Publication - Statistics

The Scottish Health Survey 2011 - volume 2: children

Published: 25 Sep 2012
Part of:
Health and social care
ISBN:
9781780458427

Annual report of the Scottish Health Survey for 2011. Volume focussing on child health.

141 page PDF

2.5 MB

141 page PDF

2.5 MB

Contents
The Scottish Health Survey 2011 - volume 2: children
Appendix A: Glossary

141 page PDF

2.5 MB

Appendix A: Glossary

This glossary explains terms used in Volume 2 of the 2011 report, other than those fully described in particular chapters.

Anthropometric
See Body mass index (BMI)

Arithmetic mean
See Mean

Body mass index
Weight in kg divided by the square of height in metres. Adults (aged 16 and over) can be classified into the following BMI groups:

BMI (kg/m2) Description

Less than 18.5 Underweight

18.5 to less than 25 Normal

25 to less than 30 Overweight

30 to less than 40 Obese

40 and above Morbidly obese

Although the BMI calculation method is the same, there are no fixed BMI cut-off points defining overweight and obesity in children. Instead, overweight and obesity are defined using several other methods including age and sex specific BMI cut-off points or BMI percentiles cut-offs based on reference populations. Children can be classified into the following groups:

Percentile cut-off Description
At or below 5th percentile Underweight
Above 5th percentile and below
85th percentile
Healthy weight
At or above 85th percentile and
below 95th percentile
Overweight
At or above 95th percentile and
below 98th percentile
Obese
At or above 98th percentile Morbidly obese

Equivalised Household income
Making precise estimates of household income, as is done for example in the Family Resources Survey, requires far more interview time than was available in the Health Survey. Household income was thus established by means of a card (see Volume 3, Appendix A) on which banded incomes were presented. Information was obtained from the household reference person (HRP) or their partner. Initially they were asked to state their own (HRP and partner) aggregate gross income, and were then asked to estimate the total household income including that of any other persons in the household. Household income can be used as an analysis variable, but there has been increasing interest recently in using measures of equivalised income that adjust income to take account of the number of persons in the household. Methods of doing this vary in detail: the starting point is usually an exact estimate of net income, rather than the banded estimate of gross income obtained in the Health Survey. The method used in the present report was as follows. It utilises the widely used McClements scoring system, described below.

1. A score was allocated to each household member, and these were added together to produce an overall household McClements score. Household members were given scores as follows.

First adult (HRP) 0.61
Spouse/partner of HRP 0.39
Other second adult 0.46
Third adult 0.42
Subsequent adults 0.36
Dependant aged 0-1 0.09
Dependant aged 2-4 0.18
Dependant aged 5-7 0.21
Dependant aged 8-10 0.23
Dependant aged 11-12 0.25
Dependant aged 13-15 0.27
Dependant aged 16+ 0.36

2 The equivalised income was derived as the annual household income divided by the McClements score.

3 This equivalised annual household income was attributed to all members of the household, including children.

4 Households were ranked by equivalised income, and quintiles q1- q5 were identified. Because income was obtained in banded form, there were clumps of households with the same income spanning the quintiles. It was decided not to split clumps but to define the quintiles as 'households with equivalised income up to q1', 'over q1 up to q2' etc.

All individuals in each household were allocated to the equivalised household income quintile to which their household had been allocated. Insofar as the mean number of persons per household may vary between tertiles, the numbers in the quintiles will be unequal. Inequalities in numbers are also introduced by the clumping referred to above, and by the fact that in any sub-group analysed the proportionate distribution across quintiles will differ from that of the total sample.

Reference: McClements, D. (1977). Equivalence scales for children. Journal of Public Economics. 8: 191-210.

Frankfort plane
The Frankfort Plane is an imaginary line passing through the external ear canal and across the top of the lower bone of the eye socket, immediately under the eye. Informants' heads are positioned with the Frankfort Plane in a horizontal position when height is measured using a stadiometer as a means of ensuring that, as far as possible, the measurements taken are standardised.

GHQ12
The General Health Questionnaire (GHQ12) is a scale designed to detect possible psychiatric morbidity in the general population. It was administered to informants aged 13 and above. The questionnaire contains 12 questions about the informant's general level of happiness, depression, anxiety and sleep disturbance over the past four weeks. Responses to these items are scored, with one point given each time a particular feeling or type of behaviour was reported to have been experienced 'more than usual' or 'much more than usual' over the past few weeks. These scores are combined to create an overall score of between zero and twelve. A score of four or more (referred to as a 'high' GHQ12 score) has been used in this report to indicate the presence of a possible psychiatric disorder.

Reference: Goldberg D, Williams PA. User's Guide to the General Health Questionnaire. NFER-NELSON, 1988.

Household
A household was defined as one person or a group of people who have the accommodation as their only or main residence and who either share at least one meal a day or share the living accommodation.

Household Reference Person
The household reference person (HRP) is defined as the householder (a person in whose name the property is owned or rented) with the highest income. If there is more than one householder and they have equal income, then the household reference person is the oldest.

Income
See Equivalised household income

Logistic regression
Logistic regression was used to investigate the effect of two or more independent or predictor variables on a two-category (binary) outcome variable. The independent variables can be continuous or categorical (grouped) variables. The parameter estimates from a logistic regression model for each independent variable give an estimate of the effect of that variable on the outcome variable, adjusted for all other independent variables in the model. For example, this was used in the analysis of children's strengths and difficulties scores.

Logistic regression models the log 'odds' of a binary outcome variable. The 'odds' of an outcome is the ratio of the probability of it occurring to the probability of it not occurring. The parameter estimates obtained from a logistic regression model have been presented as odds ratios for ease of interpretation.

For continuous independent variables, the odds ratio gives the change in the odds of the outcome occurring for a one unit change in the value of the predictor variable.

For categorical independent variables one category of the categorical variable has been selected as a baseline or reference category, with all other categories compared to it. Therefore there is no parameter estimate for the reference category and odds ratios for all other categories are the ratio of the odds of the outcome occurring between each category and the reference category, adjusted for all other variables in the model.

The statistical significance of independent variables in models was assessed by the likelihood ratio test and its associated p value. 95% confidence intervals were also calculated for the odds ratios. These can be interpreted as meaning that there is a 95% chance that the given interval for the sample will contain the true population parameter of interest. In logistic regression a 95% confidence interval which does not include 1.0 indicates the given parameter estimate is statistically significant.

Reference: Hosmer, D.W. Jr. and Lemeshow. S. (1989). Applied logistic regression. New York: John Wiley & Sons.

Long-term conditions & limiting long-term conditions
Long-term conditions were defined as a long-standing physical or mental condition or disability that has troubled the participant for at least 12 months, or that is likely to affect them for at least 12 months. Note that prior to 2008 these were described as long-standing illnesses. Long-term conditions were coded into categories defined in the International Classification of Diseases (ICD), but it should be noted that the ICD is used mostly to classify conditions according to the cause, whereas SHeS classifies according to the reported symptoms. A long-term condition was defined as limiting if the respondent reported that it limited their activities in any way.

Mean
Means in this report are Arithmetic means (the sum of the values for cases divided by the number of cases).

Median
The value of a distribution which divides it into two equal parts such that half the cases have values below the median and half the cases have values above the median.

Morbid obesity
See Body mass index.

NHS Health Board
The National Health Service (NHS) in Scotland is divided up into 14 geographically-based local NHS Boards and a number of National Special Health Boards. Health Boards in this report refers to the 14 local NHS Boards. (See Volume 3: Appendix C)

NS-SEC
The National Statistics Socio-economic Classification (NS-SEC) is a social classification system that attempts to classify groups on the basis of employment relations, based on characteristics such as career prospects, autonomy, mode of payment and period of notice. There are fourteen operational categories representing different groups of occupations (for example higher and lower managerial, higher and lower professional) and a further three 'residual' categories for full-time students, occupations that cannot be classified due to lack of information or other reasons. The operational categories may be collapsed to form a nine, eight, five or three category system. This report mostly uses the five category system in which participants are classified as managerial and professional, intermediate, small employers and own account workers, lower supervisory and technical, and semi-routine and routine occupations. In some instances where there were insufficient numbers to use the five category classification, the three category system was used instead. In analyses presented in this report it is the NS-SEC of the household reference person which is used. NS-SEC was introduced in 2001 and replaced Registrar General's Social Class (which had been used in the 1995 and 1998 surveys) as the main measure of socio-economic status.

Obesity
See Body mass index

Odds ratio
See Logistic regression

Overweight
See Body mass index

Percentile
The value of a distribution which partitions the cases into groups of a specified size. For example, the 20th percentile is the value of the distribution where 20 percent of the cases have values below the 20th percentile and 80 percent have values above it. The 50th percentile is the median.

p value
A p value is the probability of the observed result occurring due to chance alone. A p value of less than 5% is conventionally taken to indicate a statistically significant result (p<0.05). It should be noted that the p value is dependent on the sample size, so that with large samples differences or associations which are very small may still be statistically significant. Results should therefore be assessed on the magnitude of the differences or associations as well as on the p value itself. The p values given in this report take into account the clustered sampling design of the survey.

Quintile
Quintiles are percentiles which divide a distribution into fifths, i.e., the 20th, 40th, 60th and 80th percentiles.

Scottish Index of Multiple Deprivation
The Scottish Index of Multiple Deprivation (SIMD) is the Scottish Government's official measure of area based multiple deprivation. It is based on 37 indicators across 7 individual domains of current income, employment, housing, health, education, skills and training and geographic access to services and telecommunications. SIMD is calculated at data zone level, enabling small pockets of deprivation to be identified. The data zones are ranked from most deprived (1) to least deprived (6505) on the overall SIMD index. The result is a comprehensive picture of relative area deprivation across Scotland.

This report uses the SIMD 2009.
http://www.scotland.gov.uk/Topics/Statistics/SIMD

SDQ
The Strengths and Difficulties Questionnaire (SDQ) is designed to detect behavioural, emotional and relationship difficulties in children aged 4-16. The questionnaire is based on 25 items: 10 strengths, 14 difficulties and one neutral item. The 25 items are divided into 5 scales of 5 items each: hyperactivity, emotional symptoms, conduct problems, peer problems and prosocial behaviour. Each SDQ item has three possible answers which are assigned a value 0,1 or 2. The score for each scale is generated by adding up the scores on the 5 items within that scale, producing scale scores ranging from 0 to 10. A 'Total Difficulties' score is derived from the sum of scores from each of the scales except the Prosocial Behaviour scale, producing a total score from 0 to 40. The SDQ was used for children aged 4-12 in the 2008, 2009, 2010 and 2011 surveys.

The SDQ correlates highly with the Rutter questionnaire and the Child Behaviour Checklist, both of which are long established behavioural screening questionnaires for children that have been proved valid and reliable in many contexts and correlate highly with one another. The SDQ is shorter than these screening instruments and is the first to include a scale focusing on positive behaviour: the Prosocial Behaviour Scale.

Reference: Goodman, R. (1997). The Strengths and Difficulties Questionnaire: A Research Note. Journal of Child Psychology and Psychiatry. 38: 581-586.

Standard deviation
The standard deviation is a measure of the extent to which the values within a set of data are dispersed from, or close to, the mean value. In a normally distributed set of data 68% of the cases will lie within one standard deviation of the mean, 95% within two standard deviations and 99% will be within 3 standard deviations. For example, for a mean value of 50 with a standard deviation of 5, 95% of values will lie within the range 40-60.

Standard error
The standard error is a variance estimate that measures the amount of uncertainty (as a result of sampling error) associated with a survey statistic. All data presented in this report in the form of means are presented with their associated standard errors (with the exception of the WEMWBS scores which are also presented with their standard deviations). Confidence intervals are calculated from the standard error; therefore the larger the standard error, the wider the confidence interval will be.


Contact

Email: Julie Ramsay