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Racism and sexism are “alarmingly normalized” in the NHS

Evidence of impact of discrimination and inequalities is clear; what is needed now is action from government and NHS institutions, says the BMJ Commission on the Future of the NHS

BMJ Group

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Racism and sexism are “alarmingly normalised” within the structures and person-to-person interactions across the NHS, and the NHS has delayed acknowledging and learning from the evidence, says a report from the BMJ Commission on the Future of the NHS, published in The BMJ today.

There is an ethical imperative for the government and NHS institutions to act now, it concludes.

On a wider scale, discrimination and inequities related to protected characteristics, such as race and ethnicity, sex and gender, age, disability, sexuality, religion and belief have a major impact on the health of the public the NHS serves—and on staff wellbeing—says the report. 

Discrimination and inequities contribute to increased risk of physical and mental health conditions, limit access to care, shape negative experiences of illness and encounters with services, and lead to worse overall health outcomes, including mortality, it highlights.

As well as having a major impact on population health, discrimination and inequity also have huge financial consequences for the UK economy. Every year health inequity leads to productivity losses of £31-£33bn, lost taxes, and increased welfare payments of £20-£32bn, as well as direct healthcare costs of at least £5.5bn, it points out. 

After reviewing the evidence, the report makes recommendations for the UK government, healthcare leaders, the Care Quality Commission (CQC) and equivalent regulators, and the NHS on tackling discrimination and inequities across the NHS to enhance the experience of patients and staff and improve health outcomes. 

For the UK government:

• Hold NHS leaders responsible for achieving the ambitions outlined in the NHS equality, diversity, and inclusion improvement plan.
• Implement the independent Messenger report on inclusive leadership in full.
• Give the NHS Race and Health Observatory (RHO) statutory responsibility for producing equity based impact assessments of new NHS policies and programmes and make it the main repository for all matters related to race and ethnicity in the NHS, including the Workforce Race Equality Standards.
• Mandate national research and health bodies to establish equality standards (especially concerning race, ethnicity, sex, and gender) in all research grants, studies, and approvals of drugs, medical devices, and technologies.
• Ensure that biases in existing advanced technology and artificial intelligence are identified and corrected, preventing the introduction of new biases that discriminate against patients.

For the Care Quality Commission (CQC) and equivalent regulators:

• Add an explicit inspection criterion for staff wellbeing to identify and tackle racism, sexism, and other forms of discrimination.
• Hold leaders and organisations accountable for failures in addressing discrimination.

For the NHS:

• Collect and report transparent, accurate, disaggregated data on race, ethnicity, sex, and gender in all organisations.
• Prioritise equitable research, with financial support from government research funders like the National Institute for Health and Care Research (NIHR) and UK Research and Innovation (UKRI), and collaboration with charitable research funders. Withhold funding where these principles are not met.
• Implement the starkest findings of research on inequitable clinical care and ringfence funding to support improvements.
• Set national standards for diversity and inclusion to produce culture change.
• Provide statutory protection for whistleblowers and update the NHS England long term workforce plan to include the role of freedom to speak up guardians.
• Make evidence based training focused on reducing bias and discrimination (including intersectionality training which explores how various aspects of a person's identity, like race, gender, and class, intersect to create unique experiences of privilege and oppression) readily available, emphasising improved interactions with diverse colleagues, cultural safety, and cultural competency. Encourage professionals to develop awareness of clinical interactions and reflect on personal and systemic bias.

For healthcare leaders:

• Improve working conditions by facilitating flexible and remote working and reducing bullying, harassment, and discrimination.
• Model anti-racist and anti-sexist behaviour to encourage similar conduct in employees.
• Monitor staff diversity and track inequity across roles and career trajectories.

The six expert authors conclude: “NHS leaders and the public must recognise that prioritising health equity is a proved strategic investment that leads to good patient outcomes, and better retention and recruitment rates of staff. It is also an ethical and legal imperative.”

Equity in healthcare is about acknowledging that different needs require different responses with varying resources, and that inequity involves multiple characteristics in many cases. Services should be codesigned with those who struggle the most to access care and have the poorest outcomes to reduce inequality in health outcomes, they argue.

They add: “Inaction represents an unacceptable choice that increases harms to patients and costs in terms of increased staff absences, sickness, resignations, and reduced productivity. 

“The evidence and policy options are abundantly clear. Political and institutional leaders must urgently choose to prioritise the elimination of these avoidable, unhealthy, and costly injustices, or face the consequences of a disaffected NHS workforce, and widening inequalities in health outcomes in the general population.

“The recommendations we make, if implemented, will go a long way to make the NHS a happier and healthier place.”

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Journal

The BMJ

DOI

10.1136/bmj.r1334 

Method of Research

Commentary/editorial

Subject of Research

People

Article Title

NHS must tackle racism and sexism for the benefit of patients, staff, and society

Article Publication Date

1-Jul-2025

COI Statement

We have read and understood BMJ policy on declaration of interests and have the following interests to declare: CN and YC are board members of the Race and Health Observatory and JSB isco-chair of its mental health advisory group. KW receivesfunding from the Wellcome Trust.

 

Harnessing big data for apple breeding: Genomic models to meet climate challenges

Nanjing Agricultural University The Academy of Science

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Relative contribution of different model components estimated for eleven traits. A, Average proportions of phenotypic variance related to genotypic (g) and genomic (G) effects, their interactions (×) with the vector of environments (E), the enviromic effects (W), the interaction effects G × W, as well as the residual effect extracted from the statistical genomic prediction model fits. The relationship matrices for the different effects in the statistical genomic prediction models were constructed using the G-BLUP approach or, where indicated, the Gaussian kernel (GK) or Deep kernel (DK). The statistical genomic prediction models were compared with a model based on phenotypic data (Phenotypic). Error bars correspond to standard deviation around the mean. B, Average proportions of phenotypic variance related to genomic (G), additive (A), and dominance (D) effects, their interactions (×) with the vector of environments (E), and the residual effect extracted from the statistical genomic prediction model fits. The model structures G and G + D were additionally extended with the fixed effect of inbreeding (inb). The relationship matrices for the different effects were based on G-BLUP. Error bars correspond to standard deviation around the mean. The results for the benchmark model G are the same as shown in A. C Relative contribution of the SNP, PC, weather, and soil feature streams estimated using SHAP for the deep learning genomic prediction model. Error bars correspond to standard deviation around the mean.

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Credit: Horticulture Research

In the face of climate change, apple breeding programs need innovative ways to select cultivars that can thrive under diverse conditions. A recent study demonstrates how integrating genomic data with environmental factors such as weather and soil can drastically improve the selection process. By using multi-environmental genomic prediction models, including deep learning, researchers have enhanced the accuracy of predictions for key apple traits. These advancements provide a promising tool for breeding apple varieties that are not only high-yielding but also adaptable to the fluctuating climate, offering a potential breakthrough in ensuring future food security.

Traditional apple breeding methods often fail to account for the complexities of genotype-by-environment interactions, which are crucial in selecting the best cultivars for varying climates. To overcome this limitation, the latest research combines phenotypic, genomic, and environmental data into sophisticated multi-environmental prediction models. These models are designed to predict how apple cultivars will perform across different environments, a process that has historically been difficult due to the vast diversity of environmental conditions. This new approach leverages both statistical and deep learning methods to process complex datasets, offering a more robust and accurate model for future breeding efforts.

Published (DOI: 10.1093/hr/uhae319) in Horticulture Research (November 2024), this study from Agroscope, ETH Zurich, and collaborators presents a breakthrough in apple breeding by applying multi-environmental genomic prediction. The research explores the integration of genomic data with environmental variables such as soil conditions and weather patterns, providing a new method for predicting key apple traits. By incorporating genotype-by-environment interactions into the prediction models, the study paves the way for selecting apple cultivars that are better suited to different climates, ultimately helping breeders respond to the challenges posed by climate change.

The study utilized the apple REFPOP, a comprehensive genetic population, to examine how different models predict 11 important traits in apples, including harvest date, fruit weight, and acidity. Researchers employed a range of prediction techniques, from the standard G-BLUP method to more advanced deep learning models, assessing the impact of genotype-by-environment interactions. The results showed that incorporating environmental factors such as weather and soil significantly improved the prediction accuracy for most traits, particularly when the G-BLUP model was enriched with environmental data.

The study also highlighted the power of deep learning models, which outperformed traditional methods for traits with complex genetic architectures, such as harvest date and titratable acidity. For these traits, deep learning models improved predictive ability by up to 0.10, providing a clear advantage in precision. The findings emphasize that as climate variability becomes more pronounced, integrating both genomic and environmental data using advanced machine learning approaches will be key to breeding apple cultivars that can adapt to future environmental challenges.

“By combining genomic data with environmental factors, we are opening a new frontier in apple breeding,” said Dr. Michaela Jung, the lead researcher from Agroscope. “The ability to predict how different apple cultivars will perform under various environmental conditions will give breeders a powerful tool to select varieties that are not only high-yielding but also climate-resilient. Deep learning models, in particular, have shown immense potential in refining these predictions, offering a promising solution for adapting apple breeding to the challenges of a changing climate.”

This study provides a roadmap for apple breeders to develop cultivars that are more resilient to climate change, ensuring stable production despite fluctuating environmental conditions. The integration of genomic and environmental data in breeding programs will enable the selection of apple varieties that are better suited for different regions and climates, improving both yield and quality. Moreover, the application of deep learning models in multi-environmental genomic prediction can be extended to other crops, offering a broader solution for global food security. By harnessing big data and advanced algorithms, this approach could revolutionize the way crops are bred, making them more adaptable to the future.

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References

DOI

10.1093/hr/uhae319

Original Source URL

https://doi.org/10.1093/hr/uhae319

Funding information

C.Q.-T. was supported by the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No 847585 – RESPONSE. This study was partially funded by the FOAG project ‘Apfelzukunft dank Züchtung’ (2020/17/AZZ).

About Horticulture Research

Horticulture Research is an open access journal of Nanjing Agricultural University and ranked number one in the Horticulture category of the Journal Citation Reports ™ from Clarivate, 2023. The journal is committed to publishing original research articles, reviews, perspectives, comments, correspondence articles and letters to the editor related to all major horticultural plants and disciplines, including biotechnology, breeding, cellular and molecular biology, evolution, genetics, inter-species interactions, physiology, and the origination and domestication of crops.

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Journal

Horticulture Research

DOI

10.1093/hr/uhae319 

Subject of Research

Not applicable

Article Title

Integrative multi-environmental genomic prediction in apple

 

Residential segregation and lung cancer risk in African American adults

JAMA Network Open

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About The Study: 

The results of this study suggest that structural racism embedded in neighborhood conditions contributes to lung cancer development and provides evidence for policymakers and public health leaders working to reduce disparities. 

Corresponding Author: To contact the corresponding author, Loretta Erhunmwunsee, MD, email LorettaE@coh.org.

To access the embargoed study: Visit our For The Media website at this link https://media.jamanetwork.com/

(doi:10.1001/jamanetworkopen.2025.18481)

Editor’s Note: Please see the article for additional information, including other authors, author contributions and affiliations, conflict of interest and financial disclosures, and funding and support.

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Embed this link to provide your readers free access to the full-text article 

http://jamanetwork.com/journals/jamanetworkopen/fullarticle/10.1001/jamanetworkopen.2025.18481?utm_source=For_The_Media&utm_medium=referral&utm_campaign=ftm_links&utm_term=070125

About JAMA Network Open: JAMA Network Open is an online-only open access general medical journal from the JAMA Network. On weekdays, the journal publishes peer-reviewed clinical research and commentary in more than 40 medical and health subject areas. Every article is free online from the day of publication. 

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Journal

JAMA Network Open

 

Extending classical CNOP method for deep-learning atmospheric and oceanic forecasting

New approach reveals when and where input uncertainty matters most

Institute of Atmospheric Physics, Chinese Academy of Sciences

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The sea temperature in the South China Sea has a significant impact on the regional marine ecosystem as well as the high-impact ocean-atmosphere events. Therefore, accurate forecasting of ocean temperature is crucial. This study presents a method that aims to enhance the forecast skill of deep learning forecasting models by optimizing the deployment strategy of limited observational resources.

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Credit: Ziqing Zu

In recent years, deep learning methods have been increasingly applied in atmospheric and oceanic forecasting, showing superior forecast skills. Unlike time-stepping numerical models, deep learning forecasting models (DLMs) typically adopt a “multi-time-slice input” structure. This structure breaks the deterministic causality in the time dimension that exists in the numerical models. In this case, the forecast errors in DLMs should be attributed to all input slices, rather than any single one. This fundamental difference limits the applicability of the classical conditional nonlinear optimal perturbation (CNOP) method, as CNOP is defined at a single time slice, specifically, the initial time.

Recently, researchers have extended the CNOP method in the time dimension and proposed the CNOP-DL method. Designed specifically for DLMs with multi-time-slice inputs, CNOP-DL includes perturbations across multiple times of the inputs, revealing the sensitivity of forecast errors to input errors in both time and space dimensions. The new method is published in Advances in Atmospheric Sciences.

 

“CNOP-DL is useful in the targeted observation studies, as it allows us to identify not only where, but also when additional observations should be deployed to reduce the input errors, ultimately to significantly mitigate the forecast errors,” said Dr. Ziqing Zu from National Marine Environmental Forecasting Center of China, the lead author of the study. “This is especially valuable for improving the forecasts of rapidly developing systems such as typhoons and mesoscale eddies, where observational resources are often limited.”

 

To demonstrate the utility of the method, they applied CNOP-DL to a case study of sea surface temperature (SST) forecasting in the South China Sea. The CNOP-DL included six time slices in the time dimension. Therefore, the optimal time can be identified according to the temporal structure of the perturbation energies. Furthermore, the results revealed that forecast errors are more sensitive to the time of the input perturbations than to the location. In other words, determining when to deploy additional observations can be more critical than determining where.

 

“In conventional targeted observation studies, the focus is typically on identifying the optimal locations for targeted observations at the initial time. By extending CNOP in the time dimension, CNOP-DL can identify which time steps in the inputs are more critical, thereby broadening the scope of conventional targeted observation studies.” said Professor Mu Mu from Fudan University, the corresponding author of the study. “By highlighting the importance of time sensitivity, CNOP-DL holds the potential for guiding practical field campaigns that optimize both spatial and temporal deployment of observational platforms such as moored buoys, gliders, and research vessels.”

 

CNOP-DL is also useful in predictability studies. The authors demonstrate that there are significant differences between CNOP-DL and CNOP, and that CNOP-DL can lead to larger forecast errors, thereby providing a more accurate estimate of the upper bound of forecast uncertainty. This is because, essentially, CNOP searches for the optimal solution within a subset of the CNOP-DL space; thus, CNOP can be regarded as a special case of CNOP-DL.

 

Next, the authors plan to calculate CNOP-DL for a lot of forecast cases, and then conduct composite analyses of CNOP-DL results. By identifying common patterns in sensitive regions and key time windows, they aim to design an optimal observational network in the South China Sea, particularly for moored buoy arrays. Such a system could provide valuable observations to improve significantly operational forecasts, using limited observational resources.

 

Other contributors include Jiangjiang Xia from Key Laboratory of Regional Climate-Environment for Temperate East Asia at CAS IAP in Beijing, China and Qiang Wang from College of Oceanography, Hohai University in Nanjing, China.

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Journal

Advances in Atmospheric Sciences

DOI

10.1007/s00376-025-4297-9 

Article Publication Date

25-Jul-2025

 

Thunderstorms are a major driver of tree death in tropical forests

Cary-led paper reveals an underestimated and growing threat to tropical forests and the carbon they store

Cary Institute of Ecosystem Studies

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Cary Institute forest ecologist Evan Gora stands near the roots of a tree knocked over by winds from a thunderstorm. 

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Credit: Steve Yanoviak/University of Louisville

Trees in tropical forests are dying at an increased rate, with consequences for biodiversity, carbon storage, and the global climate. While deforestation is the primary cause of forest loss, intact forests are also experiencing a rise in tree death. Drought, higher temperatures, and fires have been the leading suspects, but a new paper led by Evan Gora, a forest ecologist at Cary Institute of Ecosystem Studies, identifies an underappreciated threat: thunderstorms, which are becoming more frequent with climate change.

Not to be confused with hurricanes or cyclones, these convective storms tend to be short-lived but powerful, with tree-toppling winds and lightning. In a perspective paper in Ecology Letters, Gora and colleagues lay out the case for why such storms could be a major driving force behind the rising death toll of tropical trees. As they become more common in the warming tropics, thunderstorms are a growing threat to trees and the carbon they store.

“Tropical forests have massive effects on global climate. They're like the lungs of the Earth, and we're seeing trees in them dying at higher rates than in the past, and the composition of forests is changing, too,” said Gora. “That could be really problematic for the future of not just tropical forests, but for the planet.” 

Understanding what’s causing the trends in tree death is critical to guiding decisions about which tree species to plant or conserve in a forest, so that forest managers can ensure forests continue thriving and storing carbon long into the future.

“Being in the forest during a tropical storm is unforgettable,” said coauthor Vanessa Rubio, a forest ecologist in Gora’s lab at Cary Institute. “As the storm quickly builds, the sky darkens, humidity changes drastically, and strong winds shake the trees. Then, thunder and lightning come. Leaves and branches fall to the ground, rain pours down, and your instinct is to get back to the field station as quickly as possible.” 

Despite their obvious danger to people, storms had been overlooked and understudied as a potential culprit in tree mortality trends. But when the team reanalyzed data from previous studies on tropical forest carbon stocks, they found that storms were at least as good as drought and temperature in explaining the patterns of tree mortality and forest carbon storage. 

“We were surprised to find that storms may be the largest single factor causing tree death in these forests, and they’re largely overlooked by research into carbon storage in the tropics,” said Gora. “Our estimates suggest that storms are responsible for 30 to 60% of tree mortality in the past, and that number must be increasing as storm activity increases by 5 to 25% each decade.”

The team also added storms to the largest plot-based study of forest biomass carbon dynamics to date. That study had previously concluded that when temperatures go above a certain threshold, tropical forests experience a fast decline in carbon stocks. “But when you add storms, that relationship goes away,” said Gora. “It basically shows that you have to include storms, or you might not get the answers right.”

Storms and droughts are not mutually exclusive, the scientists note — the same forests can experience both high storm activity and drought stress. They found high convective storm activity across the southern Amazon, where water stress is also high and patterns of change are among the most extreme.

“During my studies on threats to tropical forests, my professors, our textbooks, and even overall climate policy never mentioned small, convective storms as a potential source of forest mortality,” said coauthor Ian McGregor, a Cary Institute forest ecologist in Gora’s lab. “I don't remember seeing them in global climate models used to inform climate policy. Given our findings, however, it's clear we need a more thorough understanding of these storms to have more accurate climate models, and thus more effective policy.” 

There are good reasons why scientists have overlooked storms until now. Temperature and water stress can be monitored with meteorological stations and readily connected to long-term forest plot data. It is much harder to detect storms and track their highly localized damage. Mortality caused by thunderstorms is not easily detected via satellite, and it’s not practical for researchers on foot to survey large forested areas frequently enough to pinpoint the damage caused by a specific storm. 

Gigante, a project led by Gora and co-author Adriane Esquivel-Muelbert from the University of Birmingham, offers one way to overcome these challenges. The project combines a lightning location system, drone scouts, and on-the-ground experts to sample large areas of tropical forest frequently. With these tools, they are starting to quantify when, where, and why tropical trees are dying, and which species are most affected.

Understanding current and future threats to tropical forests is crucial to informing long-term conservation and restoration efforts. 

“If we make decisions about which species to plant or conserve based on an incorrect understanding of what's actually killing these trees and which species are most vulnerable, those forests won’t reach their full potential,” said Gora. Storms are most deadly to mature trees, so the consequences of misguided reforestation efforts might not be known until decades after the trees are planted.

“However,” Gora continued, “if we can build a more holistic picture of what’s driving forest change, we can be a lot more confident in guiding forest management practices for long-term sustainability.”

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Multiple trees damaged by lightning

Multiple trees damaged by lightning - tree top view



Snapped tree, storm damage

Credit

Evan Gora/Cary Institute of Ecosystem Studies

Authors

• Evan M. Gora - Cary Institute of Ecosystem Studies, Smithsonian Tropical Research Institute

• Ian R. McGregor - Cary Institute of Ecosystem Studies

• Helene C. Muller-Landau - Smithsonian Tropical Research Institute

• Jeffrey C. Burchfield - University of Alabama, Huntsville

• KC Cushman - Oak Ridge National Laboratory

• Vanessa E. Rubio - Cary Institute of Ecosystem Studies

• Gisele Biem Mori - National Institute for Amazon Research, Universidade do Estado de Mato Grosso

• Martin J. P. Sullivan -  Manchester Metropolitan University

• Matthew W. Chmielewski - University of Louisville

• Adriane Esquivel-Muelbert - University of Birmingham

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Funding was provided in part by the National Science Foundation (NSF) grants DEB-2213245 and DEB-2241507 to EMG, and NE/W003872/1 to MS and EMG. AE-M was further funded by the Royal Society Standard Grant RGS\R1\221115 ‘MegaFlora’, the UK Research and Innovation/Natural Environment Research Council (NERC) TreeScapes NE/V021346/1 ‘MEMBRA’, the NERC/NSF Gigante NE/Y003942/1, and the Foundation for Research on Biodiversity/Centre for the Synthesis and Analysis of Biodiversity ‘Syntreesys’.

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Cary Institute of Ecosystem Studies is an independent nonprofit center for environmental research. Since 1983, our scientists have been investigating the complex interactions that govern the natural world and the impacts of climate change on these systems. Our findings lead to more effective resource management, policy actions, and environmental literacy. Staff are global experts in the ecology of: cities, disease, forests, and freshwater.

 

 

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Journal

Ecology Letters

Subject of Research

Not applicable

Article Title

Storms are an important driver of change in tropical forests

Article Publication Date

1-Jul-2025