Context

Mental Health Services Monthly Statistics

This publication provides the most timely picture available of people using NHS funded secondary mental health, learning disabilities and autism services in England. These are experimental statistics which are undergoing development and evaluation. This information will be of use to people needing access to information quickly for operational decision making and other purposes. More detailed information on the quality and completeness of these statistics is made available later in our Mental Health Bulletin: Annual Report publication series.

• COVID-19 and the production of statistics

Due to the coronavirus illness (COVID-19) disruption, it would seem that this is now starting to affect the quality and coverage of some of our statistics, such as an increase in non-submissions for some datasets. We are also starting to see some different patterns in the submitted data. For example, fewer patients are being referred to hospital and more appointments being carried out via phone/telemedicine/email. Therefore, data should be interpreted with care over the COVID-19 period.

Content

Time period covered Feb 1, 2020 - April 31, 2020

Area covered England

Acknowledgements

reference: Mental Health Services Monthly Statistics

Author: Community and Mental Health Team, NHS Digital
Responsible Statistician: Tom Poupart, Principal Information Analyst
Public Enquiries: Telephone: 0300 303 5678
Email: enquiries@nhsdigital.nhs.uk
Press enquiries should be made to: Media Relations Manager: Telephone: 0300 303 3888

Published by NHS Digital part of the Government Statistical Service 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.

You may re-use this document/publication (not including logos) free of charge in any format or medium, under the terms of the Open Government Licence v3.0.
To view this licence visit To view this licence visit www.nationalarchives.gov.uk/doc/open-government-licence www.nationalarchives.gov.uk/doc/open-government-licence or write to the Information Policy Team, The National Archives, or write to the Information Policy Team, The National Archives, Kew, Richmond, Surrey, TW9 4DU Kew, Richmond, Surrey, TW9 4DU; or email: psi@nationalarchives.gsi.gov.uk or email: psi@nationalarchives.gsi.gov.uk

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Inspiration

This dataset is to solve the challenge- UNCOVER COVID-19 Challenge, United Network for COVID Data Exploration and Research. This data is scraped in hopes of solving the task - Mental health impact and support services.

Task Details Can we predict changes in demand for mental health services and how can we ensure access? (by region, social/economic/demographic factors, etc). Are there signs of shifts in mental health challenges across demographies, whether improvements or declines, as a result of COVID-19 and the various measures implement to contain the pandemic?

Data Source Description: Syndromic surveillance provides public health officials with a timely system for detecting, understanding, and monitoring health threats. By tracking symptoms and conditions reported by patients in emergency departments (EDs), public health officials can monitor trends in critical areas. When people seek treatment in the ED, the facility sends de-identified data—including chief complaint, diagnosis codes, patient characteristics, and location—to state and local health departments to share with CDC.

Indicator definitions: Definitions include both (1) visits with acute mental health crises or evaluations where the sole or primary reason for the visit is related to mental health, and (2) visits where a mental health condition may not be the sole reason for the visit but was noted to be present in the discharge diagnosis or chief complaint.

ED data are displayed as rates.

• The rate explains the number of ED visits related to a specific mental health condition out of every 100,000 ED visits. The monthly rate of ED visits related to a condition is calculated as the number of ED visits related to a condition as a fraction of the total number of ED visits in a given month multiplied by 100,000. ED visits with and without mental health conditions where patients were under 12 years old and where age, sex, and race and ethnicity were unknown were included in the denominators when calculating rates for the overall population.

• Rates are also calculated within demographic groups such as age, sex, and race and ethnicity. This is done by comparing the number of ED visits related to a condition within a demographic group as a fraction of the total number of ED visits for that demographic group in a given month multiplied by 100,000.

Data quality and methodology: More than 6,900 health care facilities covering 50 states, the District of Columbia, and Guam contribute data to NSSP daily. More than 80% of U.S. emergency departments send data to NSSP. Data are collected continuously, and for the purposes of this data channel, updated monthly. Monthly counts that are less than 10 either for the complete population or for specific demographic groups are not shown to protect confidentiality and privacy. Note, rate information may change as data are dynamically adjusted as electronic health record information is updated. However, these changes will not typically affect overall trends.

For additional information, please see: https://www.cdc.gov/mental-health/about-data/mental-health-data-sources.html" target ="_blank">Mental Health Data Sources.

Updated monthly. 

ObjectiveTo assess physician-based mental health care utilization during the COVID-19 pandemic among children and adolescents new to care and those already engaged with mental health services, and to evaluate differences by sociodemographic factors.Study designWe performed a population-based repeated cross-sectional study using linked health and administrative databases in Ontario, Canada among all children and adolescents 3–17 years. We examined outpatient visit rates per 1,000 population for mental health concerns for those new to care (no physician-based mental healthcare for ≥1 year) and those with continuing care needs (any physician-based mental healthcare <1 year) following onset of the pandemic.ResultsAmong ~2.5 million children and adolescents (48.7% female, mean age 10.1 ± 4.3 years), expected monthly mental health outpatient visits were 1.5/1,000 for those new to mental health care and 5.4/1,000 for those already engaged in care. Following onset of the pandemic, visit rates for both groups were above expected [adjusted rate ratio (aRR) 1.22, 95% CI 1.17, 1.27; aRR 1.10, 95% CI 1.07, 1.12] for new and continuing care, respectively. The greatest increase above expected was among females (new: aRR 1.33, 95% CI 1.25, 1.42; continuing: aRR 1.22 95% CI 1.17, 1.26) and adolescents ages 13–17 years (new: aRR 1.31, 95% CI 1.27, 1.34; continuing: aRR 1.15 95% CI 1.13, 1.17). Mood and anxiety concerns were prominent among those new to care.ConclusionIn the 18 months following onset of the pandemic, outpatient mental health care utilization increased for those with new and continuing care needs, especially among females and adolescents.

Latest monthly statistics on Learning Disabilities and Autism (LDA) patients from the Assuring Transformation (AT) collection and Mental Health Services Data Set (MHSDS). Data on inpatients with learning disabilities and/or autism are being collected both within the AT collection and MHSDS. There are differences in the inpatient figures between the AT and MHSDS data sets and work has been ongoing to better understand these. LDA data from MHSDS are experimental statistics. From May 2022 AT data (June publication), the following changes have been made to the tables: • Table 2.2 – new information on whether a patient was on the dynamic risk register prior to admission. • Table 2.2 – inclusion of ‘Not known’ as a breakdown within ‘Additional diagnosis since admission’. • Table 2.3 – new information on whether the patient has an Education Health and Care (EHC) plan if they are a young person. • Table 3.1 – new information on commissioner oversight visits and whether a patient is on extended leave from hospital under section 17 of the Mental Health Act 1983. From October 2021, LD MHSDS data has been collected under MHSDS version 5. A number of comparators are published each month to assess the differences in reporting between the collections. These can be found in the MHSDS datasets section. Disruption relating to the coronavirus illness (COVID-19) would seem to be affecting the quality and coverage of some of our statistics, such as an increase in non-submissions for some datasets. We have also seen some different patterns in the submitted data. For example, fewer patients are being admitted to and discharged from hospital. Therefore, data should be interpreted with care over the COVID-19 period.

The HCUP Summary Trend Tables include monthly information on hospital utilization derived from the HCUP State Inpatient Databases (SID) and HCUP State Emergency Department Databases (SEDD). Information on emergency department (ED) utilization is dependent on availability of HCUP data; not all HCUP Partners participate in the SEDD. The HCUP Summary Trend Tables include downloadable Microsoft® Excel tables with information on the following topics: Overview of monthly trends in inpatient and emergency department utilization All inpatient encounter types Inpatient stays by priority conditions -COVID-19 -Influenza -Other acute or viral respiratory infection Inpatient encounter type -Normal newborns -Deliveries -Non-elective inpatient stays, admitted through the ED -Non-elective inpatient stays, not admitted through the ED -Elective inpatient stays Inpatient service line -Maternal and neonatal conditions -Mental health and substance use disorders -Injuries -Surgeries -Other medical conditions Emergency department treat-and-release visits Emergency department treat-and-release visits by priority conditions -COVID-19 -Influenza -Other acute or viral respiratory infection Description of the data source, methodology, and clinical criteria

This is a monthly report on publicly funded community services using data from the Community Services Data Set (CSDS) reported in England for January 2019. The CSDS is a patient-level dataset providing information relating to publicly funded community services. These services can include health centres, schools, mental health trusts, and health visiting services. The data collected includes personal and demographic information, diagnoses including long-term conditions and disabilities and care events plus screening activities. It has been developed to help achieve better outcomes for children, young people and adults. It provides data that will be used to commission services in a way that improves health, reduces inequalities, and supports service improvement and clinical quality. Prior to October 2017, the predecessor Children and Young Peoples’ Health Services (CYPHS) Data Set collected data for children and young people aged 0-18. The CSDS superseded the CYPHS data set to allow adult community data to be submitted, expanding the scope of the existing data set by removing the 0-18 age restriction. The structure and content of the CSDS remains the same as the previous CYPHS data set. Further information about the CYPHS and related statistical reports is available in the related links below. References to children and young people covers records submitted for 0-18 year olds and references to adults covers records submitted for those aged over 18. Where analysis for both groups have been combined, this is referred to as all patients. These statistics are classified as experimental and should be used with caution. Experimental statistics are new official statistics undergoing evaluation. They are published in order to involve users and stakeholders in their development and as a means to build in quality at an early stage. More information about experimental statistics can be found on the UK Statistics Authority website. We hope this information is helpful and would be grateful if you could spare a couple of minutes to complete a short customer satisfaction survey. Please use the survey in the related links to provide us with any feedback or suggestions for improving the report. --------------------------------------------------------------------------------------------------------- We are reviewing our monthly and ad-hoc publications to ensure we are providing outputs that meet customer needs. We would be grateful if you could fill in the survey with your views. This survey will remain open until Friday 28th June 2019. Please take part using the link under the 'Related Links' section below. ---------------------------------------------------------------------------------------------------------

In 2008, a group of uninsured low-income adults in Oregon was selected by lottery to be given the chance to apply for Medicaid. This lottery provides an opportunity to gauge the effects of expanding access to public health insurance on the health care use, financial strain, and health of low-income adults using a randomized controlled design. The Oregon Health Insurance Experiment follows and compares those selected in the lottery (treatment group) with those not selected (control group). The data collected and provided here include data from in-person interviews, three mail surveys, emergency department records, and administrative records on Medicaid enrollment, the initial lottery sign-up list, welfare benefits, and mortality. This data collection has seven data files: Dataset 1 contains administrative data on the lottery from the state of Oregon. These data include demographic characteristics that were recorded when individuals signed up for the lottery, date of lottery draw, and information on who was selected for the lottery, applied for the lotteried Medicaid plan if selected, and whose application for the lotteried plan was approved. Also included are Oregon mortality data for 2008 and 2009. Dataset 2 contains information from the state of Oregon on the individuals' participation in Medicaid, Supplemental Nutrition Assistance Program (SNAP), and Temporary Assistance to Needy Families (TANF). Datasets 3-5 contain the data from the initial, six month, and 12 month mail surveys, respectively. Topics covered by the surveys include demographic characteristics; health insurance, access to health care and health care utilization; health care needs, experiences, and costs; overall health status and changes in health; and depression and medical conditions and use of medications to treat them. Dataset 6 contains an analysis subset of the variables from the in-person interviews. Topics covered by the survey questionnaire include overall health, health insurance coverage, health care access, health care utilization, conditions and treatments, health behaviors, medical and dental costs, and demographic characteristics. The interviewers also obtained blood pressure and anthropometric measurements and collected dried blood spots to measure levels of cholesterol, glycated hemoglobin and C-reactive protein. Dataset 7 contains an analysis subset of the variables the study obtained for all emergency department (ED) visits to twelve hospitals in the Portland area during 2007-2009. These variables capture total hospital costs, ED costs, and the number of ED visits categorized by time of the visit (daytime weekday or nighttime and weekends), necessity of the visit (emergent, ED care needed, non-preventable; emergent, ED care needed, preventable; emergent, primary care treatable), ambulatory case sensitive status, whether or not the patient was hospitalized, and the reason for the visit (e.g., injury, abdominal pain, chest pain, headache, and mental disorders). The collection also includes a ZIP archive (Dataset 8) with Stata programs that replicate analyses reported in three articles by the principal investigators and others: Finkelstein, Amy et al "The Oregon Health Insurance Experiment: Evidence from the First Year". The Quarterly Journal of Economics. August 2012. Vol 127(3). Baicker, Katherine et al "The Oregon Experiment - Effects of Medicaid on Clinical Outcomes". New England Journal of Medicine. 2 May 2013. Vol 368(18). Taubman, Sarah et al "Medicaid Increases Emergency Department Use: Evidence from Oregon's Health Insurance Experiment". Science. 2 Jan 2014.

An analysis of final 2021 DAWN data presents: (1) nationally representative weighted estimates, including percent and unadjusted rates per 100,000, for all drug-related ED visits, (2) nationally representative weighted estimates for the top five drugs in drug-related ED visits, (3) the assessment of monthly trends and drugs involved in polysubstance ED visits in a subset of sentinel hospitals, and (4) the identification of drugs new to DAWN’s Drug Reference Vocabulary.Clickhereto view the 2021 final report.

This is a monthly report on publicly funded community services for children, young people and adults using data from the Community Services Data Set (CSDS) reported in England for February 2018. The CSDS is a patient-level dataset providing information relating to publicly funded community services for children, young people and adults. These services can include health centres, schools, mental health trusts, and health visiting services. The data collected includes personal and demographic information, diagnoses including long-term conditions and disabilities and care events plus screening activities. It has been developed to help achieve better outcomes for children, young people and adults. It provides data that will be used to commission services in a way that improves health, reduces inequalities, and supports service improvement and clinical quality. Prior to October 2017, the predecessor Children and Young Peoples Health Services (CYPHS) Data Set collected data for children and young people aged 0-18. The CSDS superseded the CYPHS data set to allow adult community data to be submitted, expanding the scope of the existing data set by removing the 0-18 age restriction. The structure and content of the CSDS remains the same as the previous CYPHS data set. Further information about the CYPHS and related statistical reports is available in the related links below. References to children and young people covers records submitted for 0-18 year olds and references to adults covers records submitted for those aged over 18. Where analysis for both groups have been combined, this is referred to as all patients. These statistics are classified as experimental and should be used with caution. Experimental statistics are new official statistics undergoing evaluation. They are published in order to involve users and stakeholders in their development and as a means to build in quality at an early stage. More information about experimental statistics can be found on the UK Statistics Authority website. We hope this information is helpful and would be grateful if you could spare a couple of minutes to complete a short customer satisfaction survey. Please use the survey in the related links to provide us with any feedback or suggestions for improving the report.

Preliminary Findings from Drug-Related Emergency Department Visits, 2021. An analysis of 2021 preliminary data presents (1) nationally representative weighted estimates for the top five drugs in drug-related ED visits, (2) the assessment of monthly trends and drugs involved in polysubstance ED visits in a subset of sentinel hospitals, and (3) the identification of drugs new to DAWN’s Drug Reference Vocabulary.https://store.samhsa.gov/sites/default/files/SAMHSA_Digital_Download/PEP22-07-03-001.pdf

Physical Health score, Mental Health score and Global Person Generated Index score at baseline visit, month 3 and month 6* p value for physical health score and mental health score used the students t test and Wilcoxon rank test was used for the global person generated index scoreValues are means (SD) and median (range) for Global person generalized Index score.

This is the data spread sheet of the 1274 persons used in this study. (XLS)

This datasets presents the Mental Health Activity Data Collection (MHADC) data regarding the number of service contacts per month by the Australian Statistical Geography Standard (ASGS) 2011 Statistical Area Level 2 (SA2) boundaries for monthly periods between January 2013 and June 2015.

For more information please visit the Queensland Government Open Data Portal.

Please note:

• AURIN has spatially enabled this data. Data made available to you through a AURIN-QCIF-RIDL collaboration.

A. SUMMARY This dataset includes data on a variety of substance use services funded by the San Francisco Department of Public Health (SFDPH). This dataset only includes Drug MediCal-certified residential treatment, withdrawal management, and methadone treatment. Other private non-Drug Medi-Cal treatment providers may operate in the city. Withdrawal management discharges are inclusive of anyone who left withdrawal management after admission and may include someone who left before completing withdrawal management.

This dataset also includes naloxone distribution from the SFDPH Behavioral Health Services Naloxone Clearinghouse and the SFDPH-funded Drug Overdose Prevention and Education program. Both programs distribute naloxone to various community-based organizations who then distribute naloxone to their program participants. Programs may also receive naloxone from other sources. Data from these other sources is not included in this dataset.

Finally, this dataset includes the number of clients on medications for opioid use disorder (MOUD).

The number of people who were treated with methadone at a Drug Medi-Cal certified Opioid Treatment Program (OTP) by year is populated by the San Francisco Department of Public Health (SFDPH) Behavioral Health Services Quality Management (BHSQM) program. OTPs in San Francisco are required to submit patient billing data in an electronic medical record system called Avatar. BHSQM calculates the number of people who received methadone annually based on Avatar data. Data only from Drug MediCal certified OTPs were included in this dataset.

The number of people who receive buprenorphine by year is populated from the Controlled Substance Utilization Review and Evaluation System (CURES), administered by the California Department of Justice. All licensed prescribers in California are required to document controlled substance prescriptions in CURES. The Center on Substance Use and Health calculates the total number of people who received a buprenorphine prescription annually based on CURES data. Formulations of buprenorphine that are prescribed only for pain management are excluded.

People may receive buprenorphine and methadone in the same year, so you cannot add the Buprenorphine Clients by Year, and Methadone Clients by Year data together to get the total number of unique people receiving medications for opioid use disorder.

For more information on where to find treatment in San Francisco, visit findtreatment-sf.org. 

B. HOW THE DATASET IS CREATED This dataset is created by copying the data into this dataset from the SFDPH Behavioral Health Services Quality Management Program, the California Controlled Substance Utilization Review and Evaluation System (CURES), and the Office of Overdose Prevention.

C. UPDATE PROCESS Residential Substance Use Treatment, Withdrawal Management, Methadone, and Naloxone data are updated quarterly with a 45-day delay. Buprenorphine data are updated quarterly and when the state makes this data available, usually at a 5-month delay.

D. HOW TO USE THIS DATASET Throughout the year this dataset may include partial year data for methadone and buprenorphine treatment. As both methadone and buprenorphine are used as long-term treatments for opioid use disorder, many people on treatment at the end of one calendar year will continue into the next. For this reason, doubling (methadone), or quadrupling (buprenorphine) partial year data will not accurately project year-end totals.

E. RELATED DATASETS Overdose-Related 911 Responses by Emergency Medical Services Unintentional Overdose Death Rates by Race/Ethnicity Preliminary Unintentional Drug Overdose Deaths

F. CHANGE LOG

• 09/15/2025 - Data processing updated to capture new buprenorphine formulations.

This datasets presents the Mental Health Activity Data Collection (MHADC) data regarding the number of distinct consumers per month by the Australian Statistical Geography Standard (ASGS) 2011 Statistical Area Level 2 (SA2) boundaries for monthly periods between January 2013 and June 2015. For more information please visit the Queensland Government Open Data Portal. Please note: AURIN has spatially enabled this data. Data made available to you through a AURIN-QCIF-RIDL collaboration.

The STAMINA study examined the nutritional risks of low-income peri-urban mothers, infants and young children (IYC), and households in Peru during the COVID-19 pandemic. The study was designed to capture information through three, repeated cross-sectional surveys at approximately 6 month intervals over an 18 month period, starting in December 2020. The surveys were carried out by telephone in November-December 2020, July-August 2021 and in February-April 2022. The third survey took place over a longer period to allow for a household visit after the telephone interview.The study areas were Manchay (Lima) and Huánuco district in the Andean highlands (~ 1900m above sea level).In each study area, we purposively selected the principal health centre and one subsidiary health centre. Peri-urban communities under the jurisdiction of these health centres were then selected to participate. Systematic random sampling was employed with quotas for IYC age (6-11, 12-17 and 18-23 months) to recruit a target sample size of 250 mother-infant pairs for each survey.Data collected included: household socio-demographic characteristics; infant and young child feeding practices (IYCF), child and maternal qualitative 24-hour dietary recalls/7 day food frequency questionnaires, household food insecurity experience measured using the validated Food Insecurity Experience Scale (FIES) survey module (Cafiero, Viviani, & Nord, 2018), and maternal mental health.In addition, questions that assessed the impact of COVID-19 on households including changes in employment status, adaptations to finance, sources of financial support, household food insecurity experience as well as access to, and uptake of, well-child clinics and vaccination health services were included.This folder includes the dataset and dictionary of variables for survey 3 (English only).The survey questionnaire for survey 3 is available at 10.17028/rd.lboro.21740921.

Abstract

The Longitudinal Indian Family Health (LIFE) is a long-term research study that will examine socio-economic and environmental influences on children’s health and development in India. The main aim of the study is to understand the link between the environmental conditions in which Indian women conceive, go through their pregnancy and give birth, and their physical and mental health during this period.

The cohort comprises married women between 15 and 35 years of age (mean 22 years), recruited before pregnancy or in the first trimester of pregnancy, from 2009 to 2011. These CHVs focus on women in the village to ascertain pregnancy (by interview) and to educate and encourage the women to seek regular antenatal care and other health care services. REACH has enumerated all household members in these communities and mapped each dwelling by a geographical information system (GIS). During each visit, CHVs conduct interviews to collect and update information on demography and pregnancy. Since 2004, CHVs have been collecting data on infant deaths and birthweights in the population. Socio-demographic variables such as access to electricity, means of transportation and possession of audio-visual devices were collected from REACH database

Documentation

You can submit a proposal to collaborate with LIFE Study investigators. A written protocol must be submitted, reviewed and approved by the LIFE Data Sharing Plan Committee before initiation of new projects. For further information, contact Dr P. S. Reddy at [reddyps@verizon.net]. Updated information may be found on the research centre website at [www.sharefoundations.org].

Methodology

The LIFE study is being conducted in villages of Medchal Mandal, R.R.District, Telangana, India. Since 2009, 1227 women aged between 15 and 35 years were recruited before conception or within 14 weeks of gestation. Women were followed through pregnancy, delivery, and postpartum. Follow-up of children is ongoing. Baseline data were collected from husbands of 642 women.

Anthropometric measurements, biological samples and detailed questionnaire data were collected during registration, the first and third trimesters, delivery and at 1 month postpartum. Anthropometric measurements and health questionnaire data are obtained for each child, and a developmental assessment is done at 1, 6, 12, 18, 24, 36, 48 and 60 months. At 36 months, each child is screened for development and mental health problems. Questionnaires are completed for pregnancy loss and death of children under 5 years old. The LIFE Biobank preserves over 6000 samples.

ContextPostnatal maternal depression (PND) is a significant risk factor for infant mental health. Although often targeted alongside other factors in perinatal home-visiting programs with vulnerable families, little impact on PND has been observed.ObjectiveThis study evaluates the impact on PND symptomatology of a multifocal perinatal home-visiting intervention using psychologists in a sample of women presenting risk factors associated with infant mental health difficulties.Methods440 primiparous women were recruited at their seventh month of pregnancy. All were future first-time mothers, under 26, with at least one of three additional psychosocial risk factors: low educational level, low income, or planning to raise the child without the father. The intervention consisted of intensive multifocal home visits through to the child’s second birthday. The control group received care as usual. PND symptomatology was assessed at baseline and three months after birth using the Edinburgh Postnatal Depression Scale (EPDS).ResultsAt three months postpartum, mean (SD) EPDS scores were 9.4 (5.4) for the control group and 8.6 (5.4) for the intervention group (p = 0.18). The difference between the mean EPDS scores was 0.85 (95% CI: 0.35; 1.34). The intervention group had significantly lower EPDS scores than controls in certain subgroups: women with few depressive symptoms at inclusion (EPDS

Abstract (en): The purpose of the National Health Interview Survey (NHIS) is to obtain information about the amount and distribution of illness, its effects in terms of disability and chronic impairments, and the kinds of health services people receive. Implementation of a redesigned NHIS, consisting of a basic module, a periodic module, and a topical module, began in 1997 (See NATIONAL HEALTH INTERVIEW SURVEY, 1997 [ICPSR 2954]). This final release of the 2000 NHIS contains the Household, Family, Person, Sample Adult, Sample Child, and Immunization, and Injury and Poison data files from the basic module. The 2000 NHIS also contains the Cancer Control Module (included in the Sample Adult File, Part 4), which corresponds to the Cancer Supplements of 1987 and 1992 and examines such items as diet and nutrition, use of herbal supplements, Hispanic acculturation, genetic testing, and family history. Each record in the Household-Level File (Part 1) of the basic module contains data on the type of living quarters, number of families in the household responding and not responding, and the month and year of the interview for each eligible sampling unit. The Family-Level File (Part 2) is made up of reconstructed variables from the person-level data of the basic module and includes information on sex, age, race, marital status, Hispanic origin, education, veteran status, family income, family size, major activities, health status, activity limits, and employment status, along with industry and occupation. As part of the basic module, the Person-Level File (Part 3) provides information on all family members with respect to health status, limitation of daily activities, cognitive impairment, and health conditions. Also included are data on years at current residence, region variables, height, weight, bed days, doctor visits, hospital stays, and health care access and utilization. A randomly-selected adult in each family was interviewed for the Sample Adult File (Part 4) regarding respiratory conditions, renal conditions, AIDS, joint symptoms, health status, limitation of daily activities, and behaviors such as smoking, alcohol consumption, and physical activity. The Sample Child File (Part 5) provides information from a knowledgeable adult in the household on medical conditions of one child in the household, such as respiratory problems, seizures, allergies, and use of special equipment such as hearing aids, braces, or wheelchairs. Also included are questions regarding child behavior, the use of mental health services, and Attention Deficit Hyperactivity Disorder (ADHD). The Child Immunization File (Part 6) presents information from shot records and supplies vaccination status, along with the number and dates of shots, and information about the chicken pox vaccine. The Injury and Poison Data File (Part 7) contains episode-level data for injuries and poisonings and the Injury and Poison Verbatim File (Part 8) contains verbatim comments for both injuries and poisonings. Civilian, noninstitutionalized population of the 50 United States and the District of Columbia. The NHIS uses a stratified multistage probability design. The sample for the NHIS is redesigned every decade using population data from the most recent decennial census. A redesigned sample was implemented in 1995. This new design includes a greater number of primary sampling units (PSUs) (from 198 in 1994 to 358), and a more complicated nonresponse adjustment based on household screening and oversampling of Black and Hispanic persons, for more reliable estimates of these groups. 2006-03-30 File cb03381-all_volume_2 was removed from dataset 10 and flagged as a study-level file, so that it will accompany all downloads. Dataset 10 was then empty, and was deleted.2006-03-30 File cb03381-all_volume_1 was removed from dataset 9 and flagged as a study-level file, so that it will accompany all downloads. Dataset 9 was then empty, and was deleted.2006-03-30 File MAN3381.ALL.PDF was removed from any previous datasets and flagged as a study-level file, so that it will accompany all downloads.2006-03-30 File QU3381.ALL.PDF was removed from any previous datasets and flagged as a study-level file, so that it will accompany all downloads.2005-11-04 On 2005-03-14 new files were added to one or more datasets. These files included additional setup files as well as one or more of the following: SAS program, SAS transport, SPSS portable, and Stata system files. The metadata record was revis...

TitleInterview-Based Stress Assessment Dataset

OverviewThe dataset includes stress evaluations (6 grades) assessed by interviews of 50 Japanese workers (49 completed follow-up), as well as self-reported stress and attribute information and personality information measured at the pre and one-month follow-up.

Data SourceInterviews were conducted between December 2022 and January 2023. The average follow-up period was 34.2 days.The main variables were interview-based stress evaluation, with self-reported stress (stress load, mental symptoms and physical symptoms from the Brief Job Stress Questionnaire), well-being (life satisfaction and happiness), and burnout were measured pre and 1 month later. Interview-based stress evaluations were conducted by two occupational health professionals in addition to an evaluation by the interviewer, a psychologist.

Data Description## main variables are total (time 1 self-reported stress), burnout, wellbeing, meanStressEv (mean overall stress ratings of interviewer and two evaluators), T2_loadAll, T2_mental, T2_physical, T2_burnout, and T2_wellbeing

no: Record number or identifier.age: Age of the individual in years.gender: Gender of the individual. Possible values include 'male', 'female', etc.height_cm: Height of the individual in centimeters.weight_kg: Weight of the individual in kilograms.BMI: Body Mass Index, calculated based on height and weight.drinking_freq: Frequency of alcohol consumption. Example values might be 'daily', 'weekly', 'monthly', etc.smoking_habits: Smoking habits of the individual. Possible values include 'smoker', 'non-smoker', etc.money_spending_hobby: Attitude towards spending money on hobbies. Describes how much an individual spends on their hobbies.employment_status: Current employment status. Possible values include 'employed', 'unemployed', 'self-employed', etc.full_time: employment_statuspart_time: employment_statusdiscretionary: employment_statusside_job: This variable likely indicates whether the individual has a side job in addition to their primary employment. The values could be binary (yes/no) or provide more detail about the nature of the side job.work_type: This variable probably categorizes the type of work the individual is engaged in. It could include categories such as 'full-time', 'part-time', 'contract', 'freelance', etc.fixedHours: This variable might indicate whether the individual's work schedule has fixed hours. It could be a binary variable (yes/no) indicating the presence or absence of a fixed work schedule.rotationalShifts: This variable likely denotes whether the individual works in rotational shifts. It could be a binary (yes/no) variable or provide details on the shift rotation pattern.flexibleShifts: This variable possibly reflects if the individual has flexible shift options in their work. This could involve varying start and end times or the ability to switch shifts.flexTime: This variable might indicate the presence of 'flextime' in the individual's work arrangement, allowing them to choose their working hours within certain limits.adjustableWorkHours: This variable probably denotes whether the individual has the ability to adjust their work hours, suggesting a degree of flexibility in their work schedule.discretionaryWork: This variable could indicate whether the individual's work involves a degree of discretion or autonomy in decision-making or task execution.nightShift: This variable likely indicates if the individual works night shifts. It could be a simple binary (yes/no) or provide details about the frequency or regularity of night shifts.remote_work_freq: This variable probably measures the frequency of remote work. It could include categories like 'never', 'sometimes', 'often', or 'always'.primary_job_industry: This variable likely categorizes the industry sector of the individual's primary job. It could include sectors like 'technology', 'healthcare', 'education', 'finance', etc.ind: industryind.manu–ind.gove: binary coding of industryprimary_job_role: This variable likely represents the specific role or position held by the individual in their primary job. It could include titles like 'manager', 'engineer', 'teacher', etc.job: jobjob.admi–job.carClPa: binary coding of jobjob_duration_years: This variable probably indicates the duration of the individual's current job in years. It typically measures the length of time an individual has been in their current job role.years: Without additional context, this variable could represent various time-related aspects, such as years of experience in a particular field, age in years, or years in a specific role. It generally signifies a duration or period in years.months: Similar to 'years', this variable could refer to a duration in months. It might represent age in months (for younger individuals), months of experience, or months spent in a current role or activity.job_duration_months: This variable is likely to indicate the total duration of the individual's current job in months. It's a more precise measure compared to 'job_duration_years', especially for shorter employment periods.working_days_per_week: This variable probably denotes the number of days the individual works in a typical week. It helps to understand the work pattern, whether it's a standard five-day workweek or otherwise.work_hours_per_day: This variable likely measures the average number of hours the individual works each day. It can be used to assess work-life balance and overall workload.job_workload: This variable might represent the overall workload associated with the individual's job. This could be subjective (based on the individual's perception) or objective (based on quantifiable measures like hours worked or tasks completed).job_qualitative_load: This variable likely assesses the qualitative aspects of the job's workload, such as the level of mental or emotional stress, complexity of tasks, or level of responsibility.job_control: This variable probably measures the degree of control or autonomy the individual has in their job. It could assess how much freedom they have in making decisions, planning their work, or the flexibility in how they perform their duties.hirou_1–hirou_7: Working Conditions of Fatigue Accumulation Checklisthirou_kinmu: Sum of Working Conditions of Fatigue Accumulation ChecklistWH_1–WH_2: Items related to workaholicworkaholic: Sum of items related to workaholicWE_1–WE_3: Items related to work engagementengagement: Sum of items related to work engagementrelationship_stress: This variable likely measures stress stemming from personal relationships, possibly including family, romantic partners, or friends.future_uncertainty_stress: This variable probably captures stress related to uncertainties about the future, such as career prospects, financial stability, or life goals.discrimination_stress: This variable indicates stress experienced due to discrimination, possibly based on factors like race, gender, age, or other personal characteristics.financial_stress: This variable measures stress related to financial matters, such as income, expenses, debt, or overall financial security.health_stress: This variable likely assesses stress concerning personal health or the health of loved ones.commuting_stress: This variable measures stress associated with daily commuting, such as traffic, travel time, or transportation issues.irregular_lifestyle: This variable probably indicates the presence of an irregular lifestyle, potentially including erratic sleep patterns, eating habits, or work schedules.living_env_stress: This variable likely measures stress related to the living environment, which could include housing conditions, neighborhood safety, or noise levels.unrewarded_efforts: This variable probably assesses feelings of stress or dissatisfaction due to efforts that are perceived as unrewarded or unacknowledged.other_stressors: This variable might capture additional stress factors not covered by other specific variables.coping: This variable likely assesses the individual's coping mechanisms or strategies in response to stress.support: This variable measures the level of support the individual perceives or receives, possibly from friends, family, or professional services.weekday_bedtime: This variable likely indicates the typical bedtime of the individual on weekdays.weekday_wakeup: This variable represents the typical time the individual wakes up on weekdays.holiday_bedtime: This variable indicates the typical bedtime of the individual on holidays or non-workdays.holiday_wakeup: This variable measures the typical wake-up time of the individual on holidays or non-workdays.avg_sleep_duration: This variable likely represents the average duration of sleep the individual gets, possibly averaged over a certain period.weekday_bedtime_posix: This variable might represent the weekday bedtime in POSIX time format.weekday_wakeup_posix: Similar to bedtime, this represents the weekday wakeup time in POSIX time format.holiday_bedtime_posix: This variable likely indicates the holiday bedtime in POSIX time format.holiday_wakeup_posix: This represents the holiday wakeup time in POSIX time format.weekday_bedtime_posix_hms: This variable could be the weekday bedtime in POSIX time format, specifically in hours, minutes, and seconds.weekday_wakeup_posix_hms: This variable might represent the weekday wakeup time in POSIX time format in hours, minutes, and seconds.holiday_bedtime_posix_hms: The holiday bedtime in POSIX time format, detailed to hours, minutes, and seconds.holiday_wakeup_posix_hms: The holiday wakeup time in POSIX time format, in hours, minutes, and seconds.weekday_sleep_duration: This variable likely measures the duration of sleep on weekdays.holiday_sleep_duration: This variable measures the duration of sleep on holidays or non-workdays.delta_sleep_h_w: This variable might represent the difference in sleep duration between holidays and