Local-level mortality surveillance in resource-limited settings: a case study of Cape Town highlights disparities in health.
OBJECTIVE
To identify the leading causes of mortality and premature mortality in Cape Town, South Africa, and its subdistricts, and to compare levels of mortality between subdistricts.


METHODS
Cape Town mortality data for the period 2001-2006 were analysed by age, cause of death and sex. Cause-of-death codes were aggregated into three main cause groups: (i) pre-transitional causes (e.g. communicable diseases, maternal causes, perinatal conditions and nutritional deficiencies), (ii) noncommunicable diseases and (iii) injuries. Premature mortality was calculated in years of life lost (YLLs). Population estimates for the Cape Town Metro district were used to calculate age-specific rates per 100,000 population, which were then age-standardized and compared across subdistricts.


RESULTS
The pattern of mortality in Cape Town reflects the quadruple burden of disease observed in the national cause-of-death profile, with HIV/AIDS, other infectious diseases, injuries and noncommunicable diseases all accounting for a significant proportion of deaths. HIV/AIDS has replaced homicide as the leading cause of death. HIV/AIDS, homicide, tuberculosis and road traffic injuries accounted for 44% of all premature mortality. Khayelitsha, the poorest subdistrict, had the highest levels of mortality for all main cause groups.


CONCLUSIONS
Local mortality surveillance highlights the differential needs of the population of Cape Town and provides a wealth of data to inform planning and implementation of targeted interventions. Multisectoral interventions will be required to reduce the burden of disease.
Groenewald
Pam
P
Burden of Disease Research Unit, South African Medical Research Council, PO Box 19070, Tygerberg, 7505, South Africa. prenaud@compnet.co.za
Bradshaw
Debbie
D
Daniels
Johann
J
Zinyakatira
Nesbert
N
Matzopoulos
Richard
R
Bourne
David
D
Shaikh
Najma
N
Naledi
Tracey
T
D000293:Adolescent; D000328:Adult; D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D002423:Cause of Death / Q000639:trends; D002648:Child; D002675:Child, Preschool; D016001:Confidence Intervals; D005260:Female; D015658:HIV Infections / Q000453:epidemiology / Q000401:mortality; D006295:Health Resources / Q000706:statistics & numerical data*; D054624:Health Status Disparities; D006708:Homicide / Q000706:statistics & numerical data; D006801:Humans; D007223:Infant; D008297:Male; D008875:Middle Aged; D009026:Mortality / Q000639:trends*; D011203:Poverty / Q000706:statistics & numerical data; D012307:Risk Factors; D018571:Sentinel Surveillance; D012737:Sex Factors; D012959:Socioeconomic Factors; D013019:South Africa / Q000453:epidemiology; D055815:Young Adult