The estimated patterns of SSB, fruit, vegetables and the portions of fruit and vegetables, including 100% juice, consumed each day from the NDNS datasets are summarised in Table 1 along with respondent demographics. Here, the predictor variables that were most significant following the binary and multivariate logistic regression analysis are retained (sex, age, marital status, housing tenure and educational qualifications) while those which were less significant were dropped from the analysis (NSSEC, ethnicity). Tenure and educational qualifications were highly correlated so were not used in the same models.
Table 1: Summary of respondents and crosstabulations with outcome variables. P values for Chi-square tests shown
|
SSB consumption
|
n=5160
|
|
|
Total portions (F&V)
|
n=5150
|
|
Fruit portions
|
n=5157
|
|
|
Veg portions
|
n=5160
|
|
|
no SSB
|
1-329ml
|
330ml +
|
p value
|
< 1 portion
|
1-4.99
|
five +
|
p value
|
< 1 portion
|
1-4.99
|
five +
|
p value
|
< 1 portion
|
1-4.99
|
five +
|
p value
|
Sex
|
|
|
|
<0.001
|
|
|
|
0.353
|
|
|
|
<0.001
|
|
|
|
0.17
|
Male
|
51.1
|
32.2
|
16.7
|
|
6.4
|
69.9
|
23.7
|
|
59.9
|
38.1
|
2
|
|
18.1
|
78.4
|
3.5
|
|
Female
|
55
|
35.6
|
9.4
|
|
6.5
|
68.1
|
25.4
|
|
53
|
45.4
|
1.6
|
|
18.7
|
78.6
|
2.6
|
|
Age
|
|
|
|
<0.001
|
|
|
|
<0.001
|
|
|
|
<0.001
|
|
|
|
<0.001
|
16-24
|
26.2
|
41.9
|
31.9
|
|
10.8
|
78.5
|
10.7
|
|
76.3
|
23.1
|
0.6
|
|
30.8
|
68.2
|
1
|
|
25-34
|
43.5
|
38.5
|
18
|
|
6
|
70.1
|
23.9
|
|
60.8
|
38
|
1.1
|
|
18.2
|
78
|
3.8
|
|
35-44
|
52.2
|
37.3
|
10.5
|
|
5.4
|
70.4
|
24.2
|
|
57.8
|
41.1
|
1.1
|
|
15.8
|
80.9
|
3.3
|
|
45-54
|
61.3
|
32.3
|
6.5
|
|
5.6
|
66.9
|
27.5
|
|
51.3
|
46.2
|
2.5
|
|
14.5
|
81.5
|
4
|
|
55-64
|
68
|
28
|
4
|
|
4.1
|
61.2
|
34.7
|
|
42.4
|
54.9
|
2.7
|
|
12.9
|
83
|
4.1
|
|
65+
|
70.6
|
27.1
|
2.3
|
|
5.7
|
64.2
|
30.1
|
|
44.4
|
53.1
|
2.5
|
|
15.9
|
81.4
|
2.6
|
|
Education
|
|
|
|
<0.001
|
|
|
|
<0.001
|
|
|
|
<0.001
|
|
|
|
<0.001
|
Degree or higher
|
57.7
|
34.1
|
8.2
|
|
1.8
|
57.8
|
40.4
|
|
38.2
|
59.4
|
2.3
|
|
8.1
|
85.2
|
6.7
|
|
Below degree level
|
52.8
|
34.3
|
12.9
|
|
5.5
|
71.1
|
23.4
|
|
55.7
|
42.6
|
1.7
|
|
17.1
|
80.5
|
2.4
|
|
No qualifications
|
67.3
|
27.2
|
5.5
|
|
11.2
|
70.7
|
18.2
|
|
64.1
|
34
|
1.8
|
|
24.6
|
73.7
|
1.6
|
|
Full time student
|
27.4
|
44.5
|
28.1
|
|
10.4
|
76.4
|
13.2
|
|
73
|
26.3
|
0.7
|
|
30.6
|
68.3
|
1.1
|
|
Housing tenure
|
|
|
|
<0.001
|
|
|
|
<0.001
|
|
|
|
<0.001
|
|
|
|
<0.001
|
Owned
|
54.7
|
35.2
|
10.1
|
|
4.4
|
66.7
|
28.8
|
|
49.1
|
48.8
|
2.1
|
|
14.5
|
82.4
|
3.1
|
|
Social Rent
|
52.5
|
30.3
|
17.2
|
|
13.1
|
75
|
11.9
|
|
73.8
|
25.1
|
1.1
|
|
31
|
67.6
|
1.4
|
|
Private rent
|
48.5
|
34.4
|
17.1
|
|
7.5
|
70.8
|
21.6
|
|
64.6
|
34.5
|
0.9
|
|
21.1
|
74.4
|
4.5
|
|
Martial status
|
|
|
|
<0.001
|
|
|
|
<0.001
|
|
|
|
<0.001
|
|
|
|
<0.001
|
Single
|
38.6
|
38
|
23.3
|
|
9.3
|
72.6
|
18.1
|
|
67.9
|
30.9
|
1.2
|
|
25.8
|
71.5
|
2.7
|
|
Married
|
60.7
|
33.5
|
5.7
|
|
3.4
|
65.3
|
31.3
|
|
45.8
|
52.1
|
2.2
|
|
11.2
|
84.9
|
3.9
|
|
Separated/divorced/widowed
|
65.5
|
28.6
|
6
|
|
7.2
|
68.9
|
23.9
|
|
53.9
|
44.2
|
1.9
|
|
19.1
|
79
|
1.9
|
|
The results of the chi-square test for significance show the potential predictor variables (age, sex, marital status, tenure and educational qualifications) were all strongly correlated with diet outcomes with the exception of sex and fruit and vegetable portions and vegetable portions alone. All significant associations were confirmed with the logistic regression models. On the basis of these observed relationships, the SAE models for total fruit and vegetable portions and the model for vegetable portions only were created using age, tenure and martial status as the predictors. The model estimating the portions of fruit used age, tenure, marital status, and sex to predict this behaviour. The final model, estimating SSB consumption, used age, sex, martial status, and educational qualification.
The results in Table 2 summarise the least healthy dietary behaviour categories including high SSB consumption (more than one 330ml portion/day), lower fruit and vegetable consumption (less than one portion of fruit or one portion of vegetables, or less than one combined fruit, vegetables and 100% fruit juice portion/day).
Table 2: Mean prevalence of consumption for each diet variable by MSOA
|
Mean (%)
|
Minimum (%)
|
Maximum (%)
|
Std. Error
|
Std. Dev
|
>1 portion of fruit
|
54.25
|
44.15
|
76.10
|
0.06
|
5.24
|
< 1 portion of veg
|
17.32
|
12.78
|
30.74
|
0.04
|
2.93
|
< 1 portion of F&V
|
6.85
|
4.34
|
14.67
|
0.02
|
1.61
|
330 + ml SSB
|
11.47
|
5.69
|
30.51
|
0.03
|
2.63
|
The mean prevalence of adults consuming less than one portion of fruit, vegetables or 100% fruit juice is low overall, at 6.9% (range 4.3-14.7) of the population age 16+ years living in an MSOA. Comparing low levels of consumption of fruit or vegetables, estimates based on the NDNS dataset predict that fewer adults eat, on average, less than one portion of vegetables (17.3%, range 12.8-30.7) compared to fruit (54.3% [range 44.1-76.1]) per day. On average, 11.5% of the adult population in an MSOA are high SBB consumers (at least 330ml/day [range 5.7-30.5]). There is greater variation in the three single component variables (fruit, veg or SSB only) compared to the combined fruit and vegetable consumption (Table 2).
The results for each of these estimated dietary variables were mapped to MOSAs in quintiles with the results showing a distinct geography of higher SSB consumption, with greater prevalence (13.4-30.5% of the adult population drinking more than 330m/day) in urban areas (Figure 2). Several rural areas along the south coast, Midlands and North also have relatively high prevalence of higher SSB consumption.
The estimated prevalence of adults eating less than one portion of fruit, vegetables, or 100% fruit juice each day varies compared to the pattern of higher SSB consumption (Figure 3). Notably, northern regions and the eastern boroughs of London have higher rates of the poorest level of fruit and vegetable consumption. This is defined as MSOAs where 8.2-14.7% of the adult population consume less than a portion of fruit or veg/day, which is top quintile of MSOAs for this measure of diet quality.
A subset of areas showed low fruit and vegetable consumption predicted alongside higher prevalence of greater SSB consumption. There are 806 MSOAs (11.9% of England) where more than 8.4% of the population are estimated to consume less than a portion of fruit, vegetables or juice a day and in each of these MSOAs more than 13.4% of the population are estimated to drink >300ml of SSB a day. These MSOAs include areas from Birmingham, Leeds, Liverpool and Manchester in the north and Southampton, Portsmouth and Brighton in the south. The areas included in this combined higher risk group are predominantly urban, as clearly illustrated in the London inset map.
The variables that were used to estimate the dietary behaviours such as age and sex are also estimated in the model to provide an indication of internal model goodness-of-fit by comparing to the known population distribution from the ONS mid-year estimates. The mean TAE (mTAE) for each variable was low. For SSB the mTAE were 2.1 E-15, 0.00067, 0.009 and 0.014 with age being the variable with the highest average percent error. In the total portion variables, the mTAE values were 2.01 E-15, 0.0001 and 0.002 with age showing the most error. The values were similarly very low for portions of fruit and portions of vegetables (data not shown). This internal error is well within the suggested limits for internal goodness-of-fit, where error is less than 20% in at least 80% of the study areas(43).
Data on adult fruit and vegetable consumption at the 2011 local authority district level (n=326, with an average of 21 MSOAs in each) was available from the Active Lives Survey 2016/17 (44), however, analysis of this survey data showed that respondents were far more likely to have five portions of fruit and vegetables a day (58.1% of those age 16+ years) than respondents from the NDNS (24.7%). This disparity in descriptive statistics between the NDNS used to model the outcomes and data collected in the Active Lives survey would incorrectly indicate that there was a high proportion of error in the estimated data based on NDNS respondents. The Active Lives Survey asks only the screener question about the portions of fruit and vegetables which is prone to error when a more in-depth dietary assessment method is used such as the four-day food diary in NDSNS. This may explain the disparity between the reported prevalence of people achieving five a day between the two surveys.
The credible intervals reflect the uncertainty in the estimated values. They are wider for the outcomes where the original regression model results showed a weaker relationship between the predictor variables and the diet outcome. An example of this is the relatively tight credible intervals for low levels of fruit and vegetable consumption (less than one portion/day). On average, the estimated prevalence of adults age 16+ who did not have at least on portion a day was 6.9% with an average credible interval of 3.1% - 14.6%. In contrast, the average value for drinking at least 330ml of SSB/day was 11.5% with a wider credible interval of 5.4-22.8% (average values). The credible intervals are not symmetrical around the point estimates as they have a lower limit of zero, so the upper limit will be greater from the point estimate to reflect this value constraint (Figure 5).