Calculating Low Birth Weight from DHS

Can Mothers Help Improve Estimation?

Amos Channon, Mac McDonald, Sabu PadmadasUniversity of Southampton

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

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?

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

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

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

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?

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)

Countries in the Analysis

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

New Estimates of % LBW%LBW %LBW

Country <2500g Half ≤2500g Country <2500g Half ≤2500g

Kazakhstan 6.1 7.2 8.2 Zambia 9.1 11.5 13.6

Bolivia 6.9 8.4 9.9 Tanzania 7.6 11.7 15.9

Vietnam 6.5 8.6 10.7 Gabon 12.0 13.8 15.6

Cambodia 5.6 9.1 12.6 Mozambique 11.5 14.4 17.3

Nicaragua 9.6 9.8 10.0 Malawi 9.7 14.9 20.2

Peru 8.0 9.9 11.9 Mali 14.2 17.9 21.6

Zimbabwe 8.5 10.8 3.2 India 21.9 31.2 40.5

UK 8.0 - - Norway 5.0 - -

% 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?

How much Birth Weight is Missing?Country %Missing Country %Missin

g

Kazakhstan

2.9 Tanzania 55.7

Gabon 11.4 Malawi 55.9

Vietnam 19.9 Zambia 57.9

Zimbabwe 24.4 Mozambique

60.5

Nicaragua 29.6 India 75.1

Peru 30.0 Mali 79.4

Bolivia 41.7 Cambodia 84.1

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

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

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?

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?

Mean Weight in Perception Categories

Country V. Small Small Average Large V. Large

India 1860 2278 2806 3318 -

Mali 1925 2433 2918 3456 4075

Vietnam 1934 2456 3060 3473 4149

Zambia 2051 2474 3151 3637 3894

Bolivia 2477 2769 3414 3919 4510

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

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

Distribution of Perception

Very SmallSmall Average LargeVery LargeSize at Birth

0

10

20

30

40

50

60

Perc

ent

Birth Weight

Not Recorded

Recorded

Very Small Small Average Large Very LargeSize at Birth

0

10

20

30

40

50

Per

cent

Birth Weight

Not Recorded

Recorded

Cambodia Mali

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

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%

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%

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%

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

Estimates of % LBW

Country%LBW

BW only

% LBW

– AllCountry

%LBW

BW only% LBW

– All

Kazakhstan

7.2 7.3 Tanzania 11.7 13.7

Vietnam 8.6 9.1 Gabon 13.8 14.0

Bolivia8.4 9.2 Mozambiqu

e14.4 14.8

Nicaragua 9.8 10.9 Malawi 14.9 17.0

Zimbabwe 10.8 11.2 Mali 17.9 24.8

Zambia 11.5 11.9 India 31.2 34.5

Cambodia 9.1 12.9

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

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

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

Gabon

6 4 2 0 2 4 6

<= 11001101 - 12001201 - 13001301 - 14001401 - 15001501 - 16001601 - 17001701 - 18001801 - 19001901 - 20002001 - 21002101 - 22002201 - 23002301 - 24002401 - 25002501 - 26002601 - 27002701 - 28002801 - 29002901 - 30003001 - 31003101 - 32003201 - 33003301 - 34003401 - 35003501 - 36003601 - 37003701 - 38003801 - 39003901 - 40004001 - 41004101 - 42004201 - 43004301 - 44004401 - 45004501 - 46004601 - 47004701 - 48004801 - 49004901 - 50005001 - 51005101 - 52005201 - 53005301 - 54005401 - 55005501 - 56005601 - 57005701 - 58005801 - 5900

5901+

Wei

gh

t (g

)

Percent

Memory Recall Card Recall

India

15 10 5 0 5 10 15

<= 600601 - 700701 - 800801 - 900

901 - 10001001 - 11001101 - 12001201 - 13001301 - 14001401 - 15001501 - 16001601 - 17001701 - 18001801 - 19001901 - 20002001 - 21002101 - 22002201 - 23002301 - 24002401 - 25002501 - 26002601 - 27002701 - 28002801 - 29002901 - 30003001 - 31003101 - 32003201 - 33003301 - 34003401 - 35003501 - 36003601 - 37003701 - 38003801 - 39003901 - 40004001 - 41004101 - 42004201 - 43004301 - 44004401 - 45004501 - 46004601 - 47004701 - 48004801 - 49004901 - 50005101 - 52005201 - 53005401 - 55005501 - 5600

5901+

Memory Recall Card Recall

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

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

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

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