Anthropometric data quality assessment in multisurvey studies of child growth
Population-based surveys collect crucial data on anthropometric measures to track trends in stunting [height-for-age z score (HAZ) < −2SD] and wasting [weight-for-height z score (WHZ) < −2SD] prevalence among young children globally. However, the quality of the anthropometric data varies betwe...
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| Veröffentlicht in: | The American journal of clinical nutrition 2020-09, Vol.112 (Supplement_2), p.806S-815S |
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| creator | Perumal, Nandita Namaste, Sorrel Qamar, Huma Aimone, Ashley Bassani, Diego G Roth, Daniel E |
| description | Population-based surveys collect crucial data on anthropometric measures to track trends in stunting [height-for-age z score (HAZ) < −2SD] and wasting [weight-for-height z score (WHZ) < −2SD] prevalence among young children globally. However, the quality of the anthropometric data varies between surveys, which may affect population-based estimates of malnutrition.
We aimed to develop composite indices of anthropometric data quality for use in multisurvey analysis of child health and nutritional status.
We used anthropometric data for children 0–59 mo of age from all publicly available Demographic and Health Surveys (DHS) from 2000 onwards. We derived 6 indicators of anthropometric data quality at the survey level, including 1) date of birth completeness, 2) anthropometric measure completeness, 3) digit preference for height and age, 4) difference in mean HAZ by month of birth, 5) proportion of biologically implausible values, and 6) dispersion of HAZ and WHZ distribution. Principal component factor analysis was used to generate a composite index of anthropometric data quality for HAZ and WHZ separately. Surveys were ranked from the highest (best) to the lowest (worst) index values in anthropometric quality across countries and over time.
Of the 145 DHS included, the majority (83 of 145; 57%) were conducted in Sub-Saharan Africa. Surveys were ranked from highest to lowest anthropometric data quality relative to other surveys using the composite index for HAZ. Although slightly higher values in recent DHS suggest potential improvements in anthropometric data quality over time, there continues to be substantial heterogeneity in the quality of anthropometric data across surveys. Results were similar for the WHZ data quality index.
A composite index of anthropometric data quality using a parsimonious set of individual indicators can effectively discriminate among surveys with excellent and poor data quality. Such indices can be used to account for variations in anthropometric data quality in multisurvey epidemiologic analyses of child health. |
| doi_str_mv | 10.1093/ajcn/nqaa162 |
| format | Article |
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We aimed to develop composite indices of anthropometric data quality for use in multisurvey analysis of child health and nutritional status.
We used anthropometric data for children 0–59 mo of age from all publicly available Demographic and Health Surveys (DHS) from 2000 onwards. We derived 6 indicators of anthropometric data quality at the survey level, including 1) date of birth completeness, 2) anthropometric measure completeness, 3) digit preference for height and age, 4) difference in mean HAZ by month of birth, 5) proportion of biologically implausible values, and 6) dispersion of HAZ and WHZ distribution. Principal component factor analysis was used to generate a composite index of anthropometric data quality for HAZ and WHZ separately. Surveys were ranked from the highest (best) to the lowest (worst) index values in anthropometric quality across countries and over time.
Of the 145 DHS included, the majority (83 of 145; 57%) were conducted in Sub-Saharan Africa. Surveys were ranked from highest to lowest anthropometric data quality relative to other surveys using the composite index for HAZ. Although slightly higher values in recent DHS suggest potential improvements in anthropometric data quality over time, there continues to be substantial heterogeneity in the quality of anthropometric data across surveys. Results were similar for the WHZ data quality index.
A composite index of anthropometric data quality using a parsimonious set of individual indicators can effectively discriminate among surveys with excellent and poor data quality. Such indices can be used to account for variations in anthropometric data quality in multisurvey epidemiologic analyses of child health.</description><identifier>ISSN: 0002-9165</identifier><identifier>EISSN: 1938-3207</identifier><identifier>DOI: 10.1093/ajcn/nqaa162</identifier><identifier>PMID: 32672330</identifier><language>eng</language><publisher>United States: Elsevier Inc</publisher><subject>Anthropometry ; Body Height ; Body Weight ; Child Development ; Child, Preschool ; Data Accuracy ; Data Mining - standards ; data quality ; Demographic and Health Surveys ; Female ; Growth Disorders - epidemiology ; Growth Disorders - physiopathology ; height-for-age z score ; Humans ; Infant ; Male ; Nutritional Status ; stunting ; Supplements and Symposia ; wasting ; weight-for-height z score</subject><ispartof>The American journal of clinical nutrition, 2020-09, Vol.112 (Supplement_2), p.806S-815S</ispartof><rights>2020 American Society for Nutrition.</rights><rights>Copyright © The Author(s) on behalf of the American Society for Nutrition 2020. 2020</rights><rights>Copyright © The Author(s) on behalf of the American Society for Nutrition 2020.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c463t-91cfc10abb809c807e2b6210d3b8c4aa295465b0dc5c86a31a21c6b759f1f4923</citedby><cites>FETCH-LOGICAL-c463t-91cfc10abb809c807e2b6210d3b8c4aa295465b0dc5c86a31a21c6b759f1f4923</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>230,314,780,784,885,27924,27925</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/32672330$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Perumal, Nandita</creatorcontrib><creatorcontrib>Namaste, Sorrel</creatorcontrib><creatorcontrib>Qamar, Huma</creatorcontrib><creatorcontrib>Aimone, Ashley</creatorcontrib><creatorcontrib>Bassani, Diego G</creatorcontrib><creatorcontrib>Roth, Daniel E</creatorcontrib><title>Anthropometric data quality assessment in multisurvey studies of child growth</title><title>The American journal of clinical nutrition</title><addtitle>Am J Clin Nutr</addtitle><description>Population-based surveys collect crucial data on anthropometric measures to track trends in stunting [height-for-age z score (HAZ) < −2SD] and wasting [weight-for-height z score (WHZ) < −2SD] prevalence among young children globally. However, the quality of the anthropometric data varies between surveys, which may affect population-based estimates of malnutrition.
We aimed to develop composite indices of anthropometric data quality for use in multisurvey analysis of child health and nutritional status.
We used anthropometric data for children 0–59 mo of age from all publicly available Demographic and Health Surveys (DHS) from 2000 onwards. We derived 6 indicators of anthropometric data quality at the survey level, including 1) date of birth completeness, 2) anthropometric measure completeness, 3) digit preference for height and age, 4) difference in mean HAZ by month of birth, 5) proportion of biologically implausible values, and 6) dispersion of HAZ and WHZ distribution. Principal component factor analysis was used to generate a composite index of anthropometric data quality for HAZ and WHZ separately. Surveys were ranked from the highest (best) to the lowest (worst) index values in anthropometric quality across countries and over time.
Of the 145 DHS included, the majority (83 of 145; 57%) were conducted in Sub-Saharan Africa. Surveys were ranked from highest to lowest anthropometric data quality relative to other surveys using the composite index for HAZ. Although slightly higher values in recent DHS suggest potential improvements in anthropometric data quality over time, there continues to be substantial heterogeneity in the quality of anthropometric data across surveys. Results were similar for the WHZ data quality index.
A composite index of anthropometric data quality using a parsimonious set of individual indicators can effectively discriminate among surveys with excellent and poor data quality. Such indices can be used to account for variations in anthropometric data quality in multisurvey epidemiologic analyses of child health.</description><subject>Anthropometry</subject><subject>Body Height</subject><subject>Body Weight</subject><subject>Child Development</subject><subject>Child, Preschool</subject><subject>Data Accuracy</subject><subject>Data Mining - standards</subject><subject>data quality</subject><subject>Demographic and Health Surveys</subject><subject>Female</subject><subject>Growth Disorders - epidemiology</subject><subject>Growth Disorders - physiopathology</subject><subject>height-for-age z score</subject><subject>Humans</subject><subject>Infant</subject><subject>Male</subject><subject>Nutritional Status</subject><subject>stunting</subject><subject>Supplements and Symposia</subject><subject>wasting</subject><subject>weight-for-height z score</subject><issn>0002-9165</issn><issn>1938-3207</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>TOX</sourceid><sourceid>EIF</sourceid><recordid>eNqFkc1r20AQxZeQ0rhpbzmHvaWHutkvraRLwYR-QUov7XkZjUbxBklr765c_N9Xxm5ooNDTHOY3bx7vMXYlxXspan0LjzjejlsAadUZW8haV0utRHnOFkIItaylLS7Yq5QehZDKVPYlu9DKlkprsWDfVmNex7AJA-XokbeQgW8n6H3ec0iJUhpozNyPfJj67NMUd7TnKU-tp8RDx3Ht-5Y_xPArr1-zFx30id6c5iX7-enjj7svy_vvn7_ere6XaKzOsyXsUApomkrUWImSVGOVFK1uKjQAqi6MLRrRYoGVBS1BSbRNWdSd7Eyt9CX7cNTdTM1ALc4OI_RuE_0Ace8CePd8M_q1ewg7V5qqNKqaBd6eBGLYTpSyG3xC6nsYKUzJKaOMMYU0B_TdEcUYUorUPb2Rwh0acIcG3KmBGb_-29oT_CfyGbg5AmHa_E_KHkmao9x5ii6hpxGp9ZEwuzb4fx_-Bsp8pkA</recordid><startdate>20200901</startdate><enddate>20200901</enddate><creator>Perumal, Nandita</creator><creator>Namaste, Sorrel</creator><creator>Qamar, Huma</creator><creator>Aimone, Ashley</creator><creator>Bassani, Diego G</creator><creator>Roth, Daniel E</creator><general>Elsevier Inc</general><general>Oxford University Press</general><scope>6I.</scope><scope>AAFTH</scope><scope>TOX</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>7X8</scope><scope>5PM</scope></search><sort><creationdate>20200901</creationdate><title>Anthropometric data quality assessment in multisurvey studies of child growth</title><author>Perumal, Nandita ; Namaste, Sorrel ; Qamar, Huma ; Aimone, Ashley ; Bassani, Diego G ; Roth, Daniel E</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c463t-91cfc10abb809c807e2b6210d3b8c4aa295465b0dc5c86a31a21c6b759f1f4923</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Anthropometry</topic><topic>Body Height</topic><topic>Body Weight</topic><topic>Child Development</topic><topic>Child, Preschool</topic><topic>Data Accuracy</topic><topic>Data Mining - standards</topic><topic>data quality</topic><topic>Demographic and Health Surveys</topic><topic>Female</topic><topic>Growth Disorders - epidemiology</topic><topic>Growth Disorders - physiopathology</topic><topic>height-for-age z score</topic><topic>Humans</topic><topic>Infant</topic><topic>Male</topic><topic>Nutritional Status</topic><topic>stunting</topic><topic>Supplements and Symposia</topic><topic>wasting</topic><topic>weight-for-height z score</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Perumal, Nandita</creatorcontrib><creatorcontrib>Namaste, Sorrel</creatorcontrib><creatorcontrib>Qamar, Huma</creatorcontrib><creatorcontrib>Aimone, Ashley</creatorcontrib><creatorcontrib>Bassani, Diego G</creatorcontrib><creatorcontrib>Roth, Daniel E</creatorcontrib><collection>ScienceDirect Open Access Titles</collection><collection>Elsevier:ScienceDirect:Open Access</collection><collection>Oxford Journals Open Access Collection</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>The American journal of clinical nutrition</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Perumal, Nandita</au><au>Namaste, Sorrel</au><au>Qamar, Huma</au><au>Aimone, Ashley</au><au>Bassani, Diego G</au><au>Roth, Daniel E</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Anthropometric data quality assessment in multisurvey studies of child growth</atitle><jtitle>The American journal of clinical nutrition</jtitle><addtitle>Am J Clin Nutr</addtitle><date>2020-09-01</date><risdate>2020</risdate><volume>112</volume><issue>Supplement_2</issue><spage>806S</spage><epage>815S</epage><pages>806S-815S</pages><issn>0002-9165</issn><eissn>1938-3207</eissn><abstract>Population-based surveys collect crucial data on anthropometric measures to track trends in stunting [height-for-age z score (HAZ) < −2SD] and wasting [weight-for-height z score (WHZ) < −2SD] prevalence among young children globally. However, the quality of the anthropometric data varies between surveys, which may affect population-based estimates of malnutrition.
We aimed to develop composite indices of anthropometric data quality for use in multisurvey analysis of child health and nutritional status.
We used anthropometric data for children 0–59 mo of age from all publicly available Demographic and Health Surveys (DHS) from 2000 onwards. We derived 6 indicators of anthropometric data quality at the survey level, including 1) date of birth completeness, 2) anthropometric measure completeness, 3) digit preference for height and age, 4) difference in mean HAZ by month of birth, 5) proportion of biologically implausible values, and 6) dispersion of HAZ and WHZ distribution. Principal component factor analysis was used to generate a composite index of anthropometric data quality for HAZ and WHZ separately. Surveys were ranked from the highest (best) to the lowest (worst) index values in anthropometric quality across countries and over time.
Of the 145 DHS included, the majority (83 of 145; 57%) were conducted in Sub-Saharan Africa. Surveys were ranked from highest to lowest anthropometric data quality relative to other surveys using the composite index for HAZ. Although slightly higher values in recent DHS suggest potential improvements in anthropometric data quality over time, there continues to be substantial heterogeneity in the quality of anthropometric data across surveys. Results were similar for the WHZ data quality index.
A composite index of anthropometric data quality using a parsimonious set of individual indicators can effectively discriminate among surveys with excellent and poor data quality. Such indices can be used to account for variations in anthropometric data quality in multisurvey epidemiologic analyses of child health.</abstract><cop>United States</cop><pub>Elsevier Inc</pub><pmid>32672330</pmid><doi>10.1093/ajcn/nqaa162</doi><oa>free_for_read</oa></addata></record> |
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| subjects | Anthropometry Body Height Body Weight Child Development Child, Preschool Data Accuracy Data Mining - standards data quality Demographic and Health Surveys Female Growth Disorders - epidemiology Growth Disorders - physiopathology height-for-age z score Humans Infant Male Nutritional Status stunting Supplements and Symposia wasting weight-for-height z score |
| title | Anthropometric data quality assessment in multisurvey studies of child growth |
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