Agreement between cause of death assignment by computer-coded verbal autopsy methods and physician coding of verbal autopsy interviews in South Africa.
Agreement between cause of death assignment by computer-coded verbal autopsy methods and physician coding of verbal autopsy interviews in South Africa.
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DOI：
10.1080/16549716.2023.2285105
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发表时间：
2023-12-31
影响因子：
2.6
通讯作者：
Bradshaw, Debbie
中科院分区：
文献类型：
--
作者：
Groenewald, Pam;Thomas, Jason;Clark, Samuel J.;Morof, Diane;Joubert, Jane D.;Kabudula, Chodziwadziwa;Li, Zehang;Bradshaw, Debbie
南非国家死因验证（NCODV 2017/18）项目收集了全国口头尸检（VA）样本，并通过医生编码VA（PCVA）和计算机编码VA（CCVA）分配死因（COD）。将三种CCVA算法（InterVA-5、InSilicoVA和Tariff 2.0）在分配COD方面的性能与PCVA（参考标准）进行了比较。七个性能指标评估的COD分配的年龄，性别和死亡亚组的地方个人和人口水平的协议。阳性预测值（PPV）、灵敏度、总体一致性、kappa和机会校正一致性（CCC）评估个体水平一致性。原因特异性死亡分数（CSMF）的准确性和斯皮尔曼等级相关性评估人口水平的协议。共分析了5386条VA记录。PCVA和CCVA都将艾滋病毒/艾滋病确定为主要死因。CCVA PPV和敏感性（基于置信区间）具有可比性，但HIV/AIDS、TB、孕产妇、糖尿病、其他癌症和一些损伤除外。CCVA在识别围产期死亡、道路交通事故、自杀和他杀方面表现良好，但在识别肺炎、其他传染病和肾衰竭方面表现不佳。CCVA和PCVA之间关于最主要单一原因的总体一致性（48.2-51.6）表明两种方法之间的一致性相当弱。对于前三位原因，总体一致性显示InterVA（70.9）和InSilicoVA（73.8）的一致性中等。对于所有算法和组，基于kappa（−0.05-0.49）和CCC（0.06-0.43）的一致性较弱。CCVA在CSMF准确性方面具有中度至高度一致性，新生儿（0.90）的InterVA-5最高，成人（0.89）和男性（0.84）的Tariff 2.0最高，女性（0.88）、老年人（0.83）和机构外死亡（0.85）的InSilicoVA最高。等级相关表明成人的一致性中等（0.75-0.79）。虽然CCVA将艾滋病毒/艾滋病确定为主要COD，与PCVA一致，但在南非使用的算法仍有改进的余地。
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). The performance of three CCVA algorithms (InterVA-5, InSilicoVA and Tariff 2.0) in assigning a COD was compared with PCVA (reference standard). 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. 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). Whilst CCVAs identified HIV/AIDS as the leading COD, consistent with PCVA, there is scope for improving the algorithms for use in South Africa.
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