As of July 2024, Nigeria's population was estimated at around 229.5 million. Between 1965 and 2024, the number of people living in Nigeria increased at an average rate of over two percent. In 2024, the population grew by 2.42 percent compared to the previous year. Nigeria is the most populous country in Africa. By extension, the African continent records the highest growth rate in the world. Africa's most populous country Nigeria was the most populous country in Africa as of 2023. As of 2022, Lagos held the distinction of being Nigeria's biggest urban center, a status it also retained as the largest city across all of sub-Saharan Africa. The city boasted an excess of 17.5 million residents. Notably, Lagos assumed the pivotal roles of the nation's primary financial hub, cultural epicenter, and educational nucleus. Furthermore, Lagos was one of the largest urban agglomerations in the world. Nigeria's youthful population In Nigeria, a significant 50 percent of the populace is under the age of 19. The most prominent age bracket is constituted by those up to four years old: comprising 8.3 percent of men and eight percent of women as of 2021. Nigeria boasts one of the world's most youthful populations. On a broader scale, both within Africa and internationally, Niger maintains the lowest median age record. Nigeria secures the 20th position in global rankings. Furthermore, the life expectancy in Nigeria is an average of 62 years old. However, this is different between men and women. The main causes of death have been neonatal disorders, malaria, and diarrheal diseases.

This data set included population and expenditure breakdowns by LGA, demographic statistics, labor statistics and Socio Economis Indexes for Areas (SEIFA) LGA score and ranking per LGA. Detailed …Show full descriptionThis data set included population and expenditure breakdowns by LGA, demographic statistics, labor statistics and Socio Economis Indexes for Areas (SEIFA) LGA score and ranking per LGA. Detailed descriptions of this data set include: - Region (LGA name) - Population (18+) projection - Venue numbers - EGM numbers - Total net expenditures - Population (18+) per venue - Net EGM expenditure per adult - EGM per 1000 (18+)

265.634 (number) in 2008.

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.

This data set includes population and electronic gaming machine (EGM) expenditure breakdowns by local government area (LGA) and gaming venue, demographic statistics, labour statistics and …Show full descriptionThis data set includes population and electronic gaming machine (EGM) expenditure breakdowns by local government area (LGA) and gaming venue, demographic statistics, labour statistics and Socio-Economic Indexes for Areas (SEIFA) LGA score and ranking per LGA for the 2017/18 financial year. The data has been joined with LGA 2011 boundaries. For more information visit the Victorian Commission for Gambling and Liquor Regulation's (VCGLR) website. Please note: AURIN has spatially enabled the original data. EGM Numbers: a venue may be operating less machines than its licensed or attached numbers Gaming Machine Density calculations are based on operating gaming machines with attached entitlements divided by adult population divided by 1,000 (gaming machines per 1,000 adults). Expenditure / EGM: Refers to the average Expenditure per Gaming Machine. In 2013, these figures are based on net expenditures divided by number of gaming machines with attached entitlements. SEIFA Scores: a lower score indicates that an area is relatively disadvantaged compared to an area with a higher score. Scores should only be used in distributive analysis. Rankings are based on highest score, for example 1st ranking in index of disadvantage means the LGA is least disadvantaged. Disclaimer: Care must be taken in using any figures for an LGA involving net expenditure and population. The expenditure per person in an LGA may include an amount of expenditure coming from persons not living within the LGA. Copyright attribution: Government of Victoria - Victorian Commission for Gambling and Liquor Regulation, (2018): ; accessed from AURIN on 12/3/2020. Licence type: Creative Commons Attribution 3.0 Australia (CC BY 3.0 AU)

This data set includes population and electronic gaming machine (EGM) expenditure breakdowns by local government area (LGA) and gaming venue, demographic statistics, labour statistics and Socio-Economic Indexes for Areas (SEIFA) LGA score and ranking per LGA for the 2016/17 financial year. The data has been joined with LGA 2011 boundaries. For more information visit the Victorian Commission for Gambling and Liquor Regulation's (VCGLR) website. Please note: AURIN has spatially enabled the origi…Show full descriptionThis data set includes population and electronic gaming machine (EGM) expenditure breakdowns by local government area (LGA) and gaming venue, demographic statistics, labour statistics and Socio-Economic Indexes for Areas (SEIFA) LGA score and ranking per LGA for the 2016/17 financial year. The data has been joined with LGA 2011 boundaries. For more information visit the Victorian Commission for Gambling and Liquor Regulation's (VCGLR) website. Please note: AURIN has spatially enabled the original data. EGM Numbers: a venue may be operating less machines than its licensed or attached numbers Gaming Machine Density calculations are based on operating gaming machines with attached entitlements divided by adult population divided by 1,000 (gaming machines per 1,000 adults). Expenditure / EGM: Refers to the average Expenditure per Gaming Machine. In 2013, these figures are based on net expenditures divided by number of gaming machines with attached entitlements. SEIFA Scores: a lower score indicates that an area is relatively disadvantaged compared to an area with a higher score. Scores should only be used in distributive analysis. Rankings are based on highest score, for example 1st ranking in index of disadvantage means the LGA is least disadvantaged. Disclaimer: Care must be taken in using any figures for an LGA involving net expenditure and population. The expenditure per person in an LGA may include an amount of expenditure coming from persons not living within the LGA. Copyright attribution: Government of Victoria - Victorian Commission for Gambling and Liquor Regulation, (2017): ; accessed from AURIN on 12/3/2020. Licence type: Creative Commons Attribution 3.0 Australia (CC BY 3.0 AU)

Socio-demographic characteristics by transactional sex.