In the United States, the prevalence of mental illness in the past year is more common among females than males and more common among the young than the old. As of 2024, some 26.7 percent of females reported some type of mental illness in the past year, compared to 20 percent of males. Common forms of mental illness include depression, anxiety disorders, and mood disorders. Depression Depression is one of the most common mental illnesses in the United States. Depression is defined by prolonged feelings of sadness, hopelessness, and despair leading to a loss of interest in activities once enjoyed, a loss of energy, trouble sleeping, and thoughts of death or suicide. It is estimated that around five percent of the U.S. population suffers from depression. Depression is more common among women with around six percent of women suffering from depression compared to four percent of men. Mental illness and substance abuse Data has shown that those who suffer from mental illness are more likely to suffer from substance abuse than those without mental illness. Those with mental illness are more likely to use illicit drugs such as heroin and cocaine, and to abuse prescription drugs than those without mental illness. As of 2023, around 7.9 percent of adults in the United States suffered from co-occuring mental illness and substance use disorder.

This publication contains the official statistics about uses of the Mental Health Act(1) ('the Act') in England during 2020-21. Under the Act, people with a mental disorder may be formally detained in hospital (or 'sectioned') in the interests of their own health or safety, or for the protection of other people. They can also be treated in the community but subject to recall to hospital for assessment and/or treatment under a Community Treatment Order (CTO). In 2016-17, the way we source and produce these statistics changed. Previously these statistics were produced from the KP90 aggregate data collection. They are now primarily produced from the Mental Health Services Data Set (MHSDS). The MHSDS provides a much richer data source for these statistics, allowing for new insights into uses of the Act. However, some providers that make use of the Act are not yet submitting data to the MHSDS, or submitting incomplete data. Improvements in data quality have been made over the past year. NHS Digital is working with partners to ensure that all providers are submitting complete data and this publication includes guidance on interpreting these statistics. Please note: This publication covers the 2020-21 reporting year and, as such, it is likely the impact of COVID-19 may be evident as the national lockdown began on 23 March 2020. The time series data for people subject to detention does show a decrease in people subject to detention in March 2021 so the context of COVID-19 should be kept in mind when using and interpreting these statistics. Footnotes (1) The Mental Health Act 1983 as amended by the Mental Health Act 2007 and other legislation.

Data is based on a phone survey of 1,545 rural Indian households collected in August 2020 in 20 districts across 6 states (Rajasthan, Uttar Pradesh, Bihar, Jharkhand, Madhya Pradesh, and Maharashtra) in Northern India in August 2020. Households participated in a 20–30 min survey with two parts, a household head module and a female respondent module. In the household head module the household head surveyed about the household’s socioeconomic conditions, household head’s income, the male and female heads’ nutrition, and the number of days the respondent wished for more food for themselves or their children. The nutrition questions were taken from the National Family and Health Survey (NFHS) 2015–16, allowing to use the pre-pandemic responses to the survey from the same district to benchmark nutritional outcomes. After the head module, if the head was male, the head was asked to pass the phone to a female household member (typically the female household head). The female responded to an additional survey asking about her mental health and status within the household, as well as if this had changed since the pandemic. In cases where the respondent to the head module was female, the same respondent answered the female survey. Altogether, this allowed the female module to be conducted with 573 women. To ascertain information on women’s mental health, a selection of questions from the PHQ9 depression diagnostic scale and the GAD7 anxiety scale was asked. For a subset of questions, respondents’ were asked if outcomes have changed due to the pandemic. For example, for each of the mental health questions above (as well as the safety question), respondents were asked a follow-up question about whether their experiences have improved, worsened, or stayed the same since the pandemic. Measuring changes in these outcomes, enables to both assess the aggregate effects of the pandemic and measure the relationship between lockdowns and outcome variables, accounting for pre-pandemic differences across individuals. Additional data on case rates/deaths The phone survey data were supplemented with additional district level data on COVID-19 cases and deaths between the start of the pandemic and the time of the survey. Also hospitalization data from HMIS were used.

This study performed a repeated cross-sectional analysis to explore possible trends in mental health problems among Chinese adolescents over the years of 2016–2020. A total of 2,837 different seventh-grade students were surveyed in three waves from a junior high school in Changsha city, Hunan province in China (978 in 2016, 949 in 2019, and 910 in 2020) using the Mental Health Inventory of Middle School Students (MMHI-60). The results showed that obsessive-compulsive tendencies, interpersonal sensitivity, depression, anxiety, academic stress, and emotional disturbance problems were significantly increased in surveyed adolescents from 2016 to 2020. Moreover, positive rates of most of these problems were significantly higher in females than males, and were significantly increased in only females. These results highlight the importance of focusing on mental health problems among urban Chinese adolescents, especially among girls.

Ten to Men: The Australian Longitudinal Study on Male Health was commissioned by the Department of Health and Aged Care following the 2010 National Male Health Policy, and currently serves the National Men’s Health Strategy 2020-2030. This is Australia’s first national longitudinal study that focuses exclusively on male health and wellbeing. The cohort was recruited using a stratified, multi-stage & cluster sampling design to select males aged 10–55 years. Recruitment of eligible participants and Wave 1 of the data collection occurred between October 2013 and July 2014, resulting in a reconciled sample size of 16,021. The survey content was structured around six key research domains relevant to male health: wellbeing and mental health, use of health services, health-related behaviours, health status, health knowledge and social determinants. Wave 2 of the data collection occurred between November 2015 and May 2016. The sample size for Wave 2 was 11,936. The Wave 2 questionnaires largely retained Wave 1 items to obtain repeat longitudinal measures. New items added included additional questions on relationships, mental health, health literacy, help-seeking and resilience. Release 2.1 comprised of updated Wave 1 and Wave 2 datasets. These datasets have undergone changes to previous releases, including the renaming of variables, confidentialisation and other modifications. Release 2.1 offers General Release and Restricted Release. Wave 3 of the data collection occurred between July 2020 and February 2021. The sample size for Wave 3 was 7,919. The Wave 3 questionnaires largely retained items from previous waves to obtain repeat longitudinal measures. New items added included new questions on gambling, use of e-cigarettes, illicit drug use, gender identity, generalised anxiety, relationship quality, individual income, COVID-19 impact and natural disaster impact. Release 3.0 offers General Release and Restricted Release and linked MBS and PBS datasets. Wave 4 of the data collection occurred between August 2022 and December 2022. The sample size for Wave 4 was 7,050. The Wave 4 questionnaires largely retained items from previous waves to obtain repeat longitudinal measures. New items added included new questions on health conditions, masculinity, fathering ethnicity, gender & sexuality, intimidate partner violence, and injuries. Release 4.0 offers General Release and Restricted Release and linked MBS and PBS datasets. Release 4.0.1 is the most recent data release and offers updates to all waves of the General Release and Restricted Release datasets as explained in Change Log Registry.

Originally, the dataset come from the CDC and is a major part of the Behavioral Risk Factor Surveillance System (BRFSS), which conducts annual telephone surveys to gather data on the health status of U.S. residents. As the CDC describes: "Established in 1984 with 15 states, BRFSS now collects data in all 50 states as well as the District of Columbia and three U.S. territories. BRFSS completes more than 400,000 adult interviews each year, making it the largest continuously conducted health survey system in the world.". The most recent dataset (as of February 15, 2022) includes data from 2020. It consists of 401,958 rows and 279 columns. The vast majority of columns are questions asked to respondents about their health status, such as "Do you have serious difficulty walking or climbing stairs?" or "Have you smoked at least 100 cigarettes in your entire life? [Note: 5 packs = 100 cigarettes]".

To improve the efficiency and relevance of our analysis, we removed certain attributes from the original BRFSS dataset. Many of the 279 original attributes included administrative codes, metadata, or survey-specific variables that do not contribute meaningfully to heart disease prediction—such as respondent IDs, timestamps, state-level identifiers, and detailed lifestyle questions unrelated to cardiovascular health. By focusing on a carefully selected subset of 18 attributes directly linked to medical, behavioral, and demographic factors known to influence heart health, we streamlined the dataset. This not only reduced computational complexity but also improved model interpretability and performance by eliminating noise and irrelevant information. All predicting variables could be divided into 4 broad categories:

1. Demographic factors: sex, age category (14 levels), race, BMI (Body Mass Index)

2. Diseases: weather respondent ever had such diseases as asthma, skin cancer, diabetes, stroke or kidney disease (not including kidney stones, bladder infection or incontinence)

3. Unhealthy habits:

   • Smoking - respondents that smoked at least 100 cigarettes in their entire life (5 packs = 100 cigarettes)
   • Alcohol Drinking - heavy drinkers (adult men having more than 14 drinks per week and adult women having more than 7 drinks per week

4. General Health:

   • Difficulty Walking - weather respondent have serious difficulty walking or climbing stairs
   • Physical Activity - adults who reported doing physical activity or exercise during the past 30 days other than their regular job
   • Sleep Time - respondent’s reported average hours of sleep in a 24-hour period
   • Physical Health - number of days being physically ill or injured (0-30 days)
   • Mental Health - number of days having bad mental health (0-30 days)
   • General Health - respondents declared their health as ’Excellent’, ’Very good’, ’Good’ ,’Fair’ or ’Poor’

Below is a description of the features collected for each patient:

According to a survey conducted in Australia, around **** women and around **** men experienced a mental health issue such as stress or anxiety while working from home due to the COVID pandemic in 2020. The second leading cause of mental health issues by working from home was working in isolation.

AimsTo explore the prevalence of a mental health gender gap within a young adult sample during the COVID-19 pandemic, and to identify the impact of loneliness and domestic time use on young people’s, and particularly young women’s mental health.MethodUsing data from the UK Longitudinal Household Survey (UKHLS), this research examines mental health prior to the pandemic (2019) and during the pandemic (April 2020 until September 2021). A random-effects regression analysis was conducted to examine the effects of loneliness, and domestic factors across age and gender to ascertain their contribution to the mental health gender gap in a young adult population.ResultsAverage mental health decline was consistently higher for women compared to men, and young people (ages 16–24) saw a reduction in mental health twice as much as those in the oldest age category (over 65). Loneliness accounted for a share of the mental health gender gap, and a more decrease in mental health was recorded for young women experiencing loneliness, compared to older age groups. Domestic and familial factors did not have a significant impact on young people’s mental health.ConclusionsAlthough across all ages and genders, mental health had returned to near pre-pandemic levels by September 2021, young people and especially women continue to have worse mental health compared to other age groups, which is consistent with pre-COVID age and gender inequalities. Loneliness is a key driver in gendered mental health inequalities during the pandemic in a young adult population.

Background: Gay, bisexual, and other men who have sex with men (GBM) experience systemic marginalization and many barriers to healthcare, leading to significant healthcare disparities. It was unknown if they were more vulnerable to COVID-19, and if a failure to respond to their unique physical and mental health needs would exacerbate existing health disparities. The Engage-COVID-19 study leveraged the larger Engage Cohort Study conducted by researchers studying HIV and sexual health among GBM based at Canadian universities, public health, and community organizations. Aims of the CITF co-funded study: The study aimed to identify biomedical, behavioural, and psychosocial risk factors for contracting COVID-19, document SARS-CoV-2 immunity in HIV positive and negative participants, and characterize the clinical syndrome and severity of those with immunity. It also aimed to understand the application and understanding of COVID-19 mitigation strategies in GBM and investigate the impact of the pandemic on mental health, loneliness, sexual behaviours, substance use patterns, and access to essential healthcare. [1] Methods: This cohort study recruited individuals across Vancouver, British Columbia, Toronto, Ontario, and Montreal, Quebec who self-identified as a gay or bisexual man, including transgender GBM, and who reported having sex with another man in the past 6 months. Participants in the COVID-19 sub-study provided blood samples and responded to questionnaires in two waves of data collection, over the span of approximately 21 months. Contributed dataset contents: The datasets include 2,518 participants who completed baseline questionnaires since Feb 2017. A total of 1,564 participants had study visits during the Engage COVID-19 data collection period (09/2020–06/2022), and gave one or more blood samples in the same timeframe. A total of 2,719 serology samples were collected. Variables include data in the following areas of information: demographics (age, gender, ethnicity and indigeneity), general health (smoking; chronic disease diagnoses; flu vaccine), SARS-CoV-2 vaccination, adherence to COVID-19 prevention public health guidelines (physical distancing, remote working, wearing a mask), and serology. [1]: Please contact original study team for these mental health and behaviour data (daniel.grace@utoronto.ca).

This dataset accompanies the paper "Functional MRI connectivity accurately distinguishes cases with psychotic disorders from healthy controls, based on cortical features associated with brain network development", Morgan* and Young* et al, 2020.Pre-processed data:Regional CT, MD and FA values are provided here, as well as DTI networks. CT is given for the Maastricht, Dublin and Cobre datasets, whilst MD, FA and DTI are given for the Maastricht and Dublin datasets. Age, sex and group information is also provided (1=male, 2=female; 1=control, 2=case). Please note that fMRI data and the code to perform machine learning analyses is available on GitHub at: https://github.com/jmyoung36/fMRI_connectivity_accurately_distinguishes_cases.ML outputs:Predicted probabilities and regional ML feature weights are provided (see the paper for details).Notes:All data was parcellated according to an atlas with 308 regions, created by Dr Rafael Romero-Garcia- see Romero-Garcia et al, NeuroImage 2012 (https://doi.org/10.1016/j.neuroimage.2011.10.086). Please cite that paper, as well as Morgan* and Young* et al 2020, if you use this data for your own research.

The study was supported by grants from the European Commission (PSYSCAN - Translating neuroimaging findings from research into clinical practice; ID: 603196) and the NIHR Cambridge Biomedical Research Centre (Mental Health). The Cobre data was downloaded from the COllaborative Informatics and Neuroimaging Suite Data Exchange tool (COINS; http://coins.mrn.org/dx) and data collection was performed at the Mind Research Network, and funded by a Center of Biomedical Research Excellence (COBRE) grant 5P20RR021938/P20GM103472 from the NIH to Dr. Vince Calhoun. SEM was supported by a Henslow Fellowship at Lucy Cavendish College, University of Cambridge, funded by the Cambridge Philosophical Society. KJW was funded by an Alan Turing Institute Research Fellowship under EPSRC Research grant TU/A/000017. MPvdH was supported by a NWO VIDI and ALW open grant and a MQ fellowship. GD was supported by grants from the ERC (grant 677467) and SFI (12/IP/1359). ETB was supported by a NIHR Senior Investigator Award.

Abstract copyright UK Data Service and data collection copyright owner.Gender and Adolescence: Global Evidence (GAGE) is a ten-year (2015-2025) research programme, funded by UK Aid from the UK Foreign, Commonwealth and Development Office (FCDO), that seeks to combine longitudinal data collection and a mixed-methods approach to understand the lives of adolescents in particularly marginalized regions of the Global South, and to uncover 'what works' to support the development of their capabilities over the course of the second decade of life, when many of these individuals will go through key transitions such as finishing their education, starting to work, getting married and starting to have children.GAGE undertakes longitudinal research in seven countries in Africa (Ethiopia, Rwanda), Asia (Bangladesh, Nepal) and the Middle East (Jordan, Lebanon, Palestine). Sampling adolescent girls and boys aged between 10‐19‐year olds, the quantitative survey follows a global total of 18,000 adolescent girls and boys, and their caregivers and explores the effects that programme have on their lives. This is substantiated by in‐depth qualitative and participatory research with adolescents and their peers. Its policy and legal analysis work stream studies the processes of policy change that influence the investment in and effectiveness of adolescent programming.Further information, including publications, can be found on the Overseas Development Institute GAGE website. Gender and Adolescence: Global Evidence: Ethiopia Round 2, 2019-2020 extends the GAGE quantitative research in Ethiopia for a second round. A sample of nearly 8,600 adolescent boys and girls was sought, including nearly 7,000 adolescents surveyed in an earlier Baseline round (available from the UK Data Archive under SN 8597), as well as approximately 1,600 new adolescents. The main purpose of this survey was to gather information on the lives of Ethiopian adolescents living in urban and rural locations in the Amhara, Oromiya, and Afar regions, and to understand their changing lives and challenges. At the time of data collection, adolescents were primarily aged 12-14 and 17-19. The sample includes both randomly and purposefully sampled adolescents, and their female caregivers were also surveyed where possible. The current data release includes information for the subset of individuals who are not part of an ongoing randomized evaluation of adolescent-centric programming. A total of nearly 5,000 adolescents and their caregivers are included in the current release. Main Topics: The Core Respondent (CR) dataset contains data from the survey administered to the CR and covers education, time allocation, paid work, health and nutrition, psychosocial and mental health, mobility and voice, social inclusion, marriage and relationships, financial inclusion and economic empowerment, and information and communication technologies. The Adult Female (AF) dataset contains information on the household, including the household roster, family background, durable goods, dwelling characteristics, access to productive capital, recent positive and negative shocks, and household access to programs and support. In addition, the AF survey contains detailed information about the AF herself, such as parenting, health and nutrition, attitudes to gender equality, marriage, fertility and social norms. Purposive selection/case studies Multi-stage stratified random sample Face-to-face interview: Computer-assisted (CAPI/CAMI) 2019 2020 ACCESS TO EDUCATION ACCESS TO HEALTH SE... ACCESS TO INFORMATI... ACTIVITIES OF DAILY... ADOLESCENCE ADOLESCENTS AGE ALCOHOL USE ANIMAL HUSBANDRY ANXIETY ARRANGED MARRIAGES ATTITUDES BANK ACCOUNTS BIRTH CONTROL CHILDREN CREDIT DEVELOPING COUNTRIES DEVELOPMENT PROGRAMMES DISABILITIES EDUCATIONAL BACKGROUND EDUCATIONAL CHOICE EDUCATIONAL FACILITIES EDUCATIONAL STATUS EMOTIONAL STATES ENERGY CONSUMPTION Education Ethiopia FAMILY INFLUENCE FAMILY PLANNING FATHER S EDUCATIONA... FATHERS FINANCIAL DIFFICULTIES FOOD FOOD AND NUTRITION GENDER EQUALITY GENDER ROLE Gender and gender r... HEADS OF HOUSEHOLD HEALTH STATUS HEARING IMPAIRMENTS HOUSEHOLD BUDGETS HOUSEHOLDERS HOUSEHOLDS HOUSING CONDITIONS ILL HEALTH INFORMAL CARE INFORMATION SOURCES INTERNAL MIGRATION INTERNET ACCESS INTERNET USE LAND OWNERSHIP LAVATORIES LEISURE TIME ACTIVI... LIFE SATISFACTION LITERACY LIVESTOCK LOANS MARITAL HISTORY MARITAL STATUS MENSTRUATION MOBILE PHONES MORAL VALUES MOTHERS PARENTAL ENCOURAGEMENT PARENTAL ROLE PERSONAL FINANCE MA... PERSONAL SAFETY PHYSICAL MOBILITY PLACE OF BIRTH PREGNANCY QUALITY OF LIFE RELIGIOUS AFFILIATION RELIGIOUS BEHAVIOUR RESIDENTIAL MOBILITY ROOMS SAVINGS SCHOOL PUNISHMENTS SCHOOLS SEX SEX DISCRIMINATION SOCIAL ATTITUDES SOCIAL INEQUALITY SOCIAL VALUES STRUCTURAL ELEMENTS... STUDENT EMPLOYMENT STUDENT TRANSPORTATION Society and culture TELEVISION VIEWING TIME BUDGETS TRUANCY UNEARNED INCOME VISION IMPAIRMENTS WATER RESOURCES Youth

Objective: The COVID-19 pandemic and associated restrictions are associated with adverse psychological impacts but an assessment of positive wellbeing is required to understand the overall impacts of the pandemic. Methods: The NZ Lockdown Psychological Distress Survey measured excellent wellbeing categorised by a WHO-Five Well-being Index (WHO-5) score ≥22. The survey also contained demographic and pre-lockdown questions, subjective and objective lockdown experiences, and questions on alcohol use. The proportion of participants with excellent wellbeing is reported with multivariate analysis examining the relative importance of individual factors associated with excellent wellbeing. Results: Approximately 9% of the overall sample reported excellent wellbeing during the New Zealand lockdown. Excellent wellbeing status was associated with older age, male gender, Māori and Asian ethnicity, and lower levels of education. Excellent wellbeing was negatively associated with smoking, poor physical and mental health, and previous trauma. Conclusion: A substantial minority of New Zealanders reported excellent wellbeing during severe COVID-19 pandemic restrictions. Demographic and broader health factors predicted excellent wellbeing status. An understanding of these factors may help to enhance wellbeing during any future lockdowns.

Depression is a psychological state of mind that often influences a person in an unfavorable manner. While it can occur in people of all ages, students are especially vulnerable to it throughout their academic careers. Beginning in 2020, the COVID-19 epidemic caused major problems in people’s lives by driving them into quarantine and forcing them to be connected continually with mobile devices, such that mobile connectivity became the new norm during the pandemic and beyond. This situation is further accelerated for students as universities move towards a blended learning mode. In these circumstances, monitoring student mental health in terms of mobile and Internet connectivity is crucial for their wellbeing. This study focuses on students attending an International University of Bangladesh to investigate their mental health due to their continual use of mobile devices (e.g., smartphones, tablets, laptops etc.). A cross-sectional survey method was employed to collect data from 444 participants. Following the exploratory data analysis, eight machine learning (ML) algorithms were used to develop an automated normal-to-extreme severe depression identification and classification system. When the automated detection was incorporated with feature selection such as Chi-square test and Recursive Feature Elimination (RFE), about 3 to 5% increase in accuracy was observed by the method. Similarly, a 5 to 15% increase in accuracy has been observed when a feature extraction method such as Principal Component Analysis (PCA) was performed. Also, the SparsePCA feature extraction technique in combination with the CatBoost classifier showed the best results in terms of accuracy, F1-score, and ROC-AUC. The data analysis revealed no sign of depression in about 44% of the total participants. About 25% of students showed mild-to-moderate and 31% of students showed severe-to-extreme signs of depression. The results suggest that ML models, incorporating a proper feature engineering method can serve adequately in multi-stage depression detection among the students. This model might be utilized in other disciplines for detecting early signs of depression among people.

The Generations study is a five-year study designed to examine health and well-being across three generations of lesbians, gay men, and bisexuals (LGB). The study explored identity, stress, health outcomes, and health care and services utilization among LGBs in three generations of adults who came of age during different historical contexts. This collection includes baseline, wave 1, and wave 2 data collected as part of the Generations study. The study aimed to assess whether younger cohorts of LGBs differed from older cohorts in how they viewed their LGB identity and experienced stress related to prejudice and everyday forms of discrimination, as well as whether patterns of resilience differed between different LGB cohorts. Additionally, the study sought to examine how differences in stress experience affected mental health and well-being, including depressive and anxiety symptoms, substance and alcohol use, suicide ideation and behavior, and how younger LGBs utilized LGB-oriented social and health services, relative to older cohorts. In wave 2, respondents were re-interviewed approximately one year after completion of the baseline (wave 1) survey. Only respondents who participated in the original sample of participants were surveyed at wave 2 (i.e., the enhancement oversample was not included in the longitudinal design of this study). In wave 3, respondents were re-interviewed approximately one year after the completion of the wave 2 survey. Demographic variables collected as part of this study include questions related to age, education, race, ethnicity, sexual identity, gender identity, income, employment, and religiosity.

Abstract copyright UK Data Service and data collection copyright owner. From the early 1980s the proportion of male births in China has risen sharply with an average of 120 male births for every 100 female. These unprecedented sex ratio imbalances are now affecting the reproductive age groups, with 20 million excess men of reproductive age by 2020. Yet almost no empirical studies exist which explore this phenomenon, so the consequences of this huge surplus of excess men remains unknown. The overall objective of the study was to explore, through comparisons of urban and rural settings in three provinces, the demographic, social and psychological consequences of high sex ratios on (a) young men, (b) young women and (c) society more generally. The specific objectives were:to compare key socio-demographic indicators for areas with differing sex ratios; to explore and understand the experiences of young men and women living in environments with different sex ratios and their perceptions, if any, of the impact of excess males on society;to explore the psychological and social impact of the sex ratio for partnered and unpartnered men and women;to test hypotheses derived from the literature, including that in high sex ratio areas men are more vulnerable to depression and aggression, women have better mental health with less depression and anxiety, and violent crime and prostitution are more common.Further information may be found on the ESRC The impact of high sex ratios in urban and rural China project award webpage. Main Topics: The data cover sociodemographic, lifestyle, attitudes and mental health information, including: sociodemographic details; marriage: children; women's status in society; attitudes to sexual behaviour; unmarried older people; problems of excess men; depression; aggression; and sociosexuality. Measurement scales used: Chinese versions of: 1) The Beck Depression Inventory 2) Rosenberg self-esteem scale 3) Bryant and Smith's adaptation of the Buss/Perry aggression scale 4) Sociosexual Inventory 5) Adaptation of Spence, Helmrich and Stapp's Attitudes Towards Women scale. One-stage cluster sample Self-completion

This dataset, released January 2020, contains for the time period of 2017-2018 the Estimated population, aged 18 years and over with diabetes mellitus; Estimated male population with mental and behavioural problems; Estimated female population with mental and behavioural problems; Estimated population with mental and behavioural problems; Estimated population with heart, stroke and vascular disease; Estimated population with asthma; Estimated population with chronic obstructive pulmonary disease; Estimated population with arthritis; Estimated population with osteoporosis; The data is by Primary Health Network (PHN) 2017 geographic boundaries based on the 2016 Australian Statistical Geography Standard (ASGS). There are 31 PHNs set up by the Australian Government. Each network is controlled by a board of medical professionals and advised by a clinical council and community advisory committee. The boundaries of the PHNs closely align with the Local Hospital Networks where possible. For more information please see the data source notes on the data. Source: Estimates for Population Health Areas (PHAs) are modelled estimates and were produced by the ABS; estimates at the LGA and PHN level were derived from the PHA estimates. AURIN has spatially enabled the original data. Data that was not shown/not applicable/not published/not available for the specific area ('#', '..', '^', 'np, 'n.a.', 'n.y.a.' in original PHIDU data) was removed.It has been replaced by by Blank cells. For other keys and abbreviations refer to PHIDU Keys.

This study examines whether the experience of being bullied at school has a long-term impact on three health outcomes in adulthood in Japan: subjective health, mental health, and activity restriction due to health conditions. We employed a random effects model and the Karlson-Holm-Breen method to decompose the total effect of being bullied at school on health inequality into a direct effect and an indirect effect working through intervening factors including education, marriage, economic well-being, and social networks. We used the Japanese Life Course Panel Surveys 2007–2020 (waves 1–14), a nationally representative panel data set that includes 2,260 male and 2,608 female respondents. The results demonstrate that for both men and women, the direct effect of being bullied at school was strong and significant. Bullying experiences in childhood had a long-term impact on health outcomes in adulthood, regardless of social background and mediating factors of education, marriage, economic well-being, and social networks. Bullying victimization increased the risk of poor subjective health, low mental health scores, and activity restriction due to health conditions. Intervening factors (especially economic well-being and friendship) mediated the association between bullying experiences and all health outcomes, but their contributions were modest. Policy measures not only to prevent bullying during childhood but also to alleviate its negative consequences in adulthood should be considered to help people who have encountered adverse childhood experiences.

This table represents five variants of health expectancies: life expectancy in perceived good health. life expectancy without physical limitations. life expectancy without chronic morbidity. —without psychological complaints —life expectancy without GALI-limitations In addition, figures of ‘normal’ life expectancy are included, so the figures of health expectation can be related to them. In the table, the data on health expectancy can be split into the following characteristics: —sex (starting from the data of 2018, the category ‘total, men + women’ is added). —Age.

Using this table one can see the developments about time of health expectancies. For example it can be seen that morbidity free life expectancy of women shortened during the eighties and nineties. In the same period, the life expectancy free of moderate and severe limitations of men increased.

Data available from: 1981

Status of the figures: The figures in this table are definitive.

Changes as of 19 January 2024: In the calculation of life expectancy without psychological complaints, for the years 2020, 2021 and 2022, incorrect prevalences of ill health were used for children up to 12 years of age. That has now been adjusted. Correction leads, for children, to lower estimates of life expectancy without psychological complaints. In addition, standard errors have been added for life expectancy without psychological complaints for the ages 0, 1 and 5 years.

When will new figures be published? Third quarter of 2024.

Abstract Objectives This study aims to assess the impact of social isolation, due to the Covid-19 pandemic, on mental health, Temporomandibular Disorder (TMD) and orofacial pain in men and women. Methodology Individuals living in Brazil answered an online questionnaire on their sociodemographic and behavioral aspects, emotional scale (DASS-21), Impact of Event Scale, and Pain Screener in Temporomandibular Disorders (TMD-Pain Screener) during June 2020. Descriptive statistical analyses and logistic and linear regressions were applied (5% significance). Results Overall, 2301 individuals were included, 89.1% practiced social isolation, 72.6% were employed/studying, at least 15% presented severe or extremely severe levels of emotional distress and presence of powerful (34.1%) and severe impact event (15%). During the outbreak, 53.2% perceived feeling worse and 31.8% reported that orofacial pain started or worsened after the pandemic outbreak. Gender was associated with “social class” (P=0.036), “pain/stiffness in the jaw on awakening” (P=0.037), “change of pain during jaw habits” (P=0.034) and “perception of change in the situations mentioned in the TMD-Pain Screener” (P=0.020), “depression” (P=0.012), “anxiety” (P=0.006) and “impact of the event” (P=8.3E-11). Social isolation had a lesser chance to change the routine, to be practiced by the unemployed/not studying, and to be practiced by men (all with P

Latent and probably life-long infection with Toxoplasma, which affects about one-third of the human population, has many specific effects on the personality and behavior of infected humans. The stress-coping hypothesis was suggested to explain why many of the toxoplasmosis-associated behavioral changes go in opposite directions in men and women. This hypothesis suggests that toxoplasmosis impairs the health of humans, which results in mild chronic stress. It is known that men and women cope with stress in opposite ways. The first presumption of the hypothesis, impaired health, has been confirmed in many studies over the past 15 years. The second, higher level of stress in infected subjects, has been tested only rarely. Here we compared levels of stress, measured with the Perceived Stress Scale, and anxiety, measured with the State-Trait Anxiety Inventory X-2, in a nonclinical population of 614 Toxoplasma-free and 162 Toxoplasma-infected subjects. We found a higher level of stress in men, but not in women. However, we also found that physical health had a positive rather than negative effect on stress when mental health is controlled, which seems to contradict the prediction of the original stress-coping hypothesis. No differences were found in the anxiety of infected and noninfected men or women. In agreement with previously published data, both Toxoplasma-infected Rh-negative men and women had worse mental health than corresponding Toxoplasma-free controls.Between 28. 9. 2020 and 13. 4. 2021, the questionnaire was completed or partially completed by 6000 subjects, 5200 of them during October and November 2020. After filtering out all subjects who were younger than 18, did not provide information about toxoplasmosis or Rh phenotype, skipped more than four questions in STAI or more than two questions in PSS, or answered nearly all questions in STAI or PSS with the same code, we calculated average anxiety and stress as the arithmetic means of answers of STAI and PSS questionnaire (after the inversion of scales of questions 1, 6, 7, 10, 13, 16, 19 for STAI and 4, 5, 7, 8 for PSS). These means were multiplied by the number of questions in a particular questionnaire (20 in the case of STAI and 10 in the case of PSS) to allow comparison to published data. The final data set contained data from 776 subjects who mostly completed both questionnaires.