How to increase the value of self-reported health service data by using data linkage: a case study. (30th September 2020)

• Record Type: Journal Article
• Authors: Peng, W; Maguire, J; Hayen, A; Adams, J; Sibbritt, D
• Is Part Of: European journal of public health. Volume 30(2020)Supplement 5
• Publication Date: 2020-09-30
• Subjects: Epidemiology -- Europe -- Periodicals; Public health -- Europe -- Periodicals; 362.109405
• DOI: 10.1093/eurpub/ckaa165.1279
• Languages: English
• ISSNs: 1101-1262

Abstract

Abstract: Background: This is a case study for recurrent stroke prevention. Lifestyle factors account for about 80% of the risk of recurrent stroke. Most health services studies examining stroke prevention rely on stroke survivors' self-reported lifestyle behaviour data. How can researchers increase the value of collected self-reported data to provide additional information for more comprehensive assessments? Methods: 45 and Up Study is the largest ongoing study in the Southern Hemisphere focusing on the health of people aged 45 years and older living in NSW, Australia. This case study linked self-reported longitudinal lifestyle data in the 45 and Up Study, with corresponding mortality data (i.e. NSW Registry of Births, Deaths and Marriages & NSW Cause of Death Unit Record File) and hospital data (i.e. NSW Admitted Patient Data Collection) via the Centre for Health Record Linkage (CHeReL). The main outcome measures are health services, clinical outcomes, and mortality rates for stroke care. The analyses will include descriptive analysis, multivariate regression analysis, and survival analysis. Results: A total of 8410 stroke survivors who participated in the 45 and Up Study were included in this data linkage study. From January 2006 to December 2015, 99249 hospital claims (mean: 13 times admission to hospital per person) and 2656 death registration records have been linked to these participants. The mean age of the stroke survivors was 72 (SD = 11) years, with 56% being males. These results are preliminary and more analyses will be conducted by using quality of life status, clinical diagnosis, comorbidities, and procedures. Conclusions: Data linkage enables researchers to generate comprehensive findings on health services studies and gain a more holistic understanding of the determinants and outcomes of stroke prevention with lower data collection costs and less burden on participants. Key messages: Data linkage brings about a new opportunity for self-reported data on health services utilisation. It is a cost-effective way to enhance existing self-reported data via the data linkage approach to increase its usefulness for informing health service planning.