In the United Kingdom (UK), the share of young people who have had experiences of feeling down or depressed has in general increased from 2009 to 2021. In 2021, ** percent of the respondents reported feeling down or depressed, a significant increase from ** percent in 2010.

This report presents findings from the third (wave 3) in a series of follow up reports to the 2017 Mental Health of Children and Young People (MHCYP) survey, conducted in 2022. The sample includes 2,866 of the children and young people who took part in the MHCYP 2017 survey. The mental health of children and young people aged 7 to 24 years living in England in 2022 is examined, as well as their household circumstances, and their experiences of education, employment and services and of life in their families and communities. Comparisons are made with 2017, 2020 (wave 1) and 2021 (wave 2), where possible, to monitor changes over time.

This is the second (wave 2) in a series of follow up reports to the Mental Health and Young People Survey (MHCYP) 2017, exploring the mental health of children and young people in February/March 2021, during the Coronavirus (COVID-19) pandemic and changes since 2017. Experiences of family life, education, and services during the COVID-19 pandemic are also examined. The sample for the Mental Health Survey for Children and Young People, 2021 (MHCYP 2021), wave 2 follow up was based on 3,667 children and young people who took part in the MHCYP 2017 survey, with both surveys also drawing on information collected from parents. Cross-sectional analyses are presented, addressing three primary aims: Aim 1: Comparing mental health between 2017 and 2021 – the likelihood of a mental disorder has been assessed against completion of the Strengths and Difficulties Questionnaire (SDQ) in both years in Topic 1 by various demographics. Aim 2: Describing life during the COVID-19 pandemic - Topic 2 examines the circumstances and experiences of children and young people in February/March 2021 and the preceding months, covering: COVID-19 infection and symptoms. Feelings about social media use. Family connectedness. Family functioning. Education, including missed days of schooling, access to resources, and support for those with Special Educational Needs and Disabilities (SEND). Changes in circumstances. How lockdown and restrictions have affected children and young people’s lives. Seeking help for mental health concerns. Aim 3: Present more detailed data on the mental health, circumstances and experiences of children and young people by ethnic group during the coronavirus pandemic (where sample sizes allow). The data is broken down by gender and age bands of 6 to 10 year olds and 11 to 16 year olds for all categories, and 17 to 22 years old for certain categories where a time series is available, as well as by whether a child is unlikely to have a mental health disorder, possibly has a mental health disorder and probably has a mental health disorder. This study was funded by the Department of Health and Social Care, commissioned by NHS Digital, and carried out by the Office for National Statistics, the National Centre for Social Research, University of Cambridge and University of Exeter.

This data is from the The Early Prediction of Adolescent Depression study. This is a longitudinal prospective cohort study that began in 2007 of recurrently depressed parents and their offspring. The dataset shows the fourth wave of data collection for both parent and young person, collected from 2018 to 2020. The first three waves cannot be included due to ethical restrictions. The dataset includes symptom scores and clinical diagnoses for the young person (either self-reported, parent-reported or combined) measured using a semi-structured interview and questionnaire measures.Continuous variables are: parent and self-reported Mood and Feelings Questionnaire (MFQ) scores and self-reported Strengths and Difficulties Questionnaire (SDQ) Impact score.Binary variables: self-reported Alcohol Use Disorders Identification Test (AUDIT) score above harmful drinking threshold, combined report of anxiety disorder, combined report of broadly defined depressive disorder, combined report of mood or anxiety disorder, self-reported NEET status (not in education, employment or training), self-reported completed a university degree, self-reported low social support (only has one person or no one to rely on), self-reported recent self-harm or suicide attempt and the combined report of the SDQ impact score is abnormal or borderline.

In this project, we aimed to increase what is known about the negative effects of maternal depression and anxiety disorders (MDAD) on the mental health outcomes of children. Mental health is a topical area of research that is receiving increasing attention in the media and is one of five ESRC strategic priorities for investment. The main aim of the project was to help develop an understanding of how mental depression and anxiety disorders are transmitted from one generation to the next and ultimately help to design interventions better able to reduce the consequences of maternal mental health for children. We have used data from QResearch, a large consolidated database derived from anonymized health records from general practices in England matched with hospital administrative data, the Hospital Episode Statistics (HES). Further information is available under Related Resources.

Problems relating to Maternal Depression and Anxiety Disorders (MDAD) are common and are known to affect child health and development. In the UK, the cost of perinatal mental health problems has been estimated at £8.1 billion for each birth cohort of children, and 72 percent of this cost is related to the direct impact on the children.

The overarching aim of our proposed research is to examine the effect of MDAD on child health outcomes, with a special focus on the role that MDAD plays in the development of child depression and anxiety disorders (CDAD) in adolescence. In particular, this research will provide robust empirical evidence to understand how depression and anxiety disorders are transmitted from one generation to the next and to help design interventions aimed at reducing the negative consequences of poor maternal mental health for children.

To achieve this aim, we will address the following research questions:

1) Are the negative effects of MDAD on children exclusively explained by genetic transmission and family background characteristics? Or are these negative effects also explained by changes in the child's home environment? If the transmission of mental and anxiety disorders is explained exclusively by genetic traits and family background characteristics, then interventions targeted at reducing the negative effect of MDAD on maternal behaviour, e.g. through cognitive behavioural therapy, would be ineffective. On the contrary, evidence on significant effects of MDAD after controlling for genetic and family background characteristics would suggest that MDAD can lead to changes in the child home environment, e.g. changes in maternal behaviour, harsher parenting style and lower time investments in the child, with negative consequences on children.

2) Do school policies and health practices have a role in attenuating the negative effect of maternal depression on children? We will answer this research question by focusing on whether starting school earlier harms or protects children who are exposed to MDAD, and on whether an early diagnosis of maternal depression can attenuate the negative effects suffered by children.

We will develop and use state-of-the-art estimation methods in combination with a novel administrative dataset covering general practices and hospitals created by merging two population-based health databases from England - namely QResearch and Hospital Episode Statistics. Using this merged database, we will create a longitudinal household dataset that will allow us to study the mental health of mothers and their children at different stages of the children's lives up to adolescence.

We are a multi-disciplinary team from the Universities of Oxford and York, consisting of experts in applied econometric methods, child and maternal mental health, psychology, general practice, and on the data that we plan to utilise.

We will translate our research findings into advice for policy-makers to help them design new interventions aimed at achieving better outcomes for patients suffering from maternal mental health issues and their children. Our research will also have an impact on health practitioners, psychologists, academics and charities working with mothers and children. We will produce papers aimed at academics as well as non-technical outputs to engage with policy-makers and a non-academic audience. Furthermore, by sharing and explaining our data and estimation methods to academics, we will build capacity for further research based on large health datasets.

The final central element of the project will be to build the capacity of early career researchers to undertake and lead large interdisciplinary projects.

BackgroundEmerging research indicates that depression among parents of children/adolescents with type 1 diabetes mellitus (T1DM) has increased significantly. However, the prevalence rates reported by different studies vary substantially.MethodsSeven databases were systematically searched (Pubmed, Embase, MEDLINE, Scopus, Web of Science, Cochrane Library, PsycInfo) from the inception to 15th October 2022. We pooled prevalence rates from each study with a random-effect model. We conducted a stratified meta-analysis to identify the potential sources of heterogeneity among studies. The GRADE (Grading of Recommendations, Assessment, Development and Evaluations) approach was utilized to evaluate the quality of evidence.ResultsTwenty-two studies were included, with a total of 4639 parents living with type 1 diabetic children. Overall, the pooled prevalence rate of depression or depressive symptoms was 22.4% (95%CI 17.2% to 28.7%; I2 = 96.8%). The prevalence was higher among mothers (31.5%) than fathers (16.3%) as well as parents of children (aged < 12 years) with T1DM (32.3%) than those with adolescents (aged ≥ 12 years) (16.0%).ConclusionOur research suggests that more than 1 in 5 parents of type 1 diabetic children/adolescents worldwide suffer from depression or depressive symptom. Depression screening and interventions are required for parents of children with T1DM.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/, identifier (CRD42022368702).

According to a survey conducted in England in 2021, ** percent of young people aged between 17 and 23 years sought support or advice from friends or family for a mental health concern. While, ** percent of young people aged between 11 and 16 years used an education source for support with a mental health concern.

In England and Wales, the definition of suicide is a death with an underlying cause of intentional self-harm or an injury or poisoning with undetermined intent. In 2023, the age group with the highest rate of suicide was for those aged 50 to 54 years at 16 deaths per 100,000. The age groups 45 to 49 years with 15.9 deaths per 100,000 population had the second highest highest rate of suicides in the UK. Gender difference in suicides The suicide rate among men in England and Wales in 2023 was around three times higher than for women, the figures being 17.4 per 100,000 population for men compared to 5.7 for women. Although among both genders, the suicide rate increased in 2023 compared to 2022. Mental health in the UK Over 53 thousand people in England were detained under the Mental Health Act in the period 2020/21. Alongside this, there has also been an increase in the number of workers in Great Britain suffering from stress, depression or anxiety. In 2022/23, around 875 thousand workers reported to be suffering from these work-related issues.

BackgroundEmerging research indicates that depression among parents of children/adolescents with type 1 diabetes mellitus (T1DM) has increased significantly. However, the prevalence rates reported by different studies vary substantially.MethodsSeven databases were systematically searched (Pubmed, Embase, MEDLINE, Scopus, Web of Science, Cochrane Library, PsycInfo) from the inception to 15th October 2022. We pooled prevalence rates from each study with a random-effect model. We conducted a stratified meta-analysis to identify the potential sources of heterogeneity among studies. The GRADE (Grading of Recommendations, Assessment, Development and Evaluations) approach was utilized to evaluate the quality of evidence.ResultsTwenty-two studies were included, with a total of 4639 parents living with type 1 diabetic children. Overall, the pooled prevalence rate of depression or depressive symptoms was 22.4% (95%CI 17.2% to 28.7%; I2 = 96.8%). The prevalence was higher among mothers (31.5%) than fathers (16.3%) as well as parents of children (aged < 12 years) with T1DM (32.3%) than those with adolescents (aged ≥ 12 years) (16.0%).ConclusionOur research suggests that more than 1 in 5 parents of type 1 diabetic children/adolescents worldwide suffer from depression or depressive symptom. Depression screening and interventions are required for parents of children with T1DM.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/, identifier (CRD42022368702).

Risk of depression in child if mother had depression, adjusting for deprivation (clustering on mother ID).

BackgroundExperiencing multiple adverse childhood experiences (ACEs) is a risk factor for many adverse outcomes. We explore associations of ACEs with educational attainment and adolescent health and the role of family and socioeconomic factors in these associations.Methods and findingsUsing data from the Avon Longitudinal Study of Parents and Children (ALSPAC), a prospective cohort of children born in southwest England in 1991–1992, we assess associations of ACEs between birth and 16 years (sexual, physical, or emotional abuse; emotional neglect; parental substance abuse; parental mental illness or suicide attempt; violence between parents; parental separation; bullying; and parental criminal conviction, with data collected on multiple occasions between birth and age 16) with educational attainment at 16 years (n = 9,959) and health at age 17 years (depression, obesity, harmful alcohol use, smoking, and illicit drug use; n = 4,917). We explore the extent to which associations are robust to adjustment for family and socioeconomic factors (home ownership, mother and partner’s highest educational qualification, household social class, parity, child’s ethnicity, mother’s age, mother’s marital status, mother’s depression score at 18 and 32 weeks gestation, and mother’s partner’s depression score at 18 weeks gestation) and whether associations differ according to socioeconomic factors, and we estimate the proportion of adverse educational and health outcomes attributable to ACEs or family or socioeconomic measures. Among the 9,959 participants (49.5% female) included in analysis of educational outcomes, 84% reported at least one ACE, 24% reported 4 or more ACEs, and 54.5% received 5 or more General Certificates of Secondary Education (GCSEs) at grade C or above, including English and Maths. Among the 4,917 participants (50.1% female) included in analysis of health outcomes, 7.3% were obese, 8.7% had depression, 19.5% reported smoking, 16.1% reported drug use, and 10.9% reported harmful alcohol use. There were associations of ACEs with lower educational attainment and higher risk of depression, drug use, and smoking. For example, odds ratios (ORs) for 4+ ACEs compared with no ACEs after adjustment for confounders were depression, 2.4 (1.6–3.8, p < 0.001); drug use, 3.1 (2.1–4.4, p < 0.001); and smoking, 2.3 (1.7–3.1, p < 0.001). Associations with educational attainment attenuated after adjustment but remained strong; for example, the OR after adjustment for confounders for low educational attainment comparing 4+ ACEs with no ACEs was 2.0 (1.7–2.4, p < 0.001). Associations with depression, drug use, and smoking were not altered by adjustment. Associations of ACEs with harmful alcohol use and obesity were weak. For example, ORs for 4+ ACEs compared with no ACEs after adjustment for confounders were harmful alcohol use, 1.4 (0.9–2.0, p = 0.10) and obesity, 1.4 (0.9–2.2, p = 0.13) We found no evidence that socioeconomic factors modified the associations of ACEs with educational or health outcomes. Population attributable fractions (PAFs) for the adverse educational and health outcomes range from 5%–15% for 4+ ACEs and 1%–19% for low maternal education. Using data from multiple questionnaires across a long period of time enabled us to capture a detailed picture of the cohort members’ experience of ACEs; however, a limitation of our study is that this resulted in a high proportion of missing data, and our analyses assume data are missing at random.ConclusionsThis study demonstrates associations between ACEs and lower educational attainment and higher risks of depression, drug use, and smoking that remain after adjustment for family and socioeconomic factors. The low PAFs for both ACEs and socioeconomic factors imply that interventions that focus solely on ACEs or solely on socioeconomic deprivation, whilst beneficial, would miss most cases of adverse educational and health outcomes. This interpretation suggests that intervention strategies should target a wide range of relevant factors, including ACEs, socioeconomic deprivation, parental substance use, and mental health.

ObjectiveThe increasing occurrence of depression in children and teenagers has garnered significant social attention. Although many studies have explored the effect of exercise on alleviating depressive symptoms, substantial evidence regarding its efficacy in children and adolescents with clinical diagnoses of depression remains insufficient. This research performs a systematic review and meta-analysis aimed at assessing the true effectiveness of exercise interventions for this demographic.MethodsWe searched five databases (PubMed, Embase, Cochrane, EBSCO, and Web of Science) for studies available up to August 18, 2025. All data analyses were conducted using Review Manager software. Four subgroup analyses were carried out according to exercise frequency, session length, duration of the intervention, and control group type in order to identify the sources of variability.ResultsFollowing a thorough review of the 2,475 articles that were initially identified, a total of 15 studies were finally incorporated into the meta-analysis. These studies included 428 participants in the groups receiving exercise interventions and 403 participants in the control groups. The findings indicated that exercise produced a notable beneficial impact on children and adolescents suffering from depression (SMD = -1.14, 95% CI: -1.57 to -0.72, p < 0.001). Subgroup analyses further revealed that different weekly exercise frequencies, session durations, and intervention periods all demonstrated statistically significant beneficial effects on depressive symptoms. Moreover, exercise interventions achieved therapeutic effects comparable to those of conventional treatments.ConclusionExercise interventions were found to offer substantial therapeutic benefits for depressed children and adolescents in this study. Interventions performed more than three times per week, lasting less than 60 minutes per session, and sustained over eight weeks were found to be the most effective. Compared with traditional treatment approaches, exercise interventions achieved similarly positive outcomes. These findings provide strong evidence for optimizing exercise prescriptions and health management strategies for adolescent mental health. Educators, parents, and school administrators should incorporate age-appropriate physical activities into daily life and design exercise programs with suitable frequency, duration, and intensity.Systematic Review Registrationhttps://www.crd.york.ac.uk/PROSPERO/, identifier CRD420251121698.

Descriptive characteristics of stable male depression, offspring depression and offspring educational attainment.

Descriptive characteristics of maternal depression, offspring depression and offspring educational attainment.

BackgroundYouth adversity (e.g., abuse and bullying victimisation) is robust risk factor for later mental health problems (e.g., depression and anxiety). Research shows the prevalence of youth adversity and rates of mental health problems vary by individual characteristics, identity or social groups (e.g., gender and ethnicity). However, little is known about whether the impact of youth adversity on mental health problems differ across the intersections of these characteristics (e.g., white females). This paper reports on a component of the ATTUNE research programme (work package 2) which aims to investigate the impact and mechanisms of youth adversity on depressive and anxiety symptoms in young people by intersectionality profiles.MethodsThe data are from 4 UK adolescent cohorts: HeadStart Cornwall, Oxwell, REACH, and DASH. These cohorts were assembled for adolescents living in distinct geographical locations representing coastal, suburban and urban places in the UK. Youth adversity was assessed using a series of self-report questionnaires and official records. Validated self-report instruments measured depressive and anxiety symptoms. A range of different variables were classified as possible social and cognitive mechanisms.Results and analysisStructural equation modelling (e.g., multiple group models, latent growth models) and multilevel modelling will be used, with adaptation of methods to suit the specific available data, in accord with statistical and epidemiological conventions.DiscussionThe results from this research programme will broaden our understanding of the association between youth adversity and mental health, including new information about intersectionality and related mechanisms in young people in the UK. The findings will inform future research, clinical guidance, and policy to protect and promote the mental health of those most vulnerable to the negative consequences of youth adversity.

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

BackgroundProblematic Internet Use (PIU) has been increasingly linked to suicidal behavior among adolescents, raising significant public health concerns, particularly in Chinese youth. This study aimed to systematically review and meta-analyze the correlation between PIU and suicidal behavior in Chinese adolescents to provide a clearer understanding of this association.MethodsA comprehensive search was conducted across seven databases up to July 1, 2024. Studies investigating the relationship between PIU and suicidal behavior among Chinese adolescents were included. A random-effects meta-analysis was employed to assess pooled effect sizes, with subgroup analyses conducted to explore potential moderators, such as geographic region, age, gender, assessment tools for PIU and suicidal ideation, and the presence of depression. Data analysis was performed using STATA software (version 16).ResultsThis meta-analysis, comprising 23 studies with 353,904 participants, identified significant associations between PIU and suicidal behavior among Chinese adolescents. PIU was associated with increased risks of suicidal ideation (OR = 1.72, 95% CI: 1.45, 2.03), suicidal plans (OR = 1.50, 95% CI: 1.02, 2.20), and suicidal attempts (OR = 1.48, 95% CI: 1.16, 1.89). Subgroup analyses indicated higher risks in specific groups: adolescents from Central China (OR = 1.84, 95% CI: 1.46, 2.32), college students (OR = 2.09, 95% CI: 1.66, 2.62). The risk of suicidal ideation was particularly elevated when depression was not controlled (OR = 1.86, 95% CI: 1.53, 2.25). These findings underscore the need for targeted interventions in vulnerable populations.ConclusionThis meta-analysis demonstrated significant associations between PIU and suicidal behaviors among Chinese adolescents. The findings emphasize the need for targeted interventions, particularly for adolescents from Central and Western China, college students, and those with untreated depression. Focused strategies are required to mitigate the risks associated with PIU and to effectively address suicidal behaviors in these high-risk populations.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42024577593, identifier CRD42024577593.

The fertility rate of a country is the average number of children that women from that country would have throughout their reproductive years. In the United Kingdom in 1800, the average woman of childbearing age would have five children over the course of their lifetime. Over the next 35 years the fertility rate was quite sporadic, rising to over 5.5 in the 1810s and 1820s, then dropping to 4.9 by 1835. This was during and after the Napoleonic Wars and the War of 1812 with the US, which was a time of increased industrialization, economic depression and high unemployment after the war. As things became more stable, and the 'Pax Britannica' (a period of relative, international peace and economic prosperity for the British Empire) came into full effect, the fertility rate plateaued until 1880, before dropping gradually until the First World War. The fertility rate then jumped from 2.6 to 3.1 children per woman between 1915 and 1920, as many men returned from the war. It then resumed it's previous trajectory in the interwar years, before increasing yet again after the war (albeit, for a much longer time than after WWI), in what is known as the 'Baby Boom'. Like the US, the Baby Boom lasted until around 1980, where it then fell to 1.7 children per woman, and it has remained around this number (between 1.66 and 1.87) since then.

Characteristics of the participants included in analyses using data from multivariate multiple imputation. Because the data are from multiple imputation models, numerators and denominators are estimated by averaging across imputed data sets. All prevalences reported here relate to the data sets used for analysis of educational attainment, N = 9,959, apart from health outcomes, where N = 4,917.

BackgroundAdolescence is also accompanied by ongoing mood changes (relative to childhood and adulthood), which can trigger more extreme negative emotional responses. Physical exercise alleviates negative emotions and reduces the risk of mental illness. However, the effect of physical exercise on negative emotions in adolescents is unclear, so it is valuable to synthesize previous studies with meta-analysis.ObjectiveTo examine the influence of physical exercise (PE) intervention on negative emotions in adolescents aged 10 to 19 years.MethodsWe retrieved the articles from PubMed, Web of Science, EBSCO, Cochrane, and Embase up to April 11, 2024. The main search terms were physical exercise, negative emotions, adolescents, randomized controlled trials. The meta-analysis was conducted using Review Manager 5.3. A random-effects model was employed to calculate the standardized mean difference (SMD) and 95% confidence interval (CI). Subgroups were analysed as the type of negative emotions, type of control group, intervention type, duration, time, frequency.ResultsThe PE intervention group exhibited a significantly superior improvement in alleviating negative emotions compared to the control group (SMD = -0.59, 95% CI: -0.92 to -0.26, p < 0.01, Z = 3.50, I² = 95%). PE was particularly effective in mitigating adolescent depression (SMD = -0.67, 95% CI = -1.07 to -0.28, p < 0.01, I² = 96%) but did not yield significant results in reducing adolescent anxiety (SMD = -0.29, 95% CI = -0.63 to 0.05, p = 0.10, I² = 95%).ConclusionPE intervention can ameliorate negative emotions in adolescents.Systematic Review Registrationhttps://www.crd.york.ac.uk/prospero/, identifier CRD42024534375.