Use of quantitative molecular diagnostic methods to assess the aetiology, burden, and clinical characteristics of diarrhoea in children in low-resource settings: a reanalysis of the MAL-ED cohort study

Optimum management of childhood diarrhoea in low-resource settings has been hampered by insufficient data on aetiology, burden, and associated clinical characteristics. We used quantitative diagnostic methods to reassess and refine estimates of diarrhoea aetiology from the Etiology, Risk Factors, an...

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Published inThe Lancet global health Vol. 6; no. 12; pp. e1309 - e1318
Main Authors Platts-Mills, James A, Liu, Jie, Kabir, Furqan, Siguas, Mery, Khan, Shaila S, Praharaj, Ira, Murei, Arinao, Nshama, Rosemary, Mujaga, Buliga, Havt, Alexandre, Maciel, Irene A, Gratz, Jean, Stroup, Suzanne E, Aziz, Fatima, Qureshi, Shahida, Islam, M Ohedul, Sakpaisal, Pimmada, Rajendiran, Revathi, McCormick, Benjamin J J, Lang, Dennis, Gottlieb, Michael, Guerrant, Richard L, Lima, Aldo A M, Leite, Jose Paulo, Samie, Amidou, Bessong, Pascal O, Mason, Carl, Shrestha, Sanjaya, Mduma, Estomih R, Iqbal, Najeeha T, Ahmed, Tahmeed, Haque, Rashidul, Kang, Gagandeep, Kosek, Margaret N, Houpt, Eric R, Acosta, Angel Mendez, Rios de Burga, Rosa, Olotegui, Maribel Paredes, Pinedo, Silvia Rengifo, Trigoso, Dixner Rengifo, Ahmed, Imran, Alam, Didar, Rasheed, Muneera, Soofi, Sajid, Turab, Ali, Yousafzai, Aisha, Zaidi, Anita KM, Shrestha, Binob, Rayamajhi, Bishnu Bahadur, Strand, Tor, Ammu, Geetha, Babji, Sudhir, Jennifer, M. Steffi, John, Sushil, Kaki, Shiny, Karunakaran, Priyadarshani, Raju, Sophy, Ramadas, Rakhi, Ramanujam, Karthikeyan, Roshan, Reeba, Pan, William K, Ambikapathi, Ramya, Carreon, J Daniel, Hoest, Christel, Knobler, Stacey, Miller, Mark A, Psaki, Stephanie, Rasmussen, Zeba, Richard, Stephanie A, Tountas, Karen H, Svensen, Erling, Amour, Caroline, Dillingham, Rebecca, Petri, William A, Scharf, Rebecca, Ahmed, AM Shamsir, Alam, Md Ashraful, Mondal, Dinesh, Nahar, Baitun, Shrestha, Rita, Ulak, Manjeswori, Bauck, Aubrey, Black, Robert, Caulfield, Laura, Ross, A Catharine, Schaefer, Barbara, Simons, Suzanne, Pendergast, Laura, Abreu, Cláudia B, Leite, Álvaro M, Lima, Ila F, Maciel, Bruna LL, Medeiros, Pedro HQS, Oriá, Reinaldo B, Quetz, Josiane, Soares, Alberto M, Patil, Crystal L, Mahopo, Cloupas, Maphula, Angelina, Nyathi, Emanuel
Format Journal Article
LanguageEnglish
Published England Elsevier Ltd 01.12.2018
Elsevier
Subjects
Online AccessGet full text
ISSN2214-109X
2214-109X
DOI10.1016/S2214-109X(18)30349-8

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Abstract Optimum management of childhood diarrhoea in low-resource settings has been hampered by insufficient data on aetiology, burden, and associated clinical characteristics. We used quantitative diagnostic methods to reassess and refine estimates of diarrhoea aetiology from the Etiology, Risk Factors, and Interactions of Enteric Infections and Malnutrition and the Consequences for Child Health and Development (MAL-ED) cohort study. We re-analysed stool specimens from the multisite MAL-ED cohort study of children aged 0–2 years done at eight locations (Dhaka, Bangladesh; Vellore, India; Bhaktapur, Nepal; Naushero Feroze, Pakistan; Venda, South Africa; Haydom, Tanzania; Fortaleza, Brazil; and Loreto, Peru), which included active surveillance for diarrhoea and routine non-diarrhoeal stool collection. We used quantitative PCR to test for 29 enteropathogens, calculated population-level pathogen-specific attributable burdens, derived stringent quantitative cutoffs to identify aetiology for individual episodes, and created aetiology prediction scores using clinical characteristics. We analysed 6625 diarrhoeal and 30 968 non-diarrhoeal surveillance stools from 1715 children. Overall, 64·9% of diarrhoea episodes (95% CI 62·6–71·2) could be attributed to an aetiology by quantitative PCR compared with 32·8% (30·8–38·7) using the original study microbiology. Viral diarrhoea (36·4% of overall incidence, 95% CI 33·6–39·5) was more common than bacterial (25·0%, 23·4–28·4) and parasitic diarrhoea (3·5%, 3·0–5·2). Ten pathogens accounted for 95·7% of attributable diarrhoea: Shigella (26·1 attributable episodes per 100 child-years, 95% CI 23·8–29·9), sapovirus (22·8, 18·9–27·5), rotavirus (20·7, 18·8–23·0), adenovirus 40/41 (19·0, 16·8–23·0), enterotoxigenic Escherichia coli (18·8, 16·5–23·8), norovirus (15·4, 13·5–20·1), astrovirus (15·0, 12·0–19·5), Campylobacter jejuni or C coli (12·1, 8·5–17·2), Cryptosporidium (5·8, 4·3–8·3), and typical enteropathogenic E coli (5·4, 2·8–9·3). 86·2% of the attributable incidence for Shigella was non-dysenteric. A prediction score for shigellosis was more accurate (sensitivity 50·4% [95% CI 46·7–54·1], specificity 84·0% [83·0–84·9]) than current guidelines, which recommend treatment only of bloody diarrhoea to cover Shigella (sensitivity 14·5% [95% CI 12·1–17·3], specificity 96·5% [96·0–97·0]). Quantitative molecular diagnostics improved estimates of pathogen-specific burdens of childhood diarrhoea in the community setting. Viral causes predominated, including a substantial burden of sapovirus; however, Shigella had the highest overall burden with a high incidence in the second year of life. These data could improve the management of diarrhoea in these low-resource settings. Bill & Melinda Gates Foundation.
AbstractList Background: Optimum management of childhood diarrhoea in low-resource settings has been hampered by insufficient data on aetiology, burden, and associated clinical characteristics. We used quantitative diagnostic methods to reassess and refine estimates of diarrhoea aetiology from the Etiology, Risk Factors, and Interactions of Enteric Infections and Malnutrition and the Consequences for Child Health and Development (MAL-ED) cohort study. Methods: We re-analysed stool specimens from the multisite MAL-ED cohort study of children aged 0–2 years done at eight locations (Dhaka, Bangladesh; Vellore, India; Bhaktapur, Nepal; Naushero Feroze, Pakistan; Venda, South Africa; Haydom, Tanzania; Fortaleza, Brazil; and Loreto, Peru), which included active surveillance for diarrhoea and routine non-diarrhoeal stool collection. We used quantitative PCR to test for 29 enteropathogens, calculated population-level pathogen-specific attributable burdens, derived stringent quantitative cutoffs to identify aetiology for individual episodes, and created aetiology prediction scores using clinical characteristics. Findings: We analysed 6625 diarrhoeal and 30 968 non-diarrhoeal surveillance stools from 1715 children. Overall, 64·9% of diarrhoea episodes (95% CI 62·6–71·2) could be attributed to an aetiology by quantitative PCR compared with 32·8% (30·8–38·7) using the original study microbiology. Viral diarrhoea (36·4% of overall incidence, 95% CI 33·6–39·5) was more common than bacterial (25·0%, 23·4–28·4) and parasitic diarrhoea (3·5%, 3·0–5·2). Ten pathogens accounted for 95·7% of attributable diarrhoea: Shigella (26·1 attributable episodes per 100 child-years, 95% CI 23·8–29·9), sapovirus (22·8, 18·9–27·5), rotavirus (20·7, 18·8–23·0), adenovirus 40/41 (19·0, 16·8–23·0), enterotoxigenic Escherichia coli (18·8, 16·5–23·8), norovirus (15·4, 13·5–20·1), astrovirus (15·0, 12·0–19·5), Campylobacter jejuni or C coli (12·1, 8·5–17·2), Cryptosporidium (5·8, 4·3–8·3), and typical enteropathogenic E coli (5·4, 2·8–9·3). 86·2% of the attributable incidence for Shigella was non-dysenteric. A prediction score for shigellosis was more accurate (sensitivity 50·4% [95% CI 46·7–54·1], specificity 84·0% [83·0–84·9]) than current guidelines, which recommend treatment only of bloody diarrhoea to cover Shigella (sensitivity 14·5% [95% CI 12·1–17·3], specificity 96·5% [96·0–97·0]). Interpretation: Quantitative molecular diagnostics improved estimates of pathogen-specific burdens of childhood diarrhoea in the community setting. Viral causes predominated, including a substantial burden of sapovirus; however, Shigella had the highest overall burden with a high incidence in the second year of life. These data could improve the management of diarrhoea in these low-resource settings. Funding: Bill & Melinda Gates Foundation.
Optimum management of childhood diarrhoea in low-resource settings has been hampered by insufficient data on aetiology, burden, and associated clinical characteristics. We used quantitative diagnostic methods to reassess and refine estimates of diarrhoea aetiology from the Etiology, Risk Factors, and Interactions of Enteric Infections and Malnutrition and the Consequences for Child Health and Development (MAL-ED) cohort study. We re-analysed stool specimens from the multisite MAL-ED cohort study of children aged 0-2 years done at eight locations (Dhaka, Bangladesh; Vellore, India; Bhaktapur, Nepal; Naushero Feroze, Pakistan; Venda, South Africa; Haydom, Tanzania; Fortaleza, Brazil; and Loreto, Peru), which included active surveillance for diarrhoea and routine non-diarrhoeal stool collection. We used quantitative PCR to test for 29 enteropathogens, calculated population-level pathogen-specific attributable burdens, derived stringent quantitative cutoffs to identify aetiology for individual episodes, and created aetiology prediction scores using clinical characteristics. We analysed 6625 diarrhoeal and 30 968 non-diarrhoeal surveillance stools from 1715 children. Overall, 64·9% of diarrhoea episodes (95% CI 62·6-71·2) could be attributed to an aetiology by quantitative PCR compared with 32·8% (30·8-38·7) using the original study microbiology. Viral diarrhoea (36·4% of overall incidence, 95% CI 33·6-39·5) was more common than bacterial (25·0%, 23·4-28·4) and parasitic diarrhoea (3·5%, 3·0-5·2). Ten pathogens accounted for 95·7% of attributable diarrhoea: Shigella (26·1 attributable episodes per 100 child-years, 95% CI 23·8-29·9), sapovirus (22·8, 18·9-27·5), rotavirus (20·7, 18·8-23·0), adenovirus 40/41 (19·0, 16·8-23·0), enterotoxigenic Escherichia coli (18·8, 16·5-23·8), norovirus (15·4, 13·5-20·1), astrovirus (15·0, 12·0-19·5), Campylobacter jejuni or C coli (12·1, 8·5-17·2), Cryptosporidium (5·8, 4·3-8·3), and typical enteropathogenic E coli (5·4, 2·8-9·3). 86·2% of the attributable incidence for Shigella was non-dysenteric. A prediction score for shigellosis was more accurate (sensitivity 50·4% [95% CI 46·7-54·1], specificity 84·0% [83·0-84·9]) than current guidelines, which recommend treatment only of bloody diarrhoea to cover Shigella (sensitivity 14·5% [95% CI 12·1-17·3], specificity 96·5% [96·0-97·0]). Quantitative molecular diagnostics improved estimates of pathogen-specific burdens of childhood diarrhoea in the community setting. Viral causes predominated, including a substantial burden of sapovirus; however, Shigella had the highest overall burden with a high incidence in the second year of life. These data could improve the management of diarrhoea in these low-resource settings. Bill & Melinda Gates Foundation.
Optimum management of childhood diarrhoea in low-resource settings has been hampered by insufficient data on aetiology, burden, and associated clinical characteristics. We used quantitative diagnostic methods to reassess and refine estimates of diarrhoea aetiology from the Etiology, Risk Factors, and Interactions of Enteric Infections and Malnutrition and the Consequences for Child Health and Development (MAL-ED) cohort study.BACKGROUNDOptimum management of childhood diarrhoea in low-resource settings has been hampered by insufficient data on aetiology, burden, and associated clinical characteristics. We used quantitative diagnostic methods to reassess and refine estimates of diarrhoea aetiology from the Etiology, Risk Factors, and Interactions of Enteric Infections and Malnutrition and the Consequences for Child Health and Development (MAL-ED) cohort study.We re-analysed stool specimens from the multisite MAL-ED cohort study of children aged 0-2 years done at eight locations (Dhaka, Bangladesh; Vellore, India; Bhaktapur, Nepal; Naushero Feroze, Pakistan; Venda, South Africa; Haydom, Tanzania; Fortaleza, Brazil; and Loreto, Peru), which included active surveillance for diarrhoea and routine non-diarrhoeal stool collection. We used quantitative PCR to test for 29 enteropathogens, calculated population-level pathogen-specific attributable burdens, derived stringent quantitative cutoffs to identify aetiology for individual episodes, and created aetiology prediction scores using clinical characteristics.METHODSWe re-analysed stool specimens from the multisite MAL-ED cohort study of children aged 0-2 years done at eight locations (Dhaka, Bangladesh; Vellore, India; Bhaktapur, Nepal; Naushero Feroze, Pakistan; Venda, South Africa; Haydom, Tanzania; Fortaleza, Brazil; and Loreto, Peru), which included active surveillance for diarrhoea and routine non-diarrhoeal stool collection. We used quantitative PCR to test for 29 enteropathogens, calculated population-level pathogen-specific attributable burdens, derived stringent quantitative cutoffs to identify aetiology for individual episodes, and created aetiology prediction scores using clinical characteristics.We analysed 6625 diarrhoeal and 30 968 non-diarrhoeal surveillance stools from 1715 children. Overall, 64·9% of diarrhoea episodes (95% CI 62·6-71·2) could be attributed to an aetiology by quantitative PCR compared with 32·8% (30·8-38·7) using the original study microbiology. Viral diarrhoea (36·4% of overall incidence, 95% CI 33·6-39·5) was more common than bacterial (25·0%, 23·4-28·4) and parasitic diarrhoea (3·5%, 3·0-5·2). Ten pathogens accounted for 95·7% of attributable diarrhoea: Shigella (26·1 attributable episodes per 100 child-years, 95% CI 23·8-29·9), sapovirus (22·8, 18·9-27·5), rotavirus (20·7, 18·8-23·0), adenovirus 40/41 (19·0, 16·8-23·0), enterotoxigenic Escherichia coli (18·8, 16·5-23·8), norovirus (15·4, 13·5-20·1), astrovirus (15·0, 12·0-19·5), Campylobacter jejuni or C coli (12·1, 8·5-17·2), Cryptosporidium (5·8, 4·3-8·3), and typical enteropathogenic E coli (5·4, 2·8-9·3). 86·2% of the attributable incidence for Shigella was non-dysenteric. A prediction score for shigellosis was more accurate (sensitivity 50·4% [95% CI 46·7-54·1], specificity 84·0% [83·0-84·9]) than current guidelines, which recommend treatment only of bloody diarrhoea to cover Shigella (sensitivity 14·5% [95% CI 12·1-17·3], specificity 96·5% [96·0-97·0]).FINDINGSWe analysed 6625 diarrhoeal and 30 968 non-diarrhoeal surveillance stools from 1715 children. Overall, 64·9% of diarrhoea episodes (95% CI 62·6-71·2) could be attributed to an aetiology by quantitative PCR compared with 32·8% (30·8-38·7) using the original study microbiology. Viral diarrhoea (36·4% of overall incidence, 95% CI 33·6-39·5) was more common than bacterial (25·0%, 23·4-28·4) and parasitic diarrhoea (3·5%, 3·0-5·2). Ten pathogens accounted for 95·7% of attributable diarrhoea: Shigella (26·1 attributable episodes per 100 child-years, 95% CI 23·8-29·9), sapovirus (22·8, 18·9-27·5), rotavirus (20·7, 18·8-23·0), adenovirus 40/41 (19·0, 16·8-23·0), enterotoxigenic Escherichia coli (18·8, 16·5-23·8), norovirus (15·4, 13·5-20·1), astrovirus (15·0, 12·0-19·5), Campylobacter jejuni or C coli (12·1, 8·5-17·2), Cryptosporidium (5·8, 4·3-8·3), and typical enteropathogenic E coli (5·4, 2·8-9·3). 86·2% of the attributable incidence for Shigella was non-dysenteric. A prediction score for shigellosis was more accurate (sensitivity 50·4% [95% CI 46·7-54·1], specificity 84·0% [83·0-84·9]) than current guidelines, which recommend treatment only of bloody diarrhoea to cover Shigella (sensitivity 14·5% [95% CI 12·1-17·3], specificity 96·5% [96·0-97·0]).Quantitative molecular diagnostics improved estimates of pathogen-specific burdens of childhood diarrhoea in the community setting. Viral causes predominated, including a substantial burden of sapovirus; however, Shigella had the highest overall burden with a high incidence in the second year of life. These data could improve the management of diarrhoea in these low-resource settings.INTERPRETATIONQuantitative molecular diagnostics improved estimates of pathogen-specific burdens of childhood diarrhoea in the community setting. Viral causes predominated, including a substantial burden of sapovirus; however, Shigella had the highest overall burden with a high incidence in the second year of life. These data could improve the management of diarrhoea in these low-resource settings.Bill & Melinda Gates Foundation.FUNDINGBill & Melinda Gates Foundation.
Author Ahmed, Imran
Silapong, Sasikorn
Houpt, Eric R
Vasquez, Angel Orbe
Kabir, Furqan
Bauck, Aubrey
Ross, A Catharine
Jennifer, M. Steffi
Petri, William A
Bodhidatta, Ladaporn
Sundaram, Shanmuga
Liu, Jie
Svensen, Erling
Medeiros, Pedro HQS
Yarrot, Ladislaus
Murray-Kolb, Laura E
Olotegui, Maribel Paredes
Patil, Crystal L
Page, Nicola
Guerrant, Richard L
Roshan, Reeba
Flores, Julian Torres
Yori, Pablo P
Rayamajhi, Bishnu Bahadur
Barrett, Leah
Moraes, Milena
Muliyil, Jayaprakash
Ammu, Geetha
Maciel, Bruna LL
Nahar, Baitun
Shrestha, Prakash Sunder
Kaki, Shiny
Leite, Jose Paulo
Raju, Sophy
Hariraju, Dinesh
Gratz, Jean
Pan, William K
Dillingham, Rebecca
Filho, José Quirino
Roberts, James H
Lazarus, Robin P
Kiwelu, Ireen
Havt, Alexandre
Hossain, Md Iqbal
Ahmed, Tahmeed
Benny, Blossom
Rajendiran, Revathi
Gottlieb, Michael
Lang, Dennis
Trigoso, Dixner Rengifo
Shrestha, Rita
Schaefer, Barbara
Mota, Francisco S
Lertsethtakarn, Paphavee
Nshama, Rosemary
Bose, Anuradha
Mduma, Estomih R
Turab, Ali
Schulze, Kerry
McGrath, Monica
Mvungi, Regisiana
Leite, Álvaro M
Lima
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o Foundation for the National Institutes of Health, Bethesda, MD, USA
m Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
b Department of Public Health Sciences, University of Virginia, Charlottesville, VA, USA
h University of Venda, Thohoyandou, South Africa
i Haydom Global Health Institute, Haydom, Tanzania
q Walter Reed/AFRIMS Research Unit, Nepal, Kathmandu, Nepal
d Armed Forces Research Institute of Medical Sciences (AFRIMS), Bangkok, Thailand
c Aga Khan University, Karachi, Pakistan
f International Centre for Diarrhoeal Disease Research, Dhaka, Bangladesh
a Division of Infectious Diseases and International Health, University of Virginia, Charlottesville, VA, USA
e Asociación Benéfica PRISMA, Iquitos, Peru
r University of Bergen, Bergen, Norway
k Federal University of Ceara, Fortaleza, Brazil
l Fundação Oswaldo Cruz (Fiocruz), Rio de Janeiro, Brazil
p National Institute for Communicable Diseases, Johannesburg, South Africa
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  givenname: Angel Mendez
  surname: Acosta
  fullname: Acosta, Angel Mendez
– sequence: 53
  givenname: Rosa
  surname: Rios de Burga
  fullname: Rios de Burga, Rosa
– sequence: 56
  givenname: Maribel Paredes
  surname: Olotegui
  fullname: Olotegui, Maribel Paredes
– sequence: 57
  givenname: Silvia Rengifo
  surname: Pinedo
  fullname: Pinedo, Silvia Rengifo
– sequence: 58
  givenname: Dixner Rengifo
  surname: Trigoso
  fullname: Trigoso, Dixner Rengifo
– sequence: 60
  givenname: Imran
  surname: Ahmed
  fullname: Ahmed, Imran
– sequence: 61
  givenname: Didar
  surname: Alam
  fullname: Alam, Didar
– sequence: 63
  givenname: Muneera
  surname: Rasheed
  fullname: Rasheed, Muneera
– sequence: 64
  givenname: Sajid
  surname: Soofi
  fullname: Soofi, Sajid
– sequence: 65
  givenname: Ali
  surname: Turab
  fullname: Turab, Ali
– sequence: 66
  givenname: Aisha
  surname: Yousafzai
  fullname: Yousafzai, Aisha
– sequence: 67
  givenname: Anita KM
  surname: Zaidi
  fullname: Zaidi, Anita KM
– sequence: 68
  givenname: Binob
  surname: Shrestha
  fullname: Shrestha, Binob
– sequence: 69
  givenname: Bishnu Bahadur
  surname: Rayamajhi
  fullname: Rayamajhi, Bishnu Bahadur
– sequence: 70
  givenname: Tor
  surname: Strand
  fullname: Strand, Tor
– sequence: 71
  givenname: Geetha
  surname: Ammu
  fullname: Ammu, Geetha
– sequence: 72
  givenname: Sudhir
  surname: Babji
  fullname: Babji, Sudhir
– sequence: 76
  givenname: M. Steffi
  surname: Jennifer
  fullname: Jennifer, M. Steffi
– sequence: 77
  givenname: Sushil
  surname: John
  fullname: John, Sushil
– sequence: 78
  givenname: Shiny
  surname: Kaki
  fullname: Kaki, Shiny
– sequence: 79
  givenname: Priyadarshani
  surname: Karunakaran
  fullname: Karunakaran, Priyadarshani
– sequence: 85
  givenname: Sophy
  surname: Raju
  fullname: Raju, Sophy
– sequence: 87
  givenname: Rakhi
  surname: Ramadas
  fullname: Ramadas, Rakhi
– sequence: 88
  givenname: Karthikeyan
  surname: Ramanujam
  fullname: Ramanujam, Karthikeyan
– sequence: 90
  givenname: Reeba
  surname: Roshan
  fullname: Roshan, Reeba
– sequence: 94
  givenname: William K
  surname: Pan
  fullname: Pan, William K
– sequence: 95
  givenname: Ramya
  surname: Ambikapathi
  fullname: Ambikapathi, Ramya
– sequence: 96
  givenname: J Daniel
  surname: Carreon
  fullname: Carreon, J Daniel
– sequence: 98
  givenname: Christel
  surname: Hoest
  fullname: Hoest, Christel
– sequence: 99
  givenname: Stacey
  surname: Knobler
  fullname: Knobler, Stacey
– sequence: 100
  givenname: Mark A
  surname: Miller
  fullname: Miller, Mark A
– sequence: 101
  givenname: Stephanie
  surname: Psaki
  fullname: Psaki, Stephanie
– sequence: 102
  givenname: Zeba
  surname: Rasmussen
  fullname: Rasmussen, Zeba
– sequence: 103
  givenname: Stephanie A
  surname: Richard
  fullname: Richard, Stephanie A
– sequence: 104
  givenname: Karen H
  surname: Tountas
  fullname: Tountas, Karen H
– sequence: 105
  givenname: Erling
  surname: Svensen
  fullname: Svensen, Erling
– sequence: 106
  givenname: Caroline
  surname: Amour
  fullname: Amour, Caroline
– sequence: 112
  givenname: Rebecca
  surname: Dillingham
  fullname: Dillingham, Rebecca
– sequence: 113
  givenname: William A
  surname: Petri
  fullname: Petri, William A
– sequence: 114
  givenname: Rebecca
  surname: Scharf
  fullname: Scharf, Rebecca
– sequence: 115
  givenname: AM Shamsir
  surname: Ahmed
  fullname: Ahmed, AM Shamsir
– sequence: 116
  givenname: Md Ashraful
  surname: Alam
  fullname: Alam, Md Ashraful
– sequence: 121
  givenname: Dinesh
  surname: Mondal
  fullname: Mondal, Dinesh
– sequence: 122
  givenname: Baitun
  surname: Nahar
  fullname: Nahar, Baitun
– sequence: 126
  givenname: Rita
  surname: Shrestha
  fullname: Shrestha, Rita
– sequence: 127
  givenname: Manjeswori
  surname: Ulak
  fullname: Ulak, Manjeswori
– sequence: 128
  givenname: Aubrey
  surname: Bauck
  fullname: Bauck, Aubrey
– sequence: 129
  givenname: Robert
  surname: Black
  fullname: Black, Robert
– sequence: 130
  givenname: Laura
  surname: Caulfield
  fullname: Caulfield, Laura
– sequence: 136
  givenname: A Catharine
  surname: Ross
  fullname: Ross, A Catharine
– sequence: 137
  givenname: Barbara
  surname: Schaefer
  fullname: Schaefer, Barbara
– sequence: 138
  givenname: Suzanne
  surname: Simons
  fullname: Simons, Suzanne
– sequence: 139
  givenname: Laura
  surname: Pendergast
  fullname: Pendergast, Laura
– sequence: 140
  givenname: Cláudia B
  surname: Abreu
  fullname: Abreu, Cláudia B
– sequence: 144
  givenname: Álvaro M
  surname: Leite
  fullname: Leite, Álvaro M
– sequence: 146
  givenname: Ila F
  surname: Lima
  fullname: Lima, Ila F
– sequence: 147
  givenname: Bruna LL
  surname: Maciel
  fullname: Maciel, Bruna LL
– sequence: 148
  givenname: Pedro HQS
  surname: Medeiros
  fullname: Medeiros, Pedro HQS
– sequence: 151
  givenname: Reinaldo B
  surname: Oriá
  fullname: Oriá, Reinaldo B
– sequence: 152
  givenname: Josiane
  surname: Quetz
  fullname: Quetz, Josiane
– sequence: 153
  givenname: Alberto M
  surname: Soares
  fullname: Soares, Alberto M
– sequence: 155
  givenname: Crystal L
  surname: Patil
  fullname: Patil, Crystal L
– sequence: 156
  givenname: Cloupas
  surname: Mahopo
  fullname: Mahopo, Cloupas
– sequence: 157
  givenname: Angelina
  surname: Maphula
  fullname: Maphula, Angelina
– sequence: 158
  givenname: Emanuel
  surname: Nyathi
  fullname: Nyathi, Emanuel
BackLink https://www.ncbi.nlm.nih.gov/pubmed/30287127$$D View this record in MEDLINE/PubMed
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ContentType Journal Article
Contributor Ahmed, Imran
Vasquez, Angel Orbe
Maciel, Bruna Ll
Bauck, Aubrey
Ross, A Catharine
Petri, William A
Sundaram, Shanmuga
Svensen, Erling
Medeiros, Pedro Hqs
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Copyright © 2018 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.
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30287126 - Lancet Glob Health. 2018 Dec;6(12):e1258-e1259
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Snippet Optimum management of childhood diarrhoea in low-resource settings has been hampered by insufficient data on aetiology, burden, and associated clinical...
Background: Optimum management of childhood diarrhoea in low-resource settings has been hampered by insufficient data on aetiology, burden, and associated...
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SubjectTerms Asia, Western - epidemiology
Brazil - epidemiology
Child, Preschool
Cohort Studies
Diarrhea - epidemiology
Diarrhea - etiology
Health Resources - supply & distribution
Humans
Incidence
Infant
Infant, Newborn
Molecular Diagnostic Techniques
Peru - epidemiology
Real-Time Polymerase Chain Reaction
South Africa - epidemiology
Tanzania - epidemiology
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Title Use of quantitative molecular diagnostic methods to assess the aetiology, burden, and clinical characteristics of diarrhoea in children in low-resource settings: a reanalysis of the MAL-ED cohort study
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