Characteristics, availability and uses of vital registration and other mortality data sources in post-democracy South Africa

Figures & data

Table 1. Post-democracy data sources for mortality analysis in South Africa by enumeration years

	Enumeration year(s)
Data sources	Child mortality	Adult mortality	Maternal mortality	Causes of death
Vital Registration (VR)	1997–current*	1997–current*	1997–current*	1997–current*^,††
Rapid Mortality Surveillance System (RMS)	1998–current*	1998–current*	-	1998–current ^‡‡
Population census	1996,^† 2001,^† 2011^†	1996,^∣∣ 2001,^∣∣,¶ 2011^∣∣,¶	2001 & 2011**	2001 & 2011^‡‡
Demographic Surveillance Sites (DSS): >Agincourt	1992–current*	1992–current*	1992–current*	1991–current^§§
>ACDIS	2000–current*	2000–current*	2000–current*	2000–current^§§
> Dikgale	1996–current*	1996–current*	1996–current*	2011–current^§§
Community Survey (CS)	2007^‡	2007^‡	2007^‡	2007^‡, ^‡‡
October Household Survey (OHS)	1993–1999^‡	1993–1999^‡	–	1993–1998^‡, ^‡‡
General Household Survey (GHS)	2002^‡	2002–2011^‡	–	–
Demographic & Health Survey (DHS)	1998;^‡ 2003^‡	1998 & 2003^‡	1998 & 2003^‡	1998 & 2003^‡, ^‡‡
National Income Dynamics Study (NIDS)	2008^‡	2008^‡	–	2008^‡, ^‡‡

Source: Table created by authors from vital registration and survey information as follows: VR: Stats SA, 2012 54, Stats SA, various years 55; Census 1996: Stats SA, 2012 88; Census 2001: Stats SA, 2012 54; OHS: National Research Foundation 63; GHS and CS: Stats SA web-based Nesstar information and Stats SA electronic reports 54 69 89; DHS: Department of Health et al., 2002 47, Department of Health et al., 2007 68; NIDS: Moultrie & Dorrington, 2009 90.

Notes: *Direct estimation from routine surveillance; ^†children ever born/children surviving (CEB/CS); ^‡See Table 2; ^∣∣parental survival; ^¶deaths in the household; **deaths in the household plus pregnancy/delivery-related question; ^††cause obtained from medical certificate of cause of death on death notification form (BI-1663), or headman reporting on death report (BI-1680); ^‡‡censuses and surveys are not traditional ways to collect cause-of-death data. For the censuses, surveys and RMS, causes were broadly indicated as natural/unnatural, pregnancy/delivery-related, or accident/violence-related causes; ^§§cause ascertained via information from a verbal autopsy instrument.

Table 2. National surveys measuring mortality, by year of survey, number of households and persons enumerated, and different methods of mortality measurement

Year	Number of households	Number of persons	Deaths in the household	Parental survival	Sibling survival	Spousal survival	Full birth histories	Summary data on births, deaths of previous births, and surviving children
October Household Survey (OHS)
1993	30,233	136,468	✓12 months					✓W egb15–49^†
1994	30,279	132,469	✓12 months					✓W egb <55^†
1995	29,700	130,787	✓22 months	✓				✓W egb <55^†
1996	15,917	72,889	✓22 months	✓				✓W egb <55^†
1997	29,810	140,015	✓22 months	✓	✓(Sisterhd)*	✓		✓W egb^†
1998	18,981	82,262	✓22 months	✓	✓(Sisterhd)*	✓		✓W egb^†
1999	26,164	106,650	✓12 months					✓(W gb12mo)^‡
General Household Survey (GHS)
2002	26,243	102,461		✓				✓(W 12–49)
2003 to 2011	Varied: 24,333 to 29,236	Varied: 94,263 to 109,824		✓
Community Survey (CS)
2007	246,618	949,105	✓12 months	✓				✓(W 12–49)
Demographic and Health Survey (DHS)
1998	12,540	17,500	✓12 months	✓	✓		✓	✓(W 15–49)
2003	7,756	18,274		✓	✓		✓	✓(W 15–49)
National Income Dynamics Study (NIDS)
2008	7,305	28,255	✓24 months	✓			✓	✓(W 15–49)

Source: Table created by authors from the following information on surveys: OHS: National Research Foundation 63; GHS and CS: Stats SA web-based Nesstar information and Stats SA electronic reports 54 69 89; DHS: Department of Health et al., 2002 47, Department of Health et al., 2007 68; NIDS: Moultrie & Dorrington, 2009 90, Leibbrant et al., 2009 71.

Notes: *sisterhood method; ^†W egb: ¯omen who have ēver ¯iven ¯irth; ^‡W gb12mo: all women who have ¯iven ¯irth in the last 12 months.

Table 3. Selected facility-based data sources that may complement vital registration mortality data

Condition- or age-specific data sources
Programme	Enumeration years	Selected key information about source
Confidential Enquiry into Maternal Deaths (CEMD)	1998–current	Facility-based, structured reporting
		Compulsory reporting after maternal death has been made a notifiable death
		Systematic investigation of event, cause and modifiable factors
		Despite partial coverage, data useful for highlighting main problems and opportunities in addressing maternal mortality
		Resulted in the publication of two sets of national guidelines*
Peri-natal Problem Identification Programme (PPIP)	2000–current	Facility-based, structured clinical mortality audit of peri-natal deaths
		Voluntary participation; compulsory in some provinces
		Data set relates to nearly 3,000,000 births and 108,469 deaths
		During 2008-‘09, 275 facilities participated, representing about 963,000 births, i.e. approximately 52% of all facility births for 2008-‘09
		Data not nationally representative, but standardised data collection ensures comparable over time and participating facilities; generate recommendations for better peri-natal care, improved clinical practice; and prioritisation of clinical and public health research.
Child Healthcare Problem Identification Programme (Child PIP)	2005–current	Facility-based, structured clinical mortality audit of paediatric deaths
		Voluntary participation
		2005–2009 data related to 19,295 deaths of 343,408 admissions in 101 hospitals in all nine provinces, representing just under 30% of all hospitals
		Not nationally representative data, but standardised collection ensures comparable data;
		Recommendations address key health functions, i.e. policy, management and administration, clinical practice, and education.
National Cancer Registry (NCR)	1986–current	Passive pathology-based surveillance system, with pathology reports confirming a histological cancer diagnosis submitted by selected pathology laboratories
		Voluntary participation
		Data obtained from 79 laboratories in 2001
		Average of 70,000 cancer cases annually, incl. at least 50,000 new cases
National Injury Mortality Surveillance System (NIMSS)	1999–current	Active collation and centralisation of routinely-kept data of all non-natural deaths entering the forensic medico-legal system at participating mortuaries
		Voluntary participation
		Data collection and compilation designed in accordance to particular shortcomings in the national registration system regarding non-natural deaths
		Systematic information is collected about the incidence and causes of non-natural deaths and demographic characteristics of the deceased
		2001–2008: full coverage in a number of large cities
National Tuberculosis Registry (NTR) and Electronic Tuberculosis Register (ETR.Net)	1995–current	Facility-based reporting of case finding, sputum examination, treatment, and outcomes through standardised forms
		Compulsory reporting – TB is notifiable in terms of the National Health Act
		Tuberculosis Register (GW 20/11) introduced in 1995
		Suspect Register, Laboratory Register, Patient Treatment Card, Clinic/Hospital Card contribute to register
		Priority reporting by all facilities to DoH within 24 hours for multidrug-resistant (MDR) and extensively drug-resistant (XDR) TB
		Electronic TB Register (ETR.Net) implemented in 2004/5 – facilitates standardised recording, reporting and evaluation of programmes
		Information flow: a) Data on case finding, smear conversion and treatment outcomes captured at facility level from patient and facility records; b) collated at sub/district level in electronic TB register; c) data exported into district health information system and transmitted to provincial and national level.

Source: Table created by authors from the following: CEMD: National Committee CEMD, 2008 91; PPIP: Pattinson (ed), 2011 92, Bradshaw et al., 2008 93; Child PIP: Stephen et al., 2008 94, Bradshaw et al., 2008 93; NCR: Albrecht, 2006 95, Mqoqi et al., 2003 96; NIMSS: Matzopoulos, 2002 97; TB: Dept of Health, 2004 98, Dept of Health, 2007 99.

Note: *National Maternity Guidelines for District Hospitals and Clinics, and Essential Steps in the Management of Common Causes of Maternal Deaths in South Africa.

Fig. 1.  The probability of dying between ages 15 and 50 (₃₅ q ₁₅) from different data sources.

Source: OHS and DHS estimates extracted from Figure 6.2 in Dorrington RE, Moultrie TA, Timæus IM, 2004 44. ASSA2008 estimates calculated from ASSA life table values which are based on vital registration data 101.

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