analysing causes for under nutrition among urban poor ... · i would like to thank hemalatha jali,...
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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 women’s 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 woman’s 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
7.1 Ethnographic Study
Ethnographic 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.
7.2 Data
For 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.
7.3 Outcome measures
Two 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
7.4 Covariates
Based on earlier studies on malnutrition [3], several socioeconomic and demographic variables:
age, religion, education, caste, wealth index, occupation, partner’s occupation, water and
sanitation facilities, number of women, access to information, access to health care, consumption
levels of food, occupation status, partner’s 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 (%)
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
VIII. 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, respondent’s
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 women’s 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.
7.5 Recoding of Variables
Given 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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7.6 Analysis
Logistic 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 women’s 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 women’s groups, mahila mandal, mother’s
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.
26
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 AWW’s 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 women’s 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.
27
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.
28
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, Economic Development 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)
29
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
Total Urban Rural
Anemia level Severe Count 6 34 40
% within Type of place of
residence 2.4% 1.7% 1.8%
Moderate Count 46 334 380
% within Type of place of
residence 18.2% 17.1% 17.3%
Mild Count 109 930 1039
% within Type of place of
residence 43.1% 47.7% 47.2%
Not anemic Count 92 651 743
% within Type of place of
residence 36.4% 33.4% 33.7%
Total Count 253 1949 2202
% within Type of place of
residence 100.0% 100.0% 100.0%
30
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 week 66 .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
daughter 100 .000
More than one daughter 41 1.000
Ever any emotional violence No 102 1.000
31
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
Members 115 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.
32
Classification Tablea
Observed
Predicted
Anemia Level New Percentage
Correct Anemic 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.
33
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 week 66 .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
34
Received Benefits during
pregnancy or Breast Feeding
New
No 115 .000
Yes 25 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
Members 114 .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.
35
Classification Tablea
Observed
Predicted
Anemia Level New Percentage
Correct Anemic 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
Variables in the Equation
B S.E. Wald df Sig. Exp(B)
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
36
Variables in the Equation
B S.E. Wald df Sig. Exp(B)
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.