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

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

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

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

Does the cancer drugs fund lead to faster uptake of cost-effective drugs? A time-trend analysis comparing England and Wales

BACKGROUND: The Cancer Drugs Fund (CDF) provides £200 million annually in England for ‘anti-cancer' drugs. METHODS: We used a controlled pre-/post-intervention design to compare IMS Health dispensing data for 15 cancer drugs (2007–2012) in England vs Wales, stratified by pre-CDF NICE drug approval status (rejected, mixed recommendations, recommended, not appraised). RESULTS: The CDF was associated with increased prescribing in England for three of five drugs rejected or with mixed NICE recommendations. The prescribing volume ratios (PVR) ranged from 1.29 (95% CI 1.00, 1.67) for sorafenib to 3.28 (2.59, 4.14) for bevacizumab (NICE rejected) and 0.93 (0.81, 1.06) and 1.35 (1.21, 1.49) for sunitinib and imatinib respectively (mixed recommendations). Post CDF prescribing in England increased for both drugs awaiting NICE appraisal pre-CDF (lapatinib PVR=7.44 (5.81, 9.54), panitumumab PVR=5.40 (1.20, 24.42)) and subsequently rejected. The CDF was not associated with increased prescribing in England of NICE-recommended drugs. The three most recently launched, subsequently recommended drugs were adopted faster in Wales (from pazopanib PVR=0.51 (0.28, 0.96) to abiraterone PVR=0.78 (0.61–0.99)). INTERPRETATION: These data indicate that the CDF is used to access drugs deemed not cost-effective by NICE. The CDF did not expedite access to new cost-effective cancer agents prior to NICE approval

Who will increase their physical activity? Predictors of change in objectively measured physical activity over 12 months in the ProActive cohort.

BACKGROUND: The aim was to identify predictors of change in objectively measured physical activity over 12 months in the ProActive cohort to improve understanding of factors influencing change in physical activity. METHODS: ProActive is a physical activity promotion trial that took place in Eastern England (1999-2004). 365 offspring of people with type 2 diabetes underwent measurement of physical activity energy expenditure (PAEE) using heart rate monitoring, fitness, and anthropometric and biochemical status at baseline and 1 year (n = 321). Linear regression was used to quantify the associations between baseline demographic, clinical, psychosocial and behavioural variables and change in PAEE over 12 months. This study is registered as ISRCTN61323766. RESULTS: ProActive participants significantly increased their PAEE by 0.6 kj/min (SD 4.2, p = 0.006) over one year, the equivalent of around 20 minutes brisk walking/day. Male sex and higher fitness at baseline predicted increase in PAEE. No significant associations were found for any other variables. Very few baseline demographic, clinical, psychosocial and behavioural predictors were associated with change in objectively measured physical activity. CONCLUSIONS: Traditional baseline determinants of self-reported physical activity targeted by behavioural interventions may be relatively weak predictors of change in objectively measured physical activity. Further research is needed to improve our understanding of factors influencing change in physical activity to inform the development and targeting of interventions.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

Estimating marginal healthcare costs using genetic variants as instrumental variables:Mendelian Randomization in economic evaluation

Accurate measurement of the marginal healthcare costs associated with different diseases and health conditions is important, especially for increasingly prevalent conditions such as obesity. However, existing observational study designs cannot identify the causal impact of disease on healthcare costs. This paper explores the possibilities for causal inference offered by Mendelian randomization, a form of instrumental variable analysis that uses genetic variation as a proxy for modifiable risk exposures, to estimate the effect of health conditions on cost. Well-conducted genome-wide association studies provide robust evidence of the associations of genetic variants with health conditions or disease risk factors. The subsequent causal effects of these health conditions on cost can be estimated using genetic variants as instruments for the health conditions. This is because the approximately random allocation of genotypes at conception means that many genetic variants are orthogonal to observable and unobservable confounders. Datasets with linked genotypic and resource use information obtained from electronic medical records or from routinely collected administrative data are now becoming available and will facilitate this form of analysis. We describe some of the methodological issues that arise in this type of analysis, which we illustrate by considering how Mendelian randomization could be used to estimate the causal impact of obesity, a complex trait, on healthcare costs. We describe some of the data sources that could be used for this type of analysis. We conclude by considering the challenges and opportunities offered by Mendelian randomization for economic evaluation

Cost-Consequence Analysis Alongside a Randomised Controlled Trial of Hospital Versus Telephone Follow-Up after Treatment for Endometrial Cancer

Background Regular outpatient follow-up programmes are usually offered to patients following treatment for gynaecological and other cancers. Despite the substantial resources involved in providing these programmes, there is evidence that routine follow-up programmes do not affect survival or the likelihood of detecting recurrence and may not meet patient needs. Alternative follow-up modalities may offer the same outcomes at lower cost. We examined the costs of using telephone-based routine follow-up of women treated for endometrial cancer undertaken by specialist gynaecology oncology nurses in comparison to routine hospital-based follow-up. Methods The ENDCAT trial randomised 259 women at five centres in the north west of England with a known diagnosis of Stage I endometrial cancer who had completed primary treatment on a 1:1 basis to receive either standard hospital outpatient follow-up or a telephone follow-up intervention administered by specialist nurses. A cost-consequence analysis was undertaken in which we compared costs to the health system and to individuals with the trial’s co-primary outcomes of psychological morbidity and participant satisfaction with information received. Results Psychological morbidity, psychosocial needs, patient satisfaction and quality of life did not differ between arms. Patients randomised to telephone follow-up underwent more and longer consultations. There was no difference in total health service mean per patient costs at 6 months (mean difference £8, 95% percentile confidence interval: − £147 to £141) or 12 months (mean difference: − £77, 95% percentile confidence interval: − £334 to £154). Estimated return journey costs per patient for hospital consultations were £11.47. Productivity costs were approximately twice as high under hospital follow-up. Conclusion Telephone follow-up was estimated to be cost-neutral for the NHS and may free up clinic time for other patients. There was some evidence that telephone follow-up may be more efficient for patients and wider society, and is not associated with additional psychological morbidity, lower patient satisfaction or reduced quality of life