“Partner”, “Caregiver”, or “Co-Survivor”—Might the Label We Give the Partners of Cancer Patients Affect the Health Outcome of the Patients and Their Partners?

Having a life partner significantly extends survival for most cancer patients. The label given to the partners of cancer patients may, however, influence the health of not just the patients but their partners. “Caregiver” is an increasingly common label for the partners of patients, but it carries an implicit burden. Referring to partners as “caregivers” may be detrimental to the partnerships, as it implies that the individuals are no longer able to be co-supportive. Recognizing this, there has been some effort to relabel cancer dyads as “co-survivors”. However, many cancer patients are not comfortable being called a “survivor”, and the same may apply to their partners. Cancer survivorship, we argue, could be enhanced by helping keep the bond between patients and their partners strong. This includes educating patients and partners about diverse coping strategies that individuals use when facing challenges to their health and wellbeing. We suggest that preemptive couples’ counselling in cancer centers may benefit both patients and their partners.

Artificial Intelligence to Improve Health Outcomes in the NICU and PICU: A Systematic Review

CONTEXT: Artificial intelligence (AI) technologies are increasingly used in pediatrics and have the potential to help inpatient physicians provide high-quality care for critically ill children. OBJECTIVE: We aimed to describe the use of AI to improve any health outcome(s) in neonatal and pediatric intensive care. DATA SOURCE: PubMed, IEEE Xplore, Cochrane, and Web of Science databases. STUDY SELECTION: We used peer-reviewed studies published between June 1, 2010, and May 31, 2020, in which researchers described (1) AI, (2) pediatrics, and (3) intensive care. Studies were included if researchers assessed AI use to improve at least 1 health outcome (eg, mortality). DATA EXTRACTION: Data extraction was conducted independently by 2 researchers. Articles were categorized by direct or indirect impact of AI, defined by the European Institute of Innovation and Technology Health joint report. RESULTS: Of the 287 publications screened, 32 met inclusion criteria. Approximately 22% ( n = 7) of studies revealed a direct impact and improvement in health outcomes after AI implementation. Majority were in prototype testing, and few were deployed into an ICU setting. Among the remaining 78% ( n = 25) AI models outperformed standard clinical modalities and may have indirectly influenced patient outcomes. Quantitative assessment of health outcomes using statistical measures, such as area under the receiver operating curve (56%; n = 18) and specificity (38%; n = 12), revealed marked heterogeneity in metrics and standardization. CONCLUSIONS: Few studies have revealed that AI has directly improved health outcomes for pediatric critical care patients. Further prospective, experimental studies are needed to assess AI’s impact by using established implementation frameworks, standardized metrics, and validated outcome measures.

Time-varying associations between an exposure history and a subsequent health outcome: a landmark approach to identify critical windows

Background Long-term behavioral and health risk factors constitute a primary focus of research on the etiology of chronic diseases. Yet, identifying critical time-windows during which risk factors have the strongest impact on disease risk is challenging. To assess the trajectory of association of an exposure history with an outcome, the weighted cumulative exposure index (WCIE) has been proposed, with weights reflecting the relative importance of exposures at different times. However, WCIE is restricted to a complete observed error-free exposure whereas exposures are often measured with intermittent missingness and error. Moreover, it rarely explores exposure history that is very distant from the outcome as usually sought in life-course epidemiology. Methods We extend the WCIE methodology to (i) exposures that are intermittently measured with error, and (ii) contexts where the exposure time-window precedes the outcome time-window using a landmark approach. First, the individual exposure history up to the landmark time is estimated using a mixed model that handles missing data and error in exposure measurement, and the predicted complete error-free exposure history is derived. Then the WCIE methodology is applied to assess the trajectory of association between the predicted exposure history and the health outcome collected after the landmark time. In our context, the health outcome is a longitudinal marker analyzed using a mixed model. Results A simulation study first demonstrates the correct inference obtained with this approach. Then, applied to the Nurses’ Health Study (19,415 women) to investigate the association between body mass index history (collected from midlife) and subsequent cognitive decline (evaluated after age 70), the method identified two major critical windows of association: long before the first cognitive evaluation (roughly 24 to 12 years), higher levels of BMI were associated with poorer cognition. In contrast, adjusted for the whole history, higher levels of BMI became associated with better cognition in the last years prior to the first cognitive interview, thus reflecting reverse causation (changes in exposure due to underlying disease). Conclusions This approach, easy to implement, provides a flexible tool for studying complex dynamic relationships and identifying critical time windows while accounting for exposure measurement errors.

Visualising Inequality: Modelling disparities in health outcomes

<p>The thesis begins to integrate some contemporary theorising in sociology, frameworks for explaining social disparities in population health, disciplines from System Dynamics modelling, and, D.D. Heckathorn’s model of ‘The Dynamics and Dilemmas of Collective Action’. Wilkinson and Marmot are recognised as leading participants in public discussion of population health disparities. The priorities they advocate are reflected in public statements of intent such as the statutory objective of New Zealand District Health Boards ‘to reduce, with a view to eliminating, health outcome disparities between various population groups . . .’ Sen’s advocacy for impartial governance when allocating freedom-based capabilities is considered as a core strategy for reducing disparities and promoting justice. The main question addressed is whether sociological theory can contribute to understanding the dynamics implied by Sen’s ‘idea of justice’. The conclusion is that the work of Runciman, Coleman, Turner, Lenski, Jasso and Heckathorn can be used to analyse the influence of corporate actors and sectoral strategies, which Wilkinson and Pickett referred to as ‘the elephant in the . . . room’ in discussions about determinants and the social gradient of health.</p>

Visualising Inequality: Modelling disparities in health outcomes

<p>The thesis begins to integrate some contemporary theorising in sociology, frameworks for explaining social disparities in population health, disciplines from System Dynamics modelling, and, D.D. Heckathorn’s model of ‘The Dynamics and Dilemmas of Collective Action’. Wilkinson and Marmot are recognised as leading participants in public discussion of population health disparities. The priorities they advocate are reflected in public statements of intent such as the statutory objective of New Zealand District Health Boards ‘to reduce, with a view to eliminating, health outcome disparities between various population groups . . .’ Sen’s advocacy for impartial governance when allocating freedom-based capabilities is considered as a core strategy for reducing disparities and promoting justice. The main question addressed is whether sociological theory can contribute to understanding the dynamics implied by Sen’s ‘idea of justice’. The conclusion is that the work of Runciman, Coleman, Turner, Lenski, Jasso and Heckathorn can be used to analyse the influence of corporate actors and sectoral strategies, which Wilkinson and Pickett referred to as ‘the elephant in the . . . room’ in discussions about determinants and the social gradient of health.</p>