IRP Final Report U308059

8/3/2019 IRP Final Report U308059

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Analysing causes for under nutrition among urban poor

women in Orissa and formulating a partnership model

for intervention

An independent research report submitted to Xavier Institute of Management

By

Vijay Rangarajan

U308059, PGDM(Rural Management) 2008-10

Faculty Guide: Prof. Sandip Anand


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1.Acknowledgement

I acknowledge with deep gratitude the help, encouragement and guidance rendered by my Guide

Prof. Sandip Anand, Associate Professor, Marketing, Xavier Institute of Management in

conducting my independent research project. I really appreciate his warm behaviour and

friendliness. I thank him for spending time in clarifying all my doubts and providing insights of

his research work which really quickened the learning process facilitating better understanding of

the project.

I would like to thank Hemalatha Jali, Sabita Dighalo, Krushna Nayak, Maguni Nayak and

Saninlata Swain of Saliya Sahi slum for spending their time answering questions patiently

without considering it as an intrusion to their private life. I also thank Prof. S.S Singh, Prof.S

Peppin and Prof. Bipin Das for their guidance. Finally, I thank the institute for providing me an

opportunity to conduct this research study helping me to understand about women malnutrition

in Orissa.

Vijay Rangarajan

Date: 23-Feb-2010


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Contents

1. Acknowledgement ........................... ........................... ........................... ........................... ............... 2

2. Abbreviations...................................... ........................... ........................... ........................... ............ 5

Definitions ............................................................................................................................................... 5

3. Abstract ........................ ........................... ........................... ........................... .......................... ........ 8

4. Introduction ....................... ........................... ........................... ........................... ........................... .. 9

Figure 1: Vicious Cycle of Poverty (National Nutritional Policy, Department of Human Resource

Development, 1993) ............................................................................................................................ 9

5. Focus of the study ....................... ........................... ........................... ........................... .................. 12

6. Objectives of the study ........................ ........................... ........................... ........................... ......... 13

7. Methodology .......................... ........................... .......................... ........................... ....................... 13

7.1 Ethnographic Study ........................... .......................... ........................... ........................... ..... 13

7.2 Data ........................... .......................... ........................... ........................... ........................... . 14

7.3 Outcome measures .......................... ........................... .......................... ........................... ...... 15

Table 1: Body Mass Index ................................................................................................................ 16

Table 2: Anemia Level ...................................................................................................................... 16

7.4 Covariates .......................... ........................... ........................... ........................... ................... 16

Table 3: Covariates ............................................................................................................................ 17

7.5 Recoding of Variables .......................... ........................... ........................... ........................... .. 21

7.6 Analysis .......................... ........................... ........................... ........................... ....................... 22

Table 4: Determinants of Anaemia (Dependent Variable Anaemic = 0, Not-Anaemic = 1) Odds Ratio

from Logistic Regression .................................................................................................................... 22


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8. Findings ......................... ........................... .......................... ........................... ........................... ..... 25

9. Limitations of the study ........................... ........................... ........................... ........................... ..... 27

10. References ......................... ........................... ........................... ........................... ....................... 28

11. Annexure ....................... ........................... ........................... ........................... ........................... 29

Annexure I Rural/Urban comparision of Anaemia Levels among women who belong to poorer

and poorest wealth Index ................................................................................................................ 29

Annexure II: Unadjusted Logistic Regression Model ........................... ........................... ..................... 30

Annexure III: Adjusted Logistic regression Model ........................ ........................... ........................... 33


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2.Abbreviations

AWC Anganwadi Worker

AWW Anganwadi Worker

BMI Body Mass Index

DHS Demographic and Health Survey

ICDS Integrated Child Development Services

NFHS National Family Health Survey

Definitions

Anemia Low level of hemoglobin in the blood, as evidenced by a reduced quality

or quantity of Red Blood cells; 50 per cent of anemia in world is caused

by iron deficiency.

BMI Body Mass Index (BMI) Body Weight in Kilograms divided by height in

metres squared (Kg/m2). This is used as an index of fatness. Both high

BMI(overweight, BMI greater than 25) and low BMI (thinness, BMI less

than 18.5) are considered inadequate.


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Malnutrition Various forms of poor nutrition caused by a complex array of factors

including dietary inadequacy, infections, and sociocultural factors.

Underweight or stunting and overweight, as well as micro-nutrition

deficiencies, are forms of malnutrition

Under nutrition Low weight-for-age; that is two z-score below the international reference

for weight-for age. It implies stunting or wasting and it is an indicator or

under nutrition.


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The portion of global burden of disease (mortality and morbidity, 1990 figures) in

developing countries that would be removed by elimination of malnutrition is

estimated as 32 percent. This includes the effects of malnutrition on the most

vulnerable groups burden of mortality and morbidity from infectious disease only.

This is therefore a conservative figure...

-John Mason, Philip Musgrove, and Jean-Pierre Habicht, 2003


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3.Abstract

Purpose: This study attempts to identify the determinants of nutritional status among the

urban poor women in Orissa and suggesting a partnership model for intervention

Method: The study is mainly secondary and quantitative in nature. It included analysis of data

collected for the National Family Health Survey (2005-06). Analysis was done using cross-

tabulation and logistic regression.

Limitations: The major limitation is that the scope of the study is limited to the data collected as

a part of the survey.

Findings: Findings indicate that the womens autonomy with regard to visiting her

family/relatives and frequency of watching television enhance the probability of her being non-

anaemic.

Practical Implications: The finding can be helpful in designing interventions to reduction levels

of under nutrition among women.


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4.Introduction

The past 20 years have shown that in many developing countries where the incomes have gone

up substantially, malnutrition as not declined correspondingly [2]. This indicates that economic

growth and markets alone are alone not enough to address malnutrition.

Poor nutrition perpetuates the cycle of poverty and malnutrition through three main routes; direct

loss in productivity from poor physical status, losses caused by diseases linked with malnutrition,

indirect losses from poor cognitive development and losses in schooling. Several vitamin and

mineral deficiencies in the womb leads to blindness, dwarfism, mental retardation, and neural

tube defects.

Figure 1: Vicious Cycle of Poverty (National Nutritional Policy, Department of Human

Resource Development, 1993)


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Anemia has a direct and immediate effect on productivity of adults especially those physically

demanding occupation. Eliminating anemia results in a 5% to 17% increase in productivity

which is around 2% of GDP [2]. Malnutrition affects the immune system. About 60% of all

deaths and 47% of burden of disease can be attributed to diet related chronic disease. It has been

shown in Brazil and United States that height and weight of the adults (measured by BMI)

affects wage rate even after controlling for education.[2] The mental development of a child

happens during 0-2 Years of age. The right opportunity is to break the cycle is during pregnancy

and first few years of the childhood. So the health and nutrition of pregnant women and

preschool children assumes great importance [5].

In India, productivity losses (manual work only) from stunting, iodine deficiency and iron

deficiency together are responsible for a loss of 2.95% of GDP [2]. Malnutrition in women

causes a heightened risk of adverse pregnancy outcomes. A womans nutritional status has

important implications for her health as well as the health of her children. A woman with poor

nutritional status, as indicated by a low body mass index (BMI), short stature, anemia, or other

micronutrient deficiencies, has a greater risk of obstructed labour, having a baby with a low birth

weight, having adverse pregnancy outcomes, producing lower quality breast milk, death due to

postpartum haemorrhage, and illness for herself and her baby.

Almost all modern societies going through a transition from Agrarian to an industrial one end up

creating slums as a part of the urbanisation. The rural poor who moved to urban areas in hope of

better life actually exacerbated their hunger, misery and health hazards. The government tries to

address these issues through many programmes- the important one being the public distribution


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system. Several economic, social and systemic factors prevent the effective implementation of

these programmes.

The malnutrition of women among urban areas is comparable to that of rural areas among the

poor [1]. In fact, the percentage of women suffering from mild and severe anemia is more in

urban areas. If the problems in rural areas are accentuated by inaccessibility and lack of

infrastructure, the inadequate sanitation, hygiene and water results in more sickness, lower

school enrollment and retention rates and lower work productivity in urban areas. Many

denotified slum dwellers, construction site workers and pavement dwellers in the cities are

excluded from the benefits like ICDS, PDS etc. Issues like illegality, the fear of eviction and

social exclusion are also reasons for lack of interest among the urban poor about their health and

environment.

The low socio-economic conditions and the rising food prices make their diet monotonous and

lacking in nutrition. Their daily income cycle also forces them to buy groceries and vegetables

either in small quantities or on credit leaving them on a poor bargaining condition on quality.

The slums where the urban poor are concentrated have heterogeneous community due to

migration and are low on social capital. Thus we see that urban poor lead a life which robs them

of their dignity. It is under these circumstances that this study assumes importance.

The reason for choosing particularly women for the study is that mothers can play a significant

role in reducing the malnutrition levels of the children and it was found that the children of under

nourished mothers are most likely to be under nourished. Understanding the social causes for

under nutrition among women can also contribute towards reducing the under nutrition levels

among the children. Though, everyone knows the facts given above, further studies are required


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to analyse how these conditions are applicable to a particular region like the urban areas of

Orissa.

It was also felt that the traditional social sector approaches have made insufficient headway in

addressing the problem of malnutrition. The problems have also increased in complexity and

intensity over the years crying out for more entrepreneurial approaches that create more value

with limited resources. The government of Orissa has also realised this and has come out with

public private partnership policy in 2007. Government of Orissa successfully established

partnerships in delivering health care services with civil societies to the marginalized population

of the un-served and under-served areas.

5.Focus of the study

The study is focused on the urban poor women in Orissa since the incidence of malnutrition is

more among the lower income groups than among the privileged groups [5]. The study is a kind

of Positive Deviant Approach where it was attempted to identify the factors that determine

whether a poor women is anaemic or non-anaemic. Though improvement in livelihood and

literacy can reduce levels of malnutrition in the long run, there exists opportunities in the short

run like targeted food aid, community based nutrition and health education and micro nutrient

supplements.

The study is done with the eye of a development professional. It explores the current problem of

malnutrition and the limitations of the current approaches in solving the problem and provides an

alternative entrepreneurial approach to solve the problem. It is intended to be helpful to civil


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society organisations which are involved in the nutrition and health sectors. It explores the

business opportunity in private and civil societies adding value to the provision of public

services.

6.Objectives of the study

1. To study the under nutrition status in terms of BMI and Anemia level among the urban

poor women in Orissa.

2. To examine the impact of various background variables on the nutritional status of

women and identify the determinants of under nutrition.

3. Formulating a model of intervention involving public and private partners.

7.Methodology

7.1Ethnographic StudyEthnographic study and discussions were done with the people in the slums of Salia Shahi to

understand the problem in the context of urban poor in Orissa. Many respondents did not cook in

the morning. A few respondents ate breakfast bought from nearby shops. The reason for not

cooking is the time it takes and also to save fuel. But some households using firewood ate rice all

three times a day. For some respondents it has become a habit not to eat in the morning.


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Very few families interviewed had Ration card. Others had applied through their counselor but

of no use. Subsidized groceries through PDS helps them but since it is provided once in a month,

they do not have ready cash with them and they borrow from others to buy them. They are aware

that the retail grocery shops nearby charge higher and the quality is low. But since they buy

groceries on credit, they want to maintain the relationship. So they buy from them even when

they have money.

With Rs 4500 wage per month, one household was able to buy rice and Atta for a month, send

children to private school and save money in the bank and was not dependant on the PDS.

Though none of them were starving due to lack of food, they expressed that they could not eat

fruits, drink milk or meat often. The frequency of consumption of these items was once or twice

in a month. They are able to afford fish and it is mostly part of their diet. Some of the families

have left their children in the village. Accessibility to food is not a problem since there are

sufficient shops selling groceries, firewood apart from the mobile vendors who sell snacks,

vegetables and consumer durables. Most of the respondents were drinking water from an open

well. They do not have toilets.

7.2DataFor the purpose of the study, 2005-06 National family Health Survey (NHFS-3) dataset from the

DHS website was used. NFHS-3, is a household survey which will provide estimates of

indicators of population, health, and nutrition by background characteristics at the national and

state levels. In NFHS-3, information is collected about households, and individual interviews are

conducted with women age 15-49 and men age 15-54. NFHS-3 also includes height and weight


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measurement and blood tests for HIV and anaemia. The dataset used for analysis consists of

details of 278 women living in urban areas whose wealth index is either poorer or poorest

quintiles as defined by the survey.

The raw data in SPSS format was taken and the details of women living in urban areas in Orissa

and belonging to the poorer and poorest quintiles of wealth index were filtered and a new dataset

for further analysis was created. The women of Orissa were identified using the variable

v001(PSU Number). The households belonging to Orissa were given the state code of 21 for the

first two digits in the five digits of the PSU Number. The variable Type of Place of Residence

(v025) was used to identify the women living in the urban areas. The variable wealth index

(v190) was used to filter the poorer and poorest quintile.

7.3Outcome measuresTwo outcomes for women were analysed-Body Mass Index (v445) and Anaemia Level (v457).

Since the objective was to identify the determinants of under nutrition and not in predicting the

precise BMI value, the BMI was converted to a category variable with two categories - one for

women whose BMI falls below 18.5 Kg/m2

- and other for BMI equal and above 18.5 Kg/m2

classifying women based on thinness or acute under nutrition. The women with BMI above 25

were also considered as normal due to very low prevalence of overweight in Orissa. The existing

anaemia level had 4 categories, Severe, Moderate, Mild and No Anaemia. The levels of the

anaemia were combined and the new outcome measure contained only two categories, anaemic

and non-anaemic. As seen from the tables, we see that 42.8% of women are under nourished and

63.6% are anaemic.


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Table 1: Body Mass Index

Frequency Percent Valid Percent

Cumulative

Percent

Valid BMI < 18.5 119 42.8 42.8 42.8

BMI >= 18.5 159 57.2 57.2 100.0

Total 278 100.0 100.0

Table 2: Anemia Level

Frequency Percent Valid Percent

Cumulative

Percent

Valid Anemic 161 57.9 63.6 63.6

Not Anemic 92 33.1 36.4 100.0

Total 253 91.0 100.0

Missing 9 25 9.0

Total 278 100.0

7.4CovariatesBased on earlier studies on malnutrition [3], several socioeconomic and demographic variables:

age, religion, education, caste, wealth index, occupation, partners occupation, water and

sanitation facilities, number of women, access to information, access to health care, consumption

levels of food, occupation status, partners age, autonomy, children ever born, domestic violence


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were considered. But due to lack of adequate cell frequency, the variables were recoded by

merging two or more categories. The variables that did not have a category of frequency of 25

were excluded from the analysis.

The final variables chosen and their frequencies are given in the table. These variables are

chosen after the cross-tabulation between the outcome variables and the independent variable

tested the relationship between them as statistically significant and not due to random sampling

error.

Table 3: Covariates

Variable Description (Name in the dataset) BMI

18.5 (%)

Anaemic

(%)

Not

Anaemic

(%)

I. Frequency of watching

Television(v159n)

Not at all or less than once a Week 72.8 27.2

At least Once a Week 57.1 42.9

Daily 55.4 44.6

II. Ever Emotional Violence (d104n)

No 65.2 34.8

Yes 49.1 50.9


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III. Spouse ever insulted or make feel

bad (d103cn)

No 64 36

Yes 44.1 55.9

IV. Highest Education Level (v106n)

Primary 69.9 30.1

Above Primary 52.9 47.1

V. Type of caste or tribe of the

household (sh46n)

Scheduled Tribe 75.7 24.3

Scheduled Caste 63.3 35.7

Others 54.3 45.7

VI. Number of Women per Household

Member (WPHH)

One Women for More than three Members 58 42

One Women for three or less members 76.6 23.4

VII. Daughters at home (v203n)


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No daughter or One Daughter 67 33

More than one daughter 51.8 48.2

III. Type of facility used(s368n)

Public 41.1 58.9

Private 61.3 38.7

Did not Visit in the past three months 40.3 59.7

IX. Type of Earning(v741n)Not Paid or Paid in Kind or Paid in Cash and

Kind

30 70

In Cash 51 49

X. Final say in visiting relatives/family

(v743dn)

Respondent Involved 59.1 40.9

Respondent Not Involved 74.5 25.5

X1. Number of eligible women in the

household (v138n)

One 57.1 42.9


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More than One 75.6 24.4

It was surprising that some of the important variables like Wealth Index, respondents

Occupation, and Benefits received from ICDS, Type of heath facility visited, frequency of food

consumption, water facilities, Age were found statistically insignificant. But since the analysis

was conducted only among the poorer and poorest quintile, the assets owned would be almost the

same. Most of the variables chosen did not share a statistically significant relationship with the

outcome Variable BMI.

From the cross-tabulation it can be seen that the anaemic status reduces as the frequency of

watching television increases. This can be due to nutrition related programs and the expected

affluence of those using the asset. The anaemia levels are also found to reduce when the women

are treated well by their spouse. We also see that as womens education level increases, the

percentage of anaemic women goes down. Most of the women in Scheduled Tribe and Caste are

found to be more anaemic. The anaemic status is also dependent on the number of women in the

household and the number of daughters at home. This can be due to sharing of the burden by

other women in the household. Interestingly, anaemia is more prevalent among women who visit

private health care facilities compared to public facilities. The women who are not free to visit

their family and relatives are likely to be anaemic. This can be due to the lack of avenues to share

their difficulties and support. In urban areas, because of the heterogeneity of the community, this

assumes more importance. Women who are paid in cash are more less-likely to be under

nourished.


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It is also noted that as the number of women in the household increase, anaemia status among the

household also increases. Earlier studies have indicated the burden of work as one of the reasons

for the high prevalence of anaemia among women. But, we find that even if the number of

women per household member increases, it does not result in lower number of anaemic women.

It can be because of the increase in the number of members of the household or because of

impact of the additional women on the determinants of anemia. It is also found that as the

number of daughters increase, the number of anaemic women decreases. And also, it is seen that

there is a significant relationship between the number of eligible women and the autonomy in

decision to visit family/relatives.

7.5Recoding of VariablesGiven below are the procedures followed in recoding of the variables. The variables for which

the recoding is obvious from the name of the category are not explicitly described. Only recoding

of those variables which are included in the final analysis is explained. For the variable, Ever

Emotional Violence and Spouse ever insult or make feel bad, the categories often during the

last 12 months, sometimes during last 12 months and not in the last 12 months are recoded as

Yes. The variable, Number of women per household member is obtained by dividing the number

of women per household by the number of members in the household. The Type of facility

visited variable was recoded into either public or private based on the type of facility. In the

variable, final say in visiting relatives/family the categories are recoded based on whether the

respondent was involved in the decision making.


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7.6AnalysisLogistic regression analysis for the variables which are found to have a significant relationship

with the outcome variables was done to see the interaction of variables. The dependent variable

was anaemic level. The variable had two categories, namely: Anaemic, not-anaemic. For the

purpose of dichotomization of variable, the categories severe, moderate and mild levels of

anaemia were merged under the category Anaemic and given the value 0. The Anaemic

category was given 1. The relationship of two variables was found significant. The variables

are frequency of watching television and autonomy in visiting relatives/family. They were later

adjusted for demographic variables. Though the strength of the frequency of watching television

was attenuated by these inclusions, it was found that the demographic variables strengthened the

relationship of autonomy of women in deciding to visit her family/relatives.

Table 4: Determinants of Anaemia (Dependent Variable Anaemic = 0, Not-Anaemic = 1)

Odds Ratio from Logistic Regression

Variable Category Un Adjusted Model Adjusted Model

Exp(B) Sig. Exp(B) Sig.

Final Say in Visiting

Family/ relatives

Respondent Involved

(Reference)

Respondent Not Involved

-

3.370 .015 4.593 .004

Frequency of

Watching Television

Not at all or less than once a

Week (Reference)

-


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At least Once a Week

Daily

1.656

2.887

.302

.037

1.774

2.866

.269

.052

Ever Emotional

Violence

Yes (Reference)

No .455 .221 .201 .159

Spouse insulted or

make her feel bad

No (Reference)

Yes 1.071 .929 .782 .768

Literacy Primary (Reference)

Above Primary 1.175 .717 1.195 .705

Type of caste or tribe

of the household

Scheduled Tribe

(Reference)

Scheduled Caste

Others

1.942

2.389

.272

.131

2.154

2.405

.226

.145

Women Per

Household Member

One Women for More than

three Members (Reference)

One Women for three or

less members

1.620 .418 .566 .367


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Number of eligible

women in the

household

One(Reference)

More than One 1.985 .362 .505 .263

Daughters at home None or One(Reference)

More than one 2.108 .104 1.194 .183

The model was adjusted for wealth index, Meeting with the anganwadi/Health worker in the past

three months received any maternal benefits in the past three months, Body Mass Index and

spouse ever humiliated her. It was found that the variables Final say in the decision to visit

family/relatives was significant at 99% level of confidence and the frequency of watching

television are found to be significant at 95% level of confidence. The other variables though they

not not-significant contribute to the model. The variable Meeting anganwadi worker in the last

three months, though was not significant earlier in bivariate analysis has become significant in

the logistic regression. Maternal benefits received during the last three months and the wealth

index also were significant at 90% level of confidence.

The categorical variable codings of all the variables had a minimum frequency of 25. There were

totally 140 cases included in the analysis and 138 missing cases. The variables were able to

classify 75% of the cases as anaemic or non-anaemic based on prediction. The -2Log Likelihood

value was 142.791. The pseudo R Square values are .253 and .346 respectively for Cox & Snell

and Negelkerke methods respectively.


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A similar model for BMI could not be established because none of the variables either were

found to be significant or contributing to the model. It is interesting that the determinants of

anaemia and BMI are quite different

8. Findings

Based on the analysis, it was found that the womens autonomy in visiting her relatives/family

emerges out as a significant factor in affecting the anaemia status. Also the women who watch

television daily are also less likely to be anaemic. The women who met Anganwadi/Health

Worker in the last 3 months were also less likely to be anaemic. The food they ate in the past

three was not found to have any significant relationship with their anemia status. Since the

analysis was restricted only to people with wealth index poorer and poorest, the usual

determinants like Literacy and caste did not emerge significant. From the cross-tabulations, we

find that the number of eligible women in the household affects the autonomy of the woman and

the more the number of daughters at home, less the woman is likely to be anaemic.

The existing measures taken are mostly by the government through provision of supplementary

nutrition, food fortification and IEC through mass media and trainings. The existing

infrastructure is mainly the anganwadi centers which are meant to be the focal point of delivery

of services. They also serve as a meeting place for womens groups, mahila mandal, mothers

club promoting awareness and women empowerment. The work of the NGOs related to nutrition

include promoting production and consumption of vegetables, training for health worker,

reviving traditional knowledge, creating awareness among the community and increasing food

production.


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The AWC is an extremely important structure created exclusively for women and children. The

only attraction to visit these centers is the supplementary nutrition [6]. The AWC is ineffective

unless the women and children visit these centers and the AWW cannot leave the centre and call

on mothers in their houses. 40% of AWWs time is spent on preparing and distributing food and

30% on Pre-school education [7]. So she is not able to spend sufficient time in the more

important aspect of health and nutrition education. We need to incentivize women to visit the

centers and also ensure that the above mentioned determinants are addressed.

The above problems can be reduced through partnership with the community and NGOs. The

partnership model should take into account the core competence of the partners in addressing the

need of the clients. The government has the physical infrastructure at close proximity to the

community along with dedicated staff. Participation of the women in the coverage area of the

AWC will contribute to the success of the programme. It will help in spreading the awareness

across the women. It can also facilitate women empowerment apart from giving a platform for

women to come together and share their difficulties. In a heterogeneous community like urban

poor, this can help women in building up their social capital. The women can be incentivized to

visit the center by contracting out the supplement food preparation to the women groups. The

NGOs can play a vital role in building the capacity of the women who are involved in managing

the food preparation and related finances in AWC. The NGO can also make the woman aware of

their rights so that they could fight for their rights collectively. The womens group can also

check the mismanagement at the AWC. Provision of colour Televisions in the anganwadi

centers can also incentivize people to visit the center. Awareness can be generated through

messages in-between popular programmes.


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9.Limitations of the study

1. Some of the determinants could not be studied because they did not have adequate cell

frequency. The model also did not take into account the interaction effect of those

variables.

2. The variables chosen as determinants were limited by the data collected as a part of the

NFHS-3 survey.


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10. References

1. National Family Health Survey (2005-06)

2. The World Bank, Repositioning Nutrition as Central to Development, A strategy for

Large-Scale Action

3. R. Radhakrishna and C. Ravi, (2004), Malnutrition in India: Trends and Determinants,

Economic and Political Weekly, Vol. 39, No.7, pp. 671-676

4. Ministry of Human Resource Development, (1993), National Nutritional Policy,

Department of Women and Child Development, New Delhi,

5. Pedro Belli. (1971), The Economic Implications of Malnutrition: The Dismal Science

Revisited,EconomicDevelopment and Cultural Change, Vol. 20, No. 1, pp. 1-23

6. Economic and Political Weekly (1986), Management of Services for Mothers and

Children, , Vol. 21, No. 12, pp. 510-512

7. NCAER Concurrent Evaluation, (2001)


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11. Annexure

Annexure I Rural/Urban comparision of Anaemia Levels among women who

belong to poorer and poorest wealth Index

Anemia level * Type of place of residence

Crosstabulation

Type of place of residence

TotalUrban Rural

Anemia level Severe Count 6 34 40

% within Type of place of

residence2.4% 1.7% 1.8%

Moderate Count 46 334 380


residence18.2% 17.1% 17.3%

Mild Count 109 930 1039


residence43.1% 47.7% 47.2%

Not anemic Count 92 651 743


residence36.4% 33.4% 33.7%

Total Count 253 1949 2202


residence100.0% 100.0% 100.0%


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Annexure II: Unadjusted Logistic Regression Model

Case Processing Summary

Unweighted Casesa

N Percent

Selected Cases Included in Analysis 141 50.7

Missing Cases 137 49.3

Total 278 100.0

Unselected Cases 0 .0

Total 278 100.0

a. If weight is in effect, see classification table for the total number of

cases.

Dependent Variable Encoding

Original Value Internal Value

Anemic 0

Not Anemic 1

Categorical Variables Codings

Frequency

Parameter coding

(1) (2)

Frequency of watching

Television New

Not at all or Less than once

a week66 .000 .000

Atleast once a week 36 1.000 .000

Daily 39 .000 1.000

Type of caste or tribe of the

household

Scheduled Tribe 36 .000 .000

Scheduled Caste 46 1.000 .000

Others 59 .000 1.000

Daughters at home No Daughter or One

daughter100 .000

More than one daughter 41 1.000

Ever any emotional violence No 102 1.000


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Yes 39 .000

Spouse ever insult or make

feel bad

No 117 1.000

Yes 24 .000

Highest education level new

category

Primary 92 .000

Above Primary 49 1.000

Number of Eligible Women in

Household

One Women 124 1.000

More than One Women 17 .000

Number of women per

houshold member

Less than or equal to Three

Members115 1.000

More than Three Members 26 .000

Final say on visting

relatives/family New

Respondent involved 101 1.000

Respndent Not Involved 40 .000

Omnibus Tests of Model Coefficients

Chi-square df Sig.

Step 1 Step 28.529 11 .003

Block 28.529 11 .003

Model 28.529 11 .003

Model Summary

Step -2 Log likelihood

Cox & Snell R

Square

Nagelkerke R

Square

1 156.009a

.183 .251

a. Estimation terminated at iteration number 5 because

parameter estimates changed by less than .001.


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Classification Tablea

Observed

Predicted

Anemia Level NewPercentage

CorrectAnemic Not Anemic

Step 1 Anemia Level New Anemic 78 12 86.7

Not Anemic 27 24 47.1

Overall Percentage 72.3

a. The cut value is .500

Variables in the Equation

B S.E. Wald df Sig. Exp(B)

Step 1a

v743dn(1) 1.215 .498 5.943 1 .015 3.370

v159n 4.386 2 .112

v159n(1) .504 .489 1.064 1 .302 1.656

v159n(2) 1.060 .509 4.334 1 .037 2.887

d104(1) -.788 .644 1.497 1 .221 .455

d103cn(1) .068 .764 .008 1 .929 1.071

v106nc(1) .161 .444 .132 1 .717 1.175

sh46n 2.283 2 .319

sh46n(1) .664 .604 1.206 1 .272 1.942

sh46n(2) .871 .577 2.281 1 .131 2.389

WPHH(1) .482 .595 .656 1 .418 1.620

v138n(1) .686 .752 .831 1 .362 1.985

v203n(1) .746 .459 2.637 1 .104 2.108

Constant -3.335 1.058 9.939 1 .002 .036

a. Variable(s) entered on step 1: v743dn, v159n, d104, d103cn, v106nc, sh46n, WPHH, v138n,

v203n.


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Annexure III: Adjusted Logistic regression Model

Case Processing Summary

Unweighted Casesa

N Percent

Selected Cases Included in Analysis 140 50.4

Missing Cases 138 49.6

Total 278 100.0

Unselected Cases 0 .0

Total 278 100.0

a. If weight is in effect, see classification table for the total number of

cases.

Categorical Variables Codings

Frequency

Parameter coding

(1) (2)

Type of caste or tribe of the

household

Scheduled Tribe 36 .000 .000

Scheduled Caste 46 1.000 .000

Others 58 .000 1.000

Frequency of watching

Television New

Not at all or Less than once

a week66 .000 .000

Atleast once a week 36 1.000 .000

Daily 38 .000 1.000

Wealth index Poorest 72 .000

Poorer 68 1.000

Final say on visting

relatives/family New

Respondent involved 100 1.000

Respndent Not Involved 40 .000

In past 3 mths met with

anganwadi/comm health wkr

No 111 .000

Yes 29 1.000

Ever any emotional violence No 102 1.000

Yes 38 .000

Spouse ever insult or make

feel bad

No 116 1.000

Yes 24 .000


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Received Benefits during

pregnancy or Breast Feeding

New

No 115 .000

Yes25 1.000

Daughters at home No Daughter or One

daughter

99 .000

More than one daughter 41 1.000

Body Mass Index New BMI < 18.5 59 .000

BMI >= 18.5 81 1.000

Number of women per

houshold member

Less than or equal to Three

Members114 .000

More than Three Members 26 1.000

Highest education level new

category

Primary 92 .000

Above Primary 48 1.000

Spouse ever humiiated her

New

No 109 1.000

Yes 31 .000

Omnibus Tests of Model Coefficients

Chi-square df Sig.

Step 1 Step 40.845 15 .000

Block 40.845 15 .000

Model 40.845 15 .000

Model Summary

Step -2 Log likelihood

Cox & Snell R

Square

Nagelkerke R

Square

1 142.791a

.253 .346

a. Estimation terminated at iteration number 5 because

parameter estimates changed by less than .001.


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Classification Tablea

Observed

Predicted

Anemia Level NewPercentage

CorrectAnemic Not Anemic

Step 1 Anemia Level New Anemic 78 11 87.6

Not Anemic 24 27 52.9

Overall Percentage 75.0

a. The cut value is .500



Step 1a sh46n 2.228 2 .328

sh46n(1) .767 .634 1.463 1 .226 2.154

sh46n(2) .877 .602 2.124 1 .145 2.405

v190(1) .834 .451 3.415 1 .065 2.303

v743dn(1) 1.524 .536 8.092 1 .004 4.593

v159n 3.915 2 .141

v159n(1) .573 .519 1.220 1 .269 1.774

v159n(2) 1.053 .542 3.769 1 .052 2.866

s358(1) 1.368 .636 4.627 1 .031 3.927

d104(1) -1.604 1.140 1.981 1 .159 .201

d103an(1) .793 1.083 .536 1 .464 2.210

Mat_Ben_New(1) -1.209 .700 2.985 1 .084 .298

v106nc(1) .178 .469 .144 1 .705 1.195

WPHH(1) -.684 .611 1.253 1 .263 .505

v445n(1) .615 .432 2.030 1 .154 1.850

v203n(1) .649 .488 1.771 1 .183 1.914

d103cn(1) .246 .833 .087 1 .768 1.279

Constant -3.498 1.009 12.032 1 .001 .030


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Step 1a

sh46n 2.228 2 .328

sh46n(1) .767 .634 1.463 1 .226 2.154

sh46n(2) .877 .602 2.124 1 .145 2.405

v190(1) .834 .451 3.415 1 .065 2.303

v743dn(1) 1.524 .536 8.092 1 .004 4.593

v159n 3.915 2 .141

v159n(1) .573 .519 1.220 1 .269 1.774

v159n(2) 1.053 .542 3.769 1 .052 2.866

s358(1) 1.368 .636 4.627 1 .031 3.927

d104(1) -1.604 1.140 1.981 1 .159 .201

d103an(1) .793 1.083 .536 1 .464 2.210

Mat_Ben_New(1) -1.209 .700 2.985 1 .084 .298

v106nc(1) .178 .469 .144 1 .705 1.195

WPHH(1) -.684 .611 1.253 1 .263 .505

v445n(1) .615 .432 2.030 1 .154 1.850

v203n(1) .649 .488 1.771 1 .183 1.914

d103cn(1) .246 .833 .087 1 .768 1.279

Constant -3.498 1.009 12.032 1 .001 .030

a. Variable(s) entered on step 1: sh46n, v190, v743dn, v159n, s358, d104, d103an,

Mat_Ben_New, v106nc, WPHH, v445n, v203n, d103cn.

IRP Final Report U308059

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