National Nutrition Survey

National Nutrition AgencyOfce of the Vice President

and Ministry of Women's Affairs

NaNA

The Gambia 2015

Using STANDARDISED MONITORING AND ASSESSMENT OF RELIEF TRANSITION (SMART) Methods

Data collection September 1 to October 6, 2015.

National Nutrition Survey

The Gambia 2015

National Nutrition AgencyOfce of the Vice Presidentand Ministry of Women's Affairs

NaNA

Using STANDARDISED MONITORING AND ASSESSMENT

OF RELIEF TRANSITION (SMART) Methods

Data collection September 1 to October 6, 2015.

………………………

ACKNOWLEDGMENT

The process involved in the planning, designing and conduct of this SMART survey required

enormous expertise from, commitment of and consultation with key stakeholders. It also required

requires strong collaboration and active participation of many institutions and individuals in

health and nutrition. The National Nutrition Agency would like to express its gratitude to all

those who participated in the planning, designing and conducting of this survey, notably the

members of the National Nutrition Survey Taskforce: Ms Mam Yassin Ceesay of The Gambia

Bureau of Statistics (GBoS), Mr Stanley Mwase and Ms Aminatta Sarr of UNICEF, Mrs

Catherine Kutu-Gibba Omo, Mr Dodou Sowe, Fatou Drammeh, Mr Bakary Jallow and Mr

Malang N. Fofana of NaNA.

Special thanks and appreciation go to all the enumerators, the supervisors, coordinators, data

coders and the data entry clerks for their professionalism, quality of work and commitment at the

various levels during the process.

Our sincere thanks goes to UNICEF which, with support from the European Commission's

Humanitarian Aid and Civil Protection department (ECHO) and the United Kingdom

Department for International Development (UKaid), provided the financial and technical support

to carry out this survey. Sincere thanks also goes to the Consultant Asmelash Rezene Berhane for

his dedication and expert advice and support during the planning, training, data collection,

analysis and report writing.

Our thanks go to the respondents and their communities for their support and collaboration in

making the survey a success. It is my honest opinion that the findings of this second SMART

Survey will be useful for all the institutions and individuals that may require such data and

information for planning, targeting and other purposes.

......................................... Modou Cheyassin Phall Executive Director, NaNA

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Contents

National Nutrition Survey .......................................................................................................................... 1

The Gambia 2015....................................................................................................................................... 1

ACKNOWLEDGMENT ................................................................................................................................... 2

EXECUTIVE SUMMARY .......................................................................................................................... 7

Chapter 1: Introduc�on ....................................................................................................................... 10

1.1 Background................................ ................................ ................................ .................10

1.2 Justification of the National Nutrition Survey 2015 ................................ ........................... 11

1.3 Objective of the survey ................................ ................................ ................................ ....11

Chapter 2 Methodology ...................................................................................................................... 12

2.1. Sample Size ................................ ................................ ................................ ...................12

2.2 Sampling procedure for household survey ................................ ................................ .....14

2.3 Survey Teams ................................ ................................ ................................ .............15

2.4 Training ................................ ................................ ................................ .......................15

2.4.1 Pre -testing and revision of the survey tools ................................ ................................ .........15

2.5 Data collection ................................ ................................ ................................ ............16

2.6 Data Collection and Quality control ................................ ................................ ..................17

2.7 Data Management ................................ ................................ ................................ ......17

2.8 Survey Limitations ................................ ................................ ................................ ...........19

Chapter 3: Findings ..................................................................................................................................... 20

3.1 Demography ................................ ................................ ................................ ...............20

3.2 Child Anthropometry (6-59 months) ................................ ................................ ............24

3.3 Mortality rate ................................ ................................ ................................ ...............32

3.4 Woman’s Anthropometry (15-49 Years of age) ................................ ................................ 36

3.5 Pregnancy and Breastfeeding ................................ ................................ ..........................41

Chapter 4 Discussion and conclusion ..................................................................................................... 42

4.1 Sample characteristics ................................ ................................ ................................ .....42

4.2 Malnutrition ................................ ................................ ................................ ......................42

4.3 Mortality ................................ ................................ ................................ ........................... 44

Chapter 5 RECOMMENDATIONS ............................................................................................................ 45

5.1 Nutrition specific Response options to address malnutrition ................................ ............45

5.2 Nutrition sensitive interventions to address malnutrition ................................ ...................47

5.3 Nutrition program implementation and coordination ................................ .........................47

6. REFERENCES ............................................................................................................................................ 48

Annex 1. Local calendar of events ................................ ................................ .........................49

Annexes ....................................................................................................................................................... 49

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Annex 2. Standardization result ................................ ................................ ................................ .50

Annex 3. Age displacement in children of 4 and 5 years of age in the household composition data, NNS

2015 ................................ ................................ ................................ ................................ .......52

Annex 4. Age displacement in the age groups of children of 0-4 years and 5-9 years of age in the

household composition data, NNS 2015 ................................ ................................ ......................52

Annex 5. 2015 Survey organogram and participants ................................ ................................ .....53

Annex 6. 2015 Survey questionnaires ................................ ................................ ...................54

Annex 7. Quality check by LGA ................................ ................................ ............................. 54

TABLE 1 SUMMARY OF INDICATORS ................................................................................................................................. 8

TABLE 2 SUMMARY OF INPUTS USED FOR ANTHROPOMETRY SAMPLE SIZES CALCULATIONS............................................................... 12

TABLE 3 SUMMARY OF INPUTS USED FOR MORTALITY SAMPLE SIZES CALCULATIONS ....................................................................... 13

TABLE 4 NUMBER OF HOUSEHOLDS, HOUSEHOLDS PER CLUSTER AND CLUSTERS BY DOMAIN FOR SAMPLE ........................................... 13

TABLE 5 WHO CLASSIFICATION OF NUTRITION EMERGENCIES .................................................................................................... 18

TABLE 6 MORTALITY RATE THRESHOLDS ................................................................................................................................ 18

TABLE 7 RESULTS OF THE HOUSEHOLD INTERVIEWS AND DATA COLLECTION ON WOMAN - NUMBER OF HOUSEHOLDS, NUMBER OF

INTERVIEWS, AND RESPONSE RATES, BY DOMAIN (UNWEIGHTED), THE GAMBIA NNS 2015 ................................................... 20

TABLE 8 RESULTS OF DATA COLLECTION ON CHILDREN - RESPONSE RATES BY DOMAIN (UNWEIGHTED), THE GAMBIA NNS 2015 ........... 20

TABLE 9 SEX OF HOUSEHOLD MEMBERS BY DOMAIN (UNWEIGHTED), THE GAMBIA NNS 2015 ....................................................... 21

TABLE 10 AVERAGE NUMBER OF HOUSEHOLD MEMBERS BY SEX AND DOMAIN (UNWEIGHTED), THE GAMBIA NNS 2015 ..................... 21

TABLE 11 MINIMUM, MEAN AND MAXIMUM NUMBER OF HOUSEHOLD MEMBERS, WOMEN 15-49 YEARS AND CHILDREN UNDER FIVE BY

DOMAIN (UNWEIGHTED), THE GAMBIA NNS 2015 ........................................................................................................ 22

TABLE 12 PERCENT OF CHILDREN WITHOUT AN EXACT DATE OF BIRTH BY DOMAIN, THE GAMBIA NNS 2015 ..................................... 23

TABLE 13 DISTRIBUTION OF AGE AND SEX IN THE 6-59 MONTH SAMPLE (UNWEIGHTED) ................................................................. 24

TABLE 14 PREVALENCE OF GLOBAL AND SEVERE ACUTE MALNUTRITION IN CHILDREN 6 TO 59 MONTHS OF AGE BY SEX, REGION & NATIONAL

(WHO 2006) NNS 2015 ........................................................................................................................................ 25

TABLE 15 PREVALENCE OF GLOBAL AND SEVERE ACUTE MALNUTRITION BY MUAC IN CHILDREN 6 TO 59 MONTHS OF AGE BY SEX, REGION

& NATIONAL (WHO 2006) NNS 2015 ...................................................................................................................... 26

TABLE 16 PREVALENCE OF UNDERWEIGHT IN CHILDREN 6 TO 59 MONTHS OF AGE BY SEX, REGION & NATIONAL (WHO 2006) NNS 2015

............................................................................................................................................................................ 28

TABLE 17 PREVALENCE OF STUNTING IN CHILDREN 6 TO 59 MONTHS OF AGE BY SEX, REGION & NATIONAL (WHO 2006) NNS 2015 ... 29

TABLE 18 PREVALENCE OF UNDERWEIGHT IN CHILDREN 6 TO 59 MONTHS OF AGE BY SEX, REGION & NATIONAL (WHO 2006) NNS 2015

............................................................................................................................................................................ 30

TABLE 19 MEAN Z-SCORES, DESIGN EFFECTS AND EXCLUDED SUBJECTS ....................................................................................... 31

TABLE 20. CRUDE DEATH RATE AND UNDER FIVE DEATH RATE BY LGA/DOMAIN, NNS 2015 .......................................................... 33

TABLE 21. PREVALENCE OF LOW HEIGHT (< 145 CM) IN ALL WOMEN BY DOMAIN, THE GAMBIA NNS 2015...................................... 37

TABLE 22. PREVALENCE OF LOW BMI (< 18.5) IN NON-PREGNANT WOMEN BY DOMAIN, THE GAMBIA NNS 2015 ............................ 37

TABLE 23. PERCENT DISTRIBUTION OF NON-PREGNANT WOMEN BY BMI CATEGORY AND DOMAIN, THE GAMBIA NNS 2015 ............... 38

TABLE 24. PREVALENCE OF OBESITY IN WOMAN BY PREGNANCY STATUS AND DOMAIN, THE GAMBIA NNS 2015................................ 38

TABLE 25. ACUTE MALNUTRITION IN PREGNANT WOMEN AS MEASURED BY MUAC

Table of figures

FIGURE 1 THE GAMBIAN POPULATION PYRAMID .................................................................................................................... 22

FIGURE 2 COUNT OF CHILDREN 6- 59 MONTHS BY AGE IN THE SAMPLE NNS, 2015 ...................................................................... 23

FIGURE 5 LOCATION AND POSSIBLE CAUSES OF DEATH ................................................................................................. 33

FIGURE 3 CRUDE MORTALITY RATE BY FIVE YEAR AGE GROUPS, NNS 2015 .................................................................................. 34

FIGURE 4 GAMBIA UNDER-FIVE MORTALITY RATE (GMB_B3_UNDER-FIVE MORTALITY RATE_DEFAULT_2015.5 – TOTAL) ................. 34

FIGURE 5 COUNT OF WOMEN BY AGE IN YEARS IN THE SAMPLE, NNS 2015 ................................................................................. 36

FIGURE 6 SCATTER PLOT OF BMI OVER MUAC AND FITTED TREND AND CONFIDENCE INTERVALS ..................................................... 39

FIGURE 7 PREVALENCE AND CONFIDENCE INTERVALS OF LOW BMI AND ACUTE MALNUTRITION (MUAC

ABBREVIATIONS AND ACRONYMS

BSFP Blanket Supplementary Feeding Program

CMR Crude Mortality Rate

CSAS Centric Systema�c Area Sampling

DHS Demographic and Health Survey

ECHO European Commission Humanitarian Aid and Civil Protec�on

FAO Food and Agriculture Organiza�on

GAFNA Gambian Food and Nutri�on Associa�on

GAM Global Acute Malnutri�on

GBoS Gambia Bureau of Sta�s�cs

HH Household

HMIS Health Management Informa�on Systems

IMAM Integrated Management of Acute Malnutri�on

INGO Interna�onal Non-Governmental Organiza�on

LGA Local Government Area

MAM Moderate Acute Malnutri�on

MRC Medical Research Council

NCHS Na�onal Center for Health Sta�s�cs

NRG Nutri�on for Growth

PLW Pregnant and lacta�ng women

PPS Probability to popula�on size

S3M Simple Spa�al surveying method

SAM Severe acute malnutri�on

SDG Sustainable Development Goals

SLEAC Simplified LQAS of Access and Evalua�on

SQUEAC Semi-Quan�ta�ve Evalua�on of Access and Coverage

TSFP Targeted Supplementary Feeding Program

U5MR Under Five Mortality Rate

WCARO West Africa and Central Africa Regional office

WEC World Evangelism for Christ

WFP World Food Program

WHA World Health Assembly

WHO World Health Organiza�on

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EXECUTIVE SUMMARY

This report presents the outcomes of the nutrition assessment conducted in The Gambia across the eight

Local Government Areas to assess the nutrition and mortality situation of its population. UNICEF

Gambia commissioned the assessment, with financial support from ECHO and UKaid and in

collaboration with the National Nutrition Agency (NaNA). The fieldwork of this assessment was

conducted from 1st September to 6th October 2015.

The Gambia is located midway on the bulge of the West Africa Coast and stretches over 400 kilometres

inland from west to east on either side of the River Gambia, varying in width from about 50 km near the

mouth of the River to about 24 km upstream. The country is bounded to the north, south, and east by the

Republic of Senegal and to the west by the Atlantic Ocean. The Gambia’s climate is typically Sahelian,

with a long dry season from November to May and a short rainy season between June and October.

The Gambia continues to face rising malnutrition rates linked to chronic food insecurity and a

deteriorating ability of rural communities to cope due to recurrent drought crises. According to the

Harmonized Framework Analysis done in March 2014, roughly 14 per cent of the population were in the

emergency phase of food insecurity. Factors driving this humanitarian situation include poor harvest;

increasing commodity prices; recurrence of epidemics and other natural disasters; chronic shortage and

limited access to basic social services; and the near total absence of relief support to the affected

population.

The National Nutrition Agency, through technical support from UNICEF, conducted a National Nutrition

Survey using the Standardized Monitoring and Assessment of Relief and Transition (SMART) methods to

estimate the levels of malnutrition among children under five and pregnant and lactating women in The

Gambia as a follow up to the 2012 National Nutrition Survey.

The 2015 SMART Nutrition Survey collected and analyzed anthropometrical data from children 6-59

months of age and women of reproductive age as well as household composition and mortality data.

Specifically the survey assessed the following:

1. the prevalence of acute malnutrition (including prevalence of bilateral oedema) of children 6 to 59 months

2. the prevalence of chronic malnutrition and underweight of children from 6 to 59 months 3. the prevalence of low BMI, obesity, acute malnutrition (MUAC) in women of reproductive age

groups (15-49 years of age) 4. the crude and under-five death rate

The survey was designed to present results at the Local Government Authority (LGA) and national levels.

A SMART survey methodology with two-stage cluster sampling was used to generate nutrition and

mortality data in the 8 LGAs in The Gambia from September 1st to October 6th, 2015. A total of 251

clusters were selected using a Probability Proportionate to Size (PPS) method. Data were collected from

4017 households and 4830 children were assessed.

The national prevalence of Global Acute Malnutrition (GAM) was 10.3percent [95% CI: 9.1 –11.5] and Severe Acute Malnutrition (SAM) was 2.2 percent [95% CI: 1.7 – 3.0]. These results were higher than

those reported by the 2012 SMART survey which had a GAM prevalence of 9.9 percent [95% CI: 8.8 –

10.8] and SAM prevalence of 1.6 percent [95% CI: 1.2 – 2.0]. The prevalence of Global Acute

7

Malnutrition of 10.3percent GAM (-2 Z-score) with a mean of (-0.78) falls above the WHO classification

of serious threshold of 10 percent. The table below shows the summary of indicators assessed by this

survey:

Table1 Summary of Indicators

Indicator Results Demographic Total No. of HHs Assessed 4,017 Total number of children analyzed for Anthropometry 4,830 Male 2,432 Female 2,398 Average number of persons per households 8.6 Nutrition Status of children 6-59 months Global Acute Malnutrition (WHO 2006) 10.3% (95% C.I. 9.1 – 11.5) Boys 11.1%(95% C.I: 9.6-12.8) Girls 9.4% (95% C.I: 7.7-11.5) Severe Acute Malnutrition (WHO 2006) 2.2 % (95% C.I. 1.7–3.0) Boys 2.8% (95% C.I:2.0 -4.0) Girls 1.6% (95% C.I: 1.6 -2.4) Proportion of Children Stunted 22.9% (95% C.I: 21.0-24.8) Boys 24.5% (95% C.I: 22.1-27.1) Girls 21.2% (95% C.I: 18.9 -23.7) Proportion of children underweight 21.6% (95% C.I: 19.7-23.5) Boys 22.2% (95% C.I: 19.9 -24.8) Girls 20.9% (95% C.I: 18.5 -23.5) Nutrition status of women aged 15-49 years Proportion of non-pregnant women under-weight 17.7% Proportion of non-pregnant women over-weight 14.9% Proportion of non-pregnant women obese 9.2% Proportion of pregnant women wasted 3.7% Mortality Rates Crude Death Rate 0.67 (0.56-0.79)deaths/10,000/day Under-Five Mortality rate 0.85(0.58-1.23) deaths/10,000/day

The current acute malnutrition prevalence (10.3%) is above the 10 percent serious threshold as per WHO

classification of malnutrition. Maternal and child under nutrition was high in the LGAs of Basse,

Kuntaur, Kerewan and Janjanbureh which might contribute to increased mortality and overall disease

burden in these Local Government Areas.

The high burden of malnutrition among children under five and women of reproductive age led the author

to recommend that interventions be focused on these target groups particularly in the first 1000 days of

life. The following nutrition-specific and sensitive actions which are believed to contribute to the

improvement of health and nutritional status of populations at household levels are recommended:

v Promotion and counselling to improve early initiation of breastfeeding and increased duration of exclusive breastfeeding.

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v Appropriate complementary feeding, including a sufficient intake of micronutrients is essential v Supplementation programmes for vitamin A and zinc are effective for improving micronutrient status

of children and reducing incidence of some diseases and deaths. v Fortification of foods with micronutrients has also been shown to be effective at improving

micronutrient status of children

v Scale up treatment of SAM through the IMAM Program

v Strengthen nutrition sensitive interventions to address the underlying factors that contribute to malnutrition, including hunger, poverty, gender inequality, and poor access to safe water and health services by integrating nutrition actions into other sectors

v Strengthen production and utilization of diversified nutritious and drought -tolerant crops

v Conduct a coverage survey and bottle neck analysis to identify barriers to service delivery and uptake

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Chapter1:Introduction

1.1 Background The Gambia has a population of approximately 1,979,743 (GBoS 2013) and is a low-income country with high poverty levels that contribute to the increasing vulnerability of its population to shocks. An estimated 71 per cent of the population live below the US$2 per day, (2014 human development index)i The prevalence of undernutrition in The Gambia in all its forms has not improved over the past decade and is actually worsening. The findings from the 2012 national nutrition survey reported the Global Acute Malnutrition (GAM) and Severe Acute Malnutrition (SAM) rates of 9.9% and 1.6% respectively (Gambia NNS 2012).The survey also reported that more than 43.3% of mothers are malnourished (NNS 2012 survey), about 18.7% of women of childbearing age were underweight (low body mass index), 2.6% severely underweight, 15.1% overweight and 6.9% obese. The Gambia Demographic Health Surveys (DHS) in 2013 reported that 24.5% of children under five were stunted, 16% were underweight and 11.5% were wasted while 4.2% were severely wasted. Micronutrient deficiencies are also a common public health problem with notable ones being Vitamin A deficiency, iron deficiency anaemia and iodine deficiency. In 2013, the Demographic Health Surveys (DHS 2013) estimated that 73 percent of the children in The Gambia suffered from some level of anaemia with 4 percent being severely anemic. The prevalence of anemia was higher among children in rural areas at 78% compared to 67%in urban areas with Kuntaur having the highest proportion (85%). The study also showed that rural women were more likely to be anaemic (68%) compared to urban women (53%) with more women in Kuntaur and Janjanbureh being anaemic (74%). Major predisposing factors for under nutrition include over-dependence on subsistence rain-fed agriculture and consumption (insufficient dietary diversity) coupled with the high poverty, low literacy levels and the high prevalence of morbidity malaria, diarrhoeal diseases and acute respiratory infections,. Under nutrition weakens the immune system, stunts physical growth and cognitive development and can

have a lifelong and intergenerational effect on educational attainment and economic potential for

individuals, families and nations. It has been estimated that co-exposure to a range of related factors

which have under nutrition as their underlying cause (including a weakened immune system and

susceptibility to infectious diseases such as malaria, diarrhoea and pneumonia) combined with the effects

of growth restriction, micronutrient deficiencies, and sub-optimum breastfeeding account for 45 per cent

of all child deaths (Black et al. 2013). The consequences of stunting and cognitive development tend to be

irreversible after the age of two, with the period from conception until a child’s second birthday being the

first 1,000 days ‘window of opportunity’ to prevent irreversible damage. Children who are

undernourished in the first two years of life and who put on weight rapidly later in childhood and in

adolescence are at high risk of chronic diseases in adulthood.

Maternal under nutrition and the stress this causes upon the foetus in utero (in the womb) can increase the

risk of intrauterine growth retardation (causing babies to be born Small-for-Gestational Age - SGA), and

can have long lasting effects on the health of an individual throughout the life course, and longer term

10

implications for chronic diseases including cardiovascular diseases and type II diabetes (Victoria, C., Adair

et al2008).

The Government of The Gambia is committed to improving the nutritional status of the people of The

Gambia with special focus on the vulnerable groups such as infants, children under the age of five years,

pregnant and lactating women. The country puts adequate nutrition as a major contributing factor in

attaining the Millennium Development Goals (MDGs) and vision 2020. The revised National Nutrition

Policy (2010-2020) will contribute significantly to improving the nutritional status of the Gambian

population and thus help in achieving the Sustainable Development Goals (SDGs) and vision 2020.

1.2Justi�icationoftheNationalNutritionSurvey2015As malnutrition is a significant contributor to child morbidity and mortality, it is critical to have quality,

relevant and timely information to monitor the nutritional status of children under five and other

vulnerable groups. Thus, the need for regular monitoring of the nutritional status of children across the

country is a crucial step that helps to assess the country efforts in meeting global targets (WHA, SDG’s)

as well as national targets. The National Nutrition Agency (NaNA), in collaboration with UNICEF and

other stakeholders, proposed a national nutrition survey using the Standardized Monitoring and

Assessment in Relief and Transitions (SMART) method during the same period the 2012 National

Nutrition Survey was conducted to generate standardized data on nutrition and mortality. The National

Nutrition Survey will provide data for timely action, program planning and long-term monitoring for the

government, donor communities, public and international communities working in the health and

nutrition portfolio in the country.

1.3 Objec�ve of the survey

The general objective of the National Nutrition Survey 2015 was to provide an informed analysis about the nutrition situation in The Gambia. It would also contribute to the effective planning and implementation of nutrition interventions (nutrition specific and sensitive programs).The specific objectives of the survey were to assess:

1. the prevalence of acute malnutrition (including prevalence of bilateral oedema) of children from 6 to 59 months

2. the prevalence of chronic malnutrition and underweight of children from 6 to 59 months 3. the prevalence of low BMI, obesity, acute malnutrition (MUAC) in women of reproductive age

groups (15-49 years of age) 4. the crude and under-five death rate

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Chapter2 Methodology

2.1. Sample Size Standardized Monitoring and Assessment in Relief and Transition (SMART) methodology was employed

to undertake the nutrition and retrospective mortality survey.

The Emergency Nutrition Assessment (ENA) for SMART version (July 9th, 2015) was used to determine

the sample size after entering the sample frame data. For quantitative data collection methods, sample size

calculations were made to ensure that key indicators were statistically representative at each LGA.

For anthropometry sample size estimation, statistical parameters such as 3.0% desired precision was determined to be reasonable to detect estimated prevalence of malnutrition for all LGAs. A design effect of as low as 1.2 was also used for all LGAs with possible homogeneous composition except for Brikama which was expected to have a higher design effect, due to livelihood differences expected between urban and rural dwellers so a 1.5 design effect was used to inflate sample size estimates and to compensate for cluster sampling. Other factors such as respective average household size of each LGA and an expected 5% non-response rate was used to calculate anthropometric sample sizes.

The sample size for the mortality survey was also calculated using the ENA for SMART software. The

mortality rates from the 2012 National Nutrition Survey were used to detect expected UMR and CMR.

Additional inputs such as 407 days recall period, a design effect of 1.2 for almost all LGAs except for

Brikama with a DEFF of 1.5 were keyed into the ENA for SMART software. Other factors such as each

LGA’s respective average household size and expected 5% non-respondent rate were used to calculate the

intended sample size for the mortality survey. Tables 2 and 3 below provide a summary of inputs for

sample size calculation for both the anthropometric and mortality surveys.

Table2 Summary of inputs used for anthropometry sample sizes calcula�ons

LGA/ Domain

Es�mated Prevalence

of GAM (NNS 2012)

Precision Design Effect

Number of Children to

include

Average Number of persons per household (DHS 2013)

Percent of children U5 in

total popula�on (Census 2013)

Percent of non-

response households

Number of households to include

Banjul 9.6 3 1.2 484 4.5 10.5 5 1133

Kanifing 9.5 3 1.2 479 6.2 12.5 5 680

Brikama 7.5 3 1.5 484 8.3 14.3 5 437

Mansakonko 8.3 3 1.2 424 8.2 15.0 5 581

Kerewan 9.8 3 1.2 493 9.6 15.5 5 351

Kuntaur 13.1 3 1.2 635 10.8 16.0 5 382

Janjanbureh 11.1 3 1.2 550 10.5 15.3 5 363

Basse 10.8 3 1.2 537 15 15.2 5 239

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Table3 Summary of inputs used for mortality sample sizes calcula�ons

LGA/Domain

Es�mated Crude Death

Rate (NNS 2012)

Precision Design Effect

Recall Period

Popula�on to include

Average Number of persons per household

(2013 Census)

Percent of non-

response households

Number of households to include

Banjul 0.17 0.2 1.2 407 647 4.5 5 151

Kanifing 0.21 0.2 1.2 407 647 6.2 5 110

Brikama 0.33 0.2 1.5 407 1272 8.3 5 161

Mansakonko 0.27 0.2 1.2 407 832 8.2 5 107

Kerewan 0.31 0.2 1.2 407 956 9.6 5 105

Kuntaur 0.36 0.2 1.2 407 1110 10.8 5 108

Janjanbureh 0.31 0.2 1.2 407 956 10.5 5 96

Basse 0.28 0.2 1.2 407 863 15 5 61

Table4 Number of households, households per cluster and clusters by domain for sample

LGA/Domain Number of

Households to include

Number of Households per

cluster

Number of clusters

Banjul 1133 20 57

Kanifing 680 20 34

Mansakonko 437 17 26

Brikama 581 22 27

Kerewan 351 13 27

Kuntaur 382 14 27

Janjanbureh 363 14 26

Basse 239 9 27

Na�onal 4,166 251

The sample size for the anthropometric survey was calculated as 4,166 households while the sample size

for the retrospective mortality survey was calculated as 899 households. However, since the

anthropometric survey sample size was the higher of the two, the final sample size for this survey was

taken as 4,166 households.

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2.2 Sampling Procedure for Household Survey

2.2.1 Sampling Frame

The master list which was provided by The Gambia Bureau of Statistics (GBoS) considered as the latest

sampling frame for this survey consisted of approximately 1,979,743 population from a total of 4,838

EAs (GBoS database, 2013). A two stage cluster sampling methodology was employed. The first entailed

the selection of 251 clusters using the probability proportional to population size technique, while the

second stage entailed the random selection of households.

(1) Selection of Primary Sampling Units (Clusters)

(2) Cluster and Household Selec�on

In this survey, Enumeration Areas (EAs) were considered as the clusters and were the smallest

administrative unit. The clusters were selected using the probability proportional to population size (PPS)

where all the villages alongside their population were captured in the ENA for SMART (July 9th, 2015)

Software and 251 clusters selected. On arrival at the village, first each team leader followed the local

community entrance procedures and explained the objectives and the methodologies of the survey to the

clan/village leader known as ‘Alkalo’ and sought his approval before data collection commenced.

Households were selected using systematic random sampling. The survey teams then demarcated and

confirmed the actual location of selected EA and systematically divided the EA while at the same time

enlisting actual residents within the demarcated zone. The teams then met with their respective supervisor

and together combined all listed HHs from the cluster and prepared one sampling list for HH selection.

All children 6 to 59 months found in the selected households were included in anthropometric survey.

Respondents were primarily household heads or, in their absence, whoever the household recognised as

head was interviewed to get all relevant information.

Case Definition and Inclusion/Exclusion Criteria

The Global Acute Malnutrition (GAM) is the index used to measure the level of wasting in any given

population. In this survey, GAM was defined as the proportion of children with a z-score of less than -2

weight-for-height and/or presence of bilateral oedema. Severe Acute Malnutrition (SAM) was defined as

the proportion of children with a z-score of less than -3 and/or presence of oedema as summarized below:

Malnutrition by Z-Score: WHO (2006) Standard • Severe acute malnutrition is defined by WFH < -3 SD and/or existing bilateral oedema

• Moderate acute malnutrition is defined by WFH < -2 SD and >-3 SD and no oedema

• Global acute malnutrition is defined by WFH < -2 SD and/or existing bilateral oedema

Malnutrition by MUAC • Severe malnutrition is defined by MUAC

Body Mass Index, WHO 2004 Standards

Classifica�on of BMI

BMI (Kg/m2) cut-offs

Severe underweight =25.0

Obese >=30

All children 6 to 59 months and women of reproductive age in selected households were included in the

anthropometric survey. Primarily the inclusion criterion was age; this was first determined by looking at

the child welfare cards and birth certificates and in their absence, age was estimated using local calendar

of events. See Annex 1 for a local calendar of events prepared for the Gambian context and used during

the survey data collection.

2.3 SurveyTeamsNine teams of four people (1 supervisor, a team leader and 2 measurers) were recruited by the National

Nutrition Agency (NaNA). See Annex 5 survey team database and organogram.

2.4 TrainingA total of thirty-six surveyors were trained at NaNA conference hall from 26th to 30th August, 2015

facilitated by the Nutrition Survey Consultant and NaNA core staff. The first three days covered

theoretical concepts of the SMART methodology while the last two days were for field exercises. A

training guide and power point presentation adapted from SMART methodology were used. The training

course covered the following areas: taking anthropometric measurements, conducting mortality surveys,

sampling steps and procedure, filling out questionnaires, approaching the community, common possible

errors in surveys, data entry using SMART (ENA Software) and interpreting data quality. Before

conducting the pre-test survey, all equipment and enumerators’ skills were double checked by

standardization exercise to ensure consistency of the anthropometrical measurement using ENA for

SMART. Based on such evaluation of enumerators, none were excluded due to inadequate accuracy.

Results of standardization test is in annexed 3.

2.4.1 Pre -Testing and Revision of the Survey Tools Before the commencement of the actual survey, tools and methods were pre-tested and revised. A two-

day pre-test survey was conducted in one of the non-selected villages/EA in New Jeshwang, Old

Jeshwang and Tallinding located in the Kanifing Municipality. The pre-test included steps of sampling

and anthropometry and mortality data collection. This helped to ensure that the interviewers understood

the questions in English and could interpret them into the four major languages and were able to follow

the interview/data collection procedures as outlined during the training. Based on the pre-test survey and

standardization during the training, the nine survey teams were formed and the teams then revised the

questionnaires.

15

2.5 DatacollectionTwo sets of questionnaires with all the described variables herein attached under annex 6were used to

collect the data from all the households (annex 6). Secondary data was obtained through desk reviewing

and conducting Key Informant Interviews.

Household: A household in the Gambian context was defined as a person or a group of persons who

usually live together (within the same compound) and share the same cooking/catering arrangement.

In migration: In-migrants at the household level were described as persons found in a household who

were not usual household members but planned on staying with the household for a relatively long period

of time (6 months). This excludes visitors who just came to spend the holiday, celebrate a feast or were

visiting for a short period of time.

Out-Migrants: Out-migrants at the household level were described as usual household members who

were absent during the period of the survey who planned on staying away for a relatively long period of

time (>6 months). This excluded usual household members who had travelled for a short period of time.

2.5.1AnthropometricMeasurement

Children 6-59 months were assessed for weight, height/length, Mid Upper Arm Circumference (MUAC)

and the presence of oedema. Most children were weighed either naked or with light clothes. If the

children were measured naked or only with very light short pants, then an adjustment with a standard

correction factor of 30gm was applied for weight of clothes. The children were weighed using the Seca

881 digital scale with 100 grams precision. For children that could not stand, their weights were

measured with their mothers or caregivers using the tarring process.

For children younger than two years of age or less than 87 cm, length was measured to the nearest 0.1

centimetre in the recumbent position using a standard Shorr height board. Children 87 to 110 cm or above

2 years were measured in a standing position. Oedema was assessed by applying thumb pressure to the

feet for approximately three seconds, and then examining for the presence of a shallow imprint or pit.

Malnourished children were referred to the IMAM programs for treatment of moderate and severe acute

malnutrition. Women of reproductive age (15 - 49 years) were also measured for MUAC, weight and

height.

2.5.2RetrospectiveMortalityRecordings

Mortality was assessed using the retrospective household census method. As per the SMART guideline

for collecting mortality data, the 2015 survey collected mortality information both at the household and

individual level. The beginning of the recall period was on 4th of August 2014 (beginning of last year

Tobaski feast) and the end point was midway between the first day of the survey date and the last date of

the survey which was September 15th of 2015. Respondents were requested to list all members living in

the household as of 4th of August 2014 till the last day of the recall period. First, all members living in the

household at that time were listed by age and sex, with the head of the household listed first. The

respondents were then asked where each person was at the time of the interview. Possible responses were:

alive and living in the household, alive and living elsewhere, missing or dead. Deaths occurring in each

household in the period were recorded along with the date of occurrence. Cause of death was collected

from the respondent, if not the head of the household. Each questionnaire had descriptions of the causes

16

of death listed; and codes included illness, injury/traumatic, unknown and options for location of death

would either be at home, abroad, at health facility or others.

2.6DataCollectionandQualityControlThroughout the field work, rigorous quality control measures were adopted. The Seca 881 digital scale was checked daily with known weights before starting the field work. Supervisors checked the

questionnaires at the end of each day, identified errors and made sure data collected was correct before

signing off and keying the raw data into the software. Each team supervisor was requested to complete

and submit the cluster report together with the completed questionnaires on a daily basis. Each team was

informed before the following day about the quality of the data collected the day before. As and when

needed, a meeting between survey coordinators and supervisors was organized to review the work done,

discuss common errors, and avoid recurrences and to plan for the next day.

2.6.1 Steps Taken to Minimize Bias (on site)

The reduction of any potential bias during the implementation of a survey is vital to ensuring that the data

is of good quality. The following were measures taken to help reduce the incidence of bias:

§ Strict use of age records for inclusion of eligible children using infant welfare cards and birth

certificates and, in their absence, use of local events calendar to determine the age in months of the

children;

§ Configuring each supervisors laptop in accordance with the country use of date format

(DD/MM/YR)

§ Calibrating Seca 881 digital scale with test weight (1Kg filled sand container)

§ The use of MUAC’s tapes with thread attached to determine the mid upper arm circumference

§ Measuring of weight of a child either naked at all-time or applying a correction factor of 30 gm to all

children measured with some light clothes on.

§ Verification of extreme values (re-weighing/measuring, or returning to households to verify data);

§ Returning to the households if children were missed and/or household members were not at home;

§ Strict supervision in the field by team supervisors and on spot support or backstopping or trouble

shooting as and when needed by survey coordinators

2.7 DataManagementData was entered in both ENA Software and EPI info (3.5.3) version simultaneously. All data were

entered the same day it was collected in the field. This allowed for the data to be immediately available

for cleaning and analysis. The double data entry and validation was done at NaNA Office from 30 th of

September to 9th of October 2105 by well-trained Data Entry Clerks from NaNA Office. Consistency

checks were run to detect and correct data entry errors by referring to the hard copy questionnaires

The first step of the data cleaning was the examination of the control sheet from all submitted clusters

(number of times a household was visited, number of eligible children and status of each questionnaire

vis-à-vis the three questionnaires), the correctness of the logical relatedness between these questionnaires

to ensure smooth linking between variables from different data sheets during the tabulation work.

Frequency distributions were then performed on all key indicators to examine the frequency of responses

as well as to check for missing values. Interpretation of anthropometrical measures using WHO 2006

17

Child Growth Standards was made and introduced to the child data sheet to identify flagged records and

recheck the entered data of birth date or age, height, weight and MUAC.

Finally, after the completion of field work and data entry, data was re-checked by the Consultant and all

questionnaires were tested for errors and inconsistencies related to missing entries and feasibility/outliers.

2.7.1Consent

All household members received a verbal explanation of the survey including mortality, household

questionnaire and anthropometry. The consent or refusal was recorded on the form by the interviewer.

Households were informed that the survey was confidential, and that their responses would not affect any

food and non-food distributions. Participation was voluntary and household members had the right to

refuse to answer any or all questions as well as anthropometric assessments.

2.7.2DataQualityandAnalysis

Anthropometric and mortality data were entered and analyzed using ENA for SMART Software of July

2015, and EPI info 3.5.3. For anthropometric analysis, reference population of the WHO 2006 Child

Growth Standards was used.

Anthropometric data was collected for 4,841children nationally and was checked for outliers (values

identified by WHO flags). Weight -for-height data was analyzed for 4,530 children, height-for-age for

4,464 children, using ENA for SMART. The plausibility report generated by SMART (ENA) Software

(Annex 5) scored the overall survey at 9% nationally and by LGA ranging from 0% to 5% which means

that the survey had good quality anthropometric data for children under five.

Other anthropometric data such as MUAC and BMI were analyzed using EPI Info 3.5.3 /SPSS program

(version 21; SPSS, Chicago IL USA). The plausibility report generated by SMART (ENA) Software,

quality of both mortality and anthropometrical data, a CDC calculator of two-surveys and threshold

calculators were used. A p-value -0.40 -0.40 to –0.69 -0.70 to –0.99 < -1.00

18

2.8SurveyLimitations

Malnutrition Prevalence Estimate:

The estimates of the prevalence of stunting, underweight and overweight in children in the age range of 6-

59 months do not follow the recommended age for the indicators. These cannot be directly compared to

estimates from DHS or MICS as the elimination of the age group 0-5 months of age introduces a bias.

It was reported that some weight measures of children were taken with clothes on and not following the

WHO/CDC training or SMART methods. Data were collected on weight measures with clothes on for 54

children in Banjul. No data were recorded concerning measurement of weight in clothes for other LGAs.

The consultant entered 30 g as the average amount to subtract from the 54 cases in Banjul. If other

children were measured with clothes on, this introduces a bias that decreases malnutrition but most

critically in youngest children.

Children have recorded date of birth but the dates were not recorded in a rigorous manner. The 15 th day of

the month was preferred for many children in Kanifing, Brikama, Mansakonko and Kuntaur LGAs. In

DHS and MICS methodology, when a date of birth is unknown, it is assigned to the 15th of the month.

This could be a related method. It is odd that this trend is not found in Banjul, Kerewan, Janjanburay and

Basse. There was a preference away from the birth months of April, May, June and July in the total

sample. Mistakes in the month of birth of a child can introduce analysis bias that affects stunting,

underweight and analysis of malnutrition by age in months.

Age displacement in women has been identified in the NNS 2012 and DHS 2013. Interviewers wanting

to reduce their workload, changed the age of women to older than 49 years of age to avoid conducting

interviews. Despite the warnings made in the previous surveys, it appears that supervision was not

adequate to prevent interviewers from changing ages to reduce workload.

In the NNS 2015 sample, there was evidence of age displacement by years in children. Interviewers have

used the same method to avoid measuring children. In future surveys, the use of mobile devices such as

smart phones or tablets with immediate sending of data to a central database will help to identify and

address these errors quickly.

Retrospective Mortality Estimate:

Recall bias can affect mortality surveys. There was however no remarkable event to coincide with the

recall period used in 2012 SMART survey and hence a longer recall period of more than one year was

utilized. This helped minimize the potential recall bias. Also specific local calendars were used to

confirm the timing of events. The same calendars were also used by enumerators when asking other

questions on child age.

Households that have disbanded or where no one was present during the recall period are not included in

the mortality estimates. Only households in which members were present on the day of the survey were

included in the sample. Assurances were made during the introduction of the survey to both the

communities and households that information provided regarding household composition and deaths

would not affect any future distributions.

19

Chapter3:Findings

3.1 DemographyThe following section summarizes data from anthropometry and retrospective mortality survey.

Table7 Results of the household interviews and data collec�on on woman - Number of households, number of interviews, and response rates, by domain (unweighted), The Gambia NNS 2015

LGA/Domain Households

selected Households interviewed

Households response rate

Women measured

Women response rate

Banjul 1,133 1031 91.0 897 61.2

Kanifing 680 645 94.9 879 83.2

Mansakonko 437 458 104.8 730 78.5

Brikama 581 382 65.7 538 85.5

Kerewan 351 362 103.1 589 87.8

Kuntaur 382 390 102.1 839 94.0

Janjanbureh 363 401 110.5 692 86.2

Basse 239 348 145.6 831 82.5

Na�onal 4,166 4,017 96.4 5,995 80.4

The women’s response rates were near or above 80% in all domains except Banjul. The results for women

in Banjul could be biased as they represent the women who were at home during the interview. Other

women outside the home during the interview could be of better or worse nutritional status.

Table8 Results of data collec�on on children - response rates by domain (unweighted), The Gambia NNS 2015

LGA/Domain Children listed Children measured Child response rate

Banjul 696 619 88.9

Kanifing 538 494 91.8

Mansakonko 622 554 89.1

Brikama 590 517 87.6

Kerewan 643 570 88.6

Kuntaur 851 792 93.1

Janjanbureh 669 602 90.0

Basse 787 693 88.1

Na�onal 5,396 4,841 89.7

Note: children listed includes all children under five years of age and children measured only included children from 6-59 months of age.

The child response rate of near 90% and above is solid for making representative estimates of child’s

nutrition status.

3.1.1 StudyArea

A total of 251 clusters were selected and 4,017 households were surveyed.

20

3.1.2HouseholdCharacteristicsandtheStudyPopulation

This section summarizes demographic and socioeconomic characteristics of the population in the

households sampled in the 2015 survey. The information presented in this section is intended to facilitate

interpretation of the key nutrition and mortality results presented later in the report. Moreover, it is also

intended to assist in the assessment of the representativeness of the survey sample.

Out of the total 4,166 households sampled during the whole data collection period, 4,017 households

were examined for both anthropometry and mortality. Similarly, out of the total 5,535children under-fives

enlisted during the whole data collection period, 4,841 children under-fives anthropometrical

measurement was taken. This showed a 96.4% household response rate, and 80.4% response rate in

women and an 89.7% response rate in children.

Table9 Sex of household members by domain (unweighted), The Gambia NNS 2015

LGA/Domain Male % Female % Missing Total

Banjul 2,679 49.2 2768 50.8 1 5,448

Kanifing 1,907 47.6 2100 52.4 2 4,009

Mansakonko 1,765 46.8 2002 53.1 2 3,769

Brikama 1,412 46.8 1603 53.2 0 3,015

Kerewan 1,609 48.7 1696 51.3 0 3,305

Kuntaur 1,984 47.5 2190 52.4 3 4,177

Janjanbureh 1,877 48.0 2035 52.0 1 3,913

Basse 2,057 46.0 2409 53.9 2 4,468

Na�onal 15,290 47.6 16,803 52.3 11 32,104

Table 10: Average number of household members by sex and domain (unweighted), The Gambia NNS 2015

LGA/Domain Average number of males per

household Average number of females per

household Average household size

Banjul 2.7 2.8 5.2

Kanifing 3.0 3.3 6.2

Mansakonko 3.9 4.4 8.2

Brikama 3.7 4.2 7.9

Kerewan 4.4 4.7 9.0

Kuntaur 5.1 5.6 10.8

Janjanbureh 4.7 5.1 9.8

Basse 5.9 6.9 12.6

Na�onal 4.1 4.5 8.6

21

Table11 Minimum, mean and maximum number of household members, women 15-49 years and children under five by domain (unweighted), The Gambia NNS 2015

Household Members

Women 15- 49 years

Children under five

N house-

holds Domain/ LGA min mean max min mean max min mean max

Banjul 1 5.2 35 0 1.4 8 0.0 0.7 6 1031

Kanifing 1 6.2 39 0 1.7 8 0.0 0.8 6 645

Mansakonko 1 8.2 29 0 2.1 10 0.0 1.4 9 458

Brikama 1 7.9 34 0 1.7 8 0.0 1.6 8 382

Kerewan 1 9.0 31 0 1.9 8 0.0 1.9 9 362

Kuntaur 2 10.8 39 0 2.5 9 0.0 2.4 11 390

Janjanbureh 1 9.8 45 0 2.2 13 0.0 1.9 14 401

Basse 1 12.6 83 0 4.2 22 0.0 3.3 18 348

Na�onal 1 8.6 83 0 3.9 37 0.0 1.4 18 4,017

The average number of households from this survey was 8.6.

Figure 1: The Gambian Popula�on Pyramid

Out of the total 32,151.5 population assessed from the 2015 SMART survey, 47.6 % (n=15,318.5) were

males while 52.4 %( n=16,833) were females. As per figure 2, almost all age bands of the study

population had a uniform representation except in age band (50-54) where more females than males were

recorded.

22

From The Gambia DHS 2013 - The pyramid shows a rather sharp increase in population size between

women age 45-49 and those age 50-54. To a certain extent, this may be due to a tendency on the part of

some interviewers to estimate the ages of women as above the cut-off age of 49 for eligibility for

individual interviews, thus reducing their workload. A similar trend is observed for men aged 55 -59 and

those aged 60-64.

· Indication of age displacement in women - poor supervision

· Reduction of children under five years of age, could be effect of family planning or age displacement

DHS 2013 NNS 2015 Difference

Children under 5 17.4 % 15.4 % 2.0

Children 5-9 15.9 % 16.7% 0.8

The problems of age displacement of children under five compared to children 5-9 years of age were found in Kanifing, Janjanbureh and Basse (see annex 3 and 4) and as evidenced in the figure 3 below. These issues will be addressed in future surveys by collecting data with mobile devices and providing greater scrutiny on the household listing to ensure quality anthropometry and mortality data collection.

050

10

015

0

cou

nt

67

89

1011

1213

1415

1617

1819

2021

2223

2425

2627

2829

3031

3233

3435

3637

3839

4041

4243

4445

4647

4849

5051

5253

5455

5657

5859

Counts of children by age in months

Figure 2Count of children 6- 59 months by age in the sample NNS, 2015

3.1.3AvailabilityofChildHealthCards

Nationally out the 4,841 children 6-59 months of age examined for anthropometrical measurement, only

0.9 percent had no infant welfare cards and/or birth certificates.

Table12 Percent of children without an exact date of birth by domain, The Gambia NNS 2015

23

LGA/Domain % without date of birth Number of children

Banjul 2.1 619

Kanifing 1.2 494

Mansakonko 0.2 554

Brikama 0.0 517

Kerewan 0.4 570

Kuntaur 0.3 792

Janjanbureh 0.2 602

Basse 2.5 693

Na�onal 0.9 4,841

3.1.4 AgeandSexDistributionoftheStudyPopulation

The results in Table13 shows the number of children surveyed disaggregated by sex and age. Nationally,

the sex ratio comprised of equal proportion of males and females with the overall ratio of 1:0 female to

male which indicates that there was no significant sex bias in the survey. The overall age ratio between 6-

29 to 30-59 is 0.80 which is very close to what is normal in a stable non-emergency population (age ratio

should be about 0.85)

Table13 Distribution of age and sex in the 6-59 month sample (unweighted)

Boys Girls Total Ra�o

AGE (mo) N % N % N % Boy:girl

6-17 546 50.4 538 49.6 1084 22.4 1.0

18-29 582 51.5 549 48.5 1131 23.4 1.1

30-41 563 47.6 621 52.4 1184 24.5 0.9

42-53 525 52.0 485 48.0 1010 20.9 1.1

54-59 216 51.3 205 48.7 421 8.7 1.1

Total 2432 50.4 2398 49.6 4830 100.0 1.0

The number of children assessed from the four different age groups was almost the same indicating no

systematic bias for particular age group and sex

3.2 ChildAnthropometry(6-59months)For estimating the prevalence of wasting, chronic and underweight prevalence, WHO flags were used.

3.2.1GlobalAcuteMalnutrition

Table 13 above shows that anthropometric measurements were analysed for 4,830 children. The results

presented are based upon weight-for-height z-scores and the presence of nutritional oedema. For the

overall sampled population, the prevalence of Global Acute Malnutrition (GAM) was 10.3 percent [95%

CI: 9.1 –11.5] and Severe Acute Malnutrition (SAM) was 2.2percent [95% CI: 1.7 – 3.0]. More boys,

11.1% (95% CI; 9.6 – 12.8) were wasted than girls, 9.4% (95% CI; 7.7 - 11.5). These results are higher

than the previous levels found in the 2012 SMART survey of 9.9 percent [95% CI: 8.8 – 10.8], GAM and

SAM prevalence of 1.6 percent [95% CI: 1.2 – 2.0] SAM.

24

Table14Prevalence of global and severe acute malnutri�on in children 6 to 59 months of age by sex, region & na�onal (WHO 2006) NNS 2015

Overall Boys Girls Overall Boys Girls

% % % % % %

8.4 10.8 6.5 1 0.9 1.1

6.3 - 11.1 7.0 - 14.1 4.2 - 9.7 0.4 - 2.1 0.3 - 2.7 0.4 - 3.2

9.8 9.9 9.6 1.2 2 0.4

7.2 - 13.1 6.6 - 14.7 6.4 - 14.1 0.5 - 3.3 0.6 - 6.2 0.1 - 3.2

9.1 9.9 8.3 2.6 2.9 2.3

6.9 - 12.0 6.9 - 14.0 4.8 - 14.1 1.4 - 4.7 1.3 - 6.5 1.0 - 4.8

9.5 9.9 9.1 1.7 1.7 1.8

7.0 - 12.8 6.2 - 15.4 5.8 - 14.0 1.0 - 3.0 0.7 - 4.1 0.8 - 4.0

10.6 11.8 9 1.6 2.6 0.4

8.0 - 13.9 8.4 - 16.5 6.3 - 12.7 0.8 - 3.2 1.2 - 5.7 0.1 - 3.0

11.4 13.3 9.4 2.3 2.8 1.8

9.1 - 14.1 10.1 - 17.3 6.8 - 12.9 1.5 - 3.6 1.6 - 4.8 0.7 - 4.6

10.5 12.9 8.4 2.2 4.4 0.3

7.7 - 14.1 9.2 - 17.8 5.3 - 13.3 1.4 - 3.5 2.6 - 7.3 0.0 - 2.5

13.9 14.3 13.5 3.5 4 3

11.7 - 16.5 11.5 - 17.8 10.5 - 17.2 2.5 - 4.8 2.3 - 6.8 1.9 - 4.9

10.3 11.1 9.4 2.2 2.8 1.6

9.1-11.5 9.6-12.8 7.7-11.5 1.7-3.0 2.0-4.0 1.0-2.4

LGA N

Global Acute Malnutri�on

(WHZ

Table15 Prevalence of global and severe acute malnutri�on by MUAC in children 6 to 59 months of age by sex, region & na�onal (WHO 2006)

NNS 2015

Overall Boys Girls Overall Boys Girls

% % % % % %

1.1 1.2 1.1 0.2 0.3 0.0

0.5 - 2.5 0.4 - 3.8 0.4 - 3.2 0.0 - 1.2 0.0 - 2.1 0.0 - 0.0

3.7 2.8 4.8 0.4 0.4 0.4

2.0 - 6.8 1.1 - 6.8 2.8 - 8.1 0.1 - 1.6 0.1 - 3.0 0.1 - 3.2

3.2 2.6 3.8 0.2 0 0.4

1.7 - 5.8 1.0 - 6.3 2.0 - 7.0 0.0 - 1.5 0.0 - 0.0 0.0 - 3.0

3.3 1.6 4.7 0.8 0.8 0.7

2.0 - 5.4 0.6 - 4.3 2.9 - 7.8 0.3 - 2.0 0.2 - 3.3 0.2 - 2.9

4.1 3.3 5.1 0.4 0.7 0

2.2 - 7.6 1.4 - 7.7 2.9 - 8.8 0.1 - 1.4 0.2 - 2.6 0.0 - 0.0

5.0 5.0 4.9 1.4 1.8 1.0

3.7 - 6.6 3.4 - 7.3 3.2 - 7.4 0.7 - 2.8 0.8 - 4.0 0.3 - 3.4

5.7 5.1 6.2 1.5 1.8 1.2

4.1 - 7.9 2.9 - 8.7 4.1 - 9.3 0.7 - 3.1 0.6 - 5.2 0.5 - 3.1

7.2 3.0 11.0 1.3 0.6 1.9

5.3 - 9.7 1.9 - 4.9 7.8 - 15.4 0.7 - 2.4 0.2 - 2.4 1.0 - 3.7

4.2 3.0 5.4 0.5 0.5 0.6

3.2-5.1 1.9-4.1 4.3-6.6 0.3-0.8 0.2-0.8 0.2-0.9Na�onal 4790

Janjanbureh 598

Basse 693

Brikama 517

Kerewan 561

Kuntaur 784

Banjul 617

Kanifing 482

Mansakonko 538

LGA N

Global Acute Malnutri�on Severe Acute Malnutri�on

(MUAC

MalnutritionGraphbyAgeinMonths

3.2.2UnderweightPrevalence

Using weight-for-age, the prevalence of underweight defined as

Table16 Prevalence of underweight in children 6 to 59 months of age by sex, region & na�onal (WHO 2006) NNS 2015

Overall Boys Girls Overall Boys Girls

% % % % % %

14.5 14.6 14.4 2.4 2.4 2.5

11.8 - 17.6 10.8 - 19.5 11.0 - 18.6 1.4 - 4.3 1.0 - 5.4 1.1 - 5.4

17.7 19.5 15.8 4.2 4 4.4

13.3 - 23.2 13.7 - 27.0 11.2 - 21.9 2.3 - 7.5 2.0 - 7.7 2.0 - 9.3

19 19.1 18.8 3 3.3 2.6

15.7 - 22.7 14.7 - 24.5 14.1 - 24.6 1.6 - 5.6 1.4 - 7.7 1.3 - 5.3

24.3 21.9 26.5 5.4 3.7 7

21.2 - 27.7 17.6 - 26.9 21.1 - 32.6 3.6 - 8.2 2.1 - 6.6 4.2 - 11.4

21.4 22 20.8 4.3 4.3 4.3

17.5 - 25.9 17.5 - 27.2 15.4 - 27.4 2.9 - 6.2 2.5 - 7.3 2.3 - 8.0

29.4 31.2 27.5 7.5 8.8 6.2

25.0 - 34.2 25.9 - 37.0 21.7 - 34.2 6.0 - 9.4 6.4 - 11.9 4.4 - 8.8

25.4 28.6 22.7 6.9 9.9 4.3

21.2 - 30.1 23.5 - 34.3 17.3 - 29.2 5.4 - 8.7 7.3 - 13.2 2.6 - 7.2

29.6 30.7 28.7 7.9 8.5 7.4

24.9 - 34.8 25.6 - 36.4 23.1 - 35.0 6.0 - 10.4 5.8 - 12.3 5.3 - 10.4

21.6 22.2 20.9 4.7 4.9 4.4

19.7-23.5 19.9-24.8 18.5-23.5 3.8-5.7 3.8-6.4 3.4-5.7Na�onal 4775

Note: WHO flags used for all es�mates

Janjanbureh 595

Basse 692

Brikama 514

Kerewan 560

Kuntaur 783

Banjul 614

Kanifing 479

Mansakonko 538

LGA N

Underweight Severe Underweight

(WAZ

Table 17 Prevalence of stun�ng in children 6 to 59 months of age by sex, region & na�onal (WHO 2006) NNS 2015

When malnutrition was disaggregated by age, the prevalence of chronic malnutrition among children 6-17

months of age was highest from the rest of age bands.

Overall Boys Girls Overall Boys Girls

% % % % % %

13.8 15.2 12.2 3.1 3.9 2.2

11.3 - 16.8 11.5 - 19.8 9.1 - 16.3 2.0 - 4.8 2.2 - 6.6 0.9 - 5.1

19.6 21.9 17.1 3.8 4 3.5

15.6 - 24.4 17.0 - 27.8 12.1 - 23.6 2.1 - 6.7 2.0 - 7.8 1.3 - 8.9

18.9 20.1 17.8 4.9 5.6 4.2

15.8 - 22.5 15.3 - 25.9 13.5 - 23.0 3.3 - 7.1 3.3 - 9.4 2.4 - 7.1

29 28.1 29.7 8 8.7 7.4

25.3 - 32.9 22.8 - 34.1 24.3 - 35.8 5.8 - 11.0 5.4 - 13.8 4.6 - 11.9

23.4 25.7 20.7 7.5 9.9 4.7

19.3 - 28.2 20.8 - 31.3 15.9 - 26.5 5.4 - 10.4 6.5 - 14.7 2.4 - 9.0

33 36.7 29.1 11 14.3 7.6

29.1 - 37.1 31.3 - 42.6 25.1 - 33.5 8.5 - 14.1 10.5 - 19.2 5.4 - 10.6

29.8 34.2 26.1 9.6 10.7 8.7

25.2 - 34.8 27.9 - 41.1 20.1 - 33.1 7.4 - 12.4 7.1 - 15.7 5.4 - 13.8

29.9 30.8 29 9 10.7 7.5

24.9 - 35.3 25.7 - 36.4 22.7 - 36.3 6.9 - 11.7 7.5 - 15.0 5.3 - 10.4

22.9 24.5 21.2 6.2 7.3 5.2

21.0-24.8 22.1-27.1 18.9-23.7 5.3-7.3 6.0-8.8 4.1-6.5Na�onal 4753

Note: WHO flags used for all es�mates

Janjanbureh 594

Basse 690

Brikama 511

Kerewan 559

Kuntaur 773

Banjul 614

Kanifing 479

Mansakonko 533

LGA N

Stun�ng Severe Stun�ng

(WAZ

3.2.4.OverweightPrevalence

Child obesity in The Gambia does not seem to have a high public health significance. Only a handful of

LGAs had a few children overweighing or considered as obese as measured by their weight and height

and compared with the WHO 2006 reference population. Brikama LGA had the highest number of obese

children 1.2 % (n=2) when compared with other LGA which was positive for overweight indicator.

Banjul, Kanifing and Mansakonko had an obese prevalence of 0.3%, 0.8 and 0.6% respectively.

Table18 Prevalence of underweight in children 6 to 59 months of age by sex, region & na�onal (WHO 2006) NNS 2015

Overall Boys Girls Overall Boys Girls

% % % % % %

0.3 0.3 0.4 0 0 0

0.1 - 1.3 0.0 - 2.1 0.0 - 2.7 0.0 - 0.0 0.0 - 0.0 0.0 - 0.0

0.8 0.8 0.9 0.2 0 0.4

0.3 - 2.2 0.2 - 3.2 0.2 - 3.6 0.0 - 1.6 0.0 - 0.0 0.1 - 3.3

0.6 0.4 0.8 0 0 0

0.2 - 1.8 0.0 - 2.8 0.2 - 3.1 0.0 - 0.0 0.0 - 0.0 0.0 - 0.0

1.2 2.1 0.4 0 0 0

0.5 - 2.7 0.8 - 5.5 0.0 - 2.8 0.0 - 0.0 0.0 - 0.0 0.0 - 0.0

0.4 0.7 0 0.2 0.3 0

0.1 - 1.4 0.2 - 2.6 0.0 - 0.0 0.0 - 1.4 0.0 - 2.4 0.0 - 0.0

0.1 0.3 0 0 0 0

0.0 - 1.0 0.0 - 2.0 0.0 - 0.0 0.0 - 0.0 0.0 - 0.0 0.0 - 0.0

0.3 0 0.6 0 0 0

0.1 - 1.3 0.0 - 0.0 0.2 - 2.4 0.0 - 0.0 0.0 - 0.0 0.0 - 0.0

0.1 0 0.3 0 0 0

0.0 - 1.0 0.0 - 0.0 0.0 - 2.0 0.0 - 0.0 0.0 - 0.0 0.0 - 0.0

0.5 0.5 0.6 0.1 0 0.1

0.3-0.9 0.2-1.0 0.2-1.2 0.0-0.3 0.0-0.3 0.0-0.6Na�onal 4775

Note: WHO flags used for all es�mates

Janjanbureh 592

Basse 690

Brikama 516

Kerewan 559

Kuntaur 782

Banjul 618

Kanifing 481

Mansakonko 537

LGA N

Overweight Severe Overweight

(WHZ >+2SD) (WHZ >+3SD)

30

Table19 Mean z-scores, Design Effects and excluded subjects

LGA Indicator Valid N

Mean z-

scores ± SD

Design Effect

(z-score < -2

or MUAC <

125mm)

z-scores not

available*

z-scores out

of range

Total N

Banjul WHZ 618 -0.65±1.00 1.15 1 0 619

MUAC 617 148.2±12.1 1.14 2 0 619

WAZ 614 -0.93±1.04 1.03 5 0 619

HAZ 614 -0.88±1.17 1.0 5 0 619

Kanifing WHZ 480 -0.65±1.07 1.19 13 1 494

MUAC 482 148.3±12.5 1.64 12 0 494

WAZ 479 -1.01±1.12 1.93 15 0 494

HAZ 479 -0.97±1.29 1.4 14 1 494

Mansakonko WHZ 537 -0.68±1.05 1.0 16 1 554

MUAC 538 146.7±11.6 1.57 16 0 554

WAZ 538 -1.06±1.08 1.02 16 0 554

HAZ 533 -1.07±1.28 1.0 17 4 554

Brikama WHZ 516 -0.80±1.02 1.16 0 1 517

MUAC 517 144.8±11.5 1.02 0 0 517

WAZ 514 -1.34±1.08 1.0 0 3 517

HAZ 511 -1.41±1.20 1.0 0 6 517

Kerewan WHZ 559 -0.72±1.04 1.21 10 1 570

MUAC 561 146.1±12.2 2.2 9 0 570

WAZ 560 -1.17±1.07 1.37 9 1 570

HAZ 559 -1.19±1.29 1.48 10 1 570

Kuntaur WHZ 782 -0.87±0.99 1.15 8 2 792

MUAC 784 143.8±12.2 1.0 8 0 792

WAZ 783 -1.44±1.12 1.9 7 2 792

HAZ 773 -1.51±1.22 1.33 7 12 792

Janjanbureh WHZ 592 -0.84±0.99 1.48 7 3 602

MUAC 598 143.3±12.0 0.93 4 0 602

WAZ 595 -1.42±1.07 1.46 6 1 602

HAZ 594 -1.45±1.20 1.54 4 4 602

Basse WHZ 690 -0.92±1.03 1.0 1 2 693

MUAC 693 143.5±12.8 1.13 0 0 693

WAZ 692 -1.44±1.06 1.93 0 1 693

HAZ 690 -1.41±1.23 2.11 1 2 693

Na�onal WHZ 4774 -0.74±1.05 2.0 56 11 4841

MUAC 4790 146 ± 12 2.6 51 0 4841

WAZ 4775 -1.17±1.10 2.4 58 8 4841

HAZ 4753 -1.17±1.28 2.1 58 30 4841

Note: WHO Flags applied * contains for WHZ and WAZ the children with edema (if present).

WHZ, HAZ, WAZ and MUAC are all presented for children 6-59 months.

31

3.3 Mortalityrate A proxy indication of mortality was taken retrospectively to provide an idea on the health situation of the

U5 children and older groups. The mortality assessment was done concurrently with the nutrition survey,

in which a SMART methodology with two stage cluster sampling methodology was used. The mortality

questionnaire was administered to a responsible member of that household. All households with and

without under-five child at the time of the survey were included in the mortality survey. SMART

methodology1was used to calculate the CDR and U5DR.

N=4,159 people days at risk (PDAR) were the effective sample size planned for this particular mortality

survey from a total of two hundred and fifty one (251) clusters. In the end, N=3,781 PDAR from a total of

two hundred and fifty one (251) clusters were interrogated retrospectively for morality survey

simultaneously with the anthropometric survey.

The households were also asked if any member/s of their family either traveled out, came from elsewhere

to live with them or were born during the past 407 days.

Out of the total 32,151.5 individuals, 4, 941 under-five children made up the total population for this

mortality survey. During the recall period (i.e. from 4th of August 2014 to mid-point of survey date i.e.

September 15th 2015) 301 people joined the sampled households and 1,070 left. During the same period,

1,026 new babies were born. On the contrary, there were 199 deaths during the same recall period.

The results show the under-five death rate of 0.85 deaths per 10,000 population per day [95% CI: 0.58 –

1.23] and a crude death rate of 0.67 deaths per 10,000 population per day (95% CI: 0.56 0.79). These

results show that the CDR and U5DR increased from 0.17 to 0.67 and 0.24 to 0.85 in the 2012 SMART

survey and this year period respectively with a 100%s statistical significance when measured by CDC two

surveys probability calculator.

HOUSEHOLD INFORMATION

Total popula�on Children 0-59 months

Total number HH residents 2,782 Number 0-5 years 4,941

Total number people joined HH in

recall period

301 Number 0-5yrs joined HH during

recall period

50

Total number people le� HH in recall period

Total number births during recall

period

Total number deaths during recall

period

1,070

1,026

199

Number 0-5 years le� HH during

recall period

100

Number 0-5 years deaths during

recall period

39

Crude death rate

(deaths/10,000/day)

0.67 (0.56-0.79)

Under-5 mortality rate (deaths/10,000/day)

0.85

(0.58-1.23)

Design effect 1.47 Design effect 1.42

1 Measuring Mortality, Nutritional Status, and Food Security in Crisis Situations: SMART METHODOLOGY. Version 1 April 2006.

32

Table20. Crude death rate and under five death rate by LGA/domain, NNS 2015

LGA/ Domain Crude Death Rate (95% CI) Design Effect Under five Death Rate

(95% CI) Design Effect

Banjul 0.12 (0.08-0.19) 1.00 - -

Kanifing 0.16 (0.10-0.25) 1.21 - -

Mansakonko 0.20 (0.12-0.34) 1.81 - -

Brikama 0.14 (0.08-0.23) 1.01 0.09 (0.02-0.38) 1.00

Kerewan 0.11 (0.07-0.17) 1.00 0.04 (0.01-0.34) 1.00

Kuntaur 0.11 (0.06-0.21) 1.79 0.03 (0.00-0.25) 1.00

Janjanbureh - - - -

Basse 0.18 (0.12-0.28) 1.36 - -

Note: rates are presented in deaths per 10,000 persons per day. Rates with a precision > 0.3 and rela�ve standard error more than 30% were suppressed.

Figure 3 Location and Possible Causes of Death

33

Figure 4 Crude mortality rate by five year age groups, NNS 2015

Note: rates are presented in events per 10,000 persons per day Gambia Under-Five Mortality Rate

Figure 5 Gambia under-five mortality rate (GMB_B3_Under-five mortality rate_Default_2015.5 – Total)

34

Source: h�p://childmortality.org/ Accessed on 9 December 2015.

Table 21 Birth rate, percentage of children under five, average household size, number of clusters, number of households wit h children under five and total number of households by LGA/domain, NNS 2015

Table 22. In-migra�on rate, out-migra�on rate, mid interval popula�on size and total number of households, NNS 2015

LGA/ Domain In-migra�on Rate

(Joined) Out-migra�on Rate

(Le�) Mid Interval

Popula�on Size Total number of HHs

Banjul 0.35 0.60 5385 1032

Kanifing 0.25 0.84 4024 645

Mansakonko 0.28 1.21 3747.5 458

Brikama 0.02 1.19 3036 382

Kerewan 0.14 0.46 3254.5 361

Kuntaur 0.21 1.39 4206 389

Janjanbureh 1.26 4.15 3910.5 400

Basse 0.20 0.13 4385 348

Note: rates are presented in events per 10,000 persons per day

LGA/ Domain Birth Rate

Percentage of children under five

Average household size

Number of Clusters

Number of HHs with children under five

Total number of HHs

Banjul 0.63 13.3 5.2 56 485 1032

Kanifing 0.53 13.6 6.2 34 355 645

Mansakonko 0.85 17.4 8.2 26 319 458

Brikama 0.90 20.0 7.9 27 305 382

Kerewan 1.14 19.8 9.0 27 282 361

Kuntaur 0.87 20.9 10.8 26 343 389

Janjanbureh 3.35 17.7 9.8 26 297 400

Basse 0.96 18.2 12.6 27 275 348

Note: rates are presented in events per 10,000 persons per day

35

3.4Woman’sAnthropometry(15-49yearsofage)Women's nutrition is critical for the life of the individual, her children, community and country. It is also

important in terms of the cycle of growth failure and the need to break the links between malnourished

women giving birth to low birth weight babies. The first 1,000 days from conception to the age of 24

months is a window of opportunity for breaking the lifecycle of malnutrition.

A total of 9,829 women aged 15-49 years were surveyed in the National Nutrition Survey. All eligible

women in the household were listed on the household register. If they were not present at the time of data

collection, they have missing weight, height and MUAC in the dataset. These women with missing data

were not included in the estimates of malnutrition. These data on missing information are presented in the

tables.

Women who were pregnant at the time of data collection were excluded from the analysis of BMI but not

the analysis of middle upper arm circumference.

0

10

020

030

040

0

cou

nt

1516

1718

1920

2122

2324

2526

2728

2930

3132

3334

3536

3738

3940

4142

4344

4546

4748

49

Counts of women by age in years

Figure 6 Count of women by age in years in the sample, NNS 2015

Out of the 6,108 women interviewed, there were 5,995 with valid age in years. The age distribution of the

women in the survey sample is presented above (figure 5). There is evidence of rounding of age in years

to the nearest zero or five in five year steps. The obvious peaks representing rounding are found at 20,

25, 30, 35, 40 and 45 years of age. This complicates analysis of malnutrition by age but is not

considered to affect the estimates of malnutrition in women. The indicators of malnutrition in

women are not age dependent (low height, BMI and MUAC).

36

Table21. Prevalence of low height (< 145 cm) in all women by domain, The Gambia NNS 2015

LGA/Domain Height < 145 cm 95% Confidence

Intervals Valid N % missing Total N

Banjul 0.1 [0.0,0.8] 884 39.7 1,465

Kanifing 0.5 [0.2,1.2] 868 17.8 1,056

Mansakonko 0.4 [0.1,1.2] 722 22.4 930

Brikama 0.4 [0.1,1.5] 535 14.9 629

Kerewan 0 0 581 13.4 671

Kuntaur 0.7 [0.4,1.5] 820 8.2 893

Janjanbureh 0.3 [0.1,1.2] 684 14.8 803

Basse 0 0 826 18.0 1,007

Total 0.4 [0.2,0.9] 5,920 20.6 7,454

3.4.1BodyMassIndex(BMI)

The Body Mass Index (BMI) was calculated from the weights and heights of the non-pregnant women.

Body Mass Index (BMI) is used to classify underweight, overweight and obesity in adult. It is defined as

the weight in kilograms divided by the square of the height in meters (kg/m2). BMI are not age dependent

and same cut-offs are used for both sexes. In developing countries, malnourished individuals with a BMI

below 18.5 kg/m2 have an increased risk in mortality (Gupta 1999). Using the WHO criteria, it was found

that 58.2% of non-pregnant women had normal BMI while the rest were underweight, overweight or

obese. As shown on table 23, nationally 17.7% were underweight but 1.4% were severely underweight,

24.1% were both overweight and obese.

Underweight among women was highest in Brikama (25.8%) followed by Kerewan (25.1%) and

Janjanbureh (21.7%). The least affected LGA was Kanifing (12.6%). The reverse was observed for

overweight and obesity where Banjul had the highest prevalence at 39.3% followed by Kanifing (33.5%)

and Mansakonko (24.9%). The lowest prevalence of overweight was in Kuntaur (11.7%) followed by

Kerewan with 14.2%.

Table22. Prevalence of low BMI (< 18.5) in non-pregnant women by domain, The Gambia NNS 2015

LGA/Domain BMI < 18.5 95% Confidence

Intervals Valid N

Banjul 13.3 [11.2,15.7] 812

Kanifing 12.6 [10.3,15.4] 809

Mansakonko 17 [13.1,21.6] 648

Brikama 25.8 [21.5,30.7] 507

Kerewan 25.1 [21.6,29.0] 510

Kuntaur 21.4 [18.2,25.0] 767

Janjanbureh 21.7 [18.6,25.1] 609

Basse 21.8 [17.0,27.6] 728

Total 17.7 [15.0,20.8] 5,390

37

Table23. Percent distribu�on of non-pregnant women by BMI category and domain, The Gambia NNS 2015

LGA/Domain Severe Mild

Moderate Normal Range Overweight Obese Total % Valid N

Banjul 1.7 11.6 47.4 23.0 16.3 100.0 812

Kanifing 1.5 11.1 53.9 20.0 13.5 100.0 809

Mansakonko 0.9 16.1 58.2 14.7 10.2 100.0 648

Brikama 3.0 22.9 58.2 12.8 3.2 100.0 507

Kerewan 2.0 23.1 60.8 12.2 2.0 100.0 510

Kuntaur 3.1 18.3 66.9 9.4 2.3 100.0 767

Janjanbureh 2.5 19.2 62.7 12.6 3.0 100.0 609

Basse 3.0 18.8 62.2 11.8 4.1 100.0 728

Total 1.4 16.3 58.2 14.9 9.2 100.0 5,390

Table24. Prevalence of obesity in woman by pregnancy status and domain, The Gambia NNS 2015

MUAC is a measure of wasting and has been evaluated in relation to birth outcomes. Low MUAC readings for pregnant mothers are often associated with poor birth outcome although the association has not yet been scientifically established to date. However, as a proxy indicator for low birth weight or poor birth outcome, the current 2015 SMART survey collected MUAC measurement on pregnant mothers.

Table25. Acute malnutri�on in pregnant women as measured by MUAC

Janjanbureh 10.7 [8.5,13.4] 72

Basse 12.6 [9.1,17.2] 94

Total 9.7 [8.1,11.7] 504

Table26. Prevalence of acute malnutri�on (MUAC

The scatter plot and trend line of BMI over MUAC shows the close relation between the two indicators.

These indicators do not necessarily measure the same physical traits but the figure shows that women

with low BMI are also most likely to have low MUAC.

In the bar chart below, the prevalence and confidence intervals of underweight (low BMI) and acute

malnutrition (low MUAC) in women are represented by five year age groups. The risk of malnutrition is

higher in the youngest age group with about 12% having low BMI and 33% low MUAC. The prevalence

of undernutrition declines over the years and is lowest in the 40-44 age group with about 1% low BMI

and about 4% low MUAC. The prevalence then rises slightly in the 45-49 age group, mirroring the 2012

National Nutrition Survey results.

Figure 8 Prevalence and confidence intervals of low BMI and acute malnutri�on (MUAC

41

Chapter4Discussionandconclusion

4.1SampleCharacteristicsThe survey covers 4,017 households and 4,830 U5 children, distributed across the 251 clusters. The total

boys:girls ratio was 1:0, therefore we can note that there was no gender bias in the selection and each

gender had an equal chance of being included in the sample.

4.2MalnutritionThe prevalence of Global Acute Malnutrition of 10.3 percent, though below the emergency threshold of

15 percent (for WHO malnutrition classification, 2000), is categorized under serious level of malnutrition;

considering some of the aggravating factors such as high morbidity, mortality and poor food security

situation with poor key IYCF indicators.

The recent call from WHO2 which urges countries to intensify serious actions to contain prevalence of

malnutrition for children (6-59) exceeding 20 percent for stunting, 5 percent for wasting and 10 percent

for underweight needs to be factored while designing important nutrition-specific and nutrition-sensitive

programs tailored to the specific needs and priorities of The Gambia. Reducing prevalence of

underweight children under 5 years of age was an agreed target for MDG 1. Reducing undernutrition will

ultimately also increase economic growth.

The current survey and that of the 2012 SMART survey were conducted within the hungry season or lean

period where most rural households would be busy tending to their farms (land preparation and

cultivation) and a period for most rural household’s food stocks and reserves being exhausted. This, being

the rainy season, has huge implications on child care practices due to mother’s busy schedule attending to

farms and high incidences of diseases such as malaria, diarrhoea and worm infestations that would likely

compromise children’s wellbeing. Trends in malnutrition as a result are likely to pick up and so do

admission trends in most health facilities.

However, to evaluate the statistical significance between the 2015 and 2012 SMART survey findings, a

CDC calculator for two surveys was used and employed. The results showed that the two surveys which

were conducted in the same period but in different years yielded no significant statistical difference. We

can, therefore, say with only 54.7% certainty that the 2015 GAM rate of 10.3% has increased from the

2012 wasting prevalence of 9.9% or there is not much statistical significance between the two estimates,

and that the absolute number of malnourished population has stayed almost equal in magnitude.

From the current survey, it was noted that The Gambia faces multi-faceted malnutrition from stunting,

wasting, underweight among children under five and an emerging problem with obesity among women

aged 15-49 years. Child malnutrition (wasting, stunting and underweight) in The Gambia tends to affect

boys more than girls and this was found to be statistically significant. However, this was the opposite for

2Global Nutri�on Policy review: What does it take to scale up nutri�on (WHO 2013)

42

wasting prevalence estimated by MUAC and overweight prevalence which tended to affect more girls

than boys. With MUAC measurement, these findings are consistent to many of the research highlighting

that MUAC picks up younger children and often times girls than boys. By the same token, the 2012

SMART survey showed more boys being susceptible to malnutrition than girls, but the actual causes or

dynamics for the differences in malnutrition estimate between the two genders are beyond the scope of

this survey analysis. Comparison of malnutrition across the five age bands of children under five was

more skewed towards the younger age group (6-29 months) as compared to the older age bands (30-59

months). Ironically, these same age groups were equally affected by multifaceted burden of malnutrition

such as wasting, stunting and underweight putting an irreversible damage for the current and future lives

of younger children in the country. The major contributory factors for the consistently high level of

stunting prevalence in the country points to the high burden of micronutrient deficiencies that are of

public health concern in the country (vitamin A, zinc, calcium, iodine and anaemia). Vitamin A

deficiency increases vulnerability to a range of illnesses including diarrhoea, measles, and respiratory

infections. These are leading causes of mortality among children in low and middle income countries,

where risk of infection and risk of mortality can be compounded by coexisting under nutrition.

To understand current the nutritional status of women of reproductive age groups in the country, BMI and

MUAC measurements were taken for non-pregnant and pregnant women. The survey found that 16.3

percent of the assessed women were underweight, 1.4 percent severely were underweight and 14.9

percent were overweight and 9.2 percent were obese. Another observation noted from this survey is that

the majority of public health problems (malnutrition and mortality) are more p