• Improve our understanding of communities experiencing inequity and disadvantage
• Address distance and distrust between communities and health and care
• Build the right relationships to tackle the social determinants of health
• Support people to take greater control of their health
• Promote good health across local groups

But when she graduated in 1998, she struggled to convince others that sex differences in the immune system were a worthy topic for her postdoctoral research. 

She ultimately found a postdoctoral position in the lab of one of her thesis committee members. And in the years since, as she has established a lab of her own at the university’s Bloomberg School of Public Health, she has painstakingly made the case that sex—defined by biological attributes such as our sex chromosomes, sex hormones, and reproductive tissues—really does influence immune responses. 

Through her research, Klein has helped spearhead a shift in immunology, a field that long thought sex differences didn’t matter. Historically, most trials enrolled only males, resulting in uncounted—and likely uncountable—consequences for public health and medicine. The practice has, for example, caused women to be denied a potentially lifesaving HIV therapy and left them likely to endure worse side effects from drugs and vaccines when given the same dose as men.

Men and women don’t experience infectious or autoimmune diseases in the same way. Women are nine times more likely to get lupus than men, and they have been hospitalized at higher rates for some flu strains. Meanwhile, men are significantly more likely to get tuberculosis and to die of Covid-19 than women. 

Further reading

• Medicines, research and female hormones: a dangerous knowledge gap
• Gender bias: A threat to women’s health

• Health inequalities are complex and growing, yet firm evidence on their extent and trajectory is few and far between. There is a vital need to quantify health inequalities in order to better focus policies designed to address them.
• This analysis uses a novel approach to explore the extent of diagnosed health inequalities across different population groups in England. The Cambridge Multimorbidity Score, developed by clinicians and academics, assesses the relative impact of different patterns of illness on people and their health care needs. Access to patient data linking primary care and hospital records allowed the authors to provide a more detailed picture of variations in diagnosed illness by age, socioeconomic status, ethnicity and region in England.
• We know from previous research that, on average, people living in more deprived areas have shorter lives and spend more time living with diagnosed long-term illness. This analysis shows, on average, a 60-year-old woman in the poorest area of England has diagnosed illness equivalent to that of a 76-year-old woman in the wealthiest area. She will spend more than half (43.6 years) of her life in ill health compared with 46% (41 years) for a woman in the wealthiest areas.
• People living in poorer areas also have greater levels of multiple diagnosed illness (multimorbidity). Large inequalities in the burden of disease are concentrated within a few diagnosed conditions, including chronic pain, diabetes, COPD, anxiety and depression, alcohol problems and cardiovascular disease.
• Inequalities in health start at an early age, with higher rates of diagnosed mental health conditions, chronic pain and alcohol problems starting to develop as early as the late teens and early twenties. These health inequalities then continue to grow and change across the life cycle, through working age and into old age.
• Once we standardise for different age distributions across ethnicities, we find higher levels of diagnosed ill health among people of Pakistani, Bangladeshi and black Caribbean ethnic backgrounds than for people from white ethnic backgrounds.
• This analysis adds to previous research on the north-south health divide: people living in the north of England have the highest health care needs due to diagnosed morbidity once adjusted for age. Four conditions – chronic pain, alcohol problems, COPD and cardiovascular disease – account for 83% of the inequality in diagnosed illness between the North East (region with the highest levels of illness) and the East of England (region with the lowest levels of illness).

• The environmental scan revealed that while patient safety events, overall, were characterised by racial and ethnic disparities, methodological challenges—primarily related to data availability—limited in-depth analysis of this finding.
• The environmental scan also indicated that racism and its impact on patient safety events was more often discussed in editorials than in peer-reviewed and grey literature.
• Subject-matter expert interviews indicated that various levels of racism ranging from internalized and interpersonal to institutional and systemic directly impact the risk of patient safety events and highlighted the interplay between racism and social determinants of health.
• The authors also identified patient, provider, and systems factors that contribute to disparities in patient safety events.

Recommendations

• Health systems should collect patient safety data with equity in mind so that these systems can analyse patient safety events by sociodemographic factors and look for disparities in these events.
• Health systems and patient safety reporting vendors must develop more-efficient and user-friendly formal reporting systems so that healthcare providers are more likely to report patient safety events.
• Healthcare as an industry and medicine as a discipline need to create a culture of speaking up that prevents patient safety events caused by racism from happening.
• Health insurance reform is needed to address some of the underlying drivers of disparities in patient safety events.

This collection contains information on the following subjects:

• Health literacy widens inequalities
• What happens when health information is not clear?
• How can we help people understand health information? (Simple, balanced language; Finding the focus; Online information needs to be relevant; Visuals, videos and going viral)
• Who may face extra barriers understanding health information? (People facing language barriers; People with language difficulties and learning disabilities; People living with dementia; Communities with low literacy)

• A very small proportion of women had been taking folic acid before conception compared to the national average.
• The vast majority women in the cohort (81%) had their first antenatal care appointment beyond the recommended 10 weeks of pregnancy. More than four in ten (45%) of the women did not have any antenatal care until after 16 weeks of pregnancy, compared to just one in ten women nationally.
• Within this group more than four in ten women with undocumented, uncertain, refugee or asylum seeker status (45%) and six in ten women from Sub-Saharan Africa (62%) accessed care after 16 weeks.
• Mental health issues occurred in over a third of women, potentially exacerbated by the fact that over a third also received a bill for their maternity care of up to £14,000.
• The COVID-19 pandemic had a clear impact on this cohort of women. When stratifying antenatal and postnatal outcomes by a timestamp denoting contact with DOTW UK before or during the pandemic, differences were noted in most variables.
• More women reported feeling stressed during most of their pregnancy, and the proportion of women who felt supported reduced since the pandemic.
• The proportion of babies born with a low birthweight in the pandemic period was 11% compared to 6.5% in England and Wales in 2020. Women were almost twice as likely to report mood disturbance after birth in the pandemic period compared to pre-pandemic.

This evidence highlights the need for urgent action to address the inequalities experienced by migrant pregnant women and their babies. There is a pressing need for immigration status to be considered as part of the ethnic and racial health inequalities agenda and for independent action to be taken to review the impact of NHS charging policy.

• Research conducted by public bodies has shown that COVID-19 has had a greater impact, both directly and indirectly, on people who share certain protected characteristics (such as belonging to particular ethnicities or age categories, having a disability, or being women or from the LGBTQ+ community). Health and care services have a major role to play in both identifying the extent of these impacts as well as working together to reduce them.
• This report showcases examples of health and care systems across the country devising innovative approaches to mitigate the direct effects of COVID-19, through targeted vaccination campaigns, and support for people to recover from indirect impacts, through wellbeing and support programmes.
• These examples evidence that barriers came down during the pandemic to protect staff, patients and resources both within the NHS, and between NHS organisations and external partners in local government and the voluntary sector. They also show that tackling health inequality in an inclusive way is possible.
• The NHS and its partners must continue to gather data both on the direct and indirect impacts of the pandemic on people with different protected characteristics, and on the effectiveness of different programmes in addressing these impacts.
• Building on the achievements of the past two years, we must fund voluntary and community sector partners and put processes in place to consolidate partnership working (both within the NHS and between NHS organisations and other stakeholders) to drive continued progress in reducing inequalities affecting people with protected characteristics.

This group has been hit hardest when it comes to how long they wait for treatment and the impact this has on their mental health and wellbeing and ability to work:        

• Over half (54%) of respondents from lower income householders said they were either still waiting for NHS treatment or had to wait over four months before getting hospital treatment. This compares to 34% of people from higher income households. 
• Fewer people on lower incomes (44%) reported being given a clear point of contact with the NHS. This compares to 55% of people from higher income households.     
• Nearly twice as many people from poorer households (52%) said long waits affected their mental health when compared to responses from more affluent households (28%).   
• And 39% of people from lower income households said long waits affected their ability to work, compared to 29% from richer households.   

Impact on ethnic minorities  

The poll suggests there are ethnic differences when it comes to how easy people find to get hospital treatment. And when we combine them with factors, such as wealth and education, the disparities are far starker.  

• Over half of ethnic minority respondents (57%) faced a delay to or cancellation of hospital treatment compared to 42% of white British respondents.  
• Ethnic minority respondents from lower income households were hit hardest by having to wait longer for care. These individuals were almost twice as likely to report having faced a delay to or a cancellation of their treatment compared to white British respondents with higher wealth (63% vs 38%).
• Just half of the respondents (53%) from ethnic minority backgrounds with lower education felt well-informed about their treatment. This compared to 67% of white British people with higher education. 

Impact on disabled people  

The poll suggests that disabled people have had a worse experience of waiting for treatment than those who identified as non-disabled. Out of 20% of respondents who identified themselves as disabled, two-thirds (66%) waited more than four months for treatment, compared to 44% of those who are identified as non-disabled.   

Just over half of disabled respondents felt communications from the NHS about their care or treatment were clear and easy to understand compared to non-disabled respondents (57% vs 70%).  

Long waits also affect disabled people more than non-disabled people:  

• Ability to work (55% vs 29%).  
• Ability to carry out household tasks (51% vs 33%).
• Ability to socialise (49% vs 31%).  
• Physical fitness (60% vs 40%).

Impact on women  

• Women are 28% more likely to wait over four months for NHS treatment than men (54% vs 42%).  
• More women are also likely to experience adverse impacts of long waits compared to men. Most strikingly, women are 50% more likely to say that long waits impacted their ability to socialise than men (41% vs 28%).  
• 38% of respondents who identified as female said that waiting for treatment impacted on their ability to work, compared to male respondents (29%).