Geographic coverage

National

Analysis unit

• Household
• Individual

Universe

18 of the 37 states in Nigeria were selected using procedures described in the methodology report

Kind of data

Sample survey data [ssd]

Sampling procedure

A. Sampling Frame The sampling frame was the 2006 National Population Census. For administrative purposes, Nigeria has 36 states and the Federal Capital Territory. These states are grouped into six geopolitical zones - the North Central, North East, North West, South East, South South and South West. The states in turn are divided into 776 Local Governments. The demographic and political characteristics of the states vary considerably. For example, the number of component local government areas in the states ranges from 8 in Bayelsa State (in the South South) to 44 in Kano State (in the North West). Likewise state populations vary widely from 1.41 million in the Abuja Federal Capital Territory to 9.38 million in Kano State. The National Bureau of Statistics splits the country further into 23, 070 enumeration areas (EAs). While the enumeration areas are equally distributed across the local government areas, with each local government area having 30 enumeration areas, the differences in the number of local government areas across states implies that there are also huge differences in the number of enumeration areas across states. Appendix table 1 summarizes the population according to the 2006 population census (in absolute and proportionate numbers), number of local government areas, and number of enumeration areas in each state .

Given the above, a stratified random sampling technique was thought to be needed to select areas according to population and the expected prevalence of migrants. The National Bureau of Statistics (NBS) provided a randomly selected set of enumeration areas and households spread across all states in the Federation from the 2006 sampling frame. Every state in Nigeria has three senatorial zones (often referred to as North, Central and South or East, Central and West). The NBS sample enumeration areas were distributed such that within each state, local government areas from each senatorial zones were included in the sample, with Local Governments in each state nearly evenly distributed between rural and urban areas. In all, a total of 3188 enumeration areas were selected. These enumeration areas were unevenly spread across States; some states in the North West (Kano, Katsina, and Jigawa), and a few in the South South (Akwa Ibom and Delta) had over 100 enumeration areas selected while others such as Imo and Abia in the South East, and Borno, Gombe and Taraba in the North East, had as few as 20 enumeration areas selected. This selection partially reflected the relative population distribution and number of Local Government Areas in the component states. Annex Table B shows details of the states and geopolitical regions, their shares in population of the country, the number of Local Government Areas and enumeration areas in each state and the number of enumeration areas given in the NBS list that formed the frame for the study.

B. The Sample for the Migration Survey

a. Sample Selection of States, Local Governments and Enumeration Areas Originally, the intention was to have proportionate allocation across all states, using the population of each state in the 2006 Census to select the number of households to be included in the sample. But it was later recognized that this would not yield enough migrant households, particularly those with international migrants, especially as the total number of households that could likely be covered in the sample to was limited to 2000. Consequently, a disproportionate sampling approach was adopted, with the aim of oversampling areas of the country with more migrants. According to Bilsborrow (2006), this approach becomes necessary because migrants are rare populations for which a distinct disproportionate sampling procedure is needed to ensure they are adequately captured. Given the relative rareness of households with out-migrants to international destinations within the 10 year reference period (selected by the World Bank for all countries) prior to the planned survey, sampling methods appropriate for sampling rare elements were desirable, specifically, stratified sampling with two-phase sampling at the last stage.

Establishing the strata would require that there be previous work, say from the most recent Census, to determine migration incidence among the states. However, the needed census data could not be obtained from either the National Bureau of Statistics or the National Population Commission. Therefore, the stratification procedure had to rely on available literature, particularly Hernandez-Coss and Bun (2007), Agu (2009) and a few other recent, smaller studies on migration and remittances in Nigeria. Information from this literature was supplemented by expert judgement about migration from team members who had worked on economic surveys in Nigeria in the past. Information from the literature and the expert assessment indicated that migration from households is considerably higher in the South than in the North. Following this understanding, the states were formed into two strata- those with high and those with low incidence of migration. In all, 18 States (16 in the South and 2 in the North) were put into the high migration incidence stratum while 19 states (18 in the North and 1 in the South) were classified l into the low migration incidence stratum (column C of Appendix Table 1).

The Aggregate population of the 18 states in the high migration incidence stratum was 67.04 million, spread across 10,850 Enumeration areas. Thus, the mean population of an EA in the high migration stratum was 6179. In turn, the aggregate population of the 19 states in the low migration incidence stratum was 72.95 million spread across 12,110 EAs yielding a mean EA population of 6024. These numbers were close enough to assume the mean population of EAs was essentially the same. To oversample states in the high stratum, it was decided to select twice as high a proportion of the states as in the low stratum. To further concentrate the sample and make field work more efficient in being oriented to EAs more likely to have international migrants, we decided to select randomly twice as many LGAs in each state in the high stratum states as in the low stratum states.

Thus, 12 states were randomly selected with probabilities of selection proportionate to the population size of each state (so states with larger populations were accordingly more likely to fall in the sample) from the high stratum states. Then two LGAs were randomly selected from each sample state and 2 EAs per sample LGA (one urban, one rural) to yield a total of 12 x 2 x 2 or 48 EAs in the high stratum states. For the low stratum, 6 states were randomly selected. From each of these, 1 LGA was randomly picked and 2 EAs were selected per sample LGA to give a total of 6 x 1 x 2 or 12 EAs in the low stratum. This yielded a total of 60 EAs for both strata. Given the expected range of 2000 households to be sampled, approximately 67 households were to be sampled from each local government area or 34 households from each enumeration area.

So far, the discussion has assumed two groups of households - migrant and non-migrant households. However, the study was interested in not just lumping all migrants together, but rather in classifying migrants according to whether their destination was within or outside the country. Migrant households were thus subdivided into those with former household members who were international migrants and those with former household members who were internal migrants. Three strata of households were therefore required, namely:

1. Households with an international migrant: at least one person who was a member of the household since Jan. 1, 2000 left to live in an international destination and has remained abroad;
2. Households with an internal migrant: at least one person who was a member of the household since Jan. 1, 2000 left to live elsewhere in Nigeria (outside the sample LGA) and has not returned to the LGA; and
3. Households with no migrant: No member of the household has left to live elsewhere either within or outside the country since Jan. 1, 2000.

The selection of states to be included in the sample from both strata was based on Probabilities of Selection Proportional to (Estimated) Size or PPES. The population in each stratum was cumulated and systematic sampling was performed, with an interval of 12.16 million for the low stratum (72.95 million divided by 6 States), and 5.59 million for the high stratum (67.04 million divided by 12 States). This yields approximately double the rate of sampling in the high migration stratum, as earlier explained. Using a random start between 0 and 12.16, the following states were sampled in the low stratum: Niger, Bauchi, Yobe, Kano, Katsina, and Zamfara. In the high stratum, states sampled were Abia, Ebonyi, Imo, Akwa Ibom, Delta, Edo, Rivers, Lagos, Ondo, Osun and Oyo. Given its large population size, Lagos fell into the sample twice. The final sample, with LGAs and EAs moving from North to South (i.e. from the low to the high stratum states) is presented in Table 1 below.

The sample was concentrated in the South since that is where it was expected that more households have international migrants. It was expected that the survey would still also be reasonably representative of the whole country and of both internal migrant and non-migrant households through weighting the data. To this effect, field teams were asked to keep careful track at all stages of the numbers of people and households listed compared to the number in the

The total population in Nigeria was estimated at 232.7 million people in 2024, according to the latest census figures and projections from Trading Economics. This dataset provides the latest reported value for - Nigeria Population - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Nigeria Local Government Area Boundaries provides a 2023 boundary with a total population count. The layer is designed to be used for mapping and analysis. It can be enriched with additional attributes using data enrichment tools in ArcGIS Online.The 2023 boundaries are provided by Michael Bauer Research GmbH. They are sourced from GRID3 Nigeria. These were published in October 2023. A new layer will be published in 12-18 months. Other administrative boundaries for this country are also available: Country State

Abstract

The primary mission of the 2006 Population and Housing Census (PHC) of Nigeria was to provide data for policy-making, evidence-based planning and good governance. The Government at all tiers, researchers, the academia, civil society organizations and the international agencies will find the sets of socio-demographic data useful in formulating developmental policies and planning. The 2006 data will certainly provide benchmarks for monitoring the Millennium Development Goals (MDGs). Enumeration in the 2006 PHC was conducted between March 21st and 27th 2006. It was designed to collect information on the quality of the population and housing, under the following broad categories: demographic and social, education, disability, household composition, economic activity, migration, housing and amenities, mortality and fertility. The results of the exercise are being released as per the Commission's Tabulation Plan which began with the release of the total enumerated persons by administrative areas in the country in the Official Gazette of the Federal Republic of Nigeria No.2, Vol 96 of February 2,2009 and followed with the release of Priority Tables that provide some detailed characteristics of the population of Nigeria by State and LGA.

Geographic coverage

National

Analysis unit

Individuals Households

Kind of data

Census/enumeration data [cen]

Mode of data collection

Face-to-face [f2f]

Cleaning operations

Census 2006 Processing: The Technology and Methodology:-

Unlike the data capture method used for the country’s previous censuses, where information from the census forms are typed into the computer system, data capture for census 2006 was carried out by OMR/OCR/ICR systems where questionnaires are scanned through high speed optical scanners. The choice of the scanning system was because it is faster and more accurate than the data keying method.

OMR/OCR/ICR Technology

Definition of terms

• OMR (Optical Mark Recognition) - This means the ability of the scanning machine to detect pencil marks made on the questionnaires by the Enumerators in accordance with the responses given by the respondents.
• OCR (Optical Character Recognition) - This means the ability of the scanning machine to recognize machine printed characters on the questionnaires.
• ICR (Intelligent Character Recognition) - This means the ability of the scanner to recognize characters hand written by the Enumerators in accordance with the responses given by the respondents.

Processing Procedures of Census 2006 at the DPCs:- Data processing took place in the Commission’s seven (7) Data Processing Centres located in different geographical zones in the country. There was absolute uniformity in the processing procedures in the seven DPCs.

(a) Questionnaire Retrieval/Archiving Questionnaires from the fields were taken directly from the Local Government Areas to designated DPCs. The forms on arrival at the DPCs were counted, archived and labeled. Retrieval of the questionnaires at the DPCs were carried out based on the EA frame received from the Cartography Department. Necessary Transmittal Forms are completed on receipt of the Forms at the DPCs. The Transmittal Forms are also used to keep track of questionnaires movement within the DPC.

(b) Forms Preparation The scanning machine has been designed to handle A4 size paper. And the Census form being twice that size has to be split into two through the dotted lines at the middle of the form. This forms preparation procedure is to get the questionnaires, for each Enumeration Areas (EAs), ready for scanning. There is a Batch Header to identify each batch.

(c) Scanning Each Batch on getting to the Scanning Room was placed on joggers (a vibrating machine)to properly align the forms, and get rid of dust or particles that might be on the forms.

The forms are thereafter fed into the scanner. There were security codes in form of bar codes on each questionnaire to identify its genuineness. There was electronic editing and coding for badly coded or poorly shaded questionnaires by the Data Editors. Torn, stained or mutilated forms are rejected by the scanner. These categories of forms were later manually keyed into the system.

Re-archiving of Scanned Forms:- Scanned forms were placed in their appropriate marked envelopes in batches, and thereafter returned to the Archiving Section for re-archiving.

Data Output from the Scanning Machine:- The OMR/OCR Software interprets the output from the scanner and translates it into an XML file from where it is further translated into the desired ASCII output that is compatible for use by the CSPro Package for further processing and tabulation.

Data back-up and transfer:- After being sure that the data are edited for each EA batch in an LGA, data then was exported to the SAN (Storage Area Network) of the Server. Two copies of images of the questionnaires for each EA copied to the LTO tapes as backup and then transferred to the Headquarters. The ASCII data files for each LGA are zipped and encrypted, and thereafter transfer to the Data Validation Unit (DVU) at the Headquarters in Abuja.

Data appraisal

Data collation and validation:- The Data Validation Unit at the Headquarters was responsible for collating these data into EAs, LGAs, States and National levels. The data are edited/validated for consistency errors and invalid entries. The Census and Survey Processing (CSPro) software is used for this process. The edited, and error free data are thereafter processed into desired tables.

Activities of the Data Validation unit (DVU):-

Decryption of each LGA Data File Concatenation/merging of Data Files Check each EA batch file for EA completeness within an LGA and State Check for File/Data Structure Check for Range and Invalid Data items Check for Blank and empty questionnaire Check for inter and intra record consistency Check for Skip Patterns Perform Data Validation and Imputation Generate Statistics Report of each function/activity Generate Statistical Tables on LGA, State and National levels.

Lagos, in Nigeria, ranked as the most populated city in Africa as of 2024, with an estimated population of roughly nine million inhabitants living in the city proper. Kinshasa, in Congo, and Cairo, in Egypt, followed with some 7.8 million and 7.7 million dwellers. Among the 15 largest cities in the continent, another two, Kano, and Ibadan, were located in Nigeria, the most populated country in Africa. Population density trends in Africa As of 2022, Africa exhibited a population density of 48.3 individuals per square kilometer. At the beginning of 2000, the population density across the continent has experienced a consistent annual increment. Projections indicated that the average population residing within each square kilometer would rise to approximately 54 by the year 2027. Moreover, Mauritius stood out as the African nation with the most elevated population density, exceeding 640 individuals per square kilometre. Mauritius possesses one of the most compact territories on the continent, a factor that significantly influences its high population density. Urbanization dynamics in Africa The urbanization rate in Africa was anticipated to reach close to 44 percent in 2021. Urbanization across the continent has consistently risen since 2000, with urban areas accommodating 35 percent of the total population. This trajectory is projected to continue its ascent in the years ahead. Nevertheless, the distribution between rural and urban populations shows remarkable diversity throughout the continent. In 2021, Gabon and Libya stood out as Africa’s most urbanized nations, each surpassing 80 percent urbanization. In 2023, Africa's population was estimated to expand by 2.35 percent compared to the preceding year. Since 2000, the population growth rate across the continent has consistently exceeded 2.45 percent, reaching its pinnacle at 2.59 percent between 2012 and 2013. Although the growth rate has experienced a deceleration, Africa's population will persistently grow significantly in the forthcoming years.

Population of Lagos State, Nigeria, by Local Government Area in 1996 and 2006.

Foraging for wild resources links urban citizens to nature and biodiversity while providing resources important for local livelihoods and culture. However, the abundance and distributional (in)equity of forageable urban tree resources have rarely been examined. Consequently, this study assessed the abundance of forageable street trees and their distribution in Lagos metropolis, Nigeria. During a survey of 32 randomly selected wards across 16 local government areas (LGAs) in the metropolis, 4,017 street trees from 46 species were enumerated. The LGA with the highest number of street trees was Ikeja, with 818 trees, while Lagos Island had the lowest count, with two trees. This disparity in tree numbers could be attributed to variations in human population density within each LGA. Ninety-four percent of the street trees surveyed had at least one documented use and 76 % had two, and thus were potentially forageable. However, the most common species had relatively low forageability scores. O..., All 16 LGAs were chosen and two wards from each LGA were randomly sampled, resulting in a total of 32 wards. Street trees were defined as "trees located in or near roads or streets" (Thomsen et al. 2016) for the purpose of this research. The road network dataset for Nigeria, which includes main roads, was obtained from the OpenStreetMap data and prepared by the World Food Programme (WFP) following the United Nations Spatial Data Infrastructure standards. This dataset was used to count and identify all trees on both sides of every street in the selected wards. The size, or basal diameter, of trees on the left side of the street was subsequently measured. The location of each tree was recorded using a handheld Garmin GPS 64x device. Furthermore, the usability ratings (edible, medicinal, and other uses) of the surveyed species were recorded based on the information provided by the “Useful Tropical Plants Database†(https://tropical.theferns.info/). The edible and medicinal usability rating..., , # The abundance and distributional (in)equalities of forageable street tree resources in Lagos Metropolis, Nigeria

https://doi.org/10.5061/dryad.pzgmsbcwf

Description of the data and file structure

The data contains street tree data in the Lagos metropolis. Thirty-two wards (32) were selected in Lagos Metropolis. Each worksheet was labeled by the ward name, making 32 worksheets in the Excel workbook.Â

Each Worksheet represents:

S/N – the serial number of species surveyed in a particular ward

Family – this is the family the species belongs to

Latin name – this represents the scientific name or full name of the species. Genus and specific epithet

Common name - this is the name known to the general public

BC (cm) - means basal circumference, the measurement of a tree at 4.5ft above the ground

Pi = 3.142

BA (cm) - means basal diameter, calculated by dividing the BC by Pi

ud - represents an unidentifiable stem

Abstract

The primary objective of the 2018 NDHS is to provide up-to-date estimates of basic demographic and health indicators. Specifically, the NDHS collected information on fertility, awareness and use of family planning methods, breastfeeding practices, nutritional status of women and children, maternal and child health, adult and childhood mortality, women’s empowerment, domestic violence, female genital cutting, prevalence of malaria, awareness and behaviour regarding HIV/AIDS and other sexually transmitted infections (STIs), disability, and other health-related issues such as smoking.

The information collected through the 2018 NDHS is intended to assist policymakers and programme managers in evaluating and designing programmes and strategies for improving the health of the country’s population. The 2018 NDHS also provides indicators relevant to the Sustainable Development Goals (SDGs) for Nigeria.

Geographic coverage

National coverage

Analysis unit

• Household
• Individual
• Children age 0-5
• Woman age 15-49
• Man age 15-49

Universe

The survey covered all de jure household members (usual residents), all women aged 15-49 years resident in the household, and all children aged 0-5 years resident in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling frame used for the 2018 NDHS is the Population and Housing Census of the Federal Republic of Nigeria (NPHC), which was conducted in 2006 by the National Population Commission. Administratively, Nigeria is divided into states. Each state is subdivided into local government areas (LGAs), and each LGA is divided into wards. In addition to these administrative units, during the 2006 NPHC each locality was subdivided into convenient areas called census enumeration areas (EAs). The primary sampling unit (PSU), referred to as a cluster for the 2018 NDHS, is defined on the basis of EAs from the 2006 EA census frame. Although the 2006 NPHC did not provide the number of households and population for each EA, population estimates were published for 774 LGAs. A combination of information from cartographic material demarcating each EA and the LGA population estimates from the census was used to identify the list of EAs, estimate the number of households, and distinguish EAs as urban or rural for the survey sample frame. Before sample selection, all localities were classified separately into urban and rural areas based on predetermined minimum sizes of urban areas (cut-off points); consistent with the official definition in 2017, any locality with more than a minimum population size of 20,000 was classified as urban.

The sample for the 2018 NDHS was a stratified sample selected in two stages. Stratification was achieved by separating each of the 36 states and the Federal Capital Territory into urban and rural areas. In total, 74 sampling strata were identified. Samples were selected independently in every stratum via a two-stage selection. Implicit stratifications were achieved at each of the lower administrative levels by sorting the sampling frame before sample selection according to administrative order and by using a probability proportional to size selection during the first sampling stage.

For further details on sample selection, see Appendix A of the final report.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Four questionnaires were used for the 2018 NDHS: the Household Questionnaire, the Woman’s Questionnaire, the Man’s Questionnaire, and the Biomarker Questionnaire. The questionnaires, based on The DHS Program’s standard Demographic and Health Survey (DHS-7) questionnaires, were adapted to reflect the population and health issues relevant to Nigeria. Comments were solicited from various stakeholders representing government ministries and agencies, nongovernmental organisations, and international donors. In addition, information about the fieldworkers for the survey was collected through a self-administered Fieldworker Questionnaire.

Cleaning operations

The processing of the 2018 NDHS data began almost immediately after the fieldwork started. As data collection was completed in each cluster, all electronic data files were transferred via the IFSS to the NPC central office in Abuja. These data files were registered and checked for inconsistencies, incompleteness, and outliers. The field teams were alerted to any inconsistencies and errors. Secondary editing, carried out in the central office, involved resolving inconsistencies and coding the open-ended questions. The NPC data processor coordinated the exercise at the central office. The biomarker paper questionnaires were compared with electronic data files to check for any inconsistencies in data entry. Data entry and editing were carried out using the CSPro software package. The concurrent processing of the data offered a distinct advantage because it maximised the likelihood of the data being error-free and accurate. Timely generation of field check tables allowed for effective monitoring. The secondary editing of the data was completed in the second week of April 2019.

Response rate

A total of 41,668 households were selected for the sample, of which 40,666 were occupied. Of the occupied households, 40,427 were successfully interviewed, yielding a response rate of 99%. In the households interviewed, 42,121 women age 15-49 were identified for individual interviews; interviews were completed with 41,821 women, yielding a response rate of 99%. In the subsample of households selected for the male survey, 13,422 men age 15-59 were identified and 13,311 were successfully interviewed, yielding a response rate of 99%.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: nonsampling errors and sampling errors. Nonsampling errors are the results of mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the 2018 Nigeria Demographic and Health Survey (NDHS) to minimise this type of error, nonsampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the 2018 NDHS is only one of many samples that could have been selected from the same population, using the same design and expected size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling errors are a measure of the variability among all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results.

Sampling error is usually measured in terms of the standard error for a particular statistic (mean, percentage, etc.), which is the square root of the variance. The standard error can be used to calculate confidence intervals within which the true value for the population can reasonably be assumed to fall. For example, for any given statistic calculated from a sample survey, the value of that statistic will fall within a range of plus or minus two times the standard error of that statistic in 95% of all possible samples of identical size and design.

If the sample of respondents had been selected as a simple random sample, it would have been possible to use straightforward formulas for calculating sampling errors. However, the 2018 NDHS sample is the result of a multistage stratified design, and, consequently, it was necessary to use more complex formulas. Sampling errors are computed in SAS, using programs developed by ICF. These programs use the Taylor linearisation method to estimate variances for survey estimates that are means, proportions, or ratios. The Jackknife repeated replication method is used for variance estimation of more complex statistics such as fertility and mortality rates.

Note: A more detailed description of estimates of sampling errors are presented in APPENDIX B of the survey report.

Data appraisal

Data Quality Tables - Household age distribution - Age distribution of eligible and interviewed women - Age distribution of eligible and interviewed men - Completeness of reporting - Births by calendar years - Reporting of age at death in days - Reporting of age at death in months - Standardisation exercise results from anthropometry training - Height and weight data completeness and quality for children - Height measurements from random subsample of measured children - Sibship size and sex ratio of siblings - Pregnancy-related mortality trends - Data collection period - Malaria prevalence according to rapid diagnostic test (RDT)

Note: See detailed data quality tables in APPENDIX C of the report.

Abstract

The 2018 Nigeria Demographic and Health Survey (2018 NDHS) was implemented by the National Population Commission (NPC). Data collection took place from 14 August to 29 December 2018. ICF provided technical assistance through The DHS Program, which is funded by the United States Agency for International Development (USAID) and offers financial support and technical assistance for population and health surveys in countries worldwide. Other agencies and organisations that facilitated the successful implementation of the survey through technical or financial support were the Global Fund, the Bill and Melinda Gates Foundation (BMGF), the United Nations Population Fund (UNFPA), and the World Health Organization (WHO).

SURVEY OBJECTIVES The primary objective of the 2018 NDHS is to provide up-to-date estimates of basic demographic and health indicators. Specifically, the NDHS collected information on fertility, awareness and use of family planning methods, breastfeeding practices, nutritional status of women and children, maternal and child health, adult and childhood mortality, women’s empowerment, domestic violence, female genital cutting, prevalence of malaria, awareness and behaviour regarding HIV/AIDS and other sexually transmitted infections (STIs), disability, and other health-related issues such as smoking.

The information collected through the 2018 NDHS is intended to assist policymakers and programme managers in evaluating and designing programmes and strategies for improving the health of the country’s population. The 2018 NDHS also provides indicators relevant to the Sustainable Development Goals (SDGs) for Nigeria.

Geographic coverage

national coverage

Analysis unit

Households Women Men children

Universe

the survey covered all household members (permanent residents and visitor), all Women aged 15-49 years, all children 0-59 months and all men aged 15-59 years in one-third of households

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling frame used for the 2018 NDHS is the Population and Housing Census of the Federal Republic of Nigeria (NPHC), which was conducted in 2006 by the National Population Commission. Administratively, Nigeria is divided into states. Each state is subdivided into local government areas (LGAs), and each LGA is divided into wards. In addition to these administrative units, during the 2006 NPHC each locality was subdivided into convenient areas called census enumeration areas (EAs). The primary sampling unit (PSU), referred to as a cluster for the 2018 NDHS, is defined on the basis of EAs from the 2006 EA census frame. Although the 2006 NPHC did not provide the number of households and population for each EA, population estimates were published for 774 LGAs. A combination of information from cartographic material demarcating each EA and the LGA population estimates from the census was used to identify the list of EAs, estimate the number of households, and distinguish EAs as urban or rural for the survey sample frame. Before sample selection, all localities were classified separately into urban and rural areas based on predetermined minimum sizes of urban areas (cut-off points); consistent with the official definition in 2017, any locality with more than a minimum population size of 20,000 was classified as urban.

The sample for the 2018 NDHS was a stratified sample selected in two stages. Stratification was achieved by separating each of the 36 states and the Federal Capital Territory into urban and rural areas. In total, 74 sampling strata were identified. Samples were selected independently in every stratum via a two-stage selection. Implicit stratifications were achieved at each of the lower administrative levels by sorting the sampling frame before sample selection according to administrative order and by using a probability proportional to size selection during the first sampling stage.

In the first stage, 1,400 EAs were selected with probability proportional to EA size. EA size was the number of households in the EA. A household listing operation was carried out in all selected EAs, and the resulting lists of households served as a sampling frame for the selection of households in the second stage. In the second stage’s selection, a fixed number of 30 households was selected in every cluster through equal probability systematic sampling, resulting in a total sample size of approximately 42,000 households. The household listing was carried out using tablets, and random selection of households was carried out through computer programming. The interviewers conducted interviews only in the pre-selected households. To prevent bias, no replacements and no changes of the pre-selected households were allowed in the implementing stages.

Due to the non-proportional allocation of the sample to the different states and the possible differences in response rates, sampling weights were calculated, added to the data file, and applied so that the results would be representative at the national level as well as the domain level. Because the 2018 NDHS sample was a two-stage stratified cluster sample selected from the sampling frame, sampling weights were calculated based on sampling probabilities separately for each sampling stage and for each cluster.

The survey was successfully carried out in 1,389 clusters after 11 clusters with deteriorating law-and-order situations during fieldwork were dropped. These areas were in Zamfara (4 clusters), Lagos (1 cluster), Katsina (2 clusters), Sokoto (3 clusters), and Borno (1 cluster). In the case of Borno, 11 of the 27 LGAs were dropped due to high insecurity, and therefore the results might not represent the entire state. Please refer to Appendix A in the final report for details.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Four questionnaires were used for the 2018 NDHS: the Household Questionnaire, the Woman’s Questionnaire, the Man’s Questionnaire, and the Biomarker Questionnaire. The questionnaires, based on The DHS Program’s standard Demographic and Health Survey (DHS-7) questionnaires, were adapted to reflect the population and health issues relevant to Nigeria. Comments were solicited from various stakeholders representing government ministries and agencies, nongovernmental organisations, and international donors. In addition, information about the fieldworkers for the survey was collected through a self-administered Fieldworker Questionnaire.

The survey protocol was reviewed and approved by the National Health Research Ethics Committee of Nigeria (NHREC) and the ICF Institutional Review Board. After all questionnaires were finalised in English, they were translated into Hausa, Yoruba, and Igbo. The 2018 NDHS used computer-assisted personal interviewing (CAPI) for data collection.

The Household Questionnaire listed all members of and visitors to selected households. Basic demographic information was collected on each person listed, including age, sex, marital status, education, and relationship to the head of the household. For children under age 18, survival status of parents was determined. Data on age, sex, and marital status of household members were used to identify women and men who were eligible for individual interviews. The Household Questionnaire also collected information on characteristics of the household’s dwelling unit, such as source of drinking water; type of toilet facilities; materials used for flooring, external walls, and roofing; ownership of various durable goods; and ownership of mosquito nets. In addition, data were gathered on salt testing and disability.

The Woman’s Questionnaire was used to collect information from all eligible women age 15-49. These women were asked questions on the following topics: - Background characteristics (including age, education, and media exposure) - Birth history and child mortality - Knowledge, use, and source of family planning methods - Antenatal, delivery, and postnatal care - Vaccinations and childhood illnesses - Breastfeeding and infant feeding practices - Women’s minimum dietary diversity - Marriage and sexual activity - Fertility preferences (including desire for more children and ideal number of children) - Women’s work and husbands’ background characteristics - Knowledge, awareness, and behaviour regarding HIV/AIDS and other sexually transmitted infections (STIs) - Knowledge, attitudes, and behaviour related to other health issues (e.g., smoking) - Female genital cutting - Fistula - Adult and maternal mortality - Domestic violence

The Man’s Questionnaire was administered to all men age 15-59 in the subsample of households selected for the men’s survey. The Man’s Questionnaire collected much of the same information as the Woman’s Questionnaire but was shorter because it did not contain a detailed reproductive history or questions on maternal and child health.

The Biomarker Questionnaire was used to record the results of anthropometry measurements and other biomarkers for women and children. This questionnaire was administered only to the subsample selected for the men’s survey. All children age 0-59 months and all women age 15-49 were eligible for height and weight measurements. Women age 15-49 were also eligible for haemoglobin testing. Children age 6-59 months were also eligible for haemoglobin testing, malaria testing, and genotype testing for sickle cell disease.

The purpose of the Fieldworker Questionnaire was to collect basic background information on the people who were collecting data in the field, including the team supervisor, field editor, interviewers, and the biomarker team

Chart and table of population level and growth rate for the Gwagwalada, Nigeria metro area from 1950 to 2025.

From January 2022 and February 2023, the Nigerian government allocated the highest disbursements to Delta, Akwa Ibom, Rivers, Bayelsa, Lagos, Kano, and Enugu. These states received over 100 billion Naira each, with Delta receiving almost ***** billion Naira. The FAAC (Federal Account Allocation Committee) disbursements represent the funds given to states and regions. They are allocated in proportion to each state population and, consequently, to the number of local governments in the state.

Abstract

The 2010 Nigeria Malaria Indicator Survey (2010 NMIS) was implemented by the National Population Commission (NPC) and the National Malaria Control Programme (NMCP). ICF International provided technical assistance through the MEASURE DHS programme, a project funded by the United States Agency for International Development (USAID), which provides support and technical assistance in the implementation of population and health surveys in countries worldwide. It was carried out from October to December 2010 on a nationally representative sample of more than 6,000 households. All women age 15-49 in the selected households were eligible for individual interviews. During the interviews, they were asked questions about malaria prevention during pregnancy and the treatment of fever among their children. In addition, the survey included testing for anaemia and malaria among children age 6-59 months using finger (or heel) prick blood samples. Test results were available immediately and were provided to the children’s parents or guardians. Thick blood smears and thin blood films were also made in the field and transported to the Department of Medical Microbiology and Parasitology at the College of Medicine, University of Lagos. Microscopy was performed to determine the presence of malaria parasites and to identify the parasite species. Slide validation was carried out by the University of Calabar Teaching Hospital in Calabar.

The 2009-2013 National Strategic Plan for Malaria Control in Nigeria aims to massively scale up malaria control interventions in parts of the country. The 2010 Nigeria Malaria Indicator Survey (NMIS) was, therefore, designed to measure progress toward achieving the goals and targets of this strategic plan by providing data on key malaria indicators, including ownership and use of bed nets, diagnosis and prompt treatment of malaria using artemisinin-based therapy (ACT), indoor residual spraying, and behaviour change communication.

The following are the specific objectives of the 2010 NMIS: - To measure the extent of ownership and use of mosquito bed nets - To assess the coverage of intermittent and preventive treatment programmes for pregnant women - To identify practices used to treat malaria among children under age 5 and the use of specific antimalarial medications - To measure the prevalence of malaria and anaemia among children age 6-59 months - To determine the species of plasmodium parasite most prevalent in Nigeria - To assess knowledge, attitudes, and practices regarding malaria in the general population

Geographic coverage

National

Analysis unit

• Household,
• Individual.

Universe

The survey covered all de jure household members (usual residents), all women aged between 15-49 years, and all children age 6-59 months living in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

The 2010 Nigeria Malaria Indicator Survey (NMIS) called for a nationally representative sample of about 6,000 households. The survey is designed to provide information on key malaria-related indicators including mosquito net ownership and use, coverage of preventive treatment for pregnant women, treatment of childhood fever, and the prevalence of anaemia and malaria among children age 6-59 months. The sample for the 2010 NMIS was designed to provide most of these indicators for the country as a whole, for urban and rural areas separately, and for each of the six zones formed by grouping the 36 states and the Federal Capital Territory (FCT). The zones are as follows: 1. North Central: Benue, FCT-Abuja, Kogi, Kwara, Nasarawa, Niger, and Plateau 2. North East: Adamawa, Bauchi, Borno, Gombe, Taraba, and Yobe 3. North West: Jigawa, Kaduna, Kano, Katsina, Kebbi, Sokoto, and Zamfara 4. South East: Abia, Anambra, Ebonyi, Enugu, and Imo 5. South South: Akwa Ibom, Bayelsa, Cross River, Delta, Edo, and Rivers 6. South West: Ekiti, Lagos, Ogun, Ondo, Osun, and Oyo

SAMPLING FRAME The sampling frame used for the 2010 NMIS was the Population and Housing Census of the Federal Republic of Nigeria, which was conducted in 2006 by the National Population Commission (NPC). Administratively, Nigeria is divided into states. Each state is subdivided into local government areas (LGAs), and each LGA is divided into localities. In addition to these administrative units, during the 2006 Population Census, each locality was subdivided into convenient areas called census enumeration areas (EAs). The primary sampling unit (PSU), referred to as a cluster for the 2010 NMIS, is defined on the basis of EAs from the 2006 EA census frame.

Although the 2006 Population Census did not provide the number of households and population for each EA, population estimates were published for more than 800 LGA units. A combination of information from cartographic material demarcating each EA and the LGA population estimates from the census were used to identify the list of EAs, estimate the number of households, and distinguish EAs as urban or rual for the survey sample frame.

SAMPLE ALLOCATION The 2010 NMIS sample was selected using a stratified, two-stage cluster design consisting of 240 clusters, 83 in the urban areas and 157 in the rural areas. (The final sample included 239 clusters because access to one cluster was prevented by inter-communal disturbances.) A sample of 6,240 households was selected for the survey, with a minimum target of 920 completed individual women's interviews per zone. Within each zone, the number of households was distributed proportionately among urban and rural areas. A fixed 'take' of 26 households per cluster was adopted for both urban and rural clusters.

SAMPLING PROCEDURE AND UPDATING OF THE SAMPLING FRAME The 2010 NMIS sample is a stratified sample selected in two stages. The primary sampling units (PSUs) are the enumeration areas (EAs) from the 2006 census, and the secondary sampling units (SSUs) are the households. In the first stage of selection, the 240 EAs were selected with a probability proportional to the size of the EA, where size is the number of approximate households calculated within the sampling frame.

A complete listing of households and a mapping exercise for each cluster was carried out from August through September 2010. The lists of households resulting from this exercise served as the sampling frame for the selection of households in the second stage. In addition to listing the households, the NPC listing enumerators used global positioning system (GPS) receivers to record the coordinates of the 2010 NMIS sample clusters.

In the second stage of the selection process, 26 households were selected in each cluster by equal probability systematic sampling. All women age 15-49 who were either permanent residents of the households in the 2010 NMIS sample or visitors present in the households on the night before the survey were eligible to be interviewed. In addition, all children age 6-59 months were eligible to be tested for malaria and anaemia.

The sampling procedures are fully described in Appendix A of "Nigeria Malaria Indicator Survey 2010 - Final Report" pp.69-71.

Mode of data collection

Face-to-face [f2f]

Research instrument

Two questionnaires were used in the NMIS: a Household Questionnaire and a Woman’s Questionnaire, which was administered to all women age 15-49 in the selected households. Both instruments were based on the standard Malaria Indicator Survey Questionnaires developed by the Roll Back Malaria and DHS programmes. These questionnaires were adapted to reflect the population and health issues relevant to Nigeria during a series of meetings convened with various stakeholders from the NMCP and other government ministries and agencies, nongovernmental organisations, and international donors. The questionnaires were translated into three major Nigerian languages: Hausa, Igbo, and Yoruba.

The Household Questionnaire was used to list all the usual members and visitors in the selected households. Some basic information was collected on the characteristics of each person listed, including age, sex, and relationship to the head of the household. The main purpose of the Household Questionnaire was to identify women who were eligible for the individual interview and children age 6-59 months who were eligible for anaemia and malaria testing. The Household Questionnaire also collected information on characteristics of the household’s dwelling unit, such as the source of water; type of toilet facilities; materials used for the floor, roof, and walls of the house; ownership of various durable goods; and ownership and use of mosquito nets. In addition, the questionnaire was used to record the results of the anaemia and malaria testing as well as the signatures of the interviewer and the respondent who gave consent. Children’s temperatures were also recorded.

The Woman’s Questionnaire was used to collect information from all women age 15-49. These women were asked questions on the following main topics: - Background characteristics (such as age, residence, education, media exposure, and literacy) - Birth history and childhood mortality - Antenatal care and malaria prevention for most recent birth and pregnancy - Malaria prevention and treatment - Knowledge about malaria (symptoms, causes, prevention, and drugs used in treatment)

Cleaning operations

The processing of data for the 2010 NMIS ran concurrently with data collection. Completed questionnaires were retrieved by the zonal coordinators or the trainers and delivered to NPC in standard envelopes, labelled with the sample ID, team number, and state name. The shipment also contained a written summary of any

Abstract

The 1999 Nigeria Multiple Indicator Cluster Survey has as its primary objectives: • To provide up-to-date information for assessing the situation of children and women in Nigeria at the end of the decade and for looking forward to the next decade; • To furnish data needed for monitoring progress toward goals established at the World Summit for Children and a basis for future action; • To contribute to the improvement of data and monitoring systems in Nigeria and to strengthen technical expertise in the design, implementation, and analysis of such systems.

The Multiple Indicator Cluster Survey (MICS) is conceptualized to monitor the progress of Child Survival, Development, Protection and Participation (CSPPD) Programmes as well as goals set at the World Summit for Children in 1990. Also, at the World Summit for Social Development in 1995, the need was stressed for better social statistics if social development had to move to centre stage for the cause of the children of the world. In 1995, Federal Office of Statistics (FOS) with technical and funding assistance from UNICEF, institutionalized the Multiple Indicator Survey within the National Integrated Survey of Households (NISH) as a process of collection of regular, reliable and timely social statistics. A technical team, the Multiple Indicator Cluster Survey Intersectoral Task Force (MIT), consisting of all stakeholders was put in place for the 1999 survey to plan, conduct and monitor the survey with FOS providing the leadership. This was an innovation over the previous survey, which greatly enhanced the quality of the work and coverage of programmes.

Nevertheless, this report would have been impossible without the commitments of the following organizations and individuals. Firstly, members of the Multiple Indicator Cluster Survey Inter-sectoral Taskforce (MIT) which facilitated the conduct and over-seeing of the survey. UNICEF Nigeria which gave technical support in the areas of data processing and analysis and report writing through hiring of consultants that worked closely with FOS teams.

This report is another dream to match deeds with words. This report is also unique in the sense that the findings will allow comparison of performance at sub-national (state) and inter national levels. The report will additionally serve as statistical input into future editions of Progress of Nigerian Children Report and UNICEF's State of the World's Children. It is hoped that it will be widely used by various levels of government, Federal and State for programmes and projects monitoring and evaluation on social development and reengineering for the development of the cause of Nigerian Children. It is also an excellent report for top policy formulators and programme managers in the key social sectors.

Geographic coverage

Nigeria is Africa's most populous nation with an estimated 1999 population of 124 million. The country is located in West Africa, bordered on the west by the Republic of Benin, on the north by the Republic of Niger and on the East by the Republic of Cameroun. The country occupies a land area of 923, 768 square kilometres and the vegetation ranges from mangrove forest on the coast to desert in the far north.

Administratively, Nigeria consists of 36 states and a Federal Capital territory. These are further divided into 774 local government areas (LGA). For most of her history since independence in 1960, Nigeria has been under military rule. Nigeria returned to democratic rule in May 1999 under a presidential system of government with three tiers of government: federal, state and local. The federal government comprises an executive arm (led by the President), a bicameral legislative arm (Senate and House of Representatives) and a judiciary. Each state has its own governor and house of assembly while each local government has a chairman and council.

The country has abundant natural resources. Major agricultural products include cocoa, rubber, groundnuts, palm oil, cotton, cassava, yam, corn, millet and rice. Mineral resources include petroleum, coal, tin, columbite and gold. However, petroleum has been the mainstay of foreign exchange earnings for Nigeria in the last three decades. Indeed, Nigeria is the sixth largest producer of crude oil in the world and earns several billion US dollars annually from sales of crude oil alone. However, this has not translated to a healthy national economy due to decades of mismanagement and corruption under dictatorial government by successive military regimes. Thus, at the end of the decade (1999), GDP was only US $310. Nigeria's external debt stood at about $32 billion and it was estimated that the government spends about 40% of its earnings servicing foreign debts. The economic hardship during the 1990's meant that social sector spending was far less than required. The health and education sectors, in particular, were deprived of much needed support and funding. It is only with the advent of democratic governance at the end of the decade that the social sector started receiving increased attention.

Nigeria has one of the most ethnically-diverse populations in the world, with more than 380 distinct ethnic groups. The major ethnic groups include (in alphabetical order) the Edo, Efik, Fulani, Hausa, Igbo, Ijaw, Kanuri, Tiv, Urhobo and Yoruba. Population growth has been quite rapid, from 55.7 million in the 1963 national census to 88.5 million in the 1991 census. For much of this period, total fertility rate was over 6 per woman. The population is quite young with 47 percent being under 18 years of age. Children under the age of five years comprise about 20 percent of the population and women of child bearing age another 20 percent of the population.

Under-five mortality has remained over 100 per thousand over the 1990's, being 192 per thousand in 1990 (NDHS 1990) and 147 per thousand in 1995 (MICS 1995). Maternal mortality ratio was estimated to be 800 per thousand in 1995 (The Progress of Nations 1995). Over one-third (36 percent) of under-five children were underweight in 1990. Thus, social indicators show that Nigeria is a country with low GDP, high external debt burden, high child and maternal mortality and high fertility.

Analysis unit

Household level

Universe

2.1 Sample Design 2.1.1 Introduction of NISH Design 1993/99

The Multiple Indicator Cluster Survey (MICS) 1999 was run as a module of the National Integrated Survey of Households (NISH) design. NISH is the Nigerian version of the United Nations National Household Survey Capability Programme and is a multi-subject household based survey system. It is an ongoing programme of household based surveys enquiring into various aspects of households, including housing, health, education and employment. The programme started in 1981 after a pilot study in 1980. The design utilizes a probability sample drawn using a random sampling method at the national and sub-national levels.

The main features of the NISH design are:

Multi-Phase Sampling: In each state 800 EAs were selected with equal probability as first phase samples. A second phase sample of 200 EAs was selected with probability proportional to size.

Multi-Stage Sampling Design: A two-stage design was used. Enumeration Areas were used as the first stage sampling units and Housing Units (HUs) as the second stage sampling units.

Replicated Rotatable Design: Two hundred EAs were selected in each state in 10 independent replicates of 20 EAs per replicate. A rotation was imposed which ensured 6 replicates to be studied each survey year but in subsequent year a replicate is dropped for a new one, that is, a rotation of 1/6 was applied. This means in a survey year, 120 EAs will be covered in each state. In the Federal Capital Territory (Abuja), 60 EAs are covered.

Master Sample: The EAs and HUs selected constitute the Master Sample and subsets were taken for various surveys depending on the nature of the survey and the sample size desired. In any one-year, the 120 EAs are randomly allocated to the 12 months of the year for the survey. The General Household Survey (GHS) is the core module of NISH. Thus, every month 10 EAs are covered for the GHS. For other supplemental modules of NISH, subsets of the master sample are used. The MICS 1999 was run as a module of NISH.

2.1.2 Sample Size

The global MICS design anticipated a sample of 300-500 households per district (domain). This was based on the assumption of a cluster design with design effect of about 2, an average household size of 6, children below the age of 5 years constituting 15 percent of the population and a diarrhoea prevalence of 25 percent. Such a sample would give estimates with an error margin of about 0.1 at the district level. Such a sample would usually come from about 10 clusters of 40 to 50 households per cluster.

In Nigeria, the parameters are similar to the scenario described above. Average household size varied from 3.0 to 5.6 among the states, with a national average of about 5.5. Similarly, children below 5 years constituted between 15-16 percent of total population. Diarrhoea prevalence had been estimated at about 15 percent. These figures have led to sample sizes of between 450 and 660 for each state.

It was decided that a uniform sample of 600 households per state be chosen for the survey. Although non-response, estimated at about 5 percent from previous surveys reduced the sample further, most states had 550 or more households. The MICS sample was drawn from the National Master Sample for the 1998/99 NISH programme implemented by the Federal Office of Statistics (FOS).

The sample was drawn from 30 EAs in each state with a sub-sample of 20 households selected per EA. The design was more efficient than the global MICS design which anticipated a cluster

Abstract

The 2021 Nigeria Malaria Indicator Survey (NMIS) was implemented by the National Malaria Elimination Programme (NMEP) of the Federal Ministry of Health (FMoH) in collaboration with the National Population Commission (NPC) and National Bureau of Statistics (NBS).

The primary objective of the 2021 NMIS was to provide up-to-date estimates of basic demographic and health indicators related to malaria. Specifically, the NMIS collected information on vector control interventions (such as mosquito nets), intermittent preventive treatment of malaria in pregnant women, exposure to messages on malaria, care-seeking behaviour, treatment of fever in children, and social and behaviour change communication (SBCC). Children age 6–59 months were also tested for anaemia and malaria infection. The information collected through the NMIS is intended to assist policymakers and programme managers in evaluating and designing programmes and strategies for improving the health of the country’s population.

Geographic coverage

National coverage

Analysis unit

• Household
• Individual
• Woman age 15-49

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample for the 2021 NMIS was designed to provide most of the survey indicators for the country as a whole, for urban and rural areas separately, and for each of the country’s six geopolitical zones, which include 36 states and the Federal Capital Territory (FCT). Nigeria’s geopolitical zones are as follows: • North Central: Benue, Kogi, Kwara, Nasarawa, Niger, Plateau, and FCT • North East: Adamawa, Bauchi, Borno, Gombe, Taraba, and Yobe • North West: Jigawa, Kaduna, Kano, Katsina, Kebbi, Sokoto, and Zamfara • South East: Abia, Anambra, Ebonyi, Enugu, and Imo • South South: Akwa Ibom, Bayelsa, Cross River, Delta, Edo, and Rivers • South West: Ekiti, Lagos, Ogun, Osun, Ondo, and Oyo

The 2021 NMIS used the sample frame for the proposed 2023 Population and Housing Census (PHC) of the Federal Republic of Nigeria. Administratively, Nigeria is divided into states. Each state is subdivided into local government areas (LGAs), each LGA is divided into wards, and each ward is divided into localities. Localities are further subdivided into convenient areas called census enumeration areas (EAs). The primary sampling unit (PSU), referred to as a cluster unit for the 2021 NMIS, was defined on the basis of EAs for the proposed 2023 PHC.

A two-stage sampling strategy was adopted for the 2021 NMIS. In the first stage, 568 EAs were selected with probability proportional to the EA size. The EA size is the number of households residing in the EA. The sample selection was done in such a way that it was representative of each state. The result was a total of 568 clusters throughout the country, 195 in urban areas and 373 in rural areas.

For further details on sample design, see Appendix A of the final report.

Mode of data collection

Face-to-face [f2f]

Research instrument

Three questionnaires were used in the 2021 NMIS: the Household Questionnaire, the Woman’s Questionnaire, and the Biomarker Questionnaire. The questionnaires, based on The DHS Program’s model questionnaires, were adapted to reflect the population and health issues relevant to Nigeria. After the questionnaires were finalised in English, they were translated into Hausa, Yoruba, and Igbo.

Cleaning operations

The processing of the 2021 NMIS data began immediately after the start of fieldwork. As data collection was being completed in each cluster, all electronic data files were transferred via the IFSS to the NPC central office in Abuja. Data files were registered and checked for inconsistencies, incompleteness, and outliers. The field teams were alerted on any inconsistencies and errors. Secondary editing, carried out in the central office, involved resolving inconsistencies and coding open-ended questions. The biomarker paper questionnaires were compared with electronic data files to check for any inconsistencies in data entry. Data entry and editing were carried out using the CSPro software package. Concurrent processing of the data offered a distinct advantage because it maximised the likelihood of the data being error-free and accurate. Timely generation of field check tables also allowed for effective monitoring. Secondary editing of the data was completed in February 2022. The data processing team coordinated this exercise at the central office.

Response rate

A total of 14,185 households were selected for the survey, of which 13,887 were occupied and 13,727 were successfully interviewed, yielding a response rate of 99%. In the interviewed households, 14,647 women age 15-49 were identified for individual interviews. Interviews were completed with 14,476 women, yielding a response rate of 99%.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: nonsampling errors and sampling errors. Nonsampling errors are the results of mistakes made in implementing data collection and in data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, or incorrect data entry. Although numerous efforts were made during the implementation of the 2021 Nigeria Malaria Indicator Survey (NMIS) to minimise this type of error, nonsampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the 2021 NMIS is only one of many samples that could have been selected from the same population, using the same design and expected sample size. Each of these samples would yield results that differ somewhat from the results of the selected sample. Sampling errors are a measure of the variability among all possible samples. Although the exact degree of variability is unknown, it can be estimated from the survey results.

Sampling error is usually measured in terms of the standard error for a particular statistic (mean, percentage, and so on), which is the square root of the variance. The standard error can be used to calculate confidence intervals within which the true value for the population can reasonably be assumed to fall. For example, for any given statistic calculated from a sample survey, the value of that statistic will fall within a range of plus or minus two times the standard error of that statistic in 95% of all possible samples of identical size and design.

If the sample of respondents had been selected as a simple random sample, it would have been possible to use straightforward formulas for calculating sampling errors. However, the 2021 NMIS sample was the result of a multistage stratified design, and, consequently, it was necessary to use more complex formulas. Sampling errors are computed via SAS programmes developed by ICF. These programmes use the Taylor linearisation method to estimate variances for estimated means, proportions, and ratios. The Jackknife repeated replication method is used for variance estimation of more complex statistics such as fertility and mortality rates.

Sampling errors tables are presented in Appendix B of the final report.

Data appraisal

Data Quality Tables

• Household age distribution
• Age distribution of eligible and interviewed women
• Age displacement at ages 14/15
• Age displacement at ages 49/50
• Live births by years preceding the survey
• Completeness of reporting
• Observation of mosquito nets
• Number of enumeration areas completed by month of fieldwork and zone
• Positive rapid diagnostic test (RDT) results by month of fieldwork and zone, Nigeria MIS 2021
• Concordance and discordance between RDT and microscopy results
• Concordance and discordance between national and external quality control laboratories

See details of the data quality tables in Appendix C of the final report.

Abstract

NASC is an exercise designed to fill the existing data gap in the agricultural landscape in Nigeria. It is a comprehensive enumeration of all agricultural activities in the country, including crop production, fisheries, forestry, and livestock activities. The implementation of NASC was done in two phases, the first being the Listing Phase, and the second is the Sample Survey Phase. Under the first phase, enumerators visited all the selected Enumeration Areas (EAs) across the Local Government Areas (LGAs) and listed all the farming households in the selected enumeration areas and collected the required information. The scope of information collected under this phase includes demographic details of the holders, type of agricultural activity (crop production, fishery, poultry, or livestock), the type of produce or product (for example: rice, maize, sorghum, chicken, or cow), and the details of the contact persons. The listing exercise was conducted concurrently with the administration of a Community Questionnaire, to gather information about the general views of the communities on the agricultural and non-agricultural activities through focus group discussions.

The main objective of the listing exercise is to collect information on agricultural activities at household level in order to provide a comprehensive frame for agricultural surveys. The main objective of the community questionnaire is to obtain information about the perceptions of the community members on the agricultural and non-agricultural activities in the community.

Additional objectives of the overall NASC program include the following: · To provide data to help the government at different levels in formulating policies on agriculture aimed at attaining food security and poverty alleviation · To provide data for the proposed Gross Domestic Product (GDP) rebasing

Geographic coverage

Estimation domains are administrative areas from which reliable estimates are expected. The sample size planned for the extended listing operation allowed reporting key structural agricultural statistics at Local Government Area (LGA) level.

Analysis unit

Agricultural Households.

Universe

Population units of this operation are households with members practicing agricultural activities on their own account (farming households). However, all households in selected EAs were observed as much as possible to ensure a complete coverage of farming households.

Kind of data

Census/enumeration data [cen]

Sampling procedure

An advanced methodology was adopted in the conduct of the listing exercise. For the first time in Nigeria, the entire listing was conducted digitally. NBS secured newly demarcated digitized enumeration area (EA) maps from the National Population Commission (NPC) and utilized them for the listing exercise. This newly carved out maps served as a basis for the segmentation of the areas visited for listing exercise. With these maps, the process for identifying the boundaries of the enumeration areas by the enumerators was seamless.

The census was carried out in all the 36 States of the Federation and FCT. Forty (40) enumeration Areas (EAs) were selected to be canvassed in each LGA, the number of EAs covered varied by state, which is a function of the number of LGAs in the state. Both urban and rural EAs were canvassed. Out of 774 LGAs in the country, 767 LGAs were covered and the remaining 7 LGAs (4 in Imo and 3 in Borno States) were not covered due to insecurity (99% coverage). In all, thirty thousand, nine hundred and sixty (30,960) EAs were expected to be covered nationwide but 30,546 EAs were canvassed.

The Sampling method adopted involved three levels of stratification. The objective of this was to provide representative data on every Local Government Area (LGA) in Nigeria. Thus, the LGA became the primary reporting domain for the NASC and the first level of stratification. Within each LGA, eighty (80) EAs were systematically selected and stratified into urban and rural EAs, which then formed the second level of stratification, with the 80 EAs proportionally allocated to urban and rural according to the total share of urban/rural EAs within the LGA. These 80 EAs formed the master sample from which the main NASC sample was selected. From the 80 EAs selected across all the LGAs, 40 EAs were systematically selected per LGA to be canvassed. This additional level selection of EAs was again stratified across urban and rural areas with a target allocation of 30 rural and 10 urban EAs in each LGA. The remaining 40 EAs in each LGA from the master sample were set aside for replacement purposes in case there would be need for any inaccessible EA to be replaced.

Details of sampling procedure implemented in the NASC (LISTING COMPONENT). A stratified two-phase cluster sampling method was used. The sampling frame was stratified by urban/rural criteria in each LGA (estimation domain/analytical stratum).

First phase: in each LGA, a total sample of 80 EAs were allocated in each strata (urban/rural) proportionally to their number of EAs with reallocations as need be. In each stratum, the sample was selected with a Pareto probability proportional to size considering the number of households as measure of size.

Second phase: systematic subsampling of 40 EAs was done (10 in Urban and 30 in Rural with reallocations as needed, if there were fewer than 10 Urban or 30 Rural EAs in an LGA). This phase was implicitly stratified through sorting the first phase sample by geography.

With a total of 773 LGAs covered in the frame, the total planned sample size was 30920 EAs. However, during fieldwork 2 LGAs were unable to be covered due to insecurity and additional 4 LGAs were suspended early due to insecurity. For the same reason, replacements of some sampled EAs were needed in many LGAs. The teams were advised to select replacement units where possible considering appurtenance to the same stratum and similarity including in terms of population size. However about 609 EAs replacement units were selected from a different stratum and were discarded from data processing and reporting.

Sampling deviation

Out of 774 LGAs in the country, 767 LGAs were covered and the remaining 7 LGAs (4 in Imo and 3 in Borno states) were not covered due to insecurity (99% coverage).

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The NASC household listing questionnaire served as a meticulously designed instrument administered within every household to gather comprehensive data. It encompassed various aspects such as household demographics, agricultural activities including crops, livestock (including poultry), fisheries, and ownership of agricultural/non-agricultural enterprises.

The questionnaire was structured into the following sections: Section 0: ADMINISTRATIVE IDENTIFICATION Section 1: BUILDING LISTING Section 2: HOUSEHOLD LISTING (Administered to the Head of Household or any knowledgeable adult member aged 15 years and above).

Cleaning operations

Data processing of the NASC household listing survey included checking for inconsistencies, incompleteness, and outliers. Data editing and cleaning was carried out electronically using the Stata software package. In some cases where data inconsistencies were found a call back to the household was carried out. A pre-analysis tabulation plan was developed and the final tables for publication were created using the Stata software package.

Sampling error estimates

Given the complexity of the sample design, sampling errors were estimated through re-sampling approaches (Bootstrap/Jackknife)

Abstract

The Global Nutrition Report (GNR) 2015 places Nigeria among the countries displaying commitment to reduce hunger and improve nutrition in children and women. Although it is still one of the five large low-middle income countries where more than half of children under age 5 are either stunted or wasted, the trends in meeting the global World Health Assembly Resolution (WHAR) targets are positive and Nigeria is obtaining “some progress”.

This survey report presents the results of the National Nutrition and Health Survey conducted in all the 36 States of Nigeria and Federal Capital Territory (FCT) in July and September 2015. It is the second national survey 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), the first being conducted from February to May 2014. In order to provide reliable data for planning and monitoring of key activities, new key indicators have been added: Malaria Intermittent Preventive Treatment in pregnancy for women in the reproductive age group, antenatal care coverage and HIV testing during ANC.

The objectives of the survey are: 1. Determine the prevalence of acute malnutrition among children 6 to 59 months of age using WHZ, Mid Upper Arm Circumference (MUAC) and bilateral oedema; Determine the prevalence of chronic malnutrition and underweight among children 0 to 59 months of age; 2. Determine the prevalence of acute malnutrition among women 15 to 49 years of age using MUAC; 3. Assess the prevalence of diarrhoea and use of ORS and zinc among children under-five years two weeks preceding the survey; 4. Estimate coverage of vitamin A supplementation and deworming 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 immunization among children 12-23 months of age; 6. Determine the proportion of under five children with Acute Respiratory Infection (ARI) symptoms and proportion of children with fever received treatment; 7. Determine the ownership and universal access of mosquito nets, and utilization of mosquito nets by children 0-59 months; 8. Assess the practice of skilled birth attendants, contraceptive prevalence rate and antenatal care coverage among women 15 to 49 years; and 9. Determine the proportion of women 15 – 49 years received HIV testing and intermittent preventive treatment during antenatal care.

Geographic coverage

National Coverage Local Government

Analysis unit

Household

Universe

The survey covered all selected household members, all women aged 15-49 years resident in the household, and all children aged 0-4 years (under age 5) resident in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

The National Nutrition and Health Survey using SMART methods is designed as a cross-sectional household survey using a two stage cluster sampling to provide results representative at the state level. Data were collected from a total of 25,210 households, 20,060 children under-five years of age and 23,688 women of reproductive age. The sample for the 2015 NNHS is nationally representative and covers the entire population residing in non-institutional dwelling units in the country. The survey uses the national sample frame, which is a list of Enumeration Areas (EAs) prepared for the 2006 Population Census. Administratively Nigeria is divided into states, Local Government Areas (LGAs), and localities. In addition to these administrative units, during the 2006 population census, each locality was subdivided into census Enumeration Areas (EAs). The primary sampling unit (PSU), referred to as a cluster in this survey, is defined on the basis of EAs from the 2006 EA census frame.

The 2015 NNHS sample has been selected using a two-stage cluster design as described below.

-First stage sampling procedure: cluster selection. The PSU (clusters) for each state were randomly selected from the national master sample frame according to the probability proportional to size (PPS) method with the support from National Population Commission.

-Second stage sampling procedure: household selection. The second stage of sampling consisted of selecting households within each cluster by using systematic random selection. The team leader was responsible for the determination of the total number of households in the cluster by conducting a household listing through detailed enumeration of the selected cluster with a support from the community leader. This served as the sampling frame for the selection of households. The team leader then entered the total number of households onto the tablet and sampling interval was automatically calculated and displayed on the tablet. The sampling interval is calculated by dividing the total number of households in the cluster by the number of households to be interviewed. A random number table was used to randomly select a start number, between 1 and the sampling interval, to identify the first household. The sampling interval was used to identify all subsequent households to be included in the survey.

Mode of data collection

Computer Assisted Personal Interview [capi]

Cleaning operations

Computer data editing, table generation and analysis were carried out by the NBS staff at NBS headquarters, Abuja. Report writing was carried out by the Technical Committee of the Consultative Committee on National Nutrition and Health Survey (NNHS) 2015

Response rate

The target was to interview 26,048 households across the country. The set target was notreached and overall 25,210 households were interviewed (97 percent).

Abstract

The GHS is a cross-sectional survey of 22,000 households throughout the country. The panel component (GHS-Panel) is now being applied to 5,000 households of the GHS and covers multiple agricultural activities. The focus of this panel component is to improve data from the agriculture sector and link this to other facets of household behaviour and characteristics. The GHS-Panel drew heavily on the HNLSS and the NASS to create a new survey instrument and method to shed light on the role of agriculture in households' economic wellbeing. The NBS implemented the first stage (Post Planting) of the first wave of the GHS-Panel in 2010. This panel is a subset of the full GHS (or GHS-Cross Section) that will be finished in 2011.) It is envisaged that the GHS-Panel will be carried out every two years while the GHS-Cross Section will be carried out annually.

The specific outputs and outcomes of the revised GHS with panel component are:

• Development of an innovative model for collecting agricultural data in conjunction with household data;
• Development of a model of inter-institutional collaboration between NBS and the FMA&RD and NFRA, inter alia, to ensure the relevance and use of the new GHS;
• Building the capacity to generate a sustainable system for the production of accurate and timely information on agricultural households in Nigeria.
• Comprehensive analysis of poverty indictors and socio-economic characteristics.

Geographic coverage

National, the survey covered all the 36 states and Federal Capital Territory (FCT).

Analysis unit

Households, Individuals, Agricutural plots

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample Design The GHS-Panel (Post Planting 2010), like all household surveys in the country, is based on the Master Sample Frame, This Frame is based on the 2006 Housing and Population Census conducted by the National Population Commission (NpopC). The census includes approximately 662,000 enumeration areas (EAs) throughout the country. From the census, the Master Frame was constructed at the local government area (LGA). In 668 LGAs, 30 EAs were scientifically selected. The remaining six LGAs are found in FCT, Abuja. Forty EAs were scientifically selected in each of these 6 LGAs. This gives a total of 23,280 EAs selected nationally. This is the Master Frame.

From the Master Frame a master sample frame, called the National Integrated Survey of Households 2007/2012 Master Sample Frame (NISH-MSF) was developed. The NISHMSF was constructed by pooling the LGAs in the Master Frame by state. Thereafter, a systematic sample of 200 EAs was selected with equal probability across all LGAs within the state. Furthermore, the NISH EAs in each state were divided into 20 replicates of 10 EAs each. However, the sample EAs for most national household surveys such as the GHS are based on a sub-sample of the NISH-MSF, selected as a combination of replicates from NISH-MSF frame. For the GHS-Panel, the sample is a subset of the EAs selected for the GHS.

Sample Framework The sample frame includes all thirty-six (36) states of the federation and Federal Capital Territory (FCT), Abuja. Both urban and rural areas were covered and in all, 500 clusters/EAs were canvassed and 5,000 households were interviewed. These samples were proportionally selected in the states such that different states have different samples.

Sample Selection The GHS Panel Survey used a two stage stratified sample selection process.

First Stage The Primary Sampling Units (PSUs) were the Enumeration Areas (EAs). These were selected based on probability proportional to size (PPS) of the total EAs in each state and FCT, Abuja and the total households listed in those EAs.

Second Stage The second stage involved the systematic selection of ten (10) households per EA. This involved obtaining the total number of households listed in a particular EA, and then calculating a Sampling Interval (S.I) by dividing the total households listed by ten (10). The next step is to generate a random start 'r' from the table of random numbers which stands as the 1st selection. The second selection is obtained by adding the sampling interval to the random start. For each of the next selections, the sampling interval was added to the value of the previous selection until the 10th selection is obtained. Determination of the sample size at the household level was based on the experience gained from previous rounds of the GHS, in which 10 HHs per EA are usually selected and give robust estimates.

Mode of data collection

Face-to-face [f2f]

Cleaning operations

This survey used concurrent data entry approach. In this method, the fieldwork and data entry were handled by each team assigned to the state. Each team consisted of a field supervisor, 2-4 interviewers and a data entry operator. Immediately after the data were collected in the field by the interviewers and supervisors (the supervisors administered the community questionnaires and collected data on prices), the questionnaires were handed over to the supervisor to be checked and documented. At the end of each day of fieldwork, the questionnaires were then passed to the data entry operator for entry. After the questionnaires were entered, the data entry operator generated an error report which reported issues including out of range values and inconsistencies in the data. The supervisor then checked the report, determined what should be corrected, and decided if the field team needed to revisit the household to obtain additional information. The benefits of this method are that it allows one to: - Capture errors that might have been overlooked by a visual inspection only, - Identify errors early during the field work so that if any correction required a revisit to the household, it could be done while the team was still in the EA

The CSPro software was used to design the specialized data entry program that was used for the data entry of the questionnaires.

Abstract

The primary objectives of the 2015 NMIS are to provide information on malaria indicators and malaria prevalence, both at the national level and in each of the country’s 36 states and the Federal Capital Territory. The secondary objectives are to improve knowledge regarding best practices in implementing the survey and enhance the skills of survey-implementing partners in the areas of survey design, training, logistics, data collection monitoring, data processing, laboratory testing, analysis, report drafting, and data dissemination.

Other key objectives of the 2015 Nigeria Malaria Indicator Survey are to: • Measure the extent of ownership and use of mosquito nets • Assess the coverage of preventive treatment programmes for pregnant women • Identify practices used to treat malaria among children under age 5 and the use of specific antimalarial medications • Measure the prevalence of malaria and anaemia among children age 6-59 months • Assess knowledge, attitudes, and practices regarding malaria in the general population

Geographic coverage

National coverage

Analysis unit

• Household
• Women age 15-49
• Children age 0-5

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample for the 2015 NMIS was designed to provide most of the survey indicators for the country as a whole, for urban and rural areas separately, and for each of the country's six geopolitical zones. Some of these indicators are provided for each of the 36 states and the FCT. Nigeria's geopolitical zones are as follows: 1. North Central: Benue, Kogi, Kwara, Nasarawa, Niger, Plateau, and FCT 2. North East: Adamawa, Bauchi, Borno,1 Gombe, Taraba, and Yobe 3. North West: Jigawa, Kaduna, Kano, Katsina, Kebbi, Sokoto, and Zamfara 4. South East: Abia, Anambra, Ebonyi, Enugu, and Imo 5. South South: Akwa Ibom, Bayelsa, Cross River, Delta, Edo, and Rivers 6. South West: Ekiti, Lagos, Ogun, Ondo, Osun, and Oyo

The sampling frame for the 2015 NMIS was the 2006 National Population and Housing Census (NPHC) of the Federal Republic of Nigeria, conducted by the National Population Commission. Administratively, Nigeria is divided into states. Each state is subdivided into local government areas (LGAs), and each LGA is divided into localities. In addition to these administrative units, during the 2006 census, each locality was subdivided into convenient areas called census enumeration areas (EAs). The primary sampling unit (PSU), referred to as a cluster for the 2015 NMIS, was defined on the basis of EAs from the 2006 EA census frame.

A two-stage sampling strategy was adopted for the 2015 NMIS. In the first stage, nine clusters (EAs) were selected from each state, including the FCT. The sample selection was done in such a way that it was representative of each state. The result was a total of 333 clusters throughout the country, 138 in urban areas and 195 in rural areas.

A complete listing of households was conducted, and a mapping exercise for each cluster was carried out in June and July 2015, with the resulting lists of households serving as the sampling frame for the selection of households in the second stage. All regular households were listed. The NPopC listing enumerators used global positioning system (GPS) receivers to record the coordinates of the 2015 NMIS sample clusters.

In the second stage of the selection process, 25 households were selected in each cluster by equal probability systematic sampling. All women age 15-49 who were either permanent residents of the households in the 2015 NMIS sample or visitors present in the households on the night before the survey were eligible to be interviewed. In addition, all children age 6-59 months were eligible to be tested for malaria and anaemia. This sample size was selected to guarantee that key survey indicators could be produced for each of the country's six geopolitical zones, with approximately 1,338 women in each zone expected to complete interviews. In order to produce some of the survey indicators at the state level for each of the 36 states and the FCT, interviews were expected to be completed with approximately 217 women per state.

For further details of the sample design, see Appendix A of the final report.

Mode of data collection

Face-to-face [f2f]

Research instrument

Three questionnaires were used in the survey: the Household Questionnaire; the Woman’s Questionnaire, which was administered to all women age 15-49 in the selected households; and the Biomarker Questionnaire.

The Household Questionnaire was used to list all of the usual members and visitors in the selected households. Some basic information was collected on the characteristics of each person listed, including age, sex, education, and relationship to the head of the household. Data on age and sex were used to identify women who were eligible for the individual interview. The Household Questionnaire also collected information on characteristics of the household’s dwelling unit, such as the source of water, type of toilet facilities, materials used for the floor of the house, ownership of various durable goods, and ownership and use of mosquito nets.

The Woman’s Questionnaire was used to collect information from all women age 15-49. These women were asked questions on the following main topics: • Background characteristics (e.g., education, media exposure) • Birth history and childhood mortality • Antenatal care and malaria prevention for most recent birth and pregnancy • Malaria prevention and treatment • Knowledge about malaria (symptoms, causes, prevention, drugs used in treatment)

The Biomarker Questionnaire was used to record the results of the anaemia and malaria testing as well as the signatures of the fieldworker and the respondent who gave consent.

Cleaning operations

Data for the 2015 NMIS were collected through questionnaires programmed onto tablet computers. The computers were programmed by an ICF data processing specialist and loaded with the Household, and Woman’s Questionnaires in English and the three major local languages. The tablets were Bluetooth-enabled to facilitate electronic transfer of files, for example, transfer of data from the Household Questionnaires among survey team members and transfer of completed questionnaires to the team supervisor’s tablets. The field supervisors transferred data on a daily basis to the central data processing office using the Internet. To facilitate communication and monitoring, each field worker was assigned a unique identification number.

Two data management officers were positioned at the central data office to monitor and supervise daily submission of completed interview data from teams. They also provided technical assistance on the functioning of the tablets and constantly liaised with the central coordination and ICF teams to manage data transfers from the field teams to the central office. They made intermittent visits to assist field teams with serious situations that could not be resolved at the central office, either to replace or fix the tablets.

The Census Survey Processing (CSPro) software program was used for data editing, weighting, cleaning, and tabulation. In the NPopC central office, data received from the supervisors’ tablets were registered and checked for any inconsistencies and outliers. Data editing and cleaning included structure and internal consistency checks to ensure completeness of work in the field. Any anomalies were communicated to the respective team through field coordinators and the team supervisor. Corrected results were re-sent to the central processing unit. Data processing was completed during the first week of December 2015.

Response rate

A total of 8,148 households were selected for the sample. This does not include six rural clusters in Borno State and one cluster in Plateau State that were dropped from the sample due to security concerns. Of the households selected, 7,841 were occupied. Of the occupied households, 7,745 were successfully interviewed, yielding a response rate of 99 percent. The response rate among households in rural areas was slightly higher (99 percent) than that among households in urban areas (98 percent). No clusters in rural areas of Borno State were visited; thus, estimates for national indicators and indicators in the North East Zone do not include rural Borno State.

In the interviewed households, 8,106 women were identified as eligible for individual interviews. Interviews were completed with 8,034 women, yielding a response rate of 99 percent. The response rate among eligible women did not differ by residence (urban or rural).

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: nonsampling errors and sampling errors. Nonsampling errors are the results of mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the 2015 Nigeria Malaria Indicator Survey (NMIS) to minimize this type of error, nonsampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the 2015 NMIS is only one of many samples that could have been selected from the same population, using the same design and expected size. Each of these samples would yield results that differ somewhat from the results of the actual sample

Abstract

The 2013 Nigeria Demographic and Health Survey (NDHS) was designed to provide data to monitor the population and health situation in Nigeria with an explicit goal of providing reliable information about maternal and child health and family planning services. The primary objective of the 2013 NDHS was to provide up-to-date information on fertility levels, marriage, fertility preferences, awareness and use of family planning methods, child feeding practices, nutritional status of women and children, adult and childhood mortality, awareness and attitudes regarding HIV/AIDS, and domestic violence. This information is intended to assist policymakers and programme managers in evaluating and designing programmes and strategies for improving health and family planning services in the country.

Geographic coverage

National coverage

Analysis unit

• Household
• Individual
• Children age 0-5
• Woman age 15-49
• Man age 15-49

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample Design The sample for the 2013 NDHS was nationally representative and covered the entire population residing in non-institutional dwelling units in the country. The survey used as a sampling frame the list of enumeration areas (EAs) prepared for the 2006 Population Census of the Federal Republic of Nigeria, provided by the National Population Commission. The sample was designed to provide population and health indicator estimates at the national, zonal, and state levels. The sample design allowed for specific indicators to be calculated for each of the six zones, 36 states, and the Federal Capital Territory, Abuja.

Administratively, Nigeria is divided into states. Each state is subdivided into local government areas (LGAs), and each LGA is divided into localities. In addition to these administrative units, during the 2006 population census, each locality was subdivided into census enumeration areas. The primary sampling unit (PSU), referred to as a cluster in the 2013 NDHS, is defined on the basis of EAs from the 2006 EA census frame. The 2013 NDHS sample was selected using a stratified three-stage cluster design consisting of 904 clusters, 372 in urban areas and 532 in rural areas. A representative sample of 40,680 households was selected for the survey, with a minimum target of 943 completed interviews per state.

A complete listing of households and a mapping exercise were carried out for each cluster from December 2012 to January 2013, with the resulting lists of households serving as the sampling frame for the selection of households. All regular households were listed. The NPC listing enumerators were trained to use Global Positioning System (GPS) receivers to calculate the coordinates of the 2013 NDHS sample clusters.

A fixed sample take of 45 households were selected per cluster. All women age 15-49 who were either permanent residents of the households in the 2013 NDHS sample or visitors present in the households on the night before the survey were eligible to be interviewed. In a subsample of half of the households, all men age 15-49 who were either permanent residents of the households in the sample or visitors present in the households on the night before the survey were eligible to be interviewed. Also, a subsample of one eligible woman in each household was randomly selected to be asked additional questions regarding domestic violence.

For further details on sample size and design, see Appendix B of the final report.

Mode of data collection

Face-to-face [f2f]

Research instrument

Three questionnaires were used in the 2013 NDHS: the Household Questionnaire, the Woman’s Questionnaire, and the Man’s Questionnaire.

The Household Questionnaire was used to list all of the usual members of and visitors to the selected households. Some basic information was collected on the characteristics of each person listed, including age, sex, marital status, education, and relationship to the head of the household. Information on other characteristics of household members was collected as well, including current school attendance and survivorship of parents among those under age 18. If a child in the household had a parent who was sick for more than three consecutive months in the 12 months preceding the survey or a parent who had died, additional questions related to support for orphans and vulnerable children were asked. Furthermore, if an adult in the household was sick for more than three consecutive months in the 12 months preceding the survey or an adult in the household had died, questions were asked relating to support for sick people or people in households where a member had died.

The Household Questionnaire also collected information on characteristics of the household’s dwelling unit, such as source of water; type of toilet facilities; materials used for the floor of the house; ownership of various durable goods; ownership of agricultural land; ownership of livestock, farm animals, or poultry; and ownership and use of mosquito nets and long-lasting insecticidal nets. The Household Questionnaire was further used to record height and weight measurements for children age 0-59 months and women age 15-49. In addition, data on the age and sex of household members in the Household Questionnaire were used to identify women and men who were eligible for individual interviews.

The Woman’s Questionnaire was used to collect information from all women age 15-49. These women were asked questions on the following main topics: • Background characteristics (age, religion, education, literacy, media exposure, etc.) • Reproductive history and childhood mortality • Knowledge, source, and use of family planning methods • Fertility preferences • Antenatal, delivery, and postnatal care • Breastfeeding and infant feeding practices • Child immunisation and childhood illnesses • Marriage and sexual activity • Women’s work and husbands’ background characteristics • Malaria prevention and treatment • Women’s decision making • Awareness of AIDS and other sexually transmitted infections • Maternal mortality • Domestic violence

The Man’s Questionnaire was administered to all men age 15-49 in every second household in the 2013 NDHS sample. The Man’s Questionnaire collected much of the same information found in the Woman’s Questionnaire but was shorter because it did not contain a detailed reproductive history or questions on maternal and child health or nutrition.

Cleaning operations

The processing of the 2013 NDHS data began simultaneously with the fieldwork. Completed questionnaires were edited in the field immediately by the field editors and checked by the supervisors before being dispatched to the data processing centre in Abuja. The questionnaires were then edited and entered by 26 data processing personnel specially trained for this task. Data were entered using the CSPro computer package, and all data were entered twice to allow 100 percent verification. The concurrent processing of the data offered a distinct advantage because of the assurance that the data were error free and authentic. Moreover, the double entry of data enabled easy comparisons and identification of errors and inconsistencies. Inconsistencies were resolved by tallying results with the paper questionnaire entries. Secondary editing of the data was completed in the last week of July 2013. The final cleaning of the data set was carried out by the ICF data processing specialist and completed in August.

Response rate

A total of 40,320 households were selected from 896 sample points, of which 38,904 were found to be occupied at the time of the fieldwork. Of the occupied households, 38,522 were successfully interviewed, yielding a household response rate of 99 percent. In view of the security challenges in the country, this response rate is highly encouraging and appears to be the result of a well-coordinated team effort.

In the interviewed households, a total of 39,902 women age 15-49 were identified as eligible for individual interviews, and 98 percent of them were successfully interviewed. Among men, 18,229 were identified as eligible for interviews, and 95 percent were successfully interviewed. As expected, response rates were slightly lower in urban areas than in rural areas.

Note: See summarized response rates by residence (urban/rural) in Table 1.2 of the survey report.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: non-sampling errors and sampling errors. Non-sampling errors are the results of mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the 2013 Nigeria DHS (NDHS) to minimize this type of error, non-sampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the 2013 NDHS is only one of many samples that could have been selected from the same population, using the same design and expected size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling errors are a measure of the variability between all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results.

Sampling error is usually measured in terms of the standard error

Abstract

The main objectives of the 2018/19 NLSS are: i) to provide critical information for production of a wide range of socio-economic and demographic indicators, including for benchmarking and monitoring of SDGs; ii) to monitor progress in population’s welfare; iii) to provide statistical evidence and measure the impact on households of current and anticipated government policies. In addition, the 2018/19 NLSS could be utilized to improve other non-survey statistical information, e.g. to determine and calibrate the contribution of final consumption expenditures of households to GDP; to update the weights and determine the basket for the national Consumer Price Index (CPI); to improve the methodology and dissemination of micro-economic and welfare statistics in Nigeria.

The 2018/19 NLSS collected a comprehensive and diverse set of socio-economic and demographic data pertaining to the basic needs and conditions under which households live on a day to day basis. The 2018/19 NLSS questionnaire includes wide-ranging modules, covering demographic indicators, education, health, labour, expenditures on food and non-food goods, non-farm enterprises, household assets and durables, access to safety nets, housing conditions, economic shocks, exposure to crime and farm production indicators.

Geographic coverage

National coverage

Analysis unit

• Households
• Individuals
• Communities

Universe

The survey covered all de jure households excluding prisons, hospitals, military barracks, and school dormitories.

Kind of data

Sample survey data [ssd]

Sampling procedure

The 2018/19 NLSS sample is designed to provide representative estimates for the 36 states and the Federal Capital Territory (FCT), Abuja. By extension. The sample is also representative at the national and zonal levels. Although the sample is not explicitly stratified by urban and rural areas, it is possible to obtain urban and rural estimates from the NLSS data at the national level. At all stages, the relative proportion of urban and rural EAs as has been maintained.

Before designing the sample for the 2018/19 NLSS, the results from the 2009/10 HNLSS were analysed to extract the sampling properties (variance, design effect, etc.) and estimate the required sample size to reach a desired precision for poverty estimates in the 2018/19 NLSS.

EA SELECTION: The sampling frame for the 2018/19 NLSS was based on the national master sample developed by the NBS, referred to as the NISH2 (Nigeria Integrated Survey of Households 2). This master sample was based on the enumeration areas (EAs) defined for the 2006 Nigeria Census Housing and Population conducted by National Population Commission (NPopC). The NISH2 was developed by the NBS to use as a frame for surveys with state-level domains. NISH2 EAs were drawn from another master sample that NBS developed for surveys with LGA-level domains (referred to as the “LGA master sample”). The NISH2 contains 200 EAs per state composed of 20 replicates of 10 sample EAs for each state, selected systematically from the full LGA master sample. Since the 2018/19 NLSS required domains at the state-level, the NISH2 served as the sampling frame for the survey.

Since the NISH2 is composed of state-level replicates of 10 sample EAs, a total of 6 replicates were selected from the NISH2 for each state to provide a total sample of 60 EAs per state. The 6 replicates selected for the 2018/19 NLSS in each state were selected using random systematic sampling. This sampling procedure provides a similar distribution of the sample EAs within each state as if one systematic sample of 60 EAs had been selected directly from the census frame of EAs.

A fresh listing of households was conducted in the EAs selected for the 2018/19 NLSS. Throughout the course of the listing, 139 of the selected EAs (or about 6%) were not able to be listed by the field teams. The primary reason the teams were not able to conduct the listing in these EAs was due to security issues in the country. The fieldwork period of the 2018/19 NLSS saw events related to the insurgency in the north east of the country, clashes between farmers and herdsman, and roving groups of bandits. These events made it impossible for the interviewers to visit the EAs in the villages and areas affected by these conflict events. In addition to security issues, some EAs had been demolished or abandoned since the 2006 census was conducted. In order to not compromise the sample size and thus the statistical power of the estimates, it was decided to replace these 139 EAs. Additional EAs from the same state and sector were randomly selected from the remaining NISH2 EAs to replace each EA that could not be listed by the field teams. This necessary exclusion of conflict affected areas implies that the sample is representative of areas of Nigeria that were accessible during the 2018/19 NLSS fieldwork period. The sample will not reflect conditions in areas that were undergoing conflict at that time. This compromise was necessary to ensure the safety of interviewers.

HOUSEHOLD SELECTION: Following the listing, the 10 households to be interviewed were selected from the listed households. These households were selected systemically after sorting by the order in which the households were listed. This systematic sampling helped to ensure that the selected households were well dispersed across the EA and thereby limit the potential for clustering of the selected households within an EA.

Occasionally, interviewers would encounter selected households that were not able to be interviewed (e.g. due to migration, refusal, etc.). In order to preserve the sample size and statistical power, households that could not be interviewed were replaced with an additional randomly selected household from the EA. Replacement households had to be requested by the field teams on a case-by-case basis and the replacement household was sent by the CAPI managers from NBS headquarters. Interviewers were required to submit a record for each household that was replaced, and justification given for their replacement. These replaced households are included in the disseminated data. However, replacements were relatively rare with only 2% of sampled households not able to be interviewed and replaced.

Sampling deviation

Although a sample was initially drawn for Borno state, the ongoing insurgency in the state presented severe challenges in conducting the survey there. The situation in the state made it impossible for the field teams to reach large areas of the state without compromising their safety. Given this limitation it was clear that a representative sample for Borno was not possible. However, it was decided to proceed with conducting the survey in areas that the teams could access in order to collect some information on the parts of the state that were accessible.

The limited area that field staff could safely operate in in Borno necessitated an alternative sample selection process from the other states. The EA selection occurred in several stages. Initially, an attempt was made to limit the frame to selected LGAs that were considered accessible. However, after selection of the EAs from the identified LGAs, it was reported by the NBS listing teams that a large share of the selected EAs were not safe for them to visit. Therefore, an alternative approach was adopted that would better ensure the safety of the field team but compromise further the representativeness of the sample. First, the list of 788 EAs in the LGA master sample for Borno were reviewed by NBS staff in Borno and the EAs they deemed accessible were identified. The team identified 359 EAs (46%) that were accessible. These 359 EAs served as the frame for the Borno sample and 60 EAs were randomly selected from this frame. However, throughout the course of the NLSS fieldwork, additional insurgency related events occurred which resulted in 7 of the 60 EAs being inaccessible when they were to be visited. Unlike for the main sample, these EAs were not replaced. Therefore, 53 EAs were ultimately covered from the Borno sample. The listing and household selection process that followed was the same as for the rest of the states.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Two sets of questionnaires – household and community – were used to collect information in the NLSS2018/19. The Household Questionnaire was administered to all households in the sample. The Community Questionnaire was administered to the community to collect information on the socio-economic indicators of the enumeration areas where the sample households reside.

Household Questionnaire: The Household Questionnaire provides information on demographics; education; health; labour; food and non-food expenditure; household nonfarm income-generating activities; food security and shocks; safety nets; housing conditions; assets; information and communication technology; agriculture and land tenure; and other sources of household income.

Community Questionnaire: The Community Questionnaire solicits information on access to transported and infrastructure; community organizations; resource management; changes in the community; key events; community needs, actions and achievements; and local retail price information.

Cleaning operations

CAPI: The 2018/19 NLSS was conducted using the Survey Solutions Computer Assisted Person Interview (CAPI) platform. The Survey Solutions software was developed and maintained by the Development Economics Data Group (DECDG) at the World Bank. Each interviewer and supervisor was given a tablet