Data preparation techniques for a perinatal psychiatric study based on linked data

BMC Medical Research Methodology

Elizabeth A Sullivan²  Marie-Paule Austin¹  Lisa Hilder²  Fenglian Xu² 

[1] Perinatal & Women's Mental Health Unit, St John of God Health Care & School of Psychiatry, University of New South Wales, Burwood, NSW 2134, Australia;Perinatal and Reproductive Epidemiology Research Unit, School of Women and Children’s Health, University of New South Wales, Randwick, NSW 2031, Australia

关键词: Australia;    Psychiatric study;    Method;    Data preparation;   

Others  :  1136644
DOI  :  10.1186/1471-2288-12-71

 received in 2012-03-07, accepted in 2012-05-28,  发布年份 2012

【 摘 要 】

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.

【 授权许可】

   
2012 Xu et al.; licensee BioMed Central Ltd.

【 图 表 】

Figure 1 .

Figure 2 .

【 参考文献 】

• [1]Burns L, Mattick RP, Cooke M: Use of record linkage to examine alcohol use in pregnancy. Alcohol Clin Exp Res 2006, 30(4):642-8.
• [2]Morgan VA, Jablensky AV: From inventory to benchmark: quality of psychiatric case registers in research. Br J Psychiatry 2010, 197(1):8-10.
• [3]Mai Q, et al.: Mental illness related disparities in diabetes prevalence, quality of care and outcomes: a population-based longitudinal study. BMC Med 2011, 9:118. BioMed Central Full Text
• [4]Munk-Olsen T, Laursen TM, et al.: Psychiatric Disorders with Postpartum Onset: Possible Early Manifestations of Bipolar Affective Disorders. Arch Gen Psychiatry 2011, 69(4):428-34.
• [5]Holman CD, et al.: A decade of data linkage in Western Australia: strategic design, applications and benefits of the WA data linkage system. Aust Health Rev 2008, 32(4):766-77.
• [6]Goldsbury DE, et al.: Using linked routinely collected health data to describe prostate cancer treatment in New South Wales.Australia: A validation study. BMC Health Serv Res 2011, 11:253. BioMed Central Full Text
• [7]Yu XQ, et al.: A population-based study from New South Wales, Australia 1996–2001: area variation in survival from colorectal cancer. Eur J Cancer 2005, 41(17):2715-21.
• [8]Roberts CL, Tracy S, Peat B: Rates for obstetric intervention among private and public patients in Australia: population based descriptive study. BMJ 2000, 321(7254):137-41.
• [9]Algert CS, et al.: Regional block versus general anaesthesia for caesarean section and neonatal outcomes: a population-based study. BMC Med 2009, 7:20. BioMed Central Full Text
• [10]Xu F, et al.: Improvement of maternal Aboriginality in NSW birth data. BMC Med Res Methodol 2012, 12(1):1-8. BioMed Central Full Text
• [11]Australian Institute of Health and Welfare: Australian hospital statistics 2008–09. Health services series no. 17. Cat. no. HSE 84. AIHW, Canberra; 2010.
• [12]Centre for Epidemiology and Research: Diabetes as a principal diagnosis and as a comorbidity:hospitalizations by sex, NSW 1991–92 to 2010–2011. 2012. http://www.healthstats.nsw.gov.au/Indicator/dia_pcohos webcite
• [13]Xu F, et al.: Improvement of maternal Aboriginality in NSW birth data. BMC Med Res Methodol 2012, 12(1):8. BioMed Central Full Text
• [14]Leeds KL, et al.: Indigenous mothers and their babies, Australia 2001–2004. cat. no. PER 38. AIHW, Canberra; 2007.
• [15]Li Z, et al.: Australia’s mothers and babies 2009. AIHW National Perinatal Epidemiology and Statistics Unit, Sydney; 2011:1-122.
• [16]Lawrence D, et al.: Mortality in Western Australian psychiatric patients. Soc Psychiatry Psychiatr Epidemiol 2000, 35(8):341-7.
• [17]Lawrence D, et al.: Excess cancer mortality in Western Australian psychiatric patients due to higher case fatality rates. Acta Psychiatr Scand 2000, 101(5):382-8.
• [18]Munk-Olsen T, et al.: Perinatal mental disorders in native Danes and immigrant women. Archives of Women's Mental Health 2010, 13(4):319-326.
• [19]Mai Q, et al.: Do users of mental health services lack access to general practitioner services? Medical Journal of Australia 2010, 192(9):501-506.
• [20]Page A, Ambrose S, HDGlover J, Atlas of Avoidable Hospitalisations in Australia: Atlas of Avoidable Hospitalisations in Australia: ambulatory care-sensitive conditions. PHIDU, University of Adelaide, Adelaide; 2007.
• [21]Miettunen J, et al.: Use of Register Data for Psychiatric Epidemiology in the Nordic Countries. In Textbook in Psychiatric Epidemiology. Edited by John Wiley. 117, 131; 2011.
• [22]Munk-Olsen T, et al.: New parents and mental disorders: a population-based register study. JAMA 2006, 296(21):2582-9.
• [23]Surman G, da Silva AA, Kurinczuk JJ: Cerebral palsy registers and high-quality data: an evaluation of completeness of the 4Child register using capture-recapture techniques. Child Care Health Dev 2012, 38(1):98-107.
• [24]Ohrn A, et al.: Adverse events in spine surgery in Sweden: a comparison of patient claims data and national quality register (Swespine) data. Acta Orthop 2011, 82(6):727-31.
• [25]Labek G, et al.: Organisation, data evaluation, interpretation and effect of arthroplasty register data on the outcome in terms of revision rate in total hip arthroplasty. Int Orthop 2011, 35(2):157-63.
• [26]Mai Q, et al.: Mental illness related disparities in diabetes prevalence, quality of care and outcomes: a population-based longitudinal study. BMC Medicine 2011, 9(1):118. BioMed Central Full Text
• [27]Nummela O, et al.: Register-based data indicated nonparticipation bias in a health study among aging people. J Clin Epidemiol 2011, 64(12):1418-25.
• [28]Schuh R, et al.: Validity of published outcome data concerning Anatomic Graduated Component total knee arthroplasty: a structured literature review including arthroplasty register data. Int Orthop 2012, 36(1):51-6.
• [29]Kallen B, Nilsson E, Olausson PO: Antidepressant use during pregnancy: comparison of data obtained from a prescription register and from antenatal care records. Eur J Clin Pharmacol 2011, 67(8):839-45.