Avoiding double counting: the effect of bundling hospital events in administrative datasets for the interpretation of rural-urban differences in Aotearoa New Zealand.
Rural health
administrative data
inter-hospital transfer
rural-urban disparities
Journal
Journal of clinical epidemiology
ISSN: 1878-5921
Titre abrégé: J Clin Epidemiol
Pays: United States
ID NLM: 8801383
Informations de publication
Date de publication:
29 May 2024
29 May 2024
Historique:
received:
01
10
2023
revised:
12
05
2024
accepted:
21
05
2024
medline:
1
6
2024
pubmed:
1
6
2024
entrez:
31
5
2024
Statut:
aheadofprint
Résumé
All publicly funded hospital discharges in Aotearoa New Zealand (NZ) are recorded in the National Minimum Dataset (NMDS). Movement of patients between hospitals (and occasionally within the same hospital) results in separate records (discharge events) within the NMDS and if these consecutive health records are not accounted for hospitalisation (encounters) rates might be overestimated. The aim of this study was to determine the impact of four different methods to bundle multiple discharge events in the NMDS into encounters on the relative comparison of rural and urban Ambulatory Sensitive Hospitalisation (ASH) rates. NMDS discharge events with an admission date between July 1 2015 and December 31 2019 were bundled into encounters using either using a) no method, b) an "admission flag", c) a "discharge flag" or d) a date-based method. ASH incidence rates and rate ratios (IRR), the mean total length of stay and the percentage of inter-hospital transfers were estimated for each bundling method. These outcomes were compared across 4 categories of the Geographic Classification for Health (GCH). Compared with no bundling, using the date-based method resulted in an 8.3% reduction (150 less hospitalisations per 100 000 person years) in the estimated incidence rate for ASH in the most rural (R2-3) regions. There was no difference in the interpretation of the rural-urban IRR for any bundling methodology. Length of stay was longer for all bundling methods used. For patients that live in the most rural regions, using a date-based method identified up to twice as many inter-hospital transfers (5.7% vs. 12.4%) compared to using admission flags. Consecutive events within hospital discharge datasets should be bundled into encounters to estimate incidence. This reduces the over-estimation of incidence rates and the undercounting of inter-hospital transfers and total length of stay.
Sections du résumé
BACKGROUND
BACKGROUND
All publicly funded hospital discharges in Aotearoa New Zealand (NZ) are recorded in the National Minimum Dataset (NMDS). Movement of patients between hospitals (and occasionally within the same hospital) results in separate records (discharge events) within the NMDS and if these consecutive health records are not accounted for hospitalisation (encounters) rates might be overestimated. The aim of this study was to determine the impact of four different methods to bundle multiple discharge events in the NMDS into encounters on the relative comparison of rural and urban Ambulatory Sensitive Hospitalisation (ASH) rates.
METHODS
METHODS
NMDS discharge events with an admission date between July 1 2015 and December 31 2019 were bundled into encounters using either using a) no method, b) an "admission flag", c) a "discharge flag" or d) a date-based method. ASH incidence rates and rate ratios (IRR), the mean total length of stay and the percentage of inter-hospital transfers were estimated for each bundling method. These outcomes were compared across 4 categories of the Geographic Classification for Health (GCH).
RESULTS
RESULTS
Compared with no bundling, using the date-based method resulted in an 8.3% reduction (150 less hospitalisations per 100 000 person years) in the estimated incidence rate for ASH in the most rural (R2-3) regions. There was no difference in the interpretation of the rural-urban IRR for any bundling methodology. Length of stay was longer for all bundling methods used. For patients that live in the most rural regions, using a date-based method identified up to twice as many inter-hospital transfers (5.7% vs. 12.4%) compared to using admission flags.
CONCLUSION
CONCLUSIONS
Consecutive events within hospital discharge datasets should be bundled into encounters to estimate incidence. This reduces the over-estimation of incidence rates and the undercounting of inter-hospital transfers and total length of stay.
Identifiants
pubmed: 38821135
pii: S0895-4356(24)00155-0
doi: 10.1016/j.jclinepi.2024.111400
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
111400Informations de copyright
Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.