Administrative Data Sources Research Articles

Synthetic Business Microdata

Enhancing microdata access is one of the strategic priorities for the Australian Bureau of Statistics (ABS) in its transformation program. However, balancing the trade-off between enhancing data access and protecting confidentiality is a delicate act. The ABS could use synthetic data to make its business microdata more accessible for researchers to inform decision making while maintaining confidentiality. This study explores the synthetic data approach for the release and analysis of business data. Australian businesses in some industries are characterised by oligopoly or duopoly. This means the existing microdata protection techniques such as information reduction or perturbation may not be as effective as for household microdata. The research focuses on addressing the following questions: Can a synthetic data approach enhance microdata access for the longitudinal business data? What is the utility and protection trade-off using the synthetic data approach? The study compares confidentialised input and output approaches for protecting confidentiality and analysing Australian microdata from business survey or administrative data sources.

Assessing How Well Electronic Nursing Records Reflect Changes in the Nursing Profession's Scope of Practice in Korea.

Electronic health records, a source of key administrative data for patient care, have been used in Korea for the past 13 years. In recent years, there have been significant changes in the Korean hospital nursing care delivery system. "Comprehensive nursing service," in which nurses provide direct care to patients, including activities of daily living without a family member, is one of the changes in the nursing profession's scope of practice. Accordingly, this study attempts to determine how well the electronic nursing record reveals nursing services that are being used differently. This study analyzed 19 372 nursing records of a total of 200 patients using the random sampling method in two comprehensive nursing service wards and two noncomprehensive nursing service wards. The number of nursing activity records for all items in the comprehensive nursing service ward was higher; only three items showed significant differences. Five nursing diagnoses including "anxiety" and "pain" were more significantly used in the records of the comprehensive nursing service ward. This study found that there were significant differences in nursing records based on the nursing services provided and that the current electronic nursing recording system adequately reflects changes in nursing practice.

서울시 행정 구역과 이웃 효과 관계 탐색: 한국종합사회조사(KGSS) 및 행정 자료 연계

서울시 행정 구역과 이웃 효과 관계 탐색: 한국종합사회조사(KGSS) 및 행정 자료 연계

A national initiative in data science for health: an evaluation of the UK Farr Institute.

ObjectiveTo evaluate the extent to which the inter-institutional, inter-disciplinary mobilisation of data and skills in the Farr Institute contributed to establishing the emerging field of data science for health in the UK.Design and Outcome measuresWe evaluated evidence of six domains characterising a new field of science:defining central scientific challenges,demonstrating how the central challenges might be solved,creating novel interactions among groups of scientists,training new types of experts,re-organising universities,demonstrating impacts in society.We carried out citation, network and time trend analyses of publications, and a narrative review of infrastructure, methods and tools.SettingFour UK centres in London, North England, Scotland and Wales (23 university partners), 2013-2018.Results1. The Farr Institute helped define a central scientific challenge publishing a research corpus, demonstrating insights from electronic health record (EHR) and administrative data at each stage of the translational cycle in 593 papers with at least one Farr Institute author affiliation on PubMed. 2. The Farr Institute offered some demonstrations of how these scientific challenges might be solved: it established the first four ISO27001 certified trusted research environments in the UK, and approved more than 1000 research users, published on 102 unique EHR and administrative data sources, although there was no clear evidence of an increase in novel, sustained record linkages. The Farr Institute established open platforms for the EHR phenotyping algorithms and validations (>70 diseases, CALIBER). Sample sizes showed some evidence of increase but remained less than 10% of the UK population in primary care-hospital care linked studies. 3.The Farr Institute created novel interactions among researchers: the co-author publication network expanded from 944 unique co-authors (based on 67 publications in the first 30 months) to 3839 unique co-authors (545 papers in the final 30 months). 4. Training expanded substantially with 3 new masters courses, training >400 people at masters, short-course and leadership level and 48 PhD students. 5. Universities reorganised with 4/5 Centres established 27 new faculty (tenured) positions, 3 new university institutes. 6. Emerging evidence of impacts included: > 3200 citations for the 10 most cited papers and Farr research informed eight practice-changing clinical guidelines and policies relevant to the health of millions of UK citizens.ConclusionThe Farr Institute played a major role in establishing and growing the field of data science for health in the UK, with some initial evidence of benefits for health and healthcare. The Farr Institute has now expanded into Health Data Research (HDR) UK but key challenges remain including, how to network such activities internationally.

Advances in mapping population and demographic characteristics at small-area levels.

Temporally and spatially highly resolved information on population characteristics, including demographic profile (e.g. age and sex), ethnicity and socio-economic status (e.g. income, occupation, education), are essential for observational health studies at the small-area level. Time-relevant population data are critical as denominators for health statistics, analytics and epidemiology, to calculate rates or risks of disease. Demographic and socio-economic characteristics are key determinants of health and important confounders in the relationship between environmental contaminants and health.In many countries, census data have long been the source of small-area population denominators and confounder information. A strength of the traditional census model has been its careful design and high level of population coverage, allowing high-quality detailed data to be released for small areas periodically, e.g. every 10 years. The timeliness of data, however, becomes a challenge when temporally and spatially highly accurate annual (or even more frequent) data at high spatial resolution are needed, for example, for health surveillance and epidemiological studies. Additionally, the approach to collecting demographic population information is changing in the era of open and big data and may eventually evolve to using combinations of administrative and other data, supplemented by surveys.We discuss different approaches to address these challenges including (i) the US American Community Survey, a rolling sample of the US population census, (ii) the use of spatial analysis techniques to compile temporally and spatially high-resolution demographic data and (iii) the use of administrative and big data sources as proxies for demographic characteristics.

The Health Impact of Alcohol on American Cities: Modeling the Local Burden of Current Alcohol Use in One Jurisdiction.

Measurement of alcohol use and associated harms at the city level is often incomplete or non-existent even though such data are often critical to informing local prevention strategies. This paper models how to generate local estimates of the morbidity, mortality, and cost of current alcohol use instead of abstaining. Administrative data sources, including medical examiner records, hospital records, and police records, among others, were used to obtain local estimates of alcohol-attributable outcomes. In 2018, we used alcohol-attributable fractions and scaled national estimates to quantify the burden of current alcohol use in Baltimore, MD, in 2013. Fifty-two percent of Baltimore adults reported past 30-day drinking. There were 276 alcohol-attributable deaths in 2013, and 106 (38.4%) of these were persons other than the drinker. In 2013, current alcohol use cost $582.3 million in Baltimore City. This burden was distributed across drinkers (40.1%), persons other than the drinker (21.3%), and the government (38.6%). It is possible to quantify this burden at the local level, and these data could be used to inform evidence-based alcohol policy strategies at the local level.

Increasing Complexity of New Nursing Home Residents in Ontario, Canada: A Serial Cross-Sectional Study.

The main objective of the study was to investigate annual changes in the sociodemographic characteristics, morbidity, and functional status of new nursing home residents in Ontario, Canada, between 2000 and 2015. A secondary objective was to develop and assess the quality of an algorithm for ascertaining admissions into publicly funded nursing homes in Ontario using a combination of health administrative data sources that indirectly identifies the residential status of new nursing home residents. Population-based serial cross-sectional study with an accompanying quality assessment study of algorithms. Publicly funded nursing care homes in Ontario, Canada. The reference standard for the assessment of algorithm performance was 21 544 newly admitted nursing home residents identified from the Resident Assessment Instrument-Minimum Data Set in 2012. The selected algorithm was then used to identify serial cross-sectional cohorts of newly admitted residents between 2000 and 2015 that ranged in size between 14 651 and 23 630 residents. Sociodemographic characteristics, morbidity, and functional status of new residents were determined upon admission to examine patterns in the cohorts' profiles. The proportion of residents aged 85 years and older increased from 45.1% to 53.8% over 16 years. The proportions of individuals with seven or more chronic conditions (from 14.1% to 22.1%) and with nine or more prescription medications (from 44.9% to 64.2%) have also increased in parallel over time. Hypertension, osteoarthritis, and dementia were the most prevalent conditions captured, with the proportion of incoming residents with dementia increasing from 42.3% to 54.1% between 2000 and 2015. Newly admitted residents were more likely to have extensive physical and cognitive impairments upon admission. Admission trends show that new residents were older and had greater multimorbidity and limitations in physical functioning over time. J Am Geriatr Soc 68:1293-1300, 2020.

A scoping review of core outcome sets and their \u2018mapping\u2019 onto real-world data using prostate cancer as a case study

BackgroundA Core Outcomes Set (COS) is an agreed minimum set of outcomes that should be reported in all clinical studies related to a specific condition. Using prostate cancer as a case study, we identified, summarized, and critically appraised published COS development studies and assessed the degree of overlap between them and selected real-world data (RWD) sources.MethodsWe conducted a scoping review of the Core Outcome Measures in Effectiveness Trials (COMET) Initiative database to identify all COS studies developed for prostate cancer. Several characteristics (i.e., study type, methods for consensus, type of participants, outcomes included in COS and corresponding measurement instruments, timing, and sources) were extracted from the studies; outcomes were classified according to a predefined 38-item taxonomy. The study methodology was assessed based on the recent COS-STAndards for Development (COS-STAD) recommendations. A ‘mapping’ exercise was conducted between the COS identified and RWD routinely collected in selected European countries.ResultsEleven COS development studies published between 1995 and 2017 were retrieved, of which 8 were classified as ‘COS for clinical trials and clinical research’, 2 as ‘COS for practice’ and 1 as ‘COS patient reported outcomes’. Recommended outcomes were mainly categorized into ‘mortality and survival’ (17%), ‘outcomes related to neoplasm’ (18%), and ‘renal and urinary outcomes’ (13%) with no relevant differences among COS study types. The studies generally fulfilled the criteria for the COS-STAD ‘scope specification’ domain but not the ‘stakeholders involved’ and ‘consensus process’ domains. About 72% overlap existed between COS and linked administrative data sources, with important gaps. Linking with patient registries improved coverage (85%), but was sometimes limited to smaller follow-up patient groups.ConclusionsThis scoping review identified few COS development studies in prostate cancer, some quite dated and with a growing level of methodological quality over time. This study revealed promising overlap between COS and RWD sources, though with important limitations; linking established, national patient registries to administrative data provide the best means to additionally capture patient-reported and some clinical outcomes over time. Thus, increasing the combination of different data sources and the interoperability of systems to follow larger patient groups in RWD is required.

Assessing data linkage quality in cohort studies

Background: Linkage of administrative data sources provides an efficient means of collecting detailed data on how individuals interact with cross-sectoral services, society, and the environment. These data can be used to supplement conventional cohort studies, or to create population-level electronic cohorts generated solely from administrative data. However, errors occurring during linkage (false matches/missed matches) can lead to bias in results from linked data.Aim: This paper provides guidance on evaluating linkage quality in cohort studies.Methods: We provide an overview of methods for linkage, describe mechanisms by which linkage error can introduce bias, and draw on real-world examples to demonstrate methods for evaluating linkage quality.Results: Methods for evaluating linkage quality described in this paper provide guidance on (i) estimating linkage error rates, (ii) understanding the mechanisms by which linkage error might bias results, and (iii) information that should be shared between data providers, linkers and users, so that approaches to handling linkage error in analysis can be implemented.Conclusion: Linked administrative data can enhance conventional cohorts and offers the ability to answer questions that require large sample sizes or hard-to-reach populations. Care needs to be taken to evaluate linkage quality in order to provide robust results.

Cervical cancer screening in the Canadian armed forces: An estimation of screening participation rates using the CF-HERO surveillance system

BackgroundMilitary women are faced with unique circumstances, including frequent relocation and occupational factors that may influence their participation in routine cervical cancer screening. No data on programmatic participation in cervical cancer screening in Canadian Armed Forces women has been synthesized to date. ObjectiveTo estimate cervical cancer screening rates in Canadian military women using clinical and administrative data sources. MethodsActively serving Regular Force females who were >25 years of age between January 1st 2015 and December 31st 2017 were included in the study. Scanned documents containing Papanicolaou (Pap) test results were extracted from electronic health records and further linked to demographic data sources. Screening coverage rates were calculated over the three-year study period, and results were stratified by both military command and rank. ResultsThe study period yielded over 23,000 person-years of data. The average screening rate over this period was 77.7 %, and was highest in the 45−60 year age group. Variations in rates were observed by rank and command, with higher screening rates observed in Officers and Royal Canadian Navy staff. Overall, screening rates showed a declining trend for all groups across the study period. ConclusionsCervical cancer screening rates amongst CAF members are currently below recommended guidelines and appear to be declining. These trends mirror those observed more widely in the general Canadian population, and may be a consequence of recent changes to guidelines for both cervical cancer and human papillomavirus (HPV) screening.

Cerebral palsy in Canada, 2011-2031: results of a microsimulation modelling study of epidemiological and cost impacts

The objective of our study was to present model-based estimates and projections on current and future health and economic impacts of cerebral palsy in Canada over a 20-year time horizon (2011-2031). We used Statistics Canada's Population Health Model (POHEM)-Neurological to simulate individuals' disease states, risk factors and health determinants and to describe and project health outcomes, including disease incidence, prevalence, life expectancy, health-adjusted life expectancy, health-related quality of life and health care costs over the life cycle of Canadians. Cerebral palsy cases were identified from British Columbia's health administrative data sources. A population-based cohort was then used to generate the incidence and mortality rates, enabling the projection of future incidence and mortality rates. A utility-based measure (Health Utilities Index Mark 3) was also included in the model to reflect various states of functional health to allow projections of health-related quality of life. Finally, we estimated caregiving parameters and health care costs from Canadian national surveys and health administrative data and included them as model parameters to assess the health and economic impact of cerebral palsy. Although the overall crude incidence rate of cerebral palsy is projected to remain stable, newly diagnosed cases of cerebral palsy will rise from approximately 1800 in 2011 to nearly 2200 in 2031. In addition, the number of people with the condition is expected to increase from more than 75 000 in 2011 to more than 94 000 in 2031. Direct health care costs in constant 2010 Canadian dollars were about $11 700 for children with cerebral palsy aged 1-4 years versus about $600 for those without the condition. In addition, people with cerebral palsy tend to have longer periods in poorer health-related quality of life. Individuals with cerebral palsy will continue to face challenges related to an ongoing need for specialized medical care and a rising need for supportive services. Our study offers important insights into future costs and impacts associated with cerebral palsy and provides valuable information that could be used to develop targeted health programs and strategies for Canadians living with this condition.

Enhancing Quality Care in Ontario Long-Term Care Homes Through Audit and Feedback for Physicians

The province of Ontario in Canada is an example of a jurisdiction that is using a specific quality improvement mechanism, known as “audit and feedback,” to help clinicians enhance the care they provide to long-term care (LTC) home residents. This program, launched in 2015, is unique in that the reports are provided across an entire jurisdiction. These reports were co-designed with clinicians, including medical directors, scientists, and other stakeholders, and are updated regularly to maintain their relevance to medical practice in LTC. The data for the reports is calculated using record linkage with available administrative data sources. The reports are updated with new data 4 times each year and emailed directly to physicians who have requested their report. The reports are designed to have an overall dashboard summarizing the practice level data with a comparison to all physicians in Ontario. More detailed information on their data such as trend data and resources for quality improvement are found in subsequent pages of the report. These reports are a tool to support physicians in quality improvement efforts in their LTC practice. We believe the role of a medical director is very important in both the uptake and use of these reports as the medical director would act as a trusted advisor who can influence quality of care overall within an LTC home. We are also testing a new format for delivering the reports in an interactive online format that enables more options for viewing practice data. Initial evaluation of these reports shows that there is a statistically significant impact on reducing the prescription of antipsychotic medications in LTC homes. In future, we hope to see a larger effect on the latest topic included in the reports: antibiotic prescribing.

Demonstrating the utility of machine learning innovations in address matching to spatial socio-economic applications


 
 
 The last decade has heralded an unprecedented rise in the number, frequency and availability of data sources. Yet they are often incomplete, meaning data fusion is required to enhance their quality and scope. In the context of spatial analysis, address matching is critical to enhancing household socio-economic and demographic characteristics. Matching administrative, commercial, or lifestyle data sources to items such as household surveys has the potential benefits of improving data quality, enabling spatial data visualisation, and the lowering of respondent burden in household surveys. Typically when a practitioner has high quality data, unique identifiers are used to facilitate a direct linkage between household addresses. However, real-world databases are often absent of unique identifiers to enable a one-to-one match. Moreover, irregularities between the text representations of potential matches mean extensive cleaning of the data is often required as a pre-processing step. For this reason, practitioners have traditionally relied on two linkage techniques for facilitating matches between the text representations of addresses that are broadly divided into deterministic or mathematical approaches. Deterministic matching consists of constructing hand-crafted rules that classify address matches and non-matches based on specialist domain knowledge, while mathematical approaches have increasingly adopted machine learning techniques for resolving pairs of addresses to a match. In this notebook we demonstrate methods of the latter by demonstrating the utility of machine learning approaches to the address matching work flow. To achieve this, we construct a predictive model that resolves matches between two small datasets of restaurant addresses in the US. While the problem case may seem trivial, the intention of the notebook is to demonstrate an approach that is reproducible and extensible to larger data challenges. Thus, in the present notebook, we document an end-to-end pipeline that is replicable and instructive towards assisting future address matching problem cases faced by the regional scientist.
 
 
 

Defining Depression and Anxiety in Individuals With Rheumatic Diseases Using Administrative Health Databases: A Systematic Review.

To conduct a systematic review to describe how administrative health databases have been used to study depression and anxiety in patients with rheumatic diseases and to synthesize the case definitions that have been applied. Search strategies to identify articles evaluating depression and anxiety among individuals with rheumatic diseases were employed in Medline, Embase, CINAHL, Cochrane Database of Systematic Reviews, and PsycINFO. Studies included were those using administrative health data and reporting case definitions for depression and anxiety using International Classification of Diseases (ICD) codes. We extracted information on study design and objectives, administrative health database, specific data sources (e.g., inpatient, pharmacy records), ICD codes, operational definitions, and validity of case definitions. Of the 36 studies included in this review, all studies assessed depression, and 13 studies (36.1%) evaluated anxiety. A number of specific ICD-9/10 codes were consistently applied to identify depression and anxiety, but the overall combination of ICD codes and operational definitions varied across studies. Twenty-four studies reported operational definitions, and 19 of these studies (79.2%) combined claims from more than 1 type of administrative data source (e.g., inpatient, outpatient). Validated case definitions were used by 6 studies (16.7%), with sensitivity estimates for depression and anxiety case definitions ranging from 33% to 74% and 42% to 76%, respectively. We identified numerous case definitions used to evaluate depression and anxiety among individuals with rheumatic diseases within administrative health databases. Recommendations include using case definitions with demonstrated validity as well as operationalizing case definitions within multiple data sources.

Surgical volume estimates using national or state databases can reasonably disregard missing cases from patients undergoing multiple surgeries on the same day other than for cardiac, vascular, and trauma cases

BackgroundLarge administrative data sources such as the National Inpatient Sample are frequently used to estimate the incidence of surgery in the United States. These sources do not identify unique surgical cases (often containing multiple procedures). Investigators routinely assume that all surgical procedures on a given day comprise a single case, ignoring returns to the operating room (e.g., to address a complication). MethodsWe estimated the inaccuracy of this assumption using 11 years of electronic data from 2 large academic hospitals. ResultsAn administrative database would have underestimated the actual case count by 0.22% (99% UCL = 0.25%) and 0.19% (99% UCL = 0.22%), respectively. Patients undergoing cardiac or vascular surgery had a much greater underestimation of case counts (99% UCL 1.72% and 1.60%, and 99% UCL 1.06% and 1.09%, respectively) than other specialties (99% UCL for each specialty <1.0%, including orthopedics, otolaryngology, and urology). The trauma surgery 99% UCL undercount was 1.7% at the one hospital where this could be measured. ConclusionsFor most specialties, inferring overall surgical case frequency using administrative data based on an assumption of no returns to the surgical suite is reasonable. However, adjustment for cardiac, vascular, and trauma surgical caseloads using such sources should be considered.

Health Professional Shortage Areas and Physician Location Decisions

In order to address geographic disparities in healthcare provision, the U.S. government designates primary care Health Professional Shortage Areas (HPSAs), and the Centers for Medicare and Medicaid Services (CMS) provide ten percent bonus payments for Medicare services billed by physicians in these areas. We link together several sources of administrative data from CMS covering the near-universe of Medicare-billing physicians and employ a matched difference-in-differences design to identify the causal effects of shortage area designations on physician location decisions. We find evidence that counties designated as HPSAs experience a 23% increase in the number of early-career primary care physicians, many of whom are likely making initial location decisions for their practices. The increase is driven entirely by physicians who attended ranked medical schools, perhaps reflecting the ability of the program to attract high-quality physicians just completing residency. However, we find no evidence that HPSA designation induces physicians in later career stages to relocate to shortage areas. Overall, our findings suggest that targeting financial incentives to locate in shortage areas towards newer physicians may improve the effectiveness and cost-efficiency of policies aimed at addressing physician shortages.

A Poorly Understood Disease? The Unequal Distribution of Excess Mortality Due to COVID-19 Across French Municipalities

While COVID-19 was responsible for more than 600,000 deaths worldwide as of July 24, 2020, very little is known about the socio-economic heterogeneity of its impact on mortality. In this paper, we combine several administrative data sources to estimate the relationship between mortality due to COVID-19 and poverty at a very local level (i.e. the municipality level) in France, one of the most severely hit countries in the world. We find strong evidence of an income gradient in the impact of the pandemic on mortality rates, which is twice as large in municipalities below the 25th percentile of the national income distribution than in municipalities above this threshold. We then show that both poor housing conditions and higher occupational exposure play a key role in this heterogeneity: taken together, these mechanisms account for up to 77% of the difference observed between rich and poor municipalities.

The way to move beyond the numbers: the lesson learnt from the Italian Obstetric Surveillance System.

To describe the Italian Obstetric Surveillance System (ItOSS) investigating maternal death through incident case reporting and confidential enquiries. All maternal deaths occurred in any public and private health facility in 8 Italian regions covering 73% of national births have been notified to the ItOSS. Every incident case is confidentially reviewed to assess quality of care and establish the cause and avoidability of the death. A total of 106 maternal deaths among 1 455 545 live births have been notified to the surveillance system in 2013-17. Haemorrhage, sepsis and hypertensive disorders of pregnancy are the leading causes of direct maternal deaths due to obstetric causes. A maternal mortality surveillance system, including incidence reporting and confidential enquiries along with a retrospective analysis of administrative data sources, emerged as the best option for case ascertainment and for preventing avoidable maternal deaths.

The prevention priority: linking education and homelessness data to inform policy and practice

Childhood and youth homelessness is a seemingly intractable problem across the globe. Yet, the paradigm shift in homelessness policy towards prevention is raising new questions and new opportunities for researchers, policy makers, and practitioners; specifically, schools as sites for early intervention. The aim of this paper is to shed new light on the potential power of data linkage in both understanding the relationship between homelessness and education (particularly attainment) and in guiding prevention policy and practice. Significantly, the paper draws on data from two separate projects exploring the links between youth homelessness and education – each using a different methodology for data linkage, hence the paper offers both methodological and practical insight.&#x0D; The first project has sought to understand educational achievement of homeless children by linking local authority administrative homelessness data with national education data in Wales. The key methodological challenge here has been that the homelessness data only relate to the main applicant for homelessness assistance, so other administrative data sources have had to be drawn on to identify young people living in homeless households. Early findings suggest young people experiencing homelessness attain below average in school, but the key lesson here is largely methodological. The second part of the paper focuses on identifying which schools homeless young people have attended – with the aim of informing future interventions in these schools and providing a baseline for monitoring their impacts. The methodology differs markedly from the first project and is based upon local authority homelessness and education departments sharing data and undertaking linkage within the authority. This method proves to be highly effective in matching nearly all young people and not only identifying schools but also other key experiences of the young people (e.g. having been in care). The conclusion of the paper is that data linkage, at different scales, i.e. national infrastructure vs local linkage, offer different pathways through which knowledge can influence practice.

One Size Doesn’t Fit All: Administrative Data Quality Frameworks for Production of Official Statistics

Background with rationaleThe use of administrative data is key to achieving the UK Statistics Authority’s strategy of Better Statistics, Better Decisions. Integrating administrative data into official statistics can benefit policy decisions by allowing the possibility of greater granularity and improved timeliness in outputs, while delivering efficiency gains and reducing respondent burden. Quality assessment and communicating uncertainty of administrative data sources is critical to their effective integration into official statistical outputs.&#x0D; Main AimThis presentation will discuss the main challenges of quality assuring statistical outputs containing administrative data. The differences in existing quality frameworks and identified quality metrics will be discussed. In addition, the presentation will cover the need to tailor quality assessment to answer a specific research question that an identified source is being used for and the considerations required.&#x0D; Methods/ApproachA comprehensive literature review was carried out, bringing together existing quality frameworks and metrics from National Statistical Institutes (NSIs) and academia for production of statistics using administrative data sources.&#x0D; ResultsThe main challenges and considerations faced when quality assuring outputs produced using administrative sources have been identified. The quality requirements for different outputs across social, business and census statistics were summarised and a general quality framework for admin data developed. This framework draws on international best practices for use in the UK statistical system.&#x0D; ConclusionIntegrating administrative data presents challenges can’t be solved by a one-size fits all framework. Through unifying available guidance, an adaptable quality assurance methodology has been created, enabling the use of public data for the public good.