Open AccessHospital admissions in the National Health Survey and hospital separations in the National Hospital Morbidity Dataset: What is the difference?
Author(s) -
Ranmuthugala Geetha,
Brown Laurie,
Lymer Sharyn,
Thurecht Linc
Publication year - 2008
Publication title -
australian and new zealand journal of public health
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.946
H-Index - 76
eISSN - 1753-6405
pISSN - 1326-0200
DOI - 10.1111/j.1753-6405.2008.00229.x
Subject(s) - medicine , sampling frame , confidentiality , hospital admission , population , unit (ring theory) , medical emergency , emergency medicine , environmental health , mathematics education , political science , law , mathematics
Objective:To compare the National Health Survey (NHS) derived estimates of hospital admissions with the number of hospital separations registered in the National Hospital Morbidity Dataset (NHMD).Methods:Using the person weights in the NHS, the Expanded Confidential Unit Record File of the 2004–05 NHS was used to derive a population estimate of the number of hospital admissions in the 12 months preceding the conduct of the survey. These estimates, by age and sex categories and whether or not the admission involved an overnight stay, were compared with the number of hospital separations registered in the NHMD.Results:The number of hospital admissions estimated from the NHS was approximately two thirds the number of hospital separations registered in the NHMD. The discrepancy between the two data sources was greater when hospital episodes did not involve an overnight stay in hospital.Conclusion:There are systematic differences between the number of admissions/separations derived by the NHS and the NHMD for reasons including the technical difference between a hospital admission and a separation, and the sampling frame and scope of the NHS. Researchers looking for individual level data on hospital utilisation must take note of the differences between NHS and the NHMD, and recognise that there are methods to simulate a representative population by enhancing an existing dataset with information from multiple data sources, thus providing researchers a cost‐effective data resource.