Assessment of the PROBIT approach for estimating the prevalence of global, moderate and severe acute malnutrition from population surveys

Public Health Nutr. 2013 May;16(5):858-63. doi: 10.1017/S1368980012003345. Epub 2012 Jul 27.


Objective: Prevalence of acute malnutrition is classically estimated by the proportion of children meeting a case definition in a representative population sample. In 1995 the WHO proposed the PROBIT method, based on converting parameters of a normally distributed variable to cumulative probability, as an alternative method requiring a smaller sample size. The present study compares classical and PROBIT methods for estimating the prevalence of global, moderate and severe acute malnutrition (GAM, MAM and SAM) defined by weight-for-height Z-score (WHZ) or mid-upper arm circumference (MUAC).

Design: Bias and precision of classical and PROBIT methods were compared by simulating a total of 1·26 million surveys generated from 560 nutrition surveys.

Setting: Data used for simulation were derived from nutritional surveys of children aged 6-59 months carried out in thirty-one countries around the world.

Subjects: Data of 459 036 children aged 6-59 months from representative samples were used to generate simulated populations.

Results: The PROBIT method provided an estimate of GAM, MAM and SAM using WHZ or MUAC proportional to the true prevalence with a small systematic overestimation. The PROBIT method was more precise than the classical method for estimating the prevalence for GAM, MAM and SAM by WHZ or MUAC for small sample sizes (i.e. n<150 for SAM and GAM; n<300 for MAM), but lost this advantage when sample sizes increased.

Conclusions: The classical method is preferred for estimating acute malnutrition prevalence from large sample surveys. The PROBIT method may be useful in sentinel-site surveillance systems with small sample sizes.

Publication types

  • Comparative Study
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Acute Disease
  • Body Height
  • Body Weight
  • Child, Preschool
  • Global Health
  • Humans
  • Infant
  • Malnutrition / diagnosis*
  • Malnutrition / epidemiology*
  • Nutrition Surveys*
  • Prevalence