The Annual Population Survey (APS) is a major survey series, which aims to provide data that can produce reliable estimates at the local authority level. Key topics covered in the survey include education, employment, health and ethnicity. The APS comprises key variables from the Labour Force Survey (LFS), all its associated LFS boosts and the APS boost. The APS aims to provide enhanced annual data for England, covering a target sample of at least 510 economically active persons for each Unitary Authority (UA)/Local Authority District (LAD) and at least 450 in each Greater London Borough. In combination with local LFS boost samples, the survey provides estimates for a range of indicators down to Local Education Authority (LEA) level across the United Kingdom.

For further detailed information about methodology, users should consult the Labour Force Survey User Guide, included with the APS documentation. For variable and value labelling and coding frames that are not included either in the data or in the current APS documentation, users are advised to consult the latest versions of the LFS User Guides, which are available from the ONS Labour Force Survey - User Guidance webpages.

Occupation data for 2021 and 2022
The ONS has identified an issue with the collection of some occupational data in 2021 and 2022 data files in a number of their surveys. While they estimate any impacts will be small overall, this will affect the accuracy of the breakdowns of some detailed (four-digit Standard Occupational Classification (SOC)) occupations, and data derived from them. None of ONS' headline statistics, other than those directly sourced from occupational data, are affected and you can continue to rely on their accuracy. The affected datasets have now been updated. Further information can be found in the ONS article published on 11 July 2023: Revision of miscoded occupational data in the ONS Labour Force Survey, UK: January 2021 to September 2022

APS Well-Being Datasets
From 2012-2015, the ONS published separate APS datasets aimed at providing initial estimates of subjective well-being, based on the Integrated Household Survey. In 2015 these were discontinued. A separate set of well-being variables and a corresponding weighting variable have been added to the April-March APS person datasets from A11M12 onwards. Further information on the transition can be found in the Personal well-being in the UK: 2015 to 2016 article on the ONS website.

APS disability variables
Over time, there have been some updates to disability variables in the APS. An article explaining the quality assurance investigations on these variables that have been conducted so far is available on the ONS Methodology webpage.

• age: single year of age, year and month of birth, age completed full-time education and age obtained highest qualification, age of oldest dependent child and age of youngest dependent child
• family unit and household: including a number of variables concerning the number of dependent children in the family according to their ages, relationship to head of household and relationship to head of family
• nationality and country of origin
• geography: including county, unitary/local authority, place of work, Nomenclature of Territorial Units for Statistics 2 (NUTS2) and NUTS3 regions, and whether lives and works in same local authority district
• health: including main health problem, and current and past health problems
• education and apprenticeship: including numbers and subjects of various qualifications and variables concerning apprenticeships
• industry: including industry, industry class and industry group for main, second and last job, and industry made redundant from
• occupation: including 4-digit Standard Occupational Classification (SOC) for main, second and last job and job made redundant from
• system variables: including week number when interview took place and number of households at address

The Secure Access data have more restrictive access conditions than those made available under the standard EUL. Prospective users will need to gain ONS Accredited Researcher status, complete an extra application form and demonstrate to the data owners exactly why they need access to the additional variables. Users are strongly advised to first obtain the standard EUL version of the data to see if they are sufficient for their research requirements.

For the third edition (July 2022), the qualification variable QULNOW has been added to the data file.

The National Child Development Study (NCDS) is a continuing longitudinal study that seeks to follow the lives of all those living in Great Britain who were born in one particular week in 1958. The aim of the study is to improve understanding of the factors affecting human development over the whole lifespan.

The NCDS has its origins in the Perinatal Mortality Survey (PMS) (the original PMS study is held at the UK Data Archive under SN 2137). This study was sponsored by the National Birthday Trust Fund and designed to examine the social and obstetric factors associated with stillbirth and death in early infancy among the 17,000 children born in England, Scotland and Wales in that one week. Selected data from the PMS form NCDS sweep 0, held alongside NCDS sweeps 1-3, under SN 5565.

Survey and Biomeasures Data (GN 33004):
To date there have been nine attempts to trace all members of the birth cohort in order to monitor their physical, educational and social development. The first three sweeps were carried out by the National Children's Bureau, in 1965, when respondents were aged 7, in 1969, aged 11, and in 1974, aged 16 (these sweeps form NCDS1-3, held together with NCDS0 under SN 5565). The fourth sweep, also carried out by the National Children's Bureau, was conducted in 1981, when respondents were aged 23 (held under SN 5566). In 1985 the NCDS moved to the Social Statistics Research Unit (SSRU) - now known as the Centre for Longitudinal Studies (CLS). The fifth sweep was carried out in 1991, when respondents were aged 33 (held under SN 5567). For the sixth sweep, conducted in 1999-2000, when respondents were aged 42 (NCDS6, held under SN 5578), fieldwork was combined with the 1999-2000 wave of the 1970 Birth Cohort Study (BCS70), which was also conducted by CLS (and held under GN 33229). The seventh sweep was conducted in 2004-2005 when the respondents were aged 46 (held under SN 5579), the eighth sweep was conducted in 2008-2009 when respondents were aged 50 (held under SN 6137) and the ninth sweep was conducted in 2013 when respondents were aged 55 (held under SN 7669).

Four separate datasets covering responses to NCDS over all sweeps are available. National Child Development Deaths Dataset: Special Licence Access (SN 7717) covers deaths; National Child Development Study Response and Outcomes Dataset (SN 5560) covers all other responses and outcomes; National Child Development Study: Partnership Histories (SN 6940) includes data on live-in relationships; and National Child Development Study: Activity Histories (SN 6942) covers work and non-work activities. Users are advised to order these studies alongside the other waves of NCDS.

From 2002-2004, a Biomedical Survey was completed and is available under End User Licence (EUL) (SN 8731) and Special Licence (SL) (SN 5594). Proteomics analyses of blood samples are available under SL SN 9254.

Linked Geographical Data (GN 33497):
A number of geographical variables are available, under more restrictive access conditions, which can be linked to the NCDS EUL and SL access studies.

Linked Administrative Data (GN 33396):
A number of linked administrative datasets are available, under more restrictive access conditions, which can be linked to the NCDS EUL and SL access studies. These include a Deaths dataset (SN 7717) available under SL and the Linked Health Administrative Datasets (SN 8697) available under Secure Access.

Additional Sub-Studies (GN 33562):
In addition to the main NCDS sweeps, further studies have also been conducted on a range of subjects such as parent migration, unemployment, behavioural studies and respondent essays. The full list of NCDS studies available from the UK Data Service can be found on the NCDS series access data webpage.

How to access genetic and/or bio-medical sample data from a range of longitudinal surveys:
For information on how to access biomedical data from NCDS that are not held at the UKDS, see the CLS Genetic data and biological samples webpage.

Further information about the full NCDS series can be found on the Centre for Longitudinal Studies website.
The National Child Development Deaths Dataset, 1958-2014: Special Licence Access contains data on known deaths among members of the NCDS birth cohort from 1958 to 2013. Information on deaths has been taken from the records maintained by the organisations responsible for the study over the life time of the study: the National Birthday Trust Fund, the National Children’s Bureau (NCB), the Social Statistics Research Unit (SSRU) and the CLS. The information has been gleaned from a variety of sources, including death certificates and other information from the National Health Service Central Register (NHSCR), and from relatives and friends during survey activities and cohort maintenance work by telephone, letter and e-mail. It includes all deaths up to 31st December 2013. In only 6 cases are the date of death unknown. By the end of December 8.7 per cent of the cohort were known to have died.

The National Child Development Study Response and Outcomes Dataset, 1958-2013 (SN 5560) covers other responses and outcomes of the cohort members and should be used alongside this dataset.

For the 3rd edition (July 2018) an updated version of the data was deposited. The new edition includes data on known deaths among members of the National Child Development Study (NCDS) birth cohort up to 2016. The user guide has also been updated.

Abstract copyright UK Data Service and data collection copyright owner.The Crime Survey for England and Wales (CSEW) asks a sole adult in a random sample of households about their, or their household's, experience of crime victimisation in the previous 12 months. These are recorded in the victim form data file (VF). A wide range of questions are then asked, covering demographics and crime-related subjects such as attitudes to the police and the criminal justice system (CJS). These variables are contained within the non-victim form (NVF) data file. In 2009, the survey was extended to children aged 10-15 years old; one resident of that age range was also selected from the household and asked about their experience of crime and other related topics. The first set of children's data covered January-December 2009 and is held separately under SN 6601. From 2009-2010, the children's data cover the same period as the adult data and are included with the main study.The Telephone-operated Crime Survey for England and Wales (TCSEW) became operational on 20 May 2020. It was a replacement for the face-to-face CSEW, which was suspended on 17 March 2020 because of the coronavirus (COVID-19) pandemic. It was set up with the intention of measuring the level of crime during the pandemic. As the pandemic continued throughout the 2020/21 survey year, questions have been raised as to whether the year ending March 2021 TCSEW is comparable with estimates produced in earlier years by the face-to-face CSEW. The ONS Comparability between the Telephone-operated Crime Survey for England and Wales and the face-to-face Crime Survey for England and Wales report explores those factors that may have a bearing on the comparability of estimates between the TCSEW and the former CSEW. These include survey design, sample design, questionnaire changes and modal changes.More general information about the CSEW may be found on the ONS Crime Survey for England and Wales web page and for the previous BCS, from the GOV.UK BCS Methodology web page.History - the British Crime SurveyThe CSEW was formerly known as the British Crime Survey (BCS), and has been in existence since 1981. The 1982 and 1988 BCS waves were also conducted in Scotland (data held separately under SNs 4368 and 4599). Since 1993, separate Scottish Crime and Justice Surveys have been conducted. Up to 2001, the BCS was conducted biennially. From April 2001, the Office for National Statistics took over the survey and it became the CSEW. Interviewing was then carried out continually and reported on in financial year cycles. The crime reference period was altered to accommodate this. Secure Access CSEW dataIn addition to the main survey, a series of questions covering drinking behaviour, drug use, self-offending, gangs and personal security, and intimate personal violence (IPV) (including stalking and sexual victimisation) are asked of adults via a laptop-based self-completion module (questions may vary over the years). Children aged 10-15 years also complete a separate self-completion questionnaire. The questionnaires are included in the main documentation, but the data are only available under Secure Access conditions (see SN 7280), not with the main study. In addition, from 2011 onwards, lower-level geographic variables are also available under Secure Access conditions (see SN 7311).New methodology for capping the number of incidents from 2017-18The CSEW datasets available from 2017-18 onwards are based on a new methodology of capping the number of incidents at the 98th percentile. Incidence variables names have remained consistent with previously supplied data but due to the fact they are based on the new 98th percentile cap, and old datasets are not, comparability has been lost with years prior to 2012-2013. More information can be found in the 2017-18 User Guide (see SN 8464) and the article ‘Improving victimisation estimates derived from the Crime Survey for England and Wales’. Latest edition informationFor the second edition (November 2020), the correct version of the 2018-2019 children non-victim form data has been deposited, previously the 2017-2018 data was made available.

Background:
The Millennium Cohort Study (MCS) is a large-scale, multi-purpose longitudinal dataset providing information about babies born at the beginning of the 21st century, their progress through life, and the families who are bringing them up, for the four countries of the United Kingdom. The original objectives of the first MCS survey, as laid down in the proposal to the Economic and Social Research Council (ESRC) in March 2000, were:

• to chart the initial conditions of social, economic and health advantages and disadvantages facing children born at the start of the 21st century, capturing information that the research community of the future will require
• to provide a basis for comparing patterns of development with the preceding cohorts (the National Child Development Study, held at the UK Data Archive under GN 33004, and the 1970 Birth Cohort Study, held under GN 33229)
• to collect information on previously neglected topics, such as fathers' involvement in children's care and development
• to focus on parents as the most immediate elements of the children's 'background', charting their experience as mothers and fathers of newborn babies in the year 2000, recording how they (and any other children in the family) adapted to the newcomer, and what their aspirations for her/his future may be
• to emphasise intergenerational links including those back to the parents' own childhood
• to investigate the wider social ecology of the family, including social networks, civic engagement and community facilities and services, splicing in geo-coded data when available

Additional objectives subsequently included for MCS were:

• to provide control cases for the national evaluation of Sure Start (a government programme intended to alleviate child poverty and social exclusion)
• to provide samples of adequate size to analyse and compare the smaller countries of the United Kingdom, and include disadvantaged areas of England

Further information about the MCS can be found on the Centre for Longitudinal Studies web pages.

The content of MCS studies, including questions, topics and variables can be explored via the CLOSER Discovery website.

The first sweep (MCS1) interviewed both mothers and (where resident) fathers (or father-figures) of infants included in the sample when the babies were nine months old, and the second sweep (MCS2) was carried out with the same respondents when the children were three years of age. The third sweep (MCS3) was conducted in 2006, when the children were aged five years old, the fourth sweep (MCS4) in 2008, when they were seven years old, the fifth sweep (MCS5) in 2012-2013, when they were eleven years old, the sixth sweep (MCS6) in 2015, when they were fourteen years old, and the seventh sweep (MCS7) in 2018, when they were seventeen years old.

End User Licence versions of MCS studies:
The End User Licence (EUL) versions of MCS1, MCS2, MCS3, MCS4, MCS5, MCS6 and MCS7 are held under UK Data Archive SNs 4683, 5350, 5795, 6411, 7464, 8156 and 8682 respectively. The longitudinal family file is held under SN 8172.

Sub-sample studies:
Some studies based on sub-samples of MCS have also been conducted, including a study of MCS respondent mothers who had received assisted fertility treatment, conducted in 2003 (see EUL SN 5559). Also, birth registration and maternity hospital episodes for the MCS respondents are held as a separate dataset (see EUL SN 5614).


How to access genetic and/or bio-medical sample data from a range of longitudinal surveys:
For information on how to access biomedical data from MCS that are not held at the UKDS, see the CLS Genetic data and biological samples webpage.

Secure Access datasets:
Secure Access versions of the MCS have more restrictive access conditions than versions available under the standard End User Licence or Special Licence (see 'Access data' tab above).

Secure Access versions of the MCS include:

• detailed sensitive variables not available under EUL. These have been grouped thematically and are held under SN 8753 (socio-economic, accommodation and occupational data), SN 8754 (self-reported health, behaviour and fertility), SN 8755 (demographics, language and religion) and SN 8756 (exact participation dates). These files replace previously available studies held under SNs 8456 and 8622-8627
  
• detailed geographical identifier files which are grouped by sweep held under SN 7758 (MCS1), SN 7759 (MCS2), SN 7760 (MCS3), SN 7761 (MCS4), SN 7762 (MCS5 2001 Census Boundaries), SN 7763 (MCS5 2011 Census Boundaries), SN 8231 (MCS6 2001 Census Boundaries), SN 8232 (MCS6 2011 Census Boundaries), SN 8757 (MCS7), SN 8758 (MCS7 2001 Census Boundaries) and SN 8759 (MCS7 2011 Census Boundaries). These files replace previously available files grouped by geography SN 7049 (Ward level), SN 7050 (Lower Super Output Area level), and SN 7051 (Output Area level)
• linked education administrative datasets for Key Stages 1, 2, 4 and 5 held under SN 8481 (England). This replaces previously available datasets for Key Stage 1 (SN 6862) and Key Stage 2 (SN 7712)
  
• linked education administrative datasets for Key Stage 1 held under SN 7414 (Scotland)
• linked education administrative dataset for Key Stages 1, 2, 3 and 4 under SN 9085 (Wales)
  
• linked NHS Patient Episode Database for Wales (PEDW) for MCS1 – MCS5 held under SN 8302
• linked Scottish Medical Records data held under SNs 8709, 8710, 8711, 8712, 8713 and 8714;
• Banded Distances to English Grammar Schools for MCS5 held under SN 8394
• linked Health Administrative Datasets (Hospital Episode Statistics) for England for years 2000-2019 held under SN 9030
• linked Hospital of Birth data held under SN 5724.

The linked education administrative datasets held under SNs 8481,7414 and 9085 may be ordered alongside the MCS detailed geographical identifier files only if sufficient justification is provided in the application. Users are also only allowed access to either 2001 or 2011 of Geographical Identifiers Census Boundaries studies. So for MCS5 either SN 7762 (2001 Census Boundaries) or SN 7763 (2011 Census Boundaries), for the MCS6 users are only allowed either SN 8231 (2001 Census Boundaries) or SN 8232 (2011 Census Boundaries); and the same applies for MCS7 so either SN 8758 (2001 Census Boundaries) or SN 8759 (2011 Census Boundaries).

Researchers applying for access to the Secure Access MCS datasets should indicate on their ESRC Accredited Researcher application form the EUL dataset(s) that they also wish to access (selected from the MCS Series Access web page).

The seventh sweep of the Millennium Cohort Study (MCS7) was carried out when the cohort members were 17 years old. As 17 is a key transitional age, the sweep purposefully focused on engaging with the cohort members themselves (in addition to their parents). MCS7 marks an important transitional time in the cohort members' lives, where educational and occupational paths can diverge significantly. It is also an important age in data collection terms since it may be the last sweep at which parents are interviewed and it is an age when direct engagement with the cohort members themselves rather than their families is crucial to the long term viability of the study. To reflect this, face-to-face interviews with the cohort members have been conducted for the first time. Cohort members were also asked to do a range of other activities including filling in a self-completion questionnaire on the interviewer's tablet, completing a cognitive assessment (number activity) and having their height, weight and body fat measurements taken. In addition, they were asked to complete a short online questionnaire after the visit.

Parents were still interviewed at MCS7. Resident parents were asked to complete a household interview and a short online questionnaire, and one parent was asked to complete a Strengths and Difficulties Questionnaire (SDQ) about the cohort member. Cohort members who were either unable or unwilling to complete the main survey were asked to complete a short follow up questionnaire online after the fieldwork finished. This contained some key questions and was designed to boost response and maintain engagement.

For the second edition (March 2021), two new

Central African Republic CF: Prevalence of Underweight: Weight for Age: Female: % of Children Under 5 data was reported at 20.200 % in 2019. This records an increase from the previous number of 18.100 % for 2018. Central African Republic CF: Prevalence of Underweight: Weight for Age: Female: % of Children Under 5 data is updated yearly, averaging 20.200 % from Dec 2000 (Median) to 2019, with 7 observations. The data reached an all-time high of 23.100 % in 2006 and a record low of 18.100 % in 2018. Central African Republic CF: Prevalence of Underweight: Weight for Age: Female: % of Children Under 5 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Central African Republic – Table CF.World Bank.WDI: Social: Health Statistics. Prevalence of underweight, female, is the percentage of girls under age 5 whose weight for age is more than two standard deviations below the median for the international reference population ages 0-59 months. The data are based on the WHO's 2006 Child Growth Standards.;UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology.;;Undernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF). Estimates are from national survey data. Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition.

Cameroon CM: Prevalence of Overweight: Weight for Height: % of Children Under 5 data was reported at 11.000 % in 2018. This records an increase from the previous number of 6.700 % for 2014. Cameroon CM: Prevalence of Overweight: Weight for Height: % of Children Under 5 data is updated yearly, averaging 8.200 % from Dec 1991 (Median) to 2018, with 7 observations. The data reached an all-time high of 11.000 % in 2018 and a record low of 4.700 % in 1991. Cameroon CM: Prevalence of Overweight: Weight for Height: % of Children Under 5 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Cameroon – Table CM.World Bank.WDI: Social: Health Statistics. Prevalence of overweight children is the percentage of children under age 5 whose weight for height is more than two standard deviations above the median for the international reference population of the corresponding age as established by the WHO's 2006 Child Growth Standards.;UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology.;See SH.STA.OWGH.ME.ZS for aggregation;Estimates of overweight children are from national survey data. Once considered only a high-income economy problem, overweight children have become a growing concern in developing countries. Research shows an association between childhood obesity and a high prevalence of diabetes, respiratory disease, high blood pressure, and psychosocial and orthopedic disorders (de Onis and Blössner 2003). Childhood obesity is associated with a higher chance of obesity, premature death, and disability in adulthood. In addition to increased future risks, obese children experience breathing difficulties and increased risk of fractures, hypertension, early markers of cardiovascular disease, insulin resistance, and psychological effects. Children in low- and middle-income countries are more vulnerable to inadequate nutrition before birth and in infancy and early childhood. Many of these children are exposed to high-fat, high-sugar, high-salt, calorie-dense, micronutrient-poor foods, which tend be lower in cost than more nutritious foods. These dietary patterns, in conjunction with low levels of physical activity, result in sharp increases in childhood obesity, while under-nutrition continues.

Kiribati KI: Prevalence of Stunting: Height for Age: % of Children Under 5: Male data was reported at 16.400 % in 2018. Kiribati KI: Prevalence of Stunting: Height for Age: % of Children Under 5: Male data is updated yearly, averaging 16.400 % from Dec 2018 (Median) to 2018, with 1 observations. The data reached an all-time high of 16.400 % in 2018 and a record low of 16.400 % in 2018. Kiribati KI: Prevalence of Stunting: Height for Age: % of Children Under 5: Male data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Kiribati – Table KI.World Bank.WDI: Social: Health Statistics. Prevalence of stunting, male, is the percentage of boys under age 5 whose height for age is more than two standard deviations below the median for the international reference population ages 0-59 months. For children up to two years old height is measured by recumbent length. For older children height is measured by stature while standing. The data are based on the WHO's 2006 Child Growth Standards.;UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology.;;Undernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF). Estimates are from national survey data. Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition.

This data shows healthcare utilization for asthma by Allegheny County residents 18 years of age and younger. It counts asthma-related visits to the Emergency Department (ED), hospitalizations, urgent care visits, and asthma controller medication dispensing events. The asthma data was compiled as part of the Allegheny County Health Department’s Asthma Task Force, which was established in 2018. The Task Force was formed to identify strategies to decrease asthma inpatient and emergency utilization among children (ages 0-18), with special focus on children receiving services funded by Medicaid. Data is being used to improve the understanding of asthma in Allegheny County, and inform the recommended actions of the task force. Data will also be used to evaluate progress toward the goal of reducing asthma-related hospitalization and ED visits. Regarding this data, asthma is defined using the International Classification of Diseases, Tenth Revision (IDC-10) classification system code J45.xxx. The ICD-10 system is used to classify diagnoses, symptoms, and procedures in the U.S. healthcare system. Children seeking care for an asthma-related claim in 2017 are represented in the data. Data is compiled by the Health Department from medical claims submitted to three health plans (UPMC, Gateway Health, and Highmark). Claims may also come from people enrolled in Medicaid plans managed by these insurers. The Health Department estimates that 74% of the County’s population aged 0-18 is represented in the data. Users should be cautious of using administrative claims data as a measure of disease prevalence and interpreting trends over time. Missing from the data are the uninsured, members in participating plans enrolled for less than 90 continuous days in 2017, children with an asthma-related condition that did not file a claim in 2017, and children participating in plans managed by insurers that did not share data with the Health Department. Data users should also be aware that diagnoses may also be subject to misclassification, and that children with an asthmatic condition may not be diagnosed. It is also possible that some children may be counted more than once in the data if they are enrolled in a plan by more than one participating insurer and file a claim on each policy in the same calendar year.

Children from disadvantaged families tend to have limited language skills compared to their advantaged peers. While many factors contribute to language ability, two aspects of the early caregiving environment are known to be correlated with child language outcomes 1) caregiver-child book reading and 2) caregiver contingent talk. Contingent talk refers to a style of communication whereby the caregiver talks about what is in their infant's current focus of attention. This style of talking can be facilitated when parents read books with their babies. The aim of this research was to establish whether asking parents to engage in contingent talk in the context of book reading promotes vocabulary learning. This study compared the effects of an intervention to promote contingent talk against a control where parents were given books but not given any training in how to read them in a contingent manner. The study included children from socio-economically advantaged and disadvantaged families.The most cost-effective way to tackle the root causes of many social and educational problems is to intervene early in children's lives, before the problems have had a chance to entrench. Key to this strategy is improving children's language development in the early years. Children who enter school with good language skills have better chances in school, better chances of entering higher education, and better economic success in adulthood. Reading is very effective at boosting children's language. Children who read regularly with their parents or carers tend to learn language faster, enter school with a larger vocabulary of words and become more successful readers in school. Because of this, local authorities often commission services to promote family-based shared book reading (e.g. the Bookstart programme). However, recent studies suggest that shared book reading interventions work less effectively for children from disadvantaged backgrounds than originally thought, particularly when their parents have lower levels of education. This means that there is a danger that the benefits of shared reading will be restricted to children from more affluent homes and not get through to those who need them most. To solve this problem, we need to develop a better understanding of how reading interventions work, and of how parents use them. We need to identify what parents do and say when reading aloud with their children and why this makes reading so effective at boosting children's language. We need to find out whether differences in how parents read mean that parents from disadvantaged backgrounds use these language boosting behaviours less frequently. We need to determine how to design interventions that increase the use of these behaviours in all parents, especially those with lower levels of education. Then, once we have identified how reading interventions work, we need to determine how to help parents use them successfully in their daily lives. The aim of this project is to determine how shared reading promotes child language development, and use this knowledge to make it an effective language boosting tool for children from all social and economic backgrounds. In Work Package 1, we will identify what language boosting behaviours parents use in shared reading, and will determine how parents from different social/economic backgrounds use these behaviours during shared reading. In Work Package 2, we will create four targeted interventions, each focussed on a particular language boosting behavior, and investigate how they are implemented by parents from different backgrounds, and how they affect children's language development. In Work Package 3, we will explore what influences parents' decisions to read or not to read with their children, in order to work out why parents may be unwilling to read with their children and to identify how to make reading a more enjoyable experience. We will also evaluate the benefits of a new intervention, designed by national charity The Reader Organisation, to promote reading for pleasure. Across the project, we will study a range of language skills, covering the core language abilities that are essential for learning to read and write in school. We will produce one review article, 9 original research articles, 30 conference presentations, and activities for non-academic audiences at local and national level. We will also submit a Cochrane review on the effectiveness of shared reading interventions for language development. Our results will enable health professionals such as health visitors, early years educators such as nursery school teachers, and policy-makers in local and national government to design targeted, cost-effective interventions to improve the language of children between the ages of 0 and 5 years. The work addresses ESRC's strategic priorities Influencing Behaviour and Informing Interventions and A Vibrant amd Fair Society. This study is an educational intervention developed to promote caregiver contingent talk with their infant during picture book reading and to establish whether or not levels of caregiver contingent talk in this context have a causal relationship with later infant language outcomes. When infants were 11months, a researcher visited participating families in their home to collect baseline measures. Caregivers completed questionnaires to measure demographic information, vocabulary development and frequency of shared book reading. Participants were then randomised to either an intervention condition where they were given picture books and asked to practise daily contingent talk while looking at them with their infants or a control condition where they were given books alone. At 15 months, caregivers visited the university for post-test data collection and again completed measures of vocabulary development and frequency of shared book reading. In addition, video recordings of dyads reading books together were analysed for quantity of child words. Finally, infants' real-time comprehension of familiar words was assessed using the looking-while-listening (LWL) procedure. Infants sat in front of a computer screen showing two pictures, one on either side (e.g., a ball and a shoe). We measured the accuracy with which infants looked to the correct picture upon hearing a word that describes it and how much time they took to do this.

Kiribati KI: Prevalence of Wasting: Weight for Height: % of Children Under 5: Male data was reported at 3.700 % in 2018. Kiribati KI: Prevalence of Wasting: Weight for Height: % of Children Under 5: Male data is updated yearly, averaging 3.700 % from Dec 2018 (Median) to 2018, with 1 observations. The data reached an all-time high of 3.700 % in 2018 and a record low of 3.700 % in 2018. Kiribati KI: Prevalence of Wasting: Weight for Height: % of Children Under 5: Male data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Kiribati – Table KI.World Bank.WDI: Social: Health Statistics. Prevalence of wasting, male, is the proportion of boys under age 5 whose weight for height is more than two standard deviations below the median for the international reference population ages 0-59 months.;UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology.;;Undernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF). Estimates are from national survey data. Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition.

The Women, Infants and Children (WIC) Program is a federally-funded health and nutrition program that provides assistance to pregnant women, new mothers, infants and children under age five. WIC helps California families by providing food benefits to individual participants based on their nutritional need and risk assessment. The food benefits can be used to purchase healthy supplemental foods from about 4,000 WIC authorized vendor stores throughout the State. WIC also provides nutritional education, breastfeeding support, healthcare referrals and other community services. Participants must meet income guidelines and other criteria. Currently, 84 WIC agencies provide services monthly to approximately one million participants at over 500 sites in local communities throughout the State.

Prior to June 2019, WIC issued paper food instruments (FIs) to individual participants for purchasing supplemental, nutritious foods. Beginning in June 2019, California WIC began transitioning to a new food delivery system, replacing the FI delivery system with the Electronic Benefit Transfer (EBT) system. California WIC completed this transition in March 2020. With the previous FI delivery system, participants were issued three or four paper FIs per month listing the foods that could be redeemed at authorized vendor stores. In order to take full advantage of their benefits in the FI delivery system, participants had to purchase all of the foods listed on a FI in a single transaction or lose that benefit. In contrast, in the EBT system a family’s benefits are combined and uploaded to one EBT card. Participants can use this card to purchase WIC foods as needed through the benefit expiry date without having to purchase foods that they don’t need yet and without risking losing their benefits.

The data files provided contain monthly and annual redemption information from the FI delivery system and the EBT system by the county in which WIC participants redeemed their food benefits. Because FIs are issued and redeemed at the participant level, the FI redemption data are presented with aggregation at the participant level (e.g., participant category). However, because EBT redemptions only occur at the family level, EBT data can only be presented with aggregation at the family level. Therefore, we provide two types of aggregated data:

1. WIC Redemption by Vendor County by Participant Category contains the number of FIs redeemed, the dollar amount of FIs redeemed, and the count of unique individual participants, from 2010 to 2018. This data is no longer available beyond 2018 due to transitioning from the FI delivery system to the EBT system.

2. WIC Redemption by Vendor County with Family Counts contains data from before and after the EBT transition period (i.e., 2010 – present), and provides the count of unique families instead of participants. It comprises three parts:

   • Part A contains only FI redemption data from 2010 to 2018;
   • Part B contains both the FI redemption data and the EBT redemption data for the EBT transition period that occurred in 2019 and 2020;
   • Part C contains only EBT redemption data for 2021 and forward.

The dollar amount of redemptions and the number of families redeeming benefits are expected to vary from month to month. Many of these monthly variations can be attributed to the number of days and holidays in a month. Additionally, in June 2021 the federal government approved a Cash Value Benefits (CVB) expansion, which resulted in a large increase in monthly EBT redemption amounts. The initial CVB expansion was implemented in California from June 2021 to September 2021 and provided $35 per month for all non-infant participants, increased from $9 - $11. The CVB expansion was subsequently extended several times. Effective October 1, 2023, CVB was set at new inflation-adjusted amounts where pregnant and postpartum individuals receive $47 per month, breastfeeding individuals receive $52 per month, individuals breastfeeding more than one infant receive $78 per month, and children ages 1-5 received $26 per month.

To ensure WIC participant and vendor anonymity, the redemption data has been suppressed when number of WIC vendors were less than 3 or number of redeemed participants or families were less than 11. The suppressed cells are annotated as “–“.

This deposit contains the diverse experimental and meta-analytic datasets collected during the Understanding and Awareness project. The datasets assess psychological research questions involving the relationship between how we understand and use language, and how we attend to the world around us. For example, one dataset investigates whether words and sentences can be partially understood when they have been masked from conscious awareness. Another dataset investigates how preschool children allocate their attention when describing scenes that require them to use potentially ambiguous language. Note that this project did not collect one large dataset, but rather a range of different datasets, with many different characteristics; fuller descriptions of each dataset are provided in the uploaded documentation file. The datasets in this deposit report 1) Chronometric (response time) studies conducted with adults and with preschool children (aged 3 and 5). 2) Eye tracking studies conducted with adults and with preschool children (aged 2 through 5). 3) Psychophysical (continuous flash suppression) studies conducted with adults. 4) Looking time experiments conducted with infants (age 7 months). 5) A database containing records of a meta-analysis of infant looking time data. In 1957, the advertising executive James Vicary gathered reporters to announce a startling finding. He had taken movie reels from a local cinema and repeatedly inserted single frames containing simple messages: "Eat popcorn" or "Drink Coca-Cola". The frames were essentially invisible, rushing by too fast for anyone to see, but their effects were extreme: A huge increase in sales at the concession stand. Vicary's finding suggested a powerful role for the unconscious in our everyday lives, and a lucrative new method for advertisers. There was just one problem: He was never able to corroborate the data. His startling result was false. In the intervening fifty years, we have learned a lot about the unconscious. Techniques for "masking" the world from consciousness have revealed the complex cognitive processes that proceed without awareness. But the role of awareness in language - our primary means of understanding the world and making ourselves understood - remains surprisingly unexplored, perhaps a legacy of Vicary's controversy. This project aims to correct that imbalance, investigating how understandable linguistic meanings arise from the combination of language and awareness. I want to understand the degree to which awareness of the world is a precondition for understanding and producing language. This topic is important in many different ways. It is important for science: Language and consciousness are two critical components of the human experience; understanding their interrelation can help us understand ourselves. It is important for society: Delineating the role of conscious awareness in understanding and being understood can help us to make sense of, and predict, people's behaviour. Finally, it is important for healthy development: As children grow, they have to develop the correct relationship between language and awareness. Understanding how this process might go wrong could improve the lives of both typically and atypically developing individuals. My approach is broadly focused and experimental in nature. The project examines how language and awareness interact in healthy adults, typically developing children, and individuals with schizophrenia, a developmental disorder often associated with impaired awareness. I use sophisticated experimental techniques to mask sentences from awareness, and then test the degree to which adults can still extract some understanding. I use eye tracking to measure what things in the world children, patients and healthy adults are aware of, and then test whether differences in awareness can explain some of the difficulties that both children and patients have in crafting clear, understandable descriptions of the world. The results should be important for all of the reasons set out above: They will inform both scientific theories, and methods for alleviating linguistic difficulties. And they will set the stage for future work where, in collaboration with others, I can complement our initial measurements of behaviour with an assessment of the twin neural underpinnings of language and consciousness.

The quantitative data from the TEACh project allows us to identify the characteristics of children not learning, and factors associated with disparities in educational outcomes in India and Pakistan. Information was collected from households and schools, at the beginning of the school year (in April). Assessments were made in school again at the end of the school year to identify what learning gains have been made, and the role of teacher and other factors (such as related parental support) in these gains for children with different characteristics. The first stage of the quantitative data collection required us to identify the children whose learning we want to assess. Cross-sectional data was collected from households to enumerate key household and individual characteristics. This included information such as household size and socio-economic status, as well as individual information on all of the children within the household (irrespective of their schooling participation). The household survey provides the first step towards quantifying whether children with different characteristics are in school. For those in school, it identifies the type of school they are attending (whether a mainstream or special school, and whether run by government, private sector, or NGOs). We also assessed learning of children aged 8-12 (approximately equivalent to grades 3-5) in the selected households. The second stage was to identify primary schools within the vicinity that are accessed by a majority of the children in the sample community or village. Children in grades 3-5 were tested both at the beginning and end of the school year in order to identify learning gains, using the same instruments as used in the households. These classes contained some children from the sampled households which allows us to link them back to the household information that has been gathered. Some basic household level information was also collected from all sampled children in the school (such as parental education and household size) to ensure this information is available for all children. Questionnaires were also administered to teachers to identify their background and other characteristics commonly associated with teacher effectiveness. Existing instruments such asSchoolTELLS in India and Pakistan, were adapted to draft the teacher surveys. The teacher instruments were designed to capture the extent to which teachers are aware of, and respond to, children’s diverse learning needs, their perceptions and attitudes towards these children, and the extent to which they feel prepared to teach children of different abilities, including related to training and other forms of support that they receive. As with SchoolTELLS, teachers were also asked to mark student tests to identify teachers’ content knowledge of subjects they are teaching.Governments across the world recognize the importance of providing an education to all children within an inclusive education system. Yet, despite great progress in getting more children into school over the past decade, children from disadvantaged backgrounds are likely to experience poor quality of education limiting chances of fulfilling their learning potential. Children who face multiple disadvantages related to disability, poverty, gender, caste, religion or where they live, are amongst those least likely to be learning. The project aims to identify strategies to raise learning outcomes for all children, regardless of their background. It is widely recognized that teachers are central to a child's educational experience. Yet, in low income countries, disadvantaged learners often face poor quality teaching: many teachers are recruited without having a basic subject knowledge themselves, receive inadequate training with limited attention to strategies to support children from diverse backgrounds, and weak incentives and poor teacher governance can lead to low motivation and high levels of teacher absenteeism. The research will, therefore identify which aspects of teaching are most important for improving all children's learning, and so inform governments on the strategies needed to support children who face multiple disadvantages. The research will be conducted in India and Pakistan, countries characteristic of other poor countries in terms of wide learning inequalities. India shows some advances in identifying strategies to tackle disadvantage, while Pakistan is similar to many other low income countries in not yet having such strategies. Recognising that limited information is available on learning levels of children facing different forms of disadvantages who are not in school, the research will assess children both in the household and in schools. The focus of these tests will be on achievement of foundation skills of reading, writing, reasoning and numeracy that children are expected to acquire in primary school. This will be followed up with a test a year later in order to identify what learning gains have been made, and the extent to which these gains can be attributed to particular teacher characteristics, or other factors such as family background. The research will further provide an in-depth understanding of the problems that teachers face in supporting students from diverse backgrounds within the classroom, the teaching practices they adopt, and the kinds of support they need in order to make sure they are able to help all children fulfill their learning potential. The research will aim to make an important contribution to how measures of learning need to be enriched to include children with disabilities. In addition to adapting existing learning assessments for use in braille or using sign language, for example, it will also trial tests that measure other aspects of learning, such as self-esteem and peer relationships, taking into consideration how these could be adopted on a larger scale. This research will contribute to debates about the future of global goals on education after 2015 which are focusing on raising learning outcomes in ways that make sure no one is left behind. Achieving these goals will require better identification of the characteristics of children not learning, and the implementation of strategies within countries to strengthen the effectiveness of teaching in ways that address diversity in the classroom.

Bangladesh BD: Prevalence of Wasting: Weight for Height: % of Children Under 5 data was reported at 11.000 % in 2022. This records an increase from the previous number of 9.800 % for 2019. Bangladesh BD: Prevalence of Wasting: Weight for Height: % of Children Under 5 data is updated yearly, averaging 14.500 % from Dec 1986 (Median) to 2022, with 27 observations. The data reached an all-time high of 20.700 % in 1997 and a record low of 8.400 % in 2018. Bangladesh BD: Prevalence of Wasting: Weight for Height: % of Children Under 5 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Bangladesh – Table BD.World Bank.WDI: Social: Health Statistics. Prevalence of wasting is the proportion of children under age 5 whose weight for height is more than two standard deviations below the median for the international reference population ages 0-59 months.;UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology.;Linear mixed-effect model estimates;Undernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF). Estimates are from national survey data. Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition.

The highest prevalence of current asthma among U.S. children was reported in Connecticut, where 10.6 percent of all children were estimated to currently suffer from asthma. This statistic represents the prevalence of current asthma among children in the United States in 2022, by state.

Cameroon CM: Prevalence of Underweight: Weight for Age: % of Children Under 5 data was reported at 11.000 % in 2018. This records a decrease from the previous number of 14.800 % for 2014. Cameroon CM: Prevalence of Underweight: Weight for Age: % of Children Under 5 data is updated yearly, averaging 15.100 % from Dec 1991 (Median) to 2018, with 7 observations. The data reached an all-time high of 18.000 % in 1998 and a record low of 11.000 % in 2018. Cameroon CM: Prevalence of Underweight: Weight for Age: % of Children Under 5 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Cameroon – Table CM.World Bank.WDI: Social: Health Statistics. Prevalence of underweight children is the percentage of children under age 5 whose weight for age is more than two standard deviations below the median for the international reference population ages 0-59 months. The data are based on the WHO's 2006 Child Growth Standards.;UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology.;Linear mixed-effect model estimates;Undernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF). Estimates are from national survey data. Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition.

Abstract copyright UK Data Service and data collection copyright owner.

The Crime Survey for England and Wales (CSEW) asks a sole adult in a random sample of households about their, or their household's, experience of crime victimisation in the previous 12 months. These are recorded in the victim form data file (VF). A wide range of questions are then asked, covering demographics and crime-related subjects such as attitudes to the police and the criminal justice system (CJS). These variables are contained within the non-victim form (NVF) data file. In 2009, the survey was extended to children aged 10-15 years old; one resident of that age range was also selected from the household and asked about their experience of crime and other related topics. The first set of children's data covered January-December 2009 and is held separately under SN 6601. From 2009-2010, the children's data cover the same period as the adult data and are included with the main study.

The Telephone-operated Crime Survey for England and Wales (TCSEW) became operational on 20 May 2020. It was a replacement for the face-to-face CSEW, which was suspended on 17 March 2020 because of the coronavirus (COVID-19) pandemic. It was set up with the intention of measuring the level of crime during the pandemic. As the pandemic continued throughout the 2020/21 survey year, questions have been raised as to whether the year ending March 2021 TCSEW is comparable with estimates produced in earlier years by the face-to-face CSEW. The ONS Comparability between the Telephone-operated Crime Survey for England and Wales and the face-to-face Crime Survey for England and Wales report explores those factors that may have a bearing on the comparability of estimates between the TCSEW and the former CSEW. These include survey design, sample design, questionnaire changes and modal changes.

More general information about the CSEW may be found on the ONS Crime Survey for England and Wales web page and for the previous BCS, from the GOV.UK BCS Methodology web page.

History - the British Crime Survey

The CSEW was formerly known as the British Crime Survey (BCS), and has been in existence since 1981. The 1982 and 1988 BCS waves were also conducted in Scotland (data held separately under SNs 4368 and 4599). Since 1993, separate Scottish Crime and Justice Surveys have been conducted. Up to 2001, the BCS was conducted biennially. From April 2001, the Office for National Statistics took over the survey and it became the CSEW. Interviewing was then carried out continually and reported on in financial year cycles. The crime reference period was altered to accommodate this.

Secure Access CSEW data
In addition to the main survey, a series of questions covering drinking behaviour, drug use, self-offending, gangs and personal security, and intimate personal violence (IPV) (including stalking and sexual victimisation) are asked of adults via a laptop-based self-completion module (questions may vary over the years). Children aged 10-15 years also complete a separate self-completion questionnaire. The questionnaires are included in the main documentation, but the data are only available under Secure Access conditions (see SN 7280), not with the main study. In addition, from 2011 onwards, lower-level geographic variables are also available under Secure Access conditions (see SN 7311).

New methodology for capping the number of incidents from 2017-18
The CSEW datasets available from 2017-18 onwards are based on a new methodology of capping the number of incidents at the 98th percentile. Incidence variables names have remained consistent with previously supplied data but due to the fact they are based on the new 98th percentile cap, and old datasets are not, comparability has been lost with years prior to 2012-2013. More information can be found in the 2017-18 User Guide (see SN 8464) and the article ‘Improving victimisation estimates derived from the Crime Survey for England and Wales’.

Latest Edition Information
For the third edition (May 2022), an updated version of the technical report was deposited.
Main Topics:

The study includes information from the adult and child questionnaires. Data from the adult and child samples are available as separate files.
Adults:The adult non-victim form questionnaire covers: perceptions of crime and local area; performance of the CJS; mobile phone crime; experiences of the police (Module A); attitudes to the CJS (Module B); crime prevention and security (Module C); financial loss and fraud; anti-social behaviour; demographics and media.
The adult victim form contains offence-level data. Up to six different incidents were asked about for each respondent. Each of these constituted a separate victim form and can be matched back to the respondent-level data. Topics covered included: the nature and circumstances of...

Abstract copyright UK Data Service and data collection copyright owner.The Annual Population Survey (APS) is a major survey series, which aims to provide data that can produce reliable estimates at the local authority level. Key topics covered in the survey include education, employment, health and ethnicity. The APS comprises key variables from the Labour Force Survey (LFS), all its associated LFS boosts and the APS boost. The APS aims to provide enhanced annual data for England, covering a target sample of at least 510 economically active persons for each Unitary Authority (UA)/Local Authority District (LAD) and at least 450 in each Greater London Borough. In combination with local LFS boost samples, the survey provides estimates for a range of indicators down to Local Education Authority (LEA) level across the United Kingdom.For further detailed information about methodology, users should consult the Labour Force Survey User Guide, included with the APS documentation. For variable and value labelling and coding frames that are not included either in the data or in the current APS documentation, users are advised to consult the latest versions of the LFS User Guides, which are available from the ONS Labour Force Survey - User Guidance webpages.Occupation data for 2021 and 2022The ONS has identified an issue with the collection of some occupational data in 2021 and 2022 data files in a number of their surveys. While they estimate any impacts will be small overall, this will affect the accuracy of the breakdowns of some detailed (four-digit Standard Occupational Classification (SOC)) occupations, and data derived from them. None of ONS' headline statistics, other than those directly sourced from occupational data, are affected and you can continue to rely on their accuracy. The affected datasets have now been updated. Further information can be found in the ONS article published on 11 July 2023: Revision of miscoded occupational data in the ONS Labour Force Survey, UK: January 2021 to September 2022APS Well-Being DatasetsFrom 2012-2015, the ONS published separate APS datasets aimed at providing initial estimates of subjective well-being, based on the Integrated Household Survey. In 2015 these were discontinued. A separate set of well-being variables and a corresponding weighting variable have been added to the April-March APS person datasets from A11M12 onwards. Further information on the transition can be found in the Personal well-being in the UK: 2015 to 2016 article on the ONS website.APS disability variablesOver time, there have been some updates to disability variables in the APS. An article explaining the quality assurance investigations on these variables that have been conducted so far is available on the ONS Methodology webpage. End User Licence and Secure Access APS dataUsers should note that there are two versions of each APS dataset. One is available under the standard End User Licence (EUL) agreement, and the other is a Secure Access version. The EUL version includes Government Office Region geography, banded age, 3-digit SOC and industry sector for main, second and last job. The Secure Access version contains more detailed variables relating to: age: single year of age, year and month of birth, age completed full-time education and age obtained highest qualification, age of oldest dependent child and age of youngest dependent child family unit and household: including a number of variables concerning the number of dependent children in the family according to their ages, relationship to head of household and relationship to head of family nationality and country of origin geography: including county, unitary/local authority, place of work, Nomenclature of Territorial Units for Statistics 2 (NUTS2) and NUTS3 regions, and whether lives and works in same local authority district health: including main health problem, and current and past health problems education and apprenticeship: including numbers and subjects of various qualifications and variables concerning apprenticeships industry: including industry, industry class and industry group for main, second and last job, and industry made redundant from occupation: including 4-digit Standard Occupational Classification (SOC) for main, second and last job and job made redundant from system variables: including week number when interview took place and number of households at address The Secure Access data have more restrictive access conditions than those made available under the standard EUL. Prospective users will need to gain ONS Accredited Researcher status, complete an extra application form and demonstrate to the data owners exactly why they need access to the additional variables. Users are strongly advised to first obtain the standard EUL version of the data to see if they are sufficient for their research requirements. Latest Edition InformationFor the eighth edition (November 2019), a new version of the data file was deposited, with the 2018 person and well-being weighting variables included. Main Topics:Topics covered include: household composition and relationships, housing tenure, nationality, ethnicity and residential history, employment and training (including government schemes), workplace and location, job hunting, educational background and qualifications. Many of the variables included in the survey are the same as those in the LFS. Multi-stage stratified random sample Face-to-face interview Telephone interview 2014 2015 ACADEMIC ACHIEVEMENT ADULT EDUCATION AGE APPLICATION FOR EMP... APPOINTMENT TO JOB APPRENTICESHIP ATTITUDES BONUS PAYMENTS BUSINESSES CARDIOVASCULAR DISE... CARE OF DEPENDANTS CHILD BENEFITS CHILDREN CHRONIC ILLNESS COHABITATION COMMUTING CONDITIONS OF EMPLO... DEBILITATIVE ILLNESS DEGREES DEPRESSION DIABETES DIGESTIVE SYSTEM DI... DISABILITIES Demography population ECONOMIC ACTIVITY EDUCATIONAL BACKGROUND EDUCATIONAL CERTIFI... EDUCATIONAL COURSES EMPLOYEES EMPLOYER SPONSORED ... EMPLOYMENT EMPLOYMENT HISTORY EMPLOYMENT PROGRAMMES EMPLOYMENT SERVICES ENDOCRINE DISORDERS EPILEPSY ETHNIC GROUPS FAMILIES FAMILY BENEFITS FAMILY MEMBERS FIELDS OF STUDY FULL TIME EMPLOYMENT FURNISHED ACCOMMODA... FURTHER EDUCATION GENDER HEADS OF HOUSEHOLD HEALTH HEARING IMPAIRMENTS HIGHER EDUCATION HOME BASED WORK HOME BUYING HOME OWNERSHIP HOURS OF WORK HOUSEHOLDS HOUSING HOUSING BENEFITS HOUSING TENURE ILL HEALTH INCOME INDUSTRIES JOB CHANGING JOB HUNTING JOB SEEKER S ALLOWANCE LANDLORDS LEARNING DISABILITIES LONGTERM UNEMPLOYMENT Labour and employment MANAGERS MARITAL STATUS MENTAL DISORDERS MUSCULOSKELETAL DIS... NATIONAL IDENTITY NATIONALITY NERVOUS SYSTEM DISE... OCCUPATIONAL QUALIF... OCCUPATIONS OVERTIME PART TIME COURSES PART TIME EMPLOYMENT PLACE OF BIRTH PLACE OF RESIDENCE PRIVATE SECTOR PUBLIC SECTOR QUALIFICATIONS RECREATIONAL EDUCATION RECRUITMENT REDUNDANCY REDUNDANCY PAY RELIGIOUS AFFILIATION RENTED ACCOMMODATION RESIDENTIAL MOBILITY RESPIRATORY TRACT D... SELF EMPLOYED SICK LEAVE SICKNESS AND DISABI... SKIN DISEASES SOCIAL HOUSING SOCIAL SECURITY BEN... SOCIO ECONOMIC STATUS SPEECH IMPAIRMENTS SPOUSES SQUATS STATE RETIREMENT PE... STUDENTS SUBSIDIARY EMPLOYMENT SUPERVISORS SUPERVISORY STATUS TAX RELIEF TEMPORARY EMPLOYMENT TERMINATION OF SERVICE TIED HOUSING TRAINING TRAINING COURSES TRAVELLING TIME UNEMPLOYED UNEMPLOYMENT UNEMPLOYMENT BENEFITS UNFURNISHED ACCOMMO... UNWAGED WORKERS VISION IMPAIRMENTS VOCATIONAL EDUCATIO... WAGES WELSH LANGUAGE WORKING CONDITIONS WORKPLACE vital statistics an...

Cameroon CM: Prevalence of Stunting: Height for Age: Female: % of Children Under 5 data was reported at 26.600 % in 2018. This records a decrease from the previous number of 29.800 % for 2014. Cameroon CM: Prevalence of Stunting: Height for Age: Female: % of Children Under 5 data is updated yearly, averaging 30.200 % from Dec 1991 (Median) to 2018, with 7 observations. The data reached an all-time high of 34.500 % in 2006 and a record low of 26.600 % in 2018. Cameroon CM: Prevalence of Stunting: Height for Age: Female: % of Children Under 5 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Cameroon – Table CM.World Bank.WDI: Social: Health Statistics. Prevalence of stunting, female, is the percentage of girls under age 5 whose height for age is more than two standard deviations below the median for the international reference population ages 0-59 months. For children up to two years old height is measured by recumbent length. For older children height is measured by stature while standing. The data are based on the WHO's 2006 Child Growth Standards.;UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology.;;Undernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF). Estimates are from national survey data. Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition.