Population growth of Lusaka reduced by 2.86% from 3.5 % in 2023 to 3.4 % in 2024.

This statistic shows the total population of Zambia from 2014 to 2024 by gender. In 2024, Zambia's female population amounted to approximately 10.76 million, while the male population amounted to approximately 10.55 million inhabitants.

3.4 (%) in 2024.

Zambia Population: Age 15 and Above: Lusaka data was reported at 1,598,702.000 Person in 2014. This records an increase from the previous number of 1,466,134.000 Person for 2012. Zambia Population: Age 15 and Above: Lusaka data is updated yearly, averaging 1,466,134.000 Person from Dec 2008 (Median) to 2014, with 3 observations. The data reached an all-time high of 1,598,702.000 Person in 2014 and a record low of 996,504.000 Person in 2008. Zambia Population: Age 15 and Above: Lusaka data remains active status in CEIC and is reported by Central Statistical Office. The data is categorized under Global Database’s Zambia – Table ZM.G005: Population: by Province, Sex and Settlement Type.

A household survey (cross sectional study) was conducted to establish the consumption of fish, fish products and other food items at household level (N=714). The role of fish and fish products in the diets of urban poor households, and how fish consumption is distributed within the household between women, children and men. Women and children in the first 1,000 days of life were specifically targeted. Children aged 24 – 59 months from participating households were also enrolled in the study. Lusaka district in Lusaka Province was purposively selected as the study area for the following reasons: it is an urban area within Lusaka Province with the highest number of high density settlement townships where the majority of the urban poor live in Zambia. The study targeted low-income settlement localities as the people living in these areas are most vulnerable to food and nutrition insecurity. To derive the sample size, the formula was applied; n is the minimum required sample size, Z is the Z score for the desired level of confidence (assumed to be 95% or = 0.05), is the population proportion of interest estimated to be 11%, the prevalence of stunted growth among children in Lusaka (27) and d is the margin of error (assumed to be 5%). The calculated sample size was further adjusted for the design effect and non-response rate (predicted to be 5%), to obtain the optimal sample size of 714 households. A sampling frame was developed from the 2010 Population Census and Housing report, in consultation with the local authorities and the Central Statistics Office (CSO). The sampling process involved, firstly, purposively selecting the three constituencies (Kanyama, Matero and Munali) from Lusaka district. From each constituency, one ward was randomly selected to participate in the study. In each reporting domain, study households were selected using a three-stage randomized cluster approach, with the first two stages using the Ward and Standard Enumeration Area (SEA) sampling frame from the 2010 CSO. A total of 36 SEAs (clusters) were identified and from each, 20 households were selected. Using a determined sampling interval, systematic random sampling was used in the final sampling stage. Primary data collection was carried out through a tablet-based questionnaire and by the use of the KoBo Toolkit, a platform to customise the survey to collect specific data, in this study: a) Demographic and socio-economic characteristics, including employment and income generating activities, water and sanitation, and household assets; b) Dietary diversity questionnaires were developed and used to collect dietary data for children, women and men. Guidelines on food groups to be included in the questionnaire as provided by FAO 2013 were used in developing the questionnaire for women, men and for household level data collection. The WHO 2010 guidelines were used in developing the questionnaire for collecting dietary data for children 6–23 months of age. Dietary diversity is a proxy for adequate micronutrient-density of foods. A 24 hour recall collected data that was used to estimate food intake for two adults within the household (one male and one female), infants aged 6 – 23 months and one child aged 2 – 5 years. Development of the 24 hr recall was based on the methods described by Gibson and Ferguson (2008). In addition, a dietary diversity questionnaire (FFQ) was used collect data on various food groups women, children and men consumed in the last 24 hours prior to the study. With focus on fish in the diet of young children, information was collected on the use of fish in the initiation of complementary feeding, the age at which fish is fed to children, the perceptions of mother and fathers of the importance of fish for growth and development of the young child. c) Anthropometric measurements such as weight and length/height were taken on the children and mothers/caregivers. This was done to enable determine the nutritional status of children 6 -23 months; 24- 59 months and women aged 19 – 49 years. The weights of children were taken using the SECA electronic scale and for those children, who were unable to stand, the parents/guardians were asked to carry them and their weights were subtracted from the mothers’ weight. The children’s weights were taken to the nearest 0.1 kg with minimal clothes on them. Length/height boards were used to take the length/height to the nearest 0.1 cm. Children’s age was verified using the clinic card. The mothers’ weight and height were also taken using the SECA scales. The measurements were used to determine mothers’ BMI.

Zambia Population: Mid Year: Lusaka data was reported at 2,191,225.000 Person in 2010. This records an increase from the previous number of 1,733,830.000 Person for 2009. Zambia Population: Mid Year: Lusaka data is updated yearly, averaging 1,579,769.000 Person from Jun 2000 (Median) to 2010, with 11 observations. The data reached an all-time high of 2,191,225.000 Person in 2010 and a record low of 1,391,329.000 Person in 2000. Zambia Population: Mid Year: Lusaka data remains active status in CEIC and is reported by Central Statistical Office. The data is categorized under Global Database’s Zambia – Table ZM.G001: Population: Mid Year.

Zambia Population: Female: Age 15 and Above: Lusaka data was reported at 814,307.000 Person in 2014. This records an increase from the previous number of 748,872.000 Person for 2012. Zambia Population: Female: Age 15 and Above: Lusaka data is updated yearly, averaging 748,872.000 Person from Dec 2008 (Median) to 2014, with 3 observations. The data reached an all-time high of 814,307.000 Person in 2014 and a record low of 493,621.000 Person in 2008. Zambia Population: Female: Age 15 and Above: Lusaka data remains active status in CEIC and is reported by Central Statistical Office. The data is categorized under Global Database’s Zambia – Table ZM.G005: Population: by Province, Sex and Settlement Type.

Abstract

The primary objective of the 2018 ZDHS was to provide up-to-date estimates of basic demographic and health indicators. Specifically, the ZDHS collected information on: - Fertility levels and preferences; contraceptive use; maternal and child health; infant, child, and neonatal mortality levels; maternal mortality; and gender, nutrition, and awareness regarding HIV/AIDS and other health issues relevant to the achievement of the Sustainable Development Goals (SDGs) - Ownership and use of mosquito nets as part of the national malaria eradication programmes - Health-related matters such as breastfeeding, maternal and childcare (antenatal, delivery, and postnatal), children’s immunisations, and childhood diseases - Anaemia prevalence among women age 15-49 and children age 6-59 months - Nutritional status of children under age 5 (via weight and height measurements) - HIV prevalence among men age 15-59 and women age 15-49 and behavioural risk factors related to HIV - Assessment of situation regarding violence against women

Geographic coverage

National coverage

Analysis unit

• Household
• Individual
• Children age 0-5
• Woman age 15-49
• Man age 15-59

Universe

The survey covered all de jure household members (usual residents), all women age 15-49, all men age 15-59, and all children age 0-5 years who are usual members of the selected households or who spent the night before the survey in the selected households.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling frame used for the 2018 ZDHS is the Census of Population and Housing (CPH) of the Republic of Zambia, conducted in 2010 by ZamStats. Zambia is divided into 10 provinces. Each province is subdivided into districts, each district into constituencies, and each constituency into wards. In addition to these administrative units, during the 2010 CPH each ward was divided into convenient areas called census supervisory areas (CSAs), and in turn each CSA was divided into enumeration areas (EAs). An enumeration area is a geographical area assigned to an enumerator for the purpose of conducting a census count; according to the Zambian census frame, each EA consists of an average of 110 households.

The current version of the EA frame for the 2010 CPH was updated to accommodate some changes in districts and constituencies that occurred between 2010 and 2017. The list of EAs incorporates census information on households and population counts. Each EA has a cartographic map delineating its boundaries, with identification information and a measure of size, which is the number of residential households enumerated in the 2010 CPH. This list of EAs was used as the sampling frame for the 2018 ZDHS.

The 2018 ZDHS followed a stratified two-stage sample design. The first stage involved selecting sample points (clusters) consisting of EAs. EAs were selected with a probability proportional to their size within each sampling stratum. A total of 545 clusters were selected.

The second stage involved systematic sampling of households. A household listing operation was undertaken in all of the selected clusters. During the listing, an average of 133 households were found in each cluster, from which a fixed number of 25 households were selected through an equal probability systematic selection process, to obtain a total sample size of 13,625 households. Results from this sample are representative at the national, urban and rural, and provincial levels.

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

Mode of data collection

Face-to-face [f2f]

Research instrument

Four questionnaires were used in the 2018 ZDHS: the Household Questionnaire, the Woman’s Questionnaire, the Man’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 Zambia. Input on questionnaire content was solicited from various stakeholders representing government ministries and agencies, nongovernmental organisations, and international cooperating partners. After all questionnaires were finalised in English, they were translated into seven local languages: Bemba, Kaonde, Lozi, Lunda, Luvale, Nyanja, and Tonga. In addition, information about the fieldworkers for the survey was collected through a self-administered Fieldworker Questionnaire.

Cleaning operations

All electronic data files were transferred via a secure internet file streaming system to the ZamStats central office in Lusaka, where they were stored on a password-protected computer. The data processing operation included secondary editing, which required resolution of computer-identified inconsistencies and coding of open-ended questions. The data were processed by two IT specialists and one secondary editor who took part in the main fieldwork training; they were supervised remotely by staff from The DHS Program. Data editing was accomplished using CSPro software. During the fieldwork, field-check tables were generated to check various data quality parameters, and specific feedback was given to the teams to improve performance. Secondary editing and data processing were initiated in July 2018 and completed in March 2019.

Response rate

Of the 13,595 households in the sample, 12,943 were occupied. Of these occupied households, 12,831 were successfully interviewed, yielding a response rate of 99%.

In the interviewed households, 14,189 women age 15-49 were identified as eligible for individual interviews; 13,683 women were interviewed, yielding a response rate of 96% (the same rate achieved in the 2013-14 survey). A total of 13,251 men were eligible for individual interviews; 12,132 of these men were interviewed, producing a response rate of 92% (a 1 percentage point increase from the previous survey).

Of the households successfully interviewed, 12,505 were interviewed in 2018 and 326 in 2019. As the large majority of households were interviewed in 2018 and the year for reference indicators is 2018.

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 Zambia Demographic and Health Survey (ZDHS) 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 ZDHS 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 ZDHS sample is the result of a multi-stage 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 - Completeness of information on siblings - Sibship size and sex ratio of siblings - Height and weight data completeness and quality for children - Number of enumeration areas completed by month, according to province, Zambia DHS 2018

Note: Data quality tables are presented in APPENDIX C of the report.

Abstract

The Central Statistical Office carried out a Living Conditions Monitoring Survey in November-December, 1998. The survey was carried out nation-wide in all the 72 districts of Zambia on a sample basis. The main objectives of the survey are to:- (i) Monitor the effects of government policies on households and individuals. (ii) Measure and monitor poverty overtime in order for government to evaluate its poverty reduction programs. (iii) To monitor the living conditions of households in Zambia in the form of access to various economic and social facilities and infrastructure and access to basic needs; food, shelter, clean water and sanitation, education and health, etc. (iv) To identify vulnerable groups in society. The Living Conditions Monitoring Survey (LCMS 1998) collected data on the living standards of households and persons in the areas of education, health, income sources, income levels, food production and consumption, and access to various amenities.

Geographic coverage

National

Analysis unit

• Households
• Individuals

Universe

The LCMS 1998 was conducted nation-wide on a sample basis and covered both rural and urban areas of all the 72 districts in the country. The eligible household population consisted of all households. Excluded from the sample were institutional populations in hospitals, boarding schools, colleges, universities, prisons, hotels, refugee camps, orphanages, military camps and bases and diplomats accredited to Zambia in embassies and high commissions. Private households living around these institutions and cooking separately were included such as teachers whose houses are within the premises of a school, doctors and other workers living on or around hospital premises, police living in police camps in separate houses, etc. Persons who were in hospitals, boarding schools, etc. but were usual members of households were included in their respective households. Ordinary workers other than diplomats working in embassies and high commissions were included in the survey also. Others with diplomatic status working in the UN, World Bank etc. were included. Also included were persons or households who live in institutionalized places such as hostels, lodges, etc. but cook separately. The major distinguishing factor between eligible and non eligible households in the survey is the cooking and eating separately versus food provided by an institution in a common/communal dining hall or eating place. The former cases were included while the latter were excluded.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sampling Frame and Stratification The country is made up of 9 provinces comprising 72 districts delineated by the Local Government Administration. Previously there were 57 districts in Zambia. 15 new districts have been created. Central Statistical Office has delineated these districts into Census Supervisory Areas (CSAs) and then these into Standard Enumeration Areas (SEAs) for the purposes of conducting censuses and sampling for surveys. Each CSA is made up about 3 SEAs. The list of CSAs and SEAs by province & district constitute the sampling frame for CSO censuses and surveys. The sampling frame comprises 4,193 CSAs of which 3,231 are rural and 962 are urban and 12,999 SEAs. The frame of CSAs and SEAs is arranged by province, then by district within a province,then by rural/urban within a district, then by centrality within rural/urban, and finally by low, medium or high cost for urban SEAs. The frame also contains information on the number of households and the population size per SEA and this is what was used when selecting the sample using the probability proportional to size (PPS) method. The number of households and the population in the frame is based on the 1990 population census. To boost the data from the survey to 1998 population parameters the weights calculated were multiplied by a factor equal to the estimated population growth from 1990 to 1998. This was done at the district level.

The classification of centrality is shown below:- Centrality Classification:- 1. Areas within Lusaka city. 2. Areas within Ndola city. 3. Areas within Kitwe city. 4. Areas within 50 Kms radius outside Lusaka, or Ndola, or Kitwe cities. 5. Areas within provincial capitals. 6. Areas along Southern to Copperbelt line of Rail (within 30 Kms radius). 7. Areas along Northern line of Rail (within 30 Kms radius). 8. Areas within 30kms radius outside provincial capitals. 9. Areas within district centres. 10.Areas within 30 Kms radius outside district centres 11.Remote areas.

Areas within cities, provincial capitals and district centres is equivalent to the urban part of the town.Within the rural SEAs households have been classified on the basis of the scale of agricultural activities into small scale, medium scale, large scale, and non-agricultural households.The urban SEAs have been classified into low cost, medium cost or high cost depending on the type of housing in the area.The local government administration has classified localities into low, medium and high cost based on the required housing standard. The urban SEAs were classified into low, medium and high cost areas based on a combination of the local government and CSO criteria. All urban SEAs were physically visited by CSO mapping staff with locality classification from local government and determined whether the SEA was low, medium or high cost based on the local government definition and the actual observation of the mapper. The mappers were trained on how to make this determination. Households within rural SEAs were classified into small scale, medium scale, large scale, and non agricultural households after the listing operation.

Sample Size: Out of a total of 12,999 SEAs in the frame, a sample of 820 SEAs were selected for the Living Conditions Monitoring Survey (1998) representing about 6% of the total. The urban stratum was allocated 328 SEAs and the rural stratum was allocated 492 SEAs. The total number of households enumerated were 8520 in rural areas and 8220 in the urban areas.The total number of persons who fell in the sample were 45989 in rural areas and 47480 in urban areas.All the 72 districts in Zambia were covered in the survey on a sample basis.

Sample Allocation: Sample allocation was done using the "Probability Proportional to size" (PPS) method. This entailed allocating the total sample (820) proportionately to each province according to its population share.Thereafter, allocation of the provincial sample was done proportionately to each district according to the population share from the provincial population. Similarly allocation was done by centrality within a district. For example, Mkushi district was allocated 10 SEAs by the PPS method. The district has four centrality classifications (9, 7, 10, and11). The number of SEAs under each centrality classification in the frame were summed up. The next step was to determine the share of each centrality group of SEAs from the total number of SEAs in the frame under Mkushi district. The corresponding proportions were used to allocate the sample to each centrality category. However, the final allocation was plus or minus depending on what was obtaining in the frame. For example if 1 SEA was to be allocated to centrality 9 (District centre) by using PPS and yet there is low, medium & high cost SEAs under centrality 9 in that district, the number of SEAs selected was 3 (one from low, and the other two from the medium & high cost SEAs). Not all centrality classifications obtain in all districts, for example, Lusaka district had all the SEAs fall under centrality 1 (Lusaka city) in the frame. Therefore the entire number of SEAs allocated to Lusaka district was selected from this category. The minimum size for each district sample was 7 SEAs, meaning that even the smallest district was allocated at least 7 SEAs.

Sample Selection: Sample selection was done in two stages. In the first stage, a sample of SEAs was selected within each stratum (centrality) according to the number allocated to that stratum. The second stage comprised selection of households from each sample SEA according to the number of households recommended after a complete listing of all households in the sample SEAs. Thus SEAs formed primary sampling units. The unit of analysis was the household.

Selection of SEAS: After sample allocation was done, selection of the sample SEAs from the frame followed. The allocated number of SEAs were selected at centrality level using the PPS method.

Selection of Households:In each selected SEA, households were listed and each household given a unique sampling serial number. A circular systematic sample of households was then selected. Vacant residential housing units and noncontact households were not assigned sampling serial numbers. Selection of sample households was done by supervisors in the field and they were required to select the following numbers of households: 30 households from SEAs with sample Micro-projects (whether rural or urban). 25 households from urban SEAs (without sample micro-projects) 15 households from rural SEAs (without sample micro-projects). This number increased in rural SEAs where large scale farmers were identified.

In urban areas the required sample number of households were selected straight forwardly using the circular systematic sampling method. In the rural areas, 7 households were selected from the stratum of small scale farmers, 5 from medium scale farmers, 3 from non-agricultural households, and all large scale farmers if any were found in the SEA. Therefore, the number of selected households from a rural SEA was more than 15 where there were large scale farmers. In Micro-project areas the number of households to

Zambia CSOZ Forecast: Population: Mid Year: Lusaka data was reported at 5,465,775.000 Person in 2035. This records an increase from the previous number of 5,307,950.000 Person for 2034. Zambia CSOZ Forecast: Population: Mid Year: Lusaka data is updated yearly, averaging 3,739,872.000 Person from Jun 2011 (Median) to 2035, with 25 observations. The data reached an all-time high of 5,465,775.000 Person in 2035 and a record low of 2,362,967.000 Person in 2011. Zambia CSOZ Forecast: Population: Mid Year: Lusaka data remains active status in CEIC and is reported by Central Statistical Office. The data is categorized under Global Database’s Zambia – Table ZM.G002: Population: Mid Year: Forecast: Central Statistical Office of Zambia.