1. Calculating Low Birth Weight from DHS Can Mothers Help Improve Estimation? Amos Channon, Mac McDonald, Sabu Padmadas University of Southampton

2. Outline Why is birth weight important? Data used in the study Calculating the proportion with LBW Problems with the estimates Using mothers to improve estimation Problems with the method How is birth weight recalled? Conclusions & future direction

3. Overview United Nations targeted a fall of one-third in the proportion of children with Low Birth Weight (LBW) by 2010 Important to know the current proportions in order to measure fall in LBW accurately Problems with measuring birth weight in developing countries Are there adjustments that can be made?

4. Importance of Birth Weight Greatest predictor of infant death Can be used both as an outcome or as a predictor Many models regarding mortality at younger ages include birth weight as a proxy for health at birth Longer term health problems

5. Causes of Differences in Weight Natural variability Inter-uterine growth retardation Prematurity Distinction between the two types in developing countries? Proximate causes are medical/biological Related to many social and demographic factors

6. Low Birth Weight What is Low Birth Weight? –WHO Definition: Less than 2500g Not an absolute marker for increased risk –Risk of infants dying who weigh 2450g and 2550g is similar Continuous scale better, but issues with measurement and targeting of at-risk infants

7. Research Questions How accurate are the estimates of LBW in different countries? Can alternative methods be used to calculate proportion with LBW to improve accuracy?

8. Data Initially 15 DHS surveys used Used DHS+ conducted since 1997 Attempted to get some geographical grouping Final analysis contained 13 countries –Problems with the data in 2 countries (Haiti and Peru)

9. Countries in the Analysis

10. Calculating LBW Only single births studied Births within the five years before survey Excluded births weighing over 6kg Reported weights taken as correct Percentage LBW depends on how those weighing 2500g are treated

11. New Estimates of % LBW %LBW Country<2500gHalf≤2500gCountry<2500gHalf≤2500g Kazakhstan 6.17.28.2 Zambia 9.111.513.6 Bolivia 6.98.49.9 Tanzania 7.611.715.9 Vietnam 6.58.610.7 Gabon 12.013.815.6 Cambodia 5.69.112.6 Mozambique 11.514.417.3 Nicaragua 9.69.810.0 Malawi 9.714.920.2 Peru 8.09.911.9 Mali 14.217.921.6 Zimbabwe 8.510.83.2 India 21.931.240.5 UK 8.0-- Norway 5.0--

12. % with LBW Great variability in estimates depending on how those at 2500g are treated –% heaped at 2500g ranges from 0.4% in Nicaragua to 18.7% in India Does this represent the full story? What percentage of the sample gave a birth weight?

13. How much Birth Weight is Missing? Country%MissingCountry%Missing Kazakhstan2.9Tanzania55.7 Gabon11.4Malawi55.9 Vietnam19.9Zambia57.9 Zimbabwe24.4Mozambique60.5 Nicaragua29.6India75.1 Peru30.0Mali79.4 Bolivia41.7Cambodia84.1

14. Does the Missing Data Matter? Can the infants with reported birth weights be used to calculate proportion with LBW? Do those with a birth weight differ from those without a birth weight? Logistic regression conducted on likelihood of birth weight being missing –‘1’ Available –‘0’ Missing

15. Results Significant in nearly all countries –Place of Delivery, Survival Status; Paternal Education Significant in most countries –Prenatal Care, Urban/Rural, Maternal Education Significant in few or in no countries –Gender, Marital Status, Religion

16. Using Mothers’ Perception Most DHS+ surveys included the question: ‘When (NAME) was born, was he/she: very large, larger than average, average, smaller than average or very small?’ Can the responses to this question be used to improve estimation?

17. Missing Data in Mothers Perception Amount of Missing Data is low: –0.1% in Vietnam and Tanzania –3.5% in Mali –Most countries under 1% missing How accurate are these reports? –Do they agree with the reported birth weights?

18. Mean Weight in Perception Categories CountryV. SmallSmallAverageLargeV. Large India1860227828063318- Mali19252433291834564075 Vietnam19342456306034734149 Zambia20512474315136373894 Bolivia24772769341439194510

19. Mean Weight Clear that the mean weight is closely aligned to perception All countries’ mean weights in V Small category <2500g Half of the countries’ mean weights in Small category <2500g Perception seems to be fairly consistent with weight

20. Perception for those With and Without Birth Weight Difference in the distribution of perception between those who report a birth weight and those who do not Distribution is shifted towards the smaller categories in those without a birth weight To be expected as this group have attributes which make smaller infants more likely

21. Distribution of Perception CambodiaMali

22. Method to Combine Perception and Weight Using those with weights, calculate the proportion of LBW infants in each perception group for each country Apply these proportions to those without a birth weight Combine the % with LBW for those with a birth weight with the estimated % with LBW for those without a birth weight

23. Example - Malawi % LBW from Birth weight only = 9.7% Proportion of LBW by perception categories: –Very Small – 49.8% –Small – 41.0% –Average – 6.8% –Large – 2.8% –Very Large – 2.7%

24. Example - Malawi Distribution of Mothers’ Perception in those without birth weight: –Very Small – 4.2% –Small – 15.2% –Average – 58.7% –Large – 14.3% –Very Large– 7.6%

25. Example - Malawi Apply LBW percentages to the corresponding categories % LBW for those without birth weight = 12.9% % LBW for those with a birth weight = 9.7% Combined % with LBW = 11.5%

26. Combined Estimated % LBW All countries estimates of % LBW rise Amount of increase depends on how those weighing 2500g are treated –Treated half those with weight of 2500g as LBW

27. Estimates of % LBW Country %LBW BW only % LBW – All Country %LBW BW only % LBW – All Kazakhstan 7.27.3Tanzania11.713.7 Vietnam 8.69.1Gabon13.814.0 Bolivia 8.49.2Mozambique14.414.8 Nicaragua 9.810.9Malawi14.917.0 Zimbabwe 10.811.2Mali17.924.8 Zambia 11.511.9India31.234.5 Cambodia9.112.9

28. Problems with Method Assumes that the relationship between perception and birth weight is the same for those with and without a recorded birth weight Assumes births with a birth weight are as likely to be LBW as those without a birth weight

29. Problems with Method Assumes that the reported birth weight is correct: –A few infants reported as being over 6kg were excluded (13 ¼ pounds) –What are the mothers judging the size against? –Large differences in the distribution of birth weight between weights recalled from memory and those recalled from a birth card

30. Recall Method Weights recalled from memory are: –Greatly heaped –Show greater variability –Less reliable? –More likely to be LBW Using memory recalled weights as reference further increases % with LBW

31. Gabon

32. India

33. Memory vs. Card Recall Large differences in distribution between recall methods Card recall appears more normal –But still is heaped in some countries Memory recall can be good Proportion with LBW should be calculated with reference to recall method

34. Conclusions Calculating %LBW from available weights underestimates true proportion Mothers’ perception of the size of the baby generally agrees with recorded birth weight Use of the mothers’ perception to estimate %LBW is a useful tool to obtain more accurate estimates

35. Conclusions (2) Need to be aware of difference between birth card recalled and memory recalled weights. –Memory recalled weights are likely to be less reliable in most countries Heaping of birth weights causes uncertainty over true level of LBW

36. Future Research Which mothers are more accurate in determining the size of their child? What are the determinants of the perception of a childs’ size? Using different imputation methods to impute birth weight and the relationship with infant mortality