Water, Sanitation and Hygiene, Food Security & Livelihood and Nutrition Survey … · 2020. 4....

Water, Sanitation and

Hygiene, Food Security &

Livelihood and Nutrition

Survey in Sathkira district

June 2014

FSL – WaSH, nutrition survey Satkhira October 2013 Page 1

Acknowledgments

ACF would like to thank Satkhira District, Upazila and Union Authorities for their support and

collaborations during the planning and implementation of the FSL, WASH and nutrition survey.

ACF would also like to thank Shushilan management and staff for their support in the

implementation of this survey.

ACF would like to thank the European Commission Humanitarian Office (ECHO) for providing funds

for the FSL, WaSH and nutrition Survey in waterlogging affected areas of Satkhira district.

Finally, ACF would like to especially thank all of the enumerators and team leaders who participated

in the survey.


Table of Contents

Acknowledgments ................................................................................................................................... 1

Table of Contents .................................................................................................................................... 2

Tables ...................................................................................................................................................... 4

Figures ..................................................................................................................................................... 5

Abbreviations .......................................................................................................................................... 6

Executive Summary ................................................................................................................................. 7

Introduction .......................................................................................................................................... 13

1. Survey Objectives .......................................................................................................................... 14

2. Methodology ................................................................................................................................. 14

2.1. Survey Area ........................................................................................................................... 15

2.2. Type of survey ....................................................................................................................... 15

2.3. Sampling Size ........................................................................................................................ 15

2.4. Sampling Method: ................................................................................................................. 16

2.4.1. First Step Sampling: ....................................................................................................... 16

2.4.2. Second Sampling Stage ................................................................................................. 17

2.4.3. Last Stage Sampling: ..................................................................................................... 17

2.5. Questionnaire ....................................................................................................................... 17

2.6. Data Collection ...................................................................................................................... 17

2.7. Data Entry ............................................................................................................................. 18

2.8. Data Analysis ......................................................................................................................... 18

3. Results ........................................................................................................................................... 20

3.1 Demographics ....................................................................................................................... 20

3.1.1. Heads of Households .................................................................................................... 20

3.1.2. Heads of household Sex/Age ........................................................................................ 20

3.1.3. Physical Ability .............................................................................................................. 22

3.1.4. Dependency Ratio ......................................................................................................... 22

3.2 Food Security and Livelihoods .............................................................................................. 23

3.2.1. Household Food Insecurity Access Score ............................................................................ 23

3.2.2. Food Consumption Score ................................................................................................... 25

3.2.3. Household Dietary Diversity Score ..................................................................................... 27

3.2.4. Foods Source ....................................................................................................................... 29

3.2.5. Food Expenses..................................................................................................................... 30

3.2.6. Household Expenses ........................................................................................................... 31


3.2.7. Land Access and Agriculture / Aquaculture ........................................................................ 32

3.2.8. Land Tenure ........................................................................................................................ 33

3.2.9. Assets .................................................................................................................................. 35

3.2.10. Livestock ............................................................................................................................ 37

3.2.11. Income Source .................................................................................................................. 38

3.2.12. Shocks ............................................................................................................................... 40

3.3 Water, Sanitation and Hygiene ............................................................................................. 40

3.3.1. Water Source ...................................................................................................................... 40

3.3.2 Sanitation facility ................................................................................................................. 42

3.3.3 Waste practices .................................................................................................................... 44

3.3.4. Hygiene Practices ................................................................................................................ 45

3.3.5 Health Education .................................................................................................................. 45

3.4 Nutrition and Child Health .................................................................................................... 46

3.4.1. Wasting - Weight-for- Height .............................................................................................. 47

3.4.2. Middle Upper Arm Circumference – MUAC ....................................................................... 48

3.4.3. Underweight - Weight-for-age ............................................................................................ 49

3.4.4. Stunting - Height-for-age .................................................................................................... 49

3.4.5. Young Child and Infant Care practices ................................................................................ 51

3.4.6. Child Meal Frequencies, ...................................................................................................... 51

3.4.7. Infant Diet Diversity Score (IDDS) ....................................................................................... 51

3.4.8. Minimal Acceptable Diet ..................................................................................................... 52

Discussion.............................................................................................................................................. 53

Conclusion ............................................................................................................................................. 57

Recommendations ................................................................................................................................ 58

References ............................................................................................................................................ 59

Annex .................................................................................................................................................... 60


Tables

Table 1: Summary Table of Results ....................................................................................................... 11

Table 2: Female to Male ration of Heads of Households by Livelihood Zone ...................................... 20

Table 3: Income by Head of Household Gender and HFIAS Category .................................................. 20

Table 4: Household Composition by Age Group, Gender and Livelihood Zone.................................... 21

Table 5: Income and HDDS by household size ...................................................................................... 21

Table 6: Household size by LHZ and HFIAS Category ............................................................................ 21

Table 7: Food Concerns for previous 4 weeks ...................................................................................... 24

Table 8: Food Security by Income Quartile ........................................................................................... 25

Table 9: Food Consumption for previous 7 Days .................................................................................. 27

Table 10: Food Source ........................................................................................................................... 29

Table 11: Household Income Source .................................................................................................... 38

Table 12: Household income through multiple sources ....................................................................... 39

Table 13: Income by main income source and Household Food Insecurity Category .......................... 39

Table 14: Water Source by Livelihood .................................................................................................. 40

Table 15: Water selection by livelihood Zone ....................................................................................... 41

Table 16: Water collection times .......................................................................................................... 41

Table 17: Water Collection times .......................................................................................................... 42

Table 18: Water Collection ................................................................................................................... 42

Table 19: Barriers to hygienic sanitation .............................................................................................. 43

Table 20: Critical Junctures of hand washing by income quartiles ....................................................... 45

Table 21: Hand washing with soap by income quartile ........................................................................ 45

Table 22: Age and sex breakdown ........................................................................................................ 47

Table 23: Prevalence of acute malnutrition based on weight-for-height z-scores (and/or oedema) and

by sex .................................................................................................................................................... 47

Table24: Prevalence of acute malnutrition based on MUAC cut off's (and/or oedema) and by sex ... 48

Table 25: Prevalence of underweight based on weight-for-age z-scores by sex .................................. 49

Table 26: Prevalence of stunting based on height-for-age z-scores and by sex ................................... 50

Table 27: Child Illness reported in previous 2 weeks ............................................................................ 50

Table 28: Meal Frequency of children 6-23 months ............................................................................. 51

Table 29: IDDS of children 6-23 months ............................................................................................... 51

Table 30: Acceptable Diet ..................................................................................................................... 52

Table 31: Comparison of Nutrition Indicator between 2 surveys ......................................................... 55

Table 32: Nutrition Indicators according to WHO classification ........................................................... 55


Figures

Figure 1: Overview of population data for Satkhira survey area (Census 2011) .................................. 15

Figure 2: Income and HDDS by Physical ability ..................................................................................... 22

Figure 3: HFIAS by Livelihood Zone ....................................................................................................... 24

Figure 4: Food Consumption Score by Livelihood Zone ........................................................................ 26

Figure 5: Foods consumed in previous 7 days ...................................................................................... 26

Figure 6: Distribution of Food Groups consume in previous 24 hours ................................................. 27

Figure 7: Food consumed in previous 24 hours .................................................................................... 28

Figure 8: Dietary Diversity by LHZ and HFIAS........................................................................................ 29

Figure 9: Food Expenses by LHZ ............................................................................................................ 30

Figure 10: Food Expenses by HFIAS ...................................................................................................... 31

Figure 11: Household Expenses by LHZ ................................................................................................ 32

Figure 12: Household Expenses by HFIAS Category .............................................................................. 32

Figure 13: Land ownership by HFIAS..................................................................................................... 33

Figure 14: Land ownership by LHZ ........................................................................................................ 34

Figure 15: Land Ownership by Income Quartile ................................................................................... 34

Figure 16: Asset Ownership by LHZ ...................................................................................................... 35

Figure 17: Asset ownership by Income Quartile ................................................................................... 36

Figure 18: Asset Ownership by HFIAS ................................................................................................... 36

Figure 19: Livestock ownership by LHZ ................................................................................................. 37

Figure 20: Livestock ownership by HFIAS ............................................................................................. 38

Figure 21: Household Food Insecurity by Livelihood ............................................................................ 39

Figure 22: Household Shocks in previous year ..................................................................................... 40

Figure 23: Hygienic sanitation by Livelihood Zone ............................................................................... 42

Figure 24: Hygienic sanitation by Income Quartile ............................................................................... 43

Figure 25: Rubbish disposal by livelihood zone .................................................................................... 44

Figure 26: Rubbish Disposal by income quartile ................................................................................... 44

Figure 27: Age Pyramid ......................................................................................................................... 46

Figure 28: Weight for Height Z-score distribution compared to WHO standards ................................ 48

Figure 29: Progression of Weight for Height Z-score over the age ...................................................... 48

Figure 30: Weight for Age Z-score distribution compared to WHO standards .................................... 49

Figure 31: Progression of Weight for Age Z-score over the age ........................................................... 49

Figure 32: Height for Age Z-score distribution compared to WHO standards ...................................... 50

Figure 33: Progression of Height for Age Z-score over the age ............................................................ 50

Figure 34: IDDS and Child Acceptable Diet ........................................................................................... 52


Abbreviations

ACF

Action Contre la Faim

ARI

Acute Respiratory Infection

BDHS

Bangladesh Demographic Health Survey

BDT

Bangladeshi Taka (currency)

CI

Confidence Interval

CMAM

Community Management of Acute Malnutrition

CSI

Coping Strategy Index

ECHO

European Commission Humanitarian Office

ENA

Emergency Nutrition Assessment

FCS

Food Consumption Score

FSL

Food Security and Livelihoods

GAM

Global Acute Malnutrition

HAZ

Height-for-Age z-score

HDDS

Household Dietary Diversity Score

HFIAS

Household Food Insecurity Access Score

HH

Household

IDDS

Infant Diet Diversity Score

IGA

Income Generating Activities

IYCF

Infant and Young Child Feeding

LVZ

Livelihood Zone

MAM

Moderate acute malnutrition

MH/CP

Mental Health/Care Practices

MoH

Ministry of Health

MUAC

Mid-Upper-Arm-Circumference

NCA

Nutrition Causal Analysis

NCHS

National Center of Health Statistics

NGO

Non-Governmental Organization

SAM

Severe Acute Malnutrition

SD

Standard Deviation

SMART

Standardized Monitoring and Assessment of Relief and Transition

TW

Tube Well (shallow/ deep)

WaSH

Water, Sanitation and Hygiene

WAZ

Weight-for-Age z-score

WFP

World Food Programme

WHO

World Health Organization

WHZ

Weight-for-Height z-score


Executive Summary

Since August 2011, following the water-logging of the district, an emergency response was

implemented by ACF-Bangladesh – initiated by a food distribution and followed by Cash Transfer

Program, WaSH and Shelter activities - in order to reduce the nutritional impact of water-logging.

Following the emergency phase a comprehensive nutrition sensitive and specific program in the four

upazilas of intervention has been implemented. Programs include identification and treatment of

children 6-59 months with moderate and severe undernutrition, supplementary feeding for

pregnant and lactating women, blanket feeding for children 6-23 months, cash for work and

homestead food production during the identified lean seasons in the area of implementation.

In winter 2013 - 2014, ACF- Bangladesh started a detailed investigation over the malnutrition causes

in Satkhira District through an Integrated Nutrition Survey using SMART methodology (January

2014), as well as a Nutrition Causal Analysis – NCA and this in-depth survey. These analyses tend to

identify the socio-cultural, economic causes and the structural roots of undernutrition in the ACF-

Bangladesh working area, representing 18 Unions in 4 Upazila of Satkhira District in south east of

Bangladesh.

Overall Objective

The general objective was to assess Food Security and Livelihood, WaSH and nutrition from 4

Upazilas from Satkhira District affected by 2011 water logging for the following purpose:

� To identify current rates of Household Food Insecurity

� To identify the current Livelihoods

� To understand the Water, Sanitation and Hygiene Situation

� To identify the rates of undernutrition at the time of the survey

� To build the capacity of nutrition stakeholder in SMART Survey including nutrition risk

factors.

Methods

The survey was designed to provide statistically representative Food Security and Livelihood,

nutrition and WaSH data from 4 Upazilas from Satkhira District regularly affected by water logging.

The timing of the survey, at the end of the monsoon, corresponded with the pre-harvest period of

the district, also known as the hunger-gap period, when economy of the households are at the

poorest stage of the year, awaiting the harvesting period.

To have a greater understanding of the different area, ACF decided to establish a zoning based on

the livelihood situation. Three livelihood zones were identified based on “Land Zoning Report:

Assasuni upazila, Ministry of Land,” (January 2011). Three livelihood zones were identified: Agro-

Aquaculture, mono-Agriculture and mono aquaculture zones.

A Two stage random cluster survey was set up to achieve the desired outcomes of the survey. Thirty

clusters (villages/wards) were randomly selected using the ENA-SMART software; from the selected

clusters 12 households were randomly selected. A total sample size of 1,080 household was

identified to provide a representative sample for the selected indicators. Using two-stage random

cluster methodology provided each eligible household within the sample population an equal

opportunity of being selected to the survey.

Nutrition

The nutrition situation in Satkhira has declined since the previous survey in December 2012. The

mean weight-for-height of children overall has significantly dropped since December 2012 (13.8% vs.

7.8%), while other anthropometric indicators remain stable. The reason for the increase in the

prevalence of acute undernutrition in children 6-59 months could be due to a number of seasonal


and household factors. This could include the timing of the survey corresponds with the lean season

prior to the harvest in Satkhira.

The identification of children with MUAC alone remains significantly less than using both weight-for-

height and MUAC combined. Using MUAC alone as the sole indicator for admission into nutrition

treatment and prevention programs is excluding older children and boys, which remains a concern,

especially for the continued mental and physical development of these children.

The high rate of illness (66.7%) of children and in particular acute respiratory infection (76.1%) in

Satkhira continue as identified in the December 2012 survey.

The dietary diversity of children (IDDS:3.4) remains a concern with children only receiving 3 types of

food per day, this diet predominantly comprises of cereals. Just over half the child received some

form of meat product in the previous 24 hours which while is encouraging, protein laden foods

including milk and eggs remains limited.

Food Security and Livelihoods

The survey identified that households located in the single income livelihood zones of agriculture

and aquaculture, showed to have a higher proportion of households that were either moderately or

severely food insecure, compare to the dual income livelihood zone. In average, there were 26.6%

severe food insecure households, 46.1% moderate, 8.8% mild and 18.6% food secure.

Household having “Poor” food consumption is very limited, representing a total average of 2.1%,

while 29.0% are having “Borderline” food consumption score and 68.9% are having acceptable FCS.

Livelihood zone Agro-Aquaculture has a significant higher food consumption score compared with

the monoculture livelihood zone.

Households were required to purchase their food. Rice is the main staple of the Satkhira area. The

main food source for people was through purchasing (62.8%), with only 22.4% relying on their own

production. Overall, household spent 62.8% of household income on food, 11.7% on loan

repayments, 7.4% on education and 8.2% on health care.

Household mean Dietary Diversity at the time of the survey was at acceptable levels at 5.2 food

groups consumed in the previous 24 hours. It should be noted that 100% of households consume

rice, oil and spices. The consumption of protein foods (flesh products, milk) decreased as household

food security decreased. Both agriculture and Aquaculture had HDDS Scores scored lower than the

Agro-Aquaculture.

The main source of work for households was daily labour with 42.0%. They scored the poorest

outcomes when considering all the categories identified in the survey. 75% of households had more

than one income source to supplement their income.

Overall, 58.2% of households were landless and 19.1% classed as marginalised. Approximately half

of households were able to do cropping. As the farm size increases so does the dietary diversity of

the household and the income.

Jewellery was the most owned item. Jewellery is seen and a household investment and is able to be

sold in times of crisis. Within waterlogged areas, households had significantly less jewellery assets as

compared to non-waterlogged areas. Poultry is the most owned livestock. 15.8% of the households

owned no livestock.

WaSH

The types of drinking water source per livelihood zone are very different. In mono culture areas,

people prefer close shallow tube-wells than deep tube wells even if they are recognized as more

contaminated. Further investigations are needed to understand why people use shallow tube-.


There is clearly a link between household income and access to sanitation; despite no particular

evidence could be found per livelihood zone. Unhygienic latrines and open defecation are most

common for the first and second quintile of Households Income. The main barrier to access to

sanitation is logically the cost of the latrine.

Composting is a minor practice done in all livelihood zones. There is potential for development.

Piling is more done by the 4th quintile household income, whereas the first quintile mainly throws

their garbage anywhere. The cost for garbage collection and the need to pay small fees could be an

explanation and should be confirmed by further study.

Hand-washing at critical junctures is slightly similar in all livelihood zones. However, using soap for

hand-washing is more likely to be done for household in the 4th quintile than the 1st quintile of

income.

All livelihood zones share the same exposure to hygiene promotion with the main messages being

given by health workers. It must be noticed that mono culture zone favour as well groups discussion.

So, a key strategy to enlarge the audience and the impact of hygiene promotion will be to

strengthen the health workers’ knowledge on hygiene practices and promotion. Any behaviour

changes activities in Satkhira district must then involve health workers to ensure long term

continuity in the promotion of safe practices.

Knowing the strong causal link between a healthy environment and nutrition, improving health

needs the development of WaSH facilities access. However, from the study, poverty and livelihood

opportunities are clearly the main barriers for people to benefit from safe water and safe

environment.

Conclusion

The nutrition situation in the 4 Upazila included in the survey remains poor but less than district

levels identified in large scale nutrition surveys. Bangladesh Demographic Health Survey (BDHS) data

in 2011 identified rates of malnutrition for the Khulna Division at 14.6% GAM, therefore integrated

interventions by ACF and World Food Program (WFP) addressing the treatment and prevention of

acute malnutrition could be seen to having a positive impact. Households with increased risk factors

including their source of income, where they access food and their overall HFIAS category can be

seen to have a greater risk of having acutely malnourished children.

Households that rely on daily wages have the poorest outcomes in terms of childhood nutrition,

food security, and sanitation.

Child feeding practices continue to be an issue in childhood nutrition. This is offset by the high

percentage of children who continue to be breastfed after 6 months of age.

Childhood morbidity is high among children 6-59 months. Acute respiratory infections, which is

identified as one of the main risks factors for childhood mortality is high in the area surveyed.

Household food security even post-harvest remains a concern for the surveyed population with high

percentage of households who are food insecure.

Safe water, human waste disposal and hygiene practices continue to be issues that impact on the

population and could contribute to the burden of illness and acute malnutrition in children.

Recommendations

1. Nutrition Specific programming to address moderate and severe acuter malnutrition should

continue in Satkhira considering the high rates of GAM

2. Efforts should be made for the Ministry of Health (MoH) to take over the treatment of severe

acute malnutrition through CMAM and within the community clinics.


3. Efforts should be made to restructure the screening activities of the community volunteers to

ensure that coverage is broadened and not merely the number of children to be screened is the

primary target.

4. Advocacy for the change of admission criteria to include weight-for-height in CMAM at the

national level to ensure that children requiring treatment are admitted and that children,

specifically boys and older children are not excluded from treatment

5. Evidence needs to be collected to identify what outcomes are associated with children being

excluded from nutrition treatment programs who do not fall within the MUAC thresholds for

treatment.

6. Identify the specific barriers for caretakers to provide infants with appropriate child feeding

practices.

7. Implement long-term programming to facilitate behaviour change at the household level in terms

of maternal and child nutrition

8. Review and strengthen IYCF programs aimed at ensuring dietary diversity of infants and feeding

practices.

9. Develop and strengthen homestead food production to improve the dietary diversity, especially

through lean periods.

10. Develop and strengthen Income Generating Activities (IGA) and agro-based activities in order to

have year round activity during all seasons. This will enable the most vulnerable households

(landless, daily laborers, etc.) to have sufficient income during the lean period and strengthen

their capacity to face external shocks

11. Increase access to arsenic free water, through deep-tube or alternative water

harvesting/collection methods.

12. Increase safe water access combined with hygienic sanitation for low level socio-economic

household

13. Address the hygiene practices of the communities, through using hygiene promotion activities

rising soap (or adequate alternative) usage

14. Annual integrated SMART survey to be conducted to identify changes in the evolution of

nutrition, child and maternal care, food security and WaSH situation in Satkhira.


Table 1: Summary Table of Results

Dec-12 Sep-13 Feb-14

Food Security indicators

HDDS 7.6 5.2 6.5

Worries about Food 76.50% 72.90% 74.20%

Poor Quality 75.50% 80.40% 74.80%

Not enough Food 59.90% 58.00% 58.10%

Food Secure/Mild Insecurity 27.60% 27.4% (8582) 35.70%

Moderate/Severe Insecurity 72.40% 72.6% (6158) 64.30%

Mean Income (median) 6812 (5894) 63.20%

Expense Food 64.30% 62.80% 9.8 %

Expense Health 8.90% 8.20% 11.20%

Expense Loan 10.20% 11.70% 3.40%

Expense Education 3.50% 7.40% 6.5

Nutrition indicators

Weight for height

Prevalence of global malnutrition (<-2 z-

score and/or oedema) 7.8% (5.8 - 10.5.)

12.3% (8.9 -

16.8.) 13.8% (10.9 - 17.3)

Prevalence of moderate malnutrition (<-2

z-score and >=-3 z-score, no oedema) 6.7% (4.9 - 9.0)

11.4% (8.0 -

15.8.) 12.8% (10.0 - 16.2)

Prevalence of severe malnutrition (<-3 z-

score and/or oedema) 1.1% (0.5 - 2.8)

0.9% (0.3 -

2.9) 1% (0.4 - 2.3)

MUAC

Prevalence of global malnutrition (< 125

mm and/or oedema) 2.3% (1.2 - 4.4)

0.9% (0.3 -

2.7) 2.8% (1.6 - 4.8)

Prevalence of underweight (<-2 z-score) 23.7% (19.3 - 28.7) 28.5% (23.3 -

34.4) 30% (25.2 - 35.3)

Prevalence of stunting (<-2 z-score) 33.8% (28.9 - 39.0) 31.2% (26.2 -

36.7 29% (23.8 - 34.8)

Child feeding

Mean IDDS 350.0% 300.0% 340.0%

Acceptable IDDS 45.9% 30.0% 44.4%

Unacceptable IDDS 54.1% 70.0% 55.6%

Acceptable Meals 58.1% 8250.0% 86.5%

Unacceptable Meals 41.9% 1750.0% 13.5%

Acceptable Diet 28.8% 27.5% 41.5%

Unacceptable Diet 71.2% 72.5% 58.5%

Health indicators

Illness 72.8% 73.0% 66.7%

Diarrhoea 19.8% 4.3% 9.3%


Fever 44.6% 42.5% 50.1%

ARI 73.6% 10.3% 76.1%

Other 20.6% 15.8%

WaSH

Drinking water source

Deep tube well 60.5% 41.5% 42.3%

Shallow tube well 36.7% 54.5% 53.5%

Other 2.8% 4.0% 4.2%

Sanitation Facility **

Hygienic Latrine 48.1% 51.0% 52.4%

Un-hygienic latrine 49.5% 49.0% 47.6%

Acceptable Hand washing Knowledge

After Defecation 98.8% 86.2% 32.1%

After Child defecation 40.4% 50.4% 18.6%

Before breastfeeding 25.6% 85.8% 16.7%

Before cooking 21.1% 36.1% 13.9%

Before eating 21.1% 85.8% 29.2%


Introduction

Bangladesh consists of 64 districts (zilas) in 7 divisions. Each district is further broken down into

Upazila, unions, wards and villages. The Integrated nutrition survey took place in Satkhira district in

south-western Bangladesh from December 29th to January to 3rd of February, 2014. Satkhira district

has a total population of 1,864,704 and is one of 10 districts in the Khulna Division. It lies between

21º36' and 22º54' north latitudes and between 88º54' and 89º20' east longitudes. The total area of

Satkhira is 3,817.29 km2. (1,473.00 miles2) of which 1632.00 km is under forest. Satkhira district

consists of 7 Upazila, 2 municipalities, 79 unions and 1,436 villages. The main occupations of the

population are agriculture (37%), agricultural labourer (27%) and commerce (13%). The annual

average temperature is 12.5°C - 35.5°C with an annual rainfall of 1,710mm. The main crops in

Satkhira include rice, jute, sugarcane, mustard seed, potato, and onion and betel leaf.

Background

Heavy rainfall during the end of July and early August

2011 caused severe localized flooding in Satkhira.

While flood waters begin to recede, some unions of

Satkhira, Jessore and Khulna still remained under

water, a situation referred to as ‘prolonged water

logging’. This caused displacement of the population,

disrupted livelihoods, and damaged agricultural crops,

fisheries and housing. It was assumed that the flood

waters would recede slowly and the inundation will

continue till the end of October/November 2011 as

the runoff in the two major rivers of Satkhira

Kapotakho and Betrabati are obstructed by shrimp

farms, irrigation dams/barrages and high river levels

due to raised river beds and effects of tide.

Following an initial emergency response to the 2011

water-logging, ACF implemented its comprehensive

nutrition program within 18 unions of Satkhira, those

being considered most affected by water-logging in

2011. The response was also coordinated with WFP

with the support of local actor, Shushilan. The total

population of these 18 unions is 536 9181. The

comprehensive nutrition program incorporates

treatment for severely malnourished children,

nutritional support for children under 23 months and pregnant and lactating women, plus

prevention of SAM by treating MAM through supplementary feeding programs targeting children

under 5 years and adolescent girls. In 2013 ACF along with WFP embarked on program to provide

1 Bangladesh Bureau of Statistics, Ministry of Planning (2011). Community Report Satkhira Zila June 2012:

Population and Housing Census 2011.


greater clarity as to the potential nutrition beneficiaries in the 4 upazila of Satkhira. The program

included a comprehensive FSL and WaSH survey (September 2013), qualitative information from

current Mental Health and Care Practices (MHCP) program and an Nutrition Causal Analysis (NCA)

which included an integrated SMART (January 2014) to support the NCA with quantitative

information and to provide comparative data to previous surveys conducted in the area.

In September – October 2013, ACF- Bangladesh conducted a detailed Food Security and Livelihood -

WASH and nutrition assessment in waterlogging areas of Satkhira District. This included 4 Upazilas –

18 Unions of Satkhira District in south west of Bangladesh. The areas included have comprehensive

nutrition specific and sensitive programming to address undernutrition.

The timing of the survey, at the end of the monsoon, corresponded with the pre-harvest period of

the district, also known as an hunger-gap period, when economy of the households are at the

poorest stage of the year, awaiting the harvesting period.

At the time of the data collection, further rains have exacerbated the waterlogging in areas included

in the survey, therefore this report should be considered to be representative prior to the worsening

of the situation.

This survey has been funded by ECHO. This report contain the anthropometric results of the survey,

double entry of data was done in the second part of the analysis to confirm results identified in this

report.

This report contains analysis of morbidity, child feeding practices, food security and water, sanitation

and hygiene.

1. Survey Objectives

General Objective: To have detailed Food Security and Livelihood and WaSH data from 4 Upazila

from Satkhira District affected by 2011 water logging

Specific Objectives:

� To identify current rates of Household Food Insecurity

� To identify the current Livelihoods

� To understand the Water sanitation and Hygiene Situation

� To identify the rates of undernutrition at the time of the survey

� To build the capacity of nutrition stakeholder in SMART Survey including nutrition risk

factors.

2. Methodology

ACF- Bangladesh conducted this detailed survey on Food security, Livelihood, nutrition and WaSH in

September and October 2013 in 18 unions within 4 Upazila of Satkhira district from areas where

targeted nutrition programs are currently operating. The aim was to define the core structure local

context and economy of Satkhira.

Following this review, ACF- Bangladesh started a Nutrition Causal Analysis, including qualitative

assessment on the field about nutrition and child care practices, literature review and stakeholder


belief as well as a quantitative review presented here as the Integrated Nutrition survey using

SMART methodology as the sampling basis.

2.1. Survey Area

Samples were selected from 18 unions in Sathkira district. Above the 4 Upazila of ACF- Bangladesh

interventions, Satkhira Sadar Union was excluded due to the urban complexity of Satkhira city.

Figure 1: Overview of population data for Satkhira survey area (Census 2011)

Number of

working union Household Inhabitant

Density

(pers./km2)

Estimated number

of 6-59 children

Assasuni 1 6,907 14,120 1,076 1,865

Debhata 2 14,518 28,794 1,039 3,920

Satkhira Sadar 4 25,069 106,207 1,047 6,769

Tala 10 58,983 242,296 1,017 15,925

Total 17 105,477 391,417 1,045 28,479

2.2. Type of survey

Based on the different objectives, and due to the field constrains of Satkhira, ACF decided to run a 3

stage survey to collect representative quantitative data in the 4 Upazilas of ACF intervention area in

Satkhira. To meet with WaSH and FSL assessment need, household was considered as Basic Sample

unit, rather there is a child or not.

- 1st stage; 3 Strata were defined based on Livelihood criteria

- 2nd stage; Clusters were randomly selected within each Livelihood Strata

- 3rd stage; Simple random selection of HH within each cluster

ACF decided to exclude urban areas in the survey due to its complexity for the sampling and analysis.

2.3. Sampling Size

Using previous December 2012 Integrated SMART Survey results to determine the sample size for

FSL and WaSH sectors, sample sizes were determined using food insecure households and

unhygienic latrine coverage indicators.

Sample size calculation used the following formula

Where: n = sample size

z = linked to 95% confidence interval (1.96)

p = expected prevalence (as a fraction of 1)

p = 1-p (expected non-prevalence)

d = relative desired precision (5%)

Hypothesis Value Result n

FSL HH food insecurity 34% 344

WASH Unhygienic Latrine 49% 384

n= z² x

p x q

d²


Based on calculation presented above, it was decided to take a sample of 360 HH per strata, each

strata divided in 30 cluster of 12 Households, representing a total of 1,080 HH to be surveyed

3 strata x 30 clusters x 12 HH = 1,080 HH

Nutrition perspective:

Based on previous ACF SMART Survey held in December 2012, the Moderate Acute Malnutrition

reference prevalence to be taken into account is 7.8% and taking in count a desired precision of 4%

(DEFF=1), the number of child to be surveyed should be 188.

As Bangladesh demography has a very small child rate per family, especially Satkhira where only 30%

of family might contains eligible child 6-59 month old, representing a total of 626 households.

Thus, ACF-Bangladesh decided to record all children aged between 6 to 59 months, encountered

during the survey and to take the anthropometrical measurement, ensuring the representativeness

of the overall survey. No strata analysis can be done on nutritional indicators as the number of child

per strata is insufficient to provide representative results for each.

2.4. Sampling Method:

2.4.1. First Step Sampling:

To have a greater understanding of the differing livelihoods, ACF decided to establish a zoning based

on the livelihood situation.

Three livelihood zones were identified based on “Land Zoning Report: Assasuni upazila, Ministry of

Land,” from January 2011, as described below;

- Agricultural Strata; 41,630 HH in 7 Unions.

- Aqua cultural Strata; 27,839 HH in 4 Unions.

- Agro-aqua cultural Strata; 32340 HH in 6 Unions.

Aquaculture

Agro-Aquaculture

Agriculture


2.4.2. Second Sampling Stage

The second step consisted on the random assignment of 30 clusters per strata, by entering in ENA

software the full list of villages (Basic Geographical Unit) identified per strata, and detailed per

inhabitant. Clusters were randomly assigned to the villages

2.4.3. Last Stage Sampling:

Once the village to survey was identified, the last stage sampling was to be conducted. Household

numbers were previously assigned to each household within the survey area. Household numbers

were sequential i.e. 1-100. Household numbers were entered into ENA and a random number

generator selected the households to be surveyed.

If the population is bigger, then a segmentation approach is to be considered: listing or mapping all

the households, then dividing them into segments of equal size, and then randomly selecting one

segment for random sampling of households using random numbers tables.

Children identified with moderate or severe malnutrition where checked as to whether they were

currently in a nutrition treatment program. Those that were not, were referred to nutrition program,

where MUAC was re-checked and admitted under National Nutrition treatment protocols.

2.5. Questionnaire

ACF- Bangladesh developed a questionnaire which fit the local context of Satkhira District.

Questionnaires included information of household, such as household composition, mean of income,

livelihood and food security status, asset, land ownership, expenses, children Anthropometric

measurement and morbidity, Young Infant and Child feeding care and Practices, Breastfeeding

information and Pregnant and Lactating woman information, based on Nutrition Causal Analysis

requirement and WaSH questions: access to WaSH facilities and hygiene related questions.

The questionnaire was translated into Bangla and tested at field level to cross check eventual bias

that could be introduced during interview. Any points of confusion or misinterpretation were

modified to ensure accuracy.

2.6. Data Collection

The questionnaires took 18 days to be completed with 30 data collectors from Shushilan and some

additional staff from ACF. ACF program managers supervised the data collection to ensure good

data quality collection. Support from the Dhaka office provided additional supervision.

One week training for all data collectors and pilot testing of the questionnaires was conducted prior

to the data collection in the field.

Based on a frequency of 1 cluster / team / day, the 30 clusters were visited by the field workers

within 18 working days, with an additional day for missing data recollection.

- Verbal Consents were requested after detailing objective of survey and confidentiality

policy, in case where households refused to participate, the household was recorded and the

reason was noted. All households provided verbal consent with no refusals to participate.

- Child Age; was confirmed using Immunization cards and if not available, local events

calendar to provide the most accurate estimate of the age of the child. It is to be noted that

6.2 % of the children were not bearing any Immunization card and the age had to be defined

using the local calendar of event.


- Weight; children were weighed using Slater scale and a bamboo stick, with a precision of

±100g. Slater scales were calibrated to be accurate every night using a 2kg standard weight,

and replaced if imprecise.

- MUAC; measured on the left arm with ACF- Bangladesh MUAC measurement tape, a has

been collected with a precision of ±0.1 cm for all eligible child

- Height; the height (standing position) was measured for above 87cm and the length

(recumbent position) for children under 87cm. The measurements were done with a 160 cm

height wooden board with a precision of ±0.1cm.

- Morbidity; eligible children aged 6 to 59 months morbidity 2 weeks prior to the survey have

been recorded to identify Diarrhoea, Fever, ARI or other diseases based on clear guideline

introduce during the training.

- Child Feeding Practices; for children aged 6 to 23 months, caregivers were asked to give

details about food diversity as well as number of meal given during the past 24 hours.

The anthropometric data was collected only for children aged 6 to 59 months. Their nutrition status

was evaluated through the anthropometric measurements of height, weight, mid upper arm

circumference (MUAC) and the age of the children using WHO growth standard 2006.

Children identified with moderate or severe malnutrition where checked as to whether they were

currently in a nutrition treatment program. Those that were not, were referred to the appropriate

program, where MUAC was re-checked and admitted under National Community Management of

Acute Malnutrition protocols.

2.7. Data Entry

Anthropometric data was entered on a daily basis following field work in ENA-SMART software

(Version 2011) to monitor the precision and quality of enumerator’s data. Areas identified as

requiring follow-up were reinforced with each team prior to the next day’s data collection to ensure

as accurate information as possible. Data on children identified with flagged reference values for

WFH, WFA or HFA were checked and confirmed to be correct. Once entry was completed, cross

check for oddness was controlled and cleared before running descriptive and analytical statistics.

Plausibility report shows that surveys had “Good” data quality and conform to survey standards

(Annex 1).

Complete survey data entry was entered using EPI INFO 7 software with incorporated masks to

ensure standardization of responses.

2.8. Data Analysis

All household food security and WaSH data was entered into EpiInfo 3.5.4 database developed for

the survey. Variables were created and responses were numerically coded to restrict prevent

variations of the same response.

Data was cleaned ensuring that any discrepancies were identified, clarified and either fixed or

removed from the analysis. The finished data was exported into STATA 13 for analysis and

presentation of results.

All anthropometric data were entered in ENA in one file per segment. Analysis on anthropometry

has been made with ENA using WHO 2006 Growth Standards to calculate anthropometric indicators

such has WHZ, WAZ, HAZ and MUAC. Analysis using NCHS 1977 growth charts was done to provide

reference for previous surveys.


Data on children identified with flagged reference values for WHZ, WAZ or HAZ were checked,

confirmed to be malnourished children, and referred. Amongst all child measured, 1 child with

physical disability where no height could be measured has been excluded of the database.

NOTE: As explain above, due to the limited number of child per strata, the anthropometric results

presented are representative of the all area surveyed, and cannot be segregated into strata or

administrative division.


3. Results

3.1 Demographics

3.1.1. Heads of Households

Each household was asked to identify the make-up of the household. A household was identified as

those who eat out of the same cooking pot. Questions included the sex, physical ability of the head

of the household. The respondent was then asked to provide a breakdown of the household in terms

of the sex and age of the occupants. This information was used to determine the dependency ratio

and do cross analysis of other indicators.

3.1.2. Heads of household Sex/Age

At the household level, the female to male ratio of heads of households was 0.06:1. In percentage

terms, only 6.0% of all heads of households surveyed were female. Across the three livelihoods the

same pattern was identified.

The average age was 43.2 years of age across the three livelihood zones. There was no difference in

the age of the heads of household between the LHZ

Table 2: Female to Male ration of Heads of Households by Livelihood Zone

Agriculture Agro-aquaculture Aquaculture Total

Female/Male Ratio 0.07:1 0.08:1 0.04:1 0.06:1

Average Age (years) 43.0 42.6 43.8 43.2

Age Range 18 - 83 13 - 75 20 - 90 13 – 90

There was a significant difference in the monthly income of the household depending on the sex

(p=0.035). Female Heads of households earned significantly less than men with an average of 4,969

BDT in the previous month, whereas households with males as the head earned on average 6,928

BDT2 (p=0.0002).

Table 3: Income by Head of Household Gender and HFIAS Category

Food Secure Mild Moderate Severe

% Income % Income % Income % Income

Female 11.3 6,437 8.1 6,856 40.3 5,397 40.3 3,772

Male 19.0 9,513 8.8 6,909 46.4 6,558 25.8 5,747

While most heads of households were between 18 and 58 years, there were a number of extremes

which saw very young and very old heads of households. In two households, teenagers (13 and 15

years) were identified as the Heads of households. Similarly 5 households had heads of households

at 80 years or older.

Households were asked to provide the numbers of people living within the household and to identify

their age so that a breakdown of age groups could be tabled. The figures provided are also able to be

used to calculate the dependency ratio with each of the households.

2 1 euro is equal to 100 BDT or 1 US$ is equal to 85 BDT (June 2014)


For all livelihood zones the average household size was 4.4 people. There were no significant

differences in the households sizes between each of the settlements (p=0.93). Overall the

percentage of children was low, with only 7.6% of the household members being less than 5 years of

age or 0.4 children less than 5 years per household. This is reflected in the total number of children

included in the anthropometric component of the survey with only 319 children from 1,040

households.

Table 4: Household Composition by Age Group, Gender and Livelihood Zone


Age group % total M/F

ratio

% total M/F

ratio

% total M/F

ratio

% total M/F

ratio

< 5 Years 8.7 0.4 1.2 8.6 0.4 1.0 6.6 0.3 1.1 7.6 0.4 1.1

6-17 Years 23.4 1.1 0.9 24.0 1.1 0.8 24.0 1.1 0.9 23.8 1.1 0.8

18-59

Years

60.9 2.6 1.0 59.5 2.6 1.0 59.1 2.5 1.0 59.8 2.6 1.0

>60 Years 8.1 0.4 1.1 7.9 0.4 0.9 10.2 0.5 1.1 8.8 0.4 1.1

Total 100 4.4 1.0 100 4.4 0.9 100 4.4 1.0 100 4.4 1.0

Households were categorised into various size thresholds to underrate if the differences in monthly

income was influenced. Overall, there was a significant difference between the size of the household

and the income earned (p=0.000). Only households found in the aqua-cultural livelihood zone had a

significant difference in the income between the household sizes. In addition to income, the

household dietary diversity of the household was assessed against the size of the households within

each of the livelihood zones. Again, it was seen that as the household size increases the household

dietary diversity increases.

Table 5: Income and HDDS by household size


income HDDS income HDDS income HDDS income HDDS

<4 4,947 4.8 4,682 5.4 5,731 4.8 5,162 5.0

4-5 8,222 4.9 6,662 5.6 6,973 4.9 7,299 5.1

6-9 8,696 5.2 14,069 6.1 7,488 5.2 10,040 5.5

>9 13,623 6.0 29,801 7 7,424 8 19,368 6.5

P value 0.000 0.0613 0.000 0.013 0.573 0.026 0.000 0.000

The size of the household was then assessed against the household food insecurity scale to see

whether the size of the household impacted on the food security situation of the household. It was

identified that households that were larger in size were more food secure than the smaller

households because of larger manpower within the households.

Table 6: Household size by LHZ and HFIAS Category


Secure/

Mild

Mod/

Severe

Secure/

Mild

Mod/

Severe

Secure/

Mild

Mod/

Severe

Secure/

Mild

Mod/

Severe

<4 17.8 82.2 31.5 68.5 19.8 80.2 22.9 77.1

4-5 19.4 80.6 31.6 68.4 25.6 74.4 25.4 74.6

6-9 37.2 62.8 54.8 45.1 23.6 76.4 37.8 62.2


>9 42.9 57.1 60.0 40.0 100 0 53.9 46.2

Overall what was seen through the survey was that the larger households are able to improve their

income, dietary diversity and their food insecurity status. Ultimately households with a larger

number of people had heads of household that were older, than the smaller ones. In addition

smaller households with less than 4 people had proportionally more females than other household

categories.

3.1.3. Physical Ability

The Physical ability was assessed on each of the heads of households to understand whether this

had an impact or there was relationships between this and the households’ ability to be food secure,

income category or income.

Figure 2: Income and HDDS by Physical ability


income HDDS income HDDS income HDDS income HDDS

Able

Bodied

7,177 4.9 7,025 5.6 6,812 4.9 7,000 5.2

Chronically

Ill

8,009 5.3 21,968 5.5 5,298 4.8 11,880 5.3

Disabled 7,156 5.0 6,394 5.6 6,268 5.4 6,641 5.3

Elderly 9,160 5.1 8,100 5.8 6,892 5.2 7,990 5.4

P value 0.2607 0.464 0.0035 0.9.18 0.573 0.4346 0.0061 0.516

The physical ability of the head of the household, showed some slight differences. The income of the

chronically disable was offset by a proportion of them working in business that had very high

incomes compared to the other categories. Chronically ill people in business earned an average of

11,880 BDT in the previous month, which is significantly higher than the most of the households.

Similarly, the dietary diversity of households was not dependant on the physical ability of the head

of the household. The average monthly income is 6,375 BDT (Gross national income).3

3.1.4. Dependency Ratio

The Dependency Ratio is used as a proxy between those who are not economically active (and

therefore dependant) and those who are economically active. The dependency ratio is calculated

using the following calculation:

Percentage of population aged less than 18 years + percentage of population aged 60 years and over

Percentage of population aged 18 – 59 years

The Dependency Ratio for all livelihoods was 0.8, which indicates that within the households there

are more people able to earn money than those that that do not. The dependency ratio for each of

3 (http://data.worldbank.org/country/bangladesh)


the livelihoods was 0.8, 0.8 and 0.9 respectively. There was no significant difference between each

of the livelihoods (p=0.25).

Households with males as the head of the household had slightly more dependants than households

with women as the head of the household (p=0.0015). Women headed households earned less

income than households with men as the head with BDT4,696 compared to 7,530 BDT (p=0.0351).

Ultimately households with women as the head of the household had no difference in the household

dietary diversity or food insecurity scale, possibly due to the lower number of dependants within the

household.

Logically, the size of the household influences the dependency ration, with an increase in the ratio as

the household size increases. Households with 6-9 members have just over more than 1 person

relying on a single individual to provide inputs. This then reverses with very large households and the

ration decreases below the overall average. The means that very large households have more people

who are able to contribute to the households as compared to those who are dependant.

3.2 Food Security and Livelihoods

While food security in emergency operations is observable and definable, in many households and

contexts the measure of food security become more complex[1]. Food security is considered for

individual, household, community and country development. In developing countries nutrition and

health status and the development of children depends on the inputs and household food security

[2, 3].

The food security survey implemented by ACF was used to identify the level of food security within

its area of operation using validated indicators.

The survey included the three main livelihoods’ zones identified in Satkhira District. Each area of

livelihood was identified and clusters and households were selected to give representative

information for each livelihood zone. While it is recognised that within each livelihood there is a

mixture of livelihoods, these differences are not differentiated with the livelihood.

3.2.1. Household Food Insecurity Access Score

In order to be able to distinguish between food secure and food insecure households, ACF employed

the Household Food Insecurity Access Scale (HFIAS). HFIAS enables to present a continuum of

severity of house food security on the area of the survey. HFIAS is used to generate the prevalence

of food access insecurity, used to monitor changes over time. Each household was asked to respond

to the questions with a recall time of 30 days (1 month). If the respondent answered yes to any

question, they were subsequently asked to estimate the frequency that they worried or experienced

the situation in the households. HFAIS is broken into 3 main areas, with increasing severity to

Household Food Insecurity, these include:

- Anxiety and uncertainty about the household food supply (Question 1)

- Insufficient Quality (Question 2 - 4)

- Insufficient Food Intake and its physical consequences (Questions 5-9)

Overall 72.9% of households in the 4 upazilas were concerned about food at some point in the

previous 4 weeks to the survey. More than 80.4% of households provided food to the family that

they considered lesser quality, due to inability to access quality food due to household income and

almost 60% (58.0%) of households expressed serious concerns about not having enough food to eat

in the previous 4 weeks. The last category are considered to be more food insecure than the other

groups.


Table 7: Food Concerns for previous 4 weeks

Anxiety about Food Insufficient Quality Insufficient Food Intake

Agriculture 72.9% 82.6% 63.1%

Agro-Aquaculture 70.5% 75.9% 49.1%

Aquaculture 75.1% 82.5% 61.3%

Totals 72.9% 80.4% 58.0%

Households were then classified into their food security status according to their responses. The

Household Food Insecurity Access Scale is considered as an improved measure of the coping strategy

index (CSI) as it includes the coping mechanisms employed by the household and then how many

times the household faces a similar situation in the previous month. Households that suffered from

the extreme of insufficient quantity when household members were not able to have access to food

for any period of time are considered seriously food insecure as this starts to seriously impact on the

nutritional status of household member, especially young children and pregnant and lactating

women as this sector of the community need additional food inputs to contribute to adequate

weight gain at critical times.

Overall, 26.6% of the households with children less than 5 years were considered severely food

insecure, meaning that these households had insufficient food to feed all household members

including children. Households with pregnant women showed similar results to children less than 5

years with 25.9% of the houses being severely food insecure. These results show that these

households were increasingly at risk for members to be undernourished at critical times of growth

and development.

Each livelihood was assessed to identify if there were differences between the food security of the

households. Households located in the single income livelihood zones of agriculture and

aquaculture, showed to have a higher proportion of households that were either moderately or

severely food insecure, with 78.0% and 76.0% respectively. This was compared to the dual income

livelihood zone of agro-aquaculture which while still having a large proportion of household that

were food insecure was less than the other two with 63.7% of households being moderately or

severely food insecure. Agro-Aquaculture livelihood zone is having a significantly better food access

than the 2 others zones (p<0.001).

Figure 3: HFIAS by Livelihood Zone

020

4060

Agriculture Agro-Aquaculture Aquaculture Total

Food Secure Mild Insecurity Moderate Insecurity Severe Insecurity

Per

cent

age

of H

ouse

hold

s

Graphs by Livelihood Zone


As can be expected, households with higher income showed better food security. Households with

medium income less than 5,894 BDT per month presented lower levels of food security.

Table 8: Food Security by Income Quartile

Food Secure Mild Moderate Severe

1st

Quartile (≤4320 BDT/Month) 8.5% 5.8% 44.4% 41.3%

2nd

Quartile (>4320 to 5894 BDT/month) 9.0% 9.0% 54.1% 28.0%

3rd

Quartile (5894 to ≤8000 BDT/month) 18.5% 11.6% 45.6% 24.3%

4th

Quartile (>8000 BDT/month) 37.7% 8.7% 40.4% 13.2%

Total 18.6% 8.8% 46.1% 26.6%

3.2.2. Food Consumption Score 4

Food Consumption Score is a proxy indicator based on WFP methodology to assess the food intake.

Each household was asked to identify all food groups (from an 8 food group list) eaten during the

past 7 days answering the number of days where each food group have been consumed.

Based on information collected, the Food Consumption Score is calculated to create a score with a

range between 0 and 112, as followed;

FCS= 2xCereal + 3xPulse + 1xVegetable + 1xFruit + 4xDairy + 4xMeat + 0.5xOil + 0.5xSugar

Cut-off for Food consumption score general coincides with the kcal intake of the household. The

three thresholds created from Food Consumption Score are:

• Poor- ≤ 28

• Borderline- 28.5 - 42

• Acceptable- ≥42.5

The FCS cut-off has been increased in consideration of the high consumption of oil in the Bangladesh

diet. Oil is a primary ingredient in cooking the daily meal. To identify whether the oil used was

fortified was not considered in the survey.

The cut-offs are designed to indicate that households with Poor FCS have less than 2100kcal per

person per day, which is the minimal dietary intake to maintain basal metabolism. Studies on FCS

have indicated that Poor FCS corresponds with much lower kcal (1600 – 1800 kcal) intake per person

per day, and even acceptable FCS corresponds with the basal metabolic need of 2100kcal/day.

However no studies exists on this in Bangladesh, Food Consumption Score is internationally

acknowledged and used to compare different contexts.

4 FCS: Proxy indicator that represents the dietary diversity, energy and macro and micro (content) value of the food that

people eat. Based on the calculation of food types and food frequency over a seven-day period.


Figure 4: Food Consumption Score by Livelihood Zone

As it can be read on the figure, prevalence of household having “Poor” food consumption is very

limited, representing a total average of 2.1%, while 29.0% are having “Borderline” food consumption

score. Livelihood zone Agro-Aquaculture has a significant higher food consumption score (p<0.001)

compared with the monoculture livelihood zone.

There was relationship between the household income and the Food Consumption score of

households (p=0.00). Households that were identified as having a poor FCS, in the previous month

had an average income of 4389 BDT, Borderline– 5,494BDT and Acceptable 8,223 BDT for the

month. The average monthly income is 6,375 BDT (Gross national income).5

Figure 5: Foods consumed in previous 7 days

5 (http://data.worldbank.org/country/bangladesh)

020

4060

80Agriculture Agro-Aquaculture Aquaculture Total

Poor Borderline Acceptable

Per

cent

age


01

23

45

67


Cereal/Tuber Oil Vegetable Meat/FishPulses/Legume Dairy Sugar Fruit

Day

s C

onsu

med



Above figures presents the average number of times each food group have been served during the

last 7 days, within each livelihood zone. Almost all households in Satkhira eat cereal and oil on daily

basis. Except for Meat/Fish and Vegetable food groups, the global figure of weekly food

consumption was similar from livelihood zone.

As previously observed for Agro-aquaculture livelihood zone, the significant higher FCS can be

explained by weekly consumption of both Vegetable and Meat/Fish food groups (p<0.001).

Fruit was consumed at a limited rate in households throughout the survey. The consumption of fruit

could be seen to be seasonal and as the survey was conducted in September and October locally

available fruits were not present in markets at an affordable price.

Food Consumption Score was compared to the Household Food Security Access category.

It was seen that households, on a daily basis consumed cereals. Similarly the majority of households

consumed oil on a daily basis; only 16 (1.5%) households in the preceding 7 days did not consume

oil. The distribution of theses household was across all food security categories and the mean

income was 9,360 BDT. It could be assumed that it was by choice as the household income between

the households was not significantly different to assume that this was a dominant factor.

Households with better food security consumed more meat, dairy and pulses than households that

were moderately or severely food insecure. The lack of consumption of the protein rich foods means

that children within these households and pregnant women could be prone to protein deficiency.

Table 9: Food Consumption for previous 7 Days

Cereal Oil Vegetable Meat/Fish Pulses/Legumes Sugar Dairy Fruit

Food Secure 7.0 6.8 5.9 6.1 2.7 2.2 3.3 2.3

Mild Insecurity 7.0 6.8 5.7 5.6 2.3 2.1 2.1 1.2

Moderate Insecurity 7.0 6.9 5.1 3.8 2.1 1.3 1.1 0.7

Severe Insecurity 7.0 6.9 4.9 3.0 2.1 0.8 0.5 0.4

Total 7.0 6.9 5.3 4.2 2.2 1.4 1.5 1.0

3.2.3. Household Dietary Diversity Score

Figure 6: Distribution of Food Groups consume in previous 24 hours

010

2030

0 2 4 6 8 10 12 0 2 4 6 8 10 12 0 2 4 6 8 10 12 0 2 4 6 8 10 12


Per

cent

age

Dietary Diversity (Food Groups Consumed)Graphs by Livelihood Zone


To complement the household food consumption score, households were asked to identify the

foods that were consumed in the previous 24 hours. Due to the shorter recall time, it can provide a

clearer picture of the variety of foods consumed at the household level.

Overall the mean dietary diversity over the three livelihood zones was 5.2. There was a significant

difference between the mono-cultural zones and the Agro-Aquaculture zone (p=0.000). Both

agriculture and Aquaculture had HDDS Scores were less than 5, while Agro-Aquaculture had a mean

HDDS exceeding the overall mean.

There was similar distribution of the food groups being consumed ranging from 2 to 11 food groups

in the past 24 hours. Ideally there would hope to be higher food group consumption, but the results

reflect previous survey in Satkhira and other similar surveys in Bangladesh. There was a significant

difference in the mean income of households who ate 5 of less food groups and those that ate >5

food groups (p=0.000).

Aquaculture consumed 6 different food groups in the previous 24 hours; this was compared to Agro-

Aquaculture which had increased diversity with 11 food groups. Agriculture consumed 8.

Figure 7: Food consumed in previous 24 hours

Households were assessed on the household dietary diversity according to the household food

insecurity scale. There was an obvious downward trend between the food security categories across

all livelihood zones.

The main source of animal proteins was fish. Once again it is unclear what the individual

consumption of fish was.

Apart from Cereals, oils and Pulses, households in the agricultural zone in Satkhira consumed less of

all the other food groups. As in the FCS, flesh products (meat/fish) were consumed less in

agricultural zone, though pulses were consumed slightly more when comparing to the other 2 zones.

While legumes and pulse are an important source of protein, these must be consumed at larger

quantities to provide sufficient nutrient input, than animal proteins. Therefore while households

who consumed legumes and pulses, could still be susceptible to a deficiency in protein unless larger

quantities are eaten.

020

4060

80


Cereal Oil Vegetables Tuber Fish PulsesDairy Sugar Eggs Meat Fruit Legumes

Per

cent

age

of H

ouse

hold

s



Figure 8: Dietary Diversity by LHZ and HFIAS

3.2.4. Foods Source

The source of food for each household was assessed. Each major food group was divided into

whether a household was required to purchase a food, was able to produce its own food or had

another source which included begging, gift, bartering.

Primarily households in the surveyed area were required to purchase their food. Cereal (rice) is the

main staple of the Satkhira area and is produced in this area.

Table 10: Food Source


Cereal

Purchased 69.7 % 87.9 % 73.0 % 76.7 %

Produced 29.4 % 11.5 % 25.8 % 22.4 %

Other 0.9 % 0.6 % 1.1 % 0.9 %

Oil

Purchased 99.7 % 99.4 % 99.4 % 99.5 %

Meat

Purchased 92.0 % 90.7 % 87.2 % 90.0 %

Produced 6.4 % 5.7 % 10.2 % 7.3 %

Other 1.6 % 3.6 % 10.3 % 2.6 %

Fish

Purchased 83.8 % 62.0 % 69.7 % 71.8 %

Produced 3.0 % 18.4 % 14.4 % 12.0 %

Other 13.1 % 19.6 % 15.8 % 16.21%

Eggs

Purchased 62.2 % 57.9 % 65.9 % 62.0 %

Produced 37.1 % 39.8 % 34.1 % 37.0 %

Other 0.6 5 2.3 % 0 % 1.0 %

02

46

8

Food

Secur

e

Mild

Inse

curit

y

Mod

erat

e In

secu

rity

Sever

e In

secu

rity

Food

Secur

e

Mild

Inse

curit

y

Mod

erat

e In

secu

rity

Sever

e In

secu

rity

Food

Secur

e

Mild

Inse

curit

y

Mod

erat

e In

secu

rity

Sever

e In

secu

rity

Food

Secur

e

Mild

Inse

curit

y

Mod

erat

e In

secu

rity

Sever

e In

secu

rity


HD

DS

(fo

od g

roup

s co

nsum

e)



Dairy

Purchased 59.0 % 47.6 % 43.5 % 49.8 %

Produced 39.8 % 44.1 % 53.3 % 46.0 %

Other 1.2 % 8.1 % 3.2 % 4.2 %

Vegetable Chi2= 0.008

Purchased 87.5 % 91.0 % 81.7 % 86.7 %

Produced 4.9 % 4.2 % 8.6 % 5.9 %

Other 7.5 % 4.8 % 9.7 % 7.4 %

Pulse

Purchased 98.4 % 98.7 % 98.5 % 98.5 %

Between the food security categories many of the ways in which people source foods are similar,

depending on local availably. Purchasing remained the prominent way for household to access

foods. One of the most primary differences was the access to cereals (rice), 38.5% of households

that were more food secure produced their needs as compared to 82.6% of insecure households

that were required to purchase staples, or only 16.3% were able to produce for the household.

Homestead gardening for vegetables appears to be low as they were mainly purchased, across all

categories.

3.2.5. Food Expenses

Figure 9: Food Expenses by LHZ

Household expenses were assessed against the household food security access scale. Food insecure

households spent similar amount on cereals when compared to food secure households (p=1.00).

The insecure households spent comparatively more (24.2%) of their average income on cereal

because of the lower income and spent less in all the other food groups such as meat, oil, dairy and

vegetables. Secure and mildly insecure households spent 14.2% of their monthly income on cereals.

050

01,

000

1,50

0

Agriculture Agro-Aquaculture Aquaculture

Cereal Meat/Fish Fruit/VegOil Cooking Fuel PulsesSugar Dairy Processed Food

Foo

d E

xpen

se (

BD

T)



Figure 10: Food Expenses by HFIAS

3.2.6. Household Expenses

Household expenses were looked at to understand the household expenditure patterns of

households in the three livelihood zones.

Households in all three livelihood zones spent a similar amount on food, health, education and debt

repayments (p =≥ 0.05).

Significant differences were seen when looking at households according to their Food Insecurity

category. Households that were food secure or only mildly food insecure, spent on average just

under 60% of the household income while food insecure households spent 64% of their income on

food. In monetary terms food insecure households spent 3,971 BDT in food leaving just over

2,000BDT for other household expenses, as compared to food secure households who spent 4,857

BDT on food who had 3,725 BDT of other household expenses.

050

01,

000

1,50

0Food Secure Mild Insecurity Moderate Insecurity Severe Insecurity

Cereal Meat/Fish Fruit/VegOil Cooking Fuel PulsesSugar Dairy Processed Food

Foo

d E

xpen

se (

BD

T)

Graphs by Food Security Category


Figure 11: Household Expenses by LHZ

Figure 12: Household Expenses by HFIAS Category

3.2.7. Land Access and Agriculture / Aquaculture

Households were asked whether they owned any land from which to cultivate food. Overall, 58.2%

of households identified that they were landless, with either no land holdings or less than 50 m2.

Each household within the three livelihood zones was asked if they were able to cultivate any crop in

the previous year. Overall approximately half (48.6%) of households were able to do cropping.


Food Health Education Loan Other



Food Health Education Loan Other



Significantly more households coming from the agricultural and aquaculture zones were able to do

cropping in 2012 (p=0.0002) with 52.9% and 53.8% respectively as compared with households from

the agro-aquaculture zone that with 38.7% of households planting and harvesting crops.

3.2.8. Land Tenure

The importance of land ownership or access is clearly understood to improve the household

nutritional status and the wealth with the household.

Figure 13: Land ownership by HFIAS

This figure includes households Household land ownership was classified by the Government Lands

Ministry according to the land size:

• Landless <5 decimals

• Marginalised 0 -49 decimals

• Small Farmer 50 – 249 decimals

• Medium Farmer 250 – 749 decimals

• Large Farmer >750 decimals

The average land ownership in three livelihood zones was 39.6 dm2 placing households in Satkhira as

marginalised farm owners.

In reality 58.2% of households were classed as landless and 19.1% classed as marginalised, this was

offset by very large farms owned by a small portion of the population. Households that were

considered medium and large farmers had on average 396.8 and 973.7 decimals of land, as

compared to landless and marginalises with 0.3 and 22.8 decimals of land.

Households with large farms (<750 decimals) were all located in the agro-aquaculture zone.

050

100

150

Food

Secur

e

Mild

Inse

curit

y

Mod

erat

e In

secu

rity

Sever

e In

secu

rity

Food

Secur

e

Mild

Inse

curit

y

Mod

erat

e In

secu

rity

Sever

e In

secu

rity

Food

Secur

e

Mild

Inse

curit

y

Mod

erat

e In

secu

rity

Sever

e In

secu

rity


Land

Siz

e (D

ecim

al)



Figure 14: Land ownership by LHZ

Figure 15: Land Ownership by Income Quartile

There is an obvious trend of land ownership depending and a significant relationship between the

income of the household and the households land size ownership (p=0.0000)

As the farm size increases so does the dietary diversity of the household. There was a significant

difference between the landless and marginalised famers and those who had small farms (p=0.027),

similarly households with small farms had poorer dietary diversity than households with medium or

big farms

020

040

060

080

01,

000

Land

less

Mar

ginali

sed

Small

_Far

mer

Med

ium_F

arm

er

Larg

e_Far

mer

Land

less

Mar

ginali

sed

Small

_Far

mer

Med

ium_F

arm

er

Larg

e_Far

mer

Land

less

Mar

ginali

sed

Small

_Far

mer

Med

ium_F

arm

er

Larg

e_Far

mer


Land

Siz

e (D

ecim

al)


020

4060

80

1st Quantile 2nd Quantile 3rd Quantile 4th Quantile

Land

siz

e (D

ecim

als)

Graphs by Income Quantile


3.2.9. Assets

Overall there was little difference in the assets owned by household throughout the three livelihood

zones. Jewellery was the most owned item. Jewellery is seen and a household investment and is able

to be sold in times of crisis. No households owned boats, even in the aquaculture zone.

Apart from Jewellery the distribution of small assets including phones was approximately 1 per

household. This is an important finding as it could facilitate emergency preparedness in terms of

alerting the population of possible disasters and then providing relief in terms of mobile phone

transfer of cash to household affected.

Figure 16: Asset Ownership by LHZ

Livelihood Zone influenced the amount of jewellery the household owned. There was no significant

difference again in the ownership of small assets including phone and radio, again reinforcing the

possible of preparing for possible disaster and influencing programs for households to be able to

recover from disasters in the short term (mobile phone money transfer if the markets are

functioning as assessed after a disaster).

02

46


Radio Phone Bicycle TelevisionJewellry Sewing Machine Irrigation Motorbike

Ass

set I

tem

s C

ount



Figure 17: Asset ownership by Income Quartile

What was seen across the livelihoods is not reflected when comparing households that are food

insecure.

Assets of the households that were moderately and severely food insecure were considerably less

than the other categories. Households, in order to be able to survive post disaster, often sell off

household goods in order to provide food for household members, as Satkhira has seen a

continuous poor situation following long term water-logging. Within waterlogged areas, households

had significantly less jewellery assets as compared to non-waterlogged areas (p=0.0472)

Figure 18: Asset Ownership by HFIAS

02

46

810



Ass

set I

tem

s C

ount


05

1015



Ass

set I

tem

s C

ount



3.2.10. Livestock

Households were asked to identify their livestock ownership. Households identified that poultry as

the most owned livestock. Overall there were 15.8% of the households included in the survey that

owned no livestock. Of these daily workers were the most households that did not own any

livestock, including chickens 44.7%. Other households included those households involved with

members that were fully employed, owned businesses. These households had above average

salaries when compared to daily workers which was significantly less than these households

(p=0.000). Sheep were no a livestock kept by agriculturalist when compared with the other

livelihoods.

Figure 19: Livestock ownership by LHZ

When comparing the households by the Food Insecurity category, there are only minimal differences

in the cattle, sheep and goats. What changes is the ownership of poultry at the household level.

Households considered moderate or severely food insecure had few poultry than the other group.

Of those households, poultry was considered as a cash crop, and not used for own consumption,

regardless of the food insecurity of the household. Households who were considered moderately or

severely food insecure 33% owned no poultry.

02

46

8


Cattle Sheep Goat Poultry

Live

stoc

k O

wne

rshi

p



Figure 20: Livestock ownership by HFIAS

3.2.11. Income Source

Households were asked to identify up to 4 different income sources, to be able to understand how

households were able to access income in order to meet the needs of the household. Households

were asked to identify the percentage of their household income received in each income

generating activity.

Overall, daily workers were the predominant source of the main income in Satkhira with 42% of the

households that their main income was daily labour.

Overall, Daily labourers scored the poorest outcomes when considering all the categories identified

in the survey.

Table 11: Household Income Source

% of

population

HFIAS Score FCS Score HDDS Income Land ownership

Agriculture 5.4% 5.3 60.1 5.1 6497 105.1

Aquaculture 7.1% 4.4 67.8 6.1 7734 133.5

Business 16.6% 5.8 58.9 5.5 8692 63.7

Casual 2.2% 10.2 53.8 4.7 4806 57.3

Daily worker 42.0% 11.2 47.3 4.7 5520 11.1

Employee 24.7% 7.3 55.0 5.5 7771 28.8

Livestock 0.9% 6.3 53.6 4.6 6251 43.9

Remittance 1.2% 3.2 55.3 5.9 6506 20.3

The primary source accounted for 80.4% of the household income.

Of the households surveyed, 75% of households had more than one income source to supplement

their income. 33.7% of households had 3 incomes and, 8.8% had 4 income sources.

05

10Food Secure Mild Insecurity Moderate Insecurity Severe Insecurity

Cattle Sheep Goat Poultry

Live

stoc

k O

wne

rshi

p



The sale of grown crops and the sale of livestock in Satkhira were not considered a primary source of

income but were seen as a way to supplement income.

As expected the income of the households increased as the number of income sources increased.

Table 12: Household income through multiple sources

IGA 1st

Incom

e

Percent

of

income

HH with

2nd

income

2nd

Income

Percent

of

Income

HH with

3rd

income

3rd

Incom

e

Percent

of

Income

HH

with 4th

Income

Total

Agriculture 4258 66% 11.7% 1780 28% 7.3% 1007 14% 7.3% 6497

Aquaculture 6026 76% 14.2% 1629 22% 7.6% 701 9% 7.6% 7734

Business 6577 76% 31.2% 2081 24% 15.8% 916 10% 15.8% 8692

Begging/Irregular 3645 82% 3.2% 1305 21% 1.8% 384 6% 1.8% 4806

Daily worker 4554 84% 69.7% 1135 19% 27.8% 547 9% 27.8% 5520

Employee 6342 82% 41.3% 1617 20% 17.4% 668 8% 17.4% 7771

Livestock 3497 58% 2.1% 1794 29% 1.4% 1183 16% 1.4% 6251

Remittance 5833 91% 1.8% 791 11% 1.4% 271 3% 1.4% 6506

Total 5405 80% 1508 21% 705 9% 6813

Households that relied on begging or irregular employment such as collection or disposed rubbish

and reselling had the lowest average household income. This group made up 2.1% of all surveyed

households.

Figure 21: Household Food Insecurity by Livelihood

Table 13: Income by main income source and Household Food Insecurity Category

IGA Food Secure Mild Insecurity Moderate Insecurity Severe Insecurity Total

Agriculture 8,098 7,683 5,644 3,269 6,497

Aquaculture 8,165 8,101 7,403 5,760 7,734

Business 10,538 8,650 7,644 7,019 8,692

Begging/Irregular 10,429 4,666 6,042 2,392 4,806

020

4060

8010

0

Agriculture Aquaculture Business Casual Daily worker Employee Livestock Remittence

Food Secure Mild Insecurirty Moderate Insecurity Severe Insecurity

Per

cent

age

of H

ouse

hold

s

Graphs by inc1cat


Daily worker 6,540 4,998 5,711 5,191 5,520

Employee 10,714 6,556 7,481 6,693 7,771

Livestock 4,098 7,094 2,500 6,251

Remittance 7,274 5,860 6,032 6,506

Total 9,398 6,906 6,500 5,567 6,813

3.2.12. Shocks

Shocks place additional stress on households, specifically enabling the household to access enough

cash to be able to feed the family.

Figure 22: Household Shocks in previous year

3.3 Water, Sanitation and Hygiene

3.3.1. Water Source

Information regarding drinking water access was recorded during the interview, as well as

seasonality of the water source. The figures below present the result of drinking water source during

summer par livelihood zone.

People living in the Livelihood zone” Agro Aquaculture” has a significant higher (66.7%) access to

Deep tube well, whereas the 2 others have higher access to Shallow tube well (34% to 25%).

Table 14: Water Source by Livelihood


PSF 0.6% 0% 7.6% 2.8%

Piped 0% 0.6% 1.4% 0.7%

Rainwater Harvesting 0.3% 0% 0% 0.1%

Tube well (deep) 34.0% 66.7% 25.1% 41.5%

Water loggingUnusually high level of human disease

Severely high livestock diseaseSeverely high crop pests and disease

Serious illness or accident of household memberSalinity increase

Regular floodsReduced income of a household member

Other (Specify)Low livestock/animal/aquaculture

LandslidesLack or loss of employment

Insufficient daily labourer activitiesHigh food prices

High costs of agricultural inputsFire

Drought/irregular rainsDeath of other household member

Death of a working household memberAquaculture disease

0 5 10 15 20 25Percentage


Tube well (shallow) 64.0% 32.7% 65.8% 54.5%

Unprotected Well 1.1% 0% 0% 0.4%

The choice of drinking water was asked to understand on which criteria people chose a particular

source for drinking. The quality of the water stands out as the main reason for selecting a source

despite the time needed to collect the water. However, a high percentage of households (25.3%)

remains dissatisfied with the quality of the water they use for drinking. Households are obviously

aware of the arsenic contamination in Satkhira. 88.9% of households not satisfied with the water

quality identified arsenic contamination as the main reason. The other reasons mentioned included

taste, smell, turbidity or salinity. Even so, more than 93% of these households do no treat water,

even acknowledging it was unsafe to drink. Overall, less than 5% of households treat water, even

though more than half of the households access water from unsafe water sources.

The reason for not doing any treatment should be further investigated, and hypothesis may be done

on the cost of fuel and water purification consumable access.

Table 15: Water selection by livelihood Zone


Reason for choosing Chi2= 0.001

Distance 22.0 % 6.2 % 31.1 % 20.0 %

Only Source 11.7 % 0.9 % % 3.9 % 5.6 %

Quality 38.0 % 76.2 % 35.9 % 49. 6 %

More convenient 27.7 % 13.7 &% 25.4 % 22.4 %

Other 0.6 % 3.0 % 3.7 % 2.5 %

It should be noted that for people in mono-culture areas, convenience and distance are also

considered when choosing a water source, whereas people living in Agro-aquaculture are mainly

concerned about the quality of water.

Cleaning of water containers for water collection and storage was assessed during the survey to

understand the hygiene practices related to water. More than half of the households (57.6%) of

households clean the water container each time water collection is made. The remainder clean

infrequently, or when container appears to be dirty. Some clean their containers with water only

(46.5%) and some use soap, sand or ash only (53.6%).

Looking at the time consumption for water presented below, it was observed that population living

in livelihood zone Agro-aquaculture spend significantly more time to collect their water.

Table 16: Water collection times


<15min 11.4% 1.8% 9.3% 7.6%

15-30min 22.6% 9.5% 21.2% 17.9%

30-60min 25.7% 25.0% 22.6% 24.4%

60-<120min 7.4% 39.0% 13.0% 19.5%

>120min 1.4% 6.6% 4.5% 4.1%

Source in compound 31.4% 18.2% 29.4% 26.4%

Above the 236 households spending more than 1 hour a day on water fetching, 56 % of these family

are form livelihood zone 2 “Agro Aquaculture”, and are actually collecting water from Deep Tube

well.


Table 17: Water Collection times


236 HH spending more than 1hour / day in water fetching

Other 0 1 0

Deep TW 25 133 29

Shallow TB 6 19 23

Those are consistent with the fact that people living in mono-cultures areas are relying on water

sources close to their home despite their unsatisfactory quality. No evidence or possible lead to this

situation could be highlighted during this survey and further investigations are needed before

providing any hypothesis.

Females were, above all, the ones responsible for this domestic task.

Table 18: Water Collection


Woman 89.7 % 92.9 % 90.9 % 91.1 %

Man 7.7 % 3.2 % 7.3 % 6.1 %

Girls 2.3 % 3.6 % 1.4 5 2.4 %

Boys 0.3 % 0.3 % 0.3 % 0.3 %

Information regarding water source for other needs than drinking or cooking purposes was also

collected during the survey. The main water source the population rely on is Surface water for > 60%

of the three livelihood zones. No differences were noticed with seasonality neither with

geographical situation

3.3.2 Sanitation facility

Interviewees were also requested to report on their sanitation facility and management. The figure

below presents the Sanitation facility available per Livelihood zone:

Figure 23: Hygienic sanitation by Livelihood Zone

0.1

.2.3

.4.5


Hygienic Unhygienic Open Defication



The use of latrine was assessed by the inter-quartile income range to identify if there were

differences between the income and the type of latrine used by households. There was an obvious

trend toward the use of hygienic latrines the more the household earned.

Figure 24: Hygienic sanitation by Income Quartile

This was reinforced by the households, about the most influencing factor for not having a hygienic

latrine, where they identified that cost the influencing factor. No Significant difference between

livelihood zones has been noticed. The potential barrier to better sanitation for people having access

to Unhygienic latrine or Open defecation practices was also recoded and result are presented in the

following table.

Table 19: Barriers to hygienic sanitation


Sanitation barrier

Cost 89.0 % 83.3 % 82.2 % 84.8 %

Don’t want 5.5 % 8.6 % 8.0 % 7.4 %

Children is scared 8.5 % 10.5 % 9.7 5 9.6 %

Lack of

Professional

3.6 % 7.4 % 6.8 % 6.0 %

Above the different barrier, the Cost to bear a Hygienic latrine is the most common barrier

expressed by more than 80 % of the population with Unhygienic sanitation condition.

Lack of professionalism to empty / clean the pits was probably the barrier for only 6 % of the

population. Keeping in mind that the latrine maintenance is a huge component of the Sanitation

condition; this low result for sanitation barrier may found 2 reasons;

• People with Hygienic sanitation facility don’t know about the maintenance process

• Some part of the population are actually doing the job

Actual data collected cannot allow making this kind of clarification, and need some field qualitative

approach for a better understanding of the sanitation barrier.

0.2

.4.6


Hygienic Unhygienic Open Defication



3.3.3 Waste practices

There was no particular difference regarding the practices for waste dumping per livelihood zone.

People mostly throw garbage anywhere and piling. However, it should be mentioned that

composting is done by an average 10% of the population and therefore could be encouraged.

Figure 25: Rubbish disposal by livelihood zone

Figure 26: Rubbish Disposal by income quartile

People with more revenue are piling more than the ones with less revenue. This could be linked to

garbage collection services that have to be paid for. The compost practice is also a bit more

practiced in the 4th quintile revenue. Those have more access to land and probably more use for

homestead gardening, for example. This is reinforced by the FSL findings that there is an obvious

relation between the income of the household and the households land size ownership.

020

4060

80


Compost Discard anywhere Pile

Per

cent

age


020

4060

80


Compost Discard anywhere Pile

Per

cent

age



3.3.4. Hygiene Practices

Hand washing practices within the households was assessed to understand the hygiene practices.

Hand washing is a proven public health approach to reduce illness.

Overall, across all three livelihood zones the hand washing practices were similar. There was a slight

increase in the hand-washing at critical junctures as the household income improved.

Table 20: Critical Junctures of hand washing by income quartiles

1st Quartile 2nd Quartile 3rd Quartile 4 Quartile

Agriculture

Before Cooking 31.5% 27.8% 36.1% 35.9%

After Defecating 91.0% 94.4% 91.8% 93.5%

After eating 79.8% 83.3% 71.1% 85.9%

Before Eating 91.0% 94.4% 91.8% 90.2%

After washing Babies Bottom (if child <5 58.3% 78.8% 57.6% 50.0%

Agro-Aquaculture

Before Cooking 33.7% 42.6% 49.3% 46.0%


After eating 83.7% 78.7% 79.7% 83.9%

Before Eating 76.7% 80.9% 75.4% 72.4%

After washing Babies Bottom (if child <5 30.8% 40.0% 27.6% 42.1%

Aquaculture

Before Cooking 29.8% 35.2% 34.4% 31.4%


After eating 83.3% 80.2% 82.8% 80.2%

Before Eating 94.0% 87.9% 89.2% 86.0%

After washing Babies Bottom (if child <5) 56.3% 51.2% 51.9% 60.7%

Households were then asked what they used to wash their hands. Overall, only 33.1% of households

use soap, ash or sand to wash their hands. The cost of soap was one of the main barriers (52.4%)

identified for households to be able to employ effective hand washing. Therefore it could be

assumed that these households identify washing hands with water only as sufficient to remove

pathogens or they do not know the link between dirty hands and contamination.

Table 21: Hand washing with soap by income quartile

1st Quartile 2nd Quartile 3rd Quartile 4 Quartile Total

Agriculture 32.6% 31.9% 42.3% 50.0% 39.8%

Agro-Aquaculture 15.1% 29.8% 26.1% 51.7% 31.0%

Aquaculture 21.4% 24.2% 29.0% 39.5% 28.5%

3.3.5 Health Education

Households were asked to provide insight on where they received most of the health information for

the household. It was seen that health professionals in Satkhira provide most of the information.

This was above health education session, which could be considered concerning, understanding the

widespread community mobilisation session currently being undertaken in Satkhira area. Posters

and other forms of communication rated low, this could be due to literacy levels among the villages

and wards.


Figure 27: Health and Nutrition Information sources

3.4 Nutrition and Child Health

Anthropometry on children was collected throughout the survey. Each household that had a child

that met the criteria for inclusion in anthropometry was measured using age, weight, height and

MUAC.

In total 319 children were included throughout the three livelihood zones. The low numbers of

children corresponded to the percentage of children in national census of less than 10% of the

population in Satkhira

Figure 27: Age Pyramid

020

4060

8010

0Agriculture Agro-Aquaculture Aquaculture Total

Poster Family Newspaper Group DiscussHouse of Worship Health Professional Television

Per

cent

age


40 20 0 20 40

48-59 months

36-47 months

24-35 months

12-23 months

6-11 month

Boys Girls


Table 22: Age and sex breakdown

Boys Girls Total Ratio

AGE (mo) no. % no. % no. % Boy:girl

6-11 16 50.0 16 50.0 32 10.0 1.0

12-23 48 55.8 38 44.2 86 27.0 1.3

24-35 38 49.4 39 50.6 77 24.1 1.0

36-47 28 45.2 34 54.8 62 19.4 0.8

48-59 36 58.1 26 41.9 62 19.4 1.4

Total 166 52.0 153 48.0 319 100.0 1.1

There was no preference identified in the survey for gender or in the age breakdown of the children.

3.4.1. Wasting - Weight-for- Height

Weight-for-height is a reflection of the child’s weight relative with their height. Wasting is the

process of recent significant weight loss which is usually the consequence of acute infection or

starvation.

The mean weight-for-height z-score for Satkhira was -0.85 SD ±0.98. The mean weight-for-height z-

score corresponds with the mean rate of GAM which was 12.3% of the children surveyed. The

standard deviation indicates that the distribution of weight-for-height scores collected is within the

upper and lower limits of acceptability of 0.8 and 1.2.

Table 23: Prevalence of acute malnutrition based on weight-for-height z-scores (and/or oedema) and by sex

All

n = 319%

(95% C.I.)

Boys

n = 166

(95% C.I.)

Girls

n = 153

(95% C.I.)

Prevalence of GAM 11.0 %

(8.0 - 14.9)

13.3 %

(8.9 - 19.30)

8.5 %

(5.0 - 14.0)

Prevalence of MAM 10.3 %

(7.5 - 14.2)

12.0 %

(7.9 - 17.9)

8.5 %

(5.0 - 14.0)

Prevalence of severe malnutrition 0.6 %

(0.2 - 2.3)

1.2 %

(0.3 - 4.3)

0.0 %

(0.0 - 2.4)

Through the use of weight-for-height as the indicators for children more boys than girls were

identified with acute undernutrition, remembering that the ratio of girls and boys was equal. In

addition, when analysis by age groups, older children were identified more frequently than younger

children in Satkhira as being acutely undernourished. These results reflect those identified in

December 2012.


Figure 28: Weight for Height Z-score distribution

compared to WHO standards

Figure 29: Progression of Weight for Height Z-score

over the age

In general children of a younger age closer to the 6 month inclusion criteria showed to have better

weight for height, then there is a dramatic decrease until 23 months where there is a levelling off.

There is a slight improvement of children and the related thinness of children but this does not fully

recover to a level where children are not at risk of acute malnutrition. This could be related to high

rates of breastfeeding up to 6 months of age and beyond, but as effective complimentary feeding is

essential for continued growth and development, children’s weight decrease.

3.4.2. Middle Upper Arm Circumference – MUAC

Mid-Upper Arm Circumference (MUAC) is used to identify children who are acutely undernourished.

The MUAC of children aged between 6 -59 months is relatively stable with small variations on size as

the child ages. MUAC of less than 115mm identifies a child as being at high risk of mortality

associated with acute undernutrition. MUAC is the only current admission criteria for nutritional

treatment for children less than 59 months in Bangladesh.

The use of MUAC as an indicator for acute malnutrition has shown results in acute malnutrition that

are considerably less than weight-for-height.

Table24: Prevalence of acute malnutrition based on MUAC cut off's (and/or oedema) and by sex

All

n = 319

Boys

n = 166

Girls

n = 153

Prevalence of global malnutrition

% (95% C.I.)

0.6 %

(0.2 - 2.3.)

0.6 %

(0.1 - 3.3)

0.7 %

(0.1 - 3.6)

Prevalence of moderate malnutrition

% (95% C.I.)

0.6 %

(0.2 - 2.3.)

0.6 %

(0.1 - 3.3)

0.7 %

(0.1 - 3.6)

Prevalence of severe malnutrition

% (95% C.I.)

0.0 %

(0.0 - 1.2.)

0.0 %

(0.0 - 2.3)

0.0 %

(0.0 - 2.4)

The results of this is that children with acute undernutrition will be identified significantly less when

only using MUAC as the sole measurement in Satkhira, excluding a large percentage of children who

meet the criteria for acute undernutrition when using weight-for-height.

-4 sd -3 sd -2 sd -1 sd mean 1 sd 2 sd 3 sd

GAM prevalence WHZ distribution WHO standards

-1.5

-1-.

50

.51

WH

Z-W

HO

0 6 12 18 24 36 48 59age in month


3.4.3. Underweight - Weight-for-age

Weight-for-age is a reflection of the body mass of the child, which is relative to the age of the child

and is influenced by both the height-for-age and the weight-for-height. Weight-for-age results

exceed the WHO threshold of being ‘Very High’, this could be interpreted as children having

recurrent episodes of acute malnutrition, which is also impacting on their linear growth, creating

serious levels of stunting. These children are at high risk of developmental (physical and mental)

delays, which could have significant impact through to adulthood.

Table 25: Prevalence of underweight based on weight-for-age z-scores by sex

All

n = 319

Boys

n = 166

Girls

n = 153

Prevalence of underweight

% (95% C.I.)

25.7 %

(21.2 - 30.8)

26.5 %

(20.4 - 33.7)

24.8 %

(18.7 - 32.2)

Prevalence of moderate underweight

% (95% C.I.)

21.0 %

(16.9 - 25.8)

22.3 %

(16.6 - 29.2)

19.6 %

(14.1 - 26.6)

Prevalence of severe underweight

% (95% C.I.)

4.7 %

(2.9 - 7.6)

4.2 %

(2.1 - 8.4)

5.2 %

(2.7 - 10.0)

Figure 30: Weight for Age Z-score distribution


Figure 31: Progression of Weight for Age Z-score

over the age

3.4.4. Stunting - Height-for-age

Height-for-age shows the linear growth of the children and a deficit shows the long-term cumulative

inadequacies of health or nutrition. This could be the result of recurrent bouts of illness or chronic

shortages of nutritious food and micro-nutrients required for adequate growth. Households with

only the ability to provide stables (rice) to children through high growth periods will exacerbate the

stunting of children. Once a child reaches an age of around 3 years the capacity to reverse stunting

become very unlikely and children will continue to below the growth curve. Creating a generation of

stunted people, which is particularly dangerous for stunted women who are pregnant. Short stature

is a major contributor to pregnancy and birth related complication including low-birth weight babies

as well as maternal and neonatal death.

Very high levels of stunting in children are seen in children in Satkhira. Such high levels again are

concerning for the long-term development of these children having long-term implications of their

development.

-4 sd -3 sd -2 sd -1 sd mean 1 sd 2 sd 3 sd 4 sd

Underweight prevalence WAZ distribution WHO standards

-2.5

-2-1

.5-1

-.5

0W

AZ

-WH

O

0 6 12 18 24 36 48 59age in month


This is particularly concerning as children within the first 18 months have a rapid decrease in their

weight-for-height, but there appears to be a flattening of the graph after 18 months which may

indicate that these children do not catch up their linear growth after initial stunting has occurred.

Table 26: Prevalence of stunting based on height-for-age z-scores and by sex

All

n = 318

Boys

n = 166

Girls

n = 152

Prevalence of stunting

% (95% C.I.)

29.8 %

(25.0 - 35.0)

30.7 %

(24.2 - 38.1)

28.8 %

(22.2 - 36.4)

Prevalence of moderate stunting

% (95% C.I.)

22.6 %

(18.3 - 27.5)

22.3 %

(16.6 - 29.2)

22.9 %

(16.9 - 30.1)

Prevalence of severe stunting

% (95% C.I.)

7.2 %

(4.9 - 10.6)

8.4 %

(5.1 - 13.7)

5.9 %

(3.1 - 10.8)

Figure 32: Height for Age Z-score distribution


Figure 33: Progression of Height for Age Z-score

over the age

During the survey caretakers were asked the health of all children 6-59 months. Caretakers were

asked to ask to recall any acute infection in the 2 week preceding the survey.

Overall, 73.0% of the children included in the survey reported having an illness in the 2 weeks prior

to the survey. Of those children reporting illness fever was reported the most frequently with 42.5%.

The rates of diarrhoea reported were encouraging, understanding that the survey was conducted

during the rainy season.

Fever can be seen as a symptom of other acute illnesses, therefore other acute infections not

reported. Additionally fever can be associated with periods of growth including the eruption of teeth

in infants.

Table 27: Child Illness reported in previous 2 weeks

Illness Total

Illness report in previous 73.0%

Diarrhoea 4.3%

Fever 42.5%

Acute Respiratory Infection 10.3%

-4 sd -3 sd -2 sd -1 sd mean 1 sd 2 sd 3 sd 4 sd

Stunting prevalence WAZ distribution WHO standards

-2.5

-2-1

.5-1

-.5

HA

Z-W

HO

0 6 12 18 24 36 48 59age in month


3.4.5. Young Child and Infant Care practices6

Young Child and Infant Care practices have been assessed for children aged 6 to 23 months,

representing a total of 119 children. Due to the limited number of children eligible for this section,

results presented below are to be considered for information, keeping in mind that confidence

interval are width, forbidding the use of this result for further analysis.

Based on survey questionnaire, following core indicators have been highlighted;

- Child Meal Frequency,

- Infant Diet Diversity Score (IDDS),

- Minimal Acceptable Diet,

3.4.6. Child Meal Frequencies,

The acceptable number of meals for a child is dependent on the age of the child and whether the

child continues to be breastfed. The number of meals does not include breastfeeding as meals are

considered complimentary to breastfeeding.

Table 28: Meal Frequency of children 6-23 months

Minimum Meal

Frequency

Breastfed Non - Breast fed

6 to 8 months

n = 13

9 to 23 months

n= 102

6 to 23 months

n=4

Minimum meal / day 2 3 4

Number of child having

minimal acceptable

Meal Frequency

13

100%

83

81.3%

2

50%

3.4.7. Infant Diet Diversity Score (IDDS)

Proportion of children 6–23 months of age who receive foods from 4 or more food groups above the

following food group: Cereal/Root, Legumes/Nuts, Dairy products, Flesh food, Eggs, Vitamin A rich

fruit, Other Fruit and Vegetable.

The dietary diversity of children 6-23 months over the preceding 24 hours was extremely limited,

with children only receiving 3 food groups out of the 7 in the past 24 hours. Due to this period in

childhood being a phase of rapid growth, the lack of complementary feeding of children in this age

bracket has the potential to contribute to poor mental and physical development and exposed the

child to episodes of acute malnutrition.

The lack of food and illness again raises the child’s risk of acute malnutrition. This is a concerning

practices or household situation where children do not have sufficient nutrient input to combat

infection/illness.

Table 29: IDDS of children 6-23 months

IDDS 6 to 11 months

n=32

12 to 17 months

n= 28

18 to 23 months

n=59

Total

n=119

Unacceptable 27 20 37 84

Acceptable 5

15.6%

8

28.5%

22

37.2%

35

29.4%

6 Indicators for assessing infant and young child feeding practices: conclusions of a consensus meeting held 6–8 November 2007 in Washington D.C., USA.


Figure 34: IDDS and Child Acceptable Diet

3.4.8. Minimal Acceptable Diet

Acceptable diet is an amalgamation of three factors including breastfeeding, individual dietary

diversity and meal frequency of children aged between 6 and 23 months of age.

In Satkhira the proportion of children receiving an acceptable diet is low. This is seen throughout the

three age categories of children is a very low rate of acceptable diet for children 6-23 months of age

with less than 30% of children receiving this.

The very low rate of acceptable diet places the child at risk of malnutrition and infection. While

many children may not develop severe acute malnutrition this would increase the risk of stunting

due to long term inadequate diet.

Table 30: Acceptable Diet

6 to 8 months

n=13

9 to 11 months

n= 19

12 to 17

months

n= 28

18 to 23

months

n=59

Total

n=119

Acceptable MF 13 14 23 48 98

86.7 %

Acceptable IDDS 1 4 8 22 35

29.4 %

Acceptable diet 1

7.6%

4

21%

8

28.5%

17

28.8%

30

25.2%

92

7.7

79

21

71

29

63

37

020

4060

8010

0

6 to 8 months 9 to 11months 12 to 17 months 18 to 23months

IDDS category per Infant categrory

Unacceptable diet divertsity Acceptable diet diversity


Discussion

Methodology

ACF Bangladesh has conducted integrated SMART surveys and in-depth FSL, WaSH and nutrition

survey. It appears that “bigger is not always better”, i.e. collecting too many data is not always

better. The quality of the SMART survey is not lower of the in-depth survey.

First, it takes time to collect the data, both the interviewers and the interviewees become tired and

the quality of the information collected decreased. Due to the amount of data collected, it is more

time consuming to enter the data. Then, it is not possible to analyse the data on Excel because of the

volume of the data. Finally, there are too many variables and data to analyse.

In term of cost-efficiency and speed to finalise the survey, the integrated SMART survey should be

prioritised.

Demographics

The female to male ratio of heads of households was 0.06:1. Only 6%of heads of households are

female. They earned significantly less than men with an average 4,969 BDT whereas male head of

household earned 6,928 BDT/month. The average household size was 4.4 people with only 7.6% of

the household members were being less than 5. The survey shows that the larger households are

able to improve their income, dietary diversity and their food insecurity status.

The Dependency Ratio for all livelihoods was 0.8, which indicates that within the households there

are more people able to earn money than those that that do not. Households with women as the

head of the household had no difference in the household dietary diversity or food insecurity scale,

possibly due to the lower number of dependants within the household.

Food Security & Livelihoods

The measure of food security is complex. Food security is considered for individual, household,

community and country development. In developing countries nutrition and health status and the

development of children depends on the inputs and household food security. FSL was measured by

three indicators: HFIAS, HDDS and FCS.

HFIAS

Overall 72.9% of households in the 4 upazilas were concerned about food at some point in the

previous 4 weeks to the survey. More than 80.4% of households provided food to the family that

they considered lesser quality, due to inability to access quality food due to household income and

almost 60% (58.0%) of households expressed serious concerns about not having enough food to eat

in the previous 4 weeks.

26.6% of the households with children less than 5 years were consider severely food insecure,

meaning that these households had insufficient food to feed all household members including

children. Households with pregnant women showed similar results to children less than 5 years with

25.9% of the houses being severely food insecure. These results show that these households were

increasingly at risk for members to be undernourished at critical times of growth and development.

Households located in the single income livelihood zones of agriculture and aquaculture, showed to

have a higher proportion of households that were either moderately or severely food insecure.

There is a relation between income and HFIAS.


FCS

The prevalence of household having “Poor” food consumption is very limited, representing a total

average of 2.1%, while 29.0% are having “Borderline” food consumption score. Livelihood zone

Agro-Aquaculture has a significant higher food consumption score (p<0.001) compared with the

monoculture livelihood zone. There is a relation between FCS and income. Households having a

lower income have a poor FCS.

Almost all households in Satkhira eat cereal and oil on daily basis. Except for Meat/Fish and

Vegetable food groups, the global figure of weekly food consumption is similar from livelihood zone.

Agro-aquaculture livelihood zone has a significant higher FCS which can be explained by weekly

consumption of both vegetable and meat/fish food groups.

HDDS

There was a significant difference between the mono-cultural zones and the Agro-Aquaculture zone

(p=0.000). Both agriculture and Aquaculture had HDDS Scores less than 5, while Agro-Aquaculture

had a mean HDDS exceeding the overall mean.

Other food security related questions

Households in the surveyed area were required to purchase their food. Cereal (rice) is the main

staple of the Satkhira area. Food insecure households spent similar amount on cereals when

compared to food secure households. The insecure households spent comparatively more (24.2%) of

their average income on cereal because of the lower income and spent less in the other food groups.

Households in all three livelihood zones spent a similar amount on food, health, education and debt

repayments. Households that were food secure or only mildly food insecure, spent on average just

under 60% of the household income while food insecure households spent 64% of their income on

food. Daily workers were the predominant source of the main income in Satkhira with 42% of the

households that their main income was daily labour. They scored the poorest outcomes when

considering all the categories identified in the survey. 75% of households had more than one income

source to supplement their income.

Overall, 58.2% of households were landless and 19.1% classed as marginalised. Approximately half of

households were able to do cropping. There is an obvious trend of land ownership depending and a

significant relationship between the income of the household and the households land size

ownership. As the farm size increases so does the dietary diversity of the household.

Jewellery was the most owned item. Jewellery is seen and a household investment and is able to be

sold in times of crisis. Assets of the households that were moderately and severely food insecure

were considerably less than the other categories. Households in order to be able to survive post

disaster often sell off household goods. Within waterlogged areas, households had significantly less

jewellery assets as compared to non-waterlogged areas.

Poultry is the most owned livestock. 15.8% of the households owned no livestock. Of these daily

workers were the most households that did not own any livestock, including chickens 44.7%.

Water, Sanitation and Hygiene

The type of drinking water source per livelihood zone is very different. In mono culture areas, people

prefer close shallow tube-wells than deep tube wells even if they are recognized as more

contaminated. Further investigations are needed probably in link with livelihood activities to

understand why people use shallow tube-wells.

There is clearly a link between household income and access to sanitation; despite no particular

evidence could be found per livelihood zone. Unhygienic latrines and open defecation are most


common for the first and second quintile of Households Income. The main barrier to access to

sanitation is logically the cost of the latrine.

Composting is a minor practice done in all livelihood zones. There is potential for development.

Piling is more done by the 4th quintile household income, whereas the first quintile mainly throws

their garbage anywhere. The cost for garbage collection and the need to pay small fees could be an

explanation and should be confirmed by further study.

Hand-washing at critical junctures is slightly similar in all livelihood zones. However, using soap for

hand-washing is more likely to be done for household in the 4th quintile than the 1st quintile of

income.

All livelihood zones share the same exposure to hygiene promotion with the main messages being

given by health workers. It must be noticed that mono culture zone favour as well groups discussion.

So, a key strategy to enlarge the audience and the impact of hygiene promotion will be to

strengthen the health workers’ knowledge on hygiene practices and promotion.. Any behaviour

changes activities in Satkhira district must then involve the health workers to ensure long term

continuity in the promotion of safe practices.

Knowing the strong causal link between a healthy environment and nutrition, improving health

needs the development of WaSH facilities access. However, from the study, poverty and livelihood

opportunities are clearly the main barriers for people to benefit from safe water and safe

environment.

Nutrition

While the current and the previous prevalence rates of undernutrition do not show a significant

increase, the anthropometric indicators show there is a significant difference between the

December 2012 Integrated SMART Survey where mean weight-for height of children has decreased

from (p=0.023).

As there is no significant difference between the mean weight-for-age and the mean height-for-age,

it could be assumed that this confirms there is a worsening problem with the acute weight loss of

children in this period of the year.

Table 31: Comparison of Nutrition Indicator between 2 surveys

SMART

Dec 2012

FSL-WASH

Sept-Oct 2013

Number of Children 526 319

Prevalence of GAM % (95% C.I.) 7.8% (5.8 – 10.5) 11.0 % (8.0 - 14.9)

Mean weight-for-height -0.701 SD± 1.0 -0.855 SD± 0.98

Prevalence of SAM % (95% C.I.) 1.1 % (0.5 - 2.8) 0.6 % (0.2 - 2.3)

Prevalence of Underweight % (95% C.I.) 23.6 % (19.3 – 28.6) 25.7 % (21.2 - 30.8)

Mean weight-for-age -1.31 SD± 1.1 -1.38 SD ±0.96

Prevalence of Stunting % (95% C.I.) 33.7 % (28.8 - 38.9) 29.8 % (25.0 - 35.0)

Mean height-for-age -1.49 SD±1.1 -1.43 SD± 1.1

Based on the WHO classification the nutritional situation in Satkhira in September/October 2013 can

be defined as;

Table 32: Nutrition Indicators according to WHO classification

Indicator WHO classification

Prevalence of Global Acute Malnutrition – GAM (<-2 z-score or oedema) SERIOUS

Prevalence of Underweight (<-2 z-score) HIGH


Prevalence of Stunting (<-2 z-score) HIGH

The reasoning being hind the increase in the rates of undernutrition needs to be interpreted within

the seasonal and contextual changes since December 2012. The timing of the current survey

corresponds to the lean season (pre-harvest) in Satkhira, when households have their lowest levels

of disposable cash for the purchase of food sources other than normal staples. This is reflected in

lack of variety of foods consumed, especially in poorer households where the majority of

undernourished children have been identified.

What remains both concerning and encouraging at the same time is that in the context of broad

based nutrition specific and sensitive programing, the prevention of moderate malnutrition is

limited. At the same time we could assume by the low levels of severe acute malnutrition that once

people have been incorporated into program activities children are being prevented from becoming

severely malnourished. Whether this can be attributed solely to the combined activities of ACF and

WFP cannot be assessed. What is clear is that children are not becoming severely malnourished

increasing their risk of morbidity or mortality, this is a good thing.

Unfortunately, the scope of children being admitted into these programs appears to be very low

compared to the real need if we simply compare the rates of undernutrition by weight-for-height

and those by MUAC, which currently is the national protocol for admission into nutritional treatment

and prevention programs.

Appropriate child feeding again remains both concerning and encouraging. The level of children

continued to be breastfed is encouraging. What remains unclear due to the method of investigation

is whether the breastfeeding continues on demand or only sporadically. Unfortunately the high rates

of continued breastfeeding are not compensating for the poor diet of children during rapid growth

phases as highlighted but the high levels of stunting and underweight in children less than 23

months. This coupled with high rates of illness and low dietary diversity and low levels of acceptable

diet continue to place children at high risk of poor physical and mental development, continuing the

cycle.

Children that are most likely to be excluded from treatment according to the data from this survey

and which correlates with the December 2012 survey results, is that older children and boys are

being excluded from access to treatment of undernutrition due to the use of a single anthropometric

indicator (MUAC) as the admission criteria into outpatient treatment.

Data was collected immediately before widespread increase in waterlogging occurred, therefore it

should be recognized that a worsening on the nutrition situation as previously witness has a strong

likelihood of reoccurring. The preliminary results of this survey should have been used as a warning

for stakeholders to prepare themselves for an appropriate response to increase household

vulnerability.

This could be compounded by the reality that within these areas there is very limited nutrition

sensitive programming (WaSH and FSL) in the area. Which again from previous on the ground

experience, indicates that the possibility of a worsening of the situation is likely, driving people back

into a level of poverty of household food insecurity that will continue a cycle of undernutrition and

poor development for the area.


Conclusion

The conclusion from the current survey is that Satkhira remains in a situation of instability, where

households, especially those coming from the poorer sections remain in a precarious situation of

household food and nutrition security.

The current survey indicated that people in Satkhira continue to lack the access to land for crop

generation and household income coming from agriculture of aquaculture. The predominant source

of household income remains being daily labour and the primary source of food comes from

purchasing. These houses remain the most vulnerable within the area.

This situation places households at a high level of risk of nutrition and food security. While the

survey does not have the capacity to indicate where these factors are contributing to the levels of

undernutrition witnessed in the survey. What we do see is households that rely on daily labour, are

landless and continue to have household incomes less than the median have the highest numbers of

children who are acutely undernourished.

Household remain reliant on poor levels of access to clean water and hygienic sanitation. This

coupled with poor hygiene practices have the potential to contribute to high levels of water-borne

disease in Satkhira.

Considering the data collection was concluded at the time when a recurrent water-logging was being

witnessed, there could be a worsening of the situation for households in the affected areas,

especially if the water-logging remains in place during critical periods including harvest and planting.

This is the time of the year where households that rely on daily labour are able to access a higher

level of household income.

ACF as part of its actions will in the coming months conduct a Nutrition Causal Analysis that will

enable ACF and WFP plus other key stakeholders to be able to identify the causes of undernutrition

in Satkhira. This approach will enable to agencies to be able to target the households identified in

this survey with actions that will be aimed to prevent and reduce rates of undernutrition among

these households.


Recommendations

1. Nutrition Specific programming to address moderate and severe acuter malnutrition should

continue in Satkhira considering the high rates of GAM

2. Efforts should be made for the Ministry of Health (MoH) to take over the treatment of

severe acute malnutrition through CMAM and within the community clinics.

3. Efforts should be made to restructure the screening activities of the community volunteers

to ensure that coverage is broadened and not merely the number of children to be screened

is the primary target.

4. Advocacy for the change of admission criteria to include weight-for-height in CMAM at the

national level to ensure that children requiring treatment are admitted and that children,

specifically boys and older children are not excluded from treatment

5. Evidence needs to be collected to identify what outcomes are associated with children being

excluded from nutrition treatment programs who do not fall within the MUAC thresholds for

treatment.

6. Identify the specific barriers for caretakers to provide infants with appropriate child feeding

practices.

7. Implement long-term programming to facilitate behaviour change at the household level in

terms of maternal and child nutrition

8. Review and strengthen IYCF programs aimed at ensuring dietary diversity of infants and

feeding practices.

9. Develop and strengthen homestead food production to improve the dietary diversity,

especially through lean periods.

10. Develop and strengthen Income Generating Activities (IGA) and agro-based activities in

order to have year round activity during all seasons. This will enable the most vulnerable

households (landless, daily laborers, etc.) to have sufficient income during the lean period

and strengthen their capacity to face external shocks

11. Increase access to arsenic free water, through deep-tube or alternative water

harvesting/collection methods.

12. Increase safe water access combined with hygienic sanitation for low level socio-economic

household

13. Address the hygiene practices of the communities, through using hygiene promotion

activities rising soap (or adequate alternative) usage

14. Annual integrated SMART survey to be conducted to identify changes in the evolution of

nutrition, child and maternal care, food security and WaSH situation in Satkhira.


References

1. Webb P, Coates J, Frongillo EA, Rogers BL, Swindale A, Bilinsky P: Measuring household food

insecurity: why it's so important and yet so difficult to do. The Journal of nutrition 2006,

136(5):1404S-1408S.

2. Schiff M, Valdes A: Poverty, food intake, and malnutrition: implications for food security in

developing countries. American journal of agricultural economics 1990, 72(5):1318-1322.

3. Saha KK, Tofail F, Frongillo EA, Rasmussen KM, Arifeen SE, Persson LA, Huda SN, Hamadani JD:

Household food security is associated with early childhood language development: results from a

longitudinal study in rural Bangladesh. Child Care Health Dev 2010, 36(3):309-316.


Annex

Annex 1: Plausibility Report

Overall data quality

Criteria Flags* Unit Excel Good Accept Problematic Score

Missing/Flagged data Incl % 0-2.5 >2.5-5. 0 >5.0-7.5 >7.5

(% of in-range subjects) 0 5 10 20 0 (0.3 %)

Overall Sex ratio Incl p >0.1 >0.05 >0.001 <=0.001

(Significant chi square) 0 2 4 10 0 (p=0.501)

Overall Age distrib Incl p >0.1 >0.05 >0.001 <=0.001

(Significant chi square) 0 2 4 10 2 (p=0.069)

Dig pref score - weight Incl # 0-7 8-12 13-20 > 20

0 2 4 10 0 (7)

Dig pref score - height Incl # 0-7 8-12 13-20 > 20

0 2 4 10 2 (9)

Dig pref score - MUAC Incl # 0-7 8-12 13-20 > 20

0 2 4 10 2 (10)

Standard Dev WHZ Excl SD <1.1 <1.15 <1.20 >=1.20

. and and and or

. Excl SD >0.9 >0.85 >0.80 <=0.80

0 2 6 20 0 (0.94)

Skewness WHZ Excl # <±0.2 <±0.4 <±0.6 >=±0.6

0 1 3 5 0 (0.08)

Kurtosis WHZ Excl # <±0.2 <±0.4 <±0.6 >=±0.6

0 1 3 5 0 (-0.08)

Poisson dist WHZ-2 Excl p >0.05 >0.01 >0.001 <=0.001

0 1 3 5 0 (p=0.586)

Timing Excl Not determined yet

0 1 3 5

OVERALL SCORE WHZ = 0-9 10-14 15-24 >25 6 %

The overall score of this survey is 6 %, this is excellent.

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