All Indicators > Indicator SH2: Health capital

Component SH2_1: Obesity

Additional details

In the absence of any suitable administrative or census data, survey data was the only source of information available to construct an indicator of obesity. However there are a number of problems associated with using survey data to produce Local Authority District (LAD) estimates, including small or non-existent samples in some areas leading to large variances and unstable estimates and biases introduced by particular sampling strategies.

A great deal of work, particularly in the last twenty years, has gone into addressing these issues. Although a number of different approaches have been used, all the methods tend to fall somewhere on a continuum between using direct estimates, suitably weighted for sample design, and a modelling approach using local area covariates to estimate the indicator of interest. Some are based on only one or other of the methods. However the two methods each have their own particular problems. Direct estimates, weighted as necessary, are unbiased but may have large variances; on the other hand the modelled estimates will have small variances but will be biased. Hence many estimates attempt to combine information from both in order to solve the common problem of minimising the Mean Square Error of the final estimate.

The method used in the HPI required that a well-fitted micro level model could be identified. It also assumed that the important ways in which a group may have been over-sampled in a survey sample can be captured by covariates available in the survey and at a small area level. It involved combining all surveys available for the required year with the necessary dependent and independent variables (e.g. socio-economic status, age, gender and ethnicity). Data were gathered from the Health Survey for England (HSE) (1998 – 2003 ) for estimates for both the whole population and for the ethnic groups.

In 1999, the focus of the HSE was the health of minority ethnic groups as a means to increase understanding through the monitoring of trends that will enable us to make predictions. For this purpose a boost sample was designed in order to yield interviews with members of the most populous six minority ethnic groups: Black Caribbean, Indian, Pakistani, Bangladeshi, Chinese, and Irish. For the purpose of this estimate, Irish is included under White, and the Black African group added (although this sample was not boosted, hence the low numbers). The table below shows the number of ethnic groups available for each year that were used in the modelling of estimates for ethnic groups:

Only the main, adult sample, and not the oversampled ‘special populations’, was included in the modelling process for the whole population. For the ethnic population estimates, the adult and 1999 ethnic minority boost was used.

Step 1
Using combined survey data, with LAD geocoding, a multi-level, variable intercepts, logistic model was run, with level one being the individual i, level two the primary sampling unit j and level three the LAD k. Covariates from within the survey, shown in lower case, and LAD level data, shown in upper case, were used to predict the individual level behaviour.

Logit (Pijk) = Xijk B + Ujk + Vk + Eijk

Where P is a vector of probabilities associated with individual i in Primary Sampling Unit (PSU) j within LAD k, B a vector of regression coefficients, X a matrix of covariates associated with the individual measured within the survey, U a random vector of area effects associated with the PSU and V the LAD and E is a vector of independent random ‘noise’ elements. The matrix of covariates included PSU area measures, based on aggregated individual level survey counts within the PSU. These covariates are given in the table below:

Step 2
The fixed effects part of the model were then taken and applied to the matrix of small area covariates X held by SDRC for 100% of individuals and LADs across England, the random LAD area effect added (where it was available for an LAD), and the anti-logit applied. The probability was then summed and averaged over the LAD to produce a vector of synthetic LAD level estimates:

Yk = 1 / Nk x Sum ( anti-Logit ( Xijk B + Vk ) )

This method does not use weighting to remove bias in the parameter estimators introduced by unequal selection probabilities in the survey sampling schemes. Instead important characteristics of the sample are included in the model as covariates. The sample indicator variable S will therefore be unrelated to Y conditional on these covariates. In this case the sample can be viewed as uninformative and ignorable. There is little conflict in including theses covariates because they are, by definition, predictors of Y and so should be included in the model. If they were not, the sample design would not bias the standard estimators of the parameters.

Included in our models are measures of non-manual social classes and a ‘level’ for the primary sampling unit. Together these will capture, to a great extent, the unequal selection probabilities associated with the sample design. Other variables such as age will ensure that where a question or measure was taken of only a particular age group in a specific survey year that the estimates will not be biased.

Component SH2_2: Blood Pressure

Additional details

In the absence of any suitable administrative or census data, survey data was the only source of information available to construct an indicator of high blood pressure. However there are a number of problems associated with using survey data to produce Local Authority District (LAD) estimates, including small or non-existent samples in some areas leading to large variances and unstable estimates and biases introduced by particular sampling strategies.

A great deal of work, particularly in the last twenty years, has gone into addressing these issues. Although a number of different approaches have been used, all the methods tend to fall somewhere on a continuum between using direct estimates, suitably weighted for sample design, and a modelling approach using local area covariates to estimate the indicator of interest. Some are based on only one or other of the methods. However the two methods each have their own particular problems. Direct estimates, weighted as necessary, are unbiased but may have large variances; on the other hand the modelled estimates will have small variances but will be biased. Hence many estimates attempt to combine information from both in order to solve the common problem of minimising the Mean Square Error of the final estimate.

The method used in the HPI required that a well-fitted micro level model could be identified. It also assumed that the important ways in which a group may have been over-sampled in a survey sample can be captured by covariates available in the survey and at a small area level. It involved combining all surveys available for the required year with the necessary dependent and independent variables (e.g. socio-economic status, age, gender and ethnicity). Data were gathered from the Health Survey for England (HSE) (1998 – 2003) for estimates for both the whole population and for the ethnic groups.

In 1999, the focus of the HSE was the health of minority ethnic groups as a means to increase understanding through the monitoring of trends that will enable us to make predictions. For this purpose a boost sample was designed in order to yield interviews with members of the most populous six minority ethnic groups: Black Caribbean, Black African, Indian, Pakistani, Bangladeshi, Chinese, and Irish. For the purpose of this estimate, Irish is included under White, and the Black African group added (although this sample was not boosted, hence the low numbers). The table below shows the number of ethnic groups available for each year that were used in the modelling of estimates for ethnic groups:

Only the main, adult sample, and not the oversampled ‘special populations’, was included in the modelling process for the whole population. For the ethnic population estimates, the adult and 1999 ethnic minority boost was used.

Step 1
Using combined survey data, with LAD geocoding, a multi-level, variable intercepts, logistic model was run, with level one being the individual i, level two the primary sampling unit j and level three the LAD k. Covariates from within the survey, shown in lower case, and LAD level data, shown in upper case, were used to predict the individual level behaviour.

Logit (Pijk) = Xijk B + Ujk + Vk + Eijk

Where P is a vector of probabilities associated with individual i in Primary Sampling Unit (PSU) j within LAD k, B a vector of regression coefficients, X a matrix of covariates associated with the individual measured within the survey, U a random vector of area effects associated with the PSU and V the LAD and E is a vector of independent random ‘noise’ elements. The matrix of covariates included PSU area measures, based on aggregated individual level survey counts within the PSU. These covariates are given in the table below:

Step 2
The fixed effects part of the model were then taken and applied to the matrix of small area covariates X held by SDRC for 100% of individuals and LADs across England, the random LAD area effect added (where it was available for an LAD), and the anti-logit applied. The probability was then summed and averaged over the LAD to produce a vector of synthetic LAD level estimates:

Yk = 1 / Nk x Sum ( anti-Logit ( Xijk B + Vk ) )

This method does not use weighting to remove bias in the parameter estimators introduced by unequal selection probabilities in the survey sampling schemes. Instead important characteristics of the sample are included in the model as covariates. The sample indicator variable S will therefore be unrelated to Y conditional on these covariates. In this case the sample can be viewed as uninformative and ignorable. There is little conflict in including theses covariates because they are, by definition, predictors of Y and so should be included in the model. If they were not, the sample design would not bias the standard estimators of the parameters.

Included in our models are measures of non-manual social classes and a ‘level’ for the primary sampling unit. Together these will capture, to a great extent, the unequal selection probabilities associated with the sample design. Other variables such as age will ensure that where a question or measure was taken of only a particular age group in a specific survey year, the estimates will not be biased.

Component SH2_3: Cholesterol

Additional details

In the absence of any suitable administrative or census data, survey data was the only source of information available to construct an indicator of high cholesterol. However there are a number of problems associated with using survey data to produce Local Authority District (LAD) estimates, including small or non-existent samples in some areas leading to large variances and unstable estimates and biases introduced by particular sampling strategies.

A great deal of work, particularly in the last twenty years, has gone into addressing these issues. Although a number of different approaches have been used, all the methods tend to fall somewhere on a continuum between using direct estimates, suitably weighted for sample design, and a modelling approach using local area covariates to estimate the indicator of interest. Some are based on only one or other of the methods. However the two methods each have their own particular problems. Direct estimates, weighted as necessary, are unbiased but may have large variances; on the other hand the modelled estimates will have small variances but will be biased. Hence many estimates attempt to combine information from both in order to solve the common problem of minimising the Mean Square Error of the final estimate.

The method used in the HPI required that a well-fitted micro level model could be identified. It also assumed that the important ways in which a group may have been over-sampled in a survey sample can be captured by covariates available in the survey and at a small area level. It involved combining all surveys available for the required year with the necessary dependent and independent variables (e.g. socio-economic status, age, gender and ethnicity). Data were gathered from the Health Survey for England (HSE) (1998 – 2003) for estimates for both the whole population and for the ethnic groups.

In 1999, the focus of the HSE was the health of minority ethnic groups as a means to increase understanding through the monitoring of trends that will enable us to make predictions. For this purpose a boost sample was designed in order to yield interviews with members of the most populous six minority ethnic groups: Black Caribbean, Black African, Indian, Pakistani, Bangladeshi, Chinese and Irish. For the purpose of this estimate, Irish is included under White, and the Black African group added (although this sample was not boosted, hence the low numbers). The table below shows the number of ethnic groups available for each year that were used in the modelling estimates for ethnic groups:

Only the main, adult sample, and not the oversampled ‘special populations’, was included in the modelling process. For the ethnic population estimates, the adult and 1999 ethnic minority boost was used. Cholesterol levels were derived from the blood samples taken on the nurse visit.

Step 1
Using combined survey data, with LAD geocoding, a multi-level, variable intercepts, logistic model was run, with level one being the individual i, level two the primary sampling unit j and level three the LAD k. Covariates from within the survey, shown in lower case, and LAD level data, shown in upper case, were used to predict the individual level behaviour.

Logit (Pijk) = Xijk B + Ujk + Vk + Eijk

Where P is a vector of probabilities associated with individual i in Primary Sampling Unit (PSU) j within LAD k, B a vector of regression coefficients, X a matrix of covariates associated with the individual measured within the survey, U a random vector of area effects associated with the PSU and V the LAD and E is a vector of independent random ‘noise’ elements. The matrix of covariates included PSU area measures, based on aggregated individual level survey counts within the PSU. These covariates are given in the table below:

Step 2
The fixed effects part of the model were then taken and applied to the matrix of small area covariates X held by SDRC for 100% of individuals and LADs across England, the random LAD area effect added (where it was available for an LAD), and the anti-logit applied. The probability was then summed and averaged over the LAD to produce a vector of synthetic LAD level estimates:

Yk = 1 / Nk x Sum ( anti-Logit ( Xijk B + Vk ) )

This method does not use weighting to remove bias in the parameter estimators introduced by unequal selection probabilities in the survey sampling schemes. Instead important characteristics of the sample are included in the model as covariates. The sample indicator variable S will therefore be unrelated to Y conditional on these covariates. In this case the sample can be viewed as uninformative and ignorable. There is little conflict in including theses covariates because they are, by definition, predictors of Y and so should be included in the model. If they were not, the sample design would not bias the standard estimators of the parameters.

Included in our models are measures of non-manual social classes and a ‘level’ for the primary sampling unit. Together these will capture, to a great extent, the unequal selection probabilities associated with the sample design. Other variables such as age will ensure that where a question or measure was taken of only a particular age group in a specific survey year, the estimates will not be biased.

Component SH2_4: Low birthweight

Additional details

Births without a stated birth weight, extreme birth weight values of less than 500g and more than 6,000g and stillbirths have been excluded.