National Nutrition and Health Survey 2014
Nigeria, 2014
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Reference ID
NGA_2014_NNHS_v01_EN_M_v01_A_OCS
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National Bureau of Statistics (NBS)
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Jul 15, 2019
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Identification
Survey ID Number
NGA_2014_NNHS_v01_EN_M_v01_A_OCS
Title
National Nutrition and Health Survey 2014
Country
| Name | Country code |
|---|---|
| Nigeria | NGA |
Study type
Other Household Survey [hh/oth]
Series Information
This survey report presents the results of a national nutrition survey conducted in all the 36 States of Nigeria and federal capital territory (FCT) from 9th February to 5th May 2014 to assess the nutritional and health status of children under 5 years of age and of women in the reproductive age group (15-49 years). In Borno state where 9 local government areas were excluded at sampling stage for security reasons. Hence, result from Borno state is not representative of the whole state.
It is a second round survey aimed to provide reliable data for planning and monitoring of key activities, the first being conducted in 24 states from July to August 2013. In addition to being scaled up to the national level, this new survey presents some additional new key indicators: household access to safe drinking water and sanitation have all been reviewed.
It is a second round survey aimed to provide reliable data for planning and monitoring of key activities, the first being conducted in 24 states from July to August 2013. In addition to being scaled up to the national level, this new survey presents some additional new key indicators: household access to safe drinking water and sanitation have all been reviewed.
Abstract
Nigeria is one of the six countries that accounts for half of all child deaths from malnutrition worldwide. Every year, one million children under five die, 45% of them due to causes attributed to malnutrition. Prevalence of child malnutrition vary significantly across the six geopolitical zones:
children living in the North West and in the North East stand out as being particularly disadvantaged (percent stunted in North West and North East is 50 and 47 respectively, compared to 29 in North Central, 20 in the South South and in the South West, and 10 in the South East). Similar patterns emerge for underweight and wasting. Malnutrition prevalence among women of reproductive age are also high and geographically non homogenous. The prevalence of malnutrition among women ranges from 2 percent in the South East to 10 percent in the North East and rates are particularly high for adolescents (15-19 years) as compared to women aged 20-49 years (16 versus 3 percent). A positive association was also noted between women and child nutritional status. This situation has profound implications for health and human development, and presents a major obstacle to the attainment of the Millennium Development Goals4 (MDG) in the country.
In terms of child – and women – health and nutrition, these targets aim to reduce by two thirds the under-five mortality rate and by three quarters the maternal mortality ratio, reversing at the same time the incidence of malaria and other major diseases, and doubling the proportion of people with access to safe drinking water and sanitation facilities. In addition to targeting the MDGs, in October 2012, Nigeria launched the “Saving One Million Lives” initiative aimed to improve health outcomes by specifically saving one million lives by 2015.
The objectives of the survey are:
1. Determine the prevalence of underweight, stunting, and overweight among children 0 to 59 months of age,
2. Determine the prevalence of acute malnutrition among children 6 to 59 months of age using weight for height (WHZ) and bilateral edema and Mid Upper Arm Circumference (MUAC) and bilateral edema,
3. Assess infant and young child feeding practice: ever breastfed, early initiation of breastfeeding, exclusive breastfeeding, minimum meal frequency, minimum dietary diversity and minimum acceptable diet among children age 0-23 months,
4. Estimate coverage of vitamin A supplementation and de-worming among children 6 to 59 and 12 to 59 months of age respectively within the last six months,
5. Determine the coverage of DPT3/Penta3 and measles vaccination among children 12 to 23 months of age, and assess the prevalence of diarrhoea and Acute Respiratory Infection (ARI) and relative treatment among children under five years of age.
6. Determine the ownership and access of Mosquito Nets and anti-malarial treatment of children under age 5,
7. Determine the prevalence of acute malnutrition among women 15 to 49 years of age using MUAC,
8. Assess the practice of skilled birth attendants, contraceptive prevalence rate and use of iron supplementation during pregnancy among women 15 to 49 years,
9. Determine access to improved drinking water, and sanitation facility and under 3 years children's faeces disposal practice.
children living in the North West and in the North East stand out as being particularly disadvantaged (percent stunted in North West and North East is 50 and 47 respectively, compared to 29 in North Central, 20 in the South South and in the South West, and 10 in the South East). Similar patterns emerge for underweight and wasting. Malnutrition prevalence among women of reproductive age are also high and geographically non homogenous. The prevalence of malnutrition among women ranges from 2 percent in the South East to 10 percent in the North East and rates are particularly high for adolescents (15-19 years) as compared to women aged 20-49 years (16 versus 3 percent). A positive association was also noted between women and child nutritional status. This situation has profound implications for health and human development, and presents a major obstacle to the attainment of the Millennium Development Goals4 (MDG) in the country.
In terms of child – and women – health and nutrition, these targets aim to reduce by two thirds the under-five mortality rate and by three quarters the maternal mortality ratio, reversing at the same time the incidence of malaria and other major diseases, and doubling the proportion of people with access to safe drinking water and sanitation facilities. In addition to targeting the MDGs, in October 2012, Nigeria launched the “Saving One Million Lives” initiative aimed to improve health outcomes by specifically saving one million lives by 2015.
The objectives of the survey are:
1. Determine the prevalence of underweight, stunting, and overweight among children 0 to 59 months of age,
2. Determine the prevalence of acute malnutrition among children 6 to 59 months of age using weight for height (WHZ) and bilateral edema and Mid Upper Arm Circumference (MUAC) and bilateral edema,
3. Assess infant and young child feeding practice: ever breastfed, early initiation of breastfeeding, exclusive breastfeeding, minimum meal frequency, minimum dietary diversity and minimum acceptable diet among children age 0-23 months,
4. Estimate coverage of vitamin A supplementation and de-worming among children 6 to 59 and 12 to 59 months of age respectively within the last six months,
5. Determine the coverage of DPT3/Penta3 and measles vaccination among children 12 to 23 months of age, and assess the prevalence of diarrhoea and Acute Respiratory Infection (ARI) and relative treatment among children under five years of age.
6. Determine the ownership and access of Mosquito Nets and anti-malarial treatment of children under age 5,
7. Determine the prevalence of acute malnutrition among women 15 to 49 years of age using MUAC,
8. Assess the practice of skilled birth attendants, contraceptive prevalence rate and use of iron supplementation during pregnancy among women 15 to 49 years,
9. Determine access to improved drinking water, and sanitation facility and under 3 years children's faeces disposal practice.
Kind of Data
Sample survey data [ssd]
Unit of Analysis
Households
Scope
Notes
The indicators have been divided into five macro-areas:
- Nutritional status of children under 5 years of age [including Malnutrition, Infant and Young Child Feeding practices (IYCFP), Vitamin A supplementation and Deworming;
- Health status of children under 5 years of age [vaccination, diarrhoea, Acute Respiratory Infection (ARI), fever prevalence and diagnosis and treatment of malaria];
- Nutritional status of women in the reproductive age group (15 – 49 years);
- Health status of women in the reproductive age group (15 – 49 years);
- Household access to safe drinking water, sanitation facilities and mosquito net.
- Nutritional status of children under 5 years of age [including Malnutrition, Infant and Young Child Feeding practices (IYCFP), Vitamin A supplementation and Deworming;
- Health status of children under 5 years of age [vaccination, diarrhoea, Acute Respiratory Infection (ARI), fever prevalence and diagnosis and treatment of malaria];
- Nutritional status of women in the reproductive age group (15 – 49 years);
- Health status of women in the reproductive age group (15 – 49 years);
- Household access to safe drinking water, sanitation facilities and mosquito net.
Topics
| Topic | Vocabulary |
|---|---|
| Health | World Bank |
| Health Systems & Financing | World Bank |
| HIV/AIDS | World Bank |
| Malaria | World Bank |
| Nutrition | World Bank |
| Population & Reproductive Health | World Bank |
| Pandemic Flu (including H1N1, Avian Flu) | World Bank |
Coverage
Geographic Coverage
National Coverage
Universe
The survey covered all household members (usual residents), all women aged 15-49 years resident in the household, and all children aged 0-4 years (under age 5) resident in the household.
Producers and sponsors
Primary investigators
| Name | Affiliation |
|---|---|
| National Bureau of Statistics (NBS) | Federal Government of Nigeria (FGN) |
Producers
| Name | Affiliation | Role |
|---|---|---|
| National Population Commission | Federal Government of Nigeria (FGN) | Technical Assistance |
| Federal Ministry of Health | Federal Government of Nigeria (FGN) | Technical Assistance |
| United Nations Children’s Fund | United Nations System | Technical Assistance |
Funding Agency/Sponsor
| Name | Abbreviation | Role |
|---|---|---|
| United Nations Children’s Fund | UNICEF | Funding |
| Micronutrient Initiative | MI | Funding |
| Nigeria's Saving One Million Lives Initiative | Funding | |
| United State Agency International Development | USAID | Funding |
| UKAID | Funding |
Sampling
Sampling Procedure
The National nutrition and health survey used Standardised Monitoring and Assessment of Relief and Transitions (SMART) methods. Data were collected from a total of 25,567 households, 20,939 children under-five years of age and 23,942 women of reproductive age. The 36 states and Federal Capital Territory (FCT) constitute the domains of the survey. The domains used by MICS and DHS are similar, which allows comparison of results, the only exception being the state of Borno, where 9 Local Governmental Areas (LGA) were excluded for security reasons. Therefore, results for Borno are not representative of the whole state.
It is a cross-sectional household survey using a two stage cluster sampling representative at the state level.
At first stage, clusters were drawn randomly and independently for each survey domain from the national master sample frame with the support from National Population Commission according to the probability proportional to size (PPS) method.
The second stage of sampling consists of selecting households within each cluster by using systematic random selection. The team leader verified the population and/or number of households in the cluster by updating the cluster household listing form through detailed enumeration with a support from the village chief or community leader. With total number of households, the team leader calculated the sampling interval and drew a random start number using random number table. Within each selected household, the head of household or next adult was interviewed and all women and children were measured.
In clusters with more than 250 households, segmentation was used to divide the cluster into areas of equal number of households. One segment was randomly chosen, the second stage of sampling was completed for the segment and all selected households were interviewed.
In order to be able to estimate most of the indicators with reasonable precision, the sample size for the survey is calculated using a prevalence of Global Acute Malnutrition (GAM), based on children age 6-59 months. Indicators with narrow age range; 0-23, 6-23 and 12-23 months will be estimated with reasonable precision for each state. However, indicators with narrower age group such as 0-5, 12-15, 20-23 months and very low prevalence, such as treatment of children with ARI and Malaria, will be estimated at zonal level by pooling the data from the survey domain within each zone.
The sample size for the survey was based on sample calculation for the prevalence of Global Acute Malnutrition (GAM) in children of age 6-59 months. The indicators with age ranges of one year or more; 0-23, 6-23 and 12-23 months were found to have reasonable precision for state level estimates.
Those indicators with narrower age ranges such as 0-5, 12-15, 20-23 months and very low prevalence such as treatment of children with ARI and malaria are estimated only at zonal level by aggregating the state level data within each zone.
Significantly different health and demographic conditions are found across Nigeria. In general, the southern half of the country has smaller family sizes and better health and nutrition conditions. These differences were accounted for in two separate sample calculations (for Northern and Southern states), thus two different sample sizes were used to achieve similar level of precision at a national level.
It is a cross-sectional household survey using a two stage cluster sampling representative at the state level.
At first stage, clusters were drawn randomly and independently for each survey domain from the national master sample frame with the support from National Population Commission according to the probability proportional to size (PPS) method.
The second stage of sampling consists of selecting households within each cluster by using systematic random selection. The team leader verified the population and/or number of households in the cluster by updating the cluster household listing form through detailed enumeration with a support from the village chief or community leader. With total number of households, the team leader calculated the sampling interval and drew a random start number using random number table. Within each selected household, the head of household or next adult was interviewed and all women and children were measured.
In clusters with more than 250 households, segmentation was used to divide the cluster into areas of equal number of households. One segment was randomly chosen, the second stage of sampling was completed for the segment and all selected households were interviewed.
In order to be able to estimate most of the indicators with reasonable precision, the sample size for the survey is calculated using a prevalence of Global Acute Malnutrition (GAM), based on children age 6-59 months. Indicators with narrow age range; 0-23, 6-23 and 12-23 months will be estimated with reasonable precision for each state. However, indicators with narrower age group such as 0-5, 12-15, 20-23 months and very low prevalence, such as treatment of children with ARI and Malaria, will be estimated at zonal level by pooling the data from the survey domain within each zone.
The sample size for the survey was based on sample calculation for the prevalence of Global Acute Malnutrition (GAM) in children of age 6-59 months. The indicators with age ranges of one year or more; 0-23, 6-23 and 12-23 months were found to have reasonable precision for state level estimates.
Those indicators with narrower age ranges such as 0-5, 12-15, 20-23 months and very low prevalence such as treatment of children with ARI and malaria are estimated only at zonal level by aggregating the state level data within each zone.
Significantly different health and demographic conditions are found across Nigeria. In general, the southern half of the country has smaller family sizes and better health and nutrition conditions. These differences were accounted for in two separate sample calculations (for Northern and Southern states), thus two different sample sizes were used to achieve similar level of precision at a national level.
Response Rate
Overall 23,942 women and 20,939 children were interviewed. The response rate was 100%.
Weighting
Survey weights were calculated based on populations provided from the master sample frame and number of valid cases. The state level results were self-weighted as per the sample design. The national results were weighted by the survey weights. Three sets of survey weights were used for household, woman level, and child level results, respectively.
Data Collection
Dates of Data Collection
| Start | End | Cycle |
|---|---|---|
| 2014-02-10 | 2014-05-05 | 85 days |
Data Collection Mode
Computer Assisted Personal Interview [capi]
Data Processing
Data Editing
Data quality was reviewed daily during the first week of data collection and weekly during the remainder of field work. The review of data quality comprised downloading the raw data in CSV format, converting the data to STATA, ENA and GPS data formats and producing the plausibility checks from the ENA software and analysis of timing of data collection and missing data.
The data on the daily standardization of anthropometric tools allowed quick detection and replacement of broken or non-functioning scales, height boards or MUAC strips. All supervision teams traveled with replacement scales, height boards, MUAC strips, tablets and other survey materials to resupply teams.
The GPS points of survey data collection were mapped to compare against selected clusters to identify obvious sampling errors. The daily sign-in of the data collection team along with GPS data allowed validation that personnel were in the field in the assigned geographic point as planned.
The data were assessed to ensure that data were sent daily from the tablets to the server and that all teams were following the sampling plans as trained. The time and date stamps on each data point provided data to review the number of interviews per day and the duration of each interview. The timestamps were evaluated to determine if data were collected at appropriate times during the day, not before 7AM or after 8PM.
The data were evaluated by team for missing data. If any variable had more than 5% missing data then supervision staff were alerted and asked to pay specific attention to the data collection of those teams with missing data.
The data on the daily standardization of anthropometric tools allowed quick detection and replacement of broken or non-functioning scales, height boards or MUAC strips. All supervision teams traveled with replacement scales, height boards, MUAC strips, tablets and other survey materials to resupply teams.
The GPS points of survey data collection were mapped to compare against selected clusters to identify obvious sampling errors. The daily sign-in of the data collection team along with GPS data allowed validation that personnel were in the field in the assigned geographic point as planned.
The data were assessed to ensure that data were sent daily from the tablets to the server and that all teams were following the sampling plans as trained. The time and date stamps on each data point provided data to review the number of interviews per day and the duration of each interview. The timestamps were evaluated to determine if data were collected at appropriate times during the day, not before 7AM or after 8PM.
The data were evaluated by team for missing data. If any variable had more than 5% missing data then supervision staff were alerted and asked to pay specific attention to the data collection of those teams with missing data.
Access policy
Contacts
| Name | Affiliation | URL | |
|---|---|---|---|
| Dr. Yemi Kale (Statistician-General) | National Bureau of Statistics (NBS) | [email protected] | Link |
| Isiaka Olarewaju (HOD, RSHSD) | National Bureau of Statistics (NBS) | [email protected] | Link |
| Mr. Fafunmi E.A (HOD ICT) | National Bureau of Statistics (NBS) | [email protected]ianstat.gov.ng | Link |
| Mr. Adebisi (Sampler, NBS) | National Bureau of Statistics (NBS) | Link | |
| Stanley Chitekwe (Chief of Nutrition Section) | UNICEF | [email protected] | |
| Sara Gari-Sanchis (WCARO Nutrition Monitoring Specialist) | UNICEF | [email protected] | |
| Assaye Bulti: (UNICEF Nutrition Consultant) | UNICEF | [email protected] | |
| Irenonse Victoria (Data Archivist, National Bureau of Statistics) | National Bureau of Statistics (NBS) | [email protected] | Link |
Confidentiality
The confidentiality of the individual respondent is protected by law (Statistical Act 2007)
This is published in the Official Gazette of the Federal republic of Nigeria No. 60 vol. 94 of 11th June 2007. See section 26 para.2. Punitive measures for breeches of confidentiality are outlined in section 28 of the same Act.
Access conditions
A comprehensive data access policy is been developed by NBS, however section 27 of the Statistical Act 2007 outlines the data access obligation of data producers which includes the realease of properly anonymized micro data.
Citation requirements
National Bureau of Statistics, Nigeria, National Nutrition and Health Survey 2014-v1.0
Disclaimer and copyrights
Disclaimer
The user of the data acknowledges that the original collector of the data, the authorized distributor of the data, and the relevant funding agency bear no responsibility for use of the data or for interpretations or inferences based upon such uses.
Metadata production
DDI Document ID
DDI_NGA_2014_NNHS_v01_EN_M_v01_A_OCS_FAO
Producers
| Name | Abbreviation | Affiliation | Role |
|---|---|---|---|
| Office of Chief Statistician | OCS | Food and Agriculture Organization | Metadata adapted for FAM |
| National Bureau of Statistics | NBS | Federal Government of Nigeria (FGN) | Metadata Producer |
DDI Document version
NGA_2014_NNHS_v01_EN_M_v01_A_OCS_v01