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Adult Psychiatric Morbidity Survey (APMS)

The Adult Psychiatric Morbidity Survey (APMS) series provides data on the prevalence of both treated and untreated psychiatric disorders in the English adult population (aged 16 and over). The survey has been carried out on 4 occasions, in 1993, 2000, 2007 and 2014. The most recent survey was conducted by NatCen Social Research, in collaboration with the University of Leicester, for NHS Digital.

All the APMS surveys have used largely consistent methods and in 2014 the sample size was around 7,500 adults.

APMS assesses psychiatric morbidity using actual diagnostic criteria for a range of disorders: common mental disorders, post-traumatic stress disorder, psychotic disorder, autism spectrum disorder, personality disorder, attention-deficit/hyperactivity disorder, bipolar disorder, alcohol dependence, drug use and dependence, suicidal thoughts, suicide attempts and self-harm, and comorbidity in mental and physical illness.

Age-standardised rates

Age-standardised rates adjust for differences in the age structure of populations and allow comparisons to be made between geographical areas and through time.1 The direct method applied across this report presents the age-standardised rate for a particular condition which would have occurred if the observed age-specific rates for the condition in that population or sub group had applied in a given standard population. The European Standard Population 2013 is used.

Where data are age-standardised for a specific age range which is not the whole population (for example, those aged under 75) the European Standard Population for the age range shown is used.

Attainment 8 Score

Attainment 8 measures the educational achievement of a pupil across 8 qualifications. These qualifications are:

  1. A double weighted maths element that will contain the point score of the pupil’s English Baccalaureate (EBacc) maths qualification.

  2. An English element based on the highest point score in a pupil’s EBacc English language or English literature qualification. This will be double weighted provided a pupil has taken both qualifications

  3. An element which can include the three highest point scores from any of the EBacc qualifications in science subjects, computer science, history, geography, and languages. For more information see the list of qualifications that count in the EBacc. The qualifications can count in any combination and there is no requirement to take qualifications in each of the ‘pillars’ of the EBacc.

  4. The open element contains the three highest point scores in any three other subjects, including English language or literature (if not counted in the English slot), further GCSE qualifications (including EBacc subjects) or any other technical awards from the DfE approved list.

For more information, see the list of qualifications included in the key stage 4 performance.

If a pupil has not taken the maximum number of qualifications that count in each group then they receive a point score of zero where a slot is empty.

The data presented in the report are the average Attainment 8 score for all pupils in state-funded schools.

Child obesity

The data presented for these indicators only includes children participating in the National Child Measurement Programme (NCMP) in state maintained schools. Any measurements taken at independent and special schools are excluded from the analysis.

Deprivation deciles are derived from the postcode of child residency; only children with valid geographical coding (postcode of residence) have been included in this analysis.

England totals include all children in state-maintained schools, with a valid height and weight measurement, including those with an unknown residency.

Children are classified as overweight (including obese) if their BMI is on or above the 85th centile of the British 1990 growth reference (UK90) according to age and sex.

COVID-19 Health Inequalities Monitoring in England Tool (CHIME)

The CHIME tool brings together data relating to the direct impacts of COVID-19, such as for mortality rates, hospital admissions and confirmed cases. By presenting inequality breakdowns, including by age, sex, ethnic group, level of deprivation and region, the tool provides a single point of access in order to:

Cumulative Case Rate (COVID-19)

The cumulative case rate is calculated by summing the COVID-19 cases up to a given date and dividing by the total population. The case rates are age standardised to take account of differences in the age structure of different population groups.

Dental decay in children

This is defined as children who are not free from obvious dental decay (having one or more teeth that were decayed to dentinal level, extracted or filled because of caries).

Data for individual local authorities are not included if the authority did not take part in the survey or if the number of children examined was too small (less than 30) for a robust estimate.

Deprivation deciles or quintiles

Deprivation deciles or quintiles have been constructed using the Index of Multiple Deprivation (IMD) scores at lower super output area (LSOA) level where possible, and if not at district and unitary (UA) authority level, then county and UA authority level. In the Figures in this report, if the level is not specified, LSOA level deprivation has been used, otherwise the level is specified by ‘District/UA’ or ‘County/UA’ in brackets. Unless otherwise specified in the text, Index of Multiple Deprivation 2019 (IMD2019) scores have been used.

LSOAs are small geographic areas produced by the ONS to enable reporting of small area statistics in England and Wales. There are 32,844 LSOAs in England, each having a population of approximately 1,500.

To create LSOA deprivation deciles for use in chart presentation and also in the calculation of the slope and relative index of inequality measures, LSOAs within England were ranked from most to least deprived and then organised into ten categories with approximately equal numbers of LSOAs in each.

Since the total number of LSOAs in England is not exactly divisible by ten, the ‘extra’ LSOAs were allocated to deprivation deciles using a systematic method outlined in PHE’s Technical Guide: Assigning Deprivation Categories.

The equivalent approach is taken for deprivation quintiles, only organising LSOAs into 5 categories.

To create district and UA or county and UA deprivation deciles these local authorities were ranked from most to least deprived within England and then organised into ten categories with approximately equal numbers of local authorities in each. Further information can be found in PHE’s Technical Guide: Assigning Deprivation Categories.

Where a relationship exists between deprivation and a health outcome, this may be less apparent when deprivation deciles are constructed at district and UA or county and UA level, when compared to LSOA level. IMD scores for these geographies are calculated by averaging LSOA scores across each local area. This can be necessary if the data for the indicator is not available at LSOA level, but it may mask important in-area differences. For example, people in more deprived LSOAs in different local areas may have more similar experiences of a health outcome to each other than they do with people in less deprived LSOAs in their own local area. Where district and UA deciles are calculable, these are preferred to county and UA deciles as IMD scores are averaged across smaller areas.

Excess mortality

Excess mortality in any population group is a measure of the number of deaths, from all causes, over and above what would be expected in that population group, had the pandemic not occurred.

This comparison is presented in the form of a ratio, where a value greater than 1 in a population indicates that the number of deaths was higher than expected and a value below 1 indicates that the number of deaths registered was lower than expected.

The number of expected deaths result from statistical models based on data from 2015 to 2019. Excess deaths are estimated in PHE’s ‘Excess mortality in England’ reports by week and in total from 21 March 2020 onwards, based on the date each death was registered 2. This report presents summary data by age, sex, deprivation group, ethnic group, place of death and region. Apart from place of death, which is analysed separately, these breakdowns come from a single analysis. Therefore, data for England in the ‘place of death’ category differs slightly from data for England reported for other categories.

In addition to the analyses summarised in this report, the ‘Excess mortality in England’ reports also report by cause of death (any mention of the cause of death on the death certificate) and by upper tier local authority, from two further analyses.

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. The data in the ‘Excess mortality in England’ reports breaks down registered deaths by whether COVID-19 was mentioned to aid understanding of this breakdown.

A detailed methodology document is available on the ‘Excess mortality in England: weekly reports’ page2.

Full term babies

Babies born at full term are those born after 37 complete weeks gestation. Premature births are those born at less than 37 weeks gestation.

Global Burden of Disease study (GBD)

The Global Burden of Disease study (GBD) collects data from over 80,000 data sources from countries across the world. These are used as inputs for the GBD modelling methodology to produce comparative estimates of death and disability.

The GBD groups diseases into a 4 level cause hierarchy. Level one groups diseases at a high level: communicable diseases, non-communicable diseases, and injuries. Each subsequent level in the hierarchy presents a finer grouping of conditions that nest within the level above. For example, the second level consists of major disease or injury groups, such as musculoskeletal conditions or mental and substance use disorders. The third level, which is used for most of the analysis presented in the report, further subdivides causes into disease or injury type such as osteoarthritis, low back and neck pain, and depressive disorders.

More information on the disease hierarchy can be found on the GBD website.

Healthy life expectancy

Healthy life expectancy at birth is an estimate of the average number of years that would be lived in a state of ‘good general health’ by babies born in a given time period, given mortality levels at each age and the level of good health at each age for that time period.

The healthy life expectancy measure adds a ‘quality of life’ dimension to estimates of life expectancy by dividing it into time spent in different states of health. Health status estimates for England are based on the following survey question: ‘How is your health in general; would you say it was. very good, good, fair, bad, or very bad?’ If a respondent answered ‘very good’ or ‘good’ they were classified as having ‘good’ health. Those who answered ‘fair’, ‘bad’, or ‘very bad’ were classified as having ‘not good’ health and equate to those in ‘poor’ health in the reported figures for England.

There are known limitations to data on self-assessed health status. We know that people give subjective answers to the question used to determine health status. Responses are influenced by an individual’s expectations and there are measurable differences across sociodemographic factors such as age, sex, and deprivation.

There is also likely to be a bias arising from the way respondents are selected to take part in the survey. The data are based on surveys that are not able to select people for interview who are living in institutional accommodation (for example, care homes). This may lead to an underestimate of the level of poor health.

Poor health in the EU data is defined by responses to a question in the EU Statistics on Income and Living Conditions survey (EU-SILC) which asks people: ‘How is your health in general? Is it . very good, good, fair, bad, or very bad?’ If a respondent answered ‘very good’, ‘good’ or ‘fair’ they were classified as having ‘good’ health. Those who answered ‘bad’, or ‘very bad’ were classified as having ‘not good’ health.

The EU data, therefore, uses a different definition of ‘not good’ health to that used by ONS for England estimates. In the EU data, people who say their health is ‘fair’ are regarded as having ‘good’ health but in the ONS data people who say their health is ‘fair’ are defined as having ‘not good’ health.

The EU indicator of healthy life expectancy is calculated using the same method for all countries, but Eurostat caution that the way the survey question was worded by different EU members might hamper cross-country comparisons. In addition, it has long been known that there are cultural differences in the self-reporting of health status which are difficult to resolve and are likely to affect the comparability of data for healthy life expectancy and years in poor health between countries.

Inclusion Health

Inclusion health is a ‘catch-all’ term used to describe people who are; socially excluded, typically experience multiple overlapping risk factors for poor health (such as poverty, violence and complex trauma), experience stigma and discrimination, and are not consistently accounted for in electronic records (such as healthcare databases)3. All socially excluded population groups are included in ‘inclusion health’, for example, people who experience homelessness, drug and alcohol dependence, vulnerable migrants, Gypsy, Roma and Traveller communities, sex workers, people in contact with the justice system and victims of modern slavery.

People in inclusion health groups often suffer from multiple health issues (physical and/or mental) and may have issues with substance dependence. This places them at higher risk of poor health outcomes, lower average age of death, increasing health inequalities in comparison to the general population.

Index of Multiple Deprivation (IMD)

The Index of Multiple Deprivation (IMD) is a measure of relative deprivation for small areas. It is one element of the English Indices of Deprivation released by the Ministry of Housing, Communities and Local Government.

Life expectancy

Life expectancy at birth is a summary measure of the population. It represents the average number of years that would be lived by babies born in a given time period according to the mortality rates for that time period.

Life expectancy may be calculated for a single year (as in Figure 6 of this report) or for several years (as in Figure 14, where 3 years are pooled). Measuring for a single year is more sensitive to shorter term changes in mortality rates. In addition, life expectancy may be estimated using slightly different methods. This is the case in Figure 6a, where the data from the Office for National Statistics up to 2014 is based on mortality rates at single years of age, while the analysis from the Wider Impact of COVID-19 on Health tool is based on mortality rates for five-year age bands. This makes only a very small difference to the calculated life expectancy and therefore a single time series is presented in Figure 6a.

Life expectancy - contribution of age and cause of death to change and inequality

The contribution of different age bands or causes of death to changes in life expectancy over time (due to changes in age or cause specific death rates) can be calculated using a method of ‘life expectancy decomposition’. In this report, the Arriaga III method has been used, as described by Ponnapalli4. The method is based on a life table divided into 5-year age groups. The contributions of each age group are then distributed into causes of death using a method described by Preston and others5. Contributions are distributed proportionately according to the difference in mortality between time periods by cause of death within each age group.

Contributions to changes in life expectancy over time show the amount that life expectancy has increased or decreased in the later time period due to changes in the mortality rate since the earlier time period in a given age group or cause of death, assuming all other rates remained constant. Contributions that increased life expectancy (that is, where mortality rate has reduced over time) have a positive value, while contributions that offset the life expectancy increase (that is, where mortality rate has increased over time) have a negative value.

The same decomposition method can also be used to assess the contribution of different age bands or causes of death to differences (or the gap) between areas with different levels of deprivation.

Contributions to the gap show the amount that life expectancy would increase in the most deprived area if its mortality rate for a given age group or cause of death was changed to that of the least deprived area, assuming all other rates remained constant. Contributions that widen the inequality gap (that is, where mortality rate is higher in the most deprived area) are represented with a positive value, while contributions that offset the gap (that is, where mortality rate is higher in the least deprived area) are represented with a negative value.

Low birthweight at term

This indicator is defined as live births with a recorded birth weight under 2,500g and a gestational age of at least 37 complete weeks as a percentage of all live births with recorded birth weight and a gestational age of at least 37 complete weeks.

The ONS has linked birth registrations with NHS birth notification records to allow reporting by gestational age and birth weight. With 99.4% of records linked successfully, completeness of this dataset is very good. However, not all births are recorded with a valid birth weight and gestational age. There may be regional variations in the completeness of these fields.

Minimum Income Standard (MIS)

The Minimum Income Standard (MIS) is the income that people need in order to reach a minimum socially acceptable standard of living in the United Kingdom today, based on what members of the public think. It is calculated by specifying baskets of goods and services required by different types of household in order to meet these needs and to participate in society. The research entails a sequence of detailed deliberations by groups of members of the public, informed by expert knowledge where needed. The groups work to the following definition: ‘A minimum standard of living in the UK today includes, but is more than just, food, clothes and shelter. It is about having what you need in order to have the opportunities and choices necessary to participate in society.’6

Data is published in the source document, accompanied by text. “All rights reserved. Reproduction of this report by photocopying or electronic means for non-commercial purposes is permitted. Otherwise, no part of this report may be reproduced, adapted, stored in a retrieval system or transmitted by any means, electronic, mechanical, photocopying, or otherwise without the prior written permission of the Joseph Rowntree Foundation.” Copyright Loughborough University 2017.

Proportion of life spent in poor health

The total number of years lived in poor health divided by the total number of years lived (life expectancy), expressed as a percentage.

Relative excess mortality

The statistical method used to calculate relative excess mortality is termed the relative cumulative age-standardised mortality rate (rcASMR). There are two steps to calculate the rate:

  1. the net difference between the age-standardised mortality rate for 2020 and a baseline for the same period in 2015 to 2019 is calculated.

  2. this is presented as a percentage of the annual age-standardised mortality rate at baseline (the average rate for the five years).

A nil value for rcASMR indicates that age-standardised mortality across the period has been equal to the average. A positive value indicates worse than average mortality, and a negative value indicates better than average mortality.

In this report a measure at the end of the year is used. Therefore if the baseline age-standardised mortality rate is 1,000 per 100,000, and the 2020 age-standardised mortality rate is 1,200 per 100,000, this would be (1,200 - 1,000) / 1,000, or 20%.

Relative excess mortality differs from excess mortality, which is a ratio of the observed to expected deaths. Relative excess mortality always increases in periods where the mortality rate is greater than baseline and decreases in periods where the mortality rate is smaller the baseline. The excess mortality ratio averages across the period, and so can reduce even when there is ongoing excess mortality, if it is at a lower level than before. In this report, it should also be borne in mind that the measure of excess mortality starts in March 2020 (early in the pandemic), whereas relative excess mortality is measured from January 2020. Furthermore, the calculation of relative excess mortality requires only deaths and population data by age, allowing international comparisons to be made more readily, whereas PHE’s excess mortality data is the result of complex statistical models.

Registrations (registered deaths)

Deaths occurring in England and Wales are registered on the General Register Office’s Registration Online system (RON). Registration Online (RON) is a web-based system that enables registrars to record births, stillbirths, deaths, marriages and civil partnerships online. The underlying cause of death is “the disease or injury which initiated the train of morbid events leading directly to death, or the circumstances of the accident or violence which produced the fatal injury”.

If mortality data are described as being reported by registration year, this refers to the number of deaths registered in that year. Overall, across all causes, this would be expected to be similar to the number of deaths that occur in that year, as most deaths are registered shortly after they occur. However, for specific causes which involve coroner’s inquests, such as suicide and accidental deaths, many deaths registered in a given year will have occurred in earlier years. In England and Wales, deaths referred to a coroner can only be registered once the inquest has concluded. This should be borne in mind when interpreting mortality data for these causes.

Severe mental illness (SMI)

This is measured by recording the number of people in contact with secondary mental health services for specific conditions. SMI include schizophrenia-spectrum disorders, severe bipolar disorder, and severe major depression.

Slope index of inequality (SII) and Relative index of inequality (RII)

These are measures of inequality. The slope index of inequality (SII) is a measure of the social gradient in an indicator and shows how much the indicator varies with deprivation (by deprivation decile). It takes account of inequalities across the whole range of deprivation within England and summarises this into a single number. The measure assumes a linear relationship between the indicator and deprivation.

The SII represents the absolute difference in the indicator across the social gradient from most to least deprived.

A more detailed description of the methodology used to calculate the SII can be found in the PHOF overarching indicators technical user guide.

The relative index of inequality (RII) is a summary measure of inequality related to the SII. While the SII measures the absolute difference between the most and least deprived, the RII measures the relative difference and is presented as a ratio of the least deprived to the most deprived for an indicator. For example, the RII for cancer premature mortality in 2016 to 2018 was 2.2. This means that the cancer mortality rate is 2.2 times higher in the most deprived compared to the least deprived areas. A relative measure of 1 would indicator that there was no inequality by deprivation.

When calculating the SII and RII it is assumed that the there is a linear relationship between the indicator decile values and deprivation.

The log slope index of inequality (log SII) and log relative index of inequality (log RII) are used to measure inequality by deprivation for indicators where there is not a linear relationship between the indicator decile values and deprivation.

This method has been used for infant mortality in the Health Profile for England.

In this method, decile values for the indicator are logged before calculation of the SII and RII. This results in a regression line which better fits the data for each of the three indicators. The SII and RII based on the log scale are more difficult to interpret, so the figures in the dashboard are presented in the original units of indicator (converting back to original units by taking the anti-log of extreme values of the SII line and recalculating SII and RII.

Smoking status at the time of delivery

This is measured by the number of mothers known to be smokers at the time of delivery, as a percentage of all maternities where the smoking status is known. A maternity is defined as a pregnant woman who gives birth to one or more live or stillborn babies of at least 24 weeks gestation, where the baby is delivered by either a midwife or doctor at home or in an NHS hospital.

The indicator is based on observation and is therefore susceptible to measurement bias.

Smoking prevalence

This is defined as the percentage of adults aged 18 and over who report that they are current smokers, out of those who report a valid smoking status. The annual estimates use data from the Annual Population Survey carried out by the Office for National Statistics. The data have been weighted to improve representativeness. The weighting takes into account survey design and non-response.

Self-reported smoking may be subject to respondent bias and estimates of smoking prevalence are not age-standardised, and therefore differences between groups may result from differences in population structure.

Teenage conceptions

This is defined as conceptions in women aged under 18 per 1,000 females aged 15 to 17. Conceptions are defined as the number of pregnancies that occur in women aged under 18 and result in either one or more live or stillbirths or a legal abortion under the Abortion Act 1967.

The date of conception is estimated using recorded gestation for abortions and stillbirths, and assuming 38 weeks gestation for live births. A woman’s age at conception is calculated as the number of complete years between her date of birth and the date she conceived. The postcode of the woman’s address at time of birth or abortion is used to determine geographical area of residence at time of conception. Only about 5% of under 18 conceptions are to girls aged 14 or under and to include younger age groups in the base population would produce misleading results. The 15 to 17 age group is effectively treated as the population at risk.

Wider Impacts of COVID-19 on Health (WICH) tool

The Wider Impacts of COVID-19 on Health (WICH) monitoring tool is designed to allow you 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, you can explore grocery purchasing habits by region or social class. WICH is updated monthly and includes the addition of new metrics as they become available.

Years lived in poor health

The average number of years lived in poor health is the average life expectancy minus the average healthy life expectancy (number of years lived in good health). An increase in the average number of years lived in poor health is often referred to as ‘expansion of morbidity’, whereas a reduction is referred to as ‘compression of morbidity’7

Years lived with disability (YLD)

Years lived with disability (YLD) is a measure that summarises levels of poor health and disability in a given population. It combines the prevalence of each disease with a rating of how disabling the disease state is.

In the Global Burden of Disease (GBD), for a particular year, age and sex, the YLDs associated with a given disease or condition are calculated in 3 steps:

Disability weights are a measure of the disability a person perceives when in a particular health state. Each health state is matched to a sequel, so the disability weight represents the magnitude of health loss associated with that sequela. The weights are measured on a scale from 0 to 1, where 0 equals a state of full health and 1 equals death.

This means, for example, that conditions with a low perceived disability but high prevalence are comparable with conditions with a low prevalence and high disability, in terms of overall loss of quality of life. The YLD measure is used in this report to describe the non-fatal burden and is referred to as morbidity. Better health is associated with fewer years lived in disability.

Years of life lost (YLL)

Years of life lost (YLL) are a measure of the population impact of premature mortality. They differ from mortality rates in that deaths at a younger age are assigned a weight according to the number of years lost and therefore contribute more to the final total. Therefore, if two populations have similar mortality rates to another, the YLL measure would calculate a greater burden in the one with a younger average age at death.

All measures of YLL require a comparator age (an expected age of death) from which to subtract the age at death, and so derive the number of years lost. In some measures, a specific age is used for all persons (for example, 75 years old). This assumes that all persons would have been expected to live to at least that age, and there are no YLL assigned above this age.

The measure in the Global Burden of Disease (GBD) study uses a more complex approach to YLL, whereby the number of years assigned to a death is based on an estimate of life expectancy for the individual. This therefore estimates the number of years that person would have been expected to live, with no uniform age limit for the whole population. The estimates of life expectancy used vary by age and sex but not between different areas, and therefore can be compared as a rate of the population. In GBD, the maximum life expectancy globally by age and sex is used.

The GBD YLL measure is calculated via the following steps:

Like mortality rates, the YLL measure may also be age-standardised in order to account for differences in the age structure of different populations (as shown in Figure 8c).

References

  1. ONS (2012) Age-standardised rates Accessed 23 June 2021.↩

  2. Excess Mortality in England [Internet]. 2021 [cited 30 July 2021]. Available from: https://www.gov.uk/government/statistics/excess-mortality-in-england-weekly-reports↩

  3. Public Health England (2021) Inclusion Health: applying All Our Health Accessed 09 August 2021.↩

  4. Ponnapalli K (2005) A comparison of different methods for decomposition of changes in expectation of life at birth and differentials in life expectancy at birth. Demographic Research 12:141 to 172.↩

  5. Preston S, Heuveline P, Guillot M (2000) Demography: Measuring and Modelling Population Processes. Blackwell Publishing.↩

  6. Padley M, Valadez Martinez L and Hirsch D (2017) Households below a Minimum Income Standard: 2008/09 to 2015/16. Joseph Rowntree Foundation. Accessed 24 June 2021.↩

  7. Fries JF (1980) Ageing, natural death, and the compression of morbidity. N Engl J Medicine 303(3):130 to 135.↩