As of 2023, South Africa's population increased and counted approximately 62.3 million inhabitants in total, of which the majority inhabited Gauteng, KwaZulu-Natal, and the Western-Eastern Cape. Gauteng (includes Johannesburg) is the smallest province in South Africa, though highly urbanized with a population of over 16 million people according to the estimates. Cape Town, on the other hand, is the largest city in South Africa with nearly 3.43 million inhabitants in the same year, whereas Durban counted 3.12 million citizens. However, looking at cities including municipalities, Johannesburg ranks first. High rate of young population South Africa has a substantial population of young people. In 2024, approximately 34.3 percent of the people were aged 19 years or younger. Those aged 60 or older, on the other hand, made-up over 10 percent of the total population. Distributing South African citizens by marital status, approximately half of the males and females were classified as single in 2021. Furthermore, 29.1 percent of the men were registered as married, whereas nearly 27 percent of the women walked down the aisle. Youth unemployment Youth unemployment fluctuated heavily between 2003 and 2022. In 2003, the unemployment rate stood at 36 percent, followed by a significant increase to 45.5 percent in 2010. However, it fluctuated again and as of 2022, over 51 percent of the youth were registered as unemployed. Furthermore, based on a survey conducted on the worries of South Africans, some 64 percent reported being worried about employment and the job market situation.

South Africa is the sixth African country with the largest population, counting approximately 60.5 million individuals as of 2021. In 2023, the largest city in South Africa was Cape Town. The capital of Western Cape counted 3.4 million inhabitants, whereas South Africa's second largest city was Durban (eThekwini Municipality), with 3.1 million inhabitants. Note that when observing the number of inhabitants by municipality, Johannesburg is counted as largest city/municipality of South Africa.

From four provinces to nine provinces

Before Nelson Mandela became president in 1994, the country had four provinces, Cape of Good Hope, Natal, Orange Free State, and Transvaal and 10 “homelands” (also called Bantustans). The four larger regions were for the white population while the homelands for its black population. This system was dismantled following the new constitution of South Africa in 1996 and reorganized into nine provinces. Currently, Gauteng is the most populated province with around 15.9 million people residing there, followed by KwaZulu-Natal with 11.68 million inhabiting the province. As of 2022, Black African individuals were almost 81 percent of the total population in the country, while colored citizens followed amounting to around 5.34 million.

A diverse population

Although the majority of South Africans are identified as Black, the country’s population is far from homogenous, with different ethnic groups usually residing in the different “homelands”. This can be recognizable through the various languages used to communicate between the household members and externally. IsiZulu was the most common language of the nation with around a quarter of the population using it in- and outside of households. IsiXhosa and Afrikaans ranked second and third with roughly 15 percent and 12 percent, respectively.

SD: standard deviation.Demographics of clients participating in CVCT and completing pre- and post-CVCT surveys stratified by gender, Durban, South Africa, 2013.

ObjectiveGlycated haemoglobin (HbA1c) is recommended as an additional tool to glucose-based measures (fasting plasma glucose [FPG] and 2-hour plasma glucose [2PG] during oral glucose tolerance test [OGTT]) for the diagnosis of diabetes; however, its use in sub-Saharan African populations is not established. We assessed prevalence estimates and the diagnosis and detection of diabetes based on OGTT, FPG, and HbA1c in an urban black South African population.Research Design and MethodsWe conducted a population-based cross-sectional survey using multistage cluster sampling of adults aged ≥18 years in Durban (eThekwini municipality), KwaZulu-Natal. All participants had a 75-g OGTT and HbA1c measurements. Receiver operating characteristic (ROC) analysis was used to assess the overall diagnostic accuracy of HbA1c, using OGTT as the reference, and to determine optimal HbA1c cut-offs.ResultsAmong 1190 participants (851 women, 92.6% response rate), the age-standardised prevalence of diabetes was 12.9% based on OGTT, 11.9% based on FPG, and 13.1% based on HbA1c. In participants without a previous history of diabetes (n = 1077), using OGTT as the reference, an HbA1c ≥48 mmol/mol (6.5%) detected diabetes with 70.3% sensitivity (95%CI 52.7–87.8) and 98.7% specificity (95%CI 97.9–99.4) (AUC 0.94 [95%CI 0.89–1.00]). Additional analyses suggested the optimal HbA1c cut-off for detection of diabetes in this population was 42 mmol/mol (6.0%) (sensitivity 89.2% [95%CI 78.6–99.8], specificity 92.0% [95%CI: 90.3–93.7]).ConclusionsIn an urban black South African population, we found a high prevalence of diabetes and provide the first evidence for the utility of HbA1c for the diagnosis and detection of diabetes in black Africans in sub-Saharan Africa.

Abstract

The Asenze Study is a longitudinal, population-based cohort study conducted in a peri-urban area outside Durban in KwaZulu-Natal, South Africa. Waves 1 (2008) and 2 (2012) began with a door-to-door survey to identify all children aged 4-6 years in the study area; to obtain demographic information and to invite the child and the primary caregiver with informed consent to participate in the Asenze study. 87% of those children identified attended an assessment focusing on child neurodevelopment, cognitive function, behavioral problems, and the physical and mental health (including HIV status) of both children and their caregivers. Caregivers had their own height and weight taken, answered questionnaire about the child and their own health, wellbeing and were offered HIV testing.

Geographic coverage

The study collected data in a peri-urban area outside Durban in KwaZulu-Natal, South Africa.

Analysis unit

Individuals

Kind of data

Survey and assessment data

Mode of data collection

Face-to-face

Research instrument

The study used questionnaires and assessment sheets and assessment tools. In addition to questionnaires there was a physical exam, hearing and vision assessment and hematocrit and HIV test (if consent obtained) for the child

Baseline Clinical and Demographics Characteristics of A Cohort of HIV-Infected Children Failing 1st Line ART in Durban, South Africa Stratified by Initial Treatment Regimen and Presence of Resistance Testing.

Abstract

The survey was undertaken in 2005 and conducted face-to-face interviews with a sample of 1 000 adults from Cape Town, Durban, Pretoria and Johannesburg, where ninety percent of the country's Jewish population reside.

Geographic coverage

The survey covered selected Jewish households in Cape Town, Durban, Pretoria and Johannesburg

Analysis unit

Households and individuals

Universe

The target population of the survey consists of Jewish South Africans.

Kind of data

Sample survey data

Mode of data collection

Face-to-face [f2f]

Research instrument

A single questionnaire was used for the study

These data sets are the results of simulations exploring dolphins’ potential response to disturbance caused by whale watching vessels and record individual group membership, behaviour (Traveling [1], Resting [2], Feeding [3] and Socialising [4]) and motivations (Hunger [1], Fear [2], Social needs [3] and body condition [4]). The first part of the file name refers to the population being simulated: “DS” refers to a population of 61 dolphins in Doubtful Sound, New Zealand that is closed and food limited, “DSA” refers to a population of 350 individuals from Durban Bay, South Africa that is open and not food limited, “JB” refers to a population of 108 individuals from Jervis Bay, Australia that is open and food limited, and “SB” refers to a population of 160 dolphins from Sarasota Bay, USA that is closed and not food limited. The second part of the file name refers to the type of data recorded: “GB” means the file contains the records for group membership and behaviour, and “Mot” means the file contains the records for the individual motivations. The number at the end of the file name references the level of disturbance used in the simulation: “0” for no change of disturbance, “1” for a 10% chance of disturbance, “25” for a 25% chance of disturbance and “5” for a 50% chance of disturbance. For all populations, the simulations were run 100 times, and the length of each simulation was 365 days.

Urban areas can be attractive to certain species because of increased food abundance and nesting availability which in turn may increase productivity or breeding rates. However, there are also potential costs associated with urban living such as higher nest failure, poorer body condition or increased prevalence of disease. These costs may result in species trading off the number of young produced against the condition of their young. African Crowned Eagles (Stephanoaetus coronatus) are a rare example of large, powerful apex predators that breed in some urban areas in Africa. In this study, we explored the breeding performance of these eagles across an urbanization gradient in KwaZulu-Natal Province, South Africa, over seven breeding seasons. We predicted that living in an urban environment would increase productivity through an increase in breeding rate (shifting from typically biennial breeding to annual breeding). We then explored if there were any hidden costs associated with such a change in breeding strategy by examining the body condition of chicks from pairs which had successfully bred in the previous year. We found that pairs in more urban areas were more likely to breed annually, resulting in higher breeding rates, but were also less likely to successfully fledge a chick (i.e., lower breeding success). These two contrasting responses counteracted each other and resulted in similar productivity across the urbanization gradient. For those eagles that bred in consecutive years, annual breeding did not appear to have a negative cost on chick condition. The switch to annual breeding is thought to be a response to improved or more constant food sources in urban areas, while higher failure rates might be because of increased nest disturbances from anthropogenic sources (e.g., vegetation clearing, development of industrial areas, human and car traffic). However, although urbanization negatively affected the breeding success of African Crowned Eagles, they are able to persist and thrive in this highly transformed environment likely through an increased breeding rate.

Methods Study Area and Data Collection

The study area covered ~20,000 km2 in southern KwaZulu-Natal Province, South Africa, centered on the metropoles of Durban and Pietermaritzburg, and extended to several coastal towns both north and south of Durban (Figure 1; McPherson et al. 2016a, b, 2019). Crowned Eagle nesting sites were initially found by networking with interested individuals/groups (local birding experts, Birdlife and Falconry club members, and online community groups), and accessing unpublished databases from Durban Natural History Museum, eThekwini Municipality, and SABAP2, as well as by direct searching in suitable habitat or where territorial displays were observed.

Crowned Eagle nest monitoring was conducted from August through January the following year for the years 2011 to 2017 (i.e., 7 breeding seasons), which covers their peak annual breeding period in the area (McPherson et al. 2016a, b). Territories were visited regularly, at least twice in the first month, in order to assess occupation (e.g., nest building, incubation or brooding behavior). A nest was classified as active if nest building or fresh green leaves were seen on the nest or if the adults were present in either of these first two nest visits. A nest was classified as having a breeding attempt if incubation or brooding behavior was seen. Nests with a breeding attempt were then monitored during 2-3 nest site visits until conclusion of the breeding event (i.e., until the chicks were around 70±5 days old). Breeding success was defined as having a chick survive until banding age (70 ± 5 days old). After this age, failures in this species and most other large raptors are relatively low (Brown, 1976). Nests were observed from vantage points generally 50 – 200 m away from the nest (see details in McPherson et al. 2016a).

    Crowned Eagle chicks were banded when their estimated age was 65 - 75 days, a time window recommended by experts (S. Thomsett and B. Hoffman pers. comm.). The age estimates used in this study were ascertained by photo reference material of pulli of known age (McPherson et al. 2017) and were based on size and plumage development. During banding, chicks were weighed (with an electronic hanging scale to the nearest 5 g) and the total length and unfurled length of the 8th primary feather was taken (with a straight ruler to the nearest 1 mm). All measurements were done in accordance with the SAFRING user manual (de Beer et al. 2001).

Urbanization Score

In order to establish the percentage of urbanization around each nest site, we used the LandCover 2014 raster (GEOTERRAIMAGE, 2015), which classifies land use into 72 different categories. We chose a circular buffer area of 10 km2 (radius = 1784.1 m) based on the mean home range size of the species during the breeding season from four telemetered adults in the study area (McPherson et al. 2019). Once the percentage of each land class around each nest site had been calculated, the values for all land classes containing sealed surface (see Rose et al. 2017) were used to calculate an urban score (%) for each nest. Examples of what land classes constituted sealed surface are urban residential, industrial, townships, and mines. In territories where there was more than one nest, the mean urban score was taken to represent the territory.

Statistical Analyses

All analyses were conducted in R version 3.5.1 (R Core Team 2018) with the packages ‘lme4’ (Bates et al., 2015), ‘car’ (Fox and Weisberg, 2018), and ‘effects’ (Fox, 2003). All means are presented with standard deviations. Generalized Linear Models (GLMs) or Linear Mixed Models (LMMs) were used to analyze the data. An initial model selection for the GLMs considered both the linear or quadratic relationship between urbanization and our response variables, as a quadratic relationship could reveal changed breeding demography at intermediate levels of urbanization. In all cases, the linear relationship had the best model fit (lowest AIC) and thus only linear relationships were considered in the final analyses.

    We explored how urbanization affected several Crowned Eagle breeding parameters over seven breeding seasons. GLMs with a binomial distribution were used to investigate the effect of urbanization on three key breeding parameters using the cbind function. These three variables were i) breeding rates: modelled as the total number of attempts and number of non-attempts (i.e., no nest building activity (nest lining, mating behaviour, incubation etc.) at a previously occupied nest) across the years a territory was monitored; ii) breeding success: modelled as the total number of successful breeding attempts and number of failures across the years in which a territory was active; and iii) breeding continuity: modelled as the number of continuous breeding attempts (i.e., no gap between breeding attempts) and the number of non-continuous breeding attempts (i.e., with at least 1 year gap between breeding attempts) for the total number of years monitored. This binomial approach also accounted for differences in the number of years of data for each territory, by effectively weighting each sample according to the total number of years monitored (models i and iii) or total number of active years (model ii). Additionally, a different GLM was used to investigate Crowned Eagle productivity in relation to urbanization. Here the response variable was the total number of young fledged across all the years each territory was monitored. Models were fitted with a Poisson distribution, with an offset specified as the log of the number of years monitored.

    An LMM was used to explore whether a Crowned Eagle breeding attempt or, more importantly, a breeding success in the previous year, had an influence on the body condition of chicks. For this LMM, the response variable was the condition of each chick (n = 72), where chick condition was the residual from a linear regression of weight against the length of the 8th primary feather. The explanatory variable was either attempt (t-1), where 0 = no attempt previous year, and 1 = attempt the previous year; we also ran the same model but specifying success (t-1), where 0 = no successful chick produced in the previous year, and 1 = chick successfully produced in the previous year. ‘Year’ and ‘Territory Identity’ were included as random terms to account for the repeated measures from the same territory and from different territories in the same year. As Crowned Eagles only fledge 1 chick per breeding attempt we did not need to control for the number of chicks in a nest.

Description of the population at baseline, overall and by whether blood was drawn for CD4+ count on the day of diagnosis, 459 newly-diagnosed HIV+ women and men, Durban, South Africa, 2010–2012.

Characteristics of the adult study population presenting for HIV screening in Durban, 2013–2017 (N = 5,428).