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Background: Mortality burden in South Africa since the mid-1990s has been characterized by a quadruple disease burden: HIV/AIDS and tuberculosis (TB); other communicable diseases (excluding HIV/AIDS and TB), maternal causes, perinatal conditions and nutritional deficiencies; non-communicable diseases (NCDs); and injuries. Causes from these broad groupings have persistently constituted the top 10 causes of death. However, proportions and rankings have varied over time, alongside overall mortality levels. Objective: To provide evidence on the contributions of age and cause-of-death to changes in mortality levels in a rural South African population over a quarter century (1993–2018). Methods: Using mortality and cause-of-death data from the Agincourt Health and Socio-Demographic Surveillance System (HDSS), we derive estimates of the distribution of deaths by cause, and hazards of death by age, sex, and time period, 1993–2018. We derive estimates of life expectancies at birth and years of life expectancy gained at age 15 if most common causes of death were deleted. We compare mortality indicators and cause-of-death trends from the Agincourt HDSS with South African national indicators generated from publicly available datasets. Results: Mortality and cause-of-death transition reveals that overall mortality levels have returned to pre-HIV epidemic levels. In recent years, the concentration of mortality has shifted towards older ages, and the mortality burden from cardiovascular diseases and other chronic NCDs are more prominent as people living with HIV/AIDS access ART and live longer. Changes in life expectancy at birth, distribution of deaths by age, and major cause-of-death categories in the Agincourt population follow a similar pattern to the South African population. Conclusion: The Agincourt HDSS provides critical information about general mortality, cause-of-death, and age patterns in rural South Africa. Realigning and strengthening the South African public health and healthcare systems is needed to concurrently cater for the prevention, control, and treatment of multiple disease conditions.
We used mortality and cause of death data collected from 1993 to 2018 as part of annual updates of vital events of the population of the Agincourt Health and Socio-Demographic Surveillance System (HDSS) in rural northeast South Africa [27,28]. Similar to earlier studies conducted in Agincourt [17,29–31], a person-year file was constructed containing one record for each year lived by each individual in the study population during the period 1993–2018. Attributes contained in each record consisted of Individual ID, sex, date of birth, date of death, age, calendar year, if the person died within the year, and the most probable cause-of-death. The most probable cause-of-death was generated using the InterVA-5 probabilistic model (version 5.1) [32]. For each death, the InterVA-5 model assigns up to three likely causes of death with associated likelihoods based on information on signs and symptoms of the illness or injury prior to death collected through verbal autopsy (VA) interviews. The VA interviews were conducted with caregivers of individuals identified as having died between annual surveillance update rounds using a locally validated VA instrument until 2011 and WHO VA instruments from 2012 onwards [28,33]. The timing of the interviews ranged from 1 to 11 months after death. An indeterminate cause was assigned when the VA information was inadequate for the model to arrive at any cause of death. While causes of death derived by the InterVA model have been found to not substantially differ from those generated by physician coding [34,35], the InterVA model also offers the benefit of assigning causes of death in a standardized, automated manner that is much quicker and more consistent compared to physicians. This feature is particularly desirable for assessing changes over time and across settings. Using the person-year file, we estimated the hazards of death by age, sex and time-period using logistic regression models [36–40]. Thereafter, we used the estimated hazards of death to construct standard life tables and cause-deleted life tables to, respectively, derive estimates of life expectancies at birth and to assess potential gains in life expectancy (PGLE) at age 15 if selected insignificant. Third, the InterVAcauses of death were eliminated. The PGLE provides a hypothetical estimate of the impact of a particular disease on life expectancy by highlighting the loss of life expectancy caused by a certain disease and provides a numerical indicator of survival if the disease is eliminated [41]. We follow methods that have been used in several other settings to assess the impact on life expectancy of various diseases, including cardiovascular diseases, neoplasms, HIV/AIDS and accidents using PGLE [41–44]. We split the calendar years into the following time periods: 1993–1997, 1998–2000, 2001–2003, 2004–2007, 2008–2010, 2011–2013, 2014–2016 and 2017–2018 to contextualize the dynamics of the HIV epidemic and the rollout of prevention of mother-to-child transmission (PMTCT) and antiretroviral treatment (ART) services. Where possible, we compared the indicators of mortality and cause-of-death trends from Agincourt with South African national indicators generated from publicly available datasets as a way of assessing the generalizability of our findings. We compared estimates of life expectancy at birth from the Agincourt HDSS with estimates of life expectancy at birth for South Africa obtained from the World Bank data archive [45]. We also compared the percentage distribution of deaths due to communicable diseases (Group I), non-communicable diseases (Group II) and external causes (Group III) by year of death from the Agincourt HDSS population with those in South Africa compiled by Statistics South Africa and archived in the DataFirst online microdata library [46]. Even though deaths due to HIV/AIDS and TB dominated by far deaths due to communicable diseases during periods of increased mortality [17], we did not separate HIV/AIDS and TB from communicable diseases in making the comparison due to known misattribution of HIV/AIDS deaths to infectious conditions, such as diarrhoea, tuberculosis and pneumonia in the South African national cause of death data [47–49]. Different time periods were used to compare the indicators of mortality and cause-of-death trends from Agincourt with South African national indicators due to the availability of publicly accessible national data.
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