Employment and working conditions of nurses: where and how health inequalities have increased during the COVID-19 pandemic?

BACKGROUND: Nurses and midwives play a critical role in the provision of care and the optimization of health services resources worldwide, which is particularly relevant during the current COVID-19 pandemic. However, they can only provide quality services if their work environment provides adequate conditions to support them. Today the employment and working conditions of many nurses worldwide are precarious, and the current pandemic has prompted more visibility to the vulnerability to health-damaging factors of nurses' globally. This desk review explores how employment relations, and employment and working conditions may be negatively affecting the health of nurses in countries such as Brazil, Croatia, India, Ireland, Italy, México, Nepal, Spain, and the United Kingdom. MAIN BODY: Nurses' health is influenced by the broader social, economic, and political system and the redistribution of power relations that creates new policies regarding the labour market and the welfare state. The vulnerability faced by nurses is heightened by gender inequalities, in addition to social class, ethnicity/race (and caste), age and migrant status, that are inequality axes that explain why nurses' workers, and often their families, are exposed to multiple risks and/or poorer health. Before the COVID-19 pandemic, informalization of nurses' employment and working conditions were unfair and harmed their health. During COVID-19 pandemic, there is evidence that the employment and working conditions of nurses are associated to poor physical and mental health. CONCLUSION: The protection of nurses' health is paramount. International and national enforceable standards are needed, along with economic and health policies designed to substantially improve employment and working conditions for nurses and work-life balance. More knowledge is needed to understand the pathways and mechanisms on how precariousness might affect nurses' health and monitor the progress towards nurses' health equity.

Management of liver trauma in urban university hospitals in India: an observational multicentre cohort study.

BACKGROUND: Low- and middle-income countries (LMICs) contribute to 90% of injuries occurring in the world. The liver is one of the commonest organs injured in abdominal trauma. This study aims to highlight the demographic and management profile of liver injury patients, presenting to four urban Indian university hospitals in India. METHODS: This is a retrospective registry-based study. Data of patients with liver injury either isolated or concomitant with other injuries was used using the ICD-10 code S36.1 for liver injury. The severity of injury was graded based on the World Society of Emergency Surgery (WSES) grading for liver injuries. RESULTS: A total of 368 liver injury patients were analysed. Eighty-nine percent were males, with road traffic injuries being the commonest mechanism. As per WSES liver injury grade, there were 127 (34.5%) grade I, 96 (26.1%) grade II, 70 (19.0%) grade III and 66 (17.9%) grade IV injuries. The overall mortality was 16.6%. Two hundred sixty-two patients (71.2%) were managed non-operatively (NOM), and 106 (38.8%) were operated. 90.1% of those managed non-operatively survived. CONCLUSION: In this multicentre cohort of liver injury patients from urban university hospitals in India, the commonest profile of patient was a young male, with a blunt injury to the abdomen due to a road traffic accident. Success rate of non-operative management of liver injury is comparable to other countries.

Do the Care Process and Survival Chances Differ in Patients Arriving to a Level 1 Indian Trauma Center, during-Hours and after-Hours?

INTRODUCTION: Trauma systems vary in performance during different time periods and may affect the patient outcomes, especially in resource-limited settings. The present study was undertaken to study the pattern, epidemiological profile, processes of care variations of trauma victims presenting during-hours and after-hours in a level 1 trauma Center of a lower middle-income country. METHODOLOGY: Retrospective analyses of prospectively collected data registry at a single tertiary care center. Data collected from 2013 to 2015 were analyzed. Patients with a history of trauma and admission to the center or death between arrival and admission were included. Isolated limb injury and patients dead on arrival were excluded. RESULTS: Of 4692, 1789 (38.1%) patients arrived and were admitted during-hours and 2903 (61.9%) after-hours. The overall in-hospital mortality was 14.9% in the cohort. Moreover, it was 16.10% during after-hours in comparison to 13.0% during-hours. The Revised Trauma Score was statistically different during-hours and after-hours suggesting patients with greater physiological derangement after-hours. The Kaplan-Meier survival curves for 7 days were comparable in two groups with the log-rank test of 078. The proportion of initial radiological investigations (chest X-ray, focused assessment sonography in trauma [FAST], and computerized tomography [CT] scans) was ranged from 84.9% for CT scans in the cohort to 99.3% for FAST. CONCLUSIONS: Processes of care do not differ significantly for the patients admitted at a level 1 trauma center irrespective of time of the day. Although survival probability for the initial 7 days of follow-up is comparable between two groups; however, for 30 and 90 days of follow-up they are significantly different between during-hours and after-hours, likely due to injury severity.

Comparison of ISS, NISS, and RTS score as predictor of mortality in pediatric fall.

BACKGROUND: Studies to identify an ideal trauma score tool representing prediction of outcomes of the pediatric fall patient remains elusive. Our study was undertaken to identify better predictor of mortality in the pediatric fall patients. METHODS: Data was retrieved from prospectively maintained trauma registry project at level 1 trauma center developed as part of Multicentric Project-Towards Improving Trauma Care Outcomes (TITCO) in India. Single center data retrieved from a prospectively maintained trauma registry at a level 1 trauma center, New Delhi, for a period ranging from 1 October 2013 to 17 February 2015 was evaluated. Standard anatomic scores Injury Severity Score (ISS) and New Injury Severity Score (NISS) were compared with physiologic score Revised Trauma Score (RTS) using receiver operating curve (ROC). RESULTS: Heart rate and RTS had a statistical difference among the survivors to nonsurvivors. ISS, NISS, and RTS were having 50, 50, and 86% of area under the curve on ROCs, and RTS was statistically significant among them. CONCLUSIONS: Physiologically based trauma score systems (RTS) are much better predictors of inhospital mortality in comparison to anatomical based scoring systems (ISS and NISS) for unintentional pediatric falls.

Traumatic transfers: calibration is adversely affected when prediction models are transferred between trauma care contexts in India and the United States.

OBJECTIVE: We evaluated the transferability of prediction models between trauma care contexts in India and the United States and explored updating methods to adjust such models for new contexts. STUDY DESIGN AND SETTINGS: Using a combination of prospective cohort and registry data from 3,728 patients of Towards Improved Trauma Care Outcomes in India (TITCO) and from 18,756 patients of the US National Trauma Data Bank (NTDB), we derived models in one context and validated them in the other, assessing them for discrimination and calibration using systolic blood pressure, heart rate, and Glasgow coma scale as candidate predictors. RESULTS: Early mortality was 8% in the TITCO and 1-2% in the NTDB samples. Both models discriminated well, but the TITCO model overestimated the risk of mortality in NTDB patients, and the NTDB model underestimated the risk in TITCO patients. CONCLUSION: Transferability was good in terms of discrimination but poor in terms of calibration. It was possible to improve this miscalibration by updating the models' intercept. This updating method could be used in samples with as few as 25 events.