2. Assessing dietary diversity in South Africa: What does it tell us? NP Steyn, D Labadarios, JH Nel

3. Why do we measure dietary diversity? Diet quality Micronutrient screen Measure of food security

4. Surveys 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

5. 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)

6. Study 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

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

8. Groups 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%

9. Correlations 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.

10. Z scores with DDS

11. NARs with DDS

13. Conclusion 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.

14. Date: 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

15. 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. Introduction

16. Objective of the study To measure dietary diversity in South Africans aged 16 years and older from all population groups

17. Methods 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.

18. Individual 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

19. Cut-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

20. 24 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.

21. Results

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

23. % Population having a low DDS by area Social science that makes a difference

24. % Population having a low DDs by SES status Social science that makes a difference

25. % population having a low DDS by ethnic group

26. Mean DDS of different SES categories Living Standard Measure LowMediumHigh Sample size c 58513201219 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

27. Mean DDS by geographic area Geographic areaRSA Urban, formalUrban, informalTribalRural,All Sample size20243095993553287 Mean DDS 95%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

28. Mean DDS by ethnic group Ethnicity RSA Black / AfricanMixed ancestry Indian/AsianWhiteAll Sample size19416043893533287 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

29. Most commonly consumed food groups % Consumers95%CI Cereals99.799.5-99.9 Vitamin A rich fruit & veg1715-18 Other fruit2524-27 Other vegetables5250-54 Legumes & nuts1816-19 Fat & oils3836-40 Meat/poultry7877-80 Milk & milk products5655-58 Eggs1816-19

30. Odd ratios of factors associated with having a DDS<4 FactorOdds Ratio95% CI Casual work2.769*1.447-5298 Buys at spaza1.979*1.150-3.406 Lives in traditional house2.394*1.121-5.116 Water source is river7.060*3.096-16.101 No toilet3.350*1.061-10.562 No electricity2.310*1.198-4.453

31. Odd ratios of factors associated with having a DDS<4 FactorOdds Ratio95% CI Employed full time0.672*0.430-1.050 Supermarket close by0.584*0.349-0.939 Flush toilet0.467*0.155-1.406 Electricity in house0.389*0.208-0.727 Motor vehicle0.326*0.270-0.394 Mobile phone0.473*0.389-0.575

32. Survey 3 SANHANES Full report not yet available but preliminary data confirms data from 2 earlier studies

33. Conclusions 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

34. Recommend 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