Wednesday December 11, 2019

Pakistani Doctors Blame Quacks for Alarming Rise in HIV Cases: Report

Pakistan was considered a country of low HIV prevalence for long, but the disease is expanding at an alarming rate with about 20,000 new HIV cases reported in 2017 alone, according to the UN

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HIV
Nearly 40 individual HPV types linked to HIV infection. Pixabay

The Pakistan Medical Association (PMA) has blamed quack doctors practicing without training and professional certification for an alarming rise in the number of HIV positive patients in the country, the media reported on Wednesday.

The doctors associated with the PMA said that the quacks, especially fake dentists, spread the virus by using instruments that were not sterilized, adding that despite free-of-cost availability of life-saving antiretroviral drugs at government hospitals, mortality ratio among patients carrying the virus was also increasing.

An alarming surge in HIV cases has been witnessed in five districts of Pakistan’s Punjab province, with 70 to 90 cases being reported monthly at the main government health facility in Faisalabad city, Dawn News reported.

Earlier this month, an international team of experts from the World Health Organization kicked off an investigation into the sudden HIV outbreak in Pakistan’s Sindh province after over 700 people were diagnosed with the virus in a matter of weeks, most of them were children.

HIV
School girls light candles in the shape of a ribbon during a HIV/AIDS awareness campaign ahead of World Aids Day, in Ahmedabad, India, Nov. 30, 2016. (VOA)

Following the outbreak, authorities launched a crackdown on unqualified doctors as well as illegal blood banks and laboratories said to be involved in spreading the disease. At least 17 quack doctors were arrested and more than 70 clinics in Larkana district were shut down, according to Xinhua news agency.

According to the PMA, over 600,000 quacks are currently practicing in the country with more than 80,000 based in Punjab province alone.

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The association demanded the government to make all-out efforts to stop the menace of quackery in the country.

Pakistan was considered a country of low HIV prevalence for long, but the disease is expanding at an alarming rate with about 20,000 new HIV cases reported in 2017 alone, according to the UN. (IANS)

Next Story

Machine Learning Can Help Doctors to Improve End-Of-Life Conversation with Patients

A deeper understanding of these conversations, which are often freighted with emotion and uncertainty, will also help reveal what aspects or behaviors associated with these conversations are more valuable for patients and families

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Machine Learning
A Research used Machine Learning algorithms to analyze 354 transcripts of palliative care conversations collected by the Palliative Care Communication Research Initiative, involving 231 patients. Pixabay

Researchers at University of Vermont have used Machine Learning and natural language processing (NLP) to better understand conversations about death, which could eventually help doctors improve their end-of-life communication.

Some of the most important, and difficult, conversations in healthcare are the ones that happen amid serious and life-threatening illnesses.

Discussions of the treatment options and prognoses in these settings are a delicate balance for doctors and nurses who are dealing with people at their most vulnerable point and may not fully understand what the future holds.

“We want to understand this complex thing called a conversation. Our major goal is to scale up the measurement of conversations so we can re-engineer the healthcare system to communicate better,” said Robert Gramling, director of the Vermont Conversation Lab in the study published in the journal Patient Education and Counselling.

Gramling and his colleagues used machine learning algorithms to analyze 354 transcripts of palliative care conversations collected by the Palliative Care Communication Research Initiative, involving 231 patients.

They broke each conversation into 10 parts with an equal number of words in each, and examined how the frequency and distribution of words referring to time, illness terminology, sentiment and words indicating possibility and desirability changed between each decile.

“We picked up some strong signals,” said Gramling.

Conversations tended to progress from talking about the past to talking about the future, and from sadder to happier sentiments. “There was quite a range, they went from pretty sad to pretty happy,” Gramling added.

Machine Learning
Researchers at University of Vermont have used Machine Learning and natural language processing (NLP) to better understand conversations about death, which could eventually help doctors improve their end-of-life communication. Pixabay

The consistent results across multiple conversations show just how much people make meaning out of stories in healthcare.

“What we found supports the importance of narrative in medicine,” he said.

That knowledge could eventually help healthcare practitioners understand what makes a “good” conversation about palliative care, and how different kinds of conversations might require different responses.
That could help create interventions that are matched to what the conversation indicates the patient needs the most.

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A deeper understanding of these conversations, which are often freighted with emotion and uncertainty, will also help reveal what aspects or behaviors associated with these conversations are more valuable for patients and families. (IANS)