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Poverty transitions can be explained by two opposing theories: the traditional sociological approach that focusses on social stratification and individualisation theory, which emphasises on life course risks for all strata. Both perspectives have been investigated extensively for income poverty while neglecting other important poverty indicators, such as deprivation or the receipt of social assistance. The purpose of this paper is to focus on the latter to investigate the impact of social stratification (e.g. social class), life course risks (e.g. health problems), and their interactions on the probability of social assistance entry for Germany.

The analysis utilises survey data containing a sample of first-time social assistance entrants and a sample of the residential population. Applying case-control methodology, logistic regression is conducted to model the impact of social stratification determinants, life course risks, and their interactions on the probability of social assistance entry.

Social stratification determinants, particularly social class, have a significant effect. However, their effect is weaker than the effect of life course risks. Contrary to the prediction of individualisation theory, the poverty-triggering impact of life course risks varies substantially by social stratum. The combination of both theories yields high predictive power.

This paper is the first to comprehensively test social stratification and individualisation theory with respect to social assistance receipt as a poverty indicator. It is the first paper that investigates the entire population at risk of social assistance entry in Germany.

The purpose of this paper is to identify and analyse the readmission risk prediction tools reported in the literature and their benefits when it comes to healthcare organisations and management.

Readmission risk prediction is a growing topic of interest with the aim of identifying patients in particular those suffering from chronic diseases such as congestive heart failure, chronic obstructive pulmonary disease and diabetes, who are at risk of readmission. Several models have been developed with different levels of predictive ability. A structured and extensive literature search of several databases was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-analysis strategy, and this yielded a total of 48,984 records.

Forty-three articles were selected for full-text and extensive review after following the screening process and according to the eligibility criteria. About 34 unique readmission risk prediction models were identified, in which their predictive ability ranged from poor to good (c statistic 0.5–0.86). Readmission rates ranged between 3.1 and 74.1% depending on the risk category. This review shows that readmission risk prediction is a complex process and is still relatively new as a concept and poorly understood. It confirms that readmission prediction models hold significant accuracy at identifying patients at higher risk for such an event within specific context.

Since most prediction models were developed for specific populations, conditions or hospital settings, the generalisability and transferability of the predictions across wider or other contexts may be difficult to achieve. Therefore, the value of prediction models remains limited to hospital management. Future research is indicated in this regard.

This review is the first to cover readmission risk prediction tools that have been published in the literature since 2011, thereby providing an assessment of the relevance of this crucial KPI to health organisations and managers.

The dynamic support database (DSD) clinical support tool structures the risk of admission rating for individuals with intellectual disabilities. This study aims to investigate inter-rater reliability between multi-disciplinary health care professionals within the North West of England.

A small-scale quantitative study investigated reliability between raters on the DSD clinical support tool. A data set of 60 rating tools for 30 individuals was used. Descriptive statistics and Kappa coefficient explored agreement.

The DSD clinical support tool was found to have strong inter-rater reliability between individual items and the differences between individual scores were spread suggesting variance found could not be attributed to specific questions. Strong inter-rater reliability was found in the overall ratings.

Results suggest the DSD clinical support tool provides stratification for risk of admission ratings independent of who completes it. Future studies could investigate inter-rater reliability between organisations, i.e. health and social care professionals, and use a larger data sample to ensure generalisability. Replication of the study within child and adolescent services using the children’s DSD clinical support tool is also recommended.

The DSD clinical support tool has been implemented within the child and adult intellectual disability services across the North West. As more teams across England consider its implementation, the study provides reassurance that coding agreement is high, allowing for stratification for risk of admission independent of the rater.

The research on supply chain risk management (SCRM) is visibly on the rise, although its literature still lacks the state of the art that critically analyzes its content. The SCRM literature seems to require studies that utilize risk typology, sources of risk, etc. for reviewing the topic. The purpose of this paper is to bridge the gap by synthesizing the information obtained from 343 articles across 85 journals. This study also presents a critical analysis of the content of SCRM in a structured manner to identify the directions for future research.

A systematic literature review (SLR) was devised and adopted, which involved the selection, classification, and evaluation of 343 research articles published over a period of 11 years (2004-2014). The content of extant SCRM literature was critically analyzed and synthesized from the perspective of the risk management process (RMP).

The analysis of extant literature shows that there is a marked rise in research in the SCRM area, especially after the year 2005. It was observed that not only risk but also different forms of uncertainties make supply chain (SC) operations difficult to manage. The SCRM actions yielded most benefits when their implementation was at chain or network level and managed strategically. The analysis also reveals that the manufacturing sector is most affected by risks and highly investigated by researchers.

A complete process for SCRM based on risk stratification, objectives of risk management, and RMP will be a guiding model for firms to manage risks. The research gaps identified and future directions provided here will encourage researchers and managers to devise new methods, tools, and techniques to address the risks in modern SC operations.

An SLR and risk-based content classification of SCRM literature were performed. To identify, locate, select, and analyze the SCRM literature, a structured and systematic process was adopted with some very rarely used methods such as two levels of search keywords, and strings were formulated to locate the most relevant articles in major academic databases.

The purpose of this paper is to formulate a framework to construct a patient-specific risk score and therefore to classify these patients into various risk groups that can be used as a decision support mechanism by the medical decision makers to augment their decision-making process, allowing them to optimally use the limited resources available.

A conventional statistical model (logistic regression) and two machine learning-based (i.e. artificial neural networks (ANNs) and support vector machines) data mining models were employed by also using five-fold cross-validation in the classification phase. In order to overcome the data imbalance problem, random undersampling technique was utilized. After constructing the patient-specific risk score, k-means clustering algorithm was employed to group these patients into risk groups.

Results showed that the ANN model achieved the best results with an area under the curve score of 0.867, while the sensitivity and specificity were 0.715 and 0.892, respectively. Also, the construction of patient-specific risk scores offer useful insights to the medical experts, by helping them find a trade-off between risks, costs and resources.

The study contributes to the existing body of knowledge by constructing a framework that can be utilized to determine the risk level of the targeted patient, by employing data mining-based predictive approach.

The purpose of this paper is to develop an alcohol withdrawal syndrome risk stratification tool that could support the safe discharge of low risk patients from the emergency department.

A retrospective cohort study that included all patients referred to the acute addiction liaison nursing service over one calendar month (n=400, 1–30 April 2016) was undertaken. Bivariate and multivariate modelling identified the significant variables that supported the prediction of severe alcohol withdrawal syndrome (SAWS) in the cohort population.

The Glasgow Modified Alcohol Withdrawal Scale (GMAWS), hours since last drink, fast alcohol screening test (FAST) and systolic blood pressure correctly identified 89 per cent of patients who developed SAWS and 84 per cent of patients that did not. Increasing each component by a score of one is associated with an increase in the odds of SAWS by a factor of 2.76 (95% CI 2.21, 3.45), 1.31 (95% CI 1.24, 1.37), 1.30 (95% CI 1.08, 1.57) and 1.22 (95% CI 1.10, 1.34), respectively.

The research was conducted in a single healthcare system that had a high prevalence of alcohol dependence syndrome (ADS). Second, the developed risk stratification tool was unable to guarantee no risk and lastly, the FAST score previously aligned to severe ADS may have influenced the patients highest GMAWS score.

The tool could help redesign the care pathway for patients who attend the emergency department at risk of SAWS and link low risk patients with community alcohol services better equipped to deal with their physical and psychological needs short and long term supporting engagement, abstinence and prolongation of life.

The tool could help redesign the care pathway for emergency department patients at low risk of SAWS and link them with community alcohol services better equipped to deal with their physical and psychological needs, short and long term, supporting engagement, abstinence and prolongation of life.

The purpose of this paper is to report on the dynamics of “identity leadership” with a quality improvement project undertaken by an International Medical Graduate (IMG) from Sri Lanka, on a two year Medical Training Initiative (MTI) placement in the National Health Service (NHS) [Academy of Medical Royal Colleges (AoMRC), 2017]. A combined MTI rotation with an integrated Fellowship in Quality Improvement (Subedi et al., 2019) provided the driver to implement the HEART score (HS) in an NHS Emergency Department (ED) in the UK. The project was undertaken across ED, Acute Medicine and Cardiology at the hospital, with stakeholders emphasizing different and conflicting priorities to improve the pathway for chest pain patients.

A social identity approach to leadership provided a framework to understand the insider/outsider approach to leadership which helped RH to negotiate and navigate the conflicting priorities from each departments’ perspective. A staff survey tool was undertaken to identify reasons for the lack of implementation of a clinical protocol for chest pain patients, specifically with reference to the use of the HS. A consensus was reached to develop and implement the pathway for multi-disciplinary use of the HS and a quality improvement methodology (with the use of plan do study act (PDSA) cycles) was used over a period of nine months.

The results demonstrated significant improvements in the reduction (60%) of waiting time by chronic chest pain patients in the ED. The use of the HS as a stratified risk assessment tool resulted in a more efficient and safe way to manage patients. There are specific leadership challenges faced by an MTI doctor when they arrive in the NHS, as the MTI doctor is considered an outsider to the NHS, with reduced influence. Drawing upon the Social Identity Theory of Leadership, NHS Trusts can introduce inclusion strategies to enable greater alignment in social identity with doctors from overseas.

More than one third of doctors (40%) in the English NHS are IMGs and identify as black and minority ethnic (GMC, 2019a) a trend that sees no sign of abating as the NHS continues its international medical workforce recruitment strategy for its survival (NHS England, 2019; Beech et al., 2019). IMGs can provide significant value to improving the NHS using skills developed from their own health-care system. This paper recommends a need for reciprocal learning from low to medium income countries by UK doctors to encourage the development of an inclusive global medical social identity.

This quality improvement research combined with identity leadership provides new insights into how overseas doctors can successfully lead sustainable improvement across different departments within one hospital in the NHS.

Socioeconomic status (SES) has been known to be associated with many aspects of social life such as health. We argue that social stratification remains relevant in understanding differentials in health during a pandemic, as countries globally have encouraged or enforced social distancing and remote work measures.

By examining data sources and news reports on the COVID-19 pandemic, we aim to highlight the relationship between SES and morbidity, through the ability to adopt social distancing measures and work remotely. Utilizing publicly available data from the Maryland Transport Institute and the US Census, we performed linear regressions on median income, social distancing index and percentage of individuals working from home.

Individuals with higher SES are more likely to have jobs that provide opportunities for remote work to be performed, which allows for social distancing. Comparatively, individuals with lower SES are more likely to be involved in jobs that cannot be performed remotely. The linear regression models suggest a positive moderate and significant correlation between median income and social distancing index (R² = 0.4981, p-value < 0.001), and a positive weak and significant correlation between median income and remote work (R² = 0.2460, p-value < 0.001).

Governments need to account for SES in policymaking to reduce inequalities in health.

The paper aims to improve the understanding of social stratification and morbidity through examining data on the COVID-19 pandemic.

This illustrative case study describes and evaluates drivers of effective inter-organizational collaboration to mitigate the impact and spread of COVID-19 among homeless people in two cities in the Netherlands. The aims of this study are: (1) to explore the strategic and operational policy responses in two local integrated care settings at the start of the crisis, (2) to identify best policy practices and lessons learned. The authors interpret and evaluate the findings by combining insights from the population health management (PHM) and collaborative governance literature.

The authors describe and illustrate the experiences of two Dutch municipalities, Rotterdam and The Hague, in the early policy responses to sudden operational challenges around the impact of COVID-19 on homeless people as experienced by local decision-makers, medical doctors and clients.

The authors show that best policy practices revolve around (1) using data and risk stratification methods for identifying and targeting populations at-risk in local policy making, and (2) having an inter-organizational data sharing architecture in place ex ante. These two factors were clear prerequisites for tailor-made policy responses for newly-defined groups at risk with the existing and well-documented vulnerable population, and executing crisis-induced tasks efficiently.

This paper is among the first to illustrate the potential of combining collaborative governance and PHM perspectives to identify key drivers of effective local governance responses to a healthcare crisis in an integrated care setting.