Change in quality of malnutrition surveys between 1986 and 2015

[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

CONTEXT

Poor anthropometric data quality affect the prevalence of malnutrition and could harm public policy planning.

OBJECTIVE

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.

DATA SOURCES

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.

DATA EXTRACTION

Paired researchers selected studies, extracted data, and critically appraised the selected publications.

DATA ANALYSIS

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.

RESULTS

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.

CONCLUSIONS

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.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO registration no. CRD42020208977.

Considerations for assessment of measurement quality of mid-upper arm circumference data in anthropometric surveys and mass nutritional screenings conducted in humanitarian and refugee settings

Despite frequent use of mid-upper arm circumference (MUAC) to assess populations in humanitarian settings, no guidance exists about the ranges for excluding implausible extreme outliers (flags) from MUAC data and about the quality assessment of collected MUAC data. We analysed 701 population-representative anthropometric surveys in children aged 6-59 months from 40 countries conducted between 2011 and 2019. We explored characteristics of flags as well as changes in survey-level MUAC-for-age z-score (MUACZ) and MUAC means, SD and percentage of flags based on three flagging approaches: ±3 and ±4 MUACZ z-scores from observed MUACZ survey mean and a fixed interval 100-200 mm of MUAC. Both ±4 and 100-200 flagging approaches identified as flags approximately 0.15% of records; about 60% of all surveys had no flags and less than 1% of surveys had >2% of flags. The ±3 approach flagged 0.6% records in the data set and 3% of surveys had >2% of flags. Plausible ranges (defined as 2.5th and 97.5th percentiles) for SD of MUACZ and MUAC were 0.8-1.2 and 10.5-14.4 mm, respectively. Survey-level SDs of MUAC and MUACZ were highly correlated (r = 0.68). The average SD of MUACZ was 0.96 using the ±4 flagging approach and 0.94 with ±3 approach. Defining outliers in MUAC data based on the MUACZ approach is feasible and adjusts for different probability of extreme values based on age and nutrition status of surveyed population. In assessments where age is not recorded and therefore MUACZ cannot be generated, using 100-200 mm range for flag exclusion could be a feasible solution.

Measuring results of humanitarian action: adapting public health indicators to different contexts

Humanitarian crises represent a significant public health risk factor for affected populations exacerbating mortality, morbidity, disabilities, and reducing access to and quality of health care. Reliable and timely information on the health status of and services provided to crisis-affected populations is crucial to establish public health priorities, mobilize funds, and monitor the performance of humanitarian action. Numerous efforts have contributed to standardizing and presenting timely public health information in humanitarian settings over the last two decades. While the prominence of process and output (rather than outcome and impact) indicators in monitoring frameworks leads to adequate information on resources and activities, health outcomes are rarely measured due to the challenges of measuring them using gold-standard methods that are difficult to implement in humanitarian settings.

We argue that challenges in collecting the gold-standard performance measures should not be a rationale for neglecting outcome measures for critical health and nutrition programs in humanitarian emergencies. Alternative indicators or measurement methods that are robust, practical, and feasible in varying contexts should be used in the interim while acknowledging limitations or interpretation constraints. In this paper, we draw from existing literature, expert judgment, and operational experience to propose an approach to adapt public health indicators for measuring performance of the humanitarian response across varied contexts.

Contexts were defined in terms of parameters that capture two of the main constraints affecting the capacity to obtain performance information in humanitarian settings: (i) access to population or health facilities; and (ii) availability of resources for measurement. Consequently, 2 × 2 tables depict four possible scenarios: (A) a situation with accessible populations and with available resources; (B) a situation with available resources but limited access to affected populations; (C) a situation with accessible populations and limited resources; and (D) a situation with both limited access and limited resources.

Methods and data sources can vary from large population-based surveys, rapid assessments of populations or health facilities, routine health management information systems, or data from sentinel sites in the community or among facilities. Adapting indicators and methods to specific contexts of humanitarian settings increases the potential for measuring the performance of humanitarian programs beyond inputs and outputs by assessing health outcomes, and consequently improving program impact, reducing morbidity and mortality, and improving the quality of lives amongst persons affected by humanitarian emergencies.

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.

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Unadjusted standard deviation for child malnourishment indicators are above the recommended level of DHS data quality standards.

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Variation in stunting is directly proportional to workload measured by number of eligible children in the PSUs.

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Cross-classified multilevel models show significant team-level contribution in explaining variations in anthropometric scores.

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Team-level contribution to explaining variations in child anthropometric measures is larger than district-level factors.

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The number of days assigned to gather anthropometric measurements should be dependent on the number of eligible respondents in a PSU, which may be identified at the time of mapping & listing, rather than being a fixed number of days across all the states of India.

A method for small-area estimation of population mortality in settings affected by crises

Background

Populations affected by crises (armed conflict, food insecurity, natural disasters) are poorly covered by demographic surveillance. As such, crisis-wide estimation of population mortality is extremely challenging, resulting in a lack of evidence to inform humanitarian response and conflict resolution.

Methods

We describe here a ‘small-area estimation’ method to circumvent these data gaps and quantify both total and excess (i.e. crisis-attributable) death rates and tolls, both overall and for granular geographic (e.g. district) and time (e.g. month) strata. The method is based on analysis of data previously collected by national and humanitarian actors, including ground survey observations of mortality, displacement-adjusted population denominators and datasets of variables that may predict the death rate. We describe the six sequential steps required for the method’s implementation and illustrate its recent application in Somalia, South Sudan and northeast Nigeria, based on a generic set of analysis scripts.

Results

Descriptive analysis of ground survey data reveals informative patterns, e.g. concerning the contribution of injuries to overall mortality, or household net migration. Despite some data sparsity, for each crisis that we have applied the method to thus far, available predictor data allow the specification of reasonably predictive mixed effects models of crude and under 5 years death rate, validated using cross-validation. Assumptions about values of the predictors in the absence of a crisis provide counterfactual and excess mortality estimates.

Conclusions

The method enables retrospective estimation of crisis-attributable mortality with considerable geographic and period stratification, and can therefore contribute to better understanding and historical memorialisation of the public health effects of crises. We discuss key limitations and areas for further development.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12963-022-00283-6.

Weight-for-height is associated with an overestimation of thinness burden in comparison to BMI-for-age in under-5 populations with high stunting prevalence

BACKGROUND

Thinness at <5 years of age, also known as wasting, is used to assess the nutritional status of populations for programmatic purposes. Thinness may be defined when either weight-for-height or body-mass-index-for-age (BMI-for-age) are below -2 SD of the respective World Health Organization standards. These definitions were compared for quantifying the burden of thinness.

METHODS

Theoretical consequences of ignoring age were evaluated by comparing, at varying height-for-age z-scores, the age- and sex-specific cut-offs of BMI that would define thinness with these two metrics. Thinness prevalence was then compared in simulated populations (short, intermediate and tall) and real-life data sets from research and the National Family Health Survey-4 (NFHS-4) in India.

RESULTS

In short (-2 SD) children, the BMI cut-offs with weight-for-height criteria were higher in comparison to BMI-for-age after 1 year of age but lower at earlier ages. In Indian research and NFHS-4 data sets (short populations), thinness prevalence with weight-for-height was lower from 0.5 to 1 years but higher at subsequent ages. The absolute difference (weight-for-height - BMI-for-age) for 0.5-5 years was 4.6% (15.9-11.3%) and 2.2% (19.2-17.0%), respectively; this attenuated in the 0-5 years age group. The discrepancy was higher in boys and maximal for stunted children, reducing with increasing stature. In simulated data sets from intermediate and tall populations, there were no meaningful differences.

CONCLUSIONS

The two definitions produce cut-offs, and hence estimates of thinness, that differ with the age, sex and height of children. The relative invariance, with age and stature, of the BMI-for-age thinness definition favours its use as the preferred index for programmatic purposes.

The impact of improved data quality on the prevalence estimates of anthropometric measures using DHS datasets in India

The importance of data quality to correctly determine prevalence estimates of child anthropometric failures has been a contentious issue among policymakers and researchers. Our research objective was to ascertain the impact of improved DHS data quality on the prevalence estimates of stunting, wasting, and underweight. The study also looks for the drivers of data quality. Using five data quality indicators based on age, sex, anthropometric measurements, and normality distribution, we arrive at two datasets of differential data quality and their estimates of anthropometric failures. For this purpose, we use the 2005-2006 and 2015-2016 NFHS data covering 311,182 observations from India. The prevalence estimates of stunting and underweight were virtually unchanged after the application of quality checks. The estimate of wasting had fallen 2 percentage points, indicating an overestimation of the true prevalence. However, this differential impact on the estimate of wasting was driven by the flagging procedure's sensitivity and was in accordance with empirical evidence from existing literature. We found DHS data quality to be of sufficiently high quality for the prevalence estimates of stunting and underweight, to not change significantly after further improving the data quality. The differential estimate of wasting is attributable to the sensitivity of the flagging procedure.

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

Background

Ensuring 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.

Methods

We 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.

Results

Both 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.

Conclusions

There 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

BACKGROUND

Population-based surveys collect crucial data on anthropometric measures to track trends in stunting [height-for-age z score (HAZ) < -2SD] and wasting [weight-for-height z score (WHZ) < -2SD] prevalence among young children globally. However, the quality of the anthropometric data varies between surveys, which may affect population-based estimates of malnutrition.

OBJECTIVES

We aimed to develop composite indices of anthropometric data quality for use in multisurvey analysis of child health and nutritional status.

METHODS

We used anthropometric data for children 0-59 mo of age from all publicly available Demographic and Health Surveys (DHS) from 2000 onwards. We derived 6 indicators of anthropometric data quality at the survey level, including 1) date of birth completeness, 2) anthropometric measure completeness, 3) digit preference for height and age, 4) difference in mean HAZ by month of birth, 5) proportion of biologically implausible values, and 6) dispersion of HAZ and WHZ distribution. Principal component factor analysis was used to generate a composite index of anthropometric data quality for HAZ and WHZ separately. Surveys were ranked from the highest (best) to the lowest (worst) index values in anthropometric quality across countries and over time.

RESULTS

Of the 145 DHS included, the majority (83 of 145; 57%) were conducted in Sub-Saharan Africa. Surveys were ranked from highest to lowest anthropometric data quality relative to other surveys using the composite index for HAZ. Although slightly higher values in recent DHS suggest potential improvements in anthropometric data quality over time, there continues to be substantial heterogeneity in the quality of anthropometric data across surveys. Results were similar for the WHZ data quality index.

CONCLUSIONS

A composite index of anthropometric data quality using a parsimonious set of individual indicators can effectively discriminate among surveys with excellent and poor data quality. Such indices can be used to account for variations in anthropometric data quality in multisurvey epidemiologic analyses of child health.

Data on acute malnutrition and mortality among under-5 children of pastoralists in a humanitarian setting: a cross-sectional Standardized Monitoring and Assessment of Relief and Transitions Study

Objective

In humanitarian settings, children of pastoralists usually are the increased risk of malnutrition and its related complications. Consequently, as part of the program’s targeted response to the burgeoning malnutrition caseloads, a nutrition and mortality survey was conducted using a global standardized methodology in humanitarian settings in Ikwotos country of the Eastern Equatoria of South Sudan. Additionally, in understanding the intricacies of food diversity consumed in the households, we used infants as a proxy of household feeding and collected information on the range of foods consumed by households.

Data description

Data contained in this note is a standard cross-sectional survey conducted in South Sudan with children between the ages of 6 and 59 months, although the mortality component covered all members of the household. While data for mortality and infant feeding practices were self-reported, the assessment of nutritional status were in accordance to the World Health Organisation’s guidelines for nutrition assessment. Age, sex, height and mid-upper arm circumference data were assessment and malnourished children were classified as those with Z-score between − 2 and − 3 and those above − 3 were classified as severely malnourished.