Lung cancer varies between Caucasians and Asians. There have been differences recorded in the epidemiology, genomics, standard therapies and outcomes, with variations according to the geography and ethnicity which affect the decision for optimal treatment of the patients. To better understand the profile of lung cancer in Southeast Asia, with a focus on India, we have comprehensively reviewed the available data, and discuss the challenges and the way forward. A substantial proportion of patients with lung cancer in Southeast Asia are neversmokers, and adenocarcinoma is the common histopathologic subtype, found in approximately a third of the patients. EGFR mutations are noted in 23–30% of patients, and ALK rearrangements are noted in 5–7%. Therapies are similar to global standards, although access to newer modalities and molecules is a challenge. Collaborative research, political will with various policy changes and patient advocacy are urgently needed.
{"title":"Uniqueness of lung cancer in Southeast Asia","authors":"Vanita Noronha , Atul Budukh , Pankaj Chaturvedi , Srikanth Anne , Anshu Punjabi , Maheema Bhaskar , Tarini P. Sahoo , Nandini Menon , Minit Shah , Ullas Batra , Shrinidhi Nathany , Rajiv Kumar , Omshree Shetty , Trupti Pai Ghodke , Abhishek Mahajan , Nivedita Chakrabarty , Supriya Hait , Satyendra C. Tripathi , Anuradha Chougule , Pratik Chandrani , Kumar Prabhash","doi":"10.1016/j.lansea.2024.100430","DOIUrl":"10.1016/j.lansea.2024.100430","url":null,"abstract":"<div><p>Lung cancer varies between Caucasians and Asians. There have been differences recorded in the epidemiology, genomics, standard therapies and outcomes, with variations according to the geography and ethnicity which affect the decision for optimal treatment of the patients. To better understand the profile of lung cancer in Southeast Asia, with a focus on India, we have comprehensively reviewed the available data, and discuss the challenges and the way forward. A substantial proportion of patients with lung cancer in Southeast Asia are neversmokers, and adenocarcinoma is the common histopathologic subtype, found in approximately a third of the patients. <em>EGFR</em> mutations are noted in 23–30% of patients, and <em>ALK</em> rearrangements are noted in 5–7%. Therapies are similar to global standards, although access to newer modalities and molecules is a challenge. Collaborative research, political will with various policy changes and patient advocacy are urgently needed.</p></div>","PeriodicalId":75136,"journal":{"name":"The Lancet regional health. Southeast Asia","volume":"27 ","pages":"Article 100430"},"PeriodicalIF":5.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772368224000805/pdfft?md5=e1a7cdce2e63c2597732016a9ec5d32e&pid=1-s2.0-S2772368224000805-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141694923","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-01DOI: 10.1016/j.lansea.2024.100452
Sumon Ghosh , Mohammad Nayeem Hasan , Nirmalendu Deb Nath , Najmul Haider , Daleniece Higgins Jones , Md. Kamrul Islam , M. Mujibur Rahaman , Hasan Sayedul Mursalin , Nadim Mahmud , Md. Kamruzzaman , Md. Fazlay Rabby , Shotabdi Kar , Sayed Mohammed Ullah , Md. Rashed Ali Shah , Afsana Akter Jahan , Md. Sohel Rana , Sukanta Chowdhury , Md. Jamal Uddin , Thankam S. Sunil , Be-Nazir Ahmed , Md. Nazmul Islam
Background
Bangladesh is making progress toward achieving zero dog-mediated rabies deaths by 2030, a global goal set in 2015.
Methods
Drawing from multiple datasets, including patient immunisation record books and mass dog vaccination (MDV) databases, we conducted a comprehensive analysis between 2011 and 2023 to understand the effectiveness of rabies control programmes and predict human rabies cases in Bangladesh by 2030 using time-series forecasting models. We also compared rabies virus sequences from GenBank in Bangladesh and other South Asian countries.
Findings
The estimated dog population in Bangladesh was determined to be 1,668,140, with an average dog population density of 12.83 dogs/km2 (95% CI 11.14–14.53) and a human-to-dog ratio of 86.70 (95% CI 76.60–96.80). The MDV campaign has led to the vaccination of an average of 21,295 dogs (95% CI 18,654–23,935) per district annually out of an estimated 26,065 dogs (95% CI 22,898–29,230). A declining trend in predicted and observed human rabies cases has been identified, suggesting that Bangladesh is poised to make substantial progress towards achieving the ‘Zero by 30’ goal, provided the current trajectory continues. The phylogenetic analysis shows that rabies viruses in Bangladesh belong to the Arctic-like-1 group, which differs from those in Bhutan despite sharing a common ancestor.
Interpretation
Bangladesh's One Health approach demonstrated that an increase in MDV and anti-rabies vaccine (ARV) resulted in a decline in the relative risk of human rabies cases, indicating that eliminating dog-mediated human rabies could be achievable.
Funding
The study was supported by the Communicable Disease Control (CDC) Division of the Directorate General of Health Services (DGHS) of the People's Republic of Bangladesh.
{"title":"Rabies control in Bangladesh and prediction of human rabies cases by 2030: a One Health approach","authors":"Sumon Ghosh , Mohammad Nayeem Hasan , Nirmalendu Deb Nath , Najmul Haider , Daleniece Higgins Jones , Md. Kamrul Islam , M. Mujibur Rahaman , Hasan Sayedul Mursalin , Nadim Mahmud , Md. Kamruzzaman , Md. Fazlay Rabby , Shotabdi Kar , Sayed Mohammed Ullah , Md. Rashed Ali Shah , Afsana Akter Jahan , Md. Sohel Rana , Sukanta Chowdhury , Md. Jamal Uddin , Thankam S. Sunil , Be-Nazir Ahmed , Md. Nazmul Islam","doi":"10.1016/j.lansea.2024.100452","DOIUrl":"10.1016/j.lansea.2024.100452","url":null,"abstract":"<div><h3>Background</h3><p>Bangladesh is making progress toward achieving zero dog-mediated rabies deaths by 2030, a global goal set in 2015.</p></div><div><h3>Methods</h3><p>Drawing from multiple datasets, including patient immunisation record books and mass dog vaccination (MDV) databases, we conducted a comprehensive analysis between 2011 and 2023 to understand the effectiveness of rabies control programmes and predict human rabies cases in Bangladesh by 2030 using time-series forecasting models. We also compared rabies virus sequences from GenBank in Bangladesh and other South Asian countries.</p></div><div><h3>Findings</h3><p>The estimated dog population in Bangladesh was determined to be 1,668,140, with an average dog population density of 12.83 dogs/km<sup>2</sup> (95% CI 11.14–14.53) and a human-to-dog ratio of 86.70 (95% CI 76.60–96.80). The MDV campaign has led to the vaccination of an average of 21,295 dogs (95% CI 18,654–23,935) per district annually out of an estimated 26,065 dogs (95% CI 22,898–29,230). A declining trend in predicted and observed human rabies cases has been identified, suggesting that Bangladesh is poised to make substantial progress towards achieving the ‘Zero by 30’ goal, provided the current trajectory continues. The phylogenetic analysis shows that rabies viruses in Bangladesh belong to the Arctic-like-1 group, which differs from those in Bhutan despite sharing a common ancestor.</p></div><div><h3>Interpretation</h3><p>Bangladesh's One Health approach demonstrated that an increase in MDV and anti-rabies vaccine (ARV) resulted in a decline in the relative risk of human rabies cases, indicating that eliminating dog-mediated human rabies could be achievable.</p></div><div><h3>Funding</h3><p>The study was supported by the <span>Communicable Disease Control</span> (CDC) Division of the <span>Directorate General of Health Services</span> (DGHS) of the People's Republic of Bangladesh.</p></div>","PeriodicalId":75136,"journal":{"name":"The Lancet regional health. Southeast Asia","volume":"27 ","pages":"Article 100452"},"PeriodicalIF":5.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772368224001021/pdfft?md5=01bd8e5471b75251dc72c5c660f802b6&pid=1-s2.0-S2772368224001021-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141949909","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-01DOI: 10.1016/j.lansea.2024.100459
The Lancet Regional Health – Southeast Asia
{"title":"Time to bring patients to the core of care","authors":"The Lancet Regional Health – Southeast Asia","doi":"10.1016/j.lansea.2024.100459","DOIUrl":"10.1016/j.lansea.2024.100459","url":null,"abstract":"","PeriodicalId":75136,"journal":{"name":"The Lancet regional health. Southeast Asia","volume":"27 ","pages":"Article 100459"},"PeriodicalIF":5.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772368224001094/pdfft?md5=656334310ddd74bd2d7bcdf1dc32c005&pid=1-s2.0-S2772368224001094-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141953359","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-22DOI: 10.1016/j.lansea.2024.100453
Chittaranjan S. Yajnik
{"title":"Early life origins of the epidemic of the double burden of malnutrition: life can only be understood backwards","authors":"Chittaranjan S. Yajnik","doi":"10.1016/j.lansea.2024.100453","DOIUrl":"10.1016/j.lansea.2024.100453","url":null,"abstract":"","PeriodicalId":75136,"journal":{"name":"The Lancet regional health. Southeast Asia","volume":"28 ","pages":"Article 100453"},"PeriodicalIF":5.0,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772368224001033/pdfft?md5=2f014056bcdf2d49ecb060092d295174&pid=1-s2.0-S2772368224001033-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141960453","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
During the initial phase of the COVID-19 pandemic, the Government of India implemented a nationwide lockdown, sealing borders across states and districts. The northeastern region of India, surrounded by three international borders and connected to mainland India by a narrow passage, faced particular isolation. This isolation resulted in these states forming a relatively closed population. Consequently, the availability of population-based data from Indian Council of Medical Research, tracked through national identification cards, offered a distinctive opportunity to understand the spread of the virus among non-vaccinated and non-exposed populations. This research leverages this dataset to comprehend the repercussions within isolated populations.
Methods
The inter-district variability was visualized using geospatial analysis. The patterns do not follow any established grounded theories on disease spread. Out of 7.1 million total data weekly 0.35 million COVID-19-positive northeast data was taken from April 2020 to February 2021 including “date, test result, population density, area, latitude, longitude, district, and state” to identify the spread pattern using a modified reaction-diffusion model (MRD-Model) and Geographic Information System.
Findings
The analysis of the closed population group revealed an initial uneven yet rapidly expanding geographical spread characterized by a high diffusion rate α approximately 0.4503 and a lower reaction rate β approximately 0.0256, which indicated a slower growth trajectory of case numbers rather than exponential escalation. In the latter stages, COVID-19 incidence reached zero in numerous districts, while in others, the reported cases did not exceed 100.
Interpretation
The MRD-Model effectively captured the disease transmission dynamics in the abovementioned setting. This enhanced understanding of COVID-19 spread in remote, isolated regions provided by the MRD modelling framework can guide targeted public health strategies for similar isolated areas.
Funding
This study is Funded by Indian Council of Medical Research (ICMR).
{"title":"Geospatial analysis of contagious infection growth and cross-boundary transmission in non-vaccinated districts of North-East Indian states during the COVID-19 pandemic","authors":"Mousumi Gupta , Madhab Nirola , Arpan Sharma , Prasanna Dhungel , Harpreet Singh , Amlan Gupta","doi":"10.1016/j.lansea.2024.100451","DOIUrl":"10.1016/j.lansea.2024.100451","url":null,"abstract":"<div><h3>Background</h3><p>During the initial phase of the COVID-19 pandemic, the Government of India implemented a nationwide lockdown, sealing borders across states and districts. The northeastern region of India, surrounded by three international borders and connected to mainland India by a narrow passage, faced particular isolation. This isolation resulted in these states forming a relatively closed population. Consequently, the availability of population-based data from Indian Council of Medical Research, tracked through national identification cards, offered a distinctive opportunity to understand the spread of the virus among non-vaccinated and non-exposed populations. This research leverages this dataset to comprehend the repercussions within isolated populations.</p></div><div><h3>Methods</h3><p>The inter-district variability was visualized using geospatial analysis. The patterns do not follow any established grounded theories on disease spread. Out of 7.1 million total data weekly 0.35 million COVID-19-positive northeast data was taken from April 2020 to February 2021 including “date, test result, population density, area, latitude, longitude, district, and state” to identify the spread pattern using a modified reaction-diffusion model (MRD-Model) and Geographic Information System.</p></div><div><h3>Findings</h3><p>The analysis of the closed population group revealed an initial uneven yet rapidly expanding geographical spread characterized by a high diffusion rate α approximately 0.4503 and a lower reaction rate β approximately 0.0256, which indicated a slower growth trajectory of case numbers rather than exponential escalation. In the latter stages, COVID-19 incidence reached zero in numerous districts, while in others, the reported cases did not exceed 100.</p></div><div><h3>Interpretation</h3><p>The MRD-Model effectively captured the disease transmission dynamics in the abovementioned setting. This enhanced understanding of COVID-19 spread in remote, isolated regions provided by the MRD modelling framework can guide targeted public health strategies for similar isolated areas.</p></div><div><h3>Funding</h3><p>This study is Funded by <span>Indian Council of Medical Research</span> (ICMR).</p></div>","PeriodicalId":75136,"journal":{"name":"The Lancet regional health. Southeast Asia","volume":"28 ","pages":"Article 100451"},"PeriodicalIF":5.0,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S277236822400101X/pdfft?md5=85214a67d2f3e0d9abe586a76e8f0ee6&pid=1-s2.0-S277236822400101X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141728760","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ventilator-associated pneumonia (VAP) is a major cause of morbidity and mortality in patients receiving mechanical ventilation in India. Surveillance of VAP is essential to implement data-based preventive measures. Implementation of ventilator-associated events (VAE) criteria for surveillance has major constraints for low resource settings, which can lead to significant underreporting. Surveillance of VAP using common protocols in a large network of hospitals would give meaningful estimates of the burden of VAP in low resource settings. This study leverages a previously established healthcare-associated infections (HAI) surveillance network to develop and test a modified VAP definition adjusted for Indian settings.
Methods
In this observational pilot study, thirteen hospitals from the existing HAI surveillance network were selected for developing and testing a modified VAP definition between February 2021 and April 2023. The criteria used for diagnosing VAP were adapted from the CDC’s Pediatric VAP definition and modified to cater to the needs of Indian hospitals. Designated nurses recorded each VAP event in a case report form (CRF) and also collected denominator data. The data was entered into an indigenously developed database for validation and analysis. At the time of data analysis, a questionnaire was sent to sites to get feedback on the performance of the modified VAP definitions.
Findings
Out of 133,445 patient days and 40,533 ventilator days, 261 VAP events were recorded, with an overall VAP rate of 6.4 per 1000 ventilator days and a device utilization ratio (DUR) of 0.3. A total of 344 organisms were reported from the VAP events. Of these, Acinetobacter spp (29.6%, 102) was the most frequent, followed by Klebsiella spp (26.7%, 92). Isolates of Acinetobacter spp (98%) and Enterobacterales (85.5%) showed very high resistance against Carbapenem. Colistin resistance was observed in 6% of Enterobacterales and 3.2% of Acinetobacter spp.
Interpretation
Data from this pilot study needs to validated in the larger Indian HAI surveillance network so that it can help in wider implementation of this protocol in order to assess its applicability p VAP across India.
Funding
This work was supported by a grant received from the Indian Council of Medical Research (code I-1203).
{"title":"Surveillance of ventilator associated pneumonia in a network of indian hospitals using modified definitions: a pilot study","authors":"Purva Mathur , Aparna Ningombam , Kapil Dev Soni , Richa Aggrawal , Kumari Vandana Singh , Projoyita Samanta , Stuti Gupta , Smriti Srivastava , Bijayini Behera , Swagata Tripathy , Pallab Ray , Manisha Biswal , Camilla Rodrigues , Sanjay Bhattacharya , Sudipta Mukherjee , Satyam Mukherjee , Vimala Venkatesh , Sheetal Verma , Zia Arshad , Vibhor Tak , Kamini Walia","doi":"10.1016/j.lansea.2024.100450","DOIUrl":"10.1016/j.lansea.2024.100450","url":null,"abstract":"<div><h3>Background</h3><p>Ventilator-associated pneumonia (VAP) is a major cause of morbidity and mortality in patients receiving mechanical ventilation in India. Surveillance of VAP is essential to implement data-based preventive measures. Implementation of ventilator-associated events (VAE) criteria for surveillance has major constraints for low resource settings, which can lead to significant underreporting. Surveillance of VAP using common protocols in a large network of hospitals would give meaningful estimates of the burden of VAP in low resource settings. This study leverages a previously established healthcare-associated infections (HAI) surveillance network to develop and test a modified VAP definition adjusted for Indian settings.</p></div><div><h3>Methods</h3><p>In this observational pilot study, thirteen hospitals from the existing HAI surveillance network were selected for developing and testing a modified VAP definition between February 2021 and April 2023. The criteria used for diagnosing VAP were adapted from the CDC’s Pediatric VAP definition and modified to cater to the needs of Indian hospitals. Designated nurses recorded each VAP event in a case report form (CRF) and also collected denominator data. The data was entered into an indigenously developed database for validation and analysis. At the time of data analysis, a questionnaire was sent to sites to get feedback on the performance of the modified VAP definitions.</p></div><div><h3>Findings</h3><p>Out of 133,445 patient days and 40,533 ventilator days, 261 VAP events were recorded, with an overall VAP rate of 6.4 per 1000 ventilator days and a device utilization ratio (DUR) of 0.3. A total of 344 organisms were reported from the VAP events. Of these, <em>Acinetobacter</em> spp (29.6%, 102) was the most frequent, followed by <em>Klebsiella</em> spp (26.7%, 92). Isolates of <em>Acinetobacter</em> spp (98%) and Enterobacterales (85.5%) showed very high resistance against Carbapenem. Colistin resistance was observed in 6% of Enterobacterales and 3.2% of <em>Acinetobacter</em> spp.</p></div><div><h3>Interpretation</h3><p>Data from this pilot study needs to validated in the larger Indian HAI surveillance network so that it can help in wider implementation of this protocol in order to assess its applicability p VAP across India.</p></div><div><h3>Funding</h3><p>This work was supported by a grant received from the <span>Indian Council of Medical Research</span> (<span><span>code I-1203</span></span>).</p></div>","PeriodicalId":75136,"journal":{"name":"The Lancet regional health. Southeast Asia","volume":"28 ","pages":"Article 100450"},"PeriodicalIF":5.0,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772368224001008/pdfft?md5=326c3528347d380264c25f5b08558a42&pid=1-s2.0-S2772368224001008-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141728759","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-11DOI: 10.1016/j.lansea.2024.100447
Abhijit Poddar , S.R. Rao
{"title":"Grappling Covishield fear in India: the urgent need for strong countermeasures to build vaccine confidence","authors":"Abhijit Poddar , S.R. Rao","doi":"10.1016/j.lansea.2024.100447","DOIUrl":"https://doi.org/10.1016/j.lansea.2024.100447","url":null,"abstract":"","PeriodicalId":75136,"journal":{"name":"The Lancet regional health. Southeast Asia","volume":"27 ","pages":"Article 100447"},"PeriodicalIF":5.0,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772368224000970/pdfft?md5=f2d09cbb2f62a395e73bbb5051cb5157&pid=1-s2.0-S2772368224000970-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141596488","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}