Transcript Calculating low birth weight from DHS - can mothers' help improve estimation?

Calculating Low Birth Weight from
DHS
Can Mothers Help Improve
Estimation?
Amos Channon, Mac McDonald, Sabu Padmadas
University of Southampton
Outline
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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
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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
Country
<2500g
%LBW
≤2500g
Half
Country
<2500g
≤2500g
Half
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
-
-
5.0
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-
Norway
% 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
%Missing
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
Cambodia
Mali
Birth Weight
Birth Weight
Not Recorded
Not Recorded
Recorded
Recorded
50
60
50
40
Percent
Percent
40
30
30
20
20
10
10
0
Very SmallSmall Average LargeVery Large
Size at Birth
0
Very Small Small Average Large Very Large
Size at Birth
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
– Small
– Average
– Large
– Very Large
– 49.8%
– 41.0%
– 6.8%
– 2.8%
– 2.7%
Example - Malawi
• Distribution of Mothers’ Perception in
those without birth weight:
– Very Small
– Small
– Average
– Large
– Very Large
– 4.2%
– 15.2%
– 58.7%
– 14.3%
– 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
– All
Country
%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
Bolivia
8.4
9.2
Mozambique
14.4
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
9.1
12.9
Cambodia
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
Weight (g)
Memory Recall
Card Recall
5901+
5801 - 5900
5701 - 5800
5601 - 5700
5501 - 5600
5401 - 5500
5301 - 5400
5201 - 5300
5101 - 5200
5001 - 5100
4901 - 5000
4801 - 4900
4701 - 4800
4601 - 4700
4501 - 4600
4401 - 4500
4301 - 4400
4201 - 4300
4101 - 4200
4001 - 4100
3901 - 4000
3801 - 3900
3701 - 3800
3601 - 3700
3501 - 3600
3401 - 3500
3301 - 3400
3201 - 3300
3101 - 3200
3001 - 3100
2901 - 3000
2801 - 2900
2701 - 2800
2601 - 2700
2501 - 2600
2401 - 2500
2301 - 2400
2201 - 2300
2101 - 2200
2001 - 2100
1901 - 2000
1801 - 1900
1701 - 1800
1601 - 1700
1501 - 1600
1401 - 1500
1301 - 1400
1201 - 1300
1101 - 1200
<= 1100
6
4
2
0
Percent
2
4
6
India
Memory Recall
Card Recall
5901+
5501- 5600
5401- 5500
5201- 5300
5101- 5200
4901- 5000
4801- 4900
4701- 4800
4601- 4700
4501- 4600
4401- 4500
4301- 4400
4201- 4300
4101- 4200
4001- 4100
3901- 4000
3801- 3900
3701- 3800
3601- 3700
3501- 3600
3401- 3500
3301- 3400
3201- 3300
3101- 3200
3001- 3100
2901- 3000
2801- 2900
2701- 2800
2601- 2700
2501- 2600
2401- 2500
2301- 2400
2201- 2300
2101- 2200
2001- 2100
1901- 2000
1801- 1900
1701- 1800
1601- 1700
1501- 1600
1401- 1500
1301- 1400
1201- 1300
1101- 1200
1001- 1100
901- 1000
801- 900
701- 800
601- 700
<= 600
15
10
5
0
5
10
15
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