Abstract

BackgroundUnplanned hospital admissions place a large and increasing strain on healthcare budgets worldwide. Many admissions for ambulatory care sensitive conditions (ACSCs) are thought to be preventable, a belief supported by significant geographic variations in admission rates. We conducted a systematic review of the evidence on the magnitude and correlates of geographic variation in ACSC admission rates and length of stay (LOS).MethodsWe performed a search of Medline and Embase databases for English language cross-sectional and cohort studies on 28th March 2013 reporting geographic variation in admission rates or LOS for patients receiving unplanned care across at least 10 geographical units for one of 35 previously defined ACSCs. Forward and backward citation searches were undertaken on all included studies. We provide a narrative synthesis of study findings. Study quality was assessed using a modified Newcastle-Ottawa scale.ResultsWe included 39 studies comprising 25 on admission rates and 14 on LOS. Studies generally compared admission rates between regions (e.g. states) and LOS between hospitals. Most of the published research was undertaken in the US, UK or Canada and often focussed on patients with pneumonia, COPD or heart failure. 35 (90 %) studies concluded that geographic variation was present. Primary care quality and secondary care access were frequently suggested as drivers of admission rate variation whilst secondary care quality and adherence to clinical guidelines were often listed as contributors to LOS variation. Several different methods were used to quantify variation, some studies listed raw data, failed to control for confounders and used naive statistical methods which limited their utility.ConclusionsThe substantial geographical variations in the admission rates and LOS of potentially avoidable conditions could be a symptom of variable quality of care and should be a concern for clinicians and policymakers. Policymakers targeting a reduction in unplanned admissions could introduce initiatives to improve primary care access and quality or develop alternatives to admission. Those attempting to curb unnecessarily long LOS could introduce care pathways or guidelines. Methodological work on the quantification and reporting of geographic variation is needed to aid inter-study comparisons.Electronic supplementary materialThe online version of this article (doi:10.1186/s12913-015-0964-3) contains supplementary material, which is available to authorized users.

Highlights

  • Unplanned hospital admissions place a large and increasing strain on healthcare budgets worldwide

  • Concerns that up to 29 % of English unplanned admissions are avoidable [3] have been fuelled by the increasing proportion of short stay admissions, which could be indicative that the admission was unnecessary, and the large variations in admission rates and other process measures between healthcare units [1, 4]

  • The most common causes for exclusion were a failure to report data on admission rates or length of stay (LOS) (n = 16; 51.6 %), the study contained less than 10 geographical units (n = 7; 22.6 %) or the study did not report geographic variation (n = 4; 12.9 %)

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Summary

Introduction

Unplanned hospital admissions place a large and increasing strain on healthcare budgets worldwide. Unplanned admissions place a tremendous strain on healthcare resources worldwide They account for 67 % of hospital bed days at a cost of £12.5bn per year and have risen by 47 % over the last 15 years in England [1] and 13 % between 2000 and 2009 in the USA [2]. Concerns that up to 29 % of English unplanned admissions are avoidable [3] have been fuelled by the increasing proportion of short stay admissions, which could be indicative that the admission was unnecessary, and the large variations in admission rates and other process measures between healthcare units [1, 4]. In the UK, 830,000 bed days were lost in 2013 due to delayed discharge despite patients being medically fit to leave hospital, a figure which represents a rise of 9 % on the previous year [1]

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