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Avoiding double counting: the effect of bundling hospital events in administrative datasets for the interpretation of rural-urban differences in Aotearoa New Zealand.
Miller, Rory; Davie, Gabrielle; Crengle, Sue; Whitehead, Jesse; De Graaf, Brandon; Nixon, Garry.
Afiliação
  • Miller R; Department of General Practice and Rural Health, University of Otago; Te Whatu Ora - Waikato (Thames Hospital), 55 Hanover Street, Dunedin, New Zealand 9016. Electronic address: Rory.miller@otago.ac.nz.
  • Davie G; Department of Preventative and Social Medicine, University of Otago, Dunedin, New Zealand.
  • Crengle S; Ngai Tahu Maori Research Unit, University of Otago, Dunedin, New Zealand.
  • Whitehead J; Te Ngira Institute for Population Research, University of Waikato, Hamilton, New Zealand.
  • De Graaf B; Department of Preventative and Social Medicine, University of Otago, Dunedin, New Zealand.
  • Nixon G; Department of General Practice and Rural Health, Dunstan Hospital, University of Otago, Clyde, New Zealand.
J Clin Epidemiol ; 172: 111400, 2024 Aug.
Article em En | MEDLINE | ID: mdl-38821135
ABSTRACT
BACKGROUND AND

OBJECTIVES:

All publicly funded hospital discharges in Aotearoa New Zealand 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 hospitalization (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 Hospitalization (ASH) rates.

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 rate ratios (IRRs), the mean total length of stay and the percentage of interhospital transfers were estimated for each bundling method. These outcomes were compared across 4 categories of the Geographic Classification for Health.

RESULTS:

Compared with no bundling, using the date-based method resulted in an 8.3% reduction (150 less hospitalizations 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 interhospital transfers (5.7% vs 12.4%) compared to using admission flags.

CONCLUSION:

Consecutive events within hospital discharge datasets should be bundled into encounters to estimate incidence. This reduces the overestimation of incidence rates and the undercounting of interhospital transfers and total length of stay.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Alta do Paciente / Tempo de Internação Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Alta do Paciente / Tempo de Internação Idioma: En Ano de publicação: 2024 Tipo de documento: Article