A new tool for evaluating health equity in academic journals; the Diversity Factor.

Current methods to evaluate a journal's impact rely on the downstream citation mapping used to generate the Impact Factor. This approach is a fragile metric prone to being skewed by outlier values and does not speak to a researcher's contribution to furthering health outcomes for all populations. Therefore, we propose the implementation of a Diversity Factor to fulfill this need and supplement the current metrics. It is composed of four key elements: dataset properties, author country, author gender and departmental affiliation. Due to the significance of each individual element, they should be assessed independently of each other as opposed to being combined into a simplified score to be optimized. Herein, we discuss the necessity of such metrics, provide a framework to build upon, evaluate the current landscape through the lens of each key element and publish the findings on a freely available website that enables further evaluation. The OpenAlex database was used to extract the metadata of all papers published from 2000 until August 2022, and Natural language processing was used to identify individual elements. Features were then displayed individually on a static dashboard developed using TableauPublic, which is available at www.equitablescience.com. In total, 130,721 papers were identified from 7,462 journals where significant underrepresentation of LMIC and Female authors was demonstrated. These findings are pervasive and show no positive correlation with the Journal's Impact Factor. The systematic collection of the Diversity Factor concept would allow for more detailed analysis, highlight gaps in knowledge, and reflect confidence in the translation of related research. Conversion of this metric to an active pipeline would account for the fact that how we define those most at risk will change over time and quantify responses to particular initiatives. Therefore, continuous measurement of outcomes across groups and those investigating those outcomes will never lose importance. Moving forward, we encourage further revision and improvement by diverse author groups in order to better refine this concept.

One-year mortality after recovery from critical illness: A retrospective cohort study.

RATIONALE: Factors associated with one-year mortality after recovery from critical illness are not well understood. Clinicians generally lack information regarding post-hospital discharge outcomes of patients from the intensive care unit, which may be important when counseling patients and families. OBJECTIVE: We sought to determine which factors among patients who survived for at least 30 days post-ICU admission are associated with one-year mortality. METHODS: Single-center, longitudinal retrospective cohort study of all ICU patients admitted to a tertiary-care academic medical center from 2001-2012 who survived ≥30 days from ICU admission. Cox's proportional hazards model was used to identify the variables that are associated with one-year mortality. The primary outcome was one-year mortality. RESULTS: 32,420 patients met the inclusion criteria and were included in the study. Among patients who survived to ≥30 days, 28,583 (88.2%) survived for greater than one year, whereas 3,837 (11.8%) did not. Variables associated with decreased one-year survival include: increased age, malignancy, number of hospital admissions within the prior year, duration of mechanical ventilation and vasoactive agent use, sepsis, history of congestive heart failure, end-stage renal disease, cirrhosis, chronic obstructive pulmonary disease, and the need for renal replacement therapy. Numerous effect modifications between these factors were found. CONCLUSION: Among survivors of critical illness, a significant number survive less than one year. More research is needed to help clinicians accurately identify those patients who, despite surviving their acute illness, are likely to suffer one-year mortality, and thereby to improve the quality of the decisions and care that impact this outcome.

The hackathon model to spur innovation around global mHealth.

The challenge of providing quality healthcare to underserved populations in low- and middle-income countries (LMICs) has attracted increasing attention from information and communication technology (ICT) professionals interested in providing societal impact through their work. Sana is an organisation hosted at the Institute for Medical Engineering and Science at the Massachusetts Institute of Technology that was established out of this interest. Over the past several years, Sana has developed a model of organising mobile health bootcamp and hackathon events in LMICs with the goal of encouraging increased collaboration between ICT and medical professionals and leveraging the growing prevalence of cellphones to provide health solutions in resource limited settings. Most recently, these events have been based in Colombia, Uganda, Greece and Mexico. The lessons learned from these events can provide a framework for others working to create sustainable health solutions in the developing world.