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Publication, Part of

National Child Measurement Programme, England, Provisional 2021/22 School Year Outputs

Official statistics, National statistics

National Statistics

Appendices

Appendix A - Calculation of prevalence

The prevalence of children in a BMI classification is calculated by dividing the number of children in that BMI classification by the total number of children and multiplying the result by 100.

The BMI classification of each child is derived by calculating the child's BMI centile and assigning the BMI classification based on the following thresholds1:

  • Underweight - BMI centile less than or equal to the 2nd centile
  • Healthy weight - BMI centile greater than the 2nd centile but less than the 85th centile
  • Overweight - BMI centile greater than or equal to the 85th centile but less than the 95th centile (i.e. overweight but not obese)
  • Obese - BMI centile greater than or equal to the 95th centile
  • Severely obese - BMI centile greater than or equal to 99.6. This BMI classification is a subset of the 'obese' classification.

The child’s BMI centile is a measure of how far a child’s BMI is above or below the average BMI value for their age and sex in a reference population. In England the British 1990 growth reference (UK90) is recommended for population monitoring and clinical assessment in children aged four years and over. UK90 is a large representative sample of 37,700 children which was constructed by combining data from 17 separate surveys. The sample was rebased to 1990 levels and the data were then used to express BMI as a centile based on the BMI distribution, adjusted for skewness, age and sex using Cole's LMS method2.

The child’s BMI centile is calculated in the following way:

  1. Calculate the child’s BMI (weight(kg)/height2 (m2))
  2. Calculate the child’s BMI z-score:
    • look up child age3 and sex on the UK90 BMI centiles classification;
    • retrieve the corresponding L, M, and S values for use in the following formula (where y is the BMI score):

iii. Convert the BMI z-score to the BMI centile using the standardised normal distribution.

Footnotes

  1. These thresholds are conventionally used for population monitoring in the UK and are not the same as those used in a clinical setting. Different methodologies, such as the International Obesity Task Force (IOTF) methodology, use different thresholds and may result in different prevalence figures to those presented in this report.
  2. ‘Growth monitoring with the British 1990 growth reference’. Cole Arch Dis Child.1997; 76: 47-49.
  3. Linear interpolation is used to get more accurate L, M and S values. e.g. the formula for a child who is 4 years and 6.5 months old would use the L, M and S values halfway between those for 4 years and 6 months and 4 years and 7 months.

Appendix B - Comparing prevalence: considerations

When comparing prevalence figures between groups and over time it is important to consider how participation and data quality might affect the calculated figures.

Comparisons between two groups with differing data quality or participation may be skewed and this should be taken into account as it may partly explain any difference in prevalence figures.  

Analyses looking at the impact of data quality on prevalence were carried out by the National Obesity Observatory (now part of OHID) for the 2006/07 and 2007/08 collection years and by the National Centre for Biotechnology Information (NCBI), a division of the U.S. National Library of Medicine (NLM), for the 2007/08 collection year.

No analysis has been carried out to quantify any impact on recent years but improvements in data quality and participation since the first years of the NCMP should have lessened any impact. However, it is still important to consider data quality and participation when making comparisons. Information on the data quality of the 2021/22 provisional data is provided in the methodology and data quality section, the data quality statement, and the data quality tables accompanying this report.

It is also important to realise that, since the NCMP dataset is a sample, the prevalence figures in this report are estimates assumed to apply to the entire population. These estimates are subject to natural random variation. Confidence intervals and significance testing have been used in this report to take account of such variation. Further details are available in appendices C and D.


Appendix C - Confidence intervals

A confidence interval gives an indication of the likely error around an estimate that has been calculated from measurements based on a sample of the population. It indicates the range within which the true value for the population as a whole can be expected to lie, taking natural random variation into account. Confidence intervals should be considered when interpreting results. When confidence intervals do not overlap the differences are considered as statistically significant. When confidence intervals overlap, it is not possible to determine whether differences are statistically significant. Please refer to appendix D for a suggested methodology for such cases.

Larger sample sizes lead to narrower confidence intervals, since there is less natural random variation in the results when more individuals are measured. The NCMP has relatively narrow confidence limits because of the large size of the sample and high participation rates.

In the tables accompanying this report, 95 per cent confidence intervals have been provided around the prevalence estimates. These are known as such because if it were possible to repeat the same programme under the same conditions a number of times, we would expect 95 per cent of the confidence intervals calculated in this way to contain the true population value for that estimate.

The confidence intervals in this report have not had the finite population correction (FPC) applied and have therefore not been reduced on the basis of coverage. This approach is consistent with that used throughout the public health community. For example, census, mortality and hospital admission data represent a 100 per cent sample, yet the associated confidence intervals are routinely calculated without the FPC adjustment.

 

Methodology

Confidence intervals have been calculated using the method described by Wilson1 and Newcombe2.

The steps needed are:

  1. Calculate the estimated proportions of children with and without the feature of interest (e.g. percentage of obese children in Reception year) as follows.
    • p = r / n = proportion with feature of interest
    • r = observed number with feature of interest in each area
    • n = sample size
    • q = (1 – p) = proportion without feature of interest
       
  2. Calculate three values (A, B and C) as follows:
     

where z is 𝑧(1−∝/2)  from the standard Normal distribution.

3. Then the confidence interval for the population proportion is given by:

This method is superior to other approaches because it can be used for any data.

When there are no observed events, then r and hence p are both zero, and the recommended confidence interval simplifies to

When r = n so that p = 1, the interval becomes


Appendix D - Significance Testing

Significance tests have been used in this report to determine whether differences between prevalence estimates are genuine differences (i.e. statistically significant) or the result of random natural variation.

A quick and easy check to see if two prevalence estimates are significantly different is to compare the confidence intervals of the estimates. When the confidence intervals do not overlap the differences are considered as statistically significantly different. This approach was used in NCMP reports prior to 2009/10.

However, it is not always the case that overlapping confidence intervals indicate no significant difference. In some cases estimates with overlapping confidence intervals will still be statistically significantly different. Consequently, some significant differences may have been missed in NCMP reports prior to 2009/10. A more robust way of checking if two prevalence estimates are significantly different is to use significance testing.

The significance testing methodology used in NCMP reports since 2009/10 follows the approach outlined by Altman et al.1 This methodology is consistent with that used by the Office for Health Improvement and Disparities (OHID).

A 95 per cent level of significance has been used in the tests throughout this report. This means that when prevalence estimates are described as being different, (e.g. higher/lower or increase/decrease etc.) the probability that the difference is genuine, rather than the result of random natural variation, is 0.95.

 

Methodology

The steps for the approach outlined by Altman et al. are:

  1. Calculate the absolute difference between the two proportions

2. Then calculate the confidence limits around D as:

where p is the estimated prevalence for the year i, and li and ui are the lower and upper confidence intervals for pi respectively.

3. A significance difference exists between proportions p1 and p2 if and only if zero is not included in the range covered by the confidence limits around the difference D.

Footnotes

  1. Altman DG et al. (eds). Statistics with confidence (2nd edn). London: BMJ Books; 2000: 46-8.

Appendix E - How are the statistics used?

Users and uses of the report

There are known and unknown users of the National Child Measurement Programme reports.

Known users have been established through customer engagement including a consultation carried out in 2016 and are detailed below.

Unknown users access the report directly from our website. We seek feedback from these users to understand how to better meet their needs in future via emails to [email protected]

In 2016 we engaged with users of this report as part of the wider NHS Digital consultation on all statistical products

 

Known users and uses

Department of Health and Social Care (DHSC)
The NCMP is a key element of the Government’s approach to tackling child obesity. NCMP statistics are used to inform policy and set national ambitions such as those detailed in Childhood obesity: a plan for action.


Office for Health Improvement and Disparities (OHID)
OHID are responsible for the Public Health Outcomes Framework (PHOF) which sets out the desired outcomes for public health and how these will be measured. The NCMP provides robust data for the child excess weight indicators in the PHOF.

The OHID Population Health Analysis team conduct additional analyses on the NCMP data, including regional and local analyses, and produce a range of reports and tools:


Local Authorities
Frequently use NCMP statistics for analyses, benchmarking and to inform decision making.


Academia and researchers
Non-identifiable versions of the annual NCMP datasets have been made available on the NHS Digital website since 2013/14. Datasets for years prior to 2013/14 were deposited in the UK Data Archive. This NCMP data is used by academics in their research papers.

Some examples are provided below:


Media
NCMP data are frequently used to underpin articles in newspapers, journals and online media.


Public
Aggregated NCMP data as published in NHS Digital's national report and OHID's more detailed analyses, is freely accessible for general public use.


Public Health Campaign Groups
Data is used to inform policy and decision making and to examine trends and behaviours.


Ad-hoc requests
NCMP statistics are used by NHS Digital to answer Parliamentary Questions (PQs), Freedom of Information (FOI) requests and ad-hoc queries. Ad-hoc requests are received from health professionals; research companies; public sector organisations, and members of the public, showing the statistics are widely used and not solely within the profession.
 


Last edited: 11 August 2022 10:02 am