Global Transitions最新文献

Background

This study aimed to evaluate the longitudinal relationships between living alone, being childless, and six mental health indicators and mortality in older adults in Thailand between 2015 and 2020.

Methods

We examined prospective cohort data from the Health, Aging and Retirement in Thailand (HART) study, which included participants 45 years of age and older (N = 2863) from three successive waves in 2015, 2017, and 2020. Mental health indicators were assessed by self-report. We used Generalized Estimating Equations analysis (GEE) to evaluate the longitudinal relationships between measures of living alone, childlessness and six mental health indicators and mortality.

Results

The proportion of living alone was 6.3 % and childlessness 9.9 % in 2015, while living alone only was 3.9 %, childlessness only 7.5 % and both living alone and childlessness 2.4 % in 2015. In the adjusted model, living alone only was among men positively associated with depressive symptoms, loneliness, poor quality of life and mortality, and among women only positively associated with poor quality of life. Childlessness only was among women positively associated with depressive symptoms, insomnia symptoms, loneliness, poor quality of life, poor self-rated mental health, and mortality, and among men with depressive symptoms, loneliness, poor quality of life and poor self-rated mental health. Both living alone and childlessness was among men associated with four mental health indicators (depressive symptoms, insomnia symptoms, loneliness, and poor quality of life), and among women two mental health indicators (loneliness and poor quality of life), all with higher odds ratios than in living alone only and childlessness only.

Conclusions

Living alone only, childlessness only and/or both living alone and childlessness were associated with several poor mental health indicators and/or mortality. Enhanced screening and management of living alone and being childless may improve mental health in Thailand.

Background

Lesotho has made significant strides towards universal health coverage and yet a significant proportion of the population has difficulty accessing health care services due to mountainous terrain and long travel times to nearest health facility. There have been previous studies that utilized Geographical Information Systems (GIS) to assess access to healthcare and to identify optimal locations for placement of new health facilities, though never in Lesotho.

Methods

The authors employed a mixed-methods design. Qualitative research consisting of semi-structured interviews and focus group discussions was used to gain a deep understanding of the problem with access to healthcare and to allow for the perspectives of the people of Lesotho to guide decision-making about placement of new healthcare facilities. GIS analysis was done using ArcGIS Pro v. 2.3 software and modern satellite imagery to map current access to healthcare facilities and create site recommendations for new healthcare facilities.

Results

Qualitative research revealed that walking was the primary mode of transport to healthcare facilities and that distance was the crucial barrier in limiting access to care. Decentralization in decision-making was identified as an important element in health policy decisions and study participants recommended placing health facilities centrally in large villages. GIS analysis identified that 77.7 % of the population was currently within 3-h walking distance to nearest health facility and that the addition of fifty new healthcare facilities would increase that proportion to 90.0 %.

Conclusion

This study mapped current access to care in Lesotho in rural and urban areas. It also provided an objective strategy for identifying the location of new healthcare facilities while incorporating the voices of the people of Lesotho in the process. The findings can be used to assist policymakers, and the methodology can be employed in the allocation of other public-service facilities in different countries or regions.

Knowledge about upcoming sustainability challenges is crucial to tackle them by political incentives, not at least to reach the United Nations’ 17 Sustainable Development Goals (SDGs). SDGs are multi-dimensional and require detail beyond an aggregate household approach to assess income inequality and other differences across households in transformative processes. Incorporating these aspects, we develop an SDG indicator framework for dynamic Computable General Equilibrium Models with a total of 68 endogenous indicators related to 15 SDGs. This enables a more differentiated assessment of the SDGs in forward looking analysis compared to existing approaches, by considering additional SDG indicators and household level detail based on micro-simulation. We apply the indicator framework in a global analysis of 3 Shared Socioeconomic Pathways (SSPs) until 2050 with a focus on selected low- and lower-middle income countries from different continents. The analysis finds sustainability gaps by 2030 and 2050 for all focus countries, especially in the environmental domain. In none of the analyzed SSPs, all indicators develop in the desired direction, underlining trade-off among and within SDGs, but also across the SSPs. Based on household detail, we find increasing inequality over time for several indicators regardless of developments at average aggregate household level, pointing at the need for targeted redistribution and compensation policies. These results highlight the importance of including distributional aspects and disaggregated data in policy and socioeconomic development studies.

Sulphur dioxide (SO₂) is a hazardous air pollutant, which is mostly emitted from burning of fossil fuels, and has an adverse impact on the human health and ecosystem functioning. The COVID-19 natural anthropause (lockdown) provides a great opportunity to understand the changes in SO₂ pollution across the globe, as there was a temporary standstill for most human activities. Therefore, we analyse the changes in global SO₂ pollution during lockdown compared to pre-lockdown and identify its hotspots driven by human activities using satellite measurements, reanalysis data and emission inventory. We observe a decline in SO₂ pollution of about 2.21 % in its global average, −21.05 % in Indo-Gangatic Plain, −16 % in East China, −7.67 % in East United States of America, −3.99 % in Western Europe and −3.85 % in Middle East owing to the halt in human activities such as industrial and transport operations, as found from the emissions inventory. There are point and aerial hotspots of SO₂ pollution across the globe (e.g. cities or industrial units), which also show a decrease (20–30 %) in SO₂ pollution during the anthropause. Fossil fuel burning in thermal power plants is a major source of SO₂ pollution, and it has declined notably (1–12 %) during the lockdown in the major coal consuming countries such as the United States, China, Japan, Canada, Brazil, Australia, France, Germany, Spain, Italy and the United Kingdom. Therefore, lockdown provides a clear understanding of global human-driven hotspots of SO₂ pollution and their changes, which would help us to make better and effective air pollution mitigation strategies.

The interconnectedness between humans and ecosystems highlights the need to protect ecosystems for the well-being of humans and the environment. This has led to the emergence of holistic and interdisciplinary concepts like Planetary Health, One Health, and EcoHealth. There is a growing interest in the differences and implementation of these concepts, including their founders, fundamental questions answered, focus, global distribution of studies, and alignment. This study addresses these issues to facilitate coordinated health interventions for people and ecosystems. Using electronic databases (Web of Science, PubMed, and ProQuest) and conducting a systematic literature review using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), this paper compares the concepts of Planetary Health, One Health, and EcoHealth, providing a comprehensive overview of the findings and insights by examining each field's advocacy, conceptual application, and implementation levels and exploring the contributions of influential individuals and organizations. The results highlight each concept's global relation to applicability, challenges, and opportunities for further advancement. The study concludes by emphasizing the shared goals and interconnections among these fields in addressing complex health issues at the nexus of human health, environmental health, and ecosystem well-being.

The global burden of cancer incidence, deaths and economic costs is steadily increasing since several decades. Despite a massive allocation of research funds since the 1970s, no significant (in terms of years) improvements of survival times have been achieved for most cancer types. In this article, I argue that the failure to effectively prevent and treat cancer is partly owing to the gene-centric paradigm of the somatic mutation theory of carcinogenesis. I outline and provide evidence for a new transdisciplinary evolutionary theory of carcinogenesis according to which cancer is a phylogenetic reversal towards unicellular lifeforms triggered by the breakdown of essential cooperative interactions on important levels of human organization. These levels include the genetic, cellular, tissue and psychosocial-spiritual level of human existence. The new theory considers the emergence of eukaryotes and metazoans and – of particular importance – human evolution and in this way explains why cooperation on these different levels is so essential to maintain holistic health. It is argued that the interaction between human’s slow natural evolution and the fast cultural evolution, especially during the current Anthropocene epoch, plays an important role in making individuals susceptible towards carcinogenesis. The implications of this insight and the theory of cancer as a phylogenetic reversal are discussed with respect to prevention and treatment, and concrete practical examples are provided. It is concluded that individuals could substantially reduce their risk of cancer by respecting certain biopsychosocial-spiritual lifestyle factors which are justified by human evolution.

Background

In Wuhan, China, a stringent lockdown was implemented to contain the spread of COVID-19, transitioning later to normalised prevention and control strategy. This study examines the trends in hospital visits for acute and chronic respiratory diseases, with a focus on outpatient, inpatient, and severe condition visits.

Methods

The study used administrative health insurance data spanning from January 2018 to August 2021, an interrupted time series analysis was conducted to assess the trend in hospital visits per million population for respiratory diseases. To confirm whether the change was exclusive to respiratory diseases, neoplasms and intracerebral haemorrhage were used as controls. The impact of the pandemic was estimated by comparing by weekly admissions to pre-pandemic levels. Subgroup analyses dissected variations by disease and visit types.

Results

Hospital visits for respiratory diseases declined significantly during the lockdown and exhibited a slower recovery in the later normalised prevention and control period compared to the control conditions. As of August 2021, outpatient visits increased by over 22.2% above the pre-pandemic level, while inpatient and severe condition visits witnessed significant reductions, falling to 46.7% and 80.6% of pre-pandemic levels, respectively. Compared to three other subgroups, visits for acute lower respiratory infections experienced the most significant decline, with inpatient and severe visits dropping to 23.9% and 25.7% of pre-pandemic levels.

Interpretation

Our study revealed a persistent reduction in inpatient and severe case visits for respiratory diseases throughout the ongoing pandemic. These findings suggested the possible role of non-pharmaceutical interventions in mitigating acute and chronic non-COVID respiratory diseases.

The incidence of diabetic retinopathy globally calls for advanced and more universally applicable computer-aided diagnosis (CAD) systems. This survey paper explores the current state of vision-based CAD techniques for the detection and classification of diabetic retinopathy, a diabetes-induced eye disorder that can lead to severe visual impairment or blindness. Characterized by a variety of manifestations including microaneurysms, exudates, hemorrhages, and macular detachment, diabetic retinopathy presents substantial challenges for automated detection. This is primarily due to the heterogeneity of retinal fundus images, which display diverse spatiotextural features and intricate vascular structures. Our exhaustive review indicates that most existing methodologies predominantly concentrate on isolated diabetic retinopathy types, employing localized spatiotextural feature analysis for classification. Such specificity often results in limited accuracy and generalizability, restricting practical real-world application. Furthermore, contemporary leading methods generally focus on single retinal characteristics, necessitating patients to undergo multiple CAD procedures, thereby increasing time, costs, and possibly intensifying retinal complexities. To overcome these obstacles, we propose the adoption of multi-trait-driven CAD solutions. Utilizing the potent capabilities of deep learning, these solutions could employ high-dimensional, multi-cue sensitive feature extraction and ensemble learning for classification. This approach is designed to improve the generalizability and dependability of CAD systems, offering a holistic solution capable of effectively managing the diverse manifestations of diabetic retinopathy. Our study highlights the need for a fundamental transformation in diabetic retinopathy CAD systems, motivating further research towards robust, multi-modal methods to enhance detection, classification, and grading of this widespread ailment.

Objective

To assess changes in macrosomia prevalence following a two-stage lifestyle intervention program.

Methods

The study collected annual delivery data from singleton pregnant women at the Beijing Obstetrics and Gynecology Hospital in Beijing, China (2014–2023). The first intervention stage involved nutritional assessment and lifestyle management in pregnancy, and maternal weight and fetal growth monitoring were added in the second stage, with intensive management as necessary. Pre-intervention births (2014–2016) served as controls. The change in macrosomia and low birth weight prevalence following the intervention was assessed by an interrupted time series analysis.

Results

Among 126,824 pregnant women, macrosomia prevalence decreased from 7.11 % to 4.15 % over ten years, with an accelerated decrease post-intervention (p for slope = 0.050 and 0.004 for the first and second stages), primarily contributed by the reduction in excessive gestational weight gain (adjusted population attributable risk = 28.6 %, p for Granger cause = 0.0001). The change in the increasing rate of low-birth-weight prevalence was non-significant.

Conclusions

Macrosomia prevalence significantly decreased over a decade following the intensive intervention programs.