Towards panel data specifications of efficiency measures for English acute hospitals

This paper reports work undertaken for the Department of Health to explore different approaches of measuring hospital efficiency. The emphasis throughout is on developing adjusted cost-efficiency measures in line with NHS Trusts performance objectives. Previous work described the derivation of three residual-based cost indices (CCI, 2CCI and 3CCI), each with increasing adjustment in terms of case mix, factor prices and environmental factors for a single year’s data (1995/6) (Söderlund & van der Merwe, 1999). This study explores further options based on the previous work by: (1) supplementing hospital level with specialty level data; (2) studying a 4-year panel from 1994/5 to 1997/8; (3) estimating models with non-symmetric error terms and including Trust-specific effects when measuring inefficiency. Although the paper argues that panel data models may have certain advantages over cross-sectional ones, the results suggest that data pooling across years provide robust parameter estimates. Longitudinal fixed effect models may however be useful to construct efficiency indices while stochastic frontier models have the advantage of taking account of random noise. Specialty level models proved inferior to whole hospital estimations. The paper argues that the degree of variation between hospitals in terms of efficiency is not that great and scope for efficiency enhancement is primarily attainable by optimising capacity and activity levels in the long run. Increased activity levels may however have adverse consequences such as increased hospital infection rates, poorer quality of care and a lack of capacity to deal with emergency demand. The paper argues that the Department of Health might consider a shift from the adjusted cost index approach used in this normative benchmarking framework to the more conventional efficiency analysis approach using a total cost function, and more flexible functional forms, allowing for a more defensible interpretation of the residuals as inefficiency.efficiency

Hospital benchmarking analysis and the derivation of cost indices

This paper reports work undertaken for the UK Department of Health to explore approaches to measuring and comparing hospital productivity. The purpose of the cost indices produced in this paper has been to use them to derive productivity scores for English NHS Trusts in order to benchmark them against one another to help identify poorer performers. The work builds on previous deterministic ‘efficiency indices’ by using statistical regression adjustment techniques. This work describes the derivation of three cost indices (CCI, 2CCI and 3CCI), each with increasing adjustment in terms of case mix, factor prices and environmental factors. The analysis uses data for the year 1995/6 and specifically examines acute Trusts. The CCI cost index is a deterministic index that takes into account case mix as measured by Healthcare Resource Groups (HRGs) and inpatient, first outpatient and accident and emergency (A & E) activity. It is a weighted index of actual / expected costs where expected costs are measured as average national costs per respective attendance. 2CCI takes factors into account such as additional adjustments for case mix, age and gender mix, transfers in and out of the hospital, inter-specialty transfers, local labour and capital prices and teaching and research costs for which Trusts might be over or under compensated. The 3CCI makes additional adjustments over and above those in the 2CCI for hospital capacity, including number of beds, and number of sites, scale of inpatient and non-inpatient activity and scope of activity. It therefore tries to capture institutional characteristics amenable to change in the long, but not the short run. 2CCI and 3CCI indices are obtained from a short-run regression model using CCI as the dependent variable, and productivity scores are obtained from the residuals of the regressions. The results suggest that the statistical adjustments reduce estimates of productivity variation between providers considerably, such that there is relatively little difference between providers in terms of fully adjusted (short-run) productivity scores (3CCI). This suggests that savings from bringing poorer performers up to those with higher productivity scores, may in fact be quite small. In the long run there may be more scope for productivity enhancement and savings than in the short run, by optimising capacity and activity levels. Productivity benchmarking results should always be tempered against judgements on the quality and effectiveness of service provision which these indices are currently unable to measure. Implicitly equating high cost to inefficiency, as these indices do, may also be problematic. The paper suggests that the use of panel data and the application of alternative methodologies (such as stochastic frontiers and Data Envelopment Analysis) would be a valuable way to extend this work.cost index, productivity

Patterns and determinants of acute psychiatric readmissions

Objectives. Deinstitutionalisation and shortage of psychiatric beds worldwide has led to extensive research into the risk factors and interventions associated with rapid and recurrent admission to hospital. Little research of this nature has taken place in South Africa, particularly with regard to acute hospital admissions. This study attempted primarily to assess the effect of length of stay and administration of depot antipsychotics in hospital on time to readmission.Design. A retrospective cohort of 180 admissions was followed up for 12 months, after an index discharge, by means of multiple hospital and community-based record reviews. Each readmission was analysed as an event using a survival analysis model.Setting. Chris Hani Baragwanath Hospital, Gauteng.Subjects. A random sample of patients admitted during a 6- month period in 1996.Outcome measures. Time to readmission.Results. Two hundred and eighty-four admissions were analysed. The only factor that provided a significant protective effect was being married or cohabiting (P = 0.015). Clinic attendance showed a slight protective effect early on but conferred a significantly higher risk of readmission on those who had been out of hospital for a long period (P = 0.001). Only 21 % of discharged patients ever attended a clinic. The overall risk of readmission was significantly higher in the first 90 days post discharge.Conclusions. The lack of impact of length of hospital stay and use of depot neuroleptics on time to readmission may indicate that patients are being kept for appropriate duration and that the most ill patients are receiving depot medication.Several sampling and statistical artefacts may explain some of our findings. These results confirm the worldwide difficulty in finding consistent and accurate predictors of readmission. Low rates of successful referral to community aftercare need to be addressed before their effectiveness can be reasonably assessed. The inherent instability of the post-discharge period is a potential area for further investigation and intensive management

A practical, bioinformatic workflow system for large data sets generated by next-generation sequencing

Transcriptomics (at the level of single cells, tissues and/or whole organisms) underpins many fields of biomedical science, from understanding the basic cellular function in model organisms, to the elucidation of the biological events that govern the development and progression of human diseases, and the exploration of the mechanisms of survival, drug-resistance and virulence of pathogens. Next-generation sequencing (NGS) technologies are contributing to a massive expansion of transcriptomics in all fields and are reducing the cost, time and performance barriers presented by conventional approaches. However, bioinformatic tools for the analysis of the sequence data sets produced by these technologies can be daunting to researchers with limited or no expertise in bioinformatics. Here, we constructed a semi-automated, bioinformatic workflow system, and critically evaluated it for the analysis and annotation of large-scale sequence data sets generated by NGS. We demonstrated its utility for the exploration of differences in the transcriptomes among various stages and both sexes of an economically important parasitic worm (Oesophagostomum dentatum) as well as the prediction and prioritization of essential molecules (including GTPases, protein kinases and phosphatases) as novel drug target candidates. This workflow system provides a practical tool for the assembly, annotation and analysis of NGS data sets, also to researchers with a limited bioinformatic expertise. The custom-written Perl, Python and Unix shell computer scripts used can be readily modified or adapted to suit many different applications. This system is now utilized routinely for the analysis of data sets from pathogens of major socio-economic importance and can, in principle, be applied to transcriptomics data sets from any organism

A practical, bioinformatic workflow system for large data sets generated by next-generation sequencing

Transcriptomics (at the level of single cells, tissues and/or whole organisms) underpins many fields of biomedical science, from understanding the basic cellular function in model organisms, to the elucidation of the biological events that govern the development and progression of human diseases, and the exploration of the mechanisms of survival, drug-resistance and virulence of pathogens. Next-generation sequencing (NGS) technologies are contributing to a massive expansion of transcriptomics in all fields and are reducing the cost, time and performance barriers presented by conventional approaches. However, bioinformatic tools for the analysis of the sequence data sets produced by these technologies can be daunting to researchers with limited or no expertise in bioinformatics. Here, we constructed a semi-automated, bioinformatic workflow system, and critically evaluated it for the analysis and annotation of large-scale sequence data sets generated by NGS. We demonstrated its utility for the exploration of differences in the transcriptomes among various stages and both sexes of an economically important parasitic worm (Oesophagostomum dentatum) as well as the prediction and prioritization of essential molecules (including GTPases, protein kinases and phosphatases) as novel drug target candidates. This workflow system provides a practical tool for the assembly, annotation and analysis of NGS data sets, also to researchers with a limited bioinformatic expertise. The custom-written Perl, Python and Unix shell computer scripts used can be readily modified or adapted to suit many different applications. This system is now utilized routinely for the analysis of data sets from pathogens of major socio-economic importance and can, in principle, be applied to transcriptomics data sets from any organism

Detection of 1014F kdr mutation in four major Anopheline malaria vectors in Indonesia

Background: Malaria is a serious public health problem in Indonesia, particularly in areas outside Java and Bali. The spread of resistance to the currently available anti-malarial drugs or insecticides used for mosquito control would cause an increase in malaria transmission. To better understand patterns of transmission and resistance in Indonesia, an integrated mosquito survey was conducted in three areas with different malaria endemicities, Purworejo in Central Java, South Lampung District in Sumatera and South Halmahera District in North Mollucca.\ud Methods: Mosquitoes were collected from the three areas through indoor and outdoor human landing catches (HLC) and indoor restinging catches. Specimens were identified morphologically by species and kept individually in 1.5 ml Eppendorf microtube. A fragment of the VGSC gene from 95 mosquito samples was sequenced and kdr allelic variation determined.\ud Results: The molecular analysis of these anopheline mosquitoes revealed the existence of the 1014F allele in 4 major malaria vectors from South Lampung. These species include, Anopheles sundaicus, Anopheles aconitus, Anopheles subpictus\ud andAnopheles vagus. The 1014F allele was not found in the other areas.\ud Conclusion: The finding documents the presence of this mutant allele in Indonesia, and implies that selection pressure on the Anopheles population in this area has occurred. Further studies to determine the impact of the resistance allele on the efficacy of pyrethroids in control programmes are neede

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

An essential hospital package for South Africa selection criteria, costs and affordability

No Abstract