Assessing dietary diversity in South Africa: What does it tell us?

NP Steyn, D Labadarios, JH Nel

Assessing dietary diversity in South Africa: What does it tell us?

NP Steyn, D Labadarios, JH Nel

Why do we measure dietary diversity?Why do we measure dietary diversity?

• Diet quality• Micronutrient screen• Measure of food security

Surveys in RSASurveys in RSA

• In RSA there have been 3 studies which have measured dietary diversity at national level

• The same method has been used: an un-quantified 24 hour recall to list foods eaten prior to the interview

• Division of foods into 9 food groups• Calculation of a dietary diversity score (DDS) with • 0 = no dietary diversity and 9 = maximum dietary

diversity

• Study 1: Children 1-9 years in 1999 (N=2200)

• Study 2: Adults in 2009 (N=3287)• Study 3: SANHANES in 2012 adults and

children(N=13 357)

Study 1Study 1

• National Food Consumption Survey (NFCS) • N=2200 children 1-9 years• Quantified 24 hour recall done• NAR (nutrient adequacy ratio) calculated for each

nutrient = Average consumed over 100 % RNI• MAR (mean adequacy ratio) calculated as the Sum of

NARs divided by the number of nutrients• FVS = Food variety score = mean number of different

food items consumed from all possible items eaten• DDS calculated as the score out of 9 groups with each

group only counting once

ResultsResults

• Mean FVS = 5.5 (SD 2.5)• Mean DDS = 3.6 (SD 1.4)• Mean MAR = 50% (ideal = 100%)

Groups consumedGroups consumed

• Cereals & tubers & roots= 99.6%• Dairy =55.8%• Meats =54.1%• Eggs=13.3%• Fats =38.9%• Legumes & nuts=19.7%• Vitamin A rich fruit &vegetables=23.8%• Other fruit=22%• Other vegetables=38.8%• Other: tea, sugar, sweets, jam=87.6%

CorrelationsCorrelations

• High correlation between MAR and FVS: r=0.726, p<0.0001

• High correlation between MAR and DDS: r=0.657, p<0.0001

• MAR, DDS and FVS showed significant correlations with HFA and WFA z scores

• A DDS of 4 was shown to be best indicator of MAR less than 50% since it provided the best sensitivity and specificity.

Z scores with DDSZ scores with DDS

NARs with DDSNARs with DDS

NARs with DDSNARs with DDS

ConclusionConclusion

•

• Study showed a strong relationship between DD and indicators of child growth

• Either FVS or DDS can be used as a simple and quick indicator of the micronutrient adequacy of the diet.

Date: Date:

STUDY 2

A Simple Method of Measuring Dietary Diversity at Population Level

in adults

NP Steyn, D Labadarios, JH Nel

STUDY 2

A Simple Method of Measuring Dietary Diversity at Population Level

in adults

NP Steyn, D Labadarios, JH Nel

Social science that makes a difference

• Determining dietary diversity in RSA adults has not been possible to date since there are no national dietary data on adolescents or adults

• However a national study on food consumption in children (NFCS) in RSA showed a very monotonous type of diet with specific deficiencies including: Energy, iron, zinc, calcium, vitamins A, C, E, B6, B2, niacin and folic acid. Largely due to the majority of the population consuming large amounts of maize meal, bread and sugar with low intakes of animal protein and fruit and vegetables.

• It is known that a diverse diet is more likely to contain all the essential nutrients than a monotonous one, hence measuring dietary diversity is a simple way of identifying the likelihood of having an adequate diet in terms of essential micronutrients.

IntroductionIntroduction

Objective of the studyObjective of the study

To measure dietary diversity in South

Africans aged 16 years and older from all

population groups

MethodsMethods A cross-sectional study representative of adults from all specified

ages, provinces, geographic localities, and socio-economic strata in South Africa was used (n= 3287).

Trained interviewers visited participants at their homes during the survey. Dietary data was collected by means of a face validated 24 hour recall which was not quantified.

A dietary diversity score (DDS) was calculated by counting each of 9 food groups. A DDS <4 was regarded as reflecting poor dietary diversity and poor food security.

Individual dietary varietyIndividual dietary variety• Use of an adapted FAO (2011) method using 9 food groups.

Groups were based on outcomes of the NFCS • Starchy staples (cereals, roots, tubers)• Vitamin A rich fruit and vegetables • Other fruit• Other vegetables• Legumes and nuts• Fats and oils• Meat/poultry/fish• Milk and milk products• Eggs

• FAO have an organ meats group and a dark green leafy group and combine other fruits and vegetables

Cut-off value for dietary diversity usedCut-off value for dietary diversity used

• This was based on a validation study done with data from the NFCS with assistance from FAO (Steyn NP et al, PHN 2005).

• A dietary diversity score ( DDS) of at least 4 (groups) was shown to be the lowest minimum requirement and provided a specificity of 70% and a sensitivity of 75% of at least at 50% MAR of the overall diet in children. Furthermore, z scores for weight and height for age of children rose above zero at a DDS of 4.

• No validation data available on adults

24 Hour recall24 Hour recall

• Each participant was required to list all foods and drinks consumed on the previous day. No quantities were recorded. Each item consumed from a specific food group was counted once only. A DDS< 4 would represent poor diversity. Some studies include a minimum of 15 g per item per day.

ResultsResults

% Population in each province having a low DDS (<4 groups)

% Population in each province having a low DDS (<4 groups)

% Population having a low DDS by area% Population having a low DDS by area

Social science that makes a difference

% Population having a low DDs by SES status% Population having a low DDs by SES status

Social science that makes a difference

% population having a low DDS by ethnic group% population having a low DDS by ethnic group

Mean DDS of different SES categoriesMean DDS of different SES categories

•   Living Standard Measure

Low Medium High

Sample sizec 585 1320 1219

Mean DDS

95% CI

2.93 [C]

2.81 – 3.05

3.84 [B]

3.76 – 3.93

4.72 [A]

4.64 – 4.80

Mean DDS by geographic areaMean DDS by geographic area

Geographic area RSA

Urban, formal Urban, informal Tribal Rural, All

Sample size 2024 309 599 355 3287

Mean DDS95%CI

4.42 [A] 4.7

4.34 – 4.50

3.46 [B] 3.8

3.30 – 3.61

3.17 [C] 3.3

3.05 – 3.29

3.64 [B] 3.6

3.46 – 3.81

4.02 4.2

3.96 – 4.07

Mean DDS by ethnic groupMean DDS by ethnic group

Ethnicity

RSA

Black / African Mixed ancestry Indian/Asian White All

Sample size 1941 604 389 353 3287

Mean DDS

95% CI

3.63 [C] 4.0

3.55 – 3.71

4.43 [B] 4.5

4.30 – 4.56

4.44 [B] 4.1

4.29 – 4.58

4.96 [A] 5.6

4.82 – 5.10

4.02 4.2

3.96 – 4.07

Most commonly consumed food groupsMost commonly consumed food groups

% Consumers 95%CI

Cereals 99.7 99.5-99.9

Vitamin A rich fruit & veg 17 15-18

Other fruit 25 24-27

Other vegetables 52 50-54

Legumes & nuts 18 16-19

Fat & oils 38 36-40

Meat/poultry 78 77-80

Milk & milk products 56 55-58

Eggs 18 16-19

Odd ratios of factors associated with having a DDS<4Odd ratios of factors associated with having a DDS<4

Factor Odds Ratio 95% CI

Casual work 2.769* 1.447-5298

Buys at spaza 1.979* 1.150-3.406

Lives in traditional house 2.394* 1.121-5.116

Water source is river 7.060* 3.096-16.101

No toilet 3.350* 1.061-10.562

No electricity 2.310* 1.198-4.453

Odd ratios of factors associated with having a DDS<4Odd ratios of factors associated with having a DDS<4

Factor Odds Ratio 95% CI

Employed full time 0.672* 0.430-1.050

Supermarket close by 0.584* 0.349-0.939

Flush toilet 0.467* 0.155-1.406

Electricity in house 0.389* 0.208-0.727

Motor vehicle 0.326* 0.270-0.394

Mobile phone 0.473* 0.389-0.575

Survey 3 SANHANESSurvey 3 SANHANES

• Full report not yet available but preliminary data confirms data from 2 earlier studies

ConclusionsConclusions

• Overall the major adults consumed a diet low in variety

• Tribal areas & informal urban areas were worst affected

• Eggs, legumes and vitamin A rich fruit & vegetables were least consumed

• Will include organ meats and dark green leafy vegetables in future as groups

RecommendRecommend

• That this method is used as a screening tool at clinics and health centers to identify families at risk of malnutrition and/or having poor food security