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Validation of verbal autopsy methods using hospital medical records: a case study in Vietnam.

ABSTRACT: BACKGROUND:Information on causes of death (COD) is crucial for measuring the health outcomes of populations and progress towards the Sustainable Development Goals. In many countries such as Vietnam where the civil registration and vital statistics (CRVS) system is dysfunctional, information on vital events will continue to rely on verbal autopsy (VA) methods. This study assesses the validity of VA methods used in Vietnam, and provides recommendations on methods for implementing VA validation studies in Vietnam. METHODS:This validation study was conducted on a sample of 670 deaths from a recent VA study in Quang Ninh province. The study covered 116 cases from this sample, which met three inclusion criteria: a) the death occurred within 30 days of discharge after last hospitalisation, and b) medical records (MRs) for the deceased were available from respective hospitals, and c) the medical record mentioned that the patient was terminally ill at discharge. For each death, the underlying cause of death (UCOD) identified from MRs was compared to the UCOD from VA. The validity of VA diagnoses for major causes of death was measured using sensitivity, specificity and positive predictive value (PPV). RESULTS:The sensitivity of VA was at least 75% in identifying some leading CODs such as stroke, road traffic accidents and several site-specific cancers. However, sensitivity was less than 50% for other important causes including ischemic heart disease, chronic obstructive pulmonary diseases, and diabetes. Overall, there was 57% agreement between UCOD from VA and MR, which increased to 76% when multiple causes from VA were compared to UCOD from MR. CONCLUSIONS:Our findings suggest that VA is a valid method to ascertain UCOD in contexts such as Vietnam. Furthermore, within cultural contexts in which patients prefer to die at home instead of a healthcare facility, using the available MRs as the gold standard may be meaningful to the extent that recall bias from the interval between last hospital discharge and death can be minimized. Therefore, future studies should evaluate validity of MRs as a gold standard for VA studies in contexts similar to the Vietnamese context.

SUBMITTER: Tran HT

PROVIDER: S-EPMC5960129 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature

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Validation of verbal autopsy methods using hospital medical records: a case study in Vietnam.

Tran Hong Thi HT Nguyen Hoa Phuong HP Walker Sue M SM Hill Peter S PS Rao Chalapati C

BMC medical research methodology 20180518 1

<h4>Background</h4>Information on causes of death (COD) is crucial for measuring the health outcomes of populations and progress towards the Sustainable Development Goals. In many countries such as Vietnam where the civil registration and vital statistics (CRVS) system is dysfunctional, information on vital events will continue to rely on verbal autopsy (VA) methods. This study assesses the validity of VA methods used in Vietnam, and provides recommendations on methods for implementing VA valida ...[more]

PMID: 29776431

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Project description:BackgroundThe most common method for determining cause of death is certification by physicians based either on available medical records, or where such data are not available, through verbal autopsy (VA). The physician-certification approach is costly and inconvenient; however, recent work shows the potential of a computer-based probabilistic model (InterVA) to interpret verbal autopsy data in a more convenient, consistent, and rapid way. In this study we validate separately both physician-certified verbal autopsy (PCVA) and the InterVA probabilistic model against hospital cause of death (HCOD) in adults dying in a district hospital on the coast of Kenya.MethodsBetween March 2007 and June 2010, VA interviews were conducted for 145 adult deaths that occurred at Kilifi District Hospital. The VA data were reviewed by a physician and the cause of death established. A range of indicators (including age, gender, physical signs and symptoms, pregnancy status, medical history, and the circumstances of death) from the VA forms were included in the InterVA for interpretation. Cause-specific mortality fractions (CSMF), Cohen's kappa (κ) statistic, receiver operating characteristic (ROC) curves, sensitivity, specificity, and positive predictive values were applied to compare agreement between PCVA, InterVA, and HCOD.ResultsHCOD, InterVA, and PCVA yielded the same top five underlying causes of adult deaths. The InterVA overestimated tuberculosis as a cause of death compared to the HCOD. On the other hand, PCVA overestimated diabetes. Overall, CSMF for the five major cause groups by the InterVA, PCVA, and HCOD were 70%, 65%, and 60%, respectively. PCVA versus HCOD yielded a higher kappa value (κ = 0.52, 95% confidence interval [CI]: 0.48, 0.54) than the InterVA versus HCOD which yielded a kappa (κ) value of 0.32 (95% CI: 0.30, 0.38). Overall, (κ) agreement across the three methods was 0.41 (95% CI: 0.37, 0.48). The areas under the ROC curves were 0.82 for InterVA and 0.88 for PCVA. The observed sensitivities and specificities across the five major causes of death varied from 43% to 100% and 87% to 99%, respectively, for the InterVA/PCVA against the HCOD.ConclusionBoth the InterVA and PCVA compared well with the HCOD at a population level and determined the top five underlying causes of death in the rural community of Kilifi. We hope that our study, albeit small, provides new and useful data that will stimulate further definitive work on methods of interpreting VA data.

Project description:BackgroundVerbal Autopsy (VA) is widely viewed as the only immediate strategy for registering cause of death in much of Africa and Asia, where routine physician certification of deaths is not widely practiced. VA involves a lay interview with family or friends after a death, to record essential details of the circumstances. These data can then be processed automatically to arrive at standardized cause of death information.MethodsThe Population Health Metrics Research Consortium (PHMRC) undertook a study at six tertiary hospitals in low- and middle-income countries which documented over 12,000 deaths clinically and subsequently undertook VA interviews. This dataset, now in the public domain, was compared with the WHO 2012 VA standard and the InterVA-4 interpretative model.ResultsThe PHMRC data covered 70% of the WHO 2012 VA input indicators, and categorized cause of death according to PHMRC definitions. After eliminating some problematic or incomplete records, 11,984 VAs were compared. Some of the PHMRC cause definitions, such as 'preterm delivery', differed substantially from the International Classification of Diseases, version 10 equivalent. There were some appreciable inconsistencies between the hospital and VA data, including 20% of the hospital maternal deaths being described as non-pregnant in the VA data. A high proportion of VA cases (66%) reported respiratory symptoms, but only 18% of assigned hospital causes were respiratory-related. Despite these issues, the concordance correlation coefficient between hospital and InterVA-4 cause of death categories was 0.61.ConclusionsThe PHMRC dataset is a valuable reference source for VA methods, but has to be interpreted with care. Inherently inconsistent cases should not be included when using these data to build other VA models. Conversely, models built from these data should be independently evaluated. It is important to distinguish between the internal and external validity of VA models. The effects of using tertiary hospital data, rather than the more usual application of VA to all-community deaths, are hard to evaluate. However, it would still be of value for VA method development to have further studies of population-based post-mortem examinations.

Project description:BackgroundAccurate nationally representative statistics on total and cause-specific mortality in Vietnam are lacking due to incomplete capture in government reporting systems. This paper presents total and cause-specific mortality results from a national verbal autopsy survey conducted first time in Vietnam in conjunction with the annual population change survey and discusses methodological and logistical challenges associated with the implementation of a nation-wide assessment of mortality based on surveys.Verbal autopsy interviews, using the WHO standard questionnaire, were conducted with close relatives of the 6798 deaths identified in the 2007 population change survey in Vietnam. Data collectors were health staff recruited from the commune health station who undertook 3-day intensive training on VA interview. The Preston-Coale method assessed the level of completeness of mortality reporting from the population change survey. The number of deaths in each age-sex grouping is inflated according to the estimate of completeness to produce an adjusted number of deaths. Underlying causes of death were aggregated to the International Classification of Diseases Mortality Tabulation List 1. Leading causes of death were tabulated by sex for three broad age groups: 0-14 years; 15-59 years; and 60 years and above.FindingsCompleteness of mortality reporting was 69% for males and 54% for females with substantial regional variation. The use of VA has resulted in 10% of deaths being classified to ill-defined among males, and 15% among females. More ill-defined deaths were reported among the 60 year or above age group. Incomplete death reporting, wide geographical dispersal of deaths, extensive travel between households, and substantial variation in local responses to VA interviews challenged the implementation of a national mortality and cause of death assessment based on surveys.ConclusionsVerbal autopsy can be a viable tool to identify cause of death in Vietnam. However logistical challenges limit its use in conjunction with the national sample survey. Sentinel population clusters for mortality surveillance should be tested to develop an effective and sustainable option for routine mortality and cause of death data collection in Vietnam.

Project description:BACKGROUND:Monitoring progress with disease and injury reduction in many populations will require widespread use of verbal autopsy (VA). Multiple methods have been developed for assigning cause of death from a VA but their application is restricted by uncertainty about their reliability. METHODS:We investigated the validity of five automated VA methods for assigning cause of death: InterVA-4, Random Forest (RF), Simplified Symptom Pattern (SSP), Tariff method (Tariff), and King-Lu (KL), in addition to physician review of VA forms (PCVA), based on 12,535 cases from diverse populations for which the true cause of death had been reliably established. For adults, children, neonates and stillbirths, performance was assessed separately for individuals using sensitivity, specificity, Kappa, and chance-corrected concordance (CCC) and for populations using cause specific mortality fraction (CSMF) accuracy, with and without additional diagnostic information from prior contact with health services. A total of 500 train-test splits were used to ensure that results are robust to variation in the underlying cause of death distribution. RESULTS:Three automated diagnostic methods, Tariff, SSP, and RF, but not InterVA-4, performed better than physician review in all age groups, study sites, and for the majority of causes of death studied. For adults, CSMF accuracy ranged from 0.764 to 0.770, compared with 0.680 for PCVA and 0.625 for InterVA; CCC varied from 49.2% to 54.1%, compared with 42.2% for PCVA, and 23.8% for InterVA. For children, CSMF accuracy was 0.783 for Tariff, 0.678 for PCVA, and 0.520 for InterVA; CCC was 52.5% for Tariff, 44.5% for PCVA, and 30.3% for InterVA. For neonates, CSMF accuracy was 0.817 for Tariff, 0.719 for PCVA, and 0.629 for InterVA; CCC varied from 47.3% to 50.3% for the three automated methods, 29.3% for PCVA, and 19.4% for InterVA. The method with the highest sensitivity for a specific cause varied by cause. CONCLUSIONS:Physician review of verbal autopsy questionnaires is less accurate than automated methods in determining both individual and population causes of death. Overall, Tariff performs as well or better than other methods and should be widely applied in routine mortality surveillance systems with poor cause of death certification practices.

Project description:BackgroundComputer-coded verbal autopsy (CCVA) is a promising alternative to the standard approach of physician-certified verbal autopsy (PCVA), because of its high speed, low cost, and reliability. This study introduces a new CCVA technique and validates its performance using defined clinical diagnostic criteria as a gold standard for a multisite sample of 12,542 verbal autopsies (VAs).MethodsThe Random Forest (RF) Method from machine learning (ML) was adapted to predict cause of death by training random forests to distinguish between each pair of causes, and then combining the results through a novel ranking technique. We assessed quality of the new method at the individual level using chance-corrected concordance and at the population level using cause-specific mortality fraction (CSMF) accuracy as well as linear regression. We also compared the quality of RF to PCVA for all of these metrics. We performed this analysis separately for adult, child, and neonatal VAs. We also assessed the variation in performance with and without household recall of health care experience (HCE).ResultsFor all metrics, for all settings, RF was as good as or better than PCVA, with the exception of a nonsignificantly lower CSMF accuracy for neonates with HCE information. With HCE, the chance-corrected concordance of RF was 3.4 percentage points higher for adults, 3.2 percentage points higher for children, and 1.6 percentage points higher for neonates. The CSMF accuracy was 0.097 higher for adults, 0.097 higher for children, and 0.007 lower for neonates. Without HCE, the chance-corrected concordance of RF was 8.1 percentage points higher than PCVA for adults, 10.2 percentage points higher for children, and 5.9 percentage points higher for neonates. The CSMF accuracy was higher for RF by 0.102 for adults, 0.131 for children, and 0.025 for neonates.ConclusionsWe found that our RF Method outperformed the PCVA method in terms of chance-corrected concordance and CSMF accuracy for adult and child VA with and without HCE and for neonatal VA without HCE. It is also preferable to PCVA in terms of time and cost. Therefore, we recommend it as the technique of choice for analyzing past and current verbal autopsies.

Project description:BackgroundThe 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).ObjectiveThe performance of three CCVA algorithms (InterVA-5, InSilicoVA and Tariff 2.0) in assigning a COD was compared with PCVA (reference standard).MethodsSeven 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.ResultsA 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).ConclusionsWhilst CCVAs identified HIV/AIDS as the leading COD, consistent with PCVA, there is scope for improving the algorithms for use in South Africa.

Project description:Mortality statistics by age, sex and cause are the foundation of basic health data required for health status assessment, epidemiological research and formation of health policy. Close to half the deaths in Malaysia occur outside a health facility, are not attended by medical personnel, and are given a lay opinion as to the cause of death, leading to poor quality of data from vital registration. Verbal autopsy (VA) is a very useful tool in diagnosing broad causes of deaths for events that occur outside health facilities. This article reports the development of the VA methods and our principal finding from a validation study.A cross sectional study on nationally representative sample deaths that occurred in Malaysia during 2013 was used. A VA questionnaire suitable for local use was developed. Trained field interviewers visited the family members of the deceased at their homes and conducted face to face interviews with the next of kin. Completed questionnaires were reviewed by trained physicians who assigned multiple and underlying causes. Reference diagnoses for validation were obtained from review of medical records (MR) available for a sample of the overall study deaths.Corresponding MR diagnosis with matched sample of the VA diagnosis were available in 2172 cases for the validation study. Sensitivity scores were good (>75%) for transport accidents and certain cancers. Moderate sensitivity (50% - 75%) was obtained for ischaemic heart disease (64%) and cerebrovascular disease (72%). The validation sample for deaths due to major causes such as ischaemic heart disease, pneumonia, breast cancer and transport accidents show low cause-specific mortality fraction (CSMF) changes. The scores obtained for the top 10 leading site-specific cancers ranged from average to good.We can conclude that VA is suitable for implementation for deaths outside the health facilities in Malaysia. This would reduce ill-defined mortality causes in vital registration data, and yield more accurate national mortality statistics.

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Validation of verbal autopsy methods using hospital medical records: a case study in Vietnam.

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Validation of verbal autopsy methods using hospital medical records: a case study in Vietnam.

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