Joubert Jané, Bradshaw Debbie, Kabudula Chodziwadziwa, Rao Chalapati, Kahn Kathleen, Mee Paul, Tollman Stephen, Lopez Alan D, Vos Theo
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 University of Melbourne, Carlton, VIC, Australia and Institute of Health Metrics and Evaluation, University of Washington, Seattle, USA.
Int J Epidemiol. 2014 Dec;43(6):1945-58. doi: 10.1093/ije/dyu156. Epub 2014 Aug 21.
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.
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.
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.
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.
南非的民事登记（CR）为当地卫生决策提供了关键数据来源，并为撒哈拉以南非洲数据匮乏国家的死亡率水平和原因提供了信息。我们将CR的死亡率数据与阿金库尔健康与社会人口监测系统（阿金库尔HDSS）相链接，以检查农村CR数据的质量。
使用确定性和概率性技术将2006年至2009年的死亡数据相链接。死亡原因被汇总为世界卫生组织死亡率列表1以及一份包含15种原因的当地相关简短列表。将匹配率与信息提供者报告的死亡登记情况进行比较。以VA诊断为参考，计算简短列表的错误分类模式、敏感性、阳性预测值和死因特异性死亡率（CSMF）。
匹配率达到61%[95%置信区间（CI）：59.2至62.3]，低于信息提供者报告的登记率85%（CI：83.4至85.8）。对于2264例匹配病例，世界卫生组织列表的死因一致性为15%（kappa 0.1083，CI：0.0995至0.1171），简短列表的死因一致性为23%（kappa 0.1631，CI：0.1511至0.1751）。在所评估的15种原因中，除了四种（结核病、脑血管疾病、其他心脏病和死因不明的自然疾病）之外，所有原因的CSMF均存在显著差异。
尽管存在数据限制，但将官方CR和HDSS的口头尸检数据相链接是可行的。数据链接被证明是一种很有前景的方法，可为农村CR死亡率数据的质量和效用提供实证证据。个体死因的一致性较低，但在人群层面，对CR数据的仔细解读有助于确定卫生工作重点和规划。
Zotero 插件