Health Profile for London 2021
Overview
This report is a comprehensive review of health in the London region and builds on the findings of the Health Profile for England (HPfE) 2021.
This report includes a set of important health-related topics based on the Health Profile for England. It highlights the universal impact of COVID-19 as well as presenting inequalities between the least and most deprived areas, and variation in health behaviours seen in each region in England. As we move into the post pandemic and recovery phase, there are emerging opportunities to sustainably tackle the historical and emerging challenges through the Levelling Up and soon to be published Disparities White Papers. Action will be supported via greater integration between the NHS and social care enabled by the development of Integrated Care Systems.
The data in this report provides evidence of the impact of COVID-19 on life expectancy, increasing risk factors in our population and the impact on the determinants of health which will have an enduring and long-term impact on the health of our population.
Healthy life expectancy at birth had started to decrease or level off in most regions before the pandemic. This represents an increasing number of years lived in poor health, resulting in a reduced ability to work, a reduced sense of wellbeing, and an increased need to access services.
In all regions obesity was the largest risk factor contributing to morbidity. Smoking and diabetes were the second and third largest contributors although their placing varied in different regions.
In many areas we have seen a widening of inequalities between the most and least deprived areas. The harms to health are not uniform, data in this report tells a compelling story about widening health inequalities and variations in health behaviours.
There are also areas of improvement: the proportion of mothers smoking at the time of delivery and antibiotic prescribing both continue to decrease.
Taken together, this data confirms that we are now observing the direct and indirect impacts of the COVID-19 pandemic on all parts of society, with many chronic health conditions worsening and most historically disadvantaged areas being further so. This has resulted in greater health need and widening health inequalities in all parts of the nation. The findings reinforce the need for targeted increases of clinical and preventive service recovery programmes with a resolute focus on secondary prevention called for in the NHS Core20Plus5 approach to reducing health inequalities. As we have learned throughout the pandemic, the nature and scale of the challenges cannot be met by any single agency. We must harness the full potential of our newly transformed public health and health care systems, guided by the new national strategies and frameworks, working closely with our partners in place, and engaging and mobilising local communities. Ultimately, this data confirms areas for system-wide prioritisation, mobilisation and action.
Executive Summary
The Health Profile for London provides a comprehensive snapshot of the population and an early summary of the impact of the COVID-19 pandemic on health and health inequalities. London is the capital of England and the largest UK city. The Greater London region comprises 32 boroughs and the City of London and is home to over nine million people. The aim of this profile is to highlight some of the main factors affecting the health of the population, disparities between population groups and areas within London, and trends over time. It provides intelligence and evidence to support partners plan and commission services, with a focus on the impact of the pandemic.
The report highlights how the pandemic has directly disproportionately affected some populations including people from ethnic minority groups, those living in deprived areas, older people and those with pre-existing health conditions.
There have also been significant indirect effects, for example on education, mental health and employment opportunities, particularly for the younger workforce in the hospitality and entertainment sectors. In addition, access and use of health services has been disrupted, the long-term effects of which are not yet fully known. We will continue to monitor both the direct and indirect impacts of the pandemic on London’s health, with a focus on inequalities, as further health data becomes available. Key points from each chapter of this report are as follows.
Chapter 1: COVID-19
There was a clear and direct impact of the COVID-19 pandemic on health in London, including cases, deaths, and vaccination rates. By the end of December 2021, almost two million confirmed cases of COVID-19 had been reported. At the highest peak in December 2021, on average across a week there were approaching 30,000 cases each day. COVID-19 impacted some population groups more than others. Cumulative case rates were higher for females than males, highest in ages 25-49, and in Asian and ‘Other’ ethnic groups.
By the end of December 2021, almost 22,000 people had died within 28 days of a positive test representing a cumulative age-standardised mortality rate of 358.0 per 100,000 of the population. London’s mortality rate was higher than for any other region. There were more deaths than expected in all ethnic groups, but excess mortality was highest in the Black and Asian ethnic groups.
At this date, a greater proportion of the adult population in London remained unvaccinated against COVID-19 than in any other region.
Chapter 2: Life expectancy and mortality
Reflecting the impact of the pandemic, between 2019 and 2020 life expectancy decreased for both males (by 2.5 years) and females (by 1.6 years) to 78.8 years for males and 83.4 years for females. London had the highest decrease in life expectancy of all the regions of England. There were clear disparities, with the most deprived areas experiencing the largest decreases. Between 2019 and 2020, increases in mortality rates in all age groups over 30 contributed to these decreases in life expectancy, but the largest contribution was from the 70 to 89 age groups. For those under 75 years, the age-standardised mortality rate in the most deprived decile in 2020 was almost three times that of the least deprived decile.
Chapter 3: Child Health
In 2020, the proportion of babies born at term with a low birthweight in London was worse (higher) than the England average (3.3% for London compared to 2.9% for England) and continues the worsening trend since 2017. There are inequalities by ethnic group. Rates of low birthweight were highest for Asian and Black groups and lowest for the White group. Due to the pandemic, data on child development at the end of the Reception year was not reported for the latest academic year September 2019 to July 2020 and early years settings were closed to most children. However, it is acknowledged that young children may have experienced a lack of social activities and interactions that would normally help to prepare them to start school.
Children’s education has been severely disrupted during the pandemic. From March until June 2020, most schools in England were closed to children other than those with parents who were keyworkers or classed as vulnerable. Whilst the full impact of the pandemic on child health and development will not be known for some time, studies suggest that children who started school in the Autumn 2020 term needed additional support when compared with children in previous academic years. There are also indications that learning has suffered to some degree for most pupils and year groups, particularly for more disadvantaged students.
The 2019/20 data on child obesity is less robust than previous years as fewer measurements were taken due to school closures. Findings from the National Child Measurement Programme (NCMP) suggest that obesity increased across all regions in both reception-age children and Year 6. Closure of schools, sporting and leisure facilities, parks and recreational areas, together with an increase in screen time over the pandemic period, have led to a reduction in physical activity in children and young people.
Chapter 4: Health in adults
Emergency hospital admissions decreased in London from 7.9 per 1,000 in January 2020 to 4.5 per 1,000 in April 2020. By April 2021 they had increased again to 7.1 per 1,000. However, the latest data shows that admissions remain lower than the equivalent period pre-pandemic. Hospital outpatient attendances in London followed a similar trend.
Reduced admissions, GP consultations, A&E attendances and health seeking behaviour during the pandemic may be a factor in the increase in deaths at home. They may also represent missed opportunities to provide secondary prevention treatment to patients such as blood pressure and cholesterol control and may result in an increase in long-term health complications.
Chapter 5: Risk factors associated with ill health
Risk factors play an important role in determining whether a person becomes ill, at what age, and the associated effect on quality of life. The risk factors making the largest contribution to morbidity in London are high body mass index, blood glucose, tobacco and alcohol consumption. Risk factors making the largest contribution to mortality are tobacco, high blood pressure, blood glucose and diet. Between 2015 and 2019, adult smoking prevalence decreased in London and has consistently remained below the England average. In London, the rate of death due to drug misuse was lower than for England and lowest of any region.
More than half of London’s adult population were either overweight or obese in 2019/20. Although rates are lower than for England and lowest of the regions, there has been no improvement over time. Inequalities in risk factor prevalence contribute to inequalities in ill health and mortality. For example, inequality in smoking prevalence by deprivation group is a significant determinant of inequalities in mortality and life expectancy. In 2019, smoking prevalence in England remained far higher than average for some groups, including people in manual occupations, people with a long-term mental health condition, those living in deprived areas and the Mixed ethnic group. The prevalence of adult obesity was higher in the most deprived compared to least deprived areas. There were also wide inequalities in the proportion of adults meeting recommended levels of physical activity and fruit and vegetable consumption.
Chapter 6: The wider determinants of health
Wider determinants of health are a diverse range of social, economic and environmental factors that influence people’s mental and physical health across the life course. Inequalities in these factors are an important driver of the inequalities in risk factors and health outcomes.
London’s employment rates had been increasing through to 2019 but this plateaued within the first year of the pandemic with a downturn for males and slight increase for females. Compared to England, in 2019/20 London had lower employment rates. In 2018/19, a third of the population in London did not reach the level of income the public consider is needed for a minimum socially acceptable standard of living, the ‘Minimum Income Standard’. This proportion of the population is higher in London than the national average.
During 2017/18, London had a significantly higher rate of homelessness compared to England.
Chapter 7: Health protection
Health protection issues include the prevention and control of all types of infectious diseases, as well as chemical and environmental threats to the health of the population. The level of testing for, or detection of, many infectious diseases such as TB and sexually transmitted infections decreased during the pandemic. This may reflect a real decrease in incidence due to social distancing measures or may reflect a reluctance to be tested. In addition, as demonstrated by the reduction in MMR (measles, mumps, rubella) vaccine coverage, childhood vaccinations were also interrupted during the pandemic, whilst flu vaccination coverage was considerably higher than previous years. The full impact on infection transmission and long-term health outcomes will take time to emerge and evaluate.
Introduction
The aim of this profile is to highlight some of the main factors affecting the health of the population, disparities between population groups and areas within London, and trends over time. It provides intelligence and evidence to support partners to make the best planning and commissioning decisions for their populations with a focus on the impact of the pandemic as relevant statistics are released and become available. The Picture of Health - London tool complements this profile.
Supporting information
Charts in this report follow a standard format, with 3 sections for each topic area:
Headline - overall data for the key indicator used in London, usually as a trend over several years. Regions are defined as government regions. Where this isn’t possible other geographical region definitions are used as indicated in the supporting information.
Inequalities - how the indicator varies between different groups in London, by protected characteristics such as age, sex and ethnicity or categories of socioeconomic deprivation where possible. Some inequalities information presented at a national level in the Health Profile for England 2021 is not available at regional and sub-regional level.
Sub-regional comparison - headline information on the indicator variation is presented at the Upper Tier Local Authority (UTLA) level. UTLAs affiliated with the government region are shown unless stated otherwise. UTLA codes and boundaries are subject to change pending the data released.
It is not always possible to use the same indicator for the headline, inequalities and sub-regional comparisons within each topic area.
Orange boxes highlight key information from the Health Profile for England 2021.
This report builds on the methodology used in the HPfE 2021, with minor amendments to enable regional and sub-regional comparisons, and to account for methodological changes that have occurred since the publication of the Health Profile for England 2021. Further information on methods, data and definitions is available.
Overview of the population of London
London is the capital of England and the largest UK city. The Greater London region comprises 32 boroughs and the City of London. Thirteen local authorities and the City are designated as inner London, and the remaining 19 are outer London.
ONS mid-year estimates for 2020 indicate that the capital is home to over nine million people. Latest statistics report the smallest annual increase in the population since the 2011 census, just 0.5% from 2019 to 2020, the same as England. The proportion of the population aged 65 and over is the lowest in England whilst London has the highest percentage of children aged under 18 years. The annual population survey indicates that London has the highest percentage of people from an ethnic minority of any region.
The impact of the pandemic on routine statistics is beginning to emerge. In London, as in England, there was a significant increase in mortality. COVID-19 was a leading cause of death in all adult age groups. Migration has also been impacted by the pandemic and there was a net outflow due to migration in London taking account of both internal and international migration, but some of this effect may be reversed as recovery continues. The number of live births decreased in London in 2020, continuing the trend over the last decade and contributing to the plateau in the London population. The impact of the pandemic on the number of births in subsequent years is yet to be seen.
In line with the increase in mortality, both male and female life expectancy dipped in 2020 though remains higher than the England average. London ranked worst region in the country for some indicators including low birth weight of term babies and overweight children in Year 6, whilst in addition, cancer screening rates and childhood vaccination rates are low. Overall, under 75 mortality rates are significantly better (lower) than for England but annual rates show these getting worse for some diseases. Self-reported anxiety and low satisfaction scores have increased since 2019.
Overall, statistics for London indicate similar levels of employment and deprivation as for England but this masks disparities at small area level. Deprivation scores vary markedly between local authorities ranging from 32.8 in Barking and Dagenham (relatively more deprived) down to 9.4 in Richmond upon Thames (relatively less deprived).
COVID-19
Overview
This section examines the direct impact of the COVID-19 pandemic on health, with analysis of COVID-19 cases, vaccination rates, death rates involving COVID-19, and excess deaths during the pandemic up until 31 December 2021.
COVID-19 cases
London experienced three main waves of COVID-19 cases from March 2020 to the end of December 2021. The first wave took place in spring 2020, the second in December 2020/January 2021, and the third in December 2021. By the end of December 2021, almost two million confirmed cases of COVID-19 had been reported in London. At the highest peak in December 2021, on average across a week there were approaching 30,000 cases each day (Figure 1a).
COVID-19 has impacted some population groups more than others. In London, cumulative case rates were higher for females than males, highest in ages 25-49, and in Asian and ‘Other’ ethnic groups (Figure 1b).
Contrary to national trends, cumulative age-standardised case rates were highest in the least deprived decile in London, although the two most deprived deciles and the two least deprived deciles all had rates higher than the London average (Figure 1b). Within London, there was significant variation in case rates by local authority. By the end of December 2021, cumulative case rates ranged from 16,185.8 per 100,000 in Westminster up to 24,237.4 per 100,000 in Havering (Figure 1c).
Figure 1 - COVID-19 cases
Figure 1a - Trends
Source: UKHSA COVID-19 dashboard Date accessed: 02/03/2022 Note: Source data are updated daily and historic data may be revised. Download data
Figure 1b - Inequalities
Source: OHID COVID-19 Health Inequalities Monitoring for England (CHIME) tool Date accessed: 23/02/2022 Note: Source data are updated monthly and historic data may be revised. Download data
Deaths involving COVID-19
In London, by the end of December 2021, 21,626 people had died within 28 days of a positive COVID-10 test. This equates to a cumulative age-standardised mortality rate of 358.0 per 100,000 of the population. By the end of December 2021, the cumulative age-standardised mortality rate was higher in London than in any other region.
In April 2020, during the first wave of COVID-19, there were 5,696 deaths, the highest number of any month in the pandemic. During the second wave, deaths peaked in January 2021 when there were 5,530 deaths in the month. In contrast, in the most recent third wave, despite record numbers of cases, deaths remained lower than in previous waves with 509 deaths in December 2021.
Over the course of the pandemic, there has been wide inequality between population groups for COVID-19 mortality within London (Figure 2b and 2c). Cumulative age-standardised mortality rates were:
1.8 times higher for males than females at 474.9 per 100,000 population
13 times higher for those aged 85 and over than the London average at 4685.2 per 100,000 population
2.2 times higher in the most deprived 10% of London than in the least deprived 10% at 503.1 per 100,000 population
higher in the Black (563.0 per 100,000 population) and Asian (532.6 per 100,000) ethnic groups than the London average (357.8 per 100,000)
highest in Newham at 570.0 per 100,000 population and lowest in Camden at 204.3 per 100,000 population
Health Profile for England Highlights
Inequalities in death rates from COVID-19 largely reflect inequalities in COVID-19 case rates. However, they are also influenced by differences in survival following infection. At a national level, during the first wave of the pandemic, in England people aged over 80 years were 70 times more likely to die from COVID-19 once infected compared with those aged under 40 years. Survival was higher in females than males, and after controlling for age, deprivation and pre-existing health conditions, survival among many ethnic minority groups remained lower than the White group. The Bangladeshi ethnic group had the poorest survival rates and had 1.88 times the odds of dying once diagnosed than the White ethnic group. The Pakistani, Chinese, and Black Other ethnic groups had 1.35 to 1.45 times the odds of dying once diagnosed and the Indian group 1.16. The possible reasons for these differences and further interpretation of ethnic inequalities in COVID-19 mortality rates are discussed in depth elsewhere.
Figure 2 – COVID-19 deaths
Figure 2a - Trends
Source: UKHSA COVID-19 dashboard Date accessed: 02/03/2022 Note: Source data are updated daily and historic data may be revised. Download data
Figure 2b - Inequalities
Source: OHID COVID-19 Health Inequalities Monitoring for England (CHIME) tool Date accessed: 23/02/2022 Note: Source data are updated monthly and historic data may be revised. Download data
Excess mortality during the COVID-19 pandemic
Excess mortality is a measure of how much higher all-cause mortality was in the pandemic period than would have been expected, based on previous years, had the pandemic not occurred. Because excess mortality captures deaths from all causes, not just COVID-19, it provides an understanding of both the direct and indirect impact of COVID-19 on mortality.
Between 27 March 2020 and 31 December 2021, cumulative all-cause deaths were 1.19 times higher in London than expected. Within London, excess mortality ranged from 1.07 times higher in Camden, to 1.34 times higher in Newham. These figures reflect inequalities which existed prior to the pandemic and therefore indicate the disproportionate direct and indirect impact of the pandemic on inequalities with excess mortality higher for some population groups than others over the period, indicating that the pandemic exacerbated existing inequalities (Figure 3b).
Trends in excess mortality are in line with the three waves of COVID-19 cases, but became progressively lower in each wave. Rates were by far the highest in Spring 2020 (Figure 3a). While mortality was higher than expected for both males (1.22 times higher) and females (1.16 times higher), excess mortality was higher for males aged 50-64, males from more deprived areas, and males from the Black and Asian ethnic groups.
For those under the age of 50, mortality during the period was lower than expected. However, over the age of 50, mortality was higher than expected with the highest excess mortality rates for those aged 50-64. For females in this age group, mortality was 1.23 times higher than expected, and for males 1.42 times higher than expected.
There were more deaths than expected in all ethnic groups. However, excess mortality was highest in the Black and Asian ethnic groups. In the Black ethnic group mortality was 1.37 times higher for females, and 1.57 times higher for males than expected. In the Asian ethnic group mortality was 1.27 times higher for females, and 1.52 times higher for males than expected.
Mortality was higher than expected for all levels of deprivation in London. However, by population quintile, excess mortality was higher in the most deprived two-fifths of London and lower in the least deprived two-fifths of London. Excess mortality was higher than the London average for males in the more deprived three-fifths of the London population. However, for females, variation by deprivation was less pronounced and excess mortality fell below the London average across the board.
Figure 3 – Excess deaths
Figure 3a - Trends
Source: OHID Excess mortality in England dashboard Date accessed: 07/02/2022 Download data
Figure 3b - Inequalities & UTLA
Source: OHID Excess mortality in English regions dashboard Date accessed: 07/02/2022 Download data
COVID-19 vaccinations
By the end of December 2021, 18.9% of the adult population (18 years and over) in London remained unvaccinated against COVID-19, a greater proportion than in any other region, while 77.9% had received two doses of the COVID-19 vaccine, and 55.5% had received 3 doses. Of those aged 12 and over, 75.7% had received two doses of the vaccine and 55.1% had received three doses. Within London, coverage for two doses for those aged 12 and over ranged from 54.1% in Newham to 75.7% in Bromley.
There is inequality in vaccine coverage for two doses between different adult (18 years and over) groups within the region (Figure 4b). The most marked include differences by:
Deprivation: coverage in the most deprived areas was 69.6% compared to 86.8% in the least deprived areas.
Ethnicity: coverage was lower in the Black Caribbean (52.3%) and Black African (61.9%) ethnic groups than other ethnic groups and the London average.
Socioeconomic class: for those classified as having never worked and the long-term unemployed, coverage for two doses was 61.1% in contrast to higher managerial, administration, and professional occupations for whom coverage was 84.8%.
Housing tenure: coverage for two doses for those in socially rented accommodation (66.9%) and privately rented accommodation (68.9%) was lower than those who owned their own home (85.9%).
Country of birth: coverage for two doses was 74.4% for those who were non-UK born compared to 79.9% for those born in the UK.
English language proficiency: coverage was 72.8% for those whose main language is not English compared to 79.2% for those whose main language is English.
Sex: coverage for two doses was 76.4% in males compared to 79.2% in females.
Religion: coverage for two doses was lowest in those of Muslim faith at 70.4%.
Figure 4 – COVID-19 Vaccinations
Figure 4a - Trends
Source: UKHSA COVID-19 dashboard Date accessed: 02/03/2022 Note: Source data are updated daily and historic data may be revised. Download data
Figure 4b - Inequalities
Source: OHID COVID-19 Health Inequalities Monitoring for England (CHIME) tool Date accessed: 23/02/2022 Note: Source data are updated monthly and historic data may be revised. Download data
Mortality and life expectancy
Overview
This section examines trends and inequalities in all-cause mortality, mortality from leading causes of death, and life expectancy. It presents data for the pre-pandemic period, and 2020 data where it is available.
Life expectancy
For some years prior to the pandemic, London had experienced a slowdown in year on year improvements in life expectancy (Figure 5a). However, in 2019 life expectancy increased by 0.6 years for males and 0.5 years for females, to 81.3 years for males and 85.0 years for females.
Reflecting the impact of the pandemic in London, between 2019 and 2020 life expectancy decreased for both males (by 2.3 years) and females (by 1.5 years) to 79.0 years for males and 83.5 years for females. Although life expectancy decreased across all regions, the greatest reduction was in London. Within London, the least deprived areas saw the smallest decreases in life expectancy while the most deprived saw the largest decreases (Figure 5b).
The slope index of inequality (SII) is a measure of the social gradient in an indicator and shows how much a health outcome varies with deprivation. Inequality in life expectancy, as measured by the slope index of inequality, increased in London in 2020 by 1.4 years to 8.3 years for males, and by 1.2 years to 6.1 years for females (Figure 5b). As a result of these increases, inequality in life expectancy in London was greater in 2020 than in any year in the last decade.
Figure 5 – Life expectancy
Figure 5a - Trends
Source: OHID Public Health Outcomes Framework Date accessed: 10/06/2022 Download data
Figure 5b - Inequalities
Source: OHID COVID-19 Health Inequalities Monitoring for England (CHIME) tool Date accessed: 02/03/2022 Note: SII = Slope Index of Inequality. See data and definitions document for more details. Download data
Breakdown of trends in life expectancy by age or cause of death
This section examines which age groups and causes of death have contributed to the trend in life expectancy or inequalities in life expectancy presented earlier. Figure 6a shows a breakdown of the change in life expectancy by year in London between 2017 and 2020 according to age group or cause of death. A positive value for a cause of death or age group means that reductions in the mortality rate in that group acted to increase life expectancy. A negative value means that an increase in the mortality rate in that group acted to decrease life expectancy.
Between 2018 and 2019, most causes of death show positive values. This means that death rates from these causes reduced, contributing to an increase in life expectancy. However, between 2019 and 2020, mortality from COVID-19 was estimated to have reduced life expectancy by 1.6 years for females and 2.3 years for males in London. All other causes of death had little impact on the change in life expectancy between 2019 and 2020.
Between 2018 and 2019 most age groups had a reduction in mortality rates and therefore an increase in life expectancy in London. Between 2019 and 2020, increases in mortality rates in all age groups over 30 contributed to the decrease in life expectancy, but the largest contribution was from the 70 to 79 and 80 to 89 age groups. Most age groups under 30 generally made a small positive contribution to life expectancy between 2019 and 2020 and offset some of the overall decrease.
The inequalities data in Figure 6b show the contribution by age group or cause to the gap in life expectancy between the most deprived and least deprived areas in 2019 and 2020. In 2020, COVID-19 was estimated to contribute 1.4 years to this gap for females and 1.7 years for males in London. In addition, higher mortality from heart disease, lung cancer, and chronic lower respiratory diseases in deprived areas are important reasons for the gap in life expectancy in both 2019 and 2020. Addressing inequalities in these diseases would have a substantial impact on reducing inequalities overall in life expectancy in London.
Figure 6 – Breakdown of trends and inequalities in life expectancy
Figure 6a - Trends
Source: OHID COVID-19 Health Inequalities Monitoring for England (CHIME) tool Date accessed: 23/02/2022 Note: Source data are updated monthly and historic data may be revised. Download data
Figure 6b - Inequalities
Source: OHID COVID-19 Health Inequalities Monitoring for England (CHIME) tool Date accessed: 23/02/2022 Note: Source data are updated monthly and historic data may be revised. Download data
Trends in mortality
Figure 7a shows trends in age-standardised mortality rates in all ages and those aged under 75 for males and females in London from 2001 to 2020. It shows that the all-cause age-standardised mortality rate in London increased by 23% between 2019 and 2020 for males, and by 17% for females. These were the highest rates since 2010 and reflect the direct and indirect impact of the pandemic on mortality on all ages. For those aged under 75, age-standardised mortality rates have also increased by 25% for males, and 14% for females between 2019 and 2020.
Figure 7b shows that age-standardised mortality rates in those aged under 75 increased between 2019 and 2020 for all deprivation deciles but the biggest increase was in the most deprived areas. For persons aged under 75, the rate in the most deprived decile in 2020 was almost three times that of the least deprived decile. Compared to England, London had lower than average mortality in 2019, but was similar in 2020 reflecting the impact of COVID-19. The increase in mortality rates between 2019 and 2020 was larger in London than any other region. In comparison, between 2020 and 2021, average mortality rates in London changed very little. However, by decile, the largest increase was also in the most deprived decile, though was not statistically significant.
Figure 7 – All cause mortality
Figure 7a - Trends
Source: Office for National Statistics: Nomis Date accessed: 02/03/2022, OHID analysis of Office for National Statistics data Date accessed: 02/03/2022 Note: Nomis data used from 2013 onwards; PHE analysis used for 2001 to 2012. Download data
Figure 7b - Inequalities
Source: OHID analysis of Office for National Statistics mortality and population data Date accessed: 02/03/2022 Download data
Leading causes of death
Figure 8a shows the five leading causes of death by age group in people aged over 20 years old based on the number of deaths by underlying cause, for females and males separately in 2020. Deaths for those aged under 20 are not included in this chart as numbers are small and the causes vary from year to year.
Sudden deaths refer to those where the cause is unclear or suspected to be due to causes such as suicide or drug poisonings. They can only be registered after referral to a coroner and sometimes an inquest is required which may take months or even years to conclude [1]. Although the full impact of the pandemic will not become clear for some time, coroners have reported pressure on the system which may have resulted in lengthier death registration delays than previously [2] and which may impact on the profile for 2020.
In 2020, COVID-19 was the leading underlying cause of death for males and females closely followed by heart disease, and dementia and Alzheimer’s disease. However, many people who died from COVID-19 may have had dementia or heart disease cited on their death certificate but not as the leading cause. Between March and June 2020, dementia was the most common main pre-existing condition accounting for 25.6% of all deaths involving COVID-19 in England and Wales. Heart disease was the second most common at 9.9% [3].
As previous Health Profiles for England have reported in depth, prior to the COVID-19 pandemic the age-standardised death rates from dementia and Alzheimer’s disease were increasing. A number of factors have contributed to this increase over time including a better awareness of dementia and historical NHS policies encouraging GPs to diagnose, leading to increased recording on death certificates [4]. This means deaths classified as due to dementia in recent years may not have been classified as such in the past.
COVID-19 was among the leading causes of death in all age groups alongside the causes mentioned above. For males aged from 35 to 80 years and over, COVID-19 was the leading cause of death, and in ages 20 to 34 years the third leading cause. In the 20 to 34 age group for males, there were more deaths registered from external causes such as suicide or accidental poisoning than COVID-19. For females, COVID-19 was the leading cause of death for those aged 50 to 64 and 65 to 79 years and second leading cause for ages 20 to 34, 35 to 49, and 80 years and over. As for males, for females ages 20 to 34 there were more deaths due to suicide than COVID-19. For females aged 35 to 49, there were more deaths from breast cancer, and in those aged 80 and over, more deaths from dementia and Alzheimer’s disease than COVID-19.
The data in Figure 8b show inequalities in age-standardised mortality rates at all ages for 2015 to 2019, and for 2020, for several cause groups including cardiovascular disease, cancers, respiratory disease, and dementia and Alzheimer’s. Inequalities in mortality from these causes persisted in 2020 and except for dementia and Alzheimer’s, mortality declined across every deprivation category. However, comparisons over time should be made with caution. COVID-19 was introduced as a new cause of death in 2020 and some people that died from it may have died from another cause instead if the pandemic had not occurred. In addition, this ‘mortality displacement’ may not be consistent across deprivation categories as we know that COVID-19 disproportionately impacted on the more deprived areas.
Figure 8 – Leading causes of death
Figure 8a - Trends
Females
Males
Source: Office for National Statistics Nomis Note: Age groups should be compared with caution as there are many more deaths in older age groups than in younger age groups. Download data
Figure 8b - Inequalities
Source: OHID Wider Impacts of COVID-19 on Health (WICH) tool Date accessed: 07/02/2022 Download data
Child health
Overview
The aim that every child should have a good start in life is the foundation for the future health and wellbeing of the population. This section presents key indicators of child health: birthweight, infant mortality, early child development and child obesity. Data presented includes the pre-pandemic period and 2020/21 where available.
Low birthweight
Low birthweight is associated with an increased risk of infant mortality, developmental problems in childhood and poorer health in later life [5, 6].
In 2020, the proportion of babies born at term with a low birthweight (less than 2500 grams) in London was worse (higher) than the England average (3.3% for London compared to 2.9% for England) (Figure 9a). This is an increase from 2019 and continues the worsening trend from 2017. Within London, the proportion ranged from 2.0% (Kingston upon Thames) up to 4.5% (Redbridge). Redbridge (4.5%) and Newham (4.3%) ranked among the five local authorities in both London and England with the highest proportion of low birthweight babies (Figure 9b). Only two boroughs in London had low birthweight rates significantly better than England (Havering and Kingston upon Thames).
Health Profile for England Highlights
In England the last two decades have seen overall improvements in babies born with a low birthweight, infant deaths and child development. However, in the years leading up to the coronavirus pandemic improvements slowed down.
Figure 9 – Low birthweight
Figure 9a - Trends
Source: OHID Public Health Outcomes Framework Date accessed: 31/03/2022 Download data
Infant mortality
Infant mortality includes all deaths within the first year of life. The majority of these are neonatal deaths which occur during the first month with the main cause relating to prematurity and preterm birth, followed closely by congenital anomalies [7]. This data presents trends in infant mortality rates from 2001 to 2003 until 2018 to 2020. They are presented as a three-year rolling average to smooth out variation. Nationally, the rate of infant mortality increases as deprivation increases.
In London, infant mortality was 3.4 per 1,000 live births in 2018/20 (Figure 10a). There have been no significant improvements in recent years, but rates remain significantly lower than the England average (3.9 per 1,000). Within London rates range from 1.9 per 1,000 in Wandsworth up to 4.7 per 1,000 in Hounslow (Figure 10b).
Figure 10 – Infant mortality
Figure 10a - Trends
Source: OHID Public Health Outcomes Framework Date accessed: 31/03/2022 Download data
Child development
Starting primary school is a significant milestone in a child’s educational journey. Language and communication skills are fundamental to a child and young person’s potential development and achievements later in life [8]. Being able to express themselves, interact with peers and make themselves understood helps to build a child’s confidence and boost their self-esteem [9]. Inadequate communication skills can lead to poorer adult outcomes in literacy, mental health, and employment [10].
The proportion of children in London who achieve a good level of development at the end of Reception has improved over time and remains higher than the England average. However, improvements have levelled off in recent years. Inequalities persist for those who are most deprived with rates for children with free school meal status (64.1%) lower than the rates for all children (74.1%) (Figure 11a). Within London, the proportion of children achieving a good level of development ranged from 69.6% in Hackney up to 80.6% in Richmond upon Thames, and 85.1% in City of London. Fewer boys (68.1%) than girls (80.4%) achieved a good level of development in London (Figure 11b).
Health Profile for England Highlights
Due to the pandemic, data on child development at the end of the Reception year was not reported for the academic year of September 2019 to July 2020. In March 2020 early years settings were closed to most children, with only children from key workers and vulnerable families continuing to attend (around 7% of children aged two to four years) [11]. Outside formal settings, young children may have experienced a lack of social activities and interactions that would normally have helped to prepare them to start school, for example spending time with grandparents and play sessions with other children.
Although the full impact of the pandemic on early years development will not be known for some time, a study carried out by the Education Endowment Foundation (EEF) found that of schools surveyed in England, 76% reported that children who started school in the autumn 2020 term needed more support than previous cohorts. Almost all surveyed schools indicated concerns about pupils’ communication and language development (96%), personal, social, and emotional development (91%) and levels of literacy (89%) [12].
Figure 11 – Child development
Figure 11a - Trends
Source: OHID Child and Maternal Health Profile Date accessed: 31/03/2022 Download data
Childhood obesity
Prevention and treatment of childhood obesity presents a significant public health challenge. Obesity in childhood can lead to the early onset of cardio-metabolic, respiratory, and musculoskeletal conditions, as well as adverse psycho-social outcomes and an increased risk of obesity and associated mortality and morbidity later in life [13]. Prevalence of obesity is not distributed equally across the country. Children in the most deprived areas are more than twice as likely as children in the least deprived to be obese.
In London, in the academic year 2019 to 2020, one in ten reception-age children and more than one in five Year 6 children in London were classified as obese. There have been no significant improvements in this trend in recent years. In Reception and Year 6, there was a higher prevalence of obesity for males (10.4%, 27% respectively) than females (9.6%, 20.3% respectively) (Figure 12a).
The 2019/20 data on child obesity is less robust than previous years as fewer measurements were taken due to school closures during the pandemic. As a result, data at local authority level are not as robust and for some local authorities in London data has not been published. For those boroughs for which data is available, the prevalence of obesity in reception-age children ranged from 4.7% in Richmond upon Thames to 13.8% in Greenwich. For all local authorities, prevalence was higher in Year 6 children than in Reception, with prevalence ranging from 11.1% in Richmond upon Thames to 29.0% in Barking and Dagenham (Figure 12b).
Health Profile for England Highlights
The Health Profile for England profiles inequalities in child obesity at a national level. In both age categories (Reception and Year 6), children in more deprived areas were more likely to be obese. In 2019/20 the percentage of obese children in Reception was 6.0% in the least deprived decile compared to 13.3% in the most deprived decile. For Year 6 children the percentage of obese children was 11.9% in the least deprived decile compared to 27.5% in the most deprived decile.
There are also inequalities by ethnic group. In 2019/20 the Black African group had the highest prevalence of obesity in both Reception (15.9%) and Year 6 children (30.5%) – both statistically significantly higher than for England (9.9% and 21.0% respectively), although some other ethnic groups also had high rates.
Figure 12 – Child obesity
Figure 12a - Trends
Source: OHID Child and Maternal Health Profile Date accessed: 31/03/2022 Download data
The impact of COVID-19 on child obesity and physical activity
The latest findings from the National Child Measurement Programme (NCMP) suggest that obesity has increased across all regions in both reception-age children and children in Year 6. A link between weight gain and time spent out of school in the holidays has previously been demonstrated [14]. Closure of schools, sporting and leisure facilities, park facilities and recreational areas, together with an increase in screen time (television, phones, computers etc.) over the pandemic period have led to a reduction in physical activity in children and young people [15]. Sport England survey estimates for London suggest activity decreased during 2019/20 to 41.9% down from 46.1% in 2018/19. However, there was some improvement in 2020/21 when physical activity increased to 44.4%.
Health Profile for England Highlights
A link between weight gain and out of school time in the school holidays has previously been demonstrated [13]. Closure of schools, sporting and leisure facilities, park facilities and recreational areas, together with an increase in screen time over the pandemic period have led to a reduction in physical activity in children and young people [14].
Recent evidence from Sport England suggests that in England, there has been a reduction in physical activity in boys, and an increase in physical activity for girls during the pandemic. It also suggests that differences by deprivation have widened.
Other indicators of child health
The Health Profile for England 2019 demonstrated inequalities in many other aspects of children’s health prior to the pandemic. This section profiles some of these.
Health Profile for England Highlights
In England, prior to the pandemic, smoking among teenagers had been reducing, while drug use increased. The proportion of 15-year-olds who reported they were regular smokers decreased from 12% to 5% between 2010 and 2018. Lifetime prevalence of drug use among school pupils aged 11 to 15 increased sharply between 2014 and 2016, even accounting for a methodological change, but then remained level through to 2018 at 24%. This survey data is not available at regional level.
Obesity in early pregnancy: Women who are obese have increased risk of complications during pregnancy and birth. Babies born to obese women also have a higher risk of adverse health outcomes including stillbirth, congenital abnormalities, and subsequent obesity. In 2018/19. 17.8% of mothers in London were obese in early pregnancy, lower than the England average (22.1%). Within London, there was significant variation by borough ranging from 6.8% in Kensington and Chelsea to 27.4% in Barking and Dagenham.
Dental decay: In London, 27.0% of children aged 5 years had experience of visually obvious dental decay in 2018/19 compared to 23.4% in England as a whole. Within London, there was significant variation by local authority ranging from 12% in Bromley to 42.4% in Harrow.
Teenage conceptions: Looking at the trend for 1998 to 2019, the rate of teenage conceptions for London decreased from 51.1 per 1,000 in 1998 to 13.5 per 1,000 in 2019 and is now significantly lower than the England average (15.7 per 1,000). There is variation in the under 18s conception rate for local authorities in London. However, only Lewisham (24.1 per 1,000) and Enfield (19.5 per 1,000) had rates significantly higher than the England average.
Injuries resulting in hospitalisation: Figure 13a shows the trend for hospital admissions for unintentional and deliberate injuries in children (aged 0-14 years) between 2010/11 and 2020/21. In London, the crude rate reduced by more than a third over this time, from 89.0 per 10,000 in 2010/11 down to 55.1 per 10,000 in 2020/21, with rates consistently lower than the England average.
Figure 13 – Injuries resulting in hospitalisation
Figure 13a - Trends
Source: OHID Public health profiles Date accessed: 31/03/2022 Download data
Smoking in early pregnancy: Smoking in pregnancy has well known detrimental effects for the growth and development of the baby and health of the mother. Smoking during pregnancy increases the risk of premature births, miscarriage, stillbirth and perinatal deaths, complications in pregnancy, low birthweight and the child developing other conditions in later life. In 2018/19 there was some variation in the proportion of mothers smoking in early pregnancy for local authorities in London although all areas had significantly lower proportions than England (12.8%).
Smoking at the time of delivery: Figure 14a shows the trend in the proportion of mothers smoking at the time of delivery. For London, the proportion decreased from 6.5% in 2010/11, down to 4.6% in 2020/21. However, Figure 14b shows that in 2020/21 there was significant variation within London, with rates ranging from 2.1% in Kensington and Chelsea to 9.9% in Barking and Dagenham.
Figure 14 – Smoking in pregnancy
Figure 14a - Trends
Source: OHID Public health profiles Date accessed: 31/03/2022 Download data
COVID-19 impact on young people
The pandemic has had a profound effect on the lives of young people due to social isolation and interruptions to education. Some of these effects may be long term but data are not available to measure them yet. Some of the impacts on young people are covered elsewhere in this report, including the risk factors and wider determinants sections.
A national survey comparing aspects of mental health found that in 2020, one in six (16.0%) children aged 5 to 16 years were identified as having a probable mental disorder, an increase from one in nine (10.8%) in 2017. Compared to their peers, those with a probable mental disorder were more likely to respond that lockdown had made their life worse, 54.1% aged11 to 16 years, and 59.0% aged 17 to 22 years stating this, compared with 39.2% and 37.3% respectively.
Information about the impact of COVID-19 on the mental health of young people can be found in the COVID-19 mental health and wellbeing surveillance report and a profile of the health of young people can be found in the Young People profile.
Health in adults
Overview
As well as life expectancy (how long the population can expect to live), it is also important to consider the quality of life or length of time spent in good health. This is referred to as healthy life expectancy. Currently, the most recent healthy life expectancy data is based on figures prior to the pandemic and do not reflect its impact.
In 2017-19, healthy life expectancy was similar in London to England at 63.8 years for males and 65.0 years for females (Figure 15a). However, there had been no recent improvements for either males or females. There was wide variation across local authorities, with a difference of 12.1 years for males and 13.3 years for females between the local authorities with highest and lowest healthy life expectancy.
The gap between life expectancy and healthy life expectancy can be described as the number of years a person could be expected to live in poor health. In London, the average number of years lived in poor health is 16.5 years for males, increasing to 19.3 years for females (Figure 15a).
Figure 15 – Healthy life expectancy
Figure 15a - Trends
Source: OHID Public Health Outcomes Framework Date accessed: 31/03/2022 Download data
Leading causes of morbidity
The Global Burden of Disease (GBD) uses the measure of years lived with disability (YLDs) to attribute the burden of morbidity. YLDs is a measure of morbidity that combines the prevalence of each disease with a rating of the severity of symptoms (excluding death) to provide an overall measure of the loss of quality of life.
Figure 16a identifies the most common causes of morbidity for 1990 and 2019 according to GBD (as measured by age-standardised YLDs per 100,000 population). Overall, in London the top three leading causes of morbidity in both 1990 and 2019 were low back pain, depressive disorders, and headache disorders. For males in 2019, the top three causes were low back pain, diabetes mellitus, and depressive disorders. For females, these were low back pain, headache disorders, and depressive disorders.
Change between years should be interpreted with caution as it may reflect changes in methodology and categorisation, and uncertainty limits are wide for most causes.
Figure 16 – Leading causes of morbidity
Figure 16a - Trends
Mental health and wellbeing
According to the Global Burden of Disease, mental health conditions such as depression and anxiety were estimated to account for 17.0% of total morbidity (years lived with disability) in London in 2019.
Figure 17a shows trends in wellbeing from 2011/12 through to 2020/21, measured by four different indicators: anxiety, low happiness, low life satisfaction and low worthwhile feelings. A comparison of year 2019/20 with 2020/21 provides a pre and during COVID-19 pandemic view. In London, 22.4% of the population reported a high anxiety score in 2019/20 compared to 23.8% in 2020/21. Additionally, 8.5% of the population reported a low happiness score in 2019/20, compared to 8.3% in 2020/21. Furthermore, 4.8% of the population reported a low life satisfaction score and 3.7% of the population reported a low worthwhile score in 2019/20. In 2020/21 these were 5.7% and 3.5% respectively.
Figure 17 – Mental health and wellbeing
Figure 17a - Trends
Source: OHID Public Health Outcomes Framework Date accessed: 31/03/2022 Download data
Figure 18a shows that the rate of emergency hospital admissions for intentional self-harm in London has decreased slightly over time from 112.2 per 100,000 in 2010/11 to 82.7 per 100,000 in 2020/21. The rate for suicide was similar in 2018-20 (8.0 per 100,000) to 2010-12 (8.4 per 100,000). Both the rate of emergency hospital admissions for self-harm and the rate of suicide have remained consistently lower for London than the England average.
In London in 2020/21, the rate of emergency hospital admission for self-harm was statistically significantly higher for females (110.7 per 100,000) compared to males (56.2 per 100,000). In contrast, the rate of suicide in London in 2018-20 was significantly higher for males (15.9 per 100,000) than females (5.0 per 100,000). Within London, the rate of emergency hospital admissions for self-harm ranged from 41.5 in Kensington and Chelsea to 183.5 in Kingston upon Thames, while the rate of suicide ranged from 5.0 per 100,000 in Harrow to 12.7 per 100,000 in Camden (Figure 18b).
Figure 18 – Suicide and Self-harm
Figure 18a - Trends
Source: OHID Public health profiles , Suicide Prevention Profile Date accessed: 31/03/2022 Download data
Dementia and Alzheimer’s disease
According to the Global Burden of Disease, Alzheimer’s disease and other dementias accounted for 0.7% of total morbidity (years lived with disability) in London in 2019 and (as discussed earlier in the report) is a leading cause of death.
In London, 0.5% of all people registered with a GP had a recorded diagnosis of dementia in 2020/21. For GP patients aged 65 years and older, 4.2% had a recorded diagnosis of dementia in 2020. These statistics refer to recorded rather than actual prevalence and are therefore likely to under-report true prevalence, especially for populations less likely to be registered with a GP. As a result, caution should be applied when interpreting this indicator as a higher value may mean that the prevalence of the condition is high in the area but could also indicate that detection is better there.
Health service contact during the pandemic
Emergency hospital admissions (all cause) decreased in London from 7.9 per 1,000 in January 2020 to 4.5 per 1,000 in April 2020. In comparison, April 2021 emergency admissions had increased to 7.1 per 1,000. The latest data for July 2021, show hospital admissions remain lower (7.5 per 1,000) than the equivalent 2018-19 period pre-pandemic (7.9 per 1,000).
Hospital outpatient attendances in London followed a similar trend, falling from 160.8 per 1,000 in January 2020 to 78.4 per 1,000 in April 2020. By April 2021, the rate had increased back to a rate of 148.2 per 1,000. The most recent data for July 2021 shows that hospital outpatients attendances remain lower than the equivalent 2018-19 pre-pandemic period at 155.4 per 1,000.
Health Profile for England Highlights
Reduced admissions, GP consultations, A&E attendances and health seeking behaviour recorded during this period may be a factor in the increase in deaths at home. They may also represent missed opportunities to provide secondary prevention treatment to patients, such as blood pressure and cholesterol control, and may also result in an increase in long-term health complications.
Cancer
According to the Global Burden of Disease, cancers accounted for 1.4% of total morbidity (years lived with disability) in London in 2019 and (as discussed earlier in the report) are a leading cause of death.
Figure 19a shows that the trend in new cancer diagnoses for all sites combined and the four main cancers (breast, prostate, colorectal and lung) declined in March 2020. Cancer diagnoses began to increase again as the COVID-19 pandemic progressed. The total number of new cancers diagnoses in April and May 2020 were around a third lower than in the earlier months of 2020.
By September 2021, new monthly breast and colorectal cancer diagnoses were higher than the number of new cancer diagnoses pre-pandemic in January and February 2020. New monthly lung cancer diagnoses are similar whilst new monthly prostate cancer diagnoses were lower in September 2021 than in January and February 2020.
Figure 19 – Cancer incidence
Figure 19a - Trends
Source: National Cancer Registration & Analysis Service Date accessed: 07/02/2022 Note: Data based on Cancer Alliance 2020 geographies. See data and definitions document for more details on mapping alliances to Government Office Regions. Download data
Risk factors associated with ill health
Overview
Risk factors play an important role in determining whether a person becomes ill, at what age, and the associated effect on quality of life. The Global Burden of Disease (GBD) divides risk factors into three main groups: behavioural, metabolic, and environmental and occupational. These are underpinned by the broader social and economic risk and protective factors that shape people’s lives, such as education, income, work, and social capital. These wider determinants are discussed in the next section of this report. This section focuses on behavioural and metabolic risk factors in adults and the contribution that they make to morbidity and mortality, using GBD data. Trends and inequalities in some of those risk factors making the largest contribution are examined.
Health Profile for England Highlights
Inequalities in risk factor prevalence contribute to inequalities in ill health and mortality. For example, inequality in smoking prevalence by deprivation group is a significant determinant of inequalities in mortality and life expectancy. In 2019, smoking prevalence in England remained far higher than average for some groups, for example, people in manual occupations (23.2%), people with a long-term mental health condition (25.8%), those living in deprived areas (16.9%), and the Mixed ethnic group (19.5%). The prevalence of ‘increasing or higher risk’ drinking was greatest in the highest household income group at 34.8%. The prevalence of adult obesity was higher in the most deprived compared to least deprived areas, and there were wide inequalities in the proportion of adults meeting recommended levels of physical activity and fruit and vegetable consumption. Health Survey for England evidence suggests that prevalence of multiple risk factors is higher in men, the White ethnic group, lowest income households, most deprived areas, and people with long term health conditions.
Leading risk factors
The risk factors making the largest contribution to morbidity in London are high body mass index, high fasting plasma glucose, tobacco, and alcohol consumption (Figure 20a). The risk factors making the largest contribution to mortality in London are tobacco, high blood pressure, high fasting plasma glucose, and diet (Figure 20b).
Please note that the disease burden attributable to specific risks are independently calculated for each risk factor. Risk factors attributed to YLDs or deaths cannot be summed together.
Figure 20 – Leading risk factors
Figure 20a - Morbidity
Figure 20b - Mortality
Obesity
In London, more than half (56.0%) of the adult population were either overweight or obese in 2020/21. Although rates in London are lower than the England average and lowest of the regions, there has been no improvement over time (Figure 21a). There is some variation by local authority. Bexley (64.6%) had the highest proportion of overweight or obese adults and Islington (44.0%) had the lowest.
As for other risk factors, there are inequalities in adult obesity prevalence by age, sex and deprivation. The latest data reports that in 2021 it was lowest in those aged under 25 with a gradual increase by age through to 55-64 years, after which prevalence decreases. This pattern is for both males and females. Obesity prevalence was lowest in the least deprived areas and highest in the most deprived, with a clearer gradient for females than males.
The impact of the pandemic on adult obesity levels is not yet known. Given the changes in other risk factors presented, such as diet, physical activity, and alcohol, it is possible that there has been an increase and widening of inequalities.
Blood glucose
Increased blood glucose levels may lead to diabetes and can increase the risk of heart disease and stroke, kidney disease, vision, and nerve problems. A blood glucose level that is above normal but not in the diabetic range is referred to as non-diabetic hyperglycaemia (NDH). It is estimated that approximately five million people in England have NDH and only 23.8% are diagnosed and recorded. This proportion has increased steadily since the establishment of the Diabetes Prevention Programme. [16]
Analysis from 2015 estimated that there were approximately 749,716 people across the NHS London region with NDH. For the reporting period January 2020 to March 2021, 401,650 patients had been diagnosed, which accounted for approximately 54% of the estimated prevalence. The diagnosed prevalence increased across all CCGs in the region between 2017/18 and 2020/21. In 2020/21 the prevalence (as a percentage of the GP Practice list size for patients aged 17+) ranged from 3.1% in South West London CCG to 6.8% in North West London CCG.
The NHS Diabetes Prevention Programme (NHS DPP) delivers evidence-based behavioural interventions that can prevent or delay the onset of Type 2 diabetes in adults who have been identified as having NDA. In the NHS London region, in 2020/21, 43.3% of those registered with NDH had been offered a place on an NHS DPP course, compared to 24.2% in 2017/18. In 2020/21 values ranged from 24.1% in South West London CCG to 63.1% in North West London CCG.
Smoking
Between 2015 and 2019, adult smoking prevalence (from the Annual Population Survey) decreased in London from 16.3% to 12.9% (Figure 21a). The smoking prevalence in London has consistently remained below the England average which was 13.9% in 2019 (Figure 21a). The trend in smoking prevalence should be interpreted with caution as survey methodology switched to a telephone based survey in recent years, potentially influencing the estimates. Within London, the smoking prevalence ranged from 8.0% in Richmond upon Thames to 18.1% in Barking and Dagenham (Figure 21b).
Inequalities in smoking prevalence persist in London with marked variation by socioeconomic groups. The prevalence in routine and manual occupations (for those aged 18 to 64 years) was 20.7%, twice that of managerial and professional occupations (10.3%). Londoners who rent, either privately or from a local authority, also have increased smoking prevalence (17.1%, 19.9% respectively) compared to those who own their home either outright or with a mortgage (7.3%, 8.6% respectively).
Data from the GP Patient Survey (GPPS) shows that smoking prevalence is also higher in adults (18+) with a long term mental health condition. In 2019/20 the prevalence for London was 26.6% compared to 13.1% in the general adult population.
Health Profile for England Highlights
The Health Profile for England 2021 reported that the prevalence of ‘increasing and higher risk’ went up in April 2020 and remained above pre-pandemic levels until June 2021. There has also been a reduction in physical activity levels particularly in Black and Asian groups and lower socioeconomic groups. The number of people trying to quit smoking during the pandemic increased with over a third of smokers attempting to quit in the three months up to June 2021.
Alcohol
‘Increasing or higher risk drinking’ is defined as more than 14 units per week up to 35 units for women, and up to 50 units for men. In 2019, the Health Survey for England showed that the prevalence of ‘increasing or higher risk drinkers’ in London was 20.1% and the proportion of ‘higher risk drinkers’ (more than 35 units for women or 50 units for men per week) was 5%.
For local authorities within London, Health Survey for England estimates for adults drinking over 14 units of alcohol a week for the period 2015-18 vary from 10.0% to 41.3%.
The number of deaths related to alcohol in London was 2,197 in 2020, which represents a rate of 32.2 per 100,000 population and is significantly lower than the England average.
Health Profile for England Highlights
The Health Profile for England 2021 reported differences in drinking patterns by age and income. ‘Increasing or higher risk’ drinking was highest in the 55 to 64 age group, with the lowest rates for younger age groups, under 25s, and those aged 75 or over. Prevalence of ‘increasing or higher risk’ drinking was greatest in the highest household income group. However, greater harm, such as hospital admissions for alcohol-related conditions in 2018 to 2019, was more than double in the most deprived areas compared to the least deprived areas. The gap has changed little since 2010/2011. The inverse relationship between consumption and harms is often referred to as the ‘alcohol harm paradox’. Attempts to understand this have suggested interactions with other behaviours such as smoking, poor diet and exercise, which are among the reasons why alcohol-related harms are greater in more deprived areas [17].
Drug use
In London, the rate of death due to drug misuse was 3.5 per 100,000, lower than for England (5.0 per 100,000) and lowest of any region. Within London, rates ranged from 1.9 per 100,000 in Enfield up to 8.0 per 100,000 in Hammersmith and Fulham. The latter is the only area that has significantly higher rates than England.
Health Profile for England Highlights
Pre-pandemic survey-based estimates for recent drug use in England vary year on year. 9.4% of people aged 16 to 59 reported using any drug in the last year in 2019 to 2020. The rate of deaths due to drug misuse continue to be highest among those born in the 1970s with the highest rate in those aged 45 to 49.
Diet
In 2019/2020, the proportion of the population meeting the recommended ‘5-a-day’ (5 portions of fruit and vegetables) on a ‘usual day’ in London was 55.8%, similar to the England average (55.4%). There is some variation by local authority. City of London (66.9%) and Richmond-upon-Thames (64.4%) had the highest proportion of adults meeting the recommended level whilst Barking and Dagenham (47.9%) had the lowest.
The Health Profile for England 2021 reported wide inequalities for England overall. The recommended 5-a-day consumption was lower for people who were unemployed (45.2%), living with a disability (52.1%), working in routine and manual occupations (45.8%), Asian (47.2%), Black (45.7%), or living in the most deprived areas (45.7%).
High blood pressure
Figure 21a shows that the recorded prevalence of high blood pressure (hypertension) diagnosis in London changed little between 2015/16 (11.0%) and 2020/21 (10.8%). However, during 2020/21, GP appointments were limited and as a result there may have been fewer diagnoses. Within London there is variation by local authority. Havering (14.3%) had the highest recorded prevalence while Hammersmith and Fulham (7.1%) had the lowest. However, it is acknowledged that this variation could reflect better diagnosis rates between GPs in an area. This indicator only includes recorded prevalence of hypertension, so may not reflect true prevalence in the population. The Office for Health Improvement and Disparities (OHID) CVD Prevention Packs include the percentage of estimated hypertension prevalence which has been diagnosed.
Physical activity
The definition of being physically active is taking at least the recommended level of 150 minutes of moderate intensity physical activity or equivalent per week. In 2019/20, 65.2% of adults (aged 19+) were physically active in London, lower than the England average of 66.4%. There was variation by local authority, with the lowest proportion of physically active adults in Newham (53.4%) and the highest in Lambeth (74.9%).
Health Profile for England Highlights
As for children, findings at the national level for 2020/21 from Sport England uncover wide inequalities in physical activity in adults. The proportion was lower for: people in routine/semi-routine jobs and those who are long-term unemployed or have never worked (52%); those living with a disability or long-term health condition (45%); and Asian (48%) and Black (52%) ethnic groups.
Figure 21 – Risk factors
Figure 21a - Trends
Source: Wider Impacts of COVID-19 on Health: Wellbeing and behavioural risk factors , Wider Impacts of COVID-19 on Health: Hypertension QOF Date accessed: 31/03/2022 Download data
Wider determinants of health
Overview
Wider determinants of health are a diverse range of social, economic and environmental factors that influence people’s mental and physical health across the life course [18]. Inequalities in these factors are an important driver of the inequalities in risk factors and health outcomes presented earlier in this report.
This section presents some key indicators for a range of wider determinants of health, including the built and natural environment, education, employment and income, and communities and social capital.
The built and natural environment
The quality of the built and natural environment such as air quality, quality of and access to green spaces, and housing quality affects health. Poor housing has a negative effect on physical and mental health, particularly for older populations, children, disabled people, and those with long-term illnesses.
Homelessness and the use of temporary accommodation remain at high levels in England. During 2017/18, London had a significantly higher rate of homelessness (4.2 per 1,000 households) compared to England (2.4 per 1,000 households). The same applies to homeless 16-24 year olds.
In London 11,018 rough sleepers were seen by outreach workers during 2020/21, a 3% increase compared to 2019/20. 68% of these individuals were seen for the first time.
Fuel poverty is now measured by the new Low Income Low Energy Efficiency (LILEE) statistic. A household is defined as fuel poor if it has income (after accounting for fuel costs) below a certain level and a low energy efficient home. In 2020, the proportion of fuel poor households in London was 11.5% (403,807 households), lower than the average for England (13.2%). The average fuel poverty gap (the reduction in fuel bill that the average fuel poor household needs in order not to be classed as fuel poor) was £188 for London which was second lowest of the regions after the North East (£155), and lower than England (£223).
Living in a greener environment can promote and protect good health, aid recovery from illness and help manage poor health. Green space can help to bind communities together, reduce loneliness, and mitigate the negative effects of air pollution, excessive noise, heat and flooding. London’s green space saves £580 million and £370 million due to better physical and mental health respectively. 20% of this green space’s economic value consists of these health benefits.
Tenure
In 2020 in London 45.4% of households were in rented accommodation, of these 21.0% were rented from the Local Authority (LA) or Housing Association (HA) and 24.4% from a private landlord. London is the region with the highest proportion of renting households. A higher proportion of households in Inner London compared to Outer London were in rented accommodation (57.4% and 36.9% respectively). The Local Authority with the highest proportion was Tower Hamlets with over two thirds (69.8%) of households in rented accommodation (35.6 % from LA or HA and 34.2% private landlord), followed by Hackney (63.8%). The Local Authority with the lowest proportion was Richmond upon Thames (24.8% in total, 10.1% from LA or HA and 14.7% private landlord) followed by Bromley (26.8% in total, 12.8% from LA or HA and 14.0% private landlord). The proportion of households in London in rented accommodation has decreased during the past decade from 49.4% in 2011. Conversely, in 2020 in London, 54.6% of households owned outright (26.2%) or were buying with a mortgage (28.4%). This and further information is published by the Office for National Statistics from the Annual Population Survey and available from the London Datastore.
Employment
Good employment improves health and wellbeing across people’s lives, boosting quality of life and protecting against social exclusion.
For London, employment rates had been increasing through to 2019. However, within the first year of the pandemic, this plateaued. There was a downturn for males and slight increase for females. Compared to England (76.2%), in 2019/20, London had lower employment rates (75.1%), with differences in rates for males and females in the region:
Employment rates have historically been lower for females compared to males, ranging from 60.3% in 2011/12 to 70.1% in 2019/20. During the pandemic the employment rate increased to 71% in 2020/21.
For males, employment rates increased from 74.2% in 2011/12 to 80.1% in 2019/20. During the pandemic, the proportion of males classed as employed decreased to 78% in 2020/2021.
There is also inequality in employment rates at local authority level across London:
Havering, Lambeth and Wandsworth have significantly higher employment rates than for London overall.
Barking and Dagenham and Enfield have significantly lower employment rates than the London average.
Health Profile for England Highlights
The Health Profile for England 2021 highlighted evidence that the pandemic has had a substantial impact on employment patterns and opportunities. There is evidence that the economic impacts affected young people disproportionately. At the end of January 2021, the take-up rate of eligible employees that made a claim to HMRC under the furlough scheme was highest in those aged under 18 years (34.5%) and those aged 18 to 24 years (21.1%). There has also been a decline in the number of young people aged 16 and 17 years in employment, from 22.5% in the three-month period March to May 2020, to 13.9% in the comparable period in 2021. In November 2020, the arts, entertainment and recreation industry, and the accommodation and food service industry had the highest percentage of employees on furlough at 33.6% and 21.9% respectively. These industries have a high proportion of the workforce who are relatively young.
Figure 22 – Employment
Figure 22a - Trends
Source: OHID Public health profiles Date accessed: 31/03/2022 Download data
Figure 22b - UTLA
Source: OHID Public health profiles Date accessed: 31/03/2022 Download data
Income
Many physical and mental health outcomes improve incrementally as income rises [19, 20]. Income is related to life expectancy, disability free life expectancy [20], and self-reported health [21]. This relationship operates through a variety of mechanisms. Financial resources determine the extent to which a person can both invest in goods and services that improve health, and purchase goods and services that are bad for health. Low income can also prevent active participation in social life and day to day activities affecting feelings of self-worth and status [22]. It can also influence health through feelings of shame, low self-worth and exclusion.
The Minimum Income Standard (MIS) is defined as not having enough income to afford a ‘minimum acceptable standard of living’, based on what members of the public regard as enough money to live on [23].
In 2018/19, 33.1% of the total population and 46.4% of the child population in London did not reach the MIS, higher than the national average of 29.8% and 42.3% respectively.
Figure 23 – Minimum income standard
Figure 23a - Trends
Source: Joseph Rowntree Foundation Households below a Minimum Income Standard: 2008/09 - 2018/19 Date accessed: 31/03/2022 Download data
Education
Educational attainment is strongly linked with health behaviours and outcomes. Better-educated individuals are less likely to suffer from long term diseases, to report themselves in poor health, or to suffer from mental health conditions such as depression or anxiety [24]. Education provides knowledge and capabilities that contribute to mental, physical, and social wellbeing. Educational qualifications are also a determinant of an individual’s labour market position, which in turn influences income, housing and other material resources associated with health.
Level of development at the end of reception year: In London, 74.1% of children achieved a good level of development at the end of Reception in 2018/19. This is significantly higher than the rate for England at 71.8%. In London, there were statistically significant differences between the sexes. 80.4% of female and 68.1% of male children were achieving a good level of development at the end of reception. At local authority level there were three local authorities in London with a statistically worse proportion of children achieving a good level of development: Tower Hamlets (69.9%), Enfield (69.7%) and Hackney (69.6%).
GCSE results (attainment 8): Attainment 8 measures pupils’ results in 8 GCSE-level qualifications. The average ‘Attainment 8’ score for pupils in London was 54.3 out of 90.0 in the 2020/21 academic year, higher than the score for England (50.9 out of 90.0). A difference between the sexes was apparent with an average score for females in London of 57.0 out of 90.0 and a score for males of 51.3. At local authority level the average score ranges from 51.4 per 90.0 for Barking and Dagenham to 60.9 out of 90.0 for Richmond upon Thames, with all but the former having significant better scores than the England average. The data also shows that the average Attainment 8 score for pupils in the 2020/21 academic year varied by ethnic group. Pupils from the Chinese ethnic group had the highest average Attainment 8 score out of all ethnic groups (70.3 out of 90.0) although this estimate should be treated with caution as the denominators are small. Asian pupils has the second highest score (59.2). Black pupils had the lowest score (50.5), followed by Mixed and White ethnic pupils (53.1 and 53.5).
Not in education, employment, training, or whose activity was not known (NEET): In 2020, 4.0% of 16-17-year olds in London were not in education, employment, training or whose activity was not known (NEET). This is statistically better than the England value of 5.5%. A statistically significant difference between sexes was also observed, with 3.2% of female and 4.8% of male 16-17 year olds NEET. There was significant variance between local authorities in London in 2020, Haringey (7.9%), Wandsworth (6.5%), and Lewisham (6.2%) have statistically significantly higher proportion when compared to that of England (5.5%).
Health protection
Overview
Health protection issues include the prevention and control of all types of infectious diseases, as well as chemical and environmental threats to the health of the population.
Over the past century, there has been a considerable reduction in the number of deaths from infectious diseases. However, the COVID-19 pandemic has demonstrated how threats from new infectious diseases can emerge and will continue to do so as a result of a range of global factors.
Environmental threats include factors such as air pollution, climate change and flooding. Climate change is a risk to health both nationally and globally. It affects all aspects of our everyday life and our environment, including the places we live, the air we breathe, as well as our access to food and water.
It is not possible to cover all health protection issues in this report. This section presents specific information on air pollution, sexually transmitted infections, tuberculosis (TB), vaccinations and vaccine preventable infections, and antimicrobial resistance.
Health Profile for England Highlights
Prior to the pandemic, the incidence of many infectious diseases such as TB had been declining. During this time, they disproportionately impacted the more deprived and inclusion health groups. In 2019, incidence of TB was higher in people born outside of the UK, particularly those of Indian, Pakistani or Black African ethnicity, than in people born inside the UK. It was also higher in the most deprived than the least deprived areas and more than a fifth of UK born cases had a known social risk factor such as homelessness or drug use. Preventable bacterial sexually transmitted infections (STIs) such as chlamydia and gonorrhoea had also been increasing prior to the pandemic.
The level of testing for or detection of many infectious diseases such as TB and STIs decreased during the pandemic. This may reflect a real decrease in incidence due to social distancing measures or may reflect a reluctance to be tested. In addition, as demonstrated by the reduction in MMR (measles, mumps, rubella) vaccine coverage, childhood vaccinations were also interrupted during the pandemic whilst flu vaccination coverage was considerably higher than previous years. Flu vaccine uptake in England from 1 September 2020 to 28 February 2021 was 80.9% for patients aged 65 years and over compared with 72.4% in 2019 to 2020.
Air pollution
Air pollution can contribute to cardiovascular and respiratory conditions and shorten lives. It is estimated that long-term exposure to the air pollution mixture in the UK has an annual effect equivalent to 28,000 to 36,000 deaths [25].
Figure 24a shows the trend in the level of air pollution from man-made fine particulate matter for London. The trend between 2011 and 2020 has been decreasing. In 2020, the level of air pollution from man-made fine particulate matter was 8.9 μg/m3 in London compared to 6.9 μg/m3 for England.
There is some variation in levels of across local authorities in London ranging from 8.0 μg/m3 in Hillingdon to 10.0 μg/m3 in Newham (Figure 24b).
Health Profile for England Highlights
The Health Profile for England 2021 highlights that the highest air pollution exposures have been in deprived urban environments therefore contributing to health inequalities. During the pandemic, up to July 2021 there were fewer vehicles on the roads, which had a favourable impact on air pollution levels. This reduction in vehicles was due to social restrictions implemented. Motor vehicle use fell dramatically during the first (March 2020) and third (January 2021) national lockdowns in England, but by the end of July 2021 was similar to previous years.
Figure 24 – Air quality
Figure 24a - Trend
Source: OHID Wider Determinants of Health Date accessed: 31/03/2022 Download data
Figure 24b - UTLA
Source: OHID Wider Determinants of Health Date accessed: 31/03/2022 Download data
Sexually transmitted infections
Figure 25a shows that in London, the diagnostic rates per 100,000 for chlamydia (aged 25+), gonorrhoea and syphilis had been increasing in the London region between 2012 and 2019. The diagnostic rate per 100,000 for genital warts has been decreasing since 2012. There was a decline in the detection rates of all STIs in 2020. There is wide variation in the diagnostic rates of STI in the London region. Chlamydia (aged 25+) diagnoses are most common followed by gonorrhoea, genital warts and syphilis (Figure 25b).
Chlamydia (aged 25+): Diagnostic rate per 100,000 in the London region was 403 compared to 171 for England in 2020. The local authority with the highest chlamydia diagnostic (aged 25+) rate per 100,000 in London is Lambeth (diagnostic rate of 1084.3/100,000), whilst the lowest is Bexley (diagnostic rate of 120.6/100,000).
Gonorrhoea: Diagnostic rate per 100,000 in the London region was 309 compared to 101 for England in 2020. The local authority with the highest gonorrhoea rate per 100,000 in the London region is Lambeth (diagnostic rate of 1024.2/100,000), whilst the lowest is Sutton (diagnostic rate of 83.3/100,000).
Genital warts: Diagnostic rate per 100,000 in the London region was 76.7 compared to 48.6 for England in 2020. The local authority with the highest genital warts rate per 100,000 in the London region is Hammersmith and Fulham (diagnostic rate of 160.2/100,000), whilst the lowest is Sutton (diagnostic rate of 36.6/100,000).
Syphilis: Diagnostic rate per 100,000 in the London region was 39.6 compared to 12.2 for England in 2020. The local authority with the highest syphilis rate per 100,000 in London is Lambeth (diagnostic rate of 147.9/100,000), whilst the lowest is Havering (diagnostic rate of 8.1/100,000).
Health Profile for England Highlights
The epidemiology of sexually transmitted infections (STIs) has changed markedly over the last two decades, reflecting changes in demographics, individual behaviours, surveillance techniques, diagnostics and treatments. There has been a continued decline in the rate of new HIV diagnoses [26] due to a combination of testing, pre-exposure prophylaxis, rapid linkage to treatment and support for those diagnosed with HIV to attain viral suppression. There has also been a decline in the rate of genital warts following the introduction of the HPV vaccination programme (Figure 25a).
The Health Profile for England 2021 reported that the measures taken to control the COVID-19 pandemic resulted in a decrease in the number of people accessing services. Reduced demand for these services during this time may have been influenced by compliance with social distancing measures and changes in risk perception and behaviour but may also indicate undetected infections. The full impact on infection transmission and long-term health outcomes will take time to emerge and evaluate [27].
Figure 25 – Sexually transmitted infections
Figure 25a - Trend
Source: Sexual and Reproductive Health Profiles Date accessed: 31/03/2022 Download data
Tuberculosis
The number of new cases of tuberculosis (TB) has fallen dramatically in England over the last century. Figure 26a shows the trend in TB incidence (new cases per 100,000 population) between 2000 and 2020. In London there has been a steady decline in the TB incidence rate since 2011. In 2020, the incidence rate was 16.3 per 100,000 in London compared to 7.3 per 100,000 for England.
Health Profile for England Highlights
The Health Profile for England 2021 reported that rates of TB are higher in people born outside of the UK, particularly those of Indian, Pakistani or Black African ethnicity, than in people born inside the UK. It was also higher in the most deprived than the least deprived areas, and more than a fifth of UK born cases have a known social risk factor such as homelessness or drug use.
Figure 26 – Tuberculosis
Figure 26a - Trends
Source: OHID TB Strategy Monitoring Indicators Date accessed: 31/03/2022 Download data
Vaccines and vaccine preventable infections
As a result of effective vaccination programmes the incidence of many diseases has reduced significantly over time. The importance of vaccination in controlling infectious diseases is highlighted by the COVID-19 pandemic as discussed earlier.
Influenza uptake rates in winter 2020 to 2021 were higher than they had been in previous years. This was driven by increased efforts to reach more people and increased population awareness due to the COVID-19 pandemic. In London, influenza vaccine coverage in GP registered patients aged 65 and over, was 71.8% in 2020/21 compared with 66.2% in 2019 to 2020. In patients under 65 years in one or more clinical risk groups, coverage was 45.0% in 2020/21 compared with 41.8% in 2019/20. As a consequence of this, and the social distancing measures introduced for the COVID-19 pandemic, influenza-like illness was much lower in 2020 to 2021 than in other seasons.
Advice from the Joint Committee on Vaccination and Immunisation (JCVI) on routine childhood immunisations stated that children should continue to receive vaccinations according to the national schedule during the COVID-19 pandemic. Measles is a highly infectious disease which can only be controlled by vaccination. People who have not received two doses of the MMR (measles, mumps, rubella) vaccine are at risk of developing measles. Nationally, monthly monitoring of MMR vaccination coverage shows that the measures implemented to manage the pandemic have impacted on vaccination uptake. MMR (first dose) monthly vaccine coverage estimates measured at 18 months of age from 2019 to 2021, show a decrease from April 2020. The largest decreases were seen in data for August to November 2020, reflecting a decline in uptake within the cohort of children who would have been eligible for the vaccine during the March to May 2020 lockdown.
In London, the coverage of one dose for children aged five years has been consistently lower than the England average for the past decade. In 2019/20, London was the worst ranked region in the country with a coverage of 89.8%, compared to 90.4% in 2018/19. This was also considerably lower than the second lowest ranked region, West Midlands (94.4%). In 2019/20 only 76.9% of children aged five years had received the two doses, a slight increase from previous years.
Antimicrobial resistance
Antibiotic-resistant bloodstream infections rose by an estimated 32% between 2015 and 2019 in England. Figure 27a shows the trend in the rate of antibiotic prescribing in primary care in London and England between 2015 and 2020. Antibiotic prescribing in primary care is often measured in STAR-PU, which are weighted units to allow comparisons adjusting for the age and sex of the population. It shows that the rate of antibiotic prescribing in primary care in London has fallen every year, with the largest drop between 2019 and 2020, and has consistently remained lower than the England average. There is some variation in prescribing rates within London. However, all local authorities have lower rates than the England average as demonstrated by Figure 27b.
Figure 27 – Antibiotic prescribing
Figure 27a - Trends
Source: OHID Public health profiles Date accessed: 31/03/2022 Download data
Conclusions
The 2021 Health Profile for London provides a comprehensive snapshot of the population and provides an early summary of the impact of the COVID-19 pandemic on many aspects of health and health inequalities. The report highlights how the direct impact of COVID-19 pandemic has disproportionately affected people from ethnic minority groups, those living in deprived areas, older people, and those with pre-existing health conditions.
There have been substantial indirect effects on children’s education and mental health, and on employment opportunities across the life course, particularly for younger people working in sectors such as hospitality and entertainment. In addition, access and use of a range of health services has been disrupted during the pandemic and the long-term effects of this is not yet realised.
Data on many aspects of health during the pandemic are not yet available but will be added to the Wider Impacts of COVID-19 on Health (WICH) monitoring tool where possible. Continued monitoring of the indirect impacts of the pandemic on the nation’s health and health inequalities will remain a priority.
References
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Further resources
| Theme | Tool | Brief Description |
|---|---|---|
| Topic based geographic profiles |
Fingertips |
Fingertips profiles comprise a comprehensive source of indicators across a wide range of health and wellbeing themes. They are designed to support Joint Strategic Needs Assessment (JSNA) and commissioning to improve health and wellbeing and reduce inequalities. Fingertips facilitates users to: browse indicators at different geographical levels, benchmark against the regional or national average and export data for local use Profiles are developed by the Office for Health Improvement and Disparities (OHID) and include the Public Health Outcomes Framework (PHOF), classified as an official statistic. |
| Topic based geographic profiles |
Local Health |
Local Health comprises small area health-related data visualised in maps, charts, area profiles, and reports (down to MSOA level). It offers a set of tools to explore health-related indicators to analyse, compare, map and export for further use. 72 indicators are grouped into four domains: Our community, Behavioural Risk Factors and Child Health. Disease and Poor Health, Life Expectancy and Causes of Death The tool provides area profiles and reports for small areas and allows users to define geographies and add their own data. |
| Service configuration |
SHAPE (Strategic Health Asset Planning and Evaluation) |
SHAPE is a web enabled, evidence-based application that informs and supports the strategic planning of services and assets across a whole health economy. Its analytical and presentation features are designed to support commissioners determine the best service configuration for their area. The SHAPE tool links national datasets, clinical analysis, public health, primary care, and demographic data with information on healthcare estates performance and facilities. It includes an integrated mapping tool for travel time analysis. Access is free to NHS and Local Authority professionals with a role in Public Health or Social Care via registration. The tool’s primary aim is to facilitate scenario planning and option appraisal to support STPs to: evaluate the impact of service configuration on populations, assess the optimum location of services |
| Global health |
GBD (Global Burden of Disease) |
Originated by World Bank and WHO in 1991, GBD is a method of measuring the impact of disease by combining measures of mortality, morbidity, and disability. GBD provides a consistent and comparative description of the burden of diseases, injuries, and risks to address: the main causes of health loss in an area; which causes are getting worse or improving; what causes have significantly higher, or lower, rates; and the major risks associated with the causes of the burden Main results are presented as disability adjusted life years (DALYs), a time-based measure that combines years of life lost due to premature mortality (YLLs), and years lived with a disability (YLDs). These metrics were specifically developed to assess the burden of disease. GBD also includes a database of evidence and data. The GBD Compare tool can be used for comparing trends over time and place and includes a range of visualisations: tree maps, arrow diagrams, heat maps, patterns, and line charts. |
| COVID-19 |
The Wider Impacts of COVID-19 on Health (WICH) |
The WICH monitoring tool is designed to explore the indirect effects of the COVID-19 pandemic on the population’s health and wellbeing. WICH presents a range of health and wellbeing metrics in interactive plots that can be broken down to show differences between groups, for example by region or social class. It is updated monthly, and metrics are grouped into nine themes: access to care, air quality, behavioural risk factors, grocery purchasing and food usage, life expectancy, mental health and wellbeing, mortality, pregnancy and birth, social determinants of health |
| COVID-19 |
CHIME |
The CHIME tool combines data relating to the impacts of COVID-19, including mortality rates, hospital admissions, confirmed cases, vaccinations and life expectancy. It presents inequality breakdowns including age, sex, ethnic group, deprivation, population density, region, and can be used to: a) show changes in inequalities over the course of the pandemic and the current position; b) bring together data to enable users to access and utilise intelligence; c) provide indicators with a consistent methodology across different datasets; d) support users to identify and address inequalities within an area, and highlight priorities |
| NHS healthcare |
Fingertips atlas of variation |
The Atlases of Variation help identify unwarranted variation and assess the value that healthcare provides to populations and individuals. They are produced in collaboration with OHID, NHS England, RightCare and others. Products include Compendium and themed atlases. Each indicator map, column chart and box-and-whisker plot is accompanied by text providing context, description of variation and trend data, options for action, and evidence-based resources. Interactive Atlases services accessed via the NHS England website enable organisations to interrogate local data. The tool allows users to view maps, charts, time series data and associated statistics across all indicators within the atlas. Organisations can use this to see where they sit within the national landscape or peer groups. |
| Health economics |
SPOT (Spend and Outcomes Tool) |
SPOT aims to help local commissioners improve people’s health and wellbeing and reduce health inequalities through better information about value for money. SPOT is designed for those interested in spend and outcomes across the public sector. Examples of how SPOT can be used include to: a) assess variation by comparing a local authority to others; b) support planning and prioritisation across programmes by collectively reviewing SPOT data; c) support needs assessment; d) justify maintenance of spend by providing evidence on value for money; e) support thinking about the interface between public health and other areas |
| Regional public health profile | A Picture of Health | Health Intelligence pack for regional priorities 2022 provides updated place-based regional health needs assessment to identify regional priorities following a scheduled quarterly data refresh to the key public health indicators including analysis of causes of mortality by age group, deprivation, and demographics. |