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Data preparation techniques for a perinatal psychiatric study based on linked data
BACKGROUND: In recent years there has been an increase in the use of population-based linked data. However, there is little literature that describes the method of linked data preparation. This paper describes the method for merging data, calculating the statistical variable (SV), recoding psychiatr...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2012
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3445825/ https://www.ncbi.nlm.nih.gov/pubmed/22682616 http://dx.doi.org/10.1186/1471-2288-12-71 |
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author | Xu, Fenglian Hilder, Lisa Austin, Marie-Paule Sullivan, Elizabeth A |
author_facet | Xu, Fenglian Hilder, Lisa Austin, Marie-Paule Sullivan, Elizabeth A |
author_sort | Xu, Fenglian |
collection | PubMed |
description | BACKGROUND: In recent years there has been an increase in the use of population-based linked data. However, there is little literature that describes the method of linked data preparation. This paper describes the method for merging data, calculating the statistical variable (SV), recoding psychiatric diagnoses and summarizing hospital admissions for a perinatal psychiatric study. METHODS: The data preparation techniques described in this paper are based on linked birth data from the New South Wales (NSW) Midwives Data Collection (MDC), the Register of Congenital Conditions (RCC), the Admitted Patient Data Collection (APDC) and the Pharmaceutical Drugs of Addiction System (PHDAS). RESULTS: The master dataset is the meaningfully linked data which include all or major study data collections. The master dataset can be used to improve the data quality, calculate the SV and can be tailored for different analyses. To identify hospital admissions in the periods before pregnancy, during pregnancy and after birth, a statistical variable of time interval (SVTI) needs to be calculated. The methods and SPSS syntax for building a master dataset, calculating the SVTI, recoding the principal diagnoses of mental illness and summarizing hospital admissions are described. CONCLUSION: Linked data preparation, including building the master dataset and calculating the SV, can improve data quality and enhance data function. |
format | Online Article Text |
id | pubmed-3445825 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2012 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-34458252012-09-20 Data preparation techniques for a perinatal psychiatric study based on linked data Xu, Fenglian Hilder, Lisa Austin, Marie-Paule Sullivan, Elizabeth A BMC Med Res Methodol Correspondence BACKGROUND: In recent years there has been an increase in the use of population-based linked data. However, there is little literature that describes the method of linked data preparation. This paper describes the method for merging data, calculating the statistical variable (SV), recoding psychiatric diagnoses and summarizing hospital admissions for a perinatal psychiatric study. METHODS: The data preparation techniques described in this paper are based on linked birth data from the New South Wales (NSW) Midwives Data Collection (MDC), the Register of Congenital Conditions (RCC), the Admitted Patient Data Collection (APDC) and the Pharmaceutical Drugs of Addiction System (PHDAS). RESULTS: The master dataset is the meaningfully linked data which include all or major study data collections. The master dataset can be used to improve the data quality, calculate the SV and can be tailored for different analyses. To identify hospital admissions in the periods before pregnancy, during pregnancy and after birth, a statistical variable of time interval (SVTI) needs to be calculated. The methods and SPSS syntax for building a master dataset, calculating the SVTI, recoding the principal diagnoses of mental illness and summarizing hospital admissions are described. CONCLUSION: Linked data preparation, including building the master dataset and calculating the SV, can improve data quality and enhance data function. BioMed Central 2012-06-08 /pmc/articles/PMC3445825/ /pubmed/22682616 http://dx.doi.org/10.1186/1471-2288-12-71 Text en Copyright ©2012 Xu et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Correspondence Xu, Fenglian Hilder, Lisa Austin, Marie-Paule Sullivan, Elizabeth A Data preparation techniques for a perinatal psychiatric study based on linked data |
title | Data preparation techniques for a perinatal psychiatric study based on linked data |
title_full | Data preparation techniques for a perinatal psychiatric study based on linked data |
title_fullStr | Data preparation techniques for a perinatal psychiatric study based on linked data |
title_full_unstemmed | Data preparation techniques for a perinatal psychiatric study based on linked data |
title_short | Data preparation techniques for a perinatal psychiatric study based on linked data |
title_sort | data preparation techniques for a perinatal psychiatric study based on linked data |
topic | Correspondence |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3445825/ https://www.ncbi.nlm.nih.gov/pubmed/22682616 http://dx.doi.org/10.1186/1471-2288-12-71 |
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