Persistent burden from non-communicable diseases in South Africa needs strong action

Continued effort and politcal will must be directed towards preventing, delaying the onset of and managing non-communicable diseases in South Africa

Mortality trends and diff erentials in South Africa from 1997 to 2012: second National Burden of Disease Study

Background The poor health of South Africans is known to be associated with a quadruple disease burden. In the second National Burden of Disease (NBD) study, we aimed to analyse cause of death data for 1997–2012 and develop national, population group, and provincial estimates of the levels and causes of mortality. Method We used underlying cause of death data from death notifi cations for 1997–2012 obtained from Statistics South Africa. These data were adjusted for completeness using indirect demographic techniques for adults and comparison with survey and census estimates for child mortality. A regression approach was used to estimate misclassifi ed HIV/AIDS deaths and so-called garbage codes were proportionally redistributed by age, sex, and population group population group (black African, Indian or Asian descent, white [European descent], and coloured [of mixed ancestry according to the preceding categories]). Injury deaths were estimated from additional data sources. Age-standardised death rates were calculated with mid-year population estimates and the WHO age standard. Institute of Health Metrics and Evaluation Global Burden of Disease (IHME GBD) estimates for South Africa were obtained from the IHME GHDx website for comparison. Findings All-cause age-standardised death rates increased rapidly since 1997, peaked in 2006 and then declined, driven by changes in HIV/AIDS. Mortality from tuberculosis, non-communicable diseases, and injuries decreased slightly. In 2012, HIV/AIDS caused the most deaths (29·1%) followed by cerebrovascular disease (7·5%) and lower respiratory infections (4·9%). All-cause age-standardised death rates were 1·7 times higher in the province with the highest death rate compared to the province with the lowest death rate, 2·2 times higher in black Africans compared to whites, and 1·4 times higher in males compared with females. Comparison with the IHME GBD estimates for South Africa revealed substantial diff erences for estimated deaths from all causes, particularly HIV/AIDS and interpersonal violence. Interpretation This study shows the reversal of HIV/AIDS, non-communicable disease, and injury mortality trends in South Africa during the study period. Mortality diff erentials show the importance of social determinants, raise concerns about the quality of health services, and provide relevant information to policy makers for addressing inequalities. Diff erences between GBD estimates for South Africa and this study emphasise the need for more careful calibration of global models with local data

Agreement between cause of death assignment by computer-coded verbal autopsy methods and physician coding of verbal autopsy interviews in South Africa

Background The South African national cause of death validation (NCODV 2017/18) project collected a national sample of verbal autopsies (VA) with cause of death (COD) assignment by physician-coded VA (PCVA) and computer-coded VA (CCVA). Objective The performance of three CCVA algorithms (InterVA-5, InSilicoVA and Tariff 2.0) in assigning a COD was compared with PCVA (reference standard). Methods Seven performance metrics assessed individual and population level agreement of COD assignment by age, sex and place of death subgroups. Positive predictive value (PPV), sensitivity, overall agreement, kappa, and chance corrected concordance (CCC) assessed individual level agreement. Cause-specific mortality fraction (CSMF) accuracy and Spearman’s rank correlation assessed population level agreement. Results A total of 5386 VA records were analysed. PCVA and CCVAs all identified HIV/AIDS as the leading COD. CCVA PPV and sensitivity, based on confidence intervals, were comparable except for HIV/AIDS, TB, maternal, diabetes mellitus, other cancers, and some injuries. CCVAs performed well for identifying perinatal deaths, road traffic accidents, suicide and homicide but poorly for pneumonia, other infectious diseases and renal failure. Overall agreement between CCVAs and PCVA for the top single cause (48.2–51.6) indicated comparable weak agreement between methods. Overall agreement, for the top three causes showed moderate agreement for InterVA (70.9) and InSilicoVA (73.8). Agreement based on kappa (−0.05–0.49)and CCC (0.06–0.43) was weak to none for all algorithms and groups. CCVAs had moderate to strong agreement for CSMF accuracy, with InterVA-5 highest for neonates (0.90), Tariff 2.0 highest for adults (0.89) and males (0.84), and InSilicoVA highest for females (0.88), elders (0.83) and out-of-facility deaths (0.85). Rank correlation indicated moderate agreement for adults (0.75–0.79). Conclusions Whilst CCVAs identified HIV/AIDS as the leading COD, consistent with PCVA, there is scope for improving the algorithms for use in South Africa

Record-linkage comparison of verbal autopsy and routine civil registration death certification in rural north-east South Africa : 2006-09

Background: South African civil registration (CR) provides a key data source for local health decision making, and informs the levels and causes of mortality in data-lacking sub-Saharan African countries. We linked mortality data from CR and the Agincourt Health and Socio-demographic Surveillance System (Agincourt HDSS) to examine the quality of rural CR data. Methods: Deterministic and probabilistic techniques were used to link death data from 2006 to 2009. Causes of death were aggregated into the WHO Mortality Tabulation List 1 and a locally relevant short list of 15 causes. The matching rate was compared with informant-reported death registration. Using the VA diagnoses as reference, misclassification patterns, sensitivity, positive predictive values and cause-specific mortality fractions (CSMFs) were calculated for the short list. Results: A matching rate of 61% [95% confidence interval (CI): 59.2 to 62.3] was attained, lower than the informant-reported registration rate of 85% (CI: 83.4 to 85.8). For the 2264 matched cases, cause agreement was 15% (kappa 0.1083, CI: 0.0995 to 0.1171) for the WHO list, and 23% (kappa 0.1631, CI: 0.1511 to 0.1751) for the short list. CSMFs were significantly different for all but four (tuberculosis, cerebrovascular disease, other heart disease, and ill-defined natural) of the 15 causes evaluated. Conclusion: Despite data limitations, it is feasible to link official CR and HDSS verbal autopsy data. Data linkage proved a promising method to provide empirical evidence about the quality and utility of rural CR mortality data. Agreement of individual causes of death was low but, at the population level, careful interpretation of the CR data can assist health prioritization and planning

Editorial: Persistent burden from non-communicable diseases in South Africa needs strong action

No Abstrac

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

The value of good-quality mortality data for public health is widely acknowledged. While effective civil registration systems remains the ‘gold standard’ source for continuous mortality measurement, less than 25% of deaths are registered in most African countries. Alternative data collection systems can provide mortality data to complement those from civil registration, given an understanding of data source characteristics and data quality. We aim to document mortality data sources in post-democracy South Africa; to report on availability, limitations, strengths, and possible complementary uses of the data; and to make recommendations for improved data for mortality measurement. Civil registration and alternative mortality data collection systems, data availability, and complementary uses were assessed by reviewing blank questionnaires, death notification forms, death data capture sheets, and patient cards; legislation; electronic data archives and databases; and related information in scientific journals, research reports, statistical releases, government reports and books. Recent transformation has enhanced civil registration and official mortality data availability. Additionally, a range of mortality data items are available in three population censuses, three demographic surveillance systems, and a number of national surveys, mortality audits, and disease notification programmes. Child and adult mortality items were found in all national data sources, and maternal mortality items in most. Detailed cause-of-death data are available from civil registration and demographic surveillance. In a continent often reported as lacking the basic data to infer levels, patterns and trends of mortality, there is evidence of substantial improvement in South Africa in the availability of data for mortality assessment. Mortality data sources are many and varied, providing opportunity for comparing results and improved public health planning. However, more can and must be done to improve mortality measurement by improving data quality, triangulating data, and expanding analytic capacity. Cause data, in particular, must be improved

Mortality trends and differentials in South Africa from 1997 to 2012: second National Burden of Disease Study

Background: The poor health of South Africans is known to be associated with a quadruple disease burden. In the second National Burden of Disease (NBD) study, we aimed to analyse cause of death data for 1997–2012 and develop national, population group, and provincial estimates of the levels and causes of mortality. Method: We used underlying cause of death data from death notifications for 1997–2012 obtained from Statistics South Africa. These data were adjusted for completeness using indirect demographic techniques for adults and comparison with survey and census estimates for child mortality. A regression approach was used to estimate misclassified HIV/AIDS deaths and so-called garbage codes were proportionally redistributed by age, sex, and population group population group (black African, Indian or Asian descent, white [European descent], and coloured [of mixed ancestry according to the preceding categories]). Injury deaths were estimated from additional data sources. Age-standardised death rates were calculated with mid-year population estimates and the WHO age standard. Institute of Health Metrics and Evaluation Global Burden of Disease (IHME GBD) estimates for South Africa were obtained from the IHME GHDx website for comparison. Findings: All-cause age-standardised death rates increased rapidly since 1997, peaked in 2006 and then declined, driven by changes in HIV/AIDS. Mortality from tuberculosis, non-communicable diseases, and injuries decreased slightly. In 2012, HIV/AIDS caused the most deaths (29·1%) followed by cerebrovascular disease (7·5%) and lower respiratory infections (4·9%). All-cause age-standardised death rates were 1·7 times higher in the province with the highest death rate compared to the province with the lowest death rate, 2·2 times higher in black Africans compared to whites, and 1·4 times higher in males compared with females. Comparison with the IHME GBD estimates for South Africa revealed substantial differences for estimated deaths from all causes, particularly HIV/AIDS and interpersonal violence. Interpretation: This study shows the reversal of HIV/AIDS, non-communicable disease, and injury mortality trends in South Africa during the study period. Mortality differentials show the importance of social determinants, raise concerns about the quality of health services, and provide relevant information to policy makers for addressing inequalities. Differences between GBD estimates for South Africa and this study emphasise the need for more careful calibration of global models with local data. Funding: South African Medical Research Council's Flagships Awards Project

Evaluating the quality of national mortality statistics from civil registration in South Africa, 1997-2007

Background:Two World Health Organization comparative assessments rated the quality of South Africa's 1996 mortality data as low. Since then, focussed initiatives were introduced to improve civil registration and vital statistics. Furthermore, South African cause-of-death data are widely used by research and international development agencies as the basis for making estimates of cause-specific mortality in many African countries. It is hence important to assess the quality of more recent South African data.Methods:We employed nine criteria to evaluate the quality of civil registration mortality data. Four criteria were assessed by analysing 5.38 million deaths that occurred nationally from 1997-2007. For the remaining five criteria, we reviewed relevant legislation, data repositories, and reports to highlight developments which shaped the current status of these criteria.Findings:National mortality statistics from civil registration were rated satisfactory for coverage and completeness of death registration, temporal consistency, age/sex classification, timeliness, and sub-national availability. Epidemiological consistency could not be assessed conclusively as the model lacks the discriminatory power to enable an assessment for South Africa. Selected studies and the extent of ill-defined/non-specific codes suggest substantial shortcomings with single-cause data. The latter criterion and content validity were rated unsatisfactory.Conclusion:In a region marred by mortality data absences and deficiencies, this analysis signifies optimism by revealing considerable progress from a dysfunctional mortality data system to one that offers all-cause mortality data that can be adjusted for demographic and health analysis. Additionally, timely and disaggregated single-cause data are available, certified and coded according to international standards. However, without skillfully estimating adjustments for biases, a considerable confidence gap remains for single-cause data to inform local health planning, or to fill gaps in sparse-data countries on the continent. Improving the accuracy of single-cause data will be a critical contribution to the epidemiologic and population health evidence base in Africa