In-depth analysis of data on food and nutrition security ... In-depth analysis of data on food and...

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In-depth analysis of data on food and nutrition security situation in the Indian state of Madhya

Pradesh

Food and Nutrition Security, Enhanced Resilience Programme (FaNS), India

Archana Sarkar, Barbara Walter, Vandana Sabharwal, Michael Klingler

May 2017

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Contents Contents .....................................................................................................................................2 List of Tables ..............................................................................................................................2 List of Figures .............................................................................................................................2 List of Abbreviations ....................................................................................................................2 1) Summary ................................................................................................................................3 2) In-Depth Analysis ....................................................................................................................5

2.1 Purpose of the re-analysis .................................................................................................5 2.1 Methodological approach of the in-depth analysis ..............................................................6 2.3 Findings .............................................................................................................................6

3) Conclusion ............................................................................................................................ 19

List of Tables Table 1 Data on Caste vs. Individual Dietary Diversity of Women ...............................................7 Table 2 Data on caste vs. infants of 6 - 23 months who receive a minimum acceptable diet .......8 Table 3 Association of caste vs. Food insecurity severity experienced by households ................9 Table 4 Data on educational status vs. Individual Dietary Diversity of Women .......................... 11 Table 5: Data on educational status vs. infants of 6 - 23 months who receive a minimum acceptable diet .......................................................................................................................... 11 Table 6 Data on educational status vs. Food insecurity severity experienced by households .... 12 Table 7 Data on income category vs. Individual Dietary Diversity of Women ............................ 14 Table 8 Data on income vs. infants of 6 - 23 months who receive a minimum acceptable diet .. 14 Table 9 Data on income vs. Food insecurity severity experienced by households .................... 15 Table 10 Correlation Matrix ....................................................................................................... 16 Table 11 Logistic Regression Analysis showing the factors affecting Individual Dietary Diversity Scores of Women ..................................................................................................................... 18 Table 12 Logistic Regression Analysis Showing the Factors Affecting Minimum Adequacy of Diet for Children ........................................................................................................................ 19

List of Figures Figure 1 Results of the NBS presented according to the UNICEF Model ....................................5 Figure 2 Effect of Caste on IDDS, MAD, HFIES Scores and TPDS Scheme ............................. 10 Figure 3 Effect of Educational Status on IDDS, MAD, HFIES Scores and TPDS Scheme ......... 13

List of Abbreviations MAD Minimum Acceptable Diet

IDDS Individual Dietary Diversity Score

HFIES Household Food Insecurity Experience Scale

TPDS Targeted Public Distribution System

NBS Nutrition Baseline Survey

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The caste system was originally a first attempt to divide

labour with the society as it is based on the belief that each group was born with an unchangeable nature and capacity to do certain kind of work. Within a lifetime generally nobody is able to leave the assigned group. The exploitation of this system went so far that certain groups were seen as ‘untouchables’ not being allowed to even get close to one of the higher ranking groups. The former untouchables are today recognized as the so called “scheduled castes” and “scheduled tribes” also known as Dalits and Adivasi. Even though discrimination in any form has been officially discarded by the Indian Constitution in 1950, the system is still very important for the social and economic fabric of the country especially in rural settings. Although the Government has introduced a lot of schemes and even service quotas for improving the social and economic situation of the lowest ranking groups, discriminative practices persist. People belonging to Scheduled castes and Scheduled tribes often live in isolated and separate clusters and are denied access to common services like wells or water-taps located in the areas where higher caste people live in the villages. .

1) Summary

Why looking into the data again? The key findings of the International Baseline included low dietary

diversity among women of reproductive age and children (6-23 months of age) in the intervention districts

of Sheopur and Chhatarpur in Madhya Pradesh, India. Only 16.2% of children had achieved the Minimum

Acceptable Diet (MAD); whereas more than 80% of women had an average of less than 5 food groups

consumed daily and 77% of children had consumed less than 4 food-groups daily.

Given these alarming findings, the in-depth analysis of the International Baseline data was conducted to

have a further understanding of various socio-demographic factors, behavioral practices and access to

services affecting the dietary diversity and food availability among women and children.

The project wanted to understand better how underlying factors are influencing the nutrition diversity and

food security situation of its target group (women and children). This should also help to tailor the ongoing

activities of the project especially concerning the nutrition awareness programme better to the specific

conditions for example influence of social factors like caste or environmental circumstances such as water

shortage.

What we found:

Caste status influences every aspect of social

life especially in the Indian rural areas.

Scheduled castes and scheduled tribes in Indian

context are the most vulnerable in terms of their

social, economic characteristics and health

facilities access. The study revealed that it

further determines how many different food

groups or how many meals women have.

Women who belonged to the scheduled tribes

(isolated and so called low ranking group) had a

significantly lower value for the Individual

Dietary Diversity Score (IDDS) compared to

women belonging to the general category

(29.5% vs 9%; χ2 = 20.294; p<0.001). This was

also reflected in the food security status of

scheduled tribes who were the most food

insecure group surveyed. District wise, in

Chhatarpur, fewer respondents were severely

food insecure as compared to those in Sheopur.

However, infants who receive a minimum acceptable diet remained low irrespective of caste (χ2 = 71.0;

p<0.001).

With better educational status comes a better nutrition status for mother and children in terms of IDDS

and MAD respectively. Also, income stability matters for a stable food situation. Households having

stable income source (example: sale of own crops/goods/crafts/regular salary income) were more food

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Secure (Household Food Insecurity Experience Scale-HFIES score) than those having a marginally stable

or unstable income source (example: petty trade/shops/ begging/ remittance from family, no proper source

of income).

Access to food and nutrition services plays an important part for the project’s target group especially to

tackle food insecurity. The data shows that beneficiaries who use the Targeted Public Distribution System

(TPDS – public distribution system for subsidized food grains) are also more likely to use existing nutrition

services for their children (counselling and food rations from local health centres) and participate in public

work programmes.

Knowledge about nutrition practices can make a difference. Mothers who know how to enrich for

example porridge/soft lentil based food (khichdi) or who have received nutrition and hygiene counselling

from local health workers and who themselves have a sufficient number of meals were found to have a

better IDDS. In addition, MAD scores also correlated positively with the infant’s age, knowledge scores on

prevention of malnutrition, breastfeeding and the age of introduction of complementary foods. However,

multivariate analysis indicated caste, income and education also influenced nutrition diversity indicators.

A very encouraging finding for the approach of the programme is that women who received nutrition

counselling are more likely to have higher IDDS scores irrespective of factors such as religion, caste,

income and education level, as confirmed by multiple logistic regressions (OR: 1.679; P=0.007). The

same holds for the feeding of infants. Infants are more likely to receive adequate quantity and quality of

food if their mothers have received nutrition counselling (OR: 1.5; p=0.04); know how to enhance porridge

(OR: 1.323; p=0.001) and have been made aware about hygiene practices (OR: 1.8; p=0.002). Further,

also infants who were ever breastfed and for whom complementary foods were initiated timely (between

6-7 months) had higher odds for meeting MAD scores.

What next? The in-depth analysis has clearly demonstrated the differences between the project districts.

Even though access to food and dietary diversity is low in both the districts, Sheopur faces additional

challenges such as water scarcity, remoteness, low access to resources and facilities and a higher

population of vulnerable communities like scheduled tribes and scheduled castes. The activities need to

focus more on the specific needs of these groups which can only happen with the support of the District

authorities which for example need to ensure that even remote villages have a close access to the TPDS

services through mobile shops.

The nutrition awareness activities need to address the societal and food habits of these vulnerable

communities and to find localized answers. One big challenge which needs to be tackled is migration

which is often driven by a lack of income sources or water scarcity which threatens the sustainability of

interventions such as kitchen gardens. Greater focus is needed for communities belonging to scheduled

tribes who are often isolated from the interventions. The findings on the importance of knowledge about

healthy eating and feeding to improve nutrition habits confirms the approach of the programme which

entails the training of local health workers who then in turn train women of reproductive age on village

level.

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The in-depth analysis also shows how important it is to take into account the difference between the two

districts especially in terms of the social groups when designing a research study. For further studies it

needs to be ensured that the sample reflects the different population shares of the district so that the

situation of the scheduled tribes and scheduled castes is reflected in the collected data and analysis. An

in-depth analysis of the International Baseline data of all countries which are part of the global programme

on food and nutrition security, enhanced resilience can provide key evidence on dietary diversity and food

security context in different regions.

2) In-Depth Analysis 2.1 Purpose of the re-analysis The Nutrition Baseline Survey (NBS) was conducted among women of reproductive age (15-49 years),

infants and young children between the age of 6-23 months in India in January and February 2016. The

main objective of this survey was to describe the nutrition situation among the target groups in rural areas

of the districts Chhatarpur and Sheopur in the Indian state of Madhya Pradesh. Of special interest were

Minimum Acceptable Diet (MAD) of infants and young children and Individual Dietary Diversity Scores for

women (IDDS). The study examined linkages between dietary diversity and complementary feeding

practices with living conditions as well as with knowledge and practices in regard to hygiene and nutrition.

The key findings of the baseline included low dietary diversity in women and children (6-23 months of

age), high prevalence of diarrhoea and low levels of knowledge among women on nutritional, hygiene and

care giving practices. The major results according to the UNICEF Model are presented in Figure 1.

Figure 1 Results of the NBS presented according to the UNICEF Model

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An in-depth analysis of the International baseline data was carried out in order to foster a better

understanding of various factors (socio-economic, demographic, availability of counselling services and

mother’s feeding practices) affecting nutritional intake of women and infants in the study districts.

2.1 Methodological approach of the in-depth analysis The in-depth analysis has re-analysed the findings by the variables of caste, education and income to

understand their effects on the IDDS for women, the MAD for children (defined as receiving at least

minimum dietary diversity and minimum meal frequency), household food insecurity status as well as

usage of the TPDS scheme. Chi-square tests have been applied in order to examine statistical

differences at a 0.05 level of significance. Further, correlation analysis has been conducted to

understand factors influencing IDDS and MAD. For this, the spearman rank correlation has been

employed at 0.05 level of significance. Thereafter, advanced multivariate logistic regression analysis has

been run to understand the effect/role of vulnerability factors on MAD and IDDS. Few variables were re-

categorized for the study, these include age of the mother, income, nutritional counselling and age of

introduction of starting complementary foods:

The age of mother was reported as a continuous variable in the study, however, the re-analysis treated it as a categorical variable: <=20 years, 21-25 years, 26-30 years, and > 30 years.

The data on source of income was also re-categorized as no /marginal income, temporary income and permanent income. No /marginal income included subsistence farming and begging/rag picking; temporary income included temporary salary, petty trade/small business, remittance from relatives/husband as well as income generated by sales or exchange of public transfers; permanent income included sale of own produced crops, sale of own produced goods/crafts and regular salary.

In case of nutritional counselling recoding was done in two categories, one for those who received no counselling and the other for those who received at least one or more counselling sessions.

Since the age of starting complementary feeding should be ideally between 6-7 months, this variable was recoded into four categories: those who have not received any complementary foods, complementary foods started before 6 months, complementary foods started between 6-7 months and complementary foods started after 7 months.

2.3 Findings

a) Association of Caste (Social Group) with IDDS, MAD AND HFIES Scores

Caste had a statistically significant effect on individual dietary diversity scores of women as shown in

table 1. Women belonging to vulnerable tribal communities’ (Scheduled tribes) had significantly lower

dietary diversity scores as compared to other women in the sample. IDDS (consuming the recommended

>=5 food groups) was higher for women belonging to general category as compared to other castes.

Women belonging to scheduled tribes had the lowest minimum dietary diversity (9%). Similar trends

were seen in Sheopur. In Chhatarpur, women in the general category had better individual dietary

diversity scores however, no statistical difference was observed. In general the IDDS was better in

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women belonging to Chhatarpur as compared to Sheopur. Table 2 indicates that the share of infants

of 6- 23 months who receive a minimum acceptable diet remained low irrespective of caste (p<0.05).

However, more infants of 6 - 23 months received a minimum acceptable diet in Chhatarpur (20.3 %)

than in Sheopur (12.0%). Severe food insecurity was least prevalent among the general caste

followed by other backward classes while respondents from the group of scheduled tribes were the

most food insecure (p<0.05). Similar trends were seen in Sheopur (Table 3). In Chhatarpur, fewer

respondents were severely food insecure as compared to those in Sheopur (Fig 2). Further, in

Chhatarpur, no statistical difference was observed concerning the relation of caste and food

insecurity.

Table 1 Data on Caste vs. Individual Dietary Diversity of Women

Caste Chhatarpur Sheopur Pooled Sample#

N Women who

received 5 or more

food groups

N Women who

received 5 or more

food groups

N Women who

received 5 or more

food groups

No Yes No Yes No Yes

Scheduled

Caste

57 40 (70.2) 17 (29.8) 67 58 (86.6) 9 (13.4) 124 98 (79.0) 26 (21.0)

Scheduled

Tribe

38 31 (81.6) 7 (18.4) 139 130 (93.5) 9 (6.5) 177 161 (91) 16 (9.0)

Other

Backward

Caste

256 189

(73.8)

67 (26.2) 166 139 (83.7) 27 (16.3) 422 328

(77.7)

94 (22.3)

General 48 33 (68.8) 15 (31.3) 30 22 (73.3) 8 (26.7) 78 55 (70.5) 23 (29.5)

Total 399 293

(73.3)

106

(26.8)

402 349 (86.8) 53 (13.2) 801 642

(80.1)

159 (19.9)

Chi-Square

Value

χ2= 4.898; p = 0.298 χ2= 11.778; p = 0.019 χ2= 20.294; p = 0.000

Figure in the parenthesis indicate percentages; # Pooled Chhatarpur & Sheopur Sample

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Table 2 Data on caste vs. infants of 6 - 23 months who receive a minimum acceptable diet

Caste Chhatarpur Sheopur Pooled Sample#

N Infants of 6 - 23

months who

receive a minimum

acceptable diet

N Infants of 6 - 23

months who receive

a minimum

acceptable diet

N Infants of 6 - 23

months who receive

a minimum

acceptable diet


Scheduled

Caste

56 40 (71.4) 16 (28.6) 66 59 (89.4) 7 (10.6) 122 99 (81.1) 23 (18.9)

Scheduled

Tribe

36 30 (83.3) 6 (16.7) 133 119 (89.5) 14 (10.5) 169 149

(88.2)

20 (11.8)

Other

Backward

Caste

253 205

(81.0)

48 (19.0) 163 142 (87.1) 21 (12.9) 416 347(83.4) 69 (16.6)

General 48 39 (81.3) 9 (18.8) 29 24 (82.8) 5 (17.2) 77 63 (81.8) 14 (18.2)

Total 393 314

(79.7)

79 (20.3) 391 344 (88.0) 47 (12.0) 784 658

(83.8)

126 (16.2)

Chi-Square

Value

χ2 = 6.934; p = 0.139 χ2= 1.407; p = 0.843 χ2 = 4.966; p = 0.291


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Table 3 Association of caste vs. Food insecurity severity experienced by households

Caste Chhatarpur Sheopur Pooled Sample (Chhatarpur & Sheopur)

N Food insecurity severity experienced by

households


households


households

Food

secure

Mild food

insecure

Moderate

food

insecure

Severe

food

insecure

Food

secure

Mild food

insecure

Moderate

food

insecure

Severe

food

insecure

Food

secure

Mild

food

insecure

Moderate

food

insecure

Severe

food

insecure

Scheduled

Caste

55 22

(40.0)

21

(38.2)

8

(14.5)

4

(7.3)

67 44

(65.7)

21

(31.3)

1

(1.5)

1

(1.5)

122 66

(54.1)

42

(34.4)

9

(7.4)

5

(4.1)

Schedule

Tribe

36 10

(27.8)

17

(47.2)

7

(19.4)

2

(5.6)

139 40

(28.8)

53

(38.1)

30

(21.6)

16

(11.5)

175 50

(28.6)

70

(40.0)

37

(21.1)

18

(10.3)

Other

Backward

Caste

245 112

(45.7)

103

(42.0)

27

(11.0)

3

(1.2)

166 113

(68.1)

37

(22.3)

12

(7.2)

4

(2.4)

411 225

(54.7)

140

(34.1)

39

(9.5)

7

(1.7)

General 48 27

(56.3)

14

(29.2)

6

(12.5)

1

(2.1)

30 26

(86.7)

3

(10.0)

1

(3.3)

0

(0.0)

78 53

(67.9)

17

(21.8)

7

(9.0)

1

(1.3)

Total 384 171

(44.7)

155

(40.3)

48

(12.5)

10

(2.6)

402 223

(55.3)

114

(28.5)

44

(10.9)

21

(5.2)

786 394

(50.1)

269

(34.3)

92

(11.7)

31

(3.9)

Chi-

Square

Value

χ2= 17.135; p = 0.145 χ2 = 82.268; p = 0.000 χ2= 71.063; p = 0.000

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Figure 2 Effect of Caste on IDDS, MAD, HFIES Scores and TPDS Scheme

b) Effect of educational status on IDDS, MAD, HFIES Scores and TPDS

Although very few women in the study population (19.9%) had scored well on individual dietary diversity,

there was an increase in percentage of women consuming the recommended >=5 food groups with better

educational status (p<0.05) as shown in table 4. Similar trends were seen in Sheopur. In Chhatarpur, also

individual dietary diversity increased with better educational status, however the results were not

statistically significant. In general individual dietary diversity was better in women belonging to Chhatarpur

as compared to Sheopur. As shown in table 5, infants of 6 - 23 months who receive a minimum acceptable

diet remained low in the study population. However, more Infants of 6 - 23 months received a minimum

acceptable diet whose mother were better educated. MAD scores were better in Chhatarpur (20.3%) than in

Sheopur (12.0%). A higher percentage of household having mothers who had received education higher

than secondary were food secure (Table 6). In general food security increased with increase in educational

status (p<0.05). Similar trends were seen in Sheopur. In Chhatarpur, fewer respondents were severely food

insecure as compared to those in Sheopur. Further, in Chhatarpur, no statistical difference was observed

with education in status of food insecurity.

18.9

54.1

9 11.8

28.622.3

16.6

54.7

29.5

18.2

67.9

21

0

10

20

30

40

50

60

70

80

Individual Dietary

Diversity Scores of

Women

Minimum Acceptable

Diet given to infants

Food Secure Households

Per

cen

t (%

)

Schedule Caste

Schedule Tribes

Other Backward Classes

General

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Table 4 Data on educational status vs. Individual Dietary Diversity of Women

Educational

status

Chhatarpur Sheopur Pooled Sample#

N Women who

received 5 or more

food groups

N Women who

received 5 or more

food groups

N Women who

received 5 or more

food groups


No

Schooling

150 121

(80.7)

29

(19.3)

242 220

(90.9)

22

(9.1)

392 341

(87.0)

51

(13.0)

Primary 73 52

(71.2)

21

(28.8)

55 45

(81.8)

10

(18.2)

128 97 (75.8) 31

(24.2)

Secondary 149 102

(68.5)

47

(31.5)

88 71

(80.7)

17

(19.3)

237 173

(73.0)

64

(27.0)

More than

secondary

28 18

(64.3)

10

(35.7)

18 14

(77.8)

4

(22.2)

46 32

(69.6)

14

(30.4)

Total 400 293

(73.3)

107

(26.8)

403 350

(86.8)

53

(13.2)

803 643

(80.1)

160

(19.9)

Chi-Square

(λ2)Value

χ2 = 7.258; p = 0.064 χ2 =8.939; p = 0.030 χ2= 23.855; p = 0.000


Table 5: Data on educational status vs. infants of 6 - 23 months who receive a minimum acceptable diet

Educational

status

Chhatarpur Sheopur Pooled Sample

N Infants of 6 - 23

months who

receive a minimum

acceptable diet

N Infants of 6 - 23

months who receive

a minimum

acceptable diet

N Infants of 6 - 23

months who receive

a minimum

acceptable diet


No

Schooling

146 119

(81.5)

27 (18.5) 234 215 (91.9) 19

(8.1)

380 334

(87.9)

46

(12.1)

Primary 71 56 (78.9) 15 (21.1) 53 45

(89.5)

8

(10.5)

124 101

(81.5)

23

(18.5)

Secondary 149 122

(81.9)

27 (18.1) 87 69

(79.3)

18 (20.7) 236 191

(80.9)

45

(19.1)

More than

secondary

48 17 (60.7) 11 (39.3) 18 16 (88.9) 2 (11.1) 46 33 (71.7) 13

(28.3)

Total 394 314

(79.7)

80 (20.3) 392 345 (88.0) 47 (12.0) 786 659

(83.8)

127 (16.2)

Chi-Square

(λ2) Value

χ2 = 6.999; p = 0.072 χ2= 10.059; p = 0.018 χ2 = 11.579; p = 0.009


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Table 6 Data on educational status vs. Food insecurity severity experienced by households

Educatio

nal status

Chhatarpur Sheopur Pooled Sample (Chhatarpur & Sheopur)

N Food insecurity severity

experienced by households

N Food insecurity severity experienced

by households


households

Food

secure

Mild food

insecure

Moderate

food

insecure

Severe

food

insecure

Food

secure

Mild food

insecure

Moderate

food

insecure

Severe

food

insecure

Food

secure

Mild food

insecure

Moderate

food

insecure

Severe

food

insecure

No

Schooling

14

3

53

(37.1)

62

(43.4)

23

(16.1)

5

(3.5)

24

2

111

(45.9)

77

(31.8)

37

(15.3)

17

(7.0)

38

5

166

(42.6)

139

(36.1)

60

(15.6)

22

(5.7)

Primary 71 30

(42.7)

31

(43.7)

7

(9.9)

3

(5.6)

55 35

(63.6)

16

(29.1)

2

(3.6)

2

(3.6)

12

6

65

(51.6)

47

(37.3)

9

(7.1)

5

(4.0)

Secondar

y

14

4

73

(50.7)

54

(37.5)

16

(11.1)

1

(0.7)

88 61

(69.3)

21

(23.9)

4

(4.5)

2

(2.3)

23

2

134

(57.8)

75

(32.3)

20

(8.6)

3

(1.3)

More than

secondary

27 16

(59.3)

8

(29.6)

2

(7.4)

1

(3.7)

18 16

(88.9)

1

(5.6)

1

(5.6)

0

(0.0)

45 32

(71.1)

9

(20.0)

3

(6.7)

1

(2.2)

Total 38

5

172

(44.7)

155

(40.3)

48

(12.5)

10

(2.6)

40

3

223

(55.3)

115

(28.5)

44

(10.9)

21

(5.2)

78

8

395

(50.1)

270

(34.3)

92

(11.7)

31

(3.9)

Chi-

Square λ2

Value

χ2= 11.935; p = 0.217 χ2= 31.139; p = 0.000 χ2 = 32.330; p = 0.000


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Figure 3 Effect of Educational Status on IDDS, MAD, HFIES Scores and TPDS Scheme

c) Effect of income stability on IDDS, MAD, HFIES and TPDS

Since the collected data has no direct variable on income category, data on source of income was used

for the purpose of data analysis and was re-categorized into instable, marginally stable and stable source.

Under the instable source begging, rag picking as well as subsistence farming only was included. For

marginally stable income temporary salary, small business/ petty trade, remittance from relative/husband

and income from public transfer was included whereas for stable sources sale of own produced crops,

sale of own produced goods, regular salary and rental income was considered. Table 7 indicates that

there was an increase in percentage of women consuming the recommended >=5 food groups with better

stability in income (p<0.05). Similar trends were seen in Sheopur. In Chhatarpur, also individual dietary

diversity increased with better income stability, however the results were not statistically significant. The

households with stable income sources reported marginally better MAD status for infants of 6 - 23 months

that with marginally stable or instable source as shown in table 8. MAD scores were better in Chhatarpur

(20.4 %) than in Sheopur (14.6 %).

A higher percentage of households having stable income source were food secure than those having a

marginally stable income source and the results were statistically significant (Table 9). However majority

of respondents who had an instable income were also food secure. Similar trends were seen in Sheopur.

In Chhatarpur, fewer respondents were severely food insecure as compared to Sheopur.

12.1

42.6

24.218.5

51.6

27

19.1

57.8

30.4 28.3

71.1

13

0

10

20

30

40

50

60

70

80

Individual Dietary

Diversity Scores of

Women

Minimum Acceptable

Diet given to infants

Food Secure

Households

No Schooling

Primary

Secondary

More than secondaryPercen

t (%

)

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Table 7 Data on income category vs. Individual Dietary Diversity of Women

Income

Category


N Women who

received 5 or more

food groups

N Women who received

5 or more food

groups

N Women who

received 5 or more

food groups


Instable 3 3 (100.0) 0 (0.0) 8 7 (87.5) 1 (12.5) 11 10 (90.0) 1 (9.1)

Marginally

stable

123 98 (79.7) 25 (20.3) 133 123 (92.5) 10 (7.5) 256 221

(86.3)

35 (13.7)

Stable 272 190

(69.9)

82 (30.1) 253 211 (83.4) 42 (16.6) 525 401

(76.4)

64 (23.6)

Total 398 291

(73.1)

107

(26.9)

394 341 (86.5) 53 (13.5) 792 632

(79.8)

160 20.2)

Chi-Square

λ2 Value

χ2= 5.268; p = 0.072 χ2 =6.182; p = 0.045 χ2 = 11.417; p = 0.003


Table 8 Data on income vs. infants of 6 - 23 months who receive a minimum acceptable diet

Income

Category


N Infants of 6 - 23

months who receive

a minimum

acceptable diet

N Infants of 6 - 23

months who receive

a minimum

acceptable diet

N Infants of 6 - 23

months who receive

a minimum

acceptable diet


Instable 3 3 (100.0) 0 (0.0) 8 8 (100.0) 0 (0.0) 11 11

(100.0)

0 (0.0)

Marginally

stable

119 97 (81.5) 22 (18.5) 128 117 (91.4) 11 (8.6) 247 214

(86.6)

33 (13.4)

Stable 270 212

(78.5)

58 (21.5) 247 211 (85.4) 36 (14.6) 517 423

(81.8)

94 (18.2)

Total 392 312

(79.6)

80 (20.4) 383 336 (85.4) 47 (14.6) 775 648

(83.6)

127 (16.4)

Chi-Square

λ2 Value

χ2 = 1.231; p = 0.540 χ2= 3.945; p = 0.139 χ2 = 5.023; p = 0.081


15

Table 9 Data on income vs. Food insecurity severity experienced by households

Income

Category

Chhatarpur Sheopur Pooled Sample (Chhatarpur &

Sheopur)

n Food insecurity severity

experienced by households


by households


by households

Food

secure

Mild food

insecure

Moderate

food

insecure

Severe

food

insecure

Food

secure

Mild food

insecure

Moderate

food

insecure

Severe

food

insecure

Food

secure

Mild food

insecure

Moderate

food

insecure

Severe

food

insecure

Instable 3 2

(66.7)

0

(0.0)

1

(33.3)

0

(0.0)

8 8

(100.0

)

0

(0.0)

0

(0.0)

0

(0.0)

11 10

(90.9)

0

(0.0)

1

(9.1)

0

(0)

Marginally

stable

11

9

46

(38.7)

48

(40.3)

19

(16.0)

6

(5.0)

13

3

46

(34.6)

50

(37.6)

27

(20.3)

10

(7.5)

25

2

92

(36.5)

98

(38.9)

46

(18.3)

16

(6.3)

Stable 26

1

123

(47.1)

106

(40.6)

28

(10.7)

4

(1.5)

25

3

166

(65.6)

63

(24.9)

14

(5.5)

10

(4.0)

51

4

289

(56.2)

169

(32.9)

42

(8.2)

14

(2.7)

Total 38

3

171

(44.6)

154

(40.2)

48

(12.5)

10

(2.6)

40

3

220

(55.8)

113

(28.7)

41

(10.4)

20

(5.1)

77

7

391

(50.3)

267

(34.4)

89

(11.5)

30

(3.9)

Chi-

Square

Value

χ2= 9.627; p = 0.141 χ2 = 46.836; p = 0.000 χ2 = 43.578; p = 0.000

Figure in the parenthesis indicate percentage; # Pooled Chhatarpur & Sheopur Sample

16

d) Correlation results

The data was further treated to understand the factors affecting IDDS scores, MAD scores and TPDS

utility using Spearman Rank correlation and the results were considered statistically significant at 0.05

level of significance. The IDDS scores were positively associated with education, income stability,

Household land access, nutrition counselling received, hygienic counselling received, knowledge scores

of food consumption during pregnancy and lactation and number of meals the respondent had

yesterday.

The MAD scores of children were positively associated with the age of the infant, level of education of

mother, income stability, knowledge scores of mother (for enriching porridge & for prevention of

malnutrition), child was ever breastfed, the age of introduction of complementary foods, child’s food

prepared separately and if the mother received counselling on hygiene.

Table 10 Correlation Matrix

Factors IDD Scores MAD Scores TPDS Utility

Sex of the household head 0.046 0.018 0.013

Age of the mother -0.096** 0.032 0.090*

Age of the infant - 0.194**

Sex of the child - 0.025

Level of education of the mother 0.170** 0.114** 0.039

Income of the household 0.120** 0.072* -0.015

Caste 0.130 0.008 0.100**

Household land access 0.101** 0.052 -0.012

Dependence of home garden for food

security

0.086 -0.012 0.017

Household participation in Anganwadi

programme

-0.041 0.032 0.073*

Household benefits from public work

programme

-0.043 -0.031 0.122**

Food insecurity severity experienced by

household

-0.098** -0.037 0.049

Nutrition counselling received 0.099** 0.073 0.021

Hygienic counselling received 0.071* 0.013** 0.042

Number of meal the respondent had

yesterday

0.119** - -

Knowledge scores of food consumption

during pregnancy

0.160** - -

Knowledge scores for enriching porridge - 0.125** -

Knowledge scores for recognizing signs for

malnutrition

- 0.036 -

17

Factors IDD Scores MAD Scores TPDS Utility

Knowledge scores for recognizing reasons

for malnutrition

- 0.033 -

Knowledge scores for prevention of

malnutrition

- 0.081* -

Child was ever breastfed - 0.118** -

Age at which complementary food was

introduced

0.113** -

Child’s food is prepared separately - 0.123** -

Individual dietary diversity scores of mothers - - -0.043

Minimum adequacy of diet for infants - - -0.036

* Correlation is significant at 0.05 level; ** Correlation is significant at 0.01 level

e) Logistic regression analysis

The variables which correlated with IDDS and MAD scores were further subjected to binary logistic

regression analysis to further understand their determinants using forward step-wise analysis. The results

are discussed under:

Determinants of IDDS scores

The results of the logistic regression highlighted that household land access, hygienic counselling

received, age of the mother and food insecurity severity experienced by household did not show

considerable improvement in the IDD Scores of women. Further after controlling for religion, caste,

income and education level of women, more proportion of women who received nutrition counselling

where likely to meet the IDDW as compared to those women who did not received counselling ( OR:

1.679; p=0.007). The results of the study also indicate that odds of meeting IDDW scores were 1.305

times higher for women who had knowledge about food consumption during pregnancy and lactation and

1.626 times higher for the once who had more than one meal on the day previous to the interview (Table

11).

18

Table 11 Logistic Regression Analysis showing the factors affecting Individual Dietary Diversity Scores of Women

Factors B S.E. Sig. OR

Education

No Schooling

Primary

Secondary

More than secondary

-1.080

-0.363

-0.286

0.362

0.388

0.359

0.003

0.350

0.426

0.340

0.696

0.751

Income

Instable

Marginally Stable

Stable

-1.069

-0.552

1.062

0.251

0.314

0.070

0.343

0.567

Nutrition Counselling

No Counselling Received

Counselling Received

0.518

0.191

0.007

1.679

Knowledge Scores of food

consumption during

pregnancy and lactation

Nil

One and more than one

0.266

0.081

0.001

1.305

Number of meals respondent

had the previous day

One

More than one

0.486

0.141

0.001

1.626

Determinants of MAD scores

For understanding factors that most significantly affect MAD scores of infant age of the infant, level of

education of respondent, income stability, knowledge scores for enriching porridge, knowledge scores for

prevention of malnutrition, child was ever breastfed, the age of introduction of complementary foods,

child’s food is prepared separately and hygienic counselling received were treated for regression analysis.

The results indicate that after controlling for socio-demographic factors namely religion, caste, income and

education level of women knowledge scores for prevention of malnutrition and child’s food is prepared

separately were not associated with MAD scores. Odds of meeting MAD scores where 1.513 times higher

for infants whose mothers received nutrition counselling and 1.823 times higher for infants whose mothers

received counselling on hygiene. Infants whose mothers had knowledge of preparing porridge were more

likely to meet MAD scores (OR: 1.323; p=0.001). Also children who were ever breastfed and for whom

complementary foods were initiated timely (between 6-7 months) had higher odds for meeting MAD

scores (Table 12).

19

Table 12 Logistic Regression Analysis Showing the Factors Affecting Minimum Adequacy of Diet for Children

Factors B S.E. Sig. OR

Age of the child

6-11 months

12-17 months

18-23 months

-1.614

-0.249

0.311

0.221

0.00

0.250

0.199

0.779

Education

No Schooling

Primary

Secondary

More than secondary

-1.125

-0.633

-0.609

0.307

0.405

0.371

0.002

0.117

0.101

0.325

0.531

0.544

Nutrition counselling



0.414

0.206

0.045

1.513

Counselling about hygiene



0.600

0.197

0.002

1.823

Knowledge Scores on

enriching porridge

Nil

One and more than one

0.280

0.087

0.001

1.323

Child ever breastfed

No

yes

2.135

0.726

0.003

8.453

Complementary foods started

between 6-7 months

No

Yes

0.586

0.222

0.008

1.796

3) Conclusion In general individual dietary diversity and MAD scores was better in women belonging to Chhatarpur

as compared to Sheopur. In Chhatarpur, fewer respondents were severely food insecure as

compared to Sheopur. This could be due to the fact that

Caste had a statistical effect on individual dietary diversity scores of women with IDDS being

higher for women belonging to general category and lowest for schedule tribes. However,

infants who receive a minimum acceptable diet remained low irrespective of caste. Severe

food insecurity was least prevalent in general caste followed by other backward classes

whereas respondents belonging to schedule tribes were the most food insecure.

20

Higher percentage of household belonging to schedule caste were enrolled as TPDS

beneficiaries, followed by schedule tribes and other backward classes.

There was an increase in percentage of women consuming the recommended number of

food groups with better educational status and also higher infants received a minimum

acceptable diet whose mother were better educated. A higher percentage of households

having mothers who had received education more than secondary were food secure.

However, household enrolled as TPDS beneficiary did not significantly differ with educational

status.

There was an increase in percentage of women consuming the recommended >=5 food

groups with better stability in income. The households with stable income sources reported

marginally better MAD status for infants of 6 - 23 months. Also a higher percentage of

household having stable income source were food secure than those having a marginally

stable income source on the other hand, household enrolled as TPDS beneficiary did not

significantly differ with income stability.

IDDS scores were found to be positively associated with level of education of respondent,

income stability, household land access, nutrition counselling received, hygienic counselling

received, knowledge scores of food consumption during pregnancy and lactation and

number of meals the respondent had yesterday. A negative correlation was found with the

age of the mother and food insecurity severity experienced by household.

MAD scores were positively associated with age of the infant, level of education of

respondent, income stability, knowledge scores for enriching porridge, knowledge scores for

prevention of malnutrition, child was ever breastfed, the age of introduction of

complementary foods, child’s food is prepared separately and hygienic counselling received.

Positive association of TPDS Utility was found with age of the mother, caste, participation in

Anganwadi programme and household benefits from public work programme. The other

parameters for TPDS could not be treated since the data is only available in case the

household was a TPDS beneficiary.

The results of the logistic regression highlighted after controlling for religion, caste, income

and education level of women, more proportion of women who received nutrition counselling

where likely to meet the IDDW odds of meeting IDDW scores were higher for women who

received nutrition counselling, who had knowledge about food consumption during

pregnancy and lactation and ho had more than one meal on the day previous to the

interview.

Odds for meeting MAD scores were higher for infants whose mothers received nutrition and

hygienic counselling and infants whose mothers had knowledge of preparing porridge. Also

children who were ever breastfed and for whom complementary foods were initiated timely

(between 6-7 months) had higher odds for meeting MAD scores.

Therefore it can be concluded knowledge about nutrition and hygiene and optimal infant

feeding practices can go a way forward in improving dietary diversity scores.

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