Jané Joubert1, Debbie Bradshaw2, Chodziwadziwa Kabudula2, Chalapati Rao2, Kathleen Kahn3, Paul Mee4, Stephen Tollman3, Alan D Lopez2, Theo Vos2. 1. Burden of Disease Research Unit, South African Medical Research Council, Parow Vallei, Western Cape, South Africa, School of Population Health, The University of Queensland, Brisbane, QLD, Australia, MRC/Wits Rural Public Health and Health Transitions Research Unit (Agincourt), School of Public Health, University of the Witwatersrand, Johannesburg, South Africa, Umeå Centre for Global Health Research, Department of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden, INDEPTH Network, Accra, Ghana, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, VIC, Australia and Institute of Health Metrics and Evaluation, University of Washington, Seattle, USA Burden of Disease Research Unit, South African Medical Research Council, Parow Vallei, Western Cape, South Africa, School of Population Health, The University of Queensland, Brisbane, QLD, Australia, MRC/Wits Rural Public Health and Health Transitions Research Unit (Agincourt), School of Public Health, University of the Witwatersrand, Johannesburg, South Africa, Umeå Centre for Global Health Research, Department of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden, INDEPTH Network, Accra, Ghana, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, VIC, Australia and Institute of Health Metrics and Evaluation, University of Washington, Seattle, USA jane.joubert@mrc.ac.za. 2. Burden of Disease Research Unit, South African Medical Research Council, Parow Vallei, Western Cape, South Africa, School of Population Health, The University of Queensland, Brisbane, QLD, Australia, MRC/Wits Rural Public Health and Health Transitions Research Unit (Agincourt), School of Public Health, University of the Witwatersrand, Johannesburg, South Africa, Umeå Centre for Global Health Research, Department of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden, INDEPTH Network, Accra, Ghana, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, VIC, Australia and Institute of Health Metrics and Evaluation, University of Washington, Seattle, USA. 3. Burden of Disease Research Unit, South African Medical Research Council, Parow Vallei, Western Cape, South Africa, School of Population Health, The University of Queensland, Brisbane, QLD, Australia, MRC/Wits Rural Public Health and Health Transitions Research Unit (Agincourt), School of Public Health, University of the Witwatersrand, Johannesburg, South Africa, Umeå Centre for Global Health Research, Department of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden, INDEPTH Network, Accra, Ghana, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, VIC, Australia and Institute of Health Metrics and Evaluation, University of Washington, Seattle, USA Burden of Disease Research Unit, South African Medical Research Council, Parow Vallei, Western Cape, South Africa, School of Population Health, The University of Queensland, Brisbane, QLD, Australia, MRC/Wits Rural Public Health and Health Transitions Research Unit (Agincourt), School of Public Health, University of the Witwatersrand, Johannesburg, South Africa, Umeå Centre for Global Health Research, Department of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden, INDEPTH Network, Accra, Ghana, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, VIC, Australia and Institute of Health Metrics and Evaluation, University of Washington, Seattle, USA Burden of Disease Research Unit, South African Medical Research Council, Parow Vallei, Western Cape, South Africa, School of Population Health, The University of Queensland, Brisbane, QLD, Australia, MRC/Wits Rural Public Health and Health Transitions Research Unit (Agincourt), School of Public Health, University of the Witwatersrand, Johannesburg, South Africa, Umeå Centre for Global Health Research, Department of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden, INDEPTH Network, Accra, Ghana, Melbourne School of Population and Global Health, The Uni 4. Burden of Disease Research Unit, South African Medical Research Council, Parow Vallei, Western Cape, South Africa, School of Population Health, The University of Queensland, Brisbane, QLD, Australia, MRC/Wits Rural Public Health and Health Transitions Research Unit (Agincourt), School of Public Health, University of the Witwatersrand, Johannesburg, South Africa, Umeå Centre for Global Health Research, Department of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden, INDEPTH Network, Accra, Ghana, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, VIC, Australia and Institute of Health Metrics and Evaluation, University of Washington, Seattle, USA Burden of Disease Research Unit, South African Medical Research Council, Parow Vallei, Western Cape, South Africa, School of Population Health, The University of Queensland, Brisbane, QLD, Australia, MRC/Wits Rural Public Health and Health Transitions Research Unit (Agincourt), School of Public Health, University of the Witwatersrand, Johannesburg, South Africa, Umeå Centre for Global Health Research, Department of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden, INDEPTH Network, Accra, Ghana, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, VIC, Australia and Institute of Health Metrics and Evaluation, University of Washington, Seattle, USA.
Abstract
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.
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.
Keywords:
Agincourt Health and Demographic Surveillance System; Mortality; Statistics South Africa; causes of death; data linkage; data quality; rural South Africa; verbal autopsy; vital statistics
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