scholarly journals Accuracy of reporting of Aboriginality on administrative health data collections using linked data in NSW, Australia

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Michael A. Nelson ◽  
Kim Lim ◽  
Jason Boyd ◽  
Damien Cordery ◽  
Allan Went ◽  
...  
Keyword(s):  
Record Linkage ◽  
Linked Data ◽  
Aboriginal People ◽  
Patient Survey ◽  
Health Data ◽  
Weight Of Evidence ◽  
Cross Sectional ◽  
Predictive Values ◽  
Administrative Health Data ◽  
Data Collections

Abstract Background Aboriginal people are under-reported on administrative health data in Australia. Various approaches have been used or proposed to improve reporting of Aboriginal people using linked records. This cross-sectional study used self-reported Aboriginality from the NSW Patient Survey Program (PSP) as a reference standard to assess the accuracy of reporting of Aboriginal people on NSW Admitted Patient (APDC) and Emergency Department Data Collections (EDDC), and compare the accuracy of selected approaches to enhance reporting Aboriginality using linked data. Methods Ten PSP surveys were linked to five administrative health data collections, including APDC, EDDC, perinatal, and birth and death registration records. Accuracy of reporting of Aboriginality was assessed using sensitivity, specificity, and positive and negative predictive values (PPVs and NPVs) and F score for the EDDC and APDC as baseline and four enhancement approaches using linked records: “Most recent linked record”, “Ever reported as Aboriginal”, and two approaches using a weight of evidence, “Enhanced Reporting of Aboriginality (ERA) algorithm” and “Multi-stage median (MSM)”. Results There was substantial under-reporting of Aboriginality on APDC and EDDC records (sensitivities 84 and 77% respectively) with PPVs of 95% on both data collections. Overall, specificities and NPVs were above 98%. Of people who were reported as Aboriginal on the PSP, 16% were not reported as Aboriginal on any of their linked records. Record linkage approaches generally increased sensitivity, accompanied by decrease in PPV with little change in overall F score for the APDC and an increase in F score for the EDDC. The “ERA algorithm” and “MSM” approaches provided the best overall accuracy. Conclusions Weight of evidence approaches are preferred when record linkage is used to improve reporting of Aboriginality on administrative health data collections. However, as a substantial number of Aboriginal people are not reported as Aboriginal on any of their linked records, improvements in reporting are incomplete and should be taken into account when interpreting results of any analyses. Enhancement of reporting of Aboriginality using record linkage should not replace efforts to improve recording of Aboriginal people at the point of data collection and addressing barriers to self-identification for Aboriginal people.

scholarly journals The Norwegian Twin Registry

2019 ◽  
Vol 22 (6) ◽  
pp. 647-650
Author(s):  
Thomas Nilsen ◽  
Ingunn Brandt ◽  
Jennifer R. Harris
Keyword(s):  
Record Linkage ◽  
Potential Benefit ◽  
Data Access ◽  
Health Data ◽  
Research Use ◽  
Twin Data ◽  
Physical And Mental Health ◽  
Twin Registry ◽  
Other Information ◽  
Data Collections

AbstractThe Norwegian Twin Registry (NTR) is maintained as a research resource that was compiled by merging several panels of twin data that were established for research into physical and mental health, wellbeing and development. NTR is a consent-based registry. Where possible, data that were collected in previous studies are curated for secondary research use. A particularly valuable potential benefit associated with the Norwegian twin data lies in the opportunities to expand and enhance the data through record linkage to nationwide registries that cover a wide array of health data and other information, including socioeconomic factors. This article provides a brief description of the current NTR sample and data collections, information about data access procedures and an overview of the national registries that can be linked to the NTR for research projects.

scholarly journals P084: Hard-to-reach populations and administrative health data: a serial cross-sectional study and application of data to improve interventions for people experiencing homelessness

CJEM ◽  
2020 ◽  
Vol 22 (S1) ◽  
pp. S94-S95
Author(s):  
R. Hossain ◽  
Z. Ma ◽  
J. Dai ◽  
S. Jamani ◽  
R. Hossain ◽  
...  
Keyword(s):  
Emergency Department ◽  
Vulnerable Populations ◽  
Cross Sectional Study ◽  
Models Of Care ◽  
Health Data ◽  
Emergency Department Visits ◽  
Sectional Study ◽  
Cross Sectional ◽  
Administrative Health Data ◽  
Hard To Reach Populations

Introduction: Administrative data can aid in study and intervention design, incorporating hard-to-reach individuals who may otherwise be poorly represented. We aim to use administrative health data to examine emergency department visits by people experiencing homelessness and explore the application of this data for planning interventions. Methods: We conducted a serial cross-sectional study examining emergency department use by people experiencing homelessness and non-homeless individuals in the Niagara region of Ontario, Canada. The study period included administrative health data from April 1st, 2010 to March 31st, 2018. Outcomes included number of visits, number of unique patients; group proportions of Canadian Triage and Acuity Scale (CTAS) scores; time spent in emergency; and time to see an MD. Descriptive statistics were generated, and t-tests were performed for point estimates and a Mann-Whitney U test for distributional measures. Results: Our data included 1,486,699 emergency department visits. The number of unique people experiencing homelessness ranged from 91 in 2010 to 344 in 2017, trending higher over the study period compared to non-homeless patients. The rate of visits increased from 1.7 to 2.8 per person. People experiencing homelessness tend to present later in the day and with higher overall acuity as compared to the general population. Time in emergency department and time to see an MD were greater among people experiencing homelessness. Conclusion: Administrative health data allows researchers to enhance interventions and models of care to improve services for vulnerable populations. Given the challenging fiscal realities of research, our study provides insights to more effectively target interventions for vulnerable populations.

scholarly journals Validation of Self-Reported Race in a Canadian Provincial Renal Administrative Database

2019 ◽  
Vol 6 ◽  
pp. 205435811985952
Author(s):  
Aiza Waheed ◽  
Ognjenka Djurdjev ◽  
Jianghu Dong ◽  
Jagbir Gill ◽  
Sean Barbour
Keyword(s):  
Administrative Data ◽  
Minority Groups ◽  
Secondary Analysis ◽  
Patient Survey ◽  
Administrative Databases ◽  
Survey Study ◽  
Administrative Database ◽  
Cross Sectional ◽  
Predictive Values ◽  
Health Delivery

Background:Administrative data are commonly used to study clinical outcomes in renal disease. Race is an important determinant of renal health delivery and outcomes in Canada but is not validated in most administrative data, and the correlation with census-based definitions of race is unknown.Objectives:Validation of self-reported race (SRR) in a Canadian provincial renal administrative database (Patient Records and Outcome Management Information System [PROMIS]) and comparison with the Canadian census categories of race.Design:Prospective patient survey study to validate SRR in PROMIS.Setting:British Columbia, Canada.Patients:Adult patients registered in PROMIS.Measurements:Survey SRR was used as gold standard to validate SRR in PROMIS. Self-reported race in PROMIS was compared with census race categories.Methods:This is a cross-sectional telephone survey of a random sample of all adults in PROMIS conducted between February 2016 and November 2016. Responders selected a race category from PROMIS and from the Canadian census. Sensitivity (Sn) and specificity (Sp) were calculated with 95% confidence intervals (CIs).Results:A total of 21 039 patients met inclusion criteria, 1677 were selected for the survey and 637 participated (38% response rate). There were no differences between the PROMIS, sampled, and responder populations. PROMIS SRR had an accuracy of 95.3% (95% CI: 94.2%-97.0%) when validated against the survey SRR with Sn and Sp ≥90% in all race groups except in Aboriginals (Sn 87.5%). The positive and negative predictive values were ≥95%, except in very low and high–prevalence groups, respectively. The Canadian census had an accuracy of 95.7% (95% CI: 94.4%-97.6%) when validated against PROMIS SRR with Sn and Sp ≥90%. The results did not differ in subgroups based on age, sex, birth outside Canada, or renal group (glomerulonephritis, chronic kidney disease, hemodialysis, peritoneal dialysis, transplant recipients, or live donors).Limitations:Analysis of minority groups and lower prevalence groups is limited by sample size. Results may not be generalizable to other administrative databases.Conclusions:We have shown high accuracy of PROMIS SRR that validates its use in the secondary analysis of administrative data for research. There is high correlation between PROMIS and census race categories which allows linkage with other data sources that use census-based definitions of race.

Administrative health databases for addressing emerging issues in adults with CHD: a systematic review

2018 ◽  
Vol 28 (6) ◽  
pp. 844-853 ◽  
Author(s):  
Sarah Cohen ◽  
Harel Gilutz ◽  
Ariane J. Marelli ◽  
Laurence Iserin ◽  
Arriel Benis ◽  
...  
Keyword(s):  
Temporal Trends ◽  
The United States ◽  
Health Data ◽  
Data Sources ◽  
Resource Utilisation ◽  
Cross Sectional ◽  
Administrative Health Data ◽  
Patient Level ◽  
Administrative Health Databases ◽  
The Impact

AbstractThe need for population-based studies of adults with CHD has motivated the growing use of secondary analyses of administrative health data in a variety of jurisdictions worldwide. We aimed at systematically reviewing all studies using administrative health data sources for adult CHD research from 2006 to 2016. Using PubMed and Embase (1 January, 2006 to 1 January, 2016), we identified 2217 abstracts, from which 59 studies were included in this review. These comprised 12 different data sources from six countries. Of these, 55% originated in the United States of America, 28% in Canada, and 17% in Europe and Asia. No study was published before 2007, after which the number of publications grew exponentially. In all, 41% of the studies were cross-sectional and 25% were retrospective cohort studies with a wide variation in the availability of patient-level compared with hospitalisation-level episodes of care; 58% of studies from eight different data sources linked administrative data at a patient level; and 37% of studies reported validation procedures. Assessing resource utilisation and temporal trends of relevant epidemiological and outcome end points were the most reported objectives. The median impact factor of publication journals was 4.04, with an interquartile range of 3.15, 7.44. Although not designed for research purposes, administrative health databases have become powerful data sources for studying adult CHD populations because of their large sample sizes, comprehensive records, and long observation periods, providing a useful tool to further develop quality of care improvement programmes. Data linkage with electronic records will become important in obtaining more granular life-long adult CHD data. The health services nature of the data optimises the impact on policy and public health.

scholarly journals SOEP-RV: Linking German Socio-Economic Panel Data to Pension Records

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Holger Lüthen ◽  
Carsten Schröder ◽  
Markus M. Grabka ◽  
Jan Goebel ◽  
Tatjana Mika ◽  
...  
Keyword(s):  
Panel Data ◽  
Social Security ◽  
Longitudinal Data ◽  
Record Linkage ◽  
Linked Data ◽  
Pension Insurance ◽  
Cross Sectional ◽  
Explicit Consent ◽  
Deutsche Rentenversicherung ◽  
Link Data

Abstract The aim of the project SOEP-RV is to link data from participants in the German Socio-Economic Panel (SOEP) survey to their individual Deutsche Rentenversicherung (German Pension Insurance) records. For all SOEP respondents who give explicit consent to record linkage, SOEP-RV creates a linked dataset that combines the comprehensive multi-topic SOEP data with detailed cross-sectional and longitudinal data on social security pension records covering the individual’s entire insurance history. This article provides an overview of the record linkage project, highlights potentials for analysis of the linked data, compares key SOEP and pension insurance variables, and suggests a re-weighting procedure that corrects for selectivity. It concludes with details on the process of obtaining the data for scientific use.

scholarly journals Using linked administrative health data for palliative and end of life care research in Ireland: potential and challenges

2021 ◽  
Vol 4 ◽  
pp. 17
Author(s):  
Maria Kelly ◽  
Katie O'Brien ◽  
Ailish Hannigan
Keyword(s):  
Palliative Care ◽  
Health System ◽  
End Of Life ◽  
Data Protection ◽  
End Of Life Care ◽  
Data Access ◽  
Health Data ◽  
Life Care ◽  
Administrative Health Data ◽  
Data Collections

Background: This study aims to examine the potential of currently available administrative health data for palliative and end-of-life care (PEoLC) research in Ireland. Objectives include to i) identify administrative health data sources for PEoLC research ii) describe the challenges and opportunities of using these and iii) estimate  the impact of recent health system reforms and changes to data protection laws.  Methods: The 2017 Health Information and Quality Authority catalogue of health and social care datasets was cross-referenced with a recognised list of diseases with associated palliative care needs. Criteria to assess the datasets included population coverage, data collected, data dictionary and data model availability and mechanisms for data access. Results: Eight datasets with potential for PEoLC research were identified, including four disease registries, (cancer, cystic fibrosis, motor neurone and interstitial lung disease), death certificate data, hospital episode data, community prescription data and one national survey. The ad hoc development of the health system in Ireland has resulted in i) a fragmented information infrastructure resulting in gaps in data collections particularly in the primary and community care sector where much palliative care is delivered, ii) ill-defined data governance arrangements across service providers, many of whom are not part of the publically funded health service and iii) systemic and temporal issues that affect data quality. Initiatives to improve data collections include introduction of i) patient unique identifiers, ii) health entity identifiers and iii) integration of the eircode postcodes. Recently enacted general data protection and health research regulations will clarify legal and ethical requirements for data  use. Conclusions: With appropriate permissions, detailed knowledge of the datasets and good study design currently available administrative health data can be used for PEoLC research. Ongoing reform initiatives and recent changes to data privacy laws will facilitate future use of administrative health data for PEoLC research.

scholarly journals Wait times and patterns of care in the colorectal cancer diagnostic interval

2017 ◽  
Vol 1 (1) ◽  
Author(s):  
Colleen Webber ◽  
Jennifer Flemming ◽  
Richard Birtwhistle ◽  
Mark Rosenberg ◽  
Patti Groome
Keyword(s):  
Colorectal Cancer ◽  
Cluster Analysis ◽  
Control Charts ◽  
Population Level ◽  
Population Based ◽  
Health Data ◽  
Patterns Of Care ◽  
Cross Sectional ◽  
Administrative Health Data ◽  
Diagnostic Interval

ABSTRACTObjectiveThere is concern that patients are waiting too long to be diagnosed with colorectal cancer (CRC) after presenting to the healthcare system. A prolonged time from first presentation to diagnosis, also known as the diagnostic interval, may be harmful to patients and indicate problems with the delivery of healthcare. The purpose of this study is to measure the length of the CRC diagnostic interval and describe variations in care that patients receive within the interval. ApproachThis is a population-based, cross-sectional study of CRC patients diagnosed in Ontario, Canada between 2009 and 2012 using data from the Institute for Clinical Evaluative Sciences (ICES). The diagnostic interval will be measured using physician billing, hospital discharge, emergency room and registry data. Patients’ healthcare encounters in the 18 months before diagnosis will be analyzed using control charts to identify the earliest cancer-related encounter. The diagnostic interval will be defined as the date of this first relevant healthcare encounter to the CRC diagnosis date. Cluster analysis will be used to identify and characterize groups of patients with similar diagnostic intervals, based on the care received within the interval. Analyses will examine factors associated with the length of the diagnostic interval and care received within the diagnostic interval. Results Analyses for this project are ongoing and will be complete by August 2016. Results from this study will describe the length of the CRC diagnostic interval and relevant sub-intervals, and variations in these intervals according to patient and clinical characteristics. Results will describe the care that patients received within the interval, including the number and types of tests received and physicians involved in the interval, and whether the care received in the interval was associated with how long patients wait for diagnosis. ConclusionThe findings from this study will advance our understanding of the CRC diagnostic interval. The control chart methodology used to identify CRC-related healthcare encounters from administrative health data is an improvement on previous research that has used arbitrary time periods and encounters which likely underestimate the length of the diagnostic interval. The cluster analysis method is a novel approach to characterizing the diagnostic interval that will identify common patterns of care and diagnostic pathways using administrative health data. This study will provide population-level estimates of how long patients are waiting to be diagnosed with CRC and provide an understanding of how patterns of care influence the length of the diagnostic interval.

scholarly journals Reporting of Aboriginal people in health data collections in NSW

2012 ◽  
Vol 23 (4) ◽  
pp. 61
Author(s):  
Louise Maher ◽  
Caroline Turnour ◽  
Jessica Stewart
Keyword(s):  
Aboriginal People ◽  
Health Data ◽  
Data Collections

scholarly journals Detection of adverse drug events in e-prescribing and administrative health data: a validation study

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Bettina Habib ◽  
Robyn Tamblyn ◽  
Nadyne Girard ◽  
Tewodros Eguale ◽  
Allen Huang
Keyword(s):  
Administrative Data ◽  
Gold Standard ◽  
Adverse Drug Events ◽  
Study Drug ◽  
Health Data ◽  
Diagnostic Code ◽  
Administrative Health Data ◽  
Potential Ades ◽  
Patient Reported

Abstract Background Administrative health data are increasingly used to detect adverse drug events (ADEs). However, the few studies evaluating diagnostic codes for ADE detection demonstrated low sensitivity, likely due to narrow code sets, physician under-recognition of ADEs, and underreporting in administrative data. The objective of this study was to determine if combining an expanded ICD code set in administrative data with e-prescribing data improves ADE detection. Methods We conducted a prospective cohort study among patients newly prescribed antidepressant or antihypertensive medication in primary care and followed for 2 months. Gold standard ADEs were defined as patient-reported symptoms adjudicated as medication-related by a clinical expert. Potential ADEs in administrative data were defined as physician, ED, or hospital visits during follow-up for known adverse effects of the study medication, as identified by ICD codes. Potential ADEs in e-prescribing data were defined as study drug discontinuations or dose changes made during follow-up for safety or effectiveness reasons. Results Of 688 study participants, 445 (64.7%) were female and mean age was 64.2 (SD 13.9). The study drug for 386 (56.1%) patients was an antihypertensive, and for 302 (43.9%) an antidepressant. Using the gold standard definition, 114 (16.6%) patients experienced an ADE, with 40 (10.4%) among antihypertensive users and 74 (24.5%) among antidepressant users. The sensitivity of the expanded ICD code set was 7.0%, of e-prescribing data 9.7%, and of the two combined 14.0%. Specificities were high (86.0–95.0%). The sensitivity of the combined approach increased to 25.8% when analysis was restricted to the 27% of patients who indicated having reported symptoms to a physician. Conclusion Combining an expanded diagnostic code set with e-prescribing data improves ADE detection. As few patients report symptoms to their physician, higher detection rates may be achieved by collecting patient-reported outcomes via emerging digital technologies such as patient portals and mHealth applications.

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