Scotland’s Integrated Demographic Dataset and Administrative Data Based Ethnicity Dataset

Measuring Hospital Performance in Congenital Heart Surgery: Administrative Versus Clinical Registry Data

The use of "clinical" versus "administrative" data sets for health care quality assessment continues to be debated. This study directly compares the University HealthSystem Consortium Clinical Database (UHC CDB) and the National Surgical Quality Improvement Program (NSQIP) in terms of their assessment of complications and death for 26 322 surgery patients using analyses of variance, correlation, and multivariable logistic regression. The NSQIP had more variables with significant correlation with outcomes. The NSQIP was better at predicting death (c-index 0.94 vs 0.90, P < .05) and complications (c-index 0.78 vs 0.76, P = .07), especially for higher risk patients. The UHC CDB missed and misclassified several major complications. The data sets are similar in their explanatory power relative to outcomes, but the clinical data set is better, particularly at identifying higher risk patients and specific complications. It should prove more useful for initiating and monitoring clinical process improvements because of more clinically relevant variables.

BACKGROUND: Quantitative research on the social, demographic, and economic outcomes of sexual minorities has long been hampered by data shortfalls, with most surveys and censuses limited by sample sizes and/or a lack of direct questions on sexual identity. The growing availability of administrative data presents an opportunity to fill some of these gaps. OBJECTIVE: This article highlights the challenges and opportunities involved with using a novel administrative dataset – the Longitudinal Administrative Databank, which includes 20% of Canadian tax filers – to study sexual minority populations in Canada. We identify three sources of bias, propose strategies to adjust for this bias, and introduce a measure of “inferred sexual minority status” to improve the identification of sexual minorities in tax data. RESULTS: Administrative tax data offers significant advantages, including a large sample size, high-quality income data for individuals and linked family members, a longitudinal design, and the ability to trace individuals’ same-/different-sex partnership histories. Our adjustment strategies mitigate some biases in identifying same-sex couples, including underreporting, misclassification, and measurement errors. The estimated proportion of individuals in same-sex marriages closely aligns with Canadian census estimates from 2006–2021, while the proportion in same-sex common-law partnerships is underestimated. Finally, our earnings gaps analyses highlight the utility of the inferred sexual minority status measure. CONTRIBUTION: This article contributes to research on sexual minority data landscapes, offering new insights into the identification and measures of sexual minority populations using longitudinal administrative tax data. Our approach points to new opportunities for studying the long-term longitudinal income and family dynamics of sexual minority populations on the national level.

Research based on administrative data has advantages, including large numbers, consistent data, and low cost. This study was designed to compare different methods of stroke classification using administrative data. Administrative hospital discharge data and medical record review of 206 patients were used to evaluate 3 algorithms for classifying stroke patients. These algorithms were based on all (algorithm 1), the first 2 (algorithm 2), or the primary (algorithm 3) administrative discharge diagnosis code(s). The diagnoses after review of medical record data were considered the gold standard. Then, using a large administrative data set, we compared patients with a primary discharge diagnosis of stroke with patients with their stroke discharge diagnosis code in a nonprimary position. Compared with the gold standard, algorithm 1 had the highest kappa for classifying ischemic stroke, with a sensitivity of 86%, specificity of 95%, positive predictive value of 90%, and kappa=0.82. Algorithm 3 had the highest kappa values for intracerebral hemorrhage and subarachnoid hemorrhage. For intracerebral hemorrhage, the sensitivity was 85%, specificity was 96%, positive predictive value was 89%, and kappa=0.82. For subarachnoid hemorrhage, those values were 90%, 97%, 94%, and 0.88, respectively. Nonprimary position ischemic stroke patients had significantly greater comorbidity and 30-day mortality (odds ratio, 3.2) than primary position ischemic stroke patients. Stroke classification in these administrative data were optimal using all discharge diagnoses for ischemic stroke and primary discharge diagnosis only for intracerebral and subarachnoid hemorrhage. Selecting ischemic stroke patients on the basis of primary discharge diagnosis may bias administrative samples toward more benign, unrepresentative outcomes and should be avoided.

Integrated Data System Person Identification: Accuracy Requirements and Methods

BackgroundAdministrative health care databases may be particularly useful for injury surveillance, given that they are population-based, readily available, and relatively complete. Surveillance based on administrative data, though, is often restricted to injuries that result in hospitalization. Adding physician billing data to administrative data-based surveillance efforts may improve comprehensiveness, but the feasibility of such an approach has rarely been examined. It is also not clear how injury surveillance information obtained using administrative health care databases compares with that obtained using self-report surveys. This study explored the value of using physician billing data along with hospitalization data for the surveillance of adolescent injuries in Ontario, Canada. We aimed i) to document the burden of adolescent injury using administrative health care data, focusing on the relative contribution of physician billing information; and ii) to explore data quality issues by directly comparing adolescent injuries identified in administrative and self-report data.MethodsThe sample included adolescents aged 12 to 19 years who participated in the 1996–1997 cross-sectional Ontario Health Survey, and whose survey responses were linked to administrative health care datasets (N = 2067). Descriptive analysis was used to document the burden of injuries as a proportion of all physician care by gender and location of care, and to examine the distribution of both administratively-defined and self-reported activity-limiting injuries according to demographic characteristics. Administratively-defined and self-reported injuries were also directly compared at the individual level.ResultsApproximately 10% of physician care for the sample was identified as injury-related. While 18.8% of adolescents had self-reported injury in the previous year, 25.0% had documented administratively-defined injury. The distribution of injuries according to demographic characteristics was similar across data sources, but congruence was low at the individual level. Possible reasons for discrepancies between the data sources included recall errors in the survey data and errors in the physician billing data algorithm.ConclusionIf further validated, physician billing data could be used along with hospital inpatient data to make an important and unique contribution to adolescent injury surveillance. The limitations inherent in different datasets highlight the need to continue rely on multiple information sources for complete injury surveillance information.

The use of large administrative data sets in nursing research

There is an increasing interest in using administrative data to estimate the treatment effects of interventions. While administrative data are relatively inexpensive to obtain and provide population coverage, they are frequently characterized by lack of clinical detail, often leading to problematic confounding when they are used to conduct observational research. Propensity score methods are increasingly being used to address confounding in estimating the effects of interventions in such studies. Using data on patients discharged from hospital for whom both administrative data and detailed clinical data obtained from chart reviews were available, we examined the degree to which stratifying on the quintiles of propensity scores derived from administrative data was able to balance patient characteristics measured in clinical data. We also determined the extent to which measures of treatment effect obtained using propensity score methods were similar to those obtained using traditional regression methods. As a test case, we examined the treatment effects of ASA and beta-blockers following acute myocardial infarction. We demonstrated that propensity scores developed using administrative data do not necessarily balance patient characteristics contained in clinical data. Furthermore, measures of treatment effectiveness were attenuated when obtained using clinical data compared to when administrative data were used.

BackgroundPerforming clinical research among pediatric end-stage renal disease patients is challenging. Barriers to successful initiation and completion of clinical research projects include small sample sizes and resultant limited statistical power and lack of longitudinal follow-up for hard clinical end-points in most single center studies.DescriptionExisting longitudinal organ failure disease registry and administrative health datasets available within a universal access health care system can be used to study outcomes of end-stage renal disease among pediatric patients in Canada. To construct the Canadian Pediatric End-Stage Renal Disease database, registry data were linked to administrative health data through deterministic linkage techniques creating a research database which consists of socio-demographic variables, clinical variables, all-cause hospitalizations, and relevant outcomes (death and renal allograft loss) for this patient population. The research database also allows study of major cardiovascular events using previously validated administrative data definitions.ConclusionOrgan failure registry linked to health administrative data can be a powerful tool to perform longitudinal studies in pediatric end-stage renal disease patients. The rich clinical and demographic information found in this database will facilitate study of important medical and non-medical risk factors for death, graft loss and cardiovascular disease among pediatric end-stage renal disease patients.

Differential Case Ascertainment in Clinical Registry Versus Administrative Data and Impact on Outcomes Assessment for Pediatric Cardiac Operations

S1011 The Burden of Digestive Diseases in Korea

ObjectivesDevelop administrative linkages within a national Trusted Research Environment (TRE) that hosts Longitudinal Population Study (LPS) data for over 20 LPS. We will describe the methodological development carried out to enable linkage to administrative datasets. These linked administrative data will support research for public good, informing policy and practice.&#x0D; MethodsThe first sets of administrative data under consideration in this Feasibility Study are from the Department of Work and Pensions (DWP), the Department for Education (DfE) and HM Revenue and Customs (HMRC). Working with UK Government departments through a Task &amp; Finish group we have gathered input from DWP, HMRC and DfE and Office for National Statistics (ONS) data sharing experts. The Task &amp; Finish group identified three pragmatic data linkage and data sharing models, that would enable data to be linked via a newly designed secure data pipeline in a legal, secure, and trustworthy manner for all stakeholders.&#x0D; ResultsTo encourage sustainability and acceptability, a model designed to be maintained over a long period is based on the re-use of Departmental Personal Identifiable Information (PII) – i.e., name, date of birth, gender, National Insurance number - and attribute data already deposited by the Departments into ONS. ONS will develop for the linkage and extraction of ONS Data into the TRE a system which conducts, and quality assesses the linkage; minimises the Departmental data to participants within the TRE only and the variables specified in the agreements; and, de-identifies the data to their DEA processing standards. The minimised and functionally anonymous data extract will be securely transferred for ingest and integration into the TRE enabling researchers to address a wider range of questions for public benefit.&#x0D; ConclusionThis is a model for efficient and low-burden linkages to inform cross cutting research. It will form part of a responsive UK data science capability which can inform government research needs and be used to meet future crisis e.g. new pandemics, the impacts of climate change or economic shocks.

High-quality, typically administrative, geospatial data should adhere to established measurement and representation practices and be protected from malicious attacks. However, this kind of geospatial data may only be infrequently updated due to its often prolonged production process compared to a data source of volunteered geographic information such as OpenStreetMap. Existing approaches typically try to quality-assure geospatial data by comparing it to another reference dataset of perceived higher quality - often another administrative dataset facing a similar update cycle. In contrast, this article tries to determine whether actual changes present in volunteered geographic information data such as OpenStreetMap, which also need to be applied in an administrative dataset (i.e., consists of actual changes in the real world), can be identified automatically. To that end, we present QPredict, a machine learning approach observing changes in volunteered geospatial data such as OpenStreetMap to predict issues with a target (administrative) dataset. The algorithm is trained by exploiting geospatial object characteristics, intrinsic and extrinsic quality metrics and their respective changes over time. We evaluate the effectiveness of our approach on two datasets representing two mid-size cities in Germany and discuss our findings in terms of their applicability in use cases.

Administrative monitoring is a kind of monitoring that involves the operative collection of information about the state of the management object from various sources, followed by its processing to the required level of abstraction and further provision of it as a starting point for various decision support systems and/or to the decision maker in the organizational and technical system, in the conditions of changing requirements for the structure and composition of indicators. In article questions of automated selection of model parameters in predicting the state of complex organizational and technical systems on the basis of administrative monitoring data are considered. The linear regression method is described to find the relationship between the parameters of forecasting models and the characteristics of output sets of administrative monitoring data. For some forecasting parameters, a direct dependence on the characteristics of the output data sets of administrative monitoring is determined.

This article describes two administrative data sources - UCAS applicant data and Higher Education Statistical Agency (HESA) data - and demonstrates how they can be utilised to monitor the progression of students from vocational and educational training (VET) programmes in to higher education (HE) in the UK. First the article provides a general discussion of the administrative data sets, their coverage and resulting limitations. Second, it illustrates how a combined HESA-UCAS data set can be utilised to provide a descriptive statistical analysis that demonstrates that including VET students within HE programmes widens participation for under-represented groups in the UK but shows that VET students are at a disadvantage compared with those progressing through academic pathways in terms of their probability of gaining access, which types of higher education institution (HEI) they gain access to, and an increased risk of dropping out during their first year of study. Third, multilevel modelling indicates that drop-out risks differ across HEIs and suggests that HEIs can take actions that will reduce the risk of VET students dropping out. Finally some conclusions about the utility of administrative data to answer substantive research questions are presented, with suggestions for improving the quality and access to such data for researchers.