Somalia was the African country with the highest fertility rate in 2023. There, each woman had an average of around 6.1 children in her reproductive years. Fertility levels in Africa remain high despite a steady decline The fertility rate in Africa has gradually decreased since 2000 and is projected to decline further in the coming years. Factors including improved socio-economic conditions and educational opportunities, lower infant mortality, and decreasing poverty levels have driven the declining birth rate on the continent. Nevertheless, Africa remains the continent with the highest fertility rate worldwide. As of 2023, women in Africa had an average of 4.07 children in their reproductive years. Africa was the only continent registering a fertility rate higher than the global average, which was set at 2.4 children per woman. Worldwide, the continent also had the highest adolescent fertility rate as of 2022, with West and Central Africa leading with 105 births per 1,000 girls aged 15 to 19 years. Africa’s population keeps growing According to projections, over 46 million births will be registered in Africa in 2023. Contrary to the declining fertility rate, the absolute number of births on the continent will continue to grow in the coming years to reach around 49.4 million by 2030. In general, Africa’s population – amounting to over 1.48 billion inhabitants as of 2023 – is forecast to increase considerably and achieve 2.5 billion in 2050. Countries such as Niger, Angola, and Equatorial Guinea are key drivers of population growth in Africa, registering the highest average population growth rate on the continent between 2020 and 2025. For instance, in that period, Niger’s population was forecast to expand by 3.7 percent each year.

In 2025, there are six countries, all in Sub-Saharan Africa, where the average woman of childbearing age can expect to have between 5-6 children throughout their lifetime. In fact, of the 20 countries in the world with the highest fertility rates, Afghanistan and Yemen are the only countries not found in Sub-Saharan Africa. High fertility rates in Africa With a fertility rate of almost six children per woman, Chad is the country with the highest fertility rate in the world. Population growth in Chad is among the highest in the world. Lack of healthcare access, as well as food instability, political instability, and climate change, are all exacerbating conditions that keep Chad's infant mortality rates high, which is generally the driver behind high fertility rates. This situation is common across much of the continent, and, although there has been considerable progress in recent decades, development in Sub-Saharan Africa is not moving as quickly as it did in other regions. Demographic transition While these countries have the highest fertility rates in the world, their rates are all on a generally downward trajectory due to a phenomenon known as the demographic transition. The third stage (of five) of this transition sees birth rates drop in response to decreased infant and child mortality, as families no longer feel the need to compensate for lost children. Eventually, fertility rates fall below replacement level (approximately 2.1 children per woman), which eventually leads to natural population decline once life expectancy plateaus. In some of the most developed countries today, low fertility rates are creating severe econoic and societal challenges as workforces are shrinking while aging populations are placin a greater burden on both public and personal resources.

Niger had the highest birth rate in the world in 2024, with a birth rate of 46.6 births per 1,000 inhabitants. Angola, Benin, Mali, and Uganda followed. Except for Afghanistan, all 20 countries with the highest birth rates in the world were located in Sub-Saharan Africa. High infant mortality The reasons behind the high birth rates in many Sub-Saharan African countries are manyfold, but a major reason is that infant mortality remains high on the continent, despite decreasing steadily over the past decades, resulting in high birth rates to counter death rates. Moreover, many nations in Sub-Saharan Africa are highly reliant on small-scale farming, meaning that more hands are of importance. Additionally, polygamy is not uncommon in the region, and having many children is often seen as a symbol of status. Fastest-growing populations As the high fertility rates coincide with decreasing death rates, countries in Sub-Saharan Africa have the highest population growth rates in the world. As a result, Africa's population is forecast to increase from 1.4 billion in 2022 to over 3.9 billion by 2100.

The average for 2023 based on 196 countries was 26.11 percent. The highest value was in the Central African Republic: 49.17 percent and the lowest value was in Hong Kong: 10.7 percent. The indicator is available from 1960 to 2023. Below is a chart for all countries where data are available.

BackgroundDespite the sharp decline in global under-5 deaths since 1990, uneven progress has been achieved across and within countries. In sub-Saharan Africa (SSA), the Millennium Development Goals (MDGs) for child mortality were met only by a few countries. Valid concerns exist as to whether the region would meet new Sustainable Development Goals (SDGs) for under-5 mortality. We therefore examine further sources of variation by assessing age patterns, trends, and forecasts of mortality rates.Methods and findingsData came from 106 nationally representative Demographic and Health Surveys (DHSs) with full birth histories from 31 SSA countries from 1990 to 2017 (a total of 524 country-years of data). We assessed the distribution of age at death through the following new demographic analyses. First, we used a direct method and full birth histories to estimate under-5 mortality rates (U5MRs) on a monthly basis. Second, we smoothed raw estimates of death rates by age and time by using a two-dimensional P-Spline approach. Third, a variant of the Lee–Carter (LC) model, designed for populations with limited data, was used to fit and forecast age profiles of mortality. We used mortality estimates from the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME) to adjust, validate, and minimize the risk of bias in survival, truncation, and recall in mortality estimation. Our mortality model revealed substantive declines of death rates at every age in most countries but with notable differences in the age patterns over time. U5MRs declined from 3.3% (annual rate of reduction [ARR] 0.1%) in Lesotho to 76.4% (ARR 5.2%) in Malawi, and the pace of decline was faster on average (ARR 3.2%) than that observed for infant (IMRs) (ARR 2.7%) and neonatal (NMRs) (ARR 2.0%) mortality rates. We predict that 5 countries (Kenya, Rwanda, Senegal, Tanzania, and Uganda) are on track to achieve the under-5 sustainable development target by 2030 (25 deaths per 1,000 live births), but only Rwanda and Tanzania would meet both the neonatal (12 deaths per 1,000 live births) and under-5 targets simultaneously. Our predicted NMRs and U5MRs were in line with those estimated by the UN IGME by 2030 and 2050 (they overlapped in 27/31 countries for NMRs and 22 for U5MRs) and by the Institute for Health Metrics and Evaluation (IHME) by 2030 (26/31 and 23/31, respectively). This study has a number of limitations, including poor data quality issues that reflected bias in the report of births and deaths, preventing reliable estimates and predictions from a few countries.ConclusionsTo our knowledge, this study is the first to combine full birth histories and mortality estimates from external reliable sources to model age patterns of under-5 mortality across time in SSA. We demonstrate that countries with a rapid pace of mortality reduction (ARR ≥ 3.2%) across ages would be more likely to achieve the SDG mortality targets. However, the lower pace of neonatal mortality reduction would prevent most countries from achieving those targets: 2 countries would reach them by 2030, 13 between 2030 and 2050, and 13 after 2050.

According to a study from 2020, over 70 percent of the poorest children living in Mali, Niger, Nigeria, and Guinea dis not attend school. Overall, Sub-Saharan Africa has the highest illiteracy rate in the world. Countries in East and West Africa suffer from high levels of poverty, including health, malnutrition, lack of clean water and electricity, poor education, and other similar aspects.

In 2023, Nigeria had the highest number of child AIDS-related deaths in the world, at around 15,000 deaths. It was also the country with the second-highest number of deaths due to AIDS worldwide. This statistic presents the number of AIDS-related deaths among children aged 0 to14 years in select African countries in 2023.

Abstract

The authors combine data from 84 Demographic and Health Surveys from 46 countries to analyze trends and socioeconomic differences in adult mortality, calculating mortality based on the sibling mortality reports collected from female respondents aged 15-49.

The analysis yields four main findings. First, adult mortality is different from child mortality: while under-5 mortality shows a definite improving trend over time, adult mortality does not, especially in Sub-Saharan Africa. The second main finding is the increase in adult mortality in Sub-Saharan African countries. The increase is dramatic among those most affected by the HIV/AIDS pandemic. Mortality rates in the highest HIV-prevalence countries of southern Africa exceed those in countries that experienced episodes of civil war. Third, even in Sub-Saharan countries where HIV-prevalence is not as high, mortality rates appear to be at best stagnating, and even increasing in several cases. Finally, the main socioeconomic dimension along which mortality appears to differ in the aggregate is gender. Adult mortality rates in Sub-Saharan Africa have risen substantially higher for men than for women?especially so in the high HIV-prevalence countries. On the whole, the data do not show large gaps by urban/rural residence or by school attainment.

This paper is a product of the Human Development and Public Services Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org.

Geographic coverage

We derive estimates of adult mortality from an analysis of Demographic and Health Survey (DHS) data from 46 countries, 33 of which are from Sub-Saharan Africa and 13 of which are from countries in other regions (Annex Table). Several of the countries have been surveyed more than once and we base our estimates on the total of 84 surveys that have been carried out (59 in Sub-Saharan Africa, 25 elsewhere).

The countries covered by DHS in Sub-Saharan Africa represent almost 90 percent of the region's population. Outside of Sub-Saharan Africa the DHS surveys we use cover a far smaller share of the population-even if this is restricted to countries whose GDP per capita never exceeds $10,000: overall about 14 percent of the population is covered by these countries, although this increases to 29 percent if China and India are excluded (countries for which we cannot calculate adult mortality using the DHS). It is therefore important to keep in mind that the sample of non-Sub-Saharan African countries we have cannot be thought of as "representative" of the rest of the world, or even the rest of the developing world.

Analysis unit

Country

Kind of data

Sample survey data [ssd]

Mode of data collection

Face-to-face [f2f]

Cleaning operations

In the course of carrying out this study, the authors created two databases of adult mortality estimates based on the original DHS datasets, both of which are publicly available for analysts who wish to carry out their own analysis of the data.

The naming conventions for the adult mortality-related are as follows. Variables are named:

GGG_MC_AAAA

GGG refers to the population subgroup. The values it can take, and the corresponding definitions are in the following table:

All - All Fem - Female Mal - Male Rur - Rural Urb - Urban Rurm - Rural/Male Urbm - Urban/Male Rurf - Rural/Female Urbf - Urban/Female Noed - No education Pri - Some or completed primary only Sec - At least some secondary education Noedm - No education/Male Prim - Some or completed primary only/Male Secm - At least some secondary education/Male Noedf - No education/Female Prif - Some or completed primary only/Female Secf - At least some secondary education/Female Rch - Rural as child Uch - Urban as child Rchm - Rural as child/Male Uchm - Urban as child/Male Rchf - Rural as child/Female Uchf - Urban as child/Female Edltp - Less than primary schooling Edpom - Primary or more schooling Edltpm - Less than primary schooling/Male Edpomm - Primary or more schooling/Male Edltpf - Less than primary schooling/Female Edpomf - Primary or more schooling/Female Edltpu - Less than primary schooling/Urban Edpomu - Primary or more schooling/Urban Edltpr - Less than primary schooling/Rural Edpomr - Primary or more schooling/Rural Edltpmu - Less than primary schooling/Male/Urban Edpommu - Primary or more schooling/Male/Urban Edltpmr - Less than primary schooling/Male/Rural Edpommr - Primary or more schooling/Male/Rural Edltpfu - Less than primary schooling/Female/Urban Edpomfu - Primary or more schooling/Female/Urban Edltpfr - Less than primary schooling/Female/Rural Edpomfr - Primary or more schooling/Female/Rural

M refers to whether the variable is the number of observations used to calculate the estimate (in which case M takes on the value "n") or whether it is a mortality estimate (in which case M takes on the value "m").

C refers to whether the variable is for the unadjusted mortality rate calculation (in which case C takes on the value "u") or whether it adjusts for the number of surviving female siblings (in which case C takes on the value "a").

AAAA refers to the age group that the mortality estimate is calculated for. It takes on the values: 1554 - Ages 15-54 1524 - Ages 15-24 2534 - Ages 25-34 3544 - Ages 35-44 4554 - Ages 45-54

Other variables that are in the databases are:

period - Period for which mortality rate is calculated (takes on the values 1975-79, 1980-84 … 2000-04) svycountry - Name of country for DHS countries ccode3 - Country code u5mr - Under-5 mortality (from World Development Indicators) cname - Country name gdppc - GDP per capita (constant 2000 US$) (from World Development Indicators) gdppcppp - GDP per capita PPP (constant 2005 intl $) (from World Development Indicators) pop - Population (from World Development Indicators) hivprev2001 - HIV prevalence in 2001 (from UNAIDS 2010) region - Region

BackgroundAround 8.8 million children under-five die each year, mostly due to infectious diseases, including malaria that accounts for 16% of deaths in Africa, but the impact of international financing of malaria control on under-five mortality in sub-Saharan Africa has not been examined. Methods and FindingsWe combined multiple data sources and used panel data regression analysis to study the relationship among investment, service delivery/intervention coverage, and impact on child health by observing changes in 34 sub-Saharan African countries over 2002–2008. We used Lives Saved Tool to estimate the number of lives saved from coverage increase of insecticide-treated nets (ITNs)/indoor residual spraying (IRS). As an indicator of outcome, we also used under-five mortality rate. Global Fund investments comprised more than 70% of the Official Development Assistance (ODA) for malaria control in 34 countries. Each $1 million ODA for malaria enabled distribution of 50,478 ITNs [95%CI: 37,774–63,182] in the disbursement year. 1,000 additional ITNs distributed saved 0.625 lives [95%CI: 0.369–0.881]. Cumulatively Global Fund investments that increased ITN/IRS coverage in 2002–2008 prevented an estimated 240,000 deaths. Countries with higher malaria burden received less ODA disbursement per person-at-risk compared to lower-burden countries ($3.90 vs. $7.05). Increased ITN/IRS coverage in high-burden countries led to 3,575 lives saved per 1 million children, as compared with 914 lives in lower-burden countries. Impact of ITN/IRS coverage on under-five mortality was significant among major child health interventions such as immunisation showing that 10% increase in households with ITN/IRS would reduce 1.5 [95%CI: 0.3–2.8] child deaths per 1000 live births. ConclusionsAlong with other key child survival interventions, increased ITNs/IRS coverage has significantly contributed to child mortality reduction since 2002. ITN/IRS scale-up can be more efficiently prioritized to countries where malaria is a major cause of child deaths to save greater number of lives with available resources.

Abstract

The Survey of Activities of Young People was conducted by Statistics South Africa and commissioned by the Department of Labour, primarily to gather information necessary for formulating an effective programme of action to address the issue of harmful work done by children in South Africa. Technical assistance for the survey was provided by the International Labour Organisation (ILO) and a consultant appointed by the Department of Labour. Stats SA also worked with an advisory committee, consisting of representatives from national government departments most directly concerned with child labour (the Departments of Labour,Welfare,Education and Health), non-governmental organisations, and the United Nations Children's Fund (Unicef).

Geographic coverage

The survey has national coverage

Analysis unit

Households and individuals

Universe

The sampled population was household members in South Africa. The survey excluded all people in prison, patients in hospitals, people residing in boarding houses and hotels, and boarding schools. Any single person households were screened out in all areas before the sample was drawn. Families living in hostels were treated as households.

Kind of data

Sample survey data

Sampling procedure

The sample frame was based on the 1996 Population Census Enumerator Areas (EA) and the number of households counted in 1996 Population Census. The sampled population excluded all prisoners in prison, patients in hospitals, people residing in boarding houses and hotels (whether temporary or semi-permanent), and boarding schools. Any single person households were screened out in all areas before the sample was drawn. Families living in hostels were treated as households. Coverage rules for the survey were that all children of usual residents were to be included even if they were not present. This means that most boarding school pupils were included in their parents’ household. The 16 EA types from the 1996 Population Census were condensed into four area types. The four area types were Formal Urban, Informal Urban, Tribal, and Commercial Farms. A decision was made to drop the Institution type EAs.

The EAs were stratified by province, and within a province by the four area types defined above. The sample size (6110 households) was disproportionately allocated to strata by using the square root method. Within the strata the EAs were ordered by magisterial district and the EA-types included in the area type (implicit stratification). PSUs consisted of ONE or more EAs of size 100 households to ensure sufficient numbers for screening. Statistics SA was advised by child labour experts that there was a likelihood of high rates of child labour in the Urban Informal and Rural Farm areas. The sample allocation to Rural Commercial Farms was therefore increased to a minimum of 20 PSUs.

Mode of data collection

Face-to-face [f2f]

Research instrument

The Phase one questionnaire covered the following topics: Living conditions of the household, including the type of dwelling, fuels used for cooking, lighting and heating,water source for domestic use, land ownership,tenure and cultivation; demographic information on members of the household, both adults and children. Questions covered the age, gender and population group of each household member, their marital status, their relationships to each other, and their levels of education; migration details; household income; school attendance of children aged 5 -17 years; information on economic and non-economic activities of children aged 5-17 years in the 12 months prior to the survey

Phase two questionnaire The second phase questionnaire was administered to the sampled sub-set of households in which at least one child was involved in some form of work in the year prior to the interview. It covered activities of children in much more detail than in phase one, and the work situation of related adults in the household. Both adults and children were asked to respond.

The data files contain data from sections of the questionnaires as follows:

PERSON: Data from Section 1, 2 and 3 of the questionnaire HHOLD : Data from Section 4 ADULT : Data from Section 5 YOUNGP: Data from Section 6, 7, 8 and 9

This project involves the analysis of secondary policy and household survey macro- and micro-data that are relevant to African governments’ fulfilment of children’s rights and well-being in nutrition, healthcare and education, with a focus on gender equality. It includes national and sub-national demographic, economic and social variables obtained from various data resources. Details can be found via The African Report on Child Wellbeing 2020 (https://www.africanchild.report/index.php/home).The governments of the World have agreed to the United Nations Sustainable Development Goals (SDGs). The first five goals are no poverty, zero hunger, good health and well-being, quality education and gender equality. The African Child Policy Forum (ACPF) is the leading independent, not-for-profit, Pan-African organisation, specialising in helping African governments to improve their policies and practices to meet the SDGs for children. This project builds upon a long-term partnership between the ACPF and the University of Bristol to make better use of available data to provide policymakers with the high-quality evidence they need to help meet the first five SDGs. Agenda 2063 is Africa's blueprint and master plan for transforming Africa into the global powerhouse of the future. It is the continent's strategic framework that aims to deliver on its goal for inclusive and sustainable development. This ambitious goal cannot be achieved without improvements in the lives of African Children. However, approximately 27 million African children suffer from stunting (low height for age), 16 million are underweight (low weight for age) and 8 million suffer from wasting (low weight for height). In 2016, only two-thirds of children in Africa had been vaccinated against diphtheria, tetanus and other serious childhood diseases. Similarly, about 7 million children in Eastern and Southern Africa and 8 million in West and Central Africa are likely to receive no pre-primary education in 2030 given the current slow rates of improvement (UNICEF 2018). The combination of poverty and inadequate nutrition, healthcare and education are amongst the most intractable development challenges faced by most countries in Africa (ACPF 2018). Gender discrimination is also a significant problem in Africa, and there remain many social, economic and cultural factors contributing to the disempowerment and discriminatory practices that disadvantage women and girls. There is a pressing need for systematic assessments on the nature and extent of gender discrimination in nutrition, healthcare and education in Africa over the past decade (2008-2018). This project will analyse relevant data about children's lives and circumstances using state-of-the-art quantitative and qualitative methods to explore 'what' changes there have been in child nutrition, healthcare and education during the past ten years in Africa, 'when' these changes are related to gender disparities and the availability and quality of child protection policies and services in each country, and 'where' children at sub-national level are at the greatest risk of being left behind. Outputs from this project were published by ACPF in their flagship report For details of all the data resources used, please refer to our report The African Report on Child Wellbeing 2020 (https://www.africanchild.report/index.php/home). Given that the project only analysed secondary data, the original data was not shared. Data users can go to the original data providers for getting access to the datasets. Details can be found via The African Report on Child Wellbeing 2020 (https://www.africanchild.report/index.php/home).

ZA: Prevalence of Stunting: Height for Age: % of Children Under 5 data was reported at 27.400 % in 2016. This records an increase from the previous number of 27.200 % for 2012. ZA: Prevalence of Stunting: Height for Age: % of Children Under 5 data is updated yearly, averaging 28.700 % from Dec 1994 (Median) to 2016, with 7 observations. The data reached an all-time high of 32.800 % in 2004 and a record low of 24.900 % in 2008. ZA: Prevalence of Stunting: Height for Age: % of Children Under 5 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s South Africa – Table ZA.World Bank: Health Statistics. Prevalence of stunting is the percentage of children under age 5 whose height for age is more than two standard deviations below the median for the international reference population ages 0-59 months. For children up to two years old height is measured by recumbent length. For older children height is measured by stature while standing. The data are based on the WHO's new child growth standards released in 2006.; ; UNICEF, WHO, World Bank: Joint child malnutrition estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology.; Linear mixed-effect model estimates; Undernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF, www.childinfo.org). Estimates of child malnutrition, based on prevalence of underweight and stunting, are from national survey data. The proportion of underweight children is the most common malnutrition indicator. Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition.

In 2022, Nigeria registered the highest estimated number of children dying before reaching the age of five. Over 800,000 children died in the West African country. Pakistan and the Democratic Republic of the Congo followed behind. Meanwhile, Niger had the highest child mortality rate worldwide.

Determinants of stunting among late adolescent girls in East African countries from most recent DHS data.

Significant progress has been achieved in universal basic education in African countries since the late 1990s. This study provides empirical evidence on the within- and across-country variation in numeracy skills performance among children based on nationally representative data from eight African countries (DR Congo, The Gambia, Ghana, Lesotho, Sierra Leone, Togo, Tunisia, and Zimbabwe). We assess whether and to what extent children with disabilities lag in numeracy skills and how much it depends on their type of disabilities. More specifically, we explore whether disabled children benefit equally from better school system quality. The assessment is analysed as a natural experiment using the performance of non-disabled children as a benchmark and considering the different types of disabilities as random treatments. We first evaluate the variation in average numeracy skills in the eight African countries. They can roughly be divided into low- and high-numeracy countries. We apply Instrumental Variable (IV) methods to control the endogeneity of completed school years when assessing subjects’ school performance and heterogeneous disability effects. Children with vision and hearing disabilities are not especially challenged in numeracy skills performance. The low numeracy skills among physically and intellectually disabled children are mainly attributable to their limited school attendance. Children with multiple disabilities are constrained both by low school attendance and by poor numeracy skills return to schooling. The average differences in school performance across the high- versus low-numeracy skill country groups are larger than the within-group average differences for disabled versus non-disabled kids. This indicates that school enrolment and quality are crucial for children’s learning of numeracy skills, and that disabled children benefit equally from better school quality across these African countries.

IntroductionStunting is still a major public health problem all over the world, it affecting more than one-third of under-five children in the world that leads to growth retardation, life-threatening complication and accelerate mortality and morbidity. The evidence is scarce on prevalence and associated factors of stunting among under-five children in Sub-Saharan Africa for incorporated intervention. Therefore this study aimed to investigate the prevalence and determinants of stunting among under-five children in Sub-Saharan Africa using recent demographic and health surveys of each country.MethodsThis study was based on the most recent Demographic and Health Survey data of 36 sub-Saharan African countries. A total of 203,852(weighted sample) under-five children were included in the analysis. The multi-level ordinal logistic regression was fitted to identify determinants of stunting. Parallel line (proportional odds) assumption was cheeked by Brant test and it is satisfied (p-value = 0.68) which is greater than 0.05. Due to the nested nature of the dataset deviance was used model comparison rather than AIC and BIC. Finally the adjusted odds ratio (AOR) with 95% CI was reported identify statistical significant determinants of stunting among under-five children.ResultsIn this study, the prevalence of stunting among under-five children in Sub-Saharan Africa 34.04% (95% CI: 33.83%, 34.24%) with a large difference between specific countries which ranges from 16.14% in Gabon to 56.17% in Burundi. In the multi-level ordinal logistic regression good maternal education, born from mothers aged above 35 years, high household wealth status, small family size, being female child, being female household head, having media exposure and having consecutive ANC visit were significantly associated with lower odds of stunting. Whereas, living from rural residence, being 24–59 month children age, single or divorced marital status, higher birth order and having diarrhea in the last two weeks were significantly associated with higher odds of stunting.ConclusionStunting among under-five children is still public health problem in Sub-Saharan Africa. Therefore designing interventions to address diarrhea and other infectious disease, improving the literacy level of the area and increase the economic level of the family to reduce the prevalence of stunting in the study area.

BackgroundDietary diversity is an indicator of nutritional adequacy, which plays a significant role in child growth and development. Lack of adequate nutrition is associated with suboptimal brain development, lower school performance, and increased risk of mortality and chronic diseases. We aimed to determine the prevalence and determinants of meeting minimum dietary diversity (MDD), defined as consuming at least five out of eight basic food groups in the previous 24-h in three sub-Saharan African countries.MethodsA weighted population-based cross-sectional study was conducted using the most recent Demographic and Health Surveys (DHS). MDD data were available between 2019 and 2020 for three sub-Saharan African countries (Gambia, Liberia, and Rwanda). The study population included 5,832 children aged 6–23 months. A multivariable logistic regression model was developed to identify independent factors associated with meeting MDD.ResultsOverall, the weighted prevalence of children who met the MDD was 23.2% (95% CI: 21.7–24.8%), ranging from 8.6% in Liberia to 34.4% in Rwanda. Independent factors associated with meeting MDD were: age of the child (OR) = 1.96, 95% CI: 1.61, 2.39 for 12–17 months vs. 6–11 months], mothers from highest households' wealth status (OR = 1.86, 95% CI: 1.45–2.39) compared with the lowest, and mothers with secondary/higher education (OR = 1.69, 95% CI: 1.35–2.12) compared with those with no education. Mothers who were employed, had access to a radio, and those who visited a healthcare facility in the last 12 months were more likely to meet the MDD. There was no significant association between the child's sex and the odds of fulfilling the MDD.ConclusionsThere is substantial heterogeneity in the prevalence of MDD in these three sub-Saharan African countries. Lack of food availability or affordability may play a significant role in the low prevalence of MDD. The present analysis suggests that policies that will effectively increase the prevalence of meeting MDD should target poor households with appropriate materials or financial assistance and mothers with lower literacy. Public health interventions working with sectors such as education and radio stations to promote health education about the benefits of diverse diets is a critical step toward improving MDD in sub-Saharan Africa and preventing undernutrition.

Vulnerable population identified by the nutritional status of children (weight for age and weight for height) as indicators for food security, in sample of households in West Africa study area. Data based on DHS and MICS surveys. In defining vulnerability, WFP (2009) and IFPRI (2012) have been followed and combined with indicators for food security with health indicators that signal vulnerability in a physical sense. IFPRI's Global Hunger Index uses three indicators to measure hunger: the number of adults being undernourished, the number of children that have low weight for age, and child mortality. Other classifications of food security use the variety of the diet as an indicator, combined with anthropometric data on children. However, in the DHS data there were no information available on child mortality, nor on dietary composition. Given these data limitations, data on nutritional status of women (Body Mass Index, BMI) for women and children (weight for age and weight for height) have been used as indicators for food security. These data were combined with data on morbidity among adults and children, specifically the occurrence of malaria, cough, and diarrhea. Combinations of indicators have led to a classification of households as being very vulnerable, vulnerable, nearly vulnerable and not vulnerable.

This data set was produced in the framework of the "Climate change predictions in Sub-Saharan Africa: impacts and adaptations (ClimAfrica)" project, Work Package 5 (WP5). More information on ClimAfrica project is provided in the Supplemental Information section of this metadata.

This study in WP5 aimed to identify, locate and characterize groups that are vulnerable for climate change conditions in two country clusters; one in West Africa (Benin, Burkina Faso, Côte d’Ivoire, Ghana, and Togo) and one in East Africa (Sudan, South Sudan and Uganda). Data used for the study include the Demographic and Health Surveys (DHS) , the Multi Indicator Cluster Survey (MICS) and the Afrobarometer surveys for the socio-economic variables and grid level data on agro-ecological and climatic conditions.

Data publication: 2013-08-01

Supplemental Information:

ClimAfrica was an international project funded by European Commission under the 7th Framework Programme (FP7) for the period 2010-2014. The ClimAfrica consortium was formed by 18 institutions, 9 from Europe, 8 from Africa, and the Food and Agriculture Organization of United Nations (FAO).

ClimAfrica was conceived to respond to the urgent international need for the most appropriate and up-to-date tools and methodologies to better understand and predict climate change, assess its impact on African ecosystems and population, and develop the correct adaptation strategies. Africa is probably the most vulnerable continent to climate change and climate variability and shows diverse range of agro-ecological and geographical features. Thus the impacts of climate change can be very high and can greatly differ across the continent, and even within countries.

The project focused on the following specific objectives:

1. Develop improved climate predictions on seasonal to decadal climatic scales, especially relevant to SSA;

2. Assess climate impacts in key sectors of SSA livelihood and economy, especially water resources and agriculture;

3. Evaluate the vulnerability of ecosystems and civil population to inter-annual variations and longer trends (10 years) in climate;

4. Suggest and analyse new suited adaptation strategies, focused on local needs;

5. Develop a new concept of 10 years monitoring and forecasting warning system, useful for food security, risk management and civil protection in SSA;

6. Analyse the economic impacts of climate change on agriculture and water resources in SSA and the cost-effectiveness of potential adaptation measures.

The work of ClimAfrica project was broken down into the following work packages (WPs) closely connected. All the activities described in WP1, WP2, WP3, WP4, WP5 consider the domain of the entire South Sahara Africa region. Only WP6 has a country specific (watershed) spatial scale where models validation and detailed processes analysis are carried out.

Contact points:

Metadata Contact: FAO-Data

Resource Contact: Lia van Wesenbeeck

Resource Contact: Ben Sonneveld

Resource constraints:

copyright

Online resources:

Weight for age <-3DS, % of population - Distribution in sample of households in West Africa

Weight for age -2SD --3SD, % of population - Distribution in sample of households in West Africa

Weight for age -2SD--0, % of population - Distribution in sample of households in West Africa

Weight for age >0SD, % of population - Distribution in sample of households in West Africa

A spatially explicit assessment of specific vulnerabilities of the food system due to climate change and the identification of their causes; Technical report

Scenarios of major production systems in Africa

Climafrica - Climate Change Predictions In Sub-Saharan Africa: Impacts And Adaptations

Abstract

The Survey of Activities of Young People (SAYP) is a household-based survey that collects data on the activities of young people aged 7-17 years who live in South Africa. The survey covers involvement of children in market production activities, production for own final consumption, household chores as well as activities that children engaged in at school. Statistics South Africa collects SAYP information as part of the module of the Quarterly Labour Force Survey (QLFS) every four years. This information is gathered from respondents who are members of households living in dwellings that have been selected to take part in the QLFS and have children aged 7-17 years.

The aim of the first survey (SAYP 1999) was to collect information on children’s economic activities, including paid and unpaid work. All subsequent survey's (SAYP 2010, 2015 and 2019) are intended to provide updated information on the economic activities of children, including an analysis of child labour in South Africa. The specific objectives of the SAYP are to understand the extent of children’s involvement in economic activities, provide information for the formulation of an informed policy to combat child labour within the country and to monitor the South African Child Programme of Action (CLPA) and Sustainable Development Goal (SDG'S).

Geographic coverage

National coverage

Analysis unit

Households and individuals

Universe

The SAYP covers children aged 7-17 years resident in a household. The survey excluded all people in prison, patients in hospitals, people residing in boarding houses and hotels, and boarding schools. Any single person households were screened out in all areas before the sample was drawn. Families living in hostels were treated as households.

Kind of data

Sample survey data [ssd]

Sampling procedure

The Survey of Activities of Young People (SAYP) comprised two stages. The first stage involved identifying households with children aged 7-17 years during the Quarterly Labour Force Survey (QLFS) data collection that took place in the third quarter of 2019 (Q3:2019). The second stage involved a follow-up interview with children in those households to establish what kind of activities they were involved in and several other aspects related to the activities they engaged in.

Mode of data collection

Face-to-face [f2f]

Research instrument

The SAYP collected data in two phases using one questionnaire.

The first phase questionnaire covered the following topics: Living conditions of the household, including the type of dwelling, fuels used for cooking, lighting, and heating, water source for domestic use, land ownership, tenure, and cultivation; demographic information on members of the household, both adults and children. Questions covered the age, gender and population group of each household member, their marital status, their relationships to each other, and their levels of education; migration details; household income; school attendance of children aged 5 -17 years; information on economic and non-economic activities of children aged 5-17 years in the 12 months prior to the survey.

The second phase questionnaire was administered to the sampled sub-set of households in which at least one child was involved in some form of work in the year prior to the interview. It covered activities of children in much more detail than in phase one, and the work situation of related adults in the household. Both adults and children were asked to respond.

South Africa ZA: Prevalence of Underweight: Weight for Age: % of Children Under 5 data was reported at 5.900 % in 2016. This records a decrease from the previous number of 8.500 % for 2012. South Africa ZA: Prevalence of Underweight: Weight for Age: % of Children Under 5 data is updated yearly, averaging 8.650 % from Dec 1995 (Median) to 2016, with 6 observations. The data reached an all-time high of 11.600 % in 2004 and a record low of 5.900 % in 2016. South Africa ZA: Prevalence of Underweight: Weight for Age: % of Children Under 5 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s South Africa – Table ZA.World Bank: Health Statistics. Prevalence of underweight children is the percentage of children under age 5 whose weight for age is more than two standard deviations below the median for the international reference population ages 0-59 months. The data are based on the WHO's child growth standards released in 2006.; ; UNICEF, WHO, World Bank: Joint child malnutrition estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology.; Linear mixed-effect model estimates; Undernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF, www.childinfo.org). Estimates of child malnutrition, based on prevalence of underweight and stunting, are from national survey data. The proportion of underweight children is the most common malnutrition indicator. Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition.