IAP vs Z-Score Classification for Growth Charts

A.K.NigamDirectorInstitute of Applied Statistics and Development Studies

Prevention and management of severely malnourished children is an important component of the ICDS and IMCI (Integrated Management of Children Illness) strategy. WHO also emphasizes on the management of severely malnourished children.

Prevalence of malnutrition in children is described in terms of the percentage of individuals below a specific cut-off, such as certain per cent of the median or standard deviation (sd) in terms of z scores of the reference population. Classifications like Gomez’s or Indian Association of Pediatricians (IAP) are on the basis of cut - off points as percentage of median (reference) weight-for-age.

Classification Cut-offs

Gomez (i) Normal: >90% of standard (median) weight-for-age of the NCHS reference population;

(ii) Grade I: 90-75%; mild malnutrition;

(iii) Grade II: 75-60%; moderate malnutrition; and

(iv) Grade III : <60%; severe malnutrition.

IAP (i) Normal: >80% of standard (median) weight-for-age of the reference population;

(ii) Grade I: 71-80%;

(iii) Grade II: 61-70%;

(iv) Grade III: 51-60%; and

(v) Grade IV: <50%. Usually Grades III and IV are referred to as severe malnutrition.

Standard Deviation

(z-scores)Z=(weight-median

weight)/sd

(i) Proportion of children below -2sd of the standard (median) weight-for-age; (which is the same as a z-score of -2.0) of the reference population.

(ii) The recommended cut-off point for severe malnutrition is -3sd.

NNMB/NIN reports (UNICEF also in some studies) nutritional status using each of these classifications with no inter-comparisons and guidelines about which one should be used for programme interventions.

SD classifications are now widely used by major stake holders like WHO, USAID among others. NFHS – I, II, and the latest III, the largest national surveys, which form the basis for planning by policy makers and programme implementers, present nutritional status data in terms of sd classifications only.

One is therefore in a dilemma which classification to use.

Although sd classification has distinct statistical advantages over others, its use is not widespread, mainly because it is felt (though erroneously) that it involves relatively cumbersome calculations. Even research workers find them difficult, as it needs development of appropriate software for different indicators of nutritional status.

Because of this, ICDS uses IAP classification. At each AWC, the Anganwadi Worker has to prepare and monitor growth chart for each child. These charts are based upon IAP classification and use 60% of median (standard) weight-for-age for identifying severely malnourished children.

It is known that the prevalence of severe malnutrition as derived by ICDS functionaries is far below in comparison to that reported by NFHS and other nutrition surveys. Even for the same survey (say, NNMB/NIN) the prevalence of severe malnutrition using below –3sd cut-off is much higher in comparison to that obtained using IAP cut-off of 60% of median weight.

Age (months)

Median weight(NCHS)

-3sd (% of median)

60% of median

6 7.8 4.9 (62.8) 4.7

7 8.3 5.4 (65.1) 4.9

8 8.8 5.9 (67.0) 5.2

9 9.2 6.3 (68.5) 5.3

10 9.5 6.6 (69.5) 5.5

Gap between NCHS reference and IAP classification in assessing severe undernutrition in boys

Nigam (2003) showed that 3sd cut-off matches with 67% of median (standard) weight-for-age. One readily notices the rationale behind the gap in reporting by realizing that 3sd cut-off corresponds to 67% (instead of 60% as in IAP) of median (standard) weight-for-age.

The equivalence relation facilitates the use of sd classification under field conditions and also by research workers. The growth charts, in terms of both IAP and sd classifications are given in another paper by Nigam (2005) separately for boys and girls. These charts can replace the existing growth charts at AWC.

Growth Chart (Girls)

123456789

10111213141516

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59

Age (months)

Wei

ght (

kg)

60% (IAP) Grades III & IV 67% (3 sd)Severe Malnutrition

80% (IAP) Grades I & II 78% (2 sd)

Growth Chart (Boys)

12

3456

789

10

11121314

1516

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58

Age (months)

Wei

ght (

kg)

60% (IAP) Grades III & IV 67% (3 sd)Severe Malnutrition

80% (IAP) Grades I & II 78% (2 sd)

A comparison was made by Nigam (2005) between the two approaches, 60% and 67% of median, to evaluate the percentage of severely malnourished children being left out by IAP classification which is being used by agencies like ICDS. For this, district level results from the NIN/IASDS district level Reports from the study – Nutrition Profile of Community in Uttar Pradesh, were utilized for Uttar Pradesh and Uttaranchal states.

Districts covered in NIN/IASDS

study(i)

n(ii)

IAP (Grades III & IV, <60% of median)

(iii)

Sd (<-3sd, 67%

of median)

(iv)

Deviation (7%)

(v)=(iv)-(iii)

% left out

(vi)=[(v)/(iv)]*100

BUNDELKHAND REGION

702 12.5 26.8 14.3 53.4

Lalitpur 143 7 21.7 14.7 67.7

Jalaun 145 10.4 20 9.6 48.0

Banda 160 22.5 40 17.5 43.8

Hamirpur 120 17.5 41.7 24.2 58.0

Jhansi 134 4.5 10.4 5.9 56.7

U. P. 9983 9.1 23.4 14.3 61.1

At the State level, 61.1% of severely malnourished children were left out in UP. At the regional level, in Uttar Pradesh, these left out percentages were – 58.1 in Western region, 59.9 in Central, 67 in Eastern and 53.4 in Bundelkhand. In numbers, in UP alone, out of about 6 million estimated severely malnourished children; over 3.5 million such children were likely to be left out.

The results reveal and support earlier findings that the percent of median cut-off points under IAP do not capture a

substantial number of children identified as malnourished through sd / z- score classifications.

As in most programme intervention projects, only severely malnourished are targeted and monitored, the huge gap in the two assessments also raises ethical considerations.

As ICDS uses IAP classifications for growth monitoring and identifying severely malnourished children, it is not difficult to realize the gravity and magnitude of the problem with regard to left out severely malnourished children at the national level. Corrective measure in this direction would prove to be very effective in tackling malnutrition deaths.

Age (months)

Median weight(WHO)

-3sd (% of median)

6 7.9 5.7 (72.2)

7 8.3 5.9 (71.1)

8 8.6 6.2 (70.9)

9 8.9 6.4 (71.9)

10 9.2 6.6 (71.7)

New WHO growth standards for assessing severe undernutrition in boys

Age (months)

Median BMI -for-Age

-3sd (% of median)

6 17.3 13.6 (78.6)

7 17.3 13.7 (79.2)

8 17.3 13.6 (78.6)

9 17.2 13.6 (79.1)

10 17.0 13.5 (79.4)

New WHO growth standards for assessing severe undernutrition in boys

Growth Curves (Severe Undernutrition) For Boys From WHO And NCHS Populations (0-59 months)

0

1

2

3

4

5

6

7

8

9

10

11

12

13

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61Age (months)

Wei

gh

t (K

g)

-3sdWHO 70% WHO -3sdNCHS 67%NCHS

Growth Curves (Severe Undernutrition) For Boys From WHO And NCHS Populations (0-24 months)

0

1

2

3

4

5

6

7

8

1 3 5 7 9 11 13 15 17 19 21 23 25Age (months)

Weig

ht

(Kg

)

-3sdWHO 70% WHO -3sdNCHS 67%NCHS

Growth Curves (Severe Undernutrition) For Boys From WHO And NCHS Populations (24-59 months)

8

8.5

9

9.5

10

10.5

11

11.5

12

12.5

13

13.5

24 26 28 30 32 34 36 38 40 42 44 46 48 50 52 54 56 58 60

Wei

gh

t (K

g)

-3sdWHO 70%WHO -3sdNCHS 67%NCHS

WHO Standards – Some Observations/Concerns It is seen that in 24-59 month period -3sd cut-offs vary considerably

with average cut-off &70% of median. Because for weight-based measures outer tails are highly affected

by even few extreme data points. This could also be because of lower emphasis given to uniformity on

different aspects of complementary feeding – initiation, quantity, quality and frequency among children of cross-sectional group. This perhaps explains lower dropouts in cross-sectional design

A limitation of WHO standards is non inclusion of some important ethnic groups (South East Asia, Australia, NZ etc.), and even in India children from cities from South and East.

The overall design, a mix of longitudinal and cross-sectional generates confounding – overlapping between 18-24 months, uneven visits in cross-sectional design.

WHO standards are limited to 0-59 months children; what about older children?

Overall Conclusion

No single cut-off is entirely satisfactory

More stratification is required within each country

For some time both NCHS and WHO should be concurrently used on pilot basis.

THANK YOU

Weight For Age Tables For BOYS and GIRLS In NCHS Reference PopulationBOYS

Month Median (-3sd) as

% of median (-2sd) as

% of median

0 60.6 72.7 87.9

1 51.2 67.4 83.7

2 50.0 67.3 82.7

3 51.7 68.3 83.3

4 55.2 70.1 85.1

5 58.9 72.6 86.3

6 62.8 75.6 88.5

7 65.1 77.1 89.2

8 67.0 78.4 88.6

9 68.5 78.3 89.1

10 69.5 80.0 90.5

11 69.7 79.8 89.9

12 69.6 79.4 89.2

13 70.2 79.8 90.4

14 70.1 79.4 89.7

15 69.7 79.8 89.9

16 69.4 79.3 90.1

17 69.0 79.6 89.4

18 68.7 79.1 89.6

GIRLS Month Median

(-3sd) as % of median

(-2sd) as % of median

0 56.3 68.8 84.4

1 55.0 70.0 85.0

2 57.4 70.2 85.1

3 59.3 72.2 87.0

4 61.7 75.0 88.3

5 61.2 74.6 86.6

6 63.9 76.4 87.5

7 64.9 76.6 88.3

8 64.6 76.8 87.8

9 66.3 76.7 88.4

10 66.3 77.5 88.8

11 67.4 78.3 89.1

12 67.4 77.9 89.5

13 67.3 77.6 88.8

14 67.0 78.0 89.0

15 67.6 78.4 89.2

16 67.3 78.8 89.4

17 67.9 78.3 89.6

18 67.6 78.7 89.8

19 68.4 78.6 89.7

20 68.6 79.7 89.8

21 69.2 79.2 90.0

22 68.9 79.5 89.3

23 68.5 79.0 89.5

24 68.3 78.6 89.7

25 68.0 78.9 89.1

26 67.7 78.5 89.2

27 67.9 78.6 89.3

28 68.4 78.9 89.5

29 68.1 78.5 89.6

30 67.9 78.8 89.1

31 68.1 79.0 89.9

32 67.9 78.6 89.3

33 68.3 78.9 89.4

34 68.1 78.5 88.9

35 68.3 78.6 89.7

36 68.0 78.9 89.1

37 66.9 77.7 89.2

38 66.7 78.0 88.7

39 66.4 77.6 88.8

40 66.7 77.8 88.9

19 68.2 78.2 89.1

20 67.9 78.6 89.3

21 67.5 78.9 89.5

22 68.7 79.1 89.6

23 68.4 79.5 89.7

24 68.9 79.0 89.9

25 68.6 79.3 89.3

26 69.1 78.9 89.4

27 69.4 79.8 90.3

28 69.8 80.2 89.7

29 69.5 79.7 89.8

30 70.5 79.8 89.9

31 70.2 80.2 90.1

32 69.9 79.7 89.5

33 70.1 79.9 90.3

34 69.9 80.1 89.7

35 69.6 79.7 89.9

36 69.8 79.9 89.9

37 68.5 79.0 89.5

38 68.8 79.2 89.6

39 68.5 78.8 89.7

40 68.2 78.4 89.2

BOYS GIRLS

BOYS GIRLS

41 66.5 77.4 89.0

42 66.2 77.1 88.5

43 66.5 77.8 89.2

44 66.3 77.5 88.8

45 66.0 77.2 88.9

46 65.9 76.8 88.4

47 66.1 77.6 88.5

48 65.9 77.2 88.6

49 65.7 76.9 88.2

50 65.9 77.1 88.8

51 65.7 77.3 88.4

52 65.5 77.0 88.5

53 65.7 77.1 88.6

54 65.5 77.4 88.7

55 65.9 77.1 88.3

56 66.1 77.2 88.9

57 65.9 76.9 88.5

58 66.1 77.6 89.1

59 65.9 77.3 88.6

41 68.5 79.2 89.3

42 68.2 78.8 89.4

43 68.4 78.9 89.5

44 68.2 78.6 89.0

45 68.4 78.7 89.7

46 68.2 78.3 89.2

47 68.4 78.5 89.2

48 68.1 78.8 89.4

49 67.7 78.9 89.4

50 67.9 79.0 89.5

51 67.7 78.7 89.0

52 67.9 78.8 89.7

53 67.7 78.4 89.2

54 67.9 78.6 89.3

55 67.6 78.2 88.8

56 67.3 78.4 88.9

57 67.4 78.5 89.5

58 67.2 78.2 89.1

59 67.4 78.3 89.1

Districts covered in NIN/IASDS study

(i)

n(ii)

IAP (Grades III & IV, <60% of

median)(iii)

Sd (<-3sd, 67% of

median)(iv)

Deviation (7%)

(v)=(iv)-(iii)

% left out

(vi)=[(v)/(iv)]*100

WESTERN REGION 3526 9.3 22.2 12.9 58.1

Ghaziabad 146 2.1 4.2 2.1 50.0

Moradabad 212 12.3 29.4 17.1 58.2

Bijnour 147 4.7 16.3 11.6 71.2

Badaun 231 19.5 39.8 20.3 51.0

Mainpuri 229 8.7 25 16.3 65.2

Etah 239 9.6 22.6 13 57.5

Mathura 214 9.8 17.8 8 44.9

Bulandshahar 100 12 23 11 47.8

Firozabad 178 4.5 16.3 11.8 72.4

Meerut 155 7.7 23.9 16.2 67.8

Agra 154 3.2 11 7.8 70.9

Bareilly 219 14.2 32.5 18.3 56.3

Farukkhabad 97 9.3 16.5 7.2 43.6

Pilibhit 209 2.4 11.5 9.1 79.1

Aligarh 165 13.3 23.6 10.3 43.6

Saharanpur 158 8.2 23.4 15.2 65.0

Muzaffarnagar 217 12 29 17 58.6

Etawah 129 5.4 9.3 3.9 41.9

Rampur 110 14.5 32.8 18.3 55.8

Shahjahanpur 217 8.3 21.2 12.9 60.8

Percentage of severely malnourished children left out by IAP classification

Districtscovered in NIN/IASDS

study(i)

n(ii)

IAP (Grades III & IV, <60% of

median)(iii)

Sd (<-3sd, 67% of

median)(iv)

Deviation (7%)

(v)=(iv)-(iii)

% left out

(vi)=[(v)/(iv)]*100

EASTERN REGION 3688 7.5 22.7 15.2 67.0

Deoria 177 5.7 19.2 13.5 70.3

Ballia 104 7.7 24 16.3 67.9

Ghazipur 227 6.2 18.5 12.3 66.5

Mau 242 5.8 16.1 10.3 64.0

Allahabad 177 13 35 22 62.9

Gorakhpur 159 11.9 25.8 13.9 53.9

Mirzapur 239 7.9 24.7 16.8 68.0

Jaunpur 115 4.8 14.5 9.7 66.9

Siddharthanagar 209 5.3 21.5 16.2 75.3

Basti 181 8.3 27.1 18.8 69.4

Sultanpur 201 7.5 19.4 11.9 61.3

Varanasi 223 9 29.1 20.1 69.1

Maharajganj 198 3 13.1 10.1 77.1

Bahraich 201 8 28.2 20.2 71.6

Faizabad 192 5.7 21.4 15.7 73.4

Azamgarh 193 5.7 20.7 15 72.5

Pratapgarh 176 14.8 29.5 14.7 49.8

Sonebhadra 242 6.6 20.7 14.1 68.1

Gonda 232 7.8 23.7 15.9 67.1

Districtscovered in NIN/IASDS

study(i)

n(ii)

IAP (Grades III & IV, <60% of

median)(iii)

Sd (<-3sd, 67% of

median)(iv)

Deviation (7%)

(v)=(iv)-(iii)

% left out

(vi)=[(v)/(iv)]*100

CENTRAL REGION 2067 10.3 25.7 15.4 59.9

Barabanki 215 8.4 23.6 15.2 64.4

Kheri 189 8.5 22.2 13.7 61.7

Kanpur Nagar 157 9.6 21.6 12 55.6

Kanpur Dehat 163 7.4 28.8 21.4 74.3

Sitapur 211 12.3 32.7 20.4 62.4

Raebareli 225 13.8 31.6 17.8 56.3

Lucknow 229 7.9 20.1 12.2 60.7

Unnao 225 6.7 17.8 11.1 62.4

Hardoi 223 10.4 21.5 11.1 51.6

Fatehpur 230 16.9 36.1 19.2 53.2

BUNDELKHAND REGION

702 12.5 26.8 14.3 53.4

Lalitpur 143 7 21.7 14.7 67.7

Jalaun 145 10.4 20 9.6 48.0

Banda 160 22.5 40 17.5 43.8

Hamirpur 120 17.5 41.7 24.2 58.0

Jhansi 134 4.5 10.4 5.9 56.7

U. P. 9983 9.1 23.4 14.3 61.1