The PROBIT approach in estimating the prevalence of wasting: revisiting bias and precision

Emerging Themes in Epidemiology

Oleg O Bilukha¹  Curtis J Blanton¹ 

[1] International Emergency and Refugee Health Branch Centers for Disease Control and Prevention, 4770 Buford Hwy. MS-F-57, 30341 Atlanta, GA, USA

关键词: Surveys;    Probit;    Z-score;    Weight-for-Height;    Wasting;    Child Malnutrition;   

Others  :  803709
DOI  :  10.1186/1742-7622-10-8

 received in 2013-02-20, accepted in 2013-08-20,  发布年份 2013

【 摘 要 】

Background

The PROBIT methodology was presented in the 1995 World Health Organization Technical Report on Anthropometry as an alternative to the standard prevalence based method of measuring malnutrition in children. Theoretically the PROBIT method will always give a smaller standard error than the standard prevalence method in measuring malnutrition. A recent article by Dale et al. assessed the PROBIT method for measuring global acute malnutrition measure and found that the method was biased and the precision was superior only for sample sizes less than 150 when compared to the standard method. In a manner similar to Dale, our study further investigated the bias and precision of the PROBIT method for different sample sizes using simulated populations.

Results

The PROBIT method showed bias for each of the ten simulated populations, but the direction and magnitude of the average bias was changed depending on the simulated population. For a given simulated population, the average bias was relatively constant for all sample sizes drawn. The 95% half-width confidence interval was lower for the PROBIT method than the standard prevalence method regardless of the sample size or simulated population. The absolute difference in the confidence limits showed the most gains for the PROBIT method for the smaller samples sizes, but the ratio of confidence intervals was relatively constant across all sample sizes.

Conclusions

The PROBIT method will provide gains in precision regardless of the sample size, but the method may be biased. The direction and magnitude of the bias depends on the population it is drawn from.

【 授权许可】

   
2013 Blanton and Bilukha; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

Figure 2.

【 参考文献 】

• [1]World Health Organization: WHO technical report series 854, physical status: the use and interpretation of anthropometry: report of a WHO expert committee. Geneva: WHO; 1995.
• [2]Freund J, Walpole R: Mathematical statistics. New Jersey: Prentice Hall; 1987.
• [3]Tukey JW, McLaughlin DH: Less vulnerable confidence and significance procedures for location based on a single sample: trimming/winsorization 1. Sankhya Ser A 1963, 25(3):331-352.
• [4]Dale NM, Myatt M, Prudhon C, Briend A: Assessment of the PROBIT approach for estimating the prevalence of global, moderate and severe acute malnutrition from population surveys. Public Health Nutr 2013, 16(5):858-863.
• [5]Box GEP, Cox DR: An analysis of transformations. J Roy Stat Soc B 1964, 26(2):211-252.
• [6]Bilukha O, Howard C, Wilkinson C, Bamrah S, Husain F: Effects of multimicronutrient home fortification on anemia and growth in Bhutanese refugee children. Food Nutr Bull 2011, 32(3):264-276.
• [7]World Health Organization: WHO child growth standards: length/height-for-age, weight-for-age, weight-for-height and body mass index for- age: methods and development. Geneva: World Health Organization; 2006.
• [8]ENA for SMART 2011: Software for emergency nutrition assessment. 2011. http://www.nutrisurvey.de/ena2011/ webcite
• [9]Shapiro SS, Wilk MB: An analysis of variance test for normality (complete samples). Biometrika 1965, 52(3/4):591-611.
• [10]SAS: SAS/STAT(R) 9.3 user’s guide. 2011. http://support.sas.com/documentation/cdl/en/statug/63962/PDF/default/statug.pdf webcite