Data driven contagion risk management in low-income countries using machine learning applications with COVID-19 in South Asia.

In the absence of real-time surveillance data, it is difficult to derive an early warning system and potential outbreak locations with the existing epidemiological models, especially in resource-constrained countries. We proposed a contagion risk index (CR-Index)-based on publicly available national statistics-founded on communicable disease spreadability vectors. Utilizing the daily COVID-19 data (positive cases and deaths) from 2020 to 2022, we developed country-specific and sub-national CR-Index for South Asia (India, Pakistan, and Bangladesh) and identified potential infection hotspots-aiding policymakers with efficient mitigation planning. Across the study period, the week-by-week and fixed-effects regression estimates demonstrate a strong correlation between the proposed CR-Index and sub-national (district-level) COVID-19 statistics. We validated the CR-Index using machine learning methods by evaluating the out-of-sample predictive performance. Machine learning driven validation showed that the CR-Index can correctly predict districts with high incidents of COVID-19 cases and deaths more than 85% of the time. This proposed CR-Index is a simple, replicable, and easily interpretable tool that can help low-income countries prioritize resource mobilization to contain the disease spread and associated crisis management with global relevance and applicability. This index can also help to contain future pandemics (and epidemics) and manage their far-reaching adverse consequences.

Inequalities in the disruption of paid work during the Covid-19 pandemic: A world systems analysis of core, semi-periphery, and periphery states.

This article reveals the extent of international inequalities in the immediate impact of the COVID-19 pandemic on participation in paid work. Drawing on World Systems Theory (WST) and a novel quasi-experimental analysis of nationally representative household panel surveys across 20 countries, the study finds a much sharper increase in the likelihood of dropping out of paid work in semi-periphery and periphery states relative to core states. We establish a causal link between such international disparities and the early trajectories of state interventions in the labor market. Further analysis demonstrates that within all three world systems delayed, less stringent interventions in the labor market were enabled by right-wing populism but mitigated by the strength of active labor market policies and collective bargaining.

The impact of management practices on relative patient mortality: Evidence from public hospitals.

A small, but growing, body of empirical evidence shows that the material and persistent variation in many aspects of the performance of healthcare organisations can be related to variation in their management practices. This study uses public data on hospital patient mortality outcomes, the Summary Hospital-level Mortality Indicator (SHMI) to extend this programme of research. We assemble a five-year dataset combining SHMI with potential confounding variables for all English NHS non-specialist acute hospital trusts. The large number of providers working within a common system provides a powerful environment for such investigations. We find considerable variation in SHMI between trusts and a high degree of persistence of high- or low performance. This variation is associated with a composite metric for management practices based on the NHS National Staff Survey. We then use a machine learning technique to suggest potential clusters of individual management practices related to patient mortality performance and test some of these using traditional multivariate regression. The results support the hypothesis that such clusters do matter for patient mortality, and so we conclude that any systematic effort at improving patient mortality should consider adopting an optimal cluster of management practices.

Hospital productivity: The role of efficiency drivers.

A major feature of health-care systems is substantial variation in hospital productivity. Hospital productivity varies widely across countries. The presence of such variation suggests potential areas for improvement, which can substantially lower health-care costs. This research aims to investigate factors that may explain variations in hospital productivity by constructing a longitudinal data (panel) on English NHS hospital trusts. It also seeks to explore possible interactions among the factors in a data-driven manner. We employ unbiased panel regression tree techniques from the machine-learning literature to explore the complex interactive structure of the data. We next use econometric panel regression to deal with individual hospital effects to identify some of the determinants of hospital productivity. The findings point to the significance of efficiency-enhancing mechanisms for hospital productivity, including measures to reduce the length of stay, increase day case (outpatient) surgery rate, and to minimize errors. Further, such measures are shaped by more fundamental factors such as the availability of human capital and management practices. Our results underscore the importance of within-hospital efficiency-enhancing mechanisms to cost-adjusted hospital productivity. Improving hospital operational processes will enhance productivity. At a deeper level, human capital and management practices are likely to be most critical.

Combining regression trees and panel regression for exploring and testing the impact of complementary management practices on short-notice elective operation cancellation rates.

Variation in the performance of providers across healthcare systems is pervasive. It is recognised as both a major concern and an opportunity for learning and improvement. Variation between providers is broadly considered to be due to management practices and contextual factors such as catchment-area demographics. However, there is little understanding of the ways in which these impact on performance and how they can be measured. We use recent developments in both regression trees and panel regression techniques to explore and then statistically test complementary alignments of management practices whilst taking into account contextual factors. We apply this to 5 years of NHS hospital trust data, examining performance on short-notice cancellation rates. We find that different alignments of management practices give rise to quite different short-notice cancellation rates between trusts, with some being substantially lower. Our research offers a data-driven approach for identifying optimal clusters of management practices.

Hospital heterogeneity: what drives the quality of health care.

A major feature of health care systems is substantial variation in health care quality across hospitals. The quality of stroke care widely varies across NHS hospitals. We investigate factors that may explain variations in health care quality using measures of quality of stroke care. We combine NHS trust data from the National Sentinel Stroke Audit with other data sets from the Office for National Statistics, NHS and census data to capture hospitals' human and physical assets and organisational characteristics. We employ a class of non-parametric methods to explore the complex structure of the data and a set of correlated random effects models to identify key determinants of the quality of stroke care. The organisational quality of the process of stroke care appears as a fundamental driver of clinical quality of stroke care. There are rich complementarities amongst drivers of quality of stroke care. The findings strengthen previous research on managerial and organisational determinants of health care quality.