Month: March 2024

Intrinsic capacity trajectories and socioeconomic inequalities in health: the contributions of wealth, education, gender, and ethnicity

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Abstract

Background

Life-long health inequalities exert enduring impacts and are governed by social determinants crucial for achieving healthy aging. A fundamental aspect of healthy aging, intrinsic capacity, is the primary focus of this study. Our objective is to evaluate the social inequalities connected with the trajectories of intrinsic capacity, shedding light on the impacts of socioeconomic position, gender, and ethnicity.

Methods

Our dynamic cohort study was rooted in three waves (2009, 2014, 2017) of the World Health Organization’s Study on Global AGEing and Adult Health in Mexico. We incorporated a nationally representative sample comprising 2722 older Mexican adults aged 50 years and over. Baseline measurements of socioeconomic position, gender, and ethnicity acted as the exposure variables. We evaluated intrinsic capacity across five domains: cognition, psychological, sensory, vitality, and locomotion. The Relative Index of Inequality and Slope Index of Inequality were used to quantify socioeconomic disparities.

Results

We discerned three distinct intrinsic capacity trajectories: steep decline, moderate decline, and slight increase. Significant disparities based on wealth, educational level, gender, and ethnicity were observed. Older adults with higher wealth and education typically exhibited a trajectory of moderate decrease or slight increase in intrinsic capacity. In stark contrast, women and indigenous individuals were more likely to experience a steeply declining trajectory.

Conclusions

These findings underscore the pressing need to address social determinants, minimize gender and ethnic discrimination to ensure equal access to resources and opportunities across the lifespan. It is imperative for policies and interventions to prioritize these social determinants in order to promote healthy aging and alleviate health disparities. This approach will ensure that specific demographic groups receive customized support to sustain their intrinsic capacity during their elder years.

Physician Workforce Diversity Is Still Necessary and Achievable if It Is Intentionally Prioritized

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Abstract

The 2023 Supreme Court Decision from Students for Fair Admissions v. Harvard and Students for Fair Admissions v. University of North Carolina threatens the current progress in achieving diversity within undergraduate and graduate medical education. This is necessary to achieve a diverse healthcare workforce, which is a key to healing historical healthcare trauma, eliminating health disparities, and providing equitable healthcare access for all communities. Although the Supreme Court decision seems obstructionist, viable opportunities exist to enhance recruitment further and solidify diversity efforts in undergraduate and graduate medical education to achieve these goals.

Cross-cultural perception of strength, attractiveness, aggressiveness and helpfulness of Maasai male faces calibrated to handgrip strength

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Abstract

Previous research has demonstrated that Maasai and Europeans tend to align in their ratings of the physical strength and aggressiveness of Maasai male faces, calibrated to hand grip strength (HGS). However, perceptions of attractiveness of these faces differed among populations. In this study, three morphs of young Maasai men created by means of geometric morphometrics, and depicting the average sample and two extrema (± 4 SD of HGS), were assessed by men and women from Tanzania, Czech Republic, Russia, Pakistan, China, and Mexico (total sample = 1540). The aim of this study was to test cross-cultural differences in the perception of young Maasai men’s composites calibrated to HGS, focusing on four traits: physical strength, attractiveness, aggressiveness, and helpfulness. Individuals from all six cultures were able to distinguish between low, medium, and high HGS portraits. Across all study populations, portrait of Maasai men with lower HGS was perceived as less attractive, more aggressive, and less helpful. This suggests that people from diverse populations share similar perceptions of physical strength based on facial shape, as well as attribute similar social qualities like aggressiveness and helpfulness to these facial images. Participants from all samples rated the composite image of weak Maasai men as the least attractive.

Ethno-Racial Inequities of the COVID-19 Pandemic: Implications and Recommendations for Mental Health Professionals

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Abstract

The coronavirus disease-19 (COVID-19) pandemic has disproportionately impacted Black, Indigenous, and People of Color (BIPOC) communities due to systemic health disparities based on race, ethnicity, and systemic inequities. Among extent literature on BIPOC mental health and COVID-19, there is a pressing need for culturally responsive, trauma-informed treatment approaches that go beyond the broader impacts of the pandemic or immediate pandemic-related concerns and address the persisting impacts of the COVID-19 on BIPOC mental health. To this end, our article aims to equip professional counselors with the necessary tools to serve BIPOC clients more effectively by (1) understanding the ethno-racial inequities of the COVID-19 pandemic, (2) assessing the intersectional dimensions of stress and trauma associated with COVID-19, and (3) employing therapeutic approaches to promote physical and mental well-being in BIPOC clients.

Comparative Assessment of Image Super-Resolution Techniques for Spatial Downscaling of Gridded Rainfall Data

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Abstract

With an increasing focus on improving localized understanding of weather and climate phenomena and the computation cost involved in high-resolution modelling, spatial downscaling of data has proven to be a viable alternative method to obtain high-resolution climate data. Historically, several statistical and dynamical downscaling techniques have been employed to enhance coarse-resolution data to a finer grid scale. However, these conventional methods are either inefficient in capturing several finer scale details or are resource-heavy for recursive applications. Image Super-Resolution (SR) is a computer vision concept of using grid-based approaches to enhance the resolution of an image, analogous to spatial downscaling. In this study, we explore and compare the performance of different image super-resolution methods in downscaling the Indian Meteorological Department’s (IMD) gridded rainfall data from a low spatial resolution (1.0°) to a high resolution (0.25°). The SR methods considered include a fully connected autoencoder, four traditional convolutional neural networks, and two residual neural networks. The primary objective of this study was to make an initial assessment of the performance of such methods in downscaling gridded rainfall data over the Indian region. Furthermore, the resultant downscaled products have been compared using different objective metrics and analytical comparisons with ground truth data. One of the key findings of this study is that the residual learning-based neural networks demonstrated better performance in creating perceptually realistic rainfall maps (proven both quantitatively and qualitatively), closely followed by the traditional convolutional neural networks and the autoencoder.

Systematic review of the efficacy of data-driven urban building energy models during extreme heat in cities: Current trends and future outlook

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Abstract

Energy demand fluctuations due to low probability high impact (LPHI) micro-climatic events such as urban heat island effect (UHI) and heatwaves, pose significant challenges for urban infrastructure, particularly within urban built-clusters. Mapping short term load forecasting (STLF) of buildings in urban micro-climatic setting (UMS) is obscured by the complex interplay of surrounding morphology, micro-climate and inter-building energy dynamics. Conventional urban building energy modelling (UBEM) approaches to provide quantitative insights about building energy consumption often neglect the synergistic impacts of micro-climate and urban morphology in short temporal scale. Reduced order modelling, unavailability of rich urban datasets such as building key performance indicators for building archetypes-characterization, limit the inter-building energy dynamics consideration into UBEMs. In addition, mismatch of resolutions of spatio–temporal datasets (meso to micro scale transition), LPHI events extent prediction around UMS as well as its accurate quantitative inclusion in UBEM input organization step pose another degree of limitations. This review aims to direct attention towards an integrated-UBEM (i-UBEM) framework to capture the building load fluctuation over multi-scale spatio–temporal scenario. It highlights usage of emerging data-driven hybrid approaches, after systematically analysing developments and limitations of recent physical, data-driven artificial intelligence and machine learning (AI-ML) based modelling approaches. It also discusses the potential integration of google earth engine (GEE)-cloud computing platform in UBEM input organization step to (i) map the land surface temperature (LST) data (quantitative attribute implying LPHI event occurrence), (ii) manage and pre-process high-resolution spatio–temporal UBEM input-datasets. Further the potential of digital twin, central structed data models to integrate along UBEM workflow to reduce uncertainties related to building archetype characterizations is explored. It has also found that a trade-off between high-fidelity baseline simulation models and computationally efficient platform support or co-simulation platform integration is essential to capture LPHI induced inter-building energy dynamics.

A Systematic Review of Water Governance in Asian Countries: Challenges, Frameworks, and Pathways Toward Sustainable Development Goals

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Abstract

Water governance (WG) plays a crucial role in steering integrated water resources management (IWRM) toward the fulfillment of the Sustainable Development Goals (SDGs), particularly in developing regions. Despite this, substantial challenges hinder effective WG implementation across Asia. This study systematically reviews WG literature in Asia from 2000 to 2020, identifying prevailing challenges and proposing a modified WG framework to encourage policy reform. Utilizing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol, we searched the Scopus database and additional resources to accumulate comprehensive literature on WG in Asia. We incorporated peer-reviewed articles, gray literature, and institutional reports. These were evaluated based on their relevance to WG in Asia, use of analytical frameworks, and incorporation of performance indicators. The search identified 350 potentially relevant documents, with 145 qualifying for in-depth review after screening through rigorous selection criteria, comprising peer-reviewed articles, institutional reports, and influential gray literature. The literature revealed increasing attention to WG since the SDGs’ inception, with the most significant contributions related to Southeast Asia, South Asia, and East Asia. Critical WG issues identified include transboundary water management, irrigation challenges, water quality concerns, and water–food–energy nexus interdependencies. Predominantly, these issues stem from insufficient legal and institutional frameworks, poor stakeholder engagement, and ineffective cooperation, particularly in cross-border river basins. The analysis frequently employed legal and institutional frameworks, Ostrom’s theory, and OECD guidelines, all pointing to common challenges. We propose a modified WG framework with 13 elements, accompanied by key recommendations. The study underscores the need for an enhanced understanding of WG to support policymakers, managers, and scholars in developing effective WG strategies that align with the SDGs. This research contributes to the literature by providing a synthesized perspective on WG in Asia and a foundation for future governance improvements.

Optimization models for disaster response operations: a literature review

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Abstract

Disaster operations management (DOM) seeks to mitigate the harmful impact of natural disasters on individuals, society, infrastructure, economic activities, and the environment. Due to the increasing number of people affected worldwide, and the increase in weather-related disasters, DOM has become increasingly important. In this survey, we focus on the post-disaster stage of DOM that involves response operations. We review studies that propose optimization models to supporting the following four relief logistics operations: (i) relief items distribution, (ii) location of relief facilities and temporary shelters, (iii) integrated relief items distribution and shelter location, and (iv) transportation of affected population. Optimization models from 127 articles published between 2013 and 2022, focusing on relief logistics operations during natural disasters, are categorized by disaster type and thoroughly analyzed. Each model provides a case study illustrating its application in addressing key relief logistics operations. We also analyse the extent to which these studies address the critical assumptions and methodological gaps identified by Galindo and Batta (Eur J Oper Res 230:201–211, 2013), Caunhye et al. (Socio-econ Plan Sci 46:4–13, 2012), and Kovacs and Moshtari (Eur J Oper Res 276:395–408, 2019) and the neglected research directions noted by the authors of other relevant review papers. Based on our findings, we provide avenues for potential future research. Our analysis shows a slow increase in the total number of papers published until 2018–2019 and a sharp decrease afterwards, the latter most likely as a consequence of the COVID-19 pandemic. More than half of the papers in our selection concern earthquakes while less than ten papers deal with wildfires, cyclones, or tsunamis. The majority of the stochastic optimization models consider uncertainty in the demand and supply of relief items, while some other crucial sources of uncertainty such as funding availability and donations of relief items (e.g., blood products) remain understudied. Furthermore, most of the papers in our selection fail to incorporate key characteristics of disaster relief operations such as its dynamic nature and information updates during the response phase. Finally, a large number of studies use exact commercial software to solve their models, which may not be computationally efficient or practical for large-scale problems, specifically under uncertainty.

Optimization models for disaster response operations: a literature review

Latest Results

Abstract

Disaster operations management (DOM) seeks to mitigate the harmful impact of natural disasters on individuals, society, infrastructure, economic activities, and the environment. Due to the increasing number of people affected worldwide, and the increase in weather-related disasters, DOM has become increasingly important. In this survey, we focus on the post-disaster stage of DOM that involves response operations. We review studies that propose optimization models to supporting the following four relief logistics operations: (i) relief items distribution, (ii) location of relief facilities and temporary shelters, (iii) integrated relief items distribution and shelter location, and (iv) transportation of affected population. Optimization models from 127 articles published between 2013 and 2022, focusing on relief logistics operations during natural disasters, are categorized by disaster type and thoroughly analyzed. Each model provides a case study illustrating its application in addressing key relief logistics operations. We also analyse the extent to which these studies address the critical assumptions and methodological gaps identified by Galindo and Batta (Eur J Oper Res 230:201–211, 2013), Caunhye et al. (Socio-econ Plan Sci 46:4–13, 2012), and Kovacs and Moshtari (Eur J Oper Res 276:395–408, 2019) and the neglected research directions noted by the authors of other relevant review papers. Based on our findings, we provide avenues for potential future research. Our analysis shows a slow increase in the total number of papers published until 2018–2019 and a sharp decrease afterwards, the latter most likely as a consequence of the COVID-19 pandemic. More than half of the papers in our selection concern earthquakes while less than ten papers deal with wildfires, cyclones, or tsunamis. The majority of the stochastic optimization models consider uncertainty in the demand and supply of relief items, while some other crucial sources of uncertainty such as funding availability and donations of relief items (e.g., blood products) remain understudied. Furthermore, most of the papers in our selection fail to incorporate key characteristics of disaster relief operations such as its dynamic nature and information updates during the response phase. Finally, a large number of studies use exact commercial software to solve their models, which may not be computationally efficient or practical for large-scale problems, specifically under uncertainty.