In 2023, 75 percent of organizations in the United Kingdom (UK) reported they had in place an employee assistance program to help manage their employees' mental health. Furthermore, 66 percent of organizations said they provide support through phased return to work and/or other reasonable adjustments.

The Ohio Public Employment Risk Reduction Program (PERRP) assures state and local government employees have safe and healthful working conditions. This dataset contains PERRP Frequently Cited Standards from Chapter 4167 of the Ohio Revised Code and the Ohio Administrative Code. It includes cited standards for all PERRP enforcement and voluntary compliance assistance visits since 2015. Users can filter the dataset by an adopted regulation, visit type, public employer type/sector, or by visit location county. Please note that state agency visit information appears under Franklin County.

BackgroundThere is limited evidence about how vocational rehabilitation (VR) for people with multiple sclerosis (MS) can be delivered through the United Kingdom’s (UK) National Health Service (NHS) and how it works.AimTo understand the mechanisms and context for implementing a VR intervention for people with MS in the NHS and develop an explanatory programme theory.MethodsA realist evaluation, including a review of evidence followed by semi-structured interviews. A realist review about VR for people with MS in the NHS was conducted on six electronic databases (PubMed, MEDLINE, PsychINFO, Web of Science, CINAHL, and EMBASE) with secondary purposive searches. Included studies were assessed for relevance and rigour. Semi-structured interviews with people with MS, employers, and healthcare professionals, were conducted remotely. Data were extracted, analysed, and synthesised to refine the programme theory and produce a logic model.ResultsData from 13 studies, and 19 interviews (10 people with MS, five employers, and four healthcare professionals) contributed to producing the programme theory. The resulting programme theory explains the implementation of VR in the NHS for MS populations, uncovering the complex interplay between the healthcare and employment sectors to influence health and employment outcomes. VR programmes that offer timely support, tailored to the needs of the person with MS, and that support and empower the employee beyond the healthcare context are most likely associated with improved employment outcomes, for example, job retention.ConclusionEmbedding VR support within the NHS requires substantial cultural and organisational change (e.g., increased staff numbers, training, and awareness about the benefits of work). This study emphasises the need to routinely identify people with MS at risk of job loss and follow a collaborative approach to address employment issues. This realist evaluation provides insight on how to improve the quality of care available to people with MS.

The NIHR is one of the main funders of public health research in the UK. Public health research falls within the remit of a range of NIHR Research Programmes, NIHR Centres of Excellence and Facilities, plus the NIHR Academy. NIHR awards from all NIHR Research Programmes and the NIHR Academy that were funded between January 2006 and the present extraction date are eligible for inclusion in this dataset. An agreed inclusion/exclusion criteria is used to categorise awards as public health awards (see below). Following inclusion in the dataset, public health awards are second level coded to one of the four Public Health Outcomes Framework domains. These domains are: (1) wider determinants (2) health improvement (3) health protection (4) healthcare and premature mortality.More information on the Public Health Outcomes Framework domains can be found here.This dataset is updated quarterly to include new NIHR awards categorised as public health awards. Please note that for those Public Health Research Programme projects showing an Award Budget of £0.00, the project is undertaken by an on-call team for example, PHIRST, Public Health Review Team, or Knowledge Mobilisation Team, as part of an ongoing programme of work.Inclusion criteriaNIHR awards are categorised as public health awards if they are determined to be ‘investigations of interventions in, or studies of, populations that are anticipated to have an effect on health or on health inequity at a population level.’ This definition of public health is intentionally broad to capture the wide range of NIHR public health awards across prevention, health improvement, health protection, and healthcare services (both within and outside of NHS settings). This dataset does not reflect the NIHR’s total investment in public health research. The intention is to showcase a subset of the wider NIHR public health portfolio. This dataset includes NIHR awards categorised as public health awards from NIHR Research Programmes and the NIHR Academy. This dataset does not currently include public health awards or projects funded by any of the three NIHR Research Schools or any of the NIHR Centres of Excellence and Facilities. Therefore, awards from the NIHR Schools for Public Health, Primary Care and Social Care, NIHR Public Health Policy Research Unit and the NIHR Health Protection Research Units do not feature in this curated portfolio.DisclaimersUsers of this dataset should acknowledge the broad definition of public health that has been used to develop the inclusion criteria for this dataset. This caveat applies to all data within the dataset irrespective of the funding NIHR Research Programme or NIHR Academy award.Please note that this dataset is currently subject to a limited data quality review. We are working to improve our data collection methodologies. Please also note that some awards may also appear in other NIHR curated datasets. Further informationFurther information on the individual awards shown in the dataset can be found on the NIHR’s Funding & Awards website here. Further information on individual NIHR Research Programme’s decision making processes for funding health and social care research can be found here.Further information on NIHR’s investment in public health research can be found as follows: NIHR School for Public Health here. NIHR Public Health Policy Research Unit here. NIHR Health Protection Research Units here. NIHR Public Health Research Programme Health Determinants Research Collaborations (HDRC) here. NIHR Public Health Research Programme Public Health Intervention Responsive Studies Teams (PHIRST) here.

Background: Digital data sources have become ubiquitous in modern culture in the era of digital technology but often tend to be under-researched because of restricted access to data sources due to fragmentation, privacy issues, or industry ownership, and the methodological complexity of demonstrating their measurable impact on human health. Even though new big data sources have shown unprecedented potential for disease diagnosis and outbreak detection, we need to investigate results in the existing literature to gain a comprehensive understanding of their impact on and benefits to human health.Objective: A systematic review of systematic reviews on identifying digital data sources and their impact area on people's health, including challenges, opportunities, and good practices.Methods: A multidatabase search was performed. Peer-reviewed papers published between January 2010 and November 2020 relevant to digital data sources on health were extracted, assessed, and reviewed.Results: The 64 reviews are covered by three domains, that is, universal health coverage (UHC), public health emergencies, and healthier populations, defined in WHO's General Programme of Work, 2019–2023, and the European Programme of Work, 2020–2025. In all three categories, social media platforms are the most popular digital data source, accounting for 47% (N = 8), 84% (N = 11), and 76% (N = 26) of studies, respectively. The second most utilized data source are electronic health records (EHRs) (N = 13), followed by websites (N = 7) and mass media (N = 5). In all three categories, the most studied impact of digital data sources is on prevention, management, and intervention of diseases (N = 40), and as a tool, there are also many studies (N = 10) on early warning systems for infectious diseases. However, they could also pose health hazards (N = 13), for instance, by exacerbating mental health issues and promoting smoking and drinking behavior among young people.Conclusions: The digital data sources presented are essential for collecting and mining information about human health. The key impact of social media, electronic health records, and websites is in the area of infectious diseases and early warning systems, and in the area of personal health, that is, on mental health and smoking and drinking prevention. However, further research is required to address privacy, trust, transparency, and interoperability to leverage the potential of data held in multiple datastores and systems. This study also identified the apparent gap in systematic reviews investigating the novel big data streams, Internet of Things (IoT) data streams, and sensor, mobile, and GPS data researched using artificial intelligence, complex network, and other computer science methods, as in this domain systematic reviews are not common.

Data set of extensive information on the changing circumstances of aged and disabled beneficiaries - Living, noninstitutionalized population of the continental United States from the Social Security Administration''''s Master Benefit Record who were new recipients of Social Security benefits (first payment in mid-1980 through mid-1981) or who had established entitlement to Medicare and were eligible for, but had not received, Social Security benefits as of July 1982. Based initially on a national cross-sectional survey of new beneficiaries in 1982, the original data base was expanded with information from administrative records and a second round of interviews in 1991. Variables measured in the original New Beneficiary Survey (NBS) include demographic characteristics; employment, marital, and childbearing histories; household composition; health; income and assets; program knowledge; and information about the spouses of married respondents. The 1991 New Beneficiary Follow-up (NBF) updated marital status, household composition, and the economic profile and contains additional sections on family contacts, postretirement employment, effects of widowhood and divorce, major reasons for changes in economic status, a more extensive section on health, and information on household moves and reasons for moving. Disabled-worker beneficiaries were also asked about their efforts to return to work, experiences with rehabilitation services, and knowledge of SSA work incentive provisions. The NBDS also links to administrative files of yearly covered earnings from 1951 to 1992, Medicare expenditures from 1984 to 1999, whether an SSI application has ever been made and payment status at five points in time, and dates of death as of spring 2001. For studies of health, the Medicare expenditure variables include inpatient hospital costs, outpatient hospital costs, home health care costs, and physicians'''' charges. The survey data cover functional capacity including ADLs and IADLs. For studies of work in retirement, the survey includes yearly information on extent of work, characteristics of the current or last job, and reasons for working or not working. No other data set has such detailed baseline survey data of a population immediately after retirement or disability, enhanced with subsequent measures over an extended period of time. The data are publicly available through NACDA and the Social Security Administration Website. * Dates of Study: 1982-1991 * Study Features: Longitudinal * Sample Size: ** 18,136 (NBS 1981) ** 12,677 (NBF 1991) Links: * 1982 (ICPSR): http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/08510 * 1991 (ICPSR): http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/06118

The Ministry reports the breakdown of the BC Employment and Assistance program each month by health authority. This data is from 1995 onwards and includes counts for cases, recipients and dependent children.

In 2023, 54 percent of organizations in the United Kingdom who were taking active steps to improve employee health and well-being reported that this had a large focus on mental health, with a further 37 percent reporting that they had moderately focused on mental health. Additionally, 27 percent of organizations reported a large focus on 'good work', which involved programs such as promoting a healthy work-life balance.

Recovery Colleges are a relatively recent initiative within mental health services. The first opened in 2009 in London and since then numbers have grown both in England and globally. They are based on principles of personal recovery in mental health, co-production between people with lived experience of mental health problems and professionals, and adult learning. Student eligibility criteria vary, but all serve people who use mental health services, with empirical evidence of benefit. Previously we developed a Recovery College fidelity measure and a preliminary change model identifying the mechanisms of action and outcomes for this group, which we refer to as service user students. The Recovery Colleges Characterisation and Testing (RECOLLECT) 2 study is a five-year (2020–2025) programme of research in England. The aim of RECOLLECT 2 is to determine Recovery Colleges’ effectiveness and cost-effectiveness and identify organisational influences on fidelity and improvements in mental health outcomes.

Analysis of relationship between inclusion of community and impact indicators.

Latest London Region Data and trends for a number of core indicators of the health of London's labour market.

• Download the London data in an Excel file

Latest core indicators at a glance

• Download as a PDF

Indicator data for all boroughs

Here is the latest reliable skills and employment data that exists for London boroughs and sub-regions. It covers the last three years, where possible, to show the latest figures and trends over time.

• Download the borough data in an Excel file

Core Indicators

Number of London residents of working age in employment

Employment rate

Number of male London residents of working age in employment

Male employment rate

Number of female London residents of working age in employment

Female employment rate

Workforce jobs

Jobs density

Number of London residents of working age who are economically inactive

Economic inactivity rate

Number of London residents aged 16+ who are unemployed (model based)

Proportion of London residents aged 16+ who are unemployed (model based)

Claimant unemployment

Claimant Count as a proportion of the working age population

Incidence of skill gaps (Numbers and rates)

GCSE (5+ A*–C) attainment including English and Maths

Number of working age people in London with no qualifications

Proportion of working age people in London with no qualifications

Number of working age people in London with Level 4+ qualifications

Proportion of working age people in London with Level 4+ qualifications

Number of people of working age claiming out of work benefits

Proportion of the working age population who claim out of work benefits

Number of young people aged 16-18 who are not in employment, education or training (NEET)

Proportion of 16-18 year olds who are NEET

Additional Indicators

Economy and Productivity

Business Demography (active enterprises, births and deaths of enterprises)

Business Demography (active enterprises, births and deaths of enterprises): Index

Business Demography (National indicators)

Demand for labour: Jobs, vacancies and skills needs

Total vacancies reported by employers

Skill shortage vacancies

JobCentre vacancies - notified

JobCentre vacancies - unfilled

Number employed by industry (working age)

Employment rates by industry (working age)

Number employed by occupation

Employment rates by occupation

Working age who are self-employed

Numbers employed in the civil service

Population and supply of labour

Population estimates (working age)

National Insurance Number Registrations of overseas nationals

Employment projections

Number employed by ethnic groups (working age)

Employment rates by ethnic groups (working age)

Number employed by age groups

Employment rates by age groups

Number employed by disability (working age)

Employment rates by disability (working age)

Employment: Part time/ Full time

Inactivity by reason (working age)

Inactivity rates by reason (working age)

JSA claimants by ethnic groups

Incapacity Benefit claimants by duration

Working age benefit claimants by statistical group

Aged 18-24, claiming JSA for over 6 months

Aged 18-24, claiming JSA for over 9 months

Aged over 25, claiming JSA for over 1 year

JSA claimant flows

JSA claimant flows: index

Skills and learning

Total achieving 5+ A*-C grades inc. English & Mathematics by characteristics

Percentage achieving 5+ A*-C grades inc. English & Mathematics by characteristics

GCE A level examination results of 16-18 year olds

Working age population by qualification level and sex

Working age rates by qualification level and sex

Qualification levels of those in employment (working age)

Number with no adult learning (working age)

Proportion with no adult learning (working age)

Received job related training in last 13 wks (working age)

Apprenticeship Programme starts and achievements - summary

Apprenticeship Programme starts and achievements - index

Apprenticeship Programme starts by level and age

Apprenticeship Programme achievements by level and age

Number of 19 year olds qualified to Level 3

Proportion of 19 year olds qualified to Level 3

Worklessness and NEETS

Worklessness by sex and age (working age)

Worklessness rates by sex and age (working age)

Worklessness numbers and rates by qualification levels (working age)

Within the borough spreadsheet, statistics are shown for boroughs, inner London, outer London, Thames Gateway London, Olympic Host Boroughs, West London, and West London Alliance.

Further Labour Market Indicator tools are available from the CESI website.

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This longitudinal survey was designed to add significantly to the amount of detailed information available on the economic situation of households and persons in the United States. These data examine the level of economic well-being of the population and also provide information on how economic situations relate to the demographic and social characteristics of individuals. There are three basic elements contained in the survey. The first is a control card that records basic social and demographic characteristics for each person in a household, as well as changes in such characteristics over the course of the interviewing period. The second element is the core portion of the questionnaire, with questions repeated at each interview on labor force activity, types and amounts of income, participation in various cash and noncash benefit programs, attendance in postsecondary schools, private health insurance coverage, public or subsidized rental housing, low-income energy assistance, and school breakfast and lunch participation. The third element consists of topical modules which are series of supplemental questions asked during selected household visits. No topical modules were created for the first or second waves. The Wave III Rectangular Core and Topical Module File offers both the core data and additional data on (1) education and work history and (2) health and disability. In the areas of education and work history, data are supplied on the highest level of schooling attained, courses or programs studied in high school and after high school, whether the respondent received job training, and if so, for how long and under what program (e.g., CETA or WIN). Other items pertain to the respondent's general job history and include a description of selected previous jobs, duration of jobs, and reasons for periods spent not working. Health and disability variables present information on the general condition of the respondent's health, functional limitations, work disability, and the need for personal assistance. Data are also provided on hospital stays or periods of illness, health facilities used, and whether health insurance plans (private or Medicare) were available. Respondents whose children had physical, mental, or emotional problems were questioned about the causes of the problems and whether the children attended regular schools. The Wave IV Rectangular Core and Topical Module file contains both the core data and sets of questions exploring the subjects of (1) assets and liabilities, (2) retirement and pension coverage, and (3) housing costs, conditions, and energy usage. Some of the major assets for which data are provided are savings accounts, stocks, mutual funds, bonds, Keogh and IRA accounts, home equity, life insurance, rental property, and motor vehicles. Data on unsecured liabilities such as loans, credit cards, and medical bills also are included. Retirement and pension information covers such items as when respondents expect to stop working, whether they will receive retirement benefits, whether their employers have retirement plans, if so whether they are eligible, and how much they expect to receive per year from these plans. In the category of housing costs, conditions, and energy usage, variables pertain to mortgage payments, real estate taxes, fire insurance, principal owed, when the mortgage was obtained, interest rates, rent, type of fuel used, heating facilities, appliances, and vehicles. The Wave V topical modules explore the subject areas of (1) child care, (2) welfare history and child support, (3) reasons for not working/reservation wage, and (4) support for nonhousehold members/work-related expenses. Data on child care include items on child care arrangements such as who provides the care, the number of hours of care per week, where the care is provided, and the cost. Questions in the areas of welfare history and child support focus on receipt of aid from specific welfare programs and child support agreements and their fulfillment. The reasons for not working/reservation wage module presents data on why persons are not in the labor force and the conditions under which they might join the labor force. Additional variables cover job search activities, pay rate required, and reason for refusal of a job offer. The set of questions dealin

Background: Digital data sources have become ubiquitous in modern culture in the era of digital technology but often tend to be under-researched because of restricted access to data sources due to fragmentation, privacy issues, or industry ownership, and the methodological complexity of demonstrating their measurable impact on human health. Even though new big data sources have shown unprecedented potential for disease diagnosis and outbreak detection, we need to investigate results in the existing literature to gain a comprehensive understanding of their impact on and benefits to human health.Objective: A systematic review of systematic reviews on identifying digital data sources and their impact area on people's health, including challenges, opportunities, and good practices.Methods: A multidatabase search was performed. Peer-reviewed papers published between January 2010 and November 2020 relevant to digital data sources on health were extracted, assessed, and reviewed.Results: The 64 reviews are covered by three domains, that is, universal health coverage (UHC), public health emergencies, and healthier populations, defined in WHO's General Programme of Work, 2019–2023, and the European Programme of Work, 2020–2025. In all three categories, social media platforms are the most popular digital data source, accounting for 47% (N = 8), 84% (N = 11), and 76% (N = 26) of studies, respectively. The second most utilized data source are electronic health records (EHRs) (N = 13), followed by websites (N = 7) and mass media (N = 5). In all three categories, the most studied impact of digital data sources is on prevention, management, and intervention of diseases (N = 40), and as a tool, there are also many studies (N = 10) on early warning systems for infectious diseases. However, they could also pose health hazards (N = 13), for instance, by exacerbating mental health issues and promoting smoking and drinking behavior among young people.Conclusions: The digital data sources presented are essential for collecting and mining information about human health. The key impact of social media, electronic health records, and websites is in the area of infectious diseases and early warning systems, and in the area of personal health, that is, on mental health and smoking and drinking prevention. However, further research is required to address privacy, trust, transparency, and interoperability to leverage the potential of data held in multiple datastores and systems. This study also identified the apparent gap in systematic reviews investigating the novel big data streams, Internet of Things (IoT) data streams, and sensor, mobile, and GPS data researched using artificial intelligence, complex network, and other computer science methods, as in this domain systematic reviews are not common.

Demographic, clinical, and employment characteristics of participants.

This dataset is the result of the health extension workers survey that was conducted to gather data for the process evaluation part of the impact evaluation of the Alive & Thrive (A&T) interventions in Ethiopia. The broad objective of the impact evaluation in Ethiopia is to measure the impact of A&T’s community-based interventions (CBI), delivered through the government's health extension program (HEP) platform, in the reduction of stunting and improvement of infant and young child feeding (IYCF) practices in two regions where the integrated family health program (IFHP) operates, namely Tigray and SNNPR (Southern Nations, Nationalities, and People’s Region). A&T is a six-year initiative funded by the Bill & Melinda Gates Foundation to facilitate change for improved infant and young child feeding (IYCF) practices at scale in Bangladesh, Ethiopia, and Viet Nam. The goal of A&T is to reduce avoidable death and disability due to suboptimal IYCF in the developing world by increasing exclusive breastfeeding (EBF) until 6 months of age and reducing stunting of children 0-24 months of age. The process evaluation (PE) is intended to answer one of the major learning objectives for the overall initiative, i.e., how A&T interventions achieve their impact. In Ethiopia, the studies that have been conducted as part of the process evaluation include: 1) a case study in 2011 to explore the extent of the training rollout among frontline health workers (FHWs) and to assess FHW and household exposure to interpersonal communication (IPC) tools developed by A&T, 2) a qualitative study in 2012 that included in-depth interviews with health extension workers (HEWs), HEW supervisors, and volunteers and shorter interviews with mothers and fathers to understand modalities of service delivery under IFHP and other A&T platforms, and 3) a quantitative survey in 2013 among FHWs and households to assess different aspects of service delivery and exposure to A&T community based program interventions. Three types of FHW questionnaires (HEW, VCHP, and supervisors) applied to health staff who are closest to the community or work in the community. The health extension workers (HEW) questionnaire, along with the other two FHW questionnaires, were aimed at assessing four major issues: 1) FHW’s exposure to training and IPC tools provided by the A&T program, 2) FHW’s exposure to the A&T multi-media components (radio and television messages), 3) FHW’s knowledge and understanding of IYCF and nutrition, 4) the FHW work environment related to IYCF service delivery (motivation, supportive supervision, time commitments to different tasks, and the ability to integrate sustained IYCF counseling into daily routines).

This data collection is part of a longitudinal survey designed to provide detailed information on the economic situation of households and persons in the United States. These data examine the distribution of income, wealth, and poverty in American society and gauge the effects of federal and state programs on the well-being of families and individuals. There are three basic elements contained in the survey. The first is a control card that records basic social and demographic characteristics for each person in a household, as well as changes in such characteristics over the course of the interviewing period. These include age, sex, race, ethnic origin, marital status, household relationship, education, and veteran status. Limited data are provided on housing unit characteristics such as units in structure, tenure, access, and complete kitchen facilities. The second element is the core portion of the questionnaire, with questions repeated at each interview on labor force activity, types and amounts of income, and participation in various cash and noncash benefit programs for each month of the four-month reference period. Data for employed persons include number of hours and weeks worked, earnings, and weeks without a job. Nonworkers are classified as unemployed or not in the labor force. In addition to providing income data associated with labor force activity, the core questions cover nearly 50 other types of income. Core data also include postsecondary school attendance, public or private subsidized rental housing, low-income energy assistance, and school breakfast and lunch participation. The third element consists of topical modules, which are a series of supplemental questions asked during selected household visits. Topical modules include some core data to link individuals to the core files. The Wave 1 Topical Module covers recipiency and employment history. The Wave 2 Topical Module includes work disability, education and training, marital, migration, and fertility histories, and household relationships. The Wave 3 Topical Module covers medical expenses and utilization of health care, work-related expenses and child support, assets and liabilities, real estate, shelter costs, dependent care and vehicles, value of business, interest earning accounts, rental properties, stocks and mutual fund shares, mortgages, and other assets. The Wave 4 Topical Module covers disability, taxes, child care, and annual income and retirement accounts. Data in the Wave 5 Topical Module describe child support, school enrollment and financing, support for nonhousehold members, adult and child disability, and employer-provided health benefits. Data in the Wave 6 Topical Module provide information on medical expenses, work-related expenses and child support paid, assets and liabilities, real estate, shelter costs, dependent care and vehicles, value of business, interest-earning accounts, rental properties, stock and mutual fund shares, mortgages, other financial investments. Wave 7 Topical Module includes annual income and retirement accounts, home health care, retirement expectations and pension plan coverage, and taxes. Wave 8 Topical Module covers adult well-being and welfare reform. Wave 9 Topical Module is the same as Waves 3 and 6 Topical Modules. Wave 10 Topical Module focuses on work schedules, disablility, taxes, child care, and annual income and retirement. Wave 11 includes child support, support for nonhousehold members, and adult and child disability. Wave 12 Topical Module is the same as Waves 3, 6, and 9 but also includes child well-being.

This data collection is part of a longitudinal survey designed to provide detailed information on the economic situation of households and persons in the United States. These data examine the distribution of income, wealth, and poverty in American society and gauge the effects of federal and state programs on the well-being of families and individuals. There are three basic elements contained in the survey. The first is a control card that records basic social and demographic characteristics for each person in a household, as well as changes in such characteristics over the course of the interviewing period. These include age, sex, race, ethnic origin, marital status, household relationship, education, and veteran status. Limited data are provided on housing unit characteristics such as units in structure, tenure, access, and complete kitchen facilities. The second element is the core portion of the questionnaire, with questions repeated at each interview on labor force activity, types and amounts of income, and participation in various cash and noncash benefit programs for each month of the four- month reference period. Data for employed persons include number of hours and weeks worked, earnings, and weeks without a job. Nonworkers are classified as unemployed or not in the labor force. In addition to providing income data associated with labor force activity, the core questions cover nearly 50 other types of income. Core data also include postsecondary school attendance, public or private subsidized rental housing, low-income energy assistance, and school breakfast and lunch participation. The third element consists of topical modules, which are a series of supplemental questions asked during selected household visits. Topical modules include some core data to link individuals to the core files. The Wave 1 Topical Module covers recipiency and employment history. The Wave 2 Topical Module includes work disability, education and training, marital, migration, and fertility histories, and household relationships. The Wave 3 Topical Module covers medical expenses and utilization of health care, work-related expenses and child support, assets and liabilities, real estate, shelter costs, dependent care, vehicles, value of business, interest earning accounts, rental properties, stocks and mutual fund shares, mortgages, and other assets. The Wave 4 Topical Module covers work schedule, taxes, child care, and annual income and retirement accounts. Data in the Wave 5 Topical Module describe child support agreements, school enrollment and financing, support for non-household members, adult and child disability, and employer-provided health benefits. The Wave 6 Topical Module covers medical expenses and utilization of health care, work related expenses, child support paid and child care poverty, assets and liabilities, real estate, shelter costs, dependent care, vehicles, value of business, interest earning accounts, rental properties, stock and mutual fund shares, mortgages, and other financial investments. The Wave 7 Topical Module covers informal caregiving, children's well-being, and annual income and retirement accounts. The Wave 8 Topical Module and Wave 8 Welfare Reform Topical Module cover child support agreements, support for nonhousehold members, adult disability, child disability, adult well-being, and welfare reform. The Wave 9 Topical Module covers medical expenses and utilization of heath care (adults and children), work related expenses, child support paid and child care poverty, assets and liabilities, real estate, shelter costs, dependent care, vehicles, value of business, interest earnings accounts, rental properties, stocks and mutual fund shares mortgages, and other financial investments (Source: downloaded from ICPSR 7/13/10)