Calculating hospital length of stay using the Hospital Episode Statistics; a comparison of methodologies

Abstract Background Accurate calculation of hospital length of stay (LOS) from the English Hospital Episode Statistics (HES) is important for a wide range of audit and research purposes. The two methodologies which are commonly used to achieve this differ in their accuracy and complexity. We compare these methods and make recommendations on when each is most appropriate. Methods We calculated LOS using continuous inpatient spells (CIPS), which link care spanning across multiple hospitals, and spells, which do not, for six conditions with short (dyspepsia or other stomach function, ENT infection), medium (dehydration and gastroenteritis, perforated or bleeding ulcer), and long (stroke, fractured proximal femur) average LOS. We examined how inter-area comparisons (i.e. benchmarking) and temporal trends differed. We defined a classification system for spells and explored the causes of differences. Results Stroke LOS was 16.5 days using CIPS but 24% (95% CI: 23, 24) lower, at 12.6 days, using spells. Smaller differences existed for shorter-LOS conditions including dehydration and gastroenteritis (4.5 vs. 4.2 days) and ENT infection (0.9 vs. 0.8 days). Typical patient pathways differed markedly between areas and have evolved over time. One area had the third shortest stroke LOS (out of 151) using spells but the fourth longest using CIPS. These issues were most profound for stroke and fractured proximal femur, as patients were frequently transferred to a separate hospital for rehabilitation, however important disparities also existed for conditions with simpler secondary care pathways (e.g. ENT infections, dehydration and gastroenteritis). Conclusions Spell-based LOS is widely used by researchers and national reporting organisations, including the Health and Social Care Information Centre, however it can substantially underestimate the time patients spend in hospital. A widespread shift to a CIPS methodology is required to improve the quality of LOS estimates and the robustness of research and benchmarking findings. This is vital when investigating clinical areas with typically long, complex patient pathways. Researchers should ensure that their LOS calculation methodology is fully described and explicitly acknowledge weaknesses when appropriate

How do population, general practice and hospital factors influence ambulatory care sensitive admissions:a cross sectional study

Abstract Background Reducing unplanned hospital admissions is a key priority within the UK and other healthcare systems, however it remains uncertain how this can be achieved. This paper explores the relationship between unplanned ambulatory care sensitive condition (ACSC) admission rates and population, general practice and hospital characteristics. Additionally, we investigated if these factors had a differential impact across 28 conditions. Methods We used the English Hospital Episode Statistics to calculate the number of unplanned ACSC hospital admissions for 28 conditions at 8,029 general practices during 2011/12. We used multilevel negative binomial regression to estimate the influence of population (deprivation), general practice (size, access, continuity, quality, A&E proximity) and hospital (bed availability, % day cases) characteristics on unplanned admission rates after adjusting for age, sex and chronic disease prevalence. Results Practices in deprived areas (at the 90th centile) had 16% (95% confidence interval: 14 to 18) higher admission rates than those in affluent areas (10th centile). Practices with poorer care continuity (9%; 8 to 11), located closest to A&E (8%; 6 to 9), situated in areas with high inpatient bed availability (14%; 10 to 18) or in areas with a larger proportion of day case admissions (17%; 12 to 21) had more admissions. There were smaller associations for primary care access, clinical quality, and practice size. The strength of associations varied by ACSC. For example, deprivation was most strongly associated with alcohol related diseases and COPD admission rates, while continuity of primary care was most strongly associated with admission rates for chronic diseases such as hypertension and iron-deficiency anaemia. Conclusions The drivers of unplanned ACSC admission rates are complex and include population, practice and hospital factors. The importance of these varies markedly across conditions suggesting that multifaceted interventions are required to avoid hospital admissions and reduce costs. Several of the most important drivers of admissions are largely beyond the control of GPs. However, strategies to improve primary care continuity and avoid unnecessary short-stay admissions could lead to improved efficiency

Using geographic variation in unplanned ambulatory care sensitive condition admission rates to identify commissioning priorities:an analysis of routine data from England

Objectives To use geographic variation in unplanned ambulatory care sensitive condition admission rates to identify the clinical areas and patient subgroups where there is greatest potential to prevent admissions and improve the quality and efficiency of care. Methods We used English Hospital Episode Statistics data from 2011/2012 to describe the characteristics of patients admitted for ambulatory care sensitive condition care and estimated geographic variation in unplanned admission rates. We contrasted geographic variation across admissions with different lengths of stay which we used as a proxy for clinical severity. We estimated the number of bed days that could be saved under several scenarios. Results There were 1.8 million ambulatory care sensitive condition admissions during 2011/2012. Substantial geographic variation in ambulatory care sensitive condition admission rates was commonplace but mental health care and short-stay (&lt;2 days) admissions were particularly variable. Reducing rates in the highest use areas could lead to savings of between 0.4 and 2.8 million bed days annually. Conclusions Widespread geographic variations in admission rates for conditions where admission is potentially avoidable should concern commissioners and could be symptomatic of inefficient care. Further work to explore the causes of these differences is required and should focus on mental health and short-stay admissions. </jats:sec

A systematic review of geographical variation in access to chemotherapy

BACKGROUND: Rising cancer incidence, the cost of cancer pharmaceuticals and the introduction of the Cancer Drugs Fund in England, but not other United Kingdom(UK) countries means evidence of ‘postcode prescribing’ in cancer is important. There have been no systematic reviews considering access to cancer drugs by geographical characteristics in the UK. METHODS: Studies describing receipt of cancer drugs, according to healthcare boundaries (e.g. cancer network [UK]) were identified through a systematic search of electronic databases and grey literature. Due to study heterogeneity a meta-analysis was not possible and a narrative synthesis was performed. RESULTS: 8,780 unique studies were identified and twenty-six included following a systematic search last updated in 2015. The majority of papers demonstrated substantial variability in the likelihood of receiving chemotherapy between hospitals, health authorities, cancer networks and UK countries (England and Wales). After case-mix adjustment, there was up to a 4–5 fold difference in chemotherapy utilisation between the highest and lowest prescribing cancer networks. There was no strong evidence that rurality or distance travelled were associated with the likelihood of receiving chemotherapy and conflicting evidence for an effect of travel time. CONCLUSIONS: Considerable variation in chemotherapy prescribing between healthcare boundaries has been identified. The absence of associations with natural geographical characteristics (e.g. rurality) and receipt of chemotherapy suggests that local treatment habits, capacity and policy are more influential. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12885-015-2026-y) contains supplementary material, which is available to authorized users

A Bivariate Latent Class Correlated Generalized Ordered Probit Model with an Application to Modeling Observed Obesity Levels

Obesity is a major risk factor for several diseases including diabetes, heart disease and stroke. Increasing rates of obesity internationally are set to cost health systems increasing resources. In the US a conservative estimate puts resources already spent on obesity at $120 billion annually. Given scarce health care resources it is important that categorisation of the overweight and obese is accurate, such that health promotion and public health targeting can be as e§ective as possible. To test the accuracy of current categorisation within the overweight and obese we extend the discrete data latent class literature by explicitly deÖning a latent variable for class membership as a function of both observables and unobservables, thereby allowing the equations deÖning class membership and observed outcomes to be correlated. The procedure is then applied to modeling observed obesity outcomes, based upon an underlying ordered probit equation. We Önd the standard boundaries for converting