scholarly journals Data linkage to national Australian health insurance data to investigate exposure to environmental hazards: the example of residential asbestos

2018 ◽  
Vol 3 (4) ◽  
Author(s):  
Phil Anderson ◽  
Rosemary Korda ◽  
Hsei Di Law ◽  
Martyn Kirk ◽  
Tenniel Guiver
Keyword(s):  
Health Insurance ◽  
Environmental Risk ◽  
Data Linkage ◽  
Environmental Hazards ◽  
Sensitivity Analyses ◽  
Health Study ◽  
Health Insurance Data ◽  
Population Number ◽  
Australian Health ◽  
National Medical

IntroductionThe enrolment data for Medicare, the Australian universal health insurance provider, covers almost the entire population. Medicare data are commonly used for data linkage, usually to access national medical and pharmaceutical data. However, the enrolment data also enable the identification of geographical cohorts for studies analysing exposure to environmental hazards. Objectives and ApproachOne example of this was the ACT Asbestos Health Study examining the health risks associated with living in houses insulated with loose-fill asbestos in the Australian Capital Territory. The Medicare Enrolment File contains the personal details and addresses of all people enrolled since 1984, including all updates to these details. We linked these data to a register of ~1100 affected properties, with subsequent linkage to the national death index and the Australian Cancer Database. We estimated Standardized Incidence Ratios (SIR) for selected cancers in people living in these houses to obtain a measure of exposure to environmental risk within the population. ResultsAfter intensive cleaning and standardisation, nearly all (99.8%) of the affected addresses were linked. There were over one million people who had at least one ACT address between 1983 and 2013, and 2% of these had lived at an affected address and classified as exposed. The adjusted incidence of mesothelioma in exposed males was 2·5 times that of unexposed males (SIR 2·54, 95% CI 1·02–5·24), and there were some statistically significant results. The study population, number of deaths and cancers of interest were validated against the ACT census and registry figures. There were some limitations in coverage due to the period of available data, the frequency of address updates, and records with postal rather than residential addresses, but these were tested by sensitivity analyses. Conclusion/ImplicationsThe study demonstrates the power of data linkage to (a) obtain a measure of exposure to an environmental risk within a population, and (b) obtain outcomes for the resulting case and control cohorts. This method could be applied in other risk studies where exposure is based on geography.

scholarly journals Individual-Level Linkage of Primary and Secondary Data from Three Sources for Comprehensive Analyses of Low Birthweight Effects

2020 ◽  
Vol 82 (S 02) ◽  
pp. S108-S116
Author(s):  
Diana Druschke ◽  
Katrin Arnold ◽  
Luise Heinrich ◽  
Jörg Reichert ◽  
Mario Rüdiger ◽  
...  
Keyword(s):  
Health Insurance ◽  
Data Protection ◽  
Data Linkage ◽  
Secondary Data ◽  
Data Access ◽  
Data Sources ◽  
Primary Data ◽  
Data Validation ◽  
Health Insurance Data ◽  
Individual Level

Abstract Aim of the study The linkage of primary and secondary data is becoming an increasingly popular approach in healthcare research, but involves some challenges for all involved parties, for example due to data protection requirements. The aim of this article is to systematically outline the methods used and experiences made during a cohort study in the field of pediatric health care research (EcoCare-PIn) that involved access to and linkage of three different data sources. Particular focus is placed on the necessary regulatory measures with regard to data access and data linkage as well as on data validation to ensure a correct linkage. Methods While complying with all relevant data protection requirements, the study realized an individual-level linkage of a) pseudonymized administrative health insurance data from a statutory health insurance on Saxon children born between 2007 and 2013, b) primary data collected via postal questionnaires from parents/caregivers and c) medical data from kindergarten- and school-entry-examinations of Saxon health authorities. The fundamental principle of the concept of data linkage was to strictly separate the sites of data collection and data analysis, which was realized through the involvement of a trust center. Results Challenges especially pertained to the extensive regulatory pre-requirements for data access as well as to data protection requirements while performing the study. Technical aspects and data validation also required a considerable share of attention and resources. A number of validation routines were applied to avoid incorrect data linkage and to ensure the high quality of the final dataset. Data validation included both plausibility checks within the primary data and consistency checks of information given in primary and secondary data. Conclusion The linkage of primary and secondary data on the individual level offers great opportunities for using the strengths of different data sources synergistically and overcoming some of their limitations. Statutory health insurance data and medical data from kindergarten- and school-entry-examinations of Saxon health authorities are examples of already existing data sources that can complement cost-consuming primary data collections by valuable data sets and open up opportunities for longitudinal analysis.

scholarly journals Relevance of healthcare reimbursement data to monitor syphilis epidemic: an alternative surveillance through the national health insurance database in France, 2011–2013

BMJ Open ◽  
2018 ◽  
Vol 8 (7) ◽  
pp. e020336 ◽  
Author(s):  
Ndeindo Ndeikoundam Ngangro ◽  
Delphine Viriot ◽  
Etienne Lucas ◽  
Marjorie Boussac-Zarebska ◽  
Florence Lot ◽  
...  
Keyword(s):  
Health Insurance ◽  
Census Data ◽  
Sensitivity Analyses ◽  
Annual Number ◽  
Health Insurance Data ◽  
Transmitted Infection ◽  
Alternative Source ◽  
Sentinel Network ◽  
Paris Area ◽  
Insurance Data

ObjectiveIn France, surveillance of early syphilis (primary, secondary and early latent) relies on the clinician-based ResIST sentinel network. Although ResIST enables the monitoring of trends, a complete picture of the syphilis epidemic is not possible. More specifically, cases reported by this network are mostly diagnosed in free sexually transmitted infection clinics and hospitals (75% and 24%, respectively). This study aims to estimate the number and rate of diagnoses made outside these health facilities by exploring health insurance data.MethodsAn algorithm combining healthcare reimbursements for specific diagnostic tests and recommended treatment was fitted to identify syphilis cases. Sensitivity analyses were used to validate the algorithm. Age-standardised and gender-standardised diagnosis rates were estimated using census data.ResultsBetween 2011 and 2013, 12 644 (7.5 cases per 100 000 inhabitants) reimbursements were made for syphilis-related diagnoses. The annual number of cases increased by 22% from 2011 (n=3771, rate=6.7/100 000) to 2013 (n=4589, rate=8.2/100 000). The rate of syphilis diagnosis increased in men from 12.9/100 000 to 16.0/100 000, while it remained steady in women at approximately 1.8/100 000. The disease burden was greatest in French overseas territories (18.1/100 000) and in the Paris area (11.7 cases/100 000).ConclusionDespite the lack of data on the number of confirmed diagnoses and information on sexual behaviour, these findings demonstrate the relevance of analysing insurance data to help monitor the syphilis epidemic in patients who visit general practitioners and non-hospital-based specialists. Thus, reimbursement database might be a relevant alternative source of continuous information on syphilis in countries with similar insurance-based healthcare systems.

A study on the health effects of residents in Namhae and Hadong - Centered on health insurance data

2020 ◽  
Vol 19 (3) ◽  
pp. 268-277
Author(s):  
YoonDeok Han ◽  
◽  
Sunghyeon Jung ◽  
Kwang-tae Ha ◽  
Seung-Mi Kwon ◽  
...  
Keyword(s):  
Health Insurance ◽  
Health Effects ◽  
Health Insurance Data ◽  
Insurance Data

A Novel Approach for Labelling Health Insurance Data for Cardiovascular Disease Risk Classification

2016 ◽  
Author(s):  
César L. C. Mattos ◽  
Amauri H. Souza Júnior ◽  
Ajalmar R. Rocha Neto ◽  
Guilherme A. Barreto ◽  
Ronaldo F. Ramos ◽  
...  
Keyword(s):  
Cardiovascular Disease ◽  
Health Insurance ◽  
Disease Risk ◽  
Cardiovascular Disease Risk ◽  
Risk Classification ◽  
Health Insurance Data ◽  
Novel Approach ◽  
Insurance Data

Data Processing Improvements for Estimates of Health Insurance Coverage in the Current Population Survey Annual Social and Economic Supplement

2021 ◽  
pp. 107755872110008
Author(s):  
Edward R. Berchick ◽  
Heide Jackson
Keyword(s):  
Health Insurance ◽  
Data Processing ◽  
Current Population Survey ◽  
Population Survey ◽  
Insurance Coverage ◽  
Processing System ◽  
Health Insurance Coverage ◽  
The United States ◽  
Health Insurance Data ◽  
Current Population

Estimates of health insurance coverage in the United States rely on household-based surveys, and these surveys seek to improve data quality amid a changing health insurance landscape. We examine postcollection processing improvements to health insurance data in the Current Population Survey Annual Social and Economic Supplement (CPS ASEC), one of the leading sources of coverage estimates. The implementation of updated data extraction and imputation procedures in the CPS ASEC marks the second stage of a two-stage improvement and the beginning of a new time series for health insurance estimates. To evaluate these changes, we compared estimates from two files that introduce the updated processing system with two files that use the legacy system. We find that updates resulted in higher rates of health insurance coverage and lower rates of dual coverage, among other differences. These results indicate that the updated data processing improves coverage estimates and addresses previously noted limitations of the CPS ASEC.

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