1. Overview

  • This methodology article accompanies two bulletins presenting the Health Index results at local and national levels, and an article detailing the indicators contained within the Health Index.

  • The Health Index has been designed with the support of health experts to present a single number measuring the health of an area, with a clear breakdown of how different measures of health are combined to produce this value.

  • The development of the Health Index has followed guidance by the Competence Centre on Composite Indicators and Scoreboards (COIN) on producing composite indices.

  • Data have been selected from a wide variety of sources to allow comparisons across time and by geography, down to lower-tier local authority level.

  • Data selection has been based on important principles, such as the aim to measure health and its drivers rather than direct measures of health services.

  • Factor analysis has been used to group individual indicators of health into subdomains, guided by expert advice; factor analysis results have informed each indicator’s weight towards the total Index value.

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2. Origins of the Health Index

The Office for National Statistics (ONS) previously released an article presenting work to date on a composite Health Index. The release was a provisional, or “beta”, version covering England at upper-tier local authority (UTLA) level for the years 2015 to 2018. It provided an illustrative presentation of what the Index could look like, what the results could show and how this would enable new analysis.

In conjunction with releasing this article, a public consultation was launched to gain feedback on how the Health Index could be used and the methods used to produce it. We released a summary of the feedback received in the consultation. Details of the subsequent work to develop the Index further are described in Section 17.

We have now published two new bulletins presenting Health Index results at national and local levels. These are accompanied by this methodology article and our Health Index indicators and definitions article. These results cover England at lower-tier local authority (LTLA) level for the years 2015 to 2019.

The proposal for a Health Index was made in the 2018 annual report of the government’s then Chief Medical Officer (CMO), Dame Sally Davies, entitled Health 2040 – better health within reach. The report stated:

“We need to track progress in improving health and health outcomes, to and beyond 2040 with a new composite Health Index that reflects the multi-faceted determinants of the population’s health and equity in support of ensuring health is recognised and treated as one of our nation’s primary assets. This index should be considered by Government alongside GDP and the Measuring National Well-being programme. We regularly collect most of the datasets that have the individual measures that could be combined.”

Our aim is to develop the Health Index into a regular publication allowing differences in health to be tracked over time. Representatives from the health departments of the four UK nations have been involved in its development, with a view to extending coverage beyond England in the future.

The work to develop the Health Index so far has been completed in consultation with an Expert Advisory Group (EAG) consisting of representatives from a range of government, academic and third sector organisations. This group includes the following UK government departments and arms-length bodies:

  • Cabinet Office
  • Department for Business, Energy and Industrial Strategy (BEIS)
  • Department for Environment, Food and Rural Affairs (Defra)
  • Department for Levelling Up, Housing and Communities (DLUHC)
  • Department for Transport (DfT)
  • Department of Health and Social Care (DHSC)
  • London Health Partnership
  • NHS England
  • National Institute for Health and Care Excellence (NICE)
  • Northern Ireland Health Department
  • Office for Health Improvement and Disparities (OHID)
  • Office for National Statistics (ONS)
  • Public Health Wales
  • Scottish Government
  • Welsh Government

The group also includes the following organisations which are not part of the UK government:

  • Alan Turing Institute
  • Association of Directors of Public Health (ADPH)
  • Health Foundation
  • Institute for Fiscal Studies (IFS)
  • Institute for Social and Economic Research (ISER)
  • King’s Fund
  • Organisation for Economic Co-operation and Development (OECD)
  • Royal Society for Public Health (RSPH)
  • University College London

We extend our thanks to all members for their valuable input into the Health Index’s development.

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3. How the Health Index differs from existing products

The Chief Medical Officer’s 2018 report, Health 2040 – better health within reach, identified a need for a “single number” headline health indicator to act as a policy stimulus and public focus. There is no established example of a health index of the type we are currently developing, in England or elsewhere.

In terms of the existing health indicator “scene”, there are multiple frameworks in use in England, the UK and internationally, including:

These frameworks all have important uses, and most contain elements of all three domains defined for the Health Index, described in Section 6. What the Health Index offers, which these sources individually do not, is a single headline indicator of health that is transparent in its construction and can be compared over time. It can also be compared at different geographical levels and can be broken down into the effects that drive changes in Index scores.

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4. Potential users of and uses for the Health Index

We expect there to be three broad groups of people using the Health Index, including:

  • the media and general public
  • policymakers and analysts in government and local government
  • analysts and decision makers outside of government

The media and general public can present and see the headline measures as an indicator of change in the nation’s health, and of inequality in health between different geographical areas. In future, the Health Index may also present an indication of change between different demographic groups.

Policymakers in government and local government can clearly identify which topics related to health are not improving over time, and measure health impacts when assessing policies. The Health Index enables measurement of impacts on health to become more regular and consistent. Local government decision makers can compare health in their area with other places of their choosing, such as areas with similar characteristics, and learn about differences between them.

Analysts outside government, such as academics and those in think tanks and charities, can improve the body of evidence on different aspects of health and the stories this can tell us.

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5. Process for constructing the Health Index

Our process to construct the Health Index for England largely follows that outlined in the Organisation for Economic Co-operation and Development (OECD) and Joint Research Centre’s (JRC) Handbook on constructing composite indicators and subsequently, in the Competence Centre on Composite Indicators and Scoreboards’ (COIN) 10-step guide.

The steps included in this guide are:

  1. theoretical framework
  2. data selection
  3. imputation of missing data
  4. multivariate analysis
  5. normalisation
  6. weighting
  7. aggregating indicators
  8. sensitivity analysis
  9. link to other measures
  10. visualisation

This article will focus on Steps 1 to 8 and is structured as such. We have renamed Step 5 to “Homogenising the data” to reflect that scale-based transformations are also involved here. Links to other measures are discussed in Section 3 so are not detailed here.

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6. Theoretical framework (COIN Step 1)

The concept of health that the Health Index covers is largely derived from the Chief Medical Officer’s (CMO) original recommendation, which suggested that the Index should be “inclusive of health outcome measures, modifiable risk factors and the social determinants of health”.

This encompasses the World Health Organization’s definition of health, that “health is a state of complete physical, mental and social well-being, and not merely the absence of disease or infirmity”, and adds specificity to the idea of well-being.

The theoretical framework that the CMO alluded to is well known in public health and epidemiology. It can be summarised as dividing the factors influencing health into three categories, including:

  • health status or outcomes: mortality or life expectancy, morbidity measures such as disease prevalence; wider well-being measures
  • modifiable risk factors (MRFs): these are things that affect health that can potentially be changed at individual level, such as health-related behaviours (for example, smoking and exercise) and actionable clinical findings (for example, blood pressure), but it is important to understand that these factors are in the middle of a bigger causal chain
  • wider social drivers of health: circumstances that have a major effect on life chances including both MRFs and health outcomes, but cannot be addressed at individual level; examples include unemployment rates, quality of transport infrastructure and environmental pollution

Dahlgren and Whitehead’s “rainbow” diagram in Policies and strategies to promote social equity in health from the Institute for Future Studies, Stockholm, 1991, is often used to illustrate the relationships between these different factors impacting health.

With such a broad concept of health in scope for the Health Index, the topics included typically cover general health issues that are applicable to the whole population.

Considering the definitions mentioned previously, elements of health are divided into three domains for the Health Index, each corresponding to one of these three categories:

  • Healthy People – health outcomes, ensuring representation of the population as a whole
  • Healthy Lives – health-related behaviours and personal circumstances
  • Healthy Places – wider drivers of health that relate to the places people live, such as air pollution
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7. Data selection (COIN Step 2)

The first step in deciding what content to include was to conduct a review of existing indices and frameworks that had a relation to health. The aim was to understand what content they included, and what of that was relevant to the Health Index. This was conducted in the context of the broader definition of health explained in Section 6.

Following this we reviewed the wider literature to understand whether there was additional content the Health Index should include, as its aims, functions and purpose differ from these other products. In conjunction with both steps, a range of data sources that could potentially be used to measure these concepts were identified. We reported these initial proposals for Health Index content to the Expert Advisory Group (EAG) to gain their feedback on the concepts included, how they were measured and whether there were additional concepts we should add.

Using this feedback, a detailed review of the content proposed for inclusion was carried out. This included a critical review of how these should be measured and what data were available to construct the Index presenting those concepts. At all stages of this process the aim was to maintain the right balance between concept and data, ensuring the use of the most optimal measure without unduly compromising on data quality.

Data requirements for quality

The data that have been selected to develop the Index have largely come from already published sources as this means certain quality standards will already have been met. The data have also been checked to ensure they meet the needs for the Index, using the following criteria:

  • data must be available for enough years to make comparisons over time, which at this stage means 2015 to 2019; there may be some exceptions to this where it is reasonable to assume that large changes would not occur from year to year
  • there must be reasonable certainty that the data will continue to be produced into the future, to ensure comparisons over time are based on consistent data as far as possible
  • data must be available for lower-tier local authority areas (LTLAs), which is the smallest geographical breakdown available for most health data sources suitable for the Index’s needs; this is to allow Health Index numbers to be seen both for England as a whole and for specific geographical areas, allowing comparisons to be made between areas of interest

A central consideration during this process was ensuring that we measure health itself and its drivers, rather than healthcare activity, service performance or policy. This has been considered in both the inclusion of concepts and especially in the ways in which they are measured.

Some data sources have been ruled out because they are too directly linked to one of these aspects. For example, if the Index were to include the number of people receiving adult social care as a measure, the overall figure representing health would change if the national thresholds for social care eligibility changed, even if the nation’s health did not actually get better or worse.

There are some concepts deemed important enough to be presented in the Index, where the data could not meet all our requirements. In these instances, careful considerations have been made to understand whether the benefits of their inclusion outweigh the limitations. An example is household overcrowding, which is not currently available at our desired geographies other than in the Census, and therefore is not collected regularly enough to meet all the criteria for inclusion above. We do, however, see household overcrowding as sufficiently important to include in the Health Index with just one year of data at present.

Additional concepts were also added to the Health Index based on consultation feedback. Proposed revisions were shared with the EAG, and their suggestions also incorporated.

Data differences

For the Health Index, we want to be able to report results for calendar years for consistency with other Office for National Statistics (ONS) health statistics. However, not all data sources are published on this basis. For example, some statistics are presented in financial years or academic years.

Where data differ from calendar years, we have assigned the data to the year in which most of the source period falls. For example, data in the financial year April 2016 to March 2017 would be used in the Health Index as representing the 2016 calendar year.

For some data sources, three-year aggregates are used to present the data, where counts for individual years would risk being disclosive. In such cases, to maximise timeliness of Health Index releases in future, the three years averaged would be presented as results for the most recent of those three years in the Health Index.

A separate article, Health Index indicators and definitions, details the indicators that make up the Health Index. Details of the data that underpin these indicators are available in the Health Index datasets.

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8. Methods overview

The methods chosen to construct the Health Index result from extensive research and consultation with experts. Sensitivity analyses were conducted where alternative viable methods existed, to explore any potential impact of changing methods on Index scores.

The sections that follow detail each step taken to create the Health Index, including:

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9. Geographical aggregation

The Health Index is presented at country, region, upper-tier local authority (UTLA) and lower-tier local authority (LTLA) level and using 2021 administrative geographies.

There are 309 LTLAs, which combine to form 151 UTLAs, which combine to form the nine regions of England.

For the purposes of the Health Index, results for the Isles of Scilly and City of London LTLAs are not included for any indicators because of small sample sizes leading to unreliable underlying data. For some sources, these LTLAs’ data are grouped with nearby LTLAs in the source data. Where this is the case we have not made an adjustment to separate them.

Not all data sources present data organised by these administrative geographies. Some sources, such as the GP Patient Survey (GPPS), present data based on health geographies such as individual GP surgeries. Some education data are presented using individual schools, and the crime data used in the Health Index are presented using Community Safety Partnerships (CSPs). These data need separate handling to present them in administrative geographies consistent with the other indicators.

Data collected for individual GP surgeries have GP practice codes, which can then be aggregated using the National Statistics Postcode Lookup (NSPL) to LTLA level. Postcodes for GP practices are published by NHS Digital. Similarly, schools can be aggregated to LTLA level using the postcode the school is in. The Health Index therefore assumes the patients and children registered at GP surgeries and schools are residing in the same LTLA as the GP or school they attend. If any GP or school has missing data for the number of patients or children respectively, these are excluded from the analysis.

There are 301 CSP police force areas in England, which can be broadly mapped to individual LTLAs using the Office for National Statistics (ONS) Open Geography Portal instructions. Where one CSP represents multiple LTLAs, numbers of crimes are apportioned to the component LTLAs by population size. Where one LTLA is represented by multiple CSPs, the numbers of crimes in those CSPs are summed to receive a number for that LTLA.

There were no changes to the boundaries or structure of LTLAs in 2015 to 2018. In 2019, 2020 and 2021 there have been mergers of LTLAs to form unitary authorities (UAs) or non-metropolitan districts, and some counties have been abolished and replaced by the resulting UA. The areas that have changed are:

2019:

  • Dorset UA (E06000059) created from a merger of five non-metropolitan districts (E07000049-53)
  • Bournemouth, Christchurch and Poole UA (E06000058) created from a merge of one non-metropolitan district (E07000048) and two UAs (E06000028 and E06000029)
  • the county of Dorset (E10000009), which had comprised the non-metropolitan districts E07000048-53 that were merged into the two UAs above, was abolished
  • East Suffolk non-metropolitan district (E07000244) created from two non-metropolitan districts (E07000205 and E07000206)
  • Somerset West and Taunton (E07000246) created from two non-metropolitan districts (E07000190 and E07000191)
  • West Suffolk non-metropolitan district (E07000245) created from a merger of two non-metropolitan districts (E07000201 and E07000204)

2020:

  • Buckinghamshire UA (E06000060) created from a merger of four non-metropolitan districts (E07000004-7)
  • the county of Buckinghamshire (E10000002), which had comprised the same four non-metropolitan districts as the new UA, was abolished

2021:

  • North Northamptonshire LTLA (E06000061) created from a merger of four LTLAs (E07000150, E07000152, E07000153, E07000156)
  • West Northamptonshire LTLA (E06000062) created from a merger of three LTLAs (E07000151, E07000154, E07000155)

The new areas can be calculated or estimated from the relevant non-metropolitan districts and UAs. Values are calculated for the new non-metropolitan districts as well as the new UAs despite not being included in the Health Index, as they may be needed to calculate non-metropolitan counties using 2021 geography. Where further aggregations use the 2021 geography for earlier years, the populations of those previous geographies in those years from the ONS’s mid-year population estimates are applied.

Aggregation to higher geographies followed the following process, consistent with previous guidance for the Office for Health Improvement and Disparities (OHID) Fingertips tool.

Method 1 is used if all the areas needed to form the new area are provided in the dataset, the new numerator and denominator are calculated as the sum of the numerators and denominators respectively of the comprising areas. The statistic is then calculated appropriately from the numerator and denominator (for example, a rate per 1,000 or a percentage).

Method 2 is used if the numerator or denominator is not provided, the new value is calculated by multiplying each of the values of the comprising areas by the population of that area divided by the population of the new area, and summing the adjusted values.

Method 1 calculates an aggregated value, while Method 2 provides an estimate. Each indicator was aggregated with Method 1 or 2 as appropriate.

If an indicator’s source statistic was not a rate or a percentage, Method 2 was automatically used. Method 2 was also used to give an estimate where the statistic was an age-standardised rate. In subsequent versions of the Health Index, a calculation for age-standardised values will be provided using Method 1 where the age-breakdown of the population in each of the geographies is used to calculate the resulting value.

Some denominators are not based on the whole population of the local authority, or not based on population at all. Where the numerator and denominator are not provided, by making estimations based on population proportions we are assuming that the denominator also follows these proportions. For example, if the denominator is the number of people aged 65 years and over, we are assuming that the proportion of the population of the old area that is 65 years and over is the same as in the new area.

In the case that the old county of Dorset is provided, but non-metropolitan districts are not, the new LTLA of Dorset can be estimated from the county of Dorset, as well as the non-metropolitan borough of Christchurch, which is then used to calculate the new LTLA of Bournemouth, Christchurch and Poole. The value for the former county of Dorset is assigned to both the new LTLA of Dorset and the old non-metropolitan borough of Christchurch. Numerators and denominators can be estimated by adjusting those for the former country of Dorset by the population of the calculated area divided by the population of the former county of Dorset. This process is carried out before the aggregation to 2021 geographies so that the estimated value for Christchurch can be used to calculate the value for the LTLA of Bournemouth, Christchurch and Poole if needed.

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10. Imputation of missing data (COIN Step 3)

Our approach to imputation is typically simple imputation, in line with how other indices handle missing values. Multiple imputation is being considered to refine our methods in future, but is not expected to impact results significantly.

The detail of the approach used, presented in the order that steps were applied, is:

  • if a value was too low to be presented, it was replaced with the mean of the values it could have been; for example, if counts below three were suppressed, the true value could have been zero, one or two, in this case our imputed value would be one
  • if in the back-series we had results either side of a missing value for a lower-tier local authority (LTLA), the missing year(s) value was calculated as a linear interpolation of the values either side
  • if one or more values were missing without values available on both sides in the time series, missing values were replaced with the nearest adjacent value
  • if a value is missing for an LTLA for all years, we impute the median for the region

Once fully constructed, the Health Index consists of 56 indicators, 307 LTLA records and five time points at the time of writing (2015 to 2019). This amounts to a total of 85,960 data points. Just over 10% of all records were affected by imputation in some way (9,249, or 10.8%), either because their value was imputed or one of their components was imputed. The majority of affected records were imputed because the indicator is not available for an entire year of our time series. This was the case for over 7,300 of the affected records, or 8.6% of the total Health Index data.

The regional median was imputed for a total of 108 unique LTLA and indicator combinations. The ten LTLAs affected by this include eight which changed boundaries between 2015 and 2021 as described in Section 9. The only two other LTLAs affected were Cornwall and Hackney, which are often combined with data for the two LTLAs we exclude because of their size: Isles of Scilly and City of London respectively.

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11. Multivariate analysis (COIN Step 4)

Typically for a composite index, we would aim to avoid collinearity between indicators as that suggests they are measuring similar topics, so one or more may be redundant to include.

By the nature of the Health Index’s aims of presenting health at multiple levels, and being transparent in its construction, the Index looks to capture multiple indicators measuring similar principles and cluster these into subdomains for comparison. We also expect many of our indicators are correlated because the Health Index includes both risk factors and the outcomes that we expect are associated with those risk factors.

While conducting factor analysis, as described in Section 13, we assessed correlation matrices of all indicators within each domain. We used this in conjunction with factor analysis when multiple data options were available for indicators, to assess which was a better fit for the Index as a whole.

Factor analysis was used to produce the weights for each indicator, at which point the suitability of some indicators was assessed more thoroughly. Indicators that were removed from the Index at that stage are listed in Section 13.

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12. Homogenising the data (Normalisation, COIN Step 5)

It is necessary when constructing an index to transform all indicators to a homogenous scale.

Scaling

Population differences between lower-tier local authorities (LTLAs) and regions were accounted for in scaling through the calculation of proportions or rates. To do this, the Office for National Statistics’s (ONS’s) population estimates were applied to all indicators that measure raw counts. We have used age-standardised rates where they were applicable and available, but this was reliant on the data published and so was only possible for a minority of the data sources used.

Certain indicators needed to undergo directional adjustment such that for all indicators, a higher value corresponds with better health. This process is as simple as multiplying the indicator by negative one. For example, lower smoking prevalence is associated with better health. Therefore, the smoking prevalence indicator needed to be directionally adjusted.

Normalisation

Factor analysis is used to organise the data into subdomains in a later step, and an underlying assumption of factor analysis is that data have a normal distribution. Therefore, when homogenising the data we needed to address any outliers and then assess the skew and kurtosis of each indicator.

Winsorization was used to bring extreme outliers to within three standard deviations of the mean for that indicator. To assess whether transformations were required, we examined the skew and kurtosis of each indicator, for each year, with threshold values of plus or minus 0.5 and plus or minus 2, respectively. Where values fell outside either threshold, we explored a number of commonly used transformation methods (log, square root, cube root, cube, reciprocal) and selected the method which most effectively reduced the skewness and kurtosis of the indicator. For the majority of indicators, the log transformation was used or the data were untransformed.

Standardisation

The methods available for standardisation are narrowed greatly by the Health Index’s need to be comparable across time and geographic area, with additional years of data not affecting the back series values. For this reason, time series standardisation has been used.

Regular standardisation involves subtracting the mean value and dividing by the standard deviation, for each indicator. For the Health Index, it is not suitable to employ this method across all observations as additional years of data would change the mean and standard deviation calculated and, consequently, all data from previous years. If standardisation is applied within years, the resultant values would no longer be comparable across years, which is an important attribute of the Health Index.

Temporal comparability, without enforcing annual revisions, can be achieved using a method discussed in the COIN (2020) 10-step-guide. The standardisation method is modified, such that the mean and standard deviation for each indicator are calculated for a base year and are then applied to the whole time series. This allows for comparisons across time and only causes back series changes when the reference year is updated. This is a common practice used across a number of national statistics. For the Health Index, the base year is 2015.

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13. Weighting (COIN Step 6)

The Health Index’s hierarchical structure means there are multiple levels at which weighting can be relevant, each requiring separate consideration. Indicators must be weighted within their subdomain, subdomains must be weighted within their domain and the domains must be weighted within the overall Index.

Weighting indicators within subdomains: time series factor analysis

The fundamental assumption of factor analysis is that there is a latent factor that underpins the variables in a group. In terms of the Health Index, we assume that there is a single unobserved variable that underpins the indicators within each subdomain.

The indicators within each subdomain will likely be highly correlated, which could lead to double counting in the Index. Factor analysis directly addresses this issue, accounting for the correlation between indicators in their implied weights. It also groups indicators into subdomains based on statistical information, and not just theorised concepts.

As with the normalisation methods, factor analysis cannot be used in its regular form to meet this Index’s aims. If the factor analysis were carried out across all observations, the weights would change with each additional year of data. As such, the weights need to be calculated for a set time period, and these weights are held constant until a review date.

The indicators to be included within each domain were decided based on a theoretical approach, with input from the Expert Advisory Group (EAG) and using consultation feedback. We then conducted factor analysis on all indicators within each domain, one after another. To test the theorised indicator placement within domains and to check for alternative groupings, we then ran factor analysis on all indicators proposed for inclusion in the Index.

The theoretical position of indicators is not always clear-cut, so conducting factor analysis helped guide which domains indicators fit best. For example, some indicators are both health outcomes and risk factors, such as children’s social, emotional and mental health. As such they could be placed in Healthy People as health outcomes, or Healthy Lives or Places, as risk factors.

We assessed the most suitable results for grouping into subdomains using our correlation matrices and hypothesised indicator groupings. Where groupings were surprising, we re-ran factor analysis using only specific variables to confirm the subdomains we are presenting would not split out into separate factors (subdomains) if allowed to. Each indicator could only be included in one subdomain even if it loaded onto multiple factors, for ease of user interpretation.

Where indicators did not load as expected in our initial hypotheses, we critically considered the sources used for those indicators to check they were measuring the intended information and tested the indicator in different subdomains.

Each indicator’s factor loading is the amount of the latent factor (subdomain) variance, which that indicator can explain. Weights were constructed for each indicator within each subdomain using the scaled factor loadings within that subdomain. For example, if a subdomain had two indicators with factor loadings of 0.7 and 0.5 respectively, one indicator would receive a weight of 0.7 divided by 1.2 and the other of 0.5 divided by 1.2. The weights for each indicator are presented in our downloadable Health Index datasets.

Limitations of factor analysis

There are limitations involved with using factor analysis. This method only accounts for the collinearity between indicators and does not derive any measure of the importance of the indicators (COIN, 2020). Furthermore, this method gives lower weight to indicators that are not highly correlated with others, while the low correlation between indicators is often the exact reason why an index is being created, because it suggests the indicator that is not well correlated with others is measuring a different aspect of the whole. There are subjective choices made within the process that affect the resultant weights.

Weighting subdomains within domains: equal weighting

For the purposes of this version of the Health Index, all subdomains have equal weighting within their domain. This means that as the Healthy People domain has five subdomains, each subdomain has a weight of one fifth of the overall domain. Healthy Lives has four subdomains, so each subdomain has a weight of one fourth of the overall domain. Healthy Places has five subdomains, so each subdomain has a weight of one fifth of the overall domain.

This method will be refined following this current release, and we will use a budget allocation process for subdomain weights. This will involve asking our EAG to use their expertise in public health to assign appropriate weights of importance to each subdomain within each domain.

Weighting domains to the overall Health Index score: equal weighting

Equal weighting will be used to weight the three domains. The Health Index’s aim is to offer a broad measure of health and not focus simply on health outcomes. Weighting each of these domains equally would satisfy this.

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14. Aggregating indicators (COIN Step 7)

The Health Index is aggregated using linear aggregation. Linear aggregation involves taking the (weighted) arithmetic mean of indicators to calculate the Index. This is the simplest aggregation method; however, it introduces compensability into the composite index. This means that poor performance in one area can be offset by good performance elsewhere (COIN, 2020).

Indicators are aggregated into subdomain scores using weights described in Section 13. Subdomains are equally weighted within each domain, and each domain is equally weighted to produce an overall Health Index score.

For all levels of the Health Index, when aggregating geographically, lower-tier local authority level areas (LTLAs) are weighted by their mid-year population estimate for that year of results.

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15. Sensitivity analysis (COIN Step 8)

We performed a series of analyses to investigate whether changes to the methods used in Sections 12 to 14 would have significant effects on the Health Index results, known as sensitivity analysis.

The methods tested in this way, and the alternatives tested, are:

  • normalisation using standardisation or min-max scaling
  • indicator weighting using factor analysis or equal weights
  • indicator, subdomain and domain aggregation: linear, geometric or weighted-adjusted Mazziotta-Pareto indexing

For most steps, although small differences were seen when using different methods, these made no statistically significant difference to the Index scores. Correlations between resultant scores from each pair of methods were above 0.9.

The Office for National Statistics (ONS) has collaborated with The Alan Turing Institute to quality assure the Health Index methods. This includes more detailed sensitivity analysis, such as how ranks between local authorities change as a result of different methods. The Alan Turing Institute aim to publish their assessment of the Health Index methodology, including these findings.

In future, sensitivity analysis will also be undertaken to ensure that the indicator weights produced using factor analysis do not alter greatly when they are derived using different time periods.

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16. The scale of the Health Index

The Health Index has been scaled to a base of 100 for England, with a base year of 2015. Values higher than 100 indicate better health than England in 2015, and values below 100 indicate worse health. The scale is such that a score of 110 at indicator and lower-tier local authority level represents a score one standard deviation higher than England 2015’s score for that same indicator, a score of 120 is two standard deviations higher, and so on.

In this way comparisons both over time and within a single year are simple to understand. The Index scores should also be “future proof” such that a score must be 10 standard deviations lower than England 2015 in order to be zero or become negative.

The linear aggregation of the Health Index as described in Section 14 means the scale of plus or minus 10 representing one standard deviation lower or higher is not retained at higher levels of geography or Index construction. We are exploring alternative approaches suggested by The Alan Turing Institute to better retain the scale at these higher levels.

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17. Developments following public consultation

Our previous article Developing the Health Index for England: 2015 to 2018 presented a provisional or “beta” version of the Health Index which aimed to illustrate the concept of a health index and its potential. Following this, we ran a public consultation. We published a public consultation summary response, outlining the feedback received. All suggestions were carefully considered in terms of importance, feasibility, and whether they aligned with the Health Index’s aims. An action plan was then produced, with priority given to those judged most important to users. The action plan was agreed with our Expert Advisory Group (EAG), as were specifics of any proposed changes.

The main developments from the previous release are:

  • results are available for smaller geographical areas
  • an additional year has been added (2019)
  • the data used have been reviewed and changed
  • messaging around the Health Index in the results articles and methodology reports has been made clearer
  • the methods have been refined

Using smaller geographical areas

Previously we published the Index at upper-tier local authority (UTLA) level and we have now been able to publish at lower-tier local authority (LTLA) level.

We know there is appetite for presenting results at even smaller geographies, but we are currently limited by the availability of national data at these smaller geographies. We have been supporting work by Northumbria NHS Trust to create a Local Health Index for Northumbria, using local data to supplement gaps in the national data. We aim to develop a model from this to allow other areas to create their own versions.

Changing the data used

Our article Health Index indicators and definitions details the indicators included in the Index, and details of the data that underpin them are available in the Health Index datasets.

The following is a summary of the changes made from the previous provisional, or “beta”, article of the Health Index to this article.

Firstly, there are new concepts that we have now included. These are internet access and low-level crime (represented by bike theft and shoplifting).

Secondly, we have made changes to the way some concepts are measured, including:

  • following advice not to use data from the Quality and Outcomes Framework (QOF) for our purposes, prevalence of physical and mental health conditions is now measured using data from the GP Patient Survey (GPPS)
  • using GPPS data also means that our measure of mental health conditions is expanded from depression to the percentage of people with any self-reported mental health condition
  • hospital admissions data on drug misuse are not available at LTLA level, so instead we currently use data on drug-related crime
  • using an expanded measure of physical activity that better distinguishes between some activity and none, adding a sedentary behaviour indicator
  • being advised to use destinations of school leavers data to measure young people’s education, employment and training, in place of the proportion recorded as not in education, employment or training (NEET)
  • there are no data on volume of traffic at LTLA level, which means that it is not possible to calculate the number of road accidents per volume of traffic, and the volume of traffic per area, as we had previously included; instead, we include the number of road accidents per area (measured according to the area of the LTLA in square kilometres, not including inland water and to the average high tide mark)
  • for access to GP services, pharmacies, and sports or leisure facilities, we now have measures of travelled distance rather than simply using the direct distance between two points (sometimes referred to as “as the crow flies”)
  • healthy life expectancy (HLE) data are not currently available at LTLA level, so we instead use life expectancy

One argument for using life expectancy over HLE is that there is a potential overlap between HLE and the rest of the Index, a point which was raised in consultation feedback. However, there is also strong support for using HLE, therefore, when HLE data become available at LTLA level, we will run tests to explore this further.

Finally, there are three concepts that we included in the provisional version of the Health Index that we were unable to include because of data issues at LTLA level. These are:

  • children in state care or children in need
  • transport noise
  • low pay; that is the percentage of employees earning below the National Living Wage

We previously mentioned concepts that we would have liked to include but for which no suitable data could be sourced, and suggested some areas we planned to explore, to be able to include some of those.

Where those explorations were successful, the concepts were mentioned as new additions above. Some specific areas we have investigated with less success, or where further work is required, are:

  • job satisfaction, social support, financial difficulties and satisfaction with leisure time; the Understanding Society survey provides information on these topics, but its sample sizes are too low for robust use at LTLA level
  • multimorbidity; experts support the use of GPPS data to measure this, but the required data are not publicly available and require a special access agreement; we aim to include this in future versions of the Health Index
  • loneliness; multiple sources will provide data for this at LTLA level that we can use in future, but the current time series for all adults is not sufficient for inclusion
  • quality of, and engagement with, green space; the People and Nature Survey provides information on this topic, but its sample sizes are too low for robust use at LTLA level

Consultation feedback also resulted in an additional list of concepts we would like to include, but for which we have not been able to source suitable data. Those not previously documented are:

  • control or management of physical health conditions
  • food safety
  • access to blue spaces, such as rivers, lakes or the sea
  • ecological, environmental, and climate factors
  • economic inactivity
  • access to unhealthy food
  • access to public transport

Following the current release, we will continue to improve the data included within the Health Index. The way we aim to do so is to:

  • draw on sources that are not publicly available
  • incorporate sources that are currently in their infancy when they become more established and reliable
  • make greater use of more sophisticated data acquisition techniques; for example, using Routino software to estimate distance travelled to a greater range of places
  • consider using modelling techniques to assist where data are not available at the required geography
  • encourage data producers to provide more or better data, where there are gaps

Improving timeliness

At present, 2019 is the most recent year included. The extent to which we can directly improve timeliness is limited by when the underlying data used to construct the Health Index are available.

As we develop the Health Index, refine and streamline our methods, we will be able to improve on the current lag between data availability and publication of the Health Index with those data presented. For example, now we have developed our methods for calculating the Health Index in future, we will be able to publish results for 2020 data within 2022.

We are also developing an associated Health Projections tool, a primary use for which is to be able to predict the impact of proposed policies on future changes to the Health Index. This tool could also be used to model provisional Health Index values that are more up to date than the final Health Index.

Measuring the effects of the coronavirus (COVID-19) pandemic

The changes seen as a result of the coronavirus (COVID-19) pandemic will present some clear challenges as far as data are concerned. Data collection may have been suspended or measures used to date might have changed in meaning.

Our current plan is to:

  1. assess the impact of COVID-19 on all data used in the Index (this work is already underway)
  2. identify where alternative measures or sources may be necessary and/or appropriate, and the implications of this
  3. explore methods to account for changes to data and context
  4. produce estimates for the Health Index for 2020
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18. Future developments

For the first time, although still experimental, the March 2022 version of the Health Index is no longer provisional. This means results can be used to inform decisions and further analysis, noting the stated limitations. There are still further refinements that we will make, mainly to the data used, but also to the way we present the Health Index and the methods. We will be seeking to improve these and working towards making the Health Index a National Statistic.

Related work also aims to:

  • develop a health projections model to estimate how the Health Index and its components may change in the future, and simulate simple “what if?” scenarios
  • expand the Health Index to include analysis of all four nations in the UK
  • facilitate international comparisons
  • enable the production of local health indices
  • enable breakdowns of results by age or sex
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Contact details for this Methodology

Greg Ceely
Health.Data@ons.gov.uk
Telephone: +44 207 5928692