17. Garbage codes redistributed to maternal causes, based on proportions (ICD-10) ICD-10 code Condition R99 Other ill-defined and unspecified causes of mortality R98 Unattended death R09.2 Respiratory arrest R96.0 Instantaneous death R68.8 Other specified general symptoms and signs R55 Syncope and collapse R50.9 Fever, unspecified R96.1 Death occurring less than 24 hours from onset of symptoms, not otherwise explained R57.0 Cardiogenic shock R56.8 Other and unspecified convulsions R62.8 Other lack of expected normal physiological development R10.4 Other and unspecified abdominal pain R58 Haemorrhage, not elsewhere classified R57.1 Hypovolaemic shock R09.0 Other symptoms and signs involving the circulatory and respiratory systems R02 Gangrene, not elsewhere classified R40.2 Coma, unspecified R04.8 Haemorrhage from other sites in respiratory passages
35. Bangladesh 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. 1983 Khan AR, et al. Maternal mortality in rural Bangladesh: the Jamalpur district. Stud.Fam.Plann. 1986:7-12. 1987 Fauveau V, et al.. Effect on mortality of community-based maternity-care programme in rural Bangladesh. The Lancet 1991;338(8776):1183-1186. 2000 INDEPTH 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. 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
36.
37. Bhutan Sub-nationally representative data sources Year Source Nationally representative data sources Year Source 2005 Tabulated census household deaths data
38.
39. Cambodia Sub-nationally representative data sources Year Source 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
40.
41. India Nationally representative data sources Year Source 1982, 1997, 1999, 2001, 2002, 2004 National Sample Registration Scheme (SRS) 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)
42. 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):310-318. 1989 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. 1990-1998 Medical Certification of Causes of Death (MCCD9) 1992 Kumar R, et al. Maternal mortality inquiry in a rural community of north India. International Journal of Gynecology & Obstetrics 1989;29(4):313-319. 1992 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. 1999-2001 Medical Certification of Causes of Death (MCCD10) 2000 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. 2002 Iyengar K, et al. Pregnancy-related deaths in rural Rajasthan, India: exploring causes, context, and care-seeking through verbal autopsy. Journal of Health, Population and Nutrition 2009;27(2):293. 2004 Joshi R, et al. Verbal autopsy coding: are multiple coders better than one? Bull.World Health Organ. 2009;87:51-57. 2005 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. 2007 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).
43.
44. Indonesia Sub-nationally representative data sources Year Source 1981 Fortney JA, et al. Reproductive mortality in two developing countries. Am.J.Public Health 1986 Feb;76(2):134-138. 2006 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. 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
45.
46. Lao, People’s Democratic Republic of Sub-nationally representative data sources Year Source Nationally representative data sources Year Source 1990 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. Sources that could potentially be incorporated, with access 1995 Census data 2005 Census data
47.
48. Nepal Sub-nationally representative data sources Year Source Nationally representative data sources Year Source 1996 Demographic and Health Survey (DHS) sibling history microdata 2006 Demographic and Health Survey (DHS) sibling history microdata Sources that could potentially be incorporated, with access 2008-2009 National maternal mortality enquiry
49.
50. Pakistan Sub-nationally representative data sources Year Source 1986, 1990 Fikree FF, et al. Maternal mortality in different Pakistani sites: ratios, clinical causes and determinants. Acta Obstet.Gynecol.Scand. 1997;76(7):637-645. Nationally representative data sources Year Source 1993-1994 Vital registration 2006 Demographic and Health Survey (DHS) Verbal autopsy microdata
51.
52. The Philippines Sub-nationally representative data sources Year Source Nationally representative data sources Year Source 1981, 1992-1998, 2001-2005 Vital registration data 1993 Demographic and Health Survey (DHS) sibling history microdata 1998 Demographic and Health Survey (DHS) sibling history microdata Sources that could potentially be incorporated, with access 2006 Family Planning Survey
53.
54. Sri Lanka Sub-nationally representative data sources Year Source Nationally representative data sources Year Source 1980-1989, 1991-2006 Vital registration data Sources that could potentially be incorporated, with access ARFH Surveillance data
55.
56. Thailand Sub-nationally representative data sources Year Source Nationally representative data sources Year Source 1980-1987, 1990-2000, 2002-2007 Vital registration data 2004-2006 Chandoevwit W, et al, Using multiple data for calculating the maternal mortality ratio in Thailand, TDRI Quarterly Review. 2007;22(3):13-19 1995, 1997 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
Hinweis der Redaktion
Type 1 GCs increase gradually with age – larger numbers of deaths at the oldest ages where diagnostic detail may be absent may account for this general trend. Type 2 GCs also increase and even more markedly with age, perhaps reflecting the increasing complexity of identifying underlying causes across age in some cases, especially due to heart failure. Finally, type 4 GCs have a different age pattern. This category includes cases where there is some ambiguity about the exact underlying cause but the death clearly belongs to a particular group of causes. In particular, the larger fraction of deaths falling under this category at young ages can be traced to a substantial number of injury deaths for which full detail is not available. As injuries account for a larger fraction of deaths at younger ages, this explains the larger share of Type 4 at these age groups.
There are four different household modules. The solid black line is the gold standard, and this clearly demonstrates that there is undercounting occurring with the HH instruments.