Latest news with #TBSeWA
India Gazette
4 days ago
- Health
- India Gazette
ICMR-NIE introduce alert feature to reduce TB deaths in Tamil Nadu
By Shalini Bhardwaj Chennai (Tamil Nadu) [India], July 9 (ANI): More than half of all tuberculosis (TB) deaths occur within the first two months of treatment. In response, the Indian Council of Medical Research-National Institute of Epidemiology (ICMR-NIE) has introduced a new alert feature designed to immediately notify frontline healthcare workers when a patient is identified as severely ill following a TB diagnosis. The predictive model is expected to reduce the average time from diagnosis to hospital admission for severely ill patients with tuberculosis. The ICMR-NIE has recently launched a predictive model that helps the state reduce TB deaths. The predictive model was developed using data from 57,803 adults diagnosed with TB from public health facilities. 'In 2023, of 57,803 adults with TB diagnosed from public facilities, 57,070 (99%) were triaged and 6864 (12%) were triage-positive (eligible for referral). Of 6864 eligible, 6105(89%) were referred, comprehensively assessed and confirmed as severely ill at nodal inpatient facilities. Of 6105 confirmed, 5926 (98%) were admitted for inpatient care and 5413 (92%) were successfully discharged for ambulatory directly observed treatment. The median admission duration was seven days,' the study noted. The new feature introduced by the ICMR-NIE would merge with the existing TB SeWA (Secere TB Web Application), which was launched in 2022 and integrated into the state's TN-KET (Tamil Nadu Kasanoi Erappila Thittam). According to experts from ICMR-NIE, 'This new feature will be useful to Alert front-line staff on how to recognise severely ill TB patients to avoid delay in treatment.' They further added, 'The Majority of TB deaths are being reported early (within 2 months), India TB program's information management system (Nikshay) dependent death prediction models are not feasible for prospective use as few variables are captured at diagnosis. Utilising routinely captured triage variables for severe illness in TB SeWA that are available under TN-KET at diagnosis (body mass index, pedal oedema, respiratory rate, oxygen saturation, and ability to stand without support), robust models for prospective use were developed.' (ANI)
The Hindu
6 days ago
- Health
- The Hindu
T.N. becomes first state in the country to integrate ‘predicted possibility of TB deaths' among patients into its elimination programme
To reduce the average time from diagnosis to hospital admission for severely ill tuberculosis (TB) patients Tamil Nadu has become the first state in the country to integrate a model, which predicts the possibility of TB deaths among patients, with the existing state-wide application which screens them at diagnosis. The predictive model, developed by the ICMR National Institute of Epidemiology (ICMR-NIE), which was launched last week and will aid the State in bringing down the TB mortality rate said Asha Frederick, State TB Officer of Tamil Nadu. She added that the predictive death model has been developed using data from nearly 56,000 TB patients diagnosed in public health facilities across Tamil Nadu between July 2022 and June 2023. Globally, India has the highest burden of TB with two deaths occurring every three minutes. But these deaths can be prevented. With proper care and treatment, TB patients can be cured, notes the World Health Organisation. Research further adds that TB is among the leading causes of morbidity and mortality worldwide, and more than 70% of the deaths of TB patients occur during the first two months of treatment. Speaking about the addition of the new feature in the Tamil Nadu TB elimination programme Dr. Frederick explained that this new addition will merge with the existing TB SeWA (Severe TB Web Application) which has been in use since 2022 under the state's differentiated care model initiative Tamil Nadu - Kasanoi Erappila Thittam (TN-KET). 'What this new feature will do is to alert frontline staff on how to recognise a severely ill TB patient – from a given list of medical indications including body weight, ability to stand without support, etc -- so that he is given priority in hospital admission and that treatment is initiated without delay. The predicted probability of death ranges widely — from 10% to as high as 50%, depending on how many of the five risk factors are present. In contrast, for patients not flagged as severely ill, the predicted probability drops sharply to just 1–4 per cent,' explained Hemant Shewade, a senior scientiat at NIE. He added that while the average time from diagnosis to admission of a TB patient in Tamil Nadu is one day, around a quarter of severely ill patients still face delays of up to six days in the state. All 2,800 public health facilities in Tamil Nadu — from primary health centres to medical colleges — currently use the TB SeWA application alongside a paper-based triage tool. Old age, TB/HIV co-infection and a baseline body weight of <35 kg increased the mortality during TB treatment, notes a study titled 'Time to Death and Associated Factors among Tuberculosis Patients in Dangila Woreda, Northwest Ethiopia', which adds that a special follow up of TB patients during the intensive phase, of older patients and TB/HIV co-infected cases, as well as nutritionally supplementing for underweight patients may be important to consider as interventions to reduce deaths during TB treatment.
Time of India
6 days ago
- Health
- Time of India
Tamil Nadu first state to implement TB death prediction model
New Delhi: Tamil Nadu has become the first state in the country to implement a model which predicts the probability of deaths among adults with Tuberculosis and has integrated it with the existing state-wide application TB SeWA, which triages them at diagnosis. The predictive model, developed by ICMR's National Institute of Epidemiology (NIE) that was launched last week, aims to reduce the average time from diagnosis to hospital admission for severely ill TB patients , thereby bringing down the mortality rate further, said Dr Asha Frederick, State TB Officer of Tamil Nadu. The new feature has been added to Tamil Nadu's existing TB SeWA (Severe TB Web Application), which has been in use since 2022 under the state's differentiated care model initiative Tamil Nadu - Kasanoi Erappila Thittam (TN-KET), she told PTI. Under TN-KET, healthcare workers screen a triage every newly diagnosed adult with TB for very severe undernutrition, respiratory distress or poor physical condition using five variables -- body mass index (BMI), pedal oedema (swelling of feet and ankles), respiratory rate, oxygen saturation, and the ability to stand without support. Then, health staff feeds these variables into TB SeWA, which then tells whether a particular patient is severely ill or not. Under TN-KET, all severely ill (very severely undernourished or having respiratory distress or poor physical status) have to be prioritised for admission, Dr Frederick said. Until now, TB SeWA flagged patients as 'severely ill' based on these variables, helping health staff prioritise them for inpatient care, Dr Manoj Murhekar, Director of NIE said. "The new feature goes a step further - calculating and displaying predicted probability of death for adults with TB," Dr Murhekar said. This objective risk percentage aims to overcome any subjective inference regarding severity and guide frontline staff to act immediately and firmly for the hospital admission of severely ill adults with TB at the time of diagnosis, he said. "How this feature addition is helpful is that the predicted probability of death varies widely between a 'severely ill' and 'not severely ill' patient. The predicted probability for a severely ill adult with TB death ranges from 10 per cent to as high as 50 per cent, depending on how many of the conditions are present. "In contrast, for patients not flagged as 'severely ill', the predicted probability drops sharply to just 1-4 per cent," explained Dr Hemant Shewade, a senior Scientist at NIE. Data of last three years show that about 10- 15 per cent of adults with TB in Tamil Nadu are found to be severely ill at diagnosis, he told PTI. "This clear risk estimate will help ensure that the sickest patients are admitted to hospitals without delay," Dr Shewade stated. Elaborating further, Dr Shewade said that while the average time from diagnosis to admission of a severely ill patient under TN-KET is one day, around a quarter of severely ill patients still face delays of up to three to six days in the state. "There are still some severely ill patients - like 25 per cent roughly who get admitted after some delay. This feature will advocate and motivate healthcare workers to take immediate decisions on their referral for admission," he said. "Over the time it will also help us analyse whether the average time from diagnosis to admission for a severely ill TB patient has reduced or not and eventually this will contribute towards further reducing TB deaths in the state. Two-thirds of TB deaths occur within two months of diagnosis," he said. The predictive death model has been developed using data from nearly 56,000 TB patients diagnosed in public health facilities across Tamil Nadu between July 2022 and June 2023. Dr Frederick said that for predicting TB deaths, the five triage variables used under TN-KET alone were as accurate as all baseline variables captured in India's national TB portal Ni-kshay. Dr Shewade further added that all baseline variables in Ni-kshay are available only by around three weeks (too late to use for prediction) while the five-triage variable are captured within a day in Tamil Nadu. All 2,800 public health facilities in Tamil Nadu -- from primary health centres to medical colleges -- currently use the TB SeWA application alongside a paper-based triage tool, said Dr Frederick. "Tamil Nadu is so far the only state in India to systematically record and use these five triage variables to guide patient management," she said. According to a study by the NIE, after implementing TN-KET for around three years now, the losses in the care cascade have significantly reduced and around two-third districts have documented reduction in TB death rates. The ICMR-NIE scientists emphasised that the initiative sets an important example for other states, where TB deaths, especially the early fatalities, remain a persistent challenge despite free diagnosis and treatment.
The Hindu
6 days ago
- Health
- The Hindu
Tamil Nadu first to integrate ‘predicted possibility of TB deaths' in patients to its State TB elimination programme
To reduce the average time from diagnosis to hospital admission for severely ill tuberculosis (TB) patients, Tamil Nadu has become the first State in the country to integrate a model that predicts the possibility of TB deaths among patients with the existing State-wide application which screens them at diagnosis. The predictive model, developed by the Indian Council of Medical Research (ICMR)-National Institute of Epidemiology (NIE) and launched last week, will aid the State in bringing down the TB mortality rate, Asha Frederick, State TB Officer, Tamil Nadu, said. The predictive death model has been developed using data from nearly 56,000 TB patients diagnosed in public health facilities across Tamil Nadu between July 2022 and June 2023, Dr. Frederick said. Research shows TB is among the leading causes of morbidity and mortality worldwide, and more than 70% of TB deaths occur in the first two months of TB treatment. Globally, India has the highest burden of TB, with two deaths occurring every three minutes from TB. These deaths can be prevented. With proper care and treatment, TB patients can be cured, the World Health Organization has said. The addition of the new feature in the Tamil Nadu TB elimination programme, Dr. Frederick said, would merge it with the existing TB SeWA (Severe TB Web Application), in use since 2022 under the Tamil Nadu Kasanoi Erappila Thittam (TN-KET) differentiated care model initiative. 'What this new feature will do is to alert frontline staff on how to recognise a severely ill TB patient from a given list of medical indications, including body weight, ability to stand without support, etc., so that they are given priority in hospital admissions, and treatment is initiated without delay. The predicted probability of death ranges widely — from 10% to as high as 50%, depending on how many of the five risk factors are present. In contrast, for patients not flagged as severely ill, the predicted probability drops sharply to just 1% to 4%,' Hemant Shewade, senior scientist at the NIE, said. The average time from diagnosis to admission of a TB patient in Tamil Nadu is one day, and around a quarter of severely ill patients still face delays of up to six days in the State, Dr. Shewade said. All 2,800 public health facilities in Tamil Nadu — from primary health centres to medical colleges — currently use the TB SeWA application alongside a paper-based triage tool. Old age, TB/HIV co-infection, and a baseline body weight of <35 kg increase mortality during TB treatment, a study titled 'Time to Death and Associated Factors among Tuberculosis Patients in Dangila Woreda, Northwest Ethiopia' has found. Special follow-ups of TB patients during the intensive phase, and of older patients and TB/HIV co-infected cases, as well as nutritionally supplementing underweight patients, may be important interventions to consider in order to reduce deaths during TB treatment.
Deccan Herald
6 days ago
- Health
- Deccan Herald
Tamil Nadu first state to implement TB death prediction model
The new feature has been added to Tamil Nadu's existing TB SeWA (Severe TB Web Application), which has been in use since 2022 under the state's differentiated care model initiative Tamil Nadu.
