Data sources for measuring maternal mortality

1. Data sources for measuring maternal mortality November 1, 2010 Rafael Lozano Professor of Global Health

2. Outline • 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

3. Processing input data

4. 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)

5. 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)

6. Sources for vital registration data • WHO Mortality Database • Reported civil registration data from countries • Periodically updated and released by WHO • Country websites and official publications • Sample registration systems, such as in India or China

7. 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.

8. Correcting vital registration • Shortened cause of death list: 56 causes of interest to public health practitioners • Causes mapped across ICD revisions to these 56 causes • Maternal conditions encompass all O codes (O00 – O99)

9. 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: • Identify garbage codes • Identify target codes to which garbage codes should be reassigned • Choose the fraction of deaths assigned to a garbage code that should be reassigned to each target code

10. Fraction of deaths assigned to GCs in the latest ICD-10 year since 2000

11. Garbage codes • General approach to address problem: • Identify garbage codes • Identify target codes to which garbage codes should be reassigned • Choose the fraction of deaths assigned to a garbage code that should be reassigned to each target code

12. Redistribution of garbage codes • 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

13. Percentage of Type of Garbage Codes All country years by ICD All country years by age, only ICD 10

14. Redistribution of garbage codes • Identify target codes to which garbage codes should be reassigned • Based on pathophysiology, i.e.: Garbage code Target causes Digestive diseases Genitourinary diseases Peritonitis Maternal conditions Injuries

15. Redistribution of garbage codes • Choose the fraction of deaths assigned to a garbage code that should be reassigned to each target code 3 approaches: • Proportionate redistribution • For causes with little information content • Statistical models • For heart failure • Expert judgment • Via review of published literature and consultation with experts, taking into account time trends in causes of death

16. Garbage codes redistributed to maternal causes, based on expert judgment (ICD-10)

17. Garbage codes redistributed to maternal causes, based on proportions (ICD-10)

20. 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. • 30 studies identify either late maternal and incidental deaths, but only 5 studiesidentify both • These studies provide an opportunity to validate the GC approach to maternal death correction.

21. Published Studies on Maternal Death Misclassification

22. 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)

23. Deaths in the household • Some censuses and surveys include a module on deaths occurring in the household over a specified period of time • Was the deceased between the ages 15-49 and female? • 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

24. Household Deaths are Usually Undercounts

25. 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)

26. 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 • DHS “maternal mortality” module • CDC Reproductive Health Surveys • However, naïve analysis of sibling histories can be misleading • Survivor bias • Recall bias

27. 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 • This weight (Wf=Bf /Sf) is the inverse of the probability of surviving to the time of the survey • Similar to a population sampling weight: the inverse of the probability of selection into the sample

29. 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)

30. 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 • 25 additional verbal autopsy studies which included “maternal” in the cause list 9,659 titles 593 abstracts 209 papers 61 extracted

31. Final Database by Source • No data for 21 countries, representing 2.2% of births

32. Density of site-years of observation, 1980-2008

33. Density of site-years of observation, 1980-2008

34. Data sources in each country • National and subnational sources included • Since the time of publication, new data sources have come to light: • Italicized: incorporated into model since the Lancet 2010 publication • Italicized and in blue font: sources that we are aware of but have not yet identified and incorporated

35. Bangladesh

37. Bhutan

39. Cambodia

41. India

42. India, continued

44. Indonesia

46. Lao, People’s Democratic Republic of

48. Nepal

50. Pakistan