This table contains 94080 series, with data for years 2003 - 2003 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (70 items: Newfoundland and Labrador; Health and Community Services Eastern Region; Newfoundland and Labrador; Health and Community Services St. John's Region; Newfoundland and Labrador ...) Age group (14 items: Total; 12 years and over; 12 to 14 years; 12 to 19 years; 15 to 19 years ...) Sex (3 items: Both sexes; Females; Males ...) Probability of depression (4 items: Total population for the variable probability of depression; Probability of depression; 0.9 or greater; Probability of depression; less than 0.9 ...) Characteristics (8 items: Number of persons; High 95% confidence interval; number of persons; Coefficient of variation for number of persons; Low 95% confidence interval; number of persons ...).

This table contains 8400 series, with data for years 1994 - 1996 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (not all combinations are available): Geography (5 items: Territories; Northwest Territories; Yukon; Northwest Territories including Nunavut ...), Age group (14 items: Total; 12 years and over; 12-19 years; 12-14 years; 15-19 years ...), Sex (3 items: Both sexes; Females; Males ...), Risk level of depression (5 items: Total population for the variable risk of depression; No risk of depression; Probable risk of depression; Possible risk of depression ...), Characteristics (8 items: Number of persons; Low 95% confidence interval - number of persons; High 95% confidence interval - number of persons; Coefficient of variation for number of persons ...).

This table contains 93984 series, with data for years 2002 - 2002 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (11 items: Canada; Prince Edward Island; Newfoundland and Labrador; Nova Scotia ...), Age group (4 items: 65 years and over;25 to 64 years;15 to 24 years; Total; 15 years and over ...), Sex (3 items: Both sexes; Females; Males ...), Mental health and well-being profile (89 items: Total population for the variable major depressive episode; Major depressive episode; all measured criteria are met; Major depressive episode; measured criteria not met; Major depressive episode; not stated ...), Characteristics (8 items: Number of persons; Coefficient of variation for number of persons; Low 95% confidence interval; number of persons; High 95% confidence interval; number of persons ...).

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.

PHQ-9 Depression Assessment

14-Days of Ambulatory Mood Dynamics in a General Population

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About this dataset

This dataset contains 14 days of ambulatory assessment (AA) data related to depression symptoms and mood ratings, as well as findings from a retrospective Patient Health Questionnaire (PHQ-9) designed for depression screening purposes. Furthermore, it contains demographic information about the participants such as their age and gender.

This dataset is composed of various fields including: phq1, phq2, phq3, phq4, phq5, phq6, phq7,ph q8 ,ph q9 ,age ,sex ,q10 ,e11 ,12 w13 w14 e16 e46 e47 happiness.score time period name start time Ph Q day The data gathered through this survey allows us to gain insight into the daily fluctuations in self-reported symptoms experienced by these individuals at different stages of their lives. In addition to providing important clues about possible causes or triggers associated with depressive episodes, this type of survey can also help identify interventions that may prove successful in reducing symptom severity and frequency. Our hope is that we can use this extensive collection of data to inform treatment decisions and ultimately improve outcomes for those affected by depression

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This dataset contains information about the Patient Health Questionnaire (PHQ-9) depression screening assessment, which is used to assess the severity of depressive symptoms over the past two weeks. This dataset can be used to gain insights into depression in a general population sample.

The data is broken down into several categories: PHQ Score (1-9), Age and Gender of participant, Questions 10-47 (Numeric Scores), Happiness score, Time/Period Name/Start Time, and PHQ Day.

In order to use this dataset effectively and accurately analyze your results it is important to understand how each column impacts your results. The PHQ Score column contains information on the severity of depressive symptoms in a scale from 1-9. The Age and Gender columns contain demographic information related to participants while Questions 10-47 represent a range of mental health subject including anhedonia, fatigue, sleep disturbance and changes in appetite or weight that are rated on a numeric scale from 0-4. The Happiness score reflects individual’s subjective ratings at time of assessment with higher scores reflecting greater positivity toward life as reported by participant during study period. Finally the Time/Period Name/Start Time columns provide date and time information related to study period while the PHQ Day represents total number of days elapsed since onset of clinical trial at beginning of assessment period.

By understanding how each category contributes as well as any relationships that may exist between variables researchers can use this data set effectively when analyzing their results for more detailed insights into depression in general population samples across different lengths of time or months scoring methodologies employed reflected by total PHQ scores attained over course on particular month interval included within scope defined for particular study group being considered for analysis by researcher during evaluation protocol being employed developed data research development team assigned project develop analysis offers potential obtainable from working current model designed herein designed incorporated iteration included questionnaires offer basis obtainable utilizing utilized platform outlined herethrough model presented currently established outcome metrics thereby providing tool required necessary review evaluate found current project implementation structure framework wherein needed result may provided evaluated research rationale procedures ultimately yielding findings potentially productive goals desired analytical outcomes original objective initial efforts made implement intended protocol design methodological measures prescribed evaluator's evaluation criteria reported therewith provide result assist uncovering needed research answers discoverable platform established herein presented purpose obviate further attempts previously reviewed limitations encountered earlier trials thus executing member's logbook objectives upgraded format allow corporate setting without interruption driven process overhaul project initiation iterative systemic component procedure triage session estimation techniques management applicable foundational principles

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• Developing an AI-driven screening tool that can rapidly identify and monitor symptom...

This table contains 18480 series, with data for years 1994 - 1998 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (not all combinations are available): Geography (11 items: Canada; Newfoundland and Labrador; Prince Edward Island; Nova Scotia ...), Age group (14 items: Total; 12 years and over; 15-19 years; 12-19 years; 12-14 years ...), Sex (3 items: Both sexes; Females; Males ...), Risk level of depression (5 items: Total population for the variable risk of depression; Probable risk of depression; No risk of depression; Possible risk of depression ...), Characteristics (8 items: Number of persons; High 95% confidence interval - number of persons; Coefficient of variation for number of persons; Low 95% confidence interval - number of persons ...).

This table contains 334320 series, with data for years 2000 - 2000 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (not all combinations are available): Geography (199 items: Canada; Health and Community Services Eastern Region; Newfoundland and Labrador (Peer group D); Newfoundland and Labrador; Health and Community Services St. John's Region; Newfoundland and Labrador (Peer group H) ...), Age group (14 items: Total; 12 years and over; 12-14 years; 15-19 years; 12-19 years ...), Sex (3 items: Both sexes; Females; Males ...), Risk level of depression (5 items: Total population for the variable risk of depression; Possible risk of depression; No risk of depression; Probable risk of depression ...), Characteristics (8 items: Number of persons; Low 95% confidence interval - number of persons; Coefficient of variation for number of persons; High 95% confidence interval - number of persons ...).

This dataset (n=800) explores risk factors for postpartum depression (PPD) in Bangladesh. Data was collected from postpartum women (birth within 24 months) across various locations (health complexes, hospitals, clinics, and rural areas and residential areas in major cities). Participation was voluntary and required informed consent.

The dataset includes sociodemographic (age, residence, education, marital status, occupation, income), familial (husband's education/income, family type, household members, relationship with family), personal health (addiction, children, pregnancy history, abuse, depression history, chronic diseases), and neonatal health-related variables (pregnancy details, birth details, newborn health, postpartum feelings) variables. For depression screening, PHQ-2, EPDS, and PHQ-9 scores are included.

The data was digitized after collection. This dataset can be used to investigate the prevalence of PPD, identify risk factors, and develop predictive models. Limitations include self-reported data, screening tools vs. diagnosis, and potential sampling bias. The dataset has shown, about 44% of the participants are in the risk of high level of postpartum depression, based on the EPDS scoring and 28% of the participants are in the risk of sever and moderately severe postpartum depression, based on the PHQ-9 scoring.

These data represent the predicted (modeled) prevalence of Depression among adults (Age 18+) for each census tract in Colorado. Depression is defined as ever being told by a doctor, nurse, or other health professional that you have a depressive disorder.The estimate for each census tract represents an average that was derived from multiple years of Colorado Behavioral Risk Factor Surveillance System data (2014-2017).CDPHE used a model-based approach to measure the relationship between age, race, gender, poverty, education, location and health conditions or risk behavior indicators and applied this relationship to predict the number of persons' who have the health conditions or risk behavior for each census tract in Colorado. We then applied these probabilities, based on demographic stratification, to the 2013-2017 American Community Survey population estimates and determined the percentage of adults with the health conditions or risk behavior for each census tract in Colorado.The estimates are based on statistical models and are not direct survey estimates. Using the best available data, CDPHE was able to model census tract estimates based on demographic data and background knowledge about the distribution of specific health conditions and risk behaviors.The estimates are displayed in both the map and data table using point estimate values for each census tract and displayed using a Quintile range. The high and low value for each color on the map is calculated based on dividing the total number of census tracts in Colorado (1249) into five groups based on the total range of estimates for all Colorado census tracts. Each Quintile range represents roughly 20% of the census tracts in Colorado. No estimates are provided for census tracts with a known population of less than 50. These census tracts are displayed in the map as "No Est, Pop < 50."No estimates are provided for 7 census tracts with a known population of less than 50 or for the 2 census tracts that exclusively contain a federal correctional institution as 100% of their population. These 9 census tracts are displayed in the map as "No Estimate."

Objective: To understand the prevalence of depression in diabetes population, explore the relationship between diabetes and depression, and the impact of comprehensive psychological and behavioral intervention on depression related to diabetes and glucose metabolism.Methods: 71 middle-aged and elderly patients with type 2 diabetes were investigated and evaluated with Self Rating Depression Scale (SDS), Medical Coping Scale (MCWQ) and Social Support Scale (PSSS). Patients who met the research criteria were randomly divided into an experimental group and a control group. The number of effective cases in the two groups was 36 and 35 respectively. In addition to conventional diabetes drug treatment, the experimental group was supplemented with comprehensive psychological and behavioral intervention, while the control group was only given conventional treatment. The fasting blood glucose, 2-h postprandial blood glucose, body weight and depression index were measured before and after treatment in the two groups.Results: The prevalence of depression in patients with diabetes was as high as 60%, and that in the elderly control group was 5%; In type 2 diabetes population, depression is negatively related to the total score of social support and medical coping surface, and positively related to avoidance, blood sugar, women, course of disease, education level below junior high school, body mass index, and number of complications in medical coping; The fasting blood glucose, 2-h postprandial blood glucose, body mass index, and depression index of the two groups decreased, and the range and speed of decline in the experimental group were higher than those in the control group; There were significant differences between the two groups in fasting blood glucose, 2-h postprandial blood glucose and depression index; During the follow-up period, the blood glucose and depression index of the experimental group increased.Conclusion: Depression has a high prevalence rate in middle-aged and elderly people with type 2 diabetes, and has a negative impact on blood sugar control in diabetes patients; Psychological and behavioral comprehensive intervention can improve the glucose metabolism and depressive symptoms of middle-aged and elderly patients with type 2 diabetes.

BackgroundDepression is one of the greatest public health problems worldwide. The potential benefit of social participation (SP) on mental health has been widely acknowledged. Nevertheless, a few studies have used propensity score matching (PSM) to reduce the influence of data bias and confounding variables. This study explored the effect of social participation on depression among middle-aged and older Chinese persons through a PSM method, considering the frequency, type, and quantity of SP. Effects were compared among different age groups, genders, and places of residence.MethodsThe datasets were obtained from the 2018 wave of the China Health and Retirement Longitudinal Study. A total of 9,404 respondents aged 45 and above were included in the study. PSM and ordinary least squares methods were used to estimate the effect of social participation on depression.ResultsPSM estimation results showed that SP had a significantly positive effect on decreasing depression scores (p < 0.001) by 0.875–0.898 compared with persons without SP. All types of SP had a significantly positive effect (p < 0.001), and participating in community activities had the largest effect (β = −1.549 to −1.788, p < 0.001). Higher frequency of participation and more types of SP promoted lower depression scores; subgroup analyses revealed that the promotion effect was significantly greater among women, those aged ≥75 years, and those living in urban areas.ConclusionPSM indicated that SP could alleviate the depression of middle-aged and older Chinese persons. Targeted measures should be adopted to promote SP and thereby improve mental health and promote healthy and active aging.

(Note: Part of the content of this post was adapted from the original DIRECT Psychoradiology paper (https://academic.oup.com/psyrad/article/2/1/32/6604754) and REST-meta-MDD PNAS paper (http://www.pnas.org/cgi/doi/10.1073/pnas.1900390116) under CC BY-NC-ND license.)Major Depressive Disorder (MDD) is the second leading cause of health burden worldwide (1). Unfortunately, objective biomarkers to assist in diagnosis are still lacking, and current first-line treatments are only modestly effective (2, 3), reflecting our incomplete understanding of the pathophysiology of MDD. Characterizing the neurobiological basis of MDD promises to support developing more effective diagnostic approaches and treatments.An increasingly used approach to reveal neurobiological substrates of clinical conditions is termed resting-state functional magnetic resonance imaging (R-fMRI) (4). Despite intensive efforts to characterize the pathophysiology of MDD with R-fMRI, clinical imaging markers of diagnosis and predictors of treatment outcomes have yet to be identified. Previous reports have been inconsistent, sometimes contradictory, impeding the endeavor to translate them into clinical practice (5). One reason for inconsistent results is low statistical power from small sample size studies (6). Low-powered studies are more prone to produce false positive results, reducing the reproducibility of findings in a given field (7, 8). Of note, one recent study demonstrate that sample size of thousands of subjects may be needed to identify reproducible brain-wide association findings (9), calling for larger datasets to boost effect size. Another reason could be the high analytic flexibility (10). Recently, Botvinik-Nezer and colleagues (11) demonstrated the divergence in results when independent research teams applied different workflows to process an identical fMRI dataset, highlighting the effects of “researcher degrees of freedom” (i.e., heterogeneity in (pre-)processing methods) in producing disparate fMRI findings.To address these critical issues, we initiated the Depression Imaging REsearch ConsorTium (DIRECT) in 2017. Through a series of meetings, a group of 17 participating hospitals in China agreed to establish the first project of the DIRECT consortium, the REST-meta-MDD Project, and share 25 study cohorts, including R-fMRI data from 1300 MDD patients and 1128 normal controls. Based on prior work, a standardized preprocessing pipeline adapted from Data Processing Assistant for Resting-State fMRI (DPARSF) (12, 13) was implemented at each local participating site to minimize heterogeneity in preprocessing methods. R-fMRI metrics can be vulnerable to physiological confounds such as head motion (14, 15). Based on our previous work examination of head motion impact on R-fMRI FC connectomes (16) and other recent benchmarking studies (15, 17), DPARSF implements a regression model (Friston-24 model) on the participant-level and group-level correction for mean frame displacements (FD) as the default setting.In the REST-meta-MDD Project of the DIRECT consortium, 25 research groups from 17 hospitals in China agreed to share final R-fMRI indices from patients with MDD and matched normal controls (see Supplementary Table; henceforth “site” refers to each cohort for convenience) from studies approved by local Institutional Review Boards. The consortium contributed 2428 previously collected datasets (1300 MDDs and 1128 NCs). On average, each site contributed 52.0±52.4 patients with MDD (range 13-282) and 45.1±46.9 NCs (range 6-251). Most MDD patients were female (826 vs. 474 males), as expected. The 562 patients with first episode MDD included 318 first episode drug-naïve (FEDN) MDD and 160 scanned while receiving antidepressants (medication status unavailable for 84). Of 282 with recurrent MDD, 121 were scanned while receiving antidepressants and 76 were not being treated with medication (medication status unavailable for 85). Episodicity (first or recurrent) and medication status were unavailable for 456 patients.To improve transparency and reproducibility, our analysis code has been openly shared at https://github.com/Chaogan-Yan/PaperScripts/tree/master/Yan_2019_PNAS. In addition, we would like to share the R-fMRI indices of the 1300 MDD patients and 1128 NCs through the R-fMRI Maps Project (http://rfmri.org/REST-meta-MDD). These data derivatives will allow replication, secondary analyses and discovery efforts while protecting participant privacy and confidentiality.According to the agreement of the REST-meta-MDD consortium, there would be 2 phases for sharing the brain imaging data and phenotypic data of the 1300 MDD patients and 1128 NCs. 1) Phase 1: coordinated sharing, before January 1, 2020. To reduce conflict of the researchers, the consortium will review and coordinate the proposals submitted by interested researchers. The interested researchers first send a letter of intent to rfmrilab@gmail.com. Then the consortium will send all the approved proposals to the applicant. The applicant should submit a new innovative proposal while avoiding conflict with approved proposals. This proposal would be reviewed and approved by the consortium if no conflict. Once approved, this proposal would enter the pool of approved proposals and prevent future conflict. 2) Phase 2: unrestricted sharing, after January 1, 2020. The researchers can perform any analyses of interest while not violating ethics.The REST-meta-MDD data entered unrestricted sharing phase since January 1, 2020. The researchers can perform any analyses of interest while not violating ethics. Please visit Psychological Science Data Bank to download the data, and then sign the Data Use Agreement and email the scanned signed copy to rfmrilab@gmail.com to get unzip password and phenotypic information. ACKNOWLEDGEMENTSThis work was supported by the National Key R&D Program of China (2017YFC1309902), the National Natural Science Foundation of China (81671774, 81630031, 81471740 and 81371488), the Hundred Talents Program and the 13th Five-year Informatization Plan (XXH13505) of Chinese Academy of Sciences, Beijing Municipal Science & Technology Commission (Z161100000216152, Z171100000117016, Z161100002616023 and Z171100000117012), Department of Science and Technology, Zhejiang Province (2015C03037) and the National Basic Research (973) Program (2015CB351702). REFERENCES1. A. J. Ferrari et al., Burden of Depressive Disorders by Country, Sex, Age, and Year: Findings from the Global Burden of Disease Study 2010. PLOS Medicine 10, e1001547 (2013).2. L. M. Williams et al., International Study to Predict Optimized Treatment for Depression (iSPOT-D), a randomized clinical trial: rationale and protocol. Trials 12, 4 (2011).3. S. J. Borowsky et al., Who is at risk of nondetection of mental health problems in primary care? J Gen Intern Med 15, 381-388 (2000).4. B. B. Biswal, Resting state fMRI: a personal history. Neuroimage 62, 938-944 (2012).5. C. G. Yan et al., Reduced default mode network functional connectivity in patients with recurrent major depressive disorder. Proc Natl Acad Sci U S A 116, 9078-9083 (2019).6. K. S. Button et al., Power failure: why small sample size undermines the reliability of neuroscience. Nat Rev Neurosci 14, 365-376 (2013).7. J. P. A. Ioannidis, Why Most Published Research Findings Are False. PLOS Medicine 2, e124 (2005).8. R. A. Poldrack et al., Scanning the horizon: towards transparent and reproducible neuroimaging research. Nat Rev Neurosci 10.1038/nrn.2016.167 (2017).9. S. Marek et al., Reproducible brain-wide association studies require thousands of individuals. Nature 603, 654-660 (2022).10. J. Carp, On the Plurality of (Methodological) Worlds: Estimating the Analytic Flexibility of fMRI Experiments. Frontiers in Neuroscience 6, 149 (2012).11. R. Botvinik-Nezer et al., Variability in the analysis of a single neuroimaging dataset by many teams. Nature 10.1038/s41586-020-2314-9 (2020).12. C.-G. Yan, X.-D. Wang, X.-N. Zuo, Y.-F. Zang, DPABI: Data Processing & Analysis for (Resting-State) Brain Imaging. Neuroinformatics 14, 339-351 (2016).13. C.-G. Yan, Y.-F. Zang, DPARSF: A MATLAB Toolbox for "Pipeline" Data Analysis of Resting-State fMRI. Frontiers in systems neuroscience 4, 13 (2010).14. R. Ciric et al., Mitigating head motion artifact in functional connectivity MRI. Nature protocols 13, 2801-2826 (2018).15. R. Ciric et al., Benchmarking of participant-level confound regression strategies for the control of motion artifact in studies of functional connectivity. NeuroImage 154, 174-187 (2017).16. C.-G. Yan et al., A comprehensive assessment of regional variation in the impact of head micromovements on functional connectomics. NeuroImage 76, 183-201 (2013).17. L. Parkes, B. Fulcher, M. Yücel, A. Fornito, An evaluation of the efficacy, reliability, and sensitivity of motion correction strategies for resting-state functional MRI. NeuroImage 171, 415-436 (2018).18. L. Wang et al., Interhemispheric functional connectivity and its relationships with clinical characteristics in major depressive disorder: a resting state fMRI study. PLoS One 8, e60191 (2013).19. L. Wang et al., The effects of antidepressant treatment on resting-state functional brain networks in patients with major depressive disorder. Hum Brain Mapp 36, 768-778 (2015).20. Y. Liu et al., Regional homogeneity associated with overgeneral autobiographical memory of first-episode treatment-naive patients with major depressive disorder in the orbitofrontal cortex: A resting-state fMRI study. J Affect Disord 209, 163-168 (2017).21. X. Zhu et al., Evidence of a dissociation pattern in resting-state default mode network connectivity in first-episode, treatment-naive major depression patients. Biological psychiatry 71, 611-617 (2012).22. W. Guo et al., Abnormal default-mode

SUMMARY This table contains data about women, ages 15 to 50, pregnant people, infants, children, and youths, up to age 24. It contains information about a wide range of health topics, including medical conditions, nutrition, dehydration, oral health, mental health, safety, access to health care, and basic needs, like housing. Local, county-level prevalence rates, time trends, and health disparities about national public health priorities, including preterm birth, infant death, childhood obesity, adolescent depression and substance use, and high blood pressure, diabetes, and kidney disease in young adults. The population data is from the 2023-2024 San Francisco Maternal Child and Adolescent Health needs assessment and is published on the Open Data Portal to share with community partners, plan services, and promote health. For more information see: Maternal, Child, and Adolescent Health Homepage Maternal, Child, and Adolescent Health Reports HOW THE DATASET IS CREATED The Maternal, Child, and Adolescent Health (MCAH) Needs Assessment for San Francisco included review of a wide range of citywide population data covering a ten-year span, from 2014 to 2023. Data from over 83,000 birth records, 59,000 death records, 261,000 emergency room visits, 66,000 hospital admissions, and 90,000 newborn screening discharges were gathered, along with citywide data from child welfare records, health screenings in childcare and schools, DMV records of first-time drivers, school surveys, and a state-run mailed survey of recent births (California Department of Public Health MIHA survey). The datasets provided information about approximately 700 health conditions. Each health condition was described in terms of the number of people affected or cases, and the rate affected, stratified by age, sex, race-ethnicity, insurance status, zip code, and time period. Rates were calculated by dividing the number of people or events by the population group estimate (e.g., total births or census estimates), then multiplying by 100 or 1,000 depending on the measure. Each rate was presented with its 95% confidence interval to support users to compare any two rates, either between groups or over time. Two rates differ “significantly” if their 95% confidence intervals do not overlap. The present dataset summarizes the group-level results for any age-, sex-, race-, insurance-, zip code-, and/or period-specific group that included at least 20 people or cases. Causes of death, health conditions that affected over 1000 people in the time frame, problems that got worse over time, and health disparities by insurance, race-ethnicity and/or zip code were flagged for the MCAH Needs Assessment. UPDATE PROCESS The dataset will be updated manually, bi-annually, each December and June. HOW TO USE THIS DATASET Population data from the MCAH needs assessment are shared in several formats, including aggregated datasets on DataSF.gov, downloadable PDF summary reports by age group, interactive online visualizations, data tables, trend graphs, and maps. Information about each variable is available in a linked data dictionary. The definition of each numerator and denominator depends on data source, life stage, and time. Health conditions may not be directly comparable across life stage, if the numerator definition includes age- or pregnancy-specific diagnosis codes (e.g. diabetes hospitalization). For small groups or rare conditions, consider combining time periods and/or groups. Data are suppressed if fewer than 20 cases happened in the group and period. Group-specific rates are available if the matched group-specific census estimates (denominator) were available. Census estim

The study aimed to analyze the association between the socioeconomic profile and the Quality of Life (QoL) of elderly people with depressive symptoms assisted in Primary Health Care (PHC) in Brazil and Portugal. This is a comparative cross-sectional study with a non-probabilistic sample of elderly people from PHC in Brazil and Portugal, carried out between 2018 and 2018. To assess the variables of interest, a form containing socioeconomic data, the Geriatric Depression Scale (GDS- 15) and the Medical Outcomes Short-Form Health Survey (SF-36). We performed descriptive and multivariate analyzes to test the study hypothesis. The sample consisted of n=150 participants (Brazil n=100 and Portugal n=50). There was similarity in some variables of the socioeconomic profile of both groups, with predominance in the total sample of females (76.0% / p = 0.224) and of individuals aged between 65 and 80 years (88.0% - p = 0.594 ). However, in Brazil, less education (79.0%/ p = 0.001) and participants who did not live alone (86.0%/ p = 0.001) stood out. In Portugal, all participants had an income lower than the minimum wage (100.0% / p <0.001). There is also a predominance of symptoms in the group from Brazil (59.0%) (p=0.015 / OR= 1.81 - 95%CI= 1.12 – 2.81). When performing the multivariate association analysis between socioeconomic variables, presence of depressive symptoms and QoL, we selected and presented the most relevant results in Table 5. It is noted that the Mental Health domain was the domain that was most associated with socioeconomic variables. Among them, the female gender (p= 0.027), age group 65-80 years (p=0.042), marital status “without a partner” (p=0.029), education of up to 5 years (p=0.011) and income of up to 1 minimum wage (p=0.037). In all these variables, higher scores were observed in the group from Brazil. With higher scores in Portugal, the General Health Status domain was associated with female gender (p= 0.042) and education of up to 5 years (p=0.045). In addition, the physical aspect domain was associated with income of up to 1 minimum wage. The results revealed the existence of an association between the socioeconomic profile and the QoL in the presence of depressive symptoms. This association was observed mainly among females, low education and low income with aspects of QoL related to mental, physical and social health and self-perception of health. The group from Brazil had higher QoL scores compared to Portugal.

Recent studies suggest that accelerated aging of the brain is a neuroanatomical signature of the state of mental diseases. In major depression, this pre-aging effect is negatively associated with the duration since the first onset of the disease. The olfactory bulb (OB) shrinks with age in healthy subjects and patients with mental diseases show reduced OB volumes, especially those with major depression. It is unclear whether this OB reduction in mental diseases resembles a pre-aging process and whether it is associated to the duration since the onset of the mental disease. To this aim, we investigated OB volume in 73 patients (mean-age 40.4 years, SD = 12.1 years, 57 women) with major depression and mixed comorbid mental diseases (diagnoses ranged from 1 to 6, median: 3) and 51 healthy controls (mean-age 39.2 years, SD = 13.0 years, 26 women) matched for age and sex. Patient’s first onset of disease ranged from 15 to 53 years (mean 24.2 years). All participants underwent structural MR imaging with a spin-echo T2-wheighted sequence covering the anterior and middle segments of the skull base. All results were corrected for total intracranial volume (TIV) and sex. Individual OB volume was calculated by planimetric manual contouring and the pronounced diameter change in transition from bulb to tract was used as the distal demarcation of the OB. Inter-rater correlation between two independent persons analyzing the data was high (IRC = 0.81, p < 0.005). An age-dependent decline of the OB volume was confirmed in healthy controls (r = −0.34, p < 0.05). However, this pattern was altered in patients where the OB volume was not related to age, but to the duration since the onset of the mental disease (r = −0.25, p < 0.05). This association remained stable when controlling for age. Additionally, analyses of age sub-groups revealed that the association between duration since the onset of the mental disease and OB volume was mainly driven by the group aged 50 years and above (r = −0.68; p < 0.01). We conclude that there are time windows where the OB volume is susceptible for the effects of a mental disease, e.g., depression. These effects result in cumulative pre-aging in the OB in older patients with mental diseases.

Abstract Background. Music directly addresses the emotions and other functional systems that are impaired in major depressive disorder (MDD). Therefore, music therapy (MT) can be an effective complement in the treatment of MDD. To date, the research situation is not sufficient to provide evidence of its efficacy. Methods. The study was conducted as a randomized controlled trial with group MT (GMT) in the intervention group (IG) and a waitlist control group (CG). The study aimed to investigate group*time interaction effects regarding self-rated, observer-rated, and momentary depression. Secondary outcomes encompassed emotion and mood regulation strategies and health-related quality of life. Outcomes were measured before, after, and partly 10 weeks after the intervention period. Results. 102 women between 18 and 65 years diagnosed with current MDD took part in the study. Overall, greater health-promoting effects were shown in the IG than in the CG, particularly in the pre-to-post comparison. Significant results were shown regarding momentary depression, quality of life, and different regulation strategies, especially using music. Limitations. Limitations comprised the high dropout rate and data loss due to the coronavirus pandemic, long-term effects of GMT not being assured, limited generalizability due to the biological female sex of the sample, and conditional transferability due to the process-driven application of GMT. Conclusions. GMT is a cost-effective and non-invasive approach to treat MDD yielding health-promoting effects regarding depressive symptoms, regulatory abilities, and QoL. However, the underlying mechanisms of action should be further investigated within methodologically high-quality studies. For this purpose, manualization of MT is strongly recommended. Trial Registration: The MUSED study was pre-registered in the German Clinical Trials Register (registration code: DRKS00016616).

BackgroundLittle is known about whether people who use both tobacco and cannabis (co-use) are more or less likely to have mental health disorders than single substance users or non-users. We aimed to examine associations between use of tobacco and/or cannabis with anxiety and depression.MethodsWe analyzed data from the COVID-19 Citizen Science Study, a digital cohort study, collected via online surveys during 2020–2022 from a convenience sample of 53,843 US adults (≥ 18 years old) nationwide. Past 30-day use of tobacco and cannabis was self-reported at baseline and categorized into four exclusive patterns: tobacco-only use, cannabis-only use, co-use of both substances, and non-use. Anxiety and depression were repeatedly measured in monthly surveys. To account for multiple assessments of mental health outcomes within a participant, we used Generalized Estimating Equations to examine associations between the patterns of tobacco and cannabis use with each outcome.ResultsIn the total sample (mean age 51.0 years old, 67.9% female), 4.9% reported tobacco-only use, 6.9% cannabis-only use, 1.6% co-use, and 86.6% non-use. Proportions of reporting anxiety and depression were highest for the co-use group (26.5% and 28.3%, respectively) and lowest for the non-use group (10.6% and 11.2%, respectively). Compared to non-use, the adjusted odds of mental health disorders were highest for co-use (Anxiety: OR = 1.89, 95%CI = 1.64–2.18; Depression: OR = 1.77, 95%CI = 1.46–2.16), followed by cannabis-only use, and tobacco-only use. Compared to tobacco-only use, co-use (OR = 1.35, 95%CI = 1.08–1.69) and cannabis-only use (OR = 1.17, 95%CI = 1.00–1.37) were associated with higher adjusted odds for anxiety, but not for depression. Daily use (vs. non-daily use) of cigarettes, e-cigarettes, and cannabis were associated with higher adjusted odds for anxiety and depression.ConclusionsUse of tobacco and/or cannabis, particularly co-use of both substances, were associated with poor mental health. Integrating mental health support with tobacco and cannabis cessation may address this co-morbidity.

The Australian Longitudinal Study of Ageing, which ran from 1992 to 2014, was devised to generate longitudinal data over multiple time points. Thirteen waves were carried out. Waves 1, 3, 6, 7, 9, 11 and 12 comprised of a full face-to-face ‘household’ interview and a clinical assessment. Waves 2, 4, 5, 8, 10, 13 consisted of shorter telephone household interviews.The initial sample of the older old (70 and older) was randomly drawn from the database of the South Australian Electoral Roll. Persons in the older age groups as well as males were deliberately oversampled to compensate for the higher mortality that could be expected over the study period. In addition, spouses of primary respondents (aged 65 and over) and other household members aged 70 and over were asked to participate. 2087 participants were initially interviewed at Wave 1 in 1992. Over the years, attrition due to either death, ill health, moving out of scope, being uncontactable, or refusal has reduced the number of participants to 94 in 2014. Information covering the data, questionnaires and relevant details are openly available.Items in the household interview schedule represent a comprehensive set of measures chosen for their reliability and validity in previous studies, sensitivity to change over time, and suitability for use in a study of elderly persons. The domains assessed included demography, health, depression, morbid conditions, hospitalisation, hearing and vision difficulties, cognition, gross mobility and physical performance, activities of daily living and instrumental activities of daily living, lifestyle activities, exercise education and income.At the completion of the household interview, participants were left with self-administered questionnaires, which were mailed back in pre- paid envelopes or collected at the time of the clinical assessment. The domains covered by the questionnaires were dental health, sexual activity and psychological measures of self-esteem, morale and perceived control.The individual clinical assessment objectively measured both physical and cognitive functioning. The physical examination included measures of blood pressure, anthropometry, visual acuity, audiometry and physical performance. The cognitive assessment included measures of memory, information processing efficiency, verbal ability and executive function. The clinical assessments were conducted by nurses who received special training in the standard administration of all psychological instruments and the anthropometric measures. In addition, fasting blood samples and urine specimens were collected on the morning following the clinical assessment at Wave 1, and blood samples were again taken at Wave 3.Some data have been provided by secondary sources. Participant deaths have been systematically monitored through the government Registry of Births, Deaths and Marriages.From Wave 7 onward, collateral data were gathered from the files of the Health Insurance Commission (HIC). Permission was sought for access to the Health Insurance Commission HIC for purposes of establishing use of medical care and services and expenditure. The information sought from the HIC database included: the number of medical care services, and for each service, the nature of the service, date, charge, and benefit; the number of PBS prescriptions, and for each prescription, the drug prescribed, number of repeats, date, charge, and benefit.

2020 was the first time in the history of El Salvador that mental health was assessed on a national scale. The pandemic has increased depression and anxiety symptoms in populations such as the US (Twenge & Joiner, 2020) and UK (Smith et al., 2020). Adolescents may have been particularly affected by the pandemic because of the drastic changes in their life at an age when social interactions are highly important for development (Burns et al., 2002). Studies on mental health or wellbeing are mostly done with WEIRD populations. Large-scale studies are less common in other populations as they take high amounts of resources to implement. Previous studies have shown that family support is a protective factor associated with resilience in youths exposed to adversity, (Nearchou et al., 2020) and that family support is inversely associated with symptoms for depression at the begging of adolescence (Needham, 2008). In adolescents with diabetes, it has been shown that family support is a predictor of diabetes self-care, including following a dietary plan (Skinner & Hampson, 1998) and that poor health habits overall are predictors of mental disorders (Hoare et al., 2020). At the same time higher social support is associated with lower depression and anxiety and overall higher well-being (Khalid, 2014). Studies with young people have shown varied results regarding gender and social support and depression (Needham, 2008; Rueger et al., 2016), but girls who show lower levels of parental support show higher levels of depression (Needham, 2008); at the same time being a girl is associated with both depression and anxiety in comparison to their male counterparts (Mazza et al., 2020). This dataset provides variables that reflect the impact of the pandemic on the participants social relationships. Family and social impact will be used instead of family and social support, higher family or social impact represent a more adaptive functioning during the pandemic in either group. The analysis of this dataset will give an opportunity to test for these associations during an extraordinary event such as the pandemic which significantly modified how people interact and support each other and thus have repercussions on their mental health.

Growth mindset, the belief that abilities and attributes are changeable, has been implicated in better mental health and health behaviors and may be especially critical during challenging life events. One goal of this prospective longitudinal study was to investigate the role of growth mindset in adults’ mental health (i.e., depression, well-being, and adjustment of daily routines) over two years of the COVID-19 pandemic. We also examined this relationship in older adults who had participated in a prior learning intervention including growth mindset training (compared with those who had not). Adults ages 19 to 89 from ethnically diverse backgrounds in Southern California (n = 454) were surveyed at three timepoints between June 2020 and September 2022. In Study 1 focusing on this wide age range (n = 393), we found that growth mindset was associated with lower levels of depression and higher levels of well-being and adjustment, after accounting for various sociodemographic factors. Study 2, which focused on older adults (n = 174), largely replicated the findings from Study 1. Furthermore, the conducive effect of growth mindset on well-being was marginally greater among those who had participated in the intervention, and those who had participated in the intervention showed an increase in well-being over time, while well-being scores decreased in the control group. Together, our findings suggest that growth mindset may be an important protective factor for mental health during challenging times.