Do Reduced Hospital Mortality Rates Lead to Increased Utilization of Inpatient Emergency Care? A Population-Based Cohort Study

Socioeconomic inequality in access to timely and appropriate care in emergency departments

In publicly-funded healthcare systems, waiting times for care should be based on need rather than ability to pay. Studies have shown that individuals with lower socioeconomic status face longer waits for planned inpatient care, but there is little evidence on inequalities in waiting times for emergency care. We study waiting times in emergency departments (EDs) following arrival by ambulance, where health consequences of extended waits may be severe. Using data from all major EDs in England during the 2016/17 financial year, we find patients from more deprived areas face longer waits during some parts of the ED care pathway. Inequalities in waits are small, but more deprived individuals also receive less complex ED care, are less likely to be admitted for inpatient care, and are more likely to re-attend ED or die shortly after attendance. Patient-physician interactions and unconscious bias towards more deprived patients may be important sources of inequalities.

Age, morbidity, or something else? A residual approach using microdata to measure the impact of technological progress on health care expenditure

This study measures the increment of health care expenditure (HCE) that can be attributed to technological progress and change in medical practice by using a residual approach and microdata. We examine repeated cross-sections of individuals experiencing an initial health shock at different point in time over a 10-year window and capture the impact of unobservable technology and medical practice to which they are exposed after allowing for differences in health and socioeconomic characteristics. We decompose the residual increment in the part that is due to the effect of delaying time to death, that is, individuals surviving longer after a health shock and thus contributing longer to the demand of care, and the part that is due to increasing intensity of resource use, that is, the basket of services becoming more expensive to allow for the cost of innovation. We use data from the Danish National Health System that offers universal coverage and is free of charge at the point of access. We find that technological progress and change in medical practice can explain about 60% of the increment of HCE, in line with macroeconomic studies that traditionally investigate this subject.

E-Triage Systems for COVID-19 Outbreak: Review and Recommendations

With population growth and aging, the emergence of new diseases and immunodeficiency, the demand for emergency departments (EDs) increases, making overcrowding in these departments a global problem. Due to the disease severity and transmission rate of COVID-19, it is necessary to provide an accurate and automated triage system to classify and isolate the suspected cases. Different triage methods for COVID-19 patients have been proposed as disease symptoms vary by country. Still, several problems with triage systems remain unresolved, most notably overcrowding in EDs, lengthy waiting times and difficulty adjusting static triage systems when the nature and symptoms of a disease changes. In this paper, we conduct a comprehensive review of general ED triage systems as well as COVID-19 triage systems. We identified important parameters that we recommend considering when designing an e-Triage (electronic triage) system for EDs, namely waiting time, simplicity, reliability, validity, scalability, and adaptability. Moreover, the study proposes a scoring-based e-Triage system for COVID-19 along with several recommended solutions to enhance the overall outcome of e-Triage systems during the outbreak. The recommended solutions aim to reduce overcrowding and overheads in EDs by remotely assessing patients' conditions and identifying their severity levels.

Cured Today, Ill Tomorrow: A Method for Including Future Unrelated Medical Costs in Economic Evaluation in England and Wales

OBJECTIVES

In many countries, future unrelated medical costs occurring during life-years gained are excluded from economic evaluation, and benefits of unrelated medical care are implicitly included, leading to life-extending interventions being disproportionately favored over quality of life-improving interventions. This article provides a standardized framework for the inclusion of future unrelated medical costs and demonstrates how this framework can be applied in England and Wales.

METHODS

Data sources are combined to construct estimates of per-capita National Health Service spending by age, sex, and time to death, and a framework is developed for adjusting these estimates for costs of related diseases. Using survival curves from 3 empirical examples illustrates how our estimates for unrelated National Health Service spending can be used to include unrelated medical costs in cost-effectiveness analysis and the impact depending on age, life-years gained, and baseline costs of the target group.

RESULTS

Our results show that including future unrelated medical costs is feasible and standardizable. Empirical examples show that this inclusion leads to an increase in the ICER of between 7% and 13%.

CONCLUSIONS

This article contributes to the methodology debate over unrelated costs and how to systematically include them in economic evaluation. Results show that it is both important and possible to include future unrelated medical costs.

Comparison of Reliability and Validity of the Chinese Four-Level and Three-District Triage Standard and the Australasian Triage Scale

Emergency triage is an important tool for prioritizing urgent or critical patients, and its effect needs to be investigated and evaluated. This observational study aimed to compare the reliability and validity of the Chinese four-level and three-district triage standard (CHT) and the Australasian Triage Scale (ATS) in an adult emergency department of a general hospital in China. From 2016-01 to 2017-01, twelve nurses independently performed on-site triage of 254 patients and 1552 patients to assess the scales' reliability and validity, respectively. The interrater reliability, as assessed by the weighted k scores, was 0.686 (95% CI 0.608–0.757) for the CHT and 0.731 (95% CI 0.663–0.790) for the ATS, and the k scores between the CHT and the ATS were 0.630 (95% CI 0.594–0.669). Temperature, respiration, pulse, blood oxygen saturation, waiting time, treatment time, emergency disposition, hospitalization rate, and mortality were significantly associated with the triage levels of the CHT and ATS (p < 0.001). The area under the receiver operating characteristic (AUROC) curve values of the CHT and ATS for predicting intensive care treatment were 0.845 (95% CI: 0.825–0.866) and 0.740 (95% CI: 0.715–0.765), respectively. The reliability and validity of the CHT and ATS were moderate, and both of them can be used to identify critical patients in emergency departments. It is necessary to further improve the triage system in terms of structure and content.

Do variations in hospital admission rates bias comparisons of standardized hospital mortality rates? A population-based cohort study

BACKGROUND

Standardized mortality rates are routinely used as measures of hospital performance and quality. Such metrics may, however, be biased if hospital admission thresholds differ and patient severity is not fully measured.

AIM

To examine whether comparisons of hospital mortality rates suffer from selection bias due to variations in hospital admission rates, using the example of variations by day of the week.

DATA

12,900,687 emergency department attendances and 3,418,446 unplanned admissions to all acute non-specialist hospitals of the National Health Service in England between 1 April 2013 and 28 February 2014.

METHODS

Population-based retrospective cohort study. Mortality within 30 days of attendance is modelled as a function of weekend or weekday attendance and hospital-level predictors of admission rates using patient-level risk-adjusted probit and bivariate Heckman selection models. Robustness is supported by the use of different hospital-level predictors.

RESULTS

When examining only the admitted population, patients admitted to hospital at weekends have a 0.206 percentage point higher risk of death within 30 days compared to patients admitted during the week. However, patients attending emergency departments at weekends have a 1.390 percentage point lower probability of being admitted to hospital. Once this selection bias is accounted for, the weekend effect in mortality is reduced by two-thirds to a 0.068 percentage point increase in the risk of death.

CONCLUSIONS

Comparisons of standardized hospital mortality rates following unplanned admissions can be biased by variations in emergency department admission rates, leading to incorrect conclusions about quality. The use of mortality as a performance measure could therefore lead to misleading comparisons if admission rates vary and illness severity is not fully controlled for. Accounting for sample selection bias and dependence between admission and mortality rates is vital if accurate comparisons of hospital performance are to be made.