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Catastrophic Maternal Healthcare expenses push 47 percent Mothers in India into Poverty: Researchers

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A mother and child, Pixabay
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May 22, 2017: Catastrophic maternal healthcare expenses push 46.6 per cent mothers in India into poverty — with the illiterate being especially susceptible — according to a December 2016 study by researchers from Jawaharlal Nehru University (JNU) and Indian Institute of Technology, Roorkee (IIT-R). The expenses include childbirth, antenatal care and postnatal care expenses.

Catastrophic expenditure is greater than or equal to 40 percent of a household’s non-subsistence income, i.e. income available after basic needs have been met, according to the World Health Organisation (WHO). The threshold of 40 per cent can differ according to countries, said the WHO; the 2016 study has analyzed the data at two thresholds: 10 and 40 per cent.

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As many as 63 per cent households nationwide had a catastrophic maternal health expenditure of 40 per cent, the study — which analysed data from the National Sample Survey Office — found. Among states and Union territories (UTs), 65.7 per cent households (among those where a woman had delivered) in Telangana were pushed into poverty — more than any other state/UT — due to childbearing expenses, followed by Chhattisgarh (53.7 per cent) and Puducherry (53.4 per cent).

In the 10 years to 2014, out-of-pocket (OOP) health spending has pushed 50.6 million people back into poverty.

Households where the mothers were illiterate were the most affected, with 61 per cent of them being pushed into poverty –despite having the lowest maternal health OOP expenditure at Rs 3,600 — compared to 36.7 per cent of households where women were graduates and above, who had an OOP expenditure of Rs 19,250.

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More illiterate women prefer public hospitals for delivery in both rural (79.2 per cent) and urban areas (67.7 per cent), which possibly explains their low OOP.

Among women of different social groups, women belonging to scheduled tribes (STs) had the least maternal OOP expenditure at Rs 2,962, but 71.5 per cent of them were pushed into poverty. As many as 85 per cent ST women in rural areas delivered in public hospitals — more than any other social group.

The study holds relevance in the context of the central government announcement on May 18, 2017, that it is revising the Indira Gandhi Matritva Sahyog Yojana (Maternity Benefit Programme), announced by Prime Minister Narendra Modi on December 31, 2016, by restricting the scheme to firstborns instead of “first two live births” as applicable earlier.

The programme aims to give Rs 6,000 to pregnant women for childbearing expenses. The scheme saw an increase of 226 per cent in allocation in the 2017-18 budget from Rs 634 crore to Rs 2,700 crore. However, the government had estimated that the annual requirement for the maternity benefit scheme would be Rs 14,512 crore, according to a report in The Indian Express.

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The 2016 study revealed that, on average, a woman incurred an OOP expenditure of Rs 8,543 on childbearing. There were huge variations among states — from Rs 2,801 in Uttarakhand to Rs 15,433 in Telangana.

“The most vulnerable women who are trying to reach out for the government aid won’t be able to get it,” Tania Sheshadri, an independent community health researcher who works with rural women in Karnataka, was recently quoted as saying in news reports.

“In most parts of the country, there is a two-child norm and a scheme like this will not benefit most women. The government should concentrate on quality care for pregnant women and make available the benefits to every woman who reaches a government health care centre.”

A limitation of the 2016 study is that it does not consider the benefits of Janani Suraksha Yojana (JSY, motherhood protection scheme), a 12-year-old government programme focused specially on 10 states with low rates of institutional delivery — Uttar Pradesh, Uttarakhand, Bihar, Jharkhand, Madhya Pradesh, Chhattisgarh, Assam, Rajasthan, Odisha, and Jammu and Kashmir — termed as low-performing states (LPS).

Under the programme, pregnant women in rural areas who live below the poverty line are to be given cash assistance — Rs 700 in high performing states and Rs 1,400 in LPS — irrespective of the mother’s age and number of children so that they opt for birth in a government or accredited private health facility.

The scheme has failed to cover the poorest women, according to a 2014 analysis of JSY data by researchers from Georgetown University. As many as 60 per cent women in Uttar Pradesh said they had to pay for certain public maternal health services, according to an assessment of JSY conducted by United Nations Population Fund in Bihar, Madhya Pradesh, Odisha, Rajasthan and Uttar Pradesh in 2012. (IANS/IndiaSpend)

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With the aid of Twitter and AI, researchers to develop flood warning system

In a study, published in the journal Computers & Geosciences, the researchers showed how AI can be used to extract data from Twitter and crowdsourced information from mobile phone apps to build up hyper-resolution monitoring of urban flooding.

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AI can play a key role in future flood warning and monitoring systems
AI can play a key role in future flood warning and monitoring systems

London, Dec 26: Researchers are combining Twitter, citizen science and artificial intelligence (AI) techniques to develop an early-warning system for flood-prone communities in urban areas.

In a study, published in the journal Computers & Geosciences, the researchers showed how AI can be used to extract data from Twitter and crowdsourced information from mobile phone apps to build up hyper-resolution monitoring of urban flooding.

“By combining social media, citizen science and artificial intelligence in urban flooding research, we hope to generate accurate predictions and provide warnings days in advance,” said Roger Wang from University of Dundee in Britain.

Urban flooding is difficult to monitor due to complexities in data collection and processing.

This prevents detailed risk analysis, flooding control and the validation of numerical models.

The research team set about trying to solve this problem by exploring how the latest AI technology can be used to mine social media and apps for the data that users provide.

They found that social media and crowdsourcing can be used to complement datasets based on traditional remote sensing and witness reports.

Applying these methods in case studies, they found them to be genuinely informative and that AI can play a key role in future flood warning and monitoring systems.

“The present recording systems — remote satellite sensors, a local sensor network, witness statements and insurance reports — all have their disadvantages. Therefore, we were forced to think outside the box and one of the things that occurred to us was how Twitter users provide real-time commentary on floods,” Wang said.

“A tweet can be very informative in terms of flooding data. Key words were our first filter, then we used natural language processing to find out more about severity, location and other information,” Wang said.

The researchers applied computer vision techniques to the data collected from MyCoast, a crowdsourcing app, to automatically identify scenes of flooding from the images that users post.

“We found these big data-based flood monitoring approaches can definitely complement the existing means of data collection and demonstrate great promise for improving monitoring and warnings in future,” Wang said.

Twitter data was streamed over a one-month period in 2015, with the filtering keywords of “flood”, “inundation”, “dam”, “dike”, and “levee”. More than 7,500 tweets were analysed over this time.

“We have reached the point of 70 per cent accuracy and we are using the thousands of images available on MyCoast to further improve this,” Wang said.