Blanca Gallego

A consistent input–output formulation of shared producer and consumer responsibility

Abstract Growing interest in environmental and socio-economic accounting at the sub-regional and organisational level requires a consistent and comprehensive method for computing and reporting responsibility for impacts of industrial production such as water use, pollution, or employment. This work presents a formulation for allocating responsibility for production impacts consistently amongst all agents such as consumers, producers, workers, and investors throughout demand and supply chains, in a way that reflects their contribution to the production process. Generalised input–output theory is used to re-trace the flow of past inter-industrial transactions, and examine ex-post how, for example, inputs of resources or outputs of pollution were associated with these transactions. Introducing the concept of a responsibility share we enable the division of responsibility into mutually exclusive and collectively exhaustive portions that are assigned to the different economic sectors, and that become consistently smaller as we move away from the location of the impact within the supply or demand chain system.

MATRIX BALANCING UNDER CONFLICTING INFORMATION

We have developed a generalised iterative scaling method (KRAS) that is able to balance and reconcile input–output tables and SAMs under conflicting external information and inconsistent constraints. Like earlier RAS variants, KRAS can: (a) handle constraints on arbitrarily sized and shaped subsets of matrix elements; (b) include reliability of the initial estimate and the external constraints; and (c) deal with negative values, and preserve the sign of matrix elements. Applying KRAS in four case studies, we find that, as with constrained optimisation, KRAS is able to find a compromise solution between inconsistent constraints. This feature does not exist in conventional RAS variants such as GRAS. KRAS can constitute a major advance for the practice of balancing input–output tables and Social Accounting Matrices, in that it removes the necessity of manually tracing inconsistencies in external information. This quality does not come at the expense of substantial programming and computational requirements (of conventional constrained optimisation techniques).

Do variations in hospital mortality patterns after weekend admission reflect reduced quality of care or different patient cohorts? A population-based study

Background Proposed causes for increased mortality following weekend admission (the ‘weekend effect’) include poorer quality of care and sicker patients. The aim of this study was to analyse the 7 days post-admission time patterns of excess mortality following weekend admission to identify whether distinct patterns exist for patients depending upon the relative contribution of poorer quality of care (care effect) or a case selection bias for patients presenting on weekends (patient effect). Methods Emergency department admissions to all 501 hospitals in New South Wales, Australia, between 2000 and 2007 were linked to the Death Registry and analysed. There were a total of 3 381 962 admissions for 539 122 patients and 64 789 deaths at 1 week after admission. We computed excess mortality risk curves for weekend over weekday admissions, adjusting for age, sex, comorbidity (Charlson index) and diagnostic group. Results Weekends accounted for 27% of all admissions (917 257/3 381 962) and 28% of deaths (18 282/64 789). Sixteen of 430 diagnosis groups had a significantly increased risk of death following weekend admission. They accounted for 40% of all deaths, and demonstrated different temporal excess mortality risk patterns: early care effect (cardiac arrest); care effect washout (eg, pulmonary embolism); patient effect (eg, cancer admissions) and mixed (eg, stroke). Conclusions The excess mortality patterns of the weekend effect vary widely for different diagnostic groups. Recognising these different patterns should help identify at-risk diagnoses where quality of care can be improved in order to minimise the excess mortality associated with weekend admission.

Some Comments on the GRAS Method

Junius and Oosterhaven (2003) present a RAS matrix balancing variant that can incorporate negative elements in the balancing. There are, however, a couple of issues in the approach described – the first being the handling of zeros in the initial estimate, and the second being the formulation of their minimum-information principle. We present a corrected exposition of GRAS.

Impact of a web-based personally controlled health management system on influenza vaccination and health services utilization rates: a randomized controlled trial

OBJECTIVE To assess the impact of a web-based personally controlled health management system (PCHMS) on the uptake of seasonal influenza vaccine and primary care service utilization among university students and staff. MATERIALS AND METHODS A PCHMS called Healthy.me was developed and evaluated in a 2010 CONSORT-compliant two-group (6-month waitlist vs PCHMS) parallel randomized controlled trial (RCT) (allocation ratio 1:1). The PCHMS integrated an untethered personal health record with consumer care pathways, social forums, and messaging links with a health service provider. RESULTS 742 university students and staff met inclusion criteria and were randomized to a 6-month waitlist (n=372) or the PCHMS (n=370). Amongst the 470 participants eligible for primary analysis, PCHMS users were 6.7% (95% CI: 1.46 to 12.30) more likely than the waitlist to receive an influenza vaccine (waitlist: 4.9% (12/246, 95% CI 2.8 to 8.3) vs PCHMS: 11.6% (26/224, 95% CI 8.0 to 16.5); χ(2)=7.1, p=0.008). PCHMS participants were also 11.6% (95% CI 3.6 to 19.5) more likely to visit the health service provider (waitlist: 17.9% (44/246, 95% CI 13.6 to 23.2) vs PCHMS: 29.5% (66/224, 95% CI: 23.9 to 35.7); χ(2)=8.8, p=0.003). A dose-response effect was detected, where greater use of the PCHMS was associated with higher rates of vaccination (p=0.001) and health service provider visits (p=0.003). DISCUSSION PCHMS can significantly increase consumer participation in preventive health activities, such as influenza vaccination. CONCLUSIONS Integrating a PCHMS into routine health service delivery systems appears to be an effective mechanism for enhancing consumer engagement in preventive health measures. TRIAL REGISTRATION Australian New Zealand Clinical Trials Registry ACTRN12610000386033. http://www.anzctr.org.au/trial_view.aspx?id=335463.

A Literature Review on Care at the End-of-Life in the Emergency Department.

The hospitalisation and management of patients at the end-of-life by emergency medical services is presenting a challenge to our society as the majority of people approaching death explicitly state that they want to die at home and the transition from acute care to palliation is difficult. In addition, the escalating costs of providing care at the end-of-life in acute hospitals are unsustainable. Hospitals in general and emergency departments in particular cannot always provide the best care for patients approaching end-of-life. The main objectives of this paper are to review the existing literature in order to assess the evidence for managing patients dying in the emergency department, and to identify areas of improvement such as supporting different models of care and evaluating those models with health services research. The paper identified six main areas where there is lack of research and/or suboptimal policy implementation. These include uncertainty of treatment in the emergency department; quality of life issues, costs, ethical and social issues, interaction between ED and other health services, and strategies for out of hospital care. The paper concludes with some areas for policy development and future research.

Decadal Variability of Two Oceans and an Atmosphere

A model of the midlatitude, large-scale interaction between the upper ocean and the troposphere is used to illustrate possible mechanisms of connection between the decadal variability in the North Atlantic and in the North Pacific. The two ocean basins are connected to each other only through their coupling to the common, zonally averaged atmosphere. The ocean‐atmosphere coupling takes place via wind-driven torques and heat fluxes at the air‐sea interface. In this formulation, the decadal variability in each ocean basin consists of ocean‐ atmosphere modes and arises from a delayed feedback of the upper-ocean heat content onto the wind-driven flow mediated by the atmosphere through the requirements of global heat and momentum balances. The presence of two ocean basins leads to three basic kinds of coupling-induced behavior: phase locking, oscillation death, and chaos. In the phase-locked regime, the western boundary currents of the two basins oscillate in synchrony, with the narrower basin following the wider basin by a small time lag. In the oscillation death solutions, a steady solution is reached, even though each ocean basin, when uncoupled, would have sustained oscillations. In the chaotic regime, the interbasin coupling induces aperiodic fluctuations in both basins characterized by variability at centennial, as well as decadal, timescales.

Role of electronic health records in comparative effectiveness research

The gold standard in evaluating treatment effects are randomized controlled trials (RCTs). Their design minimizes bias and maximizes our ability to identify causality. By contrast, observational data, which is routinely collected for other purposes, has many well-known limitations, including selection bias. Why then are we seeing such enthusiasm for ‘big data’ in healthcare, driven in part by the relentless growth in adoption of electronic health records (EHRs)? The first part of the answer is that, despite their many strengths, RCTs also have limitations. First, they do not represent real-world populations or settings. Their stringent exclusion criteria mean that the evidence they produce will not directly replicate the circumstances of many at-risk patients with common comorbidities, who might also benefit from the interventions under trial [1]. Most RCTs are also geographically localized, both in terms of the demographics of the participants as well as in clinical setting. RCTs also often lack the size required to detect the small effect sizes and significant variance encountered in many comparative effectiveness studies, and tend to be too short to detect long-term effects of interventions [2,3]. These limitations are imposed by the need to create controlled conditions, as well as by funding and ethical constraints common to experimental studies. The second motivation for the renewed interest in observational data is the enormous amount of digital data now being collected by clinical institutions, industry and government, and our recent technical capacity to warehouse, link and analyze data in volumes unprecedented a decade ago. Not only are clinical data being accumulated rapidly, they are providing an increasingly detailed record of individual behaviors and journeys. Together, these new attributes of observational data may become a ‘game changer’. The reason we randomize is to deal with the effects of unmodeled variation, and current strategies to deal with such variation in observational studies remain controversial [4]. However, if we had access to health records that included deep phenotypic, genotypic and environmental data, then at some stage we should reach a crossover point where observational data and RCTs are of equivalent value. At that moment we should be able to pull together, through case-matching, a personalized ‘virtual cohort’ of individuals whose collective recorded clinical destiny is at least as predictive of treatment outcomes as any RCT for a given patient. There is ongoing discussion of the relative merits of observational studies and RCTs, and the complementary roles that different forms of evidence play in contributing to the evidence base [3,5–8]. Historically, RCTs were designed to overcome the problems encountered in observational analyses and have therefore been seen as superior to observational studies rather than complementary. However, the rapid growth in EHR data has generated an unprecedented source of information, making it essential that we reassess this artificial wedge separating RCTs and observational studies, and recognize the important complementary roles both must play. “...the recent availability of large electronic health records data sets is challenging us to reconsider the role that observational studies can play in evidence-based medicine.”

Predicting the cumulative risk of death during hospitalization by modeling weekend, weekday and diurnal mortality risks.

BackgroundCurrent prognostic models factor in patient and disease specific variables but do not consider cumulative risks of hospitalization over time. We developed risk models of the likelihood of death associated with cumulative exposure to hospitalization, based on time-varying risks of hospitalization over any given day, as well as day of the week. Model performance was evaluated alone, and in combination with simple disease-specific models.MethodPatients admitted between 2000 and 2006 from 501 public and private hospitals in NSW, Australia were used for training and 2007 data for evaluation. The impact of hospital care delivered over different days of the week and or times of the day was modeled by separating hospitalization risk into 21 separate time periods (morning, day, night across the days of the week). Three models were developed to predict death up to 7-days post-discharge: 1/a simple background risk model using age, gender; 2/a time-varying risk model for exposure to hospitalization (admission time, days in hospital); 3/disease specific models (Charlson co-morbidity index, DRG). Combining these three generated a full model. Models were evaluated by accuracy, AUC, Akaike and Bayesian information criteria.ResultsThere was a clear diurnal rhythm to hospital mortality in the data set, peaking in the evening, as well as the well-known ‘weekend-effect’ where mortality peaks with weekend admissions. Individual models had modest performance on the test data set (AUC 0.71, 0.79 and 0.79 respectively). The combined model which included time-varying risk however yielded an average AUC of 0.92. This model performed best for stays up to 7-days (93% of admissions), peaking at days 3 to 5 (AUC 0.94).ConclusionsRisks of hospitalization vary not just with the day of the week but also time of the day, and can be used to make predictions about the cumulative risk of death associated with an individual’s hospitalization. Combining disease specific models with such time varying- estimates appears to result in robust predictive performance. Such risk exposure models should find utility both in enhancing standard prognostic models as well as estimating the risk of continuation of hospitalization.

Bringing cohort studies to the bedside: framework for a 'green button' to support clinical decision-making

When providing care, clinicians are expected to take note of clinical practice guidelines, which offer recommendations based on the available evidence. However, guidelines may not apply to individual patients with comorbidities, as they are typically excluded from clinical trials. Guidelines also tend not to provide relevant evidence on risks, secondary effects and long-term outcomes. Querying the electronic health records of similar patients may for many provide an alternate source of evidence to inform decision-making. It is important to develop methods to support these personalized observational studies at the point-of-care, to understand when these methods may provide valid results, and to validate and integrate these findings with those from clinical trials.