practice’s catchment areaOf the GPs that covered part of that MSOA: the percentage of patients registered with each GP that have that illness The estimated percentage of each MSOA’s population with each illness was then combined with Office for National Statistics Mid-Year Population Estimates (2019) data for MSOAs, to estimate the number of people in each MSOA with each illness, within the relevant age range.For each illness, each MSOA was assigned a relative score between 1 and 0 (1 = worst, 0 = best) based on:A) the PERCENTAGE of the population within that MSOA who are estimated to have that illnessB) the NUMBER of people within that MSOA who are estimated to have that illnessAn average of scores A & B was taken, and converted to a relative score between 1 and 0 (1= worst, 0 = best). The closer to 1 the score, the greater both the number and percentage of the population in the MSOA predicted to have that illness, compared to other MSOAs. In other words, those are areas where a large number of people are predicted to suffer from an illness, and where those people make up a large percentage of the population, indicating there is a real issue with that illness within the population and the investment of resources to address that issue could have the greatest benefits.The scores for each of the 8 illnesses were added together then converted to a relative score between 1 – 0 (1 = worst, 0 = best), to give an overall score for each MSOA: a score close to 1 would indicate that an area has high predicted levels of all obesity/inactivity-related illnesses, and these are areas where the local population could benefit the most from interventions to address those illnesses. A score close to 0 would indicate very low predicted levels of obesity/inactivity-related illnesses and therefore interventions might not be required.2. Excess weight in children and obesity in adults (combined)For each MSOA, the number and percentage of children in Reception and Year 6 with excess weight was combined with population data (up to age 17) to estimate the total number of children with excess weight.The first part of the analysis detailed in section 1 was used to estimate the number of adults with obesity in each MSOA, based on GP-level statistics.The percentage of each MSOA’s adult population (aged 18+) with obesity was estimated, using GP-level data (see section 1 above). This was achieved by calculating a weighted average based on:The percentage of the MSOA area that was covered by each GP practice’s catchment areaOf the GPs that covered part of that MSOA: the percentage of adult patients registered with each GP that are obeseThe estimated percentage of each MSOA’s adult population with obesity was then combined with Office for National Statistics Mid-Year Population Estimates (2019) data for MSOAs, to estimate the number of adults in each MSOA with obesity.The estimated number of children with excess weight and adults with obesity were combined with population data, to give the total number and percentage of the population with excess weight.Each MSOA was assigned a relative score between 1 and 0 (1 = worst, 0 = best) based on:A) the PERCENTAGE of the population within that MSOA who are estimated to have excess weight/obesityB) the NUMBER of people within that MSOA who are estimated to have excess weight/obesityAn average of scores A & B was taken, and converted to a relative score between 1 and 0 (1= worst, 0 = best). The closer to 1 the score, the greater both the number and percentage of the population in the MSOA predicted to have excess weight/obesity, compared to other MSOAs. In other words, those are areas where a large number of people are predicted to suffer from excess weight/obesity, and where those people make up a large percentage of the population, indicating there is a real issue with that excess weight/obesity within the population and the investment of resources to address that issue could have the greatest benefits.3. Inactivity in children and adultsFor each administrative district, the number of children and adults who are inactive was combined with population data to estimate the total number and percentage of the population that are inactive.Each district was assigned a relative score between 1 and 0 (1 = worst, 0 = best) based on:A) the PERCENTAGE of the population within that district who are estimated to be inactiveB) the NUMBER of people within that district who are estimated to be inactiveAn average of scores A & B was taken, and converted to a relative score between 1 and 0 (1= worst, 0 = best). The closer to 1 the score, the greater both the number and percentage of the population in the district predicted to be inactive, compared to other districts. In other words, those are areas where a large number of people are predicted to be inactive, and where those people make up a large percentage of the population, indicating there is a real issue with that inactivity within the population and the investment of resources to address that issue could have the greatest benefits.Summary datasetAn average of the scores calculated in sections 1-3 was taken, and converted to a relative score between 1 and 0 (1= worst, 0 = best). The closer the score to 1, the greater the number and percentage of people suffering from obesity, inactivity and associated illnesses. I.e. these are areas where there are a large number of people (both children and adults) who are obese, inactive and suffer from obesity/inactivity-related illnesses, and where those people make up a large percentage of the local population. These are the locations where interventions could have the greatest health and wellbeing benefits for the local population.LIMITATIONS1. For data recorded at the GP practice level, data for the financial year 1st April 2018 – 31st March 2019 was used in preference to data for the financial year 1st April 2019 – 31st March 2020, as the onset of the COVID19 pandemic during the latter year could have affected the reporting of medical statistics by GPs. However, for 53 GPs (out of 7670) that did not submit data in 2018/19, data from 2019/20 was used instead. Note also that some GPs (997 out of 7670) did not submit data in either year. This dataset should be viewed in conjunction with the ‘Levels of obesity, inactivity and associated illnesses: Summary (England). Areas with data missing’ dataset, to determine areas where data from 2019/20 was used, where one or more GPs did not submit data in either year, or where there were large discrepancies between the 2018/19 and 2019/20 data (differences in statistics that were > mean +/- 1 St.Dev.), which suggests erroneous data in one of those years (it was not feasible for this study to investigate this further), and thus where data should be interpreted with caution. Note also that there are some rural areas (with little or no population) that do not officially fall into any GP catchment area (although this will not affect the results of this analysis if there are no people living in those areas).2. Although all of the obesity/inactivity-related illnesses listed can be caused or exacerbated by inactivity and obesity, it was not possible to distinguish from the data the cause of the illnesses in patients: obesity and inactivity are highly unlikely to be the cause of all cases of each illness. By combining the data with data relating to levels of obesity and inactivity in adults and children, we can identify where obesity/inactivity could be a contributing factor, and where interventions to reduce obesity and increase activity could be most beneficial for the health of the local population.3. It was not feasible to incorporate ultra-fine-scale geographic distribution of

GLM (interaction) analysis of the association between sunlight measures and daytime physical activity.

Longitudinal data set of a nationally representative sample of the population aged 65 and over in Japan, comparable to that collected in the US and other countries. The first two waves of data are now available to the international research community. The sample is refreshed with younger members at each wave so it remains representative of the population at each wave. The study was designed primarily to investigate health status of the Japanese elderly and changes in health status over time. An additional aim is to investigate the impact of long-term care insurance system on the use of services by the Japanese elderly and to investigate the relationship between co-residence and the use of long term care. While the focus of the survey is health and health service utilization, other topics relevant to the aging experience are included such as intergenerational exchange, living arrangements, caregiving, and labor force participation. The initial questionnaire was designed to be comparable to the (US) Longitudinal Study of Aging II (LSOAII), and to the Asset and Health Dynamics Among the Oldest Old (AHEAD, a pre-1924 birth cohort) sample of the Health and Retirement Study (HRS), which has now been merged with the HRS. The sample was selected using a multistage stratified sampling method to generate 340 primary sampling units (PSUs). The sample of individuals was selected for the most part by using the National Residents Registry System, considered to be universal and accurate because it is a legal requirement to report any move to local authorities within two weeks. From each of the 340 PSUs, 6-11 persons aged 65-74 were selected and 8-12 persons aged 75+ were sampled. The population 75+ was oversampled by a factor of 2. Weights have been developed for respondents to the first wave of the survey to reflect sampling probabilities. Weights for the second wave are under development. With these weights, the sample should be representative of the 65+ Japanese population. In fall 1999, 4,997 respondents aged 65+ were interviewed, 74.6 percent of the initial target. Twelve percent of responses were provided by proxies, because of physical or mental health problems. The second wave of data was collected in November 2001. The third wave was collected in November 2003. Questionnaire topics include family structure, and living arrangements; subjects'''' parents/spouse''''s parents/children; socioeconomic status; intergenerational exchange; health behaviors, chronic conditions, physical functioning; activities of daily living and instrumental activities of daily living; functioning in the community; mental health depression measures; vision and hearing; dental health; health care and other service utilization. A CD is available which include the codebook and data files for the first and second waves of the national sample. The third wave of data will be released at a later date. * Dates of Study: 1999-2003 * Study Features: Longitudinal, International * Sample Size: ** 4,997 Nov/Dec 1999 Wave 1 ** 3,992 Nov 2001 Wave 2 ** Nov 2003 Wave 3 Link: * ICPSR: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/00156

A spreadsheet containing the output of GLM models.

The dataset for this project is characterised by photos of individual human emotion expression and these photos are taken with the help of both digital camera and a mobile phone camera from different angles, posture, background, light exposure, and distances. This task might look and sound very easy but there were some challenges encountered along the process which are reviewed below: 1) People constraint One of the major challenges faced during this project is getting people to participate in the image capturing process as school was on vacation, and other individuals gotten around the environment were not willing to let their images be captured for personal and security reasons even after explaining the notion behind the project which is mainly for academic research purposes. Due to this challenge, we resorted to capturing the images of the researcher and just a few other willing individuals. 2) Time constraint As with all deep learning projects, the more data available the more accuracy and less error the result will produce. At the initial stage of the project, it was agreed to have 10 emotional expression photos each of at least 50 persons and we can increase the number of photos for more accurate results but due to the constraint in time of this project an agreement was later made to just capture the researcher and a few other people that are willing and available. These photos were taken for just two types of human emotion expression that is, “happy” and “sad” faces due to time constraint too. To expand our work further on this project (as future works and recommendations), photos of other facial expression such as anger, contempt, disgust, fright, and surprise can be included if time permits. 3) The approved facial emotions capture. It was agreed to capture as many angles and posture of just two facial emotions for this project with at least 10 images emotional expression per individual, but due to time and people constraints few persons were captured with as many postures as possible for this project which is stated below: Ø Happy faces: 65 images Ø Sad faces: 62 images There are many other types of facial emotions and again to expand our project in the future, we can include all the other types of the facial emotions if time permits, and people are readily available. 4) Expand Further. This project can be improved furthermore with so many abilities, again due to the limitation of time given to this project, these improvements can be implemented later as future works. In simple words, this project is to detect/predict real-time human emotion which involves creating a model that can detect the percentage confidence of any happy or sad facial image. The higher the percentage confidence the more accurate the facial fed into the model. 5) Other Questions Can the model be reproducible? the supposed response to this question should be YES. If and only if the model will be fed with the proper data (images) such as images of other types of emotional expression.

Peripartum depression (PPD) is a prevalent and serious mental health disorder that is often underdiagnosed and undertreated due to limited effective and safe treatment options. Repetitive transcranial magnetic stimulation (rTMS) has emerged as a non-invasive treatment for PPD, yet awareness among patients is low. This study aims to identify barriers and facilitators to accessing mental health treatment, particularly rTMS, for PPD. We conducted 36 interviews with individuals who experienced depressive symptoms during the peripartum period and health providers, followed by a descriptive interpretive thematic analysis. Key risk factors identified include personal (i.e., age), clinical (i.e., traumatic birth), situational (i.e., COVID-19, homelessness), and social (i.e., discrimination, domestic abuse). Five themes emerged regarding barriers and facilitators: 1) the need for mom-centered care, 2) systemic challenges, 3) the importance of mental health education, 4) stigma and custody concerns, and 5) challenges in accessing care. Eighty-three percent of participants were unaware of rTMS, but following a brief description, 75% were willing to receive or refer to rTMS if it was available to them. Addressing systemic and access-related concerns is crucial to ensuring patients with PPD have access to safe, effective, and accessible treatments.

BackgroundPoststroke depression (PSD) is a highly prevalent and serious mental health condition affecting a significant proportion of stroke survivors worldwide. While its exact causes remain under investigation, managing PSD presents a significant challenge.AimThis study aimed to evaluate the prevalence and predictors of depression among Bangladeshi stroke victims.MethodsA cross-sectional study was carried out with 725 stroke victims who were receiving medical care at three designated tertiary care hospitals in Sylhet from January to December 2023. Depression and disability were measured using the Patient Health Questionnaire-9 and the Modified Rankin Scale. Logistic regression analysis was employed to examine the predictors linked to depression.ResultsAccording to the study, 80.8% of individuals had moderate to severe disability, and 58.1% of them experienced a moderate to severe level of depression. Individuals who had hemorrhagic stroke (AOR 1.31, 95% CI: 0.77–2.25), repeated episodes (AOR 3.41, 95% CI: 1.89–6.14), tobacco use (AOR 1.76, 95% CI: 1.16–2.67), or coexisting health conditions (AOR 1.68, 95% CI: 1.00–2.82) exhibited elevated levels of depression. Participants whose medical expenses covered by relatives or others were six times more likely to experience depressive symptoms (AOR 6.32, 95% CI: 1.61–24.76). Individuals who did not receive rehabilitation services had two times greater odds of being depressed (OR 1.85, 95% CI: 1.23–2.77, p = 0.003). Consequently, individuals with low functional status had eleven times greater levels of depression (AOR 11.03, 95% CI: 7.14–17.04).ConclusionMore than half of the participants in this present study reported moderate to extreme levels of depression which is a serious health issue among Bangladeshi stroke survivors. Understanding the predictors of depression linked to stroke could enhance the effectiveness of therapeutic interventions for this condition. In addition, multidisciplinary teams should work collaboratively to address this serious issue.

Background Premenstrual Syndrome (PMS) and Premenstrual Dysphoric Disorder (PMDD) have been identified as potential risk factors for various mental health issues, such as suicidal ideation and attempts. However, few studies have examined this association among Bangladeshi university students. This study aimed to examine the potential associations between PMDD and its co-existence with depression, anxiety, stress, and various suicidal behaviors, including suicidal ideation and attempts. Methods A cross-sectional survey involving 516 university students was conducted between September and October 2023. The survey was carried out in person and employed a structured questionnaire that comprised demographic information; the Depression, Anxiety, and Stress Scale (DASS-21); and the Premenstrual Symptoms Screening Tool (PSST), a 19-item screening tool for premenstrual symptoms. Multivariable logistic regression analyses were conducted to examine the relationships between variables. Results In the present study, participants with PMDD reported a prevalence of 38.8% for past-year suicidal ideation and 28.6% for suicide attempts. Through logistic regression analysis, we found a significant association between moderate/severe PMS and PMDD and a higher likelihoods of reporting suicidal ideation (AOR =  4.73; 95% CI 2.08–10.73 and AOR =  5.42; 95% CI 2.02–10.52) and suicide attempts (AOR =  3.77; 95% CI 1.36–10.50 and AOR =  4.07; 95% CI 1.22–15.56). The association between suicidal behaviors and PMS/PMDD was mediated by co-existing conditions such as depression, anxiety, and stress. Conclusions A notable proportion of individuals diagnosed with PMDD reported experiencing suicidal ideation or engaging in suicide attempts at some point in their lives. The findings of this research support the importance of conducting regular assessments of suicidal risk among women experiencing moderate to severe premenstrual disturbance. Furthermore, it is crucial to integrate mental health screenings and implement psychosocial interventions specifically designed for women diagnosed with PMS or PMDD and those with co-existing depression, anxiety, and stress alongside PMS/PMDD.

Backround characteristics.

Background: From the beginning of March 2020, lockdown regimens prevented patients with obesity from receiving bariatric surgery. Surgical emergencies and oncological procedures were the only operations allowed in public hospitals. Consequently, patients with morbid obesity were put in a standby situation. With the aim at exploring the viewpoint of our future bariatric surgery patients, we built a questionnaire concerning obesity and COVID-19.Method: A total of 116 bariatric surgery candidates were approached using a telephonic interview during the Italian lockdown.Results: Of the total sample, 73.8% were favorable to regular bariatric surgery execution. Forty percent were concerned about their own health status due to the COVID-19 emergency, and 61.1% were troubled by the temporary closure of the bariatric unit. The majority of the sample were eating more. Forty-five percent and the 27.5% of patients reported a worsening of the emotional state and physical health, respectively. Most of the patients (52.2%) considered themselves more vulnerable to COVID-19, especially individuals with class III obesity. Patients who reported an increased consumption of food were younger (43.44 ± 12.16 vs. 49.18 ± 12.66; F = 4.28, p = 0.042). No gender difference emerged.Conclusion: The lockdown had a negative result on Italian patients' psychological well-being and eating habits. The majority of patients would have proceeded with the surgery even during the COVID-19 emergency. Effective management and bariatric surgery should be restarted as soon as possible.