Repeated and Combined Participation in Workforce, Education and Social Program Services: Preliminary Evidence from Maryland Administrative Data Sources

2012 ◽  
Author(s):  
Ting Zhang ◽  
David W. Stevens
Keyword(s):  
Administrative Data ◽  
Preliminary Evidence ◽  
Workforce Education ◽  
Social Program ◽  
Data Sources

Integration of statistical and administrative agricultural data from Namibia

2021 ◽  
pp. 1-22
Author(s):  
Emily Berg ◽  
Johgho Im ◽  
Zhengyuan Zhu ◽  
Colin Lewis-Beck ◽  
Jie Li
Keyword(s):  
Measurement Error ◽  
Data Collection ◽  
Administrative Data ◽  
Statistical Data ◽  
Data Sources ◽  
Extension Programs ◽  
Multiple Data ◽  
Administrative Agencies ◽  
Crop Area ◽  
Using Data

Statistical and administrative agencies often collect information on related parameters. Discrepancies between estimates from distinct data sources can arise due to differences in definitions, reference periods, and data collection protocols. Integrating statistical data with administrative data is appealing for saving data collection costs, reducing respondent burden, and improving the coherence of estimates produced by statistical and administrative agencies. Model based techniques, such as small area estimation and measurement error models, for combining multiple data sources have benefits of transparency, reproducibility, and the ability to provide an estimated uncertainty. Issues associated with integrating statistical data with administrative data are discussed in the context of data from Namibia. The national statistical agency in Namibia produces estimates of crop area using data from probability samples. Simultaneously, the Namibia Ministry of Agriculture, Water, and Forestry obtains crop area estimates through extension programs. We illustrate the use of a structural measurement error model for the purpose of synthesizing the administrative and survey data to form a unified estimate of crop area. Limitations on the available data preclude us from conducting a genuine, thorough application. Nonetheless, our illustration of methodology holds potential use for a general practitioner.

Severe Maternal Morbidity: A Comparison of Definitions and Data Sources

2021 ◽  
Author(s):  
Jonathan M Snowden ◽  
Audrey Lyndon ◽  
Peiyi Kan ◽  
Alison El Ayadi ◽  
Elliott Main ◽  
...  
Keyword(s):  
Administrative Data ◽  
Maternal Morbidity ◽  
Claims Data ◽  
Birth Certificate ◽  
Population Level ◽  
Kappa Statistic ◽  
Data Sources ◽  
Severe Maternal Morbidity ◽  
Fine Grained ◽  
Control And Prevention

Abstract Severe maternal morbidity (SMM) is a composite outcome measure that indicates serious, potentially life-threatening maternal health problems. There is great interest in defining SMM using administrative data for surveillance and research. In the US, one common way of defining SMM at the population level is an index developed by the Centers for Disease Control and Prevention. Modifications have been proposed to this index (e.g., excluding maternal transfusion); some research defines SMM using an index introduced by Bateman et al. Birth certificate data are also increasingly being used to define SMM. We compared commonly used US definitions of SMM to each other among all California births, 2007-2012, using the Kappa statistic and other measures. We also evaluated agreement between maternal morbidity fields on the birth certificate compared to claims data. Concordance was generally low between the 7 definitions of SMM analyzed (i.e., κ < 0.4 for 13 of 21 two-way comparisons), Low concordance was particularly driven by presence/absence of transfusion and claims data versus birth certificate definitions. Low agreement between administrative data-based definitions of SMM highlights that results can be expected to differ between them. Further research is needed on validity of SMM definitions, using more fine-grained data sources.

scholarly journals Examining Disclosure Risk and Data Utility: An Administrative Data Case Study

2014 ◽  
Vol 9 (1) ◽  
pp. 12-24
Author(s):  
Michael Comerford
Keyword(s):  
Administrative Data ◽  
Real World ◽  
Data Sources ◽  
Sufficient Data ◽  
Ethical Challenges ◽  
Data Utility ◽  
Disclosure Risk ◽  
Individual Privacy

The plethora of new data sources, combined with a growing interest in increased access to previously unpublished data, poses a set of ethical challenges regarding individual privacy. This paper sets out one aspect of those challenges: the need to anonymise data in such a form that protects the privacy of individuals while providing sufficient data utility for data users. This issue is discussed using a case study of Scottish Government’s administrative data, in which disclosure risk is examined and data utility is assessed using a potential ‘real-world’ analysis.

scholarly journals Coverage Evaluation on Probabilistically Linked Data

2015 ◽  
Vol 31 (3) ◽  
pp. 415-429 ◽  
Author(s):  
Loredana Di Consiglio ◽  
Tiziana Tuoto
Keyword(s):  
Administrative Data ◽  
Simulation Study ◽  
Record Linkage ◽  
Linked Data ◽  
Data Sources ◽  
Simple Method ◽  
Population Total ◽  
Linkage Error ◽  
Capture Recapture ◽  
Better Than

Abstract The Capture-recapture method is a well-known solution for evaluating the unknown size of a population. Administrative data represent sources of independent counts of a population and can be jointly exploited for applying the capture-recapture method. Of course, administrative sources are affected by over- or undercoverage when considered separately. The standard Petersen approach is based on strong assumptions, including perfect record linkage between lists. In reality, record linkage results can be affected by errors. A simple method for achieving linkage error-unbiased population total estimates is proposed in Ding and Fienberg (1994). In this article, an extension of the Ding and Fienberg model by relaxing their conditions is proposed. The procedures are illustrated for estimating the total number of road casualties, on the basis of a probabilistic record linkage between two administrative data sources. Moreover, a simulation study is developed, providing evidence that the adjusted estimator always performs better than the Petersen estimator.

Estimating injection drug use prevalence using state wide administrative data sources: Estonia, 2004

2007 ◽  
Vol 15 (4) ◽  
pp. 411-424 ◽  
Author(s):  
Anneli Uusküla ◽  
Kristiina Rajaleid ◽  
Ave Talu ◽  
Katri Abel ◽  
Kristi Rüütel ◽  
...  
Keyword(s):  
Drug Use ◽  
Injection Drug Use ◽  
Administrative Data ◽  
Injection Drug ◽  
Data Sources

scholarly journals Models for Combining Aggregate-Level Administrative Data in the Absence of a Traditional Census

2015 ◽  
Vol 31 (3) ◽  
pp. 431-451 ◽  
Author(s):  
Dilek Yildiz ◽  
Peter W.F. Smith
Keyword(s):  
Administrative Data ◽  
Census Data ◽  
Geographical Area ◽  
Population Data ◽  
Population Characteristics ◽  
Data Sources ◽  
Aggregate Level ◽  
Administrative Data Source ◽  
Data Source ◽  
Population Counts

Abstract Administrative data sources are an important component of population data collection and they have been used in census data production in the Nordic countries since the 1960s. A large amount of information about the population is already collected in administrative data sources by governments. However, there are some challenges to using administrative data sources to estimate population counts by age, sex, and geographical area as well as population characteristics. The main limitation with the administrative data sources is that they only collect information from a subset of the population about specific events, and this may result in either undercoverage or overcoverage of the population. Another issue with the administrative data sources is that the information may not have the same quality for all population groups. This research aims to correct an inaccurate administrative data source by combining aggregate-level administrative data with more accurate marginal distributions or two-way marginal information from an auxiliary data source and produce accurate population estimates in the absence of a traditional census. The methodology developed is applied to estimate population counts by age, sex, and local authority area in England and Wales. The administrative data source used is the Patient Register which suffers from overcoverage, particularly for people between the ages of 20 and 50.

scholarly journals NEPS Starting Cohort 6 survey data linked to administrative data of the IAB (NEPS-SC6-ADIAB)

2018 ◽  
Vol 3 (2) ◽  
Author(s):  
Nadine Bachbauer
Keyword(s):  
Survey Data ◽  
Administrative Data ◽  
Linked Data ◽  
Panel Study ◽  
Data Access ◽  
Data Sources ◽  
Employment Agency ◽  
Educational Trajectories ◽  
Analytical Potential ◽  
Site Access

BackgroundNEPS-SC6-ADIAB is a new linked data product containing survey data of Starting Cohort 6 of the German National Educational Panel Study (NEPS) and administrative employment data from the Institute for Employment Research (IAB), the research institute of the Federal Employment Agency. NEPS is provided by the Leibniz Institute for Educational Trajectories (LIfBi). Starting Cohort 6 of this panel survey includes adults in their professional life, the survey focuses on education in adulthood and lifelong learning. The administrative data in NEPS-SC6-ADIAB consist of comprehensive information on the employment histories. ObjectivesCombining these two data sources increases for example the information about individual employment history. Overall, the data volume is increased by the linkage between the survey data and the administrative data. MethodsA record linkage process was used to link the two data sources. The data access is free for the whole scientific community. In addition to a large number of On-site access locations within Germany, there are also international On-site access locations. Including London and Colchester. In addition a Remote Data Access is offered. ConclusionsThis data linkage project is very innovative and creates an extensive database, which results in extensive analytical potential. A short application example is made to exemplify the comprehensive analytical potential of NEPS-SC6-ADIAB. This ongoing project deals with nonresponse in survey data. The linked data has a variety of variables collected in both data sources, administratively and through the NEPS survey, allowing for comparative analyses. In this case an idea to compensate nonresponse in income data with administrative data is drawn.

scholarly journals Canadian Approaches to Optimizing Quality of Administrative Data for Health System Use, Research, and Linkage

2018 ◽  
Vol 3 (4) ◽  
Author(s):  
Catherine Eastwood ◽  
Keith Denny ◽  
Maureen Kelly ◽  
Hude Quan
Keyword(s):  
Data Collection ◽  
Data Quality ◽  
Quality Assessment ◽  
Administrative Data ◽  
Information Quality ◽  
Health Data ◽  
Data Sources ◽  
University Of Calgary ◽  
Quality Assessment And Improvement

Theme: Data and Linkage QualityObjectives: To define health data quality from clinical, data science, and health system perspectives To describe some of the international best practices related to quality and how they are being applied to Canada’s administrative health data. To compare methods for health data quality assessment and improvement in Canada (automated logical checks, chart quality indicators, reabstraction studies, coding manager perspectives) To highlight how data linkage can be used to provide new insights into the quality of original data sources To highlight current international initiatives for improving coded data quality including results from current ICD-11 field trials Dr. Keith Denny: Director of Clinical Data Standards and Quality, Canadian Insititute for Health Information (CIHI), Adjunct Research Professor, Carleton University, Ottawa, ON. He provides leadership for CIHI’s information quality initiatives and for the development and application of clinical classifications and terminology standards. Maureen Kelly: Manager of Information Quality at CIHI, Ottawa, ON. She leads CIHI’s corporate quality program that is focused on enhancing the quality of CIHI’s data sources and information products and to fostering CIHI’s quality culture. Dr. Cathy Eastwood: Scientific Manager, Associate Director of Alberta SPOR Methods & Development Platform, Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB. She has expertise in clinical data collection, evaluation of local and systemic data quality issues, disease classification coding with ICD-10 and ICD-11. Dr. Hude Quan: Professor, Community Health Sciences, Cumming School of Medicine, University of Calgary, Director Alberta SPOR Methods Platform; Co-Chair of Hypertension Canada, Co-Chair of Person to Population Health Collaborative of the Libin Cardiovascular Institute in Calgary, AB. He has expertise in assessing, validating, and linking administrative data sources for conducting data science research including artificial intelligence methods for evaluating and improving data quality. Intended Outcomes:“What is quality health data?” The panel of experts will address this common question by discussing how to define high quality health data, and measures being taken to ensure that they are available in Canada. Optimizing the quality of clinical-administrative data, and their use-value, first requires an understanding of the processes used to create the data. Subsequently, we can address the limitations in data collection and use these data for diverse applications. Current advances in digital data collection are providing more solutions to improve health data quality at lower cost. This panel will describe a number of quality assessment and improvement initiatives aimed at ensuring that health data are fit for a range of secondary uses including data linkage. It will also discuss how the need for the linkage and integration of data sources can influence the views of the data source’s fitness for use. CIHI content will include: Methods for optimizing the value of clinical-administrative data CIHI Information Quality Framework Reabstraction studies (e.g. physician documentation/coders’ experiences) Linkage analytics for data quality University of Calgary content will include: Defining/measuring health data quality Automated methods for quality assessment and improvement ICD-11 features and coding practices Electronic health record initiatives

scholarly journals Green-Blue Spaces and Mental Health: A Longitudinal Data Linkage Study

2020 ◽  
Vol 5 (5) ◽  
Author(s):  
Daniel A Thompson ◽  
Mark Nieuwenhuijsen ◽  
James White ◽  
Rebecca Lovell ◽  
Mathew White ◽  
...  
Keyword(s):  
Mental Health ◽  
Administrative Data ◽  
Data Linkage ◽  
Well Being ◽  
Data Sources ◽  
Household Level ◽  
Individual Level ◽  
The Impact ◽  
Over Time ◽  
Sector Data

IntroductionA growing evidence base indicates health benefits are associated with access to green-blue spaces (GBS), such as beaches and parks. However, few studies have examined associations with changes in access to GBS over time. Objectives and ApproachWe have linked cross-sector data collected within Wales, United Kingdom, quarterly from 2008 to 2019, to examine the impact of GBS access on individual-level well-being and common mental health disorders (CMD). We created a longitudinal dataset of GBS access metrics, derived from satellite and administrative data sources, for 1.4 million homes in Wales. These household-level metrics were linked to individuals using the Welsh Demographic Service Dataset within the Secure Anonymised Information Linkage (SAIL) Databank. Linkage to Welsh Longitudinal General Practice data within SAIL enabled us to identify individual-level CMD over time. We also linked individual-level self-reported GBS use and well-being data from the National Survey for Wales (NSW) to routine data for cross-sectional survey participants. ResultsWe created a longitudinal cohort panel capturing all 2.84 million adults aged 16+ living in Wales between 2008 and 2019 and with a general practitioner (GP) registration. Individual-level health data and household-level environmental metrics were linked for each quarter an individual is in the study. Household addresses were linked to 97% of the cohort, creating 110+ million rows of anonymously linked cross-sector data. The cohort provides an average follow-up period of 8 years, during which 565,168 (20%) adults received at least one CMD diagnosis or symptom. Conclusion / ImplicationsThis example of multi-sectoral data linkage across multiple environmental and administrative data sources has created a rich data source, which we will use toquantify the impact of changes in GBS access on individual–level CMD and well-being. This evidence will inform policy in the areas of health, planning and the environment.

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