Anthropometric Data Quality Research Articles

The quality of the anthropometric data on children under five years of age in information systems in the State of São Paulo, Brazil

This paper involves the analysis of the quality of anthropometric data on children under five years of age in two information systems in the State of São Paulo. The sample included 2,117,108 children from the Food and Nutrition Surveillance System (SISVAN), and 748,551 from the State Milk Project (VIVALEITE). Initially, we evaluated the frequency of missing values and others outside the equipment spectrum and calculated the digit-to-weight preference index. After calculating height-for-age (HAZ), weight-for-age (WAZ), and body mass index-for-age (BAZ), we flagged the biologically implausible values (BIV) and calculated the standard deviation (SD). For each municipality, we calculated the mean and the SD of HAZ, WAZ, and BAZ; and plotted the SD values as a function of the mean. The digit-to-weight preference index was greater among children aged between 24 and 59 months in SISVAN. The frequency of BIV for HAZ (SISVAN 2.56%; VIVALEITE 0.98%) was higher than for WAZ (SISVAN 2.10%; VIVALEITE 0.18%). For HAZ, variations among municipalities were more pronounced in VIVALEITE than in SISVAN. The height variable presents low reliability in both systems. The weight variable reveals satisfactory quality in VIVALEITE and unsatisfactory quality in SISVAN.

Statistical assessment of reliability of anthropometric measurements in the multi-site South African National Dietary Intake Survey 2022.

Anthropometric data quality in large multicentre nutrition surveys is seldom adequately assessed. In preparation for the South African National Dietary Intake Survey (NDIS-2022), this study assessed site leads' and fieldworkers' intra- and inter-rater reliability for measuring weight, length/height, mid-upper arm circumference (MUAC), waist circumference (WC) and calf circumference (CC). Standardised training materials and measurement protocols were developed, and new anthropometric equipment was procured. Following two training rounds (12 site lead teams, 46 fieldworker teams), measurement reliability was assessed for both groups, using repeated measurements of volunteers similar to the survey target population. Reliability was statistically assessed using the technical error of measurement (TEM), relative TEM (%TEM), intra-class correlation coefficient (ICC) and coefficient of reliability (R). Agreement was visualised with Bland-Altman analysis. By %TEM, the best reliability was achieved for weight (%TEM = 0.260-0.923) and length/height (%TEM = 0.434-0.855), and the poorest for MUAC by fieldworkers (%TEM = 2.592-3.199) and WC (%TEM = 2.353-2.945). Whole-sample ICC and R were excellent ( > 0.90) for all parameters except site leads' CC inter-rater reliability (ICC = 0.896, R = 0.889) and fieldworkers' inter-rater reliability for MUAC in children under two (ICC = 0.851, R = 0.881). Bland-Altman analysis revealed no significant bias except in fieldworkers' intra-rater reliability of length/height measurement in adolescents/adults ( + 0.220 (0.042, 0.400) cm). Reliability was higher for site leads vs. fieldworkers, for intra-rater vs. inter-rater assessment, and for weight and length/height vs. circumference measurements. NDIS-2022 site leads and fieldworkers displayed acceptable reliability in performing anthropometric measurements, highlighting the importance of intensive training and standardised measurement protocols. Ongoing reliability assessment during data collection is recommended.

Quality of anthropometric data of children under 5 years in the Brazilian National Food and Nutrition Surveillance System, 2008-2020

The planning, monitoring, and evaluation of food and nutrition actions depend on reliable estimates based on adequate anthropometric data. The study aimed to analyze the quality of anthropometric data of children aged under 5 years in the Brazilian National Food and Nutrition Surveillance System (SISVAN) from 2008 to 2020. The sample comprised 23,453,620 children aged under 5 years. Initially, we evaluated the distribution of missing values and values outside the spectrum of the instrument, and calculated the digit preference index for weight and height. The nutritional indexes height for age (HAZ), weight for age (WAZ), and body mass index for age (BAZ) were calculated according to the World Health Organization 2006 child growth standards. Then, we identified the biologically implausible values (BIV) and calculated the standard deviation (SD) of the nutritional indexes. For each municipality, we calculated the mean and SD of HAZ and WAZ; and plotted the SD values as a function of the mean. In all Federative Units, the digit preference index reached a minimum value of 80 for height and 20 for weight. For the three nutritional indexes, there was a reduction in the frequency of BIV in the 2008-2020 period. Even after the exclusion of BIV, we identified high variability for the three nutritional indexes. The indicators evaluated showed low quality of measurement, especially in the North and Northeast regions. Our results indicate insufficient quality of anthropometric data in children aged under 5 years, and reinforce the need to invest in actions to improve the collection and recording of anthropometric information.

Frequency of anthropometric implausible values estimated from different methodologies: a systematic review and meta-analysis.

Poor anthropometric data quality affect the prevalence of malnutrition and could harm public policy planning. This systematic review and meta-analysis was designed to identify different methods to evaluate and clean anthropometric data, and to calculate the frequency of implausible values for weight and height obtained from these methodologies. Studies about anthropometric data quality and/or anthropometric data cleaning were searched for in the MEDLINE, LILACS, SciELO, Embase, Scopus, Web of Science, and Google Scholar databases in October 2020 and updated in January 2023. In addition, references of included studies were searched for the identification of potentially eligible studies. Paired researchers selected studies, extracted data, and critically appraised the selected publications. Meta-analysis of the frequency of implausible values and 95% confidence interval (CI) was estimated. Heterogeneity (I2) and publication bias were examined by meta-regression and funnel plot, respectively. In the qualitative synthesis, 123 reports from 104 studies were included, and in the quantitative synthesis, 23 studies of weight and 14 studies of height were included. The study reports were published between 1980 and 2022. The frequency of implausible values for weight was 0.55% (95%CI, 0.29-0.91) and for height was 1.20% (95%CI, 0.44-2.33). Heterogeneity was not affected by the methodological quality score of the studies and publication bias was discarded. Height had twice the frequency of implausible values compared with weight. Using a set of indicators of quality to evaluate anthropometric data is better than using indicators singly. PROSPERO registration no. CRD42020208977.

Quality of child anthropometric data from SISVAN, Brazil, 2008-2017.

To evaluate the quality of anthropometric data of children recorded in the Food and Nutrition Surveillance System (SISVAN) from 2008 to 2017. Descriptive study on the quality of anthropometric data of children under five years of age admitted in primary care services of the Unified Health System, from the individual databases of SISVAN. Data quality was annually assessed using the indicators: coverage, completeness, sex ratio, age distribution, weight and height digit preference, implausible z-score values, standard deviation, and normality of z-scores. In total, 73,745,023 records and 29,852,480 children were identified. Coverage increased from 17.7% in 2008 to 45.4% in 2017. Completeness of birth date, weight, and height corresponded to almost 100% in all years. The sex ratio was balanced and approximately similar to the expected ratio, ranging from 0.8 to 1. The age distribution revealed higher percentages of registrations from the ages of two to four years until mid-2015. A preference for terminal digits "zero" and "five" was identified among weight and height records. The percentages of implausible z-scores exceeded 1% for all anthropometric indices, with values decreasing from 2014 onwards. A high dispersion of z-scores, including standard deviations between 1.2 and 1.6, was identified mainly in the indices including height and in the records of children under two years of age and residents in the North, Northeast, and Midwest regions. The distribution of z-scores was symmetric for all indices and platykurtic for height/age and weight/age. The quality of SISVAN anthropometric data for children under five years of age has improved substantially between 2008 and 2017. Some indicators require attention, particularly for height measurements, whose quality was lower especially among groups more vulnerable to nutritional problems.

Quality of anthropometric data in India's National Family Health Survey: Disentangling interviewer and area effect using a cross-classified multilevel model

India has adopted a target-based approach to reduce the scourge of child malnourishment. Because the monitoring and evaluation required by this approach relies primarily on large-scale data, a data quality assessment is essential. As field teams are the primary mode of data collection in large-scale surveys, this study attempts to understand their contribution to variations in child anthropometric measures. This research can help disentangle the confounding effects of regions/districts and field teams on the quality of child anthropometric data. The anthropometric z-scores of 2,25,002 children below five years were obtained from the fourth round of India's National Family and Health Survey (NFHS-4), 2015–16. Unadjusted and adjusted standard deviations (SD) of the anthropometric measures were estimated to assess the variations in measurements. In addition, a cross-classified multilevel model (CCMM) approach was adopted to estimate the contribution of geographical regions/districts and teams to variations in anthropometric measures. The unadjusted SDs of the measures of stunting, wasting, and underweight were 1.7, 1.4, and 1.2, respectively. The SD of stunting was above the World Health Organisation threshold (0.8–1.2), as well as the Demographic and Health Survey mark. After adjusting for team-level characteristics, the SDs of all three measures reduced marginally, indicating that team-level workload had a marginal but significant role in explaining the variations in anthropometric z-scores. The CCMM showed that the maximum contribution to variations in anthropometric z-scores came from community-level (Primary Sampling Unit (PSU)) characteristics. Team-level characteristics had a higher contribution to variations in anthropometric z-scores than district-level attributes. Variations in measurement were higher for child height than weight. The present study decomposes the effects of district- and team-level factors and highlights the nuances of introducing teams as a level of analysis in multilevel modelling. Population size, density, and terrain variations between PSUs should be considered when allocating field teams in large-scale surveys.

Comparison of anthropometric data quality in children aged 6-23 and 24-59 months: lessons from population-representative surveys from humanitarian settings

BackgroundEnsuring the quality of anthropometry data is paramount for getting accurate estimates of malnutrition prevalence among children aged 6–59 months in humanitarian and refugee settings. Previous reports based on data from Demographic and Health Surveys suggested systematic differences in anthropometric data quality between the younger and older groups of preschool children.MethodsWe analyzed 712 anthropometric population-representative field surveys from humanitarian and refugee settings conducted during 2011–2018. We examined and compared the quality of five anthropometric indicators in children aged 6–23 months and children aged 24–59 months: weight for height, weight for age, height for age, body mass index for age and mid-upper arm circumference (MUAC) for age. Using the z-score distribution of each indicator, we calculated the following parameters: standard deviation (SD), percentage of outliers, and measures of distribution normality. We also examined and compared the quality of height, weight, MUAC and age measurements using missing data and rounding criteria.ResultsBoth SD and percentage of flags were significantly smaller on average in older than in younger age group for all five anthropometric indicators. Differences in SD between age groups did not change meaningfully depending on overall survey quality or on the quality of age ascertainment. Over 50% of surveys overall did not deviate significantly from normality. The percentage of non-normal surveys was higher in older than in the younger age groups. Digit preference score for weight, height and MUAC was slightly higher in younger age group, and for age slightly higher in the older age group. Children with reported exact date of birth (DOB) had much lower digit preference for age than those without exact DOB. SD, percentage flags and digit preference scores were positively correlated between the two age groups at the survey level, such as those surveys showing higher anthropometry data quality in younger age group also tended to show higher quality in older age group.ConclusionsThere should be an emphasis on increased rigor of training survey measurers in taking anthropometric measurements in the youngest children. Standardization test, a mandatory component of the pre-survey measurer training and evaluation, of 10 children should include at least 4–5 children below 2 years of age.

Anthropometric data quality assessment in multisurvey studies of child growth

ABSTRACTBackgroundPopulation-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.ObjectivesWe aimed to develop composite indices of anthropometric data quality for use in multisurvey analysis of child health and nutritional status.MethodsWe 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.ResultsOf 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.ConclusionsA 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.

Alternative Metrics of Linear Growth for Tracking Global Progress in Child Undernutrition (P10-001-19)

ObjectivesCompare under-5y stunting prevalence to alternative height-for-age z-score (HAZ)-based measures of location (MoLs) in terms of the strength of their relationship with key population-level indicators known to be associated with HAZ; and, examine the modification of these associations by anthropometric data quality. MethodsData from 140 Demographic and Health Surveys (DHS) from 63 low- and middle-income countries (LMICs) (2000 to present) were used to assess pairwise associations between seven alternative HAZ-based MoLs and three population-level indicators, using absolute values of correlation coefficients (corr) and Akaike’s Information Criterion (AIC) from linear mixed effects models. Extended models with interaction terms were used to assess how survey quality (defined using a score based on principal component analysis) modified each HAZ MoL - population-level indicator relationship. All analyses were performed under three flagging approaches for excluding HAZ outliers: WHO (cut-off at -6/ + 6 SD of the reference mean); SMART (cut-off at -3/ + 3 SD of the observed mean); and no flagging. ResultsAll HAZ MoLs were highly correlated with stunting (range: 0.92 to 0.98). Correlations between HAZ MoLs and population-level indicators were moderate and similar across metrics (range: 0.44 to 0.65). Stunting and model-predicted HAZ at 3y frequently had among the highest corr and lowest AIC values. Extended models consistently showed that associations between HAZ MoLs and population-level indicators attenuate as survey quality decreases. This modifying effect was weakest for stunting and predicted HAZ at 3y across flagging scenarios, and under conditions of WHO flagging across most MoLs. ConclusionsPreliminary analyses suggest that stunting is at least as or more robust compared to most other HAZ-based MoLs based on the strength of its correlation with population-level indicators, and insensitivity of those associations to variations in survey quality. Of the MoLs that represent central tendency, predicted HAZ at 3y performed comparably to stunting, and may have conceptual advantages for comparing child undernutrition within and across LMICs. Funding SourcesSickKids Hospital Growth and Development Fellowship Program. United States Agency for International Development (USAID) through the DHS Program. Supporting Tables, Images and/or Graphs▪

Perspective: Are We Ready to Measure Child Nutritional Status with Lasers?

ABSTRACTThe continued use of basic, manual anthropometric tools (e.g., boards and tapes) leaves anthropometry susceptible to human error. A potential solution, 3-dimensional (3D) imaging systems for anthropometry, has been around since the 1950s. In the 1980s, 3D imaging technology advanced from photographs to the use of lasers for body digitization; and by the 2000s, the falling price of 3D scanners made commercial application feasible. The garment sector quickly adopted imaging technology for surveys because of the need for numerous measurements and large sample sizes. In the health sector, 3D imaging for anthropometry was not widely adopted; its use was limited to research and specialized purposes. The different cost and logistical requirements for measurement in the garment and health sectors help to explain why the technology was adopted in one sector and not the other. Despite reductions, the price of 3D imaging systems remained a barrier to the use of 3D imaging for regular nutritional assessment in the health sector. Additional barriers in the health sector were that imaging systems required dedicated space and were not designed for capturing measurements in young children. In recent years, the development of light-coding technology may have removed these barriers, and a handheld imaging system was developed specifically for young children. There are not yet recommendations to replace manual equipment with 3D imaging for nutritional assessment, and there is a need for more research on low-cost, handheld imaging systems—particularly research that evaluates the ability of 3D imaging to improve the quality of anthropometric data and indicators.

Assessment of Anthropometric Data Following Investments to Ensure Quality: Kenya Demographic Health Surveys Case Study, 2008 to 2009 and 2014.

Evidence-based nutrition programs depend on accurate estimates of malnutrition derived from data collected in population representative surveys. The feasibility of obtaining accurate anthropometric data as part of national, multisectoral surveys has been a debated issue. The study aimed to evaluate changes in anthropometric data quality corresponding to investments by the Kenya Ministry of Health and nutrition sector partners for the 2014 Kenya Demographic Health Survey. Anthropometric data collected during the 2008 to 2009 and 2014 Kenya surveys were reanalyzed to assess standard parameters of quality: standard deviation, skewness, and kurtosis of z-score values for 3 anthropometric indicators (weight for height, height for age, and weight for age), percentage of children with missing measurements and outlier values, digit preference, and heaping of age. A total of 9936 households were selected in 2008 to 2009, and 39 679 households were selected in 2014. Standard deviation of z-scores for all 3 indicators was smaller in 2014 than in 2008 to 2009. Applying original Demographic and Health Survey exclusion criteria, weight for height z-scores were 1.16 in 2014, 10.1% narrower than 2008 to 2009. The percentage of outlying values declined significantly from 2008 to 2009 to 2014 for both height for age and weight for height ( P < .001). Digit preference scores in 2014 improved for both weight ( P = .011) and height ( P < .001) suggesting less rounding of terminal digits. All tests of data quality suggest an improvement in 2014 relative to 2008 to 2009, despite the complexity implied by the larger sample. This improvement corresponds with efforts to enhance training and supervision of anthropometry, suggesting a positive effect of these enhancements.

Improving the quality of child anthropometry: Manual anthropometry in the Body Imaging for Nutritional Assessment Study (BINA).

Anthropometric data collected in clinics and surveys are often inaccurate and unreliable due to measurement error. The Body Imaging for Nutritional Assessment Study (BINA) evaluated the ability of 3D imaging to correctly measure stature, head circumference (HC) and arm circumference (MUAC) for children under five years of age. This paper describes the protocol for and the quality of manual anthropometric measurements in BINA, a study conducted in 2016–17 in Atlanta, USA. Quality was evaluated by examining digit preference, biological plausibility of z-scores, z-score standard deviations, and reliability. We calculated z-scores and analyzed plausibility based on the 2006 WHO Child Growth Standards (CGS). For reliability, we calculated intra- and inter-observer Technical Error of Measurement (TEM) and Intraclass Correlation Coefficient (ICC). We found low digit preference; 99.6% of z-scores were biologically plausible, with z-score standard deviations ranging from 0.92 to 1.07. Total TEM was 0.40 for stature, 0.28 for HC, and 0.25 for MUAC in centimeters. ICC ranged from 0.99 to 1.00. The quality of manual measurements in BINA was high and similar to that of the anthropometric data used to develop the WHO CGS. We attributed high quality to vigorous training, motivated and competent field staff, reduction of non-measurement error through the use of technology, and reduction of measurement error through adequate monitoring and supervision. Our anthropometry measurement protocol, which builds on and improves upon the protocol used for the WHO CGS, can be used to improve anthropometric data quality. The discussion illustrates the need to standardize anthropometric data quality assessment, and we conclude that BINA can provide a valuable evaluation of 3D imaging for child anthropometry because there is comparison to gold-standard, manual measurements.

Child anthropometry data quality from Demographic and Health Surveys, Multiple Indicator Cluster Surveys, and National Nutrition Surveys in the West Central Africa region: are we comparing apples and oranges?

ABSTRACT Background: There has been limited work comparing survey characteristics and assessing the quality of child anthropometric data from population-based surveys. Objective: To investigate survey characteristics and indicators of quality of anthropometric data in children aged 0–59 months from 23 countries in the West Central Africa region. Methods: Using established methodologies and criteria to examine child age, sex, height, and weight, we conducted a comprehensive assessment and scoring of the quality of anthropometric data collected in 100 national surveys. Results: The Multiple Indicator Cluster Surveys (MICS) and Demographic and Health Surveys (DHS) collected data from a greater number of younger children than older children while the opposite was found for the National Nutrition Surveys (NNS). Missing or implausible height/weight data proportions were 12% and 8% in MICS and DHS compared to 3% in NNS. Average data quality scores were 14 in NNS, 33 in DHS, and 41 in MICS. Conclusions: Although our metric of data quality suggests that data from the NNS appear more consistent and robust, it is equally important to consider its disadvantages related to access and lack of broader socioeconomic information. In comparison, the DHS and MICS are publicly-accessable for research and provide socioeconomic context essential for assessing and addressing the burden of undernutrition within and between countries. The strengths and weaknesses of data from these three sources should be carefully considered when seeking to determine the burden of child undernutrition and its variation within countries.

Admission profile and discharge outcomes for infants aged less than 6 months admitted to inpatient therapeutic care in 10 countries. A secondary data analysis.

Evidence on the management of acute malnutrition in infants aged less than 6 months (infants <6mo) is scarce. To understand outcomes using current protocols, we analysed a sample of 24 045 children aged 0-60 months from 21 datasets of inpatient therapeutic care programmes in 10 countries. We compared the proportion of admissions, the anthropometric profile at admission and the discharge outcomes between infants <6mo and children aged 6-60 months (older children). Infants <6mo accounted for 12% of admissions. The quality of anthropometric data at admission was more problematic in infants <6mo than in older children with a greater proportion of missing data (a 6.9 percentage point difference for length values, 95% CI: 6.0; 7.9, P < 0.01), anthropometric measures that could not be converted to indices (a 15.6 percentage point difference for weight-for-length z-score values, 95% CI: 14.3; 16.9, P < 0.01) and anthropometric indices that were flagged as outliers (a 2.7 percentage point difference for any anthropometric index being flagged as an outlier, 95% CI: 1.7; 3.8, P < 0.01). A high proportion of both infants <6mo and older children were discharged as recovered. Infants <6mo showed a greater risk of death during treatment (risk ratio 1.30, 95% CI: 1.09; 1.56, P < 0.01). Infants <6mo represent an important proportion of admissions to therapeutic feeding programmes, and there are crucial challenges associated with their care. Systematic compilation and analysis of routine data for infants <6mo is necessary for monitoring programme performance and should be promoted as a tool to monitor the impact of new guidelines on care.

The reliability of in-home measures of height and weight in large cohort studies: Evidence from Add Health.

With the emergence of obesity as a global health issue an increasing number of major demographic surveys are collecting measured anthropometric data. Yet little is known about the characteristics and reliability of these data. We evaluate the accuracy and reliability of anthropometric data collected in the home during Wave IV of the National Longitudinal Study of Adolescent to Adult Health (Add Health), compare our estimates to national standard, clinic-based estimates from the National Health and Nutrition Examination Survey (NHANES) and, using both sources, provide a detailed anthropometric description of young adults in the United States. The reliability of Add Health in-home anthropometric measures was estimated from repeat examinations of a random subsample of study participants. A digit preference analysis evaluated the quality of anthropometric data recorded by field interviewers. The adjusted odds of obesity and central obesity in Add Health vs. NHANES were estimated with logistic regression. Short-term reliabilities of in-home measures of height, weight, waist and arm circumference-as well as derived body mass index (BMI, kg/m2)-were excellent. Prevalence of obesity (37% vs. 29%) and central obesity (47% vs. 38%) was higher in Add Health than in NHANES while socio-demographic patterns of obesity and central obesity were comparable in the two studies. Properly trained non-medical field interviewers can collect reliable anthropometric data in a nationwide, home visit study. This national cohort of young adults in the United States faces a high risk of early-onset chronic disease and premature mortality.

Qualidade dos dados antropométricos dos usuários hipertensos atendidos no Programa de Saúde da Família e sua associação com fatores de risco

O objetivo deste trabalho foi analisar a qualidade dos dados antropométricos dos hipertensos, no Programa de Saúde da Família, e sua associação com obesidade e demais fatores de risco. Trata-se de um estudo de coorte em que foram investigados os dados cadastrais, prontuários e entrevistas dos usuários no município de João Pessoa-PB. Investigou-se a qualidade dos dados, pela imputação dos registros faltantes nos hipertensos, cuja comparação dos indicadores construídos antes e depois foi feita pelo teste t´Student, além de calcular a razão de chances, pela regressão logística, com a finalidade de estabelecer uma possível associação destas variáveis com a variável dependente. Observou-se uma associação significativa entre a obesidade dos hipertensos, com circunferência da cintura e razão cintura quadril (p&lt;0,001). Evidenciou-se, também, uma associação significativa dos níveis de hipertensão e idade (p&lt;0,01). Os resultados sugerem aumento da prevalência da pressão arterial com a idade e sua associação com os indicadores antropométricos.

Virtual Simulation of Plastic Injection Technology for Medical Devices

This paper aims to present the simulation of technological process of components made by plastic injection using a computer within a orthotic device for human knee. Also, the aim of the work is to improve the final product quality and operating properties in different types of design and minimize material requirements for different anthropometric data quality in optimal conditions. The orthotic device addresses to people with walking disabilities, and aims to rehabilitate and ameliorate the pain caused by osteoarthritis disease. For the geometric modeling of components in order to obtain virtual mathematical model study we used ProEngineer application, which allows a detailed design and a high accurate technical approach. The model was imported into the Moldex3D application, being integrated within a XYZ coordinate system.To simulate the injection process we used only the component from the upper part of orthotic device. This paper presents advanced modeling elements, geometric mesh model into an advanced form of nodes and elements, plastic injection process analysis settings. It highlights the tensions, deformations and displacements in these parts during the injection process.