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01 November 2024 | Story André Damons | Photo Supplied
Dr Nomakhuwa Tabane
Dr Nomakhuwa Tabane is the Head of the Department of Paediatrics and Child Health at the University of the Free State.

The first 1 000 days of a baby’s life, from conception to the age of two, constitute a critical period during which children’s brains form as many as 1 000 neural connections every second – a pace that will not be repeated in their lifetime.

These connections are the building blocks of every child’s future, which makes the role of a campaign like the First 1 000 Days vitally important. It highlights the importance of stimulation and learning from the earliest possible moments, good nutrition for expectant mothers, prevention of malnutrition of children, and early diagnosis of chronic, life-threatening illnesses and developmental disorders.

This is according to Dr Nomakhuwa Tabane, Head of the Department of Paediatrics and Child Health at the University of the Free State (UFS). The campaign was promoted by Dr Tabane’s department in partnership with the Mother and Child Academic Hospital (MACAH) Foundation.  The annual campaign kicks off on 1 November each year.

“There are certain factors that can interfere with this process and result in irreversible damage to children’s brain development, poor growth, and compromised immunity. Those conditions include prematurity, ischaemic brain damage, and infections. These are also the top contributors to the neonatal mortality.

“In the one-month to 49-month-old period, the causes of mortality and morbidity that affect brain development and growth include respiratory illnesses like pneumonia, diarrhoeal diseases, and malnutrition,” says Dr Tabane. 

Aims of the campaign

The First 1 000 Days initiative promotes excellent mother, infant, and child healthcare by supporting community-based programmes that drive the message of the importance of the first 1 000 days of life to teenagers, young adults, healthcare workers, and the public. This initiative aims to bring about interventions that can address the Under-5 Mortality Rates (U5MR), including Neonatal Mortality Rates (NMR), Infant Mortality Rates (IMR), and Perinatal Mortality Rates (PMR).

“The campaign also aims to improve the growth and development of children in their first 1 000 days of life from conception until they are two years old. It also aims to improve expectant mothers’ health and prevent and decrease maternal mortality in the Free State, as well as to prevent unwanted pregnancies, focusing on decreasing teenage pregnancies.”

According to Dr Tabane, the 2020 South African UN Inter-agency Group for Child Mortality Estimation (UNIGME) estimate for U5MR was 32 deaths per 1 000 live births, NMR of 11 per 1 000 live births, and infant mortality rate (IMR) of 26 per 1 000 live births as compared to the Medical Research Council (MRC) estimate of U5MR of 28 per 1 000 live births, NMR of 12 per 1 000 live births and IMR of 21 per 1 000 live births (15).

South Africa behind other BRICS countries

Based on the 2020 UNIGME report, says Dr Tabane, South Africa has achieved the Sustainable Development Goals (SDG) goals of NMR and the U5MR. South Africa’s indicators were much better than the UNIGME and the MRC 2020 estimates, but it still falls behind other BRICS countries.

“In contrast to other BRICS countries (Brazil, Russia, India, China, and South Africa), UNIGME reports that in the same reporting period of 2020, China’s U5MR was seven per 1 000 live births, Brazil's 15 per 1 000 live births, and Russia's five per 1 000 live births (16). In 2020, the South African national in-hospital neonatal mortality rate (NMR) based on DHIS data was 12,0 per 1 000 live births; the infant mortality rate (IMR) was 15.1 per 1 000 live births, and the under-5 mortality (U5 MR) rate was 16.9 per 1 000 live births, with differences amongst provinces,” says Dr Tabane.

The first 1 000 days campaign’s interventions include education to prevent illnesses and deaths and promote good health, growth, and development. While many training programmes on child survival strategies have been rolled out (e.g., MSSN, HBB, ETAT, AANC, ESMOE, and IMCI), in-service training still has significant gaps.

Other interventions include preventing unwanted and unplanned pregnancies, providing healthcare support for therapeutic and interventional care, strengthening the implementation of the existing strategies developed by the Department of Health to reduce Maternal and Child Mortalities, and monitoring and evaluating the interventions.

News Archive

Mathematical methods used to detect and classify breast cancer masses
2016-08-10

Description: Breast lesions Tags: Breast lesions

Examples of Acho’s breast mass
segmentation identification

Breast cancer is the leading cause of female mortality in developing countries. According to the World Health Organization (WHO), the low survival rates in developing countries are mainly due to the lack of early detection and adequate diagnosis programs.

Seeing the picture more clearly

Susan Acho from the University of the Free State’s Department of Medical Physics, breast cancer research focuses on using mathematical methods to delineate and classify breast masses. Advancements in medical research have led to remarkable progress in breast cancer detection, however, according to Acho, the methods of diagnosis currently available commercially, lack a detailed finesse in accurately identifying the boundaries of breast mass lesions.

Inspiration drawn from pioneer

Drawing inspiration from the Mammography Computer Aided Diagnosis Development and Implementation (CAADI) project, which was the brainchild Prof William Rae, Head of the department of Medical Physics, Acho’s MMedSc thesis titled ‘Segmentation and Quantitative Characterisation of Breast Masses Imaged using Digital Mammography’ investigates classical segmentation algorithms, texture features and classification of breast masses in mammography. It is a rare research topic in South Africa.

 Characterisation of breast masses, involves delineating and analysing the breast mass region on a mammogram in order to determine its shape, margin and texture composition. Computer-aided diagnosis (CAD) program detects the outline of the mass lesion, and uses this information together with its texture features to determine the clinical traits of the mass. CAD programs mark suspicious areas for second look or areas on a mammogram that the radiologist might have overlooked. It can act as an independent double reader of a mammogram in institutions where there is a shortage of trained mammogram readers. 

Light at the end of the tunnel

Breast cancer is one of the most common malignancies among females in South Africa. “The challenge is being able to apply these mathematical methods in the medical field to help find solutions to specific medical problems, and that’s what I hope my research will do,” she says.

By using mathematics, physics and digital imaging to understand breast masses on mammograms, her research bridges the gap between these fields to provide algorithms which are applicable in medical image interpretation.

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