Sources of measurement variation in child anthropometry in the Malawi maternal and child nutrition study

Pelletier, David; Low, Jan; Msukwa, Louis A. H.

doi:10.1002/ajhb.1310030302

Cited by 12 publications

(5 citation statements)

References 26 publications

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“…In the MMCN study special effort was made to measure children with minimal clothing, with the same scales that were calibrated regularly, and by the same enumerators within any given round of data collection. In addition, measurement technique was checked regularly by supervisors during routine visits A formal test-retest study performed under classroom-type conditions between updates I and 2 revealed that only 1.7% of the total variance in weight among these children was due to intra-observer and inter-observer error, comparable to results obtained in other field studies [12].…”

Section: Reliability Of Weight Measurementssupporting

confidence: 55%

The Validity of Clinic-Based Nutrition Surveillance Data: A Study from Selected Sites in Northern Malawi

Dl¹,

Fc²

1994

Food Nutr Bull

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Many developing countries collect on the weight for age of children attending health facilities as one element of a nutrition surveillance system. This study compares the estimates of malnutrition from seven health clinics in northern Malawi with estimates derived from nearby community-level surveys. The results show that prevalence of underweight in clinics does not accurately reflect community prevalence. Clinic estimates often differ by two-to threefold from community estimates, and the direction of the bias is not constant across clinics, making these data an invalid basis for targeting programmes according to nutritional need. Similar results were reported in five other studies in the literature, indicating that the Malawi results are not unusual. It is suggested that, contrary to current practice, cross-sectional clinic-based data should be assumed invalid for targeting purposes unless proved otherwise in a given country. Trend data at regional and country levels require further validation.

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Section: Reliability Of Weight Measurementssupporting

confidence: 55%

The Validity of Clinic-Based Nutrition Surveillance Data: A Study from Selected Sites in Northern Malawi

Dl¹,

Fc²

1994

Food Nutr Bull

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“…These can be identified using on-site digital data input or in-field checks [4]. Second, fieldworker variation has the potential to dramatically affect the output of analyses using the recorded data although this source of data error may be less apparent than error due to variation in instrument use [5]. Moreover, fieldworkers tend to have a subtle directional bias in their measurements, which may go unnoticed without undertaking specialized analyses (for example digit preference) and can often be exacerbated when there are multiple fieldworkers collecting data [6,7].…”

Section: Introductionmentioning

confidence: 99%

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?

Corsi

Perkins

Subramanian

2017

Global Health Action

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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.

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“…The earlier measurement error literature focusing on adults (see references in Jamison and Zegura, 1974;Lohman et al, 19881, generally described intraobserver and/or interobserver error, usually with two or more trials by the same or different investigators spanning a very brief period of time. In contrast, for many of the recent auxological studies (Cameron, 1986;Himes, 1989;Martorell et al, 1975;Pelletier et al, 1991) the problems were broader, i.e., multiple observers working with children of different ages and repeat measurements spanning long enough periods of time that the subject would be expected to change from one session to the next. Thus Habicht et al (1979) and others who followed their approach (see Mueller and Martorell, 1988) not only defined and discussed reliability, they also calculated unreliability, precision and imprecision, and dependability and undependability.…”

Section: Methodsmentioning

confidence: 99%

Brief communication: Measurement size, precision, and reliability in craniofacial anthropometry: Bigger is better

Jamison

Ward

1993

American J Phys Anthropol

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In this paper we examine the results of an intraobserver measurement error study involving 49 craniofacial variables that ranged in size from less than 1 cm to approximately 20 cm. Repeat measurements were taken on 10 male and 10 female adult subjects (19-59 years old). Our focus is on the relationship between measurement size and measurement error across the 49 variables. We found that the size of the variable showed no relationship with the magnitude of the error as measured by the technical error of measurement. When the error was expressed as a coefficient of relative variation (Malina et al.: Vital and Health Statistics, Series 11, No. 23. Washington, DC: US Department of Health and Human Services, 1973), this quantity was negatively associated with the size of the measurement. Conversely, reliability (Fleiss: The Design and Analysis of Experiments. New York: John Wiley & Sons, 1986) was positively correlated with measurement size. We did not find effects of scale (Marks et al.: Am. J. Epidemiol. 130:578-587, 1989) within the individual measurements. Thus, for the range of size of the craniofacial measurements in this study, measurement size must be added to the list of factors such as ease of locating landmarks, measurement technique, and systematic bias in the application of the technique that can affect precision and reliability in anthropometry.

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Sources of measurement variation in child anthropometry in the Malawi maternal and child nutrition study

Cited by 12 publications

References 26 publications

The Validity of Clinic-Based Nutrition Surveillance Data: A Study from Selected Sites in Northern Malawi

The Validity of Clinic-Based Nutrition Surveillance Data: A Study from Selected Sites in Northern Malawi

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?

Brief communication: Measurement size, precision, and reliability in craniofacial anthropometry: Bigger is better

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