Transcript Data sources for measuring maternal mortality, 2010.

Data sources for measuring
maternal mortality
November 1, 2010
Rafael Lozano
Professor of Global Health
Outline
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Input data and correction process by source
PMDF to maternal deaths to rates
Modeling approaches I: linear models
Outlier detection
Modeling approaches II: space-time regression
Predictive validity
Uncertainty
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Processing input data
Raw
• Identify and obtain micro-data from a range of
sources
• Correct raw data for known biases, using
methods dependent on the type of data
Corrected
source
• Calculate the proportion of all deaths among
women aged 15-49 which are due to maternal
Final input
causes (PMDF)
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Four major categories of data
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Vital registration
Deaths in the household data from censuses and surveys
Sibling histories from surveys
National and subnational peer reviewed studies of maternal
mortality (i.e. verbal autopsy studies, etc)
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Four major categories of data
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Vital registration
Deaths in the household data from censuses and surveys
Sibling histories from surveys
National and subnational peer reviewed studies of maternal
mortality (i.e. verbal autopsy studies, etc)
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Sources for vital registration data
• WHO Mortality Database
o Reported civil registration data from countries
o Periodically updated and released by WHO
• Country websites and official publications
• Sample registration systems, such as in India or China
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Issues with vital registration
• Changes in the International Classification of Diseases (ICD)
results in changes in coding assignments to underlying causes
of death
• The use of tabulation lists in the ICD results in the loss of
substantial detail of cause of death
• Deaths can be (and often are) assigned to causes that should
not be considered underlying causes of death (garbage codes)
Together, this means that what counts as a “maternal death” in
one country in one year, may not count as a “maternal death” in
another country or another year.
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Correcting vital registration
• Shortened cause of death list: 56 causes of interest to public
health practitioners
o Causes mapped across ICD revisions to these 56 causes
o Maternal conditions encompass all O codes (O00 – O99)
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Garbage codes
• Garbage coding is the biggest challenge to comparability
across countries and over time in vital registration data
• Garbage codes: assigned causes of death which are not useful
for public health analysis of cause-of-death data
• General approach to address problem:
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Identify garbage codes
2.
Identify target codes to which garbage codes should be
reassigned
3.
Choose the fraction of deaths assigned to a garbage code that
should be reassigned to each target code
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Fraction of deaths assigned to GCs in the latest
ICD-10 year since 2000
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Garbage codes
• General approach to address problem:
1.
Identify garbage codes
2.
Identify target codes to which garbage codes should be
reassigned
3.
Choose the fraction of deaths assigned to a garbage code that
should be reassigned to each target code
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Redistribution of garbage codes
1. Identify garbage codes
4 classifications of garbage codes:
• Type 1: Causes that should not be considered underlying causes of
death
• i.e. R95-R99: Ill-defined and unknown causes of mortality
• Type 2: Intermediate causes of death
• i.e. I51: Heart failure
• Type 3: Immediate causes of death
• i.e. E87: Other disorders of fluid, electrolyte and acid-base balance
• Type 4: Unspecified causes within a larger grouping
• i.e. Malignant neoplasm without specification of site
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Percentage of Type of Garbage Codes
All country years by ICD
All country years by age, only ICD 10
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Redistribution of garbage codes
2. Identify target codes to which garbage codes should be
reassigned
• Based on pathophysiology, i.e.:
Garbage code
Target causes
Digestive diseases
Peritonitis
Genitourinary diseases
Maternal conditions
Injuries
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Redistribution of garbage codes
3. Choose the fraction of deaths assigned to a garbage code
that should be reassigned to each target code
3 approaches:
i.Proportionate redistribution
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For causes with little information content
ii.Statistical models
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For heart failure
iii.Expert judgment
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Via review of published literature and consultation with experts,
taking into account time trends in causes of death
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Garbage codes redistributed to maternal
causes, based on expert judgment (ICD-10)
ICD-10 code Condition
Disseminated intravascular
D65
coagulation [defibrination syndrome]
K65
Peritonitis
Fraction to maternal
30%
20%
A40
Streptococcal septicaemia
A41
Other septicaemia
I26
Pulmonary embolism
10%
K66.0
Peritoneal adhesions
50%
N17
Acute renal failure
N18
Chronic renal failure
N19
Unspecified renal failure
R57.9
Shock, unspecified
25%
R57.1
Hypovolaemic shock
35%
14%
0.4%
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Garbage codes redistributed to maternal
causes, based on proportions (ICD-10)
ICD-10 code
Condition
R99
R98
R09.2
R96.0
R68.8
R55
R50.9
R96.1
R57.0
R56.8
Other ill-defined and unspecified causes of mortality
Unattended death
Respiratory arrest
Instantaneous death
Other specified general symptoms and signs
Syncope and collapse
Fever, unspecified
Death occurring less than 24 hours from onset of symptoms, not otherwise explained
Cardiogenic shock
Other and unspecified convulsions
R62.8
R10.4
R58
R57.1
R09.0
R02
R40.2
R04.8
Other lack of expected normal physiological development
Other and unspecified abdominal pain
Haemorrhage, not elsewhere classified
Hypovolaemic shock
Other symptoms and signs involving the circulatory and respiratory systems
Gangrene, not elsewhere classified
Coma, unspecified
Haemorrhage from other sites in respiratory passages
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Maternal Mortality Audit Studies
• 32 studies have been published that use detailed audits of
reproductive-aged deaths to ascertain the true number of
maternal deaths compared to those registered.
• Assessment of these studies should exclude late maternal
deaths and incidental causes to make them comparable to the
GC algorithms for maternal mortality estimation.
o 30 studies identify either late maternal and incidental deaths, but
only 5 studies identify both
• These studies provide an opportunity to validate the GC
approach to maternal death correction.
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Published Studies on Maternal Death
Misclassification
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Four major categories of data
•
•
•
•
Vital registration
Deaths in the household data from censuses and surveys
Sibling histories from surveys
National and subnational peer reviewed studies of maternal
mortality (i.e. verbal autopsy studies, etc)
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Deaths in the household
• Some censuses and surveys include a module on deaths
occurring in the household over a specified period of time
o Was the deceased between the ages 15-49 and female?
o If yes: did she die while pregnant? During child birth? In the 6
weeks after giving birth or terminating the pregnancy?
• Direct questioning about events in the household tends to lead
to undercounting of vital events
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Gold Standard
1995.5 PHL DHS
Household A
Household B
04
510 9
-1
15 4
-1
20 9
-2
25 4
-2
30 9
-3
35 4
-3
40 9
-4
45 4
-4
50 9
-5
55 4
-5
60 9
-6
65 4
70 69
-7
75 4
-8
0
04
510 9
-1
15 4
-1
20 9
-2
25 4
-2
30 9
-3
35 4
-3
40 9
-4
45 4
-4
50 9
-5
55 4
-5
60 9
-6
65 4
-6
70 9
-7
75 4
-8
0
-8
-6
-4
-2
0
Household Deaths are Usually
Undercounts
Bohol
age-specific mortality rates
male
female
age group
Household C
Household D
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Four major categories of data
•
•
•
•
Vital registration
Deaths in the household data from censuses and surveys
Sibling histories from surveys
National and subnational peer reviewed studies of maternal
mortality (i.e. verbal autopsy studies, etc)
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Survey data for maternal mortality
• Difficult to capture in a survey because maternal deaths are
rare – a very large sample size required
• Sibling histories yield high return of observations per
respondent
• Availability of large datasets with information on sibling survival
from household surveys
o DHS “maternal mortality” module
o CDC Reproductive Health Surveys
• However, naïve analysis of sibling histories can be misleading
o Survivor bias
o Recall bias
Gakidou-King weights
• An algebraic correction for underrepresentation of high mortality
families
• “Upweight” observations from high mortality families
• Calculate a family-level weight in the survey micro-data
Wf 
Bf
Sf
where W f is a familylevel weight,
B f  originalsibship size, and
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S f  number of siblings survivingto the timeof thesurvey.
This weight (Wf =Bf /Sf) is the inverse of the probability of surviving
to the time of the survey
o Similar to a population sampling weight: the inverse of the probability
of selection into the sample
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Four major categories of data
•
•
•
•
Vital registration
Sibling histories from surveys
Deaths in the household data from censuses and surveys
National and subnational peer reviewed studies of
maternal mortality (i.e. verbal autopsy studies, etc)
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Literature review to identify studies
• In PubMed, searched for “maternal mortality” and “country
name”
• Included studies had to be peer-reviewed, population-based,
and provide clear description of methods
9,659 titles
593 abstracts
209 papers
61 extracted
• 25 additional verbal autopsy studies which included “maternal”
in the cause list
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Final Database by Source
Source of Data
Site-Years of Observation
Vital registration
2186
Sibling Histories
204
Surveillance Systems
20
Census/Survey Deaths in Household
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National VA
35
Subnational VA
180
Total
2651
• No data for 21 countries, representing 2.2% of births
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Density of site-years of observation,
1980-2008
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Density of site-years of observation,
1980-2008
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Data sources in each country
• National and subnational sources included
• Since the time of publication, new data sources have come to
light:
o Italicized: incorporated into model since the Lancet 2010
publication
o Italicized and in blue font: sources that we are aware of but have
not yet identified and incorporated
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Bangladesh
Nationally representative data sources
Year
Source
2000-2001
Bangladesh Maternal Mortality and Maternal Health Services
Survey (BMMS) household deaths module, microdata
2001
Bangladesh Maternal Mortality and Maternal Health Services
Survey (BMMS) sibling history microdata
Sub-nationally representative data sources
Year
Source
1980-2006
Matlab Demographic Surveillance Site
1982
Alauddin M. Maternal mortality in rural Bangladesh: the Tangail district. Stud.Fam.Plann.
1986;17(1):13-21.
Khan AR, et al. Maternal mortality in rural Bangladesh: the Jamalpur district. Stud.Fam.Plann.
1986:7-12.
Fauveau V, et al.. Effect on mortality of community-based maternity-care programme in rural
Bangladesh. The Lancet 1991;338(8776):1183-1186.
INDEPTH
1983
1987
2000
2003
Chowdhury ME, et al. Determinants of reduction in maternal mortality in Matlab,
Bangladesh: a 30-year cohort study. The Lancet 2007;370(9595):1320-1328.
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Bhutan
Nationally representative data sources
Year
Source
2005
Tabulated census household deaths data
Sub-nationally representative data sources
Year
Source
37
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Cambodia
Nationally representative data sources
Year
Source
2000
Demographic and Health Survey (DHS) sibling history microdata
2005
Demographic and Health Survey (DHS) sibling history microdata
2008
Tabulated census household deaths data
Sub-nationally representative data sources
Year
Source
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India
Nationally representative data sources
Year
Source
1982, 1997, National Sample Registration Scheme (SRS)
1999, 2001,
2002, 2004
1992
National Family Health Survey I microdata (deaths in the
household)
1998
National Family Health Survey II microdata (deaths in the
household & VA)
1999-2004
District Level Household Survey (DLHS) II microdata (deaths in
the HH)
2002
Special Survey – Nationwide
2004-2008
District Level Household Survey (DLHS) III microdata (deaths in
the HH)
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India, continued
Sub-nationally representative data sources
Year
Source
1980-1998
Survey of Causes of death (SCD)
1986
Bhatia JC. Levels and causes of maternal mortality in southern India. Stud.Fam.Plann. 1993;24(5):310318.
Gupta N, et al. Maternal mortality in seven districts of Uttar Pradesh - an ICMR Task Force Study. Indian
Journal of Public Health 2006;50(3):173-178.
Medical Certification of Causes of Death (MCCD9)
1989
1990-1998
1992
1992
1999-2001
2000
2002
2004
2005
2007
Kumar R, et al. Maternal mortality inquiry in a rural community of north India. International Journal of
Gynecology & Obstetrics 1989;29(4):313-319.
Kakrani V, et al. A study of registration of deaths at primary health centre-with special reference, to.
Indian J.Med.Sci. 1996;50(6):196.
Medical Certification of Causes of Death (MCCD10)
Singh RB, Singh V, Kulshrestha SK, Singh S, Gupta P, Kumar R, et al. Social class and all-cause mortality in
an urban population of North India. Acta Cardiol. 2005 Dec;60(6):611-617.
Iyengar K, et al. Pregnancy-related deaths in rural Rajasthan, India: exploring causes, context, and careseeking through verbal autopsy. Journal of Health, Population and Nutrition 2009;27(2):293.
Joshi R, et al. Verbal autopsy coding: are multiple coders better than one? Bull.World Health Organ.
2009;87:51-57.
Barnett S, et al. A prospective key informant surveillance system to measure maternal mortality findings from indigenous populations in Jharkhand and Orissa, India. BMC Pregnancy Childbirth 2008
Feb 28;8:6.
Dongre A, et al. A community based cross sectional study on feasibility of lay interviewers in
ascertaining causes of adult deaths by using verbal autopsy in rural Wardha. Online Journal of Health
And Allied Sciences 2009;7(4).
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Indonesia
Nationally representative data sources
Year
Source
1994
Demographic and Health Survey (DHS) sibling history microdata
1997
Demographic and Health Survey (DHS) sibling history microdata
2002
Demographic and Health Survey (DHS) sibling history microdata
2007
Demographic and Health Survey (DHS) sibling history microdata
Sub-nationally representative data sources
Year
1981
2006
Source
Fortney JA, et al. Reproductive mortality in two developing countries. Am.J.Public Health
1986 Feb;76(2):134-138.
Ronsmans C, et al. Professional assistance during birth and maternal mortality in two
Indonesian districts. Bull.World Health Organ. 2009 Jun;87(6):416-423.
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Lao, People’s Democratic Republic of
Nationally representative data sources
Year
1990
Source
Fauveau VA. The Lao People's Democratic Republic: maternal mortality and female mortality:
determining causes of deaths. World Health Stat.Q. 1995;48(1):44-46.
Sub-nationally representative data sources
Year
Source
Sources that could potentially be incorporated, with access
1995
Census data
2005
Census data
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47
Nepal
Nationally representative data sources
Year
Source
1996
Demographic and Health Survey (DHS) sibling history microdata
2006
Demographic and Health Survey (DHS) sibling history microdata
Sub-nationally representative data sources
Year
Source
Sources that could potentially be incorporated, with access
2008-2009
National maternal mortality enquiry
48
49
Pakistan
Nationally representative data sources
Year
Source
1993-1994
Vital registration
2006
Demographic and Health Survey (DHS) Verbal autopsy microdata
Sub-nationally representative data sources
Year
1986, 1990
Source
Fikree FF, et al. Maternal mortality in different Pakistani sites: ratios, clinical causes and
determinants. Acta Obstet.Gynecol.Scand. 1997;76(7):637-645.
50
51
The Philippines
Nationally representative data sources
Year
Source
1981, 1992- Vital registration data
1998, 20012005
Demographic and Health Survey (DHS) sibling history microdata
1993
1998
Demographic and Health Survey (DHS) sibling history microdata
Sub-nationally representative data sources
Year
Source
Sources that could potentially be incorporated, with access
2006
Family Planning Survey
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53
Sri Lanka
Nationally representative data sources
Year
Source
1980-1989, Vital registration data
1991-2006
Sub-nationally representative data sources
Year
Source
Sources that could potentially be incorporated, with access
ARFH Surveillance data
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55
Thailand
Nationally representative data sources
Year
Source
1980-1987, Vital registration data
1990-2000,
2002-2007
2004-2006
1995, 1997
Chandoevwit W, et al, Using multiple data for calculating the maternal mortality ratio in
Thailand, TDRI Quarterly Review. 2007;22(3):13-19
BHP studies, via Chandoevwit W, et al, Using multiple data for calculating the maternal
mortality ratio in Thailand, TDRI Quarterly Review. 2007;22(3):13-19
Sub-nationally representative data sources
Year
Source
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