Reading Part-C

Extract One

South African Traditional Medicines Used In Health Care

Traditional medicines are part of the cultural heritage of many Africans. About 80% of the African continent’s population use these medicines for healthcare. Other reasons include affordability, accessibility, patient dissatisfaction with conventional medicine, and the common misconception that “natural” is “safe”. The growing recognition of traditional medicine resulted in the first World Health Organization global summit on the topic, in August 2023, with the theme “Health and Wellbeing for All”. Traditional medicines are widely used in South Africa, with up to 60% of South Africans estimated to be reliant on traditional medicine as a primary source of healthcare .

Conventional South African healthcare facilities struggle to cope with extremely high patient numbers. The failure to meet the basic standards of healthcare, with increasing morbidity and mortality rates, poses a threat to the South African economy. In my opinion, as a qualified pharmacist and academic with a research focus on traditional medicinal plant use in South Africa, integrating traditional medicine practices into modern healthcare systems can harness centuries of indigenous knowledge, increasing treatment options and provide better healthcare. Recognition of traditional medicine as an alternative or joint source of healthcare to that of standard, conventional medicine has proven challenging. This is due to the absence of scientific research establishing and documenting the safety and effectiveness of traditional medicines, along with the lack of regulatory controls.

Traditional medicine encompasses a number of healthcare practices aimed at either preventing or treating acute or chronic complaints through the application of indigenous knowledge, beliefs and approaches. It incorporates the use of plant, animal and mineral-based products. Plant-derived products form the majority of treatment regimens. Traditional medicine practices also have a place in ritualistic activities and communicating with ancestors. South Africa is rich in indigenous medicinal fauna and flora, with about 2,000 species of plants traded for medicinal purposes. In South Africa the provinces of KwaZulu-Natal, Gauteng, Eastern Cape, Mpumalanga and
Limpopo are trading “hotspots”. The harvested plants are most often sold at traditional medicine muthi markets.

Medicinal plants most popularly traded in South Africa include buchu, bitter aloe, African wormwood, honeybush, devil’s claw, hoodia, African potato, fever tea, African geranium, African ginger, cancer bush, pepperbark tree, milk bush and the very commonly consumed South African beverage, rooibos tea.

There are many ways in which traditional medicine may be used. It can be a drop in the eye or the ear, a poultice applied to the skin, a boiled preparation for inhalation or a tea brewed for oral administration.Roots, bulbs and bark are used most often, and leaves less frequently. Roots are available throughout the year. There’s also a belief that the roots have the strongest concentration of “medicine”. Harvesting of the roots, however, poses concerns about the conservation of these medicinal plants. The South African government, with the draft policy on African traditional medicine Notice 906 of 2008 outlines considerations aimed at ensuring the conservation of these plants through counteracting unsustainable harvesting practises.

The limited research investigating interactions posed should a patient be making use of both traditional and conventional medicine is a concern. During the COVID-19 pandemic, many patients used traditional remedies for the prevention of infection or treatment. Understanding which traditional medicines are being used and how, their therapeutic effects in the human body, and how they interact with conventional medicines, would help determine safety of their combined use. Certain combinations may have advantageous interactions, increasing the efficacy or potency of the medicines and allowing for reduced dosages, thereby reducing potential toxicity.

These combinations could assist in the development of new pharmaceutical formulations.

Patients often seek treatment from both conventional and traditional sources, which can lead to side effects or duplication in medications. A comprehensive understanding of a patient’s health profile makes care easier. This could also prevent treatment failures, promote patient safety, prevent adverse interactions and minimise risks. A harmonious healthcare landscape would combine the strengths of both systems to provide better healthcare for all.

Question: 27. What is one of the key reason for the widespread use of traditional medicines in Africa, particularly in South Africa?

27. According to the second paragraph, what is the primary challenge in integrating traditional medicine into modern healthcare systems in South Africa?

29. According to the third paragraph, what is a significant feature of traditional medicine practices in South Africa?

30. Question: Which plant is commonly consumed as a South African beverage, according to the fourth paragraph?

31. What is the underlined word refers to in the third paragraph

32. According to the fifth paragraph, what governmental action aims to address the conservation concerns related to the harvesting of medicinal plant roots in South Africa?

33. What potential benefit does understanding the interactions between traditional and conventional medicines during the COVID-19 pandemic offer?

34 What is the primary advantage of having a comprehensive understanding of a patient’s health profile, as mentioned in the last paragraph?

Ankylosing spondylitis

Ankylosing spondylitis (AS) is the second most common type of inflammatory arthritis, often affecting teenagers and young adults. Symptoms of AS can include back pain, stiffness, joint inflammation (arthritis), inflammation where tendons attach to bones (enthesitis), and fatigue. Over time, these symptoms can lead to spinal fusion, which significantly affects quality of life, particularly in young people

Unfortunately, diagnosing AS can be a lengthy process, taking up to ten years from the onset of symptoms and usually requiring X-rays. The slow progression of the condition, coupled with the lack of a definitive test, contributes to these delays.However, early detection of the condition can make a tremendous difference, halting the degenerative process and preserving a good quality of life for those affected

Our study explored the potential of using routinely collected healthcare data from GPs and hospitals, combined with advanced machine learning techniques, to identify AS at an earlier stage. Machine learning involves using algorithms to analyse sample data, enabling predictions and decisions without explicit programming.We analysed data separately for men and women, and our findings could transform the way in which GPs detect and diagnose AS.To conduct our study, we used anonymous data from a national data repository at Swansea University Medical School. Patients with AS were identified and matched with people with no record of a diagnosis.

Our analysis of this data found that factors such as lower back pain, uveitis , and use of non-steroidal anti-inflammatory drugs before the age of 20 were factors associated with an increased risk of developing AS in men.In contrast, our model revealed that women tend to experience AS symptoms at a later age, and often rely on multiple pain relief medications compared with men. This possibly indicates a higher likelihood of misdiagnosis of the condition in women.

Machine learning is a valuable tool for profiling and understanding the characteristics of people who are likely to develop AS. It performs well in test data sets with artificially high prevalence rates. However, when applied to the general population in GPs and hospitals, where AS is rare, even the best model can only achieve a low positive predictive value of 1.4%. (That’s the probability that following a positive test result, the individual will truly have AS.) So, using multiple models over time may be necessary to narrow down the population and improve this predictive value, which would result in a faster AS diagnosis. Machine learning techniques have tremendous

potential to improve patient care. But it is also crucial to acknowledge the challenges associated with using these techniques effectively.

These models depend on high-quality data that is diverse and comprehensive to produce reliable, accurate results. But healthcare data can be limited due to privacy concerns, data sensitivity and lack of standardisation. These limitations may therefore compromise the accuracy and reliability of the models. It’s important to acknowledge that machine learning in relation to this topic is still in its infancy. To develop this further, we will need to gather more detailed data to improve prediction rates and clinical usefulness.

But our study demonstrates the enormous potential that machine learning has to help identify people with AS and better understand their diagnostic journeys through the health system.We know that the early detection and diagnosis of AS is crucial to secure the best outcomes for patients. We believe machine learning could help with this. It could also empower GPs, helping them to detect and refer patients more effectively and efficiently.

35. According to the text, why does Ankylosing Spondylitis (AS) significantly affect the quality of life, particularly in young people?

36. According to the second paragraph, why is early detection of Ankylosing Spondylitis (AS) considered significant?

37. What was the primary purpose of the study described in the third paragraph?

38. What does the fourth paragraph suggest about the potential misdiagnosis of Ankylosing Spondylitis (AS) in women?

39. According to the fifth paragraph, what does a positive predictive value (PPV) of 4% mean for the machine learning model’s performance in diagnosing AS?

40. What does the underlined pronoun ‘these’ refer to?

41. According to the sixth paragraph, what are the primary challenges related to using machine learning models in healthcare data?

42. According to the paragraph, how does machine learning potentially enhance the role of GPs in the context of AS diagnosis?