Differential demographic transitions for Women of different ethnicities in Gauteng, South Africa, 1996–2011

Geyer, H.S.; Mosidi, Nhlapo

doi:10.1111/rsp3.12171

Cited by 4 publications

(5 citation statements)

References 27 publications

(47 reference statements)

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“…Such profiles are likely to differ across SSA countries at differing stages of economic, developmental and lifestyle transitions, that is, different in SSA middle income countries (MICs) from low income countries (LICs). 13 , 14 Furthermore, in addition to the aforementioned conditions, the setting-pertinent infectious disease of HIV needs to be taken into account due to the ageing HIV-positive population successfully treated with antiretroviral drugs. 15 Establishing these SSA-specific morbidity profiles of breast cancer patients will form a first step to inform priorities for feasible management strategies in constrained public health systems.…”

Section: Introductionmentioning

confidence: 99%

“…In the SSA setting, there is a paucity of data on the profile of preexisting morbidity profiles among breast cancer patients. Such profiles are likely to differ across SSA countries at differing stages of economic, developmental and lifestyle transitions, that is, different in SSA middle income countries (MICs) from low income countries (LICs) 13,14 . Furthermore, in addition to the aforementioned conditions, the setting‐pertinent infectious disease of HIV needs to be taken into account due to the ageing HIV‐positive population successfully treated with antiretroviral drugs 15 .…”

Section: Introductionmentioning

confidence: 99%

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Preexisting morbidity profile of women newly diagnosed with breast cancer in sub‐Saharan Africa: African Breast Cancer—Disparities in Outcomes study

Ayeni

Norris

Joffe

et al. 2020

Intl Journal of Cancer

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The presence of preexisting morbidities poses a challenge to cancer patient care. There is little information on the profile and prevalence of multi‐morbidities in breast cancer patients across middle income countries (MIC) to lower income countries (LIC) in sub‐Saharan Africa (SSA). The African Breast Cancer–Disparities in Outcomes (ABC‐DO) breast cancer cohort spans upper MICs South Africa and Namibia, lower MICs Zambia and Nigeria and LIC Uganda. At cancer diagnosis, seven morbidities were assessed: obesity, hypertension, diabetes, asthma/chronic obstructive pulmonary disease, heart disease, tuberculosis and HIV. Logistic regression models were used to assess determinants of morbidities and the influence of morbidities on advanced stage (stage III/IV) breast cancer diagnosis. Among 2189 women, morbidity prevalence was the highest for obesity (35%, country‐specific range 15‐57%), hypertension (32%, 15‐51%) and HIV (16%, 2‐26%) then for diabetes (7%, 4%‐10%), asthma (4%, 2%‐10%), tuberculosis (4%, 0%‐8%) and heart disease (3%, 1%‐7%). Obesity and hypertension were more common in upper MICs and in higher socioeconomic groups. Overall, 27% of women had at least two preexisting morbidities. Older women were more likely to have obesity (odds ratio: 1.09 per 10 years, 95% CI 1.01‐1.18), hypertension (1.98, 1.81‐2.17), diabetes (1.51, 1.32‐1.74) and heart disease (1.69, 1.37‐2.09) and were less likely to be HIV positive (0.64, 0.58‐0.71). Multi‐morbidity was not associated with stage at diagnosis, with the exception of earlier stage in obese and hypertensive women. Breast cancer patients in higher income countries and higher social groups in SSA face the additional burden of preexisting non‐communicable diseases, particularly obesity and hypertension, exacerbated by HIV in Southern/Eastern Africa.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Preexisting morbidity profile of women newly diagnosed with breast cancer in sub‐Saharan Africa: African Breast Cancer—Disparities in Outcomes study

Ayeni

Norris

Joffe

et al. 2020

Intl Journal of Cancer

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“…However, it is important to contextualize these findings in relation to broader policies on family planning and prevailing fertility rates within a country. In recent years, South Africa has experienced a significant demographic transition, largely due to changes in fertility ( Geyer and Mosidi, 2019 ). A range of biomedical, social and structural factors, such as child support grants, education, government population policies, employment-related migration and changing social norms related to marriage, have played a role in changing fertility desires in South Africa where couples are having fewer children and often later in life; however, the current total fertility rate is still greater than replacement level ( Burger et al, 2012 ; Geyer and Mosidi, 2019 ; Moultrie and Timæus, 2003 ).…”

Section: Discussionmentioning

confidence: 99%

Estimating the government public economic benefits attributed to investing in assisted reproductive technology: a South African case study

Connolly

Panda²,

Mburu

et al. 2021

Reproductive Biomedicine & Society Online

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Limited resources and high treatment costs are arguments often used in many public health systems in low-and middleincome countries to justify providing limited treatments for people with infertility. In this analysis, we apply a government public economic perspective to evaluate public subsidy for in-vitro fertilization (IVF) in South Africa. A fiscal model was developed that considered lifetime direct and indirect taxes paid and government transfers received by a child conceived by IVF. The model was constructed from public data sources and was adjusted for mortality, age-specific educational costs, participation in the informal economy, proportions of persons receiving social grants, and health costs. Based on current proportions of individuals receiving social grants and average payments, including education and health costs, we estimate each citizen will receive ZAR513,165 (USD35,587) in transfers over their lifetime. Based on inflated age-specific earnings, we estimate lifetime direct and indirect taxes paid per citizen of ZAR452,869 (USD31,405) and ZAR494,521 (USD34,294), respectively, which also includes adjustments for the proportions of persons participating in the informal economy. The lifetime net tax after deducting transfers was estimated to be ZAR434,225 (USD31,112) per person. Based on the average IVF investment cost needed to achieve one live birth, the fiscal return on investment (ROI) for the South African Government is 5.64. Varying the discount rate from 4% to 7%, the ROI ranged from 9.54 to 1.53, respectively. Positive economic benefits can emanate from public financing of IVF. The fiscal analytic framework described here can be a useful approach for health services to evaluate future public economic benefits.

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Section: Differential Urbanisation In South Africamentioning

confidence: 99%

“…A sub-national application of the model in the South African context has been applied to the primary metropolitan areas (Geyer, 1990;Geyer, 1993;Geyer et al, 2012;Geyer & Roux, 2017;Geyer & Mosidi, 2018). In these studies, population change in conurbations has been differentiated through a range of available socio-economic indicators, including race and income (Geyer, 1990;Geyer, 1993;Geyer et al, 2012), household size (Geyer & Roux, 2017), and gender (Geyer & Mosidi, 2018). These studies add value to the retrospective economic focus of the national application of the differential urbanisation model from a range of socio-economic and cultural perspectives, which may assist in making informed decisions regarding land-use change and infrastructure provision in metropolitan areas.…”

Section: Differential Urbanisation In South Africamentioning

confidence: 99%

Differential urbanisation for settlement planning – A Western Cape case study

2023

trp

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The differential urbanisation model is a means to assess settlement growth rates. While the model has been tested primarily at a national level, including in South Africa, this study seeks to apply the analysis to the sub-national scale in the Western Cape province and Cape Winelands district municipality. The study found that the model is applicable to both the province and the district municipality. Settlements of differing size and economic importance grew at varying rates relative to each other in a predictable sequence, which realised the urban hierarchy, over a 20-year period. This finding was unexpected, given that the urban differential model assumes economic growth as well as labour and socio-economic homogeneity -factors that have not been realised evenly sub-nationally. The applicability of the model to these locations may assist in the public division of resources, particularly in small towns, where meaningful urbanisation occurs, yet capital allocations are limited. The applicability of the study is in keeping with national, provincial, and municipal trends in planning that emphasise the interrelationship between settlements of different size and function over time, and the importance of spatial planning in guiding public infrastructure expenditure.

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Differential demographic transitions for Women of different ethnicities in Gauteng, South Africa, 1996–2011

Cited by 4 publications

References 27 publications

Preexisting morbidity profile of women newly diagnosed with breast cancer in sub‐Saharan Africa: African Breast Cancer—Disparities in Outcomes study

Preexisting morbidity profile of women newly diagnosed with breast cancer in sub‐Saharan Africa: African Breast Cancer—Disparities in Outcomes study

Estimating the government public economic benefits attributed to investing in assisted reproductive technology: a South African case study

Differential urbanisation for settlement planning – A Western Cape case study

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