Beyond personal factors: Multilevel determinants of childhood stunting in Indonesia
Stunting is still a major public health problem in low- and middle-income countries, including Indonesia. Previous studies have reported the complexities associated with understanding the determinants of stunting. This study aimed to examine the household-, subdistrict- and province-level determinan...
Gespeichert in:
| Veröffentlicht in: | PloS one 2021-11, Vol.16 (11), p.e0260265 |
|---|---|
| Hauptverfasser: | , , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | |
|---|---|
| container_issue | 11 |
| container_start_page | e0260265 |
| container_title | PloS one |
| container_volume | 16 |
| creator | Mulyaningsih, Tri Mohanty, Itismita Widyaningsih, Vitri Gebremedhin, Tesfaye Alemayehu Miranti, Riyana Wiyono, Vincent Hadi |
| description | Stunting is still a major public health problem in low- and middle-income countries, including Indonesia. Previous studies have reported the complexities associated with understanding the determinants of stunting. This study aimed to examine the household-, subdistrict- and province-level determinants of stunting in Indonesia using a multilevel hierarchical mixed effects model.
We analyzed data for 8045 children taken from the 2007 and 2014 waves of the Indonesian Family and Life Surveys (IFLS). We included individual-, family-/household- and community-level variables in the analyses. A multilevel mixed effects model was employed to take into account the hierarchical structure of the data. Moreover, the model captured the effect of unobserved household-, subdistrict- and province-level characteristics on the probability of children being stunted.
Our findings showed that the odds of childhood stunting vary significantly not only by individual child- and household-level characteristics but also by province- and subdistrict-level characteristics. Among the child-level covariates included in our model, dietary habits, neonatal weight, a history of infection, and sex significantly affected the risk of stunting. Household wealth status and parental education are significant household-level covariates associated with a higher risk of stunting. Finally, the risk of stunting is higher for children living in communities without access to water, sanitation and hygiene.
Stunting is associated with not only child-level characteristics but also family- and community-level characteristics. Hence, interventions to reduce stunting should also take into account family and community characteristics to achieve effective outcomes. |
| doi_str_mv | 10.1371/journal.pone.0260265 |
| format | Article |
| fullrecord | <record><control><sourceid>gale_plos_</sourceid><recordid>TN_cdi_plos_journals_2599573315</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><galeid>A683178924</galeid><doaj_id>oai_doaj_org_article_43f8fc4a742a4cf28c06fff0c0d8dbe3</doaj_id><sourcerecordid>A683178924</sourcerecordid><originalsourceid>FETCH-LOGICAL-c743t-2bdb51eb78bfec21f61f02cdd9ab009a839f2910c746e0150f35e77d89ed04fa3</originalsourceid><addsrcrecordid>eNqNkmtrFDEUhgdR7EX_geiAUPDDrrnMJdMPQi1eFioFq34NmeRkN0s2WZNMsf_erDstO6AgE5iQPO-bw3tOUbzAaI5pi9-u_RCcsPOtdzBHpMmrflQc446SWUMQfXywPypOYlwjVFPWNE-LI1q1Xcs6clzcvIc771S5hRB9tiu1kMmHeF5-GWwyFm7BlgoShI1xwqVYel3KlbFq5b0qYxpcMm5ZGlcunMqlRCOeFU-0sBGej__T4vvHD98uP8-urj8tLi-uZrKtaJqRXvU1hr5lvQZJsG6wRkQq1YkeoU4w2mnSYZTpBhCukaY1tK1iHShUaUFPi1d73631kY95RE7qrqtbSnGdicWeUF6s-TaYjQh33AvD_xz4sOQiJCMt8IpqpmUl2oqISmrCJGq01kgixVQPNHu9G18b-g0oCS4FYSem0xtnVnzpbzlrUEUxywavR4Pgfw4Q0z9KHqmlyFUZp302kxsTJb9oGMW7tlWZmv-Fyp-CjZG5DTq3bip4MxFkJsGvtBRDjHxx8_X_2esfU_bsgF2BsGkVvR2S8S5OwWoPyuBjDKAfksOI7wb6Pg2-G2g-DnSWvTxM_UF0P8H0Nyox8n4</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2599573315</pqid></control><display><type>article</type><title>Beyond personal factors: Multilevel determinants of childhood stunting in Indonesia</title><source>MEDLINE</source><source>DOAJ Directory of Open Access Journals</source><source>Public Library of Science (PLoS) Journals Open Access</source><source>EZB-FREE-00999 freely available EZB journals</source><source>PubMed Central</source><source>Free Full-Text Journals in Chemistry</source><creator>Mulyaningsih, Tri ; Mohanty, Itismita ; Widyaningsih, Vitri ; Gebremedhin, Tesfaye Alemayehu ; Miranti, Riyana ; Wiyono, Vincent Hadi</creator><contributor>Metwally, Ammal Mokhtar</contributor><creatorcontrib>Mulyaningsih, Tri ; Mohanty, Itismita ; Widyaningsih, Vitri ; Gebremedhin, Tesfaye Alemayehu ; Miranti, Riyana ; Wiyono, Vincent Hadi ; Metwally, Ammal Mokhtar</creatorcontrib><description>Stunting is still a major public health problem in low- and middle-income countries, including Indonesia. Previous studies have reported the complexities associated with understanding the determinants of stunting. This study aimed to examine the household-, subdistrict- and province-level determinants of stunting in Indonesia using a multilevel hierarchical mixed effects model.
We analyzed data for 8045 children taken from the 2007 and 2014 waves of the Indonesian Family and Life Surveys (IFLS). We included individual-, family-/household- and community-level variables in the analyses. A multilevel mixed effects model was employed to take into account the hierarchical structure of the data. Moreover, the model captured the effect of unobserved household-, subdistrict- and province-level characteristics on the probability of children being stunted.
Our findings showed that the odds of childhood stunting vary significantly not only by individual child- and household-level characteristics but also by province- and subdistrict-level characteristics. Among the child-level covariates included in our model, dietary habits, neonatal weight, a history of infection, and sex significantly affected the risk of stunting. Household wealth status and parental education are significant household-level covariates associated with a higher risk of stunting. Finally, the risk of stunting is higher for children living in communities without access to water, sanitation and hygiene.
Stunting is associated with not only child-level characteristics but also family- and community-level characteristics. Hence, interventions to reduce stunting should also take into account family and community characteristics to achieve effective outcomes.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0260265</identifier><identifier>PMID: 34797892</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Biology and Life Sciences ; Child ; Childhood ; Children ; Children & youth ; Demographic aspects ; Diet ; Earth Sciences ; Education ; Educational Status ; Families & family life ; Family ; Family Characteristics ; Female ; Growth Disorders - epidemiology ; Growth Disorders - etiology ; Health aspects ; Health care access ; Health risks ; Households ; Humans ; Hygiene ; Income - statistics & numerical data ; Indonesia - epidemiology ; Low income groups ; Male ; Malnutrition ; Medicine and Health Sciences ; Modelling ; Multilevel ; Neonates ; Parents ; People and Places ; Politics ; Provinces ; Public health ; Public Health - statistics & numerical data ; Risk ; Risk factors ; Sanitation ; Sanitation - statistics & numerical data ; Socioeconomic Factors ; Socioeconomics ; Structural hierarchy ; Supervision</subject><ispartof>PloS one, 2021-11, Vol.16 (11), p.e0260265</ispartof><rights>COPYRIGHT 2021 Public Library of Science</rights><rights>2021 Mulyaningsih et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2021 Mulyaningsih et al 2021 Mulyaningsih et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c743t-2bdb51eb78bfec21f61f02cdd9ab009a839f2910c746e0150f35e77d89ed04fa3</citedby><cites>FETCH-LOGICAL-c743t-2bdb51eb78bfec21f61f02cdd9ab009a839f2910c746e0150f35e77d89ed04fa3</cites><orcidid>0000-0003-0116-7120 ; 0000-0002-1679-4349</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8604318/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8604318/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,864,885,2102,2928,23866,27924,27925,53791,53793,79600,79601</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/34797892$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Metwally, Ammal Mokhtar</contributor><creatorcontrib>Mulyaningsih, Tri</creatorcontrib><creatorcontrib>Mohanty, Itismita</creatorcontrib><creatorcontrib>Widyaningsih, Vitri</creatorcontrib><creatorcontrib>Gebremedhin, Tesfaye Alemayehu</creatorcontrib><creatorcontrib>Miranti, Riyana</creatorcontrib><creatorcontrib>Wiyono, Vincent Hadi</creatorcontrib><title>Beyond personal factors: Multilevel determinants of childhood stunting in Indonesia</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>Stunting is still a major public health problem in low- and middle-income countries, including Indonesia. Previous studies have reported the complexities associated with understanding the determinants of stunting. This study aimed to examine the household-, subdistrict- and province-level determinants of stunting in Indonesia using a multilevel hierarchical mixed effects model.
We analyzed data for 8045 children taken from the 2007 and 2014 waves of the Indonesian Family and Life Surveys (IFLS). We included individual-, family-/household- and community-level variables in the analyses. A multilevel mixed effects model was employed to take into account the hierarchical structure of the data. Moreover, the model captured the effect of unobserved household-, subdistrict- and province-level characteristics on the probability of children being stunted.
Our findings showed that the odds of childhood stunting vary significantly not only by individual child- and household-level characteristics but also by province- and subdistrict-level characteristics. Among the child-level covariates included in our model, dietary habits, neonatal weight, a history of infection, and sex significantly affected the risk of stunting. Household wealth status and parental education are significant household-level covariates associated with a higher risk of stunting. Finally, the risk of stunting is higher for children living in communities without access to water, sanitation and hygiene.
Stunting is associated with not only child-level characteristics but also family- and community-level characteristics. Hence, interventions to reduce stunting should also take into account family and community characteristics to achieve effective outcomes.</description><subject>Biology and Life Sciences</subject><subject>Child</subject><subject>Childhood</subject><subject>Children</subject><subject>Children & youth</subject><subject>Demographic aspects</subject><subject>Diet</subject><subject>Earth Sciences</subject><subject>Education</subject><subject>Educational Status</subject><subject>Families & family life</subject><subject>Family</subject><subject>Family Characteristics</subject><subject>Female</subject><subject>Growth Disorders - epidemiology</subject><subject>Growth Disorders - etiology</subject><subject>Health aspects</subject><subject>Health care access</subject><subject>Health risks</subject><subject>Households</subject><subject>Humans</subject><subject>Hygiene</subject><subject>Income - statistics & numerical data</subject><subject>Indonesia - epidemiology</subject><subject>Low income groups</subject><subject>Male</subject><subject>Malnutrition</subject><subject>Medicine and Health Sciences</subject><subject>Modelling</subject><subject>Multilevel</subject><subject>Neonates</subject><subject>Parents</subject><subject>People and Places</subject><subject>Politics</subject><subject>Provinces</subject><subject>Public health</subject><subject>Public Health - statistics & numerical data</subject><subject>Risk</subject><subject>Risk factors</subject><subject>Sanitation</subject><subject>Sanitation - statistics & numerical data</subject><subject>Socioeconomic Factors</subject><subject>Socioeconomics</subject><subject>Structural hierarchy</subject><subject>Supervision</subject><issn>1932-6203</issn><issn>1932-6203</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>DOA</sourceid><recordid>eNqNkmtrFDEUhgdR7EX_geiAUPDDrrnMJdMPQi1eFioFq34NmeRkN0s2WZNMsf_erDstO6AgE5iQPO-bw3tOUbzAaI5pi9-u_RCcsPOtdzBHpMmrflQc446SWUMQfXywPypOYlwjVFPWNE-LI1q1Xcs6clzcvIc771S5hRB9tiu1kMmHeF5-GWwyFm7BlgoShI1xwqVYel3KlbFq5b0qYxpcMm5ZGlcunMqlRCOeFU-0sBGej__T4vvHD98uP8-urj8tLi-uZrKtaJqRXvU1hr5lvQZJsG6wRkQq1YkeoU4w2mnSYZTpBhCukaY1tK1iHShUaUFPi1d73631kY95RE7qrqtbSnGdicWeUF6s-TaYjQh33AvD_xz4sOQiJCMt8IpqpmUl2oqISmrCJGq01kgixVQPNHu9G18b-g0oCS4FYSem0xtnVnzpbzlrUEUxywavR4Pgfw4Q0z9KHqmlyFUZp302kxsTJb9oGMW7tlWZmv-Fyp-CjZG5DTq3bip4MxFkJsGvtBRDjHxx8_X_2esfU_bsgF2BsGkVvR2S8S5OwWoPyuBjDKAfksOI7wb6Pg2-G2g-DnSWvTxM_UF0P8H0Nyox8n4</recordid><startdate>20211119</startdate><enddate>20211119</enddate><creator>Mulyaningsih, Tri</creator><creator>Mohanty, Itismita</creator><creator>Widyaningsih, Vitri</creator><creator>Gebremedhin, Tesfaye Alemayehu</creator><creator>Miranti, Riyana</creator><creator>Wiyono, Vincent Hadi</creator><general>Public Library of Science</general><general>Public Library of Science (PLoS)</general><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>IOV</scope><scope>ISR</scope><scope>3V.</scope><scope>7QG</scope><scope>7QL</scope><scope>7QO</scope><scope>7RV</scope><scope>7SN</scope><scope>7SS</scope><scope>7T5</scope><scope>7TG</scope><scope>7TM</scope><scope>7U9</scope><scope>7X2</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8C1</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>D1I</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>H94</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>KB.</scope><scope>KB0</scope><scope>KL.</scope><scope>L6V</scope><scope>LK8</scope><scope>M0K</scope><scope>M0S</scope><scope>M1P</scope><scope>M7N</scope><scope>M7P</scope><scope>M7S</scope><scope>NAPCQ</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PATMY</scope><scope>PDBOC</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>PYCSY</scope><scope>RC3</scope><scope>5PM</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0003-0116-7120</orcidid><orcidid>https://orcid.org/0000-0002-1679-4349</orcidid></search><sort><creationdate>20211119</creationdate><title>Beyond personal factors: Multilevel determinants of childhood stunting in Indonesia</title><author>Mulyaningsih, Tri ; Mohanty, Itismita ; Widyaningsih, Vitri ; Gebremedhin, Tesfaye Alemayehu ; Miranti, Riyana ; Wiyono, Vincent Hadi</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c743t-2bdb51eb78bfec21f61f02cdd9ab009a839f2910c746e0150f35e77d89ed04fa3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Biology and Life Sciences</topic><topic>Child</topic><topic>Childhood</topic><topic>Children</topic><topic>Children & youth</topic><topic>Demographic aspects</topic><topic>Diet</topic><topic>Earth Sciences</topic><topic>Education</topic><topic>Educational Status</topic><topic>Families & family life</topic><topic>Family</topic><topic>Family Characteristics</topic><topic>Female</topic><topic>Growth Disorders - epidemiology</topic><topic>Growth Disorders - etiology</topic><topic>Health aspects</topic><topic>Health care access</topic><topic>Health risks</topic><topic>Households</topic><topic>Humans</topic><topic>Hygiene</topic><topic>Income - statistics & numerical data</topic><topic>Indonesia - epidemiology</topic><topic>Low income groups</topic><topic>Male</topic><topic>Malnutrition</topic><topic>Medicine and Health Sciences</topic><topic>Modelling</topic><topic>Multilevel</topic><topic>Neonates</topic><topic>Parents</topic><topic>People and Places</topic><topic>Politics</topic><topic>Provinces</topic><topic>Public health</topic><topic>Public Health - statistics & numerical data</topic><topic>Risk</topic><topic>Risk factors</topic><topic>Sanitation</topic><topic>Sanitation - statistics & numerical data</topic><topic>Socioeconomic Factors</topic><topic>Socioeconomics</topic><topic>Structural hierarchy</topic><topic>Supervision</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Mulyaningsih, Tri</creatorcontrib><creatorcontrib>Mohanty, Itismita</creatorcontrib><creatorcontrib>Widyaningsih, Vitri</creatorcontrib><creatorcontrib>Gebremedhin, Tesfaye Alemayehu</creatorcontrib><creatorcontrib>Miranti, Riyana</creatorcontrib><creatorcontrib>Wiyono, Vincent Hadi</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Gale In Context: Opposing Viewpoints</collection><collection>Gale In Context: Science</collection><collection>ProQuest Central (Corporate)</collection><collection>Animal Behavior Abstracts</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Biotechnology Research Abstracts</collection><collection>Nursing & Allied Health Database</collection><collection>Ecology Abstracts</collection><collection>Entomology Abstracts (Full archive)</collection><collection>Immunology Abstracts</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Virology and AIDS Abstracts</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>ProQuest Pharma Collection</collection><collection>Public Health Database</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology 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>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Materials Science Collection</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Materials Science Database</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>ProQuest Engineering Collection</collection><collection>ProQuest Biological Science Collection</collection><collection>Agricultural Science Database</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Biological Science Database</collection><collection>Engineering Database</collection><collection>Nursing & Allied Health Premium</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Environmental Science Database</collection><collection>Materials Science Collection</collection><collection>Publicly Available Content Database</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 China</collection><collection>Engineering Collection</collection><collection>Environmental Science Collection</collection><collection>Genetics Abstracts</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>PloS one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Mulyaningsih, Tri</au><au>Mohanty, Itismita</au><au>Widyaningsih, Vitri</au><au>Gebremedhin, Tesfaye Alemayehu</au><au>Miranti, Riyana</au><au>Wiyono, Vincent Hadi</au><au>Metwally, Ammal Mokhtar</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Beyond personal factors: Multilevel determinants of childhood stunting in Indonesia</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2021-11-19</date><risdate>2021</risdate><volume>16</volume><issue>11</issue><spage>e0260265</spage><pages>e0260265-</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>Stunting is still a major public health problem in low- and middle-income countries, including Indonesia. Previous studies have reported the complexities associated with understanding the determinants of stunting. This study aimed to examine the household-, subdistrict- and province-level determinants of stunting in Indonesia using a multilevel hierarchical mixed effects model.
We analyzed data for 8045 children taken from the 2007 and 2014 waves of the Indonesian Family and Life Surveys (IFLS). We included individual-, family-/household- and community-level variables in the analyses. A multilevel mixed effects model was employed to take into account the hierarchical structure of the data. Moreover, the model captured the effect of unobserved household-, subdistrict- and province-level characteristics on the probability of children being stunted.
Our findings showed that the odds of childhood stunting vary significantly not only by individual child- and household-level characteristics but also by province- and subdistrict-level characteristics. Among the child-level covariates included in our model, dietary habits, neonatal weight, a history of infection, and sex significantly affected the risk of stunting. Household wealth status and parental education are significant household-level covariates associated with a higher risk of stunting. Finally, the risk of stunting is higher for children living in communities without access to water, sanitation and hygiene.
Stunting is associated with not only child-level characteristics but also family- and community-level characteristics. Hence, interventions to reduce stunting should also take into account family and community characteristics to achieve effective outcomes.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>34797892</pmid><doi>10.1371/journal.pone.0260265</doi><tpages>e0260265</tpages><orcidid>https://orcid.org/0000-0003-0116-7120</orcidid><orcidid>https://orcid.org/0000-0002-1679-4349</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1932-6203 |
| ispartof | PloS one, 2021-11, Vol.16 (11), p.e0260265 |
| issn | 1932-6203 1932-6203 |
| language | eng |
| recordid | cdi_plos_journals_2599573315 |
| source | MEDLINE; DOAJ Directory of Open Access Journals; Public Library of Science (PLoS) Journals Open Access; EZB-FREE-00999 freely available EZB journals; PubMed Central; Free Full-Text Journals in Chemistry |
| subjects | Biology and Life Sciences Child Childhood Children Children & youth Demographic aspects Diet Earth Sciences Education Educational Status Families & family life Family Family Characteristics Female Growth Disorders - epidemiology Growth Disorders - etiology Health aspects Health care access Health risks Households Humans Hygiene Income - statistics & numerical data Indonesia - epidemiology Low income groups Male Malnutrition Medicine and Health Sciences Modelling Multilevel Neonates Parents People and Places Politics Provinces Public health Public Health - statistics & numerical data Risk Risk factors Sanitation Sanitation - statistics & numerical data Socioeconomic Factors Socioeconomics Structural hierarchy Supervision |
| title | Beyond personal factors: Multilevel determinants of childhood stunting in Indonesia |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-24T13%3A04%3A17IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_plos_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Beyond%20personal%20factors:%20Multilevel%20determinants%20of%20childhood%20stunting%20in%20Indonesia&rft.jtitle=PloS%20one&rft.au=Mulyaningsih,%20Tri&rft.date=2021-11-19&rft.volume=16&rft.issue=11&rft.spage=e0260265&rft.pages=e0260265-&rft.issn=1932-6203&rft.eissn=1932-6203&rft_id=info:doi/10.1371/journal.pone.0260265&rft_dat=%3Cgale_plos_%3EA683178924%3C/gale_plos_%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2599573315&rft_id=info:pmid/34797892&rft_galeid=A683178924&rft_doaj_id=oai_doaj_org_article_43f8fc4a742a4cf28c06fff0c0d8dbe3&rfr_iscdi=true |