Analysis of surveys • Effect of measurement error • Shape of distribution as population gets more malnourished. • Possibility of development of new methods • Age/ height profile during famine • The problem of oedema • Implications for relief programs
Effect of measurement errors on survey results Suppose an imprecise error moves a value from one segment to another (up or down) If the errors are random then the same number of values will move from one half of the distribution to the other (orange to green and green to orange). There will be no change in the mean of the distribution provided that there are as many positive as negative measurement errors
Effect of measurement errors on survey results • If there is an error that moves a value from one segment to the other in the tail then there will be more points moving from the orange to the green than from the green to the orange in relation to the respective areas • There will be an increase in the Standard Deviation. • There will be an increased prevalence of values below –2Z and also below –3Z
Effect of measurement errors on survey results • A change in the standard deviation from 1.0 to 1.2 will have a major effect upon the prevalence of moderate and severe malnutrition. • Imprecise measurements are potentially a major cause of error in surveys. • The prevalence will be exaggerated even if the positive and negative errors balance each other out.
Effect of SD of survey on prevalence of wasting • Wide SD, from measurement error can increase prevalence dramatically • Narrow SD from “over cleaning”, selection or bias can reduce the prevalence of malnutrition
Effect of measurement errors on survey results MonteCarlo Simulation 1 Take a normally distributed population with mean Z-score of minus 1 Z-score WFH and Standard deviation of 1 Z-score unit. 2 Introduce an error height with a mean of 0.0cm and an SD of 1.0cm. 3 Introduce an error in weight with a mean of 0g and an SD of 100g 4 Introduce a 5 cm height error and 500g weight error to 0.25% of population 5 Introduce a 10cm height error and 1kg weight error to 0.15 of population • Introduce a random error in height of 0.5cm to account for diurnal variation • Introduce a random error in weight of up to 200g (mean error 0, SD error 200g) to allow for meal, fluid/excreta, and diurnal changes. 5 Remove all flags (plus or minus more than 4SD from population mean)
SD does not change as the population becomes more malnourished • All individuals in the population are affected by the situation to a similar extent • The whole distribution moves downwards • As this happens more and more are “recruited to the ranks of the malnourished” • Movement in the mean WFH (and prevalence of malnutrition) is not necessarily a “trailing indicator” in population terms.
The Kurtosis of the distribution becomes more, not less, normal • Kurtosis of weight-for-height distributions in surveys of children aged 6-59 months as the population becomes progressively more wasted. • p<0.001.
There is slight Skewness at with a very thin of fat population • Skewness in weight-for-height distributions in 228 surveys of children aged 6-59 months as the population becomes progressively more wasted. • r=0.33 p < 0.001
The population remains normally distributed as wasting increases • Normal probability plots of weight-for-height z-scores of individual subjects from illustrative surveys. • The skewness of the reference population may be because of inclusion of obese individuals in the NCHS population.
Deviation from normality of the distribution curves • The maximum deviation of individual data points from a normal (Gaussian) distribution, determined by the Kolmorgorov-Smirnov procedure. SND = Standard Normal Deviate • The mathematical calculation of the prevalence of malnutrition gives the same values as with counting the number observed. • Smaller numbers are then needed to give an estimate with the same confidence interval
The relative importance of moderate (SFC) and severe (TFC) malnutrition varies with the level of malnutrition in the population
The actual Observed and theoretical prevalence of malnutrition are related within the confidence intervals of the survey – maybe calculation would be more precise?
Wasting by height group as the population nutritional state deteriorates:All groups are affected – as the situation becomes desperate older children have a high prevalence
The problem of oedema • 82% of cases in surveys are not wasted • This is so even when an adjustment is made for the weight of the oedema • A population without excess global malnutrition can develop odematous malnutrition rapidly • SFP will not prevent oedematous malnutrition in the population because moderate wasting is not a forerunner.
The problem of oedema 2 • The time course of oedema is short -- days whereas that of marasmus is weeks or months. Even a low prevalence of oedema indicates a reasonably high incidence. • Surveys can not be used to estimate the number of patients that need treatment or the “coverage” of programs. • The implications of oedema and severe wasting are different and should be reported seperately.
Anthropometric surveys give prevalence: incidence is needed to plan curative services50 red cases of Kwashiorkor and 20 cases of wasting
The problem of oedema 3 • The counts of children with wasting in each cluster follows a Poisson distribution in a well taken survey. • The counts of children with oedema in each cluster follows a negative-binomial distribution. That is there are small “pockets” of oedema with many cases in a few clusters and no cases in most clusters • The estimate of oedema prevalence is much less certain than that of wasting.
Oedematous malnutrition does not have any relationship to the prevalence of wasting
Conclusions • Surveys give a measure of the severity of a crisis • They require careful planning, experienced supervisors, good training and quality assurance • The internal structure of the data can be used to examine how reliable an anthropometric survey is. There are sufficient “good” surveys to set limits on the deviation that is acceptable • New windows-based, user-friendly software should be developed specifically to analyse anthropometric surveys. It should indicate if one team is giving aberrant results in time to correct the defects.
Conclusions 2 • Who becomes malnourished may relate more closely to location than other vulnerabilty indicators • unreliable to judge the numbers of beneficiaries who will require relief (incidence-vs-prevalence) • should be combined with surveillance data