Assessment of the PROBIT approach for estimating the prevalence of global, moderate and severe acute malnutrition from population surveys
Prevalence of acute malnutrition is classically estimated by the proportion of children meeting a case definition in a representative population sample. In 1995 the WHO proposed the PROBIT method, based on converting parameters of a normally distributed variable to cumulative probability, as an alte...
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| Veröffentlicht in: | Public health nutrition 2013-05, Vol.16 (5), p.858-863 |
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| creator | Dale, Nancy M Myatt, Mark Prudhon, Claudine Briend, André |
| description | Prevalence of acute malnutrition is classically estimated by the proportion of children meeting a case definition in a representative population sample. In 1995 the WHO proposed the PROBIT method, based on converting parameters of a normally distributed variable to cumulative probability, as an alternative method requiring a smaller sample size. The present study compares classical and PROBIT methods for estimating the prevalence of global, moderate and severe acute malnutrition (GAM, MAM and SAM) defined by weight-for-height Z-score (WHZ) or mid-upper arm circumference (MUAC).
Bias and precision of classical and PROBIT methods were compared by simulating a total of 1·26 million surveys generated from 560 nutrition surveys.
Data used for simulation were derived from nutritional surveys of children aged 6-59 months carried out in thirty-one countries around the world.
Data of 459 036 children aged 6-59 months from representative samples were used to generate simulated populations.
The PROBIT method provided an estimate of GAM, MAM and SAM using WHZ or MUAC proportional to the true prevalence with a small systematic overestimation. The PROBIT method was more precise than the classical method for estimating the prevalence for GAM, MAM and SAM by WHZ or MUAC for small sample sizes (i.e. n |
| doi_str_mv | 10.1017/S1368980012003345 |
| format | Article |
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Bias and precision of classical and PROBIT methods were compared by simulating a total of 1·26 million surveys generated from 560 nutrition surveys.
Data used for simulation were derived from nutritional surveys of children aged 6-59 months carried out in thirty-one countries around the world.
Data of 459 036 children aged 6-59 months from representative samples were used to generate simulated populations.
The PROBIT method provided an estimate of GAM, MAM and SAM using WHZ or MUAC proportional to the true prevalence with a small systematic overestimation. The PROBIT method was more precise than the classical method for estimating the prevalence for GAM, MAM and SAM by WHZ or MUAC for small sample sizes (i.e. n<150 for SAM and GAM; n<300 for MAM), but lost this advantage when sample sizes increased.
The classical method is preferred for estimating acute malnutrition prevalence from large sample surveys. The PROBIT method may be useful in sentinel-site surveillance systems with small sample sizes.</description><identifier>ISSN: 1368-9800</identifier><identifier>EISSN: 1475-2727</identifier><identifier>DOI: 10.1017/S1368980012003345</identifier><identifier>PMID: 23174145</identifier><language>eng</language><publisher>Cambridge, UK: Cambridge University Press</publisher><subject>Acute Disease ; Arm circumference ; Assessment and methodology ; Biological and medical sciences ; Body Height ; Body Weight ; Child, Preschool ; Datasets ; Edema ; Global Health ; Humans ; Infant ; Malnutrition ; Malnutrition - diagnosis ; Malnutrition - epidemiology ; Medical sciences ; Metabolic diseases ; Methods ; Miscellaneous ; Nutrition ; Nutrition Surveys ; Other nutritional diseases (malnutrition, nutritional and vitamin deficiencies...) ; Prevalence ; Public health. Hygiene ; Public health. Hygiene-occupational medicine ; Sample size ; Standard scores</subject><ispartof>Public health nutrition, 2013-05, Vol.16 (5), p.858-863</ispartof><rights>Copyright © The Authors 2012</rights><rights>2014 INIST-CNRS</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c479t-54378d4fb756d97d0dbd3ac124b7533ea8ae8ab2ff5016ee2e0adbffede355003</citedby><cites>FETCH-LOGICAL-c479t-54378d4fb756d97d0dbd3ac124b7533ea8ae8ab2ff5016ee2e0adbffede355003</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,27901,27902</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=27301287$$DView record in Pascal Francis$$Hfree_for_read</backlink><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/23174145$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Dale, Nancy M</creatorcontrib><creatorcontrib>Myatt, Mark</creatorcontrib><creatorcontrib>Prudhon, Claudine</creatorcontrib><creatorcontrib>Briend, André</creatorcontrib><title>Assessment of the PROBIT approach for estimating the prevalence of global, moderate and severe acute malnutrition from population surveys</title><title>Public health nutrition</title><addtitle>Public Health Nutr</addtitle><description>Prevalence of acute malnutrition is classically estimated by the proportion of children meeting a case definition in a representative population sample. In 1995 the WHO proposed the PROBIT method, based on converting parameters of a normally distributed variable to cumulative probability, as an alternative method requiring a smaller sample size. The present study compares classical and PROBIT methods for estimating the prevalence of global, moderate and severe acute malnutrition (GAM, MAM and SAM) defined by weight-for-height Z-score (WHZ) or mid-upper arm circumference (MUAC).
Bias and precision of classical and PROBIT methods were compared by simulating a total of 1·26 million surveys generated from 560 nutrition surveys.
Data used for simulation were derived from nutritional surveys of children aged 6-59 months carried out in thirty-one countries around the world.
Data of 459 036 children aged 6-59 months from representative samples were used to generate simulated populations.
The PROBIT method provided an estimate of GAM, MAM and SAM using WHZ or MUAC proportional to the true prevalence with a small systematic overestimation. The PROBIT method was more precise than the classical method for estimating the prevalence for GAM, MAM and SAM by WHZ or MUAC for small sample sizes (i.e. n<150 for SAM and GAM; n<300 for MAM), but lost this advantage when sample sizes increased.
The classical method is preferred for estimating acute malnutrition prevalence from large sample surveys. The PROBIT method may be useful in sentinel-site surveillance systems with small sample sizes.</description><subject>Acute Disease</subject><subject>Arm circumference</subject><subject>Assessment and methodology</subject><subject>Biological and medical sciences</subject><subject>Body Height</subject><subject>Body Weight</subject><subject>Child, Preschool</subject><subject>Datasets</subject><subject>Edema</subject><subject>Global Health</subject><subject>Humans</subject><subject>Infant</subject><subject>Malnutrition</subject><subject>Malnutrition - diagnosis</subject><subject>Malnutrition - epidemiology</subject><subject>Medical sciences</subject><subject>Metabolic diseases</subject><subject>Methods</subject><subject>Miscellaneous</subject><subject>Nutrition</subject><subject>Nutrition Surveys</subject><subject>Other nutritional diseases (malnutrition, nutritional and vitamin deficiencies...)</subject><subject>Prevalence</subject><subject>Public health. Hygiene</subject><subject>Public health. Hygiene-occupational medicine</subject><subject>Sample size</subject><subject>Standard scores</subject><issn>1368-9800</issn><issn>1475-2727</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2013</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>BENPR</sourceid><recordid>eNqNkcFu1DAQhi0EoqXwAFyQJYTEoSl2bK-TY1sBrVSpCMo5msTjbSrHDnayUh-Bt8bZLi0CVeJke-b7__HMEPKasyPOuP7wjYtVVVeM8ZIxIaR6Qva51Koodamf5ntOF0t-j7xI6YYxprTWz8leKbiWXKp98vM4JUxpQD_RYOl0jfTL18uT8ysK4xgDdNfUhkgxTf0AU-_XW2SMuAGHvsNFtHahBXdIh2AwwoQUvKEJNxjztZtzYADn5yn2Ux88tTEMdAzj7GD7TnPc4G16SZ5ZcAlf7c4D8v3Tx6vTs-Li8vP56fFF0UldT4WSQldG2larlam1YaY1AjpeyhwRAqECrKAtrVWMrxBLZGBaa9GgUCqP6YC8v_PN7f2Yc2PN0KcOnQOPYU4NF3Vd8zwq9R9oueIyCxbXt3-hN2GOPjeyULJWglU6U_yO6mJIKaJtxpjnGm8bzpplpc0_K82aNzvnuR3Q3Ct-7zAD73YApA6cjeC7Pj1wWmSzbXGxKw5DG3uzxj_--Gj5XzFSuYg</recordid><startdate>20130501</startdate><enddate>20130501</enddate><creator>Dale, Nancy M</creator><creator>Myatt, Mark</creator><creator>Prudhon, Claudine</creator><creator>Briend, André</creator><general>Cambridge University Press</general><scope>IQODW</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7QP</scope><scope>7RQ</scope><scope>7RV</scope><scope>7T2</scope><scope>7X2</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8C1</scope><scope>8FE</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>ATCPS</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>KB0</scope><scope>M0K</scope><scope>M0S</scope><scope>M1P</scope><scope>NAPCQ</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>Q9U</scope><scope>7X8</scope><scope>7U9</scope><scope>H94</scope></search><sort><creationdate>20130501</creationdate><title>Assessment of the PROBIT approach for estimating the prevalence of global, moderate and severe acute malnutrition from population surveys</title><author>Dale, Nancy M ; Myatt, Mark ; Prudhon, Claudine ; Briend, André</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c479t-54378d4fb756d97d0dbd3ac124b7533ea8ae8ab2ff5016ee2e0adbffede355003</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2013</creationdate><topic>Acute Disease</topic><topic>Arm circumference</topic><topic>Assessment and methodology</topic><topic>Biological and medical sciences</topic><topic>Body Height</topic><topic>Body Weight</topic><topic>Child, Preschool</topic><topic>Datasets</topic><topic>Edema</topic><topic>Global Health</topic><topic>Humans</topic><topic>Infant</topic><topic>Malnutrition</topic><topic>Malnutrition - diagnosis</topic><topic>Malnutrition - epidemiology</topic><topic>Medical sciences</topic><topic>Metabolic diseases</topic><topic>Methods</topic><topic>Miscellaneous</topic><topic>Nutrition</topic><topic>Nutrition Surveys</topic><topic>Other nutritional diseases (malnutrition, nutritional and vitamin deficiencies...)</topic><topic>Prevalence</topic><topic>Public health. 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Hygiene-occupational medicine</topic><topic>Sample size</topic><topic>Standard scores</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Dale, Nancy M</creatorcontrib><creatorcontrib>Myatt, Mark</creatorcontrib><creatorcontrib>Prudhon, Claudine</creatorcontrib><creatorcontrib>Briend, André</creatorcontrib><collection>Pascal-Francis</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Calcium & Calcified Tissue Abstracts</collection><collection>Career & Technical Education Database</collection><collection>Proquest Nursing & Allied Health Source</collection><collection>Health and Safety Science Abstracts (Full archive)</collection><collection>Agricultural Science Collection</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Public Health Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central UK/Ireland</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central</collection><collection>Natural Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>Agricultural Science Database</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Nursing & Allied Health Premium</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central Basic</collection><collection>MEDLINE - Academic</collection><collection>Virology and AIDS Abstracts</collection><collection>AIDS and Cancer Research Abstracts</collection><jtitle>Public health nutrition</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Dale, Nancy M</au><au>Myatt, Mark</au><au>Prudhon, Claudine</au><au>Briend, André</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Assessment of the PROBIT approach for estimating the prevalence of global, moderate and severe acute malnutrition from population surveys</atitle><jtitle>Public health nutrition</jtitle><addtitle>Public Health Nutr</addtitle><date>2013-05-01</date><risdate>2013</risdate><volume>16</volume><issue>5</issue><spage>858</spage><epage>863</epage><pages>858-863</pages><issn>1368-9800</issn><eissn>1475-2727</eissn><abstract>Prevalence of acute malnutrition is classically estimated by the proportion of children meeting a case definition in a representative population sample. In 1995 the WHO proposed the PROBIT method, based on converting parameters of a normally distributed variable to cumulative probability, as an alternative method requiring a smaller sample size. The present study compares classical and PROBIT methods for estimating the prevalence of global, moderate and severe acute malnutrition (GAM, MAM and SAM) defined by weight-for-height Z-score (WHZ) or mid-upper arm circumference (MUAC).
Bias and precision of classical and PROBIT methods were compared by simulating a total of 1·26 million surveys generated from 560 nutrition surveys.
Data used for simulation were derived from nutritional surveys of children aged 6-59 months carried out in thirty-one countries around the world.
Data of 459 036 children aged 6-59 months from representative samples were used to generate simulated populations.
The PROBIT method provided an estimate of GAM, MAM and SAM using WHZ or MUAC proportional to the true prevalence with a small systematic overestimation. The PROBIT method was more precise than the classical method for estimating the prevalence for GAM, MAM and SAM by WHZ or MUAC for small sample sizes (i.e. n<150 for SAM and GAM; n<300 for MAM), but lost this advantage when sample sizes increased.
The classical method is preferred for estimating acute malnutrition prevalence from large sample surveys. The PROBIT method may be useful in sentinel-site surveillance systems with small sample sizes.</abstract><cop>Cambridge, UK</cop><pub>Cambridge University Press</pub><pmid>23174145</pmid><doi>10.1017/S1368980012003345</doi><tpages>6</tpages><oa>free_for_read</oa></addata></record> |
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| source | MEDLINE; EZB-FREE-00999 freely available EZB journals; PubMed Central |
| subjects | Acute Disease Arm circumference Assessment and methodology Biological and medical sciences Body Height Body Weight Child, Preschool Datasets Edema Global Health Humans Infant Malnutrition Malnutrition - diagnosis Malnutrition - epidemiology Medical sciences Metabolic diseases Methods Miscellaneous Nutrition Nutrition Surveys Other nutritional diseases (malnutrition, nutritional and vitamin deficiencies...) Prevalence Public health. Hygiene Public health. Hygiene-occupational medicine Sample size Standard scores |
| title | Assessment of the PROBIT approach for estimating the prevalence of global, moderate and severe acute malnutrition from population surveys |
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