These spreadsheets contain information pooled from a meta-analytic study on attrition in stroke and cancer publications. Original publications derived from CAMARADES and STREAM databases, respectively.

Weighted descriptive statistics of socio-economic variables associated with antimalarial prescriptions from qualified sources for recent malarial fever in the pooled data.

Macs1.4, FDR%< = 5Pooled peak statistics for the hMeDIP and MeDIP data among 4 case samples (7,14,15,40) and 4 control samples(9, 10, 27, 28).

Summary statistics (pooled wave-person data).

Experimental statistics using pooled data from the Labour Force Survey 2007 – 2020, and Understanding Society Longitudinal Surveys 2009 – 2018, to understand the enabling and disabling factors that could contribute to an individual's likelihood of returning to work. These tables present modelled estimates of the differences between different groups’ likelihood of returning to work in the next three months or year long periods, over the time frame.

Hong Kong ORSO: Contribution: ME: GT: Pooled: Defined Contribution data was reported at 3,314.000 HKD mn in Jun 2018. This records a decrease from the previous number of 3,436.000 HKD mn for Mar 2018. Hong Kong ORSO: Contribution: ME: GT: Pooled: Defined Contribution data is updated quarterly, averaging 4,095.000 HKD mn from Dec 2000 (Median) to Jun 2018, with 71 observations. The data reached an all-time high of 5,717.000 HKD mn in Mar 2001 and a record low of 3,314.000 HKD mn in Jun 2018. Hong Kong ORSO: Contribution: ME: GT: Pooled: Defined Contribution data remains active status in CEIC and is reported by Mandatory Provident Fund Schemes Authority. The data is categorized under Global Database’s Hong Kong – Table HK.G119: Occupational Retirement Schemes Ordinance Statistics (ORSO).

Increasing genetic and phenotypic data size is critical for understanding the genetic determinants of diseases. Evidently, establishing practical means for collaboration and data sharing among institutions is a fundamental methodological barrier for performing high-powered studies. As the sample sizes become more heterogeneous, complex statistical approaches, such as generalized linear mixed effects models, must be used to correct for the confounders that may bias results. On another front, due to the privacy concerns around Protected Health Information (PHI), genetic information is restrictively protected by sharing according to regulations such as Health Insurance Portability and Accountability Act (HIPAA). This limits data sharing among institutions and hampers efforts around executing high-powered collaborative studies. Federated approaches are promising to alleviate the issues around privacy and performance, since sensitive data never leaves the local sites. Motivated by these, we developed FedGMMAT, a federated genetic association testing tool that utilizes a federated statistical testing approach for efficient association tests that can correct for confounding fixed and additive polygenic random effects among different collaborating sites. Genetic data is never shared among collaborating sites, and the intermediate statistics are protected by encryption. Using simulated and real datasets, we demonstrate FedGMMAT can achieve the virtually same results as pooled analysis under a privacy-preserving framework with practical resource requirements.

The Annual Population Survey (APS) is a major survey series, which aims to provide data that can produce reliable estimates at 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) (held at the UK Data Archive under GN 33246), all of its associated LFS boosts and the APS boost. Thus, the APS combines results from five different sources: the LFS (waves 1 and 5); the English Local Labour Force Survey (LLFS), the Welsh Labour Force Survey (WLFS), the Scottish Labour Force Survey (SLFS) and the Annual Population Survey Boost Sample (APS(B) - however, this ceased to exist at the end of December 2005, so APS data from January 2006 onwards will contain all the above data apart from APS(B)). Users should note that the LLFS, WLFS, SLFS and APS(B) are not held separately at the UK Data Archive. For further detailed information about methodology, users should consult the Labour Force Survey User Guide, selected volumes of which have been included with the APS documentation for reference purposes (see 'Documentation' table below).

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 such as the WLFS and SLFS, the survey provides estimates for a range of indicators down to Local Education Authority (LEA) level across the United Kingdom.

APS Well-Being data
Since April 2011, the APS has included questions about personal and subjective well-being. The responses to these questions have been made available as annual sub-sets to the APS Person level files. It is important to note that the size of the achieved sample of the well-being questions within the dataset is approximately 165,000 people. This reduction is due to the well-being questions being only asked of persons aged 16 and above, who gave a personal interview and proxy answers are not accepted. As a result some caution should be used when using analysis of responses to well-being questions at detailed geography areas and also in relation to any other variables where respondent numbers are relatively small. It is recommended that for lower level geography analysis that the variable UACNTY09 is used.

As well as annual datasets, three-year pooled datasets are available. When combining multiple APS datasets together, it is important to account for the rotational design of the APS and ensure that no person appears more than once in the multiple year dataset. This is because the well-being datasets are not designed to be longitudinal e.g. they are not designed to track individuals over time/be used for longitudinal analysis. They are instead cross-sectional, and are designed to use a cross-section of the population to make inferences about the whole population. For this reason, the three-year dataset has been designed to include only a selection of the cases from the individual year APS datasets, chosen in such a way that no individuals are included more than once, and the cases included are approximately equally spread across the three years. Further information is available in the 'Documentation' section below.

Secure Access APS Well-Being data
Secure Access datasets for the APS Well-Being include additional variables not included in either the standard End User Licence (EUL) versions (see under GN 33357) or the Special Licence (SL) access versions (see under GN 33376). Extra variables that typically can be found in the Secure Access version but not in the EUL or SL versions relate to:

• geography, including:
  • Postcodes
  • Census Area Statistics (CAS) Wards
  • Census Output Areas
  • Nomenclature of Units for Territorial Statistics (NUTS) level 2 and 3 areas
  • Lower and Middle Layer Super Output Areas
  • Travel to Work Areas
  • Unitary authority / Local Authority District of place of work (main job)
  • region of place of work for first and second jobs
• qualifications, education and training including level of highest qualification, qualifications from Government schemes, qualifications related to work, qualifications from school, qualifications from university of college and qualifications gained from outside the UK
• detailed ethnic group for Scottish respondents
• detailed religious denomination for Northern Irish respondents
• length health problem has limited activity
• learning difficulty or learning disability
• occupation in apprenticeship or second job
• number of bedrooms
• number of dependent children in household aged under 19

Prospective users of the Secure Access version of the APS Well-Being will need to fulfil additional requirements, commencing with the completion of an extra application form to demonstrate to the data owners exactly why they need access to the extra, more detailed variables, in order to obtain permission to use that version. Secure Access data users must also complete face-to-face training and agree to the Secure Access User Agreement and Licence Compliance Policy (see 'Access' section below). Therefore, users are encouraged to download and inspect the EUL version of the data prior to ordering the Secure Access (or SL) version. Further details and links to all APS studies available from the UK Data Archive can be found via the APS Key Data series webpage.

APS Well-Being Datasets: Information, July 2016
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. Users should no longer use the bespoke well-being datasets (SNs 6994, 6999, 7091, 7092, 7364, 7365, 7565, 7566 and 7961, but should now use the variables included on the April-March APS person datasets instead. Further information on the transition can be found on the Personal well-being in the UK: 2015 to 2016

Documentation and coding frames
The APS is compiled from variables present in the LFS. For variable and value labelling and coding frames that are not included either in the data or in the current APS documentation (e.g. coding frames for education, industrial and geographic variables, which are held in LFS User Guide Vol.5, Classifications), 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.

May 2018 Update
Due to a change in the Travel-to-Work Area coding structure from 2001 to 2011, the variable TTWA9D has been relabelled in the pooled data file for 2012-2015.

BackgroundAnterior odontoid screw fixation (AOSF) has been one of the most popular treatments for odontoid fractures. However, the true efficacy of AOSF remains unclear. In this study, we aimed to provide the pooled rates of non-union, reoperation, infection, and approach related complications after AOSF for odontoid fractures.MethodsWe searched studies that discussed complications after AOSF for type II or type III odontoid fractures. A proportion meta-analysis was done and potential sources of heterogeneity were explored by meta-regression analysis.ResultsOf 972 references initially identified, 63 were eligible for inclusion. 54 studies provided data regarding non-union. The pooled non-union rate was 10% (95% CI: 7%–3%). 48 citations provided re-operation information with a pooled proportion of 5% (95% CI: 3%–7%). Infection was described in 20 studies with an overall rate of 0.2% (95% CI: 0%–1.2%). The main approach related complication is postoperative dysphagia with a pooled rate of 10% (95% CI: 4%–17%). Proportions for the other approach related complications such as postoperative hoarseness (1.2%, 95% CI: 0%–3.7%), esophageal/retropharyngeal injury (0%, 95% CI: 0%–1.1%), wound hematomas (0.2%, 95% CI: 0%–1.8%), and spinal cord injury (0%, 95% CI: 0%–0.2%) were very low. Significant heterogeneities were detected when we combined the rates of non-union, re-operation, and dysphagia. Multivariate meta-regression analysis showed that old age was significantly predictive of non-union. Subgroup comparisons showed significant higher non-union rates in age ≥70 than that in age ≤40 and in age 40 to

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.

Accurate breast cancer risk prediction is essential for early detection and personalized prevention strategies. While traditional models, such as Gail and Tyrer–Cuzick, are widely utilized, machine learning-based approaches may offer enhanced predictive performance. This systematic review and meta-analysis compare the accuracy of traditional statistical models and machine learning models in breast cancer risk prediction. A total of 144 studies from 27 countries were systematically reviewed, incorporating genetic, clinical, and imaging data. Pooled C-statistics were calculated to assess model discrimination, while observed-to-expected (O/E) ratios were used to evaluate calibration. Subgroup and sensitivity analyses were conducted to examine heterogeneity and assess the influence of study bias across various populations. Machine learning-based models demonstrated superior performance, with a pooled C-statistic of 0.74, compared to 0.67 for traditional models. Models that integrated genetic and imaging data showed the highest levels of accuracy, although performance varied by population. Sensitivity analyses excluding high-bias studies showed improved discrimination in models incorporating genetic factors, with the pooled C-statistic increasing to 0.72. Traditional models, such as Gail, exhibited notably poor predictive accuracy in non-Western populations, as evidenced by a C-statistic of 0.543 in Chinese cohorts. Machine learning models provide significantly greater predictive accuracy for breast cancer risk, particularly when incorporating multidimensional data. However, issues related to model generalizability and interpretability remain, particularly in diverse populations. Future research should focus on developing more interpretable models and expanding global validation efforts to improve model applicability across different demographic groups.

Coefficients of GMMAT and FedGMMAT in experiment 2.

Legacy unique identifier: P00608

This table presents a socio-demographic and socio-economic statistical profile of the population aged 15 and older by sexual orientation, geographic region, sex and age group. The characteristics included are: marital status, presence of children under 12 in the household, education, employment, household income, Indigenous identity, belonging to a population group designated as a visible minority, language(s) spoken at home, and place of residence (urban/rural). These estimates are obtained from Canadian Community Health Survey, 2015 to 2018 pooled data.

Hong Kong ORSO: Asset Size: NM: GT: Pooled: Defined Benefit data was reported at 28.000 HKD mn in Jun 2018. This stayed constant from the previous number of 28.000 HKD mn for Mar 2018. Hong Kong ORSO: Asset Size: NM: GT: Pooled: Defined Benefit data is updated quarterly, averaging 39.000 HKD mn from Dec 2000 (Median) to Jun 2018, with 71 observations. The data reached an all-time high of 500.000 HKD mn in Dec 2000 and a record low of 25.000 HKD mn in Dec 2015. Hong Kong ORSO: Asset Size: NM: GT: Pooled: Defined Benefit data remains active status in CEIC and is reported by Mandatory Provident Fund Schemes Authority. The data is categorized under Global Database’s Hong Kong – Table HK.G119: Occupational Retirement Schemes Ordinance Statistics (ORSO).

Abstract

The Quarterly Labour Force Survey (QLFS) is a household-based sample survey conducted by Statistics South Africa (Stats SA). It collects data on the labour market activities of individuals aged 15 years or older who live in South Africa. Since 2008, Stats SA have generated an annual report which is released under the name "Labour Market Dynamics in South Africa". This report is constructed using data from a version of the pooled data from all four quarters (all four QLFS datasets in the year) and is ascribed the same nomenclature. Includes a number of extra variables (including income information) that are not available in any of the QLFS data files. This makes it distinct from a datafile that simply appends all of the QLFS datafiles in a given year.

Geographic coverage

National coverage

Analysis unit

Individuals

Universe

The QLFS sample covers the non-institutional population except for those in workers' hostels. However, persons living in private dwelling units within institutions are enumerated. For example, within a school compound, one would enumerate the schoolmaster's house and teachers' accommodation because these are private dwellings. Students living in a dormitory on the school compound would, however, be excluded.

Kind of data

Sample survey data [ssd]

Sampling procedure

The QLFS frame has been developed as a general purpose household survey frame that can be used by all other household surveys irrespective of the sample size requirement of the survey. The sample size for the QLFS is roughly 30 000 dwellings per quarter.

The sample is based on information collected during the 2001 Population Census conducted by Stats SA. In preparation for the 2001 Census, the country was divided into 80 787 enumeration areas (EAs). Stats SA's household-based surveys use a Master Sample of Primary Sampling Units (PSUs) which comprises of EAs that are drawn from across the country.

The sample is designed to be representative at the provincial level and within provinces at the metro/non-metro level. Within the metros, the sample is further distributed by geography type. The four geography types are: urban formal, urban informal, farms and tribal. This implies, for example, that within a metropolitan area the sample is representative at the different geography types that may exist within that metro.

The current sample size is 3 080 PSUs. It is divided equally into four sub-groups or panels called rotation groups. The rotation groups are designed in such a way that each of these groups has the same distribution pattern as that which is observed in the whole sample. They are numbered from one to four and these numbers also correspond to the quarters of the year in which the sample will be rotated for the particular group.

The sample for the QLFS is based on a stratified two-stage design with probability proportional to size (PPS) sampling of primary sampling units (PSUs) in the first stage, and sampling of dwelling units (DUs) with systematic sampling in the second stage.

Mode of data collection

Face-to-face [f2f]

Descriptive statistics of testcross hybrids of 85 CSSLs and H 77/833-2 under summer season control (SCN), summer season moisture stress (SMS), wet season control (WCN) in 2010 and for pooled data.