water, sanitation and hygiene, food security & livelihood and nutrition survey … · 2020. 4....
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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.
FSL – WaSH, nutrition survey Satkhira October 2013 Page 2
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
FSL – WaSH, nutrition survey Satkhira October 2013 Page 3
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
FSL – WaSH, nutrition survey Satkhira October 2013 Page 4
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
FSL – WaSH, nutrition survey Satkhira October 2013 Page 5
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
FSL – WaSH, nutrition survey Satkhira October 2013 Page 6
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
FSL – WaSH, nutrition survey Satkhira October 2013 Page 7
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
FSL – WaSH, nutrition survey Satkhira October 2013 Page 8
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-.
FSL – WaSH, nutrition survey Satkhira October 2013 Page 9
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.
FSL – WaSH, nutrition survey Satkhira October 2013 Page 10
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.
FSL – WaSH, nutrition survey Satkhira October 2013 Page 11
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%
FSL – WaSH, nutrition survey Satkhira October 2013 Page 12
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%
FSL – WaSH, nutrition survey Satkhira October 2013 Page 13
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.
FSL – WaSH, nutrition survey Satkhira October 2013 Page 14
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
FSL – WaSH, nutrition survey Satkhira October 2013 Page 15
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
FSL – WaSH, nutrition survey Satkhira October 2013 Page 16
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
FSL – WaSH, nutrition survey Satkhira October 2013 Page 17
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.
FSL – WaSH, nutrition survey Satkhira October 2013 Page 18
- 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.
FSL – WaSH, nutrition survey Satkhira October 2013 Page 19
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.
FSL – WaSH, nutrition survey Satkhira October 2013 Page 20
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)
FSL – WaSH, nutrition survey Satkhira October 2013 Page 21
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
Agriculture Agro-aquaculture Aquaculture Total
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
Agriculture Agro-aquaculture Aquaculture Total
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
Agriculture Agro-aquaculture Aquaculture Total
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
FSL – WaSH, nutrition survey Satkhira October 2013 Page 22
>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
Agriculture Agro-aquaculture Aquaculture Total
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)
FSL – WaSH, nutrition survey Satkhira October 2013 Page 23
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.
FSL – WaSH, nutrition survey Satkhira October 2013 Page 24
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
FSL – WaSH, nutrition survey Satkhira October 2013 Page 25
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.
FSL – WaSH, nutrition survey Satkhira October 2013 Page 26
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
Graphs by Livelihood Zone
01
23
45
67
Agriculture Agro-Aquaculture Aquaculture Total
Cereal/Tuber Oil Vegetable Meat/FishPulses/Legume Dairy Sugar Fruit
Day
s C
onsu
med
Graphs by Livelihood Zone
FSL – WaSH, nutrition survey Satkhira October 2013 Page 27
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
Agriculture Agro-Aquaculture Aquaculture Total
Per
cent
age
Dietary Diversity (Food Groups Consumed)Graphs by Livelihood Zone
FSL – WaSH, nutrition survey Satkhira October 2013 Page 28
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
Agriculture Agro-Aquaculture Aquaculture Total
Cereal Oil Vegetables Tuber Fish PulsesDairy Sugar Eggs Meat Fruit Legumes
Per
cent
age
of H
ouse
hold
s
Graphs by Livelihood Zone
FSL – WaSH, nutrition survey Satkhira October 2013 Page 29
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
Agriculture Agro-Aquaculture Aquaculture Total
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
Agriculture Agro-Aquaculture Aquaculture Total
HD
DS
(fo
od g
roup
s co
nsum
e)
Graphs by Livelihood Zone
FSL – WaSH, nutrition survey Satkhira October 2013 Page 30
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)
Graphs by Livelihood Zone
FSL – WaSH, nutrition survey Satkhira October 2013 Page 31
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
FSL – WaSH, nutrition survey Satkhira October 2013 Page 32
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.
Agriculture Agro-Aquaculture Aquaculture
Food Health Education Loan Other
Graphs by Livelihood Zone
Food Secure Mild Insecurity Moderate Insecurity Severe Insecurity
Food Health Education Loan Other
Graphs by Food Security Category
FSL – WaSH, nutrition survey Satkhira October 2013 Page 33
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
Agriculture Agro-Aquaculture Aquaculture
Land
Siz
e (D
ecim
al)
Graphs by Livelihood Zone
FSL – WaSH, nutrition survey Satkhira October 2013 Page 34
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
Agriculture Agro-Aquaculture Aquaculture
Land
Siz
e (D
ecim
al)
Graphs by Livelihood Zone
020
4060
80
1st Quantile 2nd Quantile 3rd Quantile 4th Quantile
Land
siz
e (D
ecim
als)
Graphs by Income Quantile
FSL – WaSH, nutrition survey Satkhira October 2013 Page 35
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
Agriculture Agro-Aquaculture Aquaculture Total
Radio Phone Bicycle TelevisionJewellry Sewing Machine Irrigation Motorbike
Ass
set I
tem
s C
ount
Graphs by Livelihood Zone
FSL – WaSH, nutrition survey Satkhira October 2013 Page 36
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
1st Quantile 2nd Quantile 3rd Quantile 4th Quantile
Radio Phone Bicycle TelevisionJewellry Sewing Machine Irrigation Motorbike
Ass
set I
tem
s C
ount
Graphs by Income Quantile
05
1015
Food Secure Mild Insecurity Moderate Insecurity Severe Insecurity
Radio Phone Bicycle TelevisionJewellry Sewing Machine Irrigation Motorbike
Ass
set I
tem
s C
ount
Graphs by Food Security Category
FSL – WaSH, nutrition survey Satkhira October 2013 Page 37
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
Agriculture Agro-Aquaculture Aquaculture Total
Cattle Sheep Goat Poultry
Live
stoc
k O
wne
rshi
p
Graphs by Livelihood Zone
FSL – WaSH, nutrition survey Satkhira October 2013 Page 38
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
Graphs by Food Security Category
FSL – WaSH, nutrition survey Satkhira October 2013 Page 39
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
FSL – WaSH, nutrition survey Satkhira October 2013 Page 40
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
Agriculture Agro-aquaculture Aquaculture Total
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
FSL – WaSH, nutrition survey Satkhira October 2013 Page 41
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
Agriculture Agro-Aquaculture Aquaculture Total
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
Agriculture Agro-aquaculture Aquaculture Total
<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.
FSL – WaSH, nutrition survey Satkhira October 2013 Page 42
Table 17: Water Collection times
Agriculture Agro-Aquaculture Aquaculture
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
Agriculture Agro-Aquaculture Aquaculture Total
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
Agriculture Agro-Aquaculture Aquaculture Total
Hygienic Unhygienic Open Defication
Graphs by Livelihood Zone
FSL – WaSH, nutrition survey Satkhira October 2013 Page 43
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
Agriculture Agro-Aquaculture Aquaculture Total
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
1st Quantile 2nd Quantile 3rd Quantile 4th Quantile
Hygienic Unhygienic Open Defication
Graphs by Income Quantile
FSL – WaSH, nutrition survey Satkhira October 2013 Page 44
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
Agriculture Agro-Aquaculture Aquaculture Total
Compost Discard anywhere Pile
Per
cent
age
Graphs by Livelihood Zone
020
4060
80
1st Quantile 2nd Quantile 3rd Quantile 4th Quantile
Compost Discard anywhere Pile
Per
cent
age
Graphs by Income Quantile
FSL – WaSH, nutrition survey Satkhira October 2013 Page 45
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 Defecating 73.3% 85.1% 81.2% 80.5%
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 Defecating 85.7% 83.5% 87.1% 87.2%
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.
FSL – WaSH, nutrition survey Satkhira October 2013 Page 46
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
Graphs by Livelihood Zone
40 20 0 20 40
48-59 months
36-47 months
24-35 months
12-23 months
6-11 month
Boys Girls
FSL – WaSH, nutrition survey Satkhira October 2013 Page 47
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.
FSL – WaSH, nutrition survey Satkhira October 2013 Page 48
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
FSL – WaSH, nutrition survey Satkhira October 2013 Page 49
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
compared to WHO standards
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
FSL – WaSH, nutrition survey Satkhira October 2013 Page 50
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
compared to WHO standards
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
FSL – WaSH, nutrition survey Satkhira October 2013 Page 51
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.
FSL – WaSH, nutrition survey Satkhira October 2013 Page 52
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
FSL – WaSH, nutrition survey Satkhira October 2013 Page 53
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.
FSL – WaSH, nutrition survey Satkhira October 2013 Page 54
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
FSL – WaSH, nutrition survey Satkhira October 2013 Page 55
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
FSL – WaSH, nutrition survey Satkhira October 2013 Page 56
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.
FSL – WaSH, nutrition survey Satkhira October 2013 Page 57
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.
FSL – WaSH, nutrition survey Satkhira October 2013 Page 58
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.
FSL – WaSH, nutrition survey Satkhira October 2013 Page 59
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.
FSL – WaSH, nutrition survey Satkhira October 2013 Page 60
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.