Anthropometry measures of the household population, by sex and age group.

The accommodation of worker anthropometric variability in the workplace and personal protective equipment (PPE) is key to safe and efficient completion of work tasks. Previously, the best data available was 46 years old, which has largely become outdated due to demographic changes. These data tables consist of 34 traditional semi-nude body dimensions without gear (e.g., chest depth, standing; foot breadth, horizontal, standing; hip circumference; stature; elbow rest height, sitting; and eye height, sitting) and 15 dimension measurements over clothing and with gear (e.g., abdominal extension depth, sitting; hip breadth, sitting; and should-grip length, sitting) of 756 male and 218 female Law Enforcement Officers (LEOs). For many LEOs, patrol vehicles are the workplace where they spend significant portions of their workday and PPE is vital gear to safeguard LEOs from the harm of assaults. Design improvements of vehicle console space, vehicle ingress/egress, and LEO body-worn equipment can result in reduced LEO fatigue, pain, or injury.

This table contains anthropometric data for 50th percentile U.S. male. This data has been used to calculate dimensions of truncated ellipsoidal finite element segments.

Table 1 summarises the anthropometric measures taken from the male and female bodies in this study.

S1 Table: Detailed items and scores in CCI; S2 Table: Baseline chart of patients grouped by 3-month survival; S3 Table: Baseline chart of patients grouped by 3-month walking ability; S4 Table: Baseline chart of patients grouped by 6-month survival; S5 Table: Baseline chart of patients grouped by 6-month walking ability; S6 Table: Baseline chart of patients grouped by 1-year survival; S7 Table: Baseline chart of patients grouped by 1-year walking ability; S8 Table: Comparison of anthropometric measurements in patients with different outcomes in all individuals; S9 Table: Detailed information of Cox analyses; S10 Table: Detailed information of logistics analyses based on all individuals; S11 Table: Detailed information of logistics analyses based on males; S12 Table: Detailed information of logistics analyses based on females; S13 Table: Detailed information of ROC curves; S14 Table: Raw data of our study.

This dataset represents a group of paper records (a "series") within the Harvard School of Public Health Longitudinal Studies of Child Health and Development records, 1918-2015 (inclusive), 1930-1989 (bulk), which can be accessed on-site at the Center for the History of Medicine at the Francis A. Countway Library of Medicine in Boston, Massachusetts. The series consists of research data and related administrative and regulatory records generated during the fifty-year follow-up study of the Harvard School of Public Health Longitudinal Studies of Child Health and Development. Research data includes raw, summarized, analyzed, and coded data, and consists of: completed data collection worksheets and survey instruments; reproductive and gynecological examination records and histories; subject case summaries; data tables and charts; 5.25" floppy disks containing analyzed and coded data related to blood pressure and nutrition; and coded data computer punch cards. Some data is from the original study, but was reused and maintained with records of the fifty-year follow-up study. Administrative records consist of: subject participation and appointment scheduling records; research plans and reports; and administrative correspondence. Regulatory records include: protocols and methodologies; codebooks; and blank data collection instruments. Frequent topics include: gynecological and reproductive health; memory of diet in the distant past; anthropometric measurements; blood pressure; and nutrition, among other topics. Data and associated records are accessible onsite at the Center for the History of Medicine per the conditions governing access described below. Conditions Governing Access to Original Collection Materials: The series represented by this dataset includes personnel and student information that is restricted for 80 years from the date of record creation, longitudinal patient information that is restricted for 80 years from the most recently dated records in the collection, and Harvard University records that are restricted for 50 years from the date of record creation. Access to electronic records in this series is premised on the availability of a computer station, requisite software, and/or the ability of Public Services staff to review and/or print out records of interest in advance of an on-site visit. Researchers should contact Public Services for more information. The Harvard School of Public Health Longitudinal Studies of Child Health and Development records were processed with grant funding from the Andrew W. Mellon Foundation, as awarded and administered by the Council on Library and Information Resources (CLIR) in 2016. An online finding aid to the collection may be accessed here: http://nrs.harvard.edu/urn-3:HMS.Count:med00211

S1 Table. Anthropometrical and metabolic biomarker measurements of participants recruited for plasma pool generation. Twenty participants are recruited for a dietary fiber study. Anthropometric measurements (weight, height, systolic blood pressure, and diastolic blood pressure) were measured and recorded. Their blood samples are collected pre- and post- dietary fiber intake. Blood plasma was immediately separated after the blood draws and a portion of the plasma was sent for biomarker measurement (glucose, insulin, total cholesterol, HDL-cholesterol, LDL-cholesterol, and triglyceride) in UC Davis Department of Pathology and Laboratory Medicine,

BackgroundAbdominal obesity is recognized as a key risk factor for developing OAB. However, traditional measures of obesity, such as the waist-to-height ratio (WHtR), waist circumference, and body mass index (BMI), may not sufficiently capture fat distribution in the body. This study aims to evaluate the relationship between novel anthropometric indices and OAB, providing a more accurate assessment of obesity-related risk factors.MethodsThe National Health and Nutrition Examination Survey (NHANES) data from 2007 to 2018 were utilized, comprising 27,560 participants. To assess the association and discriminative ability of novel anthropometric indices, including the Body Roundness Index (BRI), A Body Shape Index (ABSI), Waist-to-Weight Index (WWI), and Relative Fat Mass (RFM), with OAB, we employed multivariable logistic regression, restricted cubic spline (RCS) analysis, subgroup analysis, and receiver operating characteristic (ROC) curve methods.ResultsMultivariable logistic regression analysis indicated that higher levels of novel anthropometric indices were positively associated with OAB prevalence. One z-score increase in WWI, BRI, RFM, and ABSI was associated with a 16, 31, 57, and 5% higher likelihood of OAB, respectively. RCS analysis revealed a non-linear relationship between RFM and OAB. ROC analysis indicated that WWI (AUC = 0.680) and RFM (AUC = 0.661) provided better diagnostic accuracy than traditional measures such as BMI (AUC = 0.599). Subgroup analyses supported the robustness of these findings.ConclusionNovel anthropometric indices were positively associated with OAB prevalence. WWI and RFM demonstrated significantly better diagnostic value for OAB than BMI and WHtR. Future studies should investigate the potential of combining multiple anthropometric indices to improve predictive accuracy and conduct prospective studies to determine causality.

Fanconi anemia (FA) is often associated with poor growth due to a combination of endocrine and non-endocrine causes. There are currently no disease-specific growth curves. Despite short stature being regarded as a major characteristic of FA (Auerbach, 2009), there is currently no established method of accurately monitoring growth expectations for individuals with this condition. Use of bone age to estimate adult height is viewed as falsely inflating projected growth outcomes in FA as this assumes normal hormone production, nutrition, and puberty onset, which are not seen in FA (Petryk et al., 2015). Hormone response and duration of puberty may also be abnormal in individuals with FA. Midparental target height is frequently not appropriate for setting expected growth in patients with FA (Petryk et al., 2015). As such, we aimed to create disease-specific growth curves using a cohort of individuals with FA seeking care at the University of Minnesota.

The Bone Marrow Transplant (BMT) Registry at the University of Minnesota was used to find a large cohort of patients with FA seen at the Fanconi Anemia Comprehensive Care Center at the University of Minnesota since its inception in 1968. Age, sex, height, and weight data from birth until age 20 years old were abstracted from electronic health records (EHR) beginning in spring of 2011 when EPIC was instituted as the EHR at the FA Comprehensive Care Center. Additional data were abstracted including results of FANC gene variant testing and treatment data such as BMT status. Data was collected in a REDCap database housed by the University of Minnesota.

Patient growth trajectories were visualized by plotting, longitudinal anthropometric data for height, weight, and BMI serially by clinic visits for age (i.e., spaghetti plots). Prior to statistical modeling, all de-identified patient data were screened for data quality issues such as: same-day anthropometric measurements, duplicate measurements, last measurement carried-forward using a pediatric growth cleaner statistical tool (Daymont et al., 2017). The Lambda-Mu-Sigma (LMS) growth modeling technique was then used to fit height-for-age, weight-for-age, and BMI-for-age growth functions from which reference percentile ranges for children with FA were estimated. The LMS modeling technique is from the Generalized Additive Model for Location Scale and Shape (GAMLSS) family of statistical approaches (Rigby & Stasinopoulos, 2004). It is a modeling methodology based on penalized maximum likelihood estimation with cubic splines to smooth for roughness in age-related growth channels to calculate percentiles (Cole & Green, 1992). Extensive statistical and visual diagnostics GAMLSS tools were used in select our final models.

To compare growth differences, the FA-specific percentiles charts were then overlaid on WHO standards for ages 0-2 years old and CDC references for ages 2-20 years old.

IntroductionTo date cranial development has only been described by analyzing occipitofrontal circumference (OFC). More precise methods of determining head measurements have not been widely adopted. The use of additional measurements has the potential to better account for the three-dimensional structure of the head. Our aim was to put forward centile curves of such measurements for gestational age along with a compound head volume index.MethodsWe created generalized additive models for location, scale, and shape of two ear-to-ear distances (EED), transfontanellar (fEED) and transvertical (vEED), from birth anthropometric data. Same was done for OFC, crown-heel length, and birth weight to allow for comparison of our models with growth charts by Voigt et al. and Fenton and Kim.ResultsGrowth charts and tables of LMS parameters for fEED and vEED were derived from 6,610 patients admitted to our NICU and 625 healthy term newborns. With increasing gestational age EEDs increase about half as fast compared to OFC in absolute terms, their relative growths are fairly similar.DiscussionDifferences to the charts by Fenton and Kim are minute. Tape measurements, such as fEED or vEED can be added to routine anthropometry at little extra costs. These charts may be helpful for following and evaluating head sizes and growth of preterm and term infants in three dimensions.

BackgroundPrevious studies have explored the associations between obesity and Parkinson’s disease (PD), often using body mass index (BMI) as the main metric. However, findings remain inconsistent. Anthropometric indices—quantitative measures of body shape, size, and fat distribution—offer alternative ways to assess adiposity. This study aimed to evaluate the associations between eight anthropometric indices and PD prevalence.MethodsData were obtained from the National Health and Nutrition Examination Survey (NHANES), conducted in the U.S. from 1999 to 2020. A total of 41,374 participants aged 20 years and older were included, among whom 354 were diagnosed with PD. Eight anthropometric indices were analyzed: waist-to-weight index (WWI), conicity index (CI), a body shape index (ABSI), body roundness index (BRI), waist-to-height ratio (WHtR), BMI, waist circumference (WC), and weight (WT). Weighted multivariable logistic regression models were used to assess the association between these indices and PD. Restricted cubic spline (RCS) models were employed to examine dose–response relationships. Subgroup and sensitivity analyses were conducted to validate the robustness of the findings.ResultsSignificant differences were observed between the study groups, with positive and independent correlations identified between PD and all anthropometric measures, except BMI. After full adjustment, each 1-standard deviation increase in WWI, CI, ABSI, BRI, WHtR, WC, and WT was associated with an elevated PD risk by 34, 42, 36, 18, 21, 25, and 16%, respectively. RCS analysis revealed a linear relationship between CI, ABSI, BRI, WtHR, WC, WT, and PD prevalence, whereas WWI exhibited a nonlinear association. The subgroup and sensitivity analyses confirmed the consistency of these associations.ConclusionHigher values of several anthropometric indices, particularly the ABSI, WWI, and CI, were associated with increased PD prevalence. These findings highlight the potential role of fat distribution rather than overall adiposity in PD pathogenesis. Anthropometric measures may be valuable tools for early PD risk identification and targeted prevention strategies.

This folder contains replication files for the paper 'Including Scalable Nutrition Interventions in a Graduation Model Program: Experimental Evidence from Ethiopia'. Specifically, it includes: a) edcc_all tables.do: A do-file with replication code for Tables 1-7, and Annex Tables A1, A2, B1, C1, C2 and D1 in the paper. Note that some manual formatting still needs to be applied to the tables once the .doc file is outputted by Stata (e.g. merging table cells where row labels overflow a line); b) spir_household data.dta: Household-level dataset from all survey rounds collected as part of the Strengthen the PSNP4 Institutions and Resilience trial (2018-2021) and limited to variables relevant to the analysis. This dataset is used for all the tables except from Table 2, C1, C2 and D1; c) spir_child level data.dta: Child-level (1 row per child) anthropometrics dataset from all survey rounds. This dataset is used for the anthropometry analysis in Table 2 and Annex Tables C1 and C2; d) spir_midline data.dta Child level dataset from the midline survey round used for the first panel of Table D1, replication of results from the Alderman et al 2022 Food Policy paper. If you have any questions, reach out to Heleene Tambet at the International Food Policy Research Institute, h.tambet@cgiar.org.

These are the supplemental files for the manuscript entitled, "Eating versus skipping breakfast has no discernible effect on obesity-related anthropometric outcomes: a systematic review and meta-analysis."

To calculate the meta-analyses:

• calculations.R - Updated. Calculates individual effect sizes for each study; this file is sourced by metaanalysis with subgroup.R; update to account for updates to the data for Neumann et al. 2016.
• metaanalysis with subgroup and cumulative.R - updated from metaanalysis.R from version 1 and metaanalysis with subgroup.R from version 2. Reproduces the composite forest plot, summary table, and outputs. Includes a subgroup analysis stratified by baseline breakfast habit, and tests for the interaction between baseline habit and assigned breakfast condition. Now includes a cumulative analysis in which studies are added based on duration, from longest to shortest.
• neumann2016.csv - updated. Contains the raw data from Neumann et al. 2016, incorporating the corrections sent to us by the authors
• rho estimates for farshchi.R - Uses data from Geliebter et al. to estimate within-condition pre-post correlations; these calculations are manually included in calculations.R.

PRISMA checklist.doc is the PRISMA checklist for the article with sections listed instead of page numbers, given that page numbers may change depending on viewing format. Updated with the subgroup analysis.

Subgroup analysis - methods and results.pdf describes the methods and results of the subgroup analysis testing the interaction between baseline breakfast habits and assigned breakfast habits. Includes forest plots and summary table for the five anthropometric measurements that had enough comparisons to estimate an effect: body weight, BMI, body fat percentage, fat mass, lean mass.

Cumulative analysis - methods and results.pdf describes the methods and results of the cumulative meta-analysis in which studies were added based on study duration from longest to shortest. Includes cumulative plots for the two anthropometric measurements that had enough comparisons for a meaningful cumulative analysis: body weight and BMI.

MetaNutriUnify Collection This work is linked to the iTARGET project (https://qualiment.fr/des-projets-pour-anticiper-les-besoins-de-recherche-des-entreprises-agroalimentaires-2022/hot-topics/), aiming at performing in silico and in vitro targeting of healthy gut bacteria with fiber degrading metabolic potential. In this context, we developed MetaNutriUnify, the first collection of curated and harmonized metagenomic data with unified nutritional data from public study cohorts, with a particular attention on fiber. Overview of the public studies included in MetaNutriUnify MetaNutriUnify is composed of 21 harmonized and curated public projects with available shotgun metagenomes from human adults stool samples and nutritional and anthropometric data. It consists of 949 individuals from 15 countries, totalizing 1656 metagenomes, from which we generated microbial species and associated functional modules abundance tables. We also unified nutritional data, including diet type, study type (observational/interventional), time points for stool sampling, diet intervention and associated information, macro and micronutrients when available, and reported available anthropometric data (gender, country, age, weight, height, BMI). The list of the 21 included studies is: Bioproject PRJEB8249 (2015, SWE, 21 subjects, PMID 26244932) Bioproject PRJNA278393 (2015, TZA & ITA, 33 subjects, PMID 25981789) Bioproject PRJNA328899 (2016, MNG & CHN, 110 subjects, PMID 27708392) Bioproject PRJNA305507 (2017, USA, 33 subjects, PMID 28797298) Bioproject PRJEB28687 (2018, USA & THA, 50 subjects, PMID 30388453) Bioproject PRJEB32794 (2019, IRL, 37 subjects, PMID 31558359) Bioproject PRJNA472785 (2019, USA, 12 subjects, PMID 31235964) Bioproject PRJNA386503 (2019, USA, 4 subjects, PMID 30810441) Bioproject PRJNA397112 (2019, IND, 88 subjects, PMID 30698687) Bioproject PRJEB33500 (2020, ITA, 82 subjects, PMID 32075887) Bioproject PRJNA647720 (2021, USA, 20 subjects, PMID 33727392) Bioproject PRJNA755720 (2021, ESP, 20 subjects, PMID 34444797) Bioproject PRJNA892265 (2022, ESP, 20 subjects, PMID 36364873) Bioproject PRJEB42906 (2022, USA, 50 subjects, PMID 35312171) Bioproject PRJEB45944 (2022, NLD, 149 subjects, PMID 35115599) Bioproject PRJEB48663 (2022, FRA, 39 subjects, PMID 35311446) Bioproject PRJNA762543 (2022, SGP, 62 subjects, PMID 35549618) Bioproject PRJEB48605 (2023, DEU, 68 subjects, PMID 35760036) Bioproject PRJNA939268 (2023, SGP, 10 subjects, PMID 36997838) Bioproject PRJEB26842 (2023, GBR, 29 subjects, PMID 37587110) Bioproject PRJNA906167 (2023, ESP, 12 subjects, PMID 37457982) Method summary Metagenomic data and associated metadata were recovered from the European Nucleotide Archive, while nutritional and anthropometric data were collected from various online resources (main publication, supplementary files, GitHub or BioProject information). QC validation was performed using fastp (version 0.23.4) and host related reads were filtered out with bowtie using the human reference genome (Homo sapiens T2T-CHM13v2.0). Resulting high quality reads were mapped onto the 10.4 million gut IGC2 catalogue of the human microbiome and onto the 8.4 million human oral microbial catalogue using the METEOR software clustered into Metagenomic Species Pangenomes (MSP species) that were previously taxonomically and functionally annotated. MetaNutriUnify characteristics The provided data consists of: Metagenomic Species Pangenomes (MSP) species abundance table and related GTDB taxonomy (GTBD-tk version r220) (final_msp.7z and species_taxonomy_20241119.tab) KEGG, GMM and GBM Functional modules abundance table and related modules definition (KEGG version 107, GMM modules and GBM modules) (final_modules.tab and all_modules_definition_GMM_GBM_KEGG_107_20241119.tab) Manually curated and unified data collected from bioprojects (final_metadata.tab): Metadata from the metagenomes obtained from ENA, as well as nutritional and anthropometric data. Additional data on each sample, such as the evaluation of cross contamination within each bioproject using the CroCodeEL tool, together with the number of high quality reads, the MSP species richness and if the number of reads was below 1M. We proposed a “to_exclude” variable in the deposited MetaNutriUnify file, derived from these data. If one of the following conditions was met: low_read = YES, is.contaminated = YES or MSP_richness < 20, we propose to exclude the sample from downstream analysis. Nutritional data, carefully extracted from each study and reporting information on diet type, energy, macro- and micronutrients when available. No frequency data were included, because of the great variability between studies. We reported variables and their modalities as they were described in the different studies, and only modified units of nutritional data when appropriate. We encourage users to modulate the modalities for some variables, such as time point description, and to refer to the original studies...

Contains participant characteristics, size measures and body shape principal component scores of all 9,209 participants. (XLSX)

Tunisia TN: Prevalence of Overweight: Weight for Height: % of Children Under 5, Modeled Estimate data was reported at 17.500 % in 2024. This records an increase from the previous number of 17.400 % for 2023. Tunisia TN: Prevalence of Overweight: Weight for Height: % of Children Under 5, Modeled Estimate data is updated yearly, averaging 13.600 % from Dec 2000 (Median) to 2024, with 25 observations. The data reached an all-time high of 17.500 % in 2024 and a record low of 3.900 % in 2000. Tunisia TN: Prevalence of Overweight: Weight for Height: % of Children Under 5, Modeled Estimate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Tunisia – Table TN.World Bank.WDI: Social: Health Statistics. Prevalence of overweight children is the percentage of children under age 5 whose weight for height is more than two standard deviations above the median for the international reference population of the corresponding age as established by the WHO's 2006 Child Growth Standards.;UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME).;Weighted average;Once considered only a high-income economy problem, overweight children have become a growing concern in developing countries. Research shows an association between childhood obesity and a high prevalence of diabetes, respiratory disease, high blood pressure, and psychosocial and orthopedic disorders (de Onis and Blössner 2003). Childhood obesity is associated with a higher chance of obesity, premature death, and disability in adulthood. In addition to increased future risks, obese children experience breathing difficulties and increased risk of fractures, hypertension, early markers of cardiovascular disease, insulin resistance, and psychological effects. Children in low- and middle-income countries are more vulnerable to inadequate nutrition before birth and in infancy and early childhood. Many of these children are exposed to high-fat, high-sugar, high-salt, calorie-dense, micronutrient-poor foods, which tend be lower in cost than more nutritious foods. These dietary patterns, in conjunction with low levels of physical activity, result in sharp increases in childhood obesity, while under-nutrition continues. Estimates are modeled estimates produced by the JME. Primary data sources of the anthropometric measurements are national surveys. These surveys are administered sporadically, resulting in sparse data for many countries. Furthermore, the trend of the indicators over time is usually not a straight line and varies by country. Tracking the current level and progress of indicators helps determine if countries are on track to meet certain thresholds, such as those indicated in the SDGs. Thus the JME developed statistical models and produced the modeled estimates.

Optimal MAUC Cut off for moderate acute malnutrition among under five children using weight for height Z

Greece GR: Prevalence of Overweight: Weight for Height: % of Children Under 5, Modeled Estimate data was reported at 13.500 % in 2024. This records a decrease from the previous number of 13.600 % for 2023. Greece GR: Prevalence of Overweight: Weight for Height: % of Children Under 5, Modeled Estimate data is updated yearly, averaging 13.500 % from Dec 2000 (Median) to 2024, with 25 observations. The data reached an all-time high of 13.900 % in 2019 and a record low of 10.900 % in 2000. Greece GR: Prevalence of Overweight: Weight for Height: % of Children Under 5, Modeled Estimate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Greece – Table GR.World Bank.WDI: Social: Health Statistics. Prevalence of overweight children is the percentage of children under age 5 whose weight for height is more than two standard deviations above the median for the international reference population of the corresponding age as established by the WHO's 2006 Child Growth Standards.;UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME).;Weighted average;Once considered only a high-income economy problem, overweight children have become a growing concern in developing countries. Research shows an association between childhood obesity and a high prevalence of diabetes, respiratory disease, high blood pressure, and psychosocial and orthopedic disorders (de Onis and Blössner 2003). Childhood obesity is associated with a higher chance of obesity, premature death, and disability in adulthood. In addition to increased future risks, obese children experience breathing difficulties and increased risk of fractures, hypertension, early markers of cardiovascular disease, insulin resistance, and psychological effects. Children in low- and middle-income countries are more vulnerable to inadequate nutrition before birth and in infancy and early childhood. Many of these children are exposed to high-fat, high-sugar, high-salt, calorie-dense, micronutrient-poor foods, which tend be lower in cost than more nutritious foods. These dietary patterns, in conjunction with low levels of physical activity, result in sharp increases in childhood obesity, while under-nutrition continues. Estimates are modeled estimates produced by the JME. Primary data sources of the anthropometric measurements are national surveys. These surveys are administered sporadically, resulting in sparse data for many countries. Furthermore, the trend of the indicators over time is usually not a straight line and varies by country. Tracking the current level and progress of indicators helps determine if countries are on track to meet certain thresholds, such as those indicated in the SDGs. Thus the JME developed statistical models and produced the modeled estimates.

Abstract

The Vietnam Multiple Indicator Cluster Survey (MICS 2011) was conducted from December 2010 to January 2011 by the General Statistics Office of Vietnam, in collaboration with the Ministry of Health (MOH) and the Ministry of Labour, Invalids and Social Affairs (MOLISA). Financial and technical support for the survey was provided by the United Nations Children's Fund (UNICEF). Financial support was also provided by the United Nations Population Fund (UNFPA) in Vietnam.

MICS 2011 gives valuable information and the latest evidence on the situation of children and women in Vietnam, updating information from the previous 2006 Vietnam MICS survey as well as earlier data collected in the first two MICS rounds carried out in 1996 and 2000.

The survey presents data from an equity perspective by indicating disparities by sex, region, area, ethnicity, living standards and other characteristics. MICS 2011 is based on a sample of 11,614 households interviewed and provides a comprehensive picture of children and women in Vietnam's six regions.

Geographic coverage

National

Analysis unit

• individuals,
• households.

Universe

The survey covered all de jure household members (usual residents), all women aged between 15-49 years, all children under 5 living in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

The primary objective of the sample design for the Vietnam MICS 2011 was to produce statistically reliable estimates of most indicators, at the national level, for urban and rural areas, and for the six regions of Vietnam: Red River Delta, Northern Midlands and Mountainous areas, North Central area and Central Coastal area, Central Highlands, South East and Mekong River Delta. Urban and rural areas in each of the six regions were designated as the sampling strata.

A multi-stage, stratified cluster sampling approach was used for the selection of the survey sample.

The target sample size for the Vietnam MICS 2011 was calculated as 12,000 households. For the calculation of the sample size, the key indicator used was the underweight prevalence among children aged 0-4 years.

The resulting number of households from this exercise was 2,050 households which is the sample size needed in each region - thus yielding about 12,000 in total. The average number of households selected per cluster for the Vietnam MICS 2011 was determined as 20 households, based on a number of considerations, including the design effect, the budget available, and the time that would be needed per team to complete one cluster. Dividing the total number of households by the number of sample households per cluster, it was calculated that 100 sample clusters would need to be selected in each region.

Equal allocation of the total sample size to the six regions was used. Therefore, 100 clusters were allocated to each region, with the final sample size calculated at 12,000 households (100 clusters * 6 regions * 20 sample households per cluster). In each region, the clusters (primary sampling units) were distributed to urban and rural domains, proportional to the size of urban and rural populations in that region.

The sampling procedures are more fully described in "Multiple Indicator Cluster Survey 2011 - Final Report" pp.215-218.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaires for the Generic MICS were structured questionnaires based on the MICS4 model questionnaire with some modifications and additions. Household questionnaires were administered to a knowledgeable adult living in the household. The household questionnaire includes household listing form, education, water and sanitation, household characteristics, insecticide treated bednets, indoor residual spraying, child labour, child discipline, handwashing and salt Iodisation.

In addition to a household questionnaire, questionnaires were administered in each household for women age 15-49 and children under age five. The questionnaire for children under 5 years of age was administered to mothers or caregivers of all children under 5 years of age living in the households.

The women's questionnaire includes woman's background, child mortality, desire for last birth, maternal and newborn health, illness symptoms, contraception, unmet need, attitudes toward domestic violence, marriage/union, sexual behavior and HIV/AIDS.

The children's questionnaire includes child's age, birth registration, early childhood development, breastfeeding, care of illness, malaria, immunization and anthropometry.

Cleaning operations

Data were entered using CSPro software on eight small computers. Ten operators working in shifts performed data entry under supervision of two data entry supervisors. In order to ensure quality control, all questionnaires were double entered and internal consistency checks were performed. Procedures and standard programs developed under the global MICS 4 programme and adapted to the Viet Nam questionnaire were used throughout. Data processing began on 27 December 2010 and was completed on 21 March 2011. Data were analysed using the Statistical Package for Social Sciences (SPSS) software program, Version 19. The model syntax and tabulation plans developed by UNICEF were used for this purpose.

Response rate

Of the 12,000 households selected for the sample, 11,642 were present at the time of the survey. Of these, 11,614 successfully completed the interview, resulting in a household response rate of 99.8 percent. In the interviewed households, 12,115 women (aged 15-49 years) were identified. Of these, 11,663 completed the interview, yielding a response rate of 96.3 percent compared to eligible respondents in interviewed households. In addition, 3,729 children under 5 years were listed in the household questionnaire. Questionnaires were completed for 3,678 of these children, which corresponds to a response rate of 98.6 percent within interviewed households. The overall response rates (household response rate times the woman and child response rates within households) were 96 and 98.4 percent for the survey of women and of children under 5 years of age, respectively.

Sampling error estimates

Sampling errors are a measure of the variability between the estimates from all possible samples. The extent of variability is not known exactly, but can be estimated statistically from the survey data.

The following sampling error measures are presented in this appendix for each of the selected indicators: - Standard error (se): Sampling errors are usually measured in terms of standard errors for particular indicators (means, proportions etc). Standard error is the square root of the variance of the estimate. The Taylor linearization method is used for the estimation of standard errors. - Coefficient of variation (se/r) is the ratio of the standard error to the value of the indicator, and is a measure of the relative sampling error. - Design effect (deff) is the ratio of the actual variance of an indicator, under the sampling method used in the survey, to the variance calculated under the assumption of simple random sampling. The square root of the design effect (deft) is used to show the efficiency of the sample design in relation to the precision. A deft value of 1.0 indicates that the sample design is as efficient as a simple random sample, while a deft value above 1.0 indicates the increase in the standard error due to the use of a more complex sample design. - Confidence limits are calculated to show the interval within which the true value for the population can be reasonably assumed to fall, with a specified level of confidence. For any given statistic calculated from the survey, the value of that statistic will fall within a range of plus or minus two times the standard error (r + 2.se or r – 2.se) of the statistic in 95 percent of all possible samples of identical size and design.

For the calculation of sampling errors from MICS data, SPSS Version 18 Complex Samples module has been used. The results are shown in the tables that follow. In addition to the sampling error measures described above, the tables also include weighted and unweighted counts of denominators for each indicator.

Sampling errors are calculated for indicators of primary interest, for the national level, for the regions, and for urban and rural areas. Three of the selected indicators are based on households, 8 are based on household members, 13 are based on women, and 15 are based on children under 5. All indicators presented here are in the form of proportions.

Data appraisal

A series of data quality tables are available to review the quality of the data and include the following:

• Age distribution of the household population
• Age distribution of eligible and interviewed women
• Age distribution of children under 5 in household and children under 5 questionnaires
• Completeness of reporting
• Completeness of information for anthropometric indicators
• Heaping in anthropometric measurements
• Observation of bednets places for hand washing
• Observation of women's health cards
• Observation of children under 5 birth certificates
• Observation of vaccination cards
• Presence of mother in the household and the person interviewed for the under-5 questionnaire
• Selection of children age 2-14 years for the child discipline module
• School attendance by single age
• Sex ratio at birth among children ever born and living

The results of each of these data quality tables are shown in appendix D in document "Multiple Indicator Cluster Survey 2011 - Final Report"

Georgia GE: Prevalence of Overweight: Weight for Height: % of Children Under 5, Modeled Estimate data was reported at 4.100 % in 2024. This records a decrease from the previous number of 4.400 % for 2023. Georgia GE: Prevalence of Overweight: Weight for Height: % of Children Under 5, Modeled Estimate data is updated yearly, averaging 13.700 % from Dec 2000 (Median) to 2024, with 25 observations. The data reached an all-time high of 20.400 % in 2004 and a record low of 4.100 % in 2024. Georgia GE: Prevalence of Overweight: Weight for Height: % of Children Under 5, Modeled Estimate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Georgia – Table GE.World Bank.WDI: Social: Health Statistics. Prevalence of overweight children is the percentage of children under age 5 whose weight for height is more than two standard deviations above the median for the international reference population of the corresponding age as established by the WHO's 2006 Child Growth Standards.;UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME).;Weighted average;Once considered only a high-income economy problem, overweight children have become a growing concern in developing countries. Research shows an association between childhood obesity and a high prevalence of diabetes, respiratory disease, high blood pressure, and psychosocial and orthopedic disorders (de Onis and Blössner 2003). Childhood obesity is associated with a higher chance of obesity, premature death, and disability in adulthood. In addition to increased future risks, obese children experience breathing difficulties and increased risk of fractures, hypertension, early markers of cardiovascular disease, insulin resistance, and psychological effects. Children in low- and middle-income countries are more vulnerable to inadequate nutrition before birth and in infancy and early childhood. Many of these children are exposed to high-fat, high-sugar, high-salt, calorie-dense, micronutrient-poor foods, which tend be lower in cost than more nutritious foods. These dietary patterns, in conjunction with low levels of physical activity, result in sharp increases in childhood obesity, while under-nutrition continues. Estimates are modeled estimates produced by the JME. Primary data sources of the anthropometric measurements are national surveys. These surveys are administered sporadically, resulting in sparse data for many countries. Furthermore, the trend of the indicators over time is usually not a straight line and varies by country. Tracking the current level and progress of indicators helps determine if countries are on track to meet certain thresholds, such as those indicated in the SDGs. Thus the JME developed statistical models and produced the modeled estimates.