Using Multiple Administrative Health Datasets to Identify A Mental Illness Cohort
Introduction It is well established that mental illness is associated with poor physical health and chronic diseases possibly related to high levels of risk factors such as smoking, physical inactivity, poor diet and alcohol consumption. There is also evidence to suggest that having a mental illnes...
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doaj-d8c1bc7b80074023aa688bcae4f864d52021-02-10T16:42:21ZengSwansea UniversityInternational Journal of Population Data Science2399-49082020-12-015510.23889/ijpds.v5i5.1563Using Multiple Administrative Health Datasets to Identify A Mental Illness CohortMargo Barr0Heidi Welberry1Julie Finch2Lou Anne Blunden3Centre for Primary Health Care and Equity, University of New South WalesCentre for Primary Health Care and Equity, University of New South WalesSydney Local Health District, NSW HealthSydney Local Health District, NSW Health Introduction It is well established that mental illness is associated with poor physical health and chronic diseases possibly related to high levels of risk factors such as smoking, physical inactivity, poor diet and alcohol consumption. There is also evidence to suggest that having a mental illness may be related to poorer management of chronic diseases. Health service providers wanted to investigate this in Central and Eastern Sydney (CES). Objectives and Approach The purpose of this study was to scope the feasibility of identifying a mental illness cohort using CES residents from the 45 and Up Study (n=30,049) linked to administrative health datasets. These included Medicare Benefits Schedule (MBS) and Pharmaceutical Benefits Scheme (PBS) provided by Services Australia and hospital admissions provided by the NSW Centre for Health Record Linkage. We then compared differences in the identified cohort size, characteristics and the 8-year mortality rates. Results Using only hospitalisation data, 6% of the CES cohort were identified as having a mental illness, compared to 17% using MBS data only, 26% PBS data only, 35% using both the MBS and PBS data and 36% using all of the data sources. Crude mortality was 58% in those identified in the hospitalisation data, 27% based on the PBS data,10% using MBS data, 21% using the MBS and PBS data and 23% based on all combined sources. Conclusion / Implications We decided that the most appropriate option was to include the MBS, PBS and hospitalisation data to identify the mental illness cohort. This cohort will now be used to examine difference in the management of chronic disease, such as care plans and cycles of care, between those who do and do not have a mental illness. https://ijpds.org/article/view/1563 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Margo Barr Heidi Welberry Julie Finch Lou Anne Blunden |
spellingShingle |
Margo Barr Heidi Welberry Julie Finch Lou Anne Blunden Using Multiple Administrative Health Datasets to Identify A Mental Illness Cohort International Journal of Population Data Science |
author_facet |
Margo Barr Heidi Welberry Julie Finch Lou Anne Blunden |
author_sort |
Margo Barr |
title |
Using Multiple Administrative Health Datasets to Identify A Mental Illness Cohort |
title_short |
Using Multiple Administrative Health Datasets to Identify A Mental Illness Cohort |
title_full |
Using Multiple Administrative Health Datasets to Identify A Mental Illness Cohort |
title_fullStr |
Using Multiple Administrative Health Datasets to Identify A Mental Illness Cohort |
title_full_unstemmed |
Using Multiple Administrative Health Datasets to Identify A Mental Illness Cohort |
title_sort |
using multiple administrative health datasets to identify a mental illness cohort |
publisher |
Swansea University |
series |
International Journal of Population Data Science |
issn |
2399-4908 |
publishDate |
2020-12-01 |
description |
Introduction
It is well established that mental illness is associated with poor physical health and chronic diseases possibly related to high levels of risk factors such as smoking, physical inactivity, poor diet and alcohol consumption. There is also evidence to suggest that having a mental illness may be related to poorer management of chronic diseases. Health service providers wanted to investigate this in Central and Eastern Sydney (CES).
Objectives and Approach
The purpose of this study was to scope the feasibility of identifying a mental illness cohort using CES residents from the 45 and Up Study (n=30,049) linked to administrative health datasets. These included Medicare Benefits Schedule (MBS) and Pharmaceutical Benefits Scheme (PBS) provided by Services Australia and hospital admissions provided by the NSW Centre for Health Record Linkage. We then compared differences in the identified cohort size, characteristics and the 8-year mortality rates.
Results
Using only hospitalisation data, 6% of the CES cohort were identified as having a mental illness, compared to 17% using MBS data only, 26% PBS data only, 35%
using both the MBS and PBS data and 36% using all of the data sources. Crude mortality was 58% in those identified in the hospitalisation data, 27% based on the PBS data,10% using MBS data, 21% using the MBS and PBS data and 23% based on all combined sources.
Conclusion / Implications
We decided that the most appropriate option was to include the MBS, PBS and hospitalisation data to identify the mental illness cohort. This cohort will now be used to examine difference in the management of chronic disease, such as care plans and cycles of care, between those who do and do not have a mental illness.
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url |
https://ijpds.org/article/view/1563 |
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