Improving reproducibility and validity in measures of chronic disease incidence using large-scale linked data in Australia. Article Swipe
Kylie‐Ann Mallitt
,
Jacqueline H. Stephens
,
Eleonora Dal Grande
,
Amandi Hiyare
,
Michelle Dickson
,
Allison Jauré
,
Jonathan C. Craig
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.23889/ijpds.v9i5.2888
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.23889/ijpds.v9i5.2888
Objective and ApproachOur study aimed to improve methods for identifying incident chronic kidney disease (CKD), cardiovascular disease (CVD), and diabetes in a young cohort from the Antecedents of Renal Disease in Aboriginal Children (ARDAC) study. We linked 20 state and federal administrative health datasets, including hospital records, Medicare claims, pharmaceutical data, and the National Diabetes Services Scheme (NDSS) data for the first time. This approach facilitated the development of new algorithms for disease detection. Notably, we will make the R code for these algorithms publicly available. ResultsThe analysis, involving 3,758 ARDAC study participants, created clinically robust definitions of CKD, CVD, and diabetes (by subtype) in the Australian linked data context; and revealed that all other data sources inaccurately estimated the incidence of diabetes compared to the NDSS data. ConclusionsThis key finding illustrates the discrepancy in diabetes incidence estimates and highlights the value of integrating multiple data sources for the investigation of chronic disease. ImplicationsHundreds of published studies use data linkage to investigate CVD, CKD, and diabetes in the Australian context. Our novel robust case definitions, and the identified poor estimation of diabetes incidence among young people, underscores the need to critically evaluate linked data sources used to inform health policy and research. By sharing the developed R code, we support ongoing efforts to improve transparency and replicability in health research. Our study refines chronic disease definition methods and contributes to more effective public health strategies, with particular focus on improving outcomes for Indigenous communities.
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- OA Status
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- OpenAlex ID
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https://openalex.org/W4402405899Canonical identifier for this work in OpenAlex
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https://doi.org/10.23889/ijpds.v9i5.2888Digital Object Identifier
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Improving reproducibility and validity in measures of chronic disease incidence using large-scale linked data in Australia.Work title
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articleOpenAlex work type
- Language
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enPrimary language
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2024Year of publication
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2024-09-10Full publication date if available
- Authors
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Kylie‐Ann Mallitt, Jacqueline H. Stephens, Eleonora Dal Grande, Amandi Hiyare, Michelle Dickson, Allison Jauré, Jonathan C. CraigList of authors in order
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https://doi.org/10.23889/ijpds.v9i5.2888Publisher landing page
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.23889/ijpds.v9i5.2888Direct OA link when available
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Reproducibility, Scale (ratio), Incidence (geometry), Medicine, Data mining, Statistics, Computer science, Mathematics, Geography, Cartography, GeometryTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.ResultsThe | 86 |
| abstract_inverted_index.algorithms | 70, 83 |
| abstract_inverted_index.available. | 85 |
| abstract_inverted_index.clinically | 94 |
| abstract_inverted_index.critically | 190 |
| abstract_inverted_index.definition | 225 |
| abstract_inverted_index.detection. | 73 |
| abstract_inverted_index.estimation | 179 |
| abstract_inverted_index.highlights | 139 |
| abstract_inverted_index.identified | 177 |
| abstract_inverted_index.particular | 236 |
| abstract_inverted_index.Antecedents | 26 |
| abstract_inverted_index.ApproachOur | 2 |
| abstract_inverted_index.contributes | 228 |
| abstract_inverted_index.definitions | 96 |
| abstract_inverted_index.development | 67 |
| abstract_inverted_index.discrepancy | 133 |
| abstract_inverted_index.facilitated | 65 |
| abstract_inverted_index.identifying | 9 |
| abstract_inverted_index.illustrates | 131 |
| abstract_inverted_index.integrating | 143 |
| abstract_inverted_index.investigate | 161 |
| abstract_inverted_index.strategies, | 234 |
| abstract_inverted_index.underscores | 186 |
| abstract_inverted_index.communities. | 243 |
| abstract_inverted_index.definitions, | 174 |
| abstract_inverted_index.inaccurately | 117 |
| abstract_inverted_index.transparency | 214 |
| abstract_inverted_index.investigation | 149 |
| abstract_inverted_index.participants, | 92 |
| abstract_inverted_index.replicability | 216 |
| abstract_inverted_index.administrative | 41 |
| abstract_inverted_index.cardiovascular | 15 |
| abstract_inverted_index.pharmaceutical | 49 |
| abstract_inverted_index.ConclusionsThis | 128 |
| abstract_inverted_index.ImplicationsHundreds | 153 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 7 |
| citation_normalized_percentile.value | 0.19344748 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |