Integrated SMART Survey Nutrition, Care Practices, Food Security and Livelihoods, Water Sanitation and Hygiene. Sitakunda Upazila

Transcription

1 Integrated SMART Survey Nutrition, Care Practices, Food Security and Livelihoods, Water Sanitation and Hygiene Sitakunda Upazila Chittagong District Bangladesh January 2018 Funded By

2 Acknowledgement Action Against Hunger conducted Baseline Integrated SMART Nutrition survey in Sitakunda Upazila in collaboration with Institute of Public Health Nutrition (IPHN). Action Against Hunger would like to acknowledge and express great appreciation to the following organizations, communities and individuals for their contribution and support to carry out SMART survey: District Civil Surgeon and Upazila Health and Family Planning Officer for their assistance for successful implementation of the survey in Sitakunda Upazila. Action Against Hunger-France for provision of emergency response funding to implement the Integrated SMART survey as well as technical support. Leonie Toroitich-van Mil, Health and Nutrition Head of department of Action Against Hunger- Bangladesh for her technical support. Mohammad Lalan Miah, Survey Manager for executing the survey, developing the survey protocol, providing training, guidance and support to the survey teams as well as the data analysis and writing the final survey report. Action Against Hunger Cox s Bazar for their logistical support and survey financial management. Mothers, Fathers, Caregivers and children who took part in the assessment during data collection. Action Against Hunger would like to acknowledge the community representatives and community people who have actively participated in the survey process for successful completion of the survey. Finally, Action Against Hunger is thankful to all of the surveyors, supervisor and Survey Manager for their tremendous efforts to successfully complete the survey in Sitakunda Upazila. Statement on Copyright Action Against Hunger Action Contre la Faim Action Against Hunger (ACF) is a non-governmental, non-political and non-religious organization. Unless otherwise indicated, reproduction is authorized on condition that the source is credited. If reproduction or use of texts and visual materials (sound, images, software, etc.) is subject to prior authorization, such authorization will render null and void the above-mentioned general authorization and will clearly indicate any restrictions on use.

3 Acronyms ACF ARI BBS BDHS CI CMAM DPHE ENA EPI FAO FSL GAM HAZ HDDS HFA HH HYSAWA IDDS IPC IPHN IYCF MAM MEB MHCP MoHFW MUAC NGO ODF OTP PLW PPS rcsi SAM SD SFP SMART U5 WaSH WAZ WFH WFP WHO WHZ Action Contre La Faim Action Against Hunger Acute Respiratory Infection Bangladesh Bureau of Statistics Bangladesh Demographic and Health Survey Confidence Interval Community Based Management of Acute Malnutrition Department of Public Health Engineering Emergency Nutrition Assessment Expanded Program on Immunization Food and Agriculture Organization Food Security and Livelihoods Global Acute Malnutrition Height-for-Age z-score Household Dietary Diversity Score Health Facility Assessment Household Hygiene Sanitation and Water Supply Individual Dietary Diversity Score Integrated Food Security Phase Classification Institute of Public Health Nutrition Infant and Young Child Feeding Moderate Acute Malnutrition Minimum Expenditure Basket Mental Health and Care Practice Ministry of Health and Family Welfare Mid-Upper-Arm-Circumference. Non-Governmental Organization Open Defecation Faeces Outpatient Therapeutic Program Pregnant and Lactating Women Probability Proportion to Size Reduced Coping Strategy Index Severe Acute Malnutrition Standard Deviation Supplementary Feeding Program Standardized Monitoring and Assessment of Relief and Transition Under Five Water, Sanitation and Hygiene Weight-for-Age Z-score Weight For Height World Food Programme World Health Organization Weight-for-Height Z-score 3

4 Table of Contents Acknowledgement... 2 Acronyms... 3 Executive Summary Introduction Survey Objectives Methodology Survey Area Type of survey Sample size Survey Target Population Sampling procedure: selecting clusters Sampling procedure: selecting households and children Case definitions and inclusion criteria Questionnaire, Training and supervision Data Entry, Data management and Analysis Results Household and family composition Age and Sex Ratio in Children 6-59 Months Acute Malnutrition (Wasting) based on WHZ: Acute Malnutrition Based on MUAC Underweight Chronic Malnutrition/ Stunting Childhood Morbidity Child care practices including Infant and Young Child Feeding (IYCF) Food Security and Livelihoods Water and Sanitation Discussion and Conclusion Nutrition and Health Child Care including Infant and Young Child Feeding (IYCF) Practices Food Security and Livelihoods Water, Sanitation and Hygiene Limitation and Bias Ethical Considerations Recommendations and priorities Appendices Appendix 1: Plausibility Report Appendix 2: Assignment of Clusters Appendix 3: Evaluation of enumerators (Standardisation test results) Appendix 4: Questionnaire Appendix 5: Event Calender

5 List of Table Table 1: Summary findings of Nutrition and Health indicators based on WHO 2006 growth Standards...7 Table 2: Summary findings of IYCF practices...7 Table 3: Summary findings of Food Security and Livelihoods (FSL)...8 Table 4: Summary findings of Water Sanitation and Hygiene (WASH)...8 Table 5: Details of administrative areas with population...12 Table 6: Sampling parameters-sitakunda Upazila Table 7: Details of proposed and actual sample size achieved...15 Table 8: Calculation of households coverage/day/cluster...15 Table 9: Case definitions of Acute Malnutrition, Stunting and Underweight used for analysis...16 Table 10: Case definition for IYCF, morbidity, vitamin A and measles coverage Table 11: Case definitions of public health significance level...17 Table 12: IPC classification Global Acute Malnutrition by MUAC...17 Table 13: Thresholds level for Household Dietary Diversity (HDD) Table 14: Thresholds level for household Coping Strategy Index (CSI)...17 Table 15: Thresholds level for Minimum Dietary Diversity (MDD-W) for women (15-49yrs)...17 Table 16: Overall data quality from plausibility check...18 Table 17: Household and family composition...19 Table 18: Age and sex ratio...19 Table 19: Prevalence of Acute Malnutrition by WHZ score...20 Table 20: Prevalence of acute malnutrition by age, based on weight-for-height z-scores and/or edema...20 Table 21: Prevalence of acute malnutrition based on MUAC cut offs (and/or oedema) and by sex...21 Table 22: Prevalence of acute malnutrition based on MUAC cut offs (and/or oedema) and by sex...22 Table 23: Prevalence of underweight based on weight-for-age z-scores by sex...23 Table 24: Prevalence of underweight based on weight-for-age z-scores by age...23 Table 25: Prevalence of stunting based on height-for-age z-scores and by sex...23 Table 26: Prevalence of stunting by age based on height-for-age z-scores...24 Table 27: Mean z-scores, Design Effects and excluded subjects...24 Table 29: Prevalence of childhood (6-59 Months) morbidities...25 Table 30: Summary Findings of IYCF practices...26 Table 31: Consumption patterns of different foods groups in the previous day...32 Table 32: Washing behaviour of storage container...36 Table 33: Category of sanitary latrines by percentage of households...37 Table 34: Disposal of children's feces percentage of households...37 Table 35: Hand washing behaviour with soap...37 Table of Figure Figure 1: Maps of SitakundaUpazila...11 Figure 2: WHZ Gaussian Curve...21 Figure 3: MUAC by Age (Combine)...22 Figure 4: HAZ Gaussian Curve...24 Figure 5: Treatment received modalities...25 Figure 6: Diet in the previous day (6-23 months)...27 Figure 7: Difficulties or challenges faced during childcare...28 Figure 8: Perception of caregiver for child s optimum growth and development...28 Figure 9: The source of income of the surveyed households...29 Figure 10: Income category of the households...29 Figure 11: Food Source...30 Figure 12: Dietary Diversity Status...30 Figure 13: Dietary Diversity in Different Income Groups...31 Figure 14 : Status of women dietary diversity...32 Figure 15: Consumption patterns of food groups based on nutrient density Figure 16: Overall coping strategies Figure 17: Coping strategies by income groups...34 Figure 18: Types of coping strategies...34 Figure 19: Distances of water Source...35 Figure 20: Distances of water source from house Figure 21: Distance of water source and latrine facility.36 Figure 22 : Water Collation and Women...36 Figure 23: Hand washing material Figure 24: Soap available in household

6 Executive Summary Introduction In January 2018, Action Against Hunger (ACF) Bangladesh conducted Integrated SMART survey to provide baseline data on nutrition and morbidity status of children including care practices as well as household s food security and livelihoods, and WaSH indicators in Sitakunda Upazila. This survey was implemented following the recommendations of Rapid SMART survey conducted in Tripura Para, Sitakunda (August 2017) by Action Against Hunger after a measles outbreak associated with undernutrition in July During the Rapid assessment, the overall nutrition situation in Sitakunda Upazila could not be assessed due to nature of assessment, which only considered for Tripura communities. The rapid assessment also revealed that there are concerns of high levels of acute malnutrition (GAM-10.1%) 1 due to underlying poor nutrition status, exacerbated by measles outbreak in Tripura Para, inadequate maternal and child health care as well as household food insecurity and poor water sanitation and hygiene condition in Sitakunda Upazila. This survey was conducted using the Standardized Monitoring and Assessment of Relief and Transitions (SMART) Methodology and it was the first integrated nutrition survey conducted in Sitakunda Upazila. The overall objective of the survey was to determine the nutritional status of children aged 6-59 months, and to assess the care practices behaviours, the food security, WASH situation of Sitakunda Upazila after the July 2017 measles outbreak associated with undernutrition. Methodology A cross sectional household survey was designed to provide statistically representative of nutrition, food security & livelihoods, and WaSH indicators for Sitakunda Upazila. A two-stage cluster sampling method following SMART Methodology was used to achieve the desired outcomes of the survey. At the first stage, the required number of clusters were drawn using probability proportional to size (PPS). This PPS methods ensured that every child in the sample universe had an equal chance of being selected taking into account the population size of the villages. The clusters were defined as villages for the most part and in some cases, a village may contain more than one cluster. At the second stage, simple random sampling method was applied to select the households within the cluster. The sample size was calculated using ENA (Updated July 2015) software, which calculates the sample size; based on various parameters i.e. estimated prevalence, average household size, design effect, desired precision, percentage of children and non-response rate. The sample was then converted into number of households to be surveyed. A total sample size of 1,187 households were estimated to provide a representative sample (473 children) for the selected anthropometry indicators in Sitakunda Upazila. A total of 66 clusters were selected by PPS method using the ENA for SMART software. Each selected cluster included 18 households, regardless of the number of children interviewed. The study finally surveyed 1,174 households covering 699 children (6-59 months) for non-anthro based indicators and achieved to include children (6-59 months) for anthropometric indicators. It should be noted that IYCF indicators require a larger sample size, and therefore the results of the IYCF indicators in the Sitakunda Upazila is only an indication and is NOT representative for the whole population. 1 ACF Nutrition Rapid SMART Survey in Tripura Para, Sitakunda Upazila-August Anthropometric results disaggregated the absent children during data collection, but considered for non-anthro based indicators. 6

7 Summary Findings: Table 1: Summary findings of Nutrition and Health indicators based on WHO 2006 growth Standards Sitakunda Upazila 10 th -28 th January 2018 INDICATOR Prevalence of Acute Malnutrition by WHZ WHZscores (6-59 months) Global Acute Malnutrition W/H< -2 z and/or oedema Severe Acute Malnutrition W/H < -3 z and/or oedema 7 N N=692 Table 2: Summary findings of IYCF practices IYCF Indicators N Prevalence Early Initiation of Breastfeeding within 1 hour after birth (0-23 months) % (144) Exclusive Breastfeeding for children 0-5 months % (51) Continuation of Breastfeeding at 1 year (12-15 months) % (46) Continuation of Breastfeeding at 2 year (20-23 months) % (34) Mean Dietary Diversity Score (IDDS) 3.0 Minimum Dietary Diversity (6-23 months) (>=4 food groups) % (94) Minimum Meal Frequency (6-23 months) (>=3 full meals) % (167) Minimum Acceptable Diet (6-23 months) % (76) % ( % C.I.) 1.0 % ( % C.I.) Prevalence of Acute Malnutrition by MUAC N=698 MUAC (6-59 months) Global Acute Malnutrition (<125mm) 24 Severe Acute Malnutrition (<115mm) % ( % C.I.) 0.4 % ( % C.I.) Prevalence of Underweight N=690 WAZscores (6-59 months) Underweight (<-2 z-score) 182 Severe Underweight (<-3z-score) % ( % C.I.) 4.9 % ( % C.I.) Prevalence of Stunting N=689 HAZscores (6-59 months) Stunting (<-2 z-score) 214 Severe Stunting (<-3z-score) % ( % C.I.) 7.1 % ( % C.I.) Morbidity status (6-59 months) Overall % (415) Diarrhoea % (69) Fever % (290) Acute Respiratory Infections (ARI) % (303) Other Diseases % (15) Measles immunization coverage (9-59 months) % (595) By card % (541) By Recall % (54) Vitamin A coverage (6-59 months) % (635) 7

8 Table 3: Summary findings of Food Security and Livelihoods (FSL) FSL Indicators N Prevalence Mean Household Income Category N=1174 BDT < 5, % BDT 5,000 to 10, % BDT >10, % Main Source of Income N=1174 Unskilled wage labour (including agro) % Salaries, wages-employees % Skilled labour % Seller, commercial activity % Remittance % Main Source of Food N=1174 Purchasing % Own cultivation % Mean Household Dietary Diversity (HDDs) Poor ( 3) 0 0.0% Moderate (4 to 5) % Good ( 6) % Mean Women Dietary Diversity (WDDs) Good ( 5) % Poor (0 to 4) % Reduced Coping Strategy Index (rcsi) No or low rcsi (0-3) % Medium rcsi (4 9) % High Coping (CSI 10) % Table 4: Summary findings of Water Sanitation and Hygiene (WASH) WASH Indicators N Prevalence Main Water Source of Drinking Water N=1174 Tubewell (shallow) % Tubewell (deep) % Distance from Drinking Water Source From Home N= to 150 feet % More than 150 feet % Distance Between Water Source and Latrine Pit N= to 30 feet % 30 to 100 feet % 100+ feet % Main Water Collector N=1174 Adult women % Girls % Covering and Washing Water Container N=1174 Always cover container during transportation % Washing water container daily % 8

9 Type of Sanitation Facilities N=1174 Hygienic sanitary facility % Unhygienic sanitary facility % Hand Washing Behaviour with Soap % Before cooking % After defecation % Before eating food % After disposing child's faeces/cleaning child % After working with animals, crops etc % Before feeding child % General Handwashing Material N=1174 Water only % Water and ash 2 0.2% Water and sand/mud 2 0.2% Water and soap % Others 0 0.0% Soap Available in Latrine/ Besides Latrine % Recommendations and Priorities: Nutrition, Health and Mental Health and Care practices Reinforce the Health System in this area while integrating the nutrition treatment into existing health facilities. The results show that there is the percentage of the people who seek treatment from unregistered sources is high (63.1%). This would need to be addressed through health system strengthening. Advocate for a health system strengthening exercise. Develop a large scale integrated multi-sectorial program to address acute and chronic malnutrition among U5 children and PLW taking into account Nutrition, Health, WASH, MHCP, and FSL. Reinforce Growth Monitoring and Promotion activities in government health facilities focusing on detection, referral of severe acute malnutrition and promotion of essential nutrition actions. Continue, scale up and improve the promotion of appropriate infant and young child feeding practices, preparation of nutritious food with local foods at home, dietary diversity, childcare, safe sanitation and hygienic practices through innovative approaches. The activities can be done at community level through reinforcing the capacity of existing service providers of the health facilities in this regards. Plan for capacity building of partners staff and volunteers on comprehensive maternal and child care package including IYCF, Nutrition practices for children and PLW, improving FSL, WaSH with advocating the MoHFW, stakeholders at beneficiary level. Sensitizing and mobilizing government and non-government stakeholders at Upazila level for targeting families at risk of undernutrition during programming (nutrition sensitive) through the district multisectoral coordination platform. Follow up SMART nutrition surveys next year at the same time to document progress of the response plan and lessons learnt. 9

10 Food Security and Livelihoods (FSL) More than half (53.6%) of the surveyed population in Sitakunda Upazila have average monthly income less than or equal to BDT 10,000 and 2.3% of the population are extremely poor and earn in less than BDT 5,000 per month. They are highly food insecure. Livelihoods interventions are very crucial to enhance monthly income as well as access to nutritious food. Extreme poor households have monthly average income below the Minimum Expenditure Basket (MEB); they need immediate food security support to address the food insecurity. Around 40.0% of the women have poor dietary diversity. Special attention is needed to improve the dietary diversity among the women. Water Sanitation and Hygiene (WASH) It is very important to check the water quality of shallow tube-well, which is the only dominated source of drinking, because it has a potential threat to be contaminated. Our survey found that nearly half of the latrines did not follow the standard which means potential risk have identified of faecal contamination of water source. Ii is very important findings of the survey in deed. It is very necessary to disseminate information regarding it. Water Collection, this particular task is very gender biased which need special attention to reduce the burden of female of the area. Sensitising the community on the importance of covering the container when transporting water should be included in the development of WaSH projects Put in place faecal sludge management as half of the faecal sludge is unmanaged. Support vulnerable families to ensure low cost sanitary latrine facilities and its utilization at household level. When the national coverage of ODF is Zero and still the present situation of the targeted community is far from it. It is very important to consider during project design in this specific area. If we consider hand-washing practice as an indicator of overall sanitation practice of the area it can be concluded that the sanitation practice and knowledge regarding it is not in satisfactory level and have a necessary to improve the situation through multilevel activities. 10

11 1. Introduction Sitakunda Upazila is located in Chittagong District, Bangladesh. The Upazila is bounded on the north by Mirsharai upazila, east by Fatikchhari and Hathazari upazilas, south by Pahartali thana and west by the Sandwip channel, Sandwip Upazila and the Bay of Bengal. Sitakunda Upazila is located between 22º22' and 22º42' north latitudes and between 91º34' and 91º48' east longitudes. Sitakunda Upazila occupies an area of square kilometres ( sq. mi), which includes square kilometres (23.79 sq. mi) of forest. Figure 1: Map of Sitakunda Upazila Sitakunda Upazila in inhabited by an estimated population of 416,777 3 comprising of one Paurashava and nine unions with an. Apart from the Bengali majority, there are a number of small communities of ethnic minorities living in the area. Sitakunda has an average literacy rate of 50.7% for the population of 7 years and above. Economic development in Sitakunda is largely driven by the Dhaka-Chittagong Highway and railway. Though Sitakunda is predominantly an agricultural area, it also has the largest ship breaking industry in the world. Sitakunda's ecosystems are further threatened by deforestation, over-fishing, and groundwater contamination. The upazila is also susceptible to natural hazards such as earthquakes, cyclones and storm surges. It lies on one of the most active seismic faults in Bangladesh, the Sitakunda Teknaf fault. In July, news outlets reported that 10 children died of a mysterious disease in Tripura Para, a village in Sitakunda Upazila. In response to that, GoB Institute of Epidemiology, Disease Control and Research (IEDCR) was sent to area. It was found that the children had died due to measles and undernutrition. Governement of Bangladesh (GoB) initiated a measles vaccination campaign in Kumira and Sonaichari unions in Sitakunda Upazila. In Tripura Para village, MoH established a Community Clinic that is operational 2 days a week. Despite availability of GoB health facilities in Sitakunda district, parts like Tripura Para have been deprived from health care including immunisation and treatment of common illnesses. Especially indigenous population living in Sonaichari and Kumira unions have difficulties accessing health services due to the difficult geography (hilly), distance and barriers in language. It s assumed that, due to the existing poor health and nutrition status and poor access to health facilities of the population in Sitakunda Upazila, the nutrition status of the population is likely to further deteriorate. IPHN has requested Action Against Hunger to support GoB in the implementation of nutritional assessment in Tripura Para, baseline nutritional survey, training of GoB staff on Inpatient SAM management and establishment of a SAM unit in Sitakunda, training of GoB staff on CMAM and roll out CMAM in all health facilities in Sitakunda Upazila. As part of CMAM integration, Action against Hunger initially conducted a Rapid SMART Nutrition assessment (August 2017) in Tripura Para after July measles outbreak associated with undernutrition. The assessment revealed concerns of high levels of acute malnutrition (GAM-10.1%) 4 due to underlying poor nutrition status, exacerbated by measles outbreak in Tripura Para, inadequate maternal and child health care as well as household food insecurity and poor water sanitation and hygiene condition in Sitakunda Upazila. During the 3 Projected with annual growth rate of 1.45 from 2011 to 2016 ( BBS-2011 : ) 4 ACF Nutrition Rapid SMART Survey in Tripura Para, Sitakunda Upazila-August

12 Rapid assessment, the overall nutrition situation associated with other factors e.g. IYCF practices, household FSL and WASH situation in Sitakunda Upazila could not be assessed due to the nature of survey. Therefore, Action Against Hunger conducted an Integrated SMART survey in January 2018 to provide baseline data on nutrition and morbidity status of children, care practices, households Food Security, Livelihoods, and WaSH situation in Sitakunda Upazila. 1.1Survey Objectives Overall Survey Objective The overall objective of the survey is to determine the nutritional status of children aged 6-59 months, and to assess care practices behaviours, the food security and WASH situation of Sitakunda Upazila after the July 2017 Measles outbreak associated with undernutrition. Specific Survey Objectives To determine the current global acute malnutrition rate among children aged 6-59 months. To determine the level of chronic malnutrition and underweight among children aged 6-59 months. To estimate the prevalence of morbidity in children aged 6-59 months. To determine the level of appropriate Infant and Young Child feeding practices. To assess the minimum dietary diversity for women of reproductive age (15-49 years). To assess the current level of household dietary diversity (HDDS) and to explore the existing coping mechanism (rcsi). To identify the current water access, sanitation facility access and hygiene practices at household level. 2. Methodology In January 2018, Action Against Hunger Bangladesh conducted Integrated SMART Nutrition (including IYCF, MHCP, Food Security, Livelihood and WaSH) survey in Sitakunda Upazila. This survey was conducted using the Standardized Monitoring and Assessment of Relief and Transitions (SMART) Methodology and it was the first integrated nutrition survey conducted in Sitakunda Upazila. 2.1 Survey Area The survey was conducted in Sitakunda Upazila during 10 th January to 28 rd January 2018, which is considered winter season in Bangladesh. All the villages from 10 unions were included in the survey. Thus, the total population figures 5 for each unions were defined as follows: Table 5: Details of administrative areas with population Name of Union Total households (BBS-2011) Total Population (BBS-2011) Projected Population as of 2016 Projected U5 6 Children as of 2016 Sitakunda Paurash0va 8,764 43,555 46,806 4,400 Banshbaria Union 4,502 21,850 23,481 2,207 Barabkunda Union 7,350 33,724 36,241 3,407 Bariadyala Union 5,668 28,381 30,499 2,867 Bhatiari Union 10,924 55,434 59,571 5,600 Kumira Union 7,586 38,896 41,799 3,929 Muradpur Union 5,844 29,602 31,811 2,990 Salimpur Union 11,037 54,797 58,887 5,535 Sonaichhari Union 8,994 49,346 53,029 4,985 Saidpur Union 6,357 30,655 32,943 3,097 5 Population and U5 children are projected as of 2016 with annual growth rate (1.45) and percentage of U5 (9.4%) children (BBS 2011) respectively. 6 Estimated children U5: 9.4% 12

13 Bhatiari Cantonment Area 253 1,592 1, Total 77, , ,777 39, Type of survey A cross sectional household survey following SMART methodology with a modified integrated questionnaire was designed. Two stage cluster sampling recommended by SMART methodology was used for sampling and data collection of the survey. The key objective of the survey was to assess the nutrition situation through anthropometry while additional FSL, MHCP and WaSH indicators were incorporated for Integrated Food Security Phase Classification (IPC). Household was considered as the basic sampling unit in the second stage and each villages/segments were selected as primary sampling unit (Cluster) at the first stage. 2.3 Sample size The following assumptions (based on the given context) were used to calculate the sample size in number of children, which was then converted into number of households to be surveyed. All calculations were made using ENA for SMART software (version 9 th July 2015). The sample size calculation takes into consideration the proxy indicator: anthropometry. The parameters for the sample size calculation are as outlined in table below. Table 6: Sampling parameters-sitakunda Upazila. Parameters for Anthropometry Value Assumptions based on context (footnote any references used) Estimated Prevalence of GAM (%) 10.3% Chittagong District Prevalence: MICS , GAM-10.3 % SAM-2.5% ± Desired precision 3.5% Since the prevalence falls between 10-15%, 3.5% precision has been considered as a rule of thumb for SMART. Design Effect 1.5 Correcting the effects of heterogeneity of the population under survey. The sample size inflates by this correction factor to have a representative sampling Children to be included 473 Average HH Size 4.96 BBS 2011 : Sitakunda Upazila % Children under BBS 2011: Sitakunda Upazila % Non-response Households 5% Considering possible absentees due to movement for livelihood activities. Households to be included 1187 Sample size for additional indicators: For the additional indicators of Infant Young Child Feeding (IYCF) and care practices, food security and WASH, the same sample size as the anthropometric indicators (1187 Households) was used. It should be noted that IYCF indicators require a larger sample size, and therefore the results of the IYCF indicators in the Sitakunda Upazila is only an indication and is NOT representative for the whole population. 2.4 Survey Target Population The anthropometric results for children aged 6-59 months were based on the WHO 2006 standards. All the eligible children aged 6-59 months in the household were included for anthropometric measurements. Infant and Young Child Feeding (IYCF) and Care Practices were assessed by interviewing the mothers or primary care givers and was applicable for children aged below 2 years (under 24 months); morbidity for the preceding 14 days was applicable for children 6-59 months; and Vitamin A supplementation and measles vaccination coverage were applicable for 6-59 and 9-59 months respectively. For Vitamin A and measles, 13

14 the mother/primary caregivers recall and the child vaccination card were used. All eligible children within the same household were included for the survey. Food security and WASH information were collected for all targeted households. In case there are no children identified in the household, other household information (food security and WASH) were collected. For assessing household dietary diversity (HDDS) and Reduced Coping Strategy Index (rcsi), one adult women who is responsible for household cooking was interviewed whereas one women of reproductive age (15-49yr) was selected randomly (if more than one) for assessing minimum dietary diversity for women (MDD-W). 2.5 Sampling procedure: selecting clusters Sampling procedure: definition of cluster The survey was conducted in Sitakunda Upazila using a 2-stage cluster sampling method where the primary sampling unit were the villages. The basic sampling unit for the survey was the household because there were other indicators like IYCF and care practices, household food security and livelihoods, mental health, WASH and mortality, which were collected from household level. The SMART guideline for selection of clusters has been adapted to assign the required number of clusters for the survey in the Sitakunda Upazila to make it feasible for carrying out the survey with the concept of giving each household an equal opportunity to be selected. Based on Probability to Population Size (PPS), 66 clusters were randomly selected using ENA for SMART (July 2015) software. PPS method ensured that every household in the Upazilas were an equal chance to be selected irrespective the size of the village. Reserved clusters planned to be included only when equal or more than 10% clusters could not be surveyed and if only less than 80% of the sampled households could be reached from separately two districts. Therefore, no reserved clusters were included in the survey since we could access all 66 clusters and reached minimum number of HHs expected (equal or more than 80% of the sampled HHs). 2.6 Sampling procedure: selecting households and children At second stage, households were selected using the simple random sampling within the cluster. In each area, each team updated households list before the day of data collection. If houses were near each other, and less than 250 HHs in number, the survey team gave a number to each house, then they used the random number table to select the HHs to be surveyed. If the houses were scattered throughout a large area, and/or they were more than 250 HH in number, the following method applied: The cluster was divided into segments. As the numbers of household in each segment varied in size, PPS method was used to select a segment in the following manner: the teams drew a table including the different segments and the cumulative number of households per segment. They then used a random number table to select a number between one and the total number of households. The segment contained this number was selected to be the surveyed. Consequently, a random UX mobile application was used to generate a random number table with estimated households selected randomly for each cluster. The study targeted 1,187 households that covered 473 children under five years. The targeted number of households in each cluster was 18, regardless of the number of children interviewed. If individuals or children are absent, the team revisited the houses at the end of the day before they leave the village. A household with an absent family was not replaced as non-response is factored into the sample size calculations. 14

15 Table 7: Details of proposed and actual sample size achieved Number of households planned Number of households surveyed % surveyed Number of children 6-59 months planned Number of children 6-59 months surveyed Number of children 6-59 months measured % surveyed % % The minimum percentage of clusters surveyed (90%) and children measured (80%) stipulated by the SMART methodology to ensure representativeness was achieved for this survey. Selection of number of household per cluster / per day Based on the following points, a calculation had been done for each team to estimate the number of household to be surveyed per cluster per day at each cluster. Table 8: Calculation of households coverage/day/cluster Calculation of HH coverage/day/cluster Event Time to dedicate Total time remaining Time per day for field work (including travel time, round trip) 7:30 until 18:00 = 630 min 630 min Daily feedback session 30 min 600 min Two breaks of 10 min plus 20 min lunch 10 min X min = 40 break min 560 min Discussion with village leader and selection of 1 st HH 20 min 540 min Time to dedicate per HH and reach the next 30 min for anthropometric measurements and questionnaire and walk to next HH Total number of HH s to be covered by each team per day 540/30=18HH 2.7 Case definitions and inclusion criteria Household definition: In this survey, a household is defined as a group of people who normally live together and eat from the same pot. Polygamous families counted as one household as long as they were living together and sharing a common cooking pot. Polygamous families or any other families living in the same house but not sharing a common cooking pot were counted as separate household. In such cases if the house was been selected for the survey, both households were included in the survey with a different household number. Inclusion criteria of children: All children aged from 6-59 months were included for anthropometry, infant and children 0-23 months for IYCF and care practices, all houses for food security & livelihoods and WaSH related questions. If age could not be defined by any means i.e. birth certificate, vaccination card, then a local calendars of events was used to estimate age. For children with unknown age, the cut-off point of height between 65 and 110cm was used as secondary inclusion criteria for anthropometry. 15

16 The length of children less than 2 years old was measured lying down while the height of children more than 2 years was measured standing. In the absence of age, height 87cm was used to define whether to measure the child standing ( 87cm) or lying (<87cm). The WHO growth reference 2006 was used to estimate the prevalence of under nutrition in the area. In addition, rate of acute malnutrition by MUAC criteria was analysed and reported. Table 9: Case definitions of Acute Malnutrition, Stunting and Underweight used for analysis Nutritional Status Global Moderate Severe Nutritional Status Classification Acute Malnutrition Chronic Malnutrition Underweight Weight/Age Weight/ Height (WHZ) MUAC Height/Age (HAZ) (WAZ) WHZ< -2 SD and/or MUAC< 125 mm HAZ< -2 SD WAZ< -2 SD Oedema and /or Oedema 115 mm MUAC< WAZ <- 2SD to WHZ <- 2SD to -3 SD HAZ <- 2SD to -3 SD 125 mm -3 SD WHZ < -3 SD and/or MUAC< 115 mm HAZ < -3 SD WAZ < -3 SD Oedema and /or Oedema Table 10: Case definition for IYCF, morbidity, vitamin A and measles coverage. Indicator Definitive criteria Early Initiation of Breastfeeding Proportion of children aged 0-23 months who were put to the breast within one hour of birth. Exclusive breastfeeding Proportion of infants 0 5 months of age who are fed exclusively with breast milk IYCF 7 Continued breastfeeding at 1 year and 2yr Proportion of children aged months who are fed breast milk and Proportion of children months of age who are fed breast milk during the previous day. Minimum dietary diversity Proportion of children 6-23 months of age who received foods from 4 or more food groups during the previous day. Minimum meal frequency Proportion of breastfed and non-breastfed children 6 23 months of age who receive solid, semi-solid, or soft foods (but also including milk feeds for non-breastfed children) the minimum number of times or more. (at least 3 full meals per day) Minimum acceptable diet Proportion of children 6 23 months of age who received a minimum acceptable diet (apart from breast milk). It is a composite indicator by combining minimum dietary diversity and meal frequency. Coverage Measles vaccination Measles vaccination were assessed among children aged 9-59 months by checking for the measles vaccine on the EPI card if available or by asking the caregiver to recall if no EPI card is available. Vitamin A Coverage Whether the child aged 6-59 months received a vitamin A capsule over the past six months was recorded from the EPI card or health card if available or by asking the caregiver to recall if no card is available. 7 WHO 2010: Indicators for assessing infant and young child feeding practices: Part 3, Country profile. 16

17 Morbidity Morbidity patterns and treatment status Morbidity for the preceding 14 days and applied for children 6-59 months; for which the mother/primary care givers asked using recall response. Table 11: Case definitions of public health significance level Severity Global Acute Malnutrition (WHZ) Overall Stunting (HAZ) Overall Underweight (WAZ) Interpretation Very High 15% 40% 30% Critical/ Emergency High 10% - <15% 30% - < 40% 20% - < 30% Serious Medium 5% - < 10% 20% - < 30% 10% - < 20% Poor Low < 5% < 20% < 10% Acceptable Table 12: IPC classification8 Global Acute Malnutrition by MUAC Prevalence Global Acute Malnutrition MUAC Extreme Critical >17% Critical % Alert-Serious 6-11% Acceptable <6% Table 13: Thresholds level for Household Dietary Diversity (HDD) Household Dietary Diversity Score (HDDS) Thresholds Low dietary diversity 3 food groups Medium dietary diversity Between 4 and 5 food groups High dietary diversity 6 food groups Source: Guidelines for measuring household and individual dietary diversity, FAO Table 14: Thresholds level for household Coping Strategy Index (CSI) Coping Strategy Index (CSI) Score Thresholds No or low coping 0-3 Medium coping 4-9 High coping 10 Source: Guidance note; WFP VAM unit, Afghanistan, December 2012 Table 15: Thresholds level for Minimum Dietary Diversity (MDD-W) for women (15-49yrs) Dietary Diversity (MDD-W) for women Thresholds Good 5 Poor 0-4 Source: Minimum Dietary Diversity for Women: A Guide to Measurement, FAO, USAID, FANTA, Questionnaire, Training and supervision Questionnaire A modified version of questionnaire including additional indicators of IYCF, morbidity patterns, FSL, WaSH indicators with anthropometry was developed by Action Against Hunger Bangladesh. The survey was incorporated the use of tablets using Android operating system for data collection. The tablets replaced the 8 IPC Acute Malnutrition Addendum

18 paper questionnaires; however, all teams carried hard copies of the questionnaires as back-ups in case the tablet fails at any point. Questionnaires was first developed and adapted on paper and then deployed in KoBo Toolbox by Survey Manager and then uploaded to the ASUS tablet. Team leaders were provided with two tablets including one for back up. The questionnaire was translated into Bangla before training. The questionnaire was pre-tested in the communities by the survey team (Annex 4). Survey Teams and Supervision The integrated SMART survey implemented by six survey teams, each team consisting of a team leader cum measurer, one measurer assistant, and two interviewers. A well-experienced survey supervisor was recruited to implement and monitor the overall survey activities in the field level. Each team stayed in the field for 18 days (11 th Jan -28 rd Jan 18) and each team covered one cluster per day. The team leader cum measurer was responsible for day-to-day field supervision, household selection, and taking anthropometric measurements. To ensure the accuracy and consistency of data, joint monitoring and supervision were carried out through regular field visits, cross checking and plausibility checking through ENA software every day. Survey Manager, Nutrition and Health Head of Department and Deputy Head of Department oversaw the whole Integrated SMART survey and provided necessary support to the survey team. Training The survey team has been trained on SMART methodology in December 2017 for the implementation of Kutubdia SMART survey. Therefore, the survey team received three days refresher training (9th -11th December 2017) which included classroom training, standardization test and field test. The training for enumerators covered survey objectives, household selection techniques, questionnaire, demonstration on anthropometric measurements and standardization test, data collection and interview skills with group works & field-testing of questionnaire. The standardization test included 10 healthy 6-59 months children that took place on the 3rd day of the training. A field test were conducted a day before the actual data collection. During the field-testing, the questionnaire was administered in the local Bengali language, entered into the tab and piloted before the survey. 2.9 Data Entry, Data management and Analysis The survey incorporated the use of tablets using Android operating system, and KoBo Toolbox software for data collection and entry. Once the data collection was completed, Survey Manager extracted the data from the server into excel format and randomly crosschecked to identify errors and inconsistencies in data collection. Anthropometric data was analysed using ENA software and CDC calculator. All flagged data using SMART flagging criteria (observed mean) was excluded from the analysis. Additionally, IYCF, Care Practices, WASH and Food Security data were analysed using excel software. Eligible but non-respondent samples were excluded from analysis. Table 16: Overall data quality from plausibility check CRITERI A SCORE Missin g/ flagged data Overall sex ratio Overall age distributi on Digit preferen ce score Weight Digit preferen ce score Height Digit preferen ce score MUAC Standard deviation WHZ Skewne ss WHZ Kurtosi s WHZ Poisson distribut ion WHZ 0 (0.4%) 4(0.041) 0(p=0.123) 0 (4) 2 (8) 0 (7) 0 (0.95) 0(0.16) 0 (0.03) 1 (0.014) 7% Overal l score WHZ Interpretati on Excellent Acceptabl e Excellent Excellent Good Excellent Excellent Excellent Excellent Good Excellent 18

19 3. Results 3.1 Household and family composition Household data revealed only 6.9 % of the total households were led by women. The average household size found 5.0 and percentage of U5 children was 13.5%. Table 17: Household and family composition Category/Indicator Sample HH Value Proportion/Mean % of Women Headed Household % % of Men Headed Household % Average age of HH Head Mean Family Size % of Male members % % of Female members % % of Children 0 to 5 months % % of Children 6 to 23 months % % of Children 24 to 59 months % % of Children aged 5-17 years % % Adult members (18-50y) % % Elderly members (50 years and above) % 3.2 Age and Sex Ratio in Children 6-59 Months The overall sex distribution (1.2) of the sampled children has shown significant excess of boys (P=0.041) than girls. The overall age distribution was observed as expected (P=0.228) with significant difference for boys (P=0.021). The overall age ratio of 6-29 months and months was also found to be expected (P=0.123). Table 18: Age and sex ratio Boys Girls Total Ratio AGE (mo.) no. % no. % no. % Boy: Girl Total Anthropometric results (based on WHO standards 2006): Anthropometric data from a representative sample of 698 children was collected and analysed excluding z- scores from observed mean (SMART flags): WHZ-3 to 3; WAZ -3 to 3; HAZ -3 to 3. Therefore, a total of 3, 8 and 6 children were excluded from the analysis to estimate prevalence of GAM (WHZ), underweight (WAZ) and stunting (HAZ) respectively with SMART flag criteria. 19

20 3.3 Acute Malnutrition (Wasting) based on WHZ: Acute malnutrition or wasting occurs when an individual suffers from current, severe nutritional restrictions, a recent bout of illness, inappropriate childcare practices or a combination of various factors (flooding, cyclones, limited income opportunities etc.) in any context as weight changes rapidly compared to height. It is characterised by extreme weight loss, resulting in low weight-for-height. Wasting is a reflection of present malnutrition and is therefore used as a proxy of the current nutritional status of the population. Table 19: Prevalence of Acute Malnutrition by WHZ score All Prevalence of global malnutrition (<-2 z-score and/or oedema) Prevalence of moderate malnutrition (<-2 z-score and >=-3 z-score, no oedema) Prevalence of severe malnutrition (<-3 z-score and/or oedema) n = 692 (65) 9.4 % ( % C.I.) (58) 8.4 % ( % C.I.) (7) 1.0 % ( % C.I.) Boys n = 374 (50) 13.4 % ( % C.I.) (45) 12.0 % ( % C.I.) (5) 1.3 % ( % C.I.) Girls n = 318 (15) 4.7 % ( % C.I.) (13) 4.1 % ( % C.I.) (2) 0.6 % ( % C.I.) P Value Anthropometrics of 692 children were considered (flags excluded). The assessment found 65 children 6-59 months to be acutely malnourished. The prevalence of GAM by Weight for Height Z score and/or oedema was found to be 9.4 % ( % C.I.) with SAM rate of 1.0 % ( % C.I.). The prevalence of acute malnutrition can be interpreted as poor according to WHO thresholds. Further analysis revealed significant difference in the prevalence of GAM (P=0.000) where boys (13.4%) are more malnourished than girls (4.7%). No oedema cases found during the assessment. Table 20: Prevalence of acute malnutrition by age, based on weight-for-height z-scores and/or oedema Severe wasting (<-3 z-score) Moderate wasting (>= -3 and <-2 z-score ) Normal (> = -2 z score) Oedema Age (mo.) Total no. No. % No. % No. % No. % Total The assessment findings revealed that the prevalence of acute malnutrition considering WHZ found to be higher among younger children for SAM and MAM. About 72% (47 out of 65) of those identified as malnourished either with MAM or with SAM were below 42 months and almost 90% (58 out of 65) aged less than 54 months. It is remarkable to see that the Z scores were lowest (10.8%; 7 out of 65) in the age groups. 20

21 The sampled population curve (red curve) shows a displacement to the left of the reference curve (green curve) representing the WHO standards. This is an indication of poor nutritional status. The overall mean standard deviation (SD) is 0.95 and falls within the acceptable range of Therefore, we can statistically say with confidence that on average the children are below the average weight for height of the WHO average. This means that population as a whole is undernourished. Figure 2: WHZ Gaussian Curve 3.4 Acute Malnutrition Based on MUAC A child is identified as malnourished if the circumference is less than 125 millimetres and severely malnourished if it is less than 115 millimetres. In Bangladesh as in other countries, MUAC is the primary admission criteria for nutrition treatment for children who are less than 59 months old despite WHO recommendation to use both WH and or MUAC as admission criteria for SAM children. The Global Acute Malnutrition prevalence by MUAC is found to be acceptable according to IPC Acute Malnutrition classification. Table 21: Prevalence of acute malnutrition based on MUAC cut offs (and/or oedema) and by sex Prevalence of global malnutrition (< 125 mm and/or oedema) Prevalence of moderate malnutrition (< 125 mm and >= 115 mm, no oedema) Prevalence of severe malnutrition (< 115 mm and/or oedema) All n = 698 (24) 3.4 % ( % C.I.) (21) 3.0 % ( % C.I.) (3) 0.4 % ( % C.I.) Boys n = 376 (12) 3.2 % ( % C.I.) (11) 2.9 % ( % C.I.) (1) 0.3 % ( % C.I.) Girls n = 322 (12) 3.7 % ( % C.I.) (10) 3.1 % ( % C.I.) (2) 0.6 % ( % C.I.) P Value The prevalence of Global Acute Malnutrition by MUAC was found to be 3.4% ( % C.I.) with a low level of SAM prevalence of 0.4 % ( % C.I.) in Sitakunda Upazila. According to GAM prevalence based on MUAC, there was no statistical difference between boys and girls (p=0.679). 21

22 Figure 3: MUAC by Age (Combined) The assessment findings revealed that the prevalence of GAM by MUAC [3.4% ( % C.I.)] is lower when compared to the prevalence of GAM by WHZ [9.4 %: % C.I.]. MUAC also identified younger children for SAM and MAM. About 75% (18 out of 24) of those identified as malnourished either with MAM or SAM were in the age group of 6-17 months and all of the children were in the age group of less than 29 months. Being an absolute measure, MUAC mostly detects younger children. This discrepancy has been reported as a general phenomenon by Grellety and M. H Golden 9 based on survey data from 47 countries. The discrepancy of rates of GAM across age groups and sex supports the conclusion that MUAC is dependent on age and sex. MUAC overestimates acute malnutrition among younger children and underestimates among older children 10. Low MUAC for girls compared to boys was observed and reported by LT Hop, R Gross, S Sastroamidjojo, T GiaY and W Schultink 11, which is similar to this survey finding. Table 22: Prevalence of acute malnutrition based on MUAC cut offs (and/or oedema) and by sex Severe wasting (< 115 mm) Moderate wasting (>= 115 mm and < 125 mm) Normal (> = 125 mm ) Oedema Age Total (mo.) no. No. % No. % No. % No. % Total Underweight Underweight is an effect of both wasting and stunting, and is therefore a composite indicator of general malnutrition. It is measured by low weight-for-age in children, and is an outcome of either past or present undernutrition. The index does not indicate whether the child has a low weight-for-age because of inadequate weight or because of a small stature for his or her age, and therefore cannot distinguish between chronic and acute malnutrition. 9 Emmanuel Grellety and M H Golden: Weight-for-height and mid-upper-arm circumference should be used independently to diagnose acute malnutrition: policy implications 10 de Onis M., Yip R., and Mei Z., "The development of MUAC-for-age reference data recommended by a WHO Expert Committee," Bull World Health Organization, vol. 75, pp. 11 8, PMID: Hop le T., Gross R., Sastroamidjojo S., Giay T., and Schultink W., "Mid-upper-arm circumference development and its validity in assessment of undernutrition. 22

23 Table 23: Prevalence of underweight based on weight-for-age z-scores by sex All Prevalence of underweight (<-2 z-score) Prevalence of moderate underweight (<-2 z-score and >=-3 z-score) Prevalence of severe underweight (<-3 z-score) n = 690 (182) 26.4 % ( % C.I.) (148) 21.4 % ( % C.I.) (34) 4.9 % ( % C.I.) Boys n = 371 (105) 28.3 % ( % C.I.) (86) 23.2 % ( % C.I.) (19) 5.1 % ( % C.I.) Girls n = 319 (77) 24.1 % ( % C.I.) (62) 19.4 % ( % C.I.) (15) 4.7 % ( % C.I.) P Value The overall prevalence of underweight, based on WAZ was found to be 26.4 % ( % C.I.) with 4.9 % ( % C.I.) of the children assessed being severely underweight which is considered high according to WHO thresholds. There was no significant difference (p=0.223) in the prevalence of underweight between boys and girls. Table 24: Prevalence of underweight based on weight-for-age z-scores by age Severe underweight (<-3 z-score) Moderate underweight (>= -3 and <-2 z-score ) Normal (> = -2 z score) Age (mo.) Total no. No. % No. % Age Total (mo.) no Total Chronic Malnutrition/ Stunting Stunting is an adaptation to chronic malnutrition, and reflects the negative effects of nutritional deprivation on a child s potential growth, over time. Stunting can occur when a child suffers from long-term nutrient deficiencies and/or chronic illness, so that not only weight gain but also height is affected. It can also be an outcome of repeated episodes of acute infections, or acute malnutrition. Table 25: Prevalence of stunting based on height-for-age z-scores and by sex All Prevalence of stunting (<-2 z-score) Prevalence of moderate stunting (<-2 z-score and >=-3 z-score) Prevalence of severe stunting (<-3 z-score) n = 689 (214) 31.1 % ( % C.I.) (165) 23.9 % ( % C.I.) (49) 7.1 % ( % C.I.) Boys n = 370 (116) 31.4 % ( % C.I.) (89) 24.1 % ( % C.I.) (27) 7.3 % ( % C.I.) Girls n = 319 (98) 30.7 % ( % C.I.) (76) 23.8 % ( % C.I.) (22) 6.9 % ( % C.I.) P Value

24 The prevalence of stunting, based on HAZ was 31.1 % ( % C.I.) with severely stunted rate of 7.1 % ( % C.I.). This can be interpreted as large number of children in Sitakunda Upazila suffer from chronic malnutrition and many of them are probably at risk of permanently damaging their mental, physical health, growth, undermining their future productivity and therefore income, with many of them at risk of permanently damaging their mental, physical health, growth, undermining their future productivity and therefore income. There was no significant difference in prevalence of stunting found between boys and girls (P=0.846). Sitakunda Upazila can be categorized as an Upazila with very high level of stunting rate, a serious public health concern. Figure 4: HAZ Gaussian Curve The sampled population curve (red curve) indicates a displacement to the left of the reference curve (green curve). This is an indication of poor nutritional status. The overall mean standard deviation (SD) of 1.01 and falls within the acceptable range of Table 26: Prevalence of stunting by age based on height-for-age z-scores Severe stunting Moderate stunting (>= -3 and <-2 z-score ) Normal (> = -2 z score) (<-3 z-score) Age (mo.) Total no. No. % No. Age (mo.) Total no. No Total It is alarming to find that the prevalence of stunting does not reduce as the children age. This indicates that once a child is malnourished, the child remains malnourished and does not show improvement even after 18 months. Table 27: Mean z-scores, Design Effects and excluded subjects Indicator n Mean z- scores ± SD Design Effect (z-score < -2) z-scores not available* z-scores out of range Weight-for-Height ± Weight-for-Age ± Height-for-Age ±

25 3.7 Childhood Morbidity Table 28: Prevalence of childhood (6-59 Months) morbidities. Type of Morbidity N=415 Percentage Diarrhoea % Fever % Acute Respiratory Infection (ARI) % Other diseases % A total of 699 children were assessed to determine the morbidity status of children aged 6-59 month whereas 59.4% (415) children reported illness from infectious diseases. Overall 9.9% children had diarrhoea; 41.5% children had fever; and 43.3% had Acute Respiratory Infection (ARI) and 2.1% of the children suffered from other diseases reported as skin diseases, mouth and eye infections, tumour etc. The assessment revealed that an estimated 87.7% (364) parents/caregivers of ill children (415) received treatment from different sources whereas 12.3% children did not receive any kind of treatment. However, a low rate of children received treatment from Government hospital (9.9%) and private clinic (14.7%) and alarmingly high percentage of children (63.1%) were managed by unregistered sources through untrained village doctors, traditional healers, imam, and nearest pharmacy etc. Figure 5: Treatment received modalities 12.3% 9.9% 14.7% 63.1% Did not receive Treatment Government Hospital Private Clinic Others sources On the other hand, the coverage of vitamin A and measles vaccination found quite good, but still below than the Sphere Standard s recommendation of 95 % coverage. Overall, 90.8% (635/699) children aged 6-59 months received vitamin A in the last six months. In addition, an estimated 91.0% (595/654) of children who were 9 months old received a good coverage of measles immunization through verifying card (82.7) and recall response (8.3%). 25

26 3.8 Child care practices including Infant and Young Child Feeding (IYCF) 12 Optimal infant and young child feeding entails the initiation of breastfeeding within one hour of birth; exclusive breastfeeding for the first six months; and continued breastfeeding for two years or more, together with safe, age-appropriate feeding of solid, semi-solid and soft foods starting at 6 months of age following recommended dietary diversity and meal frequency. During the survey period, it was challenging to get adequate sample for IYCF indicators within the framework of SMART methodology. However, these results can provide an overview of the situation especially on IYCF practices in lieu of generalizing the whole population. Table 29: Summary Findings of IYCF practices Indicator Total N Prevalence Initiation of breastfeeding within 1 hour of birth (0-23 months) % (144) Exclusive breastfeeding for the first six months (0-5 months) % (51) Continuation of breastfeeding at 1 year (12-15 months) % (46) Continuation of breastfeeding at 2 year (20-23 months) % (34) Mean Dietary Diversity Score (IDDS) - 3 Minimum Dietary Diversity (6-23 months) (>=4 food groups) % (94) Minimum meal frequency (6-23 months) % (167) Minimum acceptable diet (6-23 months) % (76) Early Initiation of Breastfeeding A total 334 child aged 0-23 months from Sitakunda Upazila were considered to assess the percentage of early initiation of breastfeeding after birth. An estimated 43.1% mothers initiated breast-feeding within one hour after birth. This result indicated that a significant number of mothers did not initiated breast-feeding within 1 hour after giving birth, which represents an alarming situation in Sitakunda Upazila. This practice should be improved as it is one of the recommended practices of IYCF components and contributes to decrease neonatal mortality by up to 22% Exclusive Breastfeeding The sample size for exclusively breast-feeding aged 0-5 months was too small to statistically validate these results. The findings can be used as an indicative estimation for exclusive breastfeeding among infants. The assessment findings revealed that about 60.7% of infants were exclusively breastfed during the first six months. Therefore, more focus should be placed on behaviour change activities should be implemented to encourage the practices of exclusive breastfeeding, which is requisite for optimal growth and development, as well as to protect the child from various forms of disease Continued Breast feeding (at 1 year and 2 year) The findings analysed depicts an idea of the situation of continued breast-feeding of Sitakunda Upazila although the sample size for this indicators was too small to validate the findings. The results showed that almost all of the children (97.9%) continued breastfeeding at 1 year and 73.9% of children continued breastfeeding at 2 years. This is a very good sign, since breast milk is full of natural nutrition and is essential to form an optimal immune system against disease and illness for the children. 12 Sample size for IYCF indicators is too small to validate the results: ONLY an indication, NOT a representative. 13 World Health Organisation. Infant and young child feeding. [press release] July

27 3.8.4 Complementary feeding patterns in the previous day Adequate complementary feeding from 6 months following recommended dietary diversity and meals frequency prevent undernutrition and decrease the risk of infectious diseases, such as diarrhoea and pneumonia by strengthening the child s self-immune system. The feeding patterns showed that most of the children (94.0%) received grain, roots and tubers as staple foods and followed by 46.0% children consumed flesh foods in the previous day. On the other hand, only one third of the children received their diet as both vitamin A rich fruits and vegetables in the previous day. Consistently, food intake of dairy products, legumes or nuts were very low meaning that children were not receiving adequate diversified foods that are essential for proper growth and development. Figure 6: Diet in the previous day (6-23 months) Dietary Patterns in the Previous Day (N=250) 100.0% 94.0% 90.0% 80.0% 70.0% 60.0% 50.0% 40.0% 30.0% 20.0% 10.0% 0.0% Grains, Roots, Tubers 28.4% 26.4% 46.0% Legumes or Nuts Dairy Products Flesh foods Vitamin A rich fruits & vegetables 30.0% 31.2% Eggs 41.2% Other fruits and Vegetables Minimum Dietary Diversity The mean individual dietary diversity score for children aged 6-23 months was 3.0 whereas minimum dietary diversity was 37.6% that indicates almost two third of children aged 6-23 months did not received recommended at least four category of food groups Minimum Meal Frequency The survey findings indicated that overall minimum meal frequency rate for children aged 6-23 months was 66.8%.This indicates a good percentage of children received complementary food from 6 months as per the recommendation for optimal feeding frequency Minimum Acceptable Diet The overall minimum acceptable diet was 30.4% meaning that most of the children within age bracket of 6-23 months were not feeding recommended at least minimum four food groups following minimum meal frequency that are essential for proper growth and development. 27

28 3.8.6 Knowledge of mothers on child care practices Figure 7: Difficulties or challenges faced during childcare Do not know 0.6% Caregivers were asked to share their difficulties or challenges in terms of Others 35.9% childcare practices that is linked to Criticism from others for child care 5.1% health and nutrition practices. Among Lack of knowledge or clarity about 9.6% the total study participants (649), 86.1% (559) mothers shared that they faced Lack of interest to take care of child 5.2% difficulties to feed their child and most of Running out of energy 24.8% the participants 57.9% (376) reported Lack of support from husband or 31.7% about constraints of time to care their Managing time 57.9% children. In some of the cases (31.7%), Difficulties to feed 0.0% 20.0% 40.0% 60.0% 86.1% 80.0% 100.0% mothers did not get support from their husbands or other family members on childcare. Sometimes they (24.8%) got easily tired or run out of energy to take care of their child. About 5.2% caregivers felt lack of interest to take care of their children and 5.1% mothers faced criticism, as family and neighbours have perceived them not fully capable as a caregiver. Few mothers (9.6%) reported of having lack of knowledge on necessary childcare practices also Figure 8: Perception of caregiver for child s optimum growth and development 100.0% 90.0% 80.0% 70.0% 60.0% 50.0% 40.0% 30.0% 20.0% 10.0% 0.0% 94.1% Feeding enough food 83.8% Seeking support of health professionals while child is sick 20.6% Interaction with child 24.3% Play with child 12.3% Giving opportunity to explore new things 76.7% Clean and safe environment 34.7% Giving love, warm and assurance 61.2% Discipline and explanation 10.2% Others 0.2% Do not know Mothers were asked about necessary childcare actions for their child s optimum growth and development in this study to understand existing knowledge. Most of the caregivers (94.1%) shared about the need of adequate feeding to their child. A significant number of mothers (83.8%) reported the importance of treatment of child sickness, which shows good awareness about child health care. About 20.6% reported the importance of interaction with child and 24.3% participants reported the need of play with the children. Among them 12.3% caregivers reported of having knowledge about giving the opportunity to the child about exploration of new things, which is one of the key facilitating factors for child s cognitive development. Total, 76.7% mothers reported the need of clean and safe environment. However, 34.7% mothers reported about the importance of giving affectionate love, warm and assurance to their child. Most of the caregivers (61.2%) stated about the need of discipline and necessary explanation as an important action for child s optimum growth and development. 28

29 3.9 Food Security and Livelihoods Household Source of Income Head of the households or whosoever was the head of the house at the time of the interview were asked about the household s main income source and monthly income. Given the complexity of calculating the average monthly income of household, the total monthly income from all sources for the previous 12 months was asked and then the main source of income was identified. It was believed that there could be more than one source of income and therefore the question was specifically asked in a way that the respondent would understand that they need to give the total income from all sources of for the month. Out of total 19 income sources, 7 are identified as most common sources of income (sources those represent at least 5% of the total surveyed household) representing 94.0% of the surveyed households. The most common income sources are salaries and wages employees, skilled labour, unskilled wage labour, remittance, seller or small commercial activities, agriculture and sales of crop, fishing (in open/common waterbody). Rest of the income sources (12 income sources) are very insignificant and represents only 6.0% of the surveyed households. Those income sources are mainly petty trade, livestock and sales of animals, handicrafts, collection of natural resources, begging, gift, government allowances, land broker, agriculture and sales of crop, remittance, gift, land renting etc. Figure 9: The source of income of the surveyed households Source of Income (N=1174) 5.7% 0.2% 0.4% 0.2% 0.5% 5.0% 0.1% 0.4% 11.0% 17.5% 26.9% 17.6% 10.2% 0.1% 0.3% 3.9% Agriculture and sales of crops Livestock and sales of animals Fishing (open /common water) Aquaculture((in a pond) Unskilled wage labour (including agro Skilled labour Handicrafts/cottage industry Collection of natural resources Petty trading-less than 10,000 monthly income Seller, commercial activity Salaries, wages-employees Begging Gift Remittance Government allowance Land renting Money Lender Salt cultivation Others Salaried or wage employee is the main source of income for 26.9% of the surveyed households that is highest among the income sources. Skilled/unskilled wage labour is the second highest income source that represent total 35.1% (skilled % and unskilled 17.5%) of the surveyed households. Other remarkable income sources are remittance (11.0%), sellers or small commercial activities (10.2%), fishing in open or common water bodies (5.0%), agriculture and sales of crop (5.7%). 29

30 Figure 10: Income category of the households 60.0% 50.0% 40.0% 30.0% 20.0% Household Monthly Income (N=1174) 51.3% 46.4% 10.0% 0.0% 2.3% BDT < 5000 BDT 5000 to BDT >10000 The monthly average income of surveyed households is BDT 12,771 in Sitakunda Upazila under Chittagong district. To have deeper insight of the income level, surveyed households are categorized in three income groups monthly income <BDT 5,000, BDT 5,000 to 10,000 and > BDT 10, % of the surveyed household have average monthly income BDT 5,000 or above. Only 2.3% of them have monthly income less than BDT 5000 that is less than the emergency Minimum Expenditure Basket (MEB), meaning that they are highly food insecure. Out of surveyed households, 51.3% have monthly income in between BDT 5000 to BDT 10,000 and 46.4% have monthly income above BDT 10, Source of Food There are only two sources of food purchase and own production as mentioned by the survey respondents in Sitakunda Upazila. Out of the total surveyed households 95.5% mentioned that purchase is their main food source. Only 4.5% household s food source is own production. There is no other food source. As most of the household s food source is purchase, meaning that market access is very crucial for food security in the surveyed areas. Food price hike or drop of income due to any man made or natural disasters would have big negative impact on the food security of the low-income groups in the survey area. Figure 11: Food Source Main Source of Food ( N=1174) 4.5% 95.5% Own cultivation Cash loan Borrowing Food Aid Purchaging Begging Exchanging Others Household Dietary Diversity Households were asked to identify the foods that were consumed in the previous 24 hours by the family members. Due to the shorter recall time, the data can provide a clearer picture of the variety of foods consumed at the household level. In measuring the household dietary diversity, food items are grouped in 12 food groups. 30

31 Figure 12: Dietary Diversity Status Household Dietary Diversity (N=1174) 4.1% 95.9% Poor ( 3) Moderate (4 to 5) Good ( 6) Mean Household dietary diversity Score (HDDS) of the surveyed households is 8.3 that indicates overall good dietary diversity status among the surveyed households in Sitakunda Upazila. Out of total surveyed households, 95.9% have good dietary diversity and 4.1% households have medium dietary diversity. None of the surveyed household found with poor dietary diversity. Figure 13: Dietary Diversity in Different Income Groups Relationship Between Income Level and HDDs (N=1174) Corelation Coefficient 'r'= % 100.0% 80.0% 60.0% 40.0% 20.0% 0.0% 95.2% 97.6% 77.8% 22.2% 4.8% 2.4% 0.0% 0.0% 0.0% Good Diet Diversity (N=1126) Medium Diet Diversity (N=48) Low Diet Diversity (N=0) BDT <5,000 BDT 5,000 BDT To 10,000 BDT > 10,000 Percentage of households with good dietary diversity is highest among the households having monthly average income above BDT 10,000. In contrast, percent of households with medium dietary diversity is highest among the households whose monthly average income is less than BDT 10,000. That means dietary diversity is directly interrelated with income level. Households with lower income have lower dietary diversity. 31

32 3.9.4 Minimum Dietary Diversity for Women (MDD-W) The women dietary diversity score (WDDS) reflects the probability of micronutrient adequacy of the diet and therefore the food groups included in the score are tailored towards this purpose. Vegetables and fruits were further segregated into specific categories based on their content of micronutrients availability. Consumption of foods were grouped into 10 categories proposed by FAO Guidelines for measuring individual dietary diversity to estimate the WDDS scores. Women dietary diversity score below five was considered as poor dietary intake that recommends diversified micronutrient needs from both plant and animal source of food. Additional variable for the consumption of diversified food groups considering nutrient density of food was analysed to understand the dietary adequacy of micronutrients in their diet. Figure 14 : Status of women dietary diversity In Sitakunda Upazila, mean women dietary diversity was 5.0. The minimum dietary diversity was reported as poor among 39.7% of women of reproductive age as consumed less diversified foods in their diet lacking adequate nutrition for this age group. 60.3% of women of reproductive age consumed food in line with the good dietary diversity. This means that in Sitakunda Upazila, majority of the women are more likely to have higher (more adequate) micronutrient intakes. Women Dietary Diversity (N=1149) 39.7% 60.3% Good ( 5) Poor (0 to 4) Table 30: Consumption patterns of different foods groups in the previous day Food Groups N=1149 Overall % of Women Grains, Roots, Tubers % Pulse % Nuts and Seeds % Dairy Products % Meat or Fish % Organ Meat % Egg % Dark Green Leafy Vegetables % Vitamin A rich fruits & vegetables % Vitamin C rich fruits & vegetables % Other fruits % Other vegetables % Fats, Oil % Staple foods (99.9%), meat and fishes (79.9%) and pulses (44.6%) dominated the food consumption in last 24 hours. Consumption status of nutrition dense foods are - dark green leafy vegetables (19.1%), Vitamin A rich fruits and vegetables (18.9%), milk and dairy foods (66.5%), legumes, nuts and seeds (23.6%), egg consumption (33.2%) and organ meat consumption (11.2%) by the women in the households. 32