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Despite tremendous advances in treatment of congenital heart disease (CHD), a new global study shows that the chances for a child to survive a CHD diagnosis is significantly less in low-income countries.

The research revealed that nearly 12 million people are currently living with CHD globally, 18.7 per cent more than in 1990.

The findings, published in The Lancet, is drawn from the first comprehensive study of congenital heart disease across 195 countries, prepared using data from the Global Burden of Diseases, Injuries and Risk Factors Study 2017 (GBD).

"Previous congenital heart estimates came from few data sources, were geographically narrow and did not evaluate CHD throughout the life course," said the study authors from Children's National Hospital in the US.

This is the first time the GBD study data was used along with all available data sources and previous publications - making it the most comprehensive study on the congenital heart disease burden to date.

The study found a 34.5 per cent decline in deaths from congenital disease between 1990 to 2017. Nearly 70 per cent of deaths caused by CHD in 2017 (180,624) were in infants less than one year old.

Most CHD deaths occurred in countries within the low and low-middle socio-demographic index (SDI) quintiles.

Mortality rates get lower as a country's Socio-demographic Index (SDI) rises, the study said.

According to the researchers, birth prevalence of CHD was not related to a country's socio-demographic status, but overall prevalence was much lower in the poorest countries of the world.

This is because children in these countries do not have access to life saving surgical services, they added.

"In high income countries like the United States, we diagnose some heart conditions prenatally during the 20-week ultrasound," said Gerard Martin from Children's National Hospital who contributed to the study.

"For children born in middle- and low-income countries, these data draw stark attention to what we as cardiologists already knew from our own work in these countries -- the lack of diagnostic and treatment tools leads to lower survival rates for children born with CHD," said researcher Craig Sable.

"The UN has prioritised reduction of premature deaths from heart disease, but to meet the target of 'ending preventable deaths of newborns and children under 5 years of age,' health policy makers will need to develop specific accountability measures that address barriers and improve access to care and treatment," the authors wrote.

New York, Feb 26:  A new wearable sensor that works in conjunction with artificial intelligence (AI) technology could help doctors remotely detect critical changes in heart failure patients days before a health crisis occurs, says a study.

The researchers said the system could eventually help avert up to one in three heart failure readmissions in the weeks following initial discharge from the hospital and help patients sustain a better quality of life.

"This study shows that we can accurately predict the likelihood of hospitalisation for heart failure deterioration well before doctors and patients know that something is wrong," says the study's lead author Josef Stehlik from University of Utah in the US.

"Being able to readily detect changes in the heart sufficiently early will allow physicians to initiate prompt interventions that could prevent rehospitalisation and stave off worsening heart failure," Stehlik added.

According to the researchers, even if patients survive, they have poor functional capacity, poor exercise tolerance and low quality of life after hospitalisations.

"This patch, this new diagnostic tool, could potentially help us prevent hospitalizations and decline in patient status," Stehlik said.

For the findings, published in the journal Circulation: Heart Failure, the researchers followed 100 heart failure patients, average age 68, who were diagnosed and treated at four veterans administration (VA) hospitals in Utah, Texas, California, and Florida.

After discharge, participants wore an adhesive sensor patch on their chests 24 hours a day for up to three months.

The sensor monitored continuous electrocardiogram (ECG) and motion of each subject.

This information was transmitted from the sensor via Bluetooth to a smartphone and then passed on to an analytics platform, developed by PhysIQ, on a secure server, which derived heart rate, heart rhythm, respiratory rate, walking, sleep, body posture and other normal activities.

Using artificial intelligence, the analytics established a normal baseline for each patient. When the data deviated from normal, the platform generated an indication that the patient's heart failure was getting worse.

Overall, the system accurately predicted the impending need for hospitalization more than 80 per cent of the time.

On average, this prediction occurred 10.4 days before a readmission took place (median 6.5 days), the study said.

Lower neighbourhood socioeconomic status and greater household crowding increase the risk of becoming infected with SARS-CoV-2, the virus that causes COVID-19, warn researchers.

"Our study shows that neighbourhood socioeconomic status and household crowding are strongly associated with risk of infection," said study lead author Alexander Melamed from Columbia University in the US.

"This may explain why Black and Hispanic people living in these neighbourhoods are disproportionately at risk for contracting the virus," Melamed added.

For the findings, published in the journal JAMA, the researchers examined the relationships between COVID-19 infection and neighbourhood characteristics in 396 women who gave birth during the peak of the Covid-19 outbreak in New York City. Since March 22, all women admitted to the hospitals for delivery have been tested for the virus, which gave the researchers the opportunity to detect all infections -- including infections with no symptoms -- in a defined population

The strongest predictor of COVID-19 infection among these women was residence in a neighbourhood where households with many people are common.The findings showed that women who lived in a neighbourhood with high household membership were three times more likely to be infected with the virus. Neighbourhood poverty also appeared to be a factor, the researchers said.Women were twice as likely to get COVID-19 if they lived in neighbourhoods with a high poverty rate, although that relationship was not statistically significant due to the small sample size.

The study revealed that there was no association between infection and population density.

"New York City has the highest population density of any city in the US, but our study found that the risks are related more to density in people's domestic environments rather than density in the city or within neighbourhoods," says co-author Cynthia Gyamfi-Bannerman."

The knowledge that SARS-CoV-2 infection rates are higher in disadvantaged neighbourhoods and among people who live in crowded households could help public health officials target preventive measures," the authors wrote.

Recently, another study published in the Journal of the American Planning Association, showed that dense areas were associated with lower COVID-19 death rates.